multi-factor dimensionality reduction: Topics by Science.gov

Sample records for multi-factor dimensionality reduction

Duke Workshop on High-Dimensional Data Sensing and Analysis

DTIC Science & Technology

2015-05-06

Bayesian sparse factor analysis formulation of Chen et al . ( 2011 ) this work develops multi-label PCA (MLPCA), a generative dimension reduction...version of this problem was recently treated by Banerjee et al . [1], Ravikumar et al . [2], Kolar and Xing [3], and Ho ̈fling and Tibshirani [4]. As...Not applicable. Final Report Duke Workshop on High-Dimensional Data Sensing and Analysis Workshop Dates: July 26-28, 2011
Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

PubMed Central

Cowley, Benjamin R.; Doiron, Brent; Kohn, Adam

2016-01-01

Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction—shared dimensionality and percent shared variance—with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure. PMID:27926936
Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms.

PubMed

Bromuri, Stefano; Zufferey, Damien; Hennebert, Jean; Schumacher, Michael

2014-10-01

This research is motivated by the issue of classifying illnesses of chronically ill patients for decision support in clinical settings. Our main objective is to propose multi-label classification of multivariate time series contained in medical records of chronically ill patients, by means of quantization methods, such as bag of words (BoW), and multi-label classification algorithms. Our second objective is to compare supervised dimensionality reduction techniques to state-of-the-art multi-label classification algorithms. The hypothesis is that kernel methods and locality preserving projections make such algorithms good candidates to study multi-label medical time series. We combine BoW and supervised dimensionality reduction algorithms to perform multi-label classification on health records of chronically ill patients. The considered algorithms are compared with state-of-the-art multi-label classifiers in two real world datasets. Portavita dataset contains 525 diabetes type 2 (DT2) patients, with co-morbidities of DT2 such as hypertension, dyslipidemia, and microvascular or macrovascular issues. MIMIC II dataset contains 2635 patients affected by thyroid disease, diabetes mellitus, lipoid metabolism disease, fluid electrolyte disease, hypertensive disease, thrombosis, hypotension, chronic obstructive pulmonary disease (COPD), liver disease and kidney disease. The algorithms are evaluated using multi-label evaluation metrics such as hamming loss, one error, coverage, ranking loss, and average precision. Non-linear dimensionality reduction approaches behave well on medical time series quantized using the BoW algorithm, with results comparable to state-of-the-art multi-label classification algorithms. Chaining the projected features has a positive impact on the performance of the algorithm with respect to pure binary relevance approaches. The evaluation highlights the feasibility of representing medical health records using the BoW for multi-label classification tasks. The study also highlights that dimensionality reduction algorithms based on kernel methods, locality preserving projections or both are good candidates to deal with multi-label classification tasks in medical time series with many missing values and high label density. Copyright © 2014 Elsevier Inc. All rights reserved.
Pedagogical Factors Stimulating the Self-Development of Students' Multi-Dimensional Thinking in Terms of Subject-Oriented Teaching

ERIC Educational Resources Information Center

Andreev, Valentin I.

2014-01-01

The main aim of this research is to disclose the essence of students' multi-dimensional thinking, also to reveal the rating of factors which stimulate the raising of effectiveness of self-development of students' multi-dimensional thinking in terms of subject-oriented teaching. Subject-oriented learning is characterized as a type of learning where…
Reduction of multi-dimensional laboratory data to a two-dimensional plot: a novel technique for the identification of laboratory error.

PubMed

Kazmierczak, Steven C; Leen, Todd K; Erdogmus, Deniz; Carreira-Perpinan, Miguel A

2007-01-01

The clinical laboratory generates large amounts of patient-specific data. Detection of errors that arise during pre-analytical, analytical, and post-analytical processes is difficult. We performed a pilot study, utilizing a multidimensional data reduction technique, to assess the utility of this method for identifying errors in laboratory data. We evaluated 13,670 individual patient records collected over a 2-month period from hospital inpatients and outpatients. We utilized those patient records that contained a complete set of 14 different biochemical analytes. We used two-dimensional generative topographic mapping to project the 14-dimensional record to a two-dimensional space. The use of a two-dimensional generative topographic mapping technique to plot multi-analyte patient data as a two-dimensional graph allows for the rapid identification of potentially anomalous data. Although we performed a retrospective analysis, this technique has the benefit of being able to assess laboratory-generated data in real time, allowing for the rapid identification and correction of anomalous data before they are released to the physician. In addition, serial laboratory multi-analyte data for an individual patient can also be plotted as a two-dimensional plot. This tool might also be useful for assessing patient wellbeing and prognosis.
Categorical dimensions of human odor descriptor space revealed by non-negative matrix factorization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chennubhotla, Chakra; Castro, Jason

2013-01-01

In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain un- clear. Here, we use non-negative matrix factorization (NMF) - a dimensionality reduction technique - to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor di- mensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner.more » We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures.« less
Tensor Train Neighborhood Preserving Embedding

NASA Astrophysics Data System (ADS)

Wang, Wenqi; Aggarwal, Vaneet; Aeron, Shuchin

2018-05-01

In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace. Novel approaches to solve the optimization problem in TTNPE are proposed. For this embedding, we evaluate novel trade-off gain among classification, computation, and dimensionality reduction (storage) for supervised learning. It is shown that compared to the state-of-the-arts tensor embedding methods, TTNPE achieves superior trade-off in classification, computation, and dimensionality reduction in MNIST handwritten digits and Weizmann face datasets.
Advanced Data Visualization in Astrophysics: The X3D Pathway

NASA Astrophysics Data System (ADS)

Vogt, Frédéric P. A.; Owen, Chris I.; Verdes-Montenegro, Lourdes; Borthakur, Sanchayeeta

2016-02-01

Most modern astrophysical data sets are multi-dimensional; a characteristic that can nowadays generally be conserved and exploited scientifically during the data reduction/simulation and analysis cascades. However, the same multi-dimensional data sets are systematically cropped, sliced, and/or projected to printable two-dimensional diagrams at the publication stage. In this article, we introduce the concept of the “X3D pathway” as a mean of simplifying and easing the access to data visualization and publication via three-dimensional (3D) diagrams. The X3D pathway exploits the facts that (1) the X3D 3D file format lies at the center of a product tree that includes interactive HTML documents, 3D printing, and high-end animations, and (2) all high-impact-factor and peer-reviewed journals in astrophysics are now published (some exclusively) online. We argue that the X3D standard is an ideal vector for sharing multi-dimensional data sets because it provides direct access to a range of different data visualization techniques, is fully open source, and is a well-defined standard from the International Organization for Standardization. Unlike other earlier propositions to publish multi-dimensional data sets via 3D diagrams, the X3D pathway is not tied to specific software (prone to rapid and unexpected evolution), but instead is compatible with a range of open-source software already in use by our community. The interactive HTML branch of the X3D pathway is also actively supported by leading peer-reviewed journals in the field of astrophysics. Finally, this article provides interested readers with a detailed set of practical astrophysical examples designed to act as a stepping stone toward the implementation of the X3D pathway for any other data set.
Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures.

PubMed

Cao, Peng; Liu, Xiaoli; Yang, Jinzhu; Zhao, Dazhe; Huang, Min; Zhang, Jian; Zaiane, Osmar

2017-12-01

Alzheimer's disease (AD) has been not only a substantial financial burden to the health care system but also an emotional burden to patients and their families. Making accurate diagnosis of AD based on brain magnetic resonance imaging (MRI) is becoming more and more critical and emphasized at the earliest stages. However, the high dimensionality and imbalanced data issues are two major challenges in the study of computer aided AD diagnosis. The greatest limitations of existing dimensionality reduction and over-sampling methods are that they assume a linear relationship between the MRI features (predictor) and the disease status (response). To better capture the complicated but more flexible relationship, we propose a multi-kernel based dimensionality reduction and over-sampling approaches. We combined Marginal Fisher Analysis with ℓ 2,1 -norm based multi-kernel learning (MKMFA) to achieve the sparsity of region-of-interest (ROI), which leads to simultaneously selecting a subset of the relevant brain regions and learning a dimensionality transformation. Meanwhile, a multi-kernel over-sampling (MKOS) was developed to generate synthetic instances in the optimal kernel space induced by MKMFA, so as to compensate for the class imbalanced distribution. We comprehensively evaluate the proposed models for the diagnostic classification (binary class and multi-class classification) including all subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The experimental results not only demonstrate the proposed method has superior performance over multiple comparable methods, but also identifies relevant imaging biomarkers that are consistent with prior medical knowledge. Copyright © 2017 Elsevier Ltd. All rights reserved.
South African Universities and Human Development: Towards a Theorisation and Operationalisation of Professional Capabilities for Poverty Reduction

ERIC Educational Resources Information Center

Walker, Melanie; McLean, Monica; Dison, Arona; Peppin-Vaughan, Rosie

2009-01-01

This paper reports on a research project investigating the role of universities in South Africa in contributing to poverty reduction through the quality of their professional education programmes. The focus here is on theorising and the early operationalisation of multi-layered, multi-dimensional transformation based on ideas from Amartya Sen's…
Buckling Analysis of Single and Multi Delamination In Composite Beam Using Finite Element Method

NASA Astrophysics Data System (ADS)

Simanjorang, Hans Charles; Syamsudin, Hendri; Giri Suada, Muhammad

2018-04-01

Delamination is one type of imperfection in structure which found usually in the composite structure. Delamination may exist due to some factors namely in-service condition where the foreign objects hit the composite structure and creates inner defect and poor manufacturing that causes the initial imperfections. Composite structure is susceptible to the compressive loading. Compressive loading leads the instability phenomenon in the composite structure called buckling. The existence of delamination inside of the structure will cause reduction in buckling strength. This paper will explain the effect of delamination location to the buckling strength. The analysis will use the one-dimensional modelling approach using two- dimensional finite element method.
Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.

PubMed

Wu, Dingming; Wang, Dongfang; Zhang, Michael Q; Gu, Jin

2015-12-01

One major goal of large-scale cancer omics study is to identify molecular subtypes for more accurate cancer diagnoses and treatments. To deal with high-dimensional cancer multi-omics data, a promising strategy is to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data. In this study, we proposed a novel low-rank approximation based integrative probabilistic model to fast find the shared principal subspace across multiple data types: the convexity of the low-rank regularized likelihood function of the probabilistic model ensures efficient and stable model fitting. Candidate molecular subtypes can be identified by unsupervised clustering hundreds of cancer samples in the reduced low-dimensional subspace. On testing datasets, our method LRAcluster (low-rank approximation based multi-omics data clustering) runs much faster with better clustering performances than the existing method. Then, we applied LRAcluster on large-scale cancer multi-omics data from TCGA. The pan-cancer analysis results show that the cancers of different tissue origins are generally grouped as independent clusters, except squamous-like carcinomas. While the single cancer type analysis suggests that the omics data have different subtyping abilities for different cancer types. LRAcluster is a very useful method for fast dimension reduction and unsupervised clustering of large-scale multi-omics data. LRAcluster is implemented in R and freely available via http://bioinfo.au.tsinghua.edu.cn/software/lracluster/ .
Visualizing phylogenetic tree landscapes.

PubMed

Wilgenbusch, James C; Huang, Wen; Gallivan, Kyle A

2017-02-02

Genomic-scale sequence alignments are increasingly used to infer phylogenies in order to better understand the processes and patterns of evolution. Different partitions within these new alignments (e.g., genes, codon positions, and structural features) often favor hundreds if not thousands of competing phylogenies. Summarizing and comparing phylogenies obtained from multi-source data sets using current consensus tree methods discards valuable information and can disguise potential methodological problems. Discovery of efficient and accurate dimensionality reduction methods used to display at once in 2- or 3- dimensions the relationship among these competing phylogenies will help practitioners diagnose the limits of current evolutionary models and potential problems with phylogenetic reconstruction methods when analyzing large multi-source data sets. We introduce several dimensionality reduction methods to visualize in 2- and 3-dimensions the relationship among competing phylogenies obtained from gene partitions found in three mid- to large-size mitochondrial genome alignments. We test the performance of these dimensionality reduction methods by applying several goodness-of-fit measures. The intrinsic dimensionality of each data set is also estimated to determine whether projections in 2- and 3-dimensions can be expected to reveal meaningful relationships among trees from different data partitions. Several new approaches to aid in the comparison of different phylogenetic landscapes are presented. Curvilinear Components Analysis (CCA) and a stochastic gradient decent (SGD) optimization method give the best representation of the original tree-to-tree distance matrix for each of the three- mitochondrial genome alignments and greatly outperformed the method currently used to visualize tree landscapes. The CCA + SGD method converged at least as fast as previously applied methods for visualizing tree landscapes. We demonstrate for all three mtDNA alignments that 3D projections significantly increase the fit between the tree-to-tree distances and can facilitate the interpretation of the relationship among phylogenetic trees. We demonstrate that the choice of dimensionality reduction method can significantly influence the spatial relationship among a large set of competing phylogenetic trees. We highlight the importance of selecting a dimensionality reduction method to visualize large multi-locus phylogenetic landscapes and demonstrate that 3D projections of mitochondrial tree landscapes better capture the relationship among the trees being compared.
An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

PubMed

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-10-21

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.
An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

PubMed Central

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-01-01

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine. PMID:26506347
"Staying safe" - a narrative review of falls prevention in people with Parkinson's - "PDSAFE".

PubMed

Hulbert, Sophia; Rochester, Lynn; Nieuwboer, Alice; Goodwin, Vicki; Fitton, Carolyn; Chivers-Seymour, Kim; Ashburn, Ann

2018-05-18

Parkinson's disease demonstrates a spectrum of motor and non-motor symptoms. Falling is common and disabling. Current medical management shows minimal impact to reduce falls or fall-related risk factors, such as deficits in gait, strength, and postural instability. Despite evidence supporting rehabilitation in reducing fall risk factors, the most appropriate intervention to reduce overall fall rate remains inconclusive. This article aims to 1) synthesise current evidence and conceptual models of falls rehabilitation in Parkinson's in a narrative review; and based on this evidence, 2) introduce the treatment protocol used in the falls prevention and multi-centre clinical trial "PDSAFE". Search of four bibliographic databases using the terms "Parkinson*" and "Fall*" combined with each of the following; "Rehab*, Balanc*, Strength*, Strateg*and Exercis*" and a framework for narrative review was followed. A total of 3557 papers were identified, 416 were selected for review. The majority report the impact of rehabilitation on isolated fall risk factors. Twelve directly measure the impact on overall fall rate. Results were used to construct a narrative review with conceptual discussion based on the "International Classification of Functioning", leading to presentation of the "PDSAFE" intervention protocol. Evidence suggests training single, fall risk factors may not affect overall fall rate. Combining with behavioural and strategy training in a functional, personalised multi-dimensional model, addressing all components of the "International Classification of Functioning" is likely to provide a greater influence on falls reduction. "PDSAFE" is a multi-dimensional, physiotherapist delivered, individually tailored, progressive, home-based programme. It is designed with a strong evidence-based approach and illustrates a model for the clinical delivery of the conceptual theory discussed. Implications for Rehabilitation Parkinson's disease demonstrates a spectrum of motor and non-motor symptoms, where falling is common and disabling. Current medical and surgical management have minimal impact on falls, rehabilitation of falls risk factors has strong evidence but the most appropriate intervention to reduce overall fall rate remains inconclusive. Addressing all components of the International Classification of Function in a multifactorial model when designing falls rehabilitation interventions may be more effective at reducing fall rates in people with Parkinson's than treating isolated risk factors. The clinical model for falls rehabilitation in people with Parkinson's should be multi-dimensional.
Generation Algorithm of Discrete Line in Multi-Dimensional Grids

NASA Astrophysics Data System (ADS)

Du, L.; Ben, J.; Li, Y.; Wang, R.

2017-09-01

Discrete Global Grids System (DGGS) is a kind of digital multi-resolution earth reference model, in terms of structure, it is conducive to the geographical spatial big data integration and mining. Vector is one of the important types of spatial data, only by discretization, can it be applied in grids system to make process and analysis. Based on the some constraint conditions, this paper put forward a strict definition of discrete lines, building a mathematic model of the discrete lines by base vectors combination method. Transforming mesh discrete lines issue in n-dimensional grids into the issue of optimal deviated path in n-minus-one dimension using hyperplane, which, therefore realizing dimension reduction process in the expression of mesh discrete lines. On this basis, we designed a simple and efficient algorithm for dimension reduction and generation of the discrete lines. The experimental results show that our algorithm not only can be applied in the two-dimensional rectangular grid, also can be applied in the two-dimensional hexagonal grid and the three-dimensional cubic grid. Meanwhile, when our algorithm is applied in two-dimensional rectangular grid, it can get a discrete line which is more similar to the line in the Euclidean space.
Simulation of Fluid Flow and Collection Efficiency for an SEA Multi-element Probe

NASA Technical Reports Server (NTRS)

Rigby, David L.; Struk, Peter M.; Bidwell, Colin

2014-01-01

Numerical simulations of fluid flow and collection efficiency for a Science Engineering Associates (SEA) multi-element probe are presented. Simulation of the flow field was produced using the Glenn-HT Navier-Stokes solver. Three dimensional unsteady results were produced and then time averaged for the collection efficiency results. Three grid densities were investigated to enable an assessment of grid dependence. Collection efficiencies were generated for three spherical particle sizes, 100, 20, and 5 micron in diameter, using the codes LEWICE3D and LEWICE2D. The free stream Mach number was 0.27, representing a velocity of approximately 86 ms. It was observed that a reduction in velocity of about 15-20 occurred as the flow entered the shroud of the probe.Collection efficiency results indicate a reduction in collection efficiency as particle size is reduced. The reduction with particle size is expected, however, the results tended to be lower than previous results generated for isolated two-dimensional elements. The deviation from the two-dimensional results is more pronounced for the smaller particles and is likely due to the effect of the protective shroud.
Multi-element least square HDMR methods and their applications for stochastic multiscale model reduction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Lijian, E-mail: ljjiang@hnu.edu.cn; Li, Xinping, E-mail: exping@126.com

Stochastic multiscale modeling has become a necessary approach to quantify uncertainty and characterize multiscale phenomena for many practical problems such as flows in stochastic porous media. The numerical treatment of the stochastic multiscale models can be very challengeable as the existence of complex uncertainty and multiple physical scales in the models. To efficiently take care of the difficulty, we construct a computational reduced model. To this end, we propose a multi-element least square high-dimensional model representation (HDMR) method, through which the random domain is adaptively decomposed into a few subdomains, and a local least square HDMR is constructed in eachmore » subdomain. These local HDMRs are represented by a finite number of orthogonal basis functions defined in low-dimensional random spaces. The coefficients in the local HDMRs are determined using least square methods. We paste all the local HDMR approximations together to form a global HDMR approximation. To further reduce computational cost, we present a multi-element reduced least-square HDMR, which improves both efficiency and approximation accuracy in certain conditions. To effectively treat heterogeneity properties and multiscale features in the models, we integrate multiscale finite element methods with multi-element least-square HDMR for stochastic multiscale model reduction. This approach significantly reduces the original model's complexity in both the resolution of the physical space and the high-dimensional stochastic space. We analyze the proposed approach, and provide a set of numerical experiments to demonstrate the performance of the presented model reduction techniques. - Highlights: • Multi-element least square HDMR is proposed to treat stochastic models. • Random domain is adaptively decomposed into some subdomains to obtain adaptive multi-element HDMR. • Least-square reduced HDMR is proposed to enhance computation efficiency and approximation accuracy in certain conditions. • Integrating MsFEM and multi-element least square HDMR can significantly reduce computation complexity.« less
Development of multi-dimensional body image scale for malaysian female adolescents

PubMed Central

Taib, Mohd Nasir Mohd; Shariff, Zalilah Mohd; Khor, Geok Lin

2008-01-01

The present study was conducted to develop a Multi-dimensional Body Image Scale for Malaysian female adolescents. Data were collected among 328 female adolescents from a secondary school in Kuantan district, state of Pahang, Malaysia by using a self-administered questionnaire and anthropometric measurements. The self-administered questionnaire comprised multiple measures of body image, Eating Attitude Test (EAT-26; Garner & Garfinkel, 1979) and Rosenberg Self-esteem Inventory (Rosenberg, 1965). The 152 items from selected multiple measures of body image were examined through factor analysis and for internal consistency. Correlations between Multi-dimensional Body Image Scale and body mass index (BMI), risk of eating disorders and self-esteem were assessed for construct validity. A seven factor model of a 62-item Multi-dimensional Body Image Scale for Malaysian female adolescents with construct validity and good internal consistency was developed. The scale encompasses 1) preoccupation with thinness and dieting behavior, 2) appearance and body satisfaction, 3) body importance, 4) muscle increasing behavior, 5) extreme dieting behavior, 6) appearance importance, and 7) perception of size and shape dimensions. Besides, a multidimensional body image composite score was proposed to screen negative body image risk in female adolescents. The result found body image was correlated with BMI, risk of eating disorders and self-esteem in female adolescents. In short, the present study supports a multi-dimensional concept for body image and provides a new insight into its multi-dimensionality in Malaysian female adolescents with preliminary validity and reliability of the scale. The Multi-dimensional Body Image Scale can be used to identify female adolescents who are potentially at risk of developing body image disturbance through future intervention programs. PMID:20126371

Development of multi-dimensional body image scale for malaysian female adolescents.

PubMed

Chin, Yit Siew; Taib, Mohd Nasir Mohd; Shariff, Zalilah Mohd; Khor, Geok Lin

2008-01-01

The present study was conducted to develop a Multi-dimensional Body Image Scale for Malaysian female adolescents. Data were collected among 328 female adolescents from a secondary school in Kuantan district, state of Pahang, Malaysia by using a self-administered questionnaire and anthropometric measurements. The self-administered questionnaire comprised multiple measures of body image, Eating Attitude Test (EAT-26; Garner & Garfinkel, 1979) and Rosenberg Self-esteem Inventory (Rosenberg, 1965). The 152 items from selected multiple measures of body image were examined through factor analysis and for internal consistency. Correlations between Multi-dimensional Body Image Scale and body mass index (BMI), risk of eating disorders and self-esteem were assessed for construct validity. A seven factor model of a 62-item Multi-dimensional Body Image Scale for Malaysian female adolescents with construct validity and good internal consistency was developed. The scale encompasses 1) preoccupation with thinness and dieting behavior, 2) appearance and body satisfaction, 3) body importance, 4) muscle increasing behavior, 5) extreme dieting behavior, 6) appearance importance, and 7) perception of size and shape dimensions. Besides, a multidimensional body image composite score was proposed to screen negative body image risk in female adolescents. The result found body image was correlated with BMI, risk of eating disorders and self-esteem in female adolescents. In short, the present study supports a multi-dimensional concept for body image and provides a new insight into its multi-dimensionality in Malaysian female adolescents with preliminary validity and reliability of the scale. The Multi-dimensional Body Image Scale can be used to identify female adolescents who are potentially at risk of developing body image disturbance through future intervention programs.
Parallel Lattice Basis Reduction Using a Multi-threaded Schnorr-Euchner LLL Algorithm

NASA Astrophysics Data System (ADS)

Backes, Werner; Wetzel, Susanne

In this paper, we introduce a new parallel variant of the LLL lattice basis reduction algorithm. Our new, multi-threaded algorithm is the first to provide an efficient, parallel implementation of the Schorr-Euchner algorithm for today’s multi-processor, multi-core computer architectures. Experiments with sparse and dense lattice bases show a speed-up factor of about 1.8 for the 2-thread and about factor 3.2 for the 4-thread version of our new parallel lattice basis reduction algorithm in comparison to the traditional non-parallel algorithm.
Sparse representation of multi parametric DCE-MRI features using K-SVD for classifying gene expression based breast cancer recurrence risk

NASA Astrophysics Data System (ADS)

Mahrooghy, Majid; Ashraf, Ahmed B.; Daye, Dania; Mies, Carolyn; Rosen, Mark; Feldman, Michael; Kontos, Despina

2014-03-01

We evaluate the prognostic value of sparse representation-based features by applying the K-SVD algorithm on multiparametric kinetic, textural, and morphologic features in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). K-SVD is an iterative dimensionality reduction method that optimally reduces the initial feature space by updating the dictionary columns jointly with the sparse representation coefficients. Therefore, by using K-SVD, we not only provide sparse representation of the features and condense the information in a few coefficients but also we reduce the dimensionality. The extracted K-SVD features are evaluated by a machine learning algorithm including a logistic regression classifier for the task of classifying high versus low breast cancer recurrence risk as determined by a validated gene expression assay. The features are evaluated using ROC curve analysis and leave one-out cross validation for different sparse representation and dimensionality reduction numbers. Optimal sparse representation is obtained when the number of dictionary elements is 4 (K=4) and maximum non-zero coefficients is 2 (L=2). We compare K-SVD with ANOVA based feature selection for the same prognostic features. The ROC results show that the AUC of the K-SVD based (K=4, L=2), the ANOVA based, and the original features (i.e., no dimensionality reduction) are 0.78, 0.71. and 0.68, respectively. From the results, it can be inferred that by using sparse representation of the originally extracted multi-parametric, high-dimensional data, we can condense the information on a few coefficients with the highest predictive value. In addition, the dimensionality reduction introduced by K-SVD can prevent models from over-fitting.
Meta-modelling, visualization and emulation of multi-dimensional data for virtual production intelligence

NASA Astrophysics Data System (ADS)

Schulz, Wolfgang; Hermanns, Torsten; Al Khawli, Toufik

2017-07-01

Decision making for competitive production in high-wage countries is a daily challenge where rational and irrational methods are used. The design of decision making processes is an intriguing, discipline spanning science. However, there are gaps in understanding the impact of the known mathematical and procedural methods on the usage of rational choice theory. Following Benjamin Franklin's rule for decision making formulated in London 1772, he called "Prudential Algebra" with the meaning of prudential reasons, one of the major ingredients of Meta-Modelling can be identified finally leading to one algebraic value labelling the results (criteria settings) of alternative decisions (parameter settings). This work describes the advances in Meta-Modelling techniques applied to multi-dimensional and multi-criterial optimization by identifying the persistence level of the corresponding Morse-Smale Complex. Implementations for laser cutting and laser drilling are presented, including the generation of fast and frugal Meta-Models with controlled error based on mathematical model reduction Reduced Models are derived to avoid any unnecessary complexity. Both, model reduction and analysis of multi-dimensional parameter space are used to enable interactive communication between Discovery Finders and Invention Makers. Emulators and visualizations of a metamodel are introduced as components of Virtual Production Intelligence making applicable the methods of Scientific Design Thinking and getting the developer as well as the operator more skilled.
Bifactor Approach to Modeling Multidimensionality of Physical Self-Perception Profile

ERIC Educational Resources Information Center

Chung, ChihMing; Liao, Xiaolan; Song, Hairong; Lee, Taehun

2016-01-01

The multi-dimensionality of Physical Self-Perception Profile (PSPP) has been acknowledged by the use of correlated-factor model and second-order model. In this study, the authors critically endorse the bifactor model, as a substitute to address the multi-dimensionality of PSPP. To cross-validate the models, analyses are conducted first in…
Application of diffusion maps to identify human factors of self-reported anomalies in aviation.

PubMed

Andrzejczak, Chris; Karwowski, Waldemar; Mikusinski, Piotr

2012-01-01

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. Diffusion Maps (DM) were selected as the method of choice for performing dimensionality reduction on text records for this study. Diffusion Maps have seen successful use in other domains such as image classification and pattern recognition. High-dimensionality data in the form of narrative text reports from the NASA Aviation Safety Reporting System (ASRS) were clustered and categorized by way of dimensionality reduction. Supervised analyses were performed to create a baseline document clustering system. Dimensionality reduction techniques identified concepts or keywords within records, and allowed the creation of a framework for an unsupervised document classification system. Results from the unsupervised clustering algorithm performed similarly to the supervised methods outlined in the study. The dimensionality reduction was performed on 100 of the most commonly occurring words within 126,000 text records describing commercial aviation incidents. This study demonstrates that unsupervised machine clustering and organization of incident reports is possible based on unbiased inputs. Findings from this study reinforced traditional views on what factors contribute to civil aviation anomalies, however, new associations between previously unrelated factors and conditions were also found.
Quantification of source impact to PM using three-dimensional weighted factor model analysis on multi-site data

NASA Astrophysics Data System (ADS)

Shi, Guoliang; Peng, Xing; Huangfu, Yanqi; Wang, Wei; Xu, Jiao; Tian, Yingze; Feng, Yinchang; Ivey, Cesunica E.; Russell, Armistead G.

2017-07-01

Source apportionment technologies are used to understand the impacts of important sources of particulate matter (PM) air quality, and are widely used for both scientific studies and air quality management. Generally, receptor models apportion speciated PM data from a single sampling site. With the development of large scale monitoring networks, PM speciation are observed at multiple sites in an urban area. For these situations, the models should account for three factors, or dimensions, of the PM, including the chemical species concentrations, sampling periods and sampling site information, suggesting the potential power of a three-dimensional source apportionment approach. However, the principle of three-dimensional Parallel Factor Analysis (Ordinary PARAFAC) model does not always work well in real environmental situations for multi-site receptor datasets. In this work, a new three-way receptor model, called "multi-site three way factor analysis" model is proposed to deal with the multi-site receptor datasets. Synthetic datasets were developed and introduced into the new model to test its performance. Average absolute error (AAE, between estimated and true contributions) for extracted sources were all less than 50%. Additionally, three-dimensional ambient datasets from a Chinese mega-city, Chengdu, were analyzed using this new model to assess the application. Four factors are extracted by the multi-site WFA3 model: secondary source have the highest contributions (64.73 and 56.24 μg/m3), followed by vehicular exhaust (30.13 and 33.60 μg/m3), crustal dust (26.12 and 29.99 μg/m3) and coal combustion (10.73 and 14.83 μg/m3). The model was also compared to PMF, with general agreement, though PMF suggested a lower crustal contribution.
Dimensional Reduction for the General Markov Model on Phylogenetic Trees.

PubMed

Sumner, Jeremy G

2017-03-01

We present a method of dimensional reduction for the general Markov model of sequence evolution on a phylogenetic tree. We show that taking certain linear combinations of the associated random variables (site pattern counts) reduces the dimensionality of the model from exponential in the number of extant taxa, to quadratic in the number of taxa, while retaining the ability to statistically identify phylogenetic divergence events. A key feature is the identification of an invariant subspace which depends only bilinearly on the model parameters, in contrast to the usual multi-linear dependence in the full space. We discuss potential applications including the computation of split (edge) weights on phylogenetic trees from observed sequence data.
Multi-segmental movement patterns reflect juggling complexity and skill level.

PubMed

Zago, Matteo; Pacifici, Ilaria; Lovecchio, Nicola; Galli, Manuela; Federolf, Peter Andreas; Sforza, Chiarella

2017-08-01

The juggling action of six experts and six intermediates jugglers was recorded with a motion capture system and decomposed into its fundamental components through Principal Component Analysis. The aim was to quantify trends in movement dimensionality, multi-segmental patterns and rhythmicity as a function of proficiency level and task complexity. Dimensionality was quantified in terms of Residual Variance, while the Relative Amplitude was introduced to account for individual differences in movement components. We observed that: experience-related modifications in multi-segmental actions exist, such as the progressive reduction of error-correction movements, especially in complex task condition. The systematic identification of motor patterns sensitive to the acquisition of specific experience could accelerate the learning process. Copyright © 2017 Elsevier B.V. All rights reserved.
SPHARA--a generalized spatial Fourier analysis for multi-sensor systems with non-uniformly arranged sensors: application to EEG.

PubMed

Graichen, Uwe; Eichardt, Roland; Fiedler, Patrique; Strohmeier, Daniel; Zanow, Frank; Haueisen, Jens

2015-01-01

Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction. We propose a new approach for spatial harmonic analysis (SPHARA) that extends the classical spatial Fourier analysis to EEG sensors positioned non-uniformly on the surface of the head. The proposed method is based on the eigenanalysis of the discrete Laplace-Beltrami operator defined on a triangular mesh. We present several ways to discretize the continuous Laplace-Beltrami operator and compare the properties of the resulting basis functions computed using these discretization methods. We apply SPHARA to somatosensory evoked potential data from eleven volunteers and demonstrate the ability of the method for spatial data decomposition, dimensionality reduction and noise suppression. When employing SPHARA for dimensionality reduction, a significantly more compact representation can be achieved using the FEM approach, compared to the other discretization methods. Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary. The capability of SPHARA for noise suppression is shown using artificial data. We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications.
Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

PubMed

Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

2017-07-01

Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.
Examining the relationships between resources and online health information seeking among patients with chronic diseases and healthy people.

PubMed

Oh, Young Sam; Cho, Youngmin

2015-01-01

The Internet is increasingly used as an important source of health and medical-related information for people with chronic diseases. It is recognized that online health information seeking (OHIS) is influenced by individuals' multi-dimensional factors, such as demographics, socio-economic factors, perceptions of the Internet, and health conditions. This study applies the conservation of resource theory to examine relationships between various multi-dimensional factors, daily challenges, and OHIS depending on individuals' health conditions. The data used in this study was taken from the U.S. Health Tracking Survey (2012). In this study, Internet users aged 18 and older were classified into patients (N = 518) and healthy people (N = 677) based on their health status related to chronic diseases. Multiple regression analysis was used to examine the relationships between multi-dimensional factors (resources), self-rated health, and OHIS. Patients' various resources (e.g., age, income, education, having a smartphone, and health tracking) significantly predicted their self-rated health and OHIS; in addition, self-rated health significantly mediated the relationships between focal resources and OHIS. However, the mediating effects of self-rated health were not found in healthy people.
Dimensionality reduction based on distance preservation to local mean for symmetric positive definite matrices and its application in brain-computer interfaces

NASA Astrophysics Data System (ADS)

Davoudi, Alireza; Shiry Ghidary, Saeed; Sadatnejad, Khadijeh

2017-06-01

Objective. In this paper, we propose a nonlinear dimensionality reduction algorithm for the manifold of symmetric positive definite (SPD) matrices that considers the geometry of SPD matrices and provides a low-dimensional representation of the manifold with high class discrimination in a supervised or unsupervised manner. Approach. The proposed algorithm tries to preserve the local structure of the data by preserving distances to local means (DPLM) and also provides an implicit projection matrix. DPLM is linear in terms of the number of training samples. Main results. We performed several experiments on the multi-class dataset IIa from BCI competition IV and two other datasets from BCI competition III including datasets IIIa and IVa. The results show that our approach as dimensionality reduction technique—leads to superior results in comparison with other competitors in the related literature because of its robustness against outliers and the way it preserves the local geometry of the data. Significance. The experiments confirm that the combination of DPLM with filter geodesic minimum distance to mean as the classifier leads to superior performance compared with the state of the art on brain-computer interface competition IV dataset IIa. Also the statistical analysis shows that our dimensionality reduction method performs significantly better than its competitors.
Failure analysis of fuel cell electrodes using three-dimensional multi-length scale X-ray computed tomography

NASA Astrophysics Data System (ADS)

Pokhrel, A.; El Hannach, M.; Orfino, F. P.; Dutta, M.; Kjeang, E.

2016-10-01

X-ray computed tomography (XCT), a non-destructive technique, is proposed for three-dimensional, multi-length scale characterization of complex failure modes in fuel cell electrodes. Comparative tomography data sets are acquired for a conditioned beginning of life (BOL) and a degraded end of life (EOL) membrane electrode assembly subjected to cathode degradation by voltage cycling. Micro length scale analysis shows a five-fold increase in crack size and 57% thickness reduction in the EOL cathode catalyst layer, indicating widespread action of carbon corrosion. Complementary nano length scale analysis shows a significant reduction in porosity, increased pore size, and dramatically reduced effective diffusivity within the remaining porous structure of the catalyst layer at EOL. Collapsing of the structure is evident from the combination of thinning and reduced porosity, as uniquely determined by the multi-length scale approach. Additionally, a novel image processing based technique developed for nano scale segregation of pore, ionomer, and Pt/C dominated voxels shows an increase in ionomer volume fraction, Pt/C agglomerates, and severe carbon corrosion at the catalyst layer/membrane interface at EOL. In summary, XCT based multi-length scale analysis enables detailed information needed for comprehensive understanding of the complex failure modes observed in fuel cell electrodes.
A detailed view on Model-Based Multifactor Dimensionality Reduction for detecting gene-gene interactions in case-control data in the absence and presence of noise

PubMed Central

CATTAERT, TOM; CALLE, M. LUZ; DUDEK, SCOTT M.; MAHACHIE JOHN, JESTINAH M.; VAN LISHOUT, FRANÇOIS; URREA, VICTOR; RITCHIE, MARYLYN D.; VAN STEEN, KRISTEL

2010-01-01

SUMMARY Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and non-genetic exposures. Several data mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR), which has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both non-parametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR-analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower-order effects and important confounders, and the difficulty to highlight epistasis effects when too many multi-locus genotype cells are pooled into two new genotype groups. Whereas the true value of MB-MDR can only reveal itself by extensive applications of the method in a variety of real-life scenarios, here we investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. For the considered simulation settings, we show that the power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies. PMID:21158747
SPHARA - A Generalized Spatial Fourier Analysis for Multi-Sensor Systems with Non-Uniformly Arranged Sensors: Application to EEG

PubMed Central

Graichen, Uwe; Eichardt, Roland; Fiedler, Patrique; Strohmeier, Daniel; Zanow, Frank; Haueisen, Jens

2015-01-01

Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction. We propose a new approach for spatial harmonic analysis (SPHARA) that extends the classical spatial Fourier analysis to EEG sensors positioned non-uniformly on the surface of the head. The proposed method is based on the eigenanalysis of the discrete Laplace-Beltrami operator defined on a triangular mesh. We present several ways to discretize the continuous Laplace-Beltrami operator and compare the properties of the resulting basis functions computed using these discretization methods. We apply SPHARA to somatosensory evoked potential data from eleven volunteers and demonstrate the ability of the method for spatial data decomposition, dimensionality reduction and noise suppression. When employing SPHARA for dimensionality reduction, a significantly more compact representation can be achieved using the FEM approach, compared to the other discretization methods. Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary. The capability of SPHARA for noise suppression is shown using artificial data. We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications. PMID:25885290
Mining nutrigenetics patterns related to obesity: use of parallel multifactor dimensionality reduction.

PubMed

Karayianni, Katerina N; Grimaldi, Keith A; Nikita, Konstantina S; Valavanis, Ioannis K

2015-01-01

This paper aims to enlighten the complex etiology beneath obesity by analysing data from a large nutrigenetics study, in which nutritional and genetic factors associated with obesity were recorded for around two thousand individuals. In our previous work, these data have been analysed using artificial neural network methods, which identified optimised subsets of factors to predict one's obesity status. These methods did not reveal though how the selected factors interact with each other in the obtained predictive models. For that reason, parallel Multifactor Dimensionality Reduction (pMDR) was used here to further analyse the pre-selected subsets of nutrigenetic factors. Within pMDR, predictive models using up to eight factors were constructed, further reducing the input dimensionality, while rules describing the interactive effects of the selected factors were derived. In this way, it was possible to identify specific genetic variations and their interactive effects with particular nutritional factors, which are now under further study.
Multi-Level Reduced Order Modeling Equipped with Probabilistic Error Bounds

NASA Astrophysics Data System (ADS)

Abdo, Mohammad Gamal Mohammad Mostafa

This thesis develops robust reduced order modeling (ROM) techniques to achieve the needed efficiency to render feasible the use of high fidelity tools for routine engineering analyses. Markedly different from the state-of-the-art ROM techniques, our work focuses only on techniques which can quantify the credibility of the reduction which can be measured with the reduction errors upper-bounded for the envisaged range of ROM model application. Our objective is two-fold. First, further developments of ROM techniques are proposed when conventional ROM techniques are too taxing to be computationally practical. This is achieved via a multi-level ROM methodology designed to take advantage of the multi-scale modeling strategy typically employed for computationally taxing models such as those associated with the modeling of nuclear reactor behavior. Second, the discrepancies between the original model and ROM model predictions over the full range of model application conditions are upper-bounded in a probabilistic sense with high probability. ROM techniques may be classified into two broad categories: surrogate construction techniques and dimensionality reduction techniques, with the latter being the primary focus of this work. We focus on dimensionality reduction, because it offers a rigorous approach by which reduction errors can be quantified via upper-bounds that are met in a probabilistic sense. Surrogate techniques typically rely on fitting a parametric model form to the original model at a number of training points, with the residual of the fit taken as a measure of the prediction accuracy of the surrogate. This approach, however, does not generally guarantee that the surrogate model predictions at points not included in the training process will be bound by the error estimated from the fitting residual. Dimensionality reduction techniques however employ a different philosophy to render the reduction, wherein randomized snapshots of the model variables, such as the model parameters, responses, or state variables, are projected onto lower dimensional subspaces, referred to as the "active subspaces", which are selected to capture a user-defined portion of the snapshots variations. Once determined, the ROM model application involves constraining the variables to the active subspaces. In doing so, the contribution from the variables discarded components can be estimated using a fundamental theorem from random matrix theory which has its roots in Dixon's theory, developed in 1983. This theory was initially presented for linear matrix operators. The thesis extends this theorem's results to allow reduction of general smooth nonlinear operators. The result is an approach by which the adequacy of a given active subspace determined using a given set of snapshots, generated either using the full high fidelity model, or other models with lower fidelity, can be assessed, which provides insight to the analyst on the type of snapshots required to reach a reduction that can satisfy user-defined preset tolerance limits on the reduction errors. Reactor physics calculations are employed as a test bed for the proposed developments. The focus will be on reducing the effective dimensionality of the various data streams such as the cross-section data and the neutron flux. The developed methods will be applied to representative assembly level calculations, where the size of the cross-section and flux spaces are typically large, as required by downstream core calculations, in order to capture the broad range of conditions expected during reactor operation. (Abstract shortened by ProQuest.).
The Edinburgh Postnatal Depression Scale: Screening Tool for Postpartum Anxiety as Well? Findings from a Confirmatory Factor Analysis of the Hebrew Version.

PubMed

Bina, Rena; Harrington, Donna

2016-04-01

The Edinburgh Postnatal Depression Scale (EPDS) was originally created as a uni-dimensional scale to screen for postpartum depression (PPD); however, evidence from various studies suggests that it is a multi-dimensional scale measuring mainly anxiety in addition to depression. The factor structure of the EPDS seems to differ across various language translations, raising questions regarding its stability. This study examined the factor structure of the Hebrew version of the EPDS to assess whether it is uni- or multi-dimensional. Seven hundred and fifteen (n = 715) women were screened at 6 weeks postpartum using the Hebrew version of the EPDS. Confirmatory factor analysis (CFA) was used to test four models derived from the literature. Of the four CFA models tested, a 9-item two factor model fit the data best, with one factor representing an underlying depression construct and the other representing an underlying anxiety construct. for Practice The Hebrew version of the EPDS appears to consist of depression and anxiety sub-scales. Given the widespread PPD screening initiatives, anxiety symptoms should be addressed in addition to depressive symptoms, and a short scale, such as the EPDS, assessing both may be efficient.
Measuring Developmental Students' Mathematics Anxiety

ERIC Educational Resources Information Center

Ding, Yanqing

2016-01-01

This study conducted an item-level analysis of mathematics anxiety and examined the dimensionality of mathematics anxiety in a sample of developmental mathematics students (N = 162) by Multi-dimensional Random Coefficients Multinominal Logit Model (MRCMLM). The results indicate a moderately correlated factor structure of mathematics anxiety (r =…

A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype

PubMed Central

Lee, Seungyeoun; Kim, Yongkang; Kwon, Min-Seok; Park, Taesung

2015-01-01

Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases. However, there is still a large portion of the genetic variants left unexplained. This missing heritability problem might be due to the analytical strategy that limits analyses to only single SNPs. One of possible approaches to the missing heritability problem is to consider identifying multi-SNP effects or gene-gene interactions. The multifactor dimensionality reduction method has been widely used to detect gene-gene interactions based on the constructive induction by classifying high-dimensional genotype combinations into one-dimensional variable with two attributes of high risk and low risk for the case-control study. Many modifications of MDR have been proposed and also extended to the survival phenotype. In this study, we propose several extensions of MDR for the survival phenotype and compare the proposed extensions with earlier MDR through comprehensive simulation studies. PMID:26339630
Multi-dimensional multi-species modeling of transient electrodeposition in LIGA microfabrication.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Evans, Gregory Herbert; Chen, Ken Shuang

2004-06-01

This report documents the efforts and accomplishments of the LIGA electrodeposition modeling project which was headed by the ASCI Materials and Physics Modeling Program. A multi-dimensional framework based on GOMA was developed for modeling time-dependent diffusion and migration of multiple charged species in a dilute electrolyte solution with reduction electro-chemical reactions on moving deposition surfaces. By combining the species mass conservation equations with the electroneutrality constraint, a Poisson equation that explicitly describes the electrolyte potential was derived. The set of coupled, nonlinear equations governing species transport, electric potential, velocity, hydrodynamic pressure, and mesh motion were solved in GOMA, using themore » finite-element method and a fully-coupled implicit solution scheme via Newton's method. By treating the finite-element mesh as a pseudo solid with an arbitrary Lagrangian-Eulerian formulation and by repeatedly performing re-meshing with CUBIT and re-mapping with MAPVAR, the moving deposition surfaces were tracked explicitly from start of deposition until the trenches were filled with metal, thus enabling the computation of local current densities that potentially influence the microstructure and frictional/mechanical properties of the deposit. The multi-dimensional, multi-species, transient computational framework was demonstrated in case studies of two-dimensional nickel electrodeposition in single and multiple trenches, without and with bath stirring or forced flow. Effects of buoyancy-induced convection on deposition were also investigated. To further illustrate its utility, the framework was employed to simulate deposition in microscreen-based LIGA molds. Lastly, future needs for modeling LIGA electrodeposition are discussed.« less
Fuzzy Regression Prediction and Application Based on Multi-Dimensional Factors of Freight Volume

NASA Astrophysics Data System (ADS)

Xiao, Mengting; Li, Cheng

2018-01-01

Based on the reality of the development of air cargo, the multi-dimensional fuzzy regression method is used to determine the influencing factors, and the three most important influencing factors of GDP, total fixed assets investment and regular flight route mileage are determined. The system’s viewpoints and analogy methods, the use of fuzzy numbers and multiple regression methods to predict the civil aviation cargo volume. In comparison with the 13th Five-Year Plan for China’s Civil Aviation Development (2016-2020), it is proved that this method can effectively improve the accuracy of forecasting and reduce the risk of forecasting. It is proved that this model predicts civil aviation freight volume of the feasibility, has a high practical significance and practical operation.
Multispectral x-ray CT: multivariate statistical analysis for efficient reconstruction

NASA Astrophysics Data System (ADS)

Kheirabadi, Mina; Mustafa, Wail; Lyksborg, Mark; Lund Olsen, Ulrik; Bjorholm Dahl, Anders

2017-10-01

Recent developments in multispectral X-ray detectors allow for an efficient identification of materials based on their chemical composition. This has a range of applications including security inspection, which is our motivation. In this paper, we analyze data from a tomographic setup employing the MultiX detector, that records projection data in 128 energy bins covering the range from 20 to 160 keV. Obtaining all information from this data requires reconstructing 128 tomograms, which is computationally expensive. Instead, we propose to reduce the dimensionality of projection data prior to reconstruction and reconstruct from the reduced data. We analyze three linear methods for dimensionality reduction using a dataset with 37 equally-spaced projection angles. Four bottles with different materials are recorded for which we are able to obtain similar discrimination of their content using a very reduced subset of tomograms compared to the 128 tomograms that would otherwise be needed without dimensionality reduction.
Detection of surface cracking in steel pipes based on vibration data using a multi-class support vector machine classifier

NASA Astrophysics Data System (ADS)

Mustapha, S.; Braytee, A.; Ye, L.

2017-04-01

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.
Detecting Shielded Special Nuclear Materials Using Multi-Dimensional Neutron Source and Detector Geometries

NASA Astrophysics Data System (ADS)

Santarius, John; Navarro, Marcos; Michalak, Matthew; Fancher, Aaron; Kulcinski, Gerald; Bonomo, Richard

2016-10-01

A newly initiated research project will be described that investigates methods for detecting shielded special nuclear materials by combining multi-dimensional neutron sources, forward/adjoint calculations modeling neutron and gamma transport, and sparse data analysis of detector signals. The key tasks for this project are: (1) developing a radiation transport capability for use in optimizing adaptive-geometry, inertial-electrostatic confinement (IEC) neutron source/detector configurations for neutron pulses distributed in space and/or phased in time; (2) creating distributed-geometry, gas-target, IEC fusion neutron sources; (3) applying sparse data and noise reduction algorithms, such as principal component analysis (PCA) and wavelet transform analysis, to enhance detection fidelity; and (4) educating graduate and undergraduate students. Funded by DHS DNDO Project 2015-DN-077-ARI095.
Multi-dimensional photonic states from a quantum dot

NASA Astrophysics Data System (ADS)

Lee, J. P.; Bennett, A. J.; Stevenson, R. M.; Ellis, D. J. P.; Farrer, I.; Ritchie, D. A.; Shields, A. J.

2018-04-01

Quantum states superposed across multiple particles or degrees of freedom offer an advantage in the development of quantum technologies. Creating these states deterministically and with high efficiency is an ongoing challenge. A promising approach is the repeated excitation of multi-level quantum emitters, which have been shown to naturally generate light with quantum statistics. Here we describe how to create one class of higher dimensional quantum state, a so called W-state, which is superposed across multiple time bins. We do this by repeated Raman scattering of photons from a charged quantum dot in a pillar microcavity. We show this method can be scaled to larger dimensions with no reduction in coherence or single-photon character. We explain how to extend this work to enable the deterministic creation of arbitrary time-bin encoded qudits.
Phase reduction approach to synchronisation of nonlinear oscillators

NASA Astrophysics Data System (ADS)

Nakao, Hiroya

2016-04-01

Systems of dynamical elements exhibiting spontaneous rhythms are found in various fields of science and engineering, including physics, chemistry, biology, physiology, and mechanical and electrical engineering. Such dynamical elements are often modelled as nonlinear limit-cycle oscillators. In this article, we briefly review phase reduction theory, which is a simple and powerful method for analysing the synchronisation properties of limit-cycle oscillators exhibiting rhythmic dynamics. Through phase reduction theory, we can systematically simplify the nonlinear multi-dimensional differential equations describing a limit-cycle oscillator to a one-dimensional phase equation, which is much easier to analyse. Classical applications of this theory, i.e. the phase locking of an oscillator to a periodic external forcing and the mutual synchronisation of interacting oscillators, are explained. Further, more recent applications of this theory to the synchronisation of non-interacting oscillators induced by common noise and the dynamics of coupled oscillators on complex networks are discussed. We also comment on some recent advances in phase reduction theory for noise-driven oscillators and rhythmic spatiotemporal patterns.
A computational intelligent approach to multi-factor analysis of violent crime information system

NASA Astrophysics Data System (ADS)

Liu, Hongbo; Yang, Chao; Zhang, Meng; McLoone, Seán; Sun, Yeqing

2017-02-01

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.
Model reduction of nonsquare linear MIMO systems using multipoint matrix continued-fraction expansions

NASA Technical Reports Server (NTRS)

Guo, Tong-Yi; Hwang, Chyi; Shieh, Leang-San

1994-01-01

This paper deals with the multipoint Cauer matrix continued-fraction expansion (MCFE) for model reduction of linear multi-input multi-output (MIMO) systems with various numbers of inputs and outputs. A salient feature of the proposed MCFE approach to model reduction of MIMO systems with square transfer matrices is its equivalence to the matrix Pade approximation approach. The Cauer second form of the ordinary MCFE for a square transfer function matrix is generalized in this paper to a multipoint and nonsquare-matrix version. An interesting connection of the multipoint Cauer MCFE method to the multipoint matrix Pade approximation method is established. Also, algorithms for obtaining the reduced-degree matrix-fraction descriptions and reduced-dimensional state-space models from a transfer function matrix via the multipoint Cauer MCFE algorithm are presented. Practical advantages of using the multipoint Cauer MCFE are discussed and a numerical example is provided to illustrate the algorithms.
Decoupling Principle Analysis and Development of a Parallel Three-Dimensional Force Sensor

PubMed Central

Zhao, Yanzhi; Jiao, Leihao; Weng, Dacheng; Zhang, Dan; Zheng, Rencheng

2016-01-01

In the development of the multi-dimensional force sensor, dimension coupling is the ubiquitous factor restricting the improvement of the measurement accuracy. To effectively reduce the influence of dimension coupling on the parallel multi-dimensional force sensor, a novel parallel three-dimensional force sensor is proposed using a mechanical decoupling principle, and the influence of the friction on dimension coupling is effectively reduced by making the friction rolling instead of sliding friction. In this paper, the mathematical model is established by combining with the structure model of the parallel three-dimensional force sensor, and the modeling and analysis of mechanical decoupling are carried out. The coupling degree (ε) of the designed sensor is defined and calculated, and the calculation results show that the mechanical decoupling parallel structure of the sensor possesses good decoupling performance. A prototype of the parallel three-dimensional force sensor was developed, and FEM analysis was carried out. The load calibration and data acquisition experiment system are built, and then calibration experiments were done. According to the calibration experiments, the measurement accuracy is less than 2.86% and the coupling accuracy is less than 3.02%. The experimental results show that the sensor system possesses high measuring accuracy, which provides a basis for the applied research of the parallel multi-dimensional force sensor. PMID:27649194
Towards a European Framework to Monitor Infectious Diseases among Migrant Populations: Design and Applicability

PubMed Central

Riccardo, Flavia; Dente, Maria Grazia; Kärki, Tommi; Fabiani, Massimo; Napoli, Christian; Chiarenza, Antonio; Giorgi Rossi, Paolo; Velasco Munoz, Cesar; Noori, Teymur; Declich, Silvia

2015-01-01

There are limitations in our capacity to interpret point estimates and trends of infectious diseases occurring among diverse migrant populations living in the European Union/European Economic Area (EU/EEA). The aim of this study was to design a data collection framework that could capture information on factors associated with increased risk to infectious diseases in migrant populations in the EU/EEA. The authors defined factors associated with increased risk according to a multi-dimensional framework and performed a systematic literature review in order to identify whether those factors well reflected the reported risk factors for infectious disease in these populations. Following this, the feasibility of applying this framework to relevant available EU/EEA data sources was assessed. The proposed multidimensional framework is well suited to capture the complexity and concurrence of these risk factors and in principle applicable in the EU/EEA. The authors conclude that adopting a multi-dimensional framework to monitor infectious diseases could favor the disaggregated collection and analysis of migrant health data. PMID:26393623
Towards a European Framework to Monitor Infectious Diseases among Migrant Populations: Design and Applicability.

PubMed

Riccardo, Flavia; Dente, Maria Grazia; Kärki, Tommi; Fabiani, Massimo; Napoli, Christian; Chiarenza, Antonio; Giorgi Rossi, Paolo; Munoz, Cesar Velasco; Noori, Teymur; Declich, Silvia

2015-09-17

There are limitations in our capacity to interpret point estimates and trends of infectious diseases occurring among diverse migrant populations living in the European Union/European Economic Area (EU/EEA). The aim of this study was to design a data collection framework that could capture information on factors associated with increased risk to infectious diseases in migrant populations in the EU/EEA. The authors defined factors associated with increased risk according to a multi-dimensional framework and performed a systematic literature review in order to identify whether those factors well reflected the reported risk factors for infectious disease in these populations. Following this, the feasibility of applying this framework to relevant available EU/EEA data sources was assessed. The proposed multidimensional framework is well suited to capture the complexity and concurrence of these risk factors and in principle applicable in the EU/EEA. The authors conclude that adopting a multi-dimensional framework to monitor infectious diseases could favor the disaggregated collection and analysis of migrant health data.
A Non Local Electron Heat Transport Model for Multi-Dimensional Fluid Codes

NASA Astrophysics Data System (ADS)

Schurtz, Guy

2000-10-01

Apparent inhibition of thermal heat flow is one of the most ancient problems in computational Inertial Fusion and flux-limited Spitzer-Harm conduction has been a mainstay in multi-dimensional hydrodynamic codes for more than 25 years. Theoretical investigation of the problem indicates that heat transport in laser produced plasmas has to be considered as a non local process. Various authors contributed to the non local theory and proposed convolution formulas designed for practical implementation in one-dimensional fluid codes. Though the theory, confirmed by kinetic calculations, actually predicts a reduced heat flux, it fails to explain the very small limiters required in two-dimensional simulations. Fokker-Planck simulations by Epperlein, Rickard and Bell [PRL 61, 2453 (1988)] demonstrated that non local effects could lead to a strong reduction of heat flow in two dimensions, even in situations where a one-dimensional analysis suggests that the heat flow is nearly classical. We developed at CEA/DAM a non local electron heat transport model suitable for implementation in our two-dimensional radiation hydrodynamic code FCI2. This model may be envisionned as the first step of an iterative solution of the Fokker-Planck equations; it takes the mathematical form of multigroup diffusion equations, the solution of which yields both the heat flux and the departure of the electron distribution function to the Maxwellian. Although direct implementation of the model is straightforward, formal solutions of it can be expressed in convolution form, exhibiting a three-dimensional tensor propagator. Reduction to one dimension retrieves the original formula of Luciani, Mora and Virmont [PRL 51, 1664 (1983)]. Intense magnetic fields may be generated by thermal effects in laser targets; these fields, as well as non local effects, will inhibit electron conduction. We present simulations where both effects are taken into account and shortly discuss the coupling strategy between them.
Explanation of Educational and Cultural Dimensions of Globalization in the Views of Ayatollah Javadi-Amoli

ERIC Educational Resources Information Center

Soltaninejad, Najme; Keshtiaray, Narges; Vaezi, Seyed Hossein

2017-01-01

Globalization is a multi-dimensional phenomenon as it leads to high mobility in social, political, economic and value fields and besides reduction of the gap between time and place presents new interpretations of politics, economy, culture, government, authority and security. The present study aimed to explain the educational and cultural…
Defect-Repairable Latent Feature Extraction of Driving Behavior via a Deep Sparse Autoencoder

PubMed Central

Taniguchi, Tadahiro; Takenaka, Kazuhito; Bando, Takashi

2018-01-01

Data representing driving behavior, as measured by various sensors installed in a vehicle, are collected as multi-dimensional sensor time-series data. These data often include redundant information, e.g., both the speed of wheels and the engine speed represent the velocity of the vehicle. Redundant information can be expected to complicate the data analysis, e.g., more factors need to be analyzed; even varying the levels of redundancy can influence the results of the analysis. We assume that the measured multi-dimensional sensor time-series data of driving behavior are generated from low-dimensional data shared by the many types of one-dimensional data of which multi-dimensional time-series data are composed. Meanwhile, sensor time-series data may be defective because of sensor failure. Therefore, another important function is to reduce the negative effect of defective data when extracting low-dimensional time-series data. This study proposes a defect-repairable feature extraction method based on a deep sparse autoencoder (DSAE) to extract low-dimensional time-series data. In the experiments, we show that DSAE provides high-performance latent feature extraction for driving behavior, even for defective sensor time-series data. In addition, we show that the negative effect of defects on the driving behavior segmentation task could be reduced using the latent features extracted by DSAE. PMID:29462931
Spectral factorization of wavefields and wave operators

NASA Astrophysics Data System (ADS)

Rickett, James Edward

Spectral factorization is the problem of finding a minimum-phase function with a given power spectrum. Minimum phase functions have the property that they are causal with a causal (stable) inverse. In this thesis, I factor multidimensional systems into their minimum-phase components. Helical boundary conditions resolve any ambiguities over causality, allowing me to factor multi-dimensional systems with conventional one-dimensional spectral factorization algorithms. In the first part, I factor passive seismic wavefields recorded in two-dimensional spatial arrays. The result provides an estimate of the acoustic impulse response of the medium that has higher bandwidth than autocorrelation-derived estimates. Also, the function's minimum-phase nature mimics the physics of the system better than the zero-phase autocorrelation model. I demonstrate this on helioseismic data recorded by the satellite-based Michelson Doppler Imager (MDI) instrument, and shallow seismic data recorded at Long Beach, California. In the second part of this thesis, I take advantage of the stable-inverse property of minimum-phase functions to solve wave-equation partial differential equations. By factoring multi-dimensional finite-difference stencils into minimum-phase components, I can invert them efficiently, facilitating rapid implicit extrapolation without the azimuthal anisotropy that is observed with splitting approximations. The final part of this thesis describes how to calculate diagonal weighting functions that approximate the combined operation of seismic modeling and migration. These weighting functions capture the effects of irregular subsurface illumination, which can be the result of either the surface-recording geometry, or focusing and defocusing of the seismic wavefield as it propagates through the earth. Since they are diagonal, they can be easily both factored and inverted to compensate for uneven subsurface illumination in migrated images. Experimental results show that applying these weighting functions after migration leads to significantly improved estimates of seismic reflectivity.
Simulation of Fluid Flow and Collection Efficiency for an SEA Multi-element Probe

NASA Technical Reports Server (NTRS)

Rigby, David L.; Struk, Peter M.; Bidwell, Colin

2014-01-01

Numerical simulations of fluid flow and collection efficiency for a Science Engineering Associates (SEA) multi-element probe are presented. Simulation of the flow field was produced using the Glenn-HT Navier-Stokes solver. Three-dimensional unsteady results were produced and then time averaged for the heat transfer and collection efficiency results. Three grid densities were investigated to enable an assessment of grid dependence. Simulations were completed for free stream velocities ranging from 85-135 meters per second, and free stream total pressure of 44.8 and 93.1 kilopascals (6.5 and 13.5 pounds per square inch absolute). In addition, the effect of angle of attack and yaw were investigated by including 5 degree deviations from straight for one of the flow conditions. All but one of the cases simulated a probe in isolation (i.e. in a very large domain without any support strut). One case is included which represents a probe mounted on a support strut within a finite sized wind tunnel. Collection efficiencies were generated, using the LEWICE3D code, for four spherical particle sizes, 100, 50, 20, and 5 micron in diameter. It was observed that a reduction in velocity of about 20% occurred, for all cases, as the flow entered the shroud of the probe. The reduction in velocity within the shroud is not indicative of any error in the probe measurement accuracy. Heat transfer results are presented which agree quite well with a correlation for the circular cross section heated elements. Collection efficiency results indicate a reduction in collection efficiency as particle size is reduced. The reduction with particle size is expected, however, the results tended to be lower than the previous results generated for isolated two-dimensional elements. The deviation from the two-dimensional results is more pronounced for the smaller particles and is likely due to the reduced flow within the protective shroud. As particle size increases differences between the two-dimensional and three dimensional results become negligible. Taken as a group, the total collection efficiency of the elements including the effects of the shroud has been shown to be in the range of 0.93 to 0.99 for particles above 20 microns. The 3D model has improved the estimated collection efficiency for smaller particles where errors in previous estimates were more significant.
Measurement: The Boon and Bane of Investigating Religion.

ERIC Educational Resources Information Center

Gorsuch, Richard L.

1984-01-01

A major problem of research into religion is whether religion is uni- or multi-dimensional; a model maintaining the advantages of both approaches is suggested with general religiousness as a broad construct (higher order factor) that is subdivided into a set of more specific factors. (CMG)
Slaying Hydra: A Python-Based Reduction Pipeline for the Hydra Multi-Object Spectrograph

NASA Astrophysics Data System (ADS)

Seifert, Richard; Mann, Andrew

2018-01-01

We present a Python-based data reduction pipeline for the Hydra Multi-Object Spectrograph on the WIYN 3.5 m telescope, an instrument which enables simultaneous spectroscopy of up to 93 targets. The reduction steps carried out include flat-fielding, dynamic fiber tracing, wavelength calibration, optimal fiber extraction, and sky subtraction. The pipeline also supports the use of sky lines to correct for zero-point offsets between fibers. To account for the moving parts on the instrument and telescope, fiber positions and wavelength solutions are derived in real-time for each dataset. The end result is a one-dimensional spectrum for each target fiber. Quick and fully automated, the pipeline enables on-the-fly reduction while observing, and has been known to outperform the IRAF pipeline by more accurately reproducing known RVs. While Hydra has many configurations in both high- and low-resolution, the pipeline was developed and tested with only one high-resolution mode. In the future we plan to expand the pipeline to work in most commonly used modes.

Seismic Data Analysis throught Multi-Class Classification.

NASA Astrophysics Data System (ADS)

Anderson, P.; Kappedal, R. D.; Magana-Zook, S. A.

2017-12-01

In this research, we conducted twenty experiments of varying time and frequency bands on 5000seismic signals with the intent of finding a method to classify signals as either an explosion or anearthquake in an automated fashion. We used a multi-class approach by clustering of the data throughvarious techniques. Dimensional reduction was examined through the use of wavelet transforms withthe use of the coiflet mother wavelet and various coefficients to explore possible computational time vsaccuracy dependencies. Three and four classes were generated from the clustering techniques andexamined with the three class approach producing the most accurate and realistic results.
The staircase method: integrals for periodic reductions of integrable lattice equations

NASA Astrophysics Data System (ADS)

van der Kamp, Peter H.; Quispel, G. R. W.

2010-11-01

We show, in full generality, that the staircase method (Papageorgiou et al 1990 Phys. Lett. A 147 106-14, Quispel et al 1991 Physica A 173 243-66) provides integrals for mappings, and correspondences, obtained as traveling wave reductions of (systems of) integrable partial difference equations. We apply the staircase method to a variety of equations, including the Korteweg-De Vries equation, the five-point Bruschi-Calogero-Droghei equation, the quotient-difference (QD)-algorithm and the Boussinesq system. We show that, in all these cases, if the staircase method provides r integrals for an n-dimensional mapping, with 2r, then one can introduce q <= 2r variables, which reduce the dimension of the mapping from n to q. These dimension-reducing variables are obtained as joint invariants of k-symmetries of the mappings. Our results support the idea that often the staircase method provides sufficiently many integrals for the periodic reductions of integrable lattice equations to be completely integrable. We also study reductions on other quad-graphs than the regular {\\ Z}^2 lattice, and we prove linear growth of the multi-valuedness of iterates of high-dimensional correspondences obtained as reductions of the QD-algorithm.
Effect of randomness on multi-frequency aeroelastic responses resolved by Unsteady Adaptive Stochastic Finite Elements

NASA Astrophysics Data System (ADS)

Witteveen, Jeroen A. S.; Bijl, Hester

2009-10-01

The Unsteady Adaptive Stochastic Finite Elements (UASFE) method resolves the effect of randomness in numerical simulations of single-mode aeroelastic responses with a constant accuracy in time for a constant number of samples. In this paper, the UASFE framework is extended to multi-frequency responses and continuous structures by employing a wavelet decomposition pre-processing step to decompose the sampled multi-frequency signals into single-frequency components. The effect of the randomness on the multi-frequency response is then obtained by summing the results of the UASFE interpolation at constant phase for the different frequency components. Results for multi-frequency responses and continuous structures show a three orders of magnitude reduction of computational costs compared to crude Monte Carlo simulations in a harmonically forced oscillator, a flutter panel problem, and the three-dimensional transonic AGARD 445.6 wing aeroelastic benchmark subject to random fields and random parameters with various probability distributions.
Predisposing, Precipitating, Perpetuating, Professional Help, and Prevention Factors of Eating Disorders.

ERIC Educational Resources Information Center

Moriarty, Dick; Chanko, Cathy

This report describes an eating disorder as a multi-dimensional physiological, psychological, social, and cultural illness. A chart describing the typical anorexic and bulimic is included which has on its horizontal axis the predisposing, precipitating, perpetuating, professional help, and prevention factors of anorexia nervosa and bulimia. On its…
Modeling of Aerosols in Post-Combustor Flow Path and Sampling System

NASA Technical Reports Server (NTRS)

Wey, Thomas; Liu, Nan-Suey

2006-01-01

The development and application of a multi-dimensional capability for modeling and simulation of aviation-sourced particle emissions and their precursors are elucidated. Current focus is on the role of the flow and thermal environments. The cases investigated include a film cooled turbine blade, the first-stage of a high-pressure turbine, the sampling probes, the sampling lines, and a pressure reduction chamber.
Participatory three dimensional mapping for the preparation of landslide disaster risk reduction program

NASA Astrophysics Data System (ADS)

Kusratmoko, Eko; Wibowo, Adi; Cholid, Sofyan; Pin, Tjiong Giok

2017-07-01

This paper presents the results of applications of participatory three dimensional mapping (P3DM) method for fqcilitating the people of Cibanteng' village to compile a landslide disaster risk reduction program. Physical factors, as high rainfall, topography, geology and land use, and coupled with the condition of demographic and social-economic factors, make up the Cibanteng region highly susceptible to landslides. During the years 2013-2014 has happened 2 times landslides which caused economic losses, as a result of damage to homes and farmland. Participatory mapping is one part of the activities of community-based disaster risk reduction (CBDRR)), because of the involvement of local communities is a prerequisite for sustainable disaster risk reduction. In this activity, participatory mapping method are done in two ways, namely participatory two-dimensional mapping (P2DM) with a focus on mapping of disaster areas and participatory three-dimensional mapping (P3DM) with a focus on the entire territory of the village. Based on the results P3DM, the ability of the communities in understanding the village environment spatially well-tested and honed, so as to facilitate the preparation of the CBDRR programs. Furthermore, the P3DM method can be applied to another disaster areas, due to it becomes a medium of effective dialogue between all levels of involved communities.
The transonic multi-foil Augmentor-Wing

NASA Technical Reports Server (NTRS)

Farbridge, J. E.; Smith, R. C.

1977-01-01

The paper describes the development of a transonic blown multi-foil Augmentor-Wing airfoil section that has a thickness/chord (t/c) value of 0.18. In comparison with an unblown single-foil supercritical section of the same overall t/c the new multi-foil section is characterized by an increased drag rise Mach number, increased buffet boundaries, and a reduction in 'effective' drag due to blowing. Potential advantages of the Augmentor-Wing are considered and the testing of three high-speed models in a trisonic pressurized wind tunnel (possessing a two-dimensional transonic insert) is discussed. The data indicate that a very thick wing is feasible since separations toward the rear of the main foil can be controlled both by shroud location and augmentor blowing.
Accessing Multi-Dimensional Images and Data Cubes in the Virtual Observatory

NASA Astrophysics Data System (ADS)

Tody, Douglas; Plante, R. L.; Berriman, G. B.; Cresitello-Dittmar, M.; Good, J.; Graham, M.; Greene, G.; Hanisch, R. J.; Jenness, T.; Lazio, J.; Norris, P.; Pevunova, O.; Rots, A. H.

2014-01-01

Telescopes across the spectrum are routinely producing multi-dimensional images and datasets, such as Doppler velocity cubes, polarization datasets, and time-resolved “movies.” Examples of current telescopes producing such multi-dimensional images include the JVLA, ALMA, and the IFU instruments on large optical and near-infrared wavelength telescopes. In the near future, both the LSST and JWST will also produce such multi-dimensional images routinely. High-energy instruments such as Chandra produce event datasets that are also a form of multi-dimensional data, in effect being a very sparse multi-dimensional image. Ensuring that the data sets produced by these telescopes can be both discovered and accessed by the community is essential and is part of the mission of the Virtual Observatory (VO). The Virtual Astronomical Observatory (VAO, http://www.usvao.org/), in conjunction with its international partners in the International Virtual Observatory Alliance (IVOA), has developed a protocol and an initial demonstration service designed for the publication, discovery, and access of arbitrarily large multi-dimensional images. The protocol describing multi-dimensional images is the Simple Image Access Protocol, version 2, which provides the minimal set of metadata required to characterize a multi-dimensional image for its discovery and access. A companion Image Data Model formally defines the semantics and structure of multi-dimensional images independently of how they are serialized, while providing capabilities such as support for sparse data that are essential to deal effectively with large cubes. A prototype data access service has been deployed and tested, using a suite of multi-dimensional images from a variety of telescopes. The prototype has demonstrated the capability to discover and remotely access multi-dimensional data via standard VO protocols. The prototype informs the specification of a protocol that will be submitted to the IVOA for approval, with an operational data cube service to be delivered in mid-2014. An associated user-installable VO data service framework will provide the capabilities required to publish VO-compatible multi-dimensional images or data cubes.
Some applications of the multi-dimensional fractional order for the Riemann-Liouville derivative

NASA Astrophysics Data System (ADS)

Ahmood, Wasan Ajeel; Kiliçman, Adem

2017-01-01

In this paper, the aim of this work is to study theorem for the one-dimensional space-time fractional deriative, generalize some function for the one-dimensional fractional by table represents the fractional Laplace transforms of some elementary functions to be valid for the multi-dimensional fractional Laplace transform and give the definition of the multi-dimensional fractional Laplace transform. This study includes that, dedicate the one-dimensional fractional Laplace transform for functions of only one independent variable and develop of the one-dimensional fractional Laplace transform to multi-dimensional fractional Laplace transform based on the modified Riemann-Liouville derivative.
SOMAR-LES: A framework for multi-scale modeling of turbulent stratified oceanic flows

NASA Astrophysics Data System (ADS)

Chalamalla, Vamsi K.; Santilli, Edward; Scotti, Alberto; Jalali, Masoud; Sarkar, Sutanu

2017-12-01

A new multi-scale modeling technique, SOMAR-LES, is presented in this paper. Localized grid refinement gives SOMAR (the Stratified Ocean Model with Adaptive Resolution) access to small scales of the flow which are normally inaccessible to general circulation models (GCMs). SOMAR-LES drives a LES (Large Eddy Simulation) on SOMAR's finest grids, forced with large scale forcing from the coarser grids. Three-dimensional simulations of internal tide generation, propagation and scattering are performed to demonstrate this multi-scale modeling technique. In the case of internal tide generation at a two-dimensional bathymetry, SOMAR-LES is able to balance the baroclinic energy budget and accurately model turbulence losses at only 10% of the computational cost required by a non-adaptive solver running at SOMAR-LES's fine grid resolution. This relative cost is significantly reduced in situations with intermittent turbulence or where the location of the turbulence is not known a priori because SOMAR-LES does not require persistent, global, high resolution. To illustrate this point, we consider a three-dimensional bathymetry with grids adaptively refined along the tidally generated internal waves to capture remote mixing in regions of wave focusing. The computational cost in this case is found to be nearly 25 times smaller than that of a non-adaptive solver at comparable resolution. In the final test case, we consider the scattering of a mode-1 internal wave at an isolated two-dimensional and three-dimensional topography, and we compare the results with Legg (2014) numerical experiments. We find good agreement with theoretical estimates. SOMAR-LES is less dissipative than the closure scheme employed by Legg (2014) near the bathymetry. Depending on the flow configuration and resolution employed, a reduction of more than an order of magnitude in computational costs is expected, relative to traditional existing solvers.
Korean Early Childhood Educators' Multi-Dimensional Teacher Self-Efficacy and ECE Center Climate and Depression Severity in Teachers as Contributing Factors

ERIC Educational Resources Information Center

Kim, Yeon Ha; Kim, Yang Eun

2010-01-01

This study investigated profiles of South Korean early childhood educators' teacher self-efficacy and contributing factors to teacher self-efficacy. The contributing factors were examined with a focus on early childhood education (ECE) center climate and depression severity in teachers as well as teacher and classroom characteristics. The results…
A Multi-Level Investigation into the Antecedents of Enterprise Architecture (EA) Assimilation in the U.S. Federal Government: A Longitudinal Mixed Methods Research Study

ERIC Educational Resources Information Center

Makiya, George K.

2012-01-01

This dissertation reports on a multi-dimensional longitudinal investigation of the factors that influence Enterprise Architecture (EA) diffusion and assimilation within the U.S. federal government. The study uses publicly available datasets of 123 U.S. federal departments and agencies, as well as interview data among CIOs and EA managers within…
Improving diabetes care: Multi-component CArdiovascular Disease Risk Reduction Strategies for People with Diabetes in South Asia - The CARRS Multi-center Translation Trial

PubMed Central

Shah, Seema; Singh, Kavita; Ali, Mohammed K.; Mohan, V.; Kadir, Muhammad Masood; Unnikrishnan, A.G.; Sahay, Rakesh Kumar; Varthakavi, Premlata; Dharmalingam, Mala; Viswanathan, Vijay; Masood, Qamar; Bantwal, Ganapathi; Khadgawat, Rajesh; Desai, Ankush; Sethi, Bipin Kumar; Shivashankar, Roopa; Ajay, Vamadevan S; Reddy, K. Srinath; Narayan, K.M. Venkat; Prabhakaran, Dorairaj; Tandon, Nikhil

2012-01-01

Aims Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in people with diabetes in South Asia. The CARRS translation trial tests the effectiveness, cost-effectiveness, and sustainability of a clinic-based multi-component CVD risk reduction intervention among people with diabetes in India and Pakistan. Methods We randomly assigned 1,146 adults with diabetes recruited from 10 urban clinic sites, to receive usual care by physicians or to receive an integrated multi-component CVD risk reduction intervention. The intervention involves electronic health record management, decision-support prompts to the healthcare team, and the support of a care coordinator to actively facilitate patient and provider adherence to evidence-based guidelines. The primary outcome is a composite of multiple CVD risk factor control (blood glucose and either blood pressure or cholesterol, or all three). Other outcomes include control of the individual CVD risk factors, process and patient-centered measures, cost-effectiveness, and acceptability/feasibility. Conclusion The CARRS translation trial tests a low-cost diabetes care delivery model in urban South Asia to achieve comprehensive cardio-metabolic disease case-management of high-risk patients (clinicaltrials.gov number: NCT01212328). PMID:23084280
The Extraction of One-Dimensional Flow Properties from Multi-Dimensional Data Sets

NASA Technical Reports Server (NTRS)

Baurle, Robert A.; Gaffney, Richard L., Jr.

2007-01-01

The engineering design and analysis of air-breathing propulsion systems relies heavily on zero- or one-dimensional properties (e.g. thrust, total pressure recovery, mixing and combustion efficiency, etc.) for figures of merit. The extraction of these parameters from experimental data sets and/or multi-dimensional computational data sets is therefore an important aspect of the design process. A variety of methods exist for extracting performance measures from multi-dimensional data sets. Some of the information contained in the multi-dimensional flow is inevitably lost when any one-dimensionalization technique is applied. Hence, the unique assumptions associated with a given approach may result in one-dimensional properties that are significantly different than those extracted using alternative approaches. The purpose of this effort is to examine some of the more popular methods used for the extraction of performance measures from multi-dimensional data sets, reveal the strengths and weaknesses of each approach, and highlight various numerical issues that result when mapping data from a multi-dimensional space to a space of one dimension.
The Art of Extracting One-Dimensional Flow Properties from Multi-Dimensional Data Sets

NASA Technical Reports Server (NTRS)

Baurle, R. A.; Gaffney, R. L.

2007-01-01

The engineering design and analysis of air-breathing propulsion systems relies heavily on zero- or one-dimensional properties (e:g: thrust, total pressure recovery, mixing and combustion efficiency, etc.) for figures of merit. The extraction of these parameters from experimental data sets and/or multi-dimensional computational data sets is therefore an important aspect of the design process. A variety of methods exist for extracting performance measures from multi-dimensional data sets. Some of the information contained in the multi-dimensional flow is inevitably lost when any one-dimensionalization technique is applied. Hence, the unique assumptions associated with a given approach may result in one-dimensional properties that are significantly different than those extracted using alternative approaches. The purpose of this effort is to examine some of the more popular methods used for the extraction of performance measures from multi-dimensional data sets, reveal the strengths and weaknesses of each approach, and highlight various numerical issues that result when mapping data from a multi-dimensional space to a space of one dimension.
Laser speckle reduction due to spatial and angular diversity introduced by fast scanning micromirror.

PubMed

Akram, M Nadeem; Tong, Zhaomin; Ouyang, Guangmin; Chen, Xuyuan; Kartashov, Vladimir

2010-06-10

We utilize spatial and angular diversity to achieve speckle reduction in laser illumination. Both free-space and imaging geometry configurations are considered. A fast two-dimensional scanning micromirror is employed to steer the laser beam. A simple experimental setup is built to demonstrate the application of our technique in a two-dimensional laser picture projection. Experimental results show that the speckle contrast factor can be reduced down to 5% within the integration time of the detector.
Leadership Readiness for Flexibility and Mobility: The 4th Dimensions on Situational Leadership Styles in Educational Settings

ERIC Educational Resources Information Center

Rajbhandari, Mani Man Singh; Loock, Coert; Du Plessis, Pierre; Rajbhandari, Smriti

2014-01-01

In educational settings, leadership flexibility and mobility is essential factor for leadership readiness. This incorporates both factors concerning the situational needs and followership situational readiness. Leadership in education require multi facet dimensional approaches that enables the educational leaders to fill in the gaps and reduces…
Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Bloch, B. Nicholas; Chappelow, Jonathan; Patel, Pratik; Rofsky, Neil; Lenkinski, Robert; Genega, Elizabeth; Madabhushi, Anant

2011-03-01

Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of individual protocols, while deriving an integrated multi-parametric data representation which best captures all disease-pertinent information available. In this paper, we present a scheme called Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE); a powerful, generalizable framework applicable to a variety of domains for multi-parametric data representation and fusion. Our scheme utilizes an ensemble of embeddings (via dimensionality reduction, DR); thereby exploiting the variance amongst multiple uncorrelated embeddings in a manner similar to ensemble classifier schemes (e.g. Bagging, Boosting). We apply this framework to the problem of prostate cancer (CaP) detection on 12 3 Tesla pre-operative in vivo multi-parametric (T2-weighted, Dynamic Contrast Enhanced, and Diffusion-weighted) magnetic resonance imaging (MRI) studies, in turn comprising a total of 39 2D planar MR images. We first align the different imaging protocols via automated image registration, followed by quantification of image attributes from individual protocols. Multiple embeddings are generated from the resultant high-dimensional feature space which are then combined intelligently to yield a single stable solution. Our scheme is employed in conjunction with graph embedding (for DR) and probabilistic boosting trees (PBTs) to detect CaP on multi-parametric MRI. Finally, a probabilistic pairwise Markov Random Field algorithm is used to apply spatial constraints to the result of the PBT classifier, yielding a per-voxel classification of CaP presence. Per-voxel evaluation of detection results against ground truth for CaP extent on MRI (obtained by spatially registering pre-operative MRI with available whole-mount histological specimens) reveals that EMPrAvISE yields a statistically significant improvement (AUC=0.77) over classifiers constructed from individual protocols (AUC=0.62, 0.62, 0.65, for T2w, DCE, DWI respectively) as well as one trained using multi-parametric feature concatenation (AUC=0.67).
The use of the bi-factor model to test the uni-dimensionality of a battery of reasoning tests.

PubMed

Primi, Ricardo; Rocha da Silva, Marjorie Cristina; Rodrigues, Priscila; Muniz, Monalisa; Almeida, Leandro S

2013-02-01

The Battery of Reasoning Tests 5 (BPR-5) aims to assess the reasoning ability of individuals, using sub-tests with different formats and contents that require basic processes of inductive and deductive reasoning for their resolution. The BPR has three sequential forms: BPR-5i (for children from first to fifth grade), BPR-5 - Form A (for children from sixth to eighth grade) and BPR-5 - form B (for high school and undergraduate students). The present study analysed 412 questionnaires concerning BPR-5i, 603 questionnaires concerning BPR-5 - Form A and 1748 questionnaires concerning BPR-5 - Form B. The main goal was to test the uni-dimensionality of the battery and its tests in relation to items using the bi-factor model. Results suggest that the g factor loadings (extracted by the uni-dimensional model) do not change when the data is adjusted for a more flexible multi-factor model (bi-factor model). A general reasoning factor underlying different contents items is supported.
Some theorems and properties of multi-dimensional fractional Laplace transforms

NASA Astrophysics Data System (ADS)

Ahmood, Wasan Ajeel; Kiliçman, Adem

2016-06-01

The aim of this work is to study theorems and properties for the one-dimensional fractional Laplace transform, generalize some properties for the one-dimensional fractional Lapalce transform to be valid for the multi-dimensional fractional Lapalce transform and is to give the definition of the multi-dimensional fractional Lapalce transform. This study includes: dedicate the one-dimensional fractional Laplace transform for functions of only one independent variable with some of important theorems and properties and develop of some properties for the one-dimensional fractional Laplace transform to multi-dimensional fractional Laplace transform. Also, we obtain a fractional Laplace inversion theorem after a short survey on fractional analysis based on the modified Riemann-Liouville derivative.

Design and analysis of compound flexible skin based on deformable honeycomb

NASA Astrophysics Data System (ADS)

Zou, Tingting; Zhou, Li

2017-04-01

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.
The positive mental health instrument: development and validation of a culturally relevant scale in a multi-ethnic Asian population.

PubMed

Vaingankar, Janhavi Ajit; Subramaniam, Mythily; Chong, Siow Ann; Abdin, Edimansyah; Orlando Edelen, Maria; Picco, Louisa; Lim, Yee Wei; Phua, Mei Yen; Chua, Boon Yiang; Tee, Joseph Y S; Sherbourne, Cathy

2011-10-31

Instruments to measure mental health and well-being are largely developed and often used within Western populations and this compromises their validity in other cultures. A previous qualitative study in Singapore demonstrated the relevance of spiritual and religious practices to mental health, a dimension currently not included in exiting multi-dimensional measures. The objective of this study was to develop a self-administered measure that covers all key and culturally appropriate domains of mental health, which can be applied to compare levels of mental health across different age, gender and ethnic groups. We present the item reduction and validation of the Positive Mental Health (PMH) instrument in a community-based adult sample in Singapore. Surveys were conducted among adult (21-65 years) residents belonging to Chinese, Malay and Indian ethnicities. Exploratory and confirmatory factor analysis (EFA, CFA) were conducted and items were reduced using item response theory tests (IRT). The final version of the PMH instrument was tested for internal consistency and criterion validity. Items were tested for differential item functioning (DIF) to check if items functioned in the same way across all subgroups. EFA and CFA identified six first-order factor structure (General coping, Personal growth and autonomy, Spirituality, Interpersonal skills, Emotional support, and Global affect) under one higher-order dimension of Positive Mental Health (RMSEA=0.05, CFI=0.96, TLI=0.96). A 47-item self-administered multi-dimensional instrument with a six-point Likert response scale was constructed. The slope estimates and strength of the relation to the theta for all items in each six PMH subscales were high (range:1.39 to 5.69), suggesting good discrimination properties. The threshold estimates for the instrument ranged from -3.45 to 1.61 indicating that the instrument covers entire spectrums for the six dimensions. The instrument demonstrated high internal consistency and had significant and expected correlations with other well-being measures. Results confirmed absence of DIF. The PMH instrument is a reliable and valid instrument that can be used to measure and compare level of mental health across different age, gender and ethnic groups in Singapore.
Verification of Three Dimensional Triangular Prismatic Discrete Ordinates Transport Code ENSEMBLE-TRIZ by Comparison with Monte Carlo Code GMVP

NASA Astrophysics Data System (ADS)

Homma, Yuto; Moriwaki, Hiroyuki; Ohki, Shigeo; Ikeda, Kazumi

2014-06-01

This paper deals with verification of three dimensional triangular prismatic discrete ordinates transport calculation code ENSEMBLE-TRIZ by comparison with multi-group Monte Carlo calculation code GMVP in a large fast breeder reactor. The reactor is a 750 MWe electric power sodium cooled reactor. Nuclear characteristics are calculated at beginning of cycle of an initial core and at beginning and end of cycle of equilibrium core. According to the calculations, the differences between the two methodologies are smaller than 0.0002 Δk in the multi-plication factor, relatively about 1% in the control rod reactivity, and 1% in the sodium void reactivity.
A dynamic nuclear polarization strategy for multi-dimensional Earth's field NMR spectroscopy.

PubMed

Halse, Meghan E; Callaghan, Paul T

2008-12-01

Dynamic nuclear polarization (DNP) is introduced as a powerful tool for polarization enhancement in multi-dimensional Earth's field NMR spectroscopy. Maximum polarization enhancements, relative to thermal equilibrium in the Earth's magnetic field, are calculated theoretically and compared to the more traditional prepolarization approach for NMR sensitivity enhancement at ultra-low fields. Signal enhancement factors on the order of 3000 are demonstrated experimentally using DNP with a nitroxide free radical, TEMPO, which contains an unpaired electron which is strongly coupled to a neighboring (14)N nucleus via the hyperfine interaction. A high-quality 2D (19)F-(1)H COSY spectrum acquired in the Earth's magnetic field with DNP enhancement is presented and compared to simulation.
Inference of multi-Gaussian property fields by probabilistic inversion of crosshole ground penetrating radar data using an improved dimensionality reduction

NASA Astrophysics Data System (ADS)

Hunziker, Jürg; Laloy, Eric; Linde, Niklas

2016-04-01

Deterministic inversion procedures can often explain field data, but they only deliver one final subsurface model that depends on the initial model and regularization constraints. This leads to poor insights about the uncertainties associated with the inferred model properties. In contrast, probabilistic inversions can provide an ensemble of model realizations that accurately span the range of possible models that honor the available calibration data and prior information allowing a quantitative description of model uncertainties. We reconsider the problem of inferring the dielectric permittivity (directly related to radar velocity) structure of the subsurface by inversion of first-arrival travel times from crosshole ground penetrating radar (GPR) measurements. We rely on the DREAM_(ZS) algorithm that is a state-of-the-art Markov chain Monte Carlo (MCMC) algorithm. Such algorithms need several orders of magnitude more forward simulations than deterministic algorithms and often become infeasible in high parameter dimensions. To enable high-resolution imaging with MCMC, we use a recently proposed dimensionality reduction approach that allows reproducing 2D multi-Gaussian fields with far fewer parameters than a classical grid discretization. We consider herein a dimensionality reduction from 5000 to 257 unknowns. The first 250 parameters correspond to a spectral representation of random and uncorrelated spatial fluctuations while the remaining seven geostatistical parameters are (1) the standard deviation of the data error, (2) the mean and (3) the variance of the relative electric permittivity, (4) the integral scale along the major axis of anisotropy, (5) the anisotropy angle, (6) the ratio of the integral scale along the minor axis of anisotropy to the integral scale along the major axis of anisotropy and (7) the shape parameter of the Matérn function. The latter essentially defines the type of covariance function (e.g., exponential, Whittle, Gaussian). We present an improved formulation of the dimensionality reduction, and numerically show how it reduces artifacts in the generated models and provides better posterior estimation of the subsurface geostatistical structure. We next show that the results of the method compare very favorably against previous deterministic and stochastic inversion results obtained at the South Oyster Bacterial Transport Site in Virginia, USA. The long-term goal of this work is to enable MCMC-based full waveform inversion of crosshole GPR data.
Progress in multi-dimensional upwind differencing

NASA Technical Reports Server (NTRS)

Vanleer, Bram

1992-01-01

Multi-dimensional upwind-differencing schemes for the Euler equations are reviewed. On the basis of the first-order upwind scheme for a one-dimensional convection equation, the two approaches to upwind differencing are discussed: the fluctuation approach and the finite-volume approach. The usual extension of the finite-volume method to the multi-dimensional Euler equations is not entirely satisfactory, because the direction of wave propagation is always assumed to be normal to the cell faces. This leads to smearing of shock and shear waves when these are not grid-aligned. Multi-directional methods, in which upwind-biased fluxes are computed in a frame aligned with a dominant wave, overcome this problem, but at the expense of robustness. The same is true for the schemes incorporating a multi-dimensional wave model not based on multi-dimensional data but on an 'educated guess' of what they could be. The fluctuation approach offers the best possibilities for the development of genuinely multi-dimensional upwind schemes. Three building blocks are needed for such schemes: a wave model, a way to achieve conservation, and a compact convection scheme. Recent advances in each of these components are discussed; putting them all together is the present focus of a worldwide research effort. Some numerical results are presented, illustrating the potential of the new multi-dimensional schemes.
2.5D multi-view gait recognition based on point cloud registration.

PubMed

Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan

2014-03-28

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM.
Study on Data Clustering and Intelligent Decision Algorithm of Indoor Localization

NASA Astrophysics Data System (ADS)

Liu, Zexi

2018-01-01

Indoor positioning technology enables the human beings to have the ability of positional perception in architectural space, and there is a shortage of single network coverage and the problem of location data redundancy. So this article puts forward the indoor positioning data clustering algorithm and intelligent decision-making research, design the basic ideas of multi-source indoor positioning technology, analyzes the fingerprint localization algorithm based on distance measurement, position and orientation of inertial device integration. By optimizing the clustering processing of massive indoor location data, the data normalization pretreatment, multi-dimensional controllable clustering center and multi-factor clustering are realized, and the redundancy of locating data is reduced. In addition, the path is proposed based on neural network inference and decision, design the sparse data input layer, the dynamic feedback hidden layer and output layer, low dimensional results improve the intelligent navigation path planning.
DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

PubMed Central

Cowley, Benjamin R.; Kaufman, Matthew T.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

2013-01-01

The activity of tens to hundreds of neurons can be succinctly summarized by a smaller number of latent variables extracted using dimensionality reduction methods. These latent variables define a reduced-dimensional space in which we can study how population activity varies over time, across trials, and across experimental conditions. Ideally, we would like to visualize the population activity directly in the reduced-dimensional space, whose optimal dimensionality (as determined from the data) is typically greater than 3. However, direct plotting can only provide a 2D or 3D view. To address this limitation, we developed a Matlab graphical user interface (GUI) that allows the user to quickly navigate through a continuum of different 2D projections of the reduced-dimensional space. To demonstrate the utility and versatility of this GUI, we applied it to visualize population activity recorded in premotor and motor cortices during reaching tasks. Examples include single-trial population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded sequentially using single electrodes. Because any single 2D projection may provide a misleading impression of the data, being able to see a large number of 2D projections is critical for intuition- and hypothesis-building during exploratory data analysis. The GUI includes a suite of additional interactive tools, including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses. The use of visualization tools like the GUI developed here, in tandem with dimensionality reduction methods, has the potential to further our understanding of neural population activity. PMID:23366954
DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity.

PubMed

Cowley, Benjamin R; Kaufman, Matthew T; Churchland, Mark M; Ryu, Stephen I; Shenoy, Krishna V; Yu, Byron M

2012-01-01

The activity of tens to hundreds of neurons can be succinctly summarized by a smaller number of latent variables extracted using dimensionality reduction methods. These latent variables define a reduced-dimensional space in which we can study how population activity varies over time, across trials, and across experimental conditions. Ideally, we would like to visualize the population activity directly in the reduced-dimensional space, whose optimal dimensionality (as determined from the data) is typically greater than 3. However, direct plotting can only provide a 2D or 3D view. To address this limitation, we developed a Matlab graphical user interface (GUI) that allows the user to quickly navigate through a continuum of different 2D projections of the reduced-dimensional space. To demonstrate the utility and versatility of this GUI, we applied it to visualize population activity recorded in premotor and motor cortices during reaching tasks. Examples include single-trial population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded sequentially using single electrodes. Because any single 2D projection may provide a misleading impression of the data, being able to see a large number of 2D projections is critical for intuition-and hypothesis-building during exploratory data analysis. The GUI includes a suite of additional interactive tools, including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses. The use of visualization tools like the GUI developed here, in tandem with dimensionality reduction methods, has the potential to further our understanding of neural population activity.
Development of a Novel Two Dimensional Surface Plasmon Resonance Sensor Using Multiplied Beam Splitting Optics

PubMed Central

Hemmi, Akihide; Mizumura, Ryosuke; Kawanishi, Ryuta; Nakajima, Hizuru; Zeng, Hulie; Uchiyama, Katsumi; Kaneki, Noriaki; Imato, Toshihiko

2013-01-01

A novel two dimensional surface plasmon resonance (SPR) sensor system with a multi-point sensing region is described. The use of multiplied beam splitting optics, as a core technology, permitted multi-point sensing to be achieved. This system was capable of simultaneously measuring nine sensing points. Calibration curves for sucrose obtained on nine sensing points were linear in the range of 0–10% with a correlation factor of 0.996–0.998 with a relative standard deviation of 0.090–4.0%. The detection limits defined as S/N = 3 were 1.98 × 10−6–3.91 × 10−5 RIU. This sensitivity is comparable to that of conventional SPR sensors. PMID:23299626
Critical Perspective on Situational Leadership Theory. Leadership Readiness for Flexibility and Mobility. The 4th Dimensions on Situational Leadership Styles in Educational Settings

ERIC Educational Resources Information Center

Rajbhandari, Mani Man Singh

2015-01-01

In educational settings, leadership flexibility and mobility is essential factor for leadership readiness. This incorporates both factors concerning the situational needs and followership situational readiness. Leadership in education require multi facet dimensional approaches that enables the educational leaders to fill in the gaps and reduces…
Investigating Factors That Contribute to Effective Teaching-Learning Practices: EFL/ESL Classroom Context

ERIC Educational Resources Information Center

Islam, Rukaia

2017-01-01

This paper seeks to address some key issues, which can influence as well as determine the nature of teaching and learning practices in an ELT classroom directly or indirectly. This paper views an EFL or ESL classroom as a dynamic and multi-dimensional platform open to different interpretations of teaching and learning. Factors like teachers'…
Identifying county characteristics associated with resident well-being: A population based study.

PubMed

Roy, Brita; Riley, Carley; Herrin, Jeph; Spatz, Erica S; Arora, Anita; Kell, Kenneth P; Welsh, John; Rula, Elizabeth Y; Krumholz, Harlan M

2018-01-01

Well-being is a positively-framed, holistic assessment of health and quality of life that is associated with longevity and better health outcomes. We aimed to identify county attributes that are independently associated with a comprehensive, multi-dimensional assessment of individual well-being. We performed a cross-sectional study examining associations between 77 pre-specified county attributes and a multi-dimensional assessment of individual US residents' well-being, captured by the Gallup-Sharecare Well-Being Index. Our cohort included 338,846 survey participants, randomly sampled from 3,118 US counties or county equivalents. We identified twelve county-level factors that were independently associated with individual well-being scores. Together, these twelve factors explained 91% of the variance in individual well-being scores, and they represent four conceptually distinct categories: demographic (% black); social and economic (child poverty, education level [
A three-dimensional non-isothermal model for a membraneless direct methanol redox fuel cell

NASA Astrophysics Data System (ADS)

Wei, Lin; Yuan, Xianxia; Jiang, Fangming

2018-05-01

In the membraneless direct methanol redox fuel cell (DMRFC), three-dimensional electrodes contribute to the reduction of methanol crossover and the open separator design lowers the system cost and extends its service life. In order to better understand the mechanisms of this configuration and further optimize its performance, the development of a three-dimensional numerical model is reported in this work. The governing equations of the multi-physics field are solved based on computational fluid dynamics methodology, and the influence of the CO2 gas is taken into consideration through the effective diffusivities. The numerical results are in good agreement with experimental data, and the deviation observed for cases of large current density may be related to the single-phase assumption made. The three-dimensional electrode is found to be effective in controlling methanol crossover in its multi-layer structure, while it also increases the flow resistance for the discharging products. It is found that the current density distribution is affected by both the electronic conductivity and the concentration of reactants, and the temperature rise can be primarily attributed to the current density distribution. The sensitivity and reliability of the model are analyzed through the investigation of the effects of cell parameters, including porosity values of gas diffusion layers and catalyst layers, methanol concentration and CO2 volume fraction, on the polarization characteristics.
2.5D Multi-View Gait Recognition Based on Point Cloud Registration

PubMed Central

Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan

2014-01-01

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM. PMID:24686727
A non-linear optimization programming model for air quality planning including co-benefits for GHG emissions.

PubMed

Turrini, Enrico; Carnevale, Claudio; Finzi, Giovanna; Volta, Marialuisa

2018-04-15

This paper introduces the MAQ (Multi-dimensional Air Quality) model aimed at defining cost-effective air quality plans at different scales (urban to national) and assessing the co-benefits for GHG emissions. The model implements and solves a non-linear multi-objective, multi-pollutant decision problem where the decision variables are the application levels of emission abatement measures allowing the reduction of energy consumption, end-of pipe technologies and fuel switch options. The objectives of the decision problem are the minimization of tropospheric secondary pollution exposure and of internal costs. The model assesses CO 2 equivalent emissions in order to support decision makers in the selection of win-win policies. The methodology is tested on Lombardy region, a heavily polluted area in northern Italy. Copyright © 2017 Elsevier B.V. All rights reserved.
Holocinematographic velocimeter for measuring time-dependent, three-dimensional flows

NASA Technical Reports Server (NTRS)

Beeler, George B.; Weinstein, Leonard M.

1987-01-01

Two simulatneous, orthogonal-axis holographic movies are made of tracer particles in a low-speed water tunnel to determine the time-dependent, three-dimensional velocity field. This instrument is called a Holocinematographic Velocimeter (HCV). The holographic movies are reduced to the velocity field with an automatic data reduction system. This permits the reduction of large numbers of holograms (time steps) in a reasonable amount of time. The current version of the HCV, built for proof-of-concept tests, uses low-frame rate holographic cameras and a prototype of a new type of water tunnel. This water tunnel is a unique low-disturbance facility which has minimal wall effects on the flow. This paper presents the first flow field examined by the HCV, the two-dimensional von Karman vortex street downstream of an unswept circular cylinder. Key factors in the HCV are flow speed, spatial and temporal resolution required, measurement volume, film transport speed, and laser pulse length. The interactions between these factors are discussed.
Mechanical exfoliation of two-dimensional materials

NASA Astrophysics Data System (ADS)

Gao, Enlai; Lin, Shao-Zhen; Qin, Zhao; Buehler, Markus J.; Feng, Xi-Qiao; Xu, Zhiping

2018-06-01

Two-dimensional materials such as graphene and transition metal dichalcogenides have been identified and drawn much attention over the last few years for their unique structural and electronic properties. However, their rise begins only after these materials are successfully isolated from their layered assemblies or adhesive substrates into individual monolayers. Mechanical exfoliation and transfer are the most successful techniques to obtain high-quality single- or few-layer nanocrystals from their native multi-layer structures or their substrate for growth, which involves interfacial peeling and intralayer tearing processes that are controlled by material properties, geometry and the kinetics of exfoliation. This procedure is rationalized in this work through theoretical analysis and atomistic simulations. We propose a criterion to assess the feasibility for the exfoliation of two-dimensional sheets from an adhesive substrate without fracturing itself, and explore the effects of material and interface properties, as well as the geometrical, kinetic factors on the peeling behaviors and the torn morphology. This multi-scale approach elucidates the microscopic mechanism of the mechanical processes, offering predictive models and tools for the design of experimental procedures to obtain single- or few-layer two-dimensional materials and structures.
Performance analysis of three-dimensional-triple-level cell and two-dimensional-multi-level cell NAND flash hybrid solid-state drives

NASA Astrophysics Data System (ADS)

Sakaki, Yukiya; Yamada, Tomoaki; Matsui, Chihiro; Yamaga, Yusuke; Takeuchi, Ken

2018-04-01

In order to improve performance of solid-state drives (SSDs), hybrid SSDs have been proposed. Hybrid SSDs consist of more than two types of NAND flash memories or NAND flash memories and storage-class memories (SCMs). However, the cost of hybrid SSDs adopting SCMs is more expensive than that of NAND flash only SSDs because of the high bit cost of SCMs. This paper proposes unique hybrid SSDs with two-dimensional (2D) horizontal multi-level cell (MLC)/three-dimensional (3D) vertical triple-level cell (TLC) NAND flash memories to achieve higher cost-performance. The 2D-MLC/3D-TLC hybrid SSD achieves up to 31% higher performance than the conventional 2D-MLC/2D-TLC hybrid SSD. The factors of different performance between the proposed hybrid SSD and the conventional hybrid SSD are analyzed by changing its block size, read/write/erase latencies, and write unit of 3D-TLC NAND flash memory, by means of a transaction-level modeling simulator.

Large Reduction of Hot Spot Temperature in Graphene Electronic Devices with Heat-Spreading Hexagonal Boron Nitride.

PubMed

Choi, David; Poudel, Nirakar; Park, Saungeun; Akinwande, Deji; Cronin, Stephen B; Watanabe, Kenji; Taniguchi, Takashi; Yao, Zhen; Shi, Li

2018-04-04

Scanning thermal microscopy measurements reveal a significant thermal benefit of including a high thermal conductivity hexagonal boron nitride (h-BN) heat-spreading layer between graphene and either a SiO 2 /Si substrate or a 100 μm thick Corning flexible Willow glass (WG) substrate. At the same power density, an 80 nm thick h-BN layer on the silicon substrate can yield a factor of 2.2 reduction of the hot spot temperature, whereas a 35 nm thick h-BN layer on the WG substrate is sufficient to obtain a factor of 4.1 reduction. The larger effect of the h-BN heat spreader on WG than on SiO 2 /Si is attributed to a smaller effective heat transfer coefficient per unit area for three-dimensional heat conduction into the thick, low-thermal conductivity WG substrate than for one-dimensional heat conduction through the thin oxide layer on silicon. Consequently, the h-BN lateral heat-spreading length is much larger on WG than on SiO 2 /Si, resulting in a larger degree of temperature reduction.
A chaotic modified-DFT encryption scheme for physical layer security and PAPR reduction in OFDM-PON

NASA Astrophysics Data System (ADS)

Fu, Xiaosong; Bi, Meihua; Zhou, Xuefang; Yang, Guowei; Li, Qiliang; Zhou, Zhao; Yang, Xuelin

2018-05-01

This letter proposes a modified discrete Fourier transform (DFT) encryption scheme with multi-dimensional chaos for the physical layer security and peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing passive optical network (OFDM-PON) system. This multiple-fold encryption algorithm is mainly composed by using the column vectors permutation and the random phase encryption in the standard DFT matrix, which can create ∼10551 key space. The transmission of ∼10 Gb/s encrypted OFDM signal is verified over 20-km standard single mode fiber (SMF). Moreover, experimental results show that, the proposed scheme can achieve ∼2.6-dB PAPR reduction and ∼1-dB improvement of receiver sensitivity if compared with the common OFDM-PON.
Reduction of respiratory ghosting motion artifacts in conventional two-dimensional multi-slice Cartesian turbo spin-echo: which k-space filling order is the best?

PubMed

Inoue, Yuuji; Yoneyama, Masami; Nakamura, Masanobu; Takemura, Atsushi

2018-06-01

The two-dimensional Cartesian turbo spin-echo (TSE) sequence is widely used in routine clinical studies, but it is sensitive to respiratory motion. We investigated the k-space orders in Cartesian TSE that can effectively reduce motion artifacts. The purpose of this study was to demonstrate the relationship between k-space order and degree of motion artifacts using a moving phantom. We compared the degree of motion artifacts between linear and asymmetric k-space orders. The actual spacing of ghost artifacts in the asymmetric order was doubled compared with that in the linear order in the free-breathing situation. The asymmetric order clearly showed less sensitivity to incomplete breath-hold at the latter half of the imaging period. Because of the actual number of partitions of the k-space and the temporal filling order, the asymmetric k-space order of Cartesian TSE was superior to the linear k-space order for reduction of ghosting motion artifacts.
Diagnosis and treatment of unconsummated marriage in an Iranian couple.

PubMed

Bokaie, Mahshid; Khalesi, Zahra Bostani; Yasini-Ardekani, Seyed Mojtaba

2017-09-01

Unconsummated marriage is a problem among couples who would not be able to perform natural sexual intercourse and vaginal penetration. This disorder is more common in developing countries and sometimes couples would come up with non-technical and non-scientific methods to overcome their problem. Multi-dimensional approach and narrative exposure therapy used in this case. This study would report a case of unconsummated marriage between a couple after 6 years. The main problem of this couple was vaginismus and post-traumatic stress. Treatment with multi-dimensional approach for this couple included methods like narrative exposure therapy, educating the anatomy of female and male reproductive system, correcting misconceptions, educating foreplay, educating body exploring and non-sexual and sexual massage and penetrating the vagina first by women finger and then men's after relaxation. The entire stages of the treatment lasted for four sessions and at the one-month follow-up couple's satisfaction was desirable. Unconsummated marriage is one of the main sexual problems; it is more common in developing countries than developed countries and cultural factors are effective on intensifying this disorder. The use of multi-dimensional approach in this study led to expedite diagnosis and treatment of vaginismus.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, P.; Pai, Woei Wu; Chan, Y. -H.

Two-dimensional materials constitute a promising platform for developing nanoscale devices and systems. Their physical properties can be very different from those of the corresponding three-dimensional materials because of extreme quantum confinement and dimensional reduction. Here in this paper we report a study of TiTe 2 from the single-layer to the bulk limit. Using angle-resolved photoemission spectroscopy and scanning tunneling microscopy and spectroscopy, we observed the emergence of a (2 × 2) charge density wave order in single-layer TiTe 2 with a transition temperature of 92 ± 3 K. Also observed was a pseudogap of about 28 meV at the Fermimore » level at 4.2 K. Surprisingly, no charge density wave transitions were observed in two-layer and multi-layer TiTe 2 , despite the quasi-two-dimensional nature of the material in the bulk. The unique charge density wave phenomenon in the single layer raises intriguing questions that challenge the prevailing thinking about the mechanisms of charge density wave formation.« less
Addition of multiple limiting resources reduces grassland diversity.

PubMed

Harpole, W Stanley; Sullivan, Lauren L; Lind, Eric M; Firn, Jennifer; Adler, Peter B; Borer, Elizabeth T; Chase, Jonathan; Fay, Philip A; Hautier, Yann; Hillebrand, Helmut; MacDougall, Andrew S; Seabloom, Eric W; Williams, Ryan; Bakker, Jonathan D; Cadotte, Marc W; Chaneton, Enrique J; Chu, Chengjin; Cleland, Elsa E; D'Antonio, Carla; Davies, Kendi F; Gruner, Daniel S; Hagenah, Nicole; Kirkman, Kevin; Knops, Johannes M H; La Pierre, Kimberly J; McCulley, Rebecca L; Moore, Joslin L; Morgan, John W; Prober, Suzanne M; Risch, Anita C; Schuetz, Martin; Stevens, Carly J; Wragg, Peter D

2016-09-01

Niche dimensionality provides a general theoretical explanation for biodiversity-more niches, defined by more limiting factors, allow for more ways that species can coexist. Because plant species compete for the same set of limiting resources, theory predicts that addition of a limiting resource eliminates potential trade-offs, reducing the number of species that can coexist. Multiple nutrient limitation of plant production is common and therefore fertilization may reduce diversity by reducing the number or dimensionality of belowground limiting factors. At the same time, nutrient addition, by increasing biomass, should ultimately shift competition from belowground nutrients towards a one-dimensional competitive trade-off for light. Here we show that plant species diversity decreased when a greater number of limiting nutrients were added across 45 grassland sites from a multi-continent experimental network. The number of added nutrients predicted diversity loss, even after controlling for effects of plant biomass, and even where biomass production was not nutrient-limited. We found that elevated resource supply reduced niche dimensionality and diversity and increased both productivity and compositional turnover. Our results point to the importance of understanding dimensionality in ecological systems that are undergoing diversity loss in response to multiple global change factors.
GENERAL: Scattering Phase Correction for Semiclassical Quantization Rules in Multi-Dimensional Quantum Systems

NASA Astrophysics Data System (ADS)

Huang, Wen-Min; Mou, Chung-Yu; Chang, Cheng-Hung

2010-02-01

While the scattering phase for several one-dimensional potentials can be exactly derived, less is known in multi-dimensional quantum systems. This work provides a method to extend the one-dimensional phase knowledge to multi-dimensional quantization rules. The extension is illustrated in the example of Bogomolny's transfer operator method applied in two quantum wells bounded by step potentials of different heights. This generalized semiclassical method accurately determines the energy spectrum of the systems, which indicates the substantial role of the proposed phase correction. Theoretically, the result can be extended to other semiclassical methods, such as Gutzwiller trace formula, dynamical zeta functions, and semiclassical Landauer-Büttiker formula. In practice, this recipe enhances the applicability of semiclassical methods to multi-dimensional quantum systems bounded by general soft potentials.
A New Time-varying Concept of Risk in a Changing Climate.

PubMed

Sarhadi, Ali; Ausín, María Concepción; Wiper, Michael P

2016-10-20

In a changing climate arising from anthropogenic global warming, the nature of extreme climatic events is changing over time. Existing analytical stationary-based risk methods, however, assume multi-dimensional extreme climate phenomena will not significantly vary over time. To strengthen the reliability of infrastructure designs and the management of water systems in the changing environment, multidimensional stationary risk studies should be replaced with a new adaptive perspective. The results of a comparison indicate that current multi-dimensional stationary risk frameworks are no longer applicable to projecting the changing behaviour of multi-dimensional extreme climate processes. Using static stationary-based multivariate risk methods may lead to undesirable consequences in designing water system infrastructures. The static stationary concept should be replaced with a flexible multi-dimensional time-varying risk framework. The present study introduces a new multi-dimensional time-varying risk concept to be incorporated in updating infrastructure design strategies under changing environments arising from human-induced climate change. The proposed generalized time-varying risk concept can be applied for all stochastic multi-dimensional systems that are under the influence of changing environments.
Central Schemes for Multi-Dimensional Hamilton-Jacobi Equations

NASA Technical Reports Server (NTRS)

Bryson, Steve; Levy, Doron; Biegel, Bryan (Technical Monitor)

2002-01-01

We present new, efficient central schemes for multi-dimensional Hamilton-Jacobi equations. These non-oscillatory, non-staggered schemes are first- and second-order accurate and are designed to scale well with an increasing dimension. Efficiency is obtained by carefully choosing the location of the evolution points and by using a one-dimensional projection step. First-and second-order accuracy is verified for a variety of multi-dimensional, convex and non-convex problems.
Neural Network Machine Learning and Dimension Reduction for Data Visualization

NASA Technical Reports Server (NTRS)

Liles, Charles A.

2014-01-01

Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.
Belief in a Just What? Demystifying Just World Beliefs by Distinguishing Sources of Justice

PubMed Central

Stroebe, Katherine; Postmes, Tom; Täuber, Susanne; Stegeman, Alwin; John, Melissa-Sue

2015-01-01

People’s Belief in a Just World (BJW) plays an important role in coping with misfortune and unfairness. This paper demonstrates that understanding of the BJW concept, and its consequences for behavior, is enhanced if we specify what (or who) the source of justice might be. We introduce a new scale, the 5-Dimensional Belief in a Just Treatment Scale (BJT5), which distinguishes five causal dimensions of BJW (God, Nature, Other People, Self, Chance). We confirm the 5-factor structure of the BJT5. We then address whether the BJW should be considered a uni- and/or multi-dimensional construct and find support for our multi-dimensional approach. Finally, we demonstrate convergent and discriminant validity with respect to important correlates of BJW as well as action in response to important negative life events and societal attitudes. This work illustrates the importance of distinguishing causal dimensions with regard to who distributes justice. PMID:25803025
Research on the parallel load sharing principle of a novel self-decoupled piezoelectric six-dimensional force sensor.

PubMed

Li, Ying-Jun; Yang, Cong; Wang, Gui-Cong; Zhang, Hui; Cui, Huan-Yong; Zhang, Yong-Liang

2017-09-01

This paper presents a novel integrated piezoelectric six-dimensional force sensor which can realize dynamic measurement of multi-dimensional space load. Firstly, the composition of the sensor, the spatial layout of force-sensitive components, and measurement principle are analyzed and designed. There is no interference of piezoelectric six-dimensional force sensor in theoretical analysis. Based on the principle of actual work and deformation compatibility coherence, this paper deduces the parallel load sharing principle of the piezoelectric six-dimensional force sensor. The main effect factors which affect the load sharing ratio are obtained. The finite element model of the piezoelectric six-dimensional force sensor is established. In order to verify the load sharing principle of the sensor, a load sharing test device of piezoelectric force sensor is designed and fabricated. The load sharing experimental platform is set up. The experimental results are in accordance with the theoretical analysis and simulation results. The experiments show that the multi-dimensional and heavy force measurement can be realized by the parallel arrangement of the load sharing ring and the force sensitive element in the novel integrated piezoelectric six-dimensional force sensor. The ideal load sharing effect of the sensor can be achieved by appropriate size parameters. This paper has an important guide for the design of the force measuring device according to the load sharing mode. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Multi-stack InAs/InGaAs Sub-monolayer Quantum Dots Infrared Photodetectors

DTIC Science & Technology

2013-01-01

013110 (2013) Demonstration of high performance bias-selectable dual- band short-/mid-wavelength infrared photodetectors based on type-II InAs/ GaSb ...been used for the growth of QD structures . These include the formation of self-assembled QD, for example, Stranski-Krastanov (SK) growth mode,8,9 atomic...confinement in SML-QD and the reduction in the amount of InAs used per layer of QD can help stack more layers in a 3-dimensional QD structure . Several
Optimism and well-being: a prospective multi-method and multi-dimensional examination of optimism as a resilience factor following the occurrence of stressful life events.

PubMed

Kleiman, Evan M; Chiara, Alexandra M; Liu, Richard T; Jager-Hyman, Shari G; Choi, Jimmy Y; Alloy, Lauren B

2017-02-01

Optimism has been conceptualised variously as positive expectations (PE) for the future , optimistic attributions , illusion of control , and self-enhancing biases. Relatively little research has examined these multiple dimensions of optimism in relation to psychological and physical health. The current study assessed the multi-dimensional nature of optimism within a prospective vulnerability-stress framework. Initial principal component analyses revealed the following dimensions: PEs, Inferential Style (IS), Sense of Invulnerability (SI), and Overconfidence (O). Prospective follow-up analyses demonstrated that PE was associated with fewer depressive episodes and moderated the effect of stressful life events on depressive symptoms. SI also moderated the effect of life stress on anxiety symptoms. Generally, our findings indicated that optimism is a multifaceted construct and not all forms of optimism have the same effects on well-being. Specifically, our findings indicted that PE may be the most relevant to depression, whereas SI may be the most relevant to anxiety.
Image matrix processor for fast multi-dimensional computations

DOEpatents

Roberson, George P.; Skeate, Michael F.

1996-01-01

An apparatus for multi-dimensional computation which comprises a computation engine, including a plurality of processing modules. The processing modules are configured in parallel and compute respective contributions to a computed multi-dimensional image of respective two dimensional data sets. A high-speed, parallel access storage system is provided which stores the multi-dimensional data sets, and a switching circuit routes the data among the processing modules in the computation engine and the storage system. A data acquisition port receives the two dimensional data sets representing projections through an image, for reconstruction algorithms such as encountered in computerized tomography. The processing modules include a programmable local host, by which they may be configured to execute a plurality of different types of multi-dimensional algorithms. The processing modules thus include an image manipulation processor, which includes a source cache, a target cache, a coefficient table, and control software for executing image transformation routines using data in the source cache and the coefficient table and loading resulting data in the target cache. The local host processor operates to load the source cache with a two dimensional data set, loads the coefficient table, and transfers resulting data out of the target cache to the storage system, or to another destination.
Multi-dimensional water quality assessment of an urban drinking water source elucidated by high resolution underwater towed vehicle mapping.

PubMed

Lock, Alan; Spiers, Graeme; Hostetler, Blair; Ray, James; Wallschläger, Dirk

2016-04-15

Spatial surveys of Ramsey Lake, Sudbury, Ontario water quality were conducted using an innovative underwater towed vehicle (UTV) equipped with a multi-parameter probe providing real-time water quality data. The UTV revealed underwater vent sites through high resolution monitoring of different spatial chemical characteristics using common sensors (turbidity, chloride, dissolved oxygen, and oxidation/reduction sensors) that would not be feasible with traditional water sampling methods. Multi-parameter probe vent site identification is supported by elevated alkalinity and silica concentrations at these sites. The identified groundwater vent sites appear to be controlled by bedrock fractures that transport water from different sources with different contaminants of concern. Elevated contaminants, such as, arsenic and nickel and/or nutrient concentrations are evident at the vent sites, illustrating the potential of these sources to degrade water quality. Copyright © 2016 Elsevier Ltd. All rights reserved.
Confirmatory Factor Analysis of the Hewitt-Multidimensional Perfectionism Scale

ERIC Educational Resources Information Center

Barut, Yasar

2015-01-01

Various studies on the conceptual framework of perfectionism construct use Hewitt Multi-dimensional Perfectionism Scale (HMPS), as a basic approach. The measure has a prominent role with respect to the theoretical considerations of perfectionism dimensions. This study aimed to evaluate the psychometric properties of the Turkish version of the…
A scale-based approach to interdisciplinary research and expertise in sports.

PubMed

Ibáñez-Gijón, Jorge; Buekers, Martinus; Morice, Antoine; Rao, Guillaume; Mascret, Nicolas; Laurin, Jérome; Montagne, Gilles

2017-02-01

After more than 20 years since the introduction of ecological and dynamical approaches in sports research, their promising opportunity for interdisciplinary research has not been fulfilled yet. The complexity of the research process and the theoretical and empirical difficulties associated with an integrated ecological-dynamical approach have been the major factors hindering the generalisation of interdisciplinary projects in sports sciences. To facilitate this generalisation, we integrate the major concepts from the ecological and dynamical approaches to study behaviour as a multi-scale process. Our integration gravitates around the distinction between functional (ecological) and execution (organic) scales, and their reciprocal intra- and inter-scale constraints. We propose an (epistemological) scale-based definition of constraints that accounts for the concept of synergies as emergent coordinative structures. To illustrate how we can operationalise the notion of multi-scale synergies we use an interdisciplinary model of locomotor pointing. To conclude, we show the value of this approach for interdisciplinary research in sport sciences, as we discuss two examples of task-specific dimensionality reduction techniques in the context of an ongoing project that aims to unveil the determinants of expertise in basketball free throw shooting. These techniques provide relevant empirical evidence to help bootstrap the challenging modelling efforts required in sport sciences.
Multi-dimensional scores to predict mortality in patients with idiopathic pulmonary fibrosis undergoing lung transplantation assessment.

PubMed

Fisher, Jolene H; Al-Hejaili, Faris; Kandel, Sonja; Hirji, Alim; Shapera, Shane; Mura, Marco

2017-04-01

The heterogeneous progression of idiopathic pulmonary fibrosis (IPF) makes prognostication difficult and contributes to high mortality on the waitlist for lung transplantation (LTx). Multi-dimensional scores (Composite Physiologic index [CPI], [Gender-Age-Physiology [GAP]; RIsk Stratification scorE [RISE]) demonstrated enhanced predictive power towards outcome in IPF. The lung allocation score (LAS) is a multi-dimensional tool commonly used to stratify patients assessed for LTx. We sought to investigate whether IPF-specific multi-dimensional scores predict mortality in patients with IPF assessed for LTx. The study included 302 patients with IPF who underwent a LTx assessment (2003-2014). Multi-dimensional scores were calculated. The primary outcome was 12-month mortality after assessment. LTx was considered as competing event in all analyses. At the end of the observation period, there were 134 transplants, 63 deaths, and 105 patients were alive without LTx. Multi-dimensional scores predicted mortality with accuracy similar to LAS, and superior to that of individual variables: area under the curve (AUC) for LAS was 0.78 (sensitivity 71%, specificity 86%); CPI 0.75 (sensitivity 67%, specificity 82%); GAP 0.67 (sensitivity 59%, specificity 74%); RISE 0.78 (sensitivity 71%, specificity 84%). A separate analysis conducted only in patients actively listed for LTx (n = 247; 50 deaths) yielded similar results. In patients with IPF assessed for LTx as well as in those actually listed, multi-dimensional scores predict mortality better than individual variables, and with accuracy similar to the LAS. If validated, multi-dimensional scores may serve as inexpensive tools to guide decisions on the timing of referral and listing for LTx. Copyright © 2017 Elsevier Ltd. All rights reserved.
Dual wing, swept forward swept rearward wing, and single wing design optimization for high performance business airplanes

NASA Technical Reports Server (NTRS)

Rhodes, M. D.; Selberg, B. P.

1982-01-01

An investigation was performed to compare closely coupled dual wing and swept forward swept rearward wing aircraft to corresponding single wing 'baseline' designs to judge the advantages offered by aircraft designed with multiple wing systems. The optimum multiple wing geometry used on the multiple wing designs was determined in an analytic study which investigated the two- and three-dimensional aerodynamic behavior of a wide range of multiple wing configurations in order to find the wing geometry that created the minimum cruise drag. This analysis used a multi-element inviscid vortex panel program coupled to a momentum integral boundary layer analysis program to account for the aerodynamic coupling between the wings and to provide the two-dimensional aerodynamic data, which was then used as input for a three-dimensional vortex lattice program, which calculated the three-dimensional aerodynamic data. The low drag of the multiple wing configurations is due to a combination of two dimensional drag reductions, tailoring the three dimensional drag for the swept forward swept rearward design, and the structural advantages of the two wings that because of the structural connections permitted higher aspect ratios.

Three-dimensional weight-accumulation algorithm for generating multiple excitation spots in fast optical stimulation

NASA Astrophysics Data System (ADS)

Takiguchi, Yu; Toyoda, Haruyoshi

2017-11-01

We report here an algorithm for calculating a hologram to be employed in a high-access speed microscope for observing sensory-driven synaptic activity across all inputs to single living neurons in an intact cerebral cortex. The system is based on holographic multi-beam generation using a two-dimensional phase-only spatial light modulator to excite multiple locations in three dimensions with a single hologram. The hologram was calculated with a three-dimensional weighted iterative Fourier transform method using the Ewald sphere restriction to increase the calculation speed. Our algorithm achieved good uniformity of three dimensionally generated excitation spots; the standard deviation of the spot intensities was reduced by a factor of two compared with a conventional algorithm.
Three-dimensional weight-accumulation algorithm for generating multiple excitation spots in fast optical stimulation

NASA Astrophysics Data System (ADS)

Takiguchi, Yu; Toyoda, Haruyoshi

2018-06-01

We report here an algorithm for calculating a hologram to be employed in a high-access speed microscope for observing sensory-driven synaptic activity across all inputs to single living neurons in an intact cerebral cortex. The system is based on holographic multi-beam generation using a two-dimensional phase-only spatial light modulator to excite multiple locations in three dimensions with a single hologram. The hologram was calculated with a three-dimensional weighted iterative Fourier transform method using the Ewald sphere restriction to increase the calculation speed. Our algorithm achieved good uniformity of three dimensionally generated excitation spots; the standard deviation of the spot intensities was reduced by a factor of two compared with a conventional algorithm.
Secondary Channel Bifurcation Geometry: A Multi-dimensional Problem

NASA Astrophysics Data System (ADS)

Gaeuman, D.; Stewart, R. L.

2017-12-01

The construction of secondary channels (or side channels) is a popular strategy for increasing aquatic habitat complexity in managed rivers. Such channels, however, frequently experience aggradation that prevents surface water from entering the side channels near their bifurcation points during periods of relatively low discharge. This failure to maintain an uninterrupted surface water connection with the main channel can reduce the habitat value of side channels for fish species that prefer lotic conditions. Various factors have been proposed as potential controls on the fate of side channels, including water surface slope differences between the main and secondary channels, the presence of main channel secondary circulation, transverse bed slopes, and bifurcation angle. A quantitative assessment of more than 50 natural and constructed secondary channels in the Trinity River of northern California indicates that bifurcations can assume a variety of configurations that are formed by different processes and whose longevity is governed by different sets of factors. Moreover, factors such as bifurcation angle and water surface slope vary with discharge level and are continuously distributed in space, such that they must be viewed as a multi-dimensional field rather than a single-valued attribute that can be assigned to a particular bifurcation.
Evidencing Learning Outcomes: A Multi-Level, Multi-Dimensional Course Alignment Model

ERIC Educational Resources Information Center

Sridharan, Bhavani; Leitch, Shona; Watty, Kim

2015-01-01

This conceptual framework proposes a multi-level, multi-dimensional course alignment model to implement a contextualised constructive alignment of rubric design that authentically evidences and assesses learning outcomes. By embedding quality control mechanisms at each level for each dimension, this model facilitates the development of an aligned…
No Association Between Variant N-acetyltransferase Genes, Cigarette Smoking and Prostate Cancer Susceptibility Among Men of African Descent

PubMed Central

Kidd, La Creis Renee; VanCleave, Tiva T.; Doll, Mark A.; Srivastava, Daya S.; Thacker, Brandon; Komolafe, Oyeyemi; Pihur, Vasyl; Brock, Guy N.; Hein, David W.

2011-01-01

Objective We evaluated the individual and combination effects of NAT1, NAT2 and tobacco smoking in a case-control study of 219 incident prostate cancer (PCa) cases and 555 disease-free men. Methods Allelic discriminations for 15 NAT1 and NAT2 loci were detected in germ-line DNA samples using Taqman polymerase chain reaction (PCR) assays. Single gene, gene-gene and gene-smoking interactions were analyzed using logistic regression models and multi-factor dimensionality reduction (MDR) adjusted for age and subpopulation stratification. MDR involves a rigorous algorithm that has ample statistical power to assess and visualize gene-gene and gene-environment interactions using relatively small samples sizes (i.e., 200 cases and 200 controls). Results Despite the relatively high prevalence of NAT1*10/*10 (40.1%), NAT2 slow (30.6%), and NAT2 very slow acetylator genotypes (10.1%) among our study participants, these putative risk factors did not individually or jointly increase PCa risk among all subjects or a subset analysis restricted to tobacco smokers. Conclusion Our data do not support the use of N-acetyltransferase genetic susceptibilities as PCa risk factors among men of African descent; however, subsequent studies in larger sample populations are needed to confirm this finding. PMID:21709725
Establishing a coherent and replicable measurement model of the Edinburgh Postnatal Depression Scale.

PubMed

Martin, Colin R; Redshaw, Maggie

2018-06-01

The 10-item Edinburgh Postnatal Depression Scale (EPDS) is an established screening tool for postnatal depression. Inconsistent findings in factor structure and replication difficulties have limited the scope of development of the measure as a multi-dimensional tool. The current investigation sought to robustly determine the underlying factor structure of the EPDS and the replicability and stability of the most plausible model identified. A between-subjects design was used. EPDS data were collected postpartum from two independent cohorts using identical data capture methods. Datasets were examined with confirmatory factor analysis, model invariance testing and systematic evaluation of relational and internal aspects of the measure. Participants were two samples of postpartum women in England assessed at three months (n = 245) and six months (n = 217). The findings showed a three-factor seven-item model of the EPDS offered an excellent fit to the data, and was observed to be replicable in both datasets and invariant as a function of time point of assessment. Some EPDS sub-scale scores were significantly higher at six months. The EPDS is multi-dimensional and a robust measurement model comprises three factors that are replicable. The potential utility of the sub-scale components identified requires further research to identify a role in contemporary screening practice. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
Regional oxygen reduction and denitrification rates in groundwater from multi-model residence time distributions, San Joaquin Valley, USA

USGS Publications Warehouse

Green, Christopher T.; Jurgens, Bryant; Zhang, Yong; Starn, Jeffrey; Singleton, Michael J.; Esser, Bradley K.

2016-01-01

Rates of oxygen and nitrate reduction are key factors in determining the chemical evolution of groundwater. Little is known about how these rates vary and covary in regional groundwater settings, as few studies have focused on regional datasets with multiple tracers and methods of analysis that account for effects of mixed residence times on apparent reaction rates. This study provides insight into the characteristics of residence times and rates of O2 reduction and denitrification (NO3− reduction) by comparing reaction rates using multi-model analytical residence time distributions (RTDs) applied to a data set of atmospheric tracers of groundwater age and geochemical data from 141 well samples in the Central Eastern San Joaquin Valley, CA. The RTD approach accounts for mixtures of residence times in a single sample to provide estimates of in-situ rates. Tracers included SF6, CFCs, 3H, He from 3H (tritiogenic He),14C, and terrigenic He. Parameter estimation and multi-model averaging were used to establish RTDs with lower error variances than those produced by individual RTD models. The set of multi-model RTDs was used in combination with NO3− and dissolved gas data to estimate zero order and first order rates of O2 reduction and denitrification. Results indicated that O2 reduction and denitrification rates followed approximately log-normal distributions. Rates of O2 and NO3− reduction were correlated and, on an electron milliequivalent basis, denitrification rates tended to exceed O2 reduction rates. Estimated historical NO3− trends were similar to historical measurements. Results show that the multi-model approach can improve estimation of age distributions, and that relatively easily measured O2 rates can provide information about trends in denitrification rates, which are more difficult to estimate.
Regional oxygen reduction and denitrification rates in groundwater from multi-model residence time distributions, San Joaquin Valley, USA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Green, Christopher T.; Jurgens, Bryant C.; Zhang, Yong

Rates of oxygen and nitrate reduction are key factors in determining the chemical evolution of groundwater. Little is known about how these rates vary and covary in regional groundwater settings, as few studies have focused on regional datasets with multiple tracers and methods of analysis that account for effects of mixed residence times on apparent reaction rates. This study provides insight into the characteristics of residence times and rates of O 2 reduction and denitrification (NO 3 – reduction) by comparing reaction rates using multi-model analytical residence time distributions (RTDs) applied to a data set of atmospheric tracers of groundwatermore » age and geochemical data from 141 well samples in the Central Eastern San Joaquin Valley, CA. The RTD approach accounts for mixtures of residence times in a single sample to provide estimates of in-situ rates. Tracers included SF 6, CFCs, 3H, He from 3H (tritiogenic He), 14C, and terrigenic He. Parameter estimation and multi-model averaging were used to establish RTDs with lower error variances than those produced by individual RTD models. The set of multi-model RTDs was used in combination with NO 3 – and dissolved gas data to estimate zero order and first order rates of O 2 reduction and denitrification. Results indicated that O 2 reduction and denitrification rates followed approximately log-normal distributions. Rates of O 2 and NO 3 – reduction were correlated and, on an electron milliequivalent basis, denitrification rates tended to exceed O 2 reduction rates. Estimated historical NO 3 – trends were similar to historical measurements. Here, results show that the multi-model approach can improve estimation of age distributions, and that relatively easily measured O 2 rates can provide information about trends in denitrification rates, which are more difficult to estimate.« less
Regional oxygen reduction and denitrification rates in groundwater from multi-model residence time distributions, San Joaquin Valley, USA

DOE PAGES

Green, Christopher T.; Jurgens, Bryant C.; Zhang, Yong; ...

2016-05-14

Rates of oxygen and nitrate reduction are key factors in determining the chemical evolution of groundwater. Little is known about how these rates vary and covary in regional groundwater settings, as few studies have focused on regional datasets with multiple tracers and methods of analysis that account for effects of mixed residence times on apparent reaction rates. This study provides insight into the characteristics of residence times and rates of O 2 reduction and denitrification (NO 3 – reduction) by comparing reaction rates using multi-model analytical residence time distributions (RTDs) applied to a data set of atmospheric tracers of groundwatermore » age and geochemical data from 141 well samples in the Central Eastern San Joaquin Valley, CA. The RTD approach accounts for mixtures of residence times in a single sample to provide estimates of in-situ rates. Tracers included SF 6, CFCs, 3H, He from 3H (tritiogenic He), 14C, and terrigenic He. Parameter estimation and multi-model averaging were used to establish RTDs with lower error variances than those produced by individual RTD models. The set of multi-model RTDs was used in combination with NO 3 – and dissolved gas data to estimate zero order and first order rates of O 2 reduction and denitrification. Results indicated that O 2 reduction and denitrification rates followed approximately log-normal distributions. Rates of O 2 and NO 3 – reduction were correlated and, on an electron milliequivalent basis, denitrification rates tended to exceed O 2 reduction rates. Estimated historical NO 3 – trends were similar to historical measurements. Here, results show that the multi-model approach can improve estimation of age distributions, and that relatively easily measured O 2 rates can provide information about trends in denitrification rates, which are more difficult to estimate.« less
Image matrix processor for fast multi-dimensional computations

DOEpatents

Roberson, G.P.; Skeate, M.F.

1996-10-15

An apparatus for multi-dimensional computation is disclosed which comprises a computation engine, including a plurality of processing modules. The processing modules are configured in parallel and compute respective contributions to a computed multi-dimensional image of respective two dimensional data sets. A high-speed, parallel access storage system is provided which stores the multi-dimensional data sets, and a switching circuit routes the data among the processing modules in the computation engine and the storage system. A data acquisition port receives the two dimensional data sets representing projections through an image, for reconstruction algorithms such as encountered in computerized tomography. The processing modules include a programmable local host, by which they may be configured to execute a plurality of different types of multi-dimensional algorithms. The processing modules thus include an image manipulation processor, which includes a source cache, a target cache, a coefficient table, and control software for executing image transformation routines using data in the source cache and the coefficient table and loading resulting data in the target cache. The local host processor operates to load the source cache with a two dimensional data set, loads the coefficient table, and transfers resulting data out of the target cache to the storage system, or to another destination. 10 figs.
Correlation Between Residual Displacement and Osteonecrosis of the Femoral Head Following Cannulated Screw Fixation of Femoral Neck Fractures.

PubMed

Wang, Chen; Xu, Gui-Jun; Han, Zhe; Jiang, Xuan; Zhang, Cheng-Bao; Dong, Qiang; Ma, Jian-Xiong; Ma, Xin-Long

2015-11-01

The aim of the study was to introduce a new method for measuring the residual displacement of the femoral head after internal fixation and explore the relationship between residual displacement and osteonecrosis with femoral head, and to evaluate the risk factors associated with osteonecrosis of the femoral head in patients with femoral neck fractures treated by closed reduction and percutaneous cannulated screw fixation.One hundred and fifty patients who sustained intracapsular femoral neck fractures between January 2011 and April 2013 were enrolled in the study. All were treated with closed reduction and percutaneous cannulated screw internal fixation. The residual displacement of the femoral head after surgery was measured by 3-dimensional reconstruction that evaluated the quality of the reduction. Other data that might affect prognosis were also obtained from outpatient follow-up, telephone calls, or case reviews. Multivariate logistic regression analysis was applied to assess the intrinsic relationship between the risk factors and the osteonecrosis of the femoral head.Osteonecrosis of the femoral head occurred in 27 patients (18%). Significant differences were observed regarding the residual displacement of the femoral head and the preoperative Garden classification. Moreover, we found more or less residual displacement of femoral head in all patients with high quality of reduction based on x-ray by the new technique. There was a close relationship between residual displacement and ONFH.There exists limitation to evaluate the quality of reduction by x-ray. Three-dimensional reconstruction and digital measurement, as a new method, is a more accurate method to assess the quality of reduction. Residual displacement of the femoral head and the preoperative Garden classification were risk factors for osteonecrosis of the femoral head. High-quality reduction was necessary to avoid complications.
Adherence is a multi-dimensional construct in the POUNDS LOST trial

PubMed Central

Williamson, Donald A.; Anton, Stephen D.; Han, Hongmei; Champagne, Catherine M.; Allen, Ray; LeBlanc, Eric; Ryan, Donna H.; McManus, Katherine; Laranjo, Nancy; Carey, Vincent J.; Loria, Catherine M.; Bray, George A.; Sacks, Frank M.

2011-01-01

Research on the conceptualization of adherence to treatment has not addressed a key question: Is adherence best defined as being a uni-dimensional or multi-dimensional behavioral construct? The primary aim of this study was to test which of these conceptual models best described adherence to a weight management program. This ancillary study was conducted as a part of the POUNDS LOST trial that tested the efficacy of four dietary macro-nutrient compositions for promoting weight loss. A sample of 811 overweight/obese adults was recruited across two clinical sites, and each participant was randomly assigned to one of four macronutrient prescriptions: (1) Low fat (20% of energy), average protein (15% of energy); (2) High fat (40%), average protein (15%); (3) Low fat (20%), high protein (25%); (4) High fat (40%), high protein (25%). Throughout the first 6 months of the study, a computer tracking system collected data on eight indicators of adherence. Computer tracking data from the initial 6 months of the intervention were analyzed using exploratory and confirmatory analyses. Two factors (accounting for 66% of the variance) were identified and confirmed: (1) behavioral adherence and (2) dietary adherence. Behavioral adherence did not differ across the four interventions, but prescription of a high fat diet (vs. a low fat diet) was found to be associated with higher levels of dietary adherence. The findings of this study indicated that adherence to a weight management program was best conceptualized as being multi-dimensional, with two dimensions: behavioral and dietary adherence. PMID:19856202
Using a fuzzy DEMATEL method for analyzing the factors influencing subcontractors selection

NASA Astrophysics Data System (ADS)

Kozik, Renata

2016-06-01

Subcontracting is a long-standing practice in the construction industry. This form of project organization, if manage properly, could provide the better quality, reduction in project time and costs. Subcontractors selection is a multi-criterion problem and can be determined by many factors. Identifying the importance of each of them as well as the direction of cause-effect relations between various types of factors can improve the management process. Their values could be evaluated on the basis of the available expert opinions with the application of a fuzzy multi-stage grading scale. In this paper it is recommended to use fuzzy DEMATEL method to analyze the relationship between factors affecting subcontractors selection.
Dynamic Museum Place: Exploring the Multi-Dimensional Museum Environment

ERIC Educational Resources Information Center

Leach, Denise Blair

2007-01-01

Place is an important factor in museum education, yet describing what the museum is as place is often difficult. This article introduces the idea that museums consist of multiple physical and virtual place "domains" where interactions between people and objects occur: the origin domain, creation domain, display domain, and the experiencer-object…
Data Visualization of Item-Total Correlation by Median Smoothing

ERIC Educational Resources Information Center

Yu, Chong Ho; Douglas, Samantha; Lee, Anna; An, Min

2016-01-01

This paper aims to illustrate how data visualization could be utilized to identify errors prior to modeling, using an example with multi-dimensional item response theory (MIRT). MIRT combines item response theory and factor analysis to identify a psychometric model that investigates two or more latent traits. While it may seem convenient to…
Development of a Scale Measuring Trait Anxiety in Physical Education

ERIC Educational Resources Information Center

Barkoukis, Vassilis; Rodafinos, Angelos; Koidou, Eirini; Tsorbatzoudis, Haralambos

2012-01-01

The aim of the present study was to examine the validity and reliability of a multi-dimensional measure of trait anxiety specifically designed for the physical education lesson. The Physical Education Trait Anxiety Scale was initially completed by 774 high school students during regular school classes. A confirmatory factor analysis supported the…
AdS and stabilized extra dimensions in multi-dimensional gravitational models with nonlinear scalar curvature terms R-1 and R4

NASA Astrophysics Data System (ADS)

Günther, Uwe; Zhuk, Alexander; Bezerra, Valdir B.; Romero, Carlos

2005-08-01

We study multi-dimensional gravitational models with scalar curvature nonlinearities of types R-1 and R4. It is assumed that the corresponding higher dimensional spacetime manifolds undergo a spontaneous compactification to manifolds with a warped product structure. Special attention has been paid to the stability of the extra-dimensional factor spaces. It is shown that for certain parameter regions the systems allow for a freezing stabilization of these spaces. In particular, we find for the R-1 model that configurations with stabilized extra dimensions do not provide a late-time acceleration (they are AdS), whereas the solution branch which allows for accelerated expansion (the dS branch) is incompatible with stabilized factor spaces. In the case of the R4 model, we obtain that the stability region in parameter space depends on the total dimension D = dim(M) of the higher dimensional spacetime M. For D > 8 the stability region consists of a single (absolutely stable) sector which is shielded from a conformal singularity (and an antigravity sector beyond it) by a potential barrier of infinite height and width. This sector is smoothly connected with the stability region of a curvature-linear model. For D < 8 an additional (metastable) sector exists which is separated from the conformal singularity by a potential barrier of finite height and width so that systems in this sector are prone to collapse into the conformal singularity. This second sector is not smoothly connected with the first (absolutely stable) one. Several limiting cases and the possibility of inflation are discussed for the R4 model.
Multi-dimensional quantum state sharing based on quantum Fourier transform

NASA Astrophysics Data System (ADS)

Qin, Huawang; Tso, Raylin; Dai, Yuewei

2018-03-01

A scheme of multi-dimensional quantum state sharing is proposed. The dealer performs the quantum SUM gate and the quantum Fourier transform to encode a multi-dimensional quantum state into an entanglement state. Then the dealer distributes each participant a particle of the entanglement state, to share the quantum state among n participants. In the recovery, n-1 participants measure their particles and supply their measurement results; the last participant performs the unitary operation on his particle according to these measurement results and can reconstruct the initial quantum state. The proposed scheme has two merits: It can share the multi-dimensional quantum state and it does not need the entanglement measurement.
Ensemble based on static classifier selection for automated diagnosis of Mild Cognitive Impairment.

PubMed

Nanni, Loris; Lumini, Alessandra; Zaffonato, Nicolò

2018-05-15

Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia in the elderly population. Scientific research is very active in the challenge of designing automated approaches to achieve an early and certain diagnosis. Recently an international competition among AD predictors has been organized: "A Machine learning neuroimaging challenge for automated diagnosis of Mild Cognitive Impairment" (MLNeCh). This competition is based on pre-processed sets of T1-weighted Magnetic Resonance Images (MRI) to be classified in four categories: stable AD, individuals with MCI who converted to AD, individuals with MCI who did not convert to AD and healthy controls. In this work, we propose a method to perform early diagnosis of AD, which is evaluated on MLNeCh dataset. Since the automatic classification of AD is based on the use of feature vectors of high dimensionality, different techniques of feature selection/reduction are compared in order to avoid the curse-of-dimensionality problem, then the classification method is obtained as the combination of Support Vector Machines trained using different clusters of data extracted from the whole training set. The multi-classifier approach proposed in this work outperforms all the stand-alone method tested in our experiments. The final ensemble is based on a set of classifiers, each trained on a different cluster of the training data. The proposed ensemble has the great advantage of performing well using a very reduced version of the data (the reduction factor is more than 90%). The MATLAB code for the ensemble of classifiers will be publicly available 1 to other researchers for future comparisons. Copyright © 2017 Elsevier B.V. All rights reserved.
Three-Dimensional Model of Strengths: Examination of Invariance Across Gender, Age, Education Levels, and Marriage Status.

PubMed

Duan, Wenjie; Ho, Samuel Mun Yin

2017-02-01

Strengths are positive qualities that significantly contributed to well-being of individuals and community. Therefore, a reliable and valid measure of strengths for research and practice is needed. The Brief Strengths Scale (BSS) is a newly developed tool for measuring the three-dimensional strengths model (i.e., temperance, intellectual, and interpersonal strength). However, empirical support for the measurement invariance of the BSS has not been obtained. This study examined the three-factor structure of BSS across gender, age, education, and marriage groups in a community sample (n = 375) using multi-group confirmatory factor analysis. After removing one item of each subscale from the original version, the revised model provided a good fit to the data at different subgroups. The revised nine-item BSS indicated that measurement invariance across gender and age groups was achieved. In addition, the measurement was more influenced by social-cultural factors than biological factors.

Exploring the effects of dimensionality reduction in deep networks for force estimation in robotic-assisted surgery

NASA Astrophysics Data System (ADS)

Aviles, Angelica I.; Alsaleh, Samar; Sobrevilla, Pilar; Casals, Alicia

2016-03-01

Robotic-Assisted Surgery approach overcomes the limitations of the traditional laparoscopic and open surgeries. However, one of its major limitations is the lack of force feedback. Since there is no direct interaction between the surgeon and the tissue, there is no way of knowing how much force the surgeon is applying which can result in irreversible injuries. The use of force sensors is not practical since they impose different constraints. Thus, we make use of a neuro-visual approach to estimate the applied forces, in which the 3D shape recovery together with the geometry of motion are used as input to a deep network based on LSTM-RNN architecture. When deep networks are used in real time, pre-processing of data is a key factor to reduce complexity and improve the network performance. A common pre-processing step is dimensionality reduction which attempts to eliminate redundant and insignificant information by selecting a subset of relevant features to use in model construction. In this work, we show the effects of dimensionality reduction in a real-time application: estimating the applied force in Robotic-Assisted Surgeries. According to the results, we demonstrated positive effects of doing dimensionality reduction on deep networks including: faster training, improved network performance, and overfitting prevention. We also show a significant accuracy improvement, ranging from about 33% to 86%, over existing approaches related to force estimation.
Dynamic Load on Continuous Multi-Lane Bridge Deck from Moving Vehicles

NASA Astrophysics Data System (ADS)

ZHU, X. Q.; LAW, S. S.

2002-04-01

The dynamic loading on a multi-lane continuous bridge deck due to vehicles moving on top at a constant velocity is investigated. The bridge is modelled as a multi-span continuous orthotropic rectangular plate with line rigid intermediate supports. The vehicle is simulated as a two-axle three-dimensional vehicle model with seven degrees of freedom according to the H20-44 vehicle design loading (AASHTO LRFD Bridge Design Specifications 1998 American Association of State Highway and Transportation Officials [1]). The dynamic behavior of the bridge deck under single and several vehicles moving in different lanes is analyzed using the orthotropic plate theory and modal superposition technique. The dynamic loading is studied in terms of the dynamic impact factor of the bridge deck. The impact factor is found varying in an opposite trend as the dynamic responses for the different loading cases under study.
Measuring the Perception of the Teachers' Autonomy-Supportive Behavior in Physical Education: Development and Initial Validation of a Multi-Dimensional Instrument

ERIC Educational Resources Information Center

Tilga, Henri; Hein, Vello; Koka, Andre

2017-01-01

This research aimed to develop and validate an instrument to assess the students' perceptions of the teachers' autonomy-supportive behavior by the multi-dimensional scale (Multi-Dimensional Perceived Autonomy Support Scale for Physical Education). The participants were 1,476 students aged 12- to 15-years-old. In Study 1, a pool of 37 items was…
Emergence of charge density waves and a pseudogap in single-layer TiTe 2

DOE PAGES

Chen, P.; Pai, Woei Wu; Chan, Y. -H.; ...

2017-09-11

Two-dimensional materials constitute a promising platform for developing nanoscale devices and systems. Their physical properties can be very different from those of the corresponding three-dimensional materials because of extreme quantum confinement and dimensional reduction. Here in this paper we report a study of TiTe 2 from the single-layer to the bulk limit. Using angle-resolved photoemission spectroscopy and scanning tunneling microscopy and spectroscopy, we observed the emergence of a (2 × 2) charge density wave order in single-layer TiTe 2 with a transition temperature of 92 ± 3 K. Also observed was a pseudogap of about 28 meV at the Fermimore » level at 4.2 K. Surprisingly, no charge density wave transitions were observed in two-layer and multi-layer TiTe 2 , despite the quasi-two-dimensional nature of the material in the bulk. The unique charge density wave phenomenon in the single layer raises intriguing questions that challenge the prevailing thinking about the mechanisms of charge density wave formation.« less
Numerical and analytical modeling of the end-loaded split (ELS) test specimens made of multi-directional coupled composite laminates

NASA Astrophysics Data System (ADS)

Samborski, Sylwester; Valvo, Paolo S.

2018-01-01

The paper deals with the numerical and analytical modelling of the end-loaded split test for multi-directional laminates affected by the typical elastic couplings. Numerical analysis of three-dimensional finite element models was performed with the Abaqus software exploiting the virtual crack closure technique (VCCT). The results show possible asymmetries in the widthwise deflections of the specimen, as well as in the strain energy release rate (SERR) distributions along the delamination front. Analytical modelling based on a beam-theory approach was also conducted in simpler cases, where only bending-extension coupling is present, but no out-of-plane effects. The analytical results matched the numerical ones, thus demonstrating that the analytical models are feasible for test design and experimental data reduction.
Multi-layer membrane model for mass transport in a direct ethanol fuel cell using an alkaline anion exchange membrane

NASA Astrophysics Data System (ADS)

Bahrami, Hafez; Faghri, Amir

2012-11-01

A one-dimensional, isothermal, single-phase model is presented to investigate the mass transport in a direct ethanol fuel cell incorporating an alkaline anion exchange membrane. The electrochemistry is analytically solved and the closed-form solution is provided for two limiting cases assuming Tafel expressions for both oxygen reduction and ethanol oxidation. A multi-layer membrane model is proposed to properly account for the diffusive and electroosmotic transport of ethanol through the membrane. The fundamental differences in fuel crossover for positive and negative electroosmotic drag coefficients are discussed. It is found that ethanol crossover is significantly reduced upon using an alkaline anion exchange membrane instead of a proton exchange membrane, especially at current densities higher than 500 A m
Scaling of graphene integrated circuits.

PubMed

Bianchi, Massimiliano; Guerriero, Erica; Fiocco, Marco; Alberti, Ruggero; Polloni, Laura; Behnam, Ashkan; Carrion, Enrique A; Pop, Eric; Sordan, Roman

2015-05-07

The influence of transistor size reduction (scaling) on the speed of realistic multi-stage integrated circuits (ICs) represents the main performance metric of a given transistor technology. Despite extensive interest in graphene electronics, scaling efforts have so far focused on individual transistors rather than multi-stage ICs. Here we study the scaling of graphene ICs based on transistors from 3.3 to 0.5 μm gate lengths and with different channel widths, access lengths, and lead thicknesses. The shortest gate delay of 31 ps per stage was obtained in sub-micron graphene ROs oscillating at 4.3 GHz, which is the highest oscillation frequency obtained in any strictly low-dimensional material to date. We also derived the fundamental Johnson limit, showing that scaled graphene ICs could be used at high frequencies in applications with small voltage swing.
Assessment of Closed-Loop Control Using Multi-Mode Sensor Fusion For a High Reynolds Number Transonic Jet

NASA Astrophysics Data System (ADS)

Low, Kerwin; Elhadidi, Basman; Glauser, Mark

2009-11-01

Understanding the different noise production mechanisms caused by the free shear flows in a turbulent jet flow provides insight to improve ``intelligent'' feedback mechanisms to control the noise. Towards this effort, a control scheme is based on feedback of azimuthal pressure measurements in the near field of the jet at two streamwise locations. Previous studies suggested that noise reduction can be achieved by azimuthal actuators perturbing the shear layer at the jet lip. The closed-loop actuation will be based on a low-dimensional Fourier representation of the hydrodynamic pressure measurements. Preliminary results show that control authority and reduction in the overall sound pressure level was possible. These results provide motivation to move forward with the overall vision of developing innovative multi-mode sensing methods to improve state estimation and derive dynamical systems. It is envisioned that estimating velocity-field and dynamic pressure information from various locations both local and in the far-field regions, sensor fusion techniques can be utilized to ascertain greater overall control authority.
Nonlinear Conservation Laws and Finite Volume Methods

NASA Astrophysics Data System (ADS)

Leveque, Randall J.

Introduction Software Notation Classification of Differential Equations Derivation of Conservation Laws The Euler Equations of Gas Dynamics Dissipative Fluxes Source Terms Radiative Transfer and Isothermal Equations Multi-dimensional Conservation Laws The Shock Tube Problem Mathematical Theory of Hyperbolic Systems Scalar Equations Linear Hyperbolic Systems Nonlinear Systems The Riemann Problem for the Euler Equations Numerical Methods in One Dimension Finite Difference Theory Finite Volume Methods Importance of Conservation Form - Incorrect Shock Speeds Numerical Flux Functions Godunov's Method Approximate Riemann Solvers High-Resolution Methods Other Approaches Boundary Conditions Source Terms and Fractional Steps Unsplit Methods Fractional Step Methods General Formulation of Fractional Step Methods Stiff Source Terms Quasi-stationary Flow and Gravity Multi-dimensional Problems Dimensional Splitting Multi-dimensional Finite Volume Methods Grids and Adaptive Refinement Computational Difficulties Low-Density Flows Discrete Shocks and Viscous Profiles Start-Up Errors Wall Heating Slow-Moving Shocks Grid Orientation Effects Grid-Aligned Shocks Magnetohydrodynamics The MHD Equations One-Dimensional MHD Solving the Riemann Problem Nonstrict Hyperbolicity Stiffness The Divergence of B Riemann Problems in Multi-dimensional MHD Staggered Grids The 8-Wave Riemann Solver Relativistic Hydrodynamics Conservation Laws in Spacetime The Continuity Equation The 4-Momentum of a Particle The Stress-Energy Tensor Finite Volume Methods Multi-dimensional Relativistic Flow Gravitation and General Relativity References
Poincaré-Treshchev Mechanism in Multi-scale, Nearly Integrable Hamiltonian Systems

NASA Astrophysics Data System (ADS)

Xu, Lu; Li, Yong; Yi, Yingfei

2018-02-01

This paper is a continuation to our work (Xu et al. in Ann Henri Poincaré 18(1):53-83, 2017) concerning the persistence of lower-dimensional tori on resonant surfaces of a multi-scale, nearly integrable Hamiltonian system. This type of systems, being properly degenerate, arise naturally in planar and spatial lunar problems of celestial mechanics for which the persistence problem ties closely to the stability of the systems. For such a system, under certain non-degenerate conditions of Rüssmann type, the majority persistence of non-resonant tori and the existence of a nearly full measure set of Poincaré non-degenerate, lower-dimensional, quasi-periodic invariant tori on a resonant surface corresponding to the highest order of scale is proved in Han et al. (Ann Henri Poincaré 10(8):1419-1436, 2010) and Xu et al. (2017), respectively. In this work, we consider a resonant surface corresponding to any intermediate order of scale and show the existence of a nearly full measure set of Poincaré non-degenerate, lower-dimensional, quasi-periodic invariant tori on the resonant surface. The proof is based on a normal form reduction which consists of a finite step of KAM iterations in pushing the non-integrable perturbation to a sufficiently high order and the splitting of resonant tori on the resonant surface according to the Poincaré-Treshchev mechanism.
Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.
Multi-dimensional Fokker-Planck equation analysis using the modified finite element method

NASA Astrophysics Data System (ADS)

Náprstek, J.; Král, R.

2016-09-01

The Fokker-Planck equation (FPE) is a frequently used tool for the solution of cross probability density function (PDF) of a dynamic system response excited by a vector of random processes. FEM represents a very effective solution possibility, particularly when transition processes are investigated or a more detailed solution is needed. Actual papers deal with single degree of freedom (SDOF) systems only. So the respective FPE includes two independent space variables only. Stepping over this limit into MDOF systems a number of specific problems related to a true multi-dimensionality must be overcome. Unlike earlier studies, multi-dimensional simplex elements in any arbitrary dimension should be deployed and rectangular (multi-brick) elements abandoned. Simple closed formulae of integration in multi-dimension domain have been derived. Another specific problem represents the generation of multi-dimensional finite element mesh. Assembling of system global matrices should be subjected to newly composed algorithms due to multi-dimensionality. The system matrices are quite full and no advantages following from their sparse character can be profited from, as is commonly used in conventional FEM applications in 2D/3D problems. After verification of partial algorithms, an illustrative example dealing with a 2DOF non-linear aeroelastic system in combination with random and deterministic excitations is discussed.
Ecotoxicological assessment of oil-based paint using three-dimensional multi-species bio-testing model: pre- and post-bioremediation analysis.

PubMed

Phulpoto, Anwar Hussain; Qazi, Muneer Ahmed; Haq, Ihsan Ul; Phul, Abdul Rahman; Ahmed, Safia; Kanhar, Nisar Ahmed

2018-06-01

The present study validates the oil-based paint bioremediation potential of Bacillus subtilis NAP1 for ecotoxicological assessment using a three-dimensional multi-species bio-testing model. The model included bioassays to determine phytotoxic effect, cytotoxic effect, and antimicrobial effect of oil-based paint. Additionally, the antioxidant activity of pre- and post-bioremediation samples was also detected to confirm its detoxification. Although, the pre-bioremediation samples of oil-based paint displayed significant toxicity against all the life forms. However, post-bioremediation, the cytotoxic effect against Artemia salina revealed substantial detoxification of oil-based paint with LD 50 of 121 μl ml -1 (without glucose) and > 400 μl ml -1 (with glucose). Similarly, the reduction in toxicity against Raphanus raphanistrum seeds germination (%FG = 98 to 100%) was also evident of successful detoxification under experimental conditions. Moreover, the toxicity against test bacterial strains and fungal strains was completely removed after bioremediation. In addition, the post-bioremediation samples showed reduced antioxidant activities (% scavenging = 23.5 ± 0.35 and 28.9 ± 2.7) without and with glucose, respectively. Convincingly, the present multi-species bio-testing model in addition to antioxidant studies could be suggested as a validation tool for bioremediation experiments, especially for middle and low-income countries. Graphical abstract ᅟ.
The forced vibration of one-dimensional multi-coupled periodic structures: An application to finite element analysis

NASA Astrophysics Data System (ADS)

Mead, Denys J.

2009-01-01

A general theory for the forced vibration of multi-coupled one-dimensional periodic structures is presented as a sequel to a much earlier general theory for free vibration. Starting from the dynamic stiffness matrix of a single multi-coupled periodic element, it derives matrix equations for the magnitudes of the characteristic free waves excited in the whole structure by prescribed harmonic forces and/or displacements acting at a single periodic junction. The semi-infinite periodic system excited at its end is first analysed to provide the basis for analysing doubly infinite and finite periodic systems. In each case, total responses are found by considering just one periodic element. An already-known method of reducing the size of the computational problem is reexamined, expanded and extended in detail, involving reduction of the dynamic stiffness matrix of the periodic element through a wave-coordinate transformation. Use of the theory is illustrated in a combined periodic structure+finite element analysis of the forced harmonic in-plane motion of a uniform flat plate. Excellent agreement between the computed low-frequency responses and those predicted by simple engineering theories validates the detailed formulations of the paper. The primary purpose of the paper is not towards a specific application but to present a systematic and coherent forced vibration theory, carefully linked with the existing free-wave theory.
Multi-view non-negative tensor factorization as relation learning in healthcare data.

PubMed

Hang Wu; Wang, May D

2016-08-01

Discovering patterns in co-occurrences data between objects and groups of concepts is a useful task in many domains, such as healthcare data analysis, information retrieval, and recommender systems. These relational representations come from objects' behaviors in different views, posing a challenging task of integrating information from these views to uncover the shared latent structures. The problem is further complicated by the high dimension of data and the large ratio of missing data. We propose a new paradigm of learning semantic relations using tensor factorization, by jointly factorizing multi-view tensors and searching for a consistent underlying semantic space across each views. We formulate the idea as an optimization problem and propose efficient optimization algorithms, with a special treatment of missing data as well as high-dimensional data. Experiments results show the potential and effectiveness of our algorithms.
Simulation of springback and microstructural analysis of dual phase steels

NASA Astrophysics Data System (ADS)

Kalyan, T. Sri.; Wei, Xing; Mendiguren, Joseba; Rolfe, Bernard

2013-12-01

With increasing demand for weight reduction and better crashworthiness abilities in car development, advanced high strength Dual Phase (DP) steels have been progressively used when making automotive parts. The higher strength steels exhibit higher springback and lower dimensional accuracy after stamping. This has necessitated the use of simulation of each stamped component prior to production to estimate the part's dimensional accuracy. Understanding the micro-mechanical behaviour of AHSS sheet may provide more accuracy to stamping simulations. This work can be divided basically into two parts: first modelling a standard channel forming process; second modelling the micro-structure of the process. The standard top hat channel forming process, benchmark NUMISHEET'93, is used for investigating springback effect of WISCO Dual Phase steels. The second part of this work includes the finite element analysis of microstructures to understand the behaviour of the multi-phase steel at a more fundamental level. The outcomes of this work will help in the dimensional control of steels during manufacturing stage based on the material's microstructure.
Using learning automata to determine proper subset size in high-dimensional spaces

NASA Astrophysics Data System (ADS)

Seyyedi, Seyyed Hossein; Minaei-Bidgoli, Behrouz

2017-03-01

In this paper, we offer a new method called FSLA (Finding the best candidate Subset using Learning Automata), which combines the filter and wrapper approaches for feature selection in high-dimensional spaces. Considering the difficulties of dimension reduction in high-dimensional spaces, FSLA's multi-objective functionality is to determine, in an efficient manner, a feature subset that leads to an appropriate tradeoff between the learning algorithm's accuracy and efficiency. First, using an existing weighting function, the feature list is sorted and selected subsets of the list of different sizes are considered. Then, a learning automaton verifies the performance of each subset when it is used as the input space of the learning algorithm and estimates its fitness upon the algorithm's accuracy and the subset size, which determines the algorithm's efficiency. Finally, FSLA introduces the fittest subset as the best choice. We tested FSLA in the framework of text classification. The results confirm its promising performance of attaining the identified goal.
Facilitating Learning in SPI through Co-design

NASA Astrophysics Data System (ADS)

Seigerroth, Ulf; Lind, Mikael

Information system development (ISD) is not a stable discipline. On the contrary, ISD must constantly cope with rapidly changing and diversifying technologies, application domains, and organizational contexts [14]. ISD is a complex and a multi dimensional phenomenon [5, 15]. As a consequence of this. Software Process Improvement (SPI) can also be regarded as a complex and multi dimensional phenomenon [16]. Problems that are accentuated in relation to SPI are: SPI is in its current shape a quite young discipline [15], there is a sparse amount of SPI-theories that can guide SPI initiatives [19], SPI-initiatives often focus on the system development (SD)-process, methods and tools which is a narrow focus that leave out important aspects such as business orientation [6], organization and social factors [4, 5] and the learning process [19]. Arguments have therefore been raised that there is a need for both researchers and practitioners to better understand SD-organisations and their practice [5].
Significant characteristics of social response to noise and vibration

NASA Technical Reports Server (NTRS)

Nishinomiya, G.

1979-01-01

Several surveys made since 1971 to investigate annoyance resulting from noise and vibration, from various sources were studied in order to quantify the relation between annoyance response to noise or vibration and properties of the respondent including factors such as noise exposure, etc. Samples collected by the social surveys and physical measurements were analyzed by multi-dimensional analysis.
Rural-Urban Analyses of Health-Related Quality of Life among People with Multiple Sclerosis

ERIC Educational Resources Information Center

Buchanan, Robert J.; Zhu, Li; Schiffer, Randolph; Radin, Dagmar; James, Wesley

2008-01-01

Context: Health-related quality of life (HRQOL) is a multi-dimensional construct including aspects of life quality or function that are affected by physical health and symptoms, psychosocial factors, and psychiatric conditions. HRQOL gives a broader measure of the burden of disease than physical impairment or disability levels. Purpose: To…

Predicting Negative Discipline in Traditional Families: A Multi-Dimensional Stress Model.

ERIC Educational Resources Information Center

Fisher, Philip A.

An attempt is made to integrate existing theories of family violence by introducing the concept of family role stress. Role stressors may be defined as factors inhibiting the enactment of family roles. Multiple regression analyses were performed on data from 190 families to test a hypothesis involving the prediction of negative discipline at…
Application of Grey Relational Analysis to Decision-Making during Product Development

ERIC Educational Resources Information Center

Hsiao, Shih-Wen; Lin, Hsin-Hung; Ko, Ya-Chuan

2017-01-01

A multi-attribute decision-making (MADM) approach was proposed in this study as a prediction method that differs from the conventional production and design methods for a product. When a client has different dimensional requirements, this approach can quickly provide a company with design decisions for each product. The production factors of a…
Perceptions of Learning Effectiveness in M-Learning: Scale Development and Student Awareness

ERIC Educational Resources Information Center

Chang, Wen-Hui; Liu, Yuan-Chen; Huang, Tzu-Hua

2017-01-01

The purpose of this study is to develop a multi-dimensional scale to measure students' awareness of key competencies for M-learning and to test its reliability and validity. The Key Competencies of Mobile Learning Scale (KCMLS) was determined via confirmatory factor analysis to have four dimensions: team collaboration, creative thinking, critical…
Spider-web inspired multi-resolution graphene tactile sensor.

PubMed

Liu, Lu; Huang, Yu; Li, Fengyu; Ma, Ying; Li, Wenbo; Su, Meng; Qian, Xin; Ren, Wanjie; Tang, Kanglai; Song, Yanlin

2018-05-08

Multi-dimensional accurate response and smooth signal transmission are critical challenges in the advancement of multi-resolution recognition and complex environment analysis. Inspired by the structure-activity relationship between discrepant microstructures of the spiral and radial threads in a spider web, we designed and printed graphene with porous and densely-packed microstructures to integrate into a multi-resolution graphene tactile sensor. The three-dimensional (3D) porous graphene structure performs multi-dimensional deformation responses. The laminar densely-packed graphene structure contributes excellent conductivity with flexible stability. The spider-web inspired printed pattern inherits orientational and locational kinesis tracking. The multi-structure construction with homo-graphene material can integrate discrepant electronic properties with remarkable flexibility, which will attract enormous attention for electronic skin, wearable devices and human-machine interactions.
Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

PubMed Central

Tao, Chenyang; Nichols, Thomas E.; Hua, Xue; Ching, Christopher R.K.; Rolls, Edmund T.; Thompson, Paul M.; Feng, Jianfeng

2017-01-01

We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches. PMID:27666385
Implementation of a Multi-Robot Coverage Algorithm on a Two-Dimensional, Grid-Based Environment

DTIC Science & Technology

2017-06-01

two planar laser range finders with a 180-degree field of view , color camera, vision beacons, and wireless communicator. In their system, the robots...Master’s thesis 4. TITLE AND SUBTITLE IMPLEMENTATION OF A MULTI -ROBOT COVERAGE ALGORITHM ON A TWO -DIMENSIONAL, GRID-BASED ENVIRONMENT 5. FUNDING NUMBERS...path planning coverage algorithm for a multi -robot system in a two -dimensional, grid-based environment. We assess the applicability of a topology
Applying multi-criteria decision-making to improve the waste reduction policy in Taiwan.

PubMed

Su, Jun-Pin; Hung, Ming-Lung; Chao, Chia-Wei; Ma, Hwong-wen

2010-01-01

Over the past two decades, the waste reduction problem has been a major issue in environmental protection. Both recycling and waste reduction policies have become increasingly important. As the complexity of decision-making has increased, it has become evident that more factors must be considered in the development and implementation of policies aimed at resource recycling and waste reduction. There are many studies focused on waste management excluding waste reduction. This study paid more attention to waste reduction. Social, economic, and management aspects of waste treatment policies were considered in this study. Further, a life-cycle assessment model was applied as an evaluation system for the environmental aspect. Results of both quantitative and qualitative analyses on the social, economic, and management aspects were integrated via the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method into the comprehensive decision-making support system of multi-criteria decision-making (MCDM). A case study evaluating the waste reduction policy in Taoyuan County is presented to demonstrate the feasibility of this model. In the case study, reinforcement of MSW sorting was shown to be the best practice. The model in this study can be applied to other cities faced with the waste reduction problems.
Numerical simulation of advection fog formation on multi-disperse aerosols due to combustion-related pollutants

NASA Technical Reports Server (NTRS)

Hung, R. J.; Liaw, G. S.

1980-01-01

The effects of multi-disperse distribution of the aerosol population are presented. Single component and multi-component aerosol species on the condensation/nucleation processes which affect the reduction in visibility are described. The aerosol population with a high particle concentration provided more favorable conditions for the formation of a denser fog than the aerosol population with a greater particle size distribution when the value of the mass concentration of the aerosols was kept constant. The results were used as numerical predictions of fog formation. Two dimensional observations in horizontal and vertical coordinates, together with time-dependent measurements were needed as initial values for the following physical parameters: (1)wind profiles; (2) temperature profiles; (3) humidity profiles; (4) mass concentration of aerosol particles; (5) particle size distribution of aerosols; and (6) chemical composition of aerosols. Formation and dissipation of advection fog, thus, can be forecasted numerically by introducing initial values obtained from the observations.
Multi-dimensional simulations of core-collapse supernova explosions with CHIMERA

NASA Astrophysics Data System (ADS)

Messer, O. E. B.; Harris, J. A.; Hix, W. R.; Lentz, E. J.; Bruenn, S. W.; Mezzacappa, A.

2018-04-01

Unraveling the core-collapse supernova (CCSN) mechanism is a problem that remains essentially unsolved despite more than four decades of effort. Spherically symmetric models with otherwise high physical fidelity generally fail to produce explosions, and it is widely accepted that CCSNe are inherently multi-dimensional. Progress in realistic modeling has occurred recently through the availability of petascale platforms and the increasing sophistication of supernova codes. We will discuss our most recent work on understanding neutrino-driven CCSN explosions employing multi-dimensional neutrino-radiation hydrodynamics simulations with the Chimera code. We discuss the inputs and resulting outputs from these simulations, the role of neutrino radiation transport, and the importance of multi-dimensional fluid flows in shaping the explosions. We also highlight the production of 48Ca in long-running Chimera simulations.
Localized contourlet features in vehicle make and model recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, B. S.

2009-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance.
A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

PubMed Central

Liu, Jingxian; Wu, Kefeng

2017-01-01

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations. PMID:28777353
Studies for the 3-Dimensional Structure, Composition, and Dynamic of Io's Atmosphere

NASA Technical Reports Server (NTRS)

Smyth, William H.

2001-01-01

Research work is discussed for the following: (1) the exploration of new H and Cl chemistry in Io's atmosphere using the already developed two-dimensional multi-species hydrodynamic model of Wong and Smyth; and (2) for the development of a new three-dimensional multi-species hydrodynamic model for Io's atmosphere.
Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

NASA Astrophysics Data System (ADS)

Cui, Tiangang; Marzouk, Youssef; Willcox, Karen

2016-06-01

Two major bottlenecks to the solution of large-scale Bayesian inverse problems are the scaling of posterior sampling algorithms to high-dimensional parameter spaces and the computational cost of forward model evaluations. Yet incomplete or noisy data, the state variation and parameter dependence of the forward model, and correlations in the prior collectively provide useful structure that can be exploited for dimension reduction in this setting-both in the parameter space of the inverse problem and in the state space of the forward model. To this end, we show how to jointly construct low-dimensional subspaces of the parameter space and the state space in order to accelerate the Bayesian solution of the inverse problem. As a byproduct of state dimension reduction, we also show how to identify low-dimensional subspaces of the data in problems with high-dimensional observations. These subspaces enable approximation of the posterior as a product of two factors: (i) a projection of the posterior onto a low-dimensional parameter subspace, wherein the original likelihood is replaced by an approximation involving a reduced model; and (ii) the marginal prior distribution on the high-dimensional complement of the parameter subspace. We present and compare several strategies for constructing these subspaces using only a limited number of forward and adjoint model simulations. The resulting posterior approximations can rapidly be characterized using standard sampling techniques, e.g., Markov chain Monte Carlo. Two numerical examples demonstrate the accuracy and efficiency of our approach: inversion of an integral equation in atmospheric remote sensing, where the data dimension is very high; and the inference of a heterogeneous transmissivity field in a groundwater system, which involves a partial differential equation forward model with high dimensional state and parameters.
Numerical Study of the Reduction Process in an Oxygen Blast Furnace

NASA Astrophysics Data System (ADS)

Zhang, Zongliang; Meng, Jiale; Guo, Lei; Guo, Zhancheng

2016-02-01

Based on computational fluid dynamics, chemical reaction kinetics, principles of transfer in metallurgy, and other principles, a multi-fluid model for a traditional blast furnace was established. The furnace conditions were simulated with this multi-fluid mathematical model, and the model was verified with the comparison of calculation and measurement. Then a multi-fluid model for an oxygen blast furnace in the gasifier-full oxygen blast furnace process was established based on this traditional blast furnace model. With the established multi-fluid model for an oxygen blast furnace, the basic characteristics of iron ore reduction process in the oxygen blast furnace were summarized, including the changing process of the iron ore reduction degree and the compositions of the burden, etc. The study found that compared to the traditional blast furnace, the magnetite reserve zone in the furnace shaft under oxygen blast furnace condition was significantly reduced, which is conducive to the efficient operation of blast furnace. In order to optimize the oxygen blast furnace design and operating parameters, the iron ore reduction process in the oxygen blast furnace was researched under different shaft tuyere positions, different recycling gas temperatures, and different allocation ratios of recycling gas between the hearth tuyere and the shaft tuyere. The results indicate that these three factors all have a substantial impact on the ore reduction process in the oxygen blast furnace. Moderate shaft tuyere position, high recycling gas temperature, and high recycling gas allocation ratio between hearth and shaft could significantly promote the reduction of iron ore, reduce the scope of the magnetite reserve zone, and improve the performance of oxygen blast furnace. Based on the above findings, the recommendations for improvement of the oxygen blast furnace design and operation were proposed.
Optically transduced MEMS magnetometer

DOEpatents

Nielson, Gregory N; Langlois, Eric

2014-03-18

MEMS magnetometers with optically transduced resonator displacement are described herein. Improved sensitivity, crosstalk reduction, and extended dynamic range may be achieved with devices including a deflectable resonator suspended from the support, a first grating extending from the support and disposed over the resonator, a pair of drive electrodes to drive an alternating current through the resonator, and a second grating in the resonator overlapping the first grating to form a multi-layer grating having apertures that vary dimensionally in response to deflection occurring as the resonator mechanically resonates in a plane parallel to the first grating in the presence of a magnetic field as a function of the Lorentz force resulting from the alternating current. A plurality of such multi-layer gratings may be disposed across a length of the resonator to provide greater dynamic range and/or accommodate fabrication tolerances.
Modeling the Chagas’ disease after stem cell transplantation

NASA Astrophysics Data System (ADS)

Galvão, Viviane; Miranda, José Garcia Vivas

2009-04-01

A recent model for Chagas’ disease after stem cell transplantation is extended for a three-dimensional multi-agent-based model. The computational model includes six different types of autonomous agents: inflammatory cell, fibrosis, cardiomyocyte, proinflammatory cytokine tumor necrosis factor- α, Trypanosoma cruzi, and bone marrow stem cell. Only fibrosis is fixed and the other types of agents can move randomly through the empty spaces using the three-dimensional Moore neighborhood. Bone marrow stem cells can promote apoptosis in inflammatory cells, fibrosis regression and can differentiate in cardiomyocyte. T. cruzi can increase the number of inflammatory cells. Inflammatory cells and tumor necrosis factor- α can increase the quantity of fibrosis. Our results were compared with experimental data giving a fairly fit and they suggest that the inflammatory cells are important for the development of fibrosis.
Sufficient Forecasting Using Factor Models

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Yao, Jiawei

2017-01-01

We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional (approximate) factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting method called the sufficient forecasting, which provides a set of sufficient predictive indices, inferred from high-dimensional predictors, to deliver additional predictive power. The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric (approximate) factor model is assumed. Our method is also applicable to cross-sectional sufficient regression using extracted factors. The connection between the sufficient forecasting and the deep learning architecture is explicitly stated. The sufficient forecasting correctly estimates projection indices of the underlying factors even in the presence of a nonparametric forecasting function. The proposed method extends the sufficient dimension reduction to high-dimensional regimes by condensing the cross-sectional information through factor models. We derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices. We further show that the natural method of running multiple regression of target on estimated factors yields a linear estimate that actually falls into this central subspace. Our method and theory allow the number of predictors to be larger than the number of observations. We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables. PMID:29731537
Multi-stage phononic crystal structure for anchor-loss reduction of thin-film piezoelectric-on-silicon microelectromechanical-system resonator

NASA Astrophysics Data System (ADS)

Bao, Fei-Hong; Bao, Lei-Lei; Li, Xin-Yi; Ammar Khan, Muhammad; Wu, Hua-Ye; Qin, Feng; Zhang, Ting; Zhang, Yi; Bao, Jing-Fu; Zhang, Xiao-Sheng

2018-06-01

Thin-film piezoelectric-on-silicon acoustic wave resonators are promising for the development of system-on-chip integrated circuits with micro/nano-engineered timing reference. However, in order to realize their large potentials, a further enhancement of the quality factor (Q) is required. In this study, a novel approach, based on a multi-stage phononic crystal (PnC) structure, was proposed to achieve an ultra-high Q. A systematical study revealed that the multi-stage PnC structure formed a frequency-selective band-gap to effectively prohibit the dissipation of acoustic waves through tethers, which significantly reduced the anchor loss, leading to an insertion-loss reduction and enhancement of Q. The maximum unloaded Q u of the fabricated resonators reached the value of ∼10,000 at 109.85 MHz, indicating an enhancement by 19.4 times.
Multi-dimensional simulations of core-collapse supernova explosions with CHIMERA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Messer, Bronson; Harris, James Austin; Hix, William Raphael

Unraveling the core-collapse supernova (CCSN) mechanism is a problem that remains essentially unsolved despite more than four decades of effort. Spherically symmetric models with otherwise high physical fidelity generally fail to produce explosions, and it is widely accepted that CCSNe are inherently multi-dimensional. Progress in realistic modeling has occurred recently through the availability of petascale platforms and the increasing sophistication of supernova codes. We will discuss our most recent work on understanding neutrino-driven CCSN explosions employing multi-dimensional neutrino-radiation hydrodynamics simulations with the Chimera code. We discuss the inputs and resulting outputs from these simulations, the role of neutrino radiation transport,more » and the importance of multi-dimensional fluid flows in shaping the explosions. We also highlight the production of 48Ca in long-running Chimera simulations.« less
The development of a multi-dimensional gambling accessibility scale.

PubMed

Hing, Nerilee; Haw, John

2009-12-01

The aim of the current study was to develop a scale of gambling accessibility that would have theoretical significance to exposure theory and also serve to highlight the accessibility risk factors for problem gambling. Scale items were generated from the Productivity Commission's (Australia's Gambling Industries: Report No. 10. AusInfo, Canberra, 1999) recommendations and tested on a group with high exposure to the gambling environment. In total, 533 gaming venue employees (aged 18-70 years; 67% women) completed a questionnaire that included six 13-item scales measuring accessibility across a range of gambling forms (gaming machines, keno, casino table games, lotteries, horse and dog racing, sports betting). Also included in the questionnaire was the Problem Gambling Severity Index (PGSI) along with measures of gambling frequency and expenditure. Principal components analysis indicated that a common three factor structure existed across all forms of gambling and these were labelled social accessibility, physical accessibility and cognitive accessibility. However, convergent validity was not demonstrated with inconsistent correlations between each subscale and measures of gambling behaviour. These results are discussed in light of exposure theory and the further development of a multi-dimensional measure of gambling accessibility.

Exploring the Dimensionality of Ethnic Minority Adaptation in Britain: An Analysis across Ethnic and Generational Lines

PubMed Central

Lessard-Phillips, Laurence

2015-01-01

In this article I explore the dimensionality of the long-term experiences of the main ethnic minority groups (their adaptation) in Britain. Using recent British data, I apply factor analysis to uncover the underlying number of factors behind variables deemed to be representative of the adaptation experience within the literature. I then attempt to assess the groupings of adaptation present in the data, to see whether a typology of adaptation exists (i.e. whether adaptation in different dimensions can be concomitant with others). The analyses provide an empirical evidence base to reflect on: (1) the extent of group differences in the adaptation process, which may cut across ethnic and generational lines; and (2) whether the uncovered dimensions of adaptation match existing theoretical views and empirical evidence. Results suggest that adaptation should be regarded as a multi-dimensional phenomenon where clear typologies of adaptation based on specific trade-offs (mostly cultural) appear to exist. PMID:28502998
Ethnicity, work-related stress and subjective reports of health by migrant workers: a multi-dimensional model.

PubMed

Capasso, Roberto; Zurlo, Maria Clelia; Smith, Andrew P

2018-02-01

This study integrates different aspects of ethnicity and work-related stress dimensions (based on the Demands-Resources-Individual-Effects model, DRIVE [Mark, G. M., and A. P. Smith. 2008. "Stress Models: A Review and Suggested New Direction." In Occupational Health Psychology, edited by J. Houdmont and S. Leka, 111-144. Nottingham: Nottingham University Press]) and aims to test a multi-dimensional model that combines individual differences, ethnicity dimensions, work characteristics, and perceived job satisfaction/stress as independent variables in the prediction of subjectives reports of health by workers differing in ethnicity. A questionnaire consisting of the following sections was submitted to 900 workers in Southern Italy: for individual and cultural characteristics, coping strategies, personality behaviours, and acculturation strategies; for work characteristics, perceived job demands and job resources/rewards; for appraisals, perceived job stress/satisfaction and racial discrimination; for subjective reports of health, psychological disorders and general health. To test the reliability and construct validity of the extracted factors referred to all dimensions involved in the proposed model and logistic regression analyses to evaluate the main effects of the independent variables on the health outcomes were conducted. Principal component analysis (PCA) yielded seven factors for individual and cultural characteristics (emotional/relational coping, objective coping, Type A behaviour, negative affectivity, social inhibition, affirmation/maintenance culture, and search identity/adoption of the host culture); three factors for work characteristics (work demands, intrinsic/extrinsic rewards, and work resources); three factors for appraisals (perceived job satisfaction, perceived job stress, perceived racial discrimination) and three factors for subjective reports of health (interpersonal disorders, anxious-depressive disorders, and general health). Logistic regression analyses showed main effects of specific individual and cultural differences, work characteristics and perceived job satisfaction/stress on the risk of suffering health problems. The suggested model provides a strong framework that illustrates how psychosocial and individual variables can influence occupational health in multi-cultural workplaces.
Development and validation of the Work Conflict Appraisal Scale (WCAS).

PubMed

González-Navarro, Pilar; Llinares-Insa, Lucía; Zurriaga-Llorens, Rosario; Lloret-Segura, Susana

2017-05-01

In the context of cognitive appraisal, the Work Conflict Appraisal Scale (WCAS) was developed to assess work conflict in terms of threat and challenge. In the first study, the factorial structure of the scale was tested using confirmatory factor analysis with a Spanish multi-occupational employee sample (N= 296). In the sec-ond study, we used multi-sampling confirmatory factor analysis (N= 815) to cross-validate the results. The analyses confirm the validity of the scale and are con-sistent with the tri-dimensional conflict classification. The findings support the distinc-tion between the challenge and threat appraisals of work conflict, highlighting the im-portance of measuring these two types of appraisal separately. This scale is a valid and reliable instrument to measure conflict appraisal in organizations.
Study of single nucleotide polymorphisms of tumour necrosis factors and HSP genes in nasopharyngeal carcinoma in North East India.

PubMed

Lakhanpal, Meena; Singh, Laishram Chandreshwor; Rahman, Tashnin; Sharma, Jagnnath; Singh, M Madhumangal; Kataki, Amal Chandra; Verma, Saurabh; Pandrangi, Santhi Latha; Singh, Y Mohan; Wajid, Saima; Kapur, Sujala; Saxena, Sunita

2016-01-01

Nasopharyngeal carcinoma (NPC) is an epithelial tumour with a distinctive racial and geographical distribution. High incidence of NPC has been reported from China, Southeast Asia, and northeast (NE) region of India. The immune mechanism plays incredibly role in pathogenesis of NPC. Tumour necrosis factors (TNFs) and heat shock protein 70 (HSP 70) constitute significant components of innate as well as adaptive host immunity. Multi-analytical approaches including logistic regression (LR), classification and regression tree (CART) and multifactor dimensionality reduction (MDR) were applied in 120 NPC cases and 100 controls to explore high order interactions among TNF-α (-308 G>A), TNF β (+252 A>G), HSP 70-1 (+190 G>C), HSP 70-hom (+2437 T>C) genes and environmental risk factors. TNF β was identified as the primary etiological factor by all three analytical approaches. Individual analysis of results showed protective effect of TNF β GG genotype (adjusted odds ratio (OR2) = 0.27, 95 % CI = 0.125-0.611, P = 0.001), HSP 70 (+2437) CC genotype (OR2 = 0.17, 95 % CI = 0.0430.69, P = 0.013), while AG genotype of TNF β was found significantly associated with risk of NPC (OR2 = 1.97, 95 % CI = 1.019-3.83, P = 0.04). Analysis of environmental factors demonstrated association of alcohol consumption, living in mud houses and use of firewood for cooking as major risk factors for NPC. Individual haplotype association analysis showed significant risk associated with GTGA haplotype (OR = 68.61, 95 % CI = 2.47-190.37, P = 0.013) while a protective effect with CCAA and GCGA haplotypes (OR = 0.19, 95 % CI = 0.05-0.75, P = 0.019; OR = 0.01 95 % CI = 0.05-0.30, P = 0.007). The multi-analytical approaches applied in this study helped in identification of distinct gene-gene and gene-environment interactions significant in risk assessment of NPC.
Model Reduction of Computational Aerothermodynamics for Multi-Discipline Analysis in High Speed Flows

NASA Astrophysics Data System (ADS)

Crowell, Andrew Rippetoe

This dissertation describes model reduction techniques for the computation of aerodynamic heat flux and pressure loads for multi-disciplinary analysis of hypersonic vehicles. NASA and the Department of Defense have expressed renewed interest in the development of responsive, reusable hypersonic cruise vehicles capable of sustained high-speed flight and access to space. However, an extensive set of technical challenges have obstructed the development of such vehicles. These technical challenges are partially due to both the inability to accurately test scaled vehicles in wind tunnels and to the time intensive nature of high-fidelity computational modeling, particularly for the fluid using Computational Fluid Dynamics (CFD). The aim of this dissertation is to develop efficient and accurate models for the aerodynamic heat flux and pressure loads to replace the need for computationally expensive, high-fidelity CFD during coupled analysis. Furthermore, aerodynamic heating and pressure loads are systematically evaluated for a number of different operating conditions, including: simple two-dimensional flow over flat surfaces up to three-dimensional flows over deformed surfaces with shock-shock interaction and shock-boundary layer interaction. An additional focus of this dissertation is on the implementation and computation of results using the developed aerodynamic heating and pressure models in complex fluid-thermal-structural simulations. Model reduction is achieved using a two-pronged approach. One prong focuses on developing analytical corrections to isothermal, steady-state CFD flow solutions in order to capture flow effects associated with transient spatially-varying surface temperatures and surface pressures (e.g., surface deformation, surface vibration, shock impingements, etc.). The second prong is focused on minimizing the computational expense of computing the steady-state CFD solutions by developing an efficient surrogate CFD model. The developed two-pronged approach is found to exhibit balanced performance in terms of accuracy and computational expense, relative to several existing approaches. This approach enables CFD-based loads to be implemented into long duration fluid-thermal-structural simulations.
Emergence of charge density waves and a pseudogap in single-layer TiTe2.

PubMed

Chen, P; Pai, Woei Wu; Chan, Y-H; Takayama, A; Xu, C-Z; Karn, A; Hasegawa, S; Chou, M Y; Mo, S-K; Fedorov, A-V; Chiang, T-C

2017-09-11

Two-dimensional materials constitute a promising platform for developing nanoscale devices and systems. Their physical properties can be very different from those of the corresponding three-dimensional materials because of extreme quantum confinement and dimensional reduction. Here we report a study of TiTe 2 from the single-layer to the bulk limit. Using angle-resolved photoemission spectroscopy and scanning tunneling microscopy and spectroscopy, we observed the emergence of a (2 × 2) charge density wave order in single-layer TiTe 2 with a transition temperature of 92 ± 3 K. Also observed was a pseudogap of about 28 meV at the Fermi level at 4.2 K. Surprisingly, no charge density wave transitions were observed in two-layer and multi-layer TiTe 2 , despite the quasi-two-dimensional nature of the material in the bulk. The unique charge density wave phenomenon in the single layer raises intriguing questions that challenge the prevailing thinking about the mechanisms of charge density wave formation.Due to reduced dimensionality, the properties of 2D materials are often different from their 3D counterparts. Here, the authors identify the emergence of a unique charge density wave (CDW) order in monolayer TiTe 2 that challenges the current understanding of CDW formation.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Kishino, Katsumi, E-mail: kishino@sophia.ac.jp; Sophia Nanotechnology Research Center, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554; Ishizawa, Shunsuke

Bottom-up grown structurally graded InGaN-based nanocolumn photonic crystals, in which nanocolumns were arranged in triangular lattice and the nanocolumn diameter changed one-dimensionally from 93 to 213 nm with a fixed lattice constant of 250 nm, were fabricated. The spatial distribution of the diameter resulted in random-laser-like operation under optical excitation. A broad multi-wavelength lasing spectrum with more than 10 peaks was obtained with a full width at half maximum of 27 nm at 505 nm wavelength as well as lowering of the polarization degree, which is expected to be suitable for speckle contrast reduction in laser projection display applications.
The multi-layer multi-configuration time-dependent Hartree method for bosons: theory, implementation, and applications.

PubMed

Cao, Lushuai; Krönke, Sven; Vendrell, Oriol; Schmelcher, Peter

2013-10-07

We develop the multi-layer multi-configuration time-dependent Hartree method for bosons (ML-MCTDHB), a variational numerically exact ab initio method for studying the quantum dynamics and stationary properties of general bosonic systems. ML-MCTDHB takes advantage of the permutation symmetry of identical bosons, which allows for investigations of the quantum dynamics from few to many-body systems. Moreover, the multi-layer feature enables ML-MCTDHB to describe mixed bosonic systems consisting of arbitrary many species. Multi-dimensional as well as mixed-dimensional systems can be accurately and efficiently simulated via the multi-layer expansion scheme. We provide a detailed account of the underlying theory and the corresponding implementation. We also demonstrate the superior performance by applying the method to the tunneling dynamics of bosonic ensembles in a one-dimensional double well potential, where a single-species bosonic ensemble of various correlation strengths and a weakly interacting two-species bosonic ensemble are considered.
The Moral Injury Symptom Scale-Military Version.

PubMed

Koenig, Harold G; Ames, Donna; Youssef, Nagy A; Oliver, John P; Volk, Fred; Teng, Ellen J; Haynes, Kerry; Erickson, Zachary D; Arnold, Irina; O'Garo, Keisha; Pearce, Michelle

2018-02-01

The purpose of this study was to develop a multi-dimensional measure of moral injury symptoms that can be used as a primary outcome measure in intervention studies that target moral injury (MI) in Veterans and Active Duty Military with PTSD. This was a multi-center study of 427 Veterans and Active Duty Military with PTSD symptoms recruited from VA Medical Centers in Augusta, Los Angeles, Durham, Houston, and San Antonio, and from Liberty University in Lynchburg. Internal reliability of the Moral Injury Symptom Scale-Military Version (MISS-M) was examined along with factor analytic, discriminant, and convergent validity. Participants were randomly split into two equal samples, with exploratory factor analysis conducted in the first sample and confirmatory factor analysis in the second. Test-retest reliability was assessed in a subsample of 64 Veterans. The 45-item MISS-M consists of 10 theoretically grounded subscales assessing guilt, shame, moral concerns, religious struggles, loss of religious faith/hope, loss of meaning/purpose, difficulty forgiving, loss of trust, and self-condemnation. The Cronbach's alpha of the overall scale was .92 and of individual subscales ranged from .56 to .91. The test-retest reliability was .91 for the total scale and ranged from .78 to .90 for subscales. Discriminant validity was demonstrated by relatively weak correlations with other psychosocial, religious, and physical health constructs, and convergent validity was indicated by strong correlations with PTSD, depression, and anxiety symptoms. The MISS-M is a reliable and valid multi-dimensional symptom measure of moral injury that can be used in studies targeting MI in Veterans and Active Duty Military with PTSD symptoms and may also be used by clinicians to identify those at risk.
Factors affecting construction performance: exploratory factor analysis

NASA Astrophysics Data System (ADS)

Soewin, E.; Chinda, T.

2018-04-01

The present work attempts to develop a multidimensional performance evaluation framework for a construction company by considering all relevant measures of performance. Based on the previous studies, this study hypothesizes nine key factors, with a total of 57 associated items. The hypothesized factors, with their associated items, are then used to develop questionnaire survey to gather data. The exploratory factor analysis (EFA) was applied to the collected data which gave rise 10 factors with 57 items affecting construction performance. The findings further reveal that the items constituting ten key performance factors (KPIs) namely; 1) Time, 2) Cost, 3) Quality, 4) Safety & Health, 5) Internal Stakeholder, 6) External Stakeholder, 7) Client Satisfaction, 8) Financial Performance, 9) Environment, and 10) Information, Technology & Innovation. The analysis helps to develop multi-dimensional performance evaluation framework for an effective measurement of the construction performance. The 10 key performance factors can be broadly categorized into economic aspect, social aspect, environmental aspect, and technology aspects. It is important to understand a multi-dimension performance evaluation framework by including all key factors affecting the construction performance of a company, so that the management level can effectively plan to implement an effective performance development plan to match with the mission and vision of the company.
Adsorption of Cd, Cu and Zn from aqueous solutions onto ferronickel slag under different potentially toxic metal combination.

PubMed

Park, Jong-Hwan; Kim, Seong-Heon; Kang, Se-Won; Kang, Byung-Hwa; Cho, Ju-Sik; Heo, Jong-Soo; Delaune, Ronald D; Ok, Yong Sik; Seo, Dong-Cheol

2016-01-01

Adsorption characteristics of potentially toxic metals in single- and multi-metal forms onto ferronickel slag were evaluated. Competitive sorption of metals by ferronickel slag has never been reported previously. The maximum adsorption capacities of toxic metals on ferronickel were in the order of Cd (10.2 mg g(-1)) > Cu (8.4 mg g(-1)) > Zn (4.4 mg g(-1)) in the single-metal adsorption isotherm and Cu (6.1 mg g(-1)) > Cd (2.3 mg g(-1)) > Zn (0.3 mg g(-1)) in the multi-metal adsorption isotherm. In comparison with single-metal adsorption isotherm, the reduction rates of maximum toxic metal adsorption capacity in the multi-metal adsorption isotherm were in the following order of Zn (93%) > Cd (78%) > Cu (27%). The Freundlich isotherm provides a slightly better fit than the Langmuir isotherm equation using ferronickel slag for potentially toxic metal adsorption. Multi-metal adsorption behaviors differed from single-metal adsorption due to competition, based on data obtained from Freundlich and Langmuir adsorption models and three-dimensional simulation. Especially, Cd and Zn were easily exchanged and substituted by Cu during multi-metal adsorption. Further competitive adsorption studies are necessary in order to accurately estimate adsorption capacity of ferronickel slag for potentially toxic metals in natural environments.
A study of performance parameters on drag and heat flux reduction efficiency of combinational novel cavity and opposing jet concept in hypersonic flows

NASA Astrophysics Data System (ADS)

Sun, Xi-wan; Guo, Zhen-yun; Huang, Wei; Li, Shi-bin; Yan, Li

2017-02-01

The drag reduction and thermal protection system applied to hypersonic re-entry vehicles have attracted an increasing attention, and several novel concepts have been proposed by researchers. In the current study, the influences of performance parameters on drag and heat reduction efficiency of combinational novel cavity and opposing jet concept has been investigated numerically. The Reynolds-average Navier-Stokes (RANS) equations coupled with the SST k-ω turbulence model have been employed to calculate its surrounding flowfields, and the first-order spatially accurate upwind scheme appears to be more suitable for three-dimensional flowfields after grid independent analysis. Different cases of performance parameters, namely jet operating conditions, freestream angle of attack and physical dimensions, are simulated based on the verification of numerical method, and the effects on shock stand-off distance, drag force coefficient, surface pressure and heat flux distributions have been analyzed. This is the basic study for drag reduction and thermal protection by multi-objective optimization of the combinational novel cavity and opposing jet concept in hypersonic flows in the future.
High performance multi-spectral interrogation for surface plasmon resonance imaging sensors.

PubMed

Sereda, A; Moreau, J; Canva, M; Maillart, E

2014-04-15

Surface plasmon resonance (SPR) sensing has proven to be a valuable tool in the field of surface interactions characterization, especially for biomedical applications where label-free techniques are of particular interest. In order to approach the theoretical resolution limit, most SPR-based systems have turned to either angular or spectral interrogation modes, which both offer very accurate real-time measurements, but at the expense of the 2-dimensional imaging capability, therefore decreasing the data throughput. In this article, we show numerically and experimentally how to combine the multi-spectral interrogation technique with 2D-imaging, while finding an optimum in terms of resolution, accuracy, acquisition speed and reduction in data dispersion with respect to the classical reflectivity interrogation mode. This multi-spectral interrogation methodology is based on a robust five parameter fitting of the spectral reflectivity curve which enables monitoring of the reflectivity spectral shift with a resolution of the order of ten picometers, and using only five wavelength measurements per point. In fine, such multi-spectral based plasmonic imaging system allows biomolecular interaction monitoring in a linear regime independently of variations of buffer optical index, which is illustrated on a DNA-DNA model case. © 2013 Elsevier B.V. All rights reserved.
Simplifying silicon burning: Application of quasi-equilibrium to (alpha) network nucleosynthesis

NASA Technical Reports Server (NTRS)

Hix, W. R.; Thielemann, F.-K.; Khokhlov, A. M.; Wheeler, J. C.

1997-01-01

While the need for accurate calculation of nucleosynthesis and the resulting rate of thermonuclear energy release within hydrodynamic models of stars and supernovae is clear, the computational expense of these nucleosynthesis calculations often force a compromise in accuracy to reduce the computational cost. To redress this trade-off of accuracy for speed, the authors present an improved nuclear network which takes advantage of quasi- equilibrium in order to reduce the number of independent nuclei, and hence the computational cost of nucleosynthesis, without significant reduction in accuracy. In this paper they will discuss the first application of this method, the further reduction in size of the minimal alpha network. The resultant QSE- reduced alpha network is twice as fast as the conventional alpha network it replaces and requires the tracking of half as many abundance variables, while accurately estimating the rate of energy generation. Such reduction in cost is particularly necessary for future generation of multi-dimensional models for supernovae.
A hybrid (Monte Carlo/deterministic) approach for multi-dimensional radiation transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bal, Guillaume, E-mail: gb2030@columbia.edu; Davis, Anthony B., E-mail: Anthony.B.Davis@jpl.nasa.gov; Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106-4030

2011-08-20

Highlights: {yields} We introduce a variance reduction scheme for Monte Carlo (MC) transport. {yields} The primary application is atmospheric remote sensing. {yields} The technique first solves the adjoint problem using a deterministic solver. {yields} Next, the adjoint solution is used as an importance function for the MC solver. {yields} The adjoint problem is solved quickly since it ignores the volume. - Abstract: A novel hybrid Monte Carlo transport scheme is demonstrated in a scene with solar illumination, scattering and absorbing 2D atmosphere, a textured reflecting mountain, and a small detector located in the sky (mounted on a satellite or amore » airplane). It uses a deterministic approximation of an adjoint transport solution to reduce variance, computed quickly by ignoring atmospheric interactions. This allows significant variance and computational cost reductions when the atmospheric scattering and absorption coefficient are small. When combined with an atmospheric photon-redirection scheme, significant variance reduction (equivalently acceleration) is achieved in the presence of atmospheric interactions.« less
Percolated microstructures for multi-modal transport enhancement in porous active materials

DOEpatents

McKay, Ian Salmon; Yang, Sungwoo; Wang, Evelyn N.; Kim, Hyunho

2018-03-13

A method of forming a composite material for use in multi-modal transport includes providing three-dimensional graphene having hollow channels, enabling a polymer to wick into the hollow channels of the three-dimensional graphene, curing the polymer to form a cured three-dimensional graphene, adding an active material to the cured three-dimensional graphene to form a composite material, and removing the polymer from within the hollow channels. A composite material formed according to the method is also provided.
A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data.

PubMed

Chen Peng; Ao Li

2017-01-01

The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .
Transcending the slow bimolecular recombination in lead-halide perovskites for electroluminescence

PubMed Central

Xing, Guichuan; Wu, Bo; Wu, Xiangyang; Li, Mingjie; Du, Bin; Wei, Qi; Guo, Jia; Yeow, Edwin K. L.; Sum, Tze Chien; Huang, Wei

2017-01-01

The slow bimolecular recombination that drives three-dimensional lead-halide perovskites' outstanding photovoltaic performance is conversely a fundamental limitation for electroluminescence. Under electroluminescence working conditions with typical charge densities lower than 1015 cm−3, defect-states trapping in three-dimensional perovskites competes effectively with the bimolecular radiative recombination. Herein, we overcome this limitation using van-der-Waals-coupled Ruddlesden-Popper perovskite multi-quantum-wells. Injected charge carriers are rapidly localized from adjacent thin few layer (n≤4) multi-quantum-wells to the thick (n≥5) multi-quantum-wells with extremely high efficiency (over 85%) through quantum coupling. Light emission originates from excitonic recombination in the thick multi-quantum-wells at much higher decay rate and efficiency than bimolecular recombination in three-dimensional perovskites. These multi-quantum-wells retain the simple solution processability and high charge carrier mobility of two-dimensional lead-halide perovskites. Importantly, these Ruddlesden-Popper perovskites offer new functionalities unavailable in single phase constituents, permitting the transcendence of the slow bimolecular recombination bottleneck in lead-halide perovskites for efficient electroluminescence. PMID:28239146
Transcending the slow bimolecular recombination in lead-halide perovskites for electroluminescence.

PubMed

Xing, Guichuan; Wu, Bo; Wu, Xiangyang; Li, Mingjie; Du, Bin; Wei, Qi; Guo, Jia; Yeow, Edwin K L; Sum, Tze Chien; Huang, Wei

2017-02-27

The slow bimolecular recombination that drives three-dimensional lead-halide perovskites' outstanding photovoltaic performance is conversely a fundamental limitation for electroluminescence. Under electroluminescence working conditions with typical charge densities lower than 10 15 cm -3 , defect-states trapping in three-dimensional perovskites competes effectively with the bimolecular radiative recombination. Herein, we overcome this limitation using van-der-Waals-coupled Ruddlesden-Popper perovskite multi-quantum-wells. Injected charge carriers are rapidly localized from adjacent thin few layer (n≤4) multi-quantum-wells to the thick (n≥5) multi-quantum-wells with extremely high efficiency (over 85%) through quantum coupling. Light emission originates from excitonic recombination in the thick multi-quantum-wells at much higher decay rate and efficiency than bimolecular recombination in three-dimensional perovskites. These multi-quantum-wells retain the simple solution processability and high charge carrier mobility of two-dimensional lead-halide perovskites. Importantly, these Ruddlesden-Popper perovskites offer new functionalities unavailable in single phase constituents, permitting the transcendence of the slow bimolecular recombination bottleneck in lead-halide perovskites for efficient electroluminescence.
TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS

PubMed Central

Johndrow, James E.; Bhattacharya, Anirban; Dunson, David B.

2017-01-01

Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. We derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions. PMID:29332971

Wideband and multi-frequency infrared cloaking of spherical objects by using the graphene-based metasurface.

PubMed

Shokati, Elnaz; Granpayeh, Nosrat; Danaeifar, Mohammad

2017-04-10

The ultrathin graphene metasurface is proposed as a mantle cloak to achieve wideband tunable scattering reduction around the spherical (three-dimensional) objects. The cloaking shell over the metallic or dielectric sphere is structured by a periodic array of graphene nanodisks that operate at infrared frequencies. By using the polarizability of the graphene nanodisks and equivalent conductivity method, the metasurface reactance is obtained. To achieve the cloaking shell for both dielectric and conducting spheres, the metasurface reactance as a function of nanodisks dimensions, graphene's Fermi energy, and permittivity of the surrounding areas can be tuned from the inductive to capacitive situation. Inhomogeneous metasurfaces including graphene nanodisks with different radii provide wideband invisibility due to extra resonances. We could significantly increase the 3-dB bandwidth more than the homogenous case by simpler realistic designs compared to the multi-layer structures. The analytical results are confirmed with full-wave numerical simulations.
K-P-Burgers equation in negative ion-rich relativistic dusty plasma including the effect of kinematic viscosity

NASA Astrophysics Data System (ADS)

Dev, A. N.; Deka, M. K.; Sarma, J.; Saikia, D.; Adhikary, N. C.

2016-10-01

The stationary solution is obtained for the K-P-Burgers equation that describes the nonlinear propagations of dust ion acoustic waves in a multi-component, collisionless, un-magnetized relativistic dusty plasma consisting of electrons, positive and negative ions in the presence of charged massive dust grains. Here, the Kadomtsev-Petviashvili (K-P) equation, three-dimensional (3D) Burgers equation, and K-P-Burgers equations are derived by using the reductive perturbation method including the effects of viscosity of plasma fluid, thermal energy, ion density, and ion temperature on the structure of a dust ion acoustic shock wave (DIASW). The K-P equation predictes the existences of stationary small amplitude solitary wave, whereas the K-P-Burgers equation in the weakly relativistic regime describes the evolution of shock-like structures in such a multi-ion dusty plasma.
Hα line shape in front of the limiter in the HT-6M tokamak

NASA Astrophysics Data System (ADS)

Wan, Baonian; Li, Jiangang; Luo, Jiarong; Xie, Jikang; Wu, Zhenwei; Zhang, Xianmei; HT-6M Group

1999-11-01

The Hα line shape in front of the limiter in the HT-6M tokamak is analysed by multi-Gaussian fitting. The energy distribution of neutral hydrogen atoms reveals that Hα radiation is contributed by Franck-Condon atoms, atoms reflected at the limiter surface and charge exchange. Multi-Gaussian fitting of the Hα spectral profile indicates contributions of 60% from reflection particles and 40% from molecule dissociation to recycling. Ion temperatures in central regions are obtained from the spectral width of charge exchange components. Dissociation of hydrogen molecules and reflection of particles at the limiter surface are dominant in edge recycling. Reduction of particle reflection at the limiter surface is important for controlling edge recycling. The measured profiles of neutral hydrogen atom density are reproduced by a particle continuity equation and a simplified one dimensional Monte Carlo simulation code.
Secure coherent optical multi-carrier system with four-dimensional modulation space and Stokes vector scrambling.

PubMed

Zhang, Lijia; Liu, Bo; Xin, Xiangjun

2015-06-15

A secure enhanced coherent optical multi-carrier system based on Stokes vector scrambling is proposed and experimentally demonstrated. The optical signal with four-dimensional (4D) modulation space has been scrambled intra- and inter-subcarriers, where a multi-layer logistic map is adopted as the chaotic model. An experiment with 61.71-Gb/s encrypted multi-carrier signal is successfully demonstrated with the proposed method. The results indicate a promising solution for the physical secure optical communication.
Angular dependence of novel magnetic quantum oscillations in a quasi-two-dimensional multiband Fermi liquid with impurities

NASA Astrophysics Data System (ADS)

Bratkovsky, A. M.; Alexandrov, A. S.

2002-03-01

The semiclassical Lifshitz-Kosevich-type description is given for the angular dependence of quantum oscillations with combination frequencies in a multiband quasi-two-dimensional Fermi liquid with a constant number of electrons. The analytical expressions are found for the Dingle, thermal, spin, and amplitude (Yamaji) reduction factors of the novel combination harmonics, where the latter two strongly oscillate with the direction of the field [1]. At the magic angles those factors reduce to the purely two-dimensional expressions given earlier. The combination harmonics are suppressed in the presence of the nonquantized background states, and they decay exponentially faster with temperature and/or disorder compared to the standard harmonics, providing an additional tool for electronic structure determination. The theory is applied to Sr2RuO4. [1] A.M. Bratkovsky and A.S. Alexandrov, Phys. Rev. B 65, xxxx (2002); cond-mat/0104520.
Numerical solutions of 2-D multi-stage rotor/stator unsteady flow interactions

NASA Astrophysics Data System (ADS)

Yang, R.-J.; Lin, S.-J.

1991-01-01

The Rai method of single-stage rotor/stator flow interaction is extended to handle multistage configurations. In this study, a two-dimensional Navier-Stokes multi-zone approach was used to investigate unsteady flow interactions within two multistage axial turbines. The governing equations are solved by an iterative, factored, implicit finite-difference, upwind algorithm. Numerical accuracy is checked by investigating the effect of time step size, the effect of subiteration in the Newton-Raphson technique, and the effect of full viscous versus thin-layer approximation. Computer results compared well with experimental data. Unsteady flow interactions, wake cutting, and the associated evolution of vortical entities are discussed.
Multi-Dimensionality of Synthetic Vision Cockpit Displays: Prevention of Controlled-Flight-Into-Terrain

NASA Technical Reports Server (NTRS)

Prinzel, Lawrence J., III; Kramer, Lynda J.; Arthur, Jarvis J.; Bailey, Randall E.

2006-01-01

NASA's Synthetic Vision Systems (SVS) project is developing technologies with practical applications that will help to eliminate low visibility conditions as a causal factor to civil aircraft accidents while replicating the operational benefits of clear day flight operations, regardless of the actual outside visibility condition. The paper describes experimental evaluation of a multi-mode 3-D exocentric synthetic vision navigation display concept for commercial aircraft. Experimental results showed the situation awareness benefits of 2-D and 3-D exocentric synthetic vision displays over traditional 2-D co-planar navigation and vertical situation displays. Conclusions and future research directions are discussed.
Application of Hyperspectral Techniques to Monitoring and Management of Invasive Plant Species Infestation

DTIC Science & Technology

2008-01-01

the sensor is a data cloud in multi- dimensional space with each band generating an axis of dimension. When the data cloud is viewed in two or three...endmember of interest is not a true endmember in the data space . A ) B) Figure 8: Linear mixture models. A ) two- dimensional ...multi- dimensional space . A classifier is a computer algorithm that takes
A Multi-Resolution Data Structure for Two-Dimensional Morse Functions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bremer, P-T; Edelsbrunner, H; Hamann, B

2003-07-30

The efficient construction of simplified models is a central problem in the field of visualization. We combine topological and geometric methods to construct a multi-resolution data structure for functions over two-dimensional domains. Starting with the Morse-Smale complex we build a hierarchy by progressively canceling critical points in pairs. The data structure supports mesh traversal operations similar to traditional multi-resolution representations.
Vehicle Color Recognition with Vehicle-Color Saliency Detection and Dual-Orientational Dimensionality Reduction of CNN Deep Features

NASA Astrophysics Data System (ADS)

Zhang, Qiang; Li, Jiafeng; Zhuo, Li; Zhang, Hui; Li, Xiaoguang

2017-12-01

Color is one of the most stable attributes of vehicles and often used as a valuable cue in some important applications. Various complex environmental factors, such as illumination, weather, noise and etc., result in the visual characteristics of the vehicle color being obvious diversity. Vehicle color recognition in complex environments has been a challenging task. The state-of-the-arts methods roughly take the whole image for color recognition, but many parts of the images such as car windows; wheels and background contain no color information, which will have negative impact on the recognition accuracy. In this paper, a novel vehicle color recognition method using local vehicle-color saliency detection and dual-orientational dimensionality reduction of convolutional neural network (CNN) deep features has been proposed. The novelty of the proposed method includes two parts: (1) a local vehicle-color saliency detection method has been proposed to determine the vehicle color region of the vehicle image and exclude the influence of non-color regions on the recognition accuracy; (2) dual-orientational dimensionality reduction strategy has been designed to greatly reduce the dimensionality of deep features that are learnt from CNN, which will greatly mitigate the storage and computational burden of the subsequent processing, while improving the recognition accuracy. Furthermore, linear support vector machine is adopted as the classifier to train the dimensionality reduced features to obtain the recognition model. The experimental results on public dataset demonstrate that the proposed method can achieve superior recognition performance over the state-of-the-arts methods.
Multi-Response Optimization of Process Parameters for Imidacloprid Removal by Reverse Osmosis Using Taguchi Design.

PubMed

Genç, Nevim; Doğan, Esra Can; Narcı, Ali Oğuzhan; Bican, Emine

2017-05-01

In this study, a multi-response optimization method using Taguchi's robust design approach is proposed for imidacloprid removal by reverse osmosis. Tests were conducted with different membrane type (BW30, LFC-3, CPA-3), transmembrane pressure (TMP = 20, 25, 30 bar), volume reduction factor (VRF = 2, 3, 4), and pH (3, 7, 11). Quality and quantity of permeate are optimized with the multi-response characteristics of the total dissolved solid (TDS), conductivity, imidacloprid, and total organic carbon (TOC) rejection ratios and flux of permeate. The optimized conditions were determined as membrane type of BW30, TMP 30 bar, VRF 3, and pH 11. Under these conditions, TDS, conductivity, imidacloprid, and TOC rejections and permeate flux were 97.50 97.41, 97.80, 98.00% and 30.60 L/m2·h, respectively. Membrane type was obtained as the most effective factor; its contribution is 64%. The difference between the predicted and observed value of multi-response signal/noise (MRSN) is within the confidence interval.
Predictive brain networks for major depression in a semi-multimodal fusion hierarchical feature reduction framework.

PubMed

Yang, Jie; Yin, Yingying; Zhang, Zuping; Long, Jun; Dong, Jian; Zhang, Yuqun; Xu, Zhi; Li, Lei; Liu, Jie; Yuan, Yonggui

2018-02-05

Major depressive disorder (MDD) is characterized by dysregulation of distributed structural and functional networks. It is now recognized that structural and functional networks are related at multiple temporal scales. The recent emergence of multimodal fusion methods has made it possible to comprehensively and systematically investigate brain networks and thereby provide essential information for influencing disease diagnosis and prognosis. However, such investigations are hampered by the inconsistent dimensionality features between structural and functional networks. Thus, a semi-multimodal fusion hierarchical feature reduction framework is proposed. Feature reduction is a vital procedure in classification that can be used to eliminate irrelevant and redundant information and thereby improve the accuracy of disease diagnosis. Our proposed framework primarily consists of two steps. The first step considers the connection distances in both structural and functional networks between MDD and healthy control (HC) groups. By adding a constraint based on sparsity regularization, the second step fully utilizes the inter-relationship between the two modalities. However, in contrast to conventional multi-modality multi-task methods, the structural networks were considered to play only a subsidiary role in feature reduction and were not included in the following classification. The proposed method achieved a classification accuracy, specificity, sensitivity, and area under the curve of 84.91%, 88.6%, 81.29%, and 0.91, respectively. Moreover, the frontal-limbic system contributed the most to disease diagnosis. Importantly, by taking full advantage of the complementary information from multimodal neuroimaging data, the selected consensus connections may be highly reliable biomarkers of MDD. Copyright © 2017 Elsevier B.V. All rights reserved.
Semi-implicit integration factor methods on sparse grids for high-dimensional systems

NASA Astrophysics Data System (ADS)

Wang, Dongyong; Chen, Weitao; Nie, Qing

2015-07-01

Numerical methods for partial differential equations in high-dimensional spaces are often limited by the curse of dimensionality. Though the sparse grid technique, based on a one-dimensional hierarchical basis through tensor products, is popular for handling challenges such as those associated with spatial discretization, the stability conditions on time step size due to temporal discretization, such as those associated with high-order derivatives in space and stiff reactions, remain. Here, we incorporate the sparse grids with the implicit integration factor method (IIF) that is advantageous in terms of stability conditions for systems containing stiff reactions and diffusions. We combine IIF, in which the reaction is treated implicitly and the diffusion is treated explicitly and exactly, with various sparse grid techniques based on the finite element and finite difference methods and a multi-level combination approach. The overall method is found to be efficient in terms of both storage and computational time for solving a wide range of PDEs in high dimensions. In particular, the IIF with the sparse grid combination technique is flexible and effective in solving systems that may include cross-derivatives and non-constant diffusion coefficients. Extensive numerical simulations in both linear and nonlinear systems in high dimensions, along with applications of diffusive logistic equations and Fokker-Planck equations, demonstrate the accuracy, efficiency, and robustness of the new methods, indicating potential broad applications of the sparse grid-based integration factor method.
Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models

NASA Astrophysics Data System (ADS)

Koziel, Slawomir; Bekasiewicz, Adrian

2016-10-01

Multi-objective optimization of antenna structures is a challenging task owing to the high computational cost of evaluating the design objectives as well as the large number of adjustable parameters. Design speed-up can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation models and design refinement methods permits identification of the Pareto-optimal set of designs within a reasonable timeframe. Here, a study concerning the scalability of surrogate-assisted multi-objective antenna design is carried out based on a set of benchmark problems, with the dimensionality of the design space ranging from six to 24 and a CPU cost of the EM antenna model from 10 to 20 min per simulation. Numerical results indicate that the computational overhead of the design process increases more or less quadratically with the number of adjustable geometric parameters of the antenna structure at hand, which is a promising result from the point of view of handling even more complex problems.
Multi-GPU hybrid programming accelerated three-dimensional phase-field model in binary alloy

NASA Astrophysics Data System (ADS)

Zhu, Changsheng; Liu, Jieqiong; Zhu, Mingfang; Feng, Li

2018-03-01

In the process of dendritic growth simulation, the computational efficiency and the problem scales have extremely important influence on simulation efficiency of three-dimensional phase-field model. Thus, seeking for high performance calculation method to improve the computational efficiency and to expand the problem scales has a great significance to the research of microstructure of the material. A high performance calculation method based on MPI+CUDA hybrid programming model is introduced. Multi-GPU is used to implement quantitative numerical simulations of three-dimensional phase-field model in binary alloy under the condition of multi-physical processes coupling. The acceleration effect of different GPU nodes on different calculation scales is explored. On the foundation of multi-GPU calculation model that has been introduced, two optimization schemes, Non-blocking communication optimization and overlap of MPI and GPU computing optimization, are proposed. The results of two optimization schemes and basic multi-GPU model are compared. The calculation results show that the use of multi-GPU calculation model can improve the computational efficiency of three-dimensional phase-field obviously, which is 13 times to single GPU, and the problem scales have been expanded to 8193. The feasibility of two optimization schemes is shown, and the overlap of MPI and GPU computing optimization has better performance, which is 1.7 times to basic multi-GPU model, when 21 GPUs are used.
Implementation of Finite Volume based Navier Stokes Algorithm Within General Purpose Flow Network Code

NASA Technical Reports Server (NTRS)

Schallhorn, Paul; Majumdar, Alok

2012-01-01

This paper describes a finite volume based numerical algorithm that allows multi-dimensional computation of fluid flow within a system level network flow analysis. There are several thermo-fluid engineering problems where higher fidelity solutions are needed that are not within the capacity of system level codes. The proposed algorithm will allow NASA's Generalized Fluid System Simulation Program (GFSSP) to perform multi-dimensional flow calculation within the framework of GFSSP s typical system level flow network consisting of fluid nodes and branches. The paper presents several classical two-dimensional fluid dynamics problems that have been solved by GFSSP's multi-dimensional flow solver. The numerical solutions are compared with the analytical and benchmark solution of Poiseulle, Couette and flow in a driven cavity.
Large-scale Instability during Gravitational Collapse with Neutrino Transport and a Core-Collapse Supernova

NASA Astrophysics Data System (ADS)

Aksenov, A. G.; Chechetkin, V. M.

2018-04-01

Most of the energy released in the gravitational collapse of the cores of massive stars is carried away by neutrinos. Neutrinos play a pivotal role in explaining core-collape supernovae. Currently, mathematical models of the gravitational collapse are based on multi-dimensional gas dynamics and thermonuclear reactions, while neutrino transport is considered in a simplified way. Multidimensional gas dynamics is used with neutrino transport in the flux-limited diffusion approximation to study the role of multi-dimensional effects. The possibility of large-scale convection is discussed, which is interesting both for explaining SN II and for setting up observations to register possible high-energy (≳10MeV) neutrinos from the supernova. A new multi-dimensional, multi-temperature gas dynamics method with neutrino transport is presented.
Vortex Generators in a Two-Dimensional, External-Compression Supersonic Inlet

NASA Technical Reports Server (NTRS)

Baydar, Ezgihan; Lu, Frank K.; Slater, John W.

2016-01-01

Vortex generators within a two-dimensional, external-compression supersonic inlet for Mach 1.6 were investigated to determine their ability to increase total pressure recovery, reduce total pressure distortion, and improve the boundary layer. The vortex generators studied included vanes and ramps. The geometric factors of the vortex generators studied included height, length, spacing, and positions upstream and downstream of the inlet terminal shock. The flow through the inlet was simulated through the computational solution of the steady-state Reynolds-averaged Navier-Stokes equations on multi-block, structured grids. The vortex generators were simulated by either gridding the geometry of the vortex generators or modeling the vortices generated by the vortex generators. The inlet performance was characterized by the inlet total pressure recovery, total pressure distortion, and incompressible shape factor of the boundary-layer at the engine face. The results suggested that downstream vanes reduced the distortion and improved the boundary layer. The height of the vortex generators had the greatest effect of the geometric factors.
Approximate series solution of multi-dimensional, time fractional-order (heat-like) diffusion equations using FRDTM.

PubMed

Singh, Brajesh K; Srivastava, Vineet K

2015-04-01

The main goal of this paper is to present a new approximate series solution of the multi-dimensional (heat-like) diffusion equation with time-fractional derivative in Caputo form using a semi-analytical approach: fractional-order reduced differential transform method (FRDTM). The efficiency of FRDTM is confirmed by considering four test problems of the multi-dimensional time fractional-order diffusion equation. FRDTM is a very efficient, effective and powerful mathematical tool which provides exact or very close approximate solutions for a wide range of real-world problems arising in engineering and natural sciences, modelled in terms of differential equations.
Approximate series solution of multi-dimensional, time fractional-order (heat-like) diffusion equations using FRDTM

PubMed Central

Singh, Brajesh K.; Srivastava, Vineet K.

2015-01-01

The main goal of this paper is to present a new approximate series solution of the multi-dimensional (heat-like) diffusion equation with time-fractional derivative in Caputo form using a semi-analytical approach: fractional-order reduced differential transform method (FRDTM). The efficiency of FRDTM is confirmed by considering four test problems of the multi-dimensional time fractional-order diffusion equation. FRDTM is a very efficient, effective and powerful mathematical tool which provides exact or very close approximate solutions for a wide range of real-world problems arising in engineering and natural sciences, modelled in terms of differential equations. PMID:26064639

Dimensionality reduction of collective motion by principal manifolds

NASA Astrophysics Data System (ADS)

Gajamannage, Kelum; Butail, Sachit; Porfiri, Maurizio; Bollt, Erik M.

2015-01-01

While the existence of low-dimensional embedding manifolds has been shown in patterns of collective motion, the current battery of nonlinear dimensionality reduction methods is not amenable to the analysis of such manifolds. This is mainly due to the necessary spectral decomposition step, which limits control over the mapping from the original high-dimensional space to the embedding space. Here, we propose an alternative approach that demands a two-dimensional embedding which topologically summarizes the high-dimensional data. In this sense, our approach is closely related to the construction of one-dimensional principal curves that minimize orthogonal error to data points subject to smoothness constraints. Specifically, we construct a two-dimensional principal manifold directly in the high-dimensional space using cubic smoothing splines, and define the embedding coordinates in terms of geodesic distances. Thus, the mapping from the high-dimensional data to the manifold is defined in terms of local coordinates. Through representative examples, we show that compared to existing nonlinear dimensionality reduction methods, the principal manifold retains the original structure even in noisy and sparse datasets. The principal manifold finding algorithm is applied to configurations obtained from a dynamical system of multiple agents simulating a complex maneuver called predator mobbing, and the resulting two-dimensional embedding is compared with that of a well-established nonlinear dimensionality reduction method.
The application of a multi-dimensional assessment approach to talent identification in Australian football.

PubMed

Woods, Carl T; Raynor, Annette J; Bruce, Lyndell; McDonald, Zane; Robertson, Sam

2016-07-01

This study investigated whether a multi-dimensional assessment could assist with talent identification in junior Australian football (AF). Participants were recruited from an elite under 18 (U18) AF competition and classified into two groups; talent identified (State U18 Academy representatives; n = 42; 17.6 ± 0.4 y) and non-talent identified (non-State U18 Academy representatives; n = 42; 17.4 ± 0.5 y). Both groups completed a multi-dimensional assessment, which consisted of physical (standing height, dynamic vertical jump height and 20 m multistage fitness test), technical (kicking and handballing tests) and perceptual-cognitive (video decision-making task) performance outcome tests. A multivariate analysis of variance tested the main effect of status on the test criterions, whilst a receiver operating characteristic curve assessed the discrimination provided from the full assessment. The talent identified players outperformed their non-talent identified peers in each test (P < 0.05). The receiver operating characteristic curve reflected near perfect discrimination (AUC = 95.4%), correctly classifying 95% and 86% of the talent identified and non-talent identified participants, respectively. When compared to single assessment approaches, this multi-dimensional assessment reflects a more comprehensive means of talent identification in AF. This study further highlights the importance of assessing multi-dimensional performance qualities when identifying talented team sports.
Development of an unstructured solution adaptive method for the quasi-three-dimensional Euler and Navier-Stokes equations

NASA Technical Reports Server (NTRS)

Jiang, Yi-Tsann

1993-01-01

A general solution adaptive scheme-based on a remeshing technique is developed for solving the two-dimensional and quasi-three-dimensional Euler and Favre-averaged Navier-Stokes equations. The numerical scheme is formulated on an unstructured triangular mesh utilizing an edge-based pointer system which defines the edge connectivity of the mesh structure. Jameson's four-stage hybrid Runge-Kutta scheme is used to march the solution in time. The convergence rate is enhanced through the use of local time stepping and implicit residual averaging. As the solution evolves, the mesh is regenerated adaptively using flow field information. Mesh adaptation parameters are evaluated such that an estimated local numerical error is equally distributed over the whole domain. For inviscid flows, the present approach generates a complete unstructured triangular mesh using the advancing front method. For turbulent flows, the approach combines a local highly stretched structured triangular mesh in the boundary layer region with an unstructured mesh in the remaining regions to efficiently resolve the important flow features. One-equation and two-equation turbulence models are incorporated into the present unstructured approach. Results are presented for a wide range of flow problems including two-dimensional multi-element airfoils, two-dimensional cascades, and quasi-three-dimensional cascades. This approach is shown to gain flow resolution in the refined regions while achieving a great reduction in the computational effort and storage requirements since solution points are not wasted in regions where they are not required.
Development of an unstructured solution adaptive method for the quasi-three-dimensional Euler and Navier-Stokes equations

NASA Technical Reports Server (NTRS)

Jiang, Yi-Tsann; Usab, William J., Jr.

1993-01-01

A general solution adaptive scheme based on a remeshing technique is developed for solving the two-dimensional and quasi-three-dimensional Euler and Favre-averaged Navier-Stokes equations. The numerical scheme is formulated on an unstructured triangular mesh utilizing an edge-based pointer system which defines the edge connectivity of the mesh structure. Jameson's four-stage hybrid Runge-Kutta scheme is used to march the solution in time. The convergence rate is enhanced through the use of local time stepping and implicit residual averaging. As the solution evolves, the mesh is regenerated adaptively using flow field information. Mesh adaptation parameters are evaluated such that an estimated local numerical error is equally distributed over the whole domain. For inviscid flows, the present approach generates a complete unstructured triangular mesh using the advancing front method. For turbulent flows, the approach combines a local highly stretched structured triangular mesh in the boundary layer region with an unstructured mesh in the remaining regions to efficiently resolve the important flow features. One-equation and two-equation turbulence models are incorporated into the present unstructured approach. Results are presented for a wide range of flow problems including two-dimensional multi-element airfoils, two-dimensional cascades, and quasi-three-dimensional cascades. This approach is shown to gain flow resolution in the refined regions while achieving a great reduction in the computational effort and storage requirements since solution points are not wasted in regions where they are not required.
Assessing the weighted multi-objective adaptive surrogate model optimization to derive large-scale reservoir operating rules with sensitivity analysis

NASA Astrophysics Data System (ADS)

Zhang, Jingwen; Wang, Xu; Liu, Pan; Lei, Xiaohui; Li, Zejun; Gong, Wei; Duan, Qingyun; Wang, Hao

2017-01-01

The optimization of large-scale reservoir system is time-consuming due to its intrinsic characteristics of non-commensurable objectives and high dimensionality. One way to solve the problem is to employ an efficient multi-objective optimization algorithm in the derivation of large-scale reservoir operating rules. In this study, the Weighted Multi-Objective Adaptive Surrogate Model Optimization (WMO-ASMO) algorithm is used. It consists of three steps: (1) simplifying the large-scale reservoir operating rules by the aggregation-decomposition model, (2) identifying the most sensitive parameters through multivariate adaptive regression splines (MARS) for dimensional reduction, and (3) reducing computational cost and speeding the searching process by WMO-ASMO, embedded with weighted non-dominated sorting genetic algorithm II (WNSGAII). The intercomparison of non-dominated sorting genetic algorithm (NSGAII), WNSGAII and WMO-ASMO are conducted in the large-scale reservoir system of Xijiang river basin in China. Results indicate that: (1) WNSGAII surpasses NSGAII in the median of annual power generation, increased by 1.03% (from 523.29 to 528.67 billion kW h), and the median of ecological index, optimized by 3.87% (from 1.879 to 1.809) with 500 simulations, because of the weighted crowding distance and (2) WMO-ASMO outperforms NSGAII and WNSGAII in terms of better solutions (annual power generation (530.032 billion kW h) and ecological index (1.675)) with 1000 simulations and computational time reduced by 25% (from 10 h to 8 h) with 500 simulations. Therefore, the proposed method is proved to be more efficient and could provide better Pareto frontier.
Principal polynomial analysis.

PubMed

Laparra, Valero; Jiménez, Sandra; Tuia, Devis; Camps-Valls, Gustau; Malo, Jesus

2014-11-01

This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of maximal variance by means of curves, instead of straight lines. Contrarily to previous approaches, PPA reduces to performing simple univariate regressions, which makes it computationally feasible and robust. Moreover, PPA shows a number of interesting analytical properties. First, PPA is a volume-preserving map, which in turn guarantees the existence of the inverse. Second, such an inverse can be obtained in closed form. Invertibility is an important advantage over other learning methods, because it permits to understand the identified features in the input domain where the data has physical meaning. Moreover, it allows to evaluate the performance of dimensionality reduction in sensible (input-domain) units. Volume preservation also allows an easy computation of information theoretic quantities, such as the reduction in multi-information after the transform. Third, the analytical nature of PPA leads to a clear geometrical interpretation of the manifold: it allows the computation of Frenet-Serret frames (local features) and of generalized curvatures at any point of the space. And fourth, the analytical Jacobian allows the computation of the metric induced by the data, thus generalizing the Mahalanobis distance. These properties are demonstrated theoretically and illustrated experimentally. The performance of PPA is evaluated in dimensionality and redundancy reduction, in both synthetic and real datasets from the UCI repository.
Modeling Vascularized Bone Regeneration Within a Porous Biodegradable CaP Scaffold Loaded with Growth Factors

PubMed Central

Sun, X; Kang, Y; Bao, J; Zhang, Y; Yang, Y; Zhou, X

2013-01-01

Osteogenetic microenvironment is a complex constitution in which extracellular matrix (ECM) molecules, stem cells and growth factors each interact to direct the coordinate regulation of bone tissue development. Importantly, angiogenesis improvement and revascularization are critical for osteogenesis during bone tissue regeneration processes. In this study, we developed a three-dimensional (3D) multi-scale system model to study cell response to growth factors released from a 3D biodegradable porous calcium phosphate (CaP) scaffold. Our model reconstructed the 3D bone regeneration system and examined the effects of pore size and porosity on bone formation and angiogenesis. The results suggested that scaffold porosity played a more dominant role in affecting bone formation and angiogenesis compared with pore size, while the pore size could be controlled to tailor the growth factor release rate and release fraction. Furthermore, a combination of gradient VEGF with BMP2 and Wnt released from the multi-layer scaffold promoted angiogenesis and bone formation more readily than single growth factors. These results demonstrated that the developed model can be potentially applied to predict vascularized bone regeneration with specific scaffold and growth factors. PMID:23566802
Assessing Children's Homework Performance: Development of Multi-Dimensional, Multi-Informant Rating Scales.

PubMed

Power, Thomas J; Dombrowski, Stefan C; Watkins, Marley W; Mautone, Jennifer A; Eagle, John W

2007-06-01

Efforts to develop interventions to improve homework performance have been impeded by limitations in the measurement of homework performance. This study was conducted to develop rating scales for assessing homework performance among students in elementary and middle school. Items on the scales were intended to assess student strengths as well as deficits in homework performance. The sample included 163 students attending two school districts in the Northeast. Parents completed the 36-item Homework Performance Questionnaire - Parent Scale (HPQ-PS). Teachers completed the 22-item teacher scale (HPQ-TS) for each student for whom the HPQ-PS had been completed. A common factor analysis with principal axis extraction and promax rotation was used to analyze the findings. The results of the factor analysis of the HPQ-PS revealed three salient and meaningful factors: student task orientation/efficiency, student competence, and teacher support. The factor analysis of the HPQ-TS uncovered two salient and substantive factors: student responsibility and student competence. The findings of this study suggest that the HPQ is a promising set of measures for assessing student homework functioning and contextual factors that may influence performance. Directions for future research are presented.
Assessing Children’s Homework Performance: Development of Multi-Dimensional, Multi-Informant Rating Scales

PubMed Central

Power, Thomas J.; Dombrowski, Stefan C.; Watkins, Marley W.; Mautone, Jennifer A.; Eagle, John W.

2007-01-01

Efforts to develop interventions to improve homework performance have been impeded by limitations in the measurement of homework performance. This study was conducted to develop rating scales for assessing homework performance among students in elementary and middle school. Items on the scales were intended to assess student strengths as well as deficits in homework performance. The sample included 163 students attending two school districts in the Northeast. Parents completed the 36-item Homework Performance Questionnaire – Parent Scale (HPQ-PS). Teachers completed the 22-item teacher scale (HPQ-TS) for each student for whom the HPQ-PS had been completed. A common factor analysis with principal axis extraction and promax rotation was used to analyze the findings. The results of the factor analysis of the HPQ-PS revealed three salient and meaningful factors: student task orientation/efficiency, student competence, and teacher support. The factor analysis of the HPQ-TS uncovered two salient and substantive factors: student responsibility and student competence. The findings of this study suggest that the HPQ is a promising set of measures for assessing student homework functioning and contextual factors that may influence performance. Directions for future research are presented. PMID:18516211
Secular Trends in the Incidence of Dementia in a Multi-Ethnic Community.

PubMed

Noble, James M; Schupf, Nicole; Manly, Jennifer J; Andrews, Howard; Tang, Ming-Xin; Mayeux, Richard

2017-10-03

Determination of secular trends in cognitive aging is important for prioritization of resources, services, and research in aging populations. Prior studies have identified declining dementia incidence associated with changes in cardiovascular risk factors and increased educational attainment. However, few studies have examined these factors in multi-ethnic cohorts. To identify secular trends in the incidence rate of dementia in an elderly population. Participants in this study were drawn from the Washington Heights-Inwood Columbia Aging Project, a multi-ethnic cohort study of northern Manhattan residents aged 65 years and older. Cox proportional hazards models were used to examine differences in the incidence of dementia in cohorts recruited in 1992 and 1999, with age at dementia or age at last follow-up visit as the "time-to-event" variable. Overall, there was a 41% reduction in the hazard ratio for dementia among participants in the 1999 cohort compared with those in the 1992 cohort, adjusting for age, sex, race, and baseline memory complaints (HR = 0.59). The reduction in incidence was greatest among non-Hispanic Whites and African-Americans and lowest among Hispanic participants (HRs = 0.60, 0.52 and 0.64, respectively), and was associated with increases in level of educational attainment, especially among African-Americans. Reduction in incidence of dementia was also greater among persons 75 years or older than among younger participants (HR = 0.52 versus HR = 0.69). Our results support previous findings that secular trends in dementia incidence are changing, including in aging minority populations.
Development and Verification of Enclosure Radiation Capabilities in the CHarring Ablator Response (CHAR) Code

NASA Technical Reports Server (NTRS)

Salazar, Giovanni; Droba, Justin C.; Oliver, Brandon; Amar, Adam J.

2016-01-01

With the recent development of multi-dimensional thermal protection system (TPS) material response codes, the capability to account for surface-to-surface radiation exchange in complex geometries is critical. This paper presents recent efforts to implement such capabilities in the CHarring Ablator Response (CHAR) code developed at NASA's Johnson Space Center. This work also describes the different numerical methods implemented in the code to compute geometric view factors for radiation problems involving multiple surfaces. Verification of the code's radiation capabilities and results of a code-to-code comparison are presented. Finally, a demonstration case of a two-dimensional ablating cavity with enclosure radiation accounting for a changing geometry is shown.
The Impact of Learner Characteristics on the Multi-Dimensional Construct of Social Presence

ERIC Educational Resources Information Center

Mykota, David

2017-01-01

This study explored the impact of learner characteristics on the multi-dimensional construct of social presence as measured by the computer-mediated communication questionnaire. Using Multiple Analysis of Variance findings reveal that the number of online courses taken and computer-mediated communication experience significantly affect the…
Development of a Multi-Dimensional Scale for PDD and ADHD

ERIC Educational Resources Information Center

Funabiki, Yasuko; Kawagishi, Hisaya; Uwatoko, Teruhisa; Yoshimura, Sayaka; Murai, Toshiya

2011-01-01

A novel assessment scale, the multi-dimensional scale for pervasive developmental disorder (PDD) and attention-deficit/hyperactivity disorder (ADHD) (MSPA), is reported. Existing assessment scales are intended to establish each diagnosis. However, the diagnosis by itself does not always capture individual characteristics or indicate the level of…
Classification of holter registers by dynamic clustering using multi-dimensional particle swarm optimization.

PubMed

Kiranyaz, Serkan; Ince, Turker; Pulkkinen, Jenni; Gabbouj, Moncef

2010-01-01

In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system.
Applying the methodology of Design of Experiments to stability studies: a Partial Least Squares approach for evaluation of drug stability.

PubMed

Jordan, Nika; Zakrajšek, Jure; Bohanec, Simona; Roškar, Robert; Grabnar, Iztok

2018-05-01

The aim of the present research is to show that the methodology of Design of Experiments can be applied to stability data evaluation, as they can be seen as multi-factor and multi-level experimental designs. Linear regression analysis is usually an approach for analyzing stability data, but multivariate statistical methods could also be used to assess drug stability during the development phase. Data from a stability study for a pharmaceutical product with hydrochlorothiazide (HCTZ) as an unstable drug substance was used as a case example in this paper. The design space of the stability study was modeled using Umetrics MODDE 10.1 software. We showed that a Partial Least Squares model could be used for a multi-dimensional presentation of all data generated in a stability study and for determination of the relationship among factors that influence drug stability. It might also be used for stability predictions and potentially for the optimization of the extent of stability testing needed to determine shelf life and storage conditions, which would be time and cost-effective for the pharmaceutical industry.
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

PubMed Central

Nowicka, Malgorzata; Krieg, Carsten; Weber, Lukas M.; Hartmann, Felix J.; Guglietta, Silvia; Becher, Burkhard; Levesque, Mitchell P.; Robinson, Mark D.

2017-01-01

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals). PMID:28663787
Modeling of Multi-Tube Pulse Detonation Engine Operation

NASA Technical Reports Server (NTRS)

Ebrahimi, Houshang B.; Mohanraj, Rajendran; Merkle, Charles L.

2001-01-01

The present paper explores some preliminary issues concerning the operational characteristics of multiple-tube pulsed detonation engines (PDEs). The study is based on a two-dimensional analysis of the first-pulse operation of two detonation tubes exhausting through a common nozzle. Computations are first performed to assess isolated tube behavior followed by results for multi-tube flow phenomena. The computations are based on an eight-species, finite-rate transient flow-field model. The results serve as an important precursor to understanding appropriate propellant fill procedures and shock wave propagation in multi-tube, multi-dimensional simulations. Differences in behavior between single and multi-tube PDE models are discussed, The influence of multi-tube geometry and the preferred times for injecting the fresh propellant mixture during multi-tube PDE operation are studied.
Geiger-mode avalanche photodiode focal plane arrays for three-dimensional imaging LADAR

NASA Astrophysics Data System (ADS)

Itzler, Mark A.; Entwistle, Mark; Owens, Mark; Patel, Ketan; Jiang, Xudong; Slomkowski, Krystyna; Rangwala, Sabbir; Zalud, Peter F.; Senko, Tom; Tower, John; Ferraro, Joseph

2010-09-01

We report on the development of focal plane arrays (FPAs) employing two-dimensional arrays of InGaAsP-based Geiger-mode avalanche photodiodes (GmAPDs). These FPAs incorporate InP/InGaAs(P) Geiger-mode avalanche photodiodes (GmAPDs) to create pixels that detect single photons at shortwave infrared wavelengths with high efficiency and low dark count rates. GmAPD arrays are hybridized to CMOS read-out integrated circuits (ROICs) that enable independent laser radar (LADAR) time-of-flight measurements for each pixel, providing three-dimensional image data at frame rates approaching 200 kHz. Microlens arrays are used to maintain high fill factor of greater than 70%. We present full-array performance maps for two different types of sensors optimized for operation at 1.06 μm and 1.55 μm, respectively. For the 1.06 μm FPAs, overall photon detection efficiency of >40% is achieved at <20 kHz dark count rates with modest cooling to ~250 K using integrated thermoelectric coolers. We also describe the first evalution of these FPAs when multi-photon pulses are incident on single pixels. The effective detection efficiency for multi-photon pulses shows excellent agreement with predictions based on Poisson statistics. We also characterize the crosstalk as a function of pulse mean photon number. Relative to the intrinsic crosstalk contribution from hot carrier luminescence that occurs during avalanche current flows resulting from single incident photons, we find a modest rise in crosstalk for multi-photon incident pulses that can be accurately explained by direct optical scattering.
Differentially Private Synthesization of Multi-Dimensional Data using Copula Functions

PubMed Central

Li, Haoran; Xiong, Li; Jiang, Xiaoqian

2014-01-01

Differential privacy has recently emerged in private statistical data release as one of the strongest privacy guarantees. Most of the existing techniques that generate differentially private histograms or synthetic data only work well for single dimensional or low-dimensional histograms. They become problematic for high dimensional and large domain data due to increased perturbation error and computation complexity. In this paper, we propose DPCopula, a differentially private data synthesization technique using Copula functions for multi-dimensional data. The core of our method is to compute a differentially private copula function from which we can sample synthetic data. Copula functions are used to describe the dependence between multivariate random vectors and allow us to build the multivariate joint distribution using one-dimensional marginal distributions. We present two methods for estimating the parameters of the copula functions with differential privacy: maximum likelihood estimation and Kendall’s τ estimation. We present formal proofs for the privacy guarantee as well as the convergence property of our methods. Extensive experiments using both real datasets and synthetic datasets demonstrate that DPCopula generates highly accurate synthetic multi-dimensional data with significantly better utility than state-of-the-art techniques. PMID:25405241
A Multi Directional Perfect Reconstruction Filter Bank Designed with 2-D Eigenfilter Approach: Application to Ultrasound Speckle Reduction.

PubMed

Nagare, Mukund B; Patil, Bhushan D; Holambe, Raghunath S

2017-02-01

B-Mode ultrasound images are degraded by inherent noise called Speckle, which creates a considerable impact on image quality. This noise reduces the accuracy of image analysis and interpretation. Therefore, reduction of speckle noise is an essential task which improves the accuracy of the clinical diagnostics. In this paper, a Multi-directional perfect-reconstruction (PR) filter bank is proposed based on 2-D eigenfilter approach. The proposed method used for the design of two-dimensional (2-D) two-channel linear-phase FIR perfect-reconstruction filter bank. In this method, the fan shaped, diamond shaped and checkerboard shaped filters are designed. The quadratic measure of the error function between the passband and stopband of the filter has been used an objective function. First, the low-pass analysis filter is designed and then the PR condition has been expressed as a set of linear constraints on the corresponding synthesis low-pass filter. Subsequently, the corresponding synthesis filter is designed using the eigenfilter design method with linear constraints. The newly designed 2-D filters are used in translation invariant pyramidal directional filter bank (TIPDFB) for reduction of speckle noise in ultrasound images. The proposed 2-D filters give better symmetry, regularity and frequency selectivity of the filters in comparison to existing design methods. The proposed method is validated on synthetic and real ultrasound data which ensures improvement in the quality of ultrasound images and efficiently suppresses the speckle noise compared to existing methods.

Quadratic Frequency Modulation Signals Parameter Estimation Based on Two-Dimensional Product Modified Parameterized Chirp Rate-Quadratic Chirp Rate Distribution.

PubMed

Qu, Zhiyu; Qu, Fuxin; Hou, Changbo; Jing, Fulong

2018-05-19

In an inverse synthetic aperture radar (ISAR) imaging system for targets with complex motion, the azimuth echo signals of the target are always modeled as multicomponent quadratic frequency modulation (QFM) signals. The chirp rate (CR) and quadratic chirp rate (QCR) estimation of QFM signals is very important to solve the ISAR image defocus problem. For multicomponent QFM (multi-QFM) signals, the conventional QR and QCR estimation algorithms suffer from the cross-term and poor anti-noise ability. This paper proposes a novel estimation algorithm called a two-dimensional product modified parameterized chirp rate-quadratic chirp rate distribution (2D-PMPCRD) for QFM signals parameter estimation. The 2D-PMPCRD employs a multi-scale parametric symmetric self-correlation function and modified nonuniform fast Fourier transform-Fast Fourier transform to transform the signals into the chirp rate-quadratic chirp rate (CR-QCR) domains. It can greatly suppress the cross-terms while strengthening the auto-terms by multiplying different CR-QCR domains with different scale factors. Compared with high order ambiguity function-integrated cubic phase function and modified Lv's distribution, the simulation results verify that the 2D-PMPCRD acquires higher anti-noise performance and obtains better cross-terms suppression performance for multi-QFM signals with reasonable computation cost.
Faulting of Rocks in a Three-Dimensional Stress Field by Micro-Anticracks

PubMed Central

Ghaffari, H. O.; Nasseri, M. H. B.; Young, R. Paul

2014-01-01

Nucleation and propagation of a shear fault is known to be the result of interaction and coalescence of many microcracks. Yet the character and rate of the microcracks' interactions, and their dependence on the three-dimensional stress state are poorly understood. Here we investigate formation of microcracks during sandstone faulting under 3D-polyaxial stress fields by analyzing multi-stationary acoustic waveforms. We show that in a true three-dimensional stress state (a) faulting forms in a orthorhombic pattern, and (b) the emitted acoustic waveforms from microcracking carry a shorter rapid slip phase. The later is associated with microcracking that dominantly develops parallel to the minimum stress direction. Our results imply that due to inducing the micro-anticracks, the three-dimensional (3D) stress state can quicken dynamic weakening and rupture propagation by a factor of two relatively to simpler stress states. The results suggest a new nucleation mechanism of 3D-faulting with implications for earthquakes' instabilities, as well as the understanding of avalanches associated with dislocations. PMID:24862447
Wing download reduction using vortex trapping plates

NASA Technical Reports Server (NTRS)

Light, Jeffrey S.; Stremel, Paul M.; Bilanin, Alan J.

1994-01-01

A download reduction technique using spanwise plates on the upper and lower wing surfaces has been examined. Experimental and analytical techniques were used to determine the download reduction obtained using this technique. Simple two-dimensional wind tunnel testing confirmed the validity of the technique for reducing two-dimensional airfoil drag. Computations using a two-dimensional Navier-Stokes analysis provided insight into the mechanism causing the drag reduction. Finally, the download reduction technique was tested using a rotor and wing to determine the benefits for a semispan configuration representative of a tilt rotor aircraft.
Fukunaga-Koontz transform based dimensionality reduction for hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ochilov, S.; Alam, M. S.; Bal, A.

2006-05-01

Fukunaga-Koontz Transform based technique offers some attractive properties for desired class oriented dimensionality reduction in hyperspectral imagery. In FKT, feature selection is performed by transforming into a new space where feature classes have complimentary eigenvectors. Dimensionality reduction technique based on these complimentary eigenvector analysis can be described under two classes, desired class and background clutter, such that each basis function best represent one class while carrying the least amount of information from the second class. By selecting a few eigenvectors which are most relevant to desired class, one can reduce the dimension of hyperspectral cube. Since the FKT based technique reduces data size, it provides significant advantages for near real time detection applications in hyperspectral imagery. Furthermore, the eigenvector selection approach significantly reduces computation burden via the dimensionality reduction processes. The performance of the proposed dimensionality reduction algorithm has been tested using real-world hyperspectral dataset.
Wavepacket dynamics and the multi-configurational time-dependent Hartree approach

NASA Astrophysics Data System (ADS)

Manthe, Uwe

2017-06-01

Multi-configurational time-dependent Hartree (MCTDH) based approaches are efficient, accurate, and versatile methods for high-dimensional quantum dynamics simulations. Applications range from detailed investigations of polyatomic reaction processes in the gas phase to high-dimensional simulations studying the dynamics of condensed phase systems described by typical solid state physics model Hamiltonians. The present article presents an overview of the different areas of application and provides a comprehensive review of the underlying theory. The concepts and guiding ideas underlying the MCTDH approach and its multi-mode and multi-layer extensions are discussed in detail. The general structure of the equations of motion is highlighted. The representation of the Hamiltonian and the correlated discrete variable representation (CDVR), which provides an efficient multi-dimensional quadrature in MCTDH calculations, are discussed. Methods which facilitate the calculation of eigenstates, the evaluation of correlation functions, and the efficient representation of thermal ensembles in MCTDH calculations are described. Different schemes for the treatment of indistinguishable particles in MCTDH calculations and recent developments towards a unified multi-layer MCTDH theory for systems including bosons and fermions are discussed.
Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.

PubMed

Qu, Kaiyang; Han, Ke; Wu, Song; Wang, Guohua; Wei, Leyi

2017-09-22

DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.
Combustion Dynamics in Multi-Nozzle Combustors Operating on High-Hydrogen Fuels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Santavicca, Dom; Lieuwen, Tim

Actual gas turbine combustors for power generation applications employ multi-nozzle combustor configurations. Researchers at Penn State and Georgia Tech have extended previous work on the flame response in single-nozzle combustors to the more realistic case of multi-nozzle combustors. Research at Georgia Tech has shown that asymmetry of both the flow field and the acoustic forcing can have a significant effect on flame response and that such behavior is important in multi-flame configurations. As a result, the structure of the flame and its response to forcing is three-dimensional. Research at Penn State has led to the development of a three-dimensional chemiluminescencemore » flame imaging technique that can be used to characterize the unforced (steady) and forced (unsteady) flame structure of multi-nozzle combustors. Important aspects of the flame response in multi-nozzle combustors which are being studied include flame-flame and flame-wall interactions. Research at Penn State using the recently developed three-dimensional flame imaging technique has shown that spatial variations in local flame confinement must be accounted for to accurately predict global flame response in a multi-nozzle can combustor.« less
Fabrication of multi-well chips for spheroid cultures and implantable constructs through rapid prototyping techniques.

PubMed

Lopa, Silvia; Piraino, Francesco; Kemp, Raymond J; Di Caro, Clelia; Lovati, Arianna B; Di Giancamillo, Alessia; Moroni, Lorenzo; Peretti, Giuseppe M; Rasponi, Marco; Moretti, Matteo

2015-07-01

Three-dimensional (3D) culture models are widely used in basic and translational research. In this study, to generate and culture multiple 3D cell spheroids, we exploited laser ablation and replica molding for the fabrication of polydimethylsiloxane (PDMS) multi-well chips, which were validated using articular chondrocytes (ACs). Multi-well ACs spheroids were comparable or superior to standard spheroids, as revealed by glycosaminoglycan and type-II collagen deposition. Moreover, the use of our multi-well chips significantly reduced the operation time for cell seeding and medium refresh. Exploiting a similar approach, we used clinical-grade fibrin to generate implantable multi-well constructs allowing for the precise distribution of multiple cell types. Multi-well fibrin constructs were seeded with ACs generating high cell density regions, as shown by histology and cell fluorescent staining. Multi-well constructs were compared to standard constructs with homogeneously distributed ACs. After 7 days in vitro, expression of SOX9, ACAN, COL2A1, and COMP was increased in both constructs, with multi-well constructs expressing significantly higher levels of chondrogenic genes than standard constructs. After 5 weeks in vivo, we found that despite a dramatic size reduction, the cell distribution pattern was maintained and glycosaminoglycan content per wet weight was significantly increased respect to pre-implantation samples. In conclusion, multi-well chips for the generation and culture of multiple cell spheroids can be fabricated by low-cost rapid prototyping techniques. Furthermore, these techniques can be used to generate implantable constructs with defined architecture and controlled cell distribution, allowing for in vitro and in vivo investigation of cell interactions in a 3D environment. © 2015 Wiley Periodicals, Inc.
A Fourier dimensionality reduction model for big data interferometric imaging

NASA Astrophysics Data System (ADS)

Vijay Kartik, S.; Carrillo, Rafael E.; Thiran, Jean-Philippe; Wiaux, Yves

2017-06-01

Data dimensionality reduction in radio interferometry can provide savings of computational resources for image reconstruction through reduced memory footprints and lighter computations per iteration, which is important for the scalability of imaging methods to the big data setting of the next-generation telescopes. This article sheds new light on dimensionality reduction from the perspective of the compressed sensing theory and studies its interplay with imaging algorithms designed in the context of convex optimization. We propose a post-gridding linear data embedding to the space spanned by the left singular vectors of the measurement operator, providing a dimensionality reduction below image size. This embedding preserves the null space of the measurement operator and hence its sampling properties are also preserved in light of the compressed sensing theory. We show that this can be approximated by first computing the dirty image and then applying a weighted subsampled discrete Fourier transform to obtain the final reduced data vector. This Fourier dimensionality reduction model ensures a fast implementation of the full measurement operator, essential for any iterative image reconstruction method. The proposed reduction also preserves the independent and identically distributed Gaussian properties of the original measurement noise. For convex optimization-based imaging algorithms, this is key to justify the use of the standard ℓ2-norm as the data fidelity term. Our simulations confirm that this dimensionality reduction approach can be leveraged by convex optimization algorithms with no loss in imaging quality relative to reconstructing the image from the complete visibility data set. Reconstruction results in simulation settings with no direction dependent effects or calibration errors show promising performance of the proposed dimensionality reduction. Further tests on real data are planned as an extension of the current work. matlab code implementing the proposed reduction method is available on GitHub.
A Replication Study on the Multi-Dimensionality of Online Social Presence

ERIC Educational Resources Information Center

Mykota, David B.

2015-01-01

The purpose of the present study is to conduct an external replication into the multi-dimensionality of social presence as measured by the Computer-Mediated Communication Questionnaire (Tu, 2005). Online social presence is one of the more important constructs for determining the level of interaction and effectiveness of learning in an online…
Impact of Malaysian Polytechnics' Head of Department Multi-Dimensional Leadership Orientation towards Lecturers Work Commitment

ERIC Educational Resources Information Center

Ibrahim, Mohammed Sani; Mujir, Siti Junaidah Mohd

2012-01-01

The purpose of this study is to determine if the multi-dimensional leadership orientation of the heads of departments in Malaysian polytechnics affects their leadership effectiveness and the lecturers' commitment to work as perceived by the lecturers. The departmental heads' leadership orientation was determined by five leadership dimensions…
Multi-band transmission color filters for multi-color white LEDs based visible light communication

NASA Astrophysics Data System (ADS)

Wang, Qixia; Zhu, Zhendong; Gu, Huarong; Chen, Mengzhu; Tan, Qiaofeng

2017-11-01

Light-emitting diodes (LEDs) based visible light communication (VLC) can provide license-free bands, high data rates, and high security levels, which is a promising technique that will be extensively applied in future. Multi-band transmission color filters with enough peak transmittance and suitable bandwidth play a pivotal role for boosting signal-noise-ratio in VLC systems. In this paper, multi-band transmission color filters with bandwidth of dozens nanometers are designed by a simple analytical method. Experiment results of one-dimensional (1D) and two-dimensional (2D) tri-band color filters demonstrate the effectiveness of the multi-band transmission color filters and the corresponding analytical method.
A Corresponding Lie Algebra of a Reductive homogeneous Group and Its Applications

NASA Astrophysics Data System (ADS)

Zhang, Yu-Feng; Wu, Li-Xin; Rui, Wen-Juan

2015-05-01

With the help of a Lie algebra of a reductive homogeneous space G/K, where G is a Lie group and K is a resulting isotropy group, we introduce a Lax pair for which an expanding (2+1)-dimensional integrable hierarchy is obtained by applying the binormial-residue representation (BRR) method, whose Hamiltonian structure is derived from the trace identity for deducing (2+1)-dimensional integrable hierarchies, which was proposed by Tu, et al. We further consider some reductions of the expanding integrable hierarchy obtained in the paper. The first reduction is just right the (2+1)-dimensional AKNS hierarchy, the second-type reduction reveals an integrable coupling of the (2+1)-dimensional AKNS equation (also called the Davey-Stewartson hierarchy), a kind of (2+1)-dimensional Schrödinger equation, which was once reobtained by Tu, Feng and Zhang. It is interesting that a new (2+1)-dimensional integrable nonlinear coupled equation is generated from the reduction of the part of the (2+1)-dimensional integrable coupling, which is further reduced to the standard (2+1)-dimensional diffusion equation along with a parameter. In addition, the well-known (1+1)-dimensional AKNS hierarchy, the (1+1)-dimensional nonlinear Schrödinger equation are all special cases of the (2+1)-dimensional expanding integrable hierarchy. Finally, we discuss a few discrete difference equations of the diffusion equation whose stabilities are analyzed by making use of the von Neumann condition and the Fourier method. Some numerical solutions of a special stationary initial value problem of the (2+1)-dimensional diffusion equation are obtained and the resulting convergence and estimation formula are investigated. Supported by the Innovation Team of Jiangsu Province hosted by China University of Mining and Technology (2014), the National Natural Science Foundation of China under Grant No. 11371361, the Fundamental Research Funds for the Central Universities (2013XK03), and the Natural Science Foundation of Shandong Province under Grant No. ZR2013AL016
Three-dimensional scrape off layer transport in the helically symmetric experiment HSX

NASA Astrophysics Data System (ADS)

Akerson, A. R.; Bader, A.; Hegna, C. C.; Schmitz, O.; Stephey, L. A.; Anderson, D. T.; Anderson, F. S. B.; Likin, K. M.

2016-08-01

The edge topology of helically symmetric experiment (HSX) in the quasi-helically symmetric configuration is characterized by an 8/7 magnetic island remnant embedded in a short connection length scrape-off layer (SOL) domain. A 2D mapping of edge plasma profiles within this heterogeneous SOL has been constructed using a movable, multi-pin Langmuir probe. Comparisons of these measurements to edge simulations using the EMC3-EIRENE 3D plasma fluid and kinetic neutral gas transport model have been performed. The measurements provide strong evidence that particle transport is diffusive within the island region and dominantly convective in the SOL region. Measurements indicate that phenomenological cross-field diffusion coefficients are low in the SOL region between the last closed flux surface and edge island (i.e. {{D}\\bot}≈ 0.03 m2 s-1). This level of transport was found to increase by a factor of two when a limiter is inserted almost completely into the magnetic island. A reduction in gradients of the edge electrostatic plasma potential was also measured in this configuration, suggesting that the reduced electric field may be linked to the increased cross-field transport observed.
Three-dimensional flow characteristics of aluminum alloy in multi-pass equal channel angular pressing

NASA Astrophysics Data System (ADS)

Jin, Young-Gwan; Son, Il-Heon; Im, Yong-Taek

2010-06-01

Experiments with a square specimen made of commercially pure aluminum alloy (AA1050) were conducted to investigate deformation behaviour during a multi-pass Equal Channel Angular Pressing (ECAP) for routes A, Bc, and C up to four passes. Three-dimensional finite element numerical simulations of the multi-pass ECAP were carried out in order to evaluate the influence of processing routes and number of passes on local flow behaviour by applying a simplified saturation model of flow stress under an isothermal condition. Simulation results were investigated by comparing them with the experimentally measured data in terms of load variations and microhardness distributions. Also, transmission electron microscopy analysis was employed to investigate the microstructural changes. The present work clearly shows that the three-dimensional flow characteristics of the deformed specimen were dependent on the strain path changes due to the processing routes and number of passes that occurred during the multi-pass ECAP.
A genetically optimized kinetic model for ethanol electro-oxidation on Pt-based binary catalysts used in direct ethanol fuel cells

NASA Astrophysics Data System (ADS)

Sánchez-Monreal, Juan; García-Salaberri, Pablo A.; Vera, Marcos

2017-09-01

A one-dimensional model is proposed for the anode of a liquid-feed direct ethanol fuel cell. The complex kinetics of the ethanol electro-oxidation reaction is described using a multi-step reaction mechanism that considers free and adsorbed intermediate species on Pt-based binary catalysts. The adsorbed species are modeled using coverage factors to account for the blockage of the active reaction sites on the catalyst surface. The reaction rates are described by Butler-Volmer equations that are coupled to a one-dimensional mass transport model, which incorporates the effect of ethanol and acetaldehyde crossover. The proposed kinetic model circumvents the acetaldehyde bottleneck effect observed in previous studies by incorporating CH3CHOHads among the adsorbed intermediates. A multi-objetive genetic algorithm is used to determine the reaction constants using anode polarization and product selectivity data obtained from the literature. By adjusting the reaction constants using the methodology developed here, different catalyst layers could be modeled and their selectivities could be successfully reproduced.
Application of the multifactor dimensionality reduction method in evaluation of the roles of multiple genes/enzymes in multidrug-resistant acquisition in Pseudomonas aeruginosa strains.

PubMed

Yao, Z; Peng, Y; Bi, J; Xie, C; Chen, X; Li, Y; Ye, X; Zhou, J

2016-03-01

Multidrug-resistant Pseudomonas aeruginosa (MDRPA) infections are major threats to healthcare-associated infection control and the intrinsic molecular mechanisms of MDRPA are also unclear. We examined 348 isolates of P. aeruginosa, including 188 MDRPA and 160 non-MDRPA, obtained from five tertiary-care hospitals in Guangzhou, China. Significant correlations were found between gene/enzyme carriage and increased rates of antimicrobial resistance (P < 0·01). gyrA mutation, OprD loss and metallo-β-lactamase (MBL) presence were identified as crucial molecular risk factors for MDRPA acquisition by a combination of univariate logistic regression and a multifactor dimensionality reduction approach. The MDRPA rate was also elevated with the increase in positive numbers of those three determinants (P < 0·001). Thus, gyrA mutation, OprD loss and MBL presence may serve as predictors for early screening of MDRPA infections in clinical settings.
A three-dimensional Navier-Stokes stage analysis of the flow through a compact radial turbine

NASA Technical Reports Server (NTRS)

Heidmann, James D.

1991-01-01

A steady, three dimensional Navier-Stokes average passage computer code is used to analyze the flow through a compact radial turbine stage. The code is based upon the average passage set of equations for turbomachinery, whereby the flow fields for all passages in a given blade row are assumed to be identical while retaining their three-dimensionality. A stage solution is achieved by alternating between stator and rotor calculations, while coupling the two solutions by means of a set of axisymmetric body forces which model the absent blade row. Results from the stage calculation are compared with experimental data and with results from an isolated rotor solution having axisymmetric inlet flow quantities upstream of the vacated stator space. Although the mass-averaged loss through the rotor is comparable for both solutions, the details of the loss distribution differ due to stator effects. The stage calculation predicts smaller spanwise variations in efficiency, in closer agreement with the data. The results of the study indicate that stage analyses hold promise for improved prediction of loss mechanisms in multi-blade row turbomachinery, which could lead to improved designs through the reduction of these losses.
A three-dimensional Navier-Stokes stage analysis of the flow through a compact radial turbine

NASA Technical Reports Server (NTRS)

Heidmann, James D.

1991-01-01

A steady, three-dimensional Navier-Stokes average passage computer code is used to analyze the flow through a compact radial turbine stage. The code is based upon the average passage set of equations for turbomachinery, whereby the flow fields for all passages in a given blade row are assumed to be identical while retaining their three-dimensionality. A stage solution is achieved by alternating between stator and rotor calculations, while coupling the two solutions by means of a set of axisymmetric body forces which model the absent blade row. Results from the stage calculation are compared with experimental data and with results from an isolated rotor solution having axisymmetric inlet flow quantities upstream of the vacated stator space. Although the mass-averaged loss through the rotor is comparable for both solutions, the details of the loss distribution differ due to stator effects. The stage calculation predicts smaller spanwise variations in efficiency, in closer agreement with the data. The results of the study indicate that stage analyses hold promise for improved prediction of loss mechanisms in multi-blade row turbomachinery, which could lead to improved designs through the reduction of these losses.
A support vector machine based test for incongruence between sets of trees in tree space

PubMed Central

2012-01-01

Background The increased use of multi-locus data sets for phylogenetic reconstruction has increased the need to determine whether a set of gene trees significantly deviate from the phylogenetic patterns of other genes. Such unusual gene trees may have been influenced by other evolutionary processes such as selection, gene duplication, or horizontal gene transfer. Results Motivated by this problem we propose a nonparametric goodness-of-fit test for two empirical distributions of gene trees, and we developed the software GeneOut to estimate a p-value for the test. Our approach maps trees into a multi-dimensional vector space and then applies support vector machines (SVMs) to measure the separation between two sets of pre-defined trees. We use a permutation test to assess the significance of the SVM separation. To demonstrate the performance of GeneOut, we applied it to the comparison of gene trees simulated within different species trees across a range of species tree depths. Applied directly to sets of simulated gene trees with large sample sizes, GeneOut was able to detect very small differences between two set of gene trees generated under different species trees. Our statistical test can also include tree reconstruction into its test framework through a variety of phylogenetic optimality criteria. When applied to DNA sequence data simulated from different sets of gene trees, results in the form of receiver operating characteristic (ROC) curves indicated that GeneOut performed well in the detection of differences between sets of trees with different distributions in a multi-dimensional space. Furthermore, it controlled false positive and false negative rates very well, indicating a high degree of accuracy. Conclusions The non-parametric nature of our statistical test provides fast and efficient analyses, and makes it an applicable test for any scenario where evolutionary or other factors can lead to trees with different multi-dimensional distributions. The software GeneOut is freely available under the GNU public license. PMID:22909268

A fast multi-resolution approach to tomographic PIV

NASA Astrophysics Data System (ADS)

Discetti, Stefano; Astarita, Tommaso

2012-03-01

Tomographic particle image velocimetry (Tomo-PIV) is a recently developed three-component, three-dimensional anemometric non-intrusive measurement technique, based on an optical tomographic reconstruction applied to simultaneously recorded images of the distribution of light intensity scattered by seeding particles immersed into the flow. Nowadays, the reconstruction process is carried out mainly by iterative algebraic reconstruction techniques, well suited to handle the problem of limited number of views, but computationally intensive and memory demanding. The adoption of the multiplicative algebraic reconstruction technique (MART) has become more and more accepted. In the present work, a novel multi-resolution approach is proposed, relying on the adoption of a coarser grid in the first step of the reconstruction to obtain a fast estimation of a reliable and accurate first guess. A performance assessment, carried out on three-dimensional computer-generated distributions of particles, shows a substantial acceleration of the reconstruction process for all the tested seeding densities with respect to the standard method based on 5 MART iterations; a relevant reduction in the memory storage is also achieved. Furthermore, a slight accuracy improvement is noticed. A modified version, improved by a multiplicative line of sight estimation of the first guess on the compressed configuration, is also tested, exhibiting a further remarkable decrease in both memory storage and computational effort, mostly at the lowest tested seeding densities, while retaining the same performances in terms of accuracy.
Multi-level emulation of complex climate model responses to boundary forcing data

NASA Astrophysics Data System (ADS)

Tran, Giang T.; Oliver, Kevin I. C.; Holden, Philip B.; Edwards, Neil R.; Sóbester, András; Challenor, Peter

2018-04-01

Climate model components involve both high-dimensional input and output fields. It is desirable to efficiently generate spatio-temporal outputs of these models for applications in integrated assessment modelling or to assess the statistical relationship between such sets of inputs and outputs, for example, uncertainty analysis. However, the need for efficiency often compromises the fidelity of output through the use of low complexity models. Here, we develop a technique which combines statistical emulation with a dimensionality reduction technique to emulate a wide range of outputs from an atmospheric general circulation model, PLASIM, as functions of the boundary forcing prescribed by the ocean component of a lower complexity climate model, GENIE-1. Although accurate and detailed spatial information on atmospheric variables such as precipitation and wind speed is well beyond the capability of GENIE-1's energy-moisture balance model of the atmosphere, this study demonstrates that the output of this model is useful in predicting PLASIM's spatio-temporal fields through multi-level emulation. Meaningful information from the fast model, GENIE-1 was extracted by utilising the correlation between variables of the same type in the two models and between variables of different types in PLASIM. We present here the construction and validation of several PLASIM variable emulators and discuss their potential use in developing a hybrid model with statistical components.
A fractional factorial probabilistic collocation method for uncertainty propagation of hydrologic model parameters in a reduced dimensional space

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Huang, W.; Fan, Y. R.; Li, Z.

2015-10-01

In this study, a fractional factorial probabilistic collocation method is proposed to reveal statistical significance of hydrologic model parameters and their multi-level interactions affecting model outputs, facilitating uncertainty propagation in a reduced dimensional space. The proposed methodology is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability, as well as its capability of revealing complex and dynamic parameter interactions. A set of reduced polynomial chaos expansions (PCEs) only with statistically significant terms can be obtained based on the results of factorial analysis of variance (ANOVA), achieving a reduction of uncertainty in hydrologic predictions. The predictive performance of reduced PCEs is verified by comparing against standard PCEs and the Monte Carlo with Latin hypercube sampling (MC-LHS) method in terms of reliability, sharpness, and Nash-Sutcliffe efficiency (NSE). Results reveal that the reduced PCEs are able to capture hydrologic behaviors of the Xiangxi River watershed, and they are efficient functional representations for propagating uncertainties in hydrologic predictions.
Three-dimensional black-blood multi-contrast carotid imaging using compressed sensing: a repeatability study.

PubMed

Yuan, Jianmin; Usman, Ammara; Reid, Scott A; King, Kevin F; Patterson, Andrew J; Gillard, Jonathan H; Graves, Martin J

2018-02-01

The purpose of this work is to evaluate the repeatability of a compressed sensing (CS) accelerated multi-contrast carotid protocol at 3 T. Twelve volunteers and eight patients with carotid disease were scanned on a 3 T MRI scanner using a CS accelerated 3-D black-blood multi-contrast protocol which comprises T 1 w, T 2 w and PDw without CS, and with a CS factor of 1.5 and 2.0. The volunteers were scanned twice, the lumen/wall area and wall thickness were measured for each scan. Eight patients were scanned once, the inter/intra-observer reproducibility of the measurements was calculated. In the repeated volunteer scans, the interclass correlation coefficient (ICC) for the wall area measurement using a CS factor of 1.5 in PDw, T 1 w and T 2 w were 0.95, 0.81, and 0.97, respectively. The ICC for lumen area measurement using a CS factor of 1.5 in PDw, T 1 w and T 2 w were 0.96, 0.92, and 0.96, respectively. In patients, the ICC for inter/intra-observer measurements of lumen/wall area, and wall thickness were all above 0.81 in all sequences. The results show a CS accelerated 3-D black-blood multi-contrast protocol is a robust and reproducible method for carotid imaging. Future protocol design could use CS to reduce the scanning time.
3-Dimensional and Interactive Istanbul University Virtual Laboratory Based on Active Learning Methods

ERIC Educational Resources Information Center

Ince, Elif; Kirbaslar, Fatma Gulay; Yolcu, Ergun; Aslan, Ayse Esra; Kayacan, Zeynep Cigdem; Alkan Olsson, Johanna; Akbasli, Ayse Ceylan; Aytekin, Mesut; Bauer, Thomas; Charalambis, Dimitris; Gunes, Zeliha Ozsoy; Kandemir, Ceyhan; Sari, Umit; Turkoglu, Suleyman; Yaman, Yavuz; Yolcu, Ozgu

2014-01-01

The purpose of this study is to develop a 3-dimensional interactive multi-user and multi-admin IUVIRLAB featuring active learning methods and techniques for university students and to introduce the Virtual Laboratory of Istanbul University and to show effects of IUVIRLAB on students' attitudes on communication skills and IUVIRLAB. Although there…
Developing a Multi-Dimensional Evaluation Framework for Faculty Teaching and Service Performance

ERIC Educational Resources Information Center

Baker, Diane F.; Neely, Walter P.; Prenshaw, Penelope J.; Taylor, Patrick A.

2015-01-01

A task force was created in a small, AACSB-accredited business school to develop a more comprehensive set of standards for faculty performance. The task force relied heavily on faculty input to identify and describe key dimensions that capture effective teaching and service performance. The result is a multi-dimensional framework that will be used…
Developing Multi-Dimensional Evaluation Criteria for English Learning Websites with University Students and Professors

ERIC Educational Resources Information Center

Liu, Gi-Zen; Liu, Zih-Hui; Hwang, Gwo-Jen

2011-01-01

Many English learning websites have been developed worldwide, but little research has been conducted concerning the development of comprehensive evaluation criteria. The main purpose of this study is thus to construct a multi-dimensional set of criteria to help learners and teachers evaluate the quality of English learning websites. These…
Developing a Hypothetical Multi-Dimensional Learning Progression for the Nature of Matter

ERIC Educational Resources Information Center

Stevens, Shawn Y.; Delgado, Cesar; Krajcik, Joseph S.

2010-01-01

We describe efforts toward the development of a hypothetical learning progression (HLP) for the growth of grade 7-14 students' models of the structure, behavior and properties of matter, as it relates to nanoscale science and engineering (NSE). This multi-dimensional HLP, based on empirical research and standards documents, describes how students…
Method of multi-dimensional moment analysis for the characterization of signal peaks

DOEpatents

Pfeifer, Kent B; Yelton, William G; Kerr, Dayle R; Bouchier, Francis A

2012-10-23

A method of multi-dimensional moment analysis for the characterization of signal peaks can be used to optimize the operation of an analytical system. With a two-dimensional Peclet analysis, the quality and signal fidelity of peaks in a two-dimensional experimental space can be analyzed and scored. This method is particularly useful in determining optimum operational parameters for an analytical system which requires the automated analysis of large numbers of analyte data peaks. For example, the method can be used to optimize analytical systems including an ion mobility spectrometer that uses a temperature stepped desorption technique for the detection of explosive mixtures.
Effect of multi-dimensional ultraviolet light exposure on the growth of pentacene film and application to organic field-effect transistors.

PubMed

Bae, Jin-Hyuk; Lee, Sin-Doo; Choi, Jong Sun; Park, Jaehoon

2012-05-01

We report on the multi-dimensional alignment of pentacene molecules on a poly(methyl methacrylate)-based photosensitive polymer (PMMA-polymer) and its effect on the electrical performance of the pentacene-based field-effect transistor (FET). Pentacene molecules are shown to be preferentially aligned on the linearly polarized ultraviolet (LPUV)-exposed PMMA-polymer layer, which is contrast to an isotropic alignment on the bare PMMA-polymer layer. Multi-dimensional alignment of pentacene molecules in the film could be achieved by adjusting the direction of LPUV exposed to the PMMA-polymer. The control of pentacene molecular alignment is found to be promising for the field-effect mobility enhancement in the pentacene FET.
Wildfire Detection using by Multi Dimensional Histogram in Boreal Forest

NASA Astrophysics Data System (ADS)

Honda, K.; Kimura, K.; Honma, T.

2008-12-01

Early detection of wildfires is an issue for reduction of damage to environment and human. There are some attempts to detect wildfires by using satellite imagery, which are mainly classified into three methods: Dozier Method(1981-), Threshold Method(1986-) and Contextual Method(1994-). However, the accuracy of these methods is not enough: some commission and omission errors are included in the detected results. In addition, it is not so easy to analyze satellite imagery with high accuracy because of insufficient ground truth data. Kudoh and Hosoi (2003) developed the detection method by using three-dimensional (3D) histogram from past fire data with the NOAA-AVHRR imagery. But their method is impractical because their method depends on their handworks to pick up past fire data from huge data. Therefore, the purpose of this study is to collect fire points as hot spots efficiently from satellite imagery and to improve the method to detect wildfires with the collected data. As our method, we collect past fire data with the Alaska Fire History data obtained by the Alaska Fire Service (AFS). We select points that are expected to be wildfires, and pick up the points inside the fire area of the AFS data. Next, we make 3D histogram with the past fire data. In this study, we use Bands 1, 21 and 32 of MODIS. We calculate the likelihood to detect wildfires with the three-dimensional histogram. As our result, we select wildfires with the 3D histogram effectively. We can detect the troidally spreading wildfire. This result shows the evidence of good wildfire detection. However, the area surrounding glacier tends to rise brightness temperature. It is a false alarm. Burnt area and bare ground are sometimes indicated as false alarms, so that it is necessary to improve this method. Additionally, we are trying various combinations of MODIS bands as the better method to detect wildfire effectively. So as to adjust our method in another area, we are applying our method to tropical forest in Kalimantan, Indonesia and around Chiang Mai, Thailand. But the ground truth data in these areas is lesser than the one in Alaska. Our method needs lots of accurate observed data to make multi-dimensional histogram in the same area. In this study, we can show the system to select wildfire data efficiently from satellite imagery. Furthermore, the development of multi-dimensional histogram from past fire data makes it possible to detect wildfires accurately.
Reduced description of reactive flows with tabulation of chemistry

NASA Astrophysics Data System (ADS)

Ren, Zhuyin; Goldin, Graham M.; Hiremath, Varun; Pope, Stephen B.

2011-12-01

The direct use of large chemical mechanisms in multi-dimensional Computational Fluid Dynamics (CFD) is computationally expensive due to the large number of chemical species and the wide range of chemical time scales involved. To meet this challenge, a reduced description of reactive flows in combination with chemistry tabulation is proposed to effectively reduce the computational cost. In the reduced description, the species are partitioned into represented species and unrepresented species; the reactive system is described in terms of a smaller number of represented species instead of the full set of chemical species in the mechanism; and the evolution equations are solved only for the represented species. When required, the unrepresented species are reconstructed assuming that they are in constrained chemical equilibrium. In situ adaptive tabulation (ISAT) is employed to speed the chemistry calculation through tabulating information of the reduced system. The proposed dimension-reduction / tabulation methodology determines and tabulates in situ the necessary information of the nr-dimensional reduced system based on the ns-species detailed mechanism. Compared to the full description with ISAT, the reduced descriptions achieve additional computational speed-up by solving fewer transport equations and faster ISAT retrieving. The approach is validated in both a methane/air premixed flame and a methane/air non-premixed flame. With the GRI 1.2 mechanism consisting of 31 species, the reduced descriptions (with 12 to 16 represented species) achieve a speed-up factor of up to three compared to the full description with ISAT, with a relatively moderate decrease in accuracy compared to the full description.
Neural networks for dimensionality reduction of fluorescence spectra and prediction of drinking water disinfection by-products.

PubMed

Peleato, Nicolas M; Legge, Raymond L; Andrews, Robert C

2018-06-01

The use of fluorescence data coupled with neural networks for improved predictability of drinking water disinfection by-products (DBPs) was investigated. Novel application of autoencoders to process high-dimensional fluorescence data was related to common dimensionality reduction techniques of parallel factors analysis (PARAFAC) and principal component analysis (PCA). The proposed method was assessed based on component interpretability as well as for prediction of organic matter reactivity to formation of DBPs. Optimal prediction accuracies on a validation dataset were observed with an autoencoder-neural network approach or by utilizing the full spectrum without pre-processing. Latent representation by an autoencoder appeared to mitigate overfitting when compared to other methods. Although DBP prediction error was minimized by other pre-processing techniques, PARAFAC yielded interpretable components which resemble fluorescence expected from individual organic fluorophores. Through analysis of the network weights, fluorescence regions associated with DBP formation can be identified, representing a potential method to distinguish reactivity between fluorophore groupings. However, distinct results due to the applied dimensionality reduction approaches were observed, dictating a need for considering the role of data pre-processing in the interpretability of the results. In comparison to common organic measures currently used for DBP formation prediction, fluorescence was shown to improve prediction accuracies, with improvements to DBP prediction best realized when appropriate pre-processing and regression techniques were applied. The results of this study show promise for the potential application of neural networks to best utilize fluorescence EEM data for prediction of organic matter reactivity. Copyright © 2018 Elsevier Ltd. All rights reserved.
A novel explicit equation for the friction factor prediction in the annular flow with drag-reducing polymer

NASA Astrophysics Data System (ADS)

Lakzian, Esmail; Masoudifar, Amir; Saghi, Hassan

2017-03-01

In this paper, a novel explicit equation is presented for the friction factor prediction in the annular flow with drag reducing polymer (DRP). By using dimensional analyses and curve fitting on the published experimental data, the suggested equation is derived based on the logarithmic velocity profiles and power law in boundary layers. In the next step, a least squares method is used to calibrate the presented equation. Then, the equation is used to friction factor prediction of the gas-liquid mixture with DRP and the results are compared with the experimental data and the Al-Sarkhi ones. Finally, drag reduction (DR) is applied as the ratio of the friction factor reduction using DRP to the friction factor without DRP. The DR results show that the suggested equation has a better agreement with the experimental data in comparison with the pervious equations. The results also show that DR prediction decreases with the increase of the gas superficial velocity.
Quantum funneling in blended multi-band gap core/shell colloidal quantum dot solar cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Neo, Darren C. J.; Assender, Hazel E.; Watt, Andrew A. R., E-mail: Andrew.watt@materials.ox.ac.uk

2015-09-07

Multi-band gap heterojunction solar cells fabricated from a blend of 1.2 eV and 1.4 eV PbS colloidal quantum dots (CQDs) show poor device performance due to non-radiative recombination. To overcome this, a CdS shell is epitaxially formed around the PbS core using cation exchange. From steady state and transient photoluminescence measurements, we understand the nature of charge transfer between these quantum dots. Photoluminescence decay lifetimes are much longer in the PbS/CdS core/shell blend compared to PbS only, explained by a reduction in non-radiative recombination resulting from CdS surface passivation. PbS/CdS heterojunction devices sustain a higher open-circuit voltage and lower reverse saturation currentmore » as compared to PbS-only devices, implying lower recombination rates. Further device performance enhancement is attained by modifying the composition profile of the CQD species in the absorbing layer resulting in a three dimensional quantum cascade structure.« less
Thermal analysis of a multi-layer microchannel heat sink for cooling concentrator photovoltaic (CPV) cells

NASA Astrophysics Data System (ADS)

Siyabi, Idris Al; Shanks, Katie; Mallick, Tapas; Sundaram, Senthilarasu

2017-09-01

Concentrator Photovoltaic (CPV) technology is increasingly being considered as an alternative option for solar electricity generation. However, increasing the light concentration ratio could decrease the system output power due to the increase in the temperature of the cells. The performance of a multi-layer microchannel heat sink configuration was evaluated using numerical analysis. In this analysis, three dimensional incompressible laminar steady flow model was solved numerically. An electrical and thermal solar cell model was coupled for solar cell temperature and efficiency calculations. Thermal resistance, solar cell temperature and pumping power were used for the system efficiency evaluation. An increase in the number of microchannel layers exhibited the best overall performance in terms of the thermal resistance, solar cell temperature uniformity and pressure drop. The channel height and width has no effect on the solar cell maximum temperature. However, increasing channel height leads to a reduction in the pressure drop and hence less fluid pumping power.
Magnetic vortex state and multi-domain pattern in electrodeposited hemispherical nanogranular nickel films

NASA Astrophysics Data System (ADS)

Samardak, Alexander; Sukovatitsina, Ekaterina; Ognev, Alexey; Stebliy, Maksim; Davydenko, Alexander; Chebotkevich, Ludmila; Keun Kim, Young; Nasirpouri, Forough; Janjan, Seyed-Mehdi; Nasirpouri, Farzad

2014-12-01

Magnetic states of nickel nanogranular films were studied in two distinct structures of individual and agglomerated granules electrodeposited on n-type Si(1 1 1) surface from a modified Watts bath at a low pH of 2. Magnetic force microscopy and micromagnetic simulations revealed three-dimensional out-of-plane magnetic vortex states in stand-alone hemispherical granules and their arrays, and multi-domain patterns in large agglomerates and integrated films. Once the granules coalesce into small chains or clusters, the coercivity values increased due to the reduction of inter-granular spacing and strengthening of the magnetostatic interaction. Further growth leads to the formation of a continuous granulated film which strongly affected the coercivity and remanence. This was characterized by the domain wall nucleation and propagation leading to a stripe domain pattern. Magnetoresistance measurements as a function of external magnetic field are indicative of anisotropic magnetoresistance (AMR) for the continuous films electrodeposited on Si substrate.
Asymmetric skew Bessel processes and their applications to finance

NASA Astrophysics Data System (ADS)

Decamps, Marc; Goovaerts, Marc; Schoutens, Wim

2006-02-01

In this paper, we extend the Harrison and Shepp's construction of the skew Brownian motion (1981) and we obtain a diffusion similar to the two-dimensional Bessel process with speed and scale densities discontinuous at one point. Natural generalizations to multi-dimensional and fractional order Bessel processes are then discussed as well as invariance properties. We call this family of diffusions asymmetric skew Bessel processes in opposition to skew Bessel processes as defined in Barlow et al. [On Walsh's Brownian motions, Seminaire de Probabilities XXIII, Lecture Notes in Mathematics, vol. 1372, Springer, Berlin, New York, 1989, pp. 275-293]. We present factorizations involving (asymmetric skew) Bessel processes with random time. Finally, applications to the valuation of perpetuities and Asian options are proposed.
Broadening microwave absorption via a multi-domain structure

NASA Astrophysics Data System (ADS)

Liu, Zhengwang; Che, Renchao; Wei, Yong; Liu, Yupu; Elzatahry, Ahmed A.; Dahyan, Daifallah Al.; Zhao, Dongyuan

2017-04-01

Materials with a high saturation magnetization have gained increasing attention in the field of microwave absorption; therefore, the magnetization value depends on the magnetic configuration inside them. However, the broad-band absorption in the range of microwave frequency (2-18 GHz) is a great challenge. Herein, the three-dimensional (3D) Fe/C hollow microspheres are constructed by iron nanocrystals permeating inside carbon matrix with a saturation magnetization of 340 emu/g, which is 1.55 times as that of bulk Fe, unexpectedly. Electron tomography, electron holography, and Lorentz transmission electron microscopy imaging provide the powerful testimony about Fe/C interpenetration and multi-domain state constructed by vortex and stripe domains. Benefiting from the unique chemical and magnetic microstructures, the microwave minimum absorption is as strong as -55 dB and the bandwidth (<-10 dB) spans 12.5 GHz ranging from 5.5 to 18 GHz. Morphology and distribution of magnetic nano-domains can be facilely regulated by a controllable reduction sintering under H2/Ar gas and an optimized temperature over 450-850 °C. The findings might shed new light on the synthesis strategies of the materials with the broad-band frequency and understanding the association between multi-domain coupling and microwave absorption performance.
Information networks in the stock market based on the distance of the multi-attribute dimensions between listed companies

NASA Astrophysics Data System (ADS)

Liu, Qian; Li, Huajiao; Liu, Xueyong; Jiang, Meihui

2018-04-01

In the stock market, there are widespread information connections between economic agents. Listed companies can obtain mutual information about investment decisions from common shareholders, and the extent of sharing information often determines the relationships between listed companies. Because different shareholder compositions and investment shares lead to different formations of the company's governance mechanisms, we map the investment relationships between shareholders to the multi-attribute dimensional spaces of the listed companies (each shareholder investment in a company is a company dimension). Then, we construct the listed company's information network based on co-shareholder relationships. The weights for the edges in the information network are measured with the Euclidean distance between the listed companies in the multi-attribute dimension space. We define two indices to analyze the information network's features. We conduct an empirical study that analyzes Chinese listed companies' information networks. The results from the analysis show that with the diversification and decentralization of shareholder investments, almost all Chinese listed companies exchanged information through common shareholder relationships, and there is a gradual reduction in information sharing capacity between listed companies that have common shareholders. This network analysis has benefits for risk management and portfolio investments.

Tissue Cartography: Compressing Bio-Image Data by Dimensional Reduction

PubMed Central

Heemskerk, Idse; Streichan, Sebastian J

2017-01-01

High data volumes produced by state-of-the-art optical microscopes encumber research. Taking advantage of the laminar structure of many biological specimens we developed a method that reduces data size and processing time by orders of magnitude, while disentangling signal. The Image Surface Analysis Environment that we implemented automatically constructs an atlas of 2D images for arbitrary shaped, dynamic, and possibly multi-layered “Surfaces of Interest”. Built-in correction for cartographic distortion assures no information on the surface is lost, making it suitable for quantitative analysis. We demonstrate our approach by application to 4D imaging of the D. melanogaster embryo and D. rerio beating heart. PMID:26524242
Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning

PubMed Central

Gönen, Mehmet

2014-01-01

Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F1, and micro F1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks. PMID:24532862
Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning.

PubMed

Gönen, Mehmet

2014-03-01

Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F 1 , and micro F 1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks.
Metric dimensional reduction at singularities with implications to Quantum Gravity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stoica, Ovidiu Cristinel, E-mail: holotronix@gmail.com

2014-08-15

A series of old and recent theoretical observations suggests that the quantization of gravity would be feasible, and some problems of Quantum Field Theory would go away if, somehow, the spacetime would undergo a dimensional reduction at high energy scales. But an identification of the deep mechanism causing this dimensional reduction would still be desirable. The main contribution of this article is to show that dimensional reduction effects are due to General Relativity at singularities, and do not need to be postulated ad-hoc. Recent advances in understanding the geometry of singularities do not require modification of General Relativity, being justmore » non-singular extensions of its mathematics to the limit cases. They turn out to work fine for some known types of cosmological singularities (black holes and FLRW Big-Bang), allowing a choice of the fundamental geometric invariants and physical quantities which remain regular. The resulting equations are equivalent to the standard ones outside the singularities. One consequence of this mathematical approach to the singularities in General Relativity is a special, (geo)metric type of dimensional reduction: at singularities, the metric tensor becomes degenerate in certain spacetime directions, and some properties of the fields become independent of those directions. Effectively, it is like one or more dimensions of spacetime just vanish at singularities. This suggests that it is worth exploring the possibility that the geometry of singularities leads naturally to the spontaneous dimensional reduction needed by Quantum Gravity. - Highlights: • The singularities we introduce are described by finite geometric/physical objects. • Our singularities are accompanied by dimensional reduction effects. • They affect the metric, the measure, the topology, the gravitational DOF (Weyl = 0). • Effects proposed in other approaches to Quantum Gravity are obtained naturally. • The geometric dimensional reduction obtained opens new ways for Quantum Gravity.« less
Tunable two-dimensional acoustic meta-structure composed of funnel-shaped unit cells with multi-band negative acoustic property

NASA Astrophysics Data System (ADS)

Cho, Sungjin; Kim, Boseung; Min, Dongki; Park, Junhong

2015-10-01

This paper presents a two-dimensional heat-exhaust and sound-proof acoustic meta-structure exhibiting tunable multi-band negative effective mass density. The meta-structure was composed of periodic funnel-shaped units in a square lattice. Each unit cell operates simultaneously as a Helmholtz resonator (HR) and an extended pipe chamber resonator (EPCR), leading to a negative effective mass density creating bandgaps for incident sound energy dissipation without transmission. This structure allowed large heat-flow through the cross-sectional area of the extended pipe since the resonance was generated by acoustic elements without using solid membranes. The pipes were horizontally directed to a flow source to enable small flow resistance for cooling. Measurements of the sound transmission were performed using a two-load, four-microphone method for a unit cell and small reverberation chamber for two-dimensional panel to characterize the acoustic performance. The effective mass density showed significant frequency dependent variation exhibiting negative values at the specific bandgaps, while the effective bulk modulus was not affected by the resonator. Theoretical models incorporating local resonances in the multiple resonator units were proposed to analyze the noise reduction mechanism. The acoustic meta-structure parameters to create broader frequency bandgaps were investigated using the theoretical model. The negative effective mass density was calculated to investigate the creation of the bandgaps. The effects of design parameters such as length, cross-sectional area, and volume of the HR; length and cross-sectional area of the EPCR were analyzed. To maximize the frequency band gap, the suggested acoustic meta-structure panel, small neck length, and cross-sectional area of the HR, large EPCR length was advantageous. The bandgaps became broader when the two resonant frequencies were similar.
A sparse grid based method for generative dimensionality reduction of high-dimensional data

NASA Astrophysics Data System (ADS)

Bohn, Bastian; Garcke, Jochen; Griebel, Michael

2016-03-01

Generative dimensionality reduction methods play an important role in machine learning applications because they construct an explicit mapping from a low-dimensional space to the high-dimensional data space. We discuss a general framework to describe generative dimensionality reduction methods, where the main focus lies on a regularized principal manifold learning variant. Since most generative dimensionality reduction algorithms exploit the representer theorem for reproducing kernel Hilbert spaces, their computational costs grow at least quadratically in the number n of data. Instead, we introduce a grid-based discretization approach which automatically scales just linearly in n. To circumvent the curse of dimensionality of full tensor product grids, we use the concept of sparse grids. Furthermore, in real-world applications, some embedding directions are usually more important than others and it is reasonable to refine the underlying discretization space only in these directions. To this end, we employ a dimension-adaptive algorithm which is based on the ANOVA (analysis of variance) decomposition of a function. In particular, the reconstruction error is used to measure the quality of an embedding. As an application, the study of large simulation data from an engineering application in the automotive industry (car crash simulation) is performed.
Clustering header categories extracted from web tables

NASA Astrophysics Data System (ADS)

Nagy, George; Embley, David W.; Krishnamoorthy, Mukkai; Seth, Sharad

2015-01-01

Revealing related content among heterogeneous web tables is part of our long term objective of formulating queries over multiple sources of information. Two hundred HTML tables from institutional web sites are segmented and each table cell is classified according to the fundamental indexing property of row and column headers. The categories that correspond to the multi-dimensional data cube view of a table are extracted by factoring the (often multi-row/column) headers. To reveal commonalities between tables from diverse sources, the Jaccard distances between pairs of category headers (and also table titles) are computed. We show how about one third of our heterogeneous collection can be clustered into a dozen groups that exhibit table-title and header similarities that can be exploited for queries.
Statistical Projections for Multi-resolution, Multi-dimensional Visual Data Exploration and Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoa T. Nguyen; Stone, Daithi; E. Wes Bethel

2016-01-01

An ongoing challenge in visual exploration and analysis of large, multi-dimensional datasets is how to present useful, concise information to a user for some specific visualization tasks. Typical approaches to this problem have proposed either reduced-resolution versions of data, or projections of data, or both. These approaches still have some limitations such as consuming high computation or suffering from errors. In this work, we explore the use of a statistical metric as the basis for both projections and reduced-resolution versions of data, with a particular focus on preserving one key trait in data, namely variation. We use two different casemore » studies to explore this idea, one that uses a synthetic dataset, and another that uses a large ensemble collection produced by an atmospheric modeling code to study long-term changes in global precipitation. The primary findings of our work are that in terms of preserving the variation signal inherent in data, that using a statistical measure more faithfully preserves this key characteristic across both multi-dimensional projections and multi-resolution representations than a methodology based upon averaging.« less
A fast efficient implicit scheme for the gasdynamic equations using a matrix reduction technique

NASA Technical Reports Server (NTRS)

Barth, T. J.; Steger, J. L.

1985-01-01

An efficient implicit finite-difference algorithm for the gasdynamic equations utilizing matrix reduction techniques is presented. A significant reduction in arithmetic operations is achieved without loss of the stability characteristics generality found in the Beam and Warming approximate factorization algorithm. Steady-state solutions to the conservative Euler equations in generalized coordinates are obtained for transonic flows and used to show that the method offers computational advantages over the conventional Beam and Warming scheme. Existing Beam and Warming codes can be retrofit with minimal effort. The theoretical extension of the matrix reduction technique to the full Navier-Stokes equations in Cartesian coordinates is presented in detail. Linear stability, using a Fourier stability analysis, is demonstrated and discussed for the one-dimensional Euler equations.
A revised Thai Multi-Dimensional Scale of Perceived Social Support.

PubMed

Wongpakaran, Nahathai; Wongpakaran, Tinakon

2012-11-01

In order to ensure the construct validity of the three-factor model of the Multi-dimensional Scale of Perceived Social Support (MSPSS), and based on the assumption that it helps users differentiate between sources of social support, in this study a revised version was created and tested. The aim was to compare the level of model fit of the original version of the MSPSS against the revised version--which contains a minor change from the original. The study was conducted on 486 medical students who completed the original and revised versions of the MSPSS, as well as the Rosenberg Self-Esteem Scale (Rosenberg, 1965) and Beck Depression Inventory II (Beck, Steer, & Brown, 1996). Confirmatory factor analysis was performed to compare the results, showing that the revised version of MSPSS demonstrated a good internal consistency--with a Cronbach's alpha of .92 for the MSPSS questionnaire, and a significant correlation with the other scales, as predicted. The revised version provided better internal consistency, increasing the Cronbach's alpha for the Significant Others sub-scale from 0.86 to 0.92. Confirmatory factor analysis revealed an acceptable model fit: chi2 128.11, df 51, p < .001; TLI 0.94; CFI 0.95; GFI 0.90; PNFI 0.71; AGFI 0.85; RMSEA 0.093 (0.073-0.113) and SRMR 0.042, which is better than the original version. The tendency of the new version was to display a better level of fit with a larger sample size. The limitations of the study are discussed, as well as recommendations for further study.
electromagnetics, eddy current, computer codes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gartling, David

TORO Version 4 is designed for finite element analysis of steady, transient and time-harmonic, multi-dimensional, quasi-static problems in electromagnetics. The code allows simulation of electrostatic fields, steady current flows, magnetostatics and eddy current problems in plane or axisymmetric, two-dimensional geometries. TORO is easily coupled to heat conduction and solid mechanics codes to allow multi-physics simulations to be performed.
Effects of bathymetric lidar errors on flow properties predicted with a multi-dimensional hydraulic model

Treesearch

J. McKean; D. Tonina; C. Bohn; C. W. Wright

2014-01-01

New remote sensing technologies and improved computer performance now allow numerical flow modeling over large stream domains. However, there has been limited testing of whether channel topography can be remotely mapped with accuracy necessary for such modeling. We assessed the ability of the Experimental Advanced Airborne Research Lidar, to support a multi-dimensional...
Exploring the Educational Potential of Three-Dimensional Multi-User Virtual Worlds for STEM Education: A Mixed-Method Systematic Literature Review

ERIC Educational Resources Information Center

Pellas, Nikolaos; Kazanidis, Ioannis; Konstantinou, Nikolaos; Georgiou, Georgia

2017-01-01

The present literature review builds on the results of 50 research articles published from 2000 until 2016. All these studies have successfully accomplished various learning tasks in the domain of Science, Technology, Engineering, and Mathematics (STEM) education using three-dimensional (3-D) multi-user virtual worlds for Primary, Secondary and…
A Cross-Cultural Comparison of Singaporean and Taiwanese Eighth Graders' Science Learning Self-Efficacy from a Multi-Dimensional Perspective

ERIC Educational Resources Information Center

Lin, Tzung-Jin; Tan, Aik Ling; Tsai, Chin-Chung

2013-01-01

Due to the scarcity of cross-cultural comparative studies in exploring students' self-efficacy in science learning, this study attempted to develop a multi-dimensional science learning self-efficacy (SLSE) instrument to measure 316 Singaporean and 303 Taiwanese eighth graders' SLSE and further to examine the differences between the two student…
A multi scale multi-dimensional thermo electrochemical modelling of high capacity lithium-ion cells

NASA Astrophysics Data System (ADS)

Tourani, Abbas; White, Peter; Ivey, Paul

2014-06-01

Lithium iron phosphate (LFP) and lithium manganese oxide (LMO) are competitive and complementary to each other as cathode materials for lithium-ion batteries, especially for use in electric vehicles. A multi scale multi-dimensional physic-based model is proposed in this paper to study the thermal behaviour of the two lithium-ion chemistries. The model consists of two sub models, a one dimensional (1D) electrochemical sub model and a two dimensional (2D) thermo-electric sub model, which are coupled and solved concurrently. The 1D model predicts the heat generation rate (Qh) and voltage (V) of the battery cell through different load cycles. The 2D model of the battery cell accounts for temperature distribution and current distribution across the surface of the battery cell. The two cells are examined experimentally through 90 h load cycles including high/low charge/discharge rates. The experimental results are compared with the model results and they are in good agreement. The presented results in this paper verify the cells temperature behaviour at different operating conditions which will lead to the design of a cost effective thermal management system for the battery pack.
On Multi-Dimensional Unstructured Mesh Adaption

NASA Technical Reports Server (NTRS)

Wood, William A.; Kleb, William L.

1999-01-01

Anisotropic unstructured mesh adaption is developed for a truly multi-dimensional upwind fluctuation splitting scheme, as applied to scalar advection-diffusion. The adaption is performed locally using edge swapping, point insertion/deletion, and nodal displacements. Comparisons are made versus the current state of the art for aggressive anisotropic unstructured adaption, which is based on a posteriori error estimates. Demonstration of both schemes to model problems, with features representative of compressible gas dynamics, show the present method to be superior to the a posteriori adaption for linear advection. The performance of the two methods is more similar when applied to nonlinear advection, with a difference in the treatment of shocks. The a posteriori adaption can excessively cluster points to a shock, while the present multi-dimensional scheme tends to merely align with a shock, using fewer nodes. As a consequence of this alignment tendency, an implementation of eigenvalue limiting for the suppression of expansion shocks is developed for the multi-dimensional distribution scheme. The differences in the treatment of shocks by the adaption schemes, along with the inherently low levels of artificial dissipation in the fluctuation splitting solver, suggest the present method is a strong candidate for applications to compressible gas dynamics.
Dark-field transmission electron microscopy and the Debye-Waller factor of graphene

PubMed Central

Hubbard, William A.; White, E. R.; Dawson, Ben; Lodge, M. S.; Ishigami, Masa; Regan, B. C.

2014-01-01

Graphene's structure bears on both the material's electronic properties and fundamental questions about long range order in two-dimensional crystals. We present an analytic calculation of selected area electron diffraction from multi-layer graphene and compare it with data from samples prepared by chemical vapor deposition and mechanical exfoliation. A single layer scatters only 0.5% of the incident electrons, so this kinematical calculation can be considered reliable for five or fewer layers. Dark-field transmission electron micrographs of multi-layer graphene illustrate how knowledge of the diffraction peak intensities can be applied for rapid mapping of thickness, stacking, and grain boundaries. The diffraction peak intensities also depend on the mean-square displacement of atoms from their ideal lattice locations, which is parameterized by a Debye-Waller factor. We measure the Debye-Waller factor of a suspended monolayer of exfoliated graphene and find a result consistent with an estimate based on the Debye model. For laboratory-scale graphene samples, finite size effects are sufficient to stabilize the graphene lattice against melting, indicating that ripples in the third dimension are not necessary. PMID:25242882
Dark-field transmission electron microscopy and the Debye-Waller factor of graphene.

PubMed

Shevitski, Brian; Mecklenburg, Matthew; Hubbard, William A; White, E R; Dawson, Ben; Lodge, M S; Ishigami, Masa; Regan, B C

2013-01-15

Graphene's structure bears on both the material's electronic properties and fundamental questions about long range order in two-dimensional crystals. We present an analytic calculation of selected area electron diffraction from multi-layer graphene and compare it with data from samples prepared by chemical vapor deposition and mechanical exfoliation. A single layer scatters only 0.5% of the incident electrons, so this kinematical calculation can be considered reliable for five or fewer layers. Dark-field transmission electron micrographs of multi-layer graphene illustrate how knowledge of the diffraction peak intensities can be applied for rapid mapping of thickness, stacking, and grain boundaries. The diffraction peak intensities also depend on the mean-square displacement of atoms from their ideal lattice locations, which is parameterized by a Debye-Waller factor. We measure the Debye-Waller factor of a suspended monolayer of exfoliated graphene and find a result consistent with an estimate based on the Debye model. For laboratory-scale graphene samples, finite size effects are sufficient to stabilize the graphene lattice against melting, indicating that ripples in the third dimension are not necessary.
Reinforcing mechanism of anchors in slopes: a numerical comparison of results of LEM and FEM

NASA Astrophysics Data System (ADS)

Cai, Fei; Ugai, Keizo

2003-06-01

This paper reports the limitation of the conventional Bishop's simplified method to calculate the safety factor of slopes stabilized with anchors, and proposes a new approach to considering the reinforcing effect of anchors on the safety factor. The reinforcing effect of anchors can be explained using an additional shearing resistance on the slip surface. A three-dimensional shear strength reduction finite element method (SSRFEM), where soil-anchor interactions were simulated by three-dimensional zero-thickness elasto-plastic interface elements, was used to calculate the safety factor of slopes stabilized with anchors to verify the reinforcing mechanism of anchors. The results of SSRFEM were compared with those of the conventional and proposed approaches for Bishop's simplified method for various orientations, positions, and spacings of anchors, and shear strengths of soil-grouted body interfaces. For the safety factor, the proposed approach compared better with SSRFEM than the conventional approach. The additional shearing resistance can explain the influence of the orientation, position, and spacing of anchors, and the shear strength of soil-grouted body interfaces on the safety factor of slopes stabilized with anchors.
A multi-ingredient nutritional supplement enhances exercise training-related reductions in markers of systemic inflammation in healthy older men.

PubMed

Bell, Kirsten E; Snijders, Tim; Zulyniak, Michael A; Kumbhare, Dinesh; Parise, Gianni; Chabowski, Adrian; Phillips, Stuart M

2018-03-01

We evaluated whether twice-daily consumption of a multi-ingredient nutritional supplement (SUPP) would reduce systemic inflammatory markers following 6 weeks of supplementation alone (phase 1), and the subsequent addition of 12 weeks of exercise training (phase 2) in healthy older men, in comparison with a carbohydrate-based control (CON). Tumour necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) concentrations were progressively reduced (P-time < 0.05) in the SUPP group. No change in TNF-α or IL-6 concentrations was observed in the CON group.

ICM: a web server for integrated clustering of multi-dimensional biomedical data.

PubMed

He, Song; He, Haochen; Xu, Wenjian; Huang, Xin; Jiang, Shuai; Li, Fei; He, Fuchu; Bo, Xiaochen

2016-07-08

Large-scale efforts for parallel acquisition of multi-omics profiling continue to generate extensive amounts of multi-dimensional biomedical data. Thus, integrated clustering of multiple types of omics data is essential for developing individual-based treatments and precision medicine. However, while rapid progress has been made, methods for integrated clustering are lacking an intuitive web interface that facilitates the biomedical researchers without sufficient programming skills. Here, we present a web tool, named Integrated Clustering of Multi-dimensional biomedical data (ICM), that provides an interface from which to fuse, cluster and visualize multi-dimensional biomedical data and knowledge. With ICM, users can explore the heterogeneity of a disease or a biological process by identifying subgroups of patients. The results obtained can then be interactively modified by using an intuitive user interface. Researchers can also exchange the results from ICM with collaborators via a web link containing a Project ID number that will directly pull up the analysis results being shared. ICM also support incremental clustering that allows users to add new sample data into the data of a previous study to obtain a clustering result. Currently, the ICM web server is available with no login requirement and at no cost at http://biotech.bmi.ac.cn/icm/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
BCYCLIC: A parallel block tridiagonal matrix cyclic solver

NASA Astrophysics Data System (ADS)

Hirshman, S. P.; Perumalla, K. S.; Lynch, V. E.; Sanchez, R.

2010-09-01

A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduction algorithm that is easily parallelized. Storage of the factored blocks allows the application of the inverse to multiple right-hand sides which may not be known at factorization time. Scalability with the number of block rows is achieved with cyclic reduction, while scalability with the block size is achieved using multithreaded routines (OpenMP, GotoBLAS) for block matrix manipulation. This dual scalability is a noteworthy feature of this new solver, as well as its ability to efficiently handle arbitrary (non-powers-of-2) block row and processor numbers. Comparison with a state-of-the art parallel sparse solver is presented. It is expected that this new solver will allow many physical applications to optimally use the parallel resources on current supercomputers. Example usage of the solver in magneto-hydrodynamic (MHD), three-dimensional equilibrium solvers for high-temperature fusion plasmas is cited.
Design Strategy of Multi-electron Transfer Catalysts Based on a Bioinformatic Analysis of Oxygen Evolution and Reduction Enzymes.

PubMed

Ooka, Hideshi; Hashimoto, Kazuhito; Nakamura, Ryuhei

2018-05-14

Understanding the design strategy of photosynthetic and respiratory enzymes is important to develop efficient artificial catalysts for oxygen evolution and reduction reactions. Here, based on a bioinformatic analysis of cyanobacterial oxygen evolution and reduction enzymes (photosystem II: PS II and cytochrome c oxidase: COX, respectively), the gene encoding the catalytic D1 subunit of PS II was found to be expressed individually across 38 phylogenetically diverse strains, which is in contrast to the operon structure of the genes encoding major COX subunits. Selective synthesis of the D1 subunit minimizes the repair cost of PS II, which allows compensation for its instability by lowering the turnover number required to generate a net positive energy yield. The different bioenergetics observed between PS II and COX suggest that in addition to the catalytic activity rationalized by the Sabatier principle, stability factors have also provided a major influence on the design strategy of biological multi-electron transfer enzymes. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Fast multi-dimensional NMR by minimal sampling

NASA Astrophysics Data System (ADS)

Kupče, Ēriks; Freeman, Ray

2008-03-01

A new scheme is proposed for very fast acquisition of three-dimensional NMR spectra based on minimal sampling, instead of the customary step-wise exploration of all of evolution space. The method relies on prior experiments to determine accurate values for the evolving frequencies and intensities from the two-dimensional 'first planes' recorded by setting t1 = 0 or t2 = 0. With this prior knowledge, the entire three-dimensional spectrum can be reconstructed by an additional measurement of the response at a single location (t1∗,t2∗) where t1∗ and t2∗ are fixed values of the evolution times. A key feature is the ability to resolve problems of overlap in the acquisition dimension. Applied to a small protein, agitoxin, the three-dimensional HNCO spectrum is obtained 35 times faster than systematic Cartesian sampling of the evolution domain. The extension to multi-dimensional spectroscopy is outlined.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Steiner, J.L.; Lime, J.F.; Elson, J.S.

One dimensional TRAC transient calculations of the process inherent ultimate safety (PIUS) advanced reactor design were performed for a pump-trip SCRAM. The TRAC calculations showed that the reactor power response and shutdown were in qualitative agreement with the one-dimensional analyses presented in the PIUS Preliminary Safety Information Document (PSID) submitted by Asea Brown Boveri (ABB) to the US Nuclear Regulatory Commission for preapplication safety review. The PSID analyses were performed with the ABB-developed RIGEL code. The TRAC-calculated phenomena and trends were also similar to those calculated with another one-dimensional PIUS model, the Brookhaven National Laboratory developed PIPA code. A TRACmore » pump-trip SCRAM transient has also been calculated with a TRAC model containing a multi-dimensional representation of the PIUS intemal flow structures and core region. The results obtained using the TRAC fully one-dimensional PIUS model are compared to the RIGEL, PIPA, and TRAC multi-dimensional results.« less
Tumor Volume Reduction Rate After Preoperative Chemoradiotherapy as a Prognostic Factor in Locally Advanced Rectal Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yeo, Seung-Gu; Department of Radiation Oncology, Soonchunhyang University College of Medicine, Cheonan; Kim, Dae Yong, E-mail: radiopiakim@hanmail.net

2012-02-01

Purpose: To investigate the prognostic significance of tumor volume reduction rate (TVRR) after preoperative chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC). Methods and Materials: In total, 430 primary LARC (cT3-4) patients who were treated with preoperative CRT and curative radical surgery between May 2002 and March 2008 were analyzed retrospectively. Pre- and post-CRT tumor volumes were measured using three-dimensional region-of-interest MR volumetry. Tumor volume reduction rate was determined using the equation TVRR (%) = (pre-CRT tumor volume - post-CRT tumor volume) Multiplication-Sign 100/pre-CRT tumor volume. The median follow-up period was 64 months (range, 27-99 months) for survivors. Endpoints weremore » disease-free survival (DFS) and overall survival (OS). Results: The median TVRR was 70.2% (mean, 64.7% {+-} 22.6%; range, 0-100%). Downstaging (ypT0-2N0M0) occurred in 183 patients (42.6%). The 5-year DFS and OS rates were 77.7% and 86.3%, respectively. In the analysis that included pre-CRT and post-CRT tumor volumes and TVRR as continuous variables, only TVRR was an independent prognostic factor. Tumor volume reduction rate was categorized according to a cutoff value of 45% and included with clinicopathologic factors in the multivariate analysis; ypN status, circumferential resection margin, and TVRR were significant prognostic factors for both DFS and OS. Conclusions: Tumor volume reduction rate was a significant prognostic factor in LARC patients receiving preoperative CRT. Tumor volume reduction rate data may be useful for tailoring surgery and postoperative adjuvant therapy after preoperative CRT.« less
MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction?

PubMed

Deledalle, Charles-Alban; Denis, Loic; Tabti, Sonia; Tupin, Florence

2017-09-01

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since most current and planned SAR imaging satellites operate in polarimetric, interferometric, or tomographic modes, SAR images are multi-channel and speckle reduction techniques must jointly process all channels to recover polarimetric and interferometric information. The distinctive nature of SAR signal (complex-valued, corrupted by multiplicative fluctuations) calls for the development of specialized methods for speckle reduction. Image denoising is a very active topic in image processing with a wide variety of approaches and many denoising algorithms available, almost always designed for additive Gaussian noise suppression. This paper proposes a general scheme, called MuLoG (MUlti-channel LOgarithm with Gaussian denoising), to include such Gaussian denoisers within a multi-channel SAR speckle reduction technique. A new family of speckle reduction algorithms can thus be obtained, benefiting from the ongoing progress in Gaussian denoising, and offering several speckle reduction results often displaying method-specific artifacts that can be dismissed by comparison between results.
Mapping the Physical and Chemical Conditions of the Ring Nebula

NASA Astrophysics Data System (ADS)

Leal-Ferreira, Marcelo L.; Aleman, Isabel; Gaughan, Andrea; Ladjal, Djazia; Ueta, Toshiya; Kerber, Samuel; Conn, Blair; Gardiner, Rhiannon; Tielens, Alexander G. G. M.

2017-10-01

We observed the Planetary Nebula NGC 6720 with the Gemini Telescope and the Gemini Multi-Object Spectrographs. We obtained spatial maps of 36 emission-lines in the wavelength range between 3600 Å and 9400 Å. We derived maps of c(Hβ), electronic densities, electronic temperatures, ionic and elemental abundances, and Ionization Correction Factors (ICFs) in the source and investigated the mass-loss history of the progenitor. The elemental abundance results indicate the need for ICFs based on three-dimensional photoionization models.
A trace ratio maximization approach to multiple kernel-based dimensionality reduction.

PubMed

Jiang, Wenhao; Chung, Fu-lai

2014-01-01

Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings. Copyright © 2013 Elsevier Ltd. All rights reserved.
Multi-tiered system of support incorporating the R.E.N.E.W. process and its relationship to perception of school safety and office discipline referrals

NASA Astrophysics Data System (ADS)

Flood, Molly M.

This study examined the relationship between the fidelity of multi-tier school-wide positive behavior interventions and supports (SWPBIS) and staff perception of school safety and office discipline referrals. This research provided a case study on multi-tier supports and interventions, and the RENEW person-centered planning process in an alternative special education center following the implementation of a multi-tier SWPBIS model. Pennsylvania is one of several states looking to adopt an effective Tier III behavioral tool. The research described the results of an analysis of implementation fidelity on a multi-tiered school-wide positive behavior support model developed at a special education center operated by a public school system entity. This research explored the fidelity of SWPBIS implementation; analyzed the relationship of SWPBIS to school climate as measured by staff perceptions and reduction of office discipline referrals (ODR); explored tier III supports incorporating a process Rehabilitation and Empowerment, Natural Supports, Education and Work (RENEW); and investigated the potential sustainability of the RENEW process as a multi-tier system of support. This study investigated staff perceptions on integrated supports between schools and communities and identified the degree of relationship to school risk factors, school protective factors, and office discipline referrals following the building of cooperative partnerships between Systems of Care and Local Education Agencies.
MAI (Multi-Dimensional Activity Based Integrated Approach): A Strategy for Cognitive Development of the Learners at the Elementary Stage

ERIC Educational Resources Information Center

Basantia, Tapan Kumar; Panda, B. N.; Sahoo, Dukhabandhu

2012-01-01

Cognitive development of the learners is the prime task of each and every stage of our school education and its importance especially in elementary state is quite worth mentioning. Present study investigated the effectiveness of a new and innovative strategy (i.e., MAI (multi-dimensional activity based integrated approach)) for the development of…
A Multi-Dimensional Instrument for Evaluating Taiwanese High School Students' Science Learning Self-Efficacy in Relation to Their Approaches to Learning Science

ERIC Educational Resources Information Center

Lin, Tzung-Jin; Tsai, Chin-Chung

2013-01-01

In the past, students' science learning self-efficacy (SLSE) was usually measured by questionnaires that consisted of only a single scale, which might be insufficient to fully understand their SLSE. In this study, a multi-dimensional instrument, the SLSE instrument, was developed and validated to assess students' SLSE based on the previous…
Modeling surface-water flow and sediment mobility with the Multi-Dimensional Surface-Water Modeling System (MD_SWMS)

USGS Publications Warehouse

McDonald, Richard; Nelson, Jonathan; Kinzel, Paul; Conaway, Jeffrey S.

2006-01-01

The Multi-Dimensional Surface-Water Modeling System (MD_SWMS) is a Graphical User Interface for surface-water flow and sediment-transport models. The capabilities of MD_SWMS for developing models include: importing raw topography and other ancillary data; building the numerical grid and defining initial and boundary conditions; running simulations; visualizing results; and comparing results with measured data.
Numerical analysis of combustion characteristics of hybrid rocket motor with multi-section swirl injection

NASA Astrophysics Data System (ADS)

Li, Chengen; Cai, Guobiao; Tian, Hui

2016-06-01

This paper is aimed to analyse the combustion characteristics of hybrid rocket motor with multi-section swirl injection by simulating the combustion flow field. Numerical combustion flow field and combustion performance parameters are obtained through three-dimensional numerical simulations based on a steady numerical model proposed in this paper. The hybrid rocket motor adopts 98% hydrogen peroxide and polyethylene as the propellants. Multiple injection sections are set along the axis of the solid fuel grain, and the oxidizer enters the combustion chamber by means of tangential injection via the injector ports in the injection sections. Simulation results indicate that the combustion flow field structure of the hybrid rocket motor could be improved by multi-section swirl injection method. The transformation of the combustion flow field can greatly increase the fuel regression rate and the combustion efficiency. The average fuel regression rate of the motor with multi-section swirl injection is improved by 8.37 times compared with that of the motor with conventional head-end irrotational injection. The combustion efficiency is increased to 95.73%. Besides, the simulation results also indicate that (1) the additional injection sections can increase the fuel regression rate and the combustion efficiency; (2) the upstream offset of the injection sections reduces the combustion efficiency; and (3) the fuel regression rate and the combustion efficiency decrease with the reduction of the number of injector ports in each injection section.
Behavioral modeling and digital compensation of nonlinearity in DFB lasers for multi-band directly modulated radio-over-fiber systems

NASA Astrophysics Data System (ADS)

Li, Jianqiang; Yin, Chunjing; Chen, Hao; Yin, Feifei; Dai, Yitang; Xu, Kun

2014-11-01

The envisioned C-RAN concept in wireless communication sector replies on distributed antenna systems (DAS) which consist of a central unit (CU), multiple remote antenna units (RAUs) and the fronthaul links between them. As the legacy and emerging wireless communication standards will coexist for a long time, the fronthaul links are preferred to carry multi-band multi-standard wireless signals. Directly-modulated radio-over-fiber (ROF) links can serve as a lowcost option to make fronthaul connections conveying multi-band wireless signals. However, directly-modulated radioover- fiber (ROF) systems often suffer from inherent nonlinearities from directly-modulated lasers. Unlike ROF systems working at the single-band mode, the modulation nonlinearities in multi-band ROF systems can result in both in-band and cross-band nonlinear distortions. In order to address this issue, we have recently investigated the multi-band nonlinear behavior of directly-modulated DFB lasers based on multi-dimensional memory polynomial model. Based on this model, an efficient multi-dimensional baseband digital predistortion technique was developed and experimentally demonstrated for linearization of multi-band directly-modulated ROF systems.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Sridharan, Niyanth; Gussev, Maxim; Seibert, Rachel

Ultrasonic additive manufacturing (UAM) is a solid-state process, which uses ultrasonic vibrations at 20 kHz along with mechanized tape layering and intermittent milling operation, to build fully functional three-dimensional parts. In the literature, UAM builds made with low power (1.5 kW) exhibited poor tensile properties in Z-direction, i.e., normal to the interfaces. This reduction in properties is often attributed to the lack of bonding at faying interfaces. The generality of this conclusion is evaluated further in 6061 aluminum alloy builds made with very high power UAM (9 kW). Tensile deformation behavior along X and Z directions were evaluated with small-scalemore » in-situ mechanical testing equipped with high-resolution digital image correlation, as well as, multi-scale characterization of builds. Interestingly, even with complete metallurgical bonding across the interfaces without any discernable voids, poor Z-direction properties were observed. This reduction is correlated to coalescence of pre-existing shear bands at interfaces into micro voids, leading to strain localization and spontaneous failure on tensile loading.« less
CHROTRAN 1.0: A mathematical and computational model for in situ heavy metal remediation in heterogeneous aquifers

NASA Astrophysics Data System (ADS)

Hansen, Scott K.; Pandey, Sachin; Karra, Satish; Vesselinov, Velimir V.

2017-12-01

Groundwater contamination by heavy metals is a critical environmental problem for which in situ remediation is frequently the only viable treatment option. For such interventions, a multi-dimensional reactive transport model of relevant biogeochemical processes is invaluable. To this end, we developed a model, chrotran, for in situ treatment, which includes full dynamics for five species: a heavy metal to be remediated, an electron donor, biomass, a nontoxic conservative bio-inhibitor, and a biocide. Direct abiotic reduction by donor-metal interaction as well as donor-driven biomass growth and bio-reduction are modeled, along with crucial processes such as donor sorption, bio-fouling, and biomass death. Our software implementation handles heterogeneous flow fields, as well as arbitrarily many chemical species and amendment injection points, and features full coupling between flow and reactive transport. We describe installation and usage and present two example simulations demonstrating its unique capabilities. One simulation suggests an unorthodox approach to remediation of Cr(VI) contamination.
Parameter Optimization and Operating Strategy of a TEG System for Railway Vehicles

NASA Astrophysics Data System (ADS)

Heghmanns, A.; Wilbrecht, S.; Beitelschmidt, M.; Geradts, K.

2016-03-01

A thermoelectric generator (TEG) system demonstrator for diesel electric locomotives with the objective of reducing the mechanical load on the thermoelectric modules (TEM) is developed and constructed to validate a one-dimensional thermo-fluid flow simulation model. The model is in good agreement with the measurements and basis for the optimization of the TEG's geometry by a genetic multi objective algorithm. The best solution has a maximum power output of approx. 2.7 kW and does not exceed the maximum back pressure of the diesel engine nor the maximum TEM hot side temperature. To maximize the reduction of the fuel consumption, an operating strategy regarding the system power output for the TEG system is developed. Finally, the potential consumption reduction in passenger and freight traffic operating modes is estimated under realistic driving conditions by means of a power train and lateral dynamics model. The fuel savings are between 0.5% and 0.7%, depending on the driving style.
Lie symmetry analysis and reduction for exact solution of (2+1)-dimensional Bogoyavlensky-Konopelchenko equation by geometric approach

NASA Astrophysics Data System (ADS)

Ray, S. Saha

2018-04-01

In this paper, the symmetry analysis and similarity reduction of the (2+1)-dimensional Bogoyavlensky-Konopelchenko (B-K) equation are investigated by means of the geometric approach of an invariance group, which is equivalent to the classical Lie symmetry method. Using the extended Harrison and Estabrook’s differential forms approach, the infinitesimal generators for (2+1)-dimensional B-K equation are obtained. Firstly, the vector field associated with the Lie group of transformation is derived. Then the symmetry reduction and the corresponding explicit exact solution of (2+1)-dimensional B-K equation is obtained.
Modeling change from large-scale high-dimensional spatio-temporal array data

NASA Astrophysics Data System (ADS)

Lu, Meng; Pebesma, Edzer

2014-05-01

The massive data that come from Earth observation satellite and other sensors provide significant information for modeling global change. At the same time, the high dimensionality of the data has brought challenges in data acquisition, management, effective querying and processing. In addition, the output of earth system modeling tends to be data intensive and needs methodologies for storing, validation, analyzing and visualization, e.g. as maps. An important proportion of earth system observations and simulated data can be represented as multi-dimensional array data, which has received increasingly attention in big data management and spatial-temporal analysis. Study cases will be developed in natural science such as climate change, hydrological modeling, sediment dynamics, from which the addressing of big data problems is necessary. Multi-dimensional array-based database management and analytics system such as Rasdaman, SciDB, and R will be applied to these cases. From these studies will hope to learn the strengths and weaknesses of these systems, how they might work together or how semantics of array operations differ, through addressing the problems associated with big data. Research questions include: • How can we reduce dimensions spatially and temporally, or thematically? • How can we extend existing GIS functions to work on multidimensional arrays? • How can we combine data sets of different dimensionality or different resolutions? • Can map algebra be extended to an intelligible array algebra? • What are effective semantics for array programming of dynamic data driven applications? • In which sense are space and time special, as dimensions, compared to other properties? • How can we make the analysis of multi-spectral, multi-temporal and multi-sensor earth observation data easy?

What is integrability of discrete variational systems?

PubMed

Boll, Raphael; Petrera, Matteo; Suris, Yuri B

2014-02-08

We propose a notion of a pluri-Lagrangian problem, which should be understood as an analogue of multi-dimensional consistency for variational systems. This is a development along the line of research of discrete integrable Lagrangian systems initiated in 2009 by Lobb and Nijhoff, however, having its more remote roots in the theory of pluriharmonic functions, in the Z -invariant models of statistical mechanics and their quasiclassical limit, as well as in the theory of variational symmetries going back to Noether. A d -dimensional pluri-Lagrangian problem can be described as follows: given a d -form [Formula: see text] on an m -dimensional space (called multi-time, m > d ), whose coefficients depend on a sought-after function x of m independent variables (called field), find those fields x which deliver critical points to the action functionals [Formula: see text] for any d -dimensional manifold Σ in the multi-time. We derive the main building blocks of the multi-time Euler-Lagrange equations for a discrete pluri-Lagrangian problem with d =2, the so-called corner equations, and discuss the notion of consistency of the system of corner equations. We analyse the system of corner equations for a special class of three-point two-forms, corresponding to integrable quad-equations of the ABS list. This allows us to close a conceptual gap of the work by Lobb and Nijhoff by showing that the corresponding two-forms are closed not only on solutions of (non-variational) quad-equations, but also on general solutions of the corresponding corner equations. We also find an example of a pluri-Lagrangian system not coming from a multi-dimensionally consistent system of quad-equations.
What is integrability of discrete variational systems?

PubMed Central

Boll, Raphael; Petrera, Matteo; Suris, Yuri B.

2014-01-01

We propose a notion of a pluri-Lagrangian problem, which should be understood as an analogue of multi-dimensional consistency for variational systems. This is a development along the line of research of discrete integrable Lagrangian systems initiated in 2009 by Lobb and Nijhoff, however, having its more remote roots in the theory of pluriharmonic functions, in the Z-invariant models of statistical mechanics and their quasiclassical limit, as well as in the theory of variational symmetries going back to Noether. A d-dimensional pluri-Lagrangian problem can be described as follows: given a d-form on an m-dimensional space (called multi-time, m>d), whose coefficients depend on a sought-after function x of m independent variables (called field), find those fields x which deliver critical points to the action functionals for any d-dimensional manifold Σ in the multi-time. We derive the main building blocks of the multi-time Euler–Lagrange equations for a discrete pluri-Lagrangian problem with d=2, the so-called corner equations, and discuss the notion of consistency of the system of corner equations. We analyse the system of corner equations for a special class of three-point two-forms, corresponding to integrable quad-equations of the ABS list. This allows us to close a conceptual gap of the work by Lobb and Nijhoff by showing that the corresponding two-forms are closed not only on solutions of (non-variational) quad-equations, but also on general solutions of the corresponding corner equations. We also find an example of a pluri-Lagrangian system not coming from a multi-dimensionally consistent system of quad-equations. PMID:24511254
Multi-Scale Modeling of an Integrated 3D Braided Composite with Applications to Helicopter Arm

NASA Astrophysics Data System (ADS)

Zhang, Diantang; Chen, Li; Sun, Ying; Zhang, Yifan; Qian, Kun

2017-10-01

A study is conducted with the aim of developing multi-scale analytical method for designing the composite helicopter arm with three-dimensional (3D) five-directional braided structure. Based on the analysis of 3D braided microstructure, the multi-scale finite element modeling is developed. Finite element analysis on the load capacity of 3D five-directional braided composites helicopter arm is carried out using the software ABAQUS/Standard. The influences of the braiding angle and loading condition on the stress and strain distribution of the helicopter arm are simulated. The results show that the proposed multi-scale method is capable of accurately predicting the mechanical properties of 3D braided composites, validated by the comparison the stress-strain curves of meso-scale RVCs. Furthermore, it is found that the braiding angle is an important factor affecting the mechanical properties of 3D five-directional braided composite helicopter arm. Based on the optimized structure parameters, the nearly net-shaped composite helicopter arm is fabricated using a novel resin transfer mould (RTM) process.
The joint return period analysis of natural disasters based on monitoring and statistical modeling of multidimensional hazard factors.

PubMed

Liu, Xueqin; Li, Ning; Yuan, Shuai; Xu, Ning; Shi, Wenqin; Chen, Weibin

2015-12-15

As a random event, a natural disaster has the complex occurrence mechanism. The comprehensive analysis of multiple hazard factors is important in disaster risk assessment. In order to improve the accuracy of risk analysis and forecasting, the formation mechanism of a disaster should be considered in the analysis and calculation of multi-factors. Based on the consideration of the importance and deficiencies of multivariate analysis of dust storm disasters, 91 severe dust storm disasters in Inner Mongolia from 1990 to 2013 were selected as study cases in the paper. Main hazard factors from 500-hPa atmospheric circulation system, near-surface meteorological system, and underlying surface conditions were selected to simulate and calculate the multidimensional joint return periods. After comparing the simulation results with actual dust storm events in 54years, we found that the two-dimensional Frank Copula function showed the better fitting results at the lower tail of hazard factors and that three-dimensional Frank Copula function displayed the better fitting results at the middle and upper tails of hazard factors. However, for dust storm disasters with the short return period, three-dimensional joint return period simulation shows no obvious advantage. If the return period is longer than 10years, it shows significant advantages in extreme value fitting. Therefore, we suggest the multivariate analysis method may be adopted in forecasting and risk analysis of serious disasters with the longer return period, such as earthquake and tsunami. Furthermore, the exploration of this method laid the foundation for the prediction and warning of other nature disasters. Copyright © 2015 Elsevier B.V. All rights reserved.
Transforming Graph Data for Statistical Relational Learning

DTIC Science & Technology

2012-10-01

Jordan, 2003), PLSA (Hofmann, 1999), ? Classification via RMN (Taskar et al., 2003) or SVM (Hasan, Chaoji, Salem , & Zaki, 2006) ? Hierarchical...dimensionality reduction methods such as Principal 407 Rossi, McDowell, Aha, & Neville Component Analysis (PCA), Principal Factor Analysis ( PFA ), and...clustering algorithm. Journal of the Royal Statistical Society. Series C, Applied statistics, 28, 100–108. Hasan, M. A., Chaoji, V., Salem , S., & Zaki, M
Helical Channel Design and Technology for Cooling of Muon Beams

NASA Astrophysics Data System (ADS)

Yonehara, K.; Derbenev, Y. S.; Johnson, R. P.

2010-11-01

Novel magnetic helical channel designs for capture and cooling of bright muon beams are being developed using numerical simulations based on new inventions such as helical solenoid (HS) magnets and hydrogen-pressurized RF (HPRF) cavities. We are close to the factor of a million six-dimensional phase space (6D) reduction needed for muon colliders. Recent experimental and simulation results are presented.
A Multi-Resolution Nonlinear Mapping Technique for Design and Analysis Applications

NASA Technical Reports Server (NTRS)

Phan, Minh Q.

1998-01-01

This report describes a nonlinear mapping technique where the unknown static or dynamic system is approximated by a sum of dimensionally increasing functions (one-dimensional curves, two-dimensional surfaces, etc.). These lower dimensional functions are synthesized from a set of multi-resolution basis functions, where the resolutions specify the level of details at which the nonlinear system is approximated. The basis functions also cause the parameter estimation step to become linear. This feature is taken advantage of to derive a systematic procedure to determine and eliminate basis functions that are less significant for the particular system under identification. The number of unknown parameters that must be estimated is thus reduced and compact models obtained. The lower dimensional functions (identified curves and surfaces) permit a kind of "visualization" into the complexity of the nonlinearity itself.
A Multi-Resolution Nonlinear Mapping Technique for Design and Analysis Application

NASA Technical Reports Server (NTRS)

Phan, Minh Q.

1997-01-01

This report describes a nonlinear mapping technique where the unknown static or dynamic system is approximated by a sum of dimensionally increasing functions (one-dimensional curves, two-dimensional surfaces, etc.). These lower dimensional functions are synthesized from a set of multi-resolution basis functions, where the resolutions specify the level of details at which the nonlinear system is approximated. The basis functions also cause the parameter estimation step to become linear. This feature is taken advantage of to derive a systematic procedure to determine and eliminate basis functions that are less significant for the particular system under identification. The number of unknown parameters that must be estimated is thus reduced and compact models obtained. The lower dimensional functions (identified curves and surfaces) permit a kind of "visualization" into the complexity of the nonlinearity itself.
Multidimensional improvements induced by an intensive obesity inpatients rehabilitation programme.

PubMed

Giordano, Francesca; Berteotti, Michela; Budui, Simona; Calgaro, Nicole; Franceschini, Laura; Gilli, Federica; Masiero, Marina; Raschellà, Guido; Salvetti, Sabrina; Taddei, Micol; Schena, Federico; Busetto, Luca

2017-06-01

To analyse the short-term effectiveness of an intensive multidimensional inpatient programme specifically developed for patients with severe obesity. A multidisciplinary team managed a 3-week residential programme characterised by the integration of nutritional and physical rehabilitation with psychological and educational intervention. All patients consecutively admitted in 10 months were analysed at admission and discharge for changes in the following domains: anthropometry (weight, body mass index (BMI), waist and neck circumferences), cardiovascular risk factors (glycaemia, HbA1c, lipid profile, blood pressure), quality of life, eating behaviour, and physical performance (VO 2peak by incremental cycle ergometer test, 6-min walking test (6MWT), chair stands test). 136 subjects (61% females, median age 52.7 years) with obesity (mean BMI 43.2 kg/m 2 ) and multiple comorbidities were analysed. A 3.9% BMI reduction and a reduction in waist (-3.8%) and neck (-3.3%) circumferences were observed. Glycaemic control was achieved in 68% of patients with uncontrolled diabetes at admission. Blood pressure control was achieved in all patients with uncontrolled hypertension at admission. Total cholesterol (-16%), LDL-cholesterol (-19%) and triglycerides (-9%) were significantly reduced. Psychometric assessment showed improvements in quality of life perception and binge eating disorder. Finally, a significant improvement in physical performance (+4.7% improvement in VO 2peak , with longer distances in 6MWT and a higher number of standings) was observed. Our preliminary data prove that a 3-week programme determined a clinically significant multi-dimensional improvement in patients with severe obesity. Long-term follow-up data are needed to confirm the efficacy of our rehabilitation setting.
Test-retest reliability of the underlying latent factor structure of alcohol subjective response.

PubMed

Lutz, Joseph A; Childs, Emma

2017-04-01

Alcohol subjective experiences are multi-dimensional and demonstrate wide inter-individual variability. Recent efforts have sought to establish a clearer understanding of subjective alcohol responses by identifying core constructs derived from multiple measurement instruments. The aim of this study was to evaluate the temporal stability of this approach to conceptualizing alcohol subjective experiences across successive alcohol administrations in the same individuals. Healthy moderate alcohol drinkers (n = 104) completed six experimental sessions each, three with alcohol (0.8 g/kg), and three with a non-alcoholic control beverage. Participants reported subjective mood and drug effects using standardized questionnaires before and at repeated times after beverage consumption. We explored the underlying latent structure of subjective responses for all alcohol administrations using exploratory factor analysis and then tested measurement invariance over the three successive administrations using multi-group confirmatory factor analyses. Exploratory factor analyses on responses to alcohol across all administrations yielded four factors representing "Positive mood," "Sedation," "Stimulation/Euphoria," and "Drug effects and Urges." A confirmatory factor analysis on the separate administrations indicated acceptable configural and metric invariance and moderate scalar invariance. In this study, we demonstrate temporal stability of the underlying constructs of subjective alcohol responses derived from factor analysis. These findings strengthen the utility of this approach to conceptualizing subjective alcohol responses especially for use in prospective and longitudinal alcohol challenge studies relating subjective response to alcohol use disorder risk.
[The physiological classification of human thermal states under high environmental temperatures].

PubMed

Bobrov, A F; Kuznets, E I

1995-01-01

The paper deals with the physiological classification of human thermal states in a hot environment. A review of the basic systems of classifications of thermal states is given, their main drawbacks are discussed. On the basis of human functional state research in a broad range of environmental temperatures the system of evaluation and classification of human thermal states is proposed. New integral one-dimensional multi-parametric criteria for evaluation are used. For the development of these criteria methods of factor, cluster and canonical correlation analyses are applied. Stochastic nomograms capable of identification of human thermal state for different intensity of influence are given. In this case evaluation of intensity is estimated according to one-dimensional criteria taking into account environmental temperature, physical load and time of man's staying in overheating conditions.
Multi-GPU accelerated three-dimensional FDTD method for electromagnetic simulation.

PubMed

Nagaoka, Tomoaki; Watanabe, Soichi

2011-01-01

Numerical simulation with a numerical human model using the finite-difference time domain (FDTD) method has recently been performed in a number of fields in biomedical engineering. To improve the method's calculation speed and realize large-scale computing with the numerical human model, we adapt three-dimensional FDTD code to a multi-GPU environment using Compute Unified Device Architecture (CUDA). In this study, we used NVIDIA Tesla C2070 as GPGPU boards. The performance of multi-GPU is evaluated in comparison with that of a single GPU and vector supercomputer. The calculation speed with four GPUs was approximately 3.5 times faster than with a single GPU, and was slightly (approx. 1.3 times) slower than with the supercomputer. Calculation speed of the three-dimensional FDTD method using GPUs can significantly improve with an expanding number of GPUs.
Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing.

PubMed

Li, Shuang; Liu, Bing; Zhang, Chen

2016-01-01

Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.
Optimal dimensionality reduction of complex dynamics: the chess game as diffusion on a free-energy landscape.

PubMed

Krivov, Sergei V

2011-07-01

Dimensionality reduction is ubiquitous in the analysis of complex dynamics. The conventional dimensionality reduction techniques, however, focus on reproducing the underlying configuration space, rather than the dynamics itself. The constructed low-dimensional space does not provide a complete and accurate description of the dynamics. Here I describe how to perform dimensionality reduction while preserving the essential properties of the dynamics. The approach is illustrated by analyzing the chess game--the archetype of complex dynamics. A variable that provides complete and accurate description of chess dynamics is constructed. The winning probability is predicted by describing the game as a random walk on the free-energy landscape associated with the variable. The approach suggests a possible way of obtaining a simple yet accurate description of many important complex phenomena. The analysis of the chess game shows that the approach can quantitatively describe the dynamics of processes where human decision-making plays a central role, e.g., financial and social dynamics.
Optimal dimensionality reduction of complex dynamics: The chess game as diffusion on a free-energy landscape

NASA Astrophysics Data System (ADS)

Krivov, Sergei V.

2011-07-01

Dimensionality reduction is ubiquitous in the analysis of complex dynamics. The conventional dimensionality reduction techniques, however, focus on reproducing the underlying configuration space, rather than the dynamics itself. The constructed low-dimensional space does not provide a complete and accurate description of the dynamics. Here I describe how to perform dimensionality reduction while preserving the essential properties of the dynamics. The approach is illustrated by analyzing the chess game—the archetype of complex dynamics. A variable that provides complete and accurate description of chess dynamics is constructed. The winning probability is predicted by describing the game as a random walk on the free-energy landscape associated with the variable. The approach suggests a possible way of obtaining a simple yet accurate description of many important complex phenomena. The analysis of the chess game shows that the approach can quantitatively describe the dynamics of processes where human decision-making plays a central role, e.g., financial and social dynamics.
Four-dimensional \\mathcal{N} = 2 supersymmetric theory with boundary as a two-dimensional complex Toda theory

NASA Astrophysics Data System (ADS)

Luo, Yuan; Tan, Meng-Chwan; Vasko, Petr; Zhao, Qin

2017-05-01

We perform a series of dimensional reductions of the 6d, \\mathcal{N} = (2, 0) SCFT on S 2 × Σ × I × S 1 down to 2d on Σ. The reductions are performed in three steps: (i) a reduction on S 1 (accompanied by a topological twist along Σ) leading to a supersymmetric Yang-Mills theory on S 2 × Σ × I, (ii) a further reduction on S 2 resulting in a complex Chern-Simons theory defined on Σ × I, with the real part of the complex Chern-Simons level being zero, and the imaginary part being proportional to the ratio of the radii of S 2 and S 1, and (iii) a final reduction to the boundary modes of complex Chern-Simons theory with the Nahm pole boundary condition at both ends of the interval I, which gives rise to a complex Toda CFT on the Riemann surface Σ. As the reduction of the 6d theory on Σ would give rise to an \\mathcal{N} = 2 supersymmetric theory on S 2 × I × S 1, our results imply a 4d-2d duality between four-dimensional \\mathcal{N} = 2 supersymmetric theory with boundary and two-dimensional complex Toda theory.
towards a theory-based multi-dimensional framework for assessment in mathematics: The "SEA" framework

NASA Astrophysics Data System (ADS)

Anku, Sitsofe E.

1997-09-01

Using the reform documents of the National Council of Teachers of Mathematics (NCTM) (NCTM, 1989, 1991, 1995), a theory-based multi-dimensional assessment framework (the "SEA" framework) which should help expand the scope of assessment in mathematics is proposed. This framework uses a context based on mathematical reasoning and has components that comprise mathematical concepts, mathematical procedures, mathematical communication, mathematical problem solving, and mathematical disposition.
Ultra-high aggregate bandwidth two-dimensional multiple-wavelength diode laser arrays

NASA Astrophysics Data System (ADS)

Chang-Hasnain, Connie

1994-04-01

Two-dimensional (2D) multi-wavelength vertical cavity surface emitting laser (VCSEL) arrays is promising for ultrahigh aggregate capacity optical networks. A 2D VCSEL array emitting 140 distinct wavelengths was reported by implementing a spatially graded layer in the VCSEL structure, which in turn creates a wavelength spread. In this program, we concentrated on novel epitaxial growth techniques to make reproducible and repeatable multi-wavelength VCSEL arrays.
Consistency of clinical biomechanical measures between three different institutions: implications for multi-center biomechanical and epidemiological research.

PubMed

Myer, Gregory D; Wordeman, Samuel C; Sugimoto, Dai; Bates, Nathaniel A; Roewer, Benjamin D; Medina McKeon, Jennifer M; DiCesare, Christopher A; Di Stasi, Stephanie L; Barber Foss, Kim D; Thomas, Staci M; Hewett, Timothy E

2014-05-01

Multi-center collaborations provide a powerful alternative to overcome the inherent limitations to single-center investigations. Specifically, multi-center projects can support large-scale prospective, longitudinal studies that investigate relatively uncommon outcomes, such as anterior cruciate ligament injury. This project was conceived to assess within- and between-center reliability of an affordable, clinical nomogram utilizing two-dimensional video methods to screen for risk of knee injury. The authors hypothesized that the two-dimensional screening methods would provide good-to-excellent reliability within and between institutions for assessment of frontal and sagittal plane biomechanics. Nineteen female, high school athletes participated. Two-dimensional video kinematics of the lower extremity during a drop vertical jump task were collected on all 19 study participants at each of the three facilities. Within-center and between-center reliability were assessed with intra- and inter-class correlation coefficients. Within-center reliability of the clinical nomogram variables was consistently excellent, but between-center reliability was fair-to-good. Within-center intra-class correlation coefficient for all nomogram variables combined was 0.98, while combined between-center inter-class correlation coefficient was 0.63. Injury risk screening protocols were reliable within and repeatable between centers. These results demonstrate the feasibility of multi-site biomechanical studies and establish a framework for further dissemination of injury risk screening algorithms. Specifically, multi-center studies may allow for further validation and optimization of two-dimensional video screening tools. 2b.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Wemhoff, A P; Burnham, A K; Nichols III, A L

The reduction of the number of reactions in kinetic models for both the HMX beta-delta phase transition and thermal cookoff provides an attractive alternative to traditional multi-stage kinetic models due to reduced calibration effort requirements. In this study, we use the LLNL code ALE3D to provide calibrated kinetic parameters for a two-reaction bidirectional beta-delta HMX phase transition model based on Sandia Instrumented Thermal Ignition (SITI) and Scaled Thermal Explosion (STEX) temperature history curves, and a Prout-Tompkins cookoff model based on One-Dimensional Time to Explosion (ODTX) data. Results show that the two-reaction bidirectional beta-delta transition model presented here agrees as wellmore » with STEX and SITI temperature history curves as a reversible four-reaction Arrhenius model, yet requires an order of magnitude less computational effort. In addition, a single-reaction Prout-Tompkins model calibrated to ODTX data provides better agreement with ODTX data than a traditional multi-step Arrhenius model, and can contain up to 90% less chemistry-limited time steps for low-temperature ODTX simulations. Manual calibration methods for the Prout-Tompkins kinetics provide much better agreement with ODTX experimental data than parameters derived from Differential Scanning Calorimetry (DSC) measurements at atmospheric pressure. The predicted surface temperature at explosion for STEX cookoff simulations is a weak function of the cookoff model used, and a reduction of up to 15% of chemistry-limited time steps can be achieved by neglecting the beta-delta transition for this type of simulation. Finally, the inclusion of the beta-delta transition model in the overall kinetics model can affect the predicted time to explosion by 1% for the traditional multi-step Arrhenius approach, while up to 11% using a Prout-Tompkins cookoff model.« less

A small molecule-based strategy for endothelial differentiation and three-dimensional morphogenesis from human embryonic stem cells.

PubMed

Geng, Yijie; Feng, Bradley

2016-07-01

The emerging models of human embryonic stem cell (hESC) self-organizing organoids provide a valuable in vitro platform for studying self-organizing processes that presumably mimic in vivo human developmental events. Here we report that through a chemical screen, we identified two novel and structurally similar small molecules BIR1 and BIR2 which robustly induced the self-organization of a balloon-shaped three-dimensional structure when applied to two-dimensional adherent hESC cultures in the absence of growth factors. Gene expression analyses and functional assays demonstrated an endothelial identity of this balloon-like structure, while cell surface marker analyses revealed a VE-cadherin(+)CD31(+)CD34(+)KDR(+)CD43(-) putative endothelial progenitor population. Furthermore, molecular marker labeling and morphological examinations characterized several other distinct DiI-Ac-LDL(+) multi-cellular modules and a VEGFR3(+) sprouting structure in the balloon cultures that likely represented intermediate structures of balloon-formation.
Fluorescence Intrinsic Characterization of Excitation-Emission Matrix Using Multi-Dimensional Ensemble Empirical Mode Decomposition

PubMed Central

Chang, Chi-Ying; Chang, Chia-Chi; Hsiao, Tzu-Chien

2013-01-01

Excitation-emission matrix (EEM) fluorescence spectroscopy is a noninvasive method for tissue diagnosis and has become important in clinical use. However, the intrinsic characterization of EEM fluorescence remains unclear. Photobleaching and the complexity of the chemical compounds make it difficult to distinguish individual compounds due to overlapping features. Conventional studies use principal component analysis (PCA) for EEM fluorescence analysis, and the relationship between the EEM features extracted by PCA and diseases has been examined. The spectral features of different tissue constituents are not fully separable or clearly defined. Recently, a non-stationary method called multi-dimensional ensemble empirical mode decomposition (MEEMD) was introduced; this method can extract the intrinsic oscillations on multiple spatial scales without loss of information. The aim of this study was to propose a fluorescence spectroscopy system for EEM measurements and to describe a method for extracting the intrinsic characteristics of EEM by MEEMD. The results indicate that, although PCA provides the principal factor for the spectral features associated with chemical compounds, MEEMD can provide additional intrinsic features with more reliable mapping of the chemical compounds. MEEMD has the potential to extract intrinsic fluorescence features and improve the detection of biochemical changes. PMID:24240806
Theoretical description of quantum mechanical permeation of graphene membranes by charged hydrogen isotopes

NASA Astrophysics Data System (ADS)

Mazzuca, James W.; Haut, Nathaniel K.

2018-06-01

It has been recently shown that in the presence of an applied voltage, hydrogen and deuterium nuclei can be separated from one another using graphene membranes as a nuclear sieve, resulting in a 10-fold enhancement in the concentration of the lighter isotope. While previous studies, both experimental and theoretical, have attributed this effect mostly to differences in vibrational zero point energy (ZPE) of the various isotopes near the membrane surface, we propose that multi-dimensional quantum mechanical tunneling of nuclei through the graphene membrane influences this proton permeation process in a fundamental way. We perform ring polymer molecular dynamics calculations in which we include both ZPE and tunneling effects of various hydrogen isotopes as they permeate the graphene membrane and compute rate constants across a range of temperatures near 300 K. While capturing the experimentally observed separation factor, our calculations indicate that the transverse motion of the various isotopes across the surface of the graphene membrane is an essential part of this sieving mechanism. An understanding of the multi-dimensional quantum mechanical nature of this process could serve to guide the design of other such isotopic enrichment processes for a variety of atomic and molecular species of interest.
Theoretical description of quantum mechanical permeation of graphene membranes by charged hydrogen isotopes.

PubMed

Mazzuca, James W; Haut, Nathaniel K

2018-06-14

It has been recently shown that in the presence of an applied voltage, hydrogen and deuterium nuclei can be separated from one another using graphene membranes as a nuclear sieve, resulting in a 10-fold enhancement in the concentration of the lighter isotope. While previous studies, both experimental and theoretical, have attributed this effect mostly to differences in vibrational zero point energy (ZPE) of the various isotopes near the membrane surface, we propose that multi-dimensional quantum mechanical tunneling of nuclei through the graphene membrane influences this proton permeation process in a fundamental way. We perform ring polymer molecular dynamics calculations in which we include both ZPE and tunneling effects of various hydrogen isotopes as they permeate the graphene membrane and compute rate constants across a range of temperatures near 300 K. While capturing the experimentally observed separation factor, our calculations indicate that the transverse motion of the various isotopes across the surface of the graphene membrane is an essential part of this sieving mechanism. An understanding of the multi-dimensional quantum mechanical nature of this process could serve to guide the design of other such isotopic enrichment processes for a variety of atomic and molecular species of interest.
[Establishment and application of "multi-dimensional structure and process dynamic quality control technology system" in preparation products of traditional Chinese medicine (I)].

PubMed

Gu, Jun-Fei; Feng, Liang; Zhang, Ming-Hua; Wu, Chan; Jia, Xiao-Bin

2013-11-01

Safety is an important component of the quality control of traditional Chinese medicine (TCM) preparation products, as well as an important guarantee for clinical application. Currently, the quality control of TCMs in Chinese Pharmacopoeia mostly focuses on indicative compounds for TCM efficacy. TCM preparations are associated with multiple links, from raw materials to products, and each procedure may have impacts on the safety of preparation. We make a summary and analysis on the factors impacting safety during the preparation of TCM products, and then expound the important role of the "multi-dimensional structure and process dynamic quality control technology system" in the quality safety of TCM preparations. Because the product quality of TCM preparation is closely related to the safety, the control over safety-related material basis is an important component of the product quality control of TCM preparations. The implementation of the quality control over the dynamic process of TCM preparations from raw materials to products, and the improvement of the TCM quality safety control at the microcosmic level help lay a firm foundation for the development of the modernization process of TCM preparations.
Alternative dimensional reduction via the density matrix

NASA Astrophysics Data System (ADS)

de Carvalho, C. A.; Cornwall, J. M.; da Silva, A. J.

2001-07-01

We give graphical rules, based on earlier work for the functional Schrödinger equation, for constructing the density matrix for scalar and gauge fields in equilibrium at finite temperature T. More useful is a dimensionally reduced effective action (DREA) constructed from the density matrix by further functional integration over the arguments of the density matrix coupled to a source. The DREA is an effective action in one less dimension which may be computed order by order in perturbation theory or by dressed-loop expansions; it encodes all thermal matrix elements. We term the DREA procedure alternative dimensional reduction, to distinguish it from the conventional dimensionally reduced field theory (DRFT) which applies at infinite T. The DREA is useful because it gives a dimensionally reduced theory usable at any T including infinity, where it yields the DRFT, and because it does not and cannot have certain spurious infinities which sometimes occur in the density matrix itself or the conventional DRFT; these come from ln T factors at infinite temperature. The DREA can be constructed to all orders (in principle) and the only regularizations needed are those which control the ultraviolet behavior of the zero-T theory. An example of spurious divergences in the DRFT occurs in d=2+1φ4 theory dimensionally reduced to d=2. We study this theory and show that the rules for the DREA replace these ``wrong'' divergences in physical parameters by calculable powers of ln T; we also compute the phase transition temperature of this φ4 theory in one-loop order. Our density-matrix construction is equivalent to a construction of the Landau-Ginzburg ``coarse-grained free energy'' from a microscopic Hamiltonian.
The GRIDView Visualization Package

NASA Astrophysics Data System (ADS)

Kent, B. R.

2011-07-01

Large three-dimensional data cubes, catalogs, and spectral line archives are increasingly important elements of the data discovery process in astronomy. Visualization of large data volumes is of vital importance for the success of large spectral line surveys. Examples of data reduction utilizing the GRIDView software package are shown. The package allows users to manipulate data cubes, extract spectral profiles, and measure line properties. The package and included graphical user interfaces (GUIs) are designed with pipeline infrastructure in mind. The software has been used with great success analyzing spectral line and continuum data sets obtained from large radio survey collaborations. The tools are also important for multi-wavelength cross-correlation studies and incorporate Virtual Observatory client applications for overlaying database information in real time as cubes are examined by users.
Concept of Operations for RCO SPO

NASA Technical Reports Server (NTRS)

Matessa, Michael; Strybel, Thomas; Vu, Kim; Battiste, Vernol; Schnell, Thomas

2017-01-01

Reduced crew operations (RCO) refers to the reduction of crew members flying long-haul or military operations with more than one pilot onboard. Single pilot operations (SPO) refers to flying a commercial transport aircraft with only one pilot on board the aircraft, assisted by advanced onboard automation andor ground operators providing piloting support services. Properly implemented, RCO/SPO could provide operating cost savings while maintaining a level of safety no less than conventional two-pilot commercial operations. A concept of operations (ConOps) for any paradigm describes the characteristics of its various components and their integration in a multi-dimensional design space. This paper presents key options for humanautomation function allocation being considered by NASA in its ongoing development of RCO/SPO ConOps.
[Research on non-rigid registration of multi-modal medical image based on Demons algorithm].

PubMed

Hao, Peibo; Chen, Zhen; Jiang, Shaofeng; Wang, Yang

2014-02-01

Non-rigid medical image registration is a popular subject in the research areas of the medical image and has an important clinical value. In this paper we put forward an improved algorithm of Demons, together with the conservation of gray model and local structure tensor conservation model, to construct a new energy function processing multi-modal registration problem. We then applied the L-BFGS algorithm to optimize the energy function and solve complex three-dimensional data optimization problem. And finally we used the multi-scale hierarchical refinement ideas to solve large deformation registration. The experimental results showed that the proposed algorithm for large de formation and multi-modal three-dimensional medical image registration had good effects.
Assessment and Reduction of Model Parametric Uncertainties: A Case Study with A Distributed Hydrological Model

NASA Astrophysics Data System (ADS)

Gan, Y.; Liang, X. Z.; Duan, Q.; Xu, J.; Zhao, P.; Hong, Y.

2017-12-01

The uncertainties associated with the parameters of a hydrological model need to be quantified and reduced for it to be useful for operational hydrological forecasting and decision support. An uncertainty quantification framework is presented to facilitate practical assessment and reduction of model parametric uncertainties. A case study, using the distributed hydrological model CREST for daily streamflow simulation during the period 2008-2010 over ten watershed, was used to demonstrate the performance of this new framework. Model behaviors across watersheds were analyzed by a two-stage stepwise sensitivity analysis procedure, using LH-OAT method for screening out insensitive parameters, followed by MARS-based Sobol' sensitivity indices for quantifying each parameter's contribution to the response variance due to its first-order and higher-order effects. Pareto optimal sets of the influential parameters were then found by the adaptive surrogate-based multi-objective optimization procedure, using MARS model for approximating the parameter-response relationship and SCE-UA algorithm for searching the optimal parameter sets of the adaptively updated surrogate model. The final optimal parameter sets were validated against the daily streamflow simulation of the same watersheds during the period 2011-2012. The stepwise sensitivity analysis procedure efficiently reduced the number of parameters that need to be calibrated from twelve to seven, which helps to limit the dimensionality of calibration problem and serves to enhance the efficiency of parameter calibration. The adaptive MARS-based multi-objective calibration exercise provided satisfactory solutions to the reproduction of the observed streamflow for all watersheds. The final optimal solutions showed significant improvement when compared to the default solutions, with about 65-90% reduction in 1-NSE and 60-95% reduction in |RB|. The validation exercise indicated a large improvement in model performance with about 40-85% reduction in 1-NSE, and 35-90% reduction in |RB|. Overall, this uncertainty quantification framework is robust, effective and efficient for parametric uncertainty analysis, the results of which provide useful information that helps to understand the model behaviors and improve the model simulations.
Facile multi-dimensional profiling of chemical gradients at the millimetre scale.

PubMed

Chen, Chih-Lin; Hsieh, Kai-Ta; Hsu, Ching-Fong; Urban, Pawel L

2016-01-07

A vast number of conventional physicochemical methods are suitable for the analysis of homogeneous samples. However, in various cases, the samples exhibit intrinsic heterogeneity. Tomography allows one to record approximate distributions of chemical species in the three-dimensional space. Here we develop a simple optical tomography system which enables performing scans of non-homogeneous samples at different wavelengths. It takes advantage of inexpensive open-source electronics and simple algorithms. The analysed samples are illuminated by a miniature LCD/LED screen which emits light at three wavelengths (598, 547 and 455 nm, corresponding to the R, G, and B channels, respectively). On presentation of every wavelength, the sample vial is rotated by ∼180°, and videoed at 30 frames per s. The RGB values of pixels in the obtained digital snapshots are subsequently collated, and processed to produce sinograms. Following the inverse Radon transform, approximate quasi-three-dimensional images are reconstructed for each wavelength. Sample components with distinct visible light absorption spectra (myoglobin, methylene blue) can be resolved. The system was used to follow dynamic changes in non-homogeneous samples in real time, to visualize binary mixtures, to reconstruct reaction-diffusion fronts formed during the reduction of 2,6-dichlorophenolindophenol by ascorbic acid, and to visualize the distribution of fungal mycelium grown in a semi-solid medium.
BioXTAS RAW: improvements to a free open-source program for small-angle X-ray scattering data reduction and analysis.

PubMed

Hopkins, Jesse Bennett; Gillilan, Richard E; Skou, Soren

2017-10-01

BioXTAS RAW is a graphical-user-interface-based free open-source Python program for reduction and analysis of small-angle X-ray solution scattering (SAXS) data. The software is designed for biological SAXS data and enables creation and plotting of one-dimensional scattering profiles from two-dimensional detector images, standard data operations such as averaging and subtraction and analysis of radius of gyration and molecular weight, and advanced analysis such as calculation of inverse Fourier transforms and envelopes. It also allows easy processing of inline size-exclusion chromatography coupled SAXS data and data deconvolution using the evolving factor analysis method. It provides an alternative to closed-source programs such as Primus and ScÅtter for primary data analysis. Because it can calibrate, mask and integrate images it also provides an alternative to synchrotron beamline pipelines that scientists can install on their own computers and use both at home and at the beamline.
Automated detection of lung nodules with three-dimensional convolutional neural networks

NASA Astrophysics Data System (ADS)

Pérez, Gustavo; Arbeláez, Pablo

2017-11-01

Lung cancer is the cancer type with highest mortality rate worldwide. It has been shown that early detection with computer tomography (CT) scans can reduce deaths caused by this disease. Manual detection of cancer nodules is costly and time-consuming. We present a general framework for the detection of nodules in lung CT images. Our method consists of the pre-processing of a patient's CT with filtering and lung extraction from the entire volume using a previously calculated mask for each patient. From the extracted lungs, we perform a candidate generation stage using morphological operations, followed by the training of a three-dimensional convolutional neural network for feature representation and classification of extracted candidates for false positive reduction. We perform experiments on the publicly available LIDC-IDRI dataset. Our candidate extraction approach is effective to produce precise candidates with a recall of 99.6%. In addition, false positive reduction stage manages to successfully classify candidates and increases precision by a factor of 7.000.
Nonlinear dimensionality reduction methods for synthetic biology biobricks' visualization.

PubMed

Yang, Jiaoyun; Wang, Haipeng; Ding, Huitong; An, Ning; Alterovitz, Gil

2017-01-19

Visualizing data by dimensionality reduction is an important strategy in Bioinformatics, which could help to discover hidden data properties and detect data quality issues, e.g. data noise, inappropriately labeled data, etc. As crowdsourcing-based synthetic biology databases face similar data quality issues, we propose to visualize biobricks to tackle them. However, existing dimensionality reduction methods could not be directly applied on biobricks datasets. Hereby, we use normalized edit distance to enhance dimensionality reduction methods, including Isomap and Laplacian Eigenmaps. By extracting biobricks from synthetic biology database Registry of Standard Biological Parts, six combinations of various types of biobricks are tested. The visualization graphs illustrate discriminated biobricks and inappropriately labeled biobricks. Clustering algorithm K-means is adopted to quantify the reduction results. The average clustering accuracy for Isomap and Laplacian Eigenmaps are 0.857 and 0.844, respectively. Besides, Laplacian Eigenmaps is 5 times faster than Isomap, and its visualization graph is more concentrated to discriminate biobricks. By combining normalized edit distance with Isomap and Laplacian Eigenmaps, synthetic biology biobircks are successfully visualized in two dimensional space. Various types of biobricks could be discriminated and inappropriately labeled biobricks could be determined, which could help to assess crowdsourcing-based synthetic biology databases' quality, and make biobricks selection.
On the reduction of 4d $$ \\mathcal{N}=1 $$ theories on $$ {\\mathbb{S}}^2 $$

DOE PAGES

Gadde, Abhijit; Razamat, Shlomo S.; Willett, Brian

2015-11-24

Here, we discuss reductions of generalmore » $$ \\mathcal{N}=1 $$ four dimensional gauge theories on $$ {\\mathbb{S}}^2 $$. The effective two dimensional theory one obtains depends on the details of the coupling of the theory to background fields, which can be translated to a choice of R-symmetry. We argue that, for special choices of R-symmetry, the resulting two dimensional theory has a natural interpretation as an $$ \\mathcal{N}(0,2) $$ gauge theory. As an application of our general observations, we discuss reductions of $$ \\mathcal{N}=1 $$ and $$ \\mathcal{N}=2 $$ dualities and argue that they imply certain two dimensional dualities.« less
Adjoint Methods for Adjusting Three-Dimensional Atmosphere and Surface Properties to Fit Multi-Angle Multi-Pixel Polarimetric Measurements

NASA Technical Reports Server (NTRS)

Martin, William G.; Cairns, Brian; Bal, Guillaume

2014-01-01

This paper derives an efficient procedure for using the three-dimensional (3D) vector radiative transfer equation (VRTE) to adjust atmosphere and surface properties and improve their fit with multi-angle/multi-pixel radiometric and polarimetric measurements of scattered sunlight. The proposed adjoint method uses the 3D VRTE to compute the measurement misfit function and the adjoint 3D VRTE to compute its gradient with respect to all unknown parameters. In the remote sensing problems of interest, the scalar-valued misfit function quantifies agreement with data as a function of atmosphere and surface properties, and its gradient guides the search through this parameter space. Remote sensing of the atmosphere and surface in a three-dimensional region may require thousands of unknown parameters and millions of data points. Many approaches would require calls to the 3D VRTE solver in proportion to the number of unknown parameters or measurements. To avoid this issue of scale, we focus on computing the gradient of the misfit function as an alternative to the Jacobian of the measurement operator. The resulting adjoint method provides a way to adjust 3D atmosphere and surface properties with only two calls to the 3D VRTE solver for each spectral channel, regardless of the number of retrieval parameters, measurement view angles or pixels. This gives a procedure for adjusting atmosphere and surface parameters that will scale to the large problems of 3D remote sensing. For certain types of multi-angle/multi-pixel polarimetric measurements, this encourages the development of a new class of three-dimensional retrieval algorithms with more flexible parametrizations of spatial heterogeneity, less reliance on data screening procedures, and improved coverage in terms of the resolved physical processes in the Earth?s atmosphere.
Low-rank factorization of electron integral tensors and its application in electronic structure theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peng, Bo; Kowalski, Karol

In this letter, we introduce the reverse Cuthill-McKee (RCM) algorithm, which is often used for the bandwidth reduction of sparse tensors, to transform the two-electron integral tensors to their block diagonal forms. By further applying the pivoted Cholesky decomposition (CD) on each of the diagonal blocks, we are able to represent the high-dimensional two-electron integral tensors in terms of permutation matrices and low-rank Cholesky vectors. This representation facilitates the low-rank factorization of the high-dimensional tensor contractions that are usually encountered in post-Hartree-Fock calculations. In this letter, we discuss the second-order Møller-Plesset (MP2) method and linear coupled- cluster model with doublesmore » (L-CCD) as two simple examples to demonstrate the efficiency of the RCM-CD technique in representing two-electron integrals in a compact form.« less
Development and validation of the Bullying and Cyberbullying Scale for Adolescents: A multi-dimensional measurement model.

PubMed

Thomas, Hannah J; Scott, James G; Coates, Jason M; Connor, Jason P

2018-05-03

Intervention on adolescent bullying is reliant on valid and reliable measurement of victimization and perpetration experiences across different behavioural expressions. This study developed and validated a survey tool that integrates measurement of both traditional and cyber bullying to test a theoretically driven multi-dimensional model. Adolescents from 10 mainstream secondary schools completed a baseline and follow-up survey (N = 1,217; M age = 14 years; 66.2% male). The Bullying and cyberbullying Scale for Adolescents (BCS-A) developed for this study comprised parallel victimization and perpetration subscales, each with 20 items. Additional measures of bullying (Olweus Global Bullying and the Forms of Bullying Scale [FBS]), as well as measures of internalizing and externalizing problems, school connectedness, social support, and personality, were used to further assess validity. Factor structure was determined, and then, the suitability of items was assessed according to the following criteria: (1) factor interpretability, (2) item correlations, (3) model parsimony, and (4) measurement equivalence across victimization and perpetration experiences. The final models comprised four factors: physical, verbal, relational, and cyber. The final scale was revised to two 13-item subscales. The BCS-A demonstrated acceptable concurrent and convergent validity (internalizing and externalizing problems, school connectedness, social support, and personality), as well as predictive validity over 6 months. The BCS-A has sound psychometric properties. This tool establishes measurement equivalence across types of involvement and behavioural forms common among adolescents. An improved measurement method could add greater rigour to the evaluation of intervention programmes and also enable interventions to be tailored to subscale profiles. © 2018 The British Psychological Society.
A review on the multivariate statistical methods for dimensional reduction studies

NASA Astrophysics Data System (ADS)

Aik, Lim Eng; Kiang, Lam Chee; Mohamed, Zulkifley Bin; Hong, Tan Wei

2017-05-01

In this research study we have discussed multivariate statistical methods for dimensional reduction, which has been done by various researchers. The reduction of dimensionality is valuable to accelerate algorithm progression, as well as really may offer assistance with the last grouping/clustering precision. A lot of boisterous or even flawed info information regularly prompts a not exactly alluring algorithm progression. Expelling un-useful or dis-instructive information segments may for sure help the algorithm discover more broad grouping locales and principles and generally speaking accomplish better exhibitions on new data set.
A Three-Dimensional Target Depth-Resolution Method with a Single-Vector Sensor

PubMed Central

Zhao, Anbang; Bi, Xuejie; Hui, Juan; Zeng, Caigao; Ma, Lin

2018-01-01

This paper mainly studies and verifies the target number category-resolution method in multi-target cases and the target depth-resolution method of aerial targets. Firstly, target depth resolution is performed by using the sign distribution of the reactive component of the vertical complex acoustic intensity; the target category and the number resolution in multi-target cases is realized with a combination of the bearing-time recording information; and the corresponding simulation verification is carried out. The algorithm proposed in this paper can distinguish between the single-target multi-line spectrum case and the multi-target multi-line spectrum case. This paper presents an improved azimuth-estimation method for multi-target cases, which makes the estimation results more accurate. Using the Monte Carlo simulation, the feasibility of the proposed target number and category-resolution algorithm in multi-target cases is verified. In addition, by studying the field characteristics of the aerial and surface targets, the simulation results verify that there is only amplitude difference between the aerial target field and the surface target field under the same environmental parameters, and an aerial target can be treated as a special case of a surface target; the aerial target category resolution can then be realized based on the sign distribution of the reactive component of the vertical acoustic intensity so as to realize three-dimensional target depth resolution. By processing data from a sea experiment, the feasibility of the proposed aerial target three-dimensional depth-resolution algorithm is verified. PMID:29649173

A Three-Dimensional Target Depth-Resolution Method with a Single-Vector Sensor.

PubMed

Zhao, Anbang; Bi, Xuejie; Hui, Juan; Zeng, Caigao; Ma, Lin

2018-04-12

This paper mainly studies and verifies the target number category-resolution method in multi-target cases and the target depth-resolution method of aerial targets. Firstly, target depth resolution is performed by using the sign distribution of the reactive component of the vertical complex acoustic intensity; the target category and the number resolution in multi-target cases is realized with a combination of the bearing-time recording information; and the corresponding simulation verification is carried out. The algorithm proposed in this paper can distinguish between the single-target multi-line spectrum case and the multi-target multi-line spectrum case. This paper presents an improved azimuth-estimation method for multi-target cases, which makes the estimation results more accurate. Using the Monte Carlo simulation, the feasibility of the proposed target number and category-resolution algorithm in multi-target cases is verified. In addition, by studying the field characteristics of the aerial and surface targets, the simulation results verify that there is only amplitude difference between the aerial target field and the surface target field under the same environmental parameters, and an aerial target can be treated as a special case of a surface target; the aerial target category resolution can then be realized based on the sign distribution of the reactive component of the vertical acoustic intensity so as to realize three-dimensional target depth resolution. By processing data from a sea experiment, the feasibility of the proposed aerial target three-dimensional depth-resolution algorithm is verified.
Breaking the cycle of loneliness? Psychological effects of a friendship enrichment program for older women.

PubMed

Martina, C M S; Stevens, N L

2006-09-01

The present study examines effects of participation in the friendship enrichment program, an intervention that is designed to stimulate improvement in friendship, self-esteem and subjective well-being, as well as reduction in loneliness among older women. The intervention group was compared to a control group of women who were interested in the program or in improving their friendships. All respondents had been studied at three points in time: at a baseline, prior to the program; three months later, and 9-10 months after baseline. The results indicate that the program was successful in attracting lonely older women who were willing to work on their friendships. Many participants reported improvement in the quantity and quality of their friendships. The program was moderately successful in stimulating improvement in subjective well-being and awareness of the need for an active stance toward achieving goals in social relations, especially in friendship. Loneliness among the participants was reduced, but it also declined in the control group, and both groups continued to experience loneliness. One conclusion is that an effective intervention to help older women reduce their loneliness should be multi-dimensional focusing not only on friendship but also on other personal and situational factors contributing to loneliness.
PACSIN2 polymorphism is associated with thiopurine-induced hematological toxicity in children with acute lymphoblastic leukaemia undergoing maintenance therapy.

PubMed

Smid, Alenka; Karas-Kuzelicki, Natasa; Jazbec, Janez; Mlinaric-Rascan, Irena

2016-07-25

Adequate maintenance therapy for childhood acute lymphoblastic leukemia (ALL), with 6-mercaptopurine as an essential component, is necessary for retaining durable remission. Interruptions or discontinuations of the therapy due to drug-related toxicities, which can be life threatening, may result in an increased risk of relapse. In this retrospective study including 305 paediatric ALL patients undergoing maintenance therapy, we systematically investigated the individual and combined effects of genetic variants of folate pathway enzymes, as well as of polymorphisms in PACSIN2 and ITPA, on drug-induced toxicities by applying a multi-analytical approach including logistic regression (LR), classification and regression tree (CART) and generalized multifactor dimensionality reduction (GMDR). In addition to the TPMT genotype, confirmed to be a major determinant of drug related toxicities, we identified the PACSIN2 rs2413739TT genotype as being a significant risk factor for 6-MP-induced toxicity in wild-type TPMT patients. A gene-gene interaction between MTRR (rs1801394) and MTHFR (rs1801133) was detected by GMDR and proved to have an independent effect on the risk of stomatitis, as shown by LR analysis. To our knowledge, this is the first study showing PACSIN2 genotype association with hematological toxicity in ALL patients undergoing maintenance therapy.
Membership determination of open clusters based on a spectral clustering method

NASA Astrophysics Data System (ADS)

Gao, Xin-Hua

2018-06-01

We present a spectral clustering (SC) method aimed at segregating reliable members of open clusters in multi-dimensional space. The SC method is a non-parametric clustering technique that performs cluster division using eigenvectors of the similarity matrix; no prior knowledge of the clusters is required. This method is more flexible in dealing with multi-dimensional data compared to other methods of membership determination. We use this method to segregate the cluster members of five open clusters (Hyades, Coma Ber, Pleiades, Praesepe, and NGC 188) in five-dimensional space; fairly clean cluster members are obtained. We find that the SC method can capture a small number of cluster members (weak signal) from a large number of field stars (heavy noise). Based on these cluster members, we compute the mean proper motions and distances for the Hyades, Coma Ber, Pleiades, and Praesepe clusters, and our results are in general quite consistent with the results derived by other authors. The test results indicate that the SC method is highly suitable for segregating cluster members of open clusters based on high-precision multi-dimensional astrometric data such as Gaia data.
Multi-Band Light Curves from Two-Dimensional Simulations of Gamma-Ray Burst Afterglows

NASA Astrophysics Data System (ADS)

MacFadyen, Andrew

2010-01-01

The dynamics of gamma-ray burst outflows is inherently multi-dimensional. 1.) We present high resolution two-dimensional relativistic hydrodynamics simulations of GRBs in the afterglow phase using adaptive mesh refinement (AMR). Using standard synchrotron radiation models, we compute multi-band light curves, from the radio to X-ray, directly from the 2D hydrodynamics simulation data. We will present on-axis light curves for both constant density and wind media. We will also present off-axis light curves relevant for searches for orphan afterglows. We find that jet breaks are smoothed due to both off-axis viewing and wind media effects. 2.) Non-thermal radiation mechanisms in GRB afterglows require substantial magnetic field strengths. In turbulence driven by shear instabilities in relativistic magnetized gas, we demonstrate that magnetic field is naturally amplified to half a percent of the total energy (epsilon B = 0.005). We will show high resolution three dimensional relativistic MHD simulations of this process as well as particle in cell (PIC) simulations of mildly relativistic collisionless shocks.
Should One Use the Ray-by-Ray Approximation in Core-Collapse Supernova Simulations?

DOE PAGES

Skinner, M. Aaron; Burrows, Adam; Dolence, Joshua C.

2016-10-28

We perform the first self-consistent, time-dependent, multi-group calculations in two dimensions (2D) to address the consequences of using the ray-by-ray+ transport simplification in core-collapse supernova simulations. Such a dimensional reduction is employed by many researchers to facilitate their resource-intensive calculations. Our new code (Fornax) implements multi-D transport, and can, by zeroing out transverse flux terms, emulate the ray-by-ray+ scheme. Using the same microphysics, initial models, resolution, and code, we compare the results of simulating 12-, 15-, 20-, and 25-M⊙ progenitor models using these two transport methods. Our findings call into question the wisdom of the pervasive use of the ray-by-ray+more » approach. Employing it leads to maximum post-bounce/preexplosion shock radii that are almost universally larger by tens of kilometers than those derived using the more accurate scheme, typically leaving the post-bounce matter less bound and artificially more “explodable.” In fact, for our 25-M⊙ progenitor, the ray-by-ray+ model explodes, while the corresponding multi-D transport model does not. Therefore, in two dimensions the combination of ray-by-ray+ with the axial sloshing hydrodynamics that is a feature of 2D supernova dynamics can result in quantitatively, and perhaps qualitatively, incorrect results.« less
Should One Use the Ray-by-Ray Approximation in Core-collapse Supernova Simulations?

NASA Astrophysics Data System (ADS)

Skinner, M. Aaron; Burrows, Adam; Dolence, Joshua C.

2016-11-01

We perform the first self-consistent, time-dependent, multi-group calculations in two dimensions (2D) to address the consequences of using the ray-by-ray+ transport simplification in core-collapse supernova simulations. Such a dimensional reduction is employed by many researchers to facilitate their resource-intensive calculations. Our new code (Fornax) implements multi-D transport, and can, by zeroing out transverse flux terms, emulate the ray-by-ray+ scheme. Using the same microphysics, initial models, resolution, and code, we compare the results of simulating 12, 15, 20, and 25 M ⊙ progenitor models using these two transport methods. Our findings call into question the wisdom of the pervasive use of the ray-by-ray+ approach. Employing it leads to maximum post-bounce/pre-explosion shock radii that are almost universally larger by tens of kilometers than those derived using the more accurate scheme, typically leaving the post-bounce matter less bound and artificially more “explodable.” In fact, for our 25 M ⊙ progenitor, the ray-by-ray+ model explodes, while the corresponding multi-D transport model does not. Therefore, in two dimensions, the combination of ray-by-ray+ with the axial sloshing hydrodynamics that is a feature of 2D supernova dynamics can result in quantitatively, and perhaps qualitatively, incorrect results.
Multi-dimensional modelling of gas turbine combustion using a flame sheet model in KIVA II

NASA Technical Reports Server (NTRS)

Cheng, W. K.; Lai, M.-C.; Chue, T.-H.

1991-01-01

A flame sheet model for heat release is incorporated into a multi-dimensional fluid mechanical simulation for gas turbine application. The model assumes that the chemical reaction takes place in thin sheets compared to the length scale of mixing, which is valid for the primary combustion zone in a gas turbine combustor. In this paper, the details of the model are described and computational results are discussed.
Ionizing Shocks in Argon. Part 2: Transient and Multi-Dimensional Effects (Preprint)

DTIC Science & Technology

2010-09-09

stability in ionizing monatomic gases. Part 1. Argon ,” J. Fluid Mech., 84, 55 (1978). 2M. P. F. Bristow and I. I. Glass, “ Polarizability of singly...Article 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Ionizing Shocks in Argon . Part 2: Transient...Physics. 14. ABSTRACT We extend the computations of ionizing shocks in argon to unsteady and multi-dimensional, using a collisional-radiative
Design and Implementation of Embedded Computer Vision Systems Based on Particle Filters

DTIC Science & Technology

2010-01-01

for hardware/software implementa- tion of multi-dimensional particle filter application and we explore this in the third application which is a 3D...methodology for hardware/software implementation of multi-dimensional particle filter application and we explore this in the third application which is a...and hence multiprocessor implementation of parti- cle filters is an important option to examine. A significant body of work exists on optimizing generic
Strong efficiency improvement in dye-sensitized solar cells by novel multi-dimensional TiO2 photoelectrode

NASA Astrophysics Data System (ADS)

Zhao, Fengyang; Ma, Rong; Jiang, Yongjian

2018-03-01

Titanium dioxide (TiO2) based dye-sensitized solar cells (DSSCs) often exhibit superior power conversion performance. Here we report a DSSC with novel hierarchical TiO2 composite structure (TCS) composed of anatase TiO2 micro-spheres and rutile TiO2 nanobelt framework by hydrothermal approach for high-performance. As photoanode, the TCS based DSSC shows a strong efficiency enhancement by 58% compared with Degussa TiO2 (P25)-DSSC (4.33%). The excellent performance is mainly attribute to its special multi-dimensional structures of TiO2: much active sites of 0D nanoparticle with exposed excellent {001} facet, special electronic transmission channel of 1D nanobelt, good dye adsorption capacity of 2D nanosheet and high light scattering ability of 3D micro-spheres. The novel multi-dimensional TCS materials will open up a new avenue to the electronic devices fields.
Removal of Long-Lived Radon Daughters by Electropolishing Thin Layers of Stainless Steel

NASA Astrophysics Data System (ADS)

White, James; Schnee, Richard; Bunker, Raymond; Bowles, Michael; Cushman, Priscilla; Epland, Matthew; Pepin, Mark; Guiseppe, Vince

2012-10-01

Long-lived alpha and beta emitters in the Radon decay chain on detector surfaces may be limiting background in many experiments attempting to detect dark matter or neutrinoless double beta decay. To screen detector surfaces for this radioactive contamination, a low-radiation, multi-wire proportional chamber (the BetaCage) is under construction. Removal of Pb-210 implanted on its 25-micron stainless steel wires without causing significant variation in the diameter of the wires is critical to the BetaCage's ultimate sensitivity. An apparatus to perform electropolishing trials to remove roughly a micron of material has been assembled. These trials have shown promising results. Stainless steel square samples implanted with Pb-210 have shown counts with a reduction factor greater than 10 after electropolishing according to gamma assay. Furthermore, alpha counting has produced similar results, with a reduction factor greater than 100. Lastly, the diameters of wires after electropolishing have remained sufficiently uniform, with reduction in thickness consistent with expectations.
Exploring the CAESAR database using dimensionality reduction techniques

NASA Astrophysics Data System (ADS)

Mendoza-Schrock, Olga; Raymer, Michael L.

2012-06-01

The Civilian American and European Surface Anthropometry Resource (CAESAR) database containing over 40 anthropometric measurements on over 4000 humans has been extensively explored for pattern recognition and classification purposes using the raw, original data [1-4]. However, some of the anthropometric variables would be impossible to collect in an uncontrolled environment. Here, we explore the use of dimensionality reduction methods in concert with a variety of classification algorithms for gender classification using only those variables that are readily observable in an uncontrolled environment. Several dimensionality reduction techniques are employed to learn the underlining structure of the data. These techniques include linear projections such as the classical Principal Components Analysis (PCA) and non-linear (manifold learning) techniques, such as Diffusion Maps and the Isomap technique. This paper briefly describes all three techniques, and compares three different classifiers, Naïve Bayes, Adaboost, and Support Vector Machines (SVM), for gender classification in conjunction with each of these three dimensionality reduction approaches.
SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844
On nonlinear evolution of low-frequency Alfvén waves in weakly-expanding solar wind plasmas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nariyuki, Y.

A multi-dimensional nonlinear evolution equation for Alfvén waves in weakly-expanding solar wind plasmas is derived by using the reductive perturbation method. The expansion of solar wind plasma parcels is modeled by an expanding box model, which includes the accelerating expansion. It is shown that the resultant equation agrees with the Wentzel-Kramers-Brillouin prediction of the low-frequency Alfvén waves in the linear limit. In the cold and one-dimensional limit, a modified derivative nonlinear Schrodinger equation is obtained. Direct numerical simulations are carried out to discuss the effect of the expansion on the modulational instability of monochromatic Alfvén waves and the propagation ofmore » Alfvén solitons. By using the instantaneous frequency, it is quantitatively shown that as far as the expansion rate is much smaller than wave frequencies, effects of the expansion are almost adiabatic. It is also confirmed that while shapes of Alfvén solitons temporally change due to the expansion, some of them can stably propagate after their collision in weakly-expanding plasmas.« less
Kaluza-Klein cosmology from five-dimensional Lovelock-Cartan theory

NASA Astrophysics Data System (ADS)

Castillo-Felisola, Oscar; Corral, Cristóbal; del Pino, Simón; Ramírez, Francisca

2016-12-01

We study the Kaluza-Klein dimensional reduction of the Lovelock-Cartan theory in five-dimensional spacetime, with a compact dimension of S1 topology. We find cosmological solutions of the Friedmann-Robertson-Walker class in the reduced spacetime. The torsion and the fields arising from the dimensional reduction induce a nonvanishing energy-momentum tensor in four dimensions. We find solutions describing expanding, contracting, and bouncing universes. The model shows a dynamical compactification of the extra dimension in some regions of the parameter space.
Study of multi-dimensional radiative energy transfer in molecular gases

NASA Technical Reports Server (NTRS)

Liu, Jiwen; Tiwari, S. N.

1993-01-01

The Monte Carlo method (MCM) is applied to analyze radiative heat transfer in nongray gases. The nongray model employed is based on the statistical arrow band model with an exponential-tailed inverse intensity distribution. Consideration of spectral correlation results in some distinguishing features of the Monte Carlo formulations. Validation of the Monte Carlo formulations has been conducted by comparing results of this method with other solutions. Extension of a one-dimensional problem to a multi-dimensional problem requires some special treatments in the Monte Carlo analysis. Use of different assumptions results in different sets of Monte Carlo formulations. The nongray narrow band formulations provide the most accurate results.
Algorithm for loading shot noise microbunching in multi-dimensional, free-electron laser simulation codes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fawley, William M.

We discuss the underlying reasoning behind and the details of the numerical algorithm used in the GINGER free-electron laser(FEL) simulation code to load the initial shot noise microbunching on the electron beam. In particular, we point out that there are some additional subtleties which must be followed for multi-dimensional codes which are not necessary for one-dimensional formulations. Moreover, requiring that the higher harmonics of the microbunching also be properly initialized with the correct statistics leads to additional complexities. We present some numerical results including the predicted incoherent, spontaneous emission as tests of the shot noise algorithm's correctness.
Structural Optimization of a Force Balance Using a Computational Experiment Design

NASA Technical Reports Server (NTRS)

Parker, P. A.; DeLoach, R.

2002-01-01

This paper proposes a new approach to force balance structural optimization featuring a computational experiment design. Currently, this multi-dimensional design process requires the designer to perform a simplification by executing parameter studies on a small subset of design variables. This one-factor-at-a-time approach varies a single variable while holding all others at a constant level. Consequently, subtle interactions among the design variables, which can be exploited to achieve the design objectives, are undetected. The proposed method combines Modern Design of Experiments techniques to direct the exploration of the multi-dimensional design space, and a finite element analysis code to generate the experimental data. To efficiently search for an optimum combination of design variables and minimize the computational resources, a sequential design strategy was employed. Experimental results from the optimization of a non-traditional force balance measurement section are presented. An approach to overcome the unique problems associated with the simultaneous optimization of multiple response criteria is described. A quantitative single-point design procedure that reflects the designer's subjective impression of the relative importance of various design objectives, and a graphical multi-response optimization procedure that provides further insights into available tradeoffs among competing design objectives are illustrated. The proposed method enhances the intuition and experience of the designer by providing new perspectives on the relationships between the design variables and the competing design objectives providing a systematic foundation for advancements in structural design.
Method and structure for skewed block-cyclic distribution of lower-dimensional data arrays in higher-dimensional processor grids

DOEpatents

Chatterjee, Siddhartha [Yorktown Heights, NY; Gunnels, John A [Brewster, NY

2011-11-08

A method and structure of distributing elements of an array of data in a computer memory to a specific processor of a multi-dimensional mesh of parallel processors includes designating a distribution of elements of at least a portion of the array to be executed by specific processors in the multi-dimensional mesh of parallel processors. The pattern of the designating includes a cyclical repetitive pattern of the parallel processor mesh, as modified to have a skew in at least one dimension so that both a row of data in the array and a column of data in the array map to respective contiguous groupings of the processors such that a dimension of the contiguous groupings is greater than one.

Transport, biodegradation and isotopic fractionation of chlorinated ethenes: modeling and parameter estimation methods

NASA Astrophysics Data System (ADS)

Béranger, Sandra C.; Sleep, Brent E.; Lollar, Barbara Sherwood; Monteagudo, Fernando Perez

2005-01-01

An analytical, one-dimensional, multi-species, reactive transport model for simulating the concentrations and isotopic signatures of tetrachloroethylene (PCE) and its daughter products was developed. The simulation model was coupled to a genetic algorithm (GA) combined with a gradient-based (GB) method to estimate the first order decay coefficients and enrichment factors. In testing with synthetic data, the hybrid GA-GB method reduced the computational requirements for parameter estimation by a factor as great as 300. The isotopic signature profiles were observed to be more sensitive than the concentration profiles to estimates of both the first order decay constants and enrichment factors. Including isotopic data for parameter estimation significantly increased the GA convergence rate and slightly improved the accuracy of estimation of first order decay constants.
Brief report: Citizenship concepts among adolescents. Evidence from a survey among Belgian 16-year olds.

PubMed

Dejaeghere, Yves; Hooghe, Marc

2009-06-01

In this research note we investigate the occurrence of citizenship concepts among adolescents in Belgium. The analysis is based on the Belgian Youth Survey (2006), which is a representative survey among 6330 16-year olds in the country. Citizenship concepts were shown to be multi-dimensional, with distinct factors for conventional or electoral participation and civic engagement. A third, weaker factor could be distinguished covering obedience to the law. This structure is largely in line with earlier comparative analysis. An exploratory analysis suggests that these factors have different outcomes on actual or intended political participation behavior of adolescents. We discuss the relevance of these findings with regard to the current debates on civic education and civic engagement among younger age cohorts.
Method to Reduce Target Motion Through Needle-Tissue Interactions.

PubMed

Oldfield, Matthew J; Leibinger, Alexander; Seah, Tian En Timothy; Rodriguez Y Baena, Ferdinando

2015-11-01

During minimally invasive surgical procedures, it is often important to deliver needles to particular tissue volumes. Needles, when interacting with a substrate, cause deformation and target motion. To reduce reliance on compensatory intra-operative imaging, a needle design and novel delivery mechanism is proposed. Three-dimensional finite element simulations of a multi-segment needle inserted into a pre-existing crack are presented. The motion profiles of the needle segments are varied to identify methods that reduce target motion. Experiments are then performed by inserting a needle into a gelatine tissue phantom and measuring the internal target motion using digital image correlation. Simulations indicate that target motion is reduced when needle segments are stroked cyclically and utilise a small amount of retraction instead of being held stationary. Results are confirmed experimentally by statistically significant target motion reductions of more than 8% during cyclic strokes and 29% when also incorporating retraction, with the same net insertion speed. By using a multi-segment needle and taking advantage of frictional interactions on the needle surface, it is demonstrated that target motion ahead of an advancing needle can be substantially reduced.
VizieR Online Data Catalog: GALAH semi-automated classification scheme (Traven+, 2017)

NASA Astrophysics Data System (ADS)

Traven, G.; Matijevic, G.; Zwitter, T.; Zerjal, M.; Kos, J.; Asplund, M.; Bland-Hawthorn, J.; Casey, A. R.; de Silva, G.; Freeman, K.; Lin, J.; Martell, S. L.; Schlesinger, K. J.; Sharma, S.; Simpson, J. D.; Zucker, D. B.; Anguiano, B.; da Costa, G.; Duong, L.; Horner, J.; Hyde, E. A.; Kafle, P. R.; Munari, U.; Nataf, D.; Navin, C. A.; Reid, W.; Ting, Y.-S.

2017-04-01

The GALactic Archaeology with HERMES (GALAH) survey was the main driver for the construction of Hermes (High Efficiency and Resolution Multi-Element Spectrograph), a fiber-fed multi-object spectrograph on the 3.9m Anglo-Australian Telescope. Its spectral resolving power (R) is about 28000, and there is also an R=45000 mode using a slit mask. Hermes has four simultaneous non-contiguous spectral arms centered at 4800, 5761, 6610, and 7740Å, covering about 1000Å in total, including Hα and Hβ lines. About 300000 spectra have been taken to date, including various calibration exposures. However, we concentrate on ~210000 spectra recorded before 2016 January 30. We devise a custom classification procedure which is based on two independently developed methods, the novel dimensionality reduction technique t-SNE (t-distributed stochastic neighbor embedding; van der Maaten & Hinton 2008, Journal of Machine Learning Research 9, 2579) and the renowned clustering algorithm DBSCAN (Ester+ 1996, Proc. 2nd Int. Conf. on KDD, 226 ed. E. Simoudis, J. Han, and U. Fayyad). (4 data files).
Fabrication and stabilization of silicon-based photonic crystals with tuned morphology for multi-band optical filtering

NASA Astrophysics Data System (ADS)

Salem, Mohamed Shaker; Abdelaleem, Asmaa Mohamed; El-Gamal, Abear Abdullah; Amin, Mohamed

2017-01-01

One-dimensional silicon-based photonic crystals are formed by the electrochemical anodization of silicon substrates in hydrofluoric acid-based solution using an appropriate current density profile. In order to create a multi-band optical filter, two fabrication approaches are compared and discussed. The first approach utilizes a current profile composed of a linear combination of sinusoidal current waveforms having different frequencies. The individual frequency of the waveform maps to a characteristic stop band in the reflectance spectrum. The stopbands of the optical filter created by the second approach, on the other hand, are controlled by stacking multiple porous silicon rugate multilayers having different fabrication conditions. The morphology of the resulting optical filters is tuned by controlling the electrolyte composition and the type of the silicon substrate. The reduction of sidelobes arising from the interference in the multilayers is observed by applying an index matching current profile to the anodizing current waveform. In order to stabilize the resulting optical filters against natural oxidation, atomic layer deposition of silicon dioxide on the pore wall is employed.
Multi-dimensional Upwind Fluctuation Splitting Scheme with Mesh Adaption for Hypersonic Viscous Flow. Degree awarded by Virginia Polytechnic Inst. and State Univ., 9 Nov. 2001

NASA Technical Reports Server (NTRS)

Wood, William A., III

2002-01-01

A multi-dimensional upwind fluctuation splitting scheme is developed and implemented for two-dimensional and axisymmetric formulations of the Navier-Stokes equations on unstructured meshes. Key features of the scheme are the compact stencil, full upwinding, and non-linear discretization which allow for second-order accuracy with enforced positivity. Throughout, the fluctuation splitting scheme is compared to a current state-of-the-art finite volume approach, a second-order, dual mesh upwind flux difference splitting scheme (DMFDSFV), and is shown to produce more accurate results using fewer computer resources for a wide range of test cases. A Blasius flat plate viscous validation case reveals a more accurate upsilon-velocity profile for fluctuation splitting, and the reduced artificial dissipation production is shown relative to DMFDSFV. Remarkably, the fluctuation splitting scheme shows grid converged skin friction coefficients with only five points in the boundary layer for this case. The second half of the report develops a local, compact, anisotropic unstructured mesh adaptation scheme in conjunction with the multi-dimensional upwind solver, exhibiting a characteristic alignment behavior for scalar problems. The adaptation strategy is extended to the two-dimensional and axisymmetric Navier-Stokes equations of motion through the concept of fluctuation minimization.
Mid-late Holocene climate, demography, and cultural dynamics in Iberia: A multi-proxy approach

NASA Astrophysics Data System (ADS)

Lillios, Katina T.; Blanco-González, Antonio; Drake, Brandon Lee; López-Sáez, José Antonio

2016-03-01

Despite increasing interest in the relationship between culture transformation and abrupt climate change, their complexities are poorly understood. The local impact of global environmental fluctuations depends on multiple factors, and their effects on societal collapse are often assumed rather than demonstrated. One of the major changes in west European later prehistory was the Copper to Bronze Age transition, contemporaneous with the 4.2 ky cal. BP event. This article offers a multi-dimensional insight into this historical process in the Iberian Peninsula from a multi-proxy and comparative perspective. Three study areas, representative of diverse ecological settings and historical trajectories, are compared. Using radiocarbon dates, 13C discrimination (Δ13C) values on C3 plants, and high-resolution palynological records as palaeoclimatic and palaeodemographic proxies, this study tracks the uneven signals of Holocene climate. The wettest Northwest region features the most stable trend lines, whereas the Southwest exhibits an abrupt decrease in its demographic signals c. 4500 cal. BP, which is then followed by a subsequent rise in the neighbouring Southeast. These lines of evidence suggest the possibility, never previously noted, of demic migration from the Southwest to the Southeast in the Early Bronze Age as a contributing factor to the cultural dynamics of southern Iberia.
Self-rated health and health-strengthening factors in community-living frail older people.

PubMed

Ebrahimi, Zahra; Dahlin-Ivanoff, Synneve; Eklund, Kajsa; Jakobsson, Annika; Wilhelmson, Katarina

2015-04-01

The aim of this study was to analyse the explanatory power of variables measuring health-strengthening factors for self-rated health among community-living frail older people. Frailty is commonly constructed as a multi-dimensional geriatric syndrome ascribed to the multi-system deterioration of the reserve capacity in older age. Frailty in older people is associated with decreased physical and psychological well-being. However, knowledge about the experiences of health in frail older people is still limited. The design of the study was cross-sectional. The data were collected between October 2008 and November 2010 through face-to-face structured interviews with older people aged 65-96 years (N = 161). Binary logistic regression was used to analyse whether a set of explanatory relevant variables is associated with self-rated health. The results from the final model showed that satisfaction with one's ability to take care of oneself, having 10 or fewer symptoms and not feeling lonely had the best explanatory power for community-living frail older peoples' experiences of good health. The results indicate that a multi-disciplinary approach is desirable, where the focus should not only be on medical problems but also on providing supportive services to older people to maintain their independence and experiences of health despite frailty. © 2014 John Wiley & Sons Ltd.
Semisupervised kernel marginal Fisher analysis for face recognition.

PubMed

Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

2013-01-01

Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.
Generalizing DTW to the multi-dimensional case requires an adaptive approach

PubMed Central

Hu, Bing; Jin, Hongxia; Wang, Jun; Keogh, Eamonn

2017-01-01

In recent years Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. For example, virtually all applications that process data from wearable devices use DTW as a core sub-routine. This is the result of significant progress in improving DTW’s efficiency, together with multiple empirical studies showing that DTW-based classifiers at least equal (and generally surpass) the accuracy of all their rivals across dozens of datasets. Thus far, most of the research has considered only the one-dimensional case, with practitioners generalizing to the multi-dimensional case in one of two ways, dependent or independent warping. In general, it appears the community believes either that the two ways are equivalent, or that the choice is irrelevant. In this work, we show that this is not the case. The two most commonly used multi-dimensional DTW methods can produce different classifications, and neither one dominates over the other. This seems to suggest that one should learn the best method for a particular application. However, we will show that this is not necessary; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods we should trust at the time of classification. Our method allows us to ensure that classification results are at least as accurate as the better of the two rival methods, and, in many cases, our method is significantly more accurate. We demonstrate our ideas with the most extensive set of multi-dimensional time series classification experiments ever attempted. PMID:29104448
Parsimony and goodness-of-fit in multi-dimensional NMR inversion

NASA Astrophysics Data System (ADS)

Babak, Petro; Kryuchkov, Sergey; Kantzas, Apostolos

2017-01-01

Multi-dimensional nuclear magnetic resonance (NMR) experiments are often used for study of molecular structure and dynamics of matter in core analysis and reservoir evaluation. Industrial applications of multi-dimensional NMR involve a high-dimensional measurement dataset with complicated correlation structure and require rapid and stable inversion algorithms from the time domain to the relaxation rate and/or diffusion domains. In practice, applying existing inverse algorithms with a large number of parameter values leads to an infinite number of solutions with a reasonable fit to the NMR data. The interpretation of such variability of multiple solutions and selection of the most appropriate solution could be a very complex problem. In most cases the characteristics of materials have sparse signatures, and investigators would like to distinguish the most significant relaxation and diffusion values of the materials. To produce an easy to interpret and unique NMR distribution with the finite number of the principal parameter values, we introduce a new method for NMR inversion. The method is constructed based on the trade-off between the conventional goodness-of-fit approach to multivariate data and the principle of parsimony guaranteeing inversion with the least number of parameter values. We suggest performing the inversion of NMR data using the forward stepwise regression selection algorithm. To account for the trade-off between goodness-of-fit and parsimony, the objective function is selected based on Akaike Information Criterion (AIC). The performance of the developed multi-dimensional NMR inversion method and its comparison with conventional methods are illustrated using real data for samples with bitumen, water and clay.
Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide

NASA Astrophysics Data System (ADS)

Sangwan, Vinod K.; Lee, Hong-Sub; Bergeron, Hadallia; Balla, Itamar; Beck, Megan E.; Chen, Kan-Sheng; Hersam, Mark C.

2018-02-01

Memristors are two-terminal passive circuit elements that have been developed for use in non-volatile resistive random-access memory and may also be useful in neuromorphic computing. Memristors have higher endurance and faster read/write times than flash memory and can provide multi-bit data storage. However, although two-terminal memristors have demonstrated capacity for basic neural functions, synapses in the human brain outnumber neurons by more than a thousandfold, which implies that multi-terminal memristors are needed to perform complex functions such as heterosynaptic plasticity. Previous attempts to move beyond two-terminal memristors, such as the three-terminal Widrow-Hoff memristor and field-effect transistors with nanoionic gates or floating gates, did not achieve memristive switching in the transistor. Here we report the experimental realization of a multi-terminal hybrid memristor and transistor (that is, a memtransistor) using polycrystalline monolayer molybdenum disulfide (MoS2) in a scalable fabrication process. The two-dimensional MoS2 memtransistors show gate tunability in individual resistance states by four orders of magnitude, as well as large switching ratios, high cycling endurance and long-term retention of states. In addition to conventional neural learning behaviour of long-term potentiation/depression, six-terminal MoS2 memtransistors have gate-tunable heterosynaptic functionality, which is not achievable using two-terminal memristors. For example, the conductance between a pair of floating electrodes (pre- and post-synaptic neurons) is varied by a factor of about ten by applying voltage pulses to modulatory terminals. In situ scanning probe microscopy, cryogenic charge transport measurements and device modelling reveal that the bias-induced motion of MoS2 defects drives resistive switching by dynamically varying Schottky barrier heights. Overall, the seamless integration of a memristor and transistor into one multi-terminal device could enable complex neuromorphic learning and the study of the physics of defect kinetics in two-dimensional materials.
Metric Identification and Protocol Development for Characterizing DNAPL Source Zone Architecture and Associated Plume Response

DTIC Science & Technology

2013-09-01

M.4.1. Two-dimensional domains cropped out of three-dimensional numerically generated realizations; (a) 3D PCE-NAPL realizations generated by UTCHEM...165 Figure R.3.2. The absolute error vs relative error scatter plots of pM and gM from SGS data set- 4 using multi-task manifold...error scatter plots of pM and gM from TP/MC data set using multi- task manifold regression
Two-Dimensional Signal Processing, Optical Information Storage and Processing, and Electromagnetic Measurements

DTIC Science & Technology

1994-05-16

analysis of anisotropic grating diffraction, perfor- mance analysis of Givens rotation integrated optical interdigitated-electrode cross- channel Bragg...11. T. R. Gardos and R. M. Mersereau, "FIR filtering on a lattice with periodically deleted samples," Proc. 1991 IEEE Int. Conf. on Acoustics...pp. vol. 1, pp. 301-311, July 1992. 23. T. R. Gardos , K. Nayebi, and R. M. Mersereau, "Time domain analysis of multi- dimensional multi-rate filter
Bayesian analysis of spatially-dependent functional responses with spatially-dependent multi-dimensional functional predictors

USDA-ARS?s Scientific Manuscript database

Recent advances in technology have led to the collection of high-dimensional data not previously encountered in many scientific environments. As a result, scientists are often faced with the challenging task of including these high-dimensional data into statistical models. For example, data from sen...
A quantitative study on magnesium alloy stent biodegradation.

PubMed

Gao, Yuanming; Wang, Lizhen; Gu, Xuenan; Chu, Zhaowei; Guo, Meng; Fan, Yubo

2018-06-06

Insufficient scaffolding time in the process of rapid corrosion is the main problem of magnesium alloy stent (MAS). Finite element method had been used to investigate corrosion of MAS. However, related researches mostly described all elements suffered corrosion in view of one-dimensional corrosion. Multi-dimensional corrosions significantly influence mechanical integrity of MAS structures such as edges and corners. In this study, the effects of multi-dimensional corrosion were studied using experiment quantitatively, then a phenomenological corrosion model was developed to consider these effects. We implemented immersion test with magnesium alloy (AZ31B) cubes, which had different numbers of exposed surfaces to analyze differences of dimension. It was indicated that corrosion rates of cubes are almost proportional to their exposed-surface numbers, especially when pitting corrosions are not marked. The cubes also represented the hexahedron elements in simulation. In conclusion, corrosion rate of every element accelerates by increasing corrosion-surface numbers in multi-dimensional corrosion. The damage ratios among elements with the same size are proportional to the ratios of corrosion-surface numbers under uniform corrosion. The finite element simulation using proposed model provided more details of changes of morphology and mechanics in scaffolding time by removing 25.7% of elements of MAS. The proposed corrosion model reflected the effects of multi-dimension on corrosions. It would be used to predict degradation process of MAS quantitatively. Copyright © 2018 Elsevier Ltd. All rights reserved.
Evolution of large amplitude Alfven waves in solar wind plasmas: Kinetic-fluid models

NASA Astrophysics Data System (ADS)

Nariyuki, Y.

2014-12-01

Large amplitude Alfven waves are ubiquitously observed in solar wind plasmas. Mjolhus(JPP, 1976) and Mio et al(JPSJ, 1976) found that nonlinear evolution of the uni-directional, parallel propagating Alfven waves can be described by the derivative nonlinear Schrodinger equation (DNLS). Later, the multi-dimensional extension (Mjolhus and Wyller, JPP, 1988; Passot and Sulem, POP, 1993; Gazol et al, POP, 1999) and ion kinetic modification (Mjolhus and Wyller, JPP, 1988; Spangler, POP, 1989; Medvedev and Diamond, POP, 1996; Nariyuki et al, POP, 2013) of DNLS have been reported. Recently, Nariyuki derived multi-dimensional DNLS from an expanding box model of the Hall-MHD system (Nariyuki, submitted). The set of equations including the nonlinear evolution of compressional wave modes (TDNLS) was derived by Hada(GRL, 1993). DNLS can be derived from TDNLS by rescaling of the variables (Mjolhus, Phys. Scr., 2006). Nariyuki and Hada(JPSJ, 2007) derived a kinetically modified TDNLS by using a simple Landau closure (Hammet and Perkins, PRL, 1990; Medvedev and Diamond, POP, 1996). In the present study, we revisit the ion kinetic modification of multi-dimensional TDNLS through more rigorous derivations, which is consistent with the past kinetic modification of DNLS. Although the original TDNLS was derived in the multi-dimensional form, the evolution of waves with finite propagation angles in TDNLS has not been paid much attention. Applicability of the resultant models to solar wind turbulence is discussed.
Limited Rank Matrix Learning, discriminative dimension reduction and visualization.

PubMed

Bunte, Kerstin; Schneider, Petra; Hammer, Barbara; Schleif, Frank-Michael; Villmann, Thomas; Biehl, Michael

2012-02-01

We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently. In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two- or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method. Copyright © 2011 Elsevier Ltd. All rights reserved.
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

PubMed

Faust, Kevin; Xie, Quin; Han, Dominick; Goyle, Kartikay; Volynskaya, Zoya; Djuric, Ugljesa; Diamandis, Phedias

2018-05-16

There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.
Treatment time and occlusal outcome of orthognathic therapy in the East of England region.

PubMed

Jeremiah, Huw G; Cousley, Richard R; Newton, Tim; Abela, Stefan

2012-09-01

To evaluate the process of combined orthognathic and orthodontic care. To identify factors that affect treatment time and percentage Peer Assessment Rating (PAR) reduction, and the PAR efficiency factor for such cases. Retrospective multi centre study of patients who underwent orthognathic treatment in the East of England region. Analysis of consecutive cases that underwent orthognathic surgery in 2008. Inclusion criteria included pre- and post-surgery orthodontic treatment. Ten orthodontic units submitted data for a total of 118 patients. Within the sample, 64% were class III, 35% class II/1 and 1% class II/2. Overall extraction rate, excluding third molars, was 58%. Median age at bond up was 17 years. Mean total number of orthodontic attendances was 23. Median length of pre-surgical orthodontics was 23 months and post-surgical orthodontics was 7 months. Median length of total treatment was 29 months. Mean wait for surgery was 3·6 months. Diagnosis of incisor relationship and skeletal base, transfer of operator, total number of visits, tooth extraction and treatment unit affected treatment duration. Median pre- and post-treatment PAR scores were 43 and 4, respectively. Median change in PAR score was 38·5. Median per cent reduction in PAR was 90·6%. The median PAR efficiency factor (reduction in PAR score divided by treatment time in months) was 1·24. Diagnosis of incisor relationship and skeletal base correlated with percentage reduction in PAR score. Combined orthognathic treatment was effective. Factors affecting treatment duration and percentage reduction in PAR have been established.

Multi-perspective views of students’ difficulties with one-dimensional vector and two-dimensional vector

NASA Astrophysics Data System (ADS)

Fauzi, Ahmad; Ratna Kawuri, Kunthi; Pratiwi, Retno

2017-01-01

Researchers of students’ conceptual change usually collects data from written tests and interviews. Moreover, reports of conceptual change often simply refer to changes in concepts, such as on a test, without any identification of the learning processes that have taken place. Research has shown that students have difficulties with vectors in university introductory physics courses and high school physics courses. In this study, we intended to explore students’ understanding of one-dimensional and two-dimensional vector in multi perspective views. In this research, we explore students’ understanding through test perspective and interviews perspective. Our research study adopted the mixed-methodology design. The participants of this research were sixty students of third semester of physics education department. The data of this research were collected by testand interviews. In this study, we divided the students’ understanding of one-dimensional vector and two-dimensional vector in two categories, namely vector skills of the addition of one-dimensionaland two-dimensional vector and the relation between vector skills and conceptual understanding. From the investigation, only 44% of students provided correct answer for vector skills of the addition of one-dimensional and two-dimensional vector and only 27% students provided correct answer for the relation between vector skills and conceptual understanding.
Multi-Dimensional Optimization for Cloud Based Multi-Tier Applications

ERIC Educational Resources Information Center

Jung, Gueyoung

2010-01-01

Emerging trends toward cloud computing and virtualization have been opening new avenues to meet enormous demands of space, resource utilization, and energy efficiency in modern data centers. By being allowed to host many multi-tier applications in consolidated environments, cloud infrastructure providers enable resources to be shared among these…
Chaotic oscillator containing memcapacitor and meminductor and its dimensionality reduction analysis.

PubMed

Yuan, Fang; Wang, Guangyi; Wang, Xiaowei

2017-03-01

In this paper, smooth curve models of meminductor and memcapacitor are designed, which are generalized from a memristor. Based on these models, a new five-dimensional chaotic oscillator that contains a meminductor and memcapacitor is proposed. By dimensionality reducing, this five-dimensional system can be transformed into a three-dimensional system. The main work of this paper is to give the comparisons between the five-dimensional system and its dimensionality reduction model. To investigate dynamics behaviors of the two systems, equilibrium points and stabilities are analyzed. And the bifurcation diagrams and Lyapunov exponent spectrums are used to explore their properties. In addition, digital signal processing technologies are used to realize this chaotic oscillator, and chaotic sequences are generated by the experimental device, which can be used in encryption applications.
Applications to car bodies - Generalized layout design of three-dimensional shells

NASA Technical Reports Server (NTRS)

Fukushima, Junichi; Suzuki, Katsuyuki; Kikuchi, Noboru

1993-01-01

We shall describe applications of the homogenization method, formulated in Part 1, to design layout of car bodies represented by three-dimensional shell structures based on a multi-loading optimization.
GIXSGUI : a MATLAB toolbox for grazing-incidence X-ray scattering data visualization and reduction, and indexing of buried three-dimensional periodic nanostructured films

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Zhang

GIXSGUIis a MATLAB toolbox that offers both a graphical user interface and script-based access to visualize and process grazing-incidence X-ray scattering data from nanostructures on surfaces and in thin films. It provides routine surface scattering data reduction methods such as geometric correction, one-dimensional intensity linecut, two-dimensional intensity reshapingetc. Three-dimensional indexing is also implemented to determine the space group and lattice parameters of buried organized nanoscopic structures in supported thin films.
A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Rosen, Mark; Madabhushi, Anant

2008-03-01

Current techniques for localization of prostatic adenocarcinoma (CaP) via blinded trans-rectal ultrasound biopsy are associated with a high false negative detection rate. While high resolution endorectal in vivo Magnetic Resonance (MR) prostate imaging has been shown to have improved contrast and resolution for CaP detection over ultrasound, similarity in intensity characteristics between benign and cancerous regions on MR images contribute to a high false positive detection rate. In this paper, we present a novel unsupervised segmentation method that employs manifold learning via consensus schemes for detection of cancerous regions from high resolution 1.5 Tesla (T) endorectal in vivo prostate MRI. A significant contribution of this paper is a method to combine multiple weak, lower-dimensional representations of high dimensional feature data in a way analogous to classifier ensemble schemes, and hence create a stable and accurate reduced dimensional representation. After correcting for MR image intensity artifacts, such as bias field inhomogeneity and intensity non-standardness, our algorithm extracts over 350 3D texture features at every spatial location in the MR scene at multiple scales and orientations. Non-linear dimensionality reduction schemes such as Locally Linear Embedding (LLE) and Graph Embedding (GE) are employed to create multiple low dimensional data representations of this high dimensional texture feature space. Our novel consensus embedding method is used to average object adjacencies from within the multiple low dimensional projections so that class relationships are preserved. Unsupervised consensus clustering is then used to partition the objects in this consensus embedding space into distinct classes. Quantitative evaluation on 18 1.5 T prostate MR data against corresponding histology obtained from the multi-site ACRIN trials show a sensitivity of 92.65% and a specificity of 82.06%, which suggests that our method is successfully able to detect suspicious regions in the prostate.
Genetic Algorithm-Based Model Order Reduction of Aeroservoelastic Systems with Consistant States

NASA Technical Reports Server (NTRS)

Zhu, Jin; Wang, Yi; Pant, Kapil; Suh, Peter M.; Brenner, Martin J.

2017-01-01

This paper presents a model order reduction framework to construct linear parameter-varying reduced-order models of flexible aircraft for aeroservoelasticity analysis and control synthesis in broad two-dimensional flight parameter space. Genetic algorithms are used to automatically determine physical states for reduction and to generate reduced-order models at grid points within parameter space while minimizing the trial-and-error process. In addition, balanced truncation for unstable systems is used in conjunction with the congruence transformation technique to achieve locally optimal realization and weak fulfillment of state consistency across the entire parameter space. Therefore, aeroservoelasticity reduced-order models at any flight condition can be obtained simply through model interpolation. The methodology is applied to the pitch-plant model of the X-56A Multi-Use Technology Testbed currently being tested at NASA Armstrong Flight Research Center for flutter suppression and gust load alleviation. The present studies indicate that the reduced-order model with more than 12× reduction in the number of states relative to the original model is able to accurately predict system response among all input-output channels. The genetic-algorithm-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The interpolated aeroservoelasticity reduced order models exhibit smooth pole transition and continuously varying gains along a set of prescribed flight conditions, which verifies consistent state representation obtained by congruence transformation. The present model order reduction framework can be used by control engineers for robust aeroservoelasticity controller synthesis and novel vehicle design.
Changing drug users' risk environments: peer health advocates as multi-level community change agents.

PubMed

Weeks, Margaret R; Convey, Mark; Dickson-Gomez, Julia; Li, Jianghong; Radda, Kim; Martinez, Maria; Robles, Eduardo

2009-06-01

Peer delivered, social oriented HIV prevention intervention designs are increasingly popular for addressing broader contexts of health risk beyond a focus on individual factors. Such interventions have the potential to affect multiple social levels of risk and change, including at the individual, network, and community levels, and reflect social ecological principles of interaction across social levels over time. The iterative and feedback dynamic generated by this multi-level effect increases the likelihood for sustained health improvement initiated by those trained to deliver the peer intervention. The Risk Avoidance Partnership (RAP), conducted with heroin and cocaine/crack users in Hartford, Connecticut, exemplified this intervention design and illustrated the multi-level effect on drug users' risk and harm reduction at the individual level, the social network level, and the larger community level. Implications of the RAP program for designing effective prevention programs and for analyzing long-term change to reduce HIV transmission among high-risk groups are discussed from this ecological and multi-level intervention perspective.
Flame-Generated Vorticity Production in Premixed Flame-Vortex Interactions

NASA Technical Reports Server (NTRS)

Patnaik, G.; Kailasanath, K.

2003-01-01

In this study, we use detailed time-dependent, multi-dimensional numerical simulations to investigate the relative importance of the processes leading to FGV in flame-vortex interactions in normal gravity and microgravity and to determine if the production of vorticity in flames in gravity is the same as that in zero gravity except for the contribution of the gravity term. The numerical simulations will be performed using the computational model developed at NRL, FLAME3D. FLAME3D is a parallel, multi-dimensional (either two- or three-dimensional) flame model based on FLIC2D, which has been used extensively to study the structure and stability of premixed hydrogen and methane flames.
Two-Dimensional Computational Model for Wave Rotor Flow Dynamics

NASA Technical Reports Server (NTRS)

Welch, Gerard E.

1996-01-01

A two-dimensional (theta,z) Navier-Stokes solver for multi-port wave rotor flow simulation is described. The finite-volume form of the unsteady thin-layer Navier-Stokes equations are integrated in time on multi-block grids that represent the stationary inlet and outlet ports and the moving rotor passages of the wave rotor. Computed results are compared with three-port wave rotor experimental data. The model is applied to predict the performance of a planned four-port wave rotor experiment. Two-dimensional flow features that reduce machine performance and influence rotor blade and duct wall thermal loads are identified. The performance impact of rounding the inlet port wall, to inhibit separation during passage gradual opening, is assessed.
SU(N) affine Toda solitons and breathers from transparent Dirac potentials

NASA Astrophysics Data System (ADS)

Thies, Michael

2017-05-01

Transparent scalar and pseudoscalar potentials in the one-dimensional Dirac equation play an important role as self-consistent mean fields in 1 + 1 dimensional four-fermion theories (Gross-Neveu, Nambu-Jona Lasinio models) and quasi-one dimensional superconductors (Bogoliubov-de Gennes equation). Here, we show that they also serve as seed to generate a large class of classical multi-soliton and multi-breather solutions of su(N) affine Toda field theories, including the Lax representation and the corresponding vector. This generalizes previous findings about the relationship between real kinks in the Gross-Neveu model and classical solitons of the sinh-Gordon equation to complex twisted kinks.
Controller design for wind turbine load reduction via multiobjective parameter synthesis

NASA Astrophysics Data System (ADS)

Hoffmann, A. F.; Weiβ, F. A.

2016-09-01

During the design process for a wind turbine load reduction controller many different, sometimes conflicting requirements must be fulfilled simultaneously. If the requirements can be expressed as mathematical criteria, such a design problem can be solved by a criterion-vector and multi-objective design optimization. The software environment MOPS (Multi-Objective Parameter Synthesis) supports the engineer for such a design optimization. In this paper MOPS is applied to design a multi-objective load reduction controller for the well-known DTU 10 MW reference wind turbine. A significant reduction in the fatigue criteria especially the blade damage can be reached by the use of an additional Individual Pitch Controller (IPC) and an additional tower damper. This reduction is reached as a trade-off with an increase of actuator load.
VizieR Online Data Catalog: Investigating Tully-Fisher relation with KMOS3D (Ubler+,

NASA Astrophysics Data System (ADS)

Ubler, H.; Forster Schreiber, N. M.; Genzel, R.; Wisnioski, E.; Wuyts, S.; Lang, P.; Naab, T.; Burkert, A.; van Dokkum, P. G.; Tacconi, L. J.; Wilman, D. J.; Fossati, M.; Mendel, J. T.; Beifiori, A.; Belli, S.; Bender, R.; Brammer, G. B.; Chan, J.; Davies, R.; Fabricius, M.; Galametz, A.; Lutz, D.; Momcheva, I. G.; Nelson, E. J.; Saglia, R. P.; Seitz, S.; Tadaki, K.

2018-02-01

This work is based on the first 3yr of observations of KMOS3D multiyear near-infrared (near-IR) IFS survey of more than 600 mass-selected star-forming galaxies (SFGs) at 0.6<~z<~2.6 with the K-band Multi Object Spectrograph (KMOS; Sharples+ 2013Msngr.151...21S) on the Very Large Telescope. The KMOS3D survey and data reduction are described in detail by Wisnioski et al. 2015ApJ...799..209W The results presented in this paper build on the KMOS3D sample as of 2016 January, with 536 observed galaxies. Of these, 316 are detected in, and have spatially resolved, Hα emission free from skyline contamination from which two-dimensional velocity and dispersion maps are produced. (1 data file).
Shape reanalysis and sensitivities utilizing preconditioned iterative boundary solvers

NASA Technical Reports Server (NTRS)

Guru Prasad, K.; Kane, J. H.

1992-01-01

The computational advantages associated with the utilization of preconditined iterative equation solvers are quantified for the reanalysis of perturbed shapes using continuum structural boundary element analysis (BEA). Both single- and multi-zone three-dimensional problems are examined. Significant reductions in computer time are obtained by making use of previously computed solution vectors and preconditioners in subsequent analyses. The effectiveness of this technique is demonstrated for the computation of shape response sensitivities required in shape optimization. Computer times and accuracies achieved using the preconditioned iterative solvers are compared with those obtained via direct solvers and implicit differentiation of the boundary integral equations. It is concluded that this approach employing preconditioned iterative equation solvers in reanalysis and sensitivity analysis can be competitive with if not superior to those involving direct solvers.
MetAlign 3.0: performance enhancement by efficient use of advances in computer hardware.

PubMed

Lommen, Arjen; Kools, Harrie J

2012-08-01

A new, multi-threaded version of the GC-MS and LC-MS data processing software, metAlign, has been developed which is able to utilize multiple cores on one PC. This new version was tested using three different multi-core PCs with different operating systems. The performance of noise reduction, baseline correction and peak-picking was 8-19 fold faster compared to the previous version on a single core machine from 2008. The alignment was 5-10 fold faster. Factors influencing the performance enhancement are discussed. Our observations show that performance scales with the increase in processor core numbers we currently see in consumer PC hardware development.
Advances in Multi-Pixel Photon Counter technology: First characterization results

NASA Astrophysics Data System (ADS)

Bonanno, G.; Marano, D.; Romeo, G.; Garozzo, S.; Grillo, A.; Timpanaro, M. C.; Catalano, O.; Giarrusso, S.; Impiombato, D.; La Rosa, G.; Sottile, G.

2016-01-01

Due to the recent advances in silicon photomultiplier technology, new types of Silicon Photomultiplier (SiPM), also named Multi-Pixel Photon Counter (MPPC) detectors have become recently available, demonstrating superior performance in terms of their most important electrical and optical parameters. This paper presents the latest characterization results of the novel Low Cross-Talk (LCT) MPPC families from Hamamatsu, where a noticeable fill-factor enhancement and cross-talk reduction is achieved. In addition, the newly adopted resin coating has been proven to yield improved photon detection capabilities in the 280-320 nm spectral range, making the new LCT MPPCs particularly suitable for emerging applications like Cherenkov Telescope Array, and Astroparticle Physics.
Multi-Dimensional Asymptotically Stable 4th Order Accurate Schemes for the Diffusion Equation

NASA Technical Reports Server (NTRS)

Abarbanel, Saul; Ditkowski, Adi

1996-01-01

An algorithm is presented which solves the multi-dimensional diffusion equation on co mplex shapes to 4th-order accuracy and is asymptotically stable in time. This bounded-error result is achieved by constructing, on a rectangular grid, a differentiation matrix whose symmetric part is negative definite. The differentiation matrix accounts for the Dirichlet boundary condition by imposing penalty like terms. Numerical examples in 2-D show that the method is effective even where standard schemes, stable by traditional definitions fail.
ALE3D: An Arbitrary Lagrangian-Eulerian Multi-Physics Code

DOE Office of Scientific and Technical Information (OSTI.GOV)

Noble, Charles R.; Anderson, Andrew T.; Barton, Nathan R.

ALE3D is a multi-physics numerical simulation software tool utilizing arbitrary-Lagrangian- Eulerian (ALE) techniques. The code is written to address both two-dimensional (2D plane and axisymmetric) and three-dimensional (3D) physics and engineering problems using a hybrid finite element and finite volume formulation to model fluid and elastic-plastic response of materials on an unstructured grid. As shown in Figure 1, ALE3D is a single code that integrates many physical phenomena.
Integrating Social Work into Palliative Care for Lung Cancer Patients and Families: A Multi-Dimensional Approach

PubMed Central

Otis-Green, Shirley; Sidhu, Rupinder K.; Ferraro, Catherine Del; Ferrell, Betty

2014-01-01

Lung cancer patients and their family caregivers face a wide range of potentially distressing symptoms across the four domains of quality of life. A multi-dimensional approach to addressing these complex concerns with early integration of palliative care has proven beneficial. This article highlights opportunities to integrate social work using a comprehensive quality of life model and a composite patient scenario from a large lung cancer educational intervention National Cancer Institute-funded program project grant. PMID:24797998
Three dimensional profile measurement using multi-channel detector MVM-SEM

NASA Astrophysics Data System (ADS)

Yoshikawa, Makoto; Harada, Sumito; Ito, Keisuke; Murakawa, Tsutomu; Shida, Soichi; Matsumoto, Jun; Nakamura, Takayuki

2014-07-01

In next generation lithography (NGL) for the 1x nm node and beyond, the three dimensional (3D) shape measurements such as side wall angle (SWA) and height of feature on photomask become more critical for the process control. Until today, AFM (Atomic Force Microscope), X-SEM (cross-section Scanning Electron Microscope) and TEM (Transmission Electron Microscope) tools are normally used for 3D measurements, however, these techniques require time-consuming preparation and observation. And both X-SEM and TEM are destructive measurement techniques. This paper presents a technology for quick and non-destructive 3D shape analysis using multi-channel detector MVM-SEM (Multi Vision Metrology SEM), and also reports its accuracy and precision.

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.
The structure of the Hospital Anxiety and Depression Scale in four cohorts of community-based, healthy older people: the HALCyon program.

PubMed

Gale, Catharine R; Allerhand, Michael; Sayer, Avan Aihie; Cooper, Cyrus; Dennison, Elaine M; Starr, John M; Ben-Shlomo, Yoav; Gallacher, John E; Kuh, Diana; Deary, Ian J

2010-06-01

The Hospital Anxiety and Depression Scale (HADS) is widely used but evaluation of its psychometric properties has produced equivocal results. Little is known about its structure in non-clinical samples of older people. We used data from four cohorts in the HALCyon collaborative research program into healthy aging: the Caerphilly Prospective Study, the Hertfordshire Ageing Study, the Hertfordshire Cohort Study, and the Lothian Birth Cohort 1921. We used exploratory factor analysis and confirmatory factor analysis with multi-group comparisons to establish the structure of the HADS and test for factorial invariance between samples. Exploratory factor analysis showed a bi-dimensional structure (anxiety and depression) of the scale in men and women in each cohort. We tested a hypothesized three-factor model but high correlations between two of the factors made a two-factor model more psychologically plausible. Multi-group confirmatory factor analysis revealed that the sizes of the respective item loadings on the two factors were effectively identical in men and women from the same cohort. There was more variation between cohorts, particularly those from different parts of the U.K. and in whom the HADS was administered differently. Differences in social-class distribution accounted for part of this variation. Scoring the HADS as two subscales of anxiety and depression is appropriate in non-clinical populations of older men and women. However, there were differences between cohorts in the way that individual items were linked with the constructs of anxiety and depression, perhaps due to differences in sociocultural factors and/or in the administration of the scale.
Two component-three dimensional catalysis

DOEpatents

Schwartz, Michael; White, James H.; Sammells, Anthony F.

2002-01-01

This invention relates to catalytic reactor membranes having a gas-impermeable membrane for transport of oxygen anions. The membrane has an oxidation surface and a reduction surface. The membrane is coated on its oxidation surface with an adherent catalyst layer and is optionally coated on its reduction surface with a catalyst that promotes reduction of an oxygen-containing species (e.g., O.sub.2, NO.sub.2, SO.sub.2, etc.) to generate oxygen anions on the membrane. The reactor has an oxidation zone and a reduction zone separated by the membrane. A component of an oxygen containing gas in the reduction zone is reduced at the membrane and a reduced species in a reactant gas in the oxidation zone of the reactor is oxidized. The reactor optionally contains a three-dimensional catalyst in the oxidation zone. The adherent catalyst layer and the three-dimensional catalyst are selected to promote a desired oxidation reaction, particularly a partial oxidation of a hydrocarbon.
Coherent multi-dimensional spectroscopy at optical frequencies in a single beam with optical readout

NASA Astrophysics Data System (ADS)

Seiler, Hélène; Palato, Samuel; Kambhampati, Patanjali

2017-09-01

Ultrafast coherent multi-dimensional spectroscopies form a powerful set of techniques to unravel complex processes, ranging from light-harvesting, chemical exchange in biological systems to many-body interactions in quantum-confined materials. Yet these spectroscopies remain complex to implement at the high frequencies of vibrational and electronic transitions, thereby limiting their widespread use. Here we demonstrate the feasibility of two-dimensional spectroscopy at optical frequencies in a single beam. Femtosecond optical pulses are spectrally broadened to a relevant bandwidth and subsequently shaped into phase coherent pulse trains. By suitably modulating the phases of the pulses within the beam, we show that it is possible to directly read out the relevant optical signals. This work shows that one needs neither complex beam geometries nor complex detection schemes in order to measure two-dimensional spectra at optical frequencies. Our setup provides not only a simplified experimental design over standard two-dimensional spectrometers but its optical readout also enables novel applications in microscopy.
Empirical validation of the English version of the Fear of Cancer Recurrence Inventory.

PubMed

Lebel, Sophie; Simard, Sebastien; Harris, Cheryl; Feldstain, Andrea; Beattie, Sara; McCallum, Megan; Lefebvre, Monique; Savard, Josée; Devins, Gerald M

2016-02-01

Cancer patients report that help in managing fear of cancer recurrence (FCR) is one of their greatest unmet needs. Research on FCR has been limited by the very few validated, multi-dimensional measures of this construct. One exception is the Fear of Cancer Recurrence Inventory (FCRI), originally developed and empirically validated in French. The present study validated the English version of the FCRI. The FCRI was translated into English using a forward-backward translation procedure and pilot-tested with 17 English-speaking cancer patients. Cross-cultural equivalency of the French and English versions was established by administering both forms to 42 bilingual cancer patients. Last, 350 English-speaking breast, colon, prostate, or lung cancer patients were asked to complete the FCRI. A subsample (n = 135) was mailed the FCRI again one month later to evaluate test-retest reliability. The English translation of the FCRI was well accepted by participants. There was no item-bias when comparing bilingual participants' answers on both versions. A confirmatory factor analysis supported the hypothesized seven-factor structure. The English version has high internal consistency (α = .96 for the total scale and .71-.94 for the subscales) and test-retest reliability (r = .88 for the total scale and 56-.87 for the subscales). The English version of the FCRI is a reliable and valid measure of FCR applicable to breast, colon, prostate, and lung cancer patients. Its multi-dimensional nature makes it an attractive research and clinical tool to further our knowledge of FCR.
A mediational model of self-esteem and social problem-solving in anorexia nervosa.

PubMed

Paterson, Gillian; Power, Kevin; Collin, Paula; Greirson, David; Yellowlees, Alex; Park, Katy

2011-01-01

Poor problem-solving and low self-esteem are frequently cited as significant factors in the development and maintenance of anorexia nervosa. The current study examines the multi-dimensional elements of these measures and postulates a model whereby self-esteem mediates the relationship between social problems-solving and anorexic pathology and considers the implications of this pathway. Fifty-five inpatients with a diagnosis of anorexia nervosa and 50 non-clinical controls completed three standardised multi-dimensional questionnaires pertaining to social problem-solving, self-esteem and eating pathology. Significant differences were yielded between clinical and non-clinical samples on all measures. Within the clinical group, elements of social problem-solving most significant to anorexic pathology were positive problem orientation, negative problem orientation and avoidance. Components of self-esteem most significant to anorexic pathology were eating, weight and shape concern but not eating restraint. The mediational model was upheld with social problem-solving impacting on anorexic pathology through the existence of low self-esteem. Problem orientation, that is, the cognitive processes of social problem-solving appear to be more significant than problem-solving methods in individuals with anorexia nervosa. Negative perceptions of eating, weight and shape appear to impact on low self-esteem but level of restriction does not. Finally, results indicate that self-esteem is a significant factor in the development and execution of positive or negative social problem-solving in individuals with anorexia nervosa by mediating the relationship between those two variables. Copyright © 2010 John Wiley & Sons, Ltd and Eating Disorders Association.
Identifying associations between pig pathologies using a multi-dimensional machine learning methodology.

PubMed

Sanchez-Vazquez, Manuel J; Nielen, Mirjam; Edwards, Sandra A; Gunn, George J; Lewis, Fraser I

2012-08-31

Abattoir detected pathologies are of crucial importance to both pig production and food safety. Usually, more than one pathology coexist in a pig herd although it often remains unknown how these different pathologies interrelate to each other. Identification of the associations between different pathologies may facilitate an improved understanding of their underlying biological linkage, and support the veterinarians in encouraging control strategies aimed at reducing the prevalence of not just one, but two or more conditions simultaneously. Multi-dimensional machine learning methodology was used to identify associations between ten typical pathologies in 6485 batches of slaughtered finishing pigs, assisting the comprehension of their biological association. Pathologies potentially associated with septicaemia (e.g. pericarditis, peritonitis) appear interrelated, suggesting on-going bacterial challenges by pathogens such as Haemophilus parasuis and Streptococcus suis. Furthermore, hepatic scarring appears interrelated with both milk spot livers (Ascaris suum) and bacteria-related pathologies, suggesting a potential multi-pathogen nature for this pathology. The application of novel multi-dimensional machine learning methodology provided new insights into how typical pig pathologies are potentially interrelated at batch level. The methodology presented is a powerful exploratory tool to generate hypotheses, applicable to a wide range of studies in veterinary research.
Rigorous Model Reduction for a Damped-Forced Nonlinear Beam Model: An Infinite-Dimensional Analysis

NASA Astrophysics Data System (ADS)

Kogelbauer, Florian; Haller, George

2018-06-01

We use invariant manifold results on Banach spaces to conclude the existence of spectral submanifolds (SSMs) in a class of nonlinear, externally forced beam oscillations. SSMs are the smoothest nonlinear extensions of spectral subspaces of the linearized beam equation. Reduction in the governing PDE to SSMs provides an explicit low-dimensional model which captures the correct asymptotics of the full, infinite-dimensional dynamics. Our approach is general enough to admit extensions to other types of continuum vibrations. The model-reduction procedure we employ also gives guidelines for a mathematically self-consistent modeling of damping in PDEs describing structural vibrations.
An Integrative Platform for Three-dimensional Quantitative Analysis of Spatially Heterogeneous Metastasis Landscapes

NASA Astrophysics Data System (ADS)

Guldner, Ian H.; Yang, Lin; Cowdrick, Kyle R.; Wang, Qingfei; Alvarez Barrios, Wendy V.; Zellmer, Victoria R.; Zhang, Yizhe; Host, Misha; Liu, Fang; Chen, Danny Z.; Zhang, Siyuan

2016-04-01

Metastatic microenvironments are spatially and compositionally heterogeneous. This seemingly stochastic heterogeneity provides researchers great challenges in elucidating factors that determine metastatic outgrowth. Herein, we develop and implement an integrative platform that will enable researchers to obtain novel insights from intricate metastatic landscapes. Our two-segment platform begins with whole tissue clearing, staining, and imaging to globally delineate metastatic landscape heterogeneity with spatial and molecular resolution. The second segment of our platform applies our custom-developed SMART 3D (Spatial filtering-based background removal and Multi-chAnnel forest classifiers-based 3D ReconsTruction), a multi-faceted image analysis pipeline, permitting quantitative interrogation of functional implications of heterogeneous metastatic landscape constituents, from subcellular features to multicellular structures, within our large three-dimensional (3D) image datasets. Coupling whole tissue imaging of brain metastasis animal models with SMART 3D, we demonstrate the capability of our integrative pipeline to reveal and quantify volumetric and spatial aspects of brain metastasis landscapes, including diverse tumor morphology, heterogeneous proliferative indices, metastasis-associated astrogliosis, and vasculature spatial distribution. Collectively, our study demonstrates the utility of our novel integrative platform to reveal and quantify the global spatial and volumetric characteristics of the 3D metastatic landscape with unparalleled accuracy, opening new opportunities for unbiased investigation of novel biological phenomena in situ.
A three-dimensional muscle activity imaging technique for assessing pelvic muscle function

NASA Astrophysics Data System (ADS)

Zhang, Yingchun; Wang, Dan; Timm, Gerald W.

2010-11-01

A novel multi-channel surface electromyography (EMG)-based three-dimensional muscle activity imaging (MAI) technique has been developed by combining the bioelectrical source reconstruction approach and subject-specific finite element modeling approach. Internal muscle activities are modeled by a current density distribution and estimated from the intra-vaginal surface EMG signals with the aid of a weighted minimum norm estimation algorithm. The MAI technique was employed to minimally invasively reconstruct electrical activity in the pelvic floor muscles and urethral sphincter from multi-channel intra-vaginal surface EMG recordings. A series of computer simulations were conducted to evaluate the performance of the present MAI technique. With appropriate numerical modeling and inverse estimation techniques, we have demonstrated the capability of the MAI technique to accurately reconstruct internal muscle activities from surface EMG recordings. This MAI technique combined with traditional EMG signal analysis techniques is being used to study etiologic factors associated with stress urinary incontinence in women by correlating functional status of muscles characterized from the intra-vaginal surface EMG measurements with the specific pelvic muscle groups that generated these signals. The developed MAI technique described herein holds promise for eliminating the need to place needle electrodes into muscles to obtain accurate EMG recordings in some clinical applications.
Silicon photonic integrated circuit swept-source optical coherence tomography receiver with dual polarization, dual balanced, in-phase and quadrature detection.

PubMed

Wang, Zhao; Lee, Hsiang-Chieh; Vermeulen, Diedrik; Chen, Long; Nielsen, Torben; Park, Seo Yeon; Ghaemi, Allan; Swanson, Eric; Doerr, Chris; Fujimoto, James

2015-07-01

Optical coherence tomography (OCT) is a widely used three-dimensional (3D) optical imaging method with many biomedical and non-medical applications. Miniaturization, cost reduction, and increased functionality of OCT systems will be critical for future emerging clinical applications. We present a silicon photonic integrated circuit swept-source OCT (SS-OCT) coherent receiver with dual polarization, dual balanced, in-phase and quadrature (IQ) detection. We demonstrate multiple functional capabilities of IQ polarization resolved detection including: complex-conjugate suppressed full-range OCT, polarization diversity detection, and polarization-sensitive OCT. To our knowledge, this is the first demonstration of a silicon photonic integrated receiver for OCT. The integrated coherent receiver provides a miniaturized, low-cost solution for SS-OCT, and is also a key step towards a fully integrated high speed SS-OCT system with good performance and multi-functional capabilities. With further performance improvement and cost reduction, photonic integrated technology promises to greatly increase penetration of OCT systems in existing applications and enable new applications.
Silicon photonic integrated circuit swept-source optical coherence tomography receiver with dual polarization, dual balanced, in-phase and quadrature detection

PubMed Central

Wang, Zhao; Lee, Hsiang-Chieh; Vermeulen, Diedrik; Chen, Long; Nielsen, Torben; Park, Seo Yeon; Ghaemi, Allan; Swanson, Eric; Doerr, Chris; Fujimoto, James

2015-01-01

Optical coherence tomography (OCT) is a widely used three-dimensional (3D) optical imaging method with many biomedical and non-medical applications. Miniaturization, cost reduction, and increased functionality of OCT systems will be critical for future emerging clinical applications. We present a silicon photonic integrated circuit swept-source OCT (SS-OCT) coherent receiver with dual polarization, dual balanced, in-phase and quadrature (IQ) detection. We demonstrate multiple functional capabilities of IQ polarization resolved detection including: complex-conjugate suppressed full-range OCT, polarization diversity detection, and polarization-sensitive OCT. To our knowledge, this is the first demonstration of a silicon photonic integrated receiver for OCT. The integrated coherent receiver provides a miniaturized, low-cost solution for SS-OCT, and is also a key step towards a fully integrated high speed SS-OCT system with good performance and multi-functional capabilities. With further performance improvement and cost reduction, photonic integrated technology promises to greatly increase penetration of OCT systems in existing applications and enable new applications. PMID:26203382
Rationalization of anisotropic mechanical properties of Al-6061 fabricated using ultrasonic additive manufacturing

DOE PAGES

Sridharan, Niyanth; Gussev, Maxim; Seibert, Rachel; ...

2016-09-01

Ultrasonic additive manufacturing (UAM) is a solid-state process, which uses ultrasonic vibrations at 20 kHz along with mechanized tape layering and intermittent milling operation, to build fully functional three-dimensional parts. In the literature, UAM builds made with low power (1.5 kW) exhibited poor tensile properties in Z-direction, i.e., normal to the interfaces. This reduction in properties is often attributed to the lack of bonding at faying interfaces. The generality of this conclusion is evaluated further in 6061 aluminum alloy builds made with very high power UAM (9 kW). Tensile deformation behavior along X and Z directions were evaluated with small-scalemore » in-situ mechanical testing equipped with high-resolution digital image correlation, as well as, multi-scale characterization of builds. Interestingly, even with complete metallurgical bonding across the interfaces without any discernable voids, poor Z-direction properties were observed. This reduction is correlated to coalescence of pre-existing shear bands at interfaces into micro voids, leading to strain localization and spontaneous failure on tensile loading.« less
Dimensional flow and fuzziness in quantum gravity: Emergence of stochastic spacetime

NASA Astrophysics Data System (ADS)

Calcagni, Gianluca; Ronco, Michele

2017-10-01

We show that the uncertainty in distance and time measurements found by the heuristic combination of quantum mechanics and general relativity is reproduced in a purely classical and flat multi-fractal spacetime whose geometry changes with the probed scale (dimensional flow) and has non-zero imaginary dimension, corresponding to a discrete scale invariance at short distances. Thus, dimensional flow can manifest itself as an intrinsic measurement uncertainty and, conversely, measurement-uncertainty estimates are generally valid because they rely on this universal property of quantum geometries. These general results affect multi-fractional theories, a recent proposal related to quantum gravity, in two ways: they can fix two parameters previously left free (in particular, the value of the spacetime dimension at short scales) and point towards a reinterpretation of the ultraviolet structure of geometry as a stochastic foam or fuzziness. This is also confirmed by a correspondence we establish between Nottale scale relativity and the stochastic geometry of multi-fractional models.
Fiber-based three-dimensional multi-mode interference device as efficient power divider and vector curvature sensor

NASA Astrophysics Data System (ADS)

Zhang, Ziyang; Fiebrandt, Julia; Haynes, Dionne; Sun, Kai; Madhav, Kalaga; Stoll, Andreas; Makan, Kirill; Makan, Vadim; Roth, Martin

2018-03-01

Three-dimensional multi-mode interference devices are demonstrated using a single-mode fiber (SMF) center-spliced to a section of polygon-shaped core multimode fiber (MMF). This simple structure can effectively generate well-localized self-focusing spots that match to the layout of a chosen multi-core fiber (MCF) as a launcher device. An optimized hexagon-core MMF can provide efficient coupling from a SMF to a 7-core MCF with an insertion loss of 0.6 dB and a power imbalance of 0.5 dB, while a square-core MMF can form a self-imaging pattern with symmetrically distributed 2 × 2, 3 × 3 or 4 × 4 spots. These spots can be directly received by a two-dimensional detector array. The device can work as a vector curvature sensor by comparing the relative power among the spots with a resolution of ∼0.1° over a 1.8 mm-long MMF.
Tau lepton production and decays: perspective of multi-dimensional distributions and Monte Carlo methods

NASA Astrophysics Data System (ADS)

Was, Z.

2017-06-01

Status of τ lepton decay Monte Carlo generator TAUOLA, its main applications and recent developments are reviewed. It is underlined, that in recent efforts on development of new hadronic currents, the multi-dimensional nature of distributions of the experimental data must be taken with a great care: lesson from comparison and fits to the BaBar and Belle data is recalled. It was found, that as in the past at a time of comparisons with CLEO and ALEPH data, proper fitting, to as detailed as possible representation of the experimental data, is essential for appropriate developments of models of τ decay dynamic. This multi-dimensional nature of distributions is also important for observables where τ leptons are used to constrain experimental data. In later part of the presentation, use of the TAUOLA program for phenomenology of W, Z, H decays at LHC is addressed, in particular in the context of the Higgs boson parity measurements. Some new results, relevant for QED lepton pair emission are mentioned as well.
Climate impacts on human livelihoods at 1.5° and 2° of warming

NASA Astrophysics Data System (ADS)

Lissner, Tabea

2017-04-01

The measurement of impacts of climate change on socio-economic systems remains challenging and especially multi-dimensional outcome measures remain scarce. Climate impacts can directly affect many dimensions of human livelihoods, which cannot be addressed by monetary assessments alone. Multi-dimensional measures are essential in order to understand the full range of consequences of climate change and to understand the costs that higher levels of warming may have, not only in economic terms, but also in terms of non-market impacts on human livelihood. The AHEAD framework aims at measuring "Adequate Human livelihood conditions for wEll-being And Development" in a multi-dimensional framework, allowing to focus on resources and conditions which are a requirement to attain well-being. In this contribution we build on previous implementations of AHEAD and focus on differences in climate impacts at 1.5° and 2° of warming in order to improve our understanding of the societal consequences of these different warming levels.
Multi-dimensional construction of a novel active yolk@conductive shell nanofiber web as a self-standing anode for high-performance lithium-ion batteries

NASA Astrophysics Data System (ADS)

Liu, Hao; Chen, Luyi; Liang, Yeru; Fu, Ruowen; Wu, Dingcai

2015-11-01

A novel active yolk@conductive shell nanofiber web with a unique synergistic advantage of various hierarchical nanodimensional objects including the 0D monodisperse SiO2 yolks, the 1D continuous carbon shell and the 3D interconnected non-woven fabric web has been developed by an innovative multi-dimensional construction method, and thus demonstrates excellent electrochemical properties as a self-standing LIB anode.A novel active yolk@conductive shell nanofiber web with a unique synergistic advantage of various hierarchical nanodimensional objects including the 0D monodisperse SiO2 yolks, the 1D continuous carbon shell and the 3D interconnected non-woven fabric web has been developed by an innovative multi-dimensional construction method, and thus demonstrates excellent electrochemical properties as a self-standing LIB anode. Electronic supplementary information (ESI) available: Experimental details and additional information about material characterization. See DOI: 10.1039/c5nr06531c
High-frequency stock linkage and multi-dimensional stationary processes

NASA Astrophysics Data System (ADS)

Wang, Xi; Bao, Si; Chen, Jingchao

2017-02-01

In recent years, China's stock market has experienced dramatic fluctuations; in particular, in the second half of 2014 and 2015, the market rose sharply and fell quickly. Many classical financial phenomena, such as stock plate linkage, appeared repeatedly during this period. In general, these phenomena have usually been studied using daily-level data or minute-level data. Our paper focuses on the linkage phenomenon in Chinese stock 5-second-level data during this extremely volatile period. The method used to select the linkage points and the arbitrage strategy are both based on multi-dimensional stationary processes. A new program method for testing the multi-dimensional stationary process is proposed in our paper, and the detailed program is presented in the paper's appendix. Because of the existence of the stationary process, the strategy's logarithmic cumulative average return will converge under the condition of the strong ergodic theorem, and this ensures the effectiveness of the stocks' linkage points and the more stable statistical arbitrage strategy.
A lock-free priority queue design based on multi-dimensional linked lists

DOE PAGES

Dechev, Damian; Zhang, Deli

2015-04-03

The throughput of concurrent priority queues is pivotal to multiprocessor applications such as discrete event simulation, best-first search and task scheduling. Existing lock-free priority queues are mostly based on skiplists, which probabilistically create shortcuts in an ordered list for fast insertion of elements. The use of skiplists eliminates the need of global rebalancing in balanced search trees and ensures logarithmic sequential search time on average, but the worst-case performance is linear with respect to the input size. In this paper, we propose a quiescently consistent lock-free priority queue based on a multi-dimensional list that guarantees worst-case search time of O(logN)more » for key universe of size N. The novel multi-dimensional list (MDList) is composed of nodes that contain multiple links to child nodes arranged by their dimensionality. The insertion operation works by first injectively mapping the scalar key to a high-dimensional vector, then uniquely locating the target position by using the vector as coordinates. Nodes in MDList are ordered by their coordinate prefixes and the ordering property of the data structure is readily maintained during insertion without rebalancing nor randomization. Furthermore, in our experimental evaluation using a micro-benchmark, our priority queue achieves an average of 50% speedup over the state of the art approaches under high concurrency.« less

A lock-free priority queue design based on multi-dimensional linked lists

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dechev, Damian; Zhang, Deli

The throughput of concurrent priority queues is pivotal to multiprocessor applications such as discrete event simulation, best-first search and task scheduling. Existing lock-free priority queues are mostly based on skiplists, which probabilistically create shortcuts in an ordered list for fast insertion of elements. The use of skiplists eliminates the need of global rebalancing in balanced search trees and ensures logarithmic sequential search time on average, but the worst-case performance is linear with respect to the input size. In this paper, we propose a quiescently consistent lock-free priority queue based on a multi-dimensional list that guarantees worst-case search time of O(logN)more » for key universe of size N. The novel multi-dimensional list (MDList) is composed of nodes that contain multiple links to child nodes arranged by their dimensionality. The insertion operation works by first injectively mapping the scalar key to a high-dimensional vector, then uniquely locating the target position by using the vector as coordinates. Nodes in MDList are ordered by their coordinate prefixes and the ordering property of the data structure is readily maintained during insertion without rebalancing nor randomization. Furthermore, in our experimental evaluation using a micro-benchmark, our priority queue achieves an average of 50% speedup over the state of the art approaches under high concurrency.« less
EXPERIMENTING WITH MULTI-ATTRIBUTE UTILITY SURVEY METHODS IN A MULTI-DIMENSIONAL VALUATION PROBLEM. (R824699)

EPA Science Inventory

Abstract
The use of willingness-to-pay (WTP) survey techniques based on multi-attribute utility (MAU) approaches has been recommended by some authors as a way to deal simultaneously with two difficulties that increasingly plague environmental valuation. The first of th...
Multi-Hamiltonian structure of the Born-Infeld equation

NASA Astrophysics Data System (ADS)

Arik, Metin; Neyzi, Fahrünisa; Nutku, Yavuz; Olver, Peter J.; Verosky, John M.

1989-06-01

The multi-Hamiltonian structure, conservation laws, and higher order symmetries for the Born-Infeld equation are exhibited. A new transformation of the Born-Infeld equation to the equations of a Chaplygin gas is presented and explored. The Born-Infeld equation is distinguished among two-dimensional hyperbolic systems by its wealth of such multi-Hamiltonian structures.
On the source of stochastic volatility: Evidence from CAC40 index options during the subprime crisis

NASA Astrophysics Data System (ADS)

Slim, Skander

2016-12-01

This paper investigates the performance of time-changed Lévy processes with distinct sources of return volatility variation for modeling cross-sectional option prices on the CAC40 index during the subprime crisis. Specifically, we propose a multi-factor stochastic volatility model: one factor captures the diffusion component dynamics and two factors capture positive and negative jump variations. In-sample and out-of-sample tests show that our full-fledged model significantly outperforms nested lower-dimensional specifications. We find that all three sources of return volatility variation, with different persistence, are needed to properly account for market pricing dynamics across moneyness, maturity and volatility level. Besides, the model estimation reveals negative risk premium for both diffusive volatility and downward jump intensity whereas a positive risk premium is found to be attributed to upward jump intensity.
Accelerating three-dimensional FDTD calculations on GPU clusters for electromagnetic field simulation.

PubMed

Nagaoka, Tomoaki; Watanabe, Soichi

2012-01-01

Electromagnetic simulation with anatomically realistic computational human model using the finite-difference time domain (FDTD) method has recently been performed in a number of fields in biomedical engineering. To improve the method's calculation speed and realize large-scale computing with the computational human model, we adapt three-dimensional FDTD code to a multi-GPU cluster environment with Compute Unified Device Architecture and Message Passing Interface. Our multi-GPU cluster system consists of three nodes. The seven GPU boards (NVIDIA Tesla C2070) are mounted on each node. We examined the performance of the FDTD calculation on multi-GPU cluster environment. We confirmed that the FDTD calculation on the multi-GPU clusters is faster than that on a multi-GPU (a single workstation), and we also found that the GPU cluster system calculate faster than a vector supercomputer. In addition, our GPU cluster system allowed us to perform the large-scale FDTD calculation because were able to use GPU memory of over 100 GB.
Application of Multi-Parameter Data Visualization by Means of Multidimensional Scaling to Evaluate Possibility of Coal Gasification

NASA Astrophysics Data System (ADS)

Jamróz, Dariusz; Niedoba, Tomasz; Surowiak, Agnieszka; Tumidajski, Tadeusz; Szostek, Roman; Gajer, Mirosław

2017-09-01

The application of methods drawing upon multi-parameter visualization of data by transformation of multidimensional space into two-dimensional one allow to show multi-parameter data on computer screen. Thanks to that, it is possible to conduct a qualitative analysis of this data in the most natural way for human being, i.e. by the sense of sight. An example of such method of multi-parameter visualization is multidimensional scaling. This method was used in this paper to present and analyze a set of seven-dimensional data obtained from Janina Mining Plant and Wieczorek Coal Mine. It was decided to examine whether the method of multi-parameter data visualization allows to divide the samples space into areas of various applicability to fluidal gasification process. The "Technological applicability card for coals" was used for this purpose [Sobolewski et al., 2012; 2017], in which the key parameters, important and additional ones affecting the gasification process were described.
A Structure-Based Distance Metric for High-Dimensional Space Exploration with Multi-Dimensional Scaling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Hyun Jung; McDonnell, Kevin T.; Zelenyuk, Alla

2014-03-01

Although the Euclidean distance does well in measuring data distances within high-dimensional clusters, it does poorly when it comes to gauging inter-cluster distances. This significantly impacts the quality of global, low-dimensional space embedding procedures such as the popular multi-dimensional scaling (MDS) where one can often observe non-intuitive layouts. We were inspired by the perceptual processes evoked in the method of parallel coordinates which enables users to visually aggregate the data by the patterns the polylines exhibit across the dimension axes. We call the path of such a polyline its structure and suggest a metric that captures this structure directly inmore » high-dimensional space. This allows us to better gauge the distances of spatially distant data constellations and so achieve data aggregations in MDS plots that are more cognizant of existing high-dimensional structure similarities. Our MDS plots also exhibit similar visual relationships as the method of parallel coordinates which is often used alongside to visualize the high-dimensional data in raw form. We then cast our metric into a bi-scale framework which distinguishes far-distances from near-distances. The coarser scale uses the structural similarity metric to separate data aggregates obtained by prior classification or clustering, while the finer scale employs the appropriate Euclidean distance.« less
Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

NASA Astrophysics Data System (ADS)

Phinyomark, A.; Hu, H.; Phukpattaranont, P.; Limsakul, C.

2012-01-01

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.
Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).

PubMed

Borchani, Hanen; Bielza, Concha; Martı Nez-Martı N, Pablo; Larrañaga, Pedro

2012-12-01

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson's patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson's disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables. Copyright © 2012 Elsevier Inc. All rights reserved.
Modeling the defrost process in complex geometries - Part 1: Development of a one-dimensional defrost model

NASA Astrophysics Data System (ADS)

van Buren, Simon; Hertle, Ellen; Figueiredo, Patric; Kneer, Reinhold; Rohlfs, Wilko

2017-11-01

Frost formation is a common, often undesired phenomenon in heat exchanges such as air coolers. Thus, air coolers have to be defrosted periodically, causing significant energy consumption. For the design and optimization, prediction of defrosting by a CFD tool is desired. This paper presents a one-dimensional transient model approach suitable to be used as a zero-dimensional wall-function in CFD for modeling the defrost process at the fin and tube interfaces. In accordance to previous work a multi stage defrost model is introduced (e.g. [1, 2]). In the first instance the multi stage model is implemented and validated using MATLAB. The defrost process of a one-dimensional frost segment is investigated. Fixed boundary conditions are provided at the frost interfaces. The simulation results verify the plausibility of the designed model. The evaluation of the simulated defrost process shows the expected convergent behavior of the three-stage sequence.
Building the 3D Geological Model of Wall Rock of Salt Caverns Based on Integration Method of Multi-source data

NASA Astrophysics Data System (ADS)

Yongzhi, WANG; hui, WANG; Lixia, LIAO; Dongsen, LI

2017-02-01

In order to analyse the geological characteristics of salt rock and stability of salt caverns, rough three-dimensional (3D) models of salt rock stratum and the 3D models of salt caverns on study areas are built by 3D GIS spatial modeling technique. During implementing, multi-source data, such as basic geographic data, DEM, geological plane map, geological section map, engineering geological data, and sonar data are used. In this study, the 3D spatial analyzing and calculation methods, such as 3D GIS intersection detection method in three-dimensional space, Boolean operations between three-dimensional space entities, three-dimensional space grid discretization, are used to build 3D models on wall rock of salt caverns. Our methods can provide effective calculation models for numerical simulation and analysis of the creep characteristics of wall rock in salt caverns.
Cluster Analysis and Gaussian Mixture Estimation of Correlated Time-Series by Means of Multi-dimensional Scaling

NASA Astrophysics Data System (ADS)

Ibuki, Takero; Suzuki, Sei; Inoue, Jun-ichi

We investigate cross-correlations between typical Japanese stocks collected through Yahoo!Japan website ( http://finance.yahoo.co.jp/ ). By making use of multi-dimensional scaling (MDS) for the cross-correlation matrices, we draw two-dimensional scattered plots in which each point corresponds to each stock. To make a clustering for these data plots, we utilize the mixture of Gaussians to fit the data set to several Gaussian densities. By minimizing the so-called Akaike Information Criterion (AIC) with respect to parameters in the mixture, we attempt to specify the best possible mixture of Gaussians. It might be naturally assumed that all the two-dimensional data points of stocks shrink into a single small region when some economic crisis takes place. The justification of this assumption is numerically checked for the empirical Japanese stock data, for instance, those around 11 March 2011.
The Multi-dimensional Character of Core-collapse Supernovae

DOE PAGES

Hix, W. R.; Lentz, E. J.; Bruenn, S. W.; ...

2016-03-01

Core-collapse supernovae, the culmination of massive stellar evolution, are spectacular astronomical events and the principle actors in the story of our elemental origins. Our understanding of these events, while still incomplete, centers around a neutrino-driven central engine that is highly hydrodynamically unstable. Increasingly sophisticated simulations reveal a shock that stalls for hundreds of milliseconds before reviving. Though brought back to life by neutrino heating, the development of the supernova explosion is inextricably linked to multi-dimensional fluid flows. In this paper, the outcomes of three-dimensional simulations that include sophisticated nuclear physics and spectral neutrino transport are juxtaposed to learn about themore » nature of the three-dimensional fluid flow that shapes the explosion. Comparison is also made between the results of simulations in spherical symmetry from several groups, to give ourselves confidence in the understanding derived from this juxtaposition.« less
Reduced basis ANOVA methods for partial differential equations with high-dimensional random inputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liao, Qifeng, E-mail: liaoqf@shanghaitech.edu.cn; Lin, Guang, E-mail: guanglin@purdue.edu

2016-07-15

In this paper we present a reduced basis ANOVA approach for partial deferential equations (PDEs) with random inputs. The ANOVA method combined with stochastic collocation methods provides model reduction in high-dimensional parameter space through decomposing high-dimensional inputs into unions of low-dimensional inputs. In this work, to further reduce the computational cost, we investigate spatial low-rank structures in the ANOVA-collocation method, and develop efficient spatial model reduction techniques using hierarchically generated reduced bases. We present a general mathematical framework of the methodology, validate its accuracy and demonstrate its efficiency with numerical experiments.
Active Subspaces for Wind Plant Surrogate Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, Ryan N; Quick, Julian; Dykes, Katherine L

Understanding the uncertainty in wind plant performance is crucial to their cost-effective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utility-scale wind plants because of poor congergence rates or the curse of dimensionality. In this paper we demonstrate that wind plant power uncertainty can be well represented with a low-dimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active sub-spaces technique to UQ of plant power output with respect to uncertainty in turbine axial inductionmore » factors, and find a single active subspace direction dominates the sensitivity in power output. When this single active subspace direction is used to construct a quadratic surrogate model, the number of model unknowns can be reduced by up to 3 orders of magnitude without compromising performance on unseen test data. We conclude that the dimension reduction achieved with active subspaces makes surrogate-based UQ approaches tractable for utility-scale wind plants.« less
A low threshold nanocavity in a two-dimensional 12-fold photonic quasicrystal

NASA Astrophysics Data System (ADS)

Ren, Jie; Sun, XiaoHong; Wang, Shuai

2018-05-01

In this article, a low threshold nanocavity is built and investigated in a two-dimensional 12-fold holographic photonic quasicrystal (PQC). The cavity is formed by using the method of multi-beam common-path interference. By finely adjusting the structure parameters of the cavity, the Q factor and the mode volume are optimized, which are two keys to low-threshold on the basis of Purcell effect. Finally, an optimal cavity is obtained with Q value of 6023 and mode volume of 1.24 ×10-12cm3 . On the other hand, by Fourier Transformation of the electric field components in the cavity, the in-plane wave vectors are calculated and fitted to evaluate the cavity performance. The performance analysis of the cavity further proves the effectiveness of the optimization process. This has a guiding significance for the research of low threshold nano-laser.
Optimization of the segmented method for optical compression and multiplexing system

NASA Astrophysics Data System (ADS)

Al Falou, Ayman

2002-05-01

Because of the constant increasing demands of images exchange, and despite the ever increasing bandwidth of the networks, compression and multiplexing of images is becoming inseparable from their generation and display. For high resolution real time motion pictures, electronic performing of compression requires complex and time-consuming processing units. On the contrary, by its inherent bi-dimensional character, coherent optics is well fitted to perform such processes that are basically bi-dimensional data handling in the Fourier domain. Additionally, the main limiting factor that was the maximum frame rate is vanishing because of the recent improvement of spatial light modulator technology. The purpose of this communication is to benefit from recent optical correlation algorithms. The segmented filtering used to store multi-references in a given space bandwidth product optical filter can be applied to networks to compress and multiplex images in a given bandwidth channel.
In situ multi-length scale approach to understand the mechanics of soft and rigid binder in composite lithium ion battery electrodes

NASA Astrophysics Data System (ADS)

Jäckel, Nicolas; Dargel, Vadim; Shpigel, Netanel; Sigalov, Sergey; Levi, Mikhael D.; Daikhin, Leonid; Aurbach, Doron; Presser, Volker

2017-12-01

Intercalation-induced dimensional changes of composite battery electrodes containing either a stiff or a soft polymeric binder is one of the many factors determining the cycling performance and ageing. Herein, we report dimensional changes in bulk composite electrodes by in situ electrochemical dilatometry (eD) combined with electrochemical quartz-crystal microbalance with dissipation monitoring (EQCM-D). The latter tracks the mechanical properties on the level of the electrode particle size. Lithium iron phosphate (LiFePO4, LFP) electrodes with a stiff binder (PVdF) and a soft binder (NaCMC) were investigated by cycling in lithium sulfate (Li2SO4) aqueous solution. The electrochemical and mechanical electrode performances depend on the electrode cycling history. Based on combined eD and EQCM-D measurements we provide evidence which properties are preferred for a binder used for a composite Li-ion battery electrode.
Cardiovascular risk factors in multi-ethnic middle school students: the HEALTHY primary prevention trial.

PubMed

Willi, S M; Hirst, K; Jago, R; Buse, J; Kaufman, F; El Ghormli, L; Bassin, S; Elliot, D; Hale, D E

2012-06-01

The objective of this study was to examine the effects of an integrated, multi-component, school-based intervention programme on cardiovascular disease (CVD) risk factors among a multi-ethnic cohort of middle school students. HEALTHY was a cluster randomized, controlled, primary prevention trial. Middle school was the unit of randomization and intervention. Half of the schools were assigned to an intervention programme consisting of changes in the total school food environment and physical education classes, enhanced by educational outreach and behaviour change activities and promoted by a social marketing campaign consisting of reinforcing messages and images. Outcome data reported (anthropometrics, blood pressure and fasting lipid levels) were collected on a cohort of students enrolled at the start of 6th grade (∼11-12 years old) and followed to end of 8th grade (∼13-14 years old). Forty-two middle schools were enrolled at seven field centres; 4363 students provided both informed consent and CVD data at baseline and end of study. The sample was 52.7% female, 54.5% Hispanic, 17.6% non-Hispanic Black, 19.4% non-Hispanic White and 8.5% other racial/ethnic combinations, and 49.6% were categorized as overweight or obese (body mass index ≥ 85th percentile) at baseline. A significant intervention effect was detected in the prevalence of hypertension in non-Hispanic Black and White males. The intervention produced no significant changes in lipid levels. The prevalence of some CVD risk factors is high in minority middle school youth, particularly males. A multi-component, school-based programme achieved only modest reductions in these risk factors; however, promising findings occurred in non-Hispanic Black and White males with hypertension. © 2012 The Authors. Pediatric Obesity © 2012 International Association for the Study of Obesity.
Developing one-dimensional implosions for inertial confinement fusion science

DOE PAGES

Kline, John L.; Yi, Sunghwan A.; Simakov, Andrei Nikolaevich; ...

2016-12-12

Experiments on the National Ignition Facility show that multi-dimensional effects currently dominate the implosion performance. Low mode implosion symmetry and hydrodynamic instabilities seeded by capsule mounting features appear to be two key limiting factors for implosion performance. One reason these factors have a large impact on the performance of inertial confinement fusion implosions is the high convergence required to achieve high fusion gains. To tackle these problems, a predictable implosion platform is needed meaning experiments must trade-off high gain for performance. LANL has adopted three main approaches to develop a one-dimensional (1D) implosion platform where 1D means measured yield overmore » the 1D clean calculation. A high adiabat, low convergence platform is being developed using beryllium capsules enabling larger case-to-capsule ratios to improve symmetry. The second approach is liquid fuel layers using wetted foam targets. With liquid fuel layers, the implosion convergence can be controlled via the initial vapor pressure set by the target fielding temperature. The last method is double shell targets. For double shells, the smaller inner shell houses the DT fuel and the convergence of this cavity is relatively small compared to hot spot ignition. However, double shell targets have a different set of trade-off versus advantages. As a result, details for each of these approaches are described.« less

Polyallylamine-Rh nanosheet nanoassemblies-carbon nanotubes organic-inorganic nanohybrids: A electrocatalyst superior to Pt for the hydrogen evolution reaction

NASA Astrophysics Data System (ADS)

Bai, Juan; Xing, Shi-Hui; Zhu, Ying-Ying; Jiang, Jia-Xing; Zeng, Jing-Hui; Chen, Yu

2018-05-01

Rationally tailoring the surface/interface structures of noble metal nanostructures emerges as a highly efficient method for improving their electrocatalytic activity, selectivity, and long-term stability. Recently, hydrogen evolution reaction is attracting more and more attention due to the energy crisis and environment pollution. Herein, we successfully synthesize polyallylamine-functionalized rhodium nanosheet nanoassemblies-carbon nanotube nanohybrids via a facile one-pot hydrothermal method. Three-dimensionally branched rhodium nanosheet nanoassemblies are consisted of two dimensionally atomically thick ultrathin rhodium nanosheets. The as-prepared polyallylamine-functionalized rhodium nanosheet nanoassemblies-carbon nanotube nanohybrids show the excellent electrocatalytic activity for the hydrogen evolution reaction in acidic media, with a low onset reduction potential of -1 mV, a small overpotential of 5 mV at 10 mA cm-2, which is much superior to commercial platinum nanocrystals. Two dimensionally ultrathin morphology of rhodium nanosheet, particular rhodium-polyallylamine interface, and three-dimensionally networks induced by carbon nanotube are the key factors for the excellent hydrogen evolution reaction activity in acidic media.
Spectroscopic properties of a two-dimensional time-dependent Cepheid model. I. Description and validation of the model

NASA Astrophysics Data System (ADS)

Vasilyev, V.; Ludwig, H.-G.; Freytag, B.; Lemasle, B.; Marconi, M.

2017-10-01

Context. Standard spectroscopic analyses of Cepheid variables are based on hydrostatic one-dimensional model atmospheres, with convection treated using various formulations of mixing-length theory. Aims: This paper aims to carry out an investigation of the validity of the quasi-static approximation in the context of pulsating stars. We check the adequacy of a two-dimensional time-dependent model of a Cepheid-like variable with focus on its spectroscopic properties. Methods: With the radiation-hydrodynamics code CO5BOLD, we construct a two-dimensional time-dependent envelope model of a Cepheid with Teff = 5600 K, log g = 2.0, solar metallicity, and a 2.8-day pulsation period. Subsequently, we perform extensive spectral syntheses of a set of artificial iron lines in local thermodynamic equilibrium. The set of lines allows us to systematically study effects of line strength, ionization stage, and excitation potential. Results: We evaluate the microturbulent velocity, line asymmetry, projection factor, and Doppler shifts. The microturbulent velocity, averaged over all lines, depends on the pulsational phase and varies between 1.5 and 2.7 km s-1. The derived projection factor lies between 1.23 and 1.27, which agrees with observational results. The mean Doppler shift is non-zero and negative, -1 km s-1, after averaging over several full periods and lines. This residual line-of-sight velocity (related to the "K-term") is primarily caused by horizontal inhomogeneities, and consequently we interpret it as the familiar convective blueshift ubiquitously present in non-pulsating late-type stars. Limited statistics prevent firm conclusions on the line asymmetries. Conclusions: Our two-dimensional model provides a reasonably accurate representation of the spectroscopic properties of a short-period Cepheid-like variable star. Some properties are primarily controlled by convective inhomogeneities rather than by the Cepheid-defining pulsations. Extended multi-dimensional modelling offers new insight into the nature of pulsating stars.
Investigation of upwind, multigrid, multiblock numerical schemes for three dimensional flows. Volume 1: Runge-Kutta methods for a thin layer Navier-Stokes solver

NASA Technical Reports Server (NTRS)

Cannizzaro, Frank E.; Ash, Robert L.

1992-01-01

A state-of-the-art computer code has been developed that incorporates a modified Runge-Kutta time integration scheme, upwind numerical techniques, multigrid acceleration, and multi-block capabilities (RUMM). A three-dimensional thin-layer formulation of the Navier-Stokes equations is employed. For turbulent flow cases, the Baldwin-Lomax algebraic turbulence model is used. Two different upwind techniques are available: van Leer's flux-vector splitting and Roe's flux-difference splitting. Full approximation multi-grid plus implicit residual and corrector smoothing were implemented to enhance the rate of convergence. Multi-block capabilities were developed to provide geometric flexibility. This feature allows the developed computer code to accommodate any grid topology or grid configuration with multiple topologies. The results shown in this dissertation were chosen to validate the computer code and display its geometric flexibility, which is provided by the multi-block structure.
3-d finite element model development for biomechanics: a software demonstration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hollerbach, K.; Hollister, A.M.; Ashby, E.

1997-03-01

Finite element analysis is becoming an increasingly important part of biomechanics and orthopedic research, as computational resources become more powerful, and data handling algorithms become more sophisticated. Until recently, tools with sufficient power did not exist or were not accessible to adequately model complicated, three-dimensional, nonlinear biomechanical systems. In the past, finite element analyses in biomechanics have often been limited to two-dimensional approaches, linear analyses, or simulations of single tissue types. Today, we have the resources to model fully three-dimensional, nonlinear, multi-tissue, and even multi-joint systems. The authors will present the process of developing these kinds of finite element models,more » using human hand and knee examples, and will demonstrate their software tools.« less
High-Order Central WENO Schemes for Multi-Dimensional Hamilton-Jacobi Equations

NASA Technical Reports Server (NTRS)

Bryson, Steve; Levy, Doron; Biegel, Bryan (Technical Monitor)

2002-01-01

We present new third- and fifth-order Godunov-type central schemes for approximating solutions of the Hamilton-Jacobi (HJ) equation in an arbitrary number of space dimensions. These are the first central schemes for approximating solutions of the HJ equations with an order of accuracy that is greater than two. In two space dimensions we present two versions for the third-order scheme: one scheme that is based on a genuinely two-dimensional Central WENO reconstruction, and another scheme that is based on a simpler dimension-by-dimension reconstruction. The simpler dimension-by-dimension variant is then extended to a multi-dimensional fifth-order scheme. Our numerical examples in one, two and three space dimensions verify the expected order of accuracy of the schemes.
Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics

NASA Astrophysics Data System (ADS)

Franck, I. M.; Koutsourelakis, P. S.

2017-01-01

This paper is concerned with the numerical solution of model-based, Bayesian inverse problems. We are particularly interested in cases where the cost of each likelihood evaluation (forward-model call) is expensive and the number of unknown (latent) variables is high. This is the setting in many problems in computational physics where forward models with nonlinear PDEs are used and the parameters to be calibrated involve spatio-temporarily varying coefficients, which upon discretization give rise to a high-dimensional vector of unknowns. One of the consequences of the well-documented ill-posedness of inverse problems is the possibility of multiple solutions. While such information is contained in the posterior density in Bayesian formulations, the discovery of a single mode, let alone multiple, poses a formidable computational task. The goal of the present paper is two-fold. On one hand, we propose approximate, adaptive inference strategies using mixture densities to capture multi-modal posteriors. On the other, we extend our work in [1] with regard to effective dimensionality reduction techniques that reveal low-dimensional subspaces where the posterior variance is mostly concentrated. We validate the proposed model by employing Importance Sampling which confirms that the bias introduced is small and can be efficiently corrected if the analyst wishes to do so. We demonstrate the performance of the proposed strategy in nonlinear elastography where the identification of the mechanical properties of biological materials can inform non-invasive, medical diagnosis. The discovery of multiple modes (solutions) in such problems is critical in achieving the diagnostic objectives.
From analytical solutions of solute transport equations to multidimensional time-domain random walk (TDRW) algorithms

NASA Astrophysics Data System (ADS)

Bodin, Jacques

2015-03-01

In this study, new multi-dimensional time-domain random walk (TDRW) algorithms are derived from approximate one-dimensional (1-D), two-dimensional (2-D), and three-dimensional (3-D) analytical solutions of the advection-dispersion equation and from exact 1-D, 2-D, and 3-D analytical solutions of the pure-diffusion equation. These algorithms enable the calculation of both the time required for a particle to travel a specified distance in a homogeneous medium and the mass recovery at the observation point, which may be incomplete due to 2-D or 3-D transverse dispersion or diffusion. The method is extended to heterogeneous media, represented as a piecewise collection of homogeneous media. The particle motion is then decomposed along a series of intermediate checkpoints located on the medium interface boundaries. The accuracy of the multi-dimensional TDRW method is verified against (i) exact analytical solutions of solute transport in homogeneous media and (ii) finite-difference simulations in a synthetic 2-D heterogeneous medium of simple geometry. The results demonstrate that the method is ideally suited to purely diffusive transport and to advection-dispersion transport problems dominated by advection. Conversely, the method is not recommended for highly dispersive transport problems because the accuracy of the advection-dispersion TDRW algorithms degrades rapidly for a low Péclet number, consistent with the accuracy limit of the approximate analytical solutions. The proposed approach provides a unified methodology for deriving multi-dimensional time-domain particle equations and may be applicable to other mathematical transport models, provided that appropriate analytical solutions are available.
Ecologically and economically conscious design of the injected pultrusion process via multi-objective optimization

NASA Astrophysics Data System (ADS)

Srinivasagupta, Deepak; Kardos, John L.

2004-05-01

Injected pultrusion (IP) is an environmentally benign continuous process for low-cost manufacture of prismatic polymer composites. IP has been of recent regulatory interest as an option to achieve significant vapour emissions reduction. This work describes the design of the IP process with multiple design objectives. In our previous work (Srinivasagupta D et al 2003 J. Compos. Mater. at press), an algorithm for economic design using a validated three-dimensional physical model of the IP process was developed, subject to controllability considerations. In this work, this algorithm was used in a multi-objective optimization approach to simultaneously meet economic, quality related, and environmental objectives. The retrofit design of a bench-scale set-up was considered, and the concept of exergy loss in the process, as well as in vapour emission, was introduced. The multi-objective approach was able to determine the optimal values of the processing parameters such as heating zone temperatures and resin injection pressure, as well as the equipment specifications (die dimensions, heater, puller and pump ratings) that satisfy the various objectives in a weighted sense, and result in enhanced throughput rates. The economic objective did not coincide with the environmental objective, and a compromise became necessary. It was seen that most of the exergy loss is in the conversion of electric power into process heating. Vapour exergy loss was observed to be negligible for the most part.
SHOULD ONE USE THE RAY-BY-RAY APPROXIMATION IN CORE-COLLAPSE SUPERNOVA SIMULATIONS?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Skinner, M. Aaron; Burrows, Adam; Dolence, Joshua C., E-mail: burrows@astro.princeton.edu, E-mail: askinner@astro.princeton.edu, E-mail: jdolence@lanl.gov

2016-11-01

We perform the first self-consistent, time-dependent, multi-group calculations in two dimensions (2D) to address the consequences of using the ray-by-ray+ transport simplification in core-collapse supernova simulations. Such a dimensional reduction is employed by many researchers to facilitate their resource-intensive calculations. Our new code (Fornax) implements multi-D transport, and can, by zeroing out transverse flux terms, emulate the ray-by-ray+ scheme. Using the same microphysics, initial models, resolution, and code, we compare the results of simulating 12, 15, 20, and 25 M {sub ⊙} progenitor models using these two transport methods. Our findings call into question the wisdom of the pervasive usemore » of the ray-by-ray+ approach. Employing it leads to maximum post-bounce/pre-explosion shock radii that are almost universally larger by tens of kilometers than those derived using the more accurate scheme, typically leaving the post-bounce matter less bound and artificially more “explodable.” In fact, for our 25 M {sub ⊙} progenitor, the ray-by-ray+ model explodes, while the corresponding multi-D transport model does not. Therefore, in two dimensions, the combination of ray-by-ray+ with the axial sloshing hydrodynamics that is a feature of 2D supernova dynamics can result in quantitatively, and perhaps qualitatively, incorrect results.« less
Chemically exfoliating large sheets of phosphorene via choline chloride urea viscosity-tuning

NASA Astrophysics Data System (ADS)

Ng, A.; Sutto, T. E.; Matis, B. R.; Deng, Y.; Ye, P. D.; Stroud, R. M.; Brintlinger, T. H.; Bassim, N. D.

2017-04-01

Exfoliation of two-dimensional phosphorene from bulk black phosphorous through chemical means is demonstrated where the solvent system of choice (choline chloride urea diluted with ethanol) has the ability to successfully exfoliate large-area multi-layer phosphorene sheets and further protect the flakes from ambient degradation. The intercalant solvent molecules, aided by low-powered sonication, diffuse between the layers of the bulk black phosphorus, allowing for the exfoliation of the multi-layer phosphorene through breaking of the interlayer van der Waals bonds. Through viscosity tuning, the optimal parameters (1:1 ratio between the intercalant and the diluting solvent) at which the exfoliation takes place is determined. Our exfoliation technique is shown to produce multi-layer phosphorene flakes with surface areas greater than 3 μm2 (a factor of three larger than what has previously been reported for a similar exfoliation method) while limiting exposure to the ambient environment, thereby protecting the flakes from degradation. Characterization techniques such as optical microscopy, Raman spectroscopy, ultraviolet-visible spectroscopy, and (scanning) transmission electron microscopy are used to investigate the quality, quantity, and thickness of the exfoliated flakes.
Highly Crystalline Multimetallic Nanoframes with Three-Dimensional Electrocatalytic Surfaces

DOE PAGES

Chen, Chen; Kang, Yijin; Huo, Ziyang; ...

2014-02-27

Control of structure at the atomic level can precisely and effectively tune catalytic properties of materials, enabling enhancement in both activity and durability. We synthesized a highly active and durable class of electrocatalysts by exploiting the structural evolution of platinum-nickel (Pt-Ni) bimetallic nanocrystals. The starting material, crystalline PtNi 3 polyhedra, transforms in solution by interior erosion into Pt 3Ni nanoframes with surfaces that offer three-dimensional molecular accessibility. The edges of the Pt-rich PtNi 3 polyhedra are maintained in the final Pt 3Ni nanoframes. Both the interior and exterior catalytic surfaces of this open-framework structure are composed of the nanosegregated Pt-skinmore » structure, which exhibits enhanced oxygen reduction reaction (ORR) activity. The Pt 3Ni nanoframe catalysts achieved a factor of 36 enhancement in mass activity and a factor of 22 enhancement in specific activity, respectively, for this reaction (relative to state-of-the-art platinum-carbon catalysts) during prolonged exposure to reaction conditions.« less
Decentralised fixed modes of networked MIMO systems

NASA Astrophysics Data System (ADS)

Hao, Yuqing; Duan, Zhisheng; Chen, Guanrong

2018-04-01

In this paper, decentralised fixed modes (DFMs) of a networked system are studied. The network topology is directed and weighted and the nodes are higher-dimensional linear time-invariant (LTI) dynamical systems. The effects of the network topology, the node-system dynamics, the external control inputs, and the inner interactions on the existence of DFMs for the whole networked system are investigated. A necessary and sufficient condition for networked multi-input/multi-output (MIMO) systems in a general topology to possess no DFMs is derived. For networked single-input/single-output (SISO) LTI systems in general as well as some typical topologies, some specific conditions for having no DFMs are established. It is shown that the existence of DFMs is an integrated result of the aforementioned relevant factors which cannot be decoupled into individual DFMs of the node-systems and the properties solely determined by the network topology.
Universality of the Volume Bound in Slow-Roll Eternal Inflation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dubovsky, Sergei; Senatore, Leonardo; Villadoro, Giovanni

2012-03-28

It has recently been shown that in single field slow-roll inflation the total volume cannot grow by a factor larger than e{sup S{sub dS}/2} without becoming infinite. The bound is saturated exactly at the phase transition to eternal inflation where the probability to produce infinite volume becomes non zero. We show that the bound holds sharply also in any space-time dimensions, when arbitrary higher-dimensional operators are included and in the multi-field inflationary case. The relation with the entropy of de Sitter and the universality of the bound strengthen the case for a deeper holographic interpretation. As a spin-off we providemore » the formalism to compute the probability distribution of the volume after inflation for generic multi-field models, which might help to address questions about the population of vacua of the landscape during slow-roll inflation.« less
Multi-physics optimization of three-dimensional microvascular polymeric components

NASA Astrophysics Data System (ADS)

Aragón, Alejandro M.; Saksena, Rajat; Kozola, Brian D.; Geubelle, Philippe H.; Christensen, Kenneth T.; White, Scott R.

2013-01-01

This work discusses the computational design of microvascular polymeric materials, which aim at mimicking the behavior found in some living organisms that contain a vascular system. The optimization of the topology of the embedded three-dimensional microvascular network is carried out by coupling a multi-objective constrained genetic algorithm with a finite-element based physics solver, the latter validated through experiments. The optimization is carried out on multiple conflicting objective functions, namely the void volume fraction left by the network, the energy required to drive the fluid through the network and the maximum temperature when the material is subjected to thermal loads. The methodology presented in this work results in a viable alternative for the multi-physics optimization of these materials for active-cooling applications.
Three-Dimensional Terahertz Coded-Aperture Imaging Based on Single Input Multiple Output Technology.

PubMed

Chen, Shuo; Luo, Chenggao; Deng, Bin; Wang, Hongqiang; Cheng, Yongqiang; Zhuang, Zhaowen

2018-01-19

As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. In this paper, we propose a three-dimensional (3D) TCAI architecture based on single input multiple output (SIMO) technology, which can reduce the coding and sampling times sharply. The coded aperture applied in the proposed TCAI architecture loads either purposive or random phase modulation factor. In the transmitting process, the purposive phase modulation factor drives the terahertz beam to scan the divided 3D imaging cells. In the receiving process, the random phase modulation factor is adopted to modulate the terahertz wave to be spatiotemporally independent for high resolution. Considering human-scale targets, images of each 3D imaging cell are reconstructed one by one to decompose the global computational complexity, and then are synthesized together to obtain the complete high-resolution image. As for each imaging cell, the multi-resolution imaging method helps to reduce the computational burden on a large-scale reference-signal matrix. The experimental results demonstrate that the proposed architecture can achieve high-resolution imaging with much less time for 3D targets and has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc.
Mechanisms of Risk Reduction in the Clinical Practice of Alzheimer’s Disease Prevention

PubMed Central

Schelke, Matthew W.; Attia, Peter; Palenchar, Daniel J.; Kaplan, Bob; Mureb, Monica; Ganzer, Christine A.; Scheyer, Olivia; Rahman, Aneela; Kachko, Robert; Krikorian, Robert; Mosconi, Lisa; Isaacson, Richard S.

2018-01-01

Alzheimer’s disease (AD) is a neurodegenerative dementia that affects nearly 50 million people worldwide and is a major source of morbidity, mortality, and healthcare expenditure. While there have been many attempts to develop disease-modifying therapies for late-onset AD, none have so far shown efficacy in humans. However, the long latency between the initial neuronal changes and onset of symptoms, the ability to identify patients at risk based on family history and genetic markers, and the emergence of AD biomarkers for preclinical disease suggests that early risk-reducing interventions may be able to decrease the incidence of, delay or prevent AD. In this review, we discuss six mechanisms—dysregulation of glucose metabolism, inflammation, oxidative stress, trophic factor release, amyloid burden, and calcium toxicity—involved in AD pathogenesis that offer promising targets for risk-reducing interventions. In addition, we offer a blueprint for a multi-modality AD risk reduction program that can be clinically implemented with the current state of knowledge. Focused risk reduction aimed at particular pathological factors may transform AD to a preventable disorder in select cases. PMID:29706884
Mechanisms of Risk Reduction in the Clinical Practice of Alzheimer's Disease Prevention.

PubMed

Schelke, Matthew W; Attia, Peter; Palenchar, Daniel J; Kaplan, Bob; Mureb, Monica; Ganzer, Christine A; Scheyer, Olivia; Rahman, Aneela; Kachko, Robert; Krikorian, Robert; Mosconi, Lisa; Isaacson, Richard S

2018-01-01

Alzheimer's disease (AD) is a neurodegenerative dementia that affects nearly 50 million people worldwide and is a major source of morbidity, mortality, and healthcare expenditure. While there have been many attempts to develop disease-modifying therapies for late-onset AD, none have so far shown efficacy in humans. However, the long latency between the initial neuronal changes and onset of symptoms, the ability to identify patients at risk based on family history and genetic markers, and the emergence of AD biomarkers for preclinical disease suggests that early risk-reducing interventions may be able to decrease the incidence of, delay or prevent AD. In this review, we discuss six mechanisms-dysregulation of glucose metabolism, inflammation, oxidative stress, trophic factor release, amyloid burden, and calcium toxicity-involved in AD pathogenesis that offer promising targets for risk-reducing interventions. In addition, we offer a blueprint for a multi-modality AD risk reduction program that can be clinically implemented with the current state of knowledge. Focused risk reduction aimed at particular pathological factors may transform AD to a preventable disorder in select cases.
Multi-loop Integrand Reduction with Computational Algebraic Geometry

NASA Astrophysics Data System (ADS)

Badger, Simon; Frellesvig, Hjalte; Zhang, Yang

2014-06-01

We discuss recent progress in multi-loop integrand reduction methods. Motivated by the possibility of an automated construction of multi-loop amplitudes via generalized unitarity cuts we describe a procedure to obtain a general parameterisation of any multi-loop integrand in a renormalizable gauge theory. The method relies on computational algebraic geometry techniques such as Gröbner bases and primary decomposition of ideals. We present some results for two and three loop amplitudes obtained with the help of the MACAULAY2 computer algebra system and the Mathematica package BASISDET.
Development, Verification and Validation of Enclosure Radiation Capabilities in the CHarring Ablator Response (CHAR) Code

NASA Technical Reports Server (NTRS)

Salazar, Giovanni; Droba, Justin C.; Oliver, Brandon; Amar, Adam J.

2016-01-01

With the recent development of multi-dimensional thermal protection system (TPS) material response codes including the capabilities to account for radiative heating is a requirement. This paper presents the recent efforts to implement such capabilities in the CHarring Ablator Response (CHAR) code developed at NASA's Johnson Space Center. This work also describes the different numerical methods implemented in the code to compute view factors for radiation problems involving multiple surfaces. Furthermore, verification and validation of the code's radiation capabilities are demonstrated by comparing solutions to analytical results, to other codes, and to radiant test data.
CAFE simulation of columnar-to-equiaxed transition in Al-7wt%Si alloys directionally solidified under microgravity

NASA Astrophysics Data System (ADS)

Liu, D. R.; Mangelinck-Noël, N.; Gandin, Ch-A.; Zimmermann, G.; Sturz, L.; Nguyen Thi, H.; Billia, B.

2016-03-01

A two-dimensional multi-scale cellular automaton - finite element (CAFE) model is used to simulate grain structure evolution and microsegregation formation during solidification of refined Al-7wt%Si alloys under microgravity. The CAFE simulations are first qualitatively compared with the benchmark experimental data under microgravity. Qualitative agreement is obtained for the position of columnar to equiaxed transition (CET) and the CET transition mode (sharp or progressive). Further comparisons of the distributions of grain elongation factor and equivalent diameter are conducted and reveal a fair quantitative agreement.

Portable laser synthesizer for high-speed multi-dimensional spectroscopy

DOEpatents

Demos, Stavros G [Livermore, CA; Shverdin, Miroslav Y [Sunnyvale, CA; Shirk, Michael D [Brentwood, CA

2012-05-29

Portable, field-deployable laser synthesizer devices designed for multi-dimensional spectrometry and time-resolved and/or hyperspectral imaging include a coherent light source which simultaneously produces a very broad, energetic, discrete spectrum spanning through or within the ultraviolet, visible, and near infrared wavelengths. The light output is spectrally resolved and each wavelength is delayed with respect to each other. A probe enables light delivery to a target. For multidimensional spectroscopy applications, the probe can collect the resulting emission and deliver this radiation to a time gated spectrometer for temporal and spectral analysis.
Three-dimensional computed topography analysis of a patient with an unusual anatomy of the maxillary second and third molars.

PubMed

Zhao, Jin; Li, Yan; Yang, Zhi-Wei; Wang, Wei; Meng, Yan

2011-10-01

We present a case of a patient with rare anatomy of a maxillary second molar with three mesiobuccal root canals and a maxillary third molar with four separate roots, identified using multi-slice computed topography (CT) and three-dimensional reconstruction techniques. The described case enriched/might enrich our knowledge about possible anatomical aberrations of maxillary molars. In addition, we demonstrate the role of multi-slice CT as an objective tool for confirmatory diagnosis and successful endodontic management.
Analytic Approximations to the Free Boundary and Multi-dimensional Problems in Financial Derivatives Pricing

NASA Astrophysics Data System (ADS)

Lau, Chun Sing

This thesis studies two types of problems in financial derivatives pricing. The first type is the free boundary problem, which can be formulated as a partial differential equation (PDE) subject to a set of free boundary condition. Although the functional form of the free boundary condition is given explicitly, the location of the free boundary is unknown and can only be determined implicitly by imposing continuity conditions on the solution. Two specific problems are studied in details, namely the valuation of fixed-rate mortgages and CEV American options. The second type is the multi-dimensional problem, which involves multiple correlated stochastic variables and their governing PDE. One typical problem we focus on is the valuation of basket-spread options, whose underlying asset prices are driven by correlated geometric Brownian motions (GBMs). Analytic approximate solutions are derived for each of these three problems. For each of the two free boundary problems, we propose a parametric moving boundary to approximate the unknown free boundary, so that the original problem transforms into a moving boundary problem which can be solved analytically. The governing parameter of the moving boundary is determined by imposing the first derivative continuity condition on the solution. The analytic form of the solution allows the price and the hedging parameters to be computed very efficiently. When compared against the benchmark finite-difference method, the computational time is significantly reduced without compromising the accuracy. The multi-stage scheme further allows the approximate results to systematically converge to the benchmark results as one recasts the moving boundary into a piecewise smooth continuous function. For the multi-dimensional problem, we generalize the Kirk (1995) approximate two-asset spread option formula to the case of multi-asset basket-spread option. Since the final formula is in closed form, all the hedging parameters can also be derived in closed form. Numerical examples demonstrate that the pricing and hedging errors are in general less than 1% relative to the benchmark prices obtained by numerical integration or Monte Carlo simulation. By exploiting an explicit relationship between the option price and the underlying probability distribution, we further derive an approximate distribution function for the general basket-spread variable. It can be used to approximate the transition probability distribution of any linear combination of correlated GBMs. Finally, an implicit perturbation is applied to reduce the pricing errors by factors of up to 100. When compared against the existing methods, the basket-spread option formula coupled with the implicit perturbation turns out to be one of the most robust and accurate approximation methods.
Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.

PubMed

Wang, Li; Wang, Xiaoyi; Jin, Xuebo; Xu, Jiping; Zhang, Huiyan; Yu, Jiabin; Sun, Qian; Gao, Chong; Wang, Lingbin

2017-03-01

The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.
Stable orthogonal local discriminant embedding for linear dimensionality reduction.

PubMed

Gao, Quanxue; Ma, Jingjie; Zhang, Hailin; Gao, Xinbo; Liu, Yamin

2013-07-01

Manifold learning is widely used in machine learning and pattern recognition. However, manifold learning only considers the similarity of samples belonging to the same class and ignores the within-class variation of data, which will impair the generalization and stableness of the algorithms. For this purpose, we construct an adjacency graph to model the intraclass variation that characterizes the most important properties, such as diversity of patterns, and then incorporate the diversity into the discriminant objective function for linear dimensionality reduction. Finally, we introduce the orthogonal constraint for the basis vectors and propose an orthogonal algorithm called stable orthogonal local discriminate embedding. Experimental results on several standard image databases demonstrate the effectiveness of the proposed dimensionality reduction approach.
Optimizing the Use of LiDAR for Hydraulic and Sediment Transport Model Development: Case Studies from Marin and Sonoma Counties, CA

NASA Astrophysics Data System (ADS)

Kobor, J. S.; O'Connor, M. D.; Sherwood, M. N.

2013-12-01

Effective floodplain management and restoration requires a detailed understanding of floodplain processes not readily achieved using standard one-dimensional hydraulic modeling approaches. The application of more advanced numerical models is, however, often limited by the relatively high costs of acquiring the high-resolution topographic data needed for model development using traditional surveying methods. The increasing availability of LiDAR data has the potential to significantly reduce these costs and thus facilitate application of multi-dimensional hydraulic models where budget constraints would have otherwise prohibited their use. The accuracy and suitability of LiDAR data for supporting model development can vary widely depending on the resolution of channel and floodplain features, the data collection density, and the degree of vegetation canopy interference among other factors. More work is needed to develop guidelines for evaluating LiDAR accuracy and determining when and how best the data can be used to support numerical modeling activities. Here we present two recent case studies where LiDAR datasets were used to support floodplain and sediment transport modeling efforts. One LiDAR dataset was collected with a relatively low point density and used to study a small stream channel in coastal Marin County and a second dataset was collected with a higher point density and applied to a larger stream channel in western Sonoma County. Traditional topographic surveying was performed at both sites which provided a quantitative means of evaluating the LiDAR accuracy. We found that with the lower point density dataset, the accuracy of the LiDAR varied significantly between the active stream channel and floodplain whereas the accuracy across the channel/floodplain interface was more uniform with the higher density dataset. Accuracy also varied widely as a function of the density of the riparian vegetation canopy. We found that coupled 1- and 2-dimensional hydraulic models whereby the active channel is simulated in 1-dimension and the floodplain in 2-dimensions provided the best means of utilizing the LiDAR data to evaluate existing conditions and develop alternative flood hazard mitigation and habitat restoration strategies. Such an approach recognizes the limitations of the LiDAR data within active channel areas with dense riparian cover and is cost-effective in that it allows field survey efforts to focus primarily on characterizing active stream channel areas. The multi-dimensional modeling approach also conforms well to the physical realties of the stream system whereby in-channel flows can generally be well-described as a one-dimensional flow problem and floodplain flows are often characterized by multiple and often poorly understood flowpaths. The multi-dimensional modeling approach has the additional advantages of allowing for accurate simulation of the effects of hydraulic structures using well-tested one-dimensional formulae and minimizing the computational burden of the models by not requiring the small spatial resolutions necessary to resolve the geometries of small stream channels in two-dimensions.
A metal-free electrocatalyst for carbon dioxide reduction to multi-carbon hydrocarbons and oxygenates

NASA Astrophysics Data System (ADS)

Wu, Jingjie; Ma, Sichao; Sun, Jing; Gold, Jake I.; Tiwary, Chandrasekhar; Kim, Byoungsu; Zhu, Lingyang; Chopra, Nitin; Odeh, Ihab N.; Vajtai, Robert; Yu, Aaron Z.; Luo, Raymond; Lou, Jun; Ding, Guqiao; Kenis, Paul J. A.; Ajayan, Pulickel M.

2016-12-01

Electroreduction of carbon dioxide into higher-energy liquid fuels and chemicals is a promising but challenging renewable energy conversion technology. Among the electrocatalysts screened so far for carbon dioxide reduction, which includes metals, alloys, organometallics, layered materials and carbon nanostructures, only copper exhibits selectivity towards formation of hydrocarbons and multi-carbon oxygenates at fairly high efficiencies, whereas most others favour production of carbon monoxide or formate. Here we report that nanometre-size N-doped graphene quantum dots (NGQDs) catalyse the electrochemical reduction of carbon dioxide into multi-carbon hydrocarbons and oxygenates at high Faradaic efficiencies, high current densities and low overpotentials. The NGQDs show a high total Faradaic efficiency of carbon dioxide reduction of up to 90%, with selectivity for ethylene and ethanol conversions reaching 45%. The C2 and C3 product distribution and production rate for NGQD-catalysed carbon dioxide reduction is comparable to those obtained with copper nanoparticle-based electrocatalysts.
The factor structure and clinical utility of formal thought disorder in first episode psychosis.

PubMed

Roche, Eric; Lyne, John Paul; O'Donoghue, Brian; Segurado, Ricardo; Kinsella, Anthony; Hannigan, Ailish; Kelly, Brendan D; Malone, Kevin; Clarke, Mary

2015-10-01

Formal thought disorder (FTD) is a core feature of psychosis, however there are gaps in our knowledge about its prevalence and factor structure. We had two aims: first, to establish the factor structure of FTD; second, to explore the clinical utility of dimensions of FTD in order to further the understanding of its nosology. A cross-validation study was undertaken to establish the factor structure of FTD in first episode psychosis (FEP). The relative utility of FTD categories vs. dimensions across diagnostic categories was investigated. The prevalence of clinically significant FTD in this FEP sample was 21%, although 41% showed evidence of disorganised speech, 20% displayed verbosity and 24% displayed impoverished speech. A 3-factor model was identified as the best fit for FTD, with disorganisation, poverty and verbosity dimensions (GFI=0.99, RMR=0.07). These dimensions of FTD accurately distinguished affective from non-affective diagnostic categories. A categorical approach to FTD assessment was useful in identifying markers of clinical acuteness, as identified by short duration of untreated psychosis (OR=2.94, P<0.01) and inpatient treatment status (OR=3.98, P<0.01). FTD is moderately prevalent and multi-dimensional in FEP. Employing both a dimensional and categorical assessment of FTD gives valuable clinical information, however there may be a need to revise our conceptualisation of the nosology of FTD. The prognostic value of FTD, as well as its neural basis, requires elucidation. Copyright © 2015 Elsevier B.V. All rights reserved.
Multi-Locus Candidate Gene Analyses of Lipid Levels in a Pediatric Turkish Cohort: Lessons Learned on LPL, CETP, LIPC, ABCA1, and SHBG

PubMed Central

Eren, Fatih; Agirbasli, Deniz; White, Marquitta J.; Williams, Scott M

2013-01-01

Abstract Cardiovascular risk factors and atherosclerosis precursors were examined in 365 Turkish children and adolescents. Study participants were recruited at five different state schools. We tested single and multi-locus effects of six polymorphisms from five candidate genes, chosen based on prior known association with lipid levels in adults, for association with low (≤10th percentile) high density lipoprotein cholesterol (HDL-C) and high (≥90th percentile) triglycerides (TG), and the related continuous outcomes. We observed an association between CETP variant rs708272 and low HDL-C (allelic p=0.020, genotypic p=0.046), which was supported by an independent analysis, PRAT (PRAT control p=0.027). Sex-stratified logistic regression analysis showed that the B2 allele of rs708272 decreased odds of being in the lower tenth percentile of HDL-C measurements (OR=0.36, p=0.02) in girls; this direction of effect was also seen in boys but was not significant (OR=0.64, p=0.21). Logistic regression analysis also revealed that the T allele of rs6257 (SHBG) decreased odds of being in the top tenth percentile of TG measurements in boys (OR=0.43, p=0.03). Analysis of lipid levels as a continuous trait revealed a significant association between rs708272 (CETP) and LDL-C levels in males (p=0.02) with the B2B2 genotype group having the lowest mean LDL-C; the same direction of effect was also seen in females (p=0.05). An effect was also seen between rs708272 and HDL-C levels in girls (p=0.01), with the B2B2 genotype having the highest mean HDL-C levels. Multi-locus analysis, using quantitative multifactor dimensionality reduction (qMDR) identified the previously mentioned CETP variant as the best single locus model, and overall model, for predicting HDL-C levels in children. This study provides evidence for association between CETP and low HDL-C phenotype in children, but the results appear to be weaker in children than previous results in adults and may also be subject to gender effects. PMID:23988150
Determinants of neonatal mortality in rural and urban Nigeria: Evidence from a population-based national survey.

PubMed

Adewuyi, Emmanuel O; Zhao, Yun

2017-02-01

Significant reduction in the global burden of neonatal mortality was achieved through the millennium development goals. In Nigeria, however, only a marginal reduction was realized. This study assesses the rural-urban differences in neonatal mortality rate (NMR) and the associated risk factors in Nigeria. The dataset from the 2013 Nigeria demographic and health survey (NDHS), disaggregated by rural-urban residence (n = 20 449 and 9935, respectively), was explored using univariate, bivariate, and multivariable analysis. Complex samples analysis was applied to adjust for the unequal selection probabilities due to the multi-stage cluster sampling method used in the 2013 NDHS. The adjusted relationship between the outcome and predictor variables was assessed on multi-level logistic regression analysis. NMR for rural and urban populations was 36 and 28 deaths per 1000 live births, respectively. Risk factors in urban residence were lack of electricity access (adjusted OR [AOR], 1.555; 95%CI: 1.089-2.220), small birth size (as a proxy for low birthweight; AOR, 3.048; 95%CI: 2.047-4.537), and male gender (AOR, 1.666; 95%CI: 1.215-2.284). Risk factors in rural residence were small birth size (a proxy for low birthweight; AOR, 2.118; 95%CI: 1.600-2.804), and birth interval <2 years (AOR, 2.149; 95%CI: 1.760-2.624). Cesarean delivery was a risk factor both in rural (AOR, 5.038; 95%CI: 2.617-9.700) and urban Nigeria (AOR, 2.632; 95%CI: 1.543-4.489). Determinants of neonatal mortality were different in rural and urban Nigeria, and rural neonates had greater risk of mortality than their urban counterparts. © 2016 Japan Pediatric Society.
A new methodology for sizing and performance predictions of a rotary wing ejector

NASA Astrophysics Data System (ADS)

Moodie, Alex Montfort

The application of an ejector nozzle integrated with a reaction drive rotor configuration for a vertical takeoff and landing rotorcraft is considered in this research. The ejector nozzle is a device that imparts energy from a high speed airflow source to a lower speed secondary airflow inside a duct. The overall nozzle exhaust mass flow rate is increased through fluid entrainment, while the exhaust gas velocity is simultaneously decreased. The exhaust gas velocity is strongly correlated to the jet noise produced by the nozzle, making the ejector a good candidate for propulsion system noise reduction. Ejector nozzles are mechanically simple in that there are no moving parts. However, coupled fluid dynamic processes are involved, complicating analysis and design. Geometric definitions of the ejector nozzle are determined through a reduced fidelity, multi-disciplinary, representation of the rotary wing ejector. The resulting rotary wing ejector geometric sizing procedure relates standard vehicle and rotor design parameters to the ejector. Additionally, a rotary wing ejector performance procedure is developed to compare this rotor configuration to a conventional rotor. Performance characteristics and aerodynamic effects of the rotor and ejector nozzle are analytically studied. Ejector nozzle performance, in terms of exit velocities, is compared to the primary reaction drive nozzle; giving an indication of the potential for noise reduction. Computational fluid dynamics are paramount in predicting the aerodynamic effects of the ejector nozzle located at the rotor blade tip. Two-dimensional, steady-state, Reynolds-averaged Navier-Stokes (RANS) models are implemented for sectional lift and drag predictions required for the rotor aerodynamic model associated with both the rotary wing ejector sizing and performance procedures. A three-dimensional, unsteady, RANS simulation of the rotary wing ejector is performed to study the aerodynamic interactions between the ejector nozzle and rotor. Overall performance comparisons are made between the two- and three-dimensional models of the rotary wing ejector, and a similar conventional rotor.
Predict subcellular locations of singleplex and multiplex proteins by semi-supervised learning and dimension-reducing general mode of Chou's PseAAC.

PubMed

Pacharawongsakda, Eakasit; Theeramunkong, Thanaruk

2013-12-01

Predicting protein subcellular location is one of major challenges in Bioinformatics area since such knowledge helps us understand protein functions and enables us to select the targeted proteins during drug discovery process. While many computational techniques have been proposed to improve predictive performance for protein subcellular location, they have several shortcomings. In this work, we propose a method to solve three main issues in such techniques; i) manipulation of multiplex proteins which may exist or move between multiple cellular compartments, ii) handling of high dimensionality in input and output spaces and iii) requirement of sufficient labeled data for model training. Towards these issues, this work presents a new computational method for predicting proteins which have either single or multiple locations. The proposed technique, namely iFLAST-CORE, incorporates the dimensionality reduction in the feature and label spaces with co-training paradigm for semi-supervised multi-label classification. For this purpose, the Singular Value Decomposition (SVD) is applied to transform the high-dimensional feature space and label space into the lower-dimensional spaces. After that, due to limitation of labeled data, the co-training regression makes use of unlabeled data by predicting the target values in the lower-dimensional spaces of unlabeled data. In the last step, the component of SVD is used to project labels in the lower-dimensional space back to those in the original space and an adaptive threshold is used to map a numeric value to a binary value for label determination. A set of experiments on viral proteins and gram-negative bacterial proteins evidence that our proposed method improve the classification performance in terms of various evaluation metrics such as Aiming (or Precision), Coverage (or Recall) and macro F-measure, compared to the traditional method that uses only labeled data.
Nonlinear dimensionality reduction of CT histogram based feature space for predicting recurrence-free survival in non-small-cell lung cancer

NASA Astrophysics Data System (ADS)

Kawata, Y.; Niki, N.; Ohmatsu, H.; Aokage, K.; Kusumoto, M.; Tsuchida, T.; Eguchi, K.; Kaneko, M.

2015-03-01

Advantages of CT scanners with high resolution have allowed the improved detection of lung cancers. In the recent release of positive results from the National Lung Screening Trial (NLST) in the US showing that CT screening does in fact have a positive impact on the reduction of lung cancer related mortality. While this study does show the efficacy of CT based screening, physicians often face the problems of deciding appropriate management strategies for maximizing patient survival and for preserving lung function. Several key manifold-learning approaches efficiently reveal intrinsic low-dimensional structures latent in high-dimensional data spaces. This study was performed to investigate whether the dimensionality reduction can identify embedded structures from the CT histogram feature of non-small-cell lung cancer (NSCLC) space to improve the performance in predicting the likelihood of RFS for patients with NSCLC.
TPSLVM: a dimensionality reduction algorithm based on thin plate splines.

PubMed

Jiang, Xinwei; Gao, Junbin; Wang, Tianjiang; Shi, Daming

2014-10-01

Dimensionality reduction (DR) has been considered as one of the most significant tools for data analysis. One type of DR algorithms is based on latent variable models (LVM). LVM-based models can handle the preimage problem easily. In this paper we propose a new LVM-based DR model, named thin plate spline latent variable model (TPSLVM). Compared to the well-known Gaussian process latent variable model (GPLVM), our proposed TPSLVM is more powerful especially when the dimensionality of the latent space is low. Also, TPSLVM is robust to shift and rotation. This paper investigates two extensions of TPSLVM, i.e., the back-constrained TPSLVM (BC-TPSLVM) and TPSLVM with dynamics (TPSLVM-DM) as well as their combination BC-TPSLVM-DM. Experimental results show that TPSLVM and its extensions provide better data visualization and more efficient dimensionality reduction compared to PCA, GPLVM, ISOMAP, etc.
Vibration and stress analysis of soft-bonded shuttle insulation tiles. Modal analysis with compact widely space stringers

NASA Technical Reports Server (NTRS)

Ojalvo, I. U.; Austin, F.; Levy, A.

1974-01-01

An efficient iterative procedure is described for the vibration and modal stress analysis of reusable surface insulation (RSI) of multi-tiled space shuttle panels. The method, which is quite general, is rapidly convergent and highly useful for this application. A user-oriented computer program based upon this procedure and titled RESIST (REusable Surface Insulation Stresses) has been prepared for the analysis of compact, widely spaced, stringer-stiffened panels. RESIST, which uses finite element methods, obtains three dimensional tile stresses in the isolator, arrestor (if any) and RSI materials. Two dimensional stresses are obtained in the tile coating and the stringer-stiffened primary structure plate. A special feature of the program is that all the usual detailed finite element grid data is generated internally from a minimum of input data. The program can accommodate tile idealizations with up to 850 nodes (2550 degrees-of-freedom) and primary structure idealizations with a maximum of 10,000 degrees-of-freedom. The primary structure vibration capability is achieved through the development of a new rapid eigenvalue program named ALARM (Automatic LArge Reduction of Matrices to tridiagonal form).
Raman and Conductivity Analysis of Graphene for Biomedical Applications

PubMed Central

Qiu, Chao; Bennet, Kevin E.; Khan, Tamanna; Ciubuc, John D.; Manciu, Felicia S.

2016-01-01

In this study, we present a comprehensive investigation of graphene’s optical and conductive properties using confocal Raman and a Drude model. A comparative analysis between experimental findings and theoretical predictions of the material’s changes and improvements as it transitioned from three-dimensional graphite is also presented and discussed. Besides spectral recording by Raman, which reveals whether there is a single, a few, or multi-layers of graphene, the confocal Raman mapping allows for distinction of such domains and a direct visualization of material inhomogeneity. Drude model employment in the analysis of the far-infrared transmittance measurements demonstrates a distinct increase of the material’s conductivity with dimensionality reduction. Other particularly important material characteristics, including carrier concentration and time constant, were also determined using this model and presented here. Furthermore, the detection of micromolar concentration of dopamine on graphene surfaces not only proves that the Raman technique facilitates ultrasensitive chemical detection of analytes, besides offering high information content about the biomaterial under study, but also that carbon-based materials are biocompatible and favorable micro-environments for such detection. Such information is valuable for the development of bio-medical sensors, which is the main application envisioned for this analysis. PMID:28774016
A Three-Dimensional Coupled Internal/External Simulation of a Film-Cooled Turbine Vane

NASA Technical Reports Server (NTRS)

Heidmann, James D.; Rigby, David L.; Ameri, Ali A.

1999-01-01

A three-dimensional Navier-Stokes simulation has been performed for a realistic film-cooled turbine vane using the LeRC-HT code. The simulation includes the flow regions inside the coolant plena and film cooling holes in addition to the external flow. The vane is the subject of an upcoming NASA Glenn Research Center experiment and has both circular cross-section and shaped film cooling holes. This complex geometry is modeled using a multi-block grid which accurately discretizes the actual vane geometry including shaped holes. The simulation matches operating conditions for the planned experiment and assumes periodicity in the spanwise direction on the scale of one pitch of the film cooling hole pattern. Two computations were performed for different isothermal wall temperatures, allowing independent determination of heat transfer coefficients and film effectiveness values. The results indicate separate localized regions of high heat transfer coefficient values, while the shaped holes provide a reduction in heat flux through both parameters. Hole exit data indicate rather simple skewed profiles for the round holes, but complex profiles for the shaped holes with mass fluxes skewed strongly toward their leading edges.
Flexible robot control: Modeling and experiments

NASA Technical Reports Server (NTRS)

Oppenheim, Irving J.; Shimoyama, Isao

1989-01-01

Described here is a model and its use in experimental studies of flexible manipulators. The analytical model uses the equivalent of Rayleigh's method to approximate the displaced shape of a flexible link as the static elastic displacement which would occur under end rotations as applied at the joints. The generalized coordinates are thereby expressly compatible with joint motions and rotations in serial link manipulators, because the amplitude variables are simply the end rotations between the flexible link and the chord connecting the end points. The equations for the system dynamics are quite simple and can readily be formulated for the multi-link, three-dimensional case. When the flexible links possess mass and (polar moment of) inertia which are small compared to the concentrated mass and inertia at the joints, the analytical model is exact and displays the additional advantage of reduction in system dimension for the governing equations. Four series of pilot tests have been completed. Studies on a planar single-link system were conducted at Carnegie-Mellon University, and tests conducted at Toshiba Corporation on a planar two-link system were then incorporated into the study. A single link system under three-dimensional motion, displaying biaxial flexure, was then tested at Carnegie-Mellon.
Multi-Scale Analyses of Three Dimensional Woven Composite 3D Shell With a Cut Out Circle

NASA Astrophysics Data System (ADS)

Nguyen, Duc Hai; Wang, Hu

2018-06-01

A composite material are made by combining two or more constituent materials to obtain the desired material properties of each product type. The matrix material which can be polymer and fiber is used as reinforcing material. Currently, the polymer matrix is widely used in many different fields with differently designed structures such as automotive structures and aviation, aerospace, marine, etc. because of their excellent mechanical properties; in addition, they possess the high level of hardness and durability together with a significant reduction in weight compared to traditional materials. However, during design process of structure, there will be many interruptions created for the purpose of assembling the structures together or for many other design purposes. Therefore, when this structure is subject to load-bearing, its failure occurs at these interruptions due to stress concentration. This paper proposes multi-scale modeling and optimization strategies in evaluation of the effectiveness of fiber orientation in an E-glass/Epoxy woven composite 3D shell with circular holes at the center investigated by FEA results. A multi-scale model approach was developed to predict the mechanical behavior of woven composite 3D shell with circular holes at the center with different designs of material and structural parameters. Based on the analysis result of laminae, we have found that the 3D shell with fiber direction of 450 shows the best stress and strain bearing capacity. Thus combining several layers of 450 fiber direction in a multi-layer composite 3D shell reduces the stresses concentrated on the cuts of the structures.
Simplified method for the transverse bending analysis of twin celled concrete box girder bridges

NASA Astrophysics Data System (ADS)

Chithra, J.; Nagarajan, Praveen; S, Sajith A.

2018-03-01

Box girder bridges are one of the best options for bridges with span more than 25 m. For the study of these bridges, three-dimensional finite element analysis is the best suited method. However, performing three-dimensional analysis for routine design is difficult as well as time consuming. Also, software used for the three-dimensional analysis are very expensive. Hence designers resort to simplified analysis for predicting longitudinal and transverse bending moments. Among the many analytical methods used to find the transverse bending moments, SFA is the simplest and widely used in design offices. Results from simplified frame analysis can be used for the preliminary analysis of the concrete box girder bridges.From the review of literatures, it is found that majority of the work done using SFA is restricted to the analysis of single cell box girder bridges. Not much work has been done on the analysis multi-cell concrete box girder bridges. In this present study, a double cell concrete box girder bridge is chosen. The bridge is modelled using three- dimensional finite element software and the results are then compared with the simplified frame analysis. The study mainly focuses on establishing correction factors for transverse bending moment values obtained from SFA.

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Sample records for multi-factor dimensionality reduction

Abstract