Hierarchical virtual screening approaches in small molecule drug discovery.

PubMed

Kumar, Ashutosh; Zhang, Kam Y J

2015-01-01

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery. Copyright © 2014 Elsevier Inc. All rights reserved.

Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

NASA Astrophysics Data System (ADS)

Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

2014-03-01

This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

Synchronization of Hierarchical Time-Varying Neural Networks Based on Asynchronous and Intermittent Sampled-Data Control.

PubMed

Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing

In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

When mechanism matters: Bayesian forecasting using models of ecological diffusion

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

2017-01-01

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

Wavelet-based hierarchical surface approximation from height fields

Treesearch

Sang-Mook Lee; A. Lynn Abbott; Daniel L. Schmoldt

2004-01-01

This paper presents a novel hierarchical approach to triangular mesh generation from height fields. A wavelet-based multiresolution analysis technique is used to estimate local shape information at different levels of resolution. Using predefined templates at the coarsest level, the method constructs an initial triangulation in which underlying object shapes are well...

Meshfree truncated hierarchical refinement for isogeometric analysis

NASA Astrophysics Data System (ADS)

Atri, H. R.; Shojaee, S.

2018-05-01

In this paper truncated hierarchical B-spline (THB-spline) is coupled with reproducing kernel particle method (RKPM) to blend advantages of the isogeometric analysis and meshfree methods. Since under certain conditions, the isogeometric B-spline and NURBS basis functions are exactly represented by reproducing kernel meshfree shape functions, recursive process of producing isogeometric bases can be omitted. More importantly, a seamless link between meshfree methods and isogeometric analysis can be easily defined which provide an authentic meshfree approach to refine the model locally in isogeometric analysis. This procedure can be accomplished using truncated hierarchical B-splines to construct new bases and adaptively refine them. It is also shown that the THB-RKPM method can provide efficient approximation schemes for numerical simulations and represent a promising performance in adaptive refinement of partial differential equations via isogeometric analysis. The proposed approach for adaptive locally refinement is presented in detail and its effectiveness is investigated through well-known benchmark examples.

The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology

NASA Astrophysics Data System (ADS)

Timchenko, Leonid; Yarovyi, Andrii; Kokriatskaya, Nataliya; Nakonechna, Svitlana; Abramenko, Ludmila; Ławicki, Tomasz; Popiel, Piotr; Yesmakhanova, Laura

2016-09-01

The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

A Hierarchical Clustering Methodology for the Estimation of Toxicity

EPA Science Inventory

A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural sim...

Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

NASA Astrophysics Data System (ADS)

Alshehhi, Rasha; Marpu, Prashanth Reddy

2017-04-01

Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

Band-gap analysis of a novel lattice with a hierarchical periodicity using the spectral element method

NASA Astrophysics Data System (ADS)

Wu, Zhijing; Li, Fengming; Zhang, Chuanzeng

2018-05-01

Inspired by the hierarchical structures of butterfly wing surfaces, a new kind of lattice structures with a two-order hierarchical periodicity is proposed and designed, and the band-gap properties are investigated by the spectral element method (SEM). The equations of motion of the whole structure are established considering the macro and micro periodicities of the system. The efficiency of the SEM is exploited in the modeling process and validated by comparing the results with that of the finite element method (FEM). Based on the highly accurate results in the frequency domain, the dynamic behaviors of the proposed two-order hierarchical structures are analyzed. An original and interesting finding is the existence of the distinct macro and micro stop-bands in the given frequency domain. The mechanisms for these two types of band-gaps are also explored. Finally, the relations between the hierarchical periodicities and the different types of the stop-bands are investigated by analyzing the parametrical influences.

Using GOMS models and hypertext to create representations of medical procedures for online display

NASA Technical Reports Server (NTRS)

Gugerty, Leo; Halgren, Shannon; Gosbee, John; Rudisill, Marianne

1991-01-01

This study investigated two methods to improve organization and presentation of computer-based medical procedures. A literature review suggested that the GOMS (goals, operators, methods, and selecton rules) model can assist in rigorous task analysis, which can then help generate initial design ideas for the human-computer interface. GOMS model are hierarchical in nature, so this study also investigated the effect of hierarchical, hypertext interfaces. We used a 2 x 2 between subjects design, including the following independent variables: procedure organization - GOMS model based vs. medical-textbook based; navigation type - hierarchical vs. linear (booklike). After naive subjects studies the online procedures, measures were taken of their memory for the content and the organization of the procedures. This design was repeated for two medical procedures. For one procedure, subjects who studied GOMS-based and hierarchical procedures remembered more about the procedures than other subjects. The results for the other procedure were less clear. However, data for both procedures showed a 'GOMSification effect'. That is, when asked to do a free recall of a procedure, subjects who had studies a textbook procedure often recalled key information in a location inconsistent with the procedure they actually studied, but consistent with the GOMS-based procedure.

Fast Low-Rank Bayesian Matrix Completion With Hierarchical Gaussian Prior Models

NASA Astrophysics Data System (ADS)

Yang, Linxiao; Fang, Jun; Duan, Huiping; Li, Hongbin; Zeng, Bing

2018-06-01

The problem of low rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rank matrix are assumed to follow a Gaussian distribution with zero mean and a common precision matrix, and a Wishart distribution is specified as a hyperprior over the precision matrix. We show that such a hierarchical Gaussian prior has the potential to encourage a low-rank solution. Based on the proposed hierarchical prior model, a variational Bayesian method is developed for matrix completion, where the generalized approximate massage passing (GAMP) technique is embedded into the variational Bayesian inference in order to circumvent cumbersome matrix inverse operations. Simulation results show that our proposed method demonstrates superiority over existing state-of-the-art matrix completion methods.

Hierarchical Co(OH)2 nanostructures/glassy carbon electrode derived from Co(BTC) metal-organic frameworks for glucose sensing

NASA Astrophysics Data System (ADS)

He, Juan; Lu, Xingping; Yu, Jie; Wang, Li; Song, Yonghai

2016-07-01

A novel Co(OH)2/glassy carbon electrode (GCE) has been fabricated via metal-organic framework (MOF)-directed method. In the strategy, the Co(BTC, 1,3,5-benzentricarboxylic acid) MOFs/GCE was firstly prepared by alternately immersing GCE in Co2+ and BTC solution based on a layer-by-layer method. And then, the Co(OH)2 with hierarchical flake nanostructure/GCE was constructed by immersing Co(BTC) MOFs/GCE into 0.1 M NaOH solution at room temperature. Such strategy improves the distribution of hierarchical Co(OH)2 nanostructures on electrode surface greatly, enhances the stability of nanomaterials on the electrode surface, and increases the use efficiency of the Co(OH)2 nanostructures. Scanning electron microscopy, energy dispersive X-ray spectroscopy, X-ray powder diffraction, energy dispersive spectroscopy, Fourier transform infrared spectroscopy, and Raman spectra were used to characterize the Co(BTC) MOFs/GCE and Co(OH)2/GCE. Based on the hierarchical Co(OH)2 nanostructures/GCE, a novel and sensitive nonenzymatic glucose sensor was developed. The good performance of the resulted sensor toward the detection of glucose was ascribed to hierarchical flake nanostructures, good mechanical stability, excellent distribution, and large specific surface area of Co(OH)2 nanostructures. The proposed preparation method is simple, efficient, and cheap .

Image Search Reranking With Hierarchical Topic Awareness.

PubMed

Tian, Xinmei; Yang, Linjun; Lu, Yijuan; Tian, Qi; Tao, Dacheng

2015-10-01

With much attention from both academia and industrial communities, visual search reranking has recently been proposed to refine image search results obtained from text-based image search engines. Most of the traditional reranking methods cannot capture both relevance and diversity of the search results at the same time. Or they ignore the hierarchical topic structure of search result. Each topic is treated equally and independently. However, in real applications, images returned for certain queries are naturally in hierarchical organization, rather than simple parallel relation. In this paper, a new reranking method "topic-aware reranking (TARerank)" is proposed. TARerank describes the hierarchical topic structure of search results in one model, and seamlessly captures both relevance and diversity of the image search results simultaneously. Through a structured learning framework, relevance and diversity are modeled in TARerank by a set of carefully designed features, and then the model is learned from human-labeled training samples. The learned model is expected to predict reranking results with high relevance and diversity for testing queries. To verify the effectiveness of the proposed method, we collect an image search dataset and conduct comparison experiments on it. The experimental results demonstrate that the proposed TARerank outperforms the existing relevance-based and diversified reranking methods.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

PubMed

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-08-16

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

PubMed Central

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-01-01

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888

A new hierarchical method to find community structure in networks

NASA Astrophysics Data System (ADS)

Saoud, Bilal; Moussaoui, Abdelouahab

2018-04-01

Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

Hierarchical optimization for neutron scattering problems

DOE PAGES

Bao, Feng; Archibald, Rick; Bansal, Dipanshu; ...

2016-03-14

In this study, we present a scalable optimization method for neutron scattering problems that determines confidence regions of simulation parameters in lattice dynamics models used to fit neutron scattering data for crystalline solids. The method uses physics-based hierarchical dimension reduction in both the computational simulation domain and the parameter space. We demonstrate for silicon that after a few iterations the method converges to parameters values (interatomic force-constants) computed with density functional theory simulations.

Hierarchical optimization for neutron scattering problems

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

Bao, Feng; Archibald, Rick; Bansal, Dipanshu

In this study, we present a scalable optimization method for neutron scattering problems that determines confidence regions of simulation parameters in lattice dynamics models used to fit neutron scattering data for crystalline solids. The method uses physics-based hierarchical dimension reduction in both the computational simulation domain and the parameter space. We demonstrate for silicon that after a few iterations the method converges to parameters values (interatomic force-constants) computed with density functional theory simulations.

Hierarchical trie packet classification algorithm based on expectation-maximization clustering.

PubMed

Bi, Xia-An; Zhao, Junxia

2017-01-01

With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes. This paper proposes a new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC). Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering. Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm.

A facile approach toward transition metal oxide hierarchical structures and their lithium storage properties.

PubMed

Zhang, Cuimiao; Chen, Jing; Zeng, Yi; Rui, Xianhong; Zhu, Jixin; Zhang, Wenyu; Xu, Chen; Lim, Tuti Mariana; Hng, Huey Hoon; Yan, Qingyu

2012-06-21

A simple, non-template, non-surfactant and environmentally friendly hydrothermal method is presented based on the controlled release of the reactants into the reaction solvents to induce slow nucleation and growth of three-dimensional hierarchical nanostructures of transition metal oxides. This method is a general approach, which can be used to prepare Co(3)O(4), CuO, and Ni(OH)(2)/NiO. These metal oxides with hierarchical nanostructures can be used as anode materials for lithium-ion batteries with good Li storage performance, e.g. high specific capacities and stable cyclability.

An adaptive sampling method for variable-fidelity surrogate models using improved hierarchical kriging

NASA Astrophysics Data System (ADS)

Hu, Jiexiang; Zhou, Qi; Jiang, Ping; Shao, Xinyu; Xie, Tingli

2018-01-01

Variable-fidelity (VF) modelling methods have been widely used in complex engineering system design to mitigate the computational burden. Building a VF model generally includes two parts: design of experiments and metamodel construction. In this article, an adaptive sampling method based on improved hierarchical kriging (ASM-IHK) is proposed to refine the improved VF model. First, an improved hierarchical kriging model is developed as the metamodel, in which the low-fidelity model is varied through a polynomial response surface function to capture the characteristics of a high-fidelity model. Secondly, to reduce local approximation errors, an active learning strategy based on a sequential sampling method is introduced to make full use of the already required information on the current sampling points and to guide the sampling process of the high-fidelity model. Finally, two numerical examples and the modelling of the aerodynamic coefficient for an aircraft are provided to demonstrate the approximation capability of the proposed approach, as well as three other metamodelling methods and two sequential sampling methods. The results show that ASM-IHK provides a more accurate metamodel at the same simulation cost, which is very important in metamodel-based engineering design problems.

Models and Methods of Aggregating Linguistic Information in Multi-criteria Hierarchical Quality Assessment Systems

NASA Astrophysics Data System (ADS)

Azarnova, T. V.; Titova, I. A.; Barkalov, S. A.

2018-03-01

The article presents an algorithm for obtaining an integral assessment of the quality of an organization from the perspective of customers, based on the method of aggregating linguistic information on a multilevel hierarchical system of quality assessment. The algorithm is of a constructive nature, it provides not only the possibility of obtaining an integral evaluation, but also the development of a quality improvement strategy based on the method of linguistic decomposition, which forms the minimum set of areas of work with clients whose quality change will allow obtaining the required level of integrated quality assessment.

Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

2011-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

A new anisotropic mesh adaptation method based upon hierarchical a posteriori error estimates

NASA Astrophysics Data System (ADS)

Huang, Weizhang; Kamenski, Lennard; Lang, Jens

2010-03-01

A new anisotropic mesh adaptation strategy for finite element solution of elliptic differential equations is presented. It generates anisotropic adaptive meshes as quasi-uniform ones in some metric space, with the metric tensor being computed based on hierarchical a posteriori error estimates. A global hierarchical error estimate is employed in this study to obtain reliable directional information of the solution. Instead of solving the global error problem exactly, which is costly in general, we solve it iteratively using the symmetric Gauß-Seidel method. Numerical results show that a few GS iterations are sufficient for obtaining a reasonably good approximation to the error for use in anisotropic mesh adaptation. The new method is compared with several strategies using local error estimators or recovered Hessians. Numerical results are presented for a selection of test examples and a mathematical model for heat conduction in a thermal battery with large orthotropic jumps in the material coefficients.

A Simulation Study of Methods for Selecting Subgroup-Specific Doses in Phase I Trials

PubMed Central

Morita, Satoshi; Thall, Peter F.; Takeda, Kentaro

2016-01-01

Summary Patient heterogeneity may complicate dose-finding in phase I clinical trials if the dose-toxicity curves differ between subgroups. Conducting separate trials within subgroups may lead to infeasibly small sample sizes in subgroups having low prevalence. Alternatively, it is not obvious how to conduct a single trial while accounting for heterogeneity. To address this problem, we consider a generalization of the continual reassessment method (O’Quigley, et al., 1990) based on a hierarchical Bayesian dose-toxicity model that borrows strength between subgroups under the assumption that the subgroups are exchangeable. We evaluate a design using this model that includes subgroup-specific dose selection and safety rules. A simulation study is presented that includes comparison of this method to three alternative approaches, based on non-hierarchical models, that make different types of assumptions about within-subgroup dose-toxicity curves. The simulations show that the hierarchical model-based method is recommended in settings where the dose-toxicity curves are exchangeable between subgroups. We present practical guidelines for application, and provide computer programs for trial simulation and conduct. PMID:28111916

Three-dimensional information hierarchical encryption based on computer-generated holograms

NASA Astrophysics Data System (ADS)

Kong, Dezhao; Shen, Xueju; Cao, Liangcai; Zhang, Hao; Zong, Song; Jin, Guofan

2016-12-01

A novel approach for encrypting three-dimensional (3-D) scene information hierarchically based on computer-generated holograms (CGHs) is proposed. The CGHs of the layer-oriented 3-D scene information are produced by angular-spectrum propagation algorithm at different depths. All the CGHs are then modulated by different chaotic random phase masks generated by the logistic map. Hierarchical encryption encoding is applied when all the CGHs are accumulated one by one, and the reconstructed volume of the 3-D scene information depends on permissions of different users. The chaotic random phase masks could be encoded into several parameters of the chaotic sequences to simplify the transmission and preservation of the keys. Optical experiments verify the proposed method and numerical simulations show the high key sensitivity, high security, and application flexibility of the method.

Hydrothermal Fabrication of WO3 Hierarchical Architectures: Structure, Growth and Response

PubMed Central

Wu, Chuan-Sheng

2015-01-01

Recently hierarchical architectures, consisting of two-dimensional (2D) nanostructures, are of great interest for potential applications in energy and environmental. Here, novel rose-like WO3 hierarchical architectures were successfully synthesized via a facile hydrothermal method. The as-prepared WO3 hierarchical architectures were in fact assembled by numerous nanosheets with an average thickness of ~30 nm. We found that the oxalic acid played a significant role in governing morphologies of WO3 during hydrothermal process. Based on comparative studies, a possible formation mechanism was also proposed in detail. Furthermore, gas-sensing measurement showed that the well-defined 3D WO3 hierarchical architectures exhibited the excellent gas sensing properties towards CO. PMID:28347062

Multi person detection and tracking based on hierarchical level-set method

NASA Astrophysics Data System (ADS)

Khraief, Chadia; Benzarti, Faouzi; Amiri, Hamid

2018-04-01

In this paper, we propose an efficient unsupervised method for mutli-person tracking based on hierarchical level-set approach. The proposed method uses both edge and region information in order to effectively detect objects. The persons are tracked on each frame of the sequence by minimizing an energy functional that combines color, texture and shape information. These features are enrolled in covariance matrix as region descriptor. The present method is fully automated without the need to manually specify the initial contour of Level-set. It is based on combined person detection and background subtraction methods. The edge-based is employed to maintain a stable evolution, guide the segmentation towards apparent boundaries and inhibit regions fusion. The computational cost of level-set is reduced by using narrow band technique. Many experimental results are performed on challenging video sequences and show the effectiveness of the proposed method.

A new fast direct solver for the boundary element method

NASA Astrophysics Data System (ADS)

Huang, S.; Liu, Y. J.

2017-09-01

A new fast direct linear equation solver for the boundary element method (BEM) is presented in this paper. The idea of the new fast direct solver stems from the concept of the hierarchical off-diagonal low-rank matrix. The hierarchical off-diagonal low-rank matrix can be decomposed into the multiplication of several diagonal block matrices. The inverse of the hierarchical off-diagonal low-rank matrix can be calculated efficiently with the Sherman-Morrison-Woodbury formula. In this paper, a more general and efficient approach to approximate the coefficient matrix of the BEM with the hierarchical off-diagonal low-rank matrix is proposed. Compared to the current fast direct solver based on the hierarchical off-diagonal low-rank matrix, the proposed method is suitable for solving general 3-D boundary element models. Several numerical examples of 3-D potential problems with the total number of unknowns up to above 200,000 are presented. The results show that the new fast direct solver can be applied to solve large 3-D BEM models accurately and with better efficiency compared with the conventional BEM.

Segmentation of knee cartilage by using a hierarchical active shape model based on multi-resolution transforms in magnetic resonance images

NASA Astrophysics Data System (ADS)

León, Madeleine; Escalante-Ramirez, Boris

2013-11-01

Knee osteoarthritis (OA) is characterized by the morphological degeneration of cartilage. Efficient segmentation of cartilage is important for cartilage damage diagnosis and to support therapeutic responses. We present a method for knee cartilage segmentation in magnetic resonance images (MRI). Our method incorporates the Hermite Transform to obtain a hierarchical decomposition of contours which describe knee cartilage shapes. Then, we compute a statistical model of the contour of interest from a set of training images. Thereby, our Hierarchical Active Shape Model (HASM) captures a large range of shape variability even from a small group of training samples, improving segmentation accuracy. The method was trained with a training set of 16- MRI of knee and tested with leave-one-out method.

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

Wang, Yong; State Key Laboratory of Multiphase Complex System, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080; Zhu, Qingshan, E-mail: qszhu@home.ipe.ac.cn

{beta}-Ni(OH){sub 2} hierarchical micro-flowers, hierarchical hollow microspheres and nanosheets were synthesized via a facile, single-step and selected-control hydrothermal method. Both hierarchical micro-flowers and hierarchical hollow microspheres were built from two-dimensional nanosheets with thickness of 50-100 nm. The as-obtained products were characterized by Brunauer-Emmett-Teller (BET) surface area analysis, X-ray powder diffraction (XRD) and field emission scanning electron microscopy (FESEM). It was observed that marked morphological changes in {beta}-Ni(OH){sub 2} depended on the initial concentrations of Ni{sup 2+} ions and glycine. A possible growth mechanism was proposed based on experimental results. In addition, the effect of morphology on the electrochemical properties wasmore » also investigated. Both hierarchical micro-flowers and hierarchical hollow microspheres exhibited enhanced specific capacity and high-rate discharge ability as compared with pure Ni(OH){sub 2} nanosheets. Investigations confirmed that hierarchical structures had a pronounced influence upon the electrochemical performance of nickel hydroxide.« less

An Investigation of Possible Hierarchical Dependency of Four Piaget-Type Tasks under Two Methods of Presentation to Third-, Fifth-, and Seventh-Grade Children.

ERIC Educational Resources Information Center

Phillips, Darrell Gordon

The purpose of this study was to investigate a proposed model for the acquisition of the concept of displacement volume and to compare two methods of conservation task presentation. A 12-stage hierarchical model for the acquisition of the concept was proposed, based on four primary assumptions: (1) concept attainment can be measured by…

Personalized Medicine in Veterans with Traumatic Brain Injuries

DTIC Science & Technology

2014-07-01

9 control cases are subjected to unsupervised hierarchical clustering analysis using the UPGMA algorithm with cosine correlation as the similarity...in unsu- pervised hierarchical clustering by the Un- weighted Pair-Group Method using Arithmetic averages ( UPGMA ) based on cosine correlation of row...of log2 trans- formed MAS5.0 signal values; probe set cluster- ing was performed by the UPGMA method using Cosine correlation as the similarity

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

NASA Technical Reports Server (NTRS)

Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

2012-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

Hierarchical Bayesian Models of Subtask Learning

ERIC Educational Resources Information Center

Anglim, Jeromy; Wynton, Sarah K. A.

2015-01-01

The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking…

Hierarchical trie packet classification algorithm based on expectation-maximization clustering

PubMed Central

Bi, Xia-an; Zhao, Junxia

2017-01-01

With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes. This paper proposes a new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC). Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering. Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm. PMID:28704476

Hierarchical clustering method for improved prostate cancer imaging in diffuse optical tomography

NASA Astrophysics Data System (ADS)

Kavuri, Venkaiah C.; Liu, Hanli

2013-03-01

We investigate the feasibility of trans-rectal near infrared (NIR) based diffuse optical tomography (DOT) for early detection of prostate cancer using a transrectal ultrasound (TRUS) compatible imaging probe. For this purpose, we designed a TRUS-compatible, NIR-based image system (780nm), in which the photo diodes were placed on the trans-rectal probe. DC signals were recorded and used for estimating the absorption coefficient. We validated the system using laboratory phantoms. For further improvement, we also developed a hierarchical clustering method (HCM) to improve the accuracy of image reconstruction with limited prior information. We demonstrated the method using computer simulations laboratory phantom experiments.

Decomposition and extraction: a new framework for visual classification.

PubMed

Fang, Yuqiang; Chen, Qiang; Sun, Lin; Dai, Bin; Yan, Shuicheng

2014-08-01

In this paper, we present a novel framework for visual classification based on hierarchical image decomposition and hybrid midlevel feature extraction. Unlike most midlevel feature learning methods, which focus on the process of coding or pooling, we emphasize that the mechanism of image composition also strongly influences the feature extraction. To effectively explore the image content for the feature extraction, we model a multiplicity feature representation mechanism through meaningful hierarchical image decomposition followed by a fusion step. In particularly, we first propose a new hierarchical image decomposition approach in which each image is decomposed into a series of hierarchical semantical components, i.e, the structure and texture images. Then, different feature extraction schemes can be adopted to match the decomposed structure and texture processes in a dissociative manner. Here, two schemes are explored to produce property related feature representations. One is based on a single-stage network over hand-crafted features and the other is based on a multistage network, which can learn features from raw pixels automatically. Finally, those multiple midlevel features are incorporated by solving a multiple kernel learning task. Extensive experiments are conducted on several challenging data sets for visual classification, and experimental results demonstrate the effectiveness of the proposed method.

Bayesian hierarchical functional data analysis via contaminated informative priors.

PubMed

Scarpa, Bruno; Dunson, David B

2009-09-01

A variety of flexible approaches have been proposed for functional data analysis, allowing both the mean curve and the distribution about the mean to be unknown. Such methods are most useful when there is limited prior information. Motivated by applications to modeling of temperature curves in the menstrual cycle, this article proposes a flexible approach for incorporating prior information in semiparametric Bayesian analyses of hierarchical functional data. The proposed approach is based on specifying the distribution of functions as a mixture of a parametric hierarchical model and a nonparametric contamination. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. In the motivating application, the contamination component allows unanticipated curve shapes in unhealthy menstrual cycles. Methods are developed for posterior computation, and the approach is applied to data from a European fecundability study.

The Partition of Multi-Resolution LOD Based on Qtm

NASA Astrophysics Data System (ADS)

Hou, M.-L.; Xing, H.-Q.; Zhao, X.-S.; Chen, J.

2011-08-01

The partition hierarch of Quaternary Triangular Mesh (QTM) determine the accuracy of spatial analysis and application based on QTM. In order to resolve the problem that the partition hierarch of QTM is limited by the level of the computer hardware, the new method that Multi- Resolution LOD (Level of Details) based on QTM will be discussed in this paper. This method can make the resolution of the cells varying with the viewpoint position by partitioning the cells of QTM, selecting the particular area according to the viewpoint; dealing with the cracks caused by different subdivisions, it satisfies the request of unlimited partition in part.

Delineating the joint hierarchical structure of clinical and personality disorders in an outpatient psychiatric sample.

PubMed

Forbes, Miriam K; Kotov, Roman; Ruggero, Camilo J; Watson, David; Zimmerman, Mark; Krueger, Robert F

2017-11-01

A large body of research has focused on identifying the optimal number of dimensions - or spectra - to model individual differences in psychopathology. Recently, it has become increasingly clear that ostensibly competing models with varying numbers of spectra can be synthesized in empirically derived hierarchical structures. We examined the convergence between top-down (bass-ackwards or sequential principal components analysis) and bottom-up (hierarchical agglomerative cluster analysis) statistical methods for elucidating hierarchies to explicate the joint hierarchical structure of clinical and personality disorders. Analyses examined 24 clinical and personality disorders based on semi-structured clinical interviews in an outpatient psychiatric sample (n=2900). The two methods of hierarchical analysis converged on a three-tier joint hierarchy of psychopathology. At the lowest tier, there were seven spectra - disinhibition, antagonism, core thought disorder, detachment, core internalizing, somatoform, and compulsivity - that emerged in both methods. These spectra were nested under the same three higher-order superspectra in both methods: externalizing, broad thought dysfunction, and broad internalizing. In turn, these three superspectra were nested under a single general psychopathology spectrum, which represented the top tier of the hierarchical structure. The hierarchical structure mirrors and extends upon past research, with the inclusion of a novel compulsivity spectrum, and the finding that psychopathology is organized in three superordinate domains. This hierarchy can thus be used as a flexible and integrative framework to facilitate psychopathology research with varying levels of specificity (i.e., focusing on the optimal level of detailed information, rather than the optimal number of factors). Copyright © 2017 Elsevier Inc. All rights reserved.

Down-Regulation of Olfactory Receptors in Response to Traumatic Brain Injury Promotes Risk for Alzheimers Disease

DTIC Science & Technology

2015-12-01

group assignment of samples in unsupervised hierarchical clustering by the Unweighted Pair-Group Method using Arithmetic averages ( UPGMA ) based on...log2 transformed MAS5.0 signal values; probe set clustering was performed by the UPGMA method using Cosine correlation as the similarity met- ric. For...differentially-regulated genes identified were subjected to unsupervised hierarchical clustering analysis using the UPGMA algorithm with cosine correlation as

Chaotic Signal Denoising Based on Hierarchical Threshold Synchrosqueezed Wavelet Transform

NASA Astrophysics Data System (ADS)

Wang, Wen-Bo; Jing, Yun-yu; Zhao, Yan-chao; Zhang, Lian-Hua; Wang, Xiang-Li

2017-12-01

In order to overcoming the shortcoming of single threshold synchrosqueezed wavelet transform(SWT) denoising method, an adaptive hierarchical threshold SWT chaotic signal denoising method is proposed. Firstly, a new SWT threshold function is constructed based on Stein unbiased risk estimation, which is two order continuous derivable. Then, by using of the new threshold function, a threshold process based on the minimum mean square error was implemented, and the optimal estimation value of each layer threshold in SWT chaotic denoising is obtained. The experimental results of the simulating chaotic signal and measured sunspot signals show that, the proposed method can filter the noise of chaotic signal well, and the intrinsic chaotic characteristic of the original signal can be recovered very well. Compared with the EEMD denoising method and the single threshold SWT denoising method, the proposed method can obtain better denoising result for the chaotic signal.

Superhydrophobic Surface Based on a Coral-Like Hierarchical Structure of ZnO

PubMed Central

Wu, Jun; Xia, Jun; Lei, Wei; Wang, Baoping

2010-01-01

Background Fabrication of superhydrophobic surfaces has attracted much interest in the past decade. The fabrication methods that have been studied are chemical vapour deposition, the sol-gel method, etching technique, electrochemical deposition, the layer-by-layer deposition, and so on. Simple and inexpensive methods for manufacturing environmentally stable superhydrophobic surfaces have also been proposed lately. However, work referring to the influence of special structures on the wettability, such as hierarchical ZnO nanostructures, is rare. Methodology This study presents a simple and reproducible method to fabricate a superhydrophobic surface with micro-scale roughness based on zinc oxide (ZnO) hierarchical structure, which is grown by the hydrothermal method with an alkaline aqueous solution. Coral-like structures of ZnO were fabricated on a glass substrate with a micro-scale roughness, while the antennas of the coral formed the nano-scale roughness. The fresh ZnO films exhibited excellent superhydrophilicity (the apparent contact angle for water droplet was about 0°), while the ability to be wet could be changed to superhydrophobicity after spin-coating Teflon (the apparent contact angle greater than 168°). The procedure reported here can be applied to substrates consisting of other materials and having various shapes. Results The new process is convenient and environmentally friendly compared to conventional methods. Furthermore, the hierarchical structure generates the extraordinary solid/gas/liquid three-phase contact interface, which is the essential characteristic for a superhydrophobic surface. PMID:21209931

A hierarchical clustering methodology for the estimation of toxicity.

PubMed

Martin, Todd M; Harten, Paul; Venkatapathy, Raghuraman; Das, Shashikala; Young, Douglas M

2008-01-01

ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm-based technique is used to generate statistically valid QSAR models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using a Tetrahymena pyriformis acute toxicity data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results from the hierarchical clustering methodology were compared to the results from several different QSAR methodologies.

3D reconstruction from non-uniform point clouds via local hierarchical clustering

NASA Astrophysics Data System (ADS)

Yang, Jiaqi; Li, Ruibo; Xiao, Yang; Cao, Zhiguo

2017-07-01

Raw scanned 3D point clouds are usually irregularly distributed due to the essential shortcomings of laser sensors, which therefore poses a great challenge for high-quality 3D surface reconstruction. This paper tackles this problem by proposing a local hierarchical clustering (LHC) method to improve the consistency of point distribution. Specifically, LHC consists of two steps: 1) adaptive octree-based decomposition of 3D space, and 2) hierarchical clustering. The former aims at reducing the computational complexity and the latter transforms the non-uniform point set into uniform one. Experimental results on real-world scanned point clouds validate the effectiveness of our method from both qualitative and quantitative aspects.

Hierarchical models of animal abundance and occurrence

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, R.M.

2006-01-01

Much of animal ecology is devoted to studies of abundance and occurrence of species, based on surveys of spatially referenced sample units. These surveys frequently yield sparse counts that are contaminated by imperfect detection, making direct inference about abundance or occurrence based on observational data infeasible. This article describes a flexible hierarchical modeling framework for estimation and inference about animal abundance and occurrence from survey data that are subject to imperfect detection. Within this framework, we specify models of abundance and detectability of animals at the level of the local populations defined by the sample units. Information at the level of the local population is aggregated by specifying models that describe variation in abundance and detection among sites. We describe likelihood-based and Bayesian methods for estimation and inference under the resulting hierarchical model. We provide two examples of the application of hierarchical models to animal survey data, the first based on removal counts of stream fish and the second based on avian quadrat counts. For both examples, we provide a Bayesian analysis of the models using the software WinBUGS.

A postprocessing method in the HMC framework for predicting gene function based on biological instrumental data

NASA Astrophysics Data System (ADS)

Feng, Shou; Fu, Ping; Zheng, Wenbin

2018-03-01

Predicting gene function based on biological instrumental data is a complicated and challenging hierarchical multi-label classification (HMC) problem. When using local approach methods to solve this problem, a preliminary results processing method is usually needed. This paper proposed a novel preliminary results processing method called the nodes interaction method. The nodes interaction method revises the preliminary results and guarantees that the predictions are consistent with the hierarchy constraint. This method exploits the label dependency and considers the hierarchical interaction between nodes when making decisions based on the Bayesian network in its first phase. In the second phase, this method further adjusts the results according to the hierarchy constraint. Implementing the nodes interaction method in the HMC framework also enhances the HMC performance for solving the gene function prediction problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph that is more difficult to tackle. The experimental results validate the promising performance of the proposed method compared to state-of-the-art methods on eight benchmark yeast data sets annotated by the GO.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning.

PubMed

Lim, Kwangyong; Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).

Hierarchical LiFePO4 with a controllable growth of the (010) facet for lithium-ion batteries.

PubMed

Guo, Binbin; Ruan, Hongcheng; Zheng, Cheng; Fei, Hailong; Wei, Mingdeng

2013-09-27

Hierarchically structured LiFePO4 was successfully synthesized by ionic liquid solvothermal method. These hierarchically structured LiFePO4 samples were constructed from nanostructured platelets with their (010) facets mainly exposed. To the best of our knowledge, facet control of a hierarchical LiFePO4 crystal has not been reported yet. Based on a series of experimental results, a tentative mechanism for the formation of these hierarchical structures was proposed. After these hierarchically structured LiFePO4 samples were coated with a thin carbon layer and used as cathode materials for lithium-ion batteries, they exhibited excellent high-rate discharge capability and cycling stability. For instance, a capacity of 95% can be maintained for the LiFePO4 sample at a rate as high as 20 C, even after 1000 cycles.

Biomimetic wall-shaped hierarchical microstructure for gecko-like attachment.

PubMed

Kasem, Haytam; Tsipenyuk, Alexey; Varenberg, Michael

2015-04-21

Most biological hairy adhesive systems involved in locomotion rely on spatula-shaped terminal elements, whose operation has been actively studied during the last decade. However, though functional principles underlying their amazing performance are now well understood, due to technical difficulties in manufacturing the complex structure of hierarchical spatulate systems, a biomimetic surface structure featuring true shear-induced dynamic attachment still remains elusive. To try bridging this gap, a novel method of manufacturing gecko-like attachment surfaces is devised based on a laser-micromachining technology. This method overcomes the inherent disadvantages of photolithography techniques and opens wide perspectives for future production of gecko-like attachment systems. Advanced smart-performance surfaces featuring thin-film-based hierarchical shear-activated elements are fabricated and found capable of generating friction force of several tens of times the contact load, which makes a significant step forward towards a true gecko-like adhesive.

Highly Transparent Water-Repelling Surfaces based on Biomimetic Hierarchical Structure

NASA Astrophysics Data System (ADS)

Wooh, Sanghyuk; Koh, Jai; Yoon, Hyunsik; Char, Kookheon

2013-03-01

Nature is a great source of inspiration for creating unique structures with special functions. The representative examples of water-repelling surfaces in nature, such as lotus leaves, rose petals, and insect wings, consist of an array of bumps (or long hairs) and nanoscale surface features with different dimension scales. Herein, we introduced a method of realizing multi-dimensional hierarchical structures and water-repellancy of the surfaces with different drop impact scenarios. The multi-dimensional hierarchical structures were fabricated by soft imprinting method with TiO2 nanoparticle pastes. In order to achieve the enhanced hydrophobicity, fluorinated moieties were attached to the etched surfaces to lower the surface energy. As a result, super-hydrophobic surfaces with high transparency were realized (over 176° water contact angle), and for further investigation, these hierarchical surfaces with different drop impact scenarios were characterized by varying the impact speed, drop size, and the geometry of the surfaces.

Hierarchically nanostructured hydroxyapatite: hydrothermal synthesis, morphology control, growth mechanism, and biological activity

PubMed Central

Ma, Ming-Guo

2012-01-01

Hierarchically nanosized hydroxyapatite (HA) with flower-like structure assembled from nanosheets consisting of nanorod building blocks was successfully synthesized by using CaCl2, NaH2PO4, and potassium sodium tartrate via a hydrothermal method at 200°C for 24 hours. The effects of heating time and heating temperature on the products were investigated. As a chelating ligand and template molecule, the potassium sodium tartrate plays a key role in the formation of hierarchically nanostructured HA. On the basis of experimental results, a possible mechanism based on soft-template and self-assembly was proposed for the formation and growth of the hierarchically nanostructured HA. Cytotoxicity experiments indicated that the hierarchically nanostructured HA had good biocompatibility. It was shown by in-vitro experiments that mesenchymal stem cells could attach to the hierarchically nanostructured HA after being cultured for 48 hours. Objective The purpose of this study was to develop facile and effective methods for the synthesis of novel hydroxyapatite (HA) with hierarchical nanostructures assembled from independent and discrete nanobuilding blocks. Methods A simple hydrothermal approach was applied to synthesize HA by using CaCl2, NaH2PO4, and potassium sodium tartrate at 200°C for 24 hours. The cell cytotoxicity of the hierarchically nanostructured HA was tested by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. Results HA displayed the flower-like structure assembled from nanosheets consisting of nanorod building blocks. The potassium sodium tartrate was used as a chelating ligand, inducing the formation and self-assembly of HA nanorods. The heating time and heating temperature influenced the aggregation and morphology of HA. The cell viability did not decrease with the increasing concentration of hierarchically nanostructured HA added. Conclusion A novel, simple and reliable hydrothermal route had been developed for the synthesis of hierarchically nanosized HA with flower-like structure assembled from nanosheets consisting of nanorod building blocks. The HA with the hierarchical nanostructure was formed via a soft-template assisted self-assembly mechanism. The hierarchically nanostructured HA has a good biocompatibility and essentially no in-vitro cytotoxicity. PMID:22619527

Down-Regulation of Olfactory Receptors in Response to Traumatic Brain Injury Promotes Risk for Alzheimer’s Disease

DTIC Science & Technology

2013-10-01

correct group assignment of samples in unsupervised hierarchical clustering by the Unweighted Pair-Group Method using Arithmetic averages ( UPGMA ) based on...centering of log2 transformed MAS5.0 signal values; probe set clustering was performed by the UPGMA method using Cosine correlation as the similarity met...A) The 108 differentially-regulated genes identified were subjected to unsupervised hierarchical clustering analysis using the UPGMA algorithm with

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

PubMed Central

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

PubMed

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

Multilevel Optimization Framework for Hierarchical Stiffened Shells Accelerated by Adaptive Equivalent Strategy

NASA Astrophysics Data System (ADS)

Wang, Bo; Tian, Kuo; Zhao, Haixin; Hao, Peng; Zhu, Tianyu; Zhang, Ke; Ma, Yunlong

2017-06-01

In order to improve the post-buckling optimization efficiency of hierarchical stiffened shells, a multilevel optimization framework accelerated by adaptive equivalent strategy is presented in this paper. Firstly, the Numerical-based Smeared Stiffener Method (NSSM) for hierarchical stiffened shells is derived by means of the numerical implementation of asymptotic homogenization (NIAH) method. Based on the NSSM, a reasonable adaptive equivalent strategy for hierarchical stiffened shells is developed from the concept of hierarchy reduction. Its core idea is to self-adaptively decide which hierarchy of the structure should be equivalent according to the critical buckling mode rapidly predicted by NSSM. Compared with the detailed model, the high prediction accuracy and efficiency of the proposed model is highlighted. On the basis of this adaptive equivalent model, a multilevel optimization framework is then established by decomposing the complex entire optimization process into major-stiffener-level and minor-stiffener-level sub-optimizations, during which Fixed Point Iteration (FPI) is employed to accelerate convergence. Finally, the illustrative examples of the multilevel framework is carried out to demonstrate its efficiency and effectiveness to search for the global optimum result by contrast with the single-level optimization method. Remarkably, the high efficiency and flexibility of the adaptive equivalent strategy is indicated by compared with the single equivalent strategy.

Efficient propagation of the hierarchical equations of motion using the matrix product state method

NASA Astrophysics Data System (ADS)

Shi, Qiang; Xu, Yang; Yan, Yaming; Xu, Meng

2018-05-01

We apply the matrix product state (MPS) method to propagate the hierarchical equations of motion (HEOM). It is shown that the MPS approximation works well in different type of problems, including boson and fermion baths. The MPS method based on the time-dependent variational principle is also found to be applicable to HEOM with over one thousand effective modes. Combining the flexibility of the HEOM in defining the effective modes and the efficiency of the MPS method thus may provide a promising tool in simulating quantum dynamics in condensed phases.

Ultrasensitive, Real-time and Discriminative Detection of Improvised Explosives by Chemiresistive Thin-film Sensory Array of Mn2+ Tailored Hierarchical ZnS

NASA Astrophysics Data System (ADS)

Zhou, Chaoyu; Wu, Zhaofeng; Guo, Yanan; Li, Yushu; Cao, Hongyu; Zheng, Xuefang; Dou, Xincun

2016-05-01

A simple method combing Mn2+ doping with a hierarchical structure was developed for the improvement of thin-film sensors and efficient detection of the explosives relevant to improvised explosive devices (IEDs). ZnS hierarchical nanospheres (HNs) were prepared via a solution-based route and their sensing performances were manipulated by Mn2+ doping. The responses of the sensors based on ZnS HNs towards 8 explosives generally increase firstly and then decrease with the increase of the doped Mn2+ concentration, reaching the climate at 5% Mn2+. Furthermore, the sensory array based on ZnS HNs with different doping levels achieved the sensitive and discriminative detection of 6 analytes relevant to IEDs and 2 military explosives in less than 5 s at room temperature. Importantly, the superior sensing performances make ZnS HNs material interesting in the field of chemiresistive sensors, and this simple method could be a very promising strategy to put the sensors based on thin-films of one-dimensional (1D) nanostructures into practical IEDs detection.

Ultrasensitive, Real-time and Discriminative Detection of Improvised Explosives by Chemiresistive Thin-film Sensory Array of Mn2+ Tailored Hierarchical ZnS

PubMed Central

Zhou, Chaoyu; Wu, Zhaofeng; Guo, Yanan; Li, Yushu; Cao, Hongyu; Zheng, Xuefang; Dou, Xincun

2016-01-01

A simple method combing Mn2+ doping with a hierarchical structure was developed for the improvement of thin-film sensors and efficient detection of the explosives relevant to improvised explosive devices (IEDs). ZnS hierarchical nanospheres (HNs) were prepared via a solution-based route and their sensing performances were manipulated by Mn2+ doping. The responses of the sensors based on ZnS HNs towards 8 explosives generally increase firstly and then decrease with the increase of the doped Mn2+ concentration, reaching the climate at 5% Mn2+. Furthermore, the sensory array based on ZnS HNs with different doping levels achieved the sensitive and discriminative detection of 6 analytes relevant to IEDs and 2 military explosives in less than 5 s at room temperature. Importantly, the superior sensing performances make ZnS HNs material interesting in the field of chemiresistive sensors, and this simple method could be a very promising strategy to put the sensors based on thin-films of one-dimensional (1D) nanostructures into practical IEDs detection. PMID:27161193

Morphology evolution of hierarchical ZnO nanostructures modulated by supersaturation and growth temperature

NASA Astrophysics Data System (ADS)

Yan, Youguo; Zhou, Lixia; Yu, Lianqing; Zhang, Ye

2008-07-01

Three kinds of ZnO hierarchical structures, nanocombs with tube- and needle-shaped teeth and hierarchical nanorod arrays, were successfully synthesized through the chemical vapor deposition method. Combining the experimental parameters, the microcosmic growing conditions (growth temperature and supersaturation) along the flux was discussed at length, and, based on the conclusions, three reasonable growth processes were proposed. The results and discussions were beneficial to further realize the relation between the growing behavior of the nanomaterial and microcosmic conditions, and the hierarchical nanostructures obtained were also expected to have potential applications as functional blocks in future nanodevices. Furthermore, the study of photoluminescence further indicated that the physical properties were strongly dependent on the crystal structure.

A novel snowflake-like SnO2 hierarchical architecture with superior gas sensing properties

NASA Astrophysics Data System (ADS)

Li, Yanqiong

2018-02-01

Snowflake-like SnO2 hierarchical architecture has been synthesized via a facile hydrothermal method and followed by calcination. The SnO2 hierarchical structures are assembled with thin nanoflakes blocks, which look like snowflake shape. A possible mechanism for the formation of the SnO2 hierarchical structures is speculated. Moreover, gas sensing tests show that the sensor based on snowflake-like SnO2 architectures exhibited excellent gas sensing properties. The enhancement may be attributed to its unique structures, in which the porous feature on the snowflake surface could further increase the active surface area of the materials and provide facile pathways for the target gas.

A Hierarchical Learning Control Framework for an Aerial Manipulation System

NASA Astrophysics Data System (ADS)

Ma, Le; Chi, yanxun; Li, Jiapeng; Li, Zhongsheng; Ding, Yalei; Liu, Lixing

2017-07-01

A hierarchical learning control framework for an aerial manipulation system is proposed. Firstly, the mechanical design of aerial manipulation system is introduced and analyzed, and the kinematics and the dynamics based on Newton-Euler equation are modeled. Secondly, the framework of hierarchical learning for this system is presented, in which flight platform and manipulator are controlled by different controller respectively. The RBF (Radial Basis Function) neural networks are employed to estimate parameters and control. The Simulation and experiment demonstrate that the methods proposed effective and advanced.

Fabrication of Advanced Thermoelectric Materials by Hierarchical Nanovoid Generation

NASA Technical Reports Server (NTRS)

Park, Yeonjoon (Inventor); Elliott, James R. (Inventor); Stoakley, Diane M. (Inventor); Chu, Sang-Hyon (Inventor); King, Glen C. (Inventor); Kim, Jae-Woo (Inventor); Choi, Sang Hyouk (Inventor); Lillehei, Peter T. (Inventor)

2011-01-01

A novel method to prepare an advanced thermoelectric material has hierarchical structures embedded with nanometer-sized voids which are key to enhancement of the thermoelectric performance. Solution-based thin film deposition technique enables preparation of stable film of thermoelectric material and void generator (voigen). A subsequent thermal process creates hierarchical nanovoid structure inside the thermoelectric material. Potential application areas of this advanced thermoelectric material with nanovoid structure are commercial applications (electronics cooling), medical and scientific applications (biological analysis device, medical imaging systems), telecommunications, and defense and military applications (night vision equipments).

Hierarchical Parallelism in Finite Difference Analysis of Heat Conduction

NASA Technical Reports Server (NTRS)

Padovan, Joseph; Krishna, Lala; Gute, Douglas

1997-01-01

Based on the concept of hierarchical parallelism, this research effort resulted in highly efficient parallel solution strategies for very large scale heat conduction problems. Overall, the method of hierarchical parallelism involves the partitioning of thermal models into several substructured levels wherein an optimal balance into various associated bandwidths is achieved. The details are described in this report. Overall, the report is organized into two parts. Part 1 describes the parallel modelling methodology and associated multilevel direct, iterative and mixed solution schemes. Part 2 establishes both the formal and computational properties of the scheme.

A hierarchical classification method for finger knuckle print recognition

NASA Astrophysics Data System (ADS)

Kong, Tao; Yang, Gongping; Yang, Lu

2014-12-01

Finger knuckle print has recently been seen as an effective biometric technique. In this paper, we propose a hierarchical classification method for finger knuckle print recognition, which is rooted in traditional score-level fusion methods. In the proposed method, we firstly take Gabor feature as the basic feature for finger knuckle print recognition and then a new decision rule is defined based on the predefined threshold. Finally, the minor feature speeded-up robust feature is conducted for these users, who cannot be recognized by the basic feature. Extensive experiments are performed to evaluate the proposed method, and experimental results show that it can achieve a promising performance.

Selective Template Wetting Routes to Hierarchical Polymer Films: Polymer Nanotubes from Phase-Separated Films via Solvent Annealing.

PubMed

Ko, Hao-Wen; Cheng, Ming-Hsiang; Chi, Mu-Huan; Chang, Chun-Wei; Chen, Jiun-Tai

2016-03-01

We demonstrate a novel wetting method to prepare hierarchical polymer films with polymer nanotubes on selective regions. This strategy is based on the selective wetting abilities of polymer chains, annealed in different solvent vapors, into the nanopores of porous templates. Phase-separated films of polystyrene (PS) and poly(methyl methacrylate) (PMMA), two commonly used polymers, are prepared as a model system. After anodic aluminum oxide (AAO) templates are placed on the films, the samples are annealed in vapors of acetic acid, in which the PMMA chains are swollen and wet the nanopores of the AAO templates selectively. As a result, hierarchical polymer films containing PMMA nanotubes can be obtained after the AAO templates are removed. The distribution of the PMMA nanotubes of the hierarchical polymer films can also be controlled by changing the compositions of the polymer blends. This work not only presents a novel method to fabricate hierarchical polymer films with polymer nanotubes on selective regions, but also gives a deeper understanding in the selective wetting ability of polymer chains in solvent vapors.

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

PubMed

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

Hierarchical structures of amorphous solids characterized by persistent homology

PubMed Central

Hiraoka, Yasuaki; Nakamura, Takenobu; Hirata, Akihiko; Escolar, Emerson G.; Matsue, Kaname; Nishiura, Yasumasa

2016-01-01

This article proposes a topological method that extracts hierarchical structures of various amorphous solids. The method is based on the persistence diagram (PD), a mathematical tool for capturing shapes of multiscale data. The input to the PDs is given by an atomic configuration and the output is expressed as 2D histograms. Then, specific distributions such as curves and islands in the PDs identify meaningful shape characteristics of the atomic configuration. Although the method can be applied to a wide variety of disordered systems, it is applied here to silica glass, the Lennard-Jones system, and Cu-Zr metallic glass as standard examples of continuous random network and random packing structures. In silica glass, the method classified the atomic rings as short-range and medium-range orders and unveiled hierarchical ring structures among them. These detailed geometric characterizations clarified a real space origin of the first sharp diffraction peak and also indicated that PDs contain information on elastic response. Even in the Lennard-Jones system and Cu-Zr metallic glass, the hierarchical structures in the atomic configurations were derived in a similar way using PDs, although the glass structures and properties substantially differ from silica glass. These results suggest that the PDs provide a unified method that extracts greater depth of geometric information in amorphous solids than conventional methods. PMID:27298351

Medical Waste Disposal Method Selection Based on a Hierarchical Decision Model with Intuitionistic Fuzzy Relations

PubMed Central

Qian, Wuyong; Wang, Zhou-Jing; Li, Kevin W.

2016-01-01

Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS), where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty’s 1–9 scale, this paper proposes a cross-ratio-based bipolar 0.1–0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness. PMID:27618082

Medical Waste Disposal Method Selection Based on a Hierarchical Decision Model with Intuitionistic Fuzzy Relations.

PubMed

Qian, Wuyong; Wang, Zhou-Jing; Li, Kevin W

2016-09-09

Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS), where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty's 1-9 scale, this paper proposes a cross-ratio-based bipolar 0.1-0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning

PubMed Central

Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea). PMID:28264011

Highly efficient decomposition of organic dye by aqueous-solid phase transfer and in situ photocatalysis using hierarchical copper phthalocyanine hollow spheres.

PubMed

Zhang, Mingyi; Shao, Changlu; Guo, Zengcai; Zhang, Zhenyi; Mu, Jingbo; Zhang, Peng; Cao, Tieping; Liu, Yichun

2011-07-01

The hierarchical tetranitro copper phthalocyanine (TNCuPc) hollow spheres were fabricated by a simple solvothermal method. The formation mechanism was proposed based on the evolution of morphology as a function of solvothermal time, which involved the initial formation of nanoparticles followed by their self-aggregation to microspheres and transformation into hierarchical hollow spheres by Ostwald ripening. Furthermore, the hierarchical TNCuPc hollow spheres exhibited high adsorption capacity and excellent simultaneously visible-light-driven photocatalytic performance for Rhodamine B (RB) under visible light. A possible mechanism for the "aqueous-solid phase transfer and in situ photocatalysis" was suggested. Repetitive tests showed that the hierarchical TNCuPc hollow spheres maintained high catalytic activity over several cycles, and it had a better regeneration capability under mild conditions.

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

PubMed

Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

2018-05-01

Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2012-01-01

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

Hierarchical and coupling model of factors influencing vessel traffic flow.

PubMed

Liu, Zhao; Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi

2017-01-01

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

Hierarchical and coupling model of factors influencing vessel traffic flow

PubMed Central

Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi

2017-01-01

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system. PMID:28414747

Reinforcement learning algorithms for robotic navigation in dynamic environments.

PubMed

Yen, Gary G; Hickey, Travis W

2004-04-01

The purpose of this study was to examine improvements to reinforcement learning (RL) algorithms in order to successfully interact within dynamic environments. The scope of the research was that of RL algorithms as applied to robotic navigation. Proposed improvements include: addition of a forgetting mechanism, use of feature based state inputs, and hierarchical structuring of an RL agent. Simulations were performed to evaluate the individual merits and flaws of each proposal, to compare proposed methods to prior established methods, and to compare proposed methods to theoretically optimal solutions. Incorporation of a forgetting mechanism did considerably improve the learning times of RL agents in a dynamic environment. However, direct implementation of a feature-based RL agent did not result in any performance enhancements, as pure feature-based navigation results in a lack of positional awareness, and the inability of the agent to determine the location of the goal state. Inclusion of a hierarchical structure in an RL agent resulted in significantly improved performance, specifically when one layer of the hierarchy included a feature-based agent for obstacle avoidance, and a standard RL agent for global navigation. In summary, the inclusion of a forgetting mechanism, and the use of a hierarchically structured RL agent offer substantially increased performance when compared to traditional RL agents navigating in a dynamic environment.

Comparing hierarchical models via the marginalized deviance information criterion.

PubMed

Quintero, Adrian; Lesaffre, Emmanuel

2018-07-20

Hierarchical models are extensively used in pharmacokinetics and longitudinal studies. When the estimation is performed from a Bayesian approach, model comparison is often based on the deviance information criterion (DIC). In hierarchical models with latent variables, there are several versions of this statistic: the conditional DIC (cDIC) that incorporates the latent variables in the focus of the analysis and the marginalized DIC (mDIC) that integrates them out. Regardless of the asymptotic and coherency difficulties of cDIC, this alternative is usually used in Markov chain Monte Carlo (MCMC) methods for hierarchical models because of practical convenience. The mDIC criterion is more appropriate in most cases but requires integration of the likelihood, which is computationally demanding and not implemented in Bayesian software. Therefore, we consider a method to compute mDIC by generating replicate samples of the latent variables that need to be integrated out. This alternative can be easily conducted from the MCMC output of Bayesian packages and is widely applicable to hierarchical models in general. Additionally, we propose some approximations in order to reduce the computational complexity for large-sample situations. The method is illustrated with simulated data sets and 2 medical studies, evidencing that cDIC may be misleading whilst mDIC appears pertinent. Copyright © 2018 John Wiley & Sons, Ltd.

Functional architecture of Escherichia coli: new insights provided by a natural decomposition approach.

PubMed

Freyre-González, Julio A; Alonso-Pavón, José A; Treviño-Quintanilla, Luis G; Collado-Vides, Julio

2008-10-27

Previous studies have used different methods in an effort to extract the modular organization of transcriptional regulatory networks. However, these approaches are not natural, as they try to cluster strongly connected genes into a module or locate known pleiotropic transcription factors in lower hierarchical layers. Here, we unravel the transcriptional regulatory network of Escherichia coli by separating it into its key elements, thus revealing its natural organization. We also present a mathematical criterion, based on the topological features of the transcriptional regulatory network, to classify the network elements into one of two possible classes: hierarchical or modular genes. We found that modular genes are clustered into physiologically correlated groups validated by a statistical analysis of the enrichment of the functional classes. Hierarchical genes encode transcription factors responsible for coordinating module responses based on general interest signals. Hierarchical elements correlate highly with the previously studied global regulators, suggesting that this could be the first mathematical method to identify global regulators. We identified a new element in transcriptional regulatory networks never described before: intermodular genes. These are structural genes that integrate, at the promoter level, signals coming from different modules, and therefore from different physiological responses. Using the concept of pleiotropy, we have reconstructed the hierarchy of the network and discuss the role of feedforward motifs in shaping the hierarchical backbone of the transcriptional regulatory network. This study sheds new light on the design principles underpinning the organization of transcriptional regulatory networks, showing a novel nonpyramidal architecture composed of independent modules globally governed by hierarchical transcription factors, whose responses are integrated by intermodular genes.

Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology

NASA Astrophysics Data System (ADS)

Hadida, Jonathan; Desrosiers, Christian; Duong, Luc

2011-03-01

The segmentation of anatomical structures in Computed Tomography Angiography (CTA) is a pre-operative task useful in image guided surgery. Even though very robust and precise methods have been developed to help achieving a reliable segmentation (level sets, active contours, etc), it remains very time consuming both in terms of manual interactions and in terms of computation time. The goal of this study is to present a fast method to find coarse anatomical structures in CTA with few parameters, based on hierarchical clustering. The algorithm is organized as follows: first, a fast non-parametric histogram clustering method is proposed to compute a piecewise constant mask. A second step then indexes all the space-connected regions in the piecewise constant mask. Finally, a hierarchical clustering is achieved to build a graph representing the connections between the various regions in the piecewise constant mask. This step builds up a structural knowledge about the image. Several interactive features for segmentation are presented, for instance association or disassociation of anatomical structures. A comparison with the Mean-Shift algorithm is presented.

Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

PubMed Central

Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

2012-01-01

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate. PMID:22368464

Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.

PubMed

Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

2012-01-01

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

Create and Publish a Hierarchical Progressive Survey (HiPS)

NASA Astrophysics Data System (ADS)

Fernique, P.; Boch, T.; Pineau, F.; Oberto, A.

2014-05-01

Since 2009, the CDS promotes a method for visualizing based on the HEALPix sky tessellation. This method, called “Hierarchical Progressive Survey" or HiPS, allows one to display a survey progressively. It is particularly suited for all-sky surveys or deep fields. This visualization method is now integrated in several applications, notably Aladin, the SiTools/MIZAR CNES framework, and the recent HTML5 “Aladin Lite". Also, more than one hundred surveys are already available in this view mode. In this article, we will present the progress concerning this method and its recent adaptation to the astronomical catalogs such as the GAIA simulation.

Modular and hierarchical structure of social contact networks

NASA Astrophysics Data System (ADS)

Ge, Yuanzheng; Song, Zhichao; Qiu, Xiaogang; Song, Hongbin; Wang, Yong

2013-10-01

Social contact networks exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated nature. We propose a mixing pattern of modular and growing hierarchical structures to reconstruct social contact networks by using an individual’s geospatial distribution information in the real world. The hierarchical structure of social contact networks is defined based on the spatial distance between individuals, and edges among individuals are added in turn from the modular layer to the highest layer. It is a gradual process to construct the hierarchical structure: from the basic modular model up to the global network. The proposed model not only shows hierarchically increasing degree distribution and large clustering coefficients in communities, but also exhibits spatial clustering features of individual distributions. As an evaluation of the method, we reconstruct a hierarchical contact network based on the investigation data of a university. Transmission experiments of influenza H1N1 are carried out on the generated social contact networks, and results show that the constructed network is efficient to reproduce the dynamic process of an outbreak and evaluate interventions. The reproduced spread process exhibits that the spatial clustering of infection is accordant with the clustering of network topology. Moreover, the effect of individual topological character on the spread of influenza is analyzed, and the experiment results indicate that the spread is limited by individual daily contact patterns and local clustering topology rather than individual degree.

HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.

PubMed

Wiecki, Thomas V; Sofer, Imri; Frank, Michael J

2013-01-01

The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/

Flexible supercapacitors with high areal capacitance based on hierarchical carbon tubular nanostructures

NASA Astrophysics Data System (ADS)

Zhang, Haitao; Su, Hai; Zhang, Lei; Zhang, Binbin; Chun, Fengjun; Chu, Xiang; He, Weidong; Yang, Weiqing

2016-11-01

Hierarchical structure design can greatly enhance the unique properties of primary material(s) but suffers from complicated preparation process and difficult self-assembly of materials with different dimensionalities. Here we report on the growth of single carbon tubular nanostructures with hierarchical structure (hCTNs) through a simple method based on direct conversion of carbon dioxide. Resorting to in-situ transformation and self-assembly of carbon micro/nano-structures, the obtained hCTNs are blood-like multichannel hierarchy composed of one large channel across the hCTNs and plenty of small branches connected to each other. Due to the unique pore structure and high surface area, these hCTN-based flexible supercapacitors possess the highest areal capacitance of ∼320 mF cm-2, as well as good rate-capability and excellent cycling stability (95% retention after 2500 cycles). It was established that this method can control the morphology, size, and density of hCTNs and effectively construct hCTNs well anchored to the various substrates. Our work unambiguously demonstrated the potential of hCTNs for large flexible supercapacitors and integrated energy management electronics.

Wavelet compression of multichannel ECG data by enhanced set partitioning in hierarchical trees algorithm.

PubMed

Sharifahmadian, Ershad

2006-01-01

The set partitioning in hierarchical trees (SPIHT) algorithm is very effective and computationally simple technique for image and signal compression. Here the author modified the algorithm which provides even better performance than the SPIHT algorithm. The enhanced set partitioning in hierarchical trees (ESPIHT) algorithm has performance faster than the SPIHT algorithm. In addition, the proposed algorithm reduces the number of bits in a bit stream which is stored or transmitted. I applied it to compression of multichannel ECG data. Also, I presented a specific procedure based on the modified algorithm for more efficient compression of multichannel ECG data. This method employed on selected records from the MIT-BIH arrhythmia database. According to experiments, the proposed method attained the significant results regarding compression of multichannel ECG data. Furthermore, in order to compress one signal which is stored for a long time, the proposed multichannel compression method can be utilized efficiently.

Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

NASA Astrophysics Data System (ADS)

Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-04-01

One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community detection in complex networks.

Efficiently computing and deriving topological relation matrices between complex regions with broad boundaries

NASA Astrophysics Data System (ADS)

Du, Shihong; Guo, Luo; Wang, Qiao; Qin, Qimin

The extended 9-intersection matrix is used to formalize topological relations between uncertain regions while it is designed to satisfy the requirements at a concept level, and to deal with the complex regions with broad boundaries (CBBRs) as a whole without considering their hierarchical structures. In contrast to simple regions with broad boundaries, CBBRs have complex hierarchical structures. Therefore, it is necessary to take into account the complex hierarchical structure and to represent the topological relations between all regions in CBBRs as a relation matrix, rather than using the extended 9-intersection matrix to determine topological relations. In this study, a tree model is first used to represent the intrinsic configuration of CBBRs hierarchically. Then, the reasoning tables are presented for deriving topological relations between child, parent and sibling regions from the relations between two given regions in CBBRs. Finally, based on the reasoning, efficient methods are proposed to compute and derive the topological relation matrix. The proposed methods can be incorporated into spatial databases to facilitate geometric-oriented applications.

Carbon fiber reinforced hierarchical orthogrid stiffened cylinder: Fabrication and testing

NASA Astrophysics Data System (ADS)

Wu, Hao; Lai, Changlian; Sun, Fangfang; Li, Ming; Ji, Bin; Wei, Weiyi; Liu, Debo; Zhang, Xi; Fan, Hualin

2018-04-01

To get strong, stiff and light cylindrical shell, carbon fiber reinforced hierarchical orthogrid stiffened cylinders are designed and fabricated. The cylinder is stiffened by two-scale orthogrid. The primary orthogrid has thick and high ribs and contains several sub-orthogrid cells whose rib is much thinner and lower. The primary orthogrid stiffens the bending rigidity of the cylinder to resist the global instability while the sub-orthogrid stiffens the bending rigidity of the skin enclosed by the primary orthogrid to resist local buckling. The cylinder is fabricated by filament winding method based on a silicone rubber mandrel with hierarchical grooves. Axial compression tests are performed to reveal the failure modes. With hierarchical stiffeners, the cylinder fails at skin fracture and has high specific strength. The cylinder will fail at end crushing if the end of the cylinder is not thickened. Global instability and local buckling are well restricted by the hierarchical stiffeners.

Distinct Photovoltaic Performance of Hierarchical Nanostructures Self-Assembled from Multiblock Copolymers.

PubMed

Xu, Zhanwen; Lin, Jiaping; Zhang, Liangshun; Wang, Liquan; Wang, Gengchao; Tian, Xiaohui; Jiang, Tao

2018-06-14

We applied a multi-scale approach coupling dissipative particle dynamics method with a drift-diffusion model to elucidate the photovoltaic properties of multiblock copolymers consisting of alternating electron donor and acceptor blocks. A series of hierarchical lamellae-in-lamellar structures were obtained from the self-assembly of the multiblock copolymers. A distinct improvement in photovoltaic performance upon the morphology transformation from lamella to lamellae-in-lamella was observed. The hierarchical lamellae-in-lamellar structures significantly enhanced exciton dissociation and charge carrier transport, which consequently contributed to the improved photovoltaic performance. Based on our theoretical calculations, the hierarchical nanostructures can achieve a much enhanced energy conversion efficiency, improved by around 25% compared with that of general ones, through structure modulation on number and size of the small-length-scale domains. Our findings are supported by recent experimental evidence and yield guidelines for designing hierarchical materials with improved photovoltaic properties.

Methods of developing core collections based on the predicted genotypic value of rice ( Oryza sativa L.).

PubMed

Li, C T; Shi, C H; Wu, J G; Xu, H M; Zhang, H Z; Ren, Y L

2004-04-01

The selection of an appropriate sampling strategy and a clustering method is important in the construction of core collections based on predicted genotypic values in order to retain the greatest degree of genetic diversity of the initial collection. In this study, methods of developing rice core collections were evaluated based on the predicted genotypic values for 992 rice varieties with 13 quantitative traits. The genotypic values of the traits were predicted by the adjusted unbiased prediction (AUP) method. Based on the predicted genotypic values, Mahalanobis distances were calculated and employed to measure the genetic similarities among the rice varieties. Six hierarchical clustering methods, including the single linkage, median linkage, centroid, unweighted pair-group average, weighted pair-group average and flexible-beta methods, were combined with random, preferred and deviation sampling to develop 18 core collections of rice germplasm. The results show that the deviation sampling strategy in combination with the unweighted pair-group average method of hierarchical clustering retains the greatest degree of genetic diversities of the initial collection. The core collections sampled using predicted genotypic values had more genetic diversity than those based on phenotypic values.

Learn from every mistake! Hierarchical information combination in astronomy

NASA Astrophysics Data System (ADS)

Süveges, Maria; Fotopoulou, Sotiria; Coupon, Jean; Paltani, Stéphane; Eyer, Laurent; Rimoldini, Lorenzo

2017-06-01

Throughout the processing and analysis of survey data, a ubiquitous issue nowadays is that we are spoilt for choice when we need to select a methodology for some of its steps. The alternative methods usually fail and excel in different data regions, and have various advantages and drawbacks, so a combination that unites the strengths of all while suppressing the weaknesses is desirable. We propose to use a two-level hierarchy of learners. Its first level consists of training and applying the possible base methods on the first part of a known set. At the second level, we feed the output probability distributions from all base methods to a second learner trained on the remaining known objects. Using classification of variable stars and photometric redshift estimation as examples, we show that the hierarchical combination is capable of achieving general improvement over averaging-type combination methods, correcting systematics present in all base methods, is easy to train and apply, and thus, it is a promising tool in the astronomical ``Big Data'' era.

Computer systems and methods for the query and visualization of multidimensional databases

DOEpatents

Stolte, Chris; Tang, Diane L.; Hanrahan, Patrick

2006-08-08

A method and system for producing graphics. A hierarchical structure of a database is determined. A visual table, comprising a plurality of panes, is constructed by providing a specification that is in a language based on the hierarchical structure of the database. In some cases, this language can include fields that are in the database schema. The database is queried to retrieve a set of tuples in accordance with the specification. A subset of the set of tuples is associated with a pane in the plurality of panes.

Computer systems and methods for the query and visualization of multidimensional database

DOEpatents

Stolte, Chris; Tang, Diane L.; Hanrahan, Patrick

2010-05-11

A method and system for producing graphics. A hierarchical structure of a database is determined. A visual table, comprising a plurality of panes, is constructed by providing a specification that is in a language based on the hierarchical structure of the database. In some cases, this language can include fields that are in the database schema. The database is queried to retrieve a set of tuples in accordance with the specification. A subset of the set of tuples is associated with a pane in the plurality of panes.

Clustering PPI data by combining FA and SHC method.

PubMed

Lei, Xiujuan; Ying, Chao; Wu, Fang-Xiang; Xu, Jin

2015-01-01

Clustering is one of main methods to identify functional modules from protein-protein interaction (PPI) data. Nevertheless traditional clustering methods may not be effective for clustering PPI data. In this paper, we proposed a novel method for clustering PPI data by combining firefly algorithm (FA) and synchronization-based hierarchical clustering (SHC) algorithm. Firstly, the PPI data are preprocessed via spectral clustering (SC) which transforms the high-dimensional similarity matrix into a low dimension matrix. Then the SHC algorithm is used to perform clustering. In SHC algorithm, hierarchical clustering is achieved by enlarging the neighborhood radius of synchronized objects continuously, while the hierarchical search is very difficult to find the optimal neighborhood radius of synchronization and the efficiency is not high. So we adopt the firefly algorithm to determine the optimal threshold of the neighborhood radius of synchronization automatically. The proposed algorithm is tested on the MIPS PPI dataset. The results show that our proposed algorithm is better than the traditional algorithms in precision, recall and f-measure value.

Clustering PPI data by combining FA and SHC method

PubMed Central

2015-01-01

Clustering is one of main methods to identify functional modules from protein-protein interaction (PPI) data. Nevertheless traditional clustering methods may not be effective for clustering PPI data. In this paper, we proposed a novel method for clustering PPI data by combining firefly algorithm (FA) and synchronization-based hierarchical clustering (SHC) algorithm. Firstly, the PPI data are preprocessed via spectral clustering (SC) which transforms the high-dimensional similarity matrix into a low dimension matrix. Then the SHC algorithm is used to perform clustering. In SHC algorithm, hierarchical clustering is achieved by enlarging the neighborhood radius of synchronized objects continuously, while the hierarchical search is very difficult to find the optimal neighborhood radius of synchronization and the efficiency is not high. So we adopt the firefly algorithm to determine the optimal threshold of the neighborhood radius of synchronization automatically. The proposed algorithm is tested on the MIPS PPI dataset. The results show that our proposed algorithm is better than the traditional algorithms in precision, recall and f-measure value. PMID:25707632

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

PubMed

Paraskevopoulou, Sivylla E; Wu, Di; Eftekhar, Amir; Constandinou, Timothy G

2014-09-30

This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Adaptive variable structure hierarchical fuzzy control for a class of high-order nonlinear dynamic systems.

PubMed

Mansouri, Mohammad; Teshnehlab, Mohammad; Aliyari Shoorehdeli, Mahdi

2015-05-01

In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Hierarchically Nanostructured Transition Metal Oxides for Lithium‐Ion Batteries

PubMed Central

Zheng, Mingbo; Tang, Hao; Li, Lulu; Hu, Qin; Zhang, Li; Xue, Huaiguo

2018-01-01

Abstract Lithium‐ion batteries (LIBs) have been widely used in the field of portable electric devices because of their high energy density and long cycling life. To further improve the performance of LIBs, it is of great importance to develop new electrode materials. Various transition metal oxides (TMOs) have been extensively investigated as electrode materials for LIBs. According to the reaction mechanism, there are mainly two kinds of TMOs, one is based on conversion reaction and the other is based on intercalation/deintercalation reaction. Recently, hierarchically nanostructured TMOs have become a hot research area in the field of LIBs. Hierarchical architecture can provide numerous accessible electroactive sites for redox reactions, shorten the diffusion distance of Li‐ion during the reaction, and accommodate volume expansion during cycling. With rapid research progress in this field, a timely account of this advanced technology is highly necessary. Here, the research progress on the synthesis methods, morphological characteristics, and electrochemical performances of hierarchically nanostructured TMOs for LIBs is summarized and discussed. Some relevant prospects are also proposed. PMID:29593962

Hierarchically Nanostructured Transition Metal Oxides for Lithium-Ion Batteries.

PubMed

Zheng, Mingbo; Tang, Hao; Li, Lulu; Hu, Qin; Zhang, Li; Xue, Huaiguo; Pang, Huan

2018-03-01

Lithium-ion batteries (LIBs) have been widely used in the field of portable electric devices because of their high energy density and long cycling life. To further improve the performance of LIBs, it is of great importance to develop new electrode materials. Various transition metal oxides (TMOs) have been extensively investigated as electrode materials for LIBs. According to the reaction mechanism, there are mainly two kinds of TMOs, one is based on conversion reaction and the other is based on intercalation/deintercalation reaction. Recently, hierarchically nanostructured TMOs have become a hot research area in the field of LIBs. Hierarchical architecture can provide numerous accessible electroactive sites for redox reactions, shorten the diffusion distance of Li-ion during the reaction, and accommodate volume expansion during cycling. With rapid research progress in this field, a timely account of this advanced technology is highly necessary. Here, the research progress on the synthesis methods, morphological characteristics, and electrochemical performances of hierarchically nanostructured TMOs for LIBs is summarized and discussed. Some relevant prospects are also proposed.

A Hierarchical Object-oriented Urban Land Cover Classification Using WorldView-2 Imagery and Airborne LiDAR data

NASA Astrophysics Data System (ADS)

Wu, M. F.; Sun, Z. C.; Yang, B.; Yu, S. S.

2016-11-01

In order to reduce the “salt and pepper” in pixel-based urban land cover classification and expand the application of fusion of multi-source data in the field of urban remote sensing, WorldView-2 imagery and airborne Light Detection and Ranging (LiDAR) data were used to improve the classification of urban land cover. An approach of object- oriented hierarchical classification was proposed in our study. The processing of proposed method consisted of two hierarchies. (1) In the first hierarchy, LiDAR Normalized Digital Surface Model (nDSM) image was segmented to objects. The NDVI, Costal Blue and nDSM thresholds were set for extracting building objects. (2) In the second hierarchy, after removing building objects, WorldView-2 fused imagery was obtained by Haze-ratio-based (HR) fusion, and was segmented. A SVM classifier was applied to generate road/parking lot, vegetation and bare soil objects. (3) Trees and grasslands were split based on an nDSM threshold (2.4 meter). The results showed that compared with pixel-based and non-hierarchical object-oriented approach, proposed method provided a better performance of urban land cover classification, the overall accuracy (OA) and overall kappa (OK) improved up to 92.75% and 0.90. Furthermore, proposed method reduced “salt and pepper” in pixel-based classification, improved the extraction accuracy of buildings based on LiDAR nDSM image segmentation, and reduced the confusion between trees and grasslands through setting nDSM threshold.

Nanoscale Analysis of a Hierarchical Hybrid Solar Cell in 3D.

PubMed

Divitini, Giorgio; Stenzel, Ole; Ghadirzadeh, Ali; Guarnera, Simone; Russo, Valeria; Casari, Carlo S; Bassi, Andrea Li; Petrozza, Annamaria; Di Fonzo, Fabio; Schmidt, Volker; Ducati, Caterina

2014-05-01

A quantitative method for the characterization of nanoscale 3D morphology is applied to the investigation of a hybrid solar cell based on a novel hierarchical nanostructured photoanode. A cross section of the solar cell device is prepared by focused ion beam milling in a micropillar geometry, which allows a detailed 3D reconstruction of the titania photoanode by electron tomography. It is found that the hierarchical titania nanostructure facilitates polymer infiltration, thus favoring intermixing of the two semiconducting phases, essential for charge separation. The 3D nanoparticle network is analyzed with tools from stochastic geometry to extract information related to the charge transport in the hierarchical solar cell. In particular, the experimental dataset allows direct visualization of the percolation pathways that contribute to the photocurrent.

Nanoscale Analysis of a Hierarchical Hybrid Solar Cell in 3D

PubMed Central

Divitini, Giorgio; Stenzel, Ole; Ghadirzadeh, Ali; Guarnera, Simone; Russo, Valeria; Casari, Carlo S; Bassi, Andrea Li; Petrozza, Annamaria; Di Fonzo, Fabio; Schmidt, Volker; Ducati, Caterina

2014-01-01

A quantitative method for the characterization of nanoscale 3D morphology is applied to the investigation of a hybrid solar cell based on a novel hierarchical nanostructured photoanode. A cross section of the solar cell device is prepared by focused ion beam milling in a micropillar geometry, which allows a detailed 3D reconstruction of the titania photoanode by electron tomography. It is found that the hierarchical titania nanostructure facilitates polymer infiltration, thus favoring intermixing of the two semiconducting phases, essential for charge separation. The 3D nanoparticle network is analyzed with tools from stochastic geometry to extract information related to the charge transport in the hierarchical solar cell. In particular, the experimental dataset allows direct visualization of the percolation pathways that contribute to the photocurrent. PMID:25834481

A hierarchical model for spatial capture-recapture data

USGS Publications Warehouse

Royle, J. Andrew; Young, K.V.

2008-01-01

Estimating density is a fundamental objective of many animal population studies. Application of methods for estimating population size from ostensibly closed populations is widespread, but ineffective for estimating absolute density because most populations are subject to short-term movements or so-called temporary emigration. This phenomenon invalidates the resulting estimates because the effective sample area is unknown. A number of methods involving the adjustment of estimates based on heuristic considerations are in widespread use. In this paper, a hierarchical model of spatially indexed capture recapture data is proposed for sampling based on area searches of spatial sample units subject to uniform sampling intensity. The hierarchical model contains explicit models for the distribution of individuals and their movements, in addition to an observation model that is conditional on the location of individuals during sampling. Bayesian analysis of the hierarchical model is achieved by the use of data augmentation, which allows for a straightforward implementation in the freely available software WinBUGS. We present results of a simulation study that was carried out to evaluate the operating characteristics of the Bayesian estimator under variable densities and movement patterns of individuals. An application of the model is presented for survey data on the flat-tailed horned lizard (Phrynosoma mcallii) in Arizona, USA.

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

NASA Astrophysics Data System (ADS)

Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

2015-12-01

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

Biomimetic cellular metals-using hierarchical structuring for energy absorption.

PubMed

Bührig-Polaczek, A; Fleck, C; Speck, T; Schüler, P; Fischer, S F; Caliaro, M; Thielen, M

2016-07-19

Fruit walls as well as nut and seed shells typically perform a multitude of functions. One of the biologically most important functions consists in the direct or indirect protection of the seeds from mechanical damage or other negative environmental influences. This qualifies such biological structures as role models for the development of new materials and components that protect commodities and/or persons from damage caused for example by impacts due to rough handling or crashes. We were able to show how the mechanical properties of metal foam based components can be improved by altering their structure on various hierarchical levels inspired by features and principles important for the impact and/or puncture resistance of the biological role models, rather than by tuning the properties of the bulk material. For this various investigation methods have been established which combine mechanical testing with different imaging methods, as well as with in situ and ex situ mechanical testing methods. Different structural hierarchies especially important for the mechanical deformation and failure behaviour of the biological role models, pomelo fruit (Citrus maxima) and Macadamia integrifolia, were identified. They were abstracted and transferred into corresponding structural principles and thus hierarchically structured bio-inspired metal foams have been designed. A production route for metal based bio-inspired structures by investment casting was successfully established. This allows the production of complex and reliable structures, by implementing and combining different hierarchical structural elements found in the biological concept generators, such as strut design and integration of fibres, as well as by minimising casting defects. To evaluate the structural effects, similar investigation methods and mechanical tests were applied to both the biological role models and the metallic foams. As a result an even deeper quantitative understanding of the form-structure-function relationship of the biological concept generators as well as the bio-inspired metal foams was achieved, on deeper hierarchical levels and overarching different levels.

Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks.

PubMed

Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing

2017-07-19

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.

Hierarchical classification method and its application in shape representation

NASA Astrophysics Data System (ADS)

Ireton, M. A.; Oakley, John P.; Xydeas, Costas S.

1992-04-01

In this paper we describe a technique for performing shaped-based content retrieval of images from a large database. In order to be able to formulate such user-generated queries about visual objects, we have developed an hierarchical classification technique. This hierarchical classification technique enables similarity matching between objects, with the position in the hierarchy signifying the level of generality to be used in the query. The classification technique is unsupervised, robust, and general; it can be applied to any suitable parameter set. To establish the potential of this classifier for aiding visual querying, we have applied it to the classification of the 2-D outlines of leaves.

Fabrication of hierarchical feather-mimetic polymer nanofibres

NASA Astrophysics Data System (ADS)

Ouyang, Shenshen; Wang, Tao; Zhong, Longgang; Peng, Meiling; Yao, Juming; Wang, Sheng

2018-01-01

In this study, hierarchically feather-mimetic structures formed of poly(m-phenylene isophthalamide) (PMIA) nanofibres were prepared by electrospinning and subsequent crystallisation for superwettability applications. X-ray diffraction measurementsand scanning electron microscopy show that a feather-mimetic structure of crystallised nanoflakes was formed following a hydrothermal treatment process. The nanoflakes formed a nanosized fine texture on top of a coarser-textured membrane, which greatly improved the membrane roughness and yielded a hierarchical topography. After fluorination, the membrane exhibited superamphiphobicity, with surface contact angles of 151° and 136° for water and hexadecane, respectively. The method provides new insight for the design and development of functional bionic membranes based on PMIA.

POLLUTION PREVENTION IN THE EARLY STAGES OF HIERARCHICAL PROCESS DESIGN

EPA Science Inventory

Hierarchical methods are often used in the conceptual stages of process design to synthesize and evaluate process alternatives. In this work, the methods of hierarchical process design will be focused on environmental aspects. In particular, the design methods will be coupled to ...

Hierarchical Metal–Organic Framework Hybrids: Perturbation-Assisted Nanofusion Synthesis

DOE PAGES

Yue, Yanfeng; Fulvio, Pasquale F.; Dai, Sheng

2015-12-04

Metal–organic frameworks (MOFs) represent a new family of microporous materials; however, microporous–mesoporous hierarchical MOF materials have been less investigated because of the lack of simple, reliable methods to introduce mesopores to the crystalline microporous particles. State-of-the-art MOF hierarchical materials have been prepared by ligand extension methods or by using a template, resulting in intrinsic mesopores of longer ligands or replicated pores from template agents, respectively. However, mesoporous MOF materials obtained through ligand extension often collapse in the absence of guest molecules, which dramatically reduces the size of the pore aperture. Although the template-directed strategy allows for the preparation of hierarchicalmore » materials with larger mesopores, the latter requires a template removal step, which may result in the collapse of the implemented mesopores. Recently, a general template-free synthesis of hierarchical microporous crystalline frameworks, such as MOFs and Prussian blue analogues (PBAs), has been reported. Our new method is based on the kinetically controlled precipitation (perturbation), with simultaneous condensation and redissolution of polymorphic nanocrystallites in the mother liquor. This method further eliminates the use of extended organic ligands and the micropores do not collapse upon removal of trapped guest solvent molecules, thus yielding hierarchical MOF materials with intriguing porosity in the gram scale. The hierarchical MOF materials prepared in this way exhibited exceptional properties when tested for the adsorption of large organic dyes over their corresponding microporous frameworks, due to the enhanced pore accessibility and electrolyte diffusion within the mesopores. As for PBAs, the pore size distribution of these materials can be tailored by changing the metals substituting Fe cations in the PB lattice. For these, the textural mesopores increased from approximately 10 nm for Cu analogue (mesoCuHCF), to 16 nm in Co substituted compound (mesoCoHCF), and to as large as 30 nm for the Ni derivative (mesoNiHCF). And while bulk PB and analogues have a higher capacitance than hierarchical analogues for Na-batteries, the increased accessibility to the microporous channels of PBAs allow for faster intercalated ion exchange and diffusion than in bulk PBA crystals. Therefore, hierarchical PBAs are promising candidates for electrodes in future electrochemical energy storage devices with faster charge–discharge rates than batteries, namely pseudocapacitors. Finally, this new synthetic method opens the possibility to prepare hierarchical materials having bimodal distribution of mesopores, and to tailor the structural properties of MOFs for different applications, including contrasting agents for MRI, and drug delivery.« less

HierarchicalTopics: visually exploring large text collections using topic hierarchies.

PubMed

Dou, Wenwen; Yu, Li; Wang, Xiaoyu; Ma, Zhiqiang; Ribarsky, William

2013-12-01

Analyzing large textual collections has become increasingly challenging given the size of the data available and the rate that more data is being generated. Topic-based text summarization methods coupled with interactive visualizations have presented promising approaches to address the challenge of analyzing large text corpora. As the text corpora and vocabulary grow larger, more topics need to be generated in order to capture the meaningful latent themes and nuances in the corpora. However, it is difficult for most of current topic-based visualizations to represent large number of topics without being cluttered or illegible. To facilitate the representation and navigation of a large number of topics, we propose a visual analytics system--HierarchicalTopic (HT). HT integrates a computational algorithm, Topic Rose Tree, with an interactive visual interface. The Topic Rose Tree constructs a topic hierarchy based on a list of topics. The interactive visual interface is designed to present the topic content as well as temporal evolution of topics in a hierarchical fashion. User interactions are provided for users to make changes to the topic hierarchy based on their mental model of the topic space. To qualitatively evaluate HT, we present a case study that showcases how HierarchicalTopics aid expert users in making sense of a large number of topics and discovering interesting patterns of topic groups. We have also conducted a user study to quantitatively evaluate the effect of hierarchical topic structure. The study results reveal that the HT leads to faster identification of large number of relevant topics. We have also solicited user feedback during the experiments and incorporated some suggestions into the current version of HierarchicalTopics.

Comparing effects of fire modeling methods on simulated fire patterns and succession: a case study in the Missouri Ozarks

Treesearch

Jian Yang; Hong S. He; Brian R. Sturtevant; Brian R. Miranda; Eric J. Gustafson

2008-01-01

We compared four fire spread simulation methods (completely random, dynamic percolation. size-based minimum travel time algorithm. and duration-based minimum travel time algorithm) and two fire occurrence simulation methods (Poisson fire frequency model and hierarchical fire frequency model) using a two-way factorial design. We examined these treatment effects on...

Using cluster analysis and a classification and regression tree model to developed cover types in the Sky Islands of southeastern Arizona

Treesearch

Jose M. Iniguez; Joseph L. Ganey; Peter J. Daughtery; John D. Bailey

2005-01-01

The objective of this study was to develop a rule based cover type classification system for the forest and woodland vegetation in the Sky Islands of southeastern Arizona. In order to develop such a system we qualitatively and quantitatively compared a hierarchical (Ward’s) and a non-hierarchical (k-means) clustering method. Ecologically, unique groups represented by...

Using cluster analysis and a classification and regression tree model to developed cover types in the Sky Islands of southeastern Arizona [Abstract

Treesearch

Jose M. Iniguez; Joseph L. Ganey; Peter J. Daugherty; John D. Bailey

2005-01-01

The objective of this study was to develop a rule based cover type classification system for the forest and woodland vegetation in the Sky Islands of southeastern Arizona. In order to develop such system we qualitatively and quantitatively compared a hierarchical (Ward’s) and a non-hierarchical (k-means) clustering method. Ecologically, unique groups and plots...

A hybrid deterministic-probabilistic approach to model the mechanical response of helically arranged hierarchical strands

NASA Astrophysics Data System (ADS)

Fraldi, M.; Perrella, G.; Ciervo, M.; Bosia, F.; Pugno, N. M.

2017-09-01

Very recently, a Weibull-based probabilistic strategy has been successfully applied to bundles of wires to determine their overall stress-strain behaviour, also capturing previously unpredicted nonlinear and post-elastic features of hierarchical strands. This approach is based on the so-called "Equal Load Sharing (ELS)" hypothesis by virtue of which, when a wire breaks, the load acting on the strand is homogeneously redistributed among the surviving wires. Despite the overall effectiveness of the method, some discrepancies between theoretical predictions and in silico Finite Element-based simulations or experimental findings might arise when more complex structures are analysed, e.g. helically arranged bundles. To overcome these limitations, an enhanced hybrid approach is proposed in which the probability of rupture is combined with a deterministic mechanical model of a strand constituted by helically-arranged and hierarchically-organized wires. The analytical model is validated comparing its predictions with both Finite Element simulations and experimental tests. The results show that generalized stress-strain responses - incorporating tension/torsion coupling - are naturally found and, once one or more elements break, the competition between geometry and mechanics of the strand microstructure, i.e. the different cross sections and helical angles of the wires in the different hierarchical levels of the strand, determines the no longer homogeneous stress redistribution among the surviving wires whose fate is hence governed by a "Hierarchical Load Sharing" criterion.

Hierarchical graphs for rule-based modeling of biochemical systems

PubMed Central

2011-01-01

Background In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal) of an edge represents a class of association (dissociation) reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Results For purposes of model annotation, we propose the use of hierarchical graphs to represent structural relationships among components and subcomponents of molecules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR) complex. We also show that computational methods developed for regular graphs can be applied to hierarchical graphs. In particular, we describe a generalization of Nauty, a graph isomorphism and canonical labeling algorithm. The generalized version of the Nauty procedure, which we call HNauty, can be used to assign canonical labels to hierarchical graphs or more generally to graphs with multiple edge types. The difference between the Nauty and HNauty procedures is minor, but for completeness, we provide an explanation of the entire HNauty algorithm. Conclusions Hierarchical graphs provide more intuitive formal representations of proteins and other structured molecules with multiple functional components than do the regular graphs of current languages for specifying rule-based models, such as the BioNetGen language (BNGL). Thus, the proposed use of hierarchical graphs should promote clarity and better understanding of rule-based models. PMID:21288338

Multiple-Primitives Hierarchical Classification of Airborne Laser Scanning Data in Urban Areas

NASA Astrophysics Data System (ADS)

Ni, H.; Lin, X. G.; Zhang, J. X.

2017-09-01

A hierarchical classification method for Airborne Laser Scanning (ALS) data of urban areas is proposed in this paper. This method is composed of three stages among which three types of primitives are utilized, i.e., smooth surface, rough surface, and individual point. In the first stage, the input ALS data is divided into smooth surfaces and rough surfaces by employing a step-wise point cloud segmentation method. In the second stage, classification based on smooth surfaces and rough surfaces is performed. Points in the smooth surfaces are first classified into ground and buildings based on semantic rules. Next, features of rough surfaces are extracted. Then, points in rough surfaces are classified into vegetation and vehicles based on the derived features and Random Forests (RF). In the third stage, point-based features are extracted for the ground points, and then, an individual point classification procedure is performed to classify the ground points into bare land, artificial ground and greenbelt. Moreover, the shortages of the existing studies are analyzed, and experiments show that the proposed method overcomes these shortages and handles more types of objects.

Discriminative Hierarchical K-Means Tree for Large-Scale Image Classification.

PubMed

Chen, Shizhi; Yang, Xiaodong; Tian, Yingli

2015-09-01

A key challenge in large-scale image classification is how to achieve efficiency in terms of both computation and memory without compromising classification accuracy. The learning-based classifiers achieve the state-of-the-art accuracies, but have been criticized for the computational complexity that grows linearly with the number of classes. The nonparametric nearest neighbor (NN)-based classifiers naturally handle large numbers of categories, but incur prohibitively expensive computation and memory costs. In this brief, we present a novel classification scheme, i.e., discriminative hierarchical K-means tree (D-HKTree), which combines the advantages of both learning-based and NN-based classifiers. The complexity of the D-HKTree only grows sublinearly with the number of categories, which is much better than the recent hierarchical support vector machines-based methods. The memory requirement is the order of magnitude less than the recent Naïve Bayesian NN-based approaches. The proposed D-HKTree classification scheme is evaluated on several challenging benchmark databases and achieves the state-of-the-art accuracies, while with significantly lower computation cost and memory requirement.

Hierarchical video summarization based on context clustering

NASA Astrophysics Data System (ADS)

Tseng, Belle L.; Smith, John R.

2003-11-01

A personalized video summary is dynamically generated in our video personalization and summarization system based on user preference and usage environment. The three-tier personalization system adopts the server-middleware-client architecture in order to maintain, select, adapt, and deliver rich media content to the user. The server stores the content sources along with their corresponding MPEG-7 metadata descriptions. In this paper, the metadata includes visual semantic annotations and automatic speech transcriptions. Our personalization and summarization engine in the middleware selects the optimal set of desired video segments by matching shot annotations and sentence transcripts with user preferences. Besides finding the desired contents, the objective is to present a coherent summary. There are diverse methods for creating summaries, and we focus on the challenges of generating a hierarchical video summary based on context information. In our summarization algorithm, three inputs are used to generate the hierarchical video summary output. These inputs are (1) MPEG-7 metadata descriptions of the contents in the server, (2) user preference and usage environment declarations from the user client, and (3) context information including MPEG-7 controlled term list and classification scheme. In a video sequence, descriptions and relevance scores are assigned to each shot. Based on these shot descriptions, context clustering is performed to collect consecutively similar shots to correspond to hierarchical scene representations. The context clustering is based on the available context information, and may be derived from domain knowledge or rules engines. Finally, the selection of structured video segments to generate the hierarchical summary efficiently balances between scene representation and shot selection.

Recognizing Chinese characters in digital ink from non-native language writers using hierarchical models

NASA Astrophysics Data System (ADS)

Bai, Hao; Zhang, Xi-wen

2017-06-01

While Chinese is learned as a second language, its characters are taught step by step from their strokes to components, radicals to components, and their complex relations. Chinese Characters in digital ink from non-native language writers are deformed seriously, thus the global recognition approaches are poorer. So a progressive approach from bottom to top is presented based on hierarchical models. Hierarchical information includes strokes and hierarchical components. Each Chinese character is modeled as a hierarchical tree. Strokes in one Chinese characters in digital ink are classified with Hidden Markov Models and concatenated to the stroke symbol sequence. And then the structure of components in one ink character is extracted. According to the extraction result and the stroke symbol sequence, candidate characters are traversed and scored. Finally, the recognition candidate results are listed by descending. The method of this paper is validated by testing 19815 copies of the handwriting Chinese characters written by foreign students.

Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

PubMed

Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

2015-01-01

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

Indirect estimates of natal dispersal distance from genetic data in a stream-dwelling fish (Mogurnda adspersa).

PubMed

Shipham, Ashlee; Schmidt, Daniel J; Hughes, Jane M

2013-01-01

Recent work has highlighted the need to account for hierarchical patterns of genetic structure when estimating evolutionary and ecological parameters of interest. This caution is particularly relevant to studies of riverine organisms, where hierarchical structure appears to be commonplace. Here, we indirectly estimate dispersal distance in a hierarchically structured freshwater fish, Mogurnda adspersa. Microsatellite and mitochondrial DNA (mtDNA) data were obtained for 443 individuals across 27 sites separated by an average of 1.3 km within creeks of southeastern Queensland, Australia. Significant genetic structure was found among sites (mtDNA Φ(ST) = 0.508; microsatellite F(ST) = 0.225, F'(ST) = 0.340). Various clustering methods produced congruent patterns of hierarchical structure reflecting stream architecture. Partial mantel tests identified contiguous sets of sample sites where isolation by distance (IBD) explained F(ST) variation without significant contribution of hierarchical structure. Analysis of mean natal dispersal distance (σ) within sets of IBD-linked sample sites suggested most dispersal occurs over less than 1 km, and the average effective density (D(e)) was estimated at 11.5 individuals km(-1); indicating sedentary behavior and small effective population size are responsible for the remarkable patterns of genetic structure observed. Our results demonstrate that Rousset's regression-based method is applicable to estimating the scale of dispersal in riverine organisms and that identifying contiguous populations that satisfy the assumptions of this model is achievable with genetic clustering methods and partial correlations.

Implementation of hierarchical clustering using k-mer sparse matrix to analyze MERS-CoV genetic relationship

NASA Astrophysics Data System (ADS)

Bustamam, A.; Ulul, E. D.; Hura, H. F. A.; Siswantining, T.

2017-07-01

Hierarchical clustering is one of effective methods in creating a phylogenetic tree based on the distance matrix between DNA (deoxyribonucleic acid) sequences. One of the well-known methods to calculate the distance matrix is k-mer method. Generally, k-mer is more efficient than some distance matrix calculation techniques. The steps of k-mer method are started from creating k-mer sparse matrix, and followed by creating k-mer singular value vectors. The last step is computing the distance amongst vectors. In this paper, we analyze the sequences of MERS-CoV (Middle East Respiratory Syndrome - Coronavirus) DNA by implementing hierarchical clustering using k-mer sparse matrix in order to perform the phylogenetic analysis. Our results show that the ancestor of our MERS-CoV is coming from Egypt. Moreover, we found that the MERS-CoV infection that occurs in one country may not necessarily come from the same country of origin. This suggests that the process of MERS-CoV mutation might not only be influenced by geographical factor.

The analysis of thin walled composite laminated helicopter rotor with hierarchical warping functions and finite element method

NASA Astrophysics Data System (ADS)

Zhu, Dechao; Deng, Zhongmin; Wang, Xingwei

2001-08-01

In the present paper, a series of hierarchical warping functions is developed to analyze the static and dynamic problems of thin walled composite laminated helicopter rotors composed of several layers with single closed cell. This method is the development and extension of the traditional constrained warping theory of thin walled metallic beams, which had been proved very successful since 1940s. The warping distribution along the perimeter of each layer is expanded into a series of successively corrective warping functions with the traditional warping function caused by free torsion or free bending as the first term, and is assumed to be piecewise linear along the thickness direction of layers. The governing equations are derived based upon the variational principle of minimum potential energy for static analysis and Rayleigh Quotient for free vibration analysis. Then the hierarchical finite element method is introduced to form a numerical algorithm. Both static and natural vibration problems of sample box beams are analyzed with the present method to show the main mechanical behavior of the thin walled composite laminated helicopter rotor.

Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.

PubMed

Wang, Yong-Cui; Wang, Yong; Yang, Zhi-Xia; Deng, Nai-Yang

2011-06-20

Enzymes are known as the largest class of proteins and their functions are usually annotated by the Enzyme Commission (EC), which uses a hierarchy structure, i.e., four numbers separated by periods, to classify the function of enzymes. Automatically categorizing enzyme into the EC hierarchy is crucial to understand its specific molecular mechanism. In this paper, we introduce two key improvements in predicting enzyme function within the machine learning framework. One is to introduce the efficient sequence encoding methods for representing given proteins. The second one is to develop a structure-based prediction method with low computational complexity. In particular, we propose to use the conjoint triad feature (CTF) to represent the given protein sequences by considering not only the composition of amino acids but also the neighbor relationships in the sequence. Then we develop a support vector machine (SVM)-based method, named as SVMHL (SVM for hierarchy labels), to output enzyme function by fully considering the hierarchical structure of EC. The experimental results show that our SVMHL with the CTF outperforms SVMHL with the amino acid composition (AAC) feature both in predictive accuracy and Matthew's correlation coefficient (MCC). In addition, SVMHL with the CTF obtains the accuracy and MCC ranging from 81% to 98% and 0.82 to 0.98 when predicting the first three EC digits on a low-homologous enzyme dataset. We further demonstrate that our method outperforms the methods which do not take account of hierarchical relationship among enzyme categories and alternative methods which incorporate prior knowledge about inter-class relationships. Our structure-based prediction model, SVMHL with the CTF, reduces the computational complexity and outperforms the alternative approaches in enzyme function prediction. Therefore our new method will be a useful tool for enzyme function prediction community.

Comparison of Multiscale Method of Cells-Based Models for Predicting Elastic Properties of Filament Wound C/C-SiC

NASA Technical Reports Server (NTRS)

Pineda, Evan J.; Fassin, Marek; Bednarcyk, Brett A.; Reese, Stefanie; Simon, Jaan-Willem

2017-01-01

Three different multiscale models, based on the method of cells (generalized and high fidelity) micromechanics models were developed and used to predict the elastic properties of C/C-SiC composites. In particular, the following multiscale modeling strategies were employed: Concurrent multiscale modeling of all phases using the generalized method of cells, synergistic (two-way coupling in space) multiscale modeling with the generalized method of cells, and hierarchical (one-way coupling in space) multiscale modeling with the high fidelity generalized method of cells. The three models are validated against data from a hierarchical multiscale finite element model in the literature for a repeating unit cell of C/C-SiC. Furthermore, the multiscale models are used in conjunction with classical lamination theory to predict the stiffness of C/C-SiC plates manufactured via a wet filament winding and liquid silicon infiltration process recently developed by the German Aerospace Institute.

Hydrothermal Synthesis of Various Hierarchical ZnO Nanostructures and Their Methane Sensing Properties

PubMed Central

Zhou, Qu; Chen, Weigen; Xu, Lingna; Peng, Shudi

2013-01-01

Hierarchical flower-like ZnO nanorods, net-like ZnO nanofibers and ZnO nanobulks have been successfully synthesized via a surfactant assisted hydrothemal method. The synthesized products were characterized by X-ray powder diffraction and field emission scanning electron microscopy, respectively. A possible growth mechanism of the various hierarchical ZnO nanostructures is discussed in detail. Gas sensors based on the as-prepared ZnO nanostructures were fabricated by screen-printing on a flat ceramic substrate. Furthermore, their gas sensing characteristics towards methane were systematically investigated. Methane is an important characteristic hydrocarbon contaminant found dissolved in power transformer oil as a result of faults. We find that the hierarchical flower-like ZnO nanorods and net-like ZnO nanofibers samples show higher gas response and lower operating temperature with rapid response-recovery time compared to those of sensors based on ZnO nanobulks. These results present a feasible way of exploring high performance sensing materials for on-site detection of characteristic fault gases dissolved in transformer oil. PMID:23666136

Relation between financial market structure and the real economy: comparison between clustering methods.

PubMed

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T

2015-01-01

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].

Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods

PubMed Central

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T.

2015-01-01

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover, we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging. PMID:25786703

Model-based clustering for RNA-seq data.

PubMed

Si, Yaqing; Liu, Peng; Li, Pinghua; Brutnell, Thomas P

2014-01-15

RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis. In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models. An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org

A STUDY OF HIERARCHICAL ORGANIZATION LOCATION MODEL FOR SERVICE INDUSTRY ENTERPRISE

NASA Astrophysics Data System (ADS)

Okumura, Makoto; Takada, Naoki; Okubo, Kazuaki

The service industry has come to participate in many economic activities, but there are few studies, which analyze on the hierarchical organization location of a service industry enterprise that treats information. We propose a hierarchical organized location model, which endogenously determines the number of hierarchies. Furtheremore, We propose a MCMC based statistical method to obtain parameter distributions corresponding to the observed macro emplowment distribution as well as the saralies paid. By using our model, we analyzed the regional disparities about the number of employees and the wage. We found that the change of the regional disparities is caused by internal factors such as the industrial structural change, rather than external factors such as traffic condition changes.

Efficient Prediction Structures for H.264 Multi View Coding Using Temporal Scalability

NASA Astrophysics Data System (ADS)

Guruvareddiar, Palanivel; Joseph, Biju K.

2014-03-01

Prediction structures with "disposable view components based" hierarchical coding have been proven to be efficient for H.264 multi view coding. Though these prediction structures along with the QP cascading schemes provide superior compression efficiency when compared to the traditional IBBP coding scheme, the temporal scalability requirements of the bit stream could not be met to the fullest. On the other hand, a fully scalable bit stream, obtained by "temporal identifier based" hierarchical coding, provides a number of advantages including bit rate adaptations and improved error resilience, but lacks in compression efficiency when compared to the former scheme. In this paper it is proposed to combine the two approaches such that a fully scalable bit stream could be realized with minimal reduction in compression efficiency when compared to state-of-the-art "disposable view components based" hierarchical coding. Simulation results shows that the proposed method enables full temporal scalability with maximum BDPSNR reduction of only 0.34 dB. A novel method also has been proposed for the identification of temporal identifier for the legacy H.264/AVC base layer packets. Simulation results also show that this enables the scenario where the enhancement views could be extracted at a lower frame rate (1/2nd or 1/4th of base view) with average extraction time for a view component of only 0.38 ms.

Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks

PubMed Central

Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Li, Baoqing; Yuan, Xiaobing

2017-01-01

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum–minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms. PMID:28753962

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

PubMed

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

Sugar apple-shaped TiO2 hierarchical spheres for highly efficient dye-sensitized solar cells

NASA Astrophysics Data System (ADS)

Lei, Bing-Xin; Zeng, Li-Li; Zhang, Ping; Qiao, He-Kang; Sun, Zhen-Fan

2014-05-01

The sugar apple-shaped TiO2 hierarchical spheres are prepared by a facile hydrothermal method using polyethylene glycol 600 as stabilized reagent, (NH4)2TiF6 and urea as starting materials at 180 °C. The characterizations show that the TiO2 hierarchical sphere has well-defined pyramid-shaped crystal facets. The as-prepared TiO2 hierarchical spheres are crystalline of the anatase phase, with a diameter of about 2-4 μm and a surface area of 36.846 m2 g-1. The optical investigation evidences that the sugar apple-shaped TiO2 hierarchical sphere film exhibits a prominent light scattering effect at a wavelength range of 600-800 nm due to the unique hierarchical morphology. Furthermore, the sugar apple-shaped TiO2 hierarchical spheres are deposited as the scattering layer to balance the dye adsorption and light scattering effect in DSSCs and a 7.20% solar energy conversion efficiency is demonstrated, indicating an improvement compared with the P25 cell (6.68%). Based on the optical and electrochemical investigations, the high conversion efficiency is mainly due to the effective suppression of the back reaction of the injected electron with the I3- in the electrolyte and excellent light scattering ability.

Array-based Hierarchical Mesh Generation in Parallel

DOE PAGES

Ray, Navamita; Grindeanu, Iulian; Zhao, Xinglin; ...

2015-11-03

In this paper, we describe an array-based hierarchical mesh generation capability through uniform refinement of unstructured meshes for efficient solution of PDE's using finite element methods and multigrid solvers. A multi-degree, multi-dimensional and multi-level framework is designed to generate the nested hierarchies from an initial mesh that can be used for a number of purposes such as multi-level methods to generating large meshes. The capability is developed under the parallel mesh framework “Mesh Oriented dAtaBase” a.k.a MOAB. We describe the underlying data structures and algorithms to generate such hierarchies and present numerical results for computational efficiency and mesh quality. Inmore » conclusion, we also present results to demonstrate the applicability of the developed capability to a multigrid finite-element solver.« less

Biomedical application of hierarchically built structures based on metal oxides

NASA Astrophysics Data System (ADS)

Korovin, M. S.; Fomenko, A. N.

2017-12-01

Nowadays, the use of hierarchically built structures in biology and medicine arouses much interest. The aim of this work is to review and summarize the available literature data about hierarchically organized structures in biomedical application. Nanoparticles can serve as an example of such structures. Medicine holds a special place among various application methods of similar systems. Special attention is paid to inorganic nanoparticles based on different metal oxides and hydroxides, such as iron, zinc, copper, and aluminum. Our investigations show that low-dimensional nanostructures based on aluminum oxides and hydroxides have an inhibitory effect on tumor cells and possess an antimicrobial activity. At the same time, it is obvious that the large-scale use of nanoparticles by humans needs to thoroughly study their properties. Special attention should be paid to the study of nanoparticle interaction with living biological objects. The numerous data show that there is no clear understanding of interaction mechanisms between nanoparticles and various cell types.

Spine-like Nanostructured Carbon Interconnected by Graphene for High-performance Supercapacitors

NASA Astrophysics Data System (ADS)

Park, Sang-Hoon; Yoon, Seung-Beom; Kim, Hyun-Kyung; Han, Joong Tark; Park, Hae-Woong; Han, Joah; Yun, Seok-Min; Jeong, Han Gi; Roh, Kwang Chul; Kim, Kwang-Bum

2014-08-01

Recent studies on supercapacitors have focused on the development of hierarchical nanostructured carbons by combining two-dimensional graphene and other conductive sp2 carbons, which differ in dimensionality, to improve their electrochemical performance. Herein, we report a strategy for synthesizing a hierarchical graphene-based carbon material, which we shall refer to as spine-like nanostructured carbon, from a one-dimensional graphitic carbon nanofiber by controlling the local graphene/graphitic structure via an expanding process and a co-solvent exfoliation method. Spine-like nanostructured carbon has a unique hierarchical structure of partially exfoliated graphitic blocks interconnected by thin graphene sheets in the same manner as in the case of ligaments. Owing to the exposed graphene layers and interconnected sp2 carbon structure, this hierarchical nanostructured carbon possesses a large, electrochemically accessible surface area with high electrical conductivity and exhibits high electrochemical performance.

Spine-like nanostructured carbon interconnected by graphene for high-performance supercapacitors.

PubMed

Park, Sang-Hoon; Yoon, Seung-Beom; Kim, Hyun-Kyung; Han, Joong Tark; Park, Hae-Woong; Han, Joah; Yun, Seok-Min; Jeong, Han Gi; Roh, Kwang Chul; Kim, Kwang-Bum

2014-08-19

Recent studies on supercapacitors have focused on the development of hierarchical nanostructured carbons by combining two-dimensional graphene and other conductive sp(2) carbons, which differ in dimensionality, to improve their electrochemical performance. Herein, we report a strategy for synthesizing a hierarchical graphene-based carbon material, which we shall refer to as spine-like nanostructured carbon, from a one-dimensional graphitic carbon nanofiber by controlling the local graphene/graphitic structure via an expanding process and a co-solvent exfoliation method. Spine-like nanostructured carbon has a unique hierarchical structure of partially exfoliated graphitic blocks interconnected by thin graphene sheets in the same manner as in the case of ligaments. Owing to the exposed graphene layers and interconnected sp(2) carbon structure, this hierarchical nanostructured carbon possesses a large, electrochemically accessible surface area with high electrical conductivity and exhibits high electrochemical performance.

Spine-like Nanostructured Carbon Interconnected by Graphene for High-performance Supercapacitors

PubMed Central

Park, Sang-Hoon; Yoon, Seung-Beom; Kim, Hyun-Kyung; Han, Joong Tark; Park, Hae-Woong; Han, Joah; Yun, Seok-Min; Jeong, Han Gi; Roh, Kwang Chul; Kim, Kwang-Bum

2014-01-01

Recent studies on supercapacitors have focused on the development of hierarchical nanostructured carbons by combining two-dimensional graphene and other conductive sp2 carbons, which differ in dimensionality, to improve their electrochemical performance. Herein, we report a strategy for synthesizing a hierarchical graphene-based carbon material, which we shall refer to as spine-like nanostructured carbon, from a one-dimensional graphitic carbon nanofiber by controlling the local graphene/graphitic structure via an expanding process and a co-solvent exfoliation method. Spine-like nanostructured carbon has a unique hierarchical structure of partially exfoliated graphitic blocks interconnected by thin graphene sheets in the same manner as in the case of ligaments. Owing to the exposed graphene layers and interconnected sp2 carbon structure, this hierarchical nanostructured carbon possesses a large, electrochemically accessible surface area with high electrical conductivity and exhibits high electrochemical performance. PMID:25134517

Joint image encryption and compression scheme based on IWT and SPIHT

NASA Astrophysics Data System (ADS)

Zhang, Miao; Tong, Xiaojun

2017-03-01

A joint lossless image encryption and compression scheme based on integer wavelet transform (IWT) and set partitioning in hierarchical trees (SPIHT) is proposed to achieve lossless image encryption and compression simultaneously. Making use of the properties of IWT and SPIHT, encryption and compression are combined. Moreover, the proposed secure set partitioning in hierarchical trees (SSPIHT) via the addition of encryption in the SPIHT coding process has no effect on compression performance. A hyper-chaotic system, nonlinear inverse operation, Secure Hash Algorithm-256(SHA-256), and plaintext-based keystream are all used to enhance the security. The test results indicate that the proposed methods have high security and good lossless compression performance.

Application of hierarchical cascading technique to finite element method simulation in bulk acoustic wave devices

NASA Astrophysics Data System (ADS)

Li, Xinyi; Bao, Jingfu; Huang, Yulin; Zhang, Benfeng; Omori, Tatsuya; Hashimoto, Ken-ya

2018-07-01

In this paper, we propose the use of the hierarchical cascading technique (HCT) for the finite element method (FEM) analysis of bulk acoustic wave (BAW) devices. First, the implementation of this technique is presented for the FEM analysis of BAW devices. It is shown that the traveling-wave excitation sources proposed by the authors are fully compatible with the HCT. Furthermore, a HCT-based absorbing mechanism is also proposed to replace the perfectly matched layer (PML). Finally, it is demonstrated how the technique is much more efficient in terms of memory consumption and execution time than the full FEM analysis.

Hierarchical equations of motion method applied to nonequilibrium heat transport in model molecular junctions: Transient heat current and high-order moments of the current operator

NASA Astrophysics Data System (ADS)

Song, Linze; Shi, Qiang

2017-02-01

We present a theoretical approach to study nonequilibrium quantum heat transport in molecular junctions described by a spin-boson type model. Based on the Feynman-Vernon path integral influence functional formalism, expressions for the average value and high-order moments of the heat current operators are derived, which are further obtained directly from the auxiliary density operators (ADOs) in the hierarchical equations of motion (HEOM) method. Distribution of the heat current is then derived from the high-order moments. As the HEOM method is nonperturbative and capable of treating non-Markovian system-environment interactions, the method can be applied to various problems of nonequilibrium quantum heat transport beyond the weak coupling regime.

Best Merge Region Growing with Integrated Probabilistic Classification for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2011-01-01

A new method for spectral-spatial classification of hyperspectral images is proposed. The method is based on the integration of probabilistic classification within the hierarchical best merge region growing algorithm. For this purpose, preliminary probabilistic support vector machines classification is performed. Then, hierarchical step-wise optimization algorithm is applied, by iteratively merging regions with the smallest Dissimilarity Criterion (DC). The main novelty of this method consists in defining a DC between regions as a function of region statistical and geometrical features along with classification probabilities. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana s vegetation area and compared with those obtained by recently proposed spectral-spatial classification techniques. The proposed method improves classification accuracies when compared to other classification approaches.

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

A Multilevel, Hierarchical Sampling Technique for Spatially Correlated Random Fields

DOE PAGES

Osborn, Sarah; Vassilevski, Panayot S.; Villa, Umberto

2017-10-26

In this paper, we propose an alternative method to generate samples of a spatially correlated random field with applications to large-scale problems for forward propagation of uncertainty. A classical approach for generating these samples is the Karhunen--Loève (KL) decomposition. However, the KL expansion requires solving a dense eigenvalue problem and is therefore computationally infeasible for large-scale problems. Sampling methods based on stochastic partial differential equations provide a highly scalable way to sample Gaussian fields, but the resulting parametrization is mesh dependent. We propose a multilevel decomposition of the stochastic field to allow for scalable, hierarchical sampling based on solving amore » mixed finite element formulation of a stochastic reaction-diffusion equation with a random, white noise source function. Lastly, numerical experiments are presented to demonstrate the scalability of the sampling method as well as numerical results of multilevel Monte Carlo simulations for a subsurface porous media flow application using the proposed sampling method.« less

A Multilevel, Hierarchical Sampling Technique for Spatially Correlated Random Fields

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

Osborn, Sarah; Vassilevski, Panayot S.; Villa, Umberto

In this paper, we propose an alternative method to generate samples of a spatially correlated random field with applications to large-scale problems for forward propagation of uncertainty. A classical approach for generating these samples is the Karhunen--Loève (KL) decomposition. However, the KL expansion requires solving a dense eigenvalue problem and is therefore computationally infeasible for large-scale problems. Sampling methods based on stochastic partial differential equations provide a highly scalable way to sample Gaussian fields, but the resulting parametrization is mesh dependent. We propose a multilevel decomposition of the stochastic field to allow for scalable, hierarchical sampling based on solving amore » mixed finite element formulation of a stochastic reaction-diffusion equation with a random, white noise source function. Lastly, numerical experiments are presented to demonstrate the scalability of the sampling method as well as numerical results of multilevel Monte Carlo simulations for a subsurface porous media flow application using the proposed sampling method.« less

A hierarchical Bayesian method for vibration-based time domain force reconstruction problems

NASA Astrophysics Data System (ADS)

Li, Qiaofeng; Lu, Qiuhai

2018-05-01

Traditional force reconstruction techniques require prior knowledge on the force nature to determine the regularization term. When such information is unavailable, the inappropriate term is easily chosen and the reconstruction result becomes unsatisfactory. In this paper, we propose a novel method to automatically determine the appropriate q as in ℓq regularization and reconstruct the force history. The method incorporates all to-be-determined variables such as the force history, precision parameters and q into a hierarchical Bayesian formulation. The posterior distributions of variables are evaluated by a Metropolis-within-Gibbs sampler. The point estimates of variables and their uncertainties are given. Simulations of a cantilever beam and a space truss under various loading conditions validate the proposed method in providing adaptive determination of q and better reconstruction performance than existing Bayesian methods.

Data graphing methods, articles of manufacture, and computing devices

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

Wong, Pak Chung; Mackey, Patrick S.; Cook, Kristin A.

Data graphing methods, articles of manufacture, and computing devices are described. In one aspect, a method includes accessing a data set, displaying a graphical representation including data of the data set which is arranged according to a first of different hierarchical levels, wherein the first hierarchical level represents the data at a first of a plurality of different resolutions which respectively correspond to respective ones of the hierarchical levels, selecting a portion of the graphical representation wherein the data of the portion is arranged according to the first hierarchical level at the first resolution, modifying the graphical representation by arrangingmore » the data of the portion according to a second of the hierarchal levels at a second of the resolutions, and after the modifying, displaying the graphical representation wherein the data of the portion is arranged according to the second hierarchal level at the second resolution.« less

Advances in Applications of Hierarchical Bayesian Methods with Hydrological Models

NASA Astrophysics Data System (ADS)

Alexander, R. B.; Schwarz, G. E.; Boyer, E. W.

2017-12-01

Mechanistic and empirical watershed models are increasingly used to inform water resource decisions. Growing access to historical stream measurements and data from in-situ sensor technologies has increased the need for improved techniques for coupling models with hydrological measurements. Techniques that account for the intrinsic uncertainties of both models and measurements are especially needed. Hierarchical Bayesian methods provide an efficient modeling tool for quantifying model and prediction uncertainties, including those associated with measurements. Hierarchical methods can also be used to explore spatial and temporal variations in model parameters and uncertainties that are informed by hydrological measurements. We used hierarchical Bayesian methods to develop a hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual streamflow across diverse environmental and climatic drainages in 18 U.S. hydrological regions. Our application illustrates the use of a new generation of Bayesian methods that offer more advanced computational efficiencies than the prior generation. Evaluations of the effects of hierarchical (regional) variations in model coefficients and uncertainties on model accuracy indicates improved prediction accuracies (median of 10-50%) but primarily in humid eastern regions, where model uncertainties are one-third of those in arid western regions. Generally moderate regional variability is observed for most hierarchical coefficients. Accounting for measurement and structural uncertainties, using hierarchical state-space techniques, revealed the effects of spatially-heterogeneous, latent hydrological processes in the "localized" drainages between calibration sites; this improved model precision, with only minor changes in regional coefficients. Our study can inform advances in the use of hierarchical methods with hydrological models to improve their integration with stream measurements.

Complexity of major UK companies between 2006 and 2010: Hierarchical structure method approach

NASA Astrophysics Data System (ADS)

Ulusoy, Tolga; Keskin, Mustafa; Shirvani, Ayoub; Deviren, Bayram; Kantar, Ersin; Çaǧrı Dönmez, Cem

2012-11-01

This study reports on topology of the top 40 UK companies that have been analysed for predictive verification of markets for the period 2006-2010, applying the concept of minimal spanning tree and hierarchical tree (HT) analysis. Construction of the minimal spanning tree (MST) and the hierarchical tree (HT) is confined to a brief description of the methodology and a definition of the correlation function between a pair of companies based on the London Stock Exchange (LSE) index in order to quantify synchronization between the companies. A derivation of hierarchical organization and the construction of minimal-spanning and hierarchical trees for the 2006-2008 and 2008-2010 periods have been used and the results validate the predictive verification of applied semantics. The trees are known as useful tools to perceive and detect the global structure, taxonomy and hierarchy in financial data. From these trees, two different clusters of companies in 2006 were detected. They also show three clusters in 2008 and two between 2008 and 2010, according to their proximity. The clusters match each other as regards their common production activities or their strong interrelationship. The key companies are generally given by major economic activities as expected. This work gives a comparative approach between MST and HT methods from statistical physics and information theory with analysis of financial markets that may give new valuable and useful information of the financial market dynamics.

Virtual Surveyor based Object Extraction from Airborne LiDAR data

NASA Astrophysics Data System (ADS)

Habib, Md. Ahsan

Topographic feature detection of land cover from LiDAR data is important in various fields - city planning, disaster response and prevention, soil conservation, infrastructure or forestry. In recent years, feature classification, compliant with Object-Based Image Analysis (OBIA) methodology has been gaining traction in remote sensing and geographic information science (GIS). In OBIA, the LiDAR image is first divided into meaningful segments called object candidates. This results, in addition to spectral values, in a plethora of new information such as aggregated spectral pixel values, morphology, texture, context as well as topology. Traditional nonparametric segmentation methods rely on segmentations at different scales to produce a hierarchy of semantically significant objects. Properly tuned scale parameters are, therefore, imperative in these methods for successful subsequent classification. Recently, some progress has been made in the development of methods for tuning the parameters for automatic segmentation. However, researchers found that it is very difficult to automatically refine the tuning with respect to each object class present in the scene. Moreover, due to the relative complexity of real-world objects, the intra-class heterogeneity is very high, which leads to over-segmentation. Therefore, the method fails to deliver correctly many of the new segment features. In this dissertation, a new hierarchical 3D object segmentation algorithm called Automatic Virtual Surveyor based Object Extracted (AVSOE) is presented. AVSOE segments objects based on their distinct geometric concavity/convexity. This is achieved by strategically mapping the sloping surface, which connects the object to its background. Further analysis produces hierarchical decomposition of objects to its sub-objects at a single scale level. Extensive qualitative and qualitative results are presented to demonstrate the efficacy of this hierarchical segmentation approach.

Magnetron sputtering based direct fabrication of three dimensional CdTe hierarchical nanotrees exhibiting stable superhydrophobic property

NASA Astrophysics Data System (ADS)

Luo, Bingwei; Deng, Yuan; Wang, Yao; Shi, Yongming; Cao, Lili; Zhu, Wei

2013-09-01

Three dimensional CdTe hierarchical nanotrees are initially prepared by a simple one-step magnetron sputtering method without any templates or additives. The CdTe hierarchical nanotrees are constructed by the spear-like vertical trunks and horizontal branches with the diameters of about 100 nm at bottom and became cuspidal on the top. The particular nanostructure imparts these materials superhydrophobic property, and this property can be preserved after placing in air for 90 days, and is stable even after the ultraviolet light and X-ray irradiation, respectively. This study provides a simple strategy to achieve superhydrophobic properties for CdTe materials at lower temperature, which opens a new potential for CdTe solar cell with self-cleaning property.

Trans-dimensional and hierarchical Bayesian approaches toward rigorous estimation of seismic sources and structures in the Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, Seongryong; Tkalčić, Hrvoje; Mustać, Marija; Rhie, Junkee; Ford, Sean

2016-04-01

A framework is presented within which we provide rigorous estimations for seismic sources and structures in the Northeast Asia. We use Bayesian inversion methods, which enable statistical estimations of models and their uncertainties based on data information. Ambiguities in error statistics and model parameterizations are addressed by hierarchical and trans-dimensional (trans-D) techniques, which can be inherently implemented in the Bayesian inversions. Hence reliable estimation of model parameters and their uncertainties is possible, thus avoiding arbitrary regularizations and parameterizations. Hierarchical and trans-D inversions are performed to develop a three-dimensional velocity model using ambient noise data. To further improve the model, we perform joint inversions with receiver function data using a newly developed Bayesian method. For the source estimation, a novel moment tensor inversion method is presented and applied to regional waveform data of the North Korean nuclear explosion tests. By the combination of new Bayesian techniques and the structural model, coupled with meaningful uncertainties related to each of the processes, more quantitative monitoring and discrimination of seismic events is possible.

Method for indexing and retrieving manufacturing-specific digital imagery based on image content

DOEpatents

Ferrell, Regina K.; Karnowski, Thomas P.; Tobin, Jr., Kenneth W.

2004-06-15

A method for indexing and retrieving manufacturing-specific digital images based on image content comprises three steps. First, at least one feature vector can be extracted from a manufacturing-specific digital image stored in an image database. In particular, each extracted feature vector corresponds to a particular characteristic of the manufacturing-specific digital image, for instance, a digital image modality and overall characteristic, a substrate/background characteristic, and an anomaly/defect characteristic. Notably, the extracting step includes generating a defect mask using a detection process. Second, using an unsupervised clustering method, each extracted feature vector can be indexed in a hierarchical search tree. Third, a manufacturing-specific digital image associated with a feature vector stored in the hierarchicial search tree can be retrieved, wherein the manufacturing-specific digital image has image content comparably related to the image content of the query image. More particularly, can include two data reductions, the first performed based upon a query vector extracted from a query image. Subsequently, a user can select relevant images resulting from the first data reduction. From the selection, a prototype vector can be calculated, from which a second-level data reduction can be performed. The second-level data reduction can result in a subset of feature vectors comparable to the prototype vector, and further comparable to the query vector. An additional fourth step can include managing the hierarchical search tree by substituting a vector average for several redundant feature vectors encapsulated by nodes in the hierarchical search tree.

Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan

NASA Astrophysics Data System (ADS)

Hussain, Ijaz; Spöck, Gunter; Pilz, Jürgen; Yu, Hwa-Lung

2010-08-01

Spatio-temporal estimation of precipitation over a region is essential to the modeling of hydrologic processes for water resources management. The changes of magnitude and space-time heterogeneity of rainfall observations make space-time estimation of precipitation a challenging task. In this paper we propose a Box-Cox transformed hierarchical Bayesian multivariate spatio-temporal interpolation method for the skewed response variable. The proposed method is applied to estimate space-time monthly precipitation in the monsoon periods during 1974-2000, and 27-year monthly average precipitation data are obtained from 51 stations in Pakistan. The results of transformed hierarchical Bayesian multivariate spatio-temporal interpolation are compared to those of non-transformed hierarchical Bayesian interpolation by using cross-validation. The software developed by [11] is used for Bayesian non-stationary multivariate space-time interpolation. It is observed that the transformed hierarchical Bayesian method provides more accuracy than the non-transformed hierarchical Bayesian method.

A structure adapted multipole method for electrostatic interactions in protein dynamics

NASA Astrophysics Data System (ADS)

Niedermeier, Christoph; Tavan, Paul

1994-07-01

We present an algorithm for rapid approximate evaluation of electrostatic interactions in molecular dynamics simulations of proteins. Traditional algorithms require computational work of the order O(N2) for a system of N particles. Truncation methods which try to avoid that effort entail untolerably large errors in forces, energies and other observables. Hierarchical multipole expansion algorithms, which can account for the electrostatics to numerical accuracy, scale with O(N log N) or even with O(N) if they become augmented by a sophisticated scheme for summing up forces. To further reduce the computational effort we propose an algorithm that also uses a hierarchical multipole scheme but considers only the first two multipole moments (i.e., charges and dipoles). Our strategy is based on the consideration that numerical accuracy may not be necessary to reproduce protein dynamics with sufficient correctness. As opposed to previous methods, our scheme for hierarchical decomposition is adjusted to structural and dynamical features of the particular protein considered rather than chosen rigidly as a cubic grid. As compared to truncation methods we manage to reduce errors in the computation of electrostatic forces by a factor of 10 with only marginal additional effort.

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

PubMed Central

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-01-01

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. PMID:27618903

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

PubMed

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-08-27

Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

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

PubMed

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

2017-10-12

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

How Hierarchical Topics Evolve in Large Text Corpora.

PubMed

Cui, Weiwei; Liu, Shixia; Wu, Zhuofeng; Wei, Hao

2014-12-01

Using a sequence of topic trees to organize documents is a popular way to represent hierarchical and evolving topics in text corpora. However, following evolving topics in the context of topic trees remains difficult for users. To address this issue, we present an interactive visual text analysis approach to allow users to progressively explore and analyze the complex evolutionary patterns of hierarchical topics. The key idea behind our approach is to exploit a tree cut to approximate each tree and allow users to interactively modify the tree cuts based on their interests. In particular, we propose an incremental evolutionary tree cut algorithm with the goal of balancing 1) the fitness of each tree cut and the smoothness between adjacent tree cuts; 2) the historical and new information related to user interests. A time-based visualization is designed to illustrate the evolving topics over time. To preserve the mental map, we develop a stable layout algorithm. As a result, our approach can quickly guide users to progressively gain profound insights into evolving hierarchical topics. We evaluate the effectiveness of the proposed method on Amazon's Mechanical Turk and real-world news data. The results show that users are able to successfully analyze evolving topics in text data.

Template-free fabrication of hierarchical In2O3 hollow microspheres with superior HCHO-sensing properties

NASA Astrophysics Data System (ADS)

Zhang, Su; Song, Peng; Tian, Zhebin; Wang, Qi

2018-05-01

Hierarchical In2O3 hollow microspheres were successfully prepared via a facile and low-cost hydrothermal method. Their morphology and structure were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and the Brunauer-Emmett-Teller (BET) approach. The SEM and TEM results revealed that the as-obtained hollow In2O3 microspheres is composed of In2O3 nanospheres with 200-400 nm in diameter, and the size of In2O3 microspheres is about 2-4 μm. The specific surface area of the as-prepared In2O3 is about 40.94 m2/g. The sensor based on hierarchical In2O3 hollow microspheres displays excellent sensing properties to 10 ppm HCHO, and the optimum operating temperature is relatively low (200 °C). The response value of the as-fabricated sensor to 10 ppm HCHO is about 20. Due to the sensor based on hierarchical In2O3 hollow microspheres has many advantages, such as facile preparation and excellent gas-sensing properties, it has a wide range of prospects in practical applications.

Semantic Image Segmentation with Contextual Hierarchical Models.

PubMed

Seyedhosseini, Mojtaba; Tasdizen, Tolga

2016-05-01

Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which learns contextual information in a hierarchical framework for semantic segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM performs at par with state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500).

Activated Biomass-derived Graphene-based Carbons for Supercapacitors with High Energy and Power Density.

PubMed

Jung, SungHoon; Myung, Yusik; Kim, Bit Na; Kim, In Gyoo; You, In-Kyu; Kim, TaeYoung

2018-01-30

Here, we present a facile and low-cost method to produce hierarchically porous graphene-based carbons from a biomass source. Three-dimensional (3D) graphene-based carbons were produced through continuous sequential steps such as the formation and transformation of glucose-based polymers into 3D foam-like structures and their subsequent carbonization to form the corresponding macroporous carbons with thin graphene-based carbon walls of macropores and intersectional carbon skeletons. Physical and chemical activation was then performed on this carbon to create micro- and meso-pores, thereby producing hierarchically porous biomass-derived graphene-based carbons with a high Brunauer-Emmett-Teller specific surface area of 3,657 m 2  g -1 . Owing to its exceptionally high surface area, interconnected hierarchical pore networks, and a high degree of graphitization, this carbon exhibited a high specific capacitance of 175 F g -1 in ionic liquid electrolyte. A supercapacitor constructed with this carbon yielded a maximum energy density of 74 Wh kg -1 and a maximum power density of 408 kW kg -1 , based on the total mass of electrodes, which is comparable to those of the state-of-the-art graphene-based carbons. This approach holds promise for the low-cost and readily scalable production of high performance electrode materials for supercapacitors.

Assessment of Matrix Multiplication Learning with a Rule-Based Analytical Model--"A Bayesian Network Representation"

ERIC Educational Resources Information Center

Zhang, Zhidong

2016-01-01

This study explored an alternative assessment procedure to examine learning trajectories of matrix multiplication. It took rule-based analytical and cognitive task analysis methods specifically to break down operation rules for a given matrix multiplication. Based on the analysis results, a hierarchical Bayesian network, an assessment model,…

Design and control of active vision based mechanisms for intelligent robots

NASA Technical Reports Server (NTRS)

Wu, Liwei; Marefat, Michael M.

1994-01-01

In this paper, we propose a design of an active vision system for intelligent robot application purposes. The system has the degrees of freedom of pan, tilt, vergence, camera height adjustment, and baseline adjustment with a hierarchical control system structure. Based on this vision system, we discuss two problems involved in the binocular gaze stabilization process: fixation point selection and vergence disparity extraction. A hierarchical approach to determining point of fixation from potential gaze targets using evaluation function representing human visual behavior to outside stimuli is suggested. We also characterize different visual tasks in two cameras for vergence control purposes, and a phase-based method based on binarized images to extract vergence disparity for vergence control is presented. A control algorithm for vergence control is discussed.

Applying macromolecular crowding to 3D bioprinting: fabrication of 3D hierarchical porous collagen-based hydrogel constructs.

PubMed

Ng, Wei Long; Goh, Min Hao; Yeong, Wai Yee; Naing, May Win

2018-02-27

Native tissues and/or organs possess complex hierarchical porous structures that confer highly-specific cellular functions. Despite advances in fabrication processes, it is still very challenging to emulate the hierarchical porous collagen architecture found in most native tissues. Hence, the ability to recreate such hierarchical porous structures would result in biomimetic tissue-engineered constructs. Here, a single-step drop-on-demand (DOD) bioprinting strategy is proposed to fabricate hierarchical porous collagen-based hydrogels. Printable macromolecule-based bio-inks (polyvinylpyrrolidone, PVP) have been developed and printed in a DOD manner to manipulate the porosity within the multi-layered collagen-based hydrogels by altering the collagen fibrillogenesis process. The experimental results have indicated that hierarchical porous collagen structures could be achieved by controlling the number of macromolecule-based bio-ink droplets printed on each printed collagen layer. This facile single-step bioprinting process could be useful for the structural design of collagen-based hydrogels for various tissue engineering applications.

A Comparison of Two Approaches to Beta-Flexible Clustering.

ERIC Educational Resources Information Center

Belbin, Lee; And Others

1992-01-01

A method for hierarchical agglomerative polythetic (multivariate) clustering, based on unweighted pair group using arithmetic averages (UPGMA) is compared with the original beta-flexible technique, a weighted average method. Reasons the flexible UPGMA strategy is recommended are discussed, focusing on the ability to recover cluster structure over…

A Survey of Model Evaluation Approaches with a Tutorial on Hierarchical Bayesian Methods

ERIC Educational Resources Information Center

Shiffrin, Richard M.; Lee, Michael D.; Kim, Woojae; Wagenmakers, Eric-Jan

2008-01-01

This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues…

Chelating agent-free, vapor-assisted crystallization method to synthesize hierarchical microporous/mesoporous MIL-125 (Ti).

PubMed

McNamara, Nicholas D; Hicks, Jason C

2015-03-11

Titanium-based microporous heterogeneous catalysts are widely studied but are often limited by the accessibility of reactants to active sites. Metal-organic frameworks (MOFs), such as MIL-125 (Ti), exhibit enhanced surface areas due to their high intrinsic microporosity, but the pore diameters of most microporous MOFs are often too small to allow for the diffusion of larger reactants (>7 Å) relevant to petroleum and biomass upgrading. In this work, hierarchical microporous MIL-125 exhibiting significantly enhanced interparticle mesoporosity was synthesized using a chelating-free, vapor-assisted crystallization method. The resulting hierarchical MOF was examined as an active catalyst for the oxidation of dibenzothiophene (DBT) with tert-butyl hydroperoxide and outperformed the solely microporous analogue. This was attributed to greater access of the substrate to surface active sites, as the pores in the microporous analogues were of inadequate size to accommodate DBT. Moreover, thiophene adsorption studies suggested the mesoporous MOF contained larger amounts of unsaturated metal sites that could enhance the observed catalytic activity.

A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies

PubMed Central

Tang, Li

2014-01-01

Summary An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this paper, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, e.g., subjects with mental disorders or neurodegenerative diseases such as Parkinson’s as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation. PMID:24033125

Bayesian data analysis in population ecology: motivations, methods, and benefits

USGS Publications Warehouse

Dorazio, Robert

2016-01-01

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

PubMed

Du, Xiangjun; Shao, Fengjing; Wu, Shunyao; Zhang, Hanlin; Xu, Si

2017-07-01

Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

Point-Mass Aircraft Trajectory Prediction Using a Hierarchical, Highly-Adaptable Software Design

NASA Technical Reports Server (NTRS)

Karr, David A.; Vivona, Robert A.; Woods, Sharon E.; Wing, David J.

2017-01-01

A highly adaptable and extensible method for predicting four-dimensional trajectories of civil aircraft has been developed. This method, Behavior-Based Trajectory Prediction, is based on taxonomic concepts developed for the description and comparison of trajectory prediction software. A hierarchical approach to the "behavioral" layer of a point-mass model of aircraft flight, a clear separation between the "behavioral" and "mathematical" layers of the model, and an abstraction of the methods of integrating differential equations in the "mathematical" layer have been demonstrated to support aircraft models of different types (in particular, turbojet vs. turboprop aircraft) using performance models at different levels of detail and in different formats, and promise to be easily extensible to other aircraft types and sources of data. The resulting trajectories predict location, altitude, lateral and vertical speeds, and fuel consumption along the flight path of the subject aircraft accurately and quickly, accounting for local conditions of wind and outside air temperature. The Behavior-Based Trajectory Prediction concept was implemented in NASA's Traffic Aware Planner (TAP) flight-optimizing cockpit software application.

Risk Assessment and Hierarchical Risk Management of Enterprises in Chemical Industrial Parks Based on Catastrophe Theory

PubMed Central

Chen, Yu; Song, Guobao; Yang, Fenglin; Zhang, Shushen; Zhang, Yun; Liu, Zhenyu

2012-01-01

According to risk systems theory and the characteristics of the chemical industry, an index system was established for risk assessment of enterprises in chemical industrial parks (CIPs) based on the inherent risk of the source, effectiveness of the prevention and control mechanism, and vulnerability of the receptor. A comprehensive risk assessment method based on catastrophe theory was then proposed and used to analyze the risk levels of ten major chemical enterprises in the Songmu Island CIP, China. According to the principle of equal distribution function, the chemical enterprise risk level was divided into the following five levels: 1.0 (very safe), 0.8 (safe), 0.6 (generally recognized as safe, GRAS), 0.4 (unsafe), 0.2 (very unsafe). The results revealed five enterprises (50%) with an unsafe risk level, and another five enterprises (50%) at the generally recognized as safe risk level. This method solves the multi-objective evaluation and decision-making problem. Additionally, this method involves simple calculations and provides an effective technique for risk assessment and hierarchical risk management of enterprises in CIPs. PMID:23208298

Gene function prediction based on the Gene Ontology hierarchical structure.

PubMed

Cheng, Liangxi; Lin, Hongfei; Hu, Yuncui; Wang, Jian; Yang, Zhihao

2014-01-01

The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship.

Distributed hierarchical control architecture for integrating smart grid assets during normal and disrupted operations

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

Kalsi, Karan; Fuller, Jason C.; Somani, Abhishek

Disclosed herein are representative embodiments of methods, apparatus, and systems for facilitating operation and control of a resource distribution system (such as a power grid). Among the disclosed embodiments is a distributed hierarchical control architecture (DHCA) that enables smart grid assets to effectively contribute to grid operations in a controllable manner, while helping to ensure system stability and equitably rewarding their contribution. Embodiments of the disclosed architecture can help unify the dispatch of these resources to provide both market-based and balancing services.

A Model of Knowledge Based Information Retrieval with Hierarchical Concept Graph.

ERIC Educational Resources Information Center

Kim, Young Whan; Kim, Jin H.

1990-01-01

Proposes a model of knowledge-based information retrieval (KBIR) that is based on a hierarchical concept graph (HCG) which shows relationships between index terms and constitutes a hierarchical thesaurus as a knowledge base. Conceptual distance between a query and an object is discussed and the use of Boolean operators is described. (25…

Detection and Tracking Based on a Dynamical Hierarchical Occupancy Map in Agent-Based Simulations

DTIC Science & Technology

2008-09-01

describes various techniques for targeting with probabilty reasoning. Advantages and disadvantages of the different methods will be discussed...Psum. Such an algorithm could decrease the performance of the prototype itself and therefore was not considered. Probabilty over time 0 0.2 0.4 0.6

Action detection by double hierarchical multi-structure space-time statistical matching model

NASA Astrophysics Data System (ADS)

Han, Jing; Zhu, Junwei; Cui, Yiyin; Bai, Lianfa; Yue, Jiang

2018-03-01

Aimed at the complex information in videos and low detection efficiency, an actions detection model based on neighboring Gaussian structure and 3D LARK features is put forward. We exploit a double hierarchical multi-structure space-time statistical matching model (DMSM) in temporal action localization. First, a neighboring Gaussian structure is presented to describe the multi-scale structural relationship. Then, a space-time statistical matching method is proposed to achieve two similarity matrices on both large and small scales, which combines double hierarchical structural constraints in model by both the neighboring Gaussian structure and the 3D LARK local structure. Finally, the double hierarchical similarity is fused and analyzed to detect actions. Besides, the multi-scale composite template extends the model application into multi-view. Experimental results of DMSM on the complex visual tracker benchmark data sets and THUMOS 2014 data sets show the promising performance. Compared with other state-of-the-art algorithm, DMSM achieves superior performances.

Action detection by double hierarchical multi-structure space–time statistical matching model

NASA Astrophysics Data System (ADS)

Han, Jing; Zhu, Junwei; Cui, Yiyin; Bai, Lianfa; Yue, Jiang

2018-06-01

Aimed at the complex information in videos and low detection efficiency, an actions detection model based on neighboring Gaussian structure and 3D LARK features is put forward. We exploit a double hierarchical multi-structure space-time statistical matching model (DMSM) in temporal action localization. First, a neighboring Gaussian structure is presented to describe the multi-scale structural relationship. Then, a space-time statistical matching method is proposed to achieve two similarity matrices on both large and small scales, which combines double hierarchical structural constraints in model by both the neighboring Gaussian structure and the 3D LARK local structure. Finally, the double hierarchical similarity is fused and analyzed to detect actions. Besides, the multi-scale composite template extends the model application into multi-view. Experimental results of DMSM on the complex visual tracker benchmark data sets and THUMOS 2014 data sets show the promising performance. Compared with other state-of-the-art algorithm, DMSM achieves superior performances.

Automatic Abstraction in Planning

NASA Technical Reports Server (NTRS)

Christensen, J.

1991-01-01

Traditionally, abstraction in planning has been accomplished by either state abstraction or operator abstraction, neither of which has been fully automatic. We present a new method, predicate relaxation, for automatically performing state abstraction. PABLO, a nonlinear hierarchical planner, implements predicate relaxation. Theoretical, as well as empirical results are presented which demonstrate the potential advantages of using predicate relaxation in planning. We also present a new definition of hierarchical operators that allows us to guarantee a limited form of completeness. This new definition is shown to be, in some ways, more flexible than previous definitions of hierarchical operators. Finally, a Classical Truth Criterion is presented that is proven to be sound and complete for a planning formalism that is general enough to include most classical planning formalisms that are based on the STRIPS assumption.

Hierarchically Superstructured Metal Sulfides: Facile Perturbation-Assisted Nanofusion Synthesis and Visible Light Photocatalytic Characterizations

DOE PAGES

Yue, Yanfeng; Li, Yunchao; Bridges, Craig A.; ...

2016-11-29

A novel and simple perturbation-assisted nanofusion (PNF) synthetic strategy was developed for the fabrication of stable hierarchically superstructured metal sulfides. This promising approach, based on a kinetically controlled precipitation to simultaneously condense and re-dissolve polymorphic nanocrystallites, provides the resultant samples with a unique mesoporous framework. This PNF approach is environmentally friendly, produces gram-scale products in a matter of hours, and is complimentary to traditional hard or soft templating methods for the construction of mesoporous metal sulfides. PNF derived hierarchical porous CdS exhibited a vastly improved photocatalytic performance over its commercial bulk counterpart under visible light irradiation, demonstrating the advantage ofmore » the porous morphology for photocatalysis resulting from the enlarged surface area and the easy accessibility of the mesopores.« less

Using Nested Contractions and a Hierarchical Tensor Format To Compute Vibrational Spectra of Molecules with Seven Atoms.

PubMed

Thomas, Phillip S; Carrington, Tucker

2015-12-31

We propose a method for solving the vibrational Schrödinger equation with which one can compute hundreds of energy levels of seven-atom molecules using at most a few gigabytes of memory. It uses nested contractions in conjunction with the reduced-rank block power method (RRBPM) described in J. Chem. Phys. 2014, 140, 174111. Successive basis contractions are organized into a tree, the nodes of which are associated with eigenfunctions of reduced-dimension Hamiltonians. The RRBPM is used recursively to compute eigenfunctions of nodes in bases of products of reduced-dimension eigenfunctions of nodes with fewer coordinates. The corresponding vectors are tensors in what is called CP-format. The final wave functions are therefore represented in a hierarchical CP-format. Computational efficiency and accuracy are significantly improved by representing the Hamiltonian in the same hierarchical format as the wave function. We demonstrate that with this hierarchical RRBPM it is possible to compute energy levels of a 64-D coupled-oscillator model Hamiltonian and also of acetonitrile (CH3CN) and ethylene oxide (C2H4O), for which we use quartic potentials. The most accurate acetonitrile calculation uses 139 MB of memory and takes 3.2 h on a single processor. The most accurate ethylene oxide calculation uses 6.1 GB of memory and takes 14 d on 63 processors. The hierarchical RRBPM shatters the memory barrier that impedes the calculation of vibrational spectra.

MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

PubMed Central

Korkmaz, Selcuk; Zararsiz, Gokmen; Goksuluk, Dincer

2015-01-01

Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/. PMID:25928885

Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks.

PubMed

Chen, Ying-Chih; Wen, Chih-Yu

2012-11-08

This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.

Cascade process modeling with mechanism-based hierarchical neural networks.

PubMed

Cong, Qiumei; Yu, Wen; Chai, Tianyou

2010-02-01

Cascade process, such as wastewater treatment plant, includes many nonlinear sub-systems and many variables. When the number of sub-systems is big, the input-output relation in the first block and the last block cannot represent the whole process. In this paper we use two techniques to overcome the above problem. Firstly we propose a new neural model: hierarchical neural networks to identify the cascade process; then we use serial structural mechanism model based on the physical equations to connect with neural model. A stable learning algorithm and theoretical analysis are given. Finally, this method is used to model a wastewater treatment plant. Real operational data of wastewater treatment plant is applied to illustrate the modeling approach.

An Amperometric Acetylcholinesterase Sensor Based on the Bio-templated Synthesis of Hierarchical Mesoporous Bioactive Glass Microspheres

NASA Astrophysics Data System (ADS)

Lv, Zhuo; Luo, Ruiping; Xi, Lijuan; Chen, Yang; Wang, Hongsu

2017-11-01

This work describes the synthesis of three-dimensional hollow hierarchical mesoporous bioactive glass (HMBG) microspheres based on Herba leonuri pollen grains via a hydrothermal method. The HMBG microspheres perfectly copied the hierarchical porous structure and inner hollow structure constituting the double-layer surface of the natural Herba leonuri pollen grains. This structural mimicry of the pollen grains resulted in a higher degree of adsorption of acetylcholinesterase (AChE) on HMBG microspheres in comparison with mesoporous bioactive glass. Subsequently, an amperometric biosensor for the detection of Malathion was fabricated by immobilizing AChE onto an HMBG microspheres-modified carbon paste electrode. The biosensor response exhibited two good linear ranges during an incubation time of 10 min in the malathion concentration ranges of 0.02-50 ppb and 50-600 ppb, with a detection limit of 0.0135 ppb ( S/ N = 3). Overall, the prepared enzymatic biosensor showed high sensitivity in the rapid detection of Malathion and could be applied to detect pesticide residues in vegetable matter.

Self-assembled three-dimensional hierarchical graphene/polypyrrole nanotube hybrid aerogel and its application for supercapacitors.

PubMed

Ye, Shibing; Feng, Jiachun

2014-06-25

A three-dimensional hierarchical graphene/polypyrrole aerogel (GPA) has been fabricated using graphene oxide (GO) and already synthesized one-dimensional hollow polypyrrole nanotubes (PNTs) as the feedstock. The amphiphilic GO is helpful in effectively promoting the dispersion of well-defined PNTs to result in a stable, homogeneous GO/PNT complex solution, while the PNTs not only provide a large accessible surface area for fast transport of hydrate ions but also act as spacers to prevent the restacking of graphene sheets. By a simple one-step reduction self-assembly process, hierarchically structured, low-density, highly compressible GPAs are easily obtained, which favorably combine the advantages of graphene and PNTs. The supercapacitor electrodes based on such materials exhibit excellent electrochemical performance, including a high specific capacitance up to 253 F g(-1), good rate performance, and outstanding cycle stability. Moreover, this method may be feasible to prepare other graphene-based hybrid aerogels with structure-controllable nanostructures in large scale, thereby holding enormous potential in many application fields.

Palladium nanoparticles dispersed on the hollow aluminosilicate microsphere@hierarchical γ-AlOOH as an excellent catalyst for the hydrogenation of nitroarenes under ambient conditions

NASA Astrophysics Data System (ADS)

Tian, Meng; Cui, Xueliang; Dong, Chunxu; Dong, Zhengping

2016-12-01

In this study, a novel catalyst has been prepared through supporting Pd nanoparticles (NPs) on the surface of boehmite (γ-AlOOH) based hollow aluminosilicate microspheres (HAM@γ-AlOOH). The prepared Pd/HAM@γ-AlOOH catalyst has high catalytic activity for the hydrogenation of nitroarenes to their corresponding amino derivatives with high yields at ambient conditions. The high catalytic efficiency is attributed to the large pore size of the flower-like hierarchical flakes structure of HAM@γ-AlOOH, that gives Pd NPs on the support surface easy accessibility. Moreover, the Pd/HAM@γ-AlOOH catalyst can also be easily recycled at least five times without obvious decrease of catalytic activity. This work may provide a useful method for the fabrication of supported noble metal NP-based catalysts on the surface of mesoporous hierarchical structure materials with easy accessibility and superior activity.

Superposition-Based Analysis of First-Order Probabilistic Timed Automata

NASA Astrophysics Data System (ADS)

Fietzke, Arnaud; Hermanns, Holger; Weidenbach, Christoph

This paper discusses the analysis of first-order probabilistic timed automata (FPTA) by a combination of hierarchic first-order superposition-based theorem proving and probabilistic model checking. We develop the overall semantics of FPTAs and prove soundness and completeness of our method for reachability properties. Basically, we decompose FPTAs into their time plus first-order logic aspects on the one hand, and their probabilistic aspects on the other hand. Then we exploit the time plus first-order behavior by hierarchic superposition over linear arithmetic. The result of this analysis is the basis for the construction of a reachability equivalent (to the original FPTA) probabilistic timed automaton to which probabilistic model checking is finally applied. The hierarchic superposition calculus required for the analysis is sound and complete on the first-order formulas generated from FPTAs. It even works well in practice. We illustrate the potential behind it with a real-life DHCP protocol example, which we analyze by means of tool chain support.

Compressed-sensing-based content-driven hierarchical reconstruction: Theory and application to C-arm cone-beam tomography

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

Langet, Hélène; Laboratoire des Signaux et Systèmes, CentraleSupélec, Gif-sur-Yvette F-91192; Center for Visual Computing, CentraleSupélec, Châtenay-Malabry F-92295

2015-09-15

Purpose: This paper addresses the reconstruction of x-ray cone-beam computed tomography (CBCT) for interventional C-arm systems. Subsampling of CBCT is a significant issue with C-arms due to their slow rotation and to the low frame rate of their flat panel x-ray detectors. The aim of this work is to propose a novel method able to handle the subsampling artifacts generally observed with analytical reconstruction, through a content-driven hierarchical reconstruction based on compressed sensing. Methods: The central idea is to proceed with a hierarchical method where the most salient features (high intensities or gradients) are reconstructed first to reduce the artifactsmore » these features induce. These artifacts are addressed first because their presence contaminates less salient features. Several hierarchical schemes aiming at streak artifacts reduction are introduced for C-arm CBCT: the empirical orthogonal matching pursuit approach with the ℓ{sub 0} pseudonorm for reconstructing sparse vessels; a convex variant using homotopy with the ℓ{sub 1}-norm constraint of compressed sensing, for reconstructing sparse vessels over a nonsparse background; homotopy with total variation (TV); and a novel empirical extension to nonlinear diffusion (NLD). Such principles are implemented with penalized iterative filtered backprojection algorithms. For soft-tissue imaging, the authors compare the use of TV and NLD filters as sparsity constraints, both optimized with the alternating direction method of multipliers, using a threshold for TV and a nonlinear weighting for NLD. Results: The authors show on simulated data that their approach provides fast convergence to good approximations of the solution of the TV-constrained minimization problem introduced by the compressed sensing theory. Using C-arm CBCT clinical data, the authors show that both TV and NLD can deliver improved image quality by reducing streaks. Conclusions: A flexible compressed-sensing-based algorithmic approach is proposed that is able to accommodate for a wide range of constraints. It is successfully applied to C-arm CBCT images that may not be so well approximated by piecewise constant functions.« less

Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yu, Yongtao; Li, Jonathan; Wen, Chenglu; Guan, Haiyan; Luo, Huan; Wang, Cheng

2016-03-01

This paper presents a novel algorithm for detection and recognition of traffic signs in mobile laser scanning (MLS) data for intelligent transportation-related applications. The traffic sign detection task is accomplished based on 3-D point clouds by using bag-of-visual-phrases representations; whereas the recognition task is achieved based on 2-D images by using a Gaussian-Bernoulli deep Boltzmann machine-based hierarchical classifier. To exploit high-order feature encodings of feature regions, a deep Boltzmann machine-based feature encoder is constructed. For detecting traffic signs in 3-D point clouds, the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.956, 0.946, 0.907, and 0.951, respectively, on the four selected MLS datasets. For on-image traffic sign recognition, a recognition accuracy of 97.54% is achieved by using the proposed hierarchical classifier. Comparative studies with the existing traffic sign detection and recognition methods demonstrate that our algorithm obtains promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.

CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data.

PubMed

duVerle, David A; Yotsukura, Sohiya; Nomura, Seitaro; Aburatani, Hiroyuki; Tsuda, Koji

2016-09-13

Single-cell RNA sequencing is fast becoming one the standard method for gene expression measurement, providing unique insights into cellular processes. A number of methods, based on general dimensionality reduction techniques, have been suggested to help infer and visualise the underlying structure of cell populations from single-cell expression levels, yet their models generally lack proper biological grounding and struggle at identifying complex differentiation paths. Here we introduce cellTree: an R/Bioconductor package that uses a novel statistical approach, based on document analysis techniques, to produce tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups of genes that can provide biological insights. With cellTree, we provide experimentalists with an easy-to-use tool, based on statistically and biologically-sound algorithms, to efficiently explore and visualise single-cell RNA data. The cellTree package is publicly available in the online Bionconductor repository at: http://bioconductor.org/packages/cellTree/ .

Novel density-based and hierarchical density-based clustering algorithms for uncertain data.

PubMed

Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

2017-09-01

Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing algorithms in accuracy and efficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of Bayesian networks in a hierarchical structure for environmental risk assessment: a case study of the Gabric Dam, Iran.

PubMed

Malekmohammadi, Bahram; Tayebzadeh Moghadam, Negar

2018-04-13

Environmental risk assessment (ERA) is a commonly used, effective tool applied to reduce adverse effects of environmental risk factors. In this study, ERA was investigated using the Bayesian network (BN) model based on a hierarchical structure of variables in an influence diagram (ID). ID facilitated ranking of the different alternatives under uncertainty that were then used to evaluate comparisons of the different risk factors. BN was used to present a new model for ERA applicable to complicated development projects such as dam construction. The methodology was applied to the Gabric Dam, in southern Iran. The main environmental risk factors in the region, presented by the Gabric Dam, were identified based on the Delphi technique and specific features of the study area. These included the following: flood, water pollution, earthquake, changes in land use, erosion and sedimentation, effects on the population, and ecosensitivity. These risk factors were then categorized based on results from the output decision node of the BN, including expected utility values for risk factors in the decision node. ERA was performed for the Gabric Dam using the analytical hierarchy process (AHP) method to compare results of BN modeling with those of conventional methods. Results determined that a BN-based hierarchical structure to ERA present acceptable and reasonable risk assessment prioritization in proposing suitable solutions to reduce environmental risks and can be used as a powerful decision support system for evaluating environmental risks.

Hierarchical Interactions Model for Predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) Conversion

PubMed Central

Li, Han; Liu, Yashu; Gong, Pinghua; Zhang, Changshui; Ye, Jieping

2014-01-01

Identifying patients with Mild Cognitive Impairment (MCI) who are likely to convert to dementia has recently attracted increasing attention in Alzheimer's disease (AD) research. An accurate prediction of conversion from MCI to AD can aid clinicians to initiate treatments at early stage and monitor their effectiveness. However, existing prediction systems based on the original biosignatures are not satisfactory. In this paper, we propose to fit the prediction models using pairwise biosignature interactions, thus capturing higher-order relationship among biosignatures. Specifically, we employ hierarchical constraints and sparsity regularization to prune the high-dimensional input features. Based on the significant biosignatures and underlying interactions identified, we build classifiers to predict the conversion probability based on the selected features. We further analyze the underlying interaction effects of different biosignatures based on the so-called stable expectation scores. We have used 293 MCI subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database that have MRI measurements at the baseline to evaluate the effectiveness of the proposed method. Our proposed method achieves better classification performance than state-of-the-art methods. Moreover, we discover several significant interactions predictive of MCI-to-AD conversion. These results shed light on improving the prediction performance using interaction features. PMID:24416143

Hierarchical core-shell structure of ZnO nanorod@NiO/MoO₂ composite nanosheet arrays for high-performance supercapacitors.

PubMed

Hou, Sucheng; Zhang, Guanhua; Zeng, Wei; Zhu, Jian; Gong, Feilong; Li, Feng; Duan, Huigao

2014-08-27

A hierarchical core-shell structure of ZnO nanorod@NiO/MoO2 composite nanosheet arrays on nickel foam substrate for high-performance supercapacitors was constructed by a two-step solution-based method involving two hydrothermal processes followed by a calcination treatment. Compared to one composed of pure NiO/MoO2 composite nanosheets, the hierarchical core-shell structure electrode displays better pseudocapacitive behaviors in 2 M KOH, including high areal specific capacitance values of 1.18 F cm(-2) at 5 mA cm(-2) and 0.6 F cm(-2) at 30 mA cm(-2) as well as relatively good rate capability at high current densities. Furthermore, it also shows remarkable cycle stability, remaining at 91.7% of the initial value even after 4000 cycles at a current density of 10 mA cm(-2). The enhanced pseudocapacitive behaviors are mainly due to the unique hierarchical core-shell structure and the synergistic effect of combining ZnO nanorod arrays and NiO/MoO2 composite nanosheets. This novel hierarchical core-shell structure shows promise for use in next-generation supercapacitors.

Fabrication of hierarchical porous N-doping carbon membrane by using ;confined nanospace deposition; method for supercapacitor

NASA Astrophysics Data System (ADS)

Wang, Guoxu; Liu, Meng; Du, Juan; Liu, Lei; Yu, Yifeng; Sha, Jitong; Chen, Aibing

2018-03-01

The membrane carbon materials with hierarchical porous architecture are attractive because they can provide more channels for ion transport and shorten the ions transport path. Herein, we develop a facile way based on "confined nanospace deposition" to fabricate N-dopi-ng three dimensional hierarchical porous membrane carbon material (N-THPMC) via coating the nickel nitrate, silicate oligomers and triblock copolymer P123 on the branches of commercial polyamide membrane (PAM). During high temperature treatment, the mesoporous silica layer and Ni species serve as a "confined nanospace" and catalyst respectively, which are indispensable elements for formation of carbon framework, and the gas-phase carbon precursors which derive from the decomposition of PAM are deposited into the "confined nanospace" forming carbon framework. The N-THPMC with hierarchical macro/meso/microporous structure, N-doping (2.9%) and large specific surface area (994m2 g-1) well inherits the membrane morphology and hierarchical porous structure of PAM. The N-THPMC as electrode without binder exhibits a specific capacitance of 252 F g-1 at the current density of 1 A g-1 in 6 M KOH electrolyte and excellent cycling stability of 92.7% even after 5000 cycles.

Co3O4 based non-enzymatic glucose sensor with high sensitivity and reliable stability derived from hollow hierarchical architecture.

PubMed

Tian, Liangliang; He, Gege; Cai, Yanhua; Wu, Shenping; Su, Yongyao; Yan, Hengqing; Yang, Cong; Chen, Yanling; Li, Lu

2018-02-16

Inspired by kinetics, the design of hollow hierarchical electrocatalysts through large-scale integration of building blocks is recognized as an effective approach to the achievement of superior electrocatalytic performance. In this work, a hollow, hierarchical Co 3 O 4 architecture (Co 3 O 4 HHA) was constructed using a coordinated etching and precipitation (CEP) method followed by calcination. The resulting Co 3 O 4 HHA electrode exhibited excellent electrocatalytic activity in terms of high sensitivity (839.3 μA mM -1 cm -2 ) and reliable stability in glucose detection. The high sensitivity could be attributed to the large specific surface area (SSA), ample unimpeded penetration diffusion paths and high electron transfer rate originating from the unique two-dimensional (2D) sheet-like character and hollow porous architecture. The hollow hierarchical structure also affords sufficient interspace for accommodation of volume change and structural strain, resulting in enhanced stability. The results indicate that Co 3 O 4 HHA could have potential for application in the design of non-enzymatic glucose sensors, and that the construction of hollow hierarchical architecture provides an efficient way to design highly active, stable electrocatalysts.

Co3O4 based non-enzymatic glucose sensor with high sensitivity and reliable stability derived from hollow hierarchical architecture

NASA Astrophysics Data System (ADS)

Tian, Liangliang; He, Gege; Cai, Yanhua; Wu, Shenping; Su, Yongyao; Yan, Hengqing; Yang, Cong; Chen, Yanling; Li, Lu

2018-02-01

Inspired by kinetics, the design of hollow hierarchical electrocatalysts through large-scale integration of building blocks is recognized as an effective approach to the achievement of superior electrocatalytic performance. In this work, a hollow, hierarchical Co3O4 architecture (Co3O4 HHA) was constructed using a coordinated etching and precipitation (CEP) method followed by calcination. The resulting Co3O4 HHA electrode exhibited excellent electrocatalytic activity in terms of high sensitivity (839.3 μA mM-1 cm-2) and reliable stability in glucose detection. The high sensitivity could be attributed to the large specific surface area (SSA), ample unimpeded penetration diffusion paths and high electron transfer rate originating from the unique two-dimensional (2D) sheet-like character and hollow porous architecture. The hollow hierarchical structure also affords sufficient interspace for accommodation of volume change and structural strain, resulting in enhanced stability. The results indicate that Co3O4 HHA could have potential for application in the design of non-enzymatic glucose sensors, and that the construction of hollow hierarchical architecture provides an efficient way to design highly active, stable electrocatalysts.

Bayesian techniques for analyzing group differences in the Iowa Gambling Task: A case study of intuitive and deliberate decision-makers.

PubMed

Steingroever, Helen; Pachur, Thorsten; Šmíra, Martin; Lee, Michael D

2018-06-01

The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for comparing performance, and a suite of three complementary model-based methods for assessing the cognitive processes underlying IGT performance. The three model-based methods involve Bayesian hierarchical parameter estimation, Bayes factor model comparison, and Bayesian latent-mixture modeling. We illustrate these Bayesian methods by applying them to test the extent to which differences in intuitive versus deliberate decision style are associated with differences in IGT performance. The results show that intuitive and deliberate decision-makers behave similarly on the IGT, and the modeling analyses consistently suggest that both groups of decision-makers rely on similar cognitive processes. Our results challenge the notion that individual differences in intuitive and deliberate decision styles have a broad impact on decision-making. They also highlight the advantages of Bayesian methods, especially their ability to quantify evidence in favor of the null hypothesis, and that they allow model-based analyses to incorporate hierarchical and latent-mixture structures.

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph.

PubMed

Ma, Hong-Wu; Zhao, Xue-Ming; Yuan, Ying-Jin; Zeng, An-Ping

2004-08-12

Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level. http://genome.gbf.de/bioinformatics/

A Technique of Two-Stage Clustering Applied to Environmental and Civil Engineering and Related Methods of Citation Analysis.

ERIC Educational Resources Information Center

Miyamoto, S.; Nakayama, K.

1983-01-01

A method of two-stage clustering of literature based on citation frequency is applied to 5,065 articles from 57 journals in environmental and civil engineering. Results of related methods of citation analysis (hierarchical graph, clustering of journals, multidimensional scaling) applied to same set of articles are compared. Ten references are…

Carbon composition with hierarchical porosity, and methods of preparation

DOEpatents

Mayes, Richard T; Dai, Sheng

2014-10-21

A method for fabricating a porous carbon material possessing a hierarchical porosity, the method comprising subjecting a precursor composition to a curing step followed by a carbonization step, the precursor composition comprising: (i) a templating component comprised of a block copolymer, (ii) a phenolic component, (iii) a dione component in which carbonyl groups are adjacent, and (iv) an acidic component, wherein said carbonization step comprises heating the precursor composition at a carbonizing temperature for sufficient time to convert the precursor composition to a carbon material possessing a hierarchical porosity comprised of mesopores and macropores. Also described are the resulting hierarchical porous carbon material, a capacitive deionization device in which the porous carbon material is incorporated, as well as methods for desalinating water by use of said capacitive deionization device.

Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

EPA Science Inventory

Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

The potential of near-surface geophysical methods in a hierarchical monitoring approach for the detection of shallow CO2 seeps at geological storage sites

NASA Astrophysics Data System (ADS)

Sauer, U.; Schuetze, C.; Dietrich, P.

2013-12-01

The MONACO project (Monitoring approach for geological CO2 storage sites using a hierarchic observation concept) aims to find reliable monitoring tools that work on different spatial and temporal scales at geological CO2 storage sites. This integrative hierarchical monitoring approach based on different levels of coverage and resolutions is proposed as a means of reliably detecting CO2 degassing areas at ground surface level and for identifying CO2 leakages from storage formations into the shallow subsurface, as well as CO2 releases into the atmosphere. As part of this integrative hierarchical monitoring concept, several methods and technologies from ground-based remote sensing (Open-path Fourier-transform infrared (OP-FTIR) spectroscopy), regional measurements (near-surface geophysics, chamber-based soil CO2 flux measurement) and local in-situ measurements (using shallow boreholes) will either be combined or used complementary to one another. The proposed combination is a suitable concept for investigating CO2 release sites. This also presents the possibility of adopting a modular monitoring concept whereby our monitoring approach can be expanded to incorporate other methods in various coverage scales at any temporal resolution. The link between information obtained from large-scale surveys and local in-situ monitoring can be realized by sufficient geophysical techniques for meso-scale monitoring, such as geoelectrical and self-potential (SP) surveys. These methods are useful for characterizing fluid flow and transport processes in permeable near-surface sedimentary layers and can yield important information concerning CO2-affected subsurface structures. Results of measurements carried out a natural analogue site in the Czech Republic indicate that the hierarchical monitoring approach represents a successful multidisciplinary modular concept that can be used to monitor both physical and chemical processes taking place during CO2 migration and seepage. The application of FTIR spectroscopy in combination with soil gas surveys and geophysical investigations results in a comprehensive site characterization, including atmospheric and near-surface CO2 distribution, as well as subsurface structural features. We observed a correlation of higher CO2 concentration and flux rates at the meso-scale that coincides with distinct geophysical anomalies. Here, we found prominent SP anomalies and zones of lower resistivity in the geoelectrical images compared to undisturbed regions nearby. This presentation will discuss the results we obtained and illustrate the influence of CO2 on electrical parameters measured under field conditions in relation to environmental parameters.

Rigorous Approach in Investigation of Seismic Structure and Source Characteristicsin Northeast Asia: Hierarchical and Trans-dimensional Bayesian Inversion

NASA Astrophysics Data System (ADS)

Mustac, M.; Kim, S.; Tkalcic, H.; Rhie, J.; Chen, Y.; Ford, S. R.; Sebastian, N.

2015-12-01

Conventional approaches to inverse problems suffer from non-linearity and non-uniqueness in estimations of seismic structures and source properties. Estimated results and associated uncertainties are often biased by applied regularizations and additional constraints, which are commonly introduced to solve such problems. Bayesian methods, however, provide statistically meaningful estimations of models and their uncertainties constrained by data information. In addition, hierarchical and trans-dimensional (trans-D) techniques are inherently implemented in the Bayesian framework to account for involved error statistics and model parameterizations, and, in turn, allow more rigorous estimations of the same. Here, we apply Bayesian methods throughout the entire inference process to estimate seismic structures and source properties in Northeast Asia including east China, the Korean peninsula, and the Japanese islands. Ambient noise analysis is first performed to obtain a base three-dimensional (3-D) heterogeneity model using continuous broadband waveforms from more than 300 stations. As for the tomography of surface wave group and phase velocities in the 5-70 s band, we adopt a hierarchical and trans-D Bayesian inversion method using Voronoi partition. The 3-D heterogeneity model is further improved by joint inversions of teleseismic receiver functions and dispersion data using a newly developed high-efficiency Bayesian technique. The obtained model is subsequently used to prepare 3-D structural Green's functions for the source characterization. A hierarchical Bayesian method for point source inversion using regional complete waveform data is applied to selected events from the region. The seismic structure and source characteristics with rigorously estimated uncertainties from the novel Bayesian methods provide enhanced monitoring and discrimination of seismic events in northeast Asia.

Hierarchical Regional Disparities and Potential Sector Identification Using Modified Agglomerative Clustering

NASA Astrophysics Data System (ADS)

Munandar, T. A.; Azhari; Mushdholifah, A.; Arsyad, L.

2017-03-01

Disparities in regional development methods are commonly identified using the Klassen Typology and Location Quotient. Both methods typically use the data on the gross regional domestic product (GRDP) sectors of a particular region. The Klassen approach can identify regional disparities by classifying the GRDP sector data into four classes, namely Quadrants I, II, III, and IV. Each quadrant indicates a certain level of regional disparities based on the GRDP sector value of the said region. Meanwhile, the Location Quotient (LQ) is usually used to identify potential sectors in a particular region so as to determine which sectors are potential and which ones are not potential. LQ classifies each sector into three classes namely, the basic sector, the non-basic sector with a competitive advantage, and the non-basic sector which can only meet its own necessities. Both Klassen Typology and LQ are unable to visualize the relationship of achievements in the development clearly of each region and sector. This research aimed to develop a new approach to the identification of disparities in regional development in the form of hierarchical clustering. The method of Hierarchical Agglomerative Clustering (HAC) was employed as the basis of the hierarchical clustering model for identifying disparities in regional development. Modifications were made to HAC using the Klassen Typology and LQ. Then, HAC which had been modified using the Klassen Typology was called MHACK while HAC which had been modified using LQ was called MACLoQ. Both algorithms can be used to identify regional disparities (MHACK) and potential sectors (MACLoQ), respectively, in the form of hierarchical clusters. Based on the MHACK in 31 regencies in Central Java Province, it is identified that 3 regencies (Demak, Jepara, and Magelang City) fall into the category of developed and rapidly-growing regions, while the other 28 regencies fall into the category of developed but depressed regions. Results of the MACLoQ implementation suggest that there is only 1 regency which falls into the basic-sector category (Banyumas), while the other regencies fall into the non-basic non-competitive sector category.

Hierarchical minutiae matching for fingerprint and palmprint identification.

PubMed

Chen, Fanglin; Huang, Xiaolin; Zhou, Jie

2013-12-01

Fingerprints and palmprints are the most common authentic biometrics for personal identification, especially for forensic security. Previous research have been proposed to speed up the searching process in fingerprint and palmprint identification systems, such as those based on classification or indexing, in which the deterioration of identification accuracy is hard to avert. In this paper, a novel hierarchical minutiae matching algorithm for fingerprint and palmprint identification systems is proposed. This method decomposes the matching step into several stages and rejects many false fingerprints or palmprints on different stages, thus it can save much time while preserving a high identification rate. Experimental results show that the proposed algorithm can save almost 50% searching time compared with traditional methods and illustrate its effectiveness.

Ubiquitous Robotic Technology for Smart Manufacturing System.

PubMed

Wang, Wenshan; Zhu, Xiaoxiao; Wang, Liyu; Qiu, Qiang; Cao, Qixin

2016-01-01

As the manufacturing tasks become more individualized and more flexible, the machines in smart factory are required to do variable tasks collaboratively without reprogramming. This paper for the first time discusses the similarity between smart manufacturing systems and the ubiquitous robotic systems and makes an effort on deploying ubiquitous robotic technology to the smart factory. Specifically, a component based framework is proposed in order to enable the communication and cooperation of the heterogeneous robotic devices. Further, compared to the service robotic domain, the smart manufacturing systems are often in larger size. So a hierarchical planning method was implemented to improve the planning efficiency. A test bed of smart factory is developed. It demonstrates that the proposed framework is suitable for industrial domain, and the hierarchical planning method is able to solve large problems intractable with flat methods.

Ubiquitous Robotic Technology for Smart Manufacturing System

PubMed Central

Zhu, Xiaoxiao; Wang, Liyu; Qiu, Qiang; Cao, Qixin

2016-01-01

As the manufacturing tasks become more individualized and more flexible, the machines in smart factory are required to do variable tasks collaboratively without reprogramming. This paper for the first time discusses the similarity between smart manufacturing systems and the ubiquitous robotic systems and makes an effort on deploying ubiquitous robotic technology to the smart factory. Specifically, a component based framework is proposed in order to enable the communication and cooperation of the heterogeneous robotic devices. Further, compared to the service robotic domain, the smart manufacturing systems are often in larger size. So a hierarchical planning method was implemented to improve the planning efficiency. A test bed of smart factory is developed. It demonstrates that the proposed framework is suitable for industrial domain, and the hierarchical planning method is able to solve large problems intractable with flat methods. PMID:27446206

Connected Dominating Set Based Topology Control in Wireless Sensor Networks

ERIC Educational Resources Information Center

He, Jing

2012-01-01

Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…

Using Cluster Analysis to Compartmentalize a Large Managed Wetland Based on Physical, Biological, and Climatic Geospatial Attributes.

PubMed

Hahus, Ian; Migliaccio, Kati; Douglas-Mankin, Kyle; Klarenberg, Geraldine; Muñoz-Carpena, Rafael

2018-04-27

Hierarchical and partitional cluster analyses were used to compartmentalize Water Conservation Area 1, a managed wetland within the Arthur R. Marshall Loxahatchee National Wildlife Refuge in southeast Florida, USA, based on physical, biological, and climatic geospatial attributes. Single, complete, average, and Ward's linkages were tested during the hierarchical cluster analyses, with average linkage providing the best results. In general, the partitional method, partitioning around medoids, found clusters that were more evenly sized and more spatially aggregated than those resulting from the hierarchical analyses. However, hierarchical analysis appeared to be better suited to identify outlier regions that were significantly different from other areas. The clusters identified by geospatial attributes were similar to clusters developed for the interior marsh in a separate study using water quality attributes, suggesting that similar factors have influenced variations in both the set of physical, biological, and climatic attributes selected in this study and water quality parameters. However, geospatial data allowed further subdivision of several interior marsh clusters identified from the water quality data, potentially indicating zones with important differences in function. Identification of these zones can be useful to managers and modelers by informing the distribution of monitoring equipment and personnel as well as delineating regions that may respond similarly to future changes in management or climate.

The Common Prescription Patterns Based on the Hierarchical Clustering of Herb-Pairs Efficacies

PubMed Central

2016-01-01

Prescription patterns are rules or regularities used to generate, recognize, or judge a prescription. Most of existing studies focused on the specific prescription patterns for diverse diseases or syndromes, while little attention was paid to the common patterns, which reflect the global view of the regularities of prescriptions. In this paper, we designed a method CPPM to find the common prescription patterns. The CPPM is based on the hierarchical clustering of herb-pair efficacies (HPEs). Firstly, HPEs were hierarchically clustered; secondly, the individual herbs are labeled by the HPEC (the clusters of HPEs); and then the prescription patterns were extracted from the combinations of HPEC; finally the common patterns are recognized statistically. The results showed that HPEs have hierarchical clustering structure. When the clustering level is 2 and the HPEs were classified into two clusters, the common prescription patterns are obvious. Among 332 candidate prescriptions, 319 prescriptions follow the common patterns. The description of the patterns is that if a prescription contains the herbs of the cluster (C 1), it is very likely to have other herbs of another cluster (C 2); while a prescription has the herbs of C 2, it may have no herbs of C 1. Finally, we discussed that the common patterns are mathematically coincident with the Blood-Qi theory. PMID:27190534

Generalising Ward's Method for Use with Manhattan Distances.

PubMed

Strauss, Trudie; von Maltitz, Michael Johan

2017-01-01

The claim that Ward's linkage algorithm in hierarchical clustering is limited to use with Euclidean distances is investigated. In this paper, Ward's clustering algorithm is generalised to use with l1 norm or Manhattan distances. We argue that the generalisation of Ward's linkage method to incorporate Manhattan distances is theoretically sound and provide an example of where this method outperforms the method using Euclidean distances. As an application, we perform statistical analyses on languages using methods normally applied to biology and genetic classification. We aim to quantify differences in character traits between languages and use a statistical language signature based on relative bi-gram (sequence of two letters) frequencies to calculate a distance matrix between 32 Indo-European languages. We then use Ward's method of hierarchical clustering to classify the languages, using the Euclidean distance and the Manhattan distance. Results obtained from using the different distance metrics are compared to show that the Ward's algorithm characteristic of minimising intra-cluster variation and maximising inter-cluster variation is not violated when using the Manhattan metric.

A Concept Hierarchy Based Ontology Mapping Approach

NASA Astrophysics Data System (ADS)

Wang, Ying; Liu, Weiru; Bell, David

Ontology mapping is one of the most important tasks for ontology interoperability and its main aim is to find semantic relationships between entities (i.e. concept, attribute, and relation) of two ontologies. However, most of the current methods only consider one to one (1:1) mappings. In this paper we propose a new approach (CHM: Concept Hierarchy based Mapping approach) which can find simple (1:1) mappings and complex (m:1 or 1:m) mappings simultaneously. First, we propose a new method to represent the concept names of entities. This method is based on the hierarchical structure of an ontology such that each concept name of entity in the ontology is included in a set. The parent-child relationship in the hierarchical structure of an ontology is then extended as a set-inclusion relationship between the sets for the parent and the child. Second, we compute the similarities between entities based on the new representation of entities in ontologies. Third, after generating the mapping candidates, we select the best mapping result for each source entity. We design a new algorithm based on the Apriori algorithm for selecting the mapping results. Finally, we obtain simple (1:1) and complex (m:1 or 1:m) mappings. Our experimental results and comparisons with related work indicate that utilizing this method in dealing with ontology mapping is a promising way to improve the overall mapping results.

Hierarchical dispatch using two-stage optimisation for electricity markets in smart grid

NASA Astrophysics Data System (ADS)

Yang, Jie; Zhang, Guoshan; Ma, Kai

2016-11-01

This paper proposes a hierarchical dispatch method for the electricity markets consisting of wholesale markets and retail markets. In the wholesale markets, the generators and the retailers decide the generation and the purchase according to the market-clearing price. In the retail markets, the retailers set the retail price to adjust the electricity consumption of the consumers. Due to the two-way communications in smart grid, the retailers can decide the electricity purchase from the wholesale markets based on the information on electricity usage of consumers in the retail markets. We establish the hierarchical dispatch model for the wholesale markets and the retail markets and develop distributed algorithms to search for the optimal generation, purchase, and consumption. Numerical results show the balance between the supply and demand, the profits of the retailers, and the convergence of the distributed algorithms.

Parameterization of aquatic ecosystem functioning and its natural variation: Hierarchical Bayesian modelling of plankton food web dynamics

NASA Astrophysics Data System (ADS)

Norros, Veera; Laine, Marko; Lignell, Risto; Thingstad, Frede

2017-10-01

Methods for extracting empirically and theoretically sound parameter values are urgently needed in aquatic ecosystem modelling to describe key flows and their variation in the system. Here, we compare three Bayesian formulations for mechanistic model parameterization that differ in their assumptions about the variation in parameter values between various datasets: 1) global analysis - no variation, 2) separate analysis - independent variation and 3) hierarchical analysis - variation arising from a shared distribution defined by hyperparameters. We tested these methods, using computer-generated and empirical data, coupled with simplified and reasonably realistic plankton food web models, respectively. While all methods were adequate, the simulated example demonstrated that a well-designed hierarchical analysis can result in the most accurate and precise parameter estimates and predictions, due to its ability to combine information across datasets. However, our results also highlighted sensitivity to hyperparameter prior distributions as an important caveat of hierarchical analysis. In the more complex empirical example, hierarchical analysis was able to combine precise identification of parameter values with reasonably good predictive performance, although the ranking of the methods was less straightforward. We conclude that hierarchical Bayesian analysis is a promising tool for identifying key ecosystem-functioning parameters and their variation from empirical datasets.

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

PubMed Central

Moore, Jason H; Amos, Ryan; Kiralis, Jeff; Andrews, Peter C

2015-01-01

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects. PMID:25395175

Evaluation of B2C website based on the usability factors by using fuzzy AHP & hierarchical fuzzy TOPSIS

NASA Astrophysics Data System (ADS)

Masudin, I.; Saputro, T. E.

2016-02-01

In today's technology, electronic trading transaction via internet has been utilized properly with rapid growth. This paper intends to evaluate related to B2C e-commerce website in order to find out the one which meets the usability factors better than another. The influential factors to B2C e-commerce website are determined for two big retailer websites. The factors are investigated based on the consideration of several studies and conformed to the website characteristics. The evaluation is conducted by using different methods namely fuzzy AHP and hierarchical fuzzy TOPSIS so that the final evaluation can be compared. Fuzzy triangular number is adopted to deal with imprecise judgment under fuzzy environment.

Hierarchical analytical and simulation modelling of human-machine systems with interference

NASA Astrophysics Data System (ADS)

Braginsky, M. Ya; Tarakanov, D. V.; Tsapko, S. G.; Tsapko, I. V.; Baglaeva, E. A.

2017-01-01

The article considers the principles of building the analytical and simulation model of the human operator and the industrial control system hardware and software. E-networks as the extension of Petri nets are used as the mathematical apparatus. This approach allows simulating complex parallel distributed processes in human-machine systems. The structural and hierarchical approach is used as the building method for the mathematical model of the human operator. The upper level of the human operator is represented by the logical dynamic model of decision making based on E-networks. The lower level reflects psychophysiological characteristics of the human-operator.

Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization.

PubMed

Fang, Chunying; Li, Haifeng; Ma, Lin; Zhang, Mancai

2017-01-01

Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method. A novel method of generating feature set based on S -transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, Mel S -transform cepstrum coefficients), and chaotic features (12). Finally, radar chart and F -score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96 dimensions based on NKI-CCRT corpus and 104 dimensions based on SVD corpus. The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78.7% on SVD corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation.

Using gradient-based ray and candidate shadow maps for environmental illumination distribution estimation

NASA Astrophysics Data System (ADS)

Eem, Changkyoung; Kim, Iksu; Hong, Hyunki

2015-07-01

A method to estimate the environmental illumination distribution of a scene with gradient-based ray and candidate shadow maps is presented. In the shadow segmentation stage, we apply a Canny edge detector to the shadowed image by using a three-dimensional (3-D) augmented reality (AR) marker of a known size and shape. Then the hierarchical tree of the connected edge components representing the topological relation is constructed, and the connected components are merged, taking their hierarchical structures into consideration. A gradient-based ray that is perpendicular to the gradient of the edge pixel in the shadow image can be used to extract the shadow regions. In the light source detection stage, shadow regions with both a 3-D AR marker and the light sources are partitioned into candidate shadow maps. A simple logic operation between each candidate shadow map and the segmented shadow is used to efficiently compute the area ratio between them. The proposed method successively extracts the main light sources according to their relative contributions on the segmented shadows. The proposed method can reduce unwanted effects due to the sampling positions in the shadow region and the threshold values in the shadow edge detection.

Conceptual Chemical Process Design for Sustainability. ...

EPA Pesticide Factsheets

This chapter examines the sustainable design of chemical processes, with a focus on conceptual design, hierarchical and short-cut methods, and analyses of process sustainability for alternatives. The chapter describes a methodology for incorporating process sustainability analyses throughout the conceptual design. Hierarchical and short-cut decision-making methods will be used to approach sustainability. An example showing a sustainability-based evaluation of chlor-alkali production processes is presented with economic analysis and five pollutants described as emissions. These emissions are analyzed according to their human toxicity potential by ingestion using the Waste Reduction Algorithm and a method based on US Environmental Protection Agency reference doses, with the addition of biodegradation for suitable components. Among the emissions, mercury as an element will not biodegrade, and results show the importance of this pollutant to the potential toxicity results and therefore the sustainability of the process design. The dominance of mercury in determining the long-term toxicity results when energy use is included suggests that all process system evaluations should (re)consider the role of mercury and other non-/slow-degrading pollutants in sustainability analyses. The cycling of nondegrading pollutants through the biosphere suggests the need for a complete analysis based on the economic, environmental, and social aspects of sustainability. Chapter reviews

Tubular structured hierarchical mesoporous titania material derived from natural cellulosic substances and application as photocatalyst for degradation of methylene blue

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

Huang, Haiqing; Liu, Xiaoyan; Huang, Jianguo, E-mail: jghuang@zju.edu.cn

Graphical abstract: Bio-inspired, tubular structured hierarchical mesoporous titania material with high photocatalytic activity under UV light was fabricated employing natural cellulosic substance (cotton) as hard template and cetyltrimethylammonium bromide (CTAB) surfactant as soft template using a one-pot sol-gel method. Highlights: {yields} Tubular structured mesoporous titania material was fabricated by sol-gel method. {yields} The titania material faithfully recorded the hierarchical structure of the template substrate (cotton). {yields} The titania material exhibited high photocatalytic activity in decomposition of methylene blue. -- Abstract: Bio-inspired, tubular structured hierarchical mesoporous titania material was designed and fabricated employing natural cellulosic substance (cotton) as hard template andmore » cetyltrimethylammonium bromide (CTAB) surfactant as soft template by one-pot sol-gel method. The tubular structured hierarchical mesoporous titania material processes large specific surface area (40.23 m{sup 2}/g) and shows high photocatalytic activity in the photodegradation of methylene blue under UV light irradiation.« less

Effects of hierarchical structures and insulating liquid media on adhesion

NASA Astrophysics Data System (ADS)

Yang, Weixu; Wang, Xiaoli; Li, Hanqing; Song, Xintao

2017-11-01

Effects of hierarchical structures and insulating liquid media on adhesion are investigated through a numerical adhesive contact model established in this paper, in which hierarchical structures are considered by introducing the height distribution into the surface gap equation, and media are taken into account through the Hamaker constant in Lifshitz-Hamaker approach. Computational methods such as inexact Newton method, bi-conjugate stabilized (Bi-CGSTAB) method and fast Fourier transform (FFT) technique are employed to obtain the adhesive force. It is shown that hierarchical structured surface exhibits excellent anti-adhesive properties compared with flat, micro or nano structured surfaces. Adhesion force is more dependent on the sizes of nanostructures than those of microstructures, and the optimal ranges of nanostructure pitch and maximum height for small adhesion force are presented. Insulating liquid media effectively decrease the adhesive interaction and 1-bromonaphthalene exhibits the smallest adhesion force among the five selected media. In addition, effects of hierarchical structures with optimal sizes on reducing adhesion are more obvious than those of the selected insulating liquid media.

A reliable method of manufacturing metallic hierarchical superhydrophobic surfaces

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

Pogreb, Roman; Whyman, Gene; Barayev, Reuven

2009-06-01

A method of manufacturing hierarchical metallic surfaces demonstrating superhydrophobic properties is presented. The surfaces showed apparent contact angles as high as 153 deg. and sliding angles of 10 deg. for 50-100 {mu}l droplets. The Cassie-like model [A. B. D. Cassie and S. Baxter, Trans. Faraday Soc. 40, 546 (1944)], considering the hierarchical topography of the relief, predicts apparent contact angles in a satisfactory agreement with the measured values.

An economic growth model based on financial credits distribution to the government economy priority sectors of each regency in Indonesia using hierarchical Bayesian method

NASA Astrophysics Data System (ADS)

Yasmirullah, Septia Devi Prihastuti; Iriawan, Nur; Sipayung, Feronika Rosalinda

2017-11-01

The success of regional economic establishment could be measured by economic growth. Since the Act No. 32 of 2004 has been implemented, unbalance economic among the regency in Indonesia is increasing. This condition is contrary different with the government goal to build society welfare through the economic activity development in each region. This research aims to examine economic growth through the distribution of bank credits to each Indonesia's regency. The data analyzed in this research is hierarchically structured data which follow normal distribution in first level. Two modeling approaches are employed in this research, a global-one level Bayesian approach and two-level hierarchical Bayesian approach. The result shows that hierarchical Bayesian has succeeded to demonstrate a better estimation than a global-one level Bayesian. It proves that the different economic growth in each province is significantly influenced by the variations of micro level characteristics in each province. These variations are significantly affected by cities and province characteristics in second level.

Statistical Significance for Hierarchical Clustering

PubMed Central

Kimes, Patrick K.; Liu, Yufeng; Hayes, D. Neil; Marron, J. S.

2017-01-01

Summary Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure. A critical and challenging question in cluster analysis is whether the identified clusters represent important underlying structure or are artifacts of natural sampling variation. Few approaches have been proposed for addressing this problem in the context of hierarchical clustering, for which the problem is further complicated by the natural tree structure of the partition, and the multiplicity of tests required to parse the layers of nested clusters. In this paper, we propose a Monte Carlo based approach for testing statistical significance in hierarchical clustering which addresses these issues. The approach is implemented as a sequential testing procedure guaranteeing control of the family-wise error rate. Theoretical justification is provided for our approach, and its power to detect true clustering structure is illustrated through several simulation studies and applications to two cancer gene expression datasets. PMID:28099990

Comparative analysis of hierarchical triangulated irregular networks to represent 3D elevation in terrain databases

NASA Astrophysics Data System (ADS)

Abdelguerfi, Mahdi; Wynne, Chris; Cooper, Edgar; Ladner, Roy V.; Shaw, Kevin B.

1997-08-01

Three-dimensional terrain representation plays an important role in a number of terrain database applications. Hierarchical triangulated irregular networks (TINs) provide a variable-resolution terrain representation that is based on a nested triangulation of the terrain. This paper compares and analyzes existing hierarchical triangulation techniques. The comparative analysis takes into account how aesthetically appealing and accurate the resulting terrain representation is. Parameters, such as adjacency, slivers, and streaks, are used to provide a measure on how aesthetically appealing the terrain representation is. Slivers occur when the triangulation produces thin and slivery triangles. Streaks appear when there are too many triangulations done at a given vertex. Simple mathematical expressions are derived for these parameters, thereby providing a fairer and a more easily duplicated comparison. In addition to meeting the adjacency requirement, an aesthetically pleasant hierarchical TINs generation algorithm is expected to reduce both slivers and streaks while maintaining accuracy. A comparative analysis of a number of existing approaches shows that a variant of a method originally proposed by Scarlatos exhibits better overall performance.

Drag reduction using metallic engineered surfaces with highly ordered hierarchical topographies: nanostructures on micro-riblets

NASA Astrophysics Data System (ADS)

Kim, Taekyung; Shin, Ryung; Jung, Myungki; Lee, Jinhyung; Park, Changsu; Kang, Shinill

2016-03-01

Durable drag-reduction surfaces have recently received much attention, due to energy-saving and power-consumption issues associated with harsh environment applications, such as those experienced by piping infrastructure, ships, aviation, underwater vehicles, and high-speed ground vehicles. In this study, a durable, metallic surface with highly ordered hierarchical structures was used to enhance drag-reduction properties, by combining two passive drag-reduction strategies: an air-layer effect induced by nanostructures and secondary vortex generation by micro-riblet structures. The nanostructures and micro-riblet structures were designed to increase slip length. The top-down fabrication method used to form the metallic hierarchical structures combined laser interference lithography, photolithography, thermal reflow, nanoimprinting, and pulse-reverse-current electrochemical deposition. The surfaces were formed from nickel, which has high hardness and corrosion resistance, making it suitable for use in harsh environments. The drag-reduction properties of various metal surfaces were investigated based on the surface structure: a bare surface, a nanostructured surface, a micro-riblet surface, and a hierarchically structured surface of nanostructures on micro-riblets.

Hierarchical models and the analysis of bird survey information

USGS Publications Warehouse

Sauer, J.R.; Link, W.A.

2003-01-01

Management of birds often requires analysis of collections of estimates. We describe a hierarchical modeling approach to the analysis of these data, in which parameters associated with the individual species estimates are treated as random variables, and probability statements are made about the species parameters conditioned on the data. A Markov-Chain Monte Carlo (MCMC) procedure is used to fit the hierarchical model. This approach is computer intensive, and is based upon simulation. MCMC allows for estimation both of parameters and of derived statistics. To illustrate the application of this method, we use the case in which we are interested in attributes of a collection of estimates of population change. Using data for 28 species of grassland-breeding birds from the North American Breeding Bird Survey, we estimate the number of species with increasing populations, provide precision-adjusted rankings of species trends, and describe a measure of population stability as the probability that the trend for a species is within a certain interval. Hierarchical models can be applied to a variety of bird survey applications, and we are investigating their use in estimation of population change from survey data.

Synthesis of uniform layered protonated titanate hierarchical spheres and their transformation to anatase TiO2 for lithium-ion batteries.

PubMed

Wu, Hao Bin; Lou, Xiong Wen David; Hng, Huey Hoon

2012-02-13

Layered protonated titanates (LPTs), a class of interesting inorganic layered materials, have been widely studied because of their many unique properties and their use as precursors to many important TiO(2)-based functional materials. In this work, we have developed a facile solvothermal method to synthesize hierarchical spheres (HSs) assembled from ultrathin LPT nanosheets. These LPT hierarchical spheres possess a porous structure with a large specific surface area and high stability. Importantly, the size and morphology of the LPT hierarchical spheres are easily tunable by varying the synthesis conditions. These LPT HSs can be easily converted to anatase TiO(2) HSs without significant structural alteration. Depending on the calcination atmosphere of air or N(2), pure anatase TiO(2) HSs or carbon-supported TiO(2) HSs, respectively, can be obtained. Remarkably, both types of TiO(2) HSs manifest excellent cyclability and rate capability when evaluated as anode materials for high-power lithium-ion batteries. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Ray tracing a three-dimensional scene using a hierarchical data structure

DOEpatents

Wald, Ingo; Boulos, Solomon; Shirley, Peter

2012-09-04

Ray tracing a three-dimensional scene made up of geometric primitives that are spatially partitioned into a hierarchical data structure. One example embodiment is a method for ray tracing a three-dimensional scene made up of geometric primitives that are spatially partitioned into a hierarchical data structure. In this example embodiment, the hierarchical data structure includes at least a parent node and a corresponding plurality of child nodes. The method includes a first act of determining that a first active ray in the packet hits the parent node and a second act of descending to each of the plurality of child nodes.

Hierarchical cluster-tendency analysis of the group structure in the foreign exchange market

NASA Astrophysics Data System (ADS)

Wu, Xin-Ye; Zheng, Zhi-Gang

2013-08-01

A hierarchical cluster-tendency (HCT) method in analyzing the group structure of networks of the global foreign exchange (FX) market is proposed by combining the advantages of both the minimal spanning tree (MST) and the hierarchical tree (HT). Fifty currencies of the top 50 World GDP in 2010 according to World Bank's database are chosen as the underlying system. By using the HCT method, all nodes in the FX market network can be "colored" and distinguished. We reveal that the FX networks can be divided into two groups, i.e., the Asia-Pacific group and the Pan-European group. The results given by the hierarchical cluster-tendency method agree well with the formerly observed geographical aggregation behavior in the FX market. Moreover, an oil-resource aggregation phenomenon is discovered by using our method. We find that gold could be a better numeraire for the weekly-frequency FX data.

A hierarchical approach to reliability modeling of fault-tolerant systems. M.S. Thesis

NASA Technical Reports Server (NTRS)

Gossman, W. E.

1986-01-01

A methodology for performing fault tolerant system reliability analysis is presented. The method decomposes a system into its subsystems, evaluates vent rates derived from the subsystem's conditional state probability vector and incorporates those results into a hierarchical Markov model of the system. This is done in a manner that addresses failure sequence dependence associated with the system's redundancy management strategy. The method is derived for application to a specific system definition. Results are presented that compare the hierarchical model's unreliability prediction to that of a more complicated tandard Markov model of the system. The results for the example given indicate that the hierarchical method predicts system unreliability to a desirable level of accuracy while achieving significant computational savings relative to component level Markov model of the system.

Influences on influenza transmission within terminal based on hierarchical structure of personal contact network.

PubMed

Shao, Quan; Jia, Meng

2015-03-18

Since the outbreak of pandemics, influenza has caused extensive attention in the field of public health. It is actually hard to distinguish what is the most effective method to control the influenza transmission within airport terminal. The purpose of this study was to quantitatively evaluate the influences of passenger source, immunity difference and social relation structure on the influenza transmission in terminal. A method combining hierarchical structure of personal contact network with agent-based SEIR model was proposed to analyze the characteristics of influenza diffusion within terminal. Based on the spatial distance between individuals, the hierarchical structure of personal contact network was defined to construct a complex relationship of passengers in the real world. Moreover, the agent-based SEIR model was improved by considering the individual level of influenza spread characteristics. To evaluate the method, this process was fused in simulation based on the constructed personal contact network. In the terminal we investigated, personal contact network was defined by following four layers: social relation structure, procedure partition, procedure area, and the whole terminal. With the growing of layer, the degree distribution curves move right. The value of degree distribution p(k) reached a peak at a specific value, and then back down. Besides, with the increase of layer α, the clustering coefficients presented a tendency to exponential decay. Based on the influenza transmission experiments, the main infected areas were concluded when considering different factors. Moreover, partition of passenger sources was found to impact a lot in departure, while social relation structure imposed a great influence in arrival. Besides, immunity difference exerted no obvious effect on the spread of influenza in the transmission process both in departure and arrival. The proposed method is efficient to reproduce the evolution process of influenza transmission, and exhibits various roles of each factor in different processes, also better reflects the effect of passenger topological character on influenza spread. It contributes to proposing effective influenza measures by airport relevant department and improving the efficiency and ability of epidemic prevention on the public health.

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2004-10-01

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.

Model-based hierarchical reinforcement learning and human action control

PubMed Central

Botvinick, Matthew; Weinstein, Ari

2014-01-01

Recent work has reawakened interest in goal-directed or ‘model-based’ choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour. PMID:25267822

Method and apparatus for modeling interactions

DOEpatents

Xavier, Patrick G.

2000-08-08

A method and apparatus for modeling interactions between bodies. The method comprises representing two bodies undergoing translations and rotations by two hierarchical swept volume representations. Interactions such as nearest approach and collision can be modeled based on the swept body representations. The present invention can serve as a practical tool in motion planning, CAD systems, simulation systems, safety analysis, and applications that require modeling time-based interactions. A body can be represented in the present invention by a union of convex polygons and convex polyhedra. As used generally herein, polyhedron includes polygon, and polyhedra includes polygons. The body undergoing translation can be represented by a swept body representation, where the swept body representation comprises a hierarchical bounding volume representation whose leaves each contain a representation of the region swept by a section of the body during the translation, and where the union of the regions is a superset of the region swept by the surface of the body during translation. Interactions between two bodies thus represented can be modeled by modeling interactions between the convex hulls of the finite sets of discrete points in the swept body representations.

A Modular Hierarchical Approach to 3D Electron Microscopy Image Segmentation

PubMed Central

Liu, Ting; Jones, Cory; Seyedhosseini, Mojtaba; Tasdizen, Tolga

2014-01-01

The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy. PMID:24491638

3D deformable image matching: a hierarchical approach over nested subspaces

NASA Astrophysics Data System (ADS)

Musse, Olivier; Heitz, Fabrice; Armspach, Jean-Paul

2000-06-01

This paper presents a fast hierarchical method to perform dense deformable inter-subject matching of 3D MR Images of the brain. To recover the complex morphological variations in neuroanatomy, a hierarchy of 3D deformations fields is estimated, by minimizing a global energy function over a sequence of nested subspaces. The nested subspaces, generated from a single scaling function, consist of deformation fields constrained at different scales. The highly non linear energy function, describing the interactions between the target and the source images, is minimized using a coarse-to-fine continuation strategy over this hierarchy. The resulting deformable matching method shows low sensitivity to local minima and is able to track large non-linear deformations, with moderate computational load. The performances of the approach are assessed both on simulated 3D transformations and on a real data base of 3D brain MR Images from different individuals. The method has shown efficient in putting into correspondence the principle anatomical structures of the brain. An application to atlas-based MRI segmentation, by transporting a labeled segmentation map on patient data, is also presented.

Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation

PubMed Central

Lashkari, Danial; Sridharan, Ramesh; Vul, Edward; Hsieh, Po-Jang; Kanwisher, Nancy; Golland, Polina

2011-01-01

We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over the sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to simultaneously learn the patterns of response that are shared across the group, and to estimate the number of these patterns supported by data. Inference based on this model enables automatic discovery and characterization of salient and consistent patterns in functional signals. We apply our method to data from a study that explores the response of the visual cortex to a collection of images. The discovered profiles of activation correspond to selectivity to a number of image categories such as faces, bodies, and scenes. More generally, our results appear superior to the results of alternative data-driven methods in capturing the category structure in the space of stimuli. PMID:21841977

A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy

NASA Astrophysics Data System (ADS)

Li, Yongbo; Li, Guoyan; Yang, Yuantao; Liang, Xihui; Xu, Minqiang

2018-05-01

The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.

Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models

PubMed Central

Liu, Ziyue; Cappola, Anne R.; Crofford, Leslie J.; Guo, Wensheng

2013-01-01

The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls. PMID:24729646

Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models.

PubMed

Liu, Ziyue; Cappola, Anne R; Crofford, Leslie J; Guo, Wensheng

2014-01-01

The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls.

Gel-based morphological design of zirconium metal-organic frameworks.

PubMed

Bueken, Bart; Van Velthoven, Niels; Willhammar, Tom; Stassin, Timothée; Stassen, Ivo; Keen, David A; Baron, Gino V; Denayer, Joeri F M; Ameloot, Rob; Bals, Sara; De Vos, Dirk; Bennett, Thomas D

2017-05-01

The ability of metal-organic frameworks (MOFs) to gelate under specific synthetic conditions opens up new opportunities in the preparation and shaping of hierarchically porous MOF monoliths, which could be directly implemented for catalytic and adsorptive applications. In this work, we present the first examples of xero- or aerogel monoliths consisting solely of nanoparticles of several prototypical Zr 4+ -based MOFs: UiO-66-X (X = H, NH 2 , NO 2 , (OH) 2 ), UiO-67, MOF-801, MOF-808 and NU-1000. High reactant and water concentrations during synthesis were observed to induce the formation of gels, which were converted to monolithic materials by drying in air or supercritical CO 2 . Electron microscopy, combined with N 2 physisorption experiments, was used to show that irregular nanoparticle packing leads to pure MOF monoliths with hierarchical pore systems, featuring both intraparticle micropores and interparticle mesopores. Finally, UiO-66 gels were shaped into monolithic spheres of 600 μm diameter using an oil-drop method, creating promising candidates for packed-bed catalytic or adsorptive applications, where hierarchical pore systems can greatly mitigate mass transfer limitations.

Hierarchical image segmentation via recursive superpixel with adaptive regularity

NASA Astrophysics Data System (ADS)

Nakamura, Kensuke; Hong, Byung-Woo

2017-11-01

A fast and accurate segmentation algorithm in a hierarchical way based on a recursive superpixel technique is presented. We propose a superpixel energy formulation in which the trade-off between data fidelity and regularization is dynamically determined based on the local residual in the energy optimization procedure. We also present an energy optimization algorithm that allows a pixel to be shared by multiple regions to improve the accuracy and appropriate the number of segments. The qualitative and quantitative evaluations demonstrate that our algorithm, combining the proposed energy and optimization, outperforms the conventional k-means algorithm by up to 29.10% in F-measure. We also perform comparative analysis with state-of-the-art algorithms in the hierarchical segmentation. Our algorithm yields smooth regions throughout the hierarchy as opposed to the others that include insignificant details. Our algorithm overtakes the other algorithms in terms of balance between accuracy and computational time. Specifically, our method runs 36.48% faster than the region-merging approach, which is the fastest of the comparing algorithms, while achieving a comparable accuracy.

Method and system for knowledge discovery using non-linear statistical analysis and a 1st and 2nd tier computer program

DOEpatents

Hively, Lee M [Philadelphia, TN

2011-07-12

The invention relates to a method and apparatus for simultaneously processing different sources of test data into informational data and then processing different categories of informational data into knowledge-based data. The knowledge-based data can then be communicated between nodes in a system of multiple computers according to rules for a type of complex, hierarchical computer system modeled on a human brain.

The information extraction of Gannan citrus orchard based on the GF-1 remote sensing image

NASA Astrophysics Data System (ADS)

Wang, S.; Chen, Y. L.

2017-02-01

The production of Gannan oranges is the largest in China, which occupied an important part in the world. The extraction of citrus orchard quickly and effectively has important significance for fruit pathogen defense, fruit production and industrial planning. The traditional spectra extraction method of citrus orchard based on pixel has a lower classification accuracy, difficult to avoid the “pepper phenomenon”. In the influence of noise, the phenomenon that different spectrums of objects have the same spectrum is graveness. Taking Xunwu County citrus fruit planting area of Ganzhou as the research object, aiming at the disadvantage of the lower accuracy of the traditional method based on image element classification method, a decision tree classification method based on object-oriented rule set is proposed. Firstly, multi-scale segmentation is performed on the GF-1 remote sensing image data of the study area. Subsequently the sample objects are selected for statistical analysis of spectral features and geometric features. Finally, combined with the concept of decision tree classification, a variety of empirical values of single band threshold, NDVI, band combination and object geometry characteristics are used hierarchically to execute the information extraction of the research area, and multi-scale segmentation and hierarchical decision tree classification is implemented. The classification results are verified with the confusion matrix, and the overall Kappa index is 87.91%.

On the importance of avoiding shortcuts in applying cognitive models to hierarchical data.

PubMed

Boehm, Udo; Marsman, Maarten; Matzke, Dora; Wagenmakers, Eric-Jan

2018-06-12

Psychological experiments often yield data that are hierarchically structured. A number of popular shortcut strategies in cognitive modeling do not properly accommodate this structure and can result in biased conclusions. To gauge the severity of these biases, we conducted a simulation study for a two-group experiment. We first considered a modeling strategy that ignores the hierarchical data structure. In line with theoretical results, our simulations showed that Bayesian and frequentist methods that rely on this strategy are biased towards the null hypothesis. Secondly, we considered a modeling strategy that takes a two-step approach by first obtaining participant-level estimates from a hierarchical cognitive model and subsequently using these estimates in a follow-up statistical test. Methods that rely on this strategy are biased towards the alternative hypothesis. Only hierarchical models of the multilevel data lead to correct conclusions. Our results are particularly relevant for the use of hierarchical Bayesian parameter estimates in cognitive modeling.

CuO-Decorated ZnO Hierarchical Nanostructures as Efficient and Established Sensing Materials for H2S Gas Sensors

PubMed Central

Vuong, Nguyen Minh; Chinh, Nguyen Duc; Huy, Bui The; Lee, Yong-Ill

2016-01-01

Highly sensitive hydrogen sulfide (H2S) gas sensors were developed from CuO-decorated ZnO semiconducting hierarchical nanostructures. The ZnO hierarchical nanostructure was fabricated by an electrospinning method following hydrothermal and heat treatment. CuO decoration of ZnO hierarchical structures was carried out by a wet method. The H2S gas-sensing properties were examined at different working temperatures using various quantities of CuO as the variable. CuO decoration of the ZnO hierarchical structure was observed to promote sensitivity for H2S gas higher than 30 times at low working temperature (200 °C) compared with that in the nondecorated hierarchical structure. The sensing mechanism of the hybrid sensor structure is also discussed. The morphology and characteristics of the samples were examined by scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), UV-vis absorption, photoluminescence (PL), and electrical measurements. PMID:27231026

Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site

NASA Astrophysics Data System (ADS)

Gavish, Yoni; O'Connell, Jerome; Marsh, Charles J.; Tarantino, Cristina; Blonda, Palma; Tomaselli, Valeria; Kunin, William E.

2018-02-01

The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre-defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2-3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into "black-box" based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps.

MOF-5 decorated hierarchical ZnO nanorod arrays and its photoluminescence

NASA Astrophysics Data System (ADS)

Zhang, Yinmin; Lan, Ding; Wang, Yuren; Cao, He; Jiang, Heng

2011-04-01

The strategy to manipulate nanoscale materials into well-organized hierarchical architectures is very important to both material synthesis and nanodevice applications. Here, nanoscale MOF-5 crystallites were successfully fabricated onto ordered hierarchical ZnO arrays based on aqueous chemical synthesis and molecule self-assembly technology guided room temperature diffusion method, which has the advantages of energy saving and simple operation. The structures and morphologies of the samples were performed by X-ray powder diffraction and field emission scanning electronic microscopy. The MOF-5 crystallites have good quality and bind well to the hexagonal-patterned ZnO arrays. The photoluminescence spectrum shows that the emission of hybrid MOF-5-ZnO films displays a blue shift in green emission and intensity reduction in UV emission. This ordered hybrid semiconductor material is expected to exploit the great potentiality in sensors, micro/nanodevices, and screen displays.

Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems.

PubMed

Yu, Michael Ku; Kramer, Michael; Dutkowski, Janusz; Srivas, Rohith; Licon, Katherine; Kreisberg, Jason; Ng, Cherie T; Krogan, Nevan; Sharan, Roded; Ideker, Trey

2016-02-24

Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology's hierarchical structure, we organize genotype data into an "ontotype," that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.

Insights into the hierarchical structure and digestion rate of alkali-modulated starches with different amylose contents.

PubMed

Qiao, Dongling; Yu, Long; Liu, Hongsheng; Zou, Wei; Xie, Fengwei; Simon, George; Petinakis, Eustathios; Shen, Zhiqi; Chen, Ling

2016-06-25

Combined analytical techniques were used to explore the effects of alkali treatment on the multi-scale structure and digestion behavior of starches with different amylose/amylopectin ratios. Alkali treatment disrupted the amorphous matrix, and partial lamellae and crystallites, which weakened starch molecular packing and eventually enhanced the susceptibility of starch to alkali. Stronger alkali treatment (0.5% w/w) made this effect more prominent and even transformed the dual-phase digestion of starch into a triple-phase pattern. Compared with high-amylose starch, regular maize starch, which possesses some unique structure characteristics typically as pores and crystallite weak points, showed evident changes of hierarchical structure and in digestion rate. Thus, alkali treatment has been demonstrated as a simple method to modulate starch hierarchical structure and thus to realize the rational development of starch-based food products with desired digestibility. Copyright © 2016 Elsevier Ltd. All rights reserved.

Novel hierarchical microparticles super-assembled from nanoparticles with the induction of casein micelles

NASA Astrophysics Data System (ADS)

Xiong, Xiaopeng; Duan, Jiangjiang; Wang, Yong; Yu, Zhaoju

2013-08-01

We have demonstrated a solution-based synthesis of novel waxberry-like hierarchical ZnO microparticles in the presence casein micelles under mild conditions. The microstructures of the sub-micrometer-sized hierarchical microparticles were characterized, and the synthesis conditions were optimized. The formation mechanism of the hierarchical microparticle was analyzed through control experiments. The hierarchical ZnO microparticles are found to be super-assemblies of 30-70 nm ZnO nanoparticles, which are thought to be based on casein micelle induction followed by Ostwald ripening. In the same manner, copper-based hierarchical microparticles with a similar morphology have also been successfully synthesized. By controlling the synthetic time or temperature, solid or hollow microparticles can be fabricated. The narrowly distributed ZnO microparticles have a high specific surface area, exhibiting great potential application in fields such as photocatalytic and energy conversion. Our findings may meanwhile open a new bottom-up strategy in order to construct structurally sophisticated nanomaterials.

Model-based inference for small area estimation with sampling weights

PubMed Central

Vandendijck, Y.; Faes, C.; Kirby, R.S.; Lawson, A.; Hens, N.

2017-01-01

Obtaining reliable estimates about health outcomes for areas or domains where only few to no samples are available is the goal of small area estimation (SAE). Often, we rely on health surveys to obtain information about health outcomes. Such surveys are often characterised by a complex design, stratification, and unequal sampling weights as common features. Hierarchical Bayesian models are well recognised in SAE as a spatial smoothing method, but often ignore the sampling weights that reflect the complex sampling design. In this paper, we focus on data obtained from a health survey where the sampling weights of the sampled individuals are the only information available about the design. We develop a predictive model-based approach to estimate the prevalence of a binary outcome for both the sampled and non-sampled individuals, using hierarchical Bayesian models that take into account the sampling weights. A simulation study is carried out to compare the performance of our proposed method with other established methods. The results indicate that our proposed method achieves great reductions in mean squared error when compared with standard approaches. It performs equally well or better when compared with more elaborate methods when there is a relationship between the responses and the sampling weights. The proposed method is applied to estimate asthma prevalence across districts. PMID:28989860

Controllable synthesis of layered Co-Ni hydroxide hierarchical structures for high-performance hybrid supercapacitors

NASA Astrophysics Data System (ADS)

Yuan, Peng; Zhang, Ning; Zhang, Dan; Liu, Tao; Chen, Limiao; Ma, Renzhi; Qiu, Guanzhou; Liu, Xiaohe

2016-01-01

A facile solvothermal method is developed for synthesizing layered Co-Ni hydroxide hierarchical structures by using hexamethylenetetramine (HMT) as alkaline reagent. The electrochemical measurements reveal that the specific capacitances of layered bimetallic (Co-Ni) hydroxides are generally superior to those of layered monometallic (Co, Ni) hydroxides. The as-prepared Co0.5Ni0.5 hydroxide hierarchical structures possesses the highest specific capacitance of 1767 F g-1 at a galvanic current density of 1 A g-1 and an outstanding specific capacitance retention of 87% after 1000 cycles. In comparison with the dispersed nanosheets of Co-Ni hydroxide, layered hydroxide hierarchical structures show much superior electrochemical performance. This study provides a promising method to construct hierarchical structures with controllable transition-metal compositions for enhancing the electrochemical performance in hybrid supercapacitors.

Maslow and Motherboards: Taking a Hierarchical View of Technology Planning.

ERIC Educational Resources Information Center

Johnson, Doug

2003-01-01

Presents a planning model for educational uses of technology that is based on Maslow's hierarchy of needs. Topics include established infrastructure; effective administration; extensive resources; enhanced teaching, including creating distance learning opportunities; empowered students, including evaluation methods and information literacy skills;…

Classifying Higher Education Institutions in Korea: A Performance-Based Approach

ERIC Educational Resources Information Center

Shin, Jung Cheol

2009-01-01

The purpose of this study was to classify higher education institutions according to institutional performance rather than predetermined benchmarks. Institutional performance was defined as research performance and classified using Hierarchical Cluster Analysis, a statistical method that classifies objects according to specified classification…

Hierarchical matrices implemented into the boundary integral approaches for gravity field modelling

NASA Astrophysics Data System (ADS)

Čunderlík, Róbert; Vipiana, Francesca

2017-04-01

Boundary integral approaches applied for gravity field modelling have been recently developed to solve the geodetic boundary value problems numerically, or to process satellite observations, e.g. from the GOCE satellite mission. In order to obtain numerical solutions of "cm-level" accuracy, such approaches require very refined level of the disretization or resolution. This leads to enormous memory requirements that need to be reduced. An implementation of the Hierarchical Matrices (H-matrices) can significantly reduce a numerical complexity of these approaches. A main idea of the H-matrices is based on an approximation of the entire system matrix that is split into a family of submatrices. Large submatrices are stored in factorized representation, while small submatrices are stored in standard representation. This allows reducing memory requirements significantly while improving the efficiency. The poster presents our preliminary results of implementations of the H-matrices into the existing boundary integral approaches based on the boundary element method or the method of fundamental solution.

A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.

PubMed

Khan, Adil Mehmood; Lee, Young-Koo; Lee, Sungyoung Y; Kim, Tae-Seong

2010-09-01

Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.

3D hierarchical spatial representation and memory of multimodal sensory data

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Dow, Paul A.; Huber, David J.

2009-04-01

This paper describes an efficient method and system for representing, processing and understanding multi-modal sensory data. More specifically, it describes a computational method and system for how to process and remember multiple locations in multimodal sensory space (e.g., visual, auditory, somatosensory, etc.). The multimodal representation and memory is based on a biologically-inspired hierarchy of spatial representations implemented with novel analogues of real representations used in the human brain. The novelty of the work is in the computationally efficient and robust spatial representation of 3D locations in multimodal sensory space as well as an associated working memory for storage and recall of these representations at the desired level for goal-oriented action. We describe (1) A simple and efficient method for human-like hierarchical spatial representations of sensory data and how to associate, integrate and convert between these representations (head-centered coordinate system, body-centered coordinate, etc.); (2) a robust method for training and learning a mapping of points in multimodal sensory space (e.g., camera-visible object positions, location of auditory sources, etc.) to the above hierarchical spatial representations; and (3) a specification and implementation of a hierarchical spatial working memory based on the above for storage and recall at the desired level for goal-oriented action(s). This work is most useful for any machine or human-machine application that requires processing of multimodal sensory inputs, making sense of it from a spatial perspective (e.g., where is the sensory information coming from with respect to the machine and its parts) and then taking some goal-oriented action based on this spatial understanding. A multi-level spatial representation hierarchy means that heterogeneous sensory inputs (e.g., visual, auditory, somatosensory, etc.) can map onto the hierarchy at different levels. When controlling various machine/robot degrees of freedom, the desired movements and action can be computed from these different levels in the hierarchy. The most basic embodiment of this machine could be a pan-tilt camera system, an array of microphones, a machine with arm/hand like structure or/and a robot with some or all of the above capabilities. We describe the approach, system and present preliminary results on a real-robotic platform.

Extraordinary Thermoelectric Performance Realized in Hierarchically Structured AgSbSe2 with Ultralow Thermal Conductivity.

PubMed

Gao, Weihong; Wang, Zhenyou; Huang, Jin; Liu, Zihang

2018-05-24

Thermoelectric conversion from low-grade heat to electricity is regarded as the highly reliable and environmentally friendly technology in energy-harvesting area. However, how to develop efficient thermoelectric materials using a simple fabrication method is still a critical challenge in thermoelectric community. Here, we first fabricate the high thermoelectric performance of Ca-doped AgSbSe 2 with a hierarchical microstructure using a facile approach, namely, mechanical alloying (for only 30 min) and a quick hot-pressing method. The hierarchical microstructure, including point defects (atomic scale), dislocations, and nanoprecipitates (nanoscale) as well as grain boundaries (microscale), strongly scatters phonons with comparable sizes without deterioration of carrier mobility. Because of the higher carrier concentration of nanostructured AgSbSe 2 than that of coarse-grain AgSbSe 2 , power factor can also be improved slightly after nanostructuring. Ca doping further optimizes the carrier concentration and creates the point-defect scattering of phonons, leading to the ultralow lattice thermal conductivity ∼0.27 W m -1 K -1 at 673 K and thus largely improving the peak ZT up to 1.2. The high thermoelectric performance in combination with a facile fabrication method highlights AgSbSe 2 -based materials as robust thermoelectric candidates for energy harvesting.

A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

PubMed Central

2014-01-01

Background The inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB). Methods Based on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated. Results Results showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast. Conclusions A hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice. PMID:24981916

A robust H∞ control-based hierarchical mode transition control system for plug-in hybrid electric vehicle

NASA Astrophysics Data System (ADS)

Yang, Chao; Jiao, Xiaohong; Li, Liang; Zhang, Yuanbo; Chen, Zheng

2018-01-01

To realize a fast and smooth operating mode transition process from electric driving mode to engine-on driving mode, this paper presents a novel robust hierarchical mode transition control method for a plug-in hybrid electric bus (PHEB) with pre-transmission parallel hybrid powertrain. Firstly, the mode transition process is divided into five stages to clearly describe the powertrain dynamics. Based on the dynamics models of powertrain and clutch actuating mechanism, a hierarchical control structure including two robust H∞ controllers in both upper layer and lower layer is proposed. In upper layer, the demand clutch torque can be calculated by a robust H∞controller considering the clutch engaging time and the vehicle jerk. While in lower layer a robust tracking controller with L2-gain is designed to perform the accurate position tracking control, especially when the parameters uncertainties and external disturbance occur in the clutch actuating mechanism. Simulation and hardware-in-the-loop (HIL) test are carried out in a traditional driving condition of PHEB. Results show that the proposed hierarchical control approach can obtain the good control performance: mode transition time is greatly reduced with the acceptable jerk. Meanwhile, the designed control system shows the obvious robustness with the uncertain parameters and disturbance. Therefore, the proposed approach may offer a theoretical reference for the actual vehicle controller.

Control of discrete event systems modeled as hierarchical state machines

NASA Technical Reports Server (NTRS)

Brave, Y.; Heymann, M.

1991-01-01

The authors examine a class of discrete event systems (DESs) modeled as asynchronous hierarchical state machines (AHSMs). For this class of DESs, they provide an efficient method for testing reachability, which is an essential step in many control synthesis procedures. This method utilizes the asynchronous nature and hierarchical structure of AHSMs, thereby illustrating the advantage of the AHSM representation as compared with its equivalent (flat) state machine representation. An application of the method is presented where an online minimally restrictive solution is proposed for the problem of maintaining a controlled AHSM within prescribed legal bounds.

Universal Method for Creating Hierarchical Wrinkles on Thin-Film Surfaces.

PubMed

Jung, Woo-Bin; Cho, Kyeong Min; Lee, Won-Kyu; Odom, Teri W; Jung, Hee-Tae

2018-01-10

One of the most interesting topics in physical science and materials science is the creation of complex wrinkled structures on thin-film surfaces because of their several advantages of high surface area, localized strain, and stress tolerance. In this study, a significant step was taken toward solving limitations imposed by the fabrication of previous artificial wrinkles. A universal method for preparing hierarchical three-dimensional wrinkle structures of thin films on a multiple scale (e.g., nanometers to micrometers) by sequential wrinkling with different skin layers was developed. Notably, this method was not limited to specific materials, and it was applicable to fabricating hierarchical wrinkles on all of the thin-film surfaces tested thus far, including those of metals, two-dimensional and one-dimensional materials, and polymers. The hierarchical wrinkles with multiscale structures were prepared by sequential wrinkling, in which a sacrificial layer was used as the additional skin layer between sequences. For example, a hierarchical MoS 2 wrinkle exhibited highly enhanced catalytic behavior because of the superaerophobicity and effective surface area, which are related to topological effects. As the developed method can be adopted to a majority of thin films, it is thought to be a universal method for enhancing the physical properties of various materials.

Semantic image segmentation with fused CNN features

NASA Astrophysics Data System (ADS)

Geng, Hui-qiang; Zhang, Hua; Xue, Yan-bing; Zhou, Mian; Xu, Guang-ping; Gao, Zan

2017-09-01

Semantic image segmentation is a task to predict a category label for every image pixel. The key challenge of it is to design a strong feature representation. In this paper, we fuse the hierarchical convolutional neural network (CNN) features and the region-based features as the feature representation. The hierarchical features contain more global information, while the region-based features contain more local information. The combination of these two kinds of features significantly enhances the feature representation. Then the fused features are used to train a softmax classifier to produce per-pixel label assignment probability. And a fully connected conditional random field (CRF) is used as a post-processing method to improve the labeling consistency. We conduct experiments on SIFT flow dataset. The pixel accuracy and class accuracy are 84.4% and 34.86%, respectively.

A knowledge-base generating hierarchical fuzzy-neural controller.

PubMed

Kandadai, R M; Tien, J M

1997-01-01

We present an innovative fuzzy-neural architecture that is able to automatically generate a knowledge base, in an extractable form, for use in hierarchical knowledge-based controllers. The knowledge base is in the form of a linguistic rule base appropriate for a fuzzy inference system. First, we modify Berenji and Khedkar's (1992) GARIC architecture to enable it to automatically generate a knowledge base; a pseudosupervised learning scheme using reinforcement learning and error backpropagation is employed. Next, we further extend this architecture to a hierarchical controller that is able to generate its own knowledge base. Example applications are provided to underscore its viability.

Scale of association: hierarchical linear models and the measurement of ecological systems

Treesearch

Sean M. McMahon; Jeffrey M. Diez

2007-01-01

A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance-covariance parameters in hierarchically structured...

The Relationship among School Safety, School Liking, and Students' Self-Esteem: Based on a Multilevel Mediation Model

ERIC Educational Resources Information Center

Zhang, Xinghui; Xuan, Xin; Chen, Fumei; Zhang, Cai; Luo, Yuhan; Wang, Yun

2016-01-01

Background: Perceptions of school safety have an important effect on students' development. Based on the model of "context-process-outcomes," we examined school safety as a context variable to explore how school safety at the school level affected students' self-esteem. Methods: We used hierarchical linear modeling to examine the link…

Scalable hierarchical PDE sampler for generating spatially correlated random fields using nonmatching meshes: Scalable hierarchical PDE sampler using nonmatching meshes

DOE PAGES

Osborn, Sarah; Zulian, Patrick; Benson, Thomas; ...

2018-01-30

This work describes a domain embedding technique between two nonmatching meshes used for generating realizations of spatially correlated random fields with applications to large-scale sampling-based uncertainty quantification. The goal is to apply the multilevel Monte Carlo (MLMC) method for the quantification of output uncertainties of PDEs with random input coefficients on general and unstructured computational domains. We propose a highly scalable, hierarchical sampling method to generate realizations of a Gaussian random field on a given unstructured mesh by solving a reaction–diffusion PDE with a stochastic right-hand side. The stochastic PDE is discretized using the mixed finite element method on anmore » embedded domain with a structured mesh, and then, the solution is projected onto the unstructured mesh. This work describes implementation details on how to efficiently transfer data from the structured and unstructured meshes at coarse levels, assuming that this can be done efficiently on the finest level. We investigate the efficiency and parallel scalability of the technique for the scalable generation of Gaussian random fields in three dimensions. An application of the MLMC method is presented for quantifying uncertainties of subsurface flow problems. Here, we demonstrate the scalability of the sampling method with nonmatching mesh embedding, coupled with a parallel forward model problem solver, for large-scale 3D MLMC simulations with up to 1.9·109 unknowns.« less

Scalable hierarchical PDE sampler for generating spatially correlated random fields using nonmatching meshes: Scalable hierarchical PDE sampler using nonmatching meshes

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

Osborn, Sarah; Zulian, Patrick; Benson, Thomas

This work describes a domain embedding technique between two nonmatching meshes used for generating realizations of spatially correlated random fields with applications to large-scale sampling-based uncertainty quantification. The goal is to apply the multilevel Monte Carlo (MLMC) method for the quantification of output uncertainties of PDEs with random input coefficients on general and unstructured computational domains. We propose a highly scalable, hierarchical sampling method to generate realizations of a Gaussian random field on a given unstructured mesh by solving a reaction–diffusion PDE with a stochastic right-hand side. The stochastic PDE is discretized using the mixed finite element method on anmore » embedded domain with a structured mesh, and then, the solution is projected onto the unstructured mesh. This work describes implementation details on how to efficiently transfer data from the structured and unstructured meshes at coarse levels, assuming that this can be done efficiently on the finest level. We investigate the efficiency and parallel scalability of the technique for the scalable generation of Gaussian random fields in three dimensions. An application of the MLMC method is presented for quantifying uncertainties of subsurface flow problems. Here, we demonstrate the scalability of the sampling method with nonmatching mesh embedding, coupled with a parallel forward model problem solver, for large-scale 3D MLMC simulations with up to 1.9·109 unknowns.« less

Graphical Evaluation of Hierarchical Clustering Schemes. Technical Report No. 1.

ERIC Educational Resources Information Center

Halff, Henry M.

Graphical methods for evaluating the fit of Johnson's hierarchical clustering schemes are presented together with an example. These evaluation methods examine the extent to which the clustering algorithm can minimize the overlap of the distributions of intracluster and intercluster distances. (Author)

Abstraction of the Relational Model from a Department of Veterans Affairs DHCP Database: Bridging Theory and Working Application

PubMed Central

Levy, C.; Beauchamp, C.

1996-01-01

This poster describes the methods used and working prototype that was developed from an abstraction of the relational model from the VA's hierarchical DHCP database. Overlaying the relational model on DHCP permits multiple user views of the physical data structure, enhances access to the database by providing a link to commercial (SQL based) software, and supports a conceptual managed care data model based on primary and longitudinal patient care. The goal of this work was to create a relational abstraction of the existing hierarchical database; to construct, using SQL data definition language, user views of the database which reflect the clinical conceptual view of DHCP, and to allow the user to work directly with the logical view of the data using GUI based commercial software of their choosing. The workstation is intended to serve as a platform from which a managed care information model could be implemented and evaluated.

Hierarchical Volume Representation with 3{radical}2 Subdivision and Trivariate B-Spline Wavelets

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

Linsen, L; Gray, JT; Pascucci, V

2002-01-11

Multiresolution methods provide a means for representing data at multiple levels of detail. They are typically based on a hierarchical data organization scheme and update rules needed for data value computation. We use a data organization that is based on what we call n{radical}2 subdivision. The main advantage of subdivision, compared to quadtree (n = 2) or octree (n = 3) organizations, is that the number of vertices is only doubled in each subdivision step instead of multiplied by a factor of four or eight, respectively. To update data values we use n-variate B-spline wavelets, which yields better approximations formore » each level of detail. We develop a lifting scheme for n = 2 and n = 3 based on the n{radical}2-subdivision scheme. We obtain narrow masks that could also provide a basis for view-dependent visualization and adaptive refinement.« less

A Graph-Embedding Approach to Hierarchical Visual Word Mergence.

PubMed

Wang, Lei; Liu, Lingqiao; Zhou, Luping

2017-02-01

Appropriately merging visual words are an effective dimension reduction method for the bag-of-visual-words model in image classification. The approach of hierarchically merging visual words has been extensively employed, because it gives a fully determined merging hierarchy. Existing supervised hierarchical merging methods take different approaches and realize the merging process with various formulations. In this paper, we propose a unified hierarchical merging approach built upon the graph-embedding framework. Our approach is able to merge visual words for any scenario, where a preferred structure and an undesired structure are defined, and, therefore, can effectively attend to all kinds of requirements for the word-merging process. In terms of computational efficiency, we show that our algorithm can seamlessly integrate a fast search strategy developed in our previous work and, thus, well maintain the state-of-the-art merging speed. To the best of our survey, the proposed approach is the first one that addresses the hierarchical visual word mergence in such a flexible and unified manner. As demonstrated, it can maintain excellent image classification performance even after a significant dimension reduction, and outperform all the existing comparable visual word-merging methods. In a broad sense, our work provides an open platform for applying, evaluating, and developing new criteria for hierarchical word-merging tasks.

An Analysis of Turkey's PISA 2015 Results Using Two-Level Hierarchical Linear Modelling

ERIC Educational Resources Information Center

Atas, Dogu; Karadag, Özge

2017-01-01

In the field of education, most of the data collected are multi-level structured. Cities, city based schools, school based classes and finally students in the classrooms constitute a hierarchical structure. Hierarchical linear models give more accurate results compared to standard models when the data set has a structure going far as individuals,…

Method and system for rendering and interacting with an adaptable computing environment

DOEpatents

Osbourn, Gordon Cecil [Albuquerque, NM; Bouchard, Ann Marie [Albuquerque, NM

2012-06-12

An adaptable computing environment is implemented with software entities termed "s-machines", which self-assemble into hierarchical data structures capable of rendering and interacting with the computing environment. A hierarchical data structure includes a first hierarchical s-machine bound to a second hierarchical s-machine. The first hierarchical s-machine is associated with a first layer of a rendering region on a display screen and the second hierarchical s-machine is associated with a second layer of the rendering region overlaying at least a portion of the first layer. A screen element s-machine is linked to the first hierarchical s-machine. The screen element s-machine manages data associated with a screen element rendered to the display screen within the rendering region at the first layer.

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.

Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking.

PubMed

Ren, Ji-Xia; Li, Lin-Li; Zheng, Ren-Lin; Xie, Huan-Zhang; Cao, Zhi-Xing; Feng, Shan; Pan, You-Li; Chen, Xin; Wei, Yu-Quan; Yang, Sheng-Yong

2011-06-27

In this investigation, we describe the discovery of novel potent Pim-1 inhibitors by employing a proposed hierarchical multistage virtual screening (VS) approach, which is based on support vector machine-based (SVM-based VS or SB-VS), pharmacophore-based VS (PB-VS), and docking-based VS (DB-VS) methods. In this approach, the three VS methods are applied in an increasing order of complexity so that the first filter (SB-VS) is fast and simple, while successive ones (PB-VS and DB-VS) are more time-consuming but are applied only to a small subset of the entire database. Evaluation of this approach indicates that it can be used to screen a large chemical library rapidly with a high hit rate and a high enrichment factor. This approach was then applied to screen several large chemical libraries, including PubChem, Specs, and Enamine as well as an in-house database. From the final hits, 47 compounds were selected for further in vitro Pim-1 inhibitory assay, and 15 compounds show nanomolar level or low micromolar inhibition potency against Pim-1. In particular, four of them were found to have new scaffolds which have potential for the chemical development of Pim-1 inhibitors.

Fingerprint analysis of Hibiscus mutabilis L. leaves based on ultra performance liquid chromatography with photodiode array detector combined with similarity analysis and hierarchical clustering analysis methods

PubMed Central

Liang, Xianrui; Ma, Meiling; Su, Weike

2013-01-01

Background: A method for chemical fingerprint analysis of Hibiscus mutabilis L. leaves was developed based on ultra performance liquid chromatography with photodiode array detector (UPLC-PAD) combined with similarity analysis (SA) and hierarchical clustering analysis (HCA). Materials and Methods: 10 batches of Hibiscus mutabilis L. leaves samples were collected from different regions of China. UPLC-PAD was employed to collect chemical fingerprints of Hibiscus mutabilis L. leaves. Results: The relative standard deviations (RSDs) of the relative retention times (RRT) and relative peak areas (RPA) of 10 characteristic peaks (one of them was identified as rutin) in precision, repeatability and stability test were less than 3%, and the method of fingerprint analysis was validated to be suitable for the Hibiscus mutabilis L. leaves. Conclusions: The chromatographic fingerprints showed abundant diversity of chemical constituents qualitatively in the 10 batches of Hibiscus mutabilis L. leaves samples from different locations by similarity analysis on basis of calculating the correlation coefficients between each two fingerprints. Moreover, the HCA method clustered the samples into four classes, and the HCA dendrogram showed the close or distant relations among the 10 samples, which was consistent to the SA result to some extent. PMID:23930008

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling

NASA Astrophysics Data System (ADS)

Dai, Heng; Chen, Xingyuan; Ye, Ming; Song, Xuehang; Zachara, John M.

2017-05-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study, we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially distributed input variables.

A Geostatistics-Informed Hierarchical Sensitivity Analysis Method for Complex Groundwater Flow and Transport Modeling

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2017-12-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multi-layer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed input variables.

A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting

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

Ding, Tao; Li, Cheng; Huang, Can

Here, in order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost function of the slave model for the master model, which reflects the impacts of each slave model. Second,more » the transmission and distribution networks are decoupled at feeder buses, and all the distribution networks are coordinated by the master reactive power optimization model to achieve the global optimality. Finally, numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods.« less

A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting

DOE PAGES

Ding, Tao; Li, Cheng; Huang, Can; ...

2017-01-09

Here, in order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost function of the slave model for the master model, which reflects the impacts of each slave model. Second,more » the transmission and distribution networks are decoupled at feeder buses, and all the distribution networks are coordinated by the master reactive power optimization model to achieve the global optimality. Finally, numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods.« less

Generating global network structures by triad types

PubMed Central

Ferligoj, Anuška; Žiberna, Aleš

2018-01-01

This paper addresses the question of whether one can generate networks with a given global structure (defined by selected blockmodels, i.e., cohesive, core-periphery, hierarchical, and transitivity), considering only different types of triads. Two methods are used to generate networks: (i) the newly proposed method of relocating links; and (ii) the Monte Carlo Multi Chain algorithm implemented in the ergm package in R. Most of the selected blockmodel types can be generated by considering all types of triads. The selection of only a subset of triads can improve the generated networks’ blockmodel structure. Yet, in the case of a hierarchical blockmodel without complete blocks on the diagonal, additional local structures are needed to achieve the desired global structure of generated networks. This shows that blockmodels can emerge based only on local processes that do not take attributes into account. PMID:29847563

Design of a structural and functional hierarchy for planning and control of telerobotic systems

NASA Technical Reports Server (NTRS)

Acar, Levent; Ozguner, Umit

1989-01-01

Hierarchical structures offer numerous advantages over conventional structures for the control of telerobotic systems. A hierarchically organized system can be controlled via undetailed task assignments and can easily adapt to changing circumstances. The distributed and modular structure of these systems also enables fast response needed in most telerobotic applications. On the other hand, most of the hierarchical structures proposed in the literature are based on functional properties of a system. These structures work best for a few given functions of a large class of systems. In telerobotic applications, all functions of a single system needed to be explored. This approach requires a hierarchical organization based on physical properties of a system and such a hierarchical organization is introduced. The decomposition, organization, and control of the hierarchical structure are considered, and a system with two robot arms and a camera is presented.

Random walk hierarchy measure: What is more hierarchical, a chain, a tree or a star?

PubMed Central

Czégel, Dániel; Palla, Gergely

2015-01-01

Signs of hierarchy are prevalent in a wide range of systems in nature and society. One of the key problems is quantifying the importance of hierarchical organisation in the structure of the network representing the interactions or connections between the fundamental units of the studied system. Although a number of notable methods are already available, their vast majority is treating all directed acyclic graphs as already maximally hierarchical. Here we propose a hierarchy measure based on random walks on the network. The novelty of our approach is that directed trees corresponding to multi level pyramidal structures obtain higher hierarchy scores compared to directed chains and directed stars. Furthermore, in the thermodynamic limit the hierarchy measure of regular trees is converging to a well defined limit depending only on the branching number. When applied to real networks, our method is computationally very effective, as the result can be evaluated with arbitrary precision by subsequent multiplications of the transition matrix describing the random walk process. In addition, the tests on real world networks provided very intuitive results, e.g., the trophic levels obtained from our approach on a food web were highly consistent with former results from ecology. PMID:26657012

Random walk hierarchy measure: What is more hierarchical, a chain, a tree or a star?

NASA Astrophysics Data System (ADS)

Czégel, Dániel; Palla, Gergely

2015-12-01

Signs of hierarchy are prevalent in a wide range of systems in nature and society. One of the key problems is quantifying the importance of hierarchical organisation in the structure of the network representing the interactions or connections between the fundamental units of the studied system. Although a number of notable methods are already available, their vast majority is treating all directed acyclic graphs as already maximally hierarchical. Here we propose a hierarchy measure based on random walks on the network. The novelty of our approach is that directed trees corresponding to multi level pyramidal structures obtain higher hierarchy scores compared to directed chains and directed stars. Furthermore, in the thermodynamic limit the hierarchy measure of regular trees is converging to a well defined limit depending only on the branching number. When applied to real networks, our method is computationally very effective, as the result can be evaluated with arbitrary precision by subsequent multiplications of the transition matrix describing the random walk process. In addition, the tests on real world networks provided very intuitive results, e.g., the trophic levels obtained from our approach on a food web were highly consistent with former results from ecology.

Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform.

PubMed

De Queiroz, Ricardo; Chou, Philip A

2016-06-01

In free-viewpoint video, there is a recent trend to represent scene objects as solids rather than using multiple depth maps. Point clouds have been used in computer graphics for a long time and with the recent possibility of real time capturing and rendering, point clouds have been favored over meshes in order to save computation. Each point in the cloud is associated with its 3D position and its color. We devise a method to compress the colors in point clouds which is based on a hierarchical transform and arithmetic coding. The transform is a hierarchical sub-band transform that resembles an adaptive variation of a Haar wavelet. The arithmetic encoding of the coefficients assumes Laplace distributions, one per sub-band. The Laplace parameter for each distribution is transmitted to the decoder using a custom method. The geometry of the point cloud is encoded using the well-established octtree scanning. Results show that the proposed solution performs comparably to the current state-of-the-art, in many occasions outperforming it, while being much more computationally efficient. We believe this work represents the state-of-the-art in intra-frame compression of point clouds for real-time 3D video.

The relationships between electricity consumption and GDP in Asian countries, using hierarchical structure methods

NASA Astrophysics Data System (ADS)

Kantar, Ersin; Keskin, Mustafa

2013-11-01

This study uses hierarchical structure methods (minimal spanning tree (MST) and hierarchical tree (HT)) to examine the relationship between energy consumption and economic growth in a sample of 30 Asian countries covering the period 1971-2008. These countries are categorized into four panels based on the World Bank income classification, namely high, upper middle, lower middle, and low income. In particular, we use the data of electricity consumption and real gross domestic product (GDP) per capita to detect the topological properties of the countries. We show a relationship between electricity consumption and economic growth by using the MST and HT. We also use the bootstrap technique to investigate a value of the statistical reliability to the links of the MST. Finally, we use a clustering linkage procedure in order to observe the cluster structure. The results of the structural topologies of these trees are as follows: (i) we identified different clusters of countries according to their geographical location and economic growth, (ii) we found a strong relationship between energy consumption and economic growth for all income groups considered in this study and (iii) the results are in good agreement with the causal relationship between electricity consumption and economic growth.

Random walk hierarchy measure: What is more hierarchical, a chain, a tree or a star?

PubMed

Czégel, Dániel; Palla, Gergely

2015-12-10

Signs of hierarchy are prevalent in a wide range of systems in nature and society. One of the key problems is quantifying the importance of hierarchical organisation in the structure of the network representing the interactions or connections between the fundamental units of the studied system. Although a number of notable methods are already available, their vast majority is treating all directed acyclic graphs as already maximally hierarchical. Here we propose a hierarchy measure based on random walks on the network. The novelty of our approach is that directed trees corresponding to multi level pyramidal structures obtain higher hierarchy scores compared to directed chains and directed stars. Furthermore, in the thermodynamic limit the hierarchy measure of regular trees is converging to a well defined limit depending only on the branching number. When applied to real networks, our method is computationally very effective, as the result can be evaluated with arbitrary precision by subsequent multiplications of the transition matrix describing the random walk process. In addition, the tests on real world networks provided very intuitive results, e.g., the trophic levels obtained from our approach on a food web were highly consistent with former results from ecology.

Hydrothermal Synthesis Au-Bi2Te3 Nanocomposite Thermoelectric Film with a Hierarchical Sub-Micron Antireflection Quasi-Periodic Structure.

PubMed

Tian, Junlong; Zhang, Wang; Zhang, Yuan; Xue, Ruiyang; Wang, Yuhua; Zhang, Zhijian; Zhang, Di

2015-06-03

In this work, Au-Bi(2)Te(3) nanocomposite thermoelectric film with a hierarchical sub-micron antireflection quasi-periodic structure was synthesized via a low-temperature chemical route using Troides helena (Linnaeus) forewing (T_FW) as the biomimetic template. This method combines chemosynthesis with biomimetic techniques, without the requirement of expensive equipment and energy intensive processes. The microstructure and the morphology of the Au-Bi(2)Te(3) nanocomposite thermoelectric film was analyzed by X-ray diffraction (XRD), field-emission scanning-electron microscopy (FESEM), and transmission electron microscopy (TEM). Coupled the plasmon resonances of the Au nanoparticles with the hierarchical sub-micron antireflection quasi-periodic structure, the Au-Bi(2)Te(3) nanocomposite thermoelectric film possesses an effective infrared absorption and infrared photothermal conversion performance. Based on the finite difference time domain method and the Joule effect, the heat generation and the heat source density distribution of the Au-Bi(2)Te(3) nanocomposite thermoelectric film were studied. The heterogeneity of heat source density distribution of the Au-Bi(2)Te(3) nanocomposite thermoelectric film opens up a novel promising technique for generating thermoelectric power under illumination.

Attitude and vibration control of a large flexible space-based antenna

NASA Technical Reports Server (NTRS)

Joshi, S. M.

1982-01-01

Control systems synthesis is considered for controlling the rigid body attitude and elastic motion of a large deployable space-based antenna. Two methods for control systems synthesis are considered. The first method utilizes the stability and robustness properties of the controller consisting of torque actuators and collocated attitude and rate sensors. The second method is based on the linear-quadratic-Gaussian control theory. A combination of the two methods, which results in a two level hierarchical control system, is also briefly discussed. The performance of the controllers is analyzed by computing the variances of pointing errors, feed misalignment errors and surface contour errors in the presence of sensor and actuator noise.

Graphical Methods for Reducing, Visualizing and Analyzing Large Data Sets Using Hierarchical Terminologies

PubMed Central

Jing, Xia; Cimino, James J.

2011-01-01

Objective: To explore new graphical methods for reducing and analyzing large data sets in which the data are coded with a hierarchical terminology. Methods: We use a hierarchical terminology to organize a data set and display it in a graph. We reduce the size and complexity of the data set by considering the terminological structure and the data set itself (using a variety of thresholds) as well as contributions of child level nodes to parent level nodes. Results: We found that our methods can reduce large data sets to manageable size and highlight the differences among graphs. The thresholds used as filters to reduce the data set can be used alone or in combination. We applied our methods to two data sets containing information about how nurses and physicians query online knowledge resources. The reduced graphs make the differences between the two groups readily apparent. Conclusions: This is a new approach to reduce size and complexity of large data sets and to simplify visualization. This approach can be applied to any data sets that are coded with hierarchical terminologies. PMID:22195119

Decision making in prioritization of required operational capabilities

NASA Astrophysics Data System (ADS)

Andreeva, P.; Karev, M.; Kovacheva, Ts.

2015-10-01

The paper describes an expert heuristic approach to prioritization of required operational capabilities in the field of defense. Based on expert assessment and by application of the method of Analytical Hierarchical Process, a methodology for their prioritization has been developed. It has been applied to practical simulation decision making games.

Soft- to network hard-material for constructing both ion- and electron-conductive hierarchical porous structure to significantly boost energy density of a supercapacitor.

PubMed

Yang, Pingping; Xie, Jiale; Guo, Chunxian; Li, Chang Ming

2017-01-01

Soft-material PEDOT is used to network hard Co 3 O 4 nanowires for constructing both ion- and electron-conductive hierarchical porous structure Co 3 O 4 /PEDOT to greatly boost the capacitor energy density than sum of that of plain Co 3 O 4 nanowires and PEDOT film. Specifically, the networked hierarchical porous structure of Co 3 O 4 /PEDOT is synthesized and tailored through hydrothermal method and post-electrochemical polymerization method for the PEDOT coating onto Co 3 O 4 nanowires. Typically, Co 3 O 4 /PEDOT supercapacitor gets a highest areal capacitance of 160mFcm -2 at a current density of 0.2mAcm -2 , which is about 2.2 times larger than the sum of that of plain Co 3 O 4 NWs (0.92mFcm -2 ) and PEDOT film (69.88mFcm -2 ). Besides, if only PEDOT as active mass is counted, Co 3 O 4 /PEDOT cell can achieve a highest capacitance of 567.21Fg -1 , this is the highest capacitance value obtained by PEDOT-based supercapacitors. Furthermore, this soft-hard network porous structure also achieves a high cycling stability of 93% capacitance retention after the 20,000th cycle. This work demonstrates a new approach to constructing both ion and electron conductive hierarchical porous structure to significantly boost energy density of a supercapacitor. Copyright © 2016 Elsevier Inc. All rights reserved.

Hierarchical layered double hydroxides with Ag nanoparticle modification for ethanol sensing

NASA Astrophysics Data System (ADS)

Qin, Yuxiang; Wang, Liping; Wang, Xiaofei

2018-07-01

Layered double hydroxides (LDHs) have recently been revealed to be promising in gas sensor applications due to their compositional flexibility and unique 2D-interlayer channel for gas diffusion and adsorption. This work demonstrates highly porous hierarchical LDHs containing Mg2+ and Al3+ (MgAl-LDHs) for ethanol sensing at room temperature. These MgAl-LDHs, with unique flower-like hierarchical structure and mesoporous interlayer, were synthesized hydrothermally using sodium dodecyl sulfate as soft template as well as intercalating agent. Further modification by discrete Ag nanoparticles (NPs) was achieved via an environmentally friendly glucose-reduction method to improve the gas-sensing response of the LDH-based sensor. It is found that the hierarchical MgAl-LDHs show potential in sensing ethanol gas with rapid dynamic characteristics at room temperature; their response magnitude towards ethanol can be enhanced significantly by Ag NP modification. The gas-response value of the Ag-modified MgAl-LDH sensor is about twice that of pristine MgAl-LDH sensors, towards 5–200 ppm ethanol at room temperature. Meanwhile, rapid response-recovery characteristics are achieved, with response and recovery times shorter than 10 and 50 s, respectively. The satisfactory sensing performance and remarkable response enhancement by Ag NP modification are demonstrated in terms of the unique microstructure of the hierarchical MgAl-LDHs and a constructed conductive effect model of Ag functionalized LDHs.

Hierarchical layered double hydroxides with Ag nanoparticle modification for ethanol sensing.

PubMed

Qin, Yuxiang; Wang, Liping; Wang, Xiaofei

2018-07-06

Layered double hydroxides (LDHs) have recently been revealed to be promising in gas sensor applications due to their compositional flexibility and unique 2D-interlayer channel for gas diffusion and adsorption. This work demonstrates highly porous hierarchical LDHs containing Mg 2+ and Al 3+ (MgAl-LDHs) for ethanol sensing at room temperature. These MgAl-LDHs, with unique flower-like hierarchical structure and mesoporous interlayer, were synthesized hydrothermally using sodium dodecyl sulfate as soft template as well as intercalating agent. Further modification by discrete Ag nanoparticles (NPs) was achieved via an environmentally friendly glucose-reduction method to improve the gas-sensing response of the LDH-based sensor. It is found that the hierarchical MgAl-LDHs show potential in sensing ethanol gas with rapid dynamic characteristics at room temperature; their response magnitude towards ethanol can be enhanced significantly by Ag NP modification. The gas-response value of the Ag-modified MgAl-LDH sensor is about twice that of pristine MgAl-LDH sensors, towards 5-200 ppm ethanol at room temperature. Meanwhile, rapid response-recovery characteristics are achieved, with response and recovery times shorter than 10 and 50 s, respectively. The satisfactory sensing performance and remarkable response enhancement by Ag NP modification are demonstrated in terms of the unique microstructure of the hierarchical MgAl-LDHs and a constructed conductive effect model of Ag functionalized LDHs.

Dynamic and quantitative method of analyzing service consistency evolution based on extended hierarchical finite state automata.

PubMed

Fan, Linjun; Tang, Jun; Ling, Yunxiang; Li, Benxian

2014-01-01

This paper is concerned with the dynamic evolution analysis and quantitative measurement of primary factors that cause service inconsistency in service-oriented distributed simulation applications (SODSA). Traditional methods are mostly qualitative and empirical, and they do not consider the dynamic disturbances among factors in service's evolution behaviors such as producing, publishing, calling, and maintenance. Moreover, SODSA are rapidly evolving in terms of large-scale, reusable, compositional, pervasive, and flexible features, which presents difficulties in the usage of traditional analysis methods. To resolve these problems, a novel dynamic evolution model extended hierarchical service-finite state automata (EHS-FSA) is constructed based on finite state automata (FSA), which formally depict overall changing processes of service consistency states. And also the service consistency evolution algorithms (SCEAs) based on EHS-FSA are developed to quantitatively assess these impact factors. Experimental results show that the bad reusability (17.93% on average) is the biggest influential factor, the noncomposition of atomic services (13.12%) is the second biggest one, and the service version's confusion (1.2%) is the smallest one. Compared with previous qualitative analysis, SCEAs present good effectiveness and feasibility. This research can guide the engineers of service consistency technologies toward obtaining a higher level of consistency in SODSA.

Dynamic and Quantitative Method of Analyzing Service Consistency Evolution Based on Extended Hierarchical Finite State Automata

PubMed Central

Fan, Linjun; Tang, Jun; Ling, Yunxiang; Li, Benxian

2014-01-01

This paper is concerned with the dynamic evolution analysis and quantitative measurement of primary factors that cause service inconsistency in service-oriented distributed simulation applications (SODSA). Traditional methods are mostly qualitative and empirical, and they do not consider the dynamic disturbances among factors in service's evolution behaviors such as producing, publishing, calling, and maintenance. Moreover, SODSA are rapidly evolving in terms of large-scale, reusable, compositional, pervasive, and flexible features, which presents difficulties in the usage of traditional analysis methods. To resolve these problems, a novel dynamic evolution model extended hierarchical service-finite state automata (EHS-FSA) is constructed based on finite state automata (FSA), which formally depict overall changing processes of service consistency states. And also the service consistency evolution algorithms (SCEAs) based on EHS-FSA are developed to quantitatively assess these impact factors. Experimental results show that the bad reusability (17.93% on average) is the biggest influential factor, the noncomposition of atomic services (13.12%) is the second biggest one, and the service version's confusion (1.2%) is the smallest one. Compared with previous qualitative analysis, SCEAs present good effectiveness and feasibility. This research can guide the engineers of service consistency technologies toward obtaining a higher level of consistency in SODSA. PMID:24772033

Managing Clustered Data Using Hierarchical Linear Modeling

ERIC Educational Resources Information Center

Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.

2012-01-01

Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…

[Study of the accidental thymus involution during the formation of hierarchic communities by a novel physical method for recording the social stress].

PubMed

Kulikov, A V; Arkhipova, L V; Kulikov, D A; Smirnova, G N; Kulikova, P A

2013-01-01

A novel method for recording the aggressive behavior in newly formed hierarchic communities has been developed. A temporal and age-related dynamics of the accidental thymus involution in mammals has been studied.

Buckling Load Calculations of the Isotropic Shell A-8 Using a High-Fidelity Hierarchical Approach

NASA Technical Reports Server (NTRS)

Arbocz, Johann; Starnes, James H.

2002-01-01

As a step towards developing a new design philosophy, one that moves away from the traditional empirical approach used today in design towards a science-based design technology approach, a test series of 7 isotropic shells carried out by Aristocrat and Babcock at Caltech is used. It is shown how the hierarchical approach to buckling load calculations proposed by Arbocz et al can be used to perform an approach often called 'high fidelity analysis', where the uncertainties involved in a design are simulated by refined and accurate numerical methods. The Delft Interactive Shell DEsign COde (short, DISDECO) is employed for this hierarchical analysis to provide an accurate prediction of the critical buckling load of the given shell structure. This value is used later as a reference to establish the accuracy of the Level-3 buckling load predictions. As a final step in the hierarchical analysis approach, the critical buckling load and the estimated imperfection sensitivity of the shell are verified by conducting an analysis using a sufficiently refined finite element model with one of the current generation two-dimensional shell analysis codes with the advanced capabilities needed to represent both geometric and material nonlinearities.

Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks.

PubMed

Colclough, Giles L; Woolrich, Mark W; Harrison, Samuel J; Rojas López, Pedro A; Valdes-Sosa, Pedro A; Smith, Stephen M

2018-05-07

A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in MEG beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity. Copyright © 2018. Published by Elsevier Inc.

On a High-Fidelity Hierarchical Approach to Buckling Load Calculations

NASA Technical Reports Server (NTRS)

Arbocz, Johann; Starnes, James H.; Nemeth, Michael P.

2001-01-01

As a step towards developing a new design philosophy, one that moves away from the traditional empirical approach used today in design towards a science-based design technology approach, a recent test series of 5 composite shells carried out by Waters at NASA Langley Research Center is used. It is shown how the hierarchical approach to buckling load calculations proposed by Arbocz et al can be used to perform an approach often called "high fidelity analysis", where the uncertainties involved in a design are simulated by refined and accurate numerical methods. The Delft Interactive Shell DEsign COde (short, DISDECO) is employed for this hierarchical analysis to provide an accurate prediction of the critical buckling load of the given shell structure. This value is used later as a reference to establish the accuracy of the Level-3 buckling load predictions. As a final step in the hierarchical analysis approach, the critical buckling load and the estimated imperfection sensitivity of the shell are verified by conducting an analysis using a sufficiently refined finite element model with one of the current generation two-dimensional shell analysis codes with the advanced capabilities needed to represent both geometric and material nonlinearities.

Statistics of Weighted Brain Networks Reveal Hierarchical Organization and Gaussian Degree Distribution

PubMed Central

Ivković, Miloš; Kuceyeski, Amy; Raj, Ashish

2012-01-01

Whole brain weighted connectivity networks were extracted from high resolution diffusion MRI data of 14 healthy volunteers. A statistically robust technique was proposed for the removal of questionable connections. Unlike most previous studies our methods are completely adapted for networks with arbitrary weights. Conventional statistics of these weighted networks were computed and found to be comparable to existing reports. After a robust fitting procedure using multiple parametric distributions it was found that the weighted node degree of our networks is best described by the normal distribution, in contrast to previous reports which have proposed heavy tailed distributions. We show that post-processing of the connectivity weights, such as thresholding, can influence the weighted degree asymptotics. The clustering coefficients were found to be distributed either as gamma or power-law distribution, depending on the formula used. We proposed a new hierarchical graph clustering approach, which revealed that the brain network is divided into a regular base-2 hierarchical tree. Connections within and across this hierarchy were found to be uncommonly ordered. The combined weight of our results supports a hierarchically ordered view of the brain, whose connections have heavy tails, but whose weighted node degrees are comparable. PMID:22761649

Statistics of weighted brain networks reveal hierarchical organization and Gaussian degree distribution.

PubMed

Ivković, Miloš; Kuceyeski, Amy; Raj, Ashish

2012-01-01

Whole brain weighted connectivity networks were extracted from high resolution diffusion MRI data of 14 healthy volunteers. A statistically robust technique was proposed for the removal of questionable connections. Unlike most previous studies our methods are completely adapted for networks with arbitrary weights. Conventional statistics of these weighted networks were computed and found to be comparable to existing reports. After a robust fitting procedure using multiple parametric distributions it was found that the weighted node degree of our networks is best described by the normal distribution, in contrast to previous reports which have proposed heavy tailed distributions. We show that post-processing of the connectivity weights, such as thresholding, can influence the weighted degree asymptotics. The clustering coefficients were found to be distributed either as gamma or power-law distribution, depending on the formula used. We proposed a new hierarchical graph clustering approach, which revealed that the brain network is divided into a regular base-2 hierarchical tree. Connections within and across this hierarchy were found to be uncommonly ordered. The combined weight of our results supports a hierarchically ordered view of the brain, whose connections have heavy tails, but whose weighted node degrees are comparable.

Development of ultralight, super-elastic, hierarchical metallic meta-structures with i3DP technology

NASA Astrophysics Data System (ADS)

Zhang, Dongxing; Xiao, Junfeng; Moorlag, Carolyn; Guo, Qiuquan; Yang, Jun

2017-11-01

Lightweight and mechanically robust materials show promising applications in thermal insulation, energy absorption, and battery catalyst supports. This study demonstrates an effective method for creation of ultralight metallic structures based on initiator-integrated 3D printing technology (i3DP), which provides a possible platform to design the materials with the best geometric parameters and desired mechanical performance. In this study, ultralight Ni foams with 3D interconnected hollow tubes were fabricated, consisting of hierarchical features spanning three scale orders ranging from submicron to centimeter. The resultant materials can achieve an ultralight density of as low as 5.1 mg cm-3 and nearly recover after significant compression up to 50%. Due to a high compression ratio, the hierarchical structure exhibits superior properties in terms of energy absorption and mechanical efficiency. The relationship of structural parameters and mechanical response was established. The ability of achieving ultralight density <10 mg cm-3 and the stable \\bar{E}˜ {\\bar{ρ }}2 scaling through all range of relative density, indicates an advantage over the previous stochastic metal foams. Overall, this initiator-integrated 3D printing approach provides metallic structures with substantial benefits from the hierarchical design and fabrication flexibility to ultralight applications.

Hardware implementation of hierarchical volume subdivision-based elastic registration.

PubMed

Dandekar, Omkar; Walimbe, Vivek; Shekhar, Raj

2006-01-01

Real-time, elastic and fully automated 3D image registration is critical to the efficiency and effectiveness of many image-guided diagnostic and treatment procedures relying on multimodality image fusion or serial image comparison. True, real-time performance will make many 3D image registration-based techniques clinically viable. Hierarchical volume subdivision-based image registration techniques are inherently faster than most elastic registration techniques, e.g. free-form deformation (FFD)-based techniques, and are more amenable for achieving real-time performance through hardware acceleration. Our group has previously reported an FPGA-based architecture for accelerating FFD-based image registration. In this article we show how our existing architecture can be adapted to support hierarchical volume subdivision-based image registration. A proof-of-concept implementation of the architecture achieved speedups of 100 for elastic registration against an optimized software implementation on a 3.2 GHz Pentium III Xeon workstation. Due to inherent parallel nature of the hierarchical volume subdivision-based image registration techniques further speedup can be achieved by using several computing modules in parallel.

A hierarchical estimator development for estimation of tire-road friction coefficient

PubMed Central

Zhang, Xudong; Göhlich, Dietmar

2017-01-01

The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified “magic formula” tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method. PMID:28178332

A hierarchical estimator development for estimation of tire-road friction coefficient.

PubMed

Zhang, Xudong; Göhlich, Dietmar

2017-01-01

The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified "magic formula" tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method.

Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine.

PubMed

Kim, Jongin; Lee, Boreom

2018-05-07

Different modalities such as structural MRI, FDG-PET, and CSF have complementary information, which is likely to be very useful for diagnosis of AD and MCI. Therefore, it is possible to develop a more effective and accurate AD/MCI automatic diagnosis method by integrating complementary information of different modalities. In this paper, we propose multi-modal sparse hierarchical extreme leaning machine (MSH-ELM). We used volume and mean intensity extracted from 93 regions of interest (ROIs) as features of MRI and FDG-PET, respectively, and used p-tau, t-tau, and Aβ42 as CSF features. In detail, high-level representation was individually extracted from each of MRI, FDG-PET, and CSF using a stacked sparse extreme learning machine auto-encoder (sELM-AE). Then, another stacked sELM-AE was devised to acquire a joint hierarchical feature representation by fusing the high-level representations obtained from each modality. Finally, we classified joint hierarchical feature representation using a kernel-based extreme learning machine (KELM). The results of MSH-ELM were compared with those of conventional ELM, single kernel support vector machine (SK-SVM), multiple kernel support vector machine (MK-SVM) and stacked auto-encoder (SAE). Performance was evaluated through 10-fold cross-validation. In the classification of AD vs. HC and MCI vs. HC problem, the proposed MSH-ELM method showed mean balanced accuracies of 96.10% and 86.46%, respectively, which is much better than those of competing methods. In summary, the proposed algorithm exhibits consistently better performance than SK-SVM, ELM, MK-SVM and SAE in the two binary classification problems (AD vs. HC and MCI vs. HC). © 2018 Wiley Periodicals, Inc.

A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data

PubMed Central

Gallopin, Mélina; Rau, Andrea; Jaffrézic, Florence

2013-01-01

Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso penalty to infer gene networks from RNA-seq data; this model has the advantage of directly modelling discrete data and accounting for inter-sample variance larger than the sample mean. Using real microRNA-seq data from breast cancer tumors and simulations, we compare this method to a regularized Gaussian graphical model on log-transformed data, and a Poisson log-linear graphical model with a Lasso penalty on power-transformed data. For data simulated with large inter-sample dispersion, the proposed model performs better than the other methods in terms of sensitivity, specificity and area under the ROC curve. These results show the necessity of methods specifically designed for gene network inference from RNA-seq data. PMID:24147011

Unveiling Surface Redox Charge Storage of Interacting Two-Dimensional Hetero-Nanosheets in Hierarchical Architectures

DOE PAGES

Mahmood, Qasim; Bak, Seong-Min; Kim, Min G.; ...

2015-03-03

Two-dimensional (2D) heteronanosheets are currently the focus of intense study due to the unique properties that emerge from the interplay between two low-dimensional nanomaterials with different properties. However, the properties and new phenomena based on the two 2D heteronanosheets interacting in a 3D hierarchical architecture have yet to be explored. Here, we unveil the surface redox charge storage mechanism of surface-exposed WS2 nanosheets assembled in a 3D hierarchical heterostructure using in situ synchrotron X-ray absorption and Raman spectroscopic methods. The surface dominating redox charge storage of WS2 is manifested in a highly reversible and ultrafast capacitive fashion due to themore » interaction of heteronanosheets and the 3D connectivity of the hierarchical structure. In contrast, compositionally identical 2D WS2 structures fail to provide a fast and high capacitance with different modes of lattice vibration. The distinctive surface capacitive behavior of 3D hierarchically structured heteronanosheets is associated with rapid proton accommodation into the in-plane W–S lattice (with the softening of the E2g bands), the reversible redox transition of the surface-exposed intralayers residing in the electrochemically active 1T phase of WS2 (with the reversible change in the interatomic distance and peak intensity of W–W bonds), and the change in the oxidation state during the proton insertion/deinsertion process. This proposed mechanism agrees with the dramatic improvement in the capacitive performance of the two heteronanosheets coupled in the hierarchical structure.« less

Analysis of genetic association using hierarchical clustering and cluster validation indices.

PubMed

Pagnuco, Inti A; Pastore, Juan I; Abras, Guillermo; Brun, Marcel; Ballarin, Virginia L

2017-10-01

It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, based on some criteria of similarity. This task is usually performed by clustering algorithms, where the genes are clustered into meaningful groups based on their expression values in a set of experiment. In this work, we propose a method to find sets of co-expressed genes, based on cluster validation indices as a measure of similarity for individual gene groups, and a combination of variants of hierarchical clustering to generate the candidate groups. We evaluated its ability to retrieve significant sets on simulated correlated and real genomics data, where the performance is measured based on its detection ability of co-regulated sets against a full search. Additionally, we analyzed the quality of the best ranked groups using an online bioinformatics tool that provides network information for the selected genes. Copyright © 2017 Elsevier Inc. All rights reserved.

Hierarchical cobalt-based hydroxide microspheres for water oxidation.

PubMed

Zhang, Ye; Cui, Bai; Derr, Olivia; Yao, Zhibo; Qin, Zhaotong; Deng, Xiangyun; Li, Jianbao; Lin, Hong

2014-03-21

3D hierarchical cobalt hydroxide carbonate hydrate (Co(CO3)0.5(OH)·0.11H2O) has been synthesized featuring a hollow urchin-like structure by a one-step hydrothermal method at modest temperature on FTO glass substrates. The functionalities of precursor surfactants were isolated and analyzed. A plausible formation mechanism of the spherical urchin-like microclusters has been furnished through time-dependent investigations. Introduction of other transitional metal doping (Cu, Ni) would give rise to a substantial morphological change associated with a surface area drop. The directly grown cobalt-based hydroxide composite electrodes were found to be capable of catalyzing oxygen evolution reaction (OER) under both neutral pH and alkaline conditions. The favorable 3D dendritic morphology and porous structure provide large surface areas and possible defect sites that are likely responsible for their robust electrochemical activity.

Hierarchical structure for audio-video based semantic classification of sports video sequences

NASA Astrophysics Data System (ADS)

Kolekar, M. H.; Sengupta, S.

2005-07-01

A hierarchical structure for sports event classification based on audio and video content analysis is proposed in this paper. Compared to the event classifications in other games, those of cricket are very challenging and yet unexplored. We have successfully solved cricket video classification problem using a six level hierarchical structure. The first level performs event detection based on audio energy and Zero Crossing Rate (ZCR) of short-time audio signal. In the subsequent levels, we classify the events based on video features using a Hidden Markov Model implemented through Dynamic Programming (HMM-DP) using color or motion as a likelihood function. For some of the game-specific decisions, a rule-based classification is also performed. Our proposed hierarchical structure can easily be applied to any other sports. Our results are very promising and we have moved a step forward towards addressing semantic classification problems in general.

Microparticles with hierarchical porosity

DOEpatents

Petsev, Dimiter N; Atanassov, Plamen; Pylypenko, Svitlana; Carroll, Nick; Olson, Tim

2012-12-18

The present disclosure provides oxide microparticles with engineered hierarchical porosity and methods of manufacturing the same. Also described are structures that are formed by templating, impregnating, and/or precipitating the oxide microparticles and method for forming the same. Suitable applications include catalysts, electrocatalysts, electrocatalysts support materials, capacitors, drug delivery systems, sensors and chromatography.

Complex Applications of HLM in Studies of Science and Mathematics Achievement: Cross-Classified Random Effects Models

ERIC Educational Resources Information Center

Moreno, Mario; Harwell, Michael; Guzey, S. Selcen; Phillips, Alison; Moore, Tamara J.

2016-01-01

Hierarchical linear models have become a familiar method for accounting for a hierarchical data structure in studies of science and mathematics achievement. This paper illustrates the use of cross-classified random effects models (CCREMs), which are likely less familiar. The defining characteristic of CCREMs is a hierarchical data structure…

Progressive Dictionary Learning with Hierarchical Predictive Structure for Scalable Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Xiong, Hongkai; Jiang, Xiaoqian; Zou, Junni; Taubman, David

2017-04-12

Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers (ELs) with a guarantee of reconstruction performance. The overcomplete dictionary is trained to adaptively capture local structures along motion trajectories as well as exploit the correlations between neighboring layers of resolutions. Furthermore, progressive dictionary learning is developed to enable the scalability in temporal domain and restrict the error propagation in a close-loop predictor. Under the hierarchical predictive structure, online learning is leveraged to guarantee the training and prediction performance with an improved convergence rate. To accommodate with the stateof- the-art scalable extension of H.264/AVC and latest HEVC, standardized codec cores are utilized to encode the base and enhancement layers. Experimental results show that the proposed method outperforms the latest SHVC and HEVC simulcast over extensive test sequences with various resolutions.

Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks.

PubMed

He, Jieyue; Li, Chaojun; Ye, Baoliu; Zhong, Wei

2012-06-25

Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures. In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules. The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms. Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.

Graph pyramids for protein function prediction

PubMed Central

2015-01-01

Background Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Methods Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Results Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data. PMID:26044522

A robust and hierarchical approach for the automatic co-registration of intensity and visible images

NASA Astrophysics Data System (ADS)

González-Aguilera, Diego; Rodríguez-Gonzálvez, Pablo; Hernández-López, David; Luis Lerma, José

2012-09-01

This paper presents a new robust approach to integrate intensity and visible images which have been acquired with a terrestrial laser scanner and a calibrated digital camera, respectively. In particular, an automatic and hierarchical method for the co-registration of both sensors is developed. The approach integrates several existing solutions to improve the performance of the co-registration between range-based and visible images: the Affine Scale-Invariant Feature Transform (A-SIFT), the epipolar geometry, the collinearity equations, the Groebner basis solution and the RANdom SAmple Consensus (RANSAC), integrating a voting scheme. The approach presented herein improves the existing co-registration approaches in automation, robustness, reliability and accuracy.

Auditing the NCI Thesaurus with Semantic Web Technologies

PubMed Central

Mougin, Fleur; Bodenreider, Olivier

2008-01-01

Auditing biomedical terminologies often results in the identification of inconsistencies and thus helps to improve their quality. In this paper, we present a method based on Semantic Web technologies for auditing biomedical terminologies and apply it to the NCI thesaurus. We stored the NCI thesaurus concepts and their properties in an RDF triple store. By querying this store, we assessed the consistency of both hierarchical and associative relations from the NCI thesaurus among themselves and with corresponding relations in the UMLS Semantic Network. We show that the consistency is better for associative relations than for hierarchical relations. Causes for inconsistency and benefits from using Semantic Web technologies for auditing purposes are discussed. PMID:18999265

Auditing the NCI thesaurus with semantic web technologies.

PubMed

Mougin, Fleur; Bodenreider, Olivier

2008-11-06

Auditing biomedical terminologies often results in the identification of inconsistencies and thus helps to improve their quality. In this paper, we present a method based on Semantic Web technologies for auditing biomedical terminologies and apply it to the NCI thesaurus. We stored the NCI thesaurus concepts and their properties in an RDF triple store. By querying this store, we assessed the consistency of both hierarchical and associative relations from the NCI thesaurus among themselves and with corresponding relations in the UMLS Semantic Network. We show that the consistency is better for associative relations than for hierarchical relations. Causes for inconsistency and benefits from using Semantic Web technologies for auditing purposes are discussed.

Hierarchical laser-induced periodic surface structures induced by femtosecond laser on the surface of a ZnO film

NASA Astrophysics Data System (ADS)

Wang, Shaojun; Jiang, Lan; Han, Weina; Hu, Jie; Li, Xiaowei; Wang, Qingsong; Lu, Yongfeng

2018-05-01

We realize hierarchical laser-induced periodic surface structures (LIPSSs) on the surface of a ZnO thin film in a single step by the irradiation of femtosecond laser pulses. The structures are characterized by the high-spatial-frequency LIPSSs (HSFLs) formed on the abnormal bumped low-spatial-frequency LIPSSs (LSFLs). Localized electric-field enhancement based on the initially formed LSFLs is proposed as a potential mechanism for the formation of HSFLs. The simulation results through the finite-difference time-domain method show good agreement with experiments. Furthermore, the crucial role of the LSFLs in the formation of HSFLs is validated by an elaborate experimental design with preprocessed HSFLs.

System and method for knowledge based matching of users in a network

DOEpatents

Verspoor, Cornelia Maria [Santa Fe, NM; Sims, Benjamin Hayden [Los Alamos, NM; Ambrosiano, John Joseph [Los Alamos, NM; Cleland, Timothy James [Los Alamos, NM

2011-04-26

A knowledge-based system and methods to matchmaking and social network extension are disclosed. The system is configured to allow users to specify knowledge profiles, which are collections of concepts that indicate a certain topic or area of interest selected from an. The system utilizes the knowledge model as the semantic space within which to compare similarities in user interests. The knowledge model is hierarchical so that indications of interest in specific concepts automatically imply interest in more general concept. Similarity measures between profiles may then be calculated based on suitable distance formulas within this space.

Recognition and characterization of hierarchical interstellar structure. II - Structure tree statistics

NASA Technical Reports Server (NTRS)

Houlahan, Padraig; Scalo, John

1992-01-01

A new method of image analysis is described, in which images partitioned into 'clouds' are represented by simplified skeleton images, called structure trees, that preserve the spatial relations of the component clouds while disregarding information concerning their sizes and shapes. The method can be used to discriminate between images of projected hierarchical (multiply nested) and random three-dimensional simulated collections of clouds constructed on the basis of observed interstellar properties, and even intermediate systems formed by combining random and hierarchical simulations. For a given structure type, the method can distinguish between different subclasses of models with different parameters and reliably estimate their hierarchical parameters: average number of children per parent, scale reduction factor per level of hierarchy, density contrast, and number of resolved levels. An application to a column density image of the Taurus complex constructed from IRAS data is given. Moderately strong evidence for a hierarchical structural component is found, and parameters of the hierarchy, as well as the average volume filling factor and mass efficiency of fragmentation per level of hierarchy, are estimated. The existence of nested structure contradicts models in which large molecular clouds are supposed to fragment, in a single stage, into roughly stellar-mass cores.

Personalized Medicine in Veterans with Traumatic Brain Injuries

DTIC Science & Technology

2013-05-01

Pair-Group Method using Arithmetic averages ( UPGMA ) based on cosine correlation of row mean centered log2 signal values; this was the top 50%-tile...cluster- ing was performed by the UPGMA method using Cosine correlation as the similarity metric. For comparative purposes, clustered heat maps included...non-mTBI cases were subjected to unsupervised hierarchical clustering analysis using the UPGMA algorithm with cosine correlation as the similarity

Finite Element A Posteriori Error Estimation for Heat Conduction. Degree awarded by George Washington Univ.

NASA Technical Reports Server (NTRS)

Lang, Christapher G.; Bey, Kim S. (Technical Monitor)

2002-01-01

This research investigates residual-based a posteriori error estimates for finite element approximations of heat conduction in single-layer and multi-layered materials. The finite element approximation, based upon hierarchical modelling combined with p-version finite elements, is described with specific application to a two-dimensional, steady state, heat-conduction problem. Element error indicators are determined by solving an element equation for the error with the element residual as a source, and a global error estimate in the energy norm is computed by collecting the element contributions. Numerical results of the performance of the error estimate are presented by comparisons to the actual error. Two methods are discussed and compared for approximating the element boundary flux. The equilibrated flux method provides more accurate results for estimating the error than the average flux method. The error estimation is applied to multi-layered materials with a modification to the equilibrated flux method to approximate the discontinuous flux along a boundary at the material interfaces. A directional error indicator is developed which distinguishes between the hierarchical modeling error and the finite element error. Numerical results are presented for single-layered materials which show that the directional indicators accurately determine which contribution to the total error dominates.

A hierarchical exact accelerated stochastic simulation algorithm

NASA Astrophysics Data System (ADS)

Orendorff, David; Mjolsness, Eric

2012-12-01

A new algorithm, "HiER-leap" (hierarchical exact reaction-leaping), is derived which improves on the computational properties of the ER-leap algorithm for exact accelerated simulation of stochastic chemical kinetics. Unlike ER-leap, HiER-leap utilizes a hierarchical or divide-and-conquer organization of reaction channels into tightly coupled "blocks" and is thereby able to speed up systems with many reaction channels. Like ER-leap, HiER-leap is based on the use of upper and lower bounds on the reaction propensities to define a rejection sampling algorithm with inexpensive early rejection and acceptance steps. But in HiER-leap, large portions of intra-block sampling may be done in parallel. An accept/reject step is used to synchronize across blocks. This method scales well when many reaction channels are present and has desirable asymptotic properties. The algorithm is exact, parallelizable and achieves a significant speedup over the stochastic simulation algorithm and ER-leap on certain problems. This algorithm offers a potentially important step towards efficient in silico modeling of entire organisms.

On-demand Reporting of Risk-adjusted and Smoothed Rates for Quality Profiling in ACS NSQIP.

PubMed

Cohen, Mark E; Liu, Yaoming; Huffman, Kristopher M; Ko, Clifford Y; Hall, Bruce L

2016-12-01

Surgical quality improvement depends on hospitals having accurate and timely information about comparative performance. Profiling accuracy is improved by risk adjustment and shrinkage adjustment to stabilize estimates. These adjustments are included in ACS NSQIP reports, where hospital odds ratios (OR) are estimated using hierarchical models built on contemporaneous data. However, the timeliness of feedback remains an issue. We describe an alternative, nonhierarchical approach, which yields risk- and shrinkage-adjusted rates. In contrast to our "Traditional" NSQIP method, this approach uses preexisting equations, built on historical data, which permits hospitals to have near immediate access to profiling results. We compared our traditional method to this new "on-demand" approach with respect to outlier determinations, kappa statistics, and correlations between logged OR and standardized rates, for 12 models (4 surgical groups by 3 outcomes). When both methods used the same contemporaneous data, there were similar numbers of hospital outliers and correlations between logged OR and standardized rates were high. However, larger differences were observed when the effect of contemporaneous versus historical data was added to differences in statistical methodology. The on-demand, nonhierarchical approach provides results similar to the traditional hierarchical method and offers immediacy, an "over-time" perspective, application to a broader range of models and data subsets, and reporting of more easily understood rates. Although the nonhierarchical method results are now available "on-demand" in a web-based application, the hierarchical approach has advantages, which support its continued periodic publication as the gold standard for hospital profiling in the program.

Ultra-facile fabrication of phosphorus doped egg-like hierarchic porous carbon with superior supercapacitance performance by microwave irradiation combining with self-activation strategy

NASA Astrophysics Data System (ADS)

Zhang, Deyi; Han, Mei; Li, Yubing; He, Jingjing; Wang, Bing; Wang, Kunjie; Feng, Huixia

2017-12-01

Herein, we report an ultra-facile fabrication method for a phosphorus doped egg-like hierarchic porous carbon by microwave irradiation combining with self-activation strategy under air atmosphere. Comparing with the traditional pyrolytic carbonization method, the reported method exhibits incomparable merits, such as high energy efficiency, ultra-fast and inert atmosphere protection absent fabrication process. Similar morphology and graphitization degree with the sample fabricated by the traditional pyrolytic carbonization method under inert atmosphere protection for 2 h can be easily achieved by the reported microwave irradiation method just for 3 min under ambient atmosphere. The samples fabricated by the reported method display a unique phosphorus doped egg-like hierarchic porous structure, high specific surface area (1642 m2 g-1) and large pore volume (2.04 cm3 g-1). Specific capacitance of the samples fabricated by the reported method reaches up to 209 F g-1, and over 96.2% of initial capacitance remains as current density increasing from 0.5 to 20 A g-1, indicating the superior capacitance performance of the fabricated samples. The hierarchic porous structure, opened microporosity, additional pseudocapacitance, high electrolyte-accessible surface area and good conductivity make essential contribution to its superior capacitance performance.

From lists of behaviour change techniques (BCTs) to structured hierarchies: comparison of two methods of developing a hierarchy of BCTs.

PubMed

Cane, James; Richardson, Michelle; Johnston, Marie; Ladha, Ruhina; Michie, Susan

2015-02-01

Behaviour change technique (BCT) Taxonomy v1 is a hierarchically grouped, consensus-based taxonomy of 93 BCTs for reporting intervention content. To enhance the use and understanding of BCTs, the aims of the present study were to (1) quantitatively examine the 'bottom-up' hierarchical structure of Taxonomy v1, (2) identify whether BCTs can be reliably mapped to theoretical domains using a 'top-down' theoretically driven approach, and (3) identify any overlap between the 'bottom-up' and 'top-down' groupings. The 'bottom-up' structure was examined for higher-order groupings using a dendrogram derived from hierarchical cluster analysis. For the theory-based 'top-down' structure, 18 experts sorted BCTs into 14 theoretical domains. Discriminant Content Validity was used to identify groupings, and chi-square tests and Pearson's residuals were used to examine the overlap between groupings. Behaviour change techniques relating to 'Reward and Punishment' and 'Cues and Cue Responses' were perceived as markedly different to other BCTs. Fifty-nine of the BCTs were reliably allocated to 12 of the 14 theoretical domains; 47 were significant and 12 were of borderline significance. Thirty-four of 208 'bottom-up' × 'top-down' pairings showed greater overlap than expected by chance. However, only six combinations achieved satisfactory evidence of similarity. The moderate overlap between the groupings indicates some tendency to implicitly conceptualize BCTs in terms of the same theoretical domains. Understanding the nature of the overlap will aid the conceptualization of BCTs in terms of theory and application. Further research into different methods of developing a hierarchical taxonomic structure of BCTs for international, interdisciplinary work is now required. Statement of contribution What is already known on this subject? Behaviour change interventions are effective in improving health care and health outcomes. The 'active' components of these interventions are behaviour change techniques and over 93 have been identified. Taxonomies of behaviour change techniques require structure to enable potential applications. What does this study add? This study identifies groups of BCTs to aid the recall of BCTs for intervention coding and design. It compares two methods of grouping--'bottom-up' and theory-based 'top-down'--and finds a moderate overlap. Building on identified BCT groups, it examines relationships between theoretical domains and BCTs. © 2014 The British Psychological Society.

A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data

NASA Astrophysics Data System (ADS)

Chen, Chuanfa; Li, Yanyan; Li, Wei; Dai, Honglei

2013-08-01

We presented a multiresolution hierarchical classification (MHC) algorithm for differentiating ground from non-ground LiDAR point cloud based on point residuals from the interpolated raster surface. MHC includes three levels of hierarchy, with the simultaneous increase of cell resolution and residual threshold from the low to the high level of the hierarchy. At each level, the surface is iteratively interpolated towards the ground using thin plate spline (TPS) until no ground points are classified, and the classified ground points are used to update the surface in the next iteration. 15 groups of benchmark dataset, provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission, were used to compare the performance of MHC with those of the 17 other publicized filtering methods. Results indicated that MHC with the average total error and average Cohen’s kappa coefficient of 4.11% and 86.27% performs better than all other filtering methods.

An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling

NASA Astrophysics Data System (ADS)

Zhang, Guannan; Lu, Dan; Ye, Ming; Gunzburger, Max; Webster, Clayton

2013-10-01

Bayesian analysis has become vital to uncertainty quantification in groundwater modeling, but its application has been hindered by the computational cost associated with numerous model executions required by exploring the posterior probability density function (PPDF) of model parameters. This is particularly the case when the PPDF is estimated using Markov Chain Monte Carlo (MCMC) sampling. In this study, a new approach is developed to improve the computational efficiency of Bayesian inference by constructing a surrogate of the PPDF, using an adaptive sparse-grid high-order stochastic collocation (aSG-hSC) method. Unlike previous works using first-order hierarchical basis, this paper utilizes a compactly supported higher-order hierarchical basis to construct the surrogate system, resulting in a significant reduction in the number of required model executions. In addition, using the hierarchical surplus as an error indicator allows locally adaptive refinement of sparse grids in the parameter space, which further improves computational efficiency. To efficiently build the surrogate system for the PPDF with multiple significant modes, optimization techniques are used to identify the modes, for which high-probability regions are defined and components of the aSG-hSC approximation are constructed. After the surrogate is determined, the PPDF can be evaluated by sampling the surrogate system directly without model execution, resulting in improved efficiency of the surrogate-based MCMC compared with conventional MCMC. The developed method is evaluated using two synthetic groundwater reactive transport models. The first example involves coupled linear reactions and demonstrates the accuracy of our high-order hierarchical basis approach in approximating high-dimensional posteriori distribution. The second example is highly nonlinear because of the reactions of uranium surface complexation, and demonstrates how the iterative aSG-hSC method is able to capture multimodal and non-Gaussian features of PPDF caused by model nonlinearity. Both experiments show that aSG-hSC is an effective and efficient tool for Bayesian inference.

A Hierarchical Modeling Approach to Data Analysis and Study Design in a Multi-Site Experimental fMRI Study

ERIC Educational Resources Information Center

Zhou, Bo; Konstorum, Anna; Duong, Thao; Tieu, Kinh H.; Wells, William M.; Brown, Gregory G.; Stern, Hal S.; Shahbaba, Babak

2013-01-01

We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model…

Perceived Family Resources Based on Number of Members with ADHD

ERIC Educational Resources Information Center

Corwin, Melinda; Mulsow, Miriam; Feng, Du

2012-01-01

Objective: This study examines how the number of family members with ADHD affects other family members' perceived resources. Method: A total of 40 adolescents diagnosed with ADHD and their mothers, fathers, and adolescent siblings living in the household participated. Hierarchical linear modeling was used to analyze family-level data from a total…

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm.

PubMed

Zhou, Xiuze; Lin, Fan; Yang, Lvqing; Nie, Jing; Tan, Qian; Zeng, Wenhua; Zhang, Nian

2016-01-01

With the continuous expansion of the cloud computing platform scale and rapid growth of users and applications, how to efficiently use system resources to improve the overall performance of cloud computing has become a crucial issue. To address this issue, this paper proposes a method that uses an analytic hierarchy process group decision (AHPGD) to evaluate the load state of server nodes. Training was carried out by using a hybrid hierarchical genetic algorithm (HHGA) for optimizing a radial basis function neural network (RBFNN). The AHPGD makes the aggregative indicator of virtual machines in cloud, and become input parameters of predicted RBFNN. Also, this paper proposes a new dynamic load balancing scheduling algorithm combined with a weighted round-robin algorithm, which uses the predictive periodical load value of nodes based on AHPPGD and RBFNN optimized by HHGA, then calculates the corresponding weight values of nodes and makes constant updates. Meanwhile, it keeps the advantages and avoids the shortcomings of static weighted round-robin algorithm.

Vessel Classification in Cosmo-Skymed SAR Data Using Hierarchical Feature Selection

NASA Astrophysics Data System (ADS)

Makedonas, A.; Theoharatos, C.; Tsagaris, V.; Anastasopoulos, V.; Costicoglou, S.

2015-04-01

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

Hierarchical porous photoanode based on acid boric catalyzed sol for dye sensitized solar cells

NASA Astrophysics Data System (ADS)

Maleki, Khatereh; Abdizadeh, Hossein; Golobostanfard, Mohammad Reza; Adelfar, Razieh

2017-02-01

The hierarchical porous photoanode of the dye sensitized solar cell (DSSC) is synthesized through non-aqueous sol-gel method based on H3BO3 as an acid catalyst and the efficiencies of the fabricated DSSC based on these photoanodes are compared. The sol parameters of 0.17 M, water mole ratio of 4.5, acid mole ratio of 0.45, and solvent type of ethanol are introduced as optimum parameters for photoanode formation without any detectable cracks. The optimized hierarchical photoanode mainly contains anatase phase with slight shift toward higher angles, confirming the doping of boron into titania structure. Moreover, the porous structure involves two ranges of average pore sizes of 20 and 635 nm. The diffuse reflectance spectroscopy (DRS) shows the proper scattering and blueshift in band gap. The paste parameters of solid:liquid, TiO2:ethyl cellulose, and terpineol:ethanol equal to 11:89, 3.5:7.5, and 25:64, respectively, are assigned as optimized parameters for this novel paste. The photovoltaic properties of short circuit current density, open circuit voltage, fill factor, and efficiency of 5.89 mA/cm2, 703 mV, 0.7, and 2.91% are obtained for the optimized sample, respectively. The relatively higher short circuit current of the main sample compared to other samples is mainly due to higher dye adsorption in this sample corresponding to its higher surface area and presumably higher charge transfer confirmed by low RS and Rct in electrochemical impedance spectroscopy data. Boric acid as a catalyst in titania sol not only forms hierarchical porous structure, but also dopes the titania lattice, which results in appreciated performance in this device.

ZnO Hierarchical Nanostructure Photoanode in a CdS Quantum Dot-Sensitized Solar Cell

PubMed Central

Liu, Huan; Zhang, Gengmin; Sun, Wentao; Shen, Ziyong; Shi, Mingji

2015-01-01

A hierarchical array of ZnO nanocones covered with ZnO nanospikes was hydrothermally fabricated and employed as the photoanode in a CdS quantum dot-sensitized solar cell (QDSSC). This QDSSC outperformed the QDSSC based on a simple ZnO nanocone photoanode in all the four principal photovoltaic parameters. Using the hierarchical photoanode dramatically increased the short circuit current density and also slightly raised the open circuit voltage and the fill factor. As a result, the conversion efficiency of the QDSSC based on the hierarchical photoanode was more than twice that of the QDSSC based on the simple ZnO nanocone photoanode. This improvement is attributable to both the enlarged specific area of the photoanode and the reduction in the recombination of the photoexcited electrons. PMID:26379268

Hierarchical Dirichlet process model for gene expression clustering

PubMed Central

2013-01-01

Clustering is an important data processing tool for interpreting microarray data and genomic network inference. In this article, we propose a clustering algorithm based on the hierarchical Dirichlet processes (HDP). The HDP clustering introduces a hierarchical structure in the statistical model which captures the hierarchical features prevalent in biological data such as the gene express data. We develop a Gibbs sampling algorithm based on the Chinese restaurant metaphor for the HDP clustering. We apply the proposed HDP algorithm to both regulatory network segmentation and gene expression clustering. The HDP algorithm is shown to outperform several popular clustering algorithms by revealing the underlying hierarchical structure of the data. For the yeast cell cycle data, we compare the HDP result to the standard result and show that the HDP algorithm provides more information and reduces the unnecessary clustering fragments. PMID:23587447

Patterned Diblock Co-Polymer Thin Films as Templates for Advanced Anisotropic Metal Nanostructures.

PubMed

Roth, Stephan V; Santoro, Gonzalo; Risch, Johannes F H; Yu, Shun; Schwartzkopf, Matthias; Boese, Torsten; Döhrmann, Ralph; Zhang, Peng; Besner, Bastian; Bremer, Philipp; Rukser, Dieter; Rübhausen, Michael A; Terrill, Nick J; Staniec, Paul A; Yao, Yuan; Metwalli, Ezzeldin; Müller-Buschbaum, Peter

2015-06-17

We demonstrate glancing-angle deposition of gold on a nanostructured diblock copolymer, namely polystyrene-block-poly(methyl methacrylate) thin film. Exploiting the selective wetting of gold on the polystyrene block, we are able to fabricate directional hierarchical structures. We prove the asymmetric growth of the gold nanoparticles and are able to extract the different growth laws by in situ scattering methods. The optical anisotropy of these hierarchical hybrid materials is further probed by angular resolved spectroscopic methods. This approach enables us to tailor functional hierarchical layers in nanodevices, such as nanoantennae arrays, organic photovoltaics, and sensor electronics.

Flexible fabrication of biomimetic compound eye array via two-step thermal reflow of simply pre-modeled hierarchic microstructures

NASA Astrophysics Data System (ADS)

Huang, Shengzhou; Li, Mujun; Shen, Lianguan; Qiu, Jinfeng; Zhou, Youquan

2017-06-01

A flexible fabrication method for the biomimetic compound eye (BCE) array is proposed. In this method, a triple-layer sandwich-like coating configuration was introduced, and the required hierarchic microstructures are formed with a simple single-scan exposure in maskless digital lithography. Taking advantage of the difference of glass transition point (Tg) between photoresists of each layer, the pre-formed hierarchic microstructures are in turn reflowed to the curved substrate and the BCE ommatidia in a two-step thermal reflow process. To avoid affecting the spherical substrate formed in the first thermal reflow, a non-contact strategy was proposed in the second reflow process. The measurement results were in good agreement with the designed BCE profiles. Results also showed that the fabricated BCE had good performances in optical test. The presented method is flexible, convenient, low-cost and can easily adapt to the fabrications of other optical elements with hierarchic microstructures.

Determining object orientation with a hierarchical database of binary synthetic discriminant function filters

NASA Technical Reports Server (NTRS)

Reid, Max B.; Ma, Paul W.; Downie, John D.

1990-01-01

An optical correlation-based system is demonstrated which recognizes an object and determines its angular orientation by traversing a hierarchical data base of binary filters. The data-base architecture is made possible by the development of binary synthetic discriminant function filters.

Fabrication of hierarchical porous nickel based metal-organic framework (Ni-MOF) constructed with nanosheets as novel pseudo-capacitive material for asymmetric supercapacitor.

PubMed

Du, Pengcheng; Dong, Yuman; Liu, Chang; Wei, Wenli; Liu, Dong; Liu, Peng

2018-05-15

Hierarchical porous nickel based metal-organic framework (Ni-MOF) constructed with nanosheets is fabricated by a facile hydrothermal process with the existence of trimesic acid and nickel ions. Various structures of Ni-MOFs can be obtained through adjusting the molar ratio of trimesic acid and nickel ion, the obtained hierarchical porous Ni-MOF exhibits optimal porous structure, which also possesses largest specific surface area. The hierarchical porous structure constructed with nanosheets can supply more active sites for electrochemical reactions to realize the excellent electrochemical properties, thus the hierarchical porous Ni-MOF reveals an outstanding specific capacitance of 1057 F/g at current density of 1 A/g, and delivers high specific capacitance of 649 F/g at current density of 30 A/g, indicating that it exhibits good rate capability of 63.4% even up to 30 A/g. The hierarchical porous Ni-MOF keeps 70% of its original value up to 2 500 charge-discharge cycles at the current density of 10 A/g. Furthermore, asymmetric supercapacitors (ASCs) were assembled based on hierarchical porous Ni-MOF and activated carbon (AC), the ASCs reveal specific capacitance of 87 F/g at current density of 0.5 A/g, and exhibit high energy density of 21.05 Wh/kg and power density of 6.03 kW/kg. Additionally, the tandem ASCs can light up a red LED. The hierarchical porous Ni-MOF exhibits promising applications in high performance supercapacitors. Copyright © 2018 Elsevier Inc. All rights reserved.

A comparison of locally adaptive multigrid methods: LDC, FAC and FIC

NASA Technical Reports Server (NTRS)

Khadra, Khodor; Angot, Philippe; Caltagirone, Jean-Paul

1993-01-01

This study is devoted to a comparative analysis of three 'Adaptive ZOOM' (ZOom Overlapping Multi-level) methods based on similar concepts of hierarchical multigrid local refinement: LDC (Local Defect Correction), FAC (Fast Adaptive Composite), and FIC (Flux Interface Correction)--which we proposed recently. These methods are tested on two examples of a bidimensional elliptic problem. We compare, for V-cycle procedures, the asymptotic evolution of the global error evaluated by discrete norms, the corresponding local errors, and the convergence rates of these algorithms.

Comparing two Bayes methods based on the free energy functions in Bernoulli mixtures.

PubMed

Yamazaki, Keisuke; Kaji, Daisuke

2013-08-01

Hierarchical learning models are ubiquitously employed in information science and data engineering. The structure makes the posterior distribution complicated in the Bayes method. Then, the prediction including construction of the posterior is not tractable though advantages of the method are empirically well known. The variational Bayes method is widely used as an approximation method for application; it has the tractable posterior on the basis of the variational free energy function. The asymptotic behavior has been studied in many hierarchical models and a phase transition is observed. The exact form of the asymptotic variational Bayes energy is derived in Bernoulli mixture models and the phase diagram shows that there are three types of parameter learning. However, the approximation accuracy or interpretation of the transition point has not been clarified yet. The present paper precisely analyzes the Bayes free energy function of the Bernoulli mixtures. Comparing free energy functions in these two Bayes methods, we can determine the approximation accuracy and elucidate behavior of the parameter learning. Our results claim that the Bayes free energy has the same learning types while the transition points are different. Copyright © 2013 Elsevier Ltd. All rights reserved.

Fabrication of micro/nano hierarchical structures with analysis on the surface mechanics

NASA Astrophysics Data System (ADS)

Jheng, Yu-Sheng; Lee, Yeeu-Chang

2016-10-01

Biomimicry refers to the imitation of mechanisms and features found in living creatures using artificial methods. This study used optical lithography, colloidal lithography, and dry etching to mimic the micro/nano hierarchical structures covering the soles of gecko feet. We measured the static contact angle and contact angle hysteresis to reveal the behavior of liquid drops on the hierarchical structures. Pulling tests were also performed to measure the resistance of movement between the hierarchical structures and a testing plate. Our results reveal that hierarchical structures at the micro-/nano-scale are considerably hydrophobic, they provide good flow characteristics, and they generate more contact force than do surfaces with micro-scale cylindrical structures.

In-plane crashworthiness of bio-inspired hierarchical honeycombs

DOE PAGES

Yin, Hanfeng; Huang, Xiaofei; Scarpa, Fabrizio; ...

2018-03-13

Biological tissues like bone, wood, and sponge possess hierarchical cellular topologies, which are lightweight and feature an excellent energy absorption capability. Here we present a system of bio-inspired hierarchical honeycomb structures based on hexagonal, Kagome, and triangular tessellations. The hierarchical designs and a reference regular honeycomb configuration are subjected to simulated in-plane impact using the nonlinear finite element code LS-DYNA. The numerical simulation results show that the triangular hierarchical honeycomb provides the best performance compared to the other two hierarchical honeycombs, and features more than twice the energy absorbed by the regular honeycomb under similar loading conditions. We also proposemore » a parametric study correlating the microstructure parameters (hierarchical length ratio r and the number of sub cells N) to the energy absorption capacity of these hierarchical honeycombs. The triangular hierarchical honeycomb with N = 2 and r = 1/8 shows the highest energy absorption capacity among all the investigated cases, and this configuration could be employed as a benchmark for the design of future safety protective systems.« less

In-plane crashworthiness of bio-inspired hierarchical honeycombs

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

Yin, Hanfeng; Huang, Xiaofei; Scarpa, Fabrizio

Biological tissues like bone, wood, and sponge possess hierarchical cellular topologies, which are lightweight and feature an excellent energy absorption capability. Here we present a system of bio-inspired hierarchical honeycomb structures based on hexagonal, Kagome, and triangular tessellations. The hierarchical designs and a reference regular honeycomb configuration are subjected to simulated in-plane impact using the nonlinear finite element code LS-DYNA. The numerical simulation results show that the triangular hierarchical honeycomb provides the best performance compared to the other two hierarchical honeycombs, and features more than twice the energy absorbed by the regular honeycomb under similar loading conditions. We also proposemore » a parametric study correlating the microstructure parameters (hierarchical length ratio r and the number of sub cells N) to the energy absorption capacity of these hierarchical honeycombs. The triangular hierarchical honeycomb with N = 2 and r = 1/8 shows the highest energy absorption capacity among all the investigated cases, and this configuration could be employed as a benchmark for the design of future safety protective systems.« less

Credit networks and systemic risk of Chinese local financing platforms: Too central or too big to fail?. -based on different credit correlations using hierarchical methods

NASA Astrophysics Data System (ADS)

He, Fang; Chen, Xi

2016-11-01

The accelerating accumulation and risk concentration of Chinese local financing platforms debts have attracted wide attention throughout the world. Due to the network of financial exposures among institutions, the failure of several platforms or regions of systemic importance will probably trigger systemic risk and destabilize the financial system. However, the complex network of credit relationships in Chinese local financing platforms at the state level remains unknown. To fill this gap, we presented the first complex networks and hierarchical cluster analysis of the credit market of Chinese local financing platforms using the ;bottom up; method from firm-level data. Based on balance-sheet channel, we analyzed the topology and taxonomy by applying the analysis paradigm of subdominant ultra-metric space to an empirical data in 2013. It is remarked that we chose to extract the network of co-financed financing platforms in order to evaluate the effect of risk contagion from platforms to bank system. We used the new credit similarity measure by combining the factor of connectivity and size, to extract minimal spanning trees (MSTs) and hierarchical trees (HTs). We found that: (1) the degree distributions of credit correlation backbone structure of Chinese local financing platforms are fat tailed, and the structure is unstable with respect to targeted failures; (2) the backbone is highly hierarchical, and largely explained by the geographic region; (3) the credit correlation backbone structure based on connectivity and size is significantly heterogeneous; (4) key platforms and regions of systemic importance, and contagion path of systemic risk are obtained, which are contributed to preventing systemic risk and regional risk of Chinese local financing platforms and preserving financial stability under the framework of macro prudential supervision. Our approach of credit similarity measure provides a means of recognizing ;systemically important; institutions and regions for a targeted policy with risk minimization which gives a flexible and comprehensive consideration to both aspects of ;too big to fail; and ;too central to fail;.

Fabrication of long-term stable superoleophobic surface based on copper oxide/cobalt oxide with micro-nanoscale hierarchical roughness

NASA Astrophysics Data System (ADS)

Barthwal, Sumit; Lim, Si-Hyung

2015-02-01

We have demonstrated a simple and cost-effective technique for the large-area fabrication of a superoleophobic surface using copper as a substrate. The whole process included three simple steps: First, the copper substrate was oxidized under hot alkaline conditions to fabricate flower-like copper oxide microspheres by heating at a particular temperature for an interval of time. Second, the copper-oxide-covered copper substrate was further heated in a solution of cobalt nitrate and ammonium nitrate in the presence of an ammonia solution to fabricate cobalt oxide nanostructures. We applied this second step to increase the surface roughness because it is an important criterion for improved superoleophobicity. Finally, to reduce the surface energy of the fabricated structures, the surfaces were chemically modified with perfluorooctyltrichlorosilane. Contact-angle measurements indicate that the micro-nano binary (MNB) hierarchical structures fabricated on the copper substrate became super-repellent toward a broad range of liquids with surface tension in the range of 21.5-72 mN/m. In an attempt to significantly improve the superoleophobic property of the surface, we also examined and compared the role of nanostructures in MNB hierarchical structures with only micro-fabricated surfaces. The fabricated MNB hierarchical structures also displays thermal stability and excellent long-term stability after exposure in air for more than 9 months. Our method might provide a general route toward the preparation of novel hierarchical films on metal substrates for various industrial applications.

Metal-Organic Framework-Derived Hollow Hierarchical Co3O4 Nanocages with Tunable Size and Morphology: Ultrasensitive and Highly Selective Detection of Methylbenzenes.

PubMed

Jo, Young-Moo; Kim, Tae-Hyung; Lee, Chul-Soon; Lim, Kyeorei; Na, Chan Woong; Abdel-Hady, Faissal; Wazzan, Abdulaziz A; Lee, Jong-Heun

2018-03-14

Nearly monodisperse hollow hierarchical Co 3 O 4 nanocages of four different sizes (∼0.3, 1.0, 2.0, and 4.0 μm) consisting of nanosheets were prepared by controlled precipitation of zeolitic imidazolate framework-67 (ZIF-67) rhombic dodecahedra, followed by solvothermal synthesis of Co 3 O 4 nanocages using ZIF-67 self-sacrificial templates, and subsequent heat treatment for the development of high-performance methylbenzene sensors. The sensor based on hollow hierarchical Co 3 O 4 nanocages with the size of ∼1.0 μm exhibited not only ultrahigh responses (resistance ratios) to 5 ppm p-xylene (78.6) and toluene (43.8) but also a remarkably high selectivity to methylbenzene over the interference of ubiquitous ethanol at 225 °C. The unprecedented and high response and selectivity to methylbenzenes are attributed to the highly gas-accessible hollow hierarchical morphology with thin shells, abundant mesopores, and high surface area per unit volume as well as the high catalytic activity of Co 3 O 4 . Moreover, the size, shell thickness, mesopores, and hollow/hierarchical morphology of the nanocages, the key parameters determining the gas response and selectivity, could be well-controlled by tuning the precipitation of ZIF-67 rhombic dodecahedra and solvothermal reaction. This method can pave a new pathway for the design of high-performance methylbenzene sensors for monitoring the quality of indoor air.

Trapping in scale-free networks with hierarchical organization of modularity.

PubMed

Zhang, Zhongzhi; Lin, Yuan; Gao, Shuyang; Zhou, Shuigeng; Guan, Jihong; Li, Mo

2009-11-01

A wide variety of real-life networks share two remarkable generic topological properties: scale-free behavior and modular organization, and it is natural and important to study how these two features affect the dynamical processes taking place on such networks. In this paper, we investigate a simple stochastic process--trapping problem, a random walk with a perfect trap fixed at a given location, performed on a family of hierarchical networks that exhibit simultaneously striking scale-free and modular structure. We focus on a particular case with the immobile trap positioned at the hub node having the largest degree. Using a method based on generating functions, we determine explicitly the mean first-passage time (MFPT) for the trapping problem, which is the mean of the node-to-trap first-passage time over the entire network. The exact expression for the MFPT is calculated through the recurrence relations derived from the special construction of the hierarchical networks. The obtained rigorous formula corroborated by extensive direct numerical calculations exhibits that the MFPT grows algebraically with the network order. Concretely, the MFPT increases as a power-law function of the number of nodes with the exponent much less than 1. We demonstrate that the hierarchical networks under consideration have more efficient structure for transport by diffusion in contrast with other analytically soluble media including some previously studied scale-free networks. We argue that the scale-free and modular topologies are responsible for the high efficiency of the trapping process on the hierarchical networks.

Application of growing hierarchical SOM for visualisation of network forensics traffic data.

PubMed

Palomo, E J; North, J; Elizondo, D; Luque, R M; Watson, T

2012-08-01

Digital investigation methods are becoming more and more important due to the proliferation of digital crimes and crimes involving digital evidence. Network forensics is a research area that gathers evidence by collecting and analysing network traffic data logs. This analysis can be a difficult process, especially because of the high variability of these attacks and large amount of data. Therefore, software tools that can help with these digital investigations are in great demand. In this paper, a novel approach to analysing and visualising network traffic data based on growing hierarchical self-organising maps (GHSOM) is presented. The self-organising map (SOM) has been shown to be successful for the analysis of highly-dimensional input data in data mining applications as well as for data visualisation in a more intuitive and understandable manner. However, the SOM has some problems related to its static topology and its inability to represent hierarchical relationships in the input data. The GHSOM tries to overcome these limitations by generating a hierarchical architecture that is automatically determined according to the input data and reflects the inherent hierarchical relationships among them. Moreover, the proposed GHSOM has been modified to correctly treat the qualitative features that are present in the traffic data in addition to the quantitative features. Experimental results show that this approach can be very useful for a better understanding of network traffic data, making it easier to search for evidence of attacks or anomalous behaviour in a network environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Two and three-dimensional segmentation of hyperpolarized 3He magnetic resonance imaging of pulmonary gas distribution

NASA Astrophysics Data System (ADS)

Heydarian, Mohammadreza; Kirby, Miranda; Wheatley, Andrew; Fenster, Aaron; Parraga, Grace

2012-03-01

A semi-automated method for generating hyperpolarized helium-3 (3He) measurements of individual slice (2D) or whole lung (3D) gas distribution was developed. 3He MRI functional images were segmented using two-dimensional (2D) and three-dimensional (3D) hierarchical K-means clustering of the 3He MRI signal and in addition a seeded region-growing algorithm was employed for segmentation of the 1H MRI thoracic cavity volume. 3He MRI pulmonary function measurements were generated following two-dimensional landmark-based non-rigid registration of the 3He and 1H pulmonary images. We applied this method to MRI of healthy subjects and subjects with chronic obstructive lung disease (COPD). The results of hierarchical K-means 2D and 3D segmentation were compared to an expert observer's manual segmentation results using linear regression, Pearson correlations and the Dice similarity coefficient. 2D hierarchical K-means segmentation of ventilation volume (VV) and ventilation defect volume (VDV) was strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.97, p<.0001) and mean Dice coefficients were greater than 92% for all subjects. 3D hierarchical K-means segmentation of VV and VDV was also strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.64, p<.0001) and the mean Dice coefficients were greater than 91% for all subjects. Both 2D and 3D semi-automated segmentation of 3He MRI gas distribution provides a way to generate novel pulmonary function measurements.

Retrenchment in Education: Hierarchical Decision Models for Instructional Program Termination, District Consolidations and School Closures.

ERIC Educational Resources Information Center

Wholeben, Brent Edward

A number of key issues facing elementary, secondary, and postsecondary educational administrators during retrenchment require a hierarchical decision-modeling approach. This paper identifies and discusses the use of a hierarchical multiple-alternatives modeling formulation (computer-based) that compares and evaluates a group of solution…

One-pot synthesis of hierarchical Cu{sub 2}O/Cu hollow microspheres with enhanced visible-light photocatalytic activity

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

Hong, Tianjie; Tao, Feifei, E-mail: feifeitao@usx.edu.cn; Lin, Jiudong

2015-08-15

The hierarchical Cu{sub 2}O/Cu hollow microspheres have been fabricated by the one-pot solvothermal redox method, which is one-step approach without any surfactant and template. By using the HRTEM, XRD, XPS and UV–vis spectroscopy, the as-prepared product is composed of Cu{sub 2}O and Cu with energy band gap of 1.72 eV. Based on the time-dependent experiments, the content of Cu{sub 2}O and Cu compositions can be effectively controlled by adjusting the reaction time and a possible mechanism is proposed. In addition, using various dye molecules to stimulate pollutants, the hierarchical Cu{sub 2}O/Cu hollow microspheres reacted for 8 h exhibit excellent visible-lightmore » photocatalytic activities, which is much higher than those of the Cu{sub 2}O/Cu catalysts formed at the shorter reaction time, commercial Cu{sub 2}O powder and the mixture of alone Cu{sub 2}O and Cu. This enhanced photocatalytic performance makes these hierarchical Cu{sub 2}O/Cu hollow microspheres a kind of efficient visible-light photocatalyst in removing some organic compounds in wastewater. - Graphical abstract: The hierarchical Cu{sub 2}O/Cu hollow microspheres with adjustable components have been synthesized by one-step solvothermal redox approach. The special structures and composition lead to the excellent visible-light photocatalytic activity. - Highlights: • The hierarchical Cu{sub 2}O/Cu hollow microspheres are fabricated by one-step approach. • The content of Cu{sub 2}O and Cu can be controlled by adjusting the reaction time. • The material exhibits a better visible-light photocatalytic activity and stability. • Degradation kinetics of MO by Cu{sub 2}O/Cu fits the pseudo first-order model.« less

Asymmetric supercapacitors with high energy density based on helical hierarchical porous Na x MnO2 and MoO2.

PubMed

Lu, Xue-Feng; Huang, Zhi-Xiang; Tong, Ye-Xiang; Li, Gao-Ren

2016-01-01

Helical hierarchical porous Na x MnO 2 /CC and MoO 2 /CC, which are assembled from nanosheets and nanoparticles, respectively, are fabricated using a simple electrodeposition method. These unique helical porous structures enable electrodes to have a high capacitance and an outstanding cycling performance. Based on the helical Na x MnO 2 /CC as the positive electrodes and helical MoO 2 /CC as the negative electrodes, high performance Na x MnO 2 /CC//MoO 2 /CC asymmetric supercapacitors (ASCs) are successfully assembled, and they achieve a maximum volume C sp of 2.04 F cm -3 and a maximum energy density of 0.92 mW h cm -3 for the whole device and an excellent cycling stability with 97.22% C sp retention after 6000 cycles.

Multicolor emission from large-area porous thin films constructed of nanowires of small organic molecules

NASA Astrophysics Data System (ADS)

Wang, Zhe-Chen; Xiao, Wen-Chang; Ding, Xun-Lei; Ma, Yan-Ping; Xue, Wei; He, Sheng-Gui

2008-12-01

We describe a facile low-temperature physical vapor deposition approach to fabricate porous network thin films constructed of nanowires of small organic molecules on a large area. Supermolecular assemblies of pyrene nanowires based on a combination of van der Waals forces and π-π stacking tend to hierarchically self-assemble to form uniform porous films using our techniques. The morphology of the films is studied and we also study several reasons influencing the process of assembly such as evaporation temperature, deposition temperature, and different kinds of substrate. The deposition temperature is determined to be the main reason for hierarchical aggregation. Typically prepared films exhibit unique optical properties, that is, multicolor red-green-blue emissions. This novel method can be applied to other organic molecular systems and may be potentially used to place nanoscaled building blocks directly on solid surfaces for fabricating large-area nanostructure-based flat screens.

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

PubMed Central

Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel; Meze-Hausken, Elisabeth

2013-01-01

Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models. PMID:23396890

Hierarchical silicon nanowires-carbon textiles matrix as a binder-free anode for high-performance advanced lithium-ion batteries

PubMed Central

Liu, Bin; Wang, Xianfu; Chen, Haitian; Wang, Zhuoran; Chen, Di; Cheng, Yi-Bing; Zhou, Chongwu; Shen, Guozhen

2013-01-01

Toward the increasing demands of portable energy storage and electric vehicle applications, the widely used graphite anodes with significant drawbacks become more and more unsuitable. Herein, we report a novel scaffold of hierarchical silicon nanowires-carbon textiles anodes fabricated via a facile method. Further, complete lithium-ion batteries based on Si and commercial LiCoO2 materials were assembled to investigate their corresponding across-the-aboard performances, demonstrating their enhanced specific capacity (2950 mAh g−1 at 0.2 C), good repeatability/rate capability (even >900 mAh g−1 at high rate of 5 C), long cycling life, and excellent stability in various external conditions (curvature, temperature, and humidity). Above results light the way to principally replacing graphite anodes with silicon-based electrodes which was confirmed to have better comprehensive performances. PMID:23572030

Trimethylamine Sensors Based on Au-Modified Hierarchical Porous Single-Crystalline ZnO Nanosheets.

PubMed

Meng, Fanli; Zheng, Hanxiong; Sun, Yufeng; Li, Minqiang; Liu, Jinhuai

2017-06-22

It is of great significance for dynamic monitoring of foods in storage or during the transportation process through on-line detecting trimethylamine (TMA). Here, TMA were sensitively detected by Au-modified hierarchical porous single-crystalline ZnO nanosheets (HPSCZNs)-based sensors. The HPSCZNs were synthesized through a one-pot wet-chemical method followed by an annealing treatment. Polyethyleneimine (PEI) was used to modify the surface of the HPSCZNs, and then the PEI-modified samples were mixed with Au nanoparticles (NPs) sol solution. Electrostatic interactions drive Au nanoparticles loading onto the surface of the HPSCZNs. The Au-modified HPSCZNs were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and energy dispersive spectrum (EDS), respectively. The results show that Au-modified HPSCZNs-based sensors exhibit a high response to TMA. The linear range is from 10 to 300 ppb; while the detection limit is 10 ppb, which is the lowest value to our knowledge.

Trimethylamine Sensors Based on Au-Modified Hierarchical Porous Single-Crystalline ZnO Nanosheets

PubMed Central

Zheng, Hanxiong; Sun, Yufeng; Li, Minqiang; Liu, Jinhuai

2017-01-01

It is of great significance for dynamic monitoring of foods in storage or during the transportation process through on-line detecting trimethylamine (TMA). Here, TMA were sensitively detected by Au-modified hierarchical porous single-crystalline ZnO nanosheets (HPSCZNs)-based sensors. The HPSCZNs were synthesized through a one-pot wet-chemical method followed by an annealing treatment. Polyethyleneimine (PEI) was used to modify the surface of the HPSCZNs, and then the PEI-modified samples were mixed with Au nanoparticles (NPs) sol solution. Electrostatic interactions drive Au nanoparticles loading onto the surface of the HPSCZNs. The Au-modified HPSCZNs were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and energy dispersive spectrum (EDS), respectively. The results show that Au-modified HPSCZNs-based sensors exhibit a high response to TMA. The linear range is from 10 to 300 ppb; while the detection limit is 10 ppb, which is the lowest value to our knowledge. PMID:28640226

High-Dimensional Bayesian Geostatistics

PubMed Central

Banerjee, Sudipto

2017-01-01

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatiotemporal process models have become widely deployed statistical tools for researchers to better understand the complex nature of spatial and temporal variability. However, fitting hierarchical spatiotemporal models often involves expensive matrix computations with complexity increasing in cubic order for the number of spatial locations and temporal points. This renders such models unfeasible for large data sets. This article offers a focused review of two methods for constructing well-defined highly scalable spatiotemporal stochastic processes. Both these processes can be used as “priors” for spatiotemporal random fields. The first approach constructs a low-rank process operating on a lower-dimensional subspace. The second approach constructs a Nearest-Neighbor Gaussian Process (NNGP) that ensures sparse precision matrices for its finite realizations. Both processes can be exploited as a scalable prior embedded within a rich hierarchical modeling framework to deliver full Bayesian inference. These approaches can be described as model-based solutions for big spatiotemporal datasets. The models ensure that the algorithmic complexity has ~ n floating point operations (flops), where n the number of spatial locations (per iteration). We compare these methods and provide some insight into their methodological underpinnings. PMID:29391920

Self-assembled hierarchical direct Z-scheme g-C3N4/ZnO microspheres with enhanced photocatalytic CO2 reduction performance

NASA Astrophysics Data System (ADS)

Nie, Ning; Zhang, Liuyang; Fu, Junwei; Cheng, Bei; Yu, Jiaguo

2018-05-01

Photocatalytic reduction of CO2 into hydrocarbon fuels has been regarded as a promising approach to ease the greenhouse effect and the energy shortage. Herein, an electrostatic self-assembly method was exploited to prepare g-C3N4/ZnO composite microsphere. This method simply utilized the opposite surface charge of each component, achieving a hierarchical structure with intimate contact between them. A much improved photocatalytic CO2 reduction activity was attained. The CH3OH production rate was 1.32 μmol h-1 g-1, which was 2.1 and 4.1 times more than that of the pristine ZnO and g-C3N4, respectively. This facile design bestowed the g-C3N4/ZnO composite an extended light adsorption caused by multi-light scattering effect. It also guaranteed the uniform distribution of g-C3N4 nanosheets on the surface of ZnO microspheres, maximizing their advantage and synergistic effect. Most importantly, the preeminent performance was proposed and validated based on the direct Z-scheme. The recombination rate was considerably suppressed. This work features the meliority of constructing hierarchical direct Z-scheme structures in photocatalytic CO2 reduction reactions.

An improved spatial contour tree constructed method

NASA Astrophysics Data System (ADS)

Zheng, Yi; Zhang, Ling; Guilbert, Eric; Long, Yi

2018-05-01

Contours are important data to delineate the landform on a map. A contour tree provides an object-oriented description of landforms and can be used to enrich the topological information. The traditional contour tree is used to store topological relationships between contours in a hierarchical structure and allows for the identification of eminences and depressions as sets of nested contours. This research proposes an improved contour tree so-called spatial contour tree that contains not only the topological but also the geometric information. It can be regarded as a terrain skeleton in 3-dimention, and it is established based on the spatial nodes of contours which have the latitude, longitude and elevation information. The spatial contour tree is built by connecting spatial nodes from low to high elevation for a positive landform, and from high to low elevation for a negative landform to form a hierarchical structure. The connection between two spatial nodes can provide the real distance and direction as a Euclidean vector in 3-dimention. In this paper, the construction method is tested in the experiment, and the results are discussed. The proposed hierarchical structure is in 3-demintion and can show the skeleton inside a terrain. The structure, where all nodes have geo-information, can be used to distinguish different landforms and applied for contour generalization with consideration of geographic characteristics.

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

High-Dimensional Bayesian Geostatistics.

PubMed

Banerjee, Sudipto

2017-06-01

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatiotemporal process models have become widely deployed statistical tools for researchers to better understand the complex nature of spatial and temporal variability. However, fitting hierarchical spatiotemporal models often involves expensive matrix computations with complexity increasing in cubic order for the number of spatial locations and temporal points. This renders such models unfeasible for large data sets. This article offers a focused review of two methods for constructing well-defined highly scalable spatiotemporal stochastic processes. Both these processes can be used as "priors" for spatiotemporal random fields. The first approach constructs a low-rank process operating on a lower-dimensional subspace. The second approach constructs a Nearest-Neighbor Gaussian Process (NNGP) that ensures sparse precision matrices for its finite realizations. Both processes can be exploited as a scalable prior embedded within a rich hierarchical modeling framework to deliver full Bayesian inference. These approaches can be described as model-based solutions for big spatiotemporal datasets. The models ensure that the algorithmic complexity has ~ n floating point operations (flops), where n the number of spatial locations (per iteration). We compare these methods and provide some insight into their methodological underpinnings.

Graph pyramids for protein function prediction.

PubMed

Sandhan, Tushar; Yoo, Youngjun; Choi, Jin; Kim, Sun

2015-01-01

Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data.

Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine

NASA Astrophysics Data System (ADS)

Graham, James; Ternovskiy, Igor V.

2013-06-01

We applied a two stage unsupervised hierarchical learning system to model complex dynamic surveillance and cyber space monitoring systems using a non-commercial version of the NeoAxis visualization software. The hierarchical scene learning and recognition approach is based on hierarchical expectation maximization, and was linked to a 3D graphics engine for validation of learning and classification results and understanding the human - autonomous system relationship. Scene recognition is performed by taking synthetically generated data and feeding it to a dynamic logic algorithm. The algorithm performs hierarchical recognition of the scene by first examining the features of the objects to determine which objects are present, and then determines the scene based on the objects present. This paper presents a framework within which low level data linked to higher-level visualization can provide support to a human operator and be evaluated in a detailed and systematic way.

Structural Group-based Auditing of Missing Hierarchical Relationships in UMLS

PubMed Central

Chen, Yan; Gu, Huanying(Helen); Perl, Yehoshua; Geller, James

2009-01-01

The Metathesaurus of the UMLS was created by integrating various source terminologies. The inter-concept relationships were either integrated into the UMLS from the source terminologies or specially generated. Due to the extensive size and inherent complexity of the Metathesaurus, the accidental omission of some hierarchical relationships was inevitable. We present a recursive procedure which allows a human expert, with the support of an algorithm, to locate missing hierarchical relationships. The procedure starts with a group of concepts with exactly the same (correct) semantic type assignments. It then partitions the concepts, based on child-of hierarchical relationships, into smaller, singly rooted, hierarchically connected subgroups. The auditor only needs to focus on the subgroups with very few concepts and their concepts with semantic type reassignments. The procedure was evaluated by comparing it with a comprehensive manual audit and it exhibits a perfect error recall. PMID:18824248

Panoscopic approach for high-performance Te-doped skutterudite

DOE PAGES

Liang, Tao; Su, Xianli; Yan, Yonggao; ...

2017-02-24

One-step plasma-activated sintering (OS-PAS) fabrication of single-phase high-performance CoSb 3-based skutterudite thermoelectric material with a hierarchical structure on a time scale of a few minutes is first reported here. The formation mechanism of the CoSb 3 phase and the effects of the current and pressure fields on the phase transformation and microstructure evolution are studied in the one-step PAS process. The application of the panoscopic approach to this system and its effect on the transport properties are investigated. The results show that the hierarchical structure forms during the formation of the skutterudite phase under the effects of both current andmore » sintering pressure. The samples fabricated by the OS-PAS technique have defined hierarchical structures, which scatter phonons more intensely over a broader range of frequencies and significantly reduce the lattice thermal conductivity. High-performance bulk Te-doped skutterudite with the maximum ZT of 1.1 at 820 K for the composition CoSb 2.875Te 0.125 was obtained. Such high ZT values rival those obtained from single filled skutterudites. As a result, this newly developed OS-PAS technique enhances the thermoelectric performance, dramatically shortens the synthesis period and provides a facile method for obtaining hierarchical thermoelectric materials on a large scale.« less

Freestanding hierarchically porous carbon framework decorated by polyaniline as binder-free electrodes for high performance supercapacitors

NASA Astrophysics Data System (ADS)

Miao, Fujun; Shao, Changlu; Li, Xinghua; Wang, Kexin; Lu, Na; Liu, Yichun

2016-10-01

Freestanding hierarchically porous carbon electrode materials with favorable features of large surface areas, hierarchical porosity and continuous conducting pathways are very attractive for practical applications in electrochemical devices. Herein, three-dimensional freestanding hierarchically porous carbon (HPC) materials have been fabricated successfully mainly by the facile phase separation method. In order to further improve the energy storage ability, polyaniline (PANI) with high pseudocapacitance has been decorated on HPC through in situ chemical polymerization of aniline monomers. Benefiting from the synergistic effects between HPC and PANI, the resulting HPC/PANI composites as electrode materials present dramatic electrochemical performance with high specific capacitance up to 290 F g-1 at 0.5 A g-1 and good rate capability with ∼86% (248 F g-1) capacitance retention at 64 A g-1 of initial capacitance in three-electrode configuration. Moreover, the as-assembled symmetric supercapacitor based on HPC/PANI composites also demonstrates good capacitive properties with high energy density of 9.6 Wh kg-1 at 223 W kg-1 and long-term cycling stability with 78% capacitance retention after 10 000 cycles. Therefore, this work provides a new approach for designing high-performance electrodes with exceptional electrochemical performance, which are very promising for practical application in the energy storage field.

Hierarchical auto-configuration addressing in mobile ad hoc networks (HAAM)

NASA Astrophysics Data System (ADS)

Ram Srikumar, P.; Sumathy, S.

2017-11-01

Addressing plays a vital role in networking to identify devices uniquely. A device must be assigned with a unique address in order to participate in the data communication in any network. Different protocols defining different types of addressing are proposed in literature. Address auto-configuration is a key requirement for self organizing networks. Existing auto-configuration based addressing protocols require broadcasting probes to all the nodes in the network before assigning a proper address to a new node. This needs further broadcasts to reflect the status of the acquired address in the network. Such methods incur high communication overheads due to repetitive flooding. To address this overhead, a new partially stateful address allocation scheme, namely Hierarchical Auto-configuration Addressing (HAAM) scheme is extended and proposed. Hierarchical addressing basically reduces latency and overhead caused during address configuration. Partially stateful addressing algorithm assigns addresses without the need for flooding and global state awareness, which in turn reduces the communication overhead and space complexity respectively. Nodes are assigned addresses hierarchically to maintain the graph of the network as a spanning tree which helps in effectively avoiding the broadcast storm problem. Proposed algorithm for HAAM handles network splits and merges efficiently in large scale mobile ad hoc networks incurring low communication overheads.

A Bayesian Hierarchical Model for Glacial Dynamics Based on the Shallow Ice Approximation and its Evaluation Using Analytical Solutions

NASA Astrophysics Data System (ADS)

Gopalan, Giri; Hrafnkelsson, Birgir; Aðalgeirsdóttir, Guðfinna; Jarosch, Alexander H.; Pálsson, Finnur

2018-03-01

Bayesian hierarchical modeling can assist the study of glacial dynamics and ice flow properties. This approach will allow glaciologists to make fully probabilistic predictions for the thickness of a glacier at unobserved spatio-temporal coordinates, and it will also allow for the derivation of posterior probability distributions for key physical parameters such as ice viscosity and basal sliding. The goal of this paper is to develop a proof of concept for a Bayesian hierarchical model constructed, which uses exact analytical solutions for the shallow ice approximation (SIA) introduced by Bueler et al. (2005). A suite of test simulations utilizing these exact solutions suggests that this approach is able to adequately model numerical errors and produce useful physical parameter posterior distributions and predictions. A byproduct of the development of the Bayesian hierarchical model is the derivation of a novel finite difference method for solving the SIA partial differential equation (PDE). An additional novelty of this work is the correction of numerical errors induced through a numerical solution using a statistical model. This error correcting process models numerical errors that accumulate forward in time and spatial variation of numerical errors between the dome, interior, and margin of a glacier.

Hierarchical Order Parameters for Macromolecular Assembly Simulations I: Construction and Dynamical Properties of Order Parameters

PubMed Central

Singharoy, Abhishek; Sereda, Yuriy

2012-01-01

Macromolecular assemblies often display a hierarchical organization of macromolecules or their sub-assemblies. To model this, we have formulated a space warping method that enables capturing overall macromolecular structure and dynamics via a set of coarse-grained order parameters (OPs). This article is the first of two describing the construction and computational implementation of an additional class of OPs that has built into them the hierarchical architecture of macromolecular assemblies. To accomplish this, first, the system is divided into subsystems, each of which is described via a representative set of OPs. Then, a global set of variables is constructed from these subsystem-centered OPs to capture overall system organization. Dynamical properties of the resulting OPs are compared to those of our previous nonhierarchical ones, and implied conceptual and computational advantages are discussed for a 100ns, 2 million atom solvated Human Papillomavirus-like particle simulation. In the second article, the hierarchical OPs are shown to enable a multiscale analysis that starts with the N-atom Liouville equation and yields rigorous Langevin equations of stochastic OP dynamics. The latter is demonstrated via a force-field based simulation algorithm that probes key structural transition pathways, simultaneously accounting for all-atom details and overall structure. PMID:22661911

Bayesian Hierarchical Random Intercept Model Based on Three Parameter Gamma Distribution

NASA Astrophysics Data System (ADS)

Wirawati, Ika; Iriawan, Nur; Irhamah

2017-06-01

Hierarchical data structures are common throughout many areas of research. Beforehand, the existence of this type of data was less noticed in the analysis. The appropriate statistical analysis to handle this type of data is the hierarchical linear model (HLM). This article will focus only on random intercept model (RIM), as a subclass of HLM. This model assumes that the intercept of models in the lowest level are varied among those models, and their slopes are fixed. The differences of intercepts were suspected affected by some variables in the upper level. These intercepts, therefore, are regressed against those upper level variables as predictors. The purpose of this paper would demonstrate a proven work of the proposed two level RIM of the modeling on per capita household expenditure in Maluku Utara, which has five characteristics in the first level and three characteristics of districts/cities in the second level. The per capita household expenditure data in the first level were captured by the three parameters Gamma distribution. The model, therefore, would be more complex due to interaction of many parameters for representing the hierarchical structure and distribution pattern of the data. To simplify the estimation processes of parameters, the computational Bayesian method couple with Markov Chain Monte Carlo (MCMC) algorithm and its Gibbs Sampling are employed.

Hierarchical Modeling and Robust Synthesis for the Preliminary Design of Large Scale Complex Systems

NASA Technical Reports Server (NTRS)

Koch, Patrick N.

1997-01-01

Large-scale complex systems are characterized by multiple interacting subsystems and the analysis of multiple disciplines. The design and development of such systems inevitably requires the resolution of multiple conflicting objectives. The size of complex systems, however, prohibits the development of comprehensive system models, and thus these systems must be partitioned into their constituent parts. Because simultaneous solution of individual subsystem models is often not manageable iteration is inevitable and often excessive. In this dissertation these issues are addressed through the development of a method for hierarchical robust preliminary design exploration to facilitate concurrent system and subsystem design exploration, for the concurrent generation of robust system and subsystem specifications for the preliminary design of multi-level, multi-objective, large-scale complex systems. This method is developed through the integration and expansion of current design techniques: Hierarchical partitioning and modeling techniques for partitioning large-scale complex systems into more tractable parts, and allowing integration of subproblems for system synthesis; Statistical experimentation and approximation techniques for increasing both the efficiency and the comprehensiveness of preliminary design exploration; and Noise modeling techniques for implementing robust preliminary design when approximate models are employed. Hierarchical partitioning and modeling techniques including intermediate responses, linking variables, and compatibility constraints are incorporated within a hierarchical compromise decision support problem formulation for synthesizing subproblem solutions for a partitioned system. Experimentation and approximation techniques are employed for concurrent investigations and modeling of partitioned subproblems. A modified composite experiment is introduced for fitting better predictive models across the ranges of the factors, and an approach for constructing partitioned response surfaces is developed to reduce the computational expense of experimentation for fitting models in a large number of factors. Noise modeling techniques are compared and recommendations are offered for the implementation of robust design when approximate models are sought. These techniques, approaches, and recommendations are incorporated within the method developed for hierarchical robust preliminary design exploration. This method as well as the associated approaches are illustrated through their application to the preliminary design of a commercial turbofan turbine propulsion system. The case study is developed in collaboration with Allison Engine Company, Rolls Royce Aerospace, and is based on the Allison AE3007 existing engine designed for midsize commercial, regional business jets. For this case study, the turbofan system-level problem is partitioned into engine cycle design and configuration design and a compressor modules integrated for more detailed subsystem-level design exploration, improving system evaluation. The fan and low pressure turbine subsystems are also modeled, but in less detail. Given the defined partitioning, these subproblems are investigated independently and concurrently, and response surface models are constructed to approximate the responses of each. These response models are then incorporated within a commercial turbofan hierarchical compromise decision support problem formulation. Five design scenarios are investigated, and robust solutions are identified. The method and solutions identified are verified by comparison with the AE3007 engine. The solutions obtained are similar to the AE3007 cycle and configuration, but are better with respect to many of the requirements.

Does the process map influence the outcome of quality improvement work? A comparison of a sequential flow diagram and a hierarchical task analysis diagram.

PubMed

Colligan, Lacey; Anderson, Janet E; Potts, Henry W W; Berman, Jonathan

2010-01-07

Many quality and safety improvement methods in healthcare rely on a complete and accurate map of the process. Process mapping in healthcare is often achieved using a sequential flow diagram, but there is little guidance available in the literature about the most effective type of process map to use. Moreover there is evidence that the organisation of information in an external representation affects reasoning and decision making. This exploratory study examined whether the type of process map - sequential or hierarchical - affects healthcare practitioners' judgments. A sequential and a hierarchical process map of a community-based anti coagulation clinic were produced based on data obtained from interviews, talk-throughs, attendance at a training session and examination of protocols and policies. Clinic practitioners were asked to specify the parts of the process that they judged to contain quality and safety concerns. The process maps were then shown to them in counter-balanced order and they were asked to circle on the diagrams the parts of the process where they had the greatest quality and safety concerns. A structured interview was then conducted, in which they were asked about various aspects of the diagrams. Quality and safety concerns cited by practitioners differed depending on whether they were or were not looking at a process map, and whether they were looking at a sequential diagram or a hierarchical diagram. More concerns were identified using the hierarchical diagram compared with the sequential diagram and more concerns were identified in relation to clinical work than administrative work. Participants' preference for the sequential or hierarchical diagram depended on the context in which they would be using it. The difficulties of determining the boundaries for the analysis and the granularity required were highlighted. The results indicated that the layout of a process map does influence perceptions of quality and safety problems in a process. In quality improvement work it is important to carefully consider the type of process map to be used and to consider using more than one map to ensure that different aspects of the process are captured.

Extension of mixture-of-experts networks for binary classification of hierarchical data.

PubMed

Ng, Shu-Kay; McLachlan, Geoffrey J

2007-09-01

For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another extension is to quantify cluster-specific information on data hierarchy by random effects via the generalized linear mixed-effects model (GLMM). The extension of ME networks is implemented by allowing for correlation in the hierarchical data in both the gating and expert networks via the GLMM. The proposed model is illustrated using a real thyroid disease data set. In our study, we consider 7652 thyroid diagnosis records from 1984 to early 1987 with complete information on 20 attribute values. We obtain 10 independent random splits of the data into a training set and a test set in the proportions 85% and 15%. The test sets are used to assess the generalization performance of the proposed model, based on the percentage of misclassifications. For comparison, the results obtained from the ME network with independence assumption are also included. With the thyroid disease data, the misclassification rate on test sets for the extended ME network is 8.9%, compared to 13.9% for the ME network. In addition, based on model selection methods described in Section 2, a network with two experts is selected. These two expert networks can be considered as modeling two groups of patients with high and low incidence rates. Significant variation among the predicted cluster-specific random effects is detected in the patient group with low incidence rate. It is shown that the extended ME network outperforms the ME network for binary classification of hierarchical data. With the thyroid disease data, useful information on the relative log odds of patients with diagnosed conditions at different periods can be evaluated. This information can be taken into consideration for the assessment of treatment planning of the disease. The proposed extended ME network thus facilitates a more general approach to incorporate data hierarchy mechanism in network modeling.

Hydrothermal preparation of hierarchical ZIF-L nanostructures for enhanced CO2 capture.

PubMed

Ding, Bing; Wang, Xianbiao; Xu, Yongfei; Feng, Shaojie; Ding, Yi; Pan, Yang; Xu, Weifan; Wang, Huanting

2018-06-01

A zeolitic imidazolate framework (ZIF-L) with hierarchical morphology was synthesized through hydrothermal method. The hierarchical product consists of ZIF-L leaves with length of several micrometers, width of 1 ∼ 2 μm and thickness of ∼300 nm cross connected symmetrically. It was found that the hydrothermal temperature is crucial for the formation of such hierarchical nanostructure. The formation mechanism was investigated to be a secondary crystal growth process. The hierarchical ZIF-L has larger surface area compared with the two-dimensional (2D) ZIF-L leaves. Subsequently, the hierarchical ZIF-L exhibited enhanced CO 2 adsorption capacity (1.56 mmol·g -1 ) as compared with that of the reported two-dimensional ZIF-L leaves (0.94 mmol·g -1 ). This work not only reveals a new strategy for the formation of hierarchical ZIF-L nanostructures, but also supplies a potential material for CO 2 capture. Copyright © 2018 Elsevier Inc. All rights reserved.

A Factor Graph Approach to Automated GO Annotation

PubMed Central

Spetale, Flavio E.; Tapia, Elizabeth; Krsticevic, Flavia; Roda, Fernando; Bulacio, Pilar

2016-01-01

As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum. PMID:26771463

A Factor Graph Approach to Automated GO Annotation.

PubMed

Spetale, Flavio E; Tapia, Elizabeth; Krsticevic, Flavia; Roda, Fernando; Bulacio, Pilar

2016-01-01

As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum.

Hierarchical coarse-graining transform.

PubMed

Pancaldi, Vera; King, Peter R; Christensen, Kim

2009-03-01

We present a hierarchical transform that can be applied to Laplace-like differential equations such as Darcy's equation for single-phase flow in a porous medium. A finite-difference discretization scheme is used to set the equation in the form of an eigenvalue problem. Within the formalism suggested, the pressure field is decomposed into an average value and fluctuations of different kinds and at different scales. The application of the transform to the equation allows us to calculate the unknown pressure with a varying level of detail. A procedure is suggested to localize important features in the pressure field based only on the fine-scale permeability, and hence we develop a form of adaptive coarse graining. The formalism and method are described and demonstrated using two synthetic toy problems.

Validity of the t-plot method to assess microporosity in hierarchical micro/mesoporous materials.

PubMed

Galarneau, Anne; Villemot, François; Rodriguez, Jeremy; Fajula, François; Coasne, Benoit

2014-11-11

The t-plot method is a well-known technique which allows determining the micro- and/or mesoporous volumes and the specific surface area of a sample by comparison with a reference adsorption isotherm of a nonporous material having the same surface chemistry. In this paper, the validity of the t-plot method is discussed in the case of hierarchical porous materials exhibiting both micro- and mesoporosities. Different hierarchical zeolites with MCM-41 type ordered mesoporosity are prepared using pseudomorphic transformation. For comparison, we also consider simple mechanical mixtures of microporous and mesoporous materials. We first show an intrinsic failure of the t-plot method; this method does not describe the fact that, for a given surface chemistry and pressure, the thickness of the film adsorbed in micropores or small mesopores (< 10σ, σ being the diameter of the adsorbate) increases with decreasing the pore size (curvature effect). We further show that such an effect, which arises from the fact that the surface area and, hence, the free energy of the curved gas/liquid interface decreases with increasing the film thickness, is captured using the simple thermodynamical model by Derjaguin. The effect of such a drawback on the ability of the t-plot method to estimate the micro- and mesoporous volumes of hierarchical samples is then discussed, and an abacus is given to correct the underestimated microporous volume by the t-plot method.

Hierarchical Factoring Based On Image Analysis And Orthoblique Rotations.

PubMed

Stankov, L

1979-07-01

The procedure for hierarchical factoring suggested by Schmid and Leiman (1957) is applied within the framework of image analysis and orthoblique rotational procedures. It is shown that this approach necessarily leads to correlated higher order factors. Also, one can obtain a smaller number of factors than produced by typical hierarchical procedures.

Lexical architecture based on a hierarchy of codes for high-speed string correction

NASA Astrophysics Data System (ADS)

de Bertrand de Beuvron, Francois; Trigano, Philippe

1992-03-01

AI systems for the general public have to be really tolerant to errors. These errors could be of several kinds: typographic, phonetic, grammatical, or semantic. A special lexical dictionary architecture has been designed to deal with the first two. It extends the hierarchical file method of E. Tanaka and Y. Kojima.

Improved Adhesion and Compliancy of Hierarchical Fibrillar Adhesives.

PubMed

Li, Yasong; Gates, Byron D; Menon, Carlo

2015-08-05

The gecko relies on van der Waals forces to cling onto surfaces with a variety of topography and composition. The hierarchical fibrillar structures on their climbing feet, ranging from mesoscale to nanoscale, are hypothesized to be key elements for the animal to conquer both smooth and rough surfaces. An epoxy-based artificial hierarchical fibrillar adhesive was prepared to study the influence of the hierarchical structures on the properties of a dry adhesive. The presented experiments highlight the advantages of a hierarchical structure despite a reduction of overall density and aspect ratio of nanofibrils. In contrast to an adhesive containing only nanometer-size fibrils, the hierarchical fibrillar adhesives exhibited a higher adhesion force and better compliancy when tested on an identical substrate.

What are hierarchical models and how do we analyze them?

USGS Publications Warehouse

Royle, Andy

2016-01-01

In this chapter we provide a basic definition of hierarchical models and introduce the two canonical hierarchical models in this book: site occupancy and N-mixture models. The former is a hierarchical extension of logistic regression and the latter is a hierarchical extension of Poisson regression. We introduce basic concepts of probability modeling and statistical inference including likelihood and Bayesian perspectives. We go through the mechanics of maximizing the likelihood and characterizing the posterior distribution by Markov chain Monte Carlo (MCMC) methods. We give a general perspective on topics such as model selection and assessment of model fit, although we demonstrate these topics in practice in later chapters (especially Chapters 5, 6, 7, and 10 Chapter 5 Chapter 6 Chapter 7 Chapter 10)

Hierarchical porous ZnO microflowers with ultra-high ethanol gas-sensing at low concentration

NASA Astrophysics Data System (ADS)

Song, Liming; Yue, He; Li, Haiying; Liu, Li; Li, Yu; Du, Liting; Duan, Haojie; Klyui, N. I.

2018-05-01

Hierarchical porous and non-porous ZnO microflowers have been successfully fabricated by hydrothermal method. Their crystal structure, morphology and gas-sensing properties were studied by X-ray diffraction (XRD), scanning electron microscopy (SEM), and chemical gas sensing intelligent analysis system (CGS). Compared with hierarchical non-porous ZnO microflowers, hierarchical porous ZnO microflowers exhibited ultra-high sensitivity with 50 ppm ethanol at 260 °C and the response is 110, which is 1.8 times higher than that of non-porous ZnO microflowers. Moreover, the lowest concentration limit of hierarchical porous ZnO microflowers (non-porous ZnO microflowers) to ethanol is 0.1 (1) ppm, the response value is 1.6 (1).

Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains.

PubMed

Bulashevska, Alla; Eils, Roland

2006-06-14

The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction. A novel method called HensBC is introduced to predict protein subcellular location. HensBC is a recursive algorithm which constructs a hierarchical ensemble of classifiers. The classifiers used are Bayesian classifiers based on Markov chain models. We tested our method on six various datasets; among them are Gram-negative bacteria dataset, data for discriminating outer membrane proteins and apoptosis proteins dataset. We observed that our method can predict the subcellular location with high accuracy. Another advantage of the proposed method is that it can improve the accuracy of the prediction of some classes with few sequences in training and is therefore useful for datasets with imbalanced distribution of classes. This study introduces an algorithm which uses only the primary sequence of a protein to predict its subcellular location. The proposed recursive scheme represents an interesting methodology for learning and combining classifiers. The method is computationally efficient and competitive with the previously reported approaches in terms of prediction accuracies as empirical results indicate. The code for the software is available upon request.

Hierarchical extraction of urban objects from mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Zhao, Gang; Dai, Wenxia

2015-01-01

Point clouds collected in urban scenes contain a huge number of points (e.g., billions), numerous objects with significant size variability, complex and incomplete structures, and variable point densities, raising great challenges for the automated extraction of urban objects in the field of photogrammetry, computer vision, and robotics. This paper addresses these challenges by proposing an automated method to extract urban objects robustly and efficiently. The proposed method generates multi-scale supervoxels from 3D point clouds using the point attributes (e.g., colors, intensities) and spatial distances between points, and then segments the supervoxels rather than individual points by combining graph based segmentation with multiple cues (e.g., principal direction, colors) of the supervoxels. The proposed method defines a set of rules for merging segments into meaningful units according to types of urban objects and forms the semantic knowledge of urban objects for the classification of objects. Finally, the proposed method extracts and classifies urban objects in a hierarchical order ranked by the saliency of the segments. Experiments show that the proposed method is efficient and robust for extracting buildings, streetlamps, trees, telegraph poles, traffic signs, cars, and enclosures from mobile laser scanning (MLS) point clouds, with an overall accuracy of 92.3%.

A rule based computer aided design system

NASA Technical Reports Server (NTRS)

Premack, T.

1986-01-01

A Computer Aided Design (CAD) system is presented which supports the iterative process of design, the dimensional continuity between mating parts, and the hierarchical structure of the parts in their assembled configuration. Prolog, an interactive logic programming language, is used to represent and interpret the data base. The solid geometry representing the parts is defined in parameterized form using the swept volume method. The system is demonstrated with a design of a spring piston.

Topology of foreign exchange markets using hierarchical structure methods

NASA Astrophysics Data System (ADS)

Naylor, Michael J.; Rose, Lawrence C.; Moyle, Brendan J.

2007-08-01

This paper uses two physics derived hierarchical techniques, a minimal spanning tree and an ultrametric hierarchical tree, to extract a topological influence map for major currencies from the ultrametric distance matrix for 1995-2001. We find that these two techniques generate a defined and robust scale free network with meaningful taxonomy. The topology is shown to be robust with respect to method, to time horizon and is stable during market crises. This topology, appropriately used, gives a useful guide to determining the underlying economic or regional causal relationships for individual currencies and to understanding the dynamics of exchange rate price determination as part of a complex network.

Hierarchical porous poly (ether sulfone) membranes with excellent capacity retention for vanadium flow battery application

NASA Astrophysics Data System (ADS)

Chen, Dongju; Li, Dandan; Li, Xianfeng

2017-06-01

A hierarchical poly (ether sulfone) (PES) porous membrane is facilely fabricated via a hard template method for vanadium flow battery (VFB) application. The construction of this hierarchical porous membrane is prepared via removing templates (phenolphthalein). The pore size can be well controlled by optimizing the template content in the cast solution, ensuring the membrane conductivity and selectively. The prepared hierarchical porous membrane can combine high ion selectivity with high proton conductivity, which renders a good electrochemical performance in a VFB. The optimized hierarchical porous membrane shows a columbic efficiency of 94.52% and energy efficiency of 81.66% along with a superior ability to maintain stable capacity over extended cycling at a current density of 80 mA cm-2. The characteristics of low cost, proven chemical stability and high electrochemical performance afford the hierarchical PES porous membrane great prospect in VFB application.

MnO2 Nanowire/Biomass-Derived Carbon from Hemp Stem for High-Performance Supercapacitors.

PubMed

Yang, MinHo; Kim, Dong Seok; Hong, Seok Bok; Sim, Jae-Wook; Kim, Jinsoo; Kim, Seung-Soo; Choi, Bong Gill

2017-05-30

Hierarchical 3D nanostructures based on waste biomass are being offered as promising materials for energy storage due to their processabilities, multifunctionalities, environmental benignities, and low cost. Here we report a facile, inexpensive, and scalable strategy for the fabrication of hierarchical porous 3D structure as electrode materials for supercapacitors based on MnO 2 nanowires and hemp-derived activated carbon (HC). Vertical MnO 2 wires are uniformly deposited onto the surface of HC using a one-step hydrothermal method to produce hierarchical porous structures with conductive interconnected 3D networks. HC acts as a near-ideal 3D current collector and anchors electroactive materials, and this confers a specific capacitance of 340 F g -1 at 1 A g -1 with a high rate capability (88% retention) of the 3D MnO 2 /HC composite because of its open-pore system, which facilitates ion and electron transports and synergistic contribution of two energy-storage materials. Moreover, asymmetric supercapacitors fabricated using 3D HC as the anode and 3D MnO 2 /HC as the cathode are able to store 33.3 Wh kg -1 of energy and have a power delivery of 14.8 kW kg -1 .

Gel-based morphological design of zirconium metal–organic frameworks† †Electronic supplementary information (ESI) available: Optical microscopy, PDF TGA, Hg intrusion, nanoindentation, gels of various Zr-MOFs, oil-drop process. See DOI: 10.1039/c6sc05602d Click here for additional data file.

PubMed Central

Bueken, Bart; Van Velthoven, Niels; Willhammar, Tom; Stassin, Timothée; Stassen, Ivo; Keen, David A.; Baron, Gino V.; Denayer, Joeri F. M.; Ameloot, Rob; Bals, Sara

2017-01-01

The ability of metal–organic frameworks (MOFs) to gelate under specific synthetic conditions opens up new opportunities in the preparation and shaping of hierarchically porous MOF monoliths, which could be directly implemented for catalytic and adsorptive applications. In this work, we present the first examples of xero- or aerogel monoliths consisting solely of nanoparticles of several prototypical Zr4+-based MOFs: UiO-66-X (X = H, NH2, NO2, (OH)2), UiO-67, MOF-801, MOF-808 and NU-1000. High reactant and water concentrations during synthesis were observed to induce the formation of gels, which were converted to monolithic materials by drying in air or supercritical CO2. Electron microscopy, combined with N2 physisorption experiments, was used to show that irregular nanoparticle packing leads to pure MOF monoliths with hierarchical pore systems, featuring both intraparticle micropores and interparticle mesopores. Finally, UiO-66 gels were shaped into monolithic spheres of 600 μm diameter using an oil-drop method, creating promising candidates for packed-bed catalytic or adsorptive applications, where hierarchical pore systems can greatly mitigate mass transfer limitations. PMID:28553536

Bio-inspired configurable multiscale extracellular matrix-like structures for functional alignment and guided orientation of cells.

PubMed

Bae, Won-Gyu; Kim, Jangho; Choung, Yun-Hoon; Chung, Yesol; Suh, Kahp Y; Pang, Changhyun; Chung, Jong Hoon; Jeong, Hoon Eui

2015-11-01

Inspired by the hierarchically organized protein fibers in extracellular matrix (ECM) as well as the physiological importance of multiscale topography, we developed a simple but robust method for the design and manipulation of precisely controllable multiscale hierarchical structures using capillary force lithography in combination with an original wrinkling technique. In this study, based on our proposed fabrication technology, we approached a conceptual platform that can mimic the hierarchically multiscale topographical and orientation cues of the ECM for controlling cell structure and function. We patterned the polyurethane acrylate-based nanotopography with various orientations on the microgrooves, which could provide multiscale topography signals of ECM to control single and multicellular morphology and orientation with precision. Using our platforms, we found that the structures and orientations of fibroblast cells were greatly influenced by the nanotopography, rather than the microtopography. We also proposed a new approach that enables the generation of native ECM having nanofibers in specific three-dimensional (3D) configurations by culturing fibroblast cells on the multiscale substrata. We suggest that our methodology could be used as efficient strategies for the design and manipulation of various functional platforms, including well-defined 3D tissue structures for advanced regenerative medicine applications. Copyright © 2015 Elsevier Ltd. All rights reserved.

Merging K-means with hierarchical clustering for identifying general-shaped groups.

PubMed

Peterson, Anna D; Ghosh, Arka P; Maitra, Ranjan

2018-01-01

Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and K -means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while K -means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using K -means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.

A novel label-free photoelectrochemical sensor based on N,S-GQDs and CdS co-sensitized hierarchical Zn2SnO4 cube for detection of cardiac troponin I.

PubMed

Fan, Dawei; Bao, Chunzhu; Khan, Malik Saddam; Wang, Chuanlei; Zhang, Yong; Liu, Qinze; Zhang, Xian; Wei, Qin

2018-05-30

A novel label-free photoelectrochemical (PEC) sensor based on graphene quantum dots doped with nitrogen and sulfur (N,S-GQDs) and CdS co-sensitized hierarchical Zn 2 SnO 4 cube was fabricated to detect cardiac troponin I (cTnI). The unique hierarchical Zn 2 SnO 4 cube was synthesized successfully by the solvothermal method, which has a large specific surface to load functional materials. N,S-GQDs nanoparticles were assembled to the surface of cubic Zn 2 SnO 4 coated ITO electrode, which efficiently accelerated the electronic transition and improved photo-to-current conversion efficiency. Then, CdS nanoparticles further were modified by in-situ growth method to form Zn 2 SnO 4 /N,S-GQDs/CdS composite with prominent photocurrent, which was 30 times that of the Zn 2 SnO 4 cube alone. In this work, the specific immune recognition between cTnI antigens and cTnI antibodies (anti-cTnI) reduced the intensity of the photoelectric signal. And the intensity decreased linearly with the logarithm of cTnI concentration range from 0.001 ng/mL to 50 ng/mL with a detection limit of 0.3 pg/mL. With high sensitivity, excellent selectivity, good stability and reproducibility, the fabricated PEC sensor showed promising applications in the sensor, clinical diagnosis of myocardial infarction and PEC analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

Couplings between hierarchical conformational dynamics from multi-time correlation functions and two-dimensional lifetime spectra: Application to adenylate kinase

NASA Astrophysics Data System (ADS)

Ono, Junichi; Takada, Shoji; Saito, Shinji

2015-06-01

An analytical method based on a three-time correlation function and the corresponding two-dimensional (2D) lifetime spectrum is developed to elucidate the time-dependent couplings between the multi-timescale (i.e., hierarchical) conformational dynamics in heterogeneous systems such as proteins. In analogy with 2D NMR, IR, electronic, and fluorescence spectroscopies, the waiting-time dependence of the off-diagonal peaks in the 2D lifetime spectra can provide a quantitative description of the dynamical correlations between the conformational motions with different lifetimes. The present method is applied to intrinsic conformational changes of substrate-free adenylate kinase (AKE) using long-time coarse-grained molecular dynamics simulations. It is found that the hierarchical conformational dynamics arise from the intra-domain structural transitions among conformational substates of AKE by analyzing the one-time correlation functions and one-dimensional lifetime spectra for the donor-acceptor distances corresponding to single-molecule Förster resonance energy transfer experiments with the use of the principal component analysis. In addition, the complicated waiting-time dependence of the off-diagonal peaks in the 2D lifetime spectra for the donor-acceptor distances is attributed to the fact that the time evolution of the couplings between the conformational dynamics depends upon both the spatial and temporal characters of the system. The present method is expected to shed light on the biological relationship among the structure, dynamics, and function.

Construction of hierarchical porous NiCo{sub 2}O{sub 4} films composed of nanowalls as cathode materials for high-performance supercapacitor

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

Zheng, Qingyun, E-mail: hizhengqingyun@126.com; Zhang, Xiangyang; Shen, Youming

Graphical abstract: Hydrothermal-synthesized NiCo{sub 2}O{sub 4} mesowall films exhibit porous structure and high capacity as well as good cycling life for supercapacitor application. - Highlights: • Hierarchical porous NiCo{sub 2}O{sub 4} nanowall films are prepared by a hydrothermal method. • NiCo{sub 2}O{sub 4} nanowall films show excellent electrochemical performance. • Hierarchical porous film structure is favorable for fast ion/electron transfer. - Abstract: Hierarchical porous NiCo{sub 2}O{sub 4} films composed of nanowalls on nickel foam are synthesized via a facile hydrothermal method. Besides the mesoporous walls, the NiCo{sub 2}O{sub 4} nanowalls are interconnected with each other to form hierarchical porous structure.more » These unique porous structured films possess a high specific surface area. The supercapacitor performance of the hierarchical porous NiCo{sub 2}O{sub 4} film is fully characterized. A high capacity of 130 mA h g{sup −1} is achieved at 2 A g{sup −1} with 97% capacity maintained after 2,000 cycles. Importantly, 75.6% of capacity is retained when the current density changes from 3 A g{sup −1} to 36 A g{sup −1}. The superior electrochemical performance is mainly due to the unique hierarchical porous structure with large surface area as well as shorter diffusion length for ion and charge transport.« less

The Origin of Hierarchical Structure in Self-Assembled Graphene Oxide Papers and the Effect on Mechanical Properties

NASA Astrophysics Data System (ADS)

Nandy, Krishanu

The quest for new materials with ever improving properties has motivated interest in bulk nanostructured materials. Graphene, a two-dimensional sheet of hexagonally arranged carbon atoms, has been of particular interest given its exceptional mechanical, thermal, optical and electrical properties. Graphene oxide is a chemically modified form of graphene in which the honeycomb lattice of carbon atoms is decorated with oxygen bearing functional groups. Graphene oxide represents a facile route for the production of large quantities of graphene based materials, is stable in aqueous and polar organic solvents and has the potential for further chemical modification. In this dissertation, the origin and influence of hierarchical structure on the mechanical properties of graphene oxide paper and graphene oxide paper based materials has been investigated. Free-standing papers derived from graphene oxide are of interest as structural materials due to their impressive mechanical properties. While studies have investigated the mechanical properties of graphene oxide papers, little is known about the formation mechanism. Using a series of flash-freezing experiments on graphene oxide papers undergoing formation, a stop-motion animation of the fabrication process was obtained. The results explain the origin of the hierarchical nature of graphene oxide papers and provide a method for the tailoring of graphene oxide based materials. An in depth study of fusion of graphene oxide papers demonstrates a simple single-step route for the fabrication of practical materials derived from graphene oxide papers. Fused papers retain the properties of constituent papers allowing for the fabrication of mechanical heterostructures that replicate the hierarchical nature of natural materials. The contribution of the hierarchical nature of graphene oxide papers to the mechanical properties was examined by comparing papers formed by two different mechanisms. The intermediate length scale structures were found to play a key role in yielding tough papers with high failure stress. Finally, efforts to investigate the microstructural mechanisms that govern the mechanical properties of graphene oxide papers by 3D printing of a tensile tester are detailed. It is intended to release the design of the tensile tester to the community in an effort to reduce cost and improve availability of lab equipment.

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

PubMed Central

Chen, Gary K.; Chi, Eric C.; Ranola, John Michael O.; Lange, Kenneth

2015-01-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/ PMID:25965340

Convex clustering: an attractive alternative to hierarchical clustering.

PubMed

Chen, Gary K; Chi, Eric C; Ranola, John Michael O; Lange, Kenneth

2015-05-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/.

Hierarchical Medical System Based on Big Data and Mobile Internet: A New Strategic Choice in Health Care

PubMed Central

2017-01-01

China is setting up a hierarchical medical system to solve the problems of biased resource allocation and high patient flows to large hospitals. The development of big data and mobile Internet technology provides a new perspective for the establishment of hierarchical medical system. This viewpoint discusses the challenges with the hierarchical medical system in China and how big data and mobile Internet can be used to mitigate these challenges. PMID:28790024

High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

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

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguishmore » between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.« less

High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

DOE PAGES

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; ...

2015-07-09

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguishmore » between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.« less

IC-Finder: inferring robustly the hierarchical organization of chromatin folding

PubMed Central

Haddad, Noelle

2017-01-01

Abstract The spatial organization of the genome plays a crucial role in the regulation of gene expression. Recent experimental techniques like Hi-C have emphasized the segmentation of genomes into interaction compartments that constitute conserved functional domains participating in the maintenance of a proper cell identity. Here, we propose a novel method, IC-Finder, to identify interaction compartments (IC) from experimental Hi-C maps. IC-Finder is based on a hierarchical clustering approach that we adapted to account for the polymeric nature of chromatin. Based on a benchmark of realistic in silico Hi-C maps, we show that IC-Finder is one of the best methods in terms of reliability and is the most efficient numerically. IC-Finder proposes two original options: a probabilistic description of the inferred compartments and the possibility to explore the various hierarchies of chromatin organization. Applying the method to experimental data in fly and human, we show how the predicted segmentation may depend on the normalization scheme and how 3D compartmentalization is tightly associated with epigenomic information. IC-Finder provides a robust and generic ‘all-in-one’ tool to uncover the general principles of 3D chromatin folding and their influence on gene regulation. The software is available at http://membres-timc.imag.fr/Daniel.Jost/DJ-TIMC/Software.html. PMID:28130423

A High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

PubMed Central

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijfhout, Falko P.

2015-01-01

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. A range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. Here, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required. PMID:26156000

A High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

NASA Astrophysics Data System (ADS)

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijfhout, Falko P.

2015-07-01

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. A range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. Here, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.

Parameterizations for ensemble Kalman inversion

NASA Astrophysics Data System (ADS)

Chada, Neil K.; Iglesias, Marco A.; Roininen, Lassi; Stuart, Andrew M.

2018-05-01

The use of ensemble methods to solve inverse problems is attractive because it is a derivative-free methodology which is also well-adapted to parallelization. In its basic iterative form the method produces an ensemble of solutions which lie in the linear span of the initial ensemble. Choice of the parameterization of the unknown field is thus a key component of the success of the method. We demonstrate how both geometric ideas and hierarchical ideas can be used to design effective parameterizations for a number of applied inverse problems arising in electrical impedance tomography, groundwater flow and source inversion. In particular we show how geometric ideas, including the level set method, can be used to reconstruct piecewise continuous fields, and we show how hierarchical methods can be used to learn key parameters in continuous fields, such as length-scales, resulting in improved reconstructions. Geometric and hierarchical ideas are combined in the level set method to find piecewise constant reconstructions with interfaces of unknown topology.

A hierarchical transition state search algorithm

NASA Astrophysics Data System (ADS)

del Campo, Jorge M.; Köster, Andreas M.

2008-07-01

A hierarchical transition state search algorithm is developed and its implementation in the density functional theory program deMon2k is described. This search algorithm combines the double ended saddle interpolation method with local uphill trust region optimization. A new formalism for the incorporation of the distance constrain in the saddle interpolation method is derived. The similarities between the constrained optimizations in the local trust region method and the saddle interpolation are highlighted. The saddle interpolation and local uphill trust region optimizations are validated on a test set of 28 representative reactions. The hierarchical transition state search algorithm is applied to an intramolecular Diels-Alder reaction with several internal rotors, which makes automatic transition state search rather challenging. The obtained reaction mechanism is discussed in the context of the experimentally observed product distribution.

Rapidly differentiating grape seeds from different sources based on characteristic fingerprints using direct analysis in real time coupled with time-of-flight mass spectrometry combined with chemometrics.

PubMed

Song, Yuqiao; Liao, Jie; Dong, Junxing; Chen, Li

2015-09-01

The seeds of grapevine (Vitis vinifera) are a byproduct of wine production. To examine the potential value of grape seeds, grape seeds from seven sources were subjected to fingerprinting using direct analysis in real time coupled with time-of-flight mass spectrometry combined with chemometrics. Firstly, we listed all reported components (56 components) from grape seeds and calculated the precise m/z values of the deprotonated ions [M-H](-) . Secondly, the experimental conditions were systematically optimized based on the peak areas of total ion chromatograms of the samples. Thirdly, the seven grape seed samples were examined using the optimized method. Information about 20 grape seed components was utilized to represent characteristic fingerprints. Finally, hierarchical clustering analysis and principal component analysis were performed to analyze the data. Grape seeds from seven different sources were classified into two clusters; hierarchical clustering analysis and principal component analysis yielded similar results. The results of this study lay the foundation for appropriate utilization and exploitation of grape seed samples. Due to the absence of complicated sample preparation methods and chromatographic separation, the method developed in this study represents one of the simplest and least time-consuming methods for grape seed fingerprinting. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer

NASA Astrophysics Data System (ADS)

Luiza Bondar, M.; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben

2013-08-01

For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.

Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer.

PubMed

Bondar, M Luiza; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben

2013-08-07

For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.

Iris Image Classification Based on Hierarchical Visual Codebook.

PubMed

Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

2014-06-01

Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

A review of surrogate models and their application to groundwater modeling

NASA Astrophysics Data System (ADS)

Asher, M. J.; Croke, B. F. W.; Jakeman, A. J.; Peeters, L. J. M.

2015-08-01

The spatially and temporally variable parameters and inputs to complex groundwater models typically result in long runtimes which hinder comprehensive calibration, sensitivity, and uncertainty analysis. Surrogate modeling aims to provide a simpler, and hence faster, model which emulates the specified output of a more complex model in function of its inputs and parameters. In this review paper, we summarize surrogate modeling techniques in three categories: data-driven, projection, and hierarchical-based approaches. Data-driven surrogates approximate a groundwater model through an empirical model that captures the input-output mapping of the original model. Projection-based models reduce the dimensionality of the parameter space by projecting the governing equations onto a basis of orthonormal vectors. In hierarchical or multifidelity methods the surrogate is created by simplifying the representation of the physical system, such as by ignoring certain processes, or reducing the numerical resolution. In discussing the application to groundwater modeling of these methods, we note several imbalances in the existing literature: a large body of work on data-driven approaches seemingly ignores major drawbacks to the methods; only a fraction of the literature focuses on creating surrogates to reproduce outputs of fully distributed groundwater models, despite these being ubiquitous in practice; and a number of the more advanced surrogate modeling methods are yet to be fully applied in a groundwater modeling context.

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

2008-01-01

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

Emulsion-Assisted Polymerization-Induced Hierarchical Self-Assembly of Giant Sea Urchin-like Aggregates in a Large Scale.

PubMed

Xu, Qingsong; Huang, Tong; Li, Shanlong; Li, Ke; Li, Chuanlong; Liu, Yannan; Wang, Yuling; Yu, Chunyang; Zhou, Yongfeng

2018-05-09

Hierarchical solution self-assembly has nowadays become an important biomimetic method to prepare highly complex and multifunctional supramolecular structures. However, despites the great progress, it is still highly challenging to prepare hierarchical self-assemblies in a large scale since the self-assembly processes are generally performed at high dilution. Herein, we report an emulsion-assisted polymerization-induced self-assembly (EAPISA) method with the advantages of in-situ self-assembly process, scalable preparation and facile functionalization to prepare hierarchical multiscale sea urchin-like aggregates (SUAs). It also extends horizons of PISA in monomers and in polymerization method. The obtained SUAs from amphiphilic alternating copolymers represent a novel self-assembled structure with micron-sized rattan ball-like capsule (RBC) acting as the hollow core body and radiating nanotubes tens of micrometers in length as the hollow spines. They can effectively capture model proteins at an ultra-low concentration (≈10 nM) after functionalized with amino groups through click copolymerization. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Global/local processing of hierarchical visual stimuli in a conflict-choice task by capuchin monkeys (Sapajus spp.).

PubMed

Truppa, Valentina; Carducci, Paola; De Simone, Diego Antonio; Bisazza, Angelo; De Lillo, Carlo

2017-03-01

In the last two decades, comparative research has addressed the issue of how the global and local levels of structure of visual stimuli are processed by different species, using Navon-type hierarchical figures, i.e. smaller local elements that form larger global configurations. Determining whether or not the variety of procedures adopted to test different species with hierarchical figures are equivalent is of crucial importance to ensure comparability of results. Among non-human species, global/local processing has been extensively studied in tufted capuchin monkeys using matching-to-sample tasks with hierarchical patterns. Local dominance has emerged consistently in these New World primates. In the present study, we assessed capuchins' processing of hierarchical stimuli with a method frequently adopted in studies of global/local processing in non-primate species: the conflict-choice task. Different from the matching-to-sample procedure, this task involved processing local and global information retained in long-term memory. Capuchins were trained to discriminate between consistent hierarchical stimuli (similar global and local shape) and then tested with inconsistent hierarchical stimuli (different global and local shapes). We found that capuchins preferred the hierarchical stimuli featuring the correct local elements rather than those with the correct global configuration. This finding confirms that capuchins' local dominance, typically observed using matching-to-sample procedures, is also expressed as a local preference in the conflict-choice task. Our study adds to the growing body of comparative studies on visual grouping functions by demonstrating that the methods most frequently used in the literature on global/local processing produce analogous results irrespective of extent of the involvement of memory processes.

Improved Convergence and Robustness of USM3D Solutions on Mixed-Element Grids

NASA Technical Reports Server (NTRS)

Pandya, Mohagna J.; Diskin, Boris; Thomas, James L.; Frink, Neal T.

2016-01-01

Several improvements to the mixed-element USM3D discretization and defect-correction schemes have been made. A new methodology for nonlinear iterations, called the Hierarchical Adaptive Nonlinear Iteration Method, has been developed and implemented. The Hierarchical Adaptive Nonlinear Iteration Method provides two additional hierarchies around a simple and approximate preconditioner of USM3D. The hierarchies are a matrix-free linear solver for the exact linearization of Reynolds-averaged Navier-Stokes equations and a nonlinear control of the solution update. Two variants of the Hierarchical Adaptive Nonlinear Iteration Method are assessed on four benchmark cases, namely, a zero-pressure-gradient flat plate, a bump-in-channel configuration, the NACA 0012 airfoil, and a NASA Common Research Model configuration. The new methodology provides a convergence acceleration factor of 1.4 to 13 over the preconditioner-alone method representing the baseline solver technology.

Improved Convergence and Robustness of USM3D Solutions on Mixed-Element Grids

NASA Technical Reports Server (NTRS)

Pandya, Mohagna J.; Diskin, Boris; Thomas, James L.; Frinks, Neal T.

2016-01-01

Several improvements to the mixed-elementUSM3Ddiscretization and defect-correction schemes have been made. A new methodology for nonlinear iterations, called the Hierarchical Adaptive Nonlinear Iteration Method, has been developed and implemented. The Hierarchical Adaptive Nonlinear Iteration Method provides two additional hierarchies around a simple and approximate preconditioner of USM3D. The hierarchies are a matrix-free linear solver for the exact linearization of Reynolds-averaged Navier-Stokes equations and a nonlinear control of the solution update. Two variants of the Hierarchical Adaptive Nonlinear Iteration Method are assessed on four benchmark cases, namely, a zero-pressure-gradient flat plate, a bump-in-channel configuration, the NACA 0012 airfoil, and a NASA Common Research Model configuration. The new methodology provides a convergence acceleration factor of 1.4 to 13 over the preconditioner-alone method representing the baseline solver technology.

The effect of calcination temperature on the microstructure and photocatalytic activity of TiO2-based composite nanotubes prepared by an in situ template dissolution method

NASA Astrophysics Data System (ADS)

Fan, Jiajie; Zhao, Li; Yu, Jiaguo; Liu, Gang

2012-09-01

TiO2-based composite nanotubes, based on an in situ template dissolution method, were one-step fabricated in a mixed aqueous solution of ammonium hexafluorotitanate and boric acid using ZnO nanorods as templates, and then the samples were calcined at different temperatures. The photocatalytic activity of the samples was evaluated by photocatalytic decoloration of Methyl Orange (MO) aqueous solution at ambient temperature under UV light. The results showed that the prepared sample possessed nanoscale tubular morphology with a wall thickness of ca. 30-50 nm, inner diameters of ca. 50-150 nm and lengths of ca. 400-2000 nm. The calcined samples exhibited excellent stabilization of the anatase phase in a wide temperature range of 300-800 °C. The un-calcined and calcined samples possessed hierarchically macro-mesoporous structures. The sample calcined at 600 °C exhibited the highest photocatalytic activity, corresponding to the maximal formation rate of \\z.rad OH on the photocatalyst. This is attributed to the improvement of anatase TiO2 crystallization, the formation of multi-phase structures including anatase, cubic Zn2TiO4, hexagonal ZnTiO3 and cubic ZnTiO3, and the presence of hierarchically macro-mesoporous structures.

Mind the Gap! A Multilevel Analysis of Factors Related to Variation in Published Cost-Effectiveness Estimates within and between Countries

PubMed Central

Boehler, Christian E. H.; Lord, Joanne

2016-01-01

Background. Published cost-effectiveness estimates can vary considerably, both within and between countries. Despite extensive discussion, little is known empirically about factors relating to these variations. Objectives. To use multilevel statistical modeling to integrate cost-effectiveness estimates from published economic evaluations to investigate potential causes of variation. Methods. Cost-effectiveness studies of statins for cardiovascular disease prevention were identified by systematic review. Estimates of incremental costs and effects were extracted from reported base case, sensitivity, and subgroup analyses, with estimates grouped in studies and in countries. Three bivariate models were developed: a cross-classified model to accommodate data from multinational studies, a hierarchical model with multinational data allocated to a single category at country level, and a hierarchical model excluding multinational data. Covariates at different levels were drawn from a long list of factors suggested in the literature. Results. We found 67 studies reporting 2094 cost-effectiveness estimates relating to 23 countries (6 studies reporting for more than 1 country). Data and study-level covariates included patient characteristics, intervention and comparator cost, and some study methods (e.g., discount rates and time horizon). After adjusting for these factors, the proportion of variation attributable to countries was negligible in the cross-classified model but moderate in the hierarchical models (14%−19% of total variance). Country-level variables that improved the fit of the hierarchical models included measures of income and health care finance, health care resources, and population risks. Conclusions. Our analysis suggested that variability in published cost-effectiveness estimates is related more to differences in study methods than to differences in national context. Multinational studies were associated with much lower country-level variation than single-country studies. These findings are for a single clinical question and may be atypical. PMID:25878194

Bayesian Variable Selection for Hierarchical Gene-Environment and Gene-Gene Interactions

PubMed Central

Liu, Changlu; Ma, Jianzhong; Amos, Christopher I.

2014-01-01

We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions and gene by environment interactions in the same model. Our approach incorporates the natural hierarchical structure between the main effects and interaction effects into a mixture model, such that our methods tend to remove the irrelevant interaction effects more effectively, resulting in more robust and parsimonious models. We consider both strong and weak hierarchical models. For a strong hierarchical model, both of the main effects between interacting factors must be present for the interactions to be considered in the model development, while for a weak hierarchical model, only one of the two main effects is required to be present for the interaction to be evaluated. Our simulation results show that the proposed strong and weak hierarchical mixture models work well in controlling false positive rates and provide a powerful approach for identifying the predisposing effects and interactions in gene-environment interaction studies, in comparison with the naive model that does not impose this hierarchical constraint in most of the scenarios simulated. We illustrated our approach using data for lung cancer and cutaneous melanoma. PMID:25154630

Macro and micro wettability of hydrophobic siloxane films with hierarchical surface roughness

NASA Astrophysics Data System (ADS)

Terpilowski, Konrad; Goncharuk, Olena; Gun’ko, Vladimir M.

2018-07-01

A method has been proposed to control the macro- and micro-wetting properties of hydrophobic surfaces through changes in the roughness due to modifying siloxane films with silica microparticles (MP). An experimental and theoretical analysis of macro- and micro-wettability dependence on the roughness of a film surface was carried out by combination of SEM and XPS methods with evaluation of equilibrium contact angles from Tadmor’s equation. SEM images (environmental mode) allowed characterizing the mosaic hydrophobicity/hydrophilicity of the siloxane film surface. Hydrophobic siloxane films filled with silica MP were synthesized on the plasma activated and non-activated glass substrates by the sol-gel dip-coating method using tetraethylorthosilicate based precursor compositions with subsequent reaction with hexamethyldisilazane. The values of water contact angles higher than 150° indicating a superhydrophobic effect were observed for films with combining nano- and micro-hierarchical roughness. Moreover, considering wettability on the micro scale the hybrid effect was discovered and confirmed by the SEM and XPS studies showing the presence of not only hydrophobic but also hydrophilic surface domains.

Organic Nanoflowers from a Wide Variety of Molecules Templated by a Hierarchical Supramolecular Scaffold.

PubMed

Negrón, Luis M; Díaz, Tanya L; Ortiz-Quiles, Edwin O; Dieppa-Matos, Diómedes; Madera-Soto, Bismark; Rivera, José M

2016-03-15

Nanoflowers (NFs) are flowered-shaped particles with overall sizes or features in the nanoscale. Beyond their pleasing aesthetics, NFs have found a number of applications ranging from catalysis, to sensing, to drug delivery. Compared to inorganic based NFs, their organic and hybrid counterparts are relatively underdeveloped mostly because of the lack of a reliable and versatile method for their construction. We report here a method for constructing NFs from a wide variety of biologically relevant molecules (guests), ranging from small molecules, like doxorubicin, to biomacromolecules, like various proteins and plasmid DNA. The method relies on the encapsulation of the guests within a hierarchically structured particle made from supramolecular G-quadruplexes. The size and overall flexibility of the guests dictate the broad morphological features of the resulting NFs, specifically, small and rigid guests favor the formation of NFs with spiky petals, while large and/or flexible guests promote NFs with wide petals. The results from experiments using confocal fluorescence microscopy, and scanning electron microscopy provides the basis for the proposed mechanism for the NF formation.

Linguistic steganography on Twitter: hierarchical language modeling with manual interaction

NASA Astrophysics Data System (ADS)

Wilson, Alex; Blunsom, Phil; Ker, Andrew D.

2014-02-01

This work proposes a natural language stegosystem for Twitter, modifying tweets as they are written to hide 4 bits of payload per tweet, which is a greater payload than previous systems have achieved. The system, CoverTweet, includes novel components, as well as some already developed in the literature. We believe that the task of transforming covers during embedding is equivalent to unilingual machine translation (paraphrasing), and we use this equivalence to de ne a distortion measure based on statistical machine translation methods. The system incorporates this measure of distortion to rank possible tweet paraphrases, using a hierarchical language model; we use human interaction as a second distortion measure to pick the best. The hierarchical language model is designed to model the speci c language of the covers, which in this setting is the language of the Twitter user who is embedding. This is a change from previous work, where general-purpose language models have been used. We evaluate our system by testing the output against human judges, and show that humans are unable to distinguish stego tweets from cover tweets any better than random guessing.

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data

PubMed Central

Tian, Ting; McLachlan, Geoffrey J.; Dieters, Mark J.; Basford, Kaye E.

2015-01-01

It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a novel approach in terms of hierarchical clustering. Multiple imputation (MI) was used in four ways, multiple agglomerative hierarchical clustering, normal distribution model, normal regression model, and predictive mean match. The later three models used both Bayesian analysis and non-Bayesian analysis, while the first approach used a clustering procedure with randomly selected attributes and assigned real values from the nearest neighbour to the one with missing observations. Different proportions of data entries in six complete datasets were randomly selected to be missing and the MI methods were compared based on the efficiency and accuracy of estimating those values. The results indicated that the models using Bayesian analysis had slightly higher accuracy of estimation performance than those using non-Bayesian analysis but they were more time-consuming. However, the novel approach of multiple agglomerative hierarchical clustering demonstrated the overall best performances. PMID:26689369

Durable and mass producible polymer surface structures with different combinations of micro-micro hierarchy

NASA Astrophysics Data System (ADS)

Jiang, Yu; Suvanto, Mika; Pakkanen, Tapani A.

2016-01-01

Extensive studies have been performed with the aim of fabricating hierarchical surface structures inspired by nature. However, synthetic hierarchical structures have to sacrifice mechanical resistance to functionality by introducing finer scaled structures. Therefore, surfaces are less durable. Surface micro-micro hierarchy has been proven to be effective in replacing micro-nano hierarchy in the sense of superhydrophobicity. However, less attention has been paid to the combined micro-micro hierarchies with surface pillars and pits incorporated together. The fabrication of this type of hierarchy may be less straightforward, with the possibility of being a complicated multi-step process. In this study, we present a simple yet mass producible fabrication method for hierarchical structures with different combinations of surface pillars and pits. The fabrication was based on only one aluminum (Al) mold with sequential mountings. The fabricated structures exhibit high mechanical durability and structural stabilities with a normal load up to 100 kg. In addition, the theoretical estimation of the wetting state shows a promising way of stabilizing a water droplet on the surface pit structures with a more stable Cassie-Baxter state.

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data.

PubMed

Tian, Ting; McLachlan, Geoffrey J; Dieters, Mark J; Basford, Kaye E

2015-01-01

It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a novel approach in terms of hierarchical clustering. Multiple imputation (MI) was used in four ways, multiple agglomerative hierarchical clustering, normal distribution model, normal regression model, and predictive mean match. The later three models used both Bayesian analysis and non-Bayesian analysis, while the first approach used a clustering procedure with randomly selected attributes and assigned real values from the nearest neighbour to the one with missing observations. Different proportions of data entries in six complete datasets were randomly selected to be missing and the MI methods were compared based on the efficiency and accuracy of estimating those values. The results indicated that the models using Bayesian analysis had slightly higher accuracy of estimation performance than those using non-Bayesian analysis but they were more time-consuming. However, the novel approach of multiple agglomerative hierarchical clustering demonstrated the overall best performances.

HiTAD: detecting the structural and functional hierarchies of topologically associating domains from chromatin interactions

PubMed Central

Wang, Xiao-Tao; Cui, Wang

2017-01-01

Abstract A current question in the high-order organization of chromatin is whether topologically associating domains (TADs) are distinct from other hierarchical chromatin domains. However, due to the unclear TAD definition in tradition, the structural and functional uniqueness of TAD is not well studied. In this work, we refined TAD definition by further constraining TADs to the optimal separation on global intra-chromosomal interactions. Inspired by this constraint, we developed a novel method, called HiTAD, to detect hierarchical TADs from Hi-C chromatin interactions. HiTAD performs well in domain sensitivity, replicate reproducibility and inter cell-type conservation. With a novel domain-based alignment proposed by us, we defined several types of hierarchical TAD changes which were not systematically studied previously, and subsequently used them to reveal that TADs and sub-TADs differed statistically in correlating chromosomal compartment, replication timing and gene transcription. Finally, our work also has the implication that the refinement of TAD definition could be achieved by only utilizing chromatin interactions, at least in part. HiTAD is freely available online. PMID:28977529

The Intolerance of Uncertainty Inventory: Validity and Comparison of Scoring Methods to Assess Individuals Screening Positive for Anxiety and Depression.

PubMed

Lauriola, Marco; Mosca, Oriana; Trentini, Cristina; Foschi, Renato; Tambelli, Renata; Carleton, R Nicholas

2018-01-01

Intolerance of Uncertainty is a fundamental transdiagnostic personality construct hierarchically organized with a core general factor underlying diverse clinical manifestations. The current study evaluated the construct validity of the Intolerance of Uncertainty Inventory, a two-part scale separately assessing a unitary Intolerance of Uncertainty disposition to consider uncertainties to be unacceptable and threatening (Part A) and the consequences of such disposition, regarding experiential avoidance, chronic doubt, overestimation of threat, worrying, control of uncertain situations, and seeking reassurance (Part B). Community members ( N = 1046; Mean age = 36.69 ± 12.31 years; 61% females) completed the Intolerance of Uncertainty Inventory with the Beck Depression Inventory-II and the State-Trait Anxiety Inventory. Part A demonstrated a robust unidimensional structure and an excellent convergent validity with Part B. A bifactor model was the best fitting model for Part B. Based on these results, we compared the hierarchical factor scores with summated ratings clinical proxy groups reporting anxiety and depression symptoms. Summated rating scores were associated with both depression and anxiety and proportionally increased with the co-occurrence of depressive and anxious symptoms. By contrast, hierarchical scores were useful to detect which facets mostly separated between for depression and anxiety groups. In sum, Part A was a reliable and valid transdiagnostic measure of Intolerance of Uncertainty. The Part B was arguably more useful for assessing clinical manifestations of Intolerance of Uncertainty for specific disorders, provided that hierarchical scores are used. Overall, our study suggest that clinical assessments might need to shift toward hierarchical factor scores.

The Intolerance of Uncertainty Inventory: Validity and Comparison of Scoring Methods to Assess Individuals Screening Positive for Anxiety and Depression

PubMed Central

Lauriola, Marco; Mosca, Oriana; Trentini, Cristina; Foschi, Renato; Tambelli, Renata; Carleton, R. Nicholas

2018-01-01

Intolerance of Uncertainty is a fundamental transdiagnostic personality construct hierarchically organized with a core general factor underlying diverse clinical manifestations. The current study evaluated the construct validity of the Intolerance of Uncertainty Inventory, a two-part scale separately assessing a unitary Intolerance of Uncertainty disposition to consider uncertainties to be unacceptable and threatening (Part A) and the consequences of such disposition, regarding experiential avoidance, chronic doubt, overestimation of threat, worrying, control of uncertain situations, and seeking reassurance (Part B). Community members (N = 1046; Mean age = 36.69 ± 12.31 years; 61% females) completed the Intolerance of Uncertainty Inventory with the Beck Depression Inventory-II and the State-Trait Anxiety Inventory. Part A demonstrated a robust unidimensional structure and an excellent convergent validity with Part B. A bifactor model was the best fitting model for Part B. Based on these results, we compared the hierarchical factor scores with summated ratings clinical proxy groups reporting anxiety and depression symptoms. Summated rating scores were associated with both depression and anxiety and proportionally increased with the co-occurrence of depressive and anxious symptoms. By contrast, hierarchical scores were useful to detect which facets mostly separated between for depression and anxiety groups. In sum, Part A was a reliable and valid transdiagnostic measure of Intolerance of Uncertainty. The Part B was arguably more useful for assessing clinical manifestations of Intolerance of Uncertainty for specific disorders, provided that hierarchical scores are used. Overall, our study suggest that clinical assessments might need to shift toward hierarchical factor scores. PMID:29632505

Methods Used to Support a Life Cycle of Complex Engineering Products

NASA Astrophysics Data System (ADS)

Zakharova, Alexandra A.; Kolegova, Olga A.; Nekrasova, Maria E.; Eremenko, Andrey O.

2016-08-01

Management of companies involved in the design, development and operation of complex engineering products recognize the relevance of creating systems for product lifecycle management. A system of methods is proposed to support life cycles of complex engineering products, based on fuzzy set theory and hierarchical analysis. The system of methods serves to demonstrate the grounds for making strategic decisions in an environment of uncertainty, allows the use of expert knowledge, and provides interconnection of decisions at all phases of strategic management and all stages of a complex engineering product lifecycle.

InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions

PubMed Central

Morton, Lindsay M.; Linet, Martha S.; Clarke, Christina A.; Kadin, Marshall E.; Vajdic, Claire M.; Monnereau, Alain; Maynadié, Marc; Chiu, Brian C.-H.; Marcos-Gragera, Rafael; Costantini, Adele Seniori; Cerhan, James R.; Weisenburger, Dennis D.

2010-01-01

After publication of the updated World Health Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues in 2008, the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph) now presents an update of the hierarchical classification of lymphoid neoplasms for epidemiologic research based on the 2001 WHO classification, which we published in 2007. The updated hierarchical classification incorporates all of the major and provisional entities in the 2008 WHO classification, including newly defined entities based on age, site, certain infections, and molecular characteristics, as well as borderline categories, early and “in situ” lesions, disorders with limited capacity for clinical progression, lesions without current International Classification of Diseases for Oncology, 3rd Edition codes, and immunodeficiency-associated lymphoproliferative disorders. WHO subtypes are defined in hierarchical groupings, with newly defined groups for small B-cell lymphomas with plasmacytic differentiation and for primary cutaneous T-cell lymphomas. We suggest approaches for applying the hierarchical classification in various epidemiologic settings, including strategies for dealing with multiple coexisting lymphoma subtypes in one patient, and cases with incomplete pathologic information. The pathology materials useful for state-of-the-art epidemiology studies are also discussed. We encourage epidemiologists to adopt the updated InterLymph hierarchical classification, which incorporates the most recent WHO entities while demonstrating their relationship to older classifications. PMID:20699439

InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions.

PubMed

Turner, Jennifer J; Morton, Lindsay M; Linet, Martha S; Clarke, Christina A; Kadin, Marshall E; Vajdic, Claire M; Monnereau, Alain; Maynadié, Marc; Chiu, Brian C-H; Marcos-Gragera, Rafael; Costantini, Adele Seniori; Cerhan, James R; Weisenburger, Dennis D

2010-11-18

After publication of the updated World Health Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues in 2008, the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph) now presents an update of the hierarchical classification of lymphoid neoplasms for epidemiologic research based on the 2001 WHO classification, which we published in 2007. The updated hierarchical classification incorporates all of the major and provisional entities in the 2008 WHO classification, including newly defined entities based on age, site, certain infections, and molecular characteristics, as well as borderline categories, early and "in situ" lesions, disorders with limited capacity for clinical progression, lesions without current International Classification of Diseases for Oncology, 3rd Edition codes, and immunodeficiency-associated lymphoproliferative disorders. WHO subtypes are defined in hierarchical groupings, with newly defined groups for small B-cell lymphomas with plasmacytic differentiation and for primary cutaneous T-cell lymphomas. We suggest approaches for applying the hierarchical classification in various epidemiologic settings, including strategies for dealing with multiple coexisting lymphoma subtypes in one patient, and cases with incomplete pathologic information. The pathology materials useful for state-of-the-art epidemiology studies are also discussed. We encourage epidemiologists to adopt the updated InterLymph hierarchical classification, which incorporates the most recent WHO entities while demonstrating their relationship to older classifications.

Mind the Gap! A Multilevel Analysis of Factors Related to Variation in Published Cost-Effectiveness Estimates within and between Countries.

PubMed

Boehler, Christian E H; Lord, Joanne

2016-01-01

Published cost-effectiveness estimates can vary considerably, both within and between countries. Despite extensive discussion, little is known empirically about factors relating to these variations. To use multilevel statistical modeling to integrate cost-effectiveness estimates from published economic evaluations to investigate potential causes of variation. Cost-effectiveness studies of statins for cardiovascular disease prevention were identified by systematic review. Estimates of incremental costs and effects were extracted from reported base case, sensitivity, and subgroup analyses, with estimates grouped in studies and in countries. Three bivariate models were developed: a cross-classified model to accommodate data from multinational studies, a hierarchical model with multinational data allocated to a single category at country level, and a hierarchical model excluding multinational data. Covariates at different levels were drawn from a long list of factors suggested in the literature. We found 67 studies reporting 2094 cost-effectiveness estimates relating to 23 countries (6 studies reporting for more than 1 country). Data and study-level covariates included patient characteristics, intervention and comparator cost, and some study methods (e.g., discount rates and time horizon). After adjusting for these factors, the proportion of variation attributable to countries was negligible in the cross-classified model but moderate in the hierarchical models (14%-19% of total variance). Country-level variables that improved the fit of the hierarchical models included measures of income and health care finance, health care resources, and population risks. Our analysis suggested that variability in published cost-effectiveness estimates is related more to differences in study methods than to differences in national context. Multinational studies were associated with much lower country-level variation than single-country studies. These findings are for a single clinical question and may be atypical. © The Author(s) 2015.

COHeRE: Cross-Ontology Hierarchical Relation Examination for Ontology Quality Assurance.

PubMed

Cui, Licong

Biomedical ontologies play a vital role in healthcare information management, data integration, and decision support. Ontology quality assurance (OQA) is an indispensable part of the ontology engineering cycle. Most existing OQA methods are based on the knowledge provided within the targeted ontology. This paper proposes a novel cross-ontology analysis method, Cross-Ontology Hierarchical Relation Examination (COHeRE), to detect inconsistencies and possible errors in hierarchical relations across multiple ontologies. COHeRE leverages the Unified Medical Language System (UMLS) knowledge source and the MapReduce cloud computing technique for systematic, large-scale ontology quality assurance work. COHeRE consists of three main steps with the UMLS concepts and relations as the input. First, the relations claimed in source vocabularies are filtered and aggregated for each pair of concepts. Second, inconsistent relations are detected if a concept pair is related by different types of relations in different source vocabularies. Finally, the uncovered inconsistent relations are voted according to their number of occurrences across different source vocabularies. The voting result together with the inconsistent relations serve as the output of COHeRE for possible ontological change. The highest votes provide initial suggestion on how such inconsistencies might be fixed. In UMLS, 138,987 concept pairs were found to have inconsistent relationships across multiple source vocabularies. 40 inconsistent concept pairs involving hierarchical relationships were randomly selected and manually reviewed by a human expert. 95.8% of the inconsistent relations involved in these concept pairs indeed exist in their source vocabularies rather than being introduced by mistake in the UMLS integration process. 73.7% of the concept pairs with suggested relationship were agreed by the human expert. The effectiveness of COHeRE indicates that UMLS provides a promising environment to enhance qualities of biomedical ontologies by performing cross-ontology examination.

Tree Testing of Hierarchical Menu Structures for Health Applications

PubMed Central

Le, Thai; Chaudhuri, Shomir; Chung, Jane; Thompson, Hilaire J; Demiris, George

2014-01-01

To address the need for greater evidence-based evaluation of Health Information Technology (HIT) systems we introduce a method of usability testing termed tree testing. In a tree test, participants are presented with an abstract hierarchical tree of the system taxonomy and asked to navigate through the tree in completing representative tasks. We apply tree testing to a commercially available health application, demonstrating a use case and providing a comparison with more traditional in-person usability testing methods. Online tree tests (N=54) and in-person usability tests (N=15) were conducted from August to September 2013. Tree testing provided a method to quantitatively evaluate the information structure of a system using various navigational metrics including completion time, task accuracy, and path length. The results of the analyses compared favorably to the results seen from the traditional usability test. Tree testing provides a flexible, evidence-based approach for researchers to evaluate the information structure of HITs. In addition, remote tree testing provides a quick, flexible, and high volume method of acquiring feedback in a structured format that allows for quantitative comparisons. With the diverse nature and often large quantities of health information available, addressing issues of terminology and concept classifications during the early development process of a health information system will improve navigation through the system and save future resources. Tree testing is a usability method that can be used to quickly and easily assess information hierarchy of health information systems. PMID:24582924

Enhanced lithium storage performance of hierarchical CuO nanomaterials with surface fractal characteristics

NASA Astrophysics Data System (ADS)

Li, Ang; He, Renyue; Bian, Zhuo; Song, Huaihe; Chen, Xiaohong; Zhou, Jisheng

2018-06-01

Self-assembled hierarchical CuO nanostructures with fractal structures were prepared by a mild method and exhibited excellent lithium storage properties, certain of which even demonstrated a high reversible capacity of 827 mAh g-1 at a rate of 0.1 C. An interesting phenomenon was observed that the electrochemical performance varies along with the structure complexity, and the products with higher surface factal dimensions exhibited larger capability and better cyclability. Structural and electrochemical analysis methods were used to explore the lithiation kinetics of the samples and the reasons for the outstanding electrochemical performances related to the complexities of hierarchical nanostructures and the irregularities of surface and mass distribution.

Chosen aspects of multi-criteria analysis applied to support the choice of materials for building structures

NASA Astrophysics Data System (ADS)

Szafranko, E.

2017-08-01

When planning a building structure, dilemmas arise as to what construction and material solutions are feasible. The decisions are not always obvious. A procedure for selecting the variant that will best satisfy the expectations of the investor and future users of a structure must be founded on mathematical methods. The following deserve special attention: the MCE methods, Hierarchical Analysis Methods and Weighting Methods. Another interesting solution, particularly useful when dealing with evaluations which take into account negative values, is the Indicator Method. MCE methods are relatively popular owing to the simplicity of the calculations and ease of the interpretation of the results. Having prepared the input data properly, they enable the user to compare them on the same level. In a situation where an analysis involves a large number of data, it is more convenient to divide them into groups according to main criteria and subcriteria. This option is provided by hierarchical analysis methods. They are based on ordered sets of criteria, which are evaluated in groups. In some cases, this approach yields the results that are superior and easier to read. If an analysis encompasses direct and indirect effects, an Indicator Method seems to be a justified choice for selecting the right solution. The Indicator Method is different in character and relies on weights and assessments of effects. It allows the user to evaluate effectively the analyzed variants. This article explains the methodology of conducting a multi-criteria analysis, showing its advantages and disadvantages. An example of calculations contained in the article shows what problems can be encountered when making an assessment of various solutions regarding building materials and structures. For comparison, an analysis based on graphical methods developed by the author was presented.

Mothe-Diniz Asteroid Dynamical Families V1.0

NASA Astrophysics Data System (ADS)

Mothe-Diniz, T.; Roig, F.; Carvano, J. M.

2006-03-01

This dataset contains an updated compilation of asteroid families and clusters, resulting from the application of the Hierarchical Clustering Method (HCM) on a set of around 120,000 asteroids with available proper elements. Whenever available, the classification in the Bus taxonomy is provided for family members, based on spectra from the SMASS, SMASS2 and S3OS2 spectroscopic surveys.

A New Malaysian Quality of Life Index Based on Fuzzy Sets and Hierarchical Needs

ERIC Educational Resources Information Center

Lazim, M. Abdullah; Abu Osman, M. Tap

2009-01-01

The Malaysian Quality of Life Index (MQLI) released by the Economic Planning Unit (EPU), has led authors to search for alternative method of expressing this index. One of the limitations in MQLI computations is the failure to recognise unequal weights for each accounted component. This paper offers a new way of expressing the quality of life index…

Utilizing Hierarchical Clustering to improve Efficiency of Self-Organizing Feature Map to Identify Hydrological Homogeneous Regions

NASA Astrophysics Data System (ADS)

Farsadnia, Farhad; Ghahreman, Bijan

2016-04-01

Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self-Organizing feature Map (SOM) method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces. At first by principal component analysis, we reduced SOM input matrix dimension, then the SOM was used to form a two-dimensional features map. To determine homogeneous regions for flood frequency analysis, SOM output nodes were used as input into the Ward method. Generally, the regions identified by the clustering algorithms are not statistically homogeneous. Consequently, they have to be adjusted to improve their homogeneity. After adjustment of the homogeneity regions by L-moment tests, five hydrologic homogeneous regions were identified. Finally, adjusted regions were created by a two-level SOM and then the best regional distribution function and associated parameters were selected by the L-moment approach. The results showed that the combination of self-organizing maps and Ward hierarchical clustering by principal components as input is more effective than the hierarchical method, by principal components or standardized inputs to achieve hydrologic homogeneous regions.

A Hierarchical Auction-Based Mechanism for Real-Time Resource Allocation in Cloud Robotic Systems.

PubMed

Wang, Lujia; Liu, Ming; Meng, Max Q-H

2017-02-01

Cloud computing enables users to share computing resources on-demand. The cloud computing framework cannot be directly mapped to cloud robotic systems with ad hoc networks since cloud robotic systems have additional constraints such as limited bandwidth and dynamic structure. However, most multirobotic applications with cooperative control adopt this decentralized approach to avoid a single point of failure. Robots need to continuously update intensive data to execute tasks in a coordinated manner, which implies real-time requirements. Thus, a resource allocation strategy is required, especially in such resource-constrained environments. This paper proposes a hierarchical auction-based mechanism, namely link quality matrix (LQM) auction, which is suitable for ad hoc networks by introducing a link quality indicator. The proposed algorithm produces a fast and robust method that is accurate and scalable. It reduces both global communication and unnecessary repeated computation. The proposed method is designed for firm real-time resource retrieval for physical multirobot systems. A joint surveillance scenario empirically validates the proposed mechanism by assessing several practical metrics. The results show that the proposed LQM auction outperforms state-of-the-art algorithms for resource allocation.

Leveraging Hierarchical Population Structure in Discrete Association Studies

PubMed Central

Carlson, Jonathan; Kadie, Carl; Mallal, Simon; Heckerman, David

2007-01-01

Population structure can confound the identification of correlations in biological data. Such confounding has been recognized in multiple biological disciplines, resulting in a disparate collection of proposed solutions. We examine several methods that correct for confounding on discrete data with hierarchical population structure and identify two distinct confounding processes, which we call coevolution and conditional influence. We describe these processes in terms of generative models and show that these generative models can be used to correct for the confounding effects. Finally, we apply the models to three applications: identification of escape mutations in HIV-1 in response to specific HLA-mediated immune pressure, prediction of coevolving residues in an HIV-1 peptide, and a search for genotypes that are associated with bacterial resistance traits in Arabidopsis thaliana. We show that coevolution is a better description of confounding in some applications and conditional influence is better in others. That is, we show that no single method is best for addressing all forms of confounding. Analysis tools based on these models are available on the internet as both web based applications and downloadable source code at http://atom.research.microsoft.com/bio/phylod.aspx. PMID:17611623

Building simplification algorithms based on user cognition in mobile environment

NASA Astrophysics Data System (ADS)

Shen, Jie; Shi, Junfei; Wang, Meizhen; Wu, Chenyan

2008-10-01

With the development of LBS, mobile map should adaptively satisfy the cognitive requirement of user. User cognition in mobile environment is much more objective oriented and also seem to be a heavier burden than the user in static environment. The holistic idea and methods of map generalization can not fully suitable for the mobile map. This paper took the building simplification in habitation generalization as example, analyzed the characteristic of user cognition in mobile environment and the basic rules of building simplification, collected and studied the state-of-the-art of algorithms of building simplification in the static and mobile environment, put forward the idea of hierarchical building simplification based on user cognition. This paper took Hunan road business district of Nanjing as test area and took the building data with shapfile format of ESRI as test data and realized the simplification algorithm. The method took user as center, calculated the distance between user and the building which will be simplified and took the distance as the basis for choosing different simplification algorithm for different spaces. This contribution aimed to hierarchically present the building in different level of detail by real-time simplification.

Organizational and Spatial Dynamics of Attentional Focusing in Hierarchically Structured Objects

ERIC Educational Resources Information Center

Yeari, Menahem; Goldsmith, Morris

2011-01-01

Is the focusing of visual attention object-based, space-based, both, or neither? Attentional focusing latencies in hierarchically structured compound-letter objects were examined, orthogonally manipulating global size (larger vs. smaller) and organizational complexity (two-level structure vs. three-level structure). In a dynamic focusing task,…

Hierarchical biointerfaces assembled by leukocyte-inspired particles for specifically recognizing cancer cells.

PubMed

Meng, Jingxin; Liu, Hongliang; Liu, Xueli; Yang, Gao; Zhang, Pengchao; Wang, Shutao; Jiang, Lei

2014-09-24

By mimicking certain biochemical and physical attributes of biological cells, bio-inspired particles have attracted great attention for potential biomedical applications based on cell-like biological functions. Inspired by leukocytes, hierarchical biointerfaces are designed and prepared based on specific molecules-modified leukocyte-inspired particles. These biointerfaces can efficiently recognize cancer cells from whole blood samples through the synergistic effect of molecular recognition and topographical interaction. Compared to flat, mono-micro or nano-biointerfaces, these micro/nano hierarchical biointerfaces are better able to promote specific recognition interactions, resulting in an enhanced cell-capture efficiency. It is anticipated that this study may provide promising guidance to develop new bio-inspired hierarchical biointerfaces for biomedical applications. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

AptRank: an adaptive PageRank model for protein function prediction on   bi-relational graphs.

PubMed

Jiang, Biaobin; Kloster, Kyle; Gleich, David F; Gribskov, Michael

2017-06-15

Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model. We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training. The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank . gribskov@purdue.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

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.

Hierarchical structural health monitoring system combining a fiber optic spinal cord network and distributed nerve cell devices

NASA Astrophysics Data System (ADS)

Minakuchi, Shu; Tsukamoto, Haruka; Takeda, Nobuo

2009-03-01

This study proposes novel hierarchical sensing concept for detecting damages in composite structures. In the hierarchical system, numerous three-dimensionally structured sensor devices are distributed throughout the whole structural area and connected with the optical fiber network through transducing mechanisms. The distributed "sensory nerve cell" devices detect the damage, and the fiber optic "spinal cord" network gathers damage signals and transmits the information to a measuring instrument. This study began by discussing the basic concept of the hierarchical sensing system thorough comparison with existing fiber optic based systems and nerve systems in the animal kingdom. Then, in order to validate the proposed sensing concept, impact damage detection system for the composite structure was proposed. The sensor devices were developed based on Comparative Vacuum Monitoring (CVM) system and the Brillouin based distributed strain sensing was utilized to gather the damage signals from the distributed devices. Finally a verification test was conducted using prototype devices. Occurrence of barely visible impact damage was successfully detected and it was clearly indicated that the hierarchical system has better repairability, higher robustness, and wider monitorable area compared to existing systems utilizing embedded optical fiber sensors.

Hierarchical multistage MCMC follow-up of continuous gravitational wave candidates

NASA Astrophysics Data System (ADS)

Ashton, G.; Prix, R.

2018-05-01

Leveraging Markov chain Monte Carlo optimization of the F statistic, we introduce a method for the hierarchical follow-up of continuous gravitational wave candidates identified by wide-parameter space semicoherent searches. We demonstrate parameter estimation for continuous wave sources and develop a framework and tools to understand and control the effective size of the parameter space, critical to the success of the method. Monte Carlo tests of simulated signals in noise demonstrate that this method is close to the theoretical optimal performance.

Multidisciplinary optimization for engineering systems - Achievements and potential

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw

1989-01-01

The currently common sequential design process for engineering systems is likely to lead to suboptimal designs. Recently developed decomposition methods offer an alternative for coming closer to optimum by breaking the large task of system optimization into smaller, concurrently executed and, yet, coupled tasks, identified with engineering disciplines or subsystems. The hierarchic and non-hierarchic decompositions are discussed and illustrated by examples. An organization of a design process centered on the non-hierarchic decomposition is proposed.

Multidisciplinary optimization for engineering systems: Achievements and potential

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw

1989-01-01

The currently common sequential design process for engineering systems is likely to lead to suboptimal designs. Recently developed decomposition methods offer an alternative for coming closer to optimum by breaking the large task of system optimization into smaller, concurrently executed and, yet, coupled tasks, identified with engineering disciplines or subsystems. The hierarchic and non-hierarchic decompositions are discussed and illustrated by examples. An organization of a design process centered on the non-hierarchic decomposition is proposed.

Modelling habitat associations with fingernail clam (Family: Sphaeriidae) counts at multiple spatial scales using hierarchical count models

USGS Publications Warehouse

Gray, B.R.; Haro, R.J.; Rogala, J.T.; Sauer, J.S.

2005-01-01

1. Macroinvertebrate count data often exhibit nested or hierarchical structure. Examples include multiple measurements along each of a set of streams, and multiple synoptic measurements from each of a set of ponds. With data exhibiting hierarchical structure, outcomes at both sampling (e.g. Within stream) and aggregated (e.g. Stream) scales are often of interest. Unfortunately, methods for modelling hierarchical count data have received little attention in the ecological literature. 2. We demonstrate the use of hierarchical count models using fingernail clam (Family: Sphaeriidae) count data and habitat predictors derived from sampling and aggregated spatial scales. The sampling scale corresponded to that of a standard Ponar grab (0.052 m(2)) and the aggregated scale to impounded and backwater regions within 38-197 km reaches of the Upper Mississippi River. Impounded and backwater regions were resampled annually for 10 years. Consequently, measurements on clams were nested within years. Counts were treated as negative binomial random variates, and means from each resampling event as random departures from the impounded and backwater region grand means. 3. Clam models were improved by the addition of covariates that varied at both the sampling and regional scales. Substrate composition varied at the sampling scale and was associated with model improvements, and reductions (for a given mean) in variance at the sampling scale. Inorganic suspended solids (ISS) levels, measured in the summer preceding sampling, also yielded model improvements and were associated with reductions in variances at the regional rather than sampling scales. ISS levels were negatively associated with mean clam counts. 4. Hierarchical models allow hierarchically structured data to be modelled without ignoring information specific to levels of the hierarchy. In addition, information at each hierarchical level may be modelled as functions of covariates that themselves vary by and within levels. As a result, hierarchical models provide researchers and resource managers with a method for modelling hierarchical data that explicitly recognises both the sampling design and the information contained in the corresponding data.

Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials

PubMed Central

Hobbs, Brian P.; Carlin, Bradley P.; Mandrekar, Sumithra J.; Sargent, Daniel J.

2011-01-01

Summary Bayesian clinical trial designs offer the possibility of a substantially reduced sample size, increased statistical power, and reductions in cost and ethical hazard. However when prior and current information conflict, Bayesian methods can lead to higher than expected Type I error, as well as the possibility of a costlier and lengthier trial. This motivates an investigation of the feasibility of hierarchical Bayesian methods for incorporating historical data that are adaptively robust to prior information that reveals itself to be inconsistent with the accumulating experimental data. In this paper, we present several models that allow for the commensurability of the information in the historical and current data to determine how much historical information is used. A primary tool is elaborating the traditional power prior approach based upon a measure of commensurability for Gaussian data. We compare the frequentist performance of several methods using simulations, and close with an example of a colon cancer trial that illustrates a linear models extension of our adaptive borrowing approach. Our proposed methods produce more precise estimates of the model parameters, in particular conferring statistical significance to the observed reduction in tumor size for the experimental regimen as compared to the control regimen. PMID:21361892

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets.

PubMed

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O; Gelfand, Alan E

2016-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online.

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets

PubMed Central

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O.; Gelfand, Alan E.

2018-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online. PMID:29720777

Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers.

PubMed

Campbell, Kieran R; Yau, Christopher

2017-03-15

Modeling bifurcations in single-cell transcriptomics data has become an increasingly popular field of research. Several methods have been proposed to infer bifurcation structure from such data, but all rely on heuristic non-probabilistic inference. Here we propose the first generative, fully probabilistic model for such inference based on a Bayesian hierarchical mixture of factor analyzers. Our model exhibits competitive performance on large datasets despite implementing full Markov-Chain Monte Carlo sampling, and its unique hierarchical prior structure enables automatic determination of genes driving the bifurcation process. We additionally propose an Empirical-Bayes like extension that deals with the high levels of zero-inflation in single-cell RNA-seq data and quantify when such models are useful. We apply or model to both real and simulated single-cell gene expression data and compare the results to existing pseudotime methods. Finally, we discuss both the merits and weaknesses of such a unified, probabilistic approach in the context practical bioinformatics analyses.

Region-Oriented Placement Algorithm for Coarse-Grained Power-Gating FPGA Architecture

NASA Astrophysics Data System (ADS)

Li, Ce; Dong, Yiping; Watanabe, Takahiro

An FPGA plays an essential role in industrial products due to its fast, stable and flexible features. But the power consumption of FPGAs used in portable devices is one of critical issues. Top-down hierarchical design method is commonly used in both ASIC and FPGA design. But, in the case where plural modules are integrated in an FPGA and some of them might be in sleep-mode, current FPGA architecture cannot be fully effective. In this paper, coarse-grained power gating FPGA architecture is proposed where a whole area of an FPGA is partitioned into several regions and power supply is controlled for each region, so that modules in sleep mode can be effectively power-off. We also propose a region oriented FPGA placement algorithm fitted to this user's hierarchical design based on VPR[1]. Simulation results show that this proposed method could reduce power consumption of FPGA by 38% on average by setting unused modules or regions in sleep mode.

Exploring Dance Movement Data Using Sequence Alignment Methods

PubMed Central

Chavoshi, Seyed Hossein; De Baets, Bernard; Neutens, Tijs; De Tré, Guy; Van de Weghe, Nico

2015-01-01

Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers. PMID:26181435

[Comparative study on promoting blood effects of Danshen-Honghua herb pair with different preparations based on chemometrics and multi-attribute comprehensive index methods].

PubMed

Qu, Cheng; Tang, Yu-Ping; Shi, Xu-Qin; Zhou, Gui-Sheng; Shang, Er-Xin; Shang, Li-Li; Guo, Jian-Ming; Liu, Pei; Zhao, Jing; Zhao, Bu-Chang; Duan, Jin-Ao

2017-08-01

To evaluate the promoting blood circulation and removing blood stasis effects of Danshen-Honghua(DH) herb pair with different preparations (alcohol, 50% alcohol and water) on blood rheology and coagulation functions in acute blood stasis rats, and optimize the best preparation method of DH based on principal component analysis(PCA), hierarchical cluster heatmap analysis and multi-attribute comprehensive index methods. Ice water bath and subcutaneous injection of adrenaline were both used to establish the acute blood stasis rat model. Then the blood stasis rats were administrated intragastrically with DH (alcohol, 50% alcohol and water) extracts. The whole blood viscosity(WBV), plasma viscosity(PV), erythrocyte sedimentation rate(ESR) and haematocrit(HCT) were tested to observe the effects of DH herb pair with different preparations and doses on hemorheology of blood stasis rats; the activated partial thromboplastin time(APTT), thrombin time(TT), prothrombin time(PT), and plasma fibrinogen(FIB) were tested to observe the effects of DH herb pair with different preparations on blood coagulation function and platelet aggregation of blood stasis rats. Then PCA, hierarchical cluster heatmap analysis and multi-attribute comprehensive index methods were all used to comprehensively evaluate the total promoting blood circulation and removing blood stasis effects of DH herb pair with different preparations. The hemorheological indexes and coagulation parameters of model group had significant differences with normal blank group. As compared with the model group, the DH herb pair with different preparations at low, middle and high doses could improve the blood hemorheology indexes and coagulation parameters in acute blood stasis rats with dose-effect relation. Based on the PCA, hierarchical cluster heatmap analysis and multi-attribute comprehensive index methods, the high dose group of 50% alcohol extract had the best effect of promoting blood circulation and removing blood stasis. Under the same dose but different preparations, 50% alcohol DH could obviously improve the hemorheology and blood coagulation function in acute blood stasis rats. These results suggested that DH herb pair with different preparations could obviously ameliorate the abnormality of hemorheology and blood coagulation function in acute blood stasis rats, and the optimized preparation of DH herb pair on promoting blood effects was 50% alcohol extract, providing scientific basis for more effective application of the DH herb pair in modern clinic medicine. Copyright© by the Chinese Pharmaceutical Association.

Recombination reduction on lead halide perovskite solar cells based on low temperature synthesized hierarchical TiO₂ nanorods.

PubMed

Jaramillo-Quintero, Oscar A; Solís de la Fuente, Mauricio; Sanchez, Rafael S; Recalde, Ileana B; Juarez-Perez, Emilio J; Rincón, Marina E; Mora-Seró, Iván

2016-03-28

Intensive research on the electron transport material (ETM) has been pursued to improve the efficiency of perovskite solar cells (PSCs) and decrease their cost. More importantly, the role of the ETM layer is not yet fully understood, and research on new device architectures is still needed. Here, we report the use of three-dimensional (3D) TiO2 with a hierarchical architecture based on rutile nanorods (NR) as photoanode material for PSCs. The proposed hierarchical nanorod (HNR) films were synthesized by a two-step low temperature (180 °C) hydrothermal method, and consist of TiO2 nanorod trunks with optimal lengths of 540 nm and TiO2 nanobranches with lengths of 45 nm. Different device configurations were fabricated with TiO2 structures (compact layer, NR and HNR) and CH3NH3PbI3, using different synthetic routes, as the active material. PSCs based on HNR-CH3NH3PbI3 achieved the highest power conversion efficiency compared to PSCs with other TiO2 structures. This result can be ascribed mainly to lower charge recombination as determined by impedance spectroscopy. Furthermore, we have observed that the CH3NH3PbI3 perovskite deposited by the two-step route shows higher efficiency, surface coverage and infiltration within the structure of 3D HNR than the one-step CH3NH3PbI(3-x)Cl(x) perovskite.

Deferred discrimination algorithm (nibbling) for target filter management

NASA Astrophysics Data System (ADS)

Caulfield, H. John; Johnson, John L.

1999-07-01

A new method of classifying objects is presented. Rather than trying to form the classifier in one step or in one training algorithm, it is done in a series of small steps, or nibbles. This leads to an efficient and versatile system that is trained in series with single one-shot examples but applied in parallel, is implemented with single layer perceptrons, yet maintains its fully sequential hierarchical structure. Based on the nibbling algorithm, a basic new method of target reference filter management is described.