Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model.

PubMed

Damij, Nadja; Boškoski, Pavle; Bohanec, Marko; Mileva Boshkoska, Biljana

2016-01-01

The omnipresent need for optimisation requires constant improvements of companies' business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and "what-if" scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results.

Deep Learning with Hierarchical Convolutional Factor Analysis

PubMed Central

Chen, Bo; Polatkan, Gungor; Sapiro, Guillermo; Blei, David; Dunson, David; Carin, Lawrence

2013-01-01

Unsupervised multi-layered (“deep”) models are considered for general data, with a particular focus on imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The computation of layer-dependent model parameters is implemented within a Bayesian setting, employing a Gibbs sampler and variational Bayesian (VB) analysis, that explicitly exploit the convolutional nature of the expansion. In order to address large-scale and streaming data, an online version of VB is also developed. The number of basis functions or dictionary elements at each layer is inferred from the data, based on a beta-Bernoulli implementation of the Indian buffet process. Example results are presented for several image-processing applications, with comparisons to related models in the literature. PMID:23787342

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).

Evaluating the Impacts of ICT Use: A Multi-Level Analysis with Hierarchical Linear Modeling

ERIC Educational Resources Information Center

Song, Hae-Deok; Kang, Taehoon

2012-01-01

The purpose of this study is to evaluate the impacts of ICT use on achievements by considering not only ICT use, but also the process and background variables that influence ICT use at both the student- and school-level. This study was conducted using data from the 2010 Survey of Seoul Education Longitudinal Research. A Hierarchical Linear…

Hierarchical organization of macaque and cat cortical sensory systems explored with a novel network processor.

PubMed

Hilgetag, C C; O'Neill, M A; Young, M P

2000-01-29

Neuroanatomists have described a large number of connections between the various structures of monkey and cat cortical sensory systems. Because of the complexity of the connection data, analysis is required to unravel what principles of organization they imply. To date, analysis of laminar origin and termination connection data to reveal hierarchical relationships between the cortical areas has been the most widely acknowledged approach. We programmed a network processor that searches for optimal hierarchical orderings of cortical areas given known hierarchical constraints and rules for their interpretation. For all cortical systems and all cost functions, the processor found a multitude of equally low-cost hierarchies. Laminar hierarchical constraints that are presently available in the anatomical literature were therefore insufficient to constrain a unique ordering for any of the sensory systems we analysed. Hierarchical orderings of the monkey visual system that have been widely reported, but which were derived by hand, were not among the optimal orderings. All the cortical systems we studied displayed a significant degree of hierarchical organization, and the anatomical constraints from the monkey visual and somato-motor systems were satisfied with very few constraint violations in the optimal hierarchies. The visual and somato-motor systems in that animal were therefore surprisingly strictly hierarchical. Most inconsistencies between the constraints and the hierarchical relationships in the optimal structures for the visual system were related to connections of area FST (fundus of superior temporal sulcus). We found that the hierarchical solutions could be further improved by assuming that FST consists of two areas, which differ in the nature of their projections. Indeed, we found that perfect hierarchical arrangements of the primate visual system, without any violation of anatomical constraints, could be obtained under two reasonable conditions, namely the subdivision of FST into two distinct areas, whose connectivity we predict, and the abolition of at least one of the less reliable rule constraints. Our analyses showed that the future collection of the same type of laminar constraints, or the inclusion of new hierarchical constraints from thalamocortical connections, will not resolve the problem of multiple optimal hierarchical representations for the primate visual system. Further data, however, may help to specify the relative ordering of some more areas. This indeterminacy of the visual hierarchy is in part due to the reported absence of some connections between cortical areas. These absences are consistent with limited cross-talk between differentiated processing streams in the system. Hence, hierarchical representation of the visual system is affected by, and must take into account, other organizational features, such as processing streams.

Information Acquisition, Analysis and Integration

DTIC Science & Technology

2016-08-03

of sensing and processing, theory, applications, signal processing, image and video processing, machine learning , technology transfer. 16. SECURITY... learning . 5. Solved elegantly old problems like image and video debluring, intro- ducing new revolutionary approaches. 1 DISTRIBUTION A: Distribution...Polatkan, G. Sapiro, D. Blei, D. B. Dunson, and L. Carin, “ Deep learning with hierarchical convolution factor analysis,” IEEE 6 DISTRIBUTION A

Photocatalytic properties of hierarchical ZnO flowers synthesized by a sucrose-assisted hydrothermal method

NASA Astrophysics Data System (ADS)

Lv, Wei; Wei, Bo; Xu, Lingling; Zhao, Yan; Gao, Hong; Liu, Jia

2012-10-01

In this work, hierarchical ZnO flowers were synthesized via a sucrose-assisted urea hydrothermal method. The thermogravimetric analysis/differential thermal analysis (TGA-DTA) and Fourier transform infrared spectra (FTIR) showed that sucrose acted as a complexing agent in the synthesis process and assisted combustion during annealing. Photocatalytic activity was evaluated using the degradation of organic dye methyl orange. The sucrose added ZnO flowers showed improved activity, which was mainly attributed to the better crystallinity as confirmed by X-ray photoelectron spectroscopy (XPS) analysis. The effect of sucrose amount on photocatalytic activity was also studied.

Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model

PubMed Central

2016-01-01

The omnipresent need for optimisation requires constant improvements of companies’ business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and “what-if” scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results. PMID:26871694

A hierarchical approach to forest landscape pattern characterization.

PubMed

Wang, Jialing; Yang, Xiaojun

2012-01-01

Landscape spatial patterns have increasingly been considered to be essential for environmental planning and resources management. In this study, we proposed a hierarchical approach for landscape classification and evaluation by characterizing landscape spatial patterns across different hierarchical levels. The case study site is the Red Hills region of northern Florida and southwestern Georgia, well known for its biodiversity, historic resources, and scenic beauty. We used one Landsat Enhanced Thematic Mapper image to extract land-use/-cover information. Then, we employed principal-component analysis to help identify key class-level landscape metrics for forests at different hierarchical levels, namely, open pine, upland pine, and forest as a whole. We found that the key class-level landscape metrics varied across different hierarchical levels. Compared with forest as a whole, open pine forest is much more fragmented. The landscape metric, such as CONTIG_MN, which measures whether pine patches are contiguous or not, is more important to characterize the spatial pattern of pine forest than to forest as a whole. This suggests that different metric sets should be used to characterize landscape patterns at different hierarchical levels. We further used these key metrics, along with the total class area, to classify and evaluate subwatersheds through cluster analysis. This study demonstrates a promising approach that can be used to integrate spatial patterns and processes for hierarchical forest landscape planning and management.

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.

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/.

POLLUTION PREVENTION IN THE DESIGN OF CHEMICAL PROCESSES USING HIERARCHICAL DESIGN AND SIMULATION

EPA Science Inventory

The design of chemical processes is normally an interactive process of synthesis and analysis. When one also desires or needs to limit the amount of pollution generated by the process the difficulty of the task can increase substantially. In this work, we show how combining hier...

Tracking Hierarchical Processing in Morphological Decomposition with Brain Potentials

ERIC Educational Resources Information Center

Lavric, Aureliu; Elchlepp, Heike; Rastle, Kathleen

2012-01-01

One important debate in psycholinguistics concerns the nature of morphological decomposition processes in visual word recognition (e.g., darkness = {dark} + {-ness}). One theory claims that these processes arise during orthographic analysis and prior to accessing meaning (Rastle & Davis, 2008), and another argues that these processes arise through…

Hierarchical Heteroclinics in Dynamical Model of Cognitive Processes: Chunking

NASA Astrophysics Data System (ADS)

Afraimovich, Valentin S.; Young, Todd R.; Rabinovich, Mikhail I.

Combining the results of brain imaging and nonlinear dynamics provides a new hierarchical vision of brain network functionality that is helpful in understanding the relationship of the network to different mental tasks. Using these ideas it is possible to build adequate models for the description and prediction of different cognitive activities in which the number of variables is usually small enough for analysis. The dynamical images of different mental processes depend on their temporal organization and, as a rule, cannot be just simple attractors since cognition is characterized by transient dynamics. The mathematical image for a robust transient is a stable heteroclinic channel consisting of a chain of saddles connected by unstable separatrices. We focus here on hierarchical chunking dynamics that can represent several cognitive activities. Chunking is the dynamical phenomenon that means dividing a long information chain into shorter items. Chunking is known to be important in many processes of perception, learning, memory and cognition. We prove that in the phase space of the model that describes chunking there exists a new mathematical object — heteroclinic sequence of heteroclinic cycles — using the technique of slow-fast approximations. This new object serves as a skeleton of motions reflecting sequential features of hierarchical chunking dynamics and is an adequate image of the chunking processing.

[The structure of interaction in romantic relationships: hierarchical data analysis of inter-subjectivity between partners].

PubMed

Shimizu, Hiroshi; Daibo, Ikuo

2008-02-01

A hierarchical data analysis was conducted using data from couples to examine how self-reports of interactions between partners in romantic relationships predict the quality of the relationships. Whereas the social exchange theory has elucidated the quality of relationships from the individual level of subjectivity, this study focused on the structure of interactions between the partners (i.e., the frequency, strength, and diversity) through a process of inter-subjectivity at the couple level. A multilevel covariance structure analysis of 194 university students involved in romantic relationships revealed that the quality of relationships was mainly related to the strength and the diversity of interactions at the couple level, rather than the strength of interactions at the individual level. These results indicate that the inter-subjective process in romantic relationships may primarily explain the quality of relationships.

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 ...

Random walk on p-adics and hierarchical systems

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

Lukierska-Walasek, K.; Topolski, K.; Institute of Mathematics, Wroclaw University, pl. Grunwaldzki 2/4, 50-384 Wroclaw

2006-02-01

We show that p-adic analysis provides a quite natural basis for the description of relaxation in hierarchical systems. For our purposes, we specify the Markov stochastic process considered by Albeverio and Karwowski. As a result we have obtained a random walk on the p-adic integer numbers, which provides the generalization of Cayley tree proposed by Ogielski and Stein. The temperature-dependent power-law decay and the Kohlrausch law are derived.

Hierarchical analysis of species distributions and abundance across environmental gradients

Treesearch

Jeffery Diez; Ronald H. Pulliam

2007-01-01

Abiotic and biotic processes operate at multiple spatial and temporal scales to shape many ecological processes, including species distributions and demography. Current debate about the relative roles of niche-based and stochastic processes in shaping species distributions and community composition reflects, in part, the challenge of understanding how these processes...

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.

Hierarchical analysis of spatial pattern and processes of Douglas-fir forests. Ph.D. Thesis, 10 Sep. 1991 Abstract Only

NASA Technical Reports Server (NTRS)

Bradshaw, G. A.

1995-01-01

There has been an increased interest in the quantification of pattern in ecological systems over the past years. This interest is motivated by the desire to construct valid models which extend across many scales. Spatial methods must quantify pattern, discriminate types of pattern, and relate hierarchical phenomena across scales. Wavelet analysis is introduced as a method to identify spatial structure in ecological transect data. The main advantage of the wavelet transform over other methods is its ability to preserve and display hierarchical information while allowing for pattern decomposition. Two applications of wavelet analysis are illustrated, as a means to: (1) quantify known spatial patterns in Douglas-fir forests at several scales, and (2) construct spatially-explicit hypotheses regarding pattern generating mechanisms. Application of the wavelet variance, derived from the wavelet transform, is developed for forest ecosystem analysis to obtain additional insight into spatially-explicit data. Specifically, the resolution capabilities of the wavelet variance are compared to the semi-variogram and Fourier power spectra for the description of spatial data using a set of one-dimensional stationary and non-stationary processes. The wavelet cross-covariance function is derived from the wavelet transform and introduced as a alternative method for the analysis of multivariate spatial data of understory vegetation and canopy in Douglas-fir forests of the western Cascades of Oregon.

Evolutionary analysis of groundwater flow: Application of multivariate statistical analysis to hydrochemical data in the Densu Basin, Ghana

NASA Astrophysics Data System (ADS)

Yidana, Sandow Mark; Bawoyobie, Patrick; Sakyi, Patrick; Fynn, Obed Fiifi

2018-02-01

An evolutionary trend has been postulated through the analysis of hydrochemical data of a crystalline rock aquifer system in the Densu Basin, Southern Ghana. Hydrochemcial data from 63 groundwater samples, taken from two main groundwater outlets (Boreholes and hand dug wells) were used to postulate an evolutionary theory for the basin. Sequential factor and hierarchical cluster analysis were used to disintegrate the data into three factors and five clusters (spatial associations). These were used to characterize the controls on groundwater hydrochemistry and its evolution in the terrain. The dissolution of soluble salts and cation exchange processes are the dominant processes controlling groundwater hydrochemistry in the terrain. The trend of evolution of this set of processes follows the pattern of groundwater flow predicted by a calibrated transient groundwater model in the area. The data suggest that anthropogenic activities represent the second most important process in the hydrochemistry. Silicate mineral weathering is the third most important set of processes. Groundwater associations resulting from Q-mode hierarchical cluster analysis indicate an evolutionary pattern consistent with the general groundwater flow pattern in the basin. These key findings are at variance with results of previous investigations and indicate that when carefully done, groundwater hydrochemical data can be very useful for conceptualizing groundwater flow in basins.

Processing of hierarchical syntactic structure in music.

PubMed

Koelsch, Stefan; Rohrmeier, Martin; Torrecuso, Renzo; Jentschke, Sebastian

2013-09-17

Hierarchical structure with nested nonlocal dependencies is a key feature of human language and can be identified theoretically in most pieces of tonal music. However, previous studies have argued against the perception of such structures in music. Here, we show processing of nonlocal dependencies in music. We presented chorales by J. S. Bach and modified versions in which the hierarchical structure was rendered irregular whereas the local structure was kept intact. Brain electric responses differed between regular and irregular hierarchical structures, in both musicians and nonmusicians. This finding indicates that, when listening to music, humans apply cognitive processes that are capable of dealing with long-distance dependencies resulting from hierarchically organized syntactic structures. Our results reveal that a brain mechanism fundamental for syntactic processing is engaged during the perception of music, indicating that processing of hierarchical structure with nested nonlocal dependencies is not just a key component of human language, but a multidomain capacity of human cognition.

Hierarchical classification strategy for Phenotype extraction from epidermal growth factor receptor endocytosis screening.

PubMed

Cao, Lu; Graauw, Marjo de; Yan, Kuan; Winkel, Leah; Verbeek, Fons J

2016-05-03

Endocytosis is regarded as a mechanism of attenuating the epidermal growth factor receptor (EGFR) signaling and of receptor degradation. There is increasing evidence becoming available showing that breast cancer progression is associated with a defect in EGFR endocytosis. In order to find related Ribonucleic acid (RNA) regulators in this process, high-throughput imaging with fluorescent markers is used to visualize the complex EGFR endocytosis process. Subsequently a dedicated automatic image and data analysis system is developed and applied to extract the phenotype measurement and distinguish different developmental episodes from a huge amount of images acquired through high-throughput imaging. For the image analysis, a phenotype measurement quantifies the important image information into distinct features or measurements. Therefore, the manner in which prominent measurements are chosen to represent the dynamics of the EGFR process becomes a crucial step for the identification of the phenotype. In the subsequent data analysis, classification is used to categorize each observation by making use of all prominent measurements obtained from image analysis. Therefore, a better construction for a classification strategy will support to raise the performance level in our image and data analysis system. In this paper, we illustrate an integrated analysis method for EGFR signalling through image analysis of microscopy images. Sophisticated wavelet-based texture measurements are used to obtain a good description of the characteristic stages in the EGFR signalling. A hierarchical classification strategy is designed to improve the recognition of phenotypic episodes of EGFR during endocytosis. Different strategies for normalization, feature selection and classification are evaluated. The results of performance assessment clearly demonstrate that our hierarchical classification scheme combined with a selected set of features provides a notable improvement in the temporal analysis of EGFR endocytosis. Moreover, it is shown that the addition of the wavelet-based texture features contributes to this improvement. Our workflow can be applied to drug discovery to analyze defected EGFR endocytosis processes.

The MIL-88A-Derived Fe3O4-Carbon Hierarchical Nanocomposites for Electrochemical Sensing

PubMed Central

Wang, Li; Zhang, Yayun; Li, Xia; Xie, Yingzhen; He, Juan; Yu, Jie; Song, Yonghai

2015-01-01

Metal or metal oxides/carbon nanocomposites with hierarchical superstructures have become one of the most promising functional materials in sensor, catalysis, energy conversion, etc. In this work, novel hierarchical Fe3O4/carbon superstructures have been fabricated based on metal-organic frameworks (MOFs)-derived method. Three kinds of Fe-MOFs (MIL-88A) with different morphologies were prepared beforehand as templates, and then pyrolyzed to fabricate the corresponding novel hierarchical Fe3O4/carbon superstructures. The systematic studies on the thermal decomposition process of the three kinds of MIL-88A and the effect of template morphology on the products were carried out in detail. Scanning electron microscopy, transmission electron microscopy, X-ray powder diffraction, X-ray photoelectron spectroscopy and thermal analysis were employed to investigate the hierarchical Fe3O4/carbon superstructures. Based on these resulted hierarchical Fe3O4/carbon superstructures, a novel and sensitive nonenzymatic N-acetyl cysteine sensor was developed. The porous and hierarchical superstructures and large surface area of the as-formed Fe3O4/carbon superstructures eventually contributed to the good electrocatalytic activity of the prepared sensor towards the oxidation of N-acetyl cysteine. The proposed preparation method of the hierarchical Fe3O4/carbon superstructures is simple, efficient, cheap and easy to mass production. It might open up a new way for hierarchical superstructures preparation. PMID:26387535

unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance

USGS Publications Warehouse

Fiske, Ian J.; Chandler, Richard B.

2011-01-01

Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientific questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mechanisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unified modeling interface. The R package unmarked provides such a unified modeling framework, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.

Fabrication of free-standing hierarchical carbon nanofiber/graphene oxide/polyaniline films for supercapacitors.

PubMed

Xu, Dongdong; Xu, Qun; Wang, Kaixi; Chen, Jun; Chen, Zhimin

2014-01-08

A hierarchical high-performance electrode with nanoacanthine-style polyaniline (PANI) deposited onto a carbon nanofiber/graphene oxide (CNF/GO) template was successfully prepared via an in situ polymerization process. The morphology analysis shows that introducing one-dimensional (1D) CNF could significantly decrease/inhibit the staking of laminated GO to form an open-porous CNF/GO architecture. Followed with in situ facial deposition of PANI, the as-synthesized PANI modified CNF/GO exhibits three-dimensional (3D) hierarchical layered nanoarchitecture, which favors the diffusion of the electrolyte ions into the inner region of active materials. The hierarchical free-standing electrodes were directly fabricated into sandwich structured supercapacitors using 1 M H2SO4 as the electrolyte showing a significant specific capacitance of 450.2 F/g at the voltage scan rate of 10 mV/s. The electrochemical properties of the hierarchical structure can be further improved by a reduction procedure of GO before the deposition of PANI.

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.

Hierarchical Time-Lagged Independent Component Analysis: Computing Slow Modes and Reaction Coordinates for Large Molecular Systems.

PubMed

Pérez-Hernández, Guillermo; Noé, Frank

2016-12-13

Analysis of molecular dynamics, for example using Markov models, often requires the identification of order parameters that are good indicators of the rare events, i.e. good reaction coordinates. Recently, it has been shown that the time-lagged independent component analysis (TICA) finds the linear combinations of input coordinates that optimally represent the slow kinetic modes and may serve in order to define reaction coordinates between the metastable states of the molecular system. A limitation of the method is that both computing time and memory requirements scale with the square of the number of input features. For large protein systems, this exacerbates the use of extensive feature sets such as the distances between all pairs of residues or even heavy atoms. Here we derive a hierarchical TICA (hTICA) method that approximates the full TICA solution by a hierarchical, divide-and-conquer calculation. By using hTICA on distances between heavy atoms we identify previously unknown relaxation processes in the bovine pancreatic trypsin inhibitor.

Processing Cones: A Computational Structure for Image Analysis.

DTIC Science & Technology

1981-12-01

image analysis applications, referred to as a processing cone, is described and sample algorithms are presented. A fundamental characteristic of the structure is its hierarchical organization into two-dimensional arrays of decreasing resolution. In this architecture, a protypical function is defined on a local window of data and applied uniformly to all windows in a parallel manner. Three basic modes of processing are supported in the cone: reduction operations (upward processing), horizontal operations (processing at a single level) and projection operations (downward

Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

NASA Astrophysics Data System (ADS)

Wu, Qiusheng; Lane, Charles R.

2017-07-01

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling-spilling-merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.

Planning applications in image analysis

NASA Technical Reports Server (NTRS)

Boddy, Mark; White, Jim; Goldman, Robert; Short, Nick, Jr.

1994-01-01

We describe two interim results from an ongoing effort to automate the acquisition, analysis, archiving, and distribution of satellite earth science data. Both results are applications of Artificial Intelligence planning research to the automatic generation of processing steps for image analysis tasks. First, we have constructed a linear conditional planner (CPed), used to generate conditional processing plans. Second, we have extended an existing hierarchical planning system to make use of durations, resources, and deadlines, thus supporting the automatic generation of processing steps in time and resource-constrained environments.

Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC

NASA Astrophysics Data System (ADS)

Susiluoto, Jouni; Raivonen, Maarit; Backman, Leif; Laine, Marko; Makela, Jarmo; Peltola, Olli; Vesala, Timo; Aalto, Tuula

2018-03-01

Estimating methane (CH4) emissions from natural wetlands is complex, and the estimates contain large uncertainties. The models used for the task are typically heavily parameterized and the parameter values are not well known. In this study, we perform a Bayesian model calibration for a new wetland CH4 emission model to improve the quality of the predictions and to understand the limitations of such models.The detailed process model that we analyze contains descriptions for CH4 production from anaerobic respiration, CH4 oxidation, and gas transportation by diffusion, ebullition, and the aerenchyma cells of vascular plants. The processes are controlled by several tunable parameters. We use a hierarchical statistical model to describe the parameters and obtain the posterior distributions of the parameters and uncertainties in the processes with adaptive Markov chain Monte Carlo (MCMC), importance resampling, and time series analysis techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in southern Finland. The uncertainties related to the parameters and the modeled processes are described quantitatively. At the process level, the flux measurement data are able to constrain the CH4 production processes, methane oxidation, and the different gas transport processes. The posterior covariance structures explain how the parameters and the processes are related. Additionally, the flux and flux component uncertainties are analyzed both at the annual and daily levels. The parameter posterior densities obtained provide information regarding importance of the different processes, which is also useful for development of wetland methane emission models other than the square root HelsinkI Model of MEthane buiLd-up and emIssion for peatlands (sqHIMMELI). The hierarchical modeling allows us to assess the effects of some of the parameters on an annual basis. The results of the calibration and the cross validation suggest that the early spring net primary production could be used to predict parameters affecting the annual methane production. Even though the calibration is specific to the Siikaneva site, the hierarchical modeling approach is well suited for larger-scale studies and the results of the estimation pave way for a regional or global-scale Bayesian calibration of wetland emission models.

Uniform Cu{sub 2}Cl(OH){sub 3} hierarchical microspheres: A novel adsorbent for methylene blue adsorptive removal from aqueous solution

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

Wei, Wei; Gao, Pin; Xie, Jimin, E-mail: xiejm391@sohu.com

2013-08-15

Using the solution phase method without any surfactants or templates, the hierarchical of Cu{sub 2}Cl(OH){sub 3} microspheres were synthesized by freeze drying. The size and surface area of the microspheres are ca. 1–2 µm and 76.61 m{sup 2} g{sup −1}, respectively. A possible formation mechanism is presented based on the experimental results. Methylene blue was chosen to investigate the adsorption capacity of the as-prepared adsorbent. The effects of various experimental parameters, such as pH, initial dye concentration, and contact time were investigated. The results showed that the dye removal increased with the increasing in the initial concentration of the dyemore » and also increased in the amount of microspheres used and initial pH. Adsorption data fitted well with the Freundlich adsorption isotherm. The thermodynamic analysis presented the exothermic, spontaneous and more ordered arrangement process. The microspheres could be employed effective for removal of dyes from aqueous solution. - Graphical abstract: The single-crystalline hierarchical Cu{sub 2}Cl(OH){sub 3} spheres can be prepared for the first time by using a template-free process through freeze-drying. Meanwhile, the hierarchical spheres exhibited high adsorption capacity to methylene blue. Display Omitted - Highlights: • Cu{sub 2}Cl(OH){sub 3} microspheres were successfully synthesized through a freeze drying process. • A possible formation mechanism of hierarchical microspheres was presented. • The Cu{sub 2}Cl(OH){sub 3} microspheres have high methylene blue adsorption capacity. • Methylene blue adsorption is a spontaneous and exothermic process. • The adsorption mechanism of microspheres onto dye was proposed in detail.« less

DECISION-COMPONENTS OF NICE'S TECHNOLOGY APPRAISALS ASSESSMENT FRAMEWORK.

PubMed

de Folter, Joost; Trusheim, Mark; Jonsson, Pall; Garner, Sarah

2018-01-01

Value assessment frameworks have gained prominence recently in the context of U.S. healthcare. Such frameworks set out a series of factors that are considered in funding decisions. The UK's National Institute of Health and Care Excellence (NICE) is an established health technology assessment (HTA) agency. We present a novel application of text analysis that characterizes NICE's Technology Appraisals in the context of the newer assessment frameworks and present the results in a visual way. A total of 243 documents of NICE's medicines guidance from 2007 to 2016 were analyzed. Text analysis was used to identify a hierarchical set of decision factors considered in the assessments. The frequency of decision factors stated in the documents was determined and their association with terms related to uncertainty. The results were incorporated into visual representations of hierarchical factors. We identified 125 decision factors, and hierarchically grouped these into eight domains: Clinical Effectiveness, Cost Effectiveness, Condition, Current Practice, Clinical Need, New Treatment, Studies, and Other Factors. Textual analysis showed all domains appeared consistently in the guidance documents. Many factors were commonly associated with terms relating to uncertainty. A series of visual representations was created. This study reveals the complexity and consistency of NICE's decision-making processes and demonstrates that cost effectiveness is not the only decision-criteria. The study highlights the importance of processes and methodology that can take both quantitative and qualitative information into account. Visualizations can help effectively communicate this complex information during the decision-making process and subsequently to stakeholders.

School Decentralization as a Process of Differentiation, Hierarchization and Selection

ERIC Educational Resources Information Center

Altrichter, Herbert; Heinrich, Martin; Soukup-Altrichter, Katharina

2014-01-01

The Austrian "school autonomy policy", which allowed schools to develop specific "curricular profiles", is taken as an example for discussing processes and effects of school decentralization policies. Data from school case studies (based on qualitative interviews and document analysis) are used to analyse and interpret the…

Hierarchical coarse-graining model for photosystem II including electron and excitation-energy transfer processes.

PubMed

Matsuoka, Takeshi; Tanaka, Shigenori; Ebina, Kuniyoshi

2014-03-01

We propose a hierarchical reduction scheme to cope with coupled rate equations that describe the dynamics of multi-time-scale photosynthetic reactions. To numerically solve nonlinear dynamical equations containing a wide temporal range of rate constants, we first study a prototypical three-variable model. Using a separation of the time scale of rate constants combined with identified slow variables as (quasi-)conserved quantities in the fast process, we achieve a coarse-graining of the dynamical equations reduced to those at a slower time scale. By iteratively employing this reduction method, the coarse-graining of broadly multi-scale dynamical equations can be performed in a hierarchical manner. We then apply this scheme to the reaction dynamics analysis of a simplified model for an illuminated photosystem II, which involves many processes of electron and excitation-energy transfers with a wide range of rate constants. We thus confirm a good agreement between the coarse-grained and fully (finely) integrated results for the population dynamics. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A comparative analysis of global and local processing of hierarchical visual stimuli in young children (Homo sapiens) and monkeys (Cebus apella).

PubMed

De Lillo, Carlo; Spinozzi, Giovanna; Truppa, Valentina; Naylor, Donna M

2005-05-01

Results obtained with preschool children (Homo sapiens) were compared with results previously obtained from capuchin monkeys (Cebus apella) in matching-to-sample tasks featuring hierarchical visual stimuli. In Experiment 1, monkeys, in contrast with children, showed an advantage in matching the stimuli on the basis of their local features. These results were replicated in a 2nd experiment in which control trials enabled the authors to rule out that children used spurious cues to solve the matching task. In a 3rd experiment featuring conditions in which the density of the stimuli was manipulated, monkeys' accuracy in the processing of the global shape of the stimuli was negatively affected by the separation of the local elements, whereas children's performance was robust across testing conditions. Children's response latencies revealed a global precedence in the 2nd and 3rd experiments. These results show differences in the processing of hierarchical stimuli by humans and monkeys that emerge early during childhood. 2005 APA, all rights reserved

A new artefacts resistant method for automatic lineament extraction using Multi-Hillshade Hierarchic Clustering (MHHC)

NASA Astrophysics Data System (ADS)

Šilhavý, Jakub; Minár, Jozef; Mentlík, Pavel; Sládek, Ján

2016-07-01

This paper presents a new method of automatic lineament extraction which includes the removal of the 'artefacts effect' which is associated with the process of raster based analysis. The core of the proposed Multi-Hillshade Hierarchic Clustering (MHHC) method incorporates a set of variously illuminated and rotated hillshades in combination with hierarchic clustering of derived 'protolineaments'. The algorithm also includes classification into positive and negative lineaments. MHHC was tested in two different territories in Bohemian Forest and Central Western Carpathians. The original vector-based algorithm was developed for comparison of the individual lineaments proximity. Its use confirms the compatibility of manual and automatic extraction and their similar relationships to structural data in the study areas.

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

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.

Mining a Web Citation Database for Author Co-Citation Analysis.

ERIC Educational Resources Information Center

He, Yulan; Hui, Siu Cheung

2002-01-01

Proposes a mining process to automate author co-citation analysis based on the Web Citation Database, a data warehouse for storing citation indices of Web publications. Describes the use of agglomerative hierarchical clustering for author clustering and multidimensional scaling for displaying author cluster maps, and explains PubSearch, a…

Hierarchical cortical transcriptome disorganization in autism.

PubMed

Lombardo, Michael V; Courchesne, Eric; Lewis, Nathan E; Pramparo, Tiziano

2017-01-01

Autism spectrum disorders (ASD) are etiologically heterogeneous and complex. Functional genomics work has begun to identify a diverse array of dysregulated transcriptomic programs (e.g., synaptic, immune, cell cycle, DNA damage, WNT signaling, cortical patterning and differentiation) potentially involved in ASD brain abnormalities during childhood and adulthood. However, it remains unclear whether such diverse dysregulated pathways are independent of each other or instead reflect coordinated hierarchical systems-level pathology. Two ASD cortical transcriptome datasets were re-analyzed using consensus weighted gene co-expression network analysis (WGCNA) to identify common co-expression modules across datasets. Linear mixed-effect models and Bayesian replication statistics were used to identify replicable differentially expressed modules. Eigengene network analysis was then utilized to identify between-group differences in how co-expression modules interact and cluster into hierarchical meta-modular organization. Protein-protein interaction analyses were also used to determine whether dysregulated co-expression modules show enhanced interactions. We find replicable evidence for 10 gene co-expression modules that are differentially expressed in ASD cortex. Rather than being independent non-interacting sources of pathology, these dysregulated co-expression modules work in synergy and physically interact at the protein level. These systems-level transcriptional signals are characterized by downregulation of synaptic processes coordinated with upregulation of immune/inflammation, response to other organism, catabolism, viral processes, translation, protein targeting and localization, cell proliferation, and vasculature development. Hierarchical organization of meta-modules (clusters of highly correlated modules) is also highly affected in ASD. These findings highlight that dysregulation of the ASD cortical transcriptome is characterized by the dysregulation of multiple coordinated transcriptional programs producing synergistic systems-level effects that cannot be fully appreciated by studying the individual component biological processes in isolation.

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

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

Nee, K.; Bryan, S.; Levitskaia, T.

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

DOE PAGES

Nee, K.; Bryan, S.; Levitskaia, T.; ...

2017-12-28

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Stochastic dynamics of virus capsid formation: direct versus hierarchical self-assembly

PubMed Central

2012-01-01

Background In order to replicate within their cellular host, many viruses have developed self-assembly strategies for their capsids which are sufficiently robust as to be reconstituted in vitro. Mathematical models for virus self-assembly usually assume that the bonds leading to cluster formation have constant reactivity over the time course of assembly (direct assembly). In some cases, however, binding sites between the capsomers have been reported to be activated during the self-assembly process (hierarchical assembly). Results In order to study possible advantages of such hierarchical schemes for icosahedral virus capsid assembly, we use Brownian dynamics simulations of a patchy particle model that allows us to switch binding sites on and off during assembly. For T1 viruses, we implement a hierarchical assembly scheme where inter-capsomer bonds become active only if a complete pentamer has been assembled. We find direct assembly to be favorable for reversible bonds allowing for repeated structural reorganizations, while hierarchical assembly is favorable for strong bonds with small dissociation rate, as this situation is less prone to kinetic trapping. However, at the same time it is more vulnerable to monomer starvation during the final phase. Increasing the number of initial monomers does have only a weak effect on these general features. The differences between the two assembly schemes become more pronounced for more complex virus geometries, as shown here for T3 viruses, which assemble through homogeneous pentamers and heterogeneous hexamers in the hierarchical scheme. In order to complement the simulations for this more complicated case, we introduce a master equation approach that agrees well with the simulation results. Conclusions Our analysis shows for which molecular parameters hierarchical assembly schemes can outperform direct ones and suggests that viruses with high bond stability might prefer hierarchical assembly schemes. These insights increase our physical understanding of an essential biological process, with many interesting potential applications in medicine and materials science. PMID:23244740

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

Unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance

USGS Publications Warehouse

Fiske, I.J.; Chandler, R.B.

2011-01-01

Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientic questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mecha- nisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unied modeling in- terface. The R package unmarked provides such a unied modeling framework, including tools for data exploration, model tting, model criticism, post-hoc analysis, and model comparison.

Multivariate Analysis of the Visual Information Processing of Numbers

ERIC Educational Resources Information Center

Levine, David M.

1977-01-01

Nonmetric multidimensional scaling and hierarchical clustering procedures are applied to a confusion matrix of numerals. Two dimensions were interpreted: straight versus curved, and locus of curvature. Four major clusters of numerals were developed. (Author/JKS)

A Coding Scheme for Analysing Problem-Solving Processes of First-Year Engineering Students

ERIC Educational Resources Information Center

Grigg, Sarah J.; Benson, Lisa C.

2014-01-01

This study describes the development and structure of a coding scheme for analysing solutions to well-structured problems in terms of cognitive processes and problem-solving deficiencies for first-year engineering students. A task analysis approach was used to assess students' problem solutions using the hierarchical structure from a…

Progressive data transmission for anatomical landmark detection in a cloud.

PubMed

Sofka, M; Ralovich, K; Zhang, J; Zhou, S K; Comaniciu, D

2012-01-01

In the concept of cloud-computing-based systems, various authorized users have secure access to patient records from a number of care delivery organizations from any location. This creates a growing need for remote visualization, advanced image processing, state-of-the-art image analysis, and computer aided diagnosis. This paper proposes a system of algorithms for automatic detection of anatomical landmarks in 3D volumes in the cloud computing environment. The system addresses the inherent problem of limited bandwidth between a (thin) client, data center, and data analysis server. The problem of limited bandwidth is solved by a hierarchical sequential detection algorithm that obtains data by progressively transmitting only image regions required for processing. The client sends a request to detect a set of landmarks for region visualization or further analysis. The algorithm running on the data analysis server obtains a coarse level image from the data center and generates landmark location candidates. The candidates are then used to obtain image neighborhood regions at a finer resolution level for further detection. This way, the landmark locations are hierarchically and sequentially detected and refined. Only image regions surrounding landmark location candidates need to be trans- mitted during detection. Furthermore, the image regions are lossy compressed with JPEG 2000. Together, these properties amount to at least 30 times bandwidth reduction while achieving similar accuracy when compared to an algorithm using the original data. The hierarchical sequential algorithm with progressive data transmission considerably reduces bandwidth requirements in cloud-based detection systems.

Long-range correlations and patterns of recurrence in children and adults' attention to hierarchical displays

PubMed Central

Castillo, Ramon D.; Kloos, Heidi; Holden, John G.; Richardson, Michael J.

2015-01-01

In order to make sense of a scene, a person must pay attention to several levels of nested order, ranging from the most differentiated details of the display to the integrated whole. In adults, research shows that the processes of integration and differentiation have the signature of self-organization. Does the same hold for children? The current study addresses this question with children between 6 and 9 years of age, using two tasks that require attention to hierarchical displays. A group of adults were tested as well, for control purposes. To get at the question of self-organization, reaction times were submitted to a detrended fluctuation analysis and a recurrence quantification analysis. H exponents show a long-range correlations (1/f noise), and recurrence measures (percent determinism, maximum line, entropy, and trend), show a deterministic structure of variability being characteristic of self-organizing systems. Findings are discussed in terms of organism-environment coupling that gives rise to fluid attention to hierarchical displays. PMID:25999862

Elemental Learning as a Framework for E-Learning

ERIC Educational Resources Information Center

Dempsey, John V.; Litchfield, Brenda C.

2013-01-01

Analysis of learning outcomes can be a complex and esoteric instructional design process that is often ignored by educators and e-learning designers. This paper describes a model of analysis that fosters the real-life application of learning outcomes and explains why the model may be needed. The Elemental Learning taxonomy is a hierarchical model…

Hierarchical coordinate systems for understanding complexity and its evolution, with applications to genetic regulatory networks.

PubMed

Egri-Nagy, Attila; Nehaniv, Chrystopher L

2008-01-01

Beyond complexity measures, sometimes it is worthwhile in addition to investigate how complexity changes structurally, especially in artificial systems where we have complete knowledge about the evolutionary process. Hierarchical decomposition is a useful way of assessing structural complexity changes of organisms modeled as automata, and we show how recently developed computational tools can be used for this purpose, by computing holonomy decompositions and holonomy complexity. To gain insight into the evolution of complexity, we investigate the smoothness of the landscape structure of complexity under minimal transitions. As a proof of concept, we illustrate how the hierarchical complexity analysis reveals symmetries and irreversible structure in biological networks by applying the methods to the lac operon mechanism in the genetic regulatory network of Escherichia coli.

Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach

ERIC Educational Resources Information Center

Klauer, Karl Christoph

2010-01-01

Multinomial processing tree models are widely used in many areas of psychology. A hierarchical extension of the model class is proposed, using a multivariate normal distribution of person-level parameters with the mean and covariance matrix to be estimated from the data. The hierarchical model allows one to take variability between persons into…

A hierarchical modeling methodology for the definition and selection of requirements

NASA Astrophysics Data System (ADS)

Dufresne, Stephane

This dissertation describes the development of a requirements analysis methodology that takes into account the concept of operations and the hierarchical decomposition of aerospace systems. At the core of the methodology, the Analytic Network Process (ANP) is used to ensure the traceability between the qualitative and quantitative information present in the hierarchical model. The proposed methodology is implemented to the requirements definition of a hurricane tracker Unmanned Aerial Vehicle. Three research objectives are identified in this work; (1) improve the requirements mapping process by matching the stakeholder expectations with the concept of operations, systems and available resources; (2) reduce the epistemic uncertainty surrounding the requirements and requirements mapping; and (3) improve the requirements down-selection process by taking into account the level of importance of the criteria and the available resources. Several challenges are associated with the identification and definition of requirements. The complexity of the system implies that a large number of requirements are needed to define the systems. These requirements are defined early in the conceptual design, where the level of knowledge is relatively low and the level of uncertainty is large. The proposed methodology intends to increase the level of knowledge and reduce the level of uncertainty by guiding the design team through a structured process. To address these challenges, a new methodology is created to flow-down the requirements from the stakeholder expectations to the systems alternatives. A taxonomy of requirements is created to classify the information gathered during the problem definition. Subsequently, the operational and systems functions and measures of effectiveness are integrated to a hierarchical model to allow the traceability of the information. Monte Carlo methods are used to evaluate the variations of the hierarchical model elements and consequently reduce the epistemic uncertainty. The proposed methodology is applied to the design of a hurricane tracker Unmanned Aerial Vehicles to demonstrate the origin and impact of requirements on the concept of operations and systems alternatives. This research demonstrates that the hierarchical modeling methodology provides a traceable flow-down of the requirements from the problem definition to the systems alternatives phases of conceptual design.

Hierarchical adaptation scheme for multiagent data fusion and resource management in situation analysis

NASA Astrophysics Data System (ADS)

Benaskeur, Abder R.; Roy, Jean

2001-08-01

Sensor Management (SM) has to do with how to best manage, coordinate and organize the use of sensing resources in a manner that synergistically improves the process of data fusion. Based on the contextual information, SM develops options for collecting further information, allocates and directs the sensors towards the achievement of the mission goals and/or tunes the parameters for the realtime improvement of the effectiveness of the sensing process. Conscious of the important role that SM has to play in modern data fusion systems, we are currently studying advanced SM Concepts that would help increase the survivability of the current Halifax and Iroquois Class ships, as well as their possible future upgrades. For this purpose, a hierarchical scheme has been proposed for data fusion and resource management adaptation, based on the control theory and within the process refinement paradigm of the JDL data fusion model, and taking into account the multi-agent model put forward by the SASS Group for the situation analysis process. The novelty of this work lies in the unified framework that has been defined for tackling the adaptation of both the fusion process and the sensor/weapon management.

Hierarchical cultural values predict success and mortality in high-stakes teams.

PubMed

Anicich, Eric M; Swaab, Roderick I; Galinsky, Adam D

2015-02-03

Functional accounts of hierarchy propose that hierarchy increases group coordination and reduces conflict. In contrast, dysfunctional accounts claim that hierarchy impairs performance by preventing low-ranking team members from voicing their potentially valuable perspectives and insights. The current research presents evidence for both the functional and dysfunctional accounts of hierarchy within the same dataset. Specifically, we offer empirical evidence that hierarchical cultural values affect the outcomes of teams in high-stakes environments through group processes. Experimental data from a sample of expert mountain climbers from 27 countries confirmed that climbers expect that a hierarchical culture leads to improved team coordination among climbing teams, but impaired psychological safety and information sharing compared with an egalitarian culture. An archival analysis of 30,625 Himalayan mountain climbers from 56 countries on 5,104 expeditions found that hierarchy both elevated and killed in the Himalayas: Expeditions from more hierarchical countries had more climbers reach the summit, but also more climbers die along the way. Importantly, we established the role of group processes by showing that these effects occurred only for group, but not solo, expeditions. These findings were robust to controlling for environmental factors, risk preferences, expedition-level characteristics, country-level characteristics, and other cultural values. Overall, this research demonstrates that endorsing cultural values related to hierarchy can simultaneously improve and undermine group performance.

A hierarchical-multiobjective framework for risk management

NASA Technical Reports Server (NTRS)

Haimes, Yacov Y.; Li, Duan

1991-01-01

A broad hierarchical-multiobjective framework is established and utilized to methodologically address the management of risk. United into the framework are the hierarchical character of decision-making, the multiple decision-makers at separate levels within the hierarchy, the multiobjective character of large-scale systems, the quantitative/empirical aspects, and the qualitative/normative/judgmental aspects. The methodological components essentially consist of hierarchical-multiobjective coordination, risk of extreme events, and impact analysis. Examples of applications of the framework are presented. It is concluded that complex and interrelated forces require an analysis of trade-offs between engineering analysis and societal preferences, as in the hierarchical-multiobjective framework, to successfully address inherent risk.

iSBatch: a batch-processing platform for data analysis and exploration of live-cell single-molecule microscopy images and other hierarchical datasets.

PubMed

Caldas, Victor E A; Punter, Christiaan M; Ghodke, Harshad; Robinson, Andrew; van Oijen, Antoine M

2015-10-01

Recent technical advances have made it possible to visualize single molecules inside live cells. Microscopes with single-molecule sensitivity enable the imaging of low-abundance proteins, allowing for a quantitative characterization of molecular properties. Such data sets contain information on a wide spectrum of important molecular properties, with different aspects highlighted in different imaging strategies. The time-lapsed acquisition of images provides information on protein dynamics over long time scales, giving insight into expression dynamics and localization properties. Rapid burst imaging reveals properties of individual molecules in real-time, informing on their diffusion characteristics, binding dynamics and stoichiometries within complexes. This richness of information, however, adds significant complexity to analysis protocols. In general, large datasets of images must be collected and processed in order to produce statistically robust results and identify rare events. More importantly, as live-cell single-molecule measurements remain on the cutting edge of imaging, few protocols for analysis have been established and thus analysis strategies often need to be explored for each individual scenario. Existing analysis packages are geared towards either single-cell imaging data or in vitro single-molecule data and typically operate with highly specific algorithms developed for particular situations. Our tool, iSBatch, instead allows users to exploit the inherent flexibility of the popular open-source package ImageJ, providing a hierarchical framework in which existing plugins or custom macros may be executed over entire datasets or portions thereof. This strategy affords users freedom to explore new analysis protocols within large imaging datasets, while maintaining hierarchical relationships between experiments, samples, fields of view, cells, and individual molecules.

Delineating wetland catchments and modeling hydrologic ...

EPA Pesticide Factsheets

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling–spilling–merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that

Mental structures and hierarchical brain processing. Comment on “Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition” by W. Tecumseh Fitch

NASA Astrophysics Data System (ADS)

Petkov, C. I.

2014-09-01

Fitch proposes an appealing hypothesis that humans are dendrophiles, who constantly build mental trees supported by analogous hierarchical brain processes [1]. Moreover, it is argued that, by comparison, nonhuman animals build flat or more compact behaviorally-relevant structures. Should we thus expect less impressive hierarchical brain processes in other animals? Not necessarily.

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.

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.

Quantitative nanoscopy: Tackling sampling limitations in (S)TEM imaging of polymers and composites.

PubMed

Gnanasekaran, Karthikeyan; Snel, Roderick; de With, Gijsbertus; Friedrich, Heiner

2016-01-01

Sampling limitations in electron microscopy questions whether the analysis of a bulk material is representative, especially while analyzing hierarchical morphologies that extend over multiple length scales. We tackled this problem by automatically acquiring a large series of partially overlapping (S)TEM images with sufficient resolution, subsequently stitched together to generate a large-area map using an in-house developed acquisition toolbox (TU/e Acquisition ToolBox) and stitching module (TU/e Stitcher). In addition, we show that quantitative image analysis of the large scale maps provides representative information that can be related to the synthesis and process conditions of hierarchical materials, which moves electron microscopy analysis towards becoming a bulk characterization tool. We demonstrate the power of such an analysis by examining two different multi-phase materials that are structured over multiple length scales. Copyright © 2015 Elsevier B.V. All rights reserved.

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

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.

Multi-Sensor Scene Synthesis and Analysis

DTIC Science & Technology

1981-09-01

Quad Trees for Image Representation and Processing ...... ... 126 2.6.2 Databases ..... ..... ... ..... ... ..... ..... 138 2.6.2.1 Definitions and...Basic Concepts ....... 138 2.6.3 Use of Databases in Hierarchical Scene Analysis ...... ... ..................... 147 2.6.4 Use of Relational Tables...Multisensor Image Database Systems (MIDAS) . 161 2.7.2 Relational Database System for Pictures .... ..... 168 2.7.3 Relational Pictorial Database

The Advantages of Hierarchical Linear Modeling. ERIC/AE Digest.

ERIC Educational Resources Information Center

Osborne, Jason W.

This digest introduces hierarchical data structure, describes how hierarchical models work, and presents three approaches to analyzing hierarchical data. Hierarchical, or nested data, present several problems for analysis. People or creatures that exist within hierarchies tend to be more similar to each other than people randomly sampled from the…

A Study of Hierarchical Classification in Concrete and Formal Thought.

ERIC Educational Resources Information Center

Lowell, Walter E.

This researcher investigated the relationship of hierarchical classification processes in subjects categorized as to developmental level as defined by Piaget's theory, and explored the validity of the hierarchical model and test used in the study. A hierarchical classification test and a battery of four Piaget-type tasks were administered…

Structural analysis of hierarchically organized zeolites

PubMed Central

Mitchell, Sharon; Pinar, Ana B.; Kenvin, Jeffrey; Crivelli, Paolo; Kärger, Jörg; Pérez-Ramírez, Javier

2015-01-01

Advances in materials synthesis bring about many opportunities for technological applications, but are often accompanied by unprecedented complexity. This is clearly illustrated by the case of hierarchically organized zeolite catalysts, a class of crystalline microporous solids that has been revolutionized by the engineering of multilevel pore architectures, which combine unique chemical functionality with efficient molecular transport. Three key attributes, the crystal, the pore and the active site structure, can be expected to dominate the design process. This review examines the adequacy of the palette of techniques applied to characterize these distinguishing features and their catalytic impact. PMID:26482337

Pushing Typists Back on the Learning Curve: Memory Chunking in the Hierarchical Control of Skilled Typewriting

ERIC Educational Resources Information Center

Yamaguchi, Motonori; Logan, Gordon D.

2016-01-01

Hierarchical control of skilled performance depends on the ability of higher level control to process several lower level units as a single chunk. The present study investigated the development of hierarchical control of skilled typewriting, focusing on the process of memory chunking. In the first 3 experiments, skilled typists typed words or…

Hierarchical model analysis of the Atlantic Flyway Breeding Waterfowl Survey

USGS Publications Warehouse

Sauer, John R.; Zimmerman, Guthrie S.; Klimstra, Jon D.; Link, William A.

2014-01-01

We used log-linear hierarchical models to analyze data from the Atlantic Flyway Breeding Waterfowl Survey. The survey has been conducted by state biologists each year since 1989 in the northeastern United States from Virginia north to New Hampshire and Vermont. Although yearly population estimates from the survey are used by the United States Fish and Wildlife Service for estimating regional waterfowl population status for mallards (Anas platyrhynchos), black ducks (Anas rubripes), wood ducks (Aix sponsa), and Canada geese (Branta canadensis), they are not routinely adjusted to control for time of day effects and other survey design issues. The hierarchical model analysis permits estimation of year effects and population change while accommodating the repeated sampling of plots and controlling for time of day effects in counting. We compared population estimates from the current stratified random sample analysis to population estimates from hierarchical models with alternative model structures that describe year to year changes as random year effects, a trend with random year effects, or year effects modeled as 1-year differences. Patterns of population change from the hierarchical model results generally were similar to the patterns described by stratified random sample estimates, but significant visibility differences occurred between twilight to midday counts in all species. Controlling for the effects of time of day resulted in larger population estimates for all species in the hierarchical model analysis relative to the stratified random sample analysis. The hierarchical models also provided a convenient means of estimating population trend as derived statistics from the analysis. We detected significant declines in mallard and American black ducks and significant increases in wood ducks and Canada geese, a trend that had not been significant for 3 of these 4 species in the prior analysis. We recommend using hierarchical models for analysis of the Atlantic Flyway Breeding Waterfowl Survey.

A hierarchical approach employing metabolic and gene expression profiles to identify the pathways that confer cytotoxicity in HepG2 cells

PubMed Central

Li, Zheng; Srivastava, Shireesh; Yang, Xuerui; Mittal, Sheenu; Norton, Paul; Resau, James; Haab, Brian; Chan, Christina

2007-01-01

Background Free fatty acids (FFA) and tumor necrosis factor alpha (TNF-α) have been implicated in the pathogenesis of many obesity-related metabolic disorders. When human hepatoblastoma cells (HepG2) were exposed to different types of FFA and TNF-α, saturated fatty acid was found to be cytotoxic and its toxicity was exacerbated by TNF-α. In order to identify the processes associated with the toxicity of saturated FFA and TNF-α, the metabolic and gene expression profiles were measured to characterize the cellular states. A computational model was developed to integrate these disparate data to reveal the underlying pathways and mechanisms involved in saturated fatty acid toxicity. Results A hierarchical framework consisting of three stages was developed to identify the processes and genes that regulate the toxicity. First, discriminant analysis identified that fatty acid oxidation and intracellular triglyceride accumulation were the most relevant in differentiating the cytotoxic phenotype. Second, gene set enrichment analysis (GSEA) was applied to the cDNA microarray data to identify the transcriptionally altered pathways and processes. Finally, the genes and gene sets that regulate the metabolic responses identified in step 1 were identified by integrating the expression of the enriched gene sets and the metabolic profiles with a multi-block partial least squares (MBPLS) regression model. Conclusion The hierarchical approach suggested potential mechanisms involved in mediating the cytotoxic and cytoprotective pathways, as well as identified novel targets, such as NADH dehydrogenases, aldehyde dehydrogenases 1A1 (ALDH1A1) and endothelial membrane protein 3 (EMP3) as modulator of the toxic phenotypes. These predictions, as well as, some specific targets that were suggested by the analysis were experimentally validated. PMID:17498300

Hierarchical nanostructures of copper(II) phthalocyanine on electrospun TiO(2) nanofibers: controllable solvothermal-fabrication and enhanced visible photocatalytic properties.

PubMed

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

2011-02-01

In the present work, 2,9,16,23-tetranitrophthalocyanine copper(II) (TNCuPc)/TiO(2) hierarchical nanostructures were successfully fabricated by a simple combination method of electrospinning technique and solvothermal processing. Scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy, X-ray diffraction (XRD), UV-vis diffuse reflectance (DR), Fourier transform infrared spectrum (FT-IR), X-ray photoelectron spectroscopy (XPS), and thermal gravimetric and differential thermal analysis (TG-DTA) were used to characterize the as-synthesized TNCuPc/TiO(2) hierarchical nanostructures. The results showed that the secondary TNCuPc nanostructures were not only successfully grown on the primary TiO(2) nanofibers substrates but also uniformly distributed without aggregation. By adjusting the solvothermal fabrication parameters, the TNCuPc nanowires or nanoflowers were facilely fabricated, and also the loading amounts of TNCuPc could be controlled on the TNCuPc/TiO(2) hierarchical nanostructural nanofibers. And, there might exist the interaction between TNCuPc and TiO(2). A possible mechanism for the formation of TNCuPc/TiO(2) hierarchical nanostructures was suggested. The photocatalytic studies revealed that the TNCuPc/TiO(2) hierarchical nanostructures exhibited enhanced photocatalytic efficiency of photodegradation of Rhodamine B (RB) compared with the pure TNCuPc or TiO(2) nanofibers under visible-light irradiation.

Requirements for implementing real-time control functional modules on a hierarchical parallel pipelined system

NASA Technical Reports Server (NTRS)

Wheatley, Thomas E.; Michaloski, John L.; Lumia, Ronald

1989-01-01

Analysis of a robot control system leads to a broad range of processing requirements. One fundamental requirement of a robot control system is the necessity of a microcomputer system in order to provide sufficient processing capability.The use of multiple processors in a parallel architecture is beneficial for a number of reasons, including better cost performance, modular growth, increased reliability through replication, and flexibility for testing alternate control strategies via different partitioning. A survey of the progression from low level control synchronizing primitives to higher level communication tools is presented. The system communication and control mechanisms of existing robot control systems are compared to the hierarchical control model. The impact of this design methodology on the current robot control systems is explored.

A Survey of Research Methods to Study Design

DTIC Science & Technology

1989-06-01

Integrated Computer-Aided Manufacturing (ICAM) Definition Languages (IDEFO, IDEF 1, and IDEF 2) Nijssen’s Information Analysis Method (NIAM) * Michael ... Jackson Diagrams 1I-1 • Warnier-Orr Di.grams Hierarchical Input Process Output (HIPO) charts * Nassi Schneiderman Charts. These techniques were intended

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.

Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service.

PubMed

Bao, Shunxing; Plassard, Andrew J; Landman, Bennett A; Gokhale, Aniruddha

2017-04-01

Traditional in-house, laboratory-based medical imaging studies use hierarchical data structures (e.g., NFS file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance from these approaches is, however, impeded by standard network switches since they can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. To that end, a cloud-based "medical image processing-as-a-service" offers promise in utilizing the ecosystem of Apache Hadoop, which is a flexible framework providing distributed, scalable, fault tolerant storage and parallel computational modules, and HBase, which is a NoSQL database built atop Hadoop's distributed file system. Despite this promise, HBase's load distribution strategy of region split and merge is detrimental to the hierarchical organization of imaging data (e.g., project, subject, session, scan, slice). This paper makes two contributions to address these concerns by describing key cloud engineering principles and technology enhancements we made to the Apache Hadoop ecosystem for medical imaging applications. First, we propose a row-key design for HBase, which is a necessary step that is driven by the hierarchical organization of imaging data. Second, we propose a novel data allocation policy within HBase to strongly enforce collocation of hierarchically related imaging data. The proposed enhancements accelerate data processing by minimizing network usage and localizing processing to machines where the data already exist. Moreover, our approach is amenable to the traditional scan, subject, and project-level analysis procedures, and is compatible with standard command line/scriptable image processing software. Experimental results for an illustrative sample of imaging data reveals that our new HBase policy results in a three-fold time improvement in conversion of classic DICOM to NiFTI file formats when compared with the default HBase region split policy, and nearly a six-fold improvement over a commonly available network file system (NFS) approach even for relatively small file sets. Moreover, file access latency is lower than network attached storage.

Hierarchical cultural values predict success and mortality in high-stakes teams

PubMed Central

Anicich, Eric M.; Swaab, Roderick I.; Galinsky, Adam D.

2015-01-01

Functional accounts of hierarchy propose that hierarchy increases group coordination and reduces conflict. In contrast, dysfunctional accounts claim that hierarchy impairs performance by preventing low-ranking team members from voicing their potentially valuable perspectives and insights. The current research presents evidence for both the functional and dysfunctional accounts of hierarchy within the same dataset. Specifically, we offer empirical evidence that hierarchical cultural values affect the outcomes of teams in high-stakes environments through group processes. Experimental data from a sample of expert mountain climbers from 27 countries confirmed that climbers expect that a hierarchical culture leads to improved team coordination among climbing teams, but impaired psychological safety and information sharing compared with an egalitarian culture. An archival analysis of 30,625 Himalayan mountain climbers from 56 countries on 5,104 expeditions found that hierarchy both elevated and killed in the Himalayas: Expeditions from more hierarchical countries had more climbers reach the summit, but also more climbers die along the way. Importantly, we established the role of group processes by showing that these effects occurred only for group, but not solo, expeditions. These findings were robust to controlling for environmental factors, risk preferences, expedition-level characteristics, country-level characteristics, and other cultural values. Overall, this research demonstrates that endorsing cultural values related to hierarchy can simultaneously improve and undermine group performance. PMID:25605883

Structural modeling and analysis of an effluent treatment process for electroplating--a graph theoretic approach.

PubMed

Kumar, Abhishek; Clement, Shibu; Agrawal, V P

2010-07-15

An attempt is made to address a few ecological and environment issues by developing different structural models for effluent treatment system for electroplating. The effluent treatment system is defined with the help of different subsystems contributing to waste minimization. Hierarchical tree and block diagram showing all possible interactions among subsystems are proposed. These non-mathematical diagrams are converted into mathematical models for design improvement, analysis, comparison, storage retrieval and commercially off-the-shelf purchases of different subsystems. This is achieved by developing graph theoretic model, matrix models and variable permanent function model. Analysis is carried out by permanent function, hierarchical tree and block diagram methods. Storage and retrieval is done using matrix models. The methodology is illustrated with the help of an example. Benefits to the electroplaters/end user are identified. 2010 Elsevier B.V. All rights reserved.

HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere

NASA Technical Reports Server (NTRS)

Gorski, K. M.; Hivon, Eric; Banday, A. J.; Wandelt, Benjamin D.; Hansen, Frode K.; Reinecke, Mstvos; Bartelmann, Matthia

2005-01-01

HEALPix the Hierarchical Equal Area isoLatitude Pixelization is a versatile structure for the pixelization of data on the sphere. An associated library of computational algorithms and visualization software supports fast scientific applications executable directly on discretized spherical maps generated from very large volumes of astronomical data. Originally developed to address the data processing and analysis needs of the present generation of cosmic microwave background experiments (e.g., BOOMERANG, WMAP), HEALPix can be expanded to meet many of the profound challenges that will arise in confrontation with the observational output of future missions and experiments, including, e.g., Planck, Herschel, SAFIR, and the Beyond Einstein inflation probe. In this paper we consider the requirements and implementation constraints on a framework that simultaneously enables an efficient discretization with associated hierarchical indexation and fast analysis/synthesis of functions defined on the sphere. We demonstrate how these are explicitly satisfied by HEALPix.

Hierarchical analysis of bridge decision makers : the role of new technology adoption in the timber bridge market : special project fiscal year 1992

DOT National Transportation Integrated Search

1995-08-01

Bridge design engineers and local highway officials make bridge replacement decisions across the : United States. The Analytical Hierarchy Process was used to characterize the bridge material selection : decision of these individuals. State Departmen...

High- and low-level hierarchical classification algorithm based on source separation process

NASA Astrophysics Data System (ADS)

Loghmari, Mohamed Anis; Karray, Emna; Naceur, Mohamed Saber

2016-10-01

High-dimensional data applications have earned great attention in recent years. We focus on remote sensing data analysis on high-dimensional space like hyperspectral data. From a methodological viewpoint, remote sensing data analysis is not a trivial task. Its complexity is caused by many factors, such as large spectral or spatial variability as well as the curse of dimensionality. The latter describes the problem of data sparseness. In this particular ill-posed problem, a reliable classification approach requires appropriate modeling of the classification process. The proposed approach is based on a hierarchical clustering algorithm in order to deal with remote sensing data in high-dimensional space. Indeed, one obvious method to perform dimensionality reduction is to use the independent component analysis process as a preprocessing step. The first particularity of our method is the special structure of its cluster tree. Most of the hierarchical algorithms associate leaves to individual clusters, and start from a large number of individual classes equal to the number of pixels; however, in our approach, leaves are associated with the most relevant sources which are represented according to mutually independent axes to specifically represent some land covers associated with a limited number of clusters. These sources contribute to the refinement of the clustering by providing complementary rather than redundant information. The second particularity of our approach is that at each level of the cluster tree, we combine both a high-level divisive clustering and a low-level agglomerative clustering. This approach reduces the computational cost since the high-level divisive clustering is controlled by a simple Boolean operator, and optimizes the clustering results since the low-level agglomerative clustering is guided by the most relevant independent sources. Then at each new step we obtain a new finer partition that will participate in the clustering process to enhance semantic capabilities and give good identification rates.

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.

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis.

PubMed

Cohen, Mitchell J; Grossman, Adam D; Morabito, Diane; Knudson, M Margaret; Butte, Atul J; Manley, Geoffrey T

2010-01-01

Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome. Multivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality. We identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters. Here we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients.

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.

Models, Analysis, and Recommendations Pertaining to the Retention of Naval Special Warfare s Mid-Level Officers

DTIC Science & Technology

2013-12-01

The Analytic Hierarch/Network Process,” in Rev. R. Acad. Cien. Serie A. Mat (RACSAM), submitted by Francisco Javier Giron (Real Academia de Ciencias ...Academia de Ciencias : Spain. Scott, Nathan. Naval Special Warfare Officer Retention Survey. Monterey, CA: NPS Press, September 2013. Whittenberger

Educational Attainment as Process: Using Hierarchical Discrete-Time Event History Analysis to Model Rate of Progress

ERIC Educational Resources Information Center

Bahr, Peter Riley

2009-01-01

Variables that address student enrollment patterns (e.g., persistence, enrollment inconsistency, completed credit hours, course credit load, course completion rate, procrastination) constitute a longstanding fixture of analytical strategies in educational research, particularly research that focuses on explaining variation in academic outcomes.…

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles

PubMed Central

Peterson, Leif E

2002-01-01

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 is a Windows-based computer program for hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles. CLUSFAVOR 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis. PMID:12184816

Parallel-hierarchical processing and classification of laser beam profile images based on the GPU-oriented architecture

NASA Astrophysics Data System (ADS)

Yarovyi, Andrii A.; Timchenko, Leonid I.; Kozhemiako, Volodymyr P.; Kokriatskaia, Nataliya I.; Hamdi, Rami R.; Savchuk, Tamara O.; Kulyk, Oleksandr O.; Surtel, Wojciech; Amirgaliyev, Yedilkhan; Kashaganova, Gulzhan

2017-08-01

The paper deals with a problem of insufficient productivity of existing computer means for large image processing, which do not meet modern requirements posed by resource-intensive computing tasks of laser beam profiling. The research concentrated on one of the profiling problems, namely, real-time processing of spot images of the laser beam profile. Development of a theory of parallel-hierarchic transformation allowed to produce models for high-performance parallel-hierarchical processes, as well as algorithms and software for their implementation based on the GPU-oriented architecture using GPGPU technologies. The analyzed performance of suggested computerized tools for processing and classification of laser beam profile images allows to perform real-time processing of dynamic images of various sizes.

Power and resistance within the hospital's hierarchical system: the experiences of chronically ill patients.

PubMed

Griscti, Odette; Aston, Megan; Warner, Grace; Martin-Misener, Ruth; McLeod, Deborah

2017-01-01

To explore experiences of chronically ill patients and registered nurses when they negotiate patient care in hospital settings. Specifically, we explored how social and institutional discourses shape power relations during the negotiation process. The hospital system is embedded in a hierarchical structure where the voice of the healthcare provider as expert is often given more importance than the patient. This system has been criticised as being oppressive to patients who are perceived to be lower in the hierarchy. In this study, we illustrate how the hospital's hierarchical system is not always oppressing but can also create moments of empowerment for patients. A feminist poststructuralist approach informed by the teaching of Foucault was used to explore power relations between nurses and patients when negotiating patient care in hospital settings. Eight individuals who suffered from chronic illness shared their stories about how they negotiated their care with nurses in hospital settings. The interviews were tape-recorded. Discourse analysis was used to analyse the data. Patients recounted various experiences when their voices were not heard because the current hospital system privileged the healthcare provider experts' advice over the patients' voice. The hierarchical structure of hospital supported these dynamics by privileging nurses as gatekeepers of service, by excluding the patients' input in the nursing notes and through a process of self-regulation. However, patients in this study were not passive recipients of care and used their agency creatively to resist these discourses. Nurses need to be mindful of how the hospital's hierarchical system tends to place nurses in a position of power, and how their authoritative position may positively or adversely affect the negotiation of patient care. © 2016 John Wiley & Sons Ltd.

Visual communications and image processing '92; Proceedings of the Meeting, Boston, MA, Nov. 18-20, 1992

NASA Astrophysics Data System (ADS)

Maragos, Petros

The topics discussed at the conference include hierarchical image coding, motion analysis, feature extraction and image restoration, video coding, and morphological and related nonlinear filtering. Attention is also given to vector quantization, morphological image processing, fractals and wavelets, architectures for image and video processing, image segmentation, biomedical image processing, and model-based analysis. Papers are presented on affine models for motion and shape recovery, filters for directly detecting surface orientation in an image, tracking of unresolved targets in infrared imagery using a projection-based method, adaptive-neighborhood image processing, and regularized multichannel restoration of color images using cross-validation. (For individual items see A93-20945 to A93-20951)

Hierarchical Discriminant Analysis.

PubMed

Lu, Di; Ding, Chuntao; Xu, Jinliang; Wang, Shangguang

2018-01-18

The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA). It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

An Integrative Object-Based Image Analysis Workflow for Uav Images

NASA Astrophysics Data System (ADS)

Yu, Huai; Yan, Tianheng; Yang, Wen; Zheng, Hong

2016-06-01

In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA). More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT) representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC). Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya'an earthquake demonstrate the effectiveness and efficiency of our proposed method.

A comparison of hierarchical cluster analysis and league table rankings as methods for analysis and presentation of district health system performance data in Uganda.

PubMed

Tashobya, Christine K; Dubourg, Dominique; Ssengooba, Freddie; Speybroeck, Niko; Macq, Jean; Criel, Bart

2016-03-01

In 2003, the Uganda Ministry of Health introduced the district league table for district health system performance assessment. The league table presents district performance against a number of input, process and output indicators and a composite index to rank districts. This study explores the use of hierarchical cluster analysis for analysing and presenting district health systems performance data and compares this approach with the use of the league table in Uganda. Ministry of Health and district plans and reports, and published documents were used to provide information on the development and utilization of the Uganda district league table. Quantitative data were accessed from the Ministry of Health databases. Statistical analysis using SPSS version 20 and hierarchical cluster analysis, utilizing Wards' method was used. The hierarchical cluster analysis was conducted on the basis of seven clusters determined for each year from 2003 to 2010, ranging from a cluster of good through moderate-to-poor performers. The characteristics and membership of clusters varied from year to year and were determined by the identity and magnitude of performance of the individual variables. Criticisms of the league table include: perceived unfairness, as it did not take into consideration district peculiarities; and being oversummarized and not adequately informative. Clustering organizes the many data points into clusters of similar entities according to an agreed set of indicators and can provide the beginning point for identifying factors behind the observed performance of districts. Although league table ranking emphasize summation and external control, clustering has the potential to encourage a formative, learning approach. More research is required to shed more light on factors behind observed performance of the different clusters. Other countries especially low-income countries that share many similarities with Uganda can learn from these experiences. © The Author 2015. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.

A comparison of hierarchical cluster analysis and league table rankings as methods for analysis and presentation of district health system performance data in Uganda†

PubMed Central

Tashobya, Christine K; Dubourg, Dominique; Ssengooba, Freddie; Speybroeck, Niko; Macq, Jean; Criel, Bart

2016-01-01

In 2003, the Uganda Ministry of Health introduced the district league table for district health system performance assessment. The league table presents district performance against a number of input, process and output indicators and a composite index to rank districts. This study explores the use of hierarchical cluster analysis for analysing and presenting district health systems performance data and compares this approach with the use of the league table in Uganda. Ministry of Health and district plans and reports, and published documents were used to provide information on the development and utilization of the Uganda district league table. Quantitative data were accessed from the Ministry of Health databases. Statistical analysis using SPSS version 20 and hierarchical cluster analysis, utilizing Wards’ method was used. The hierarchical cluster analysis was conducted on the basis of seven clusters determined for each year from 2003 to 2010, ranging from a cluster of good through moderate-to-poor performers. The characteristics and membership of clusters varied from year to year and were determined by the identity and magnitude of performance of the individual variables. Criticisms of the league table include: perceived unfairness, as it did not take into consideration district peculiarities; and being oversummarized and not adequately informative. Clustering organizes the many data points into clusters of similar entities according to an agreed set of indicators and can provide the beginning point for identifying factors behind the observed performance of districts. Although league table ranking emphasize summation and external control, clustering has the potential to encourage a formative, learning approach. More research is required to shed more light on factors behind observed performance of the different clusters. Other countries especially low-income countries that share many similarities with Uganda can learn from these experiences. PMID:26024882

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.

Culture Modulates the Brain Response to Harmonic Violations: An EEG Study on Hierarchical Syntactic Structure in Music.

PubMed

Akrami, Haleh; Moghimi, Sahar

2017-01-01

We investigated the role of culture in processing hierarchical syntactic structures in music. We examined whether violation of non-local dependencies manifest in event related potentials (ERP) for Western and Iranian excerpts by recording EEG while participants passively listened to sequences of modified/original excerpts. We also investigated oscillatory and synchronization properties of brain responses during processing of hierarchical structures. For the Western excerpt, subjective ratings of conclusiveness were marginally significant and the difference in the ERP components fell short of significance. However, ERP and behavioral results showed that while listening to culturally familiar music, subjects comprehended whether or not the hierarchical syntactic structure was fulfilled. Irregularities in the hierarchical structures of the Iranian excerpt elicited an early negativity in the central regions bilaterally, followed by two later negativities from 450-700 to 750-950 ms. The latter manifested throughout the scalp. Moreover, violations of hierarchical structure in the Iranian excerpt were associated with (i) an early decrease in the long range alpha phase synchronization, (ii) an early increase in the oscillatory activity in the beta band over the central areas, and (iii) a late decrease in the theta band phase synchrony between left anterior and right posterior regions. Results suggest that rhythmic structures and melodic fragments, representative of Iranian music, created a familiar context in which recognition of complex non-local syntactic structures was feasible for Iranian listeners. Processing of neural responses to the Iranian excerpt indicated neural mechanisms for processing of hierarchical syntactic structures in music at different levels of cortical integration.

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.

Robust Sensitivity Analysis for Multi-Attribute Deterministic Hierarchical Value Models

DTIC Science & Technology

2002-03-01

such as weighted sum method, weighted 5 product method, and the Analytic Hierarchy Process ( AHP ). This research focuses on only weighted sum...different groups. They can be termed as deterministic, stochastic, or fuzzy multi-objective decision methods if they are classified according to the...weighted product model (WPM), and analytic hierarchy process ( AHP ). His method attempts to identify the most important criteria weight and the most

A novel method for a multi-level hierarchical composite with brick-and-mortar structure

PubMed Central

Brandt, Kristina; Wolff, Michael F. H.; Salikov, Vitalij; Heinrich, Stefan; Schneider, Gerold A.

2013-01-01

The fascination for hierarchically structured hard tissues such as enamel or nacre arises from their unique structure-properties-relationship. During the last decades this numerously motivated the synthesis of composites, mimicking the brick-and-mortar structure of nacre. However, there is still a lack in synthetic engineering materials displaying a true hierarchical structure. Here, we present a novel multi-step processing route for anisotropic 2-level hierarchical composites by combining different coating techniques on different length scales. It comprises polymer-encapsulated ceramic particles as building blocks for the first level, followed by spouted bed spray granulation for a second level, and finally directional hot pressing to anisotropically consolidate the composite. The microstructure achieved reveals a brick-and-mortar hierarchical structure with distinct, however not yet optimized mechanical properties on each level. It opens up a completely new processing route for the synthesis of multi-level hierarchically structured composites, giving prospects to multi-functional structure-properties relationships. PMID:23900554

A novel method for a multi-level hierarchical composite with brick-and-mortar structure.

PubMed

Brandt, Kristina; Wolff, Michael F H; Salikov, Vitalij; Heinrich, Stefan; Schneider, Gerold A

2013-01-01

The fascination for hierarchically structured hard tissues such as enamel or nacre arises from their unique structure-properties-relationship. During the last decades this numerously motivated the synthesis of composites, mimicking the brick-and-mortar structure of nacre. However, there is still a lack in synthetic engineering materials displaying a true hierarchical structure. Here, we present a novel multi-step processing route for anisotropic 2-level hierarchical composites by combining different coating techniques on different length scales. It comprises polymer-encapsulated ceramic particles as building blocks for the first level, followed by spouted bed spray granulation for a second level, and finally directional hot pressing to anisotropically consolidate the composite. The microstructure achieved reveals a brick-and-mortar hierarchical structure with distinct, however not yet optimized mechanical properties on each level. It opens up a completely new processing route for the synthesis of multi-level hierarchically structured composites, giving prospects to multi-functional structure-properties relationships.

A novel method for a multi-level hierarchical composite with brick-and-mortar structure

NASA Astrophysics Data System (ADS)

Brandt, Kristina; Wolff, Michael F. H.; Salikov, Vitalij; Heinrich, Stefan; Schneider, Gerold A.

2013-07-01

The fascination for hierarchically structured hard tissues such as enamel or nacre arises from their unique structure-properties-relationship. During the last decades this numerously motivated the synthesis of composites, mimicking the brick-and-mortar structure of nacre. However, there is still a lack in synthetic engineering materials displaying a true hierarchical structure. Here, we present a novel multi-step processing route for anisotropic 2-level hierarchical composites by combining different coating techniques on different length scales. It comprises polymer-encapsulated ceramic particles as building blocks for the first level, followed by spouted bed spray granulation for a second level, and finally directional hot pressing to anisotropically consolidate the composite. The microstructure achieved reveals a brick-and-mortar hierarchical structure with distinct, however not yet optimized mechanical properties on each level. It opens up a completely new processing route for the synthesis of multi-level hierarchically structured composites, giving prospects to multi-functional structure-properties relationships.

Bayesian Hierarchical Classes Analysis

ERIC Educational Resources Information Center

Leenen, Iwin; Van Mechelen, Iven; Gelman, Andrew; De Knop, Stijn

2008-01-01

Hierarchical classes models are models for "N"-way "N"-mode data that represent the association among the "N" modes and simultaneously yield, for each mode, a hierarchical classification of its elements. In this paper we present a stochastic extension of the hierarchical classes model for two-way two-mode binary data. In line with the original…

Construction of mammographic examination process ontology using bottom-up hierarchical task analysis.

PubMed

Yagahara, Ayako; Yokooka, Yuki; Jiang, Guoqian; Tsuji, Shintarou; Fukuda, Akihisa; Nishimoto, Naoki; Kurowarabi, Kunio; Ogasawara, Katsuhiko

2018-03-01

Describing complex mammography examination processes is important for improving the quality of mammograms. It is often difficult for experienced radiologic technologists to explain the process because their techniques depend on their experience and intuition. In our previous study, we analyzed the process using a new bottom-up hierarchical task analysis and identified key components of the process. Leveraging the results of the previous study, the purpose of this study was to construct a mammographic examination process ontology to formally describe the relationships between the process and image evaluation criteria to improve the quality of mammograms. First, we identified and created root classes: task, plan, and clinical image evaluation (CIE). Second, we described an "is-a" relation referring to the result of the previous study and the structure of the CIE. Third, the procedural steps in the ontology were described using the new properties: "isPerformedBefore," "isPerformedAfter," and "isPerformedAfterIfNecessary." Finally, the relationships between tasks and CIEs were described using the "isAffectedBy" property to represent the influence of the process on image quality. In total, there were 219 classes in the ontology. By introducing new properties related to the process flow, a sophisticated mammography examination process could be visualized. In relationships between tasks and CIEs, it became clear that the tasks affecting the evaluation criteria related to positioning were greater in number than those for image quality. We developed a mammographic examination process ontology that makes knowledge explicit for a comprehensive mammography process. Our research will support education and help promote knowledge sharing about mammography examination expertise.

INCORPORATING INDUSTRIAL ECOLOGY INTO HIERARCHICAL CHEMICAL PROCESS DESIGN

EPA Science Inventory

Incorporating Industrial Ecology into Hierarchical Chemical Process Design: Determining Targets for the Exchange of Waste

The exchange of waste to be used as a recycled feed has long been encouraged by practitioners of industrial ecology. Industrial ecology is a field t...

Understanding movement data and movement processes: current and emerging directions.

PubMed

Schick, Robert S; Loarie, Scott R; Colchero, Fernando; Best, Benjamin D; Boustany, Andre; Conde, Dalia A; Halpin, Patrick N; Joppa, Lucas N; McClellan, Catherine M; Clark, James S

2008-12-01

Animal movement has been the focus on much theoretical and empirical work in ecology over the last 25 years. By studying the causes and consequences of individual movement, ecologists have gained greater insight into the behavior of individuals and the spatial dynamics of populations at increasingly higher levels of organization. In particular, ecologists have focused on the interaction between individuals and their environment in an effort to understand future impacts from habitat loss and climate change. Tools to examine this interaction have included: fractal analysis, first passage time, Lévy flights, multi-behavioral analysis, hidden markov models, and state-space models. Concurrent with the development of movement models has been an increase in the sophistication and availability of hierarchical bayesian models. In this review we bring these two threads together by using hierarchical structures as a framework for reviewing individual models. We synthesize emerging themes in movement ecology, and propose a new hierarchical model for animal movement that builds on these emerging themes. This model moves away from traditional random walks, and instead focuses inference on how moving animals with complex behavior interact with their landscape and make choices about its suitability.

The architecture of amyloid-like peptide fibrils revealed by X-ray scattering, diffraction and electron microscopy

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

Langkilde, Annette E., E-mail: annette.langkilde@sund.ku.dk; Morris, Kyle L.; Serpell, Louise C.

The aggregation process and the fibril state of an amyloidogenic peptide suggest monomer addition to be the prevailing mechanism of elongation and a model of the peptide packing in the fibrils has been obtained. Structural analysis of protein fibrillation is inherently challenging. Given the crucial role of fibrils in amyloid diseases, method advancement is urgently needed. A hybrid modelling approach is presented enabling detailed analysis of a highly ordered and hierarchically organized fibril of the GNNQQNY peptide fragment of a yeast prion protein. Data from small-angle X-ray solution scattering, fibre diffraction and electron microscopy are combined with existing high-resolution X-raymore » crystallographic structures to investigate the fibrillation process and the hierarchical fibril structure of the peptide fragment. The elongation of these fibrils proceeds without the accumulation of any detectable amount of intermediate oligomeric species, as is otherwise reported for, for example, glucagon, insulin and α-synuclein. Ribbons constituted of linearly arranged protofilaments are formed. An additional hierarchical layer is generated via the pairing of ribbons during fibril maturation. Based on the complementary data, a quasi-atomic resolution model of the protofilament peptide arrangement is suggested. The peptide structure appears in a β-sheet arrangement reminiscent of the β-zipper structures evident from high-resolution crystal structures, with specific differences in the relative peptide orientation. The complexity of protein fibrillation and structure emphasizes the need to use multiple complementary methods.« less

Settlement-Size Scaling among Prehistoric Hunter-Gatherer Settlement Systems in the New World

PubMed Central

Haas, W. Randall; Klink, Cynthia J.; Maggard, Greg J.; Aldenderfer, Mark S.

2015-01-01

Settlement size predicts extreme variation in the rates and magnitudes of many social and ecological processes in human societies. Yet, the factors that drive human settlement-size variation remain poorly understood. Size variation among economically integrated settlements tends to be heavy tailed such that the smallest settlements are extremely common and the largest settlements extremely large and rare. The upper tail of this size distribution is often formalized mathematically as a power-law function. Explanations for this scaling structure in human settlement systems tend to emphasize complex socioeconomic processes including agriculture, manufacturing, and warfare—behaviors that tend to differentially nucleate and disperse populations hierarchically among settlements. But, the degree to which heavy-tailed settlement-size variation requires such complex behaviors remains unclear. By examining the settlement patterns of eight prehistoric New World hunter-gatherer settlement systems spanning three distinct environmental contexts, this analysis explores the degree to which heavy-tailed settlement-size scaling depends on the aforementioned socioeconomic complexities. Surprisingly, the analysis finds that power-law models offer plausible and parsimonious statistical descriptions of prehistoric hunter-gatherer settlement-size variation. This finding reveals that incipient forms of hierarchical settlement structure may have preceded socioeconomic complexity in human societies and points to a need for additional research to explicate how mobile foragers came to exhibit settlement patterns that are more commonly associated with hierarchical organization. We propose that hunter-gatherer mobility with preferential attachment to previously occupied locations may account for the observed structure in site-size variation. PMID:26536241

Control, responses and modularity of cellular regulatory networks: a control analysis perspective.

PubMed

Bruggeman, F J; Snoep, J L; Westerhoff, H V

2008-11-01

Cells adapt to changes in environmental conditions through the concerted action of signalling, gene expression and metabolic subsystems. The authors will discuss a theoretical framework addressing such integrated systems. This 'hierarchical analysis' was first developed as an extension to a metabolic control analysis. It builds on the phenomenon that often the communication between signalling, gene expression and metabolic subsystems is almost exclusively via regulatory interactions and not via mass flow interactions. This allows for the treatment of the said subsystems as 'levels' in a hierarchical view of the organisation of the molecular reaction network of cells. Such a hierarchical approach has as a major advantage that levels can be analysed conceptually in isolation of each other (from a local intra-level perspective) and at a later stage integrated via their interactions (from a global inter-level perspective). Hereby, it allows for a modular approach with variable scope. A number of different approaches have been developed for the analysis of hierarchical systems, for example hierarchical control analysis and modular response analysis. The authors, here, review these methods and illustrate the strength of these types of analyses using a core model of a system with gene expression, metabolic and signal transduction levels.

A Hierarchical Model and Analysis of Factors Affecting the Adoption of Timber as a Bridge

Treesearch

Robert L. Smith; Robert J. Bush; Daniel L. Schmoldt

1995-01-01

The Analytical Hierarchy Process was used to characterize the bridge material selection decisions of highway engineers and local highway officials across the United States. State Department of Transportation engineers, private consulting engineers, and local highway officials were personally interviewed in Mississippi, Virginia, Washington, and Wisconsin to identify...

Stopover habitat: management implications and guidelines

Treesearch

Frank R. Moore; Sidney A. Gauthreaux; Paul Kerlinger; Ted R. Simons

1993-01-01

If persistence of migrant populations depends on the ability to find favorable conditions for survival throughout the annual cycle, factors associated with the en-route ecology of migrants must figure in any analysis of population change and in development of a comprehensive conservation "strategy." We view en-route habitat selection as a hierarchical process...

Argument structure hierarchy system and method for facilitating analysis and decision-making processes

DOEpatents

Janssen, Terry

2000-01-01

A system and method for facilitating decision-making comprising a computer program causing linkage of data representing a plurality of argument structure units into a hierarchical argument structure. Each argument structure unit comprises data corresponding to a hypothesis and its corresponding counter-hypothesis, data corresponding to grounds that provide a basis for inference of the hypothesis or its corresponding counter-hypothesis, data corresponding to a warrant linking the grounds to the hypothesis or its corresponding counter-hypothesis, and data corresponding to backing that certifies the warrant. The hierarchical argument structure comprises a top level argument structure unit and a plurality of subordinate level argument structure units. Each of the plurality of subordinate argument structure units comprises at least a portion of the grounds of the argument structure unit to which it is subordinate. Program code located on each of a plurality of remote computers accepts input from one of a plurality of contributors. Each input comprises data corresponding to an argument structure unit in the hierarchical argument structure and supports the hypothesis or its corresponding counter-hypothesis. A second programming code is adapted to combine the inputs into a single hierarchical argument structure. A third computer program code is responsive to the second computer program code and is adapted to represent a degree of support for the hypothesis and its corresponding counter-hypothesis in the single hierarchical argument structure.

Is it time to Leave Behind the Revised Hierarchical Model of Bilingual Language Processing after Fifteen Years of Service?

ERIC Educational Resources Information Center

Brysbaert, Marc; Duyck, Wouter

2010-01-01

The Revised Hierarchical Model (RHM) of bilingual language processing dominates current thinking on bilingual language processing. Recently, basic tenets of the model have been called into question. First, there is little evidence for separate lexicons. Second, there is little evidence for language selective access. Third, the inclusion of…

Cognitive mechanisms underlying third graders' arithmetic skills: Expanding the pathways to mathematics model.

PubMed

Träff, Ulf; Olsson, Linda; Skagerlund, Kenny; Östergren, Rickard

2018-03-01

A modified pathways to mathematics model was used to examine the cognitive mechanisms underlying arithmetic skills in third graders. A total of 269 children were assessed on tasks tapping the four pathways and arithmetic skills. A path analysis showed that symbolic number processing was directly supported by the linguistic and approximate quantitative pathways. The direct contribution from the four pathways to arithmetic proficiency varied; the linguistic pathway supported single-digit arithmetic and word problem solving, whereas the approximate quantitative pathway supported only multi-digit calculation. The spatial processing and verbal working memory pathways supported only arithmetic word problem solving. The notion of hierarchical levels of arithmetic was supported by the results, and the different levels were supported by different constellations of pathways. However, the strongest support to the hierarchical levels of arithmetic were provided by the proximal arithmetic skills. Copyright © 2017 Elsevier Inc. All rights reserved.

Possibilities of Utilizing the Method of Analytical Hierarchy Process Within the Strategy of Corporate Social Business

NASA Astrophysics Data System (ADS)

Drieniková, Katarína; Hrdinová, Gabriela; Naňo, Tomáš; Sakál, Peter

2010-01-01

The paper deals with the analysis of the theory of corporate social responsibility, risk management and the exact method of analytic hierarchic process that is used in the decision-making processes. The Chapters 2 and 3 focus on presentation of the experience with the application of the method in formulating the stakeholders' strategic goals within the Corporate Social Responsibility (CSR) and simultaneously its utilization in minimizing the environmental risks. The major benefit of this paper is the application of Analytical Hierarchy Process (AHP).

Methodological aspects of fuel performance system analysis at raw hydrocarbon processing plants

NASA Astrophysics Data System (ADS)

Kulbjakina, A. V.; Dolotovskij, I. V.

2018-01-01

The article discusses the methodological aspects of fuel performance system analysis at raw hydrocarbon (RH) processing plants. Modern RH processing facilities are the major consumers of energy resources (ER) for their own needs. To reduce ER, including fuel consumption, and to develop rational fuel system structure are complex and relevant scientific tasks that can only be done using system analysis and complex system synthesis. In accordance with the principles of system analysis, the hierarchical structure of the fuel system, the block scheme for the synthesis of the most efficient alternative of the fuel system using mathematical models and the set of performance criteria have been developed on the main stages of the study. The results from the introduction of specific engineering solutions to develop their own energy supply sources for RH processing facilities have been provided.

Revisiting the variation of clustering coefficient of biological networks suggests new modular structure.

PubMed

Hao, Dapeng; Ren, Cong; Li, Chuanxing

2012-05-01

A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn't show dependence of degree. Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to "deterministic model" of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.

Chemical Structure and Molecular Dimension As Controls on the Inherent Stability of Charcoal in Boreal Forest Soil

NASA Astrophysics Data System (ADS)

Hockaday, W. C.; Kane, E. S.; Ohlson, M.; Huang, R.; Von Bargen, J.; Davis, R.

2014-12-01

Efforts have been made by various scientific disciplines to study hyporheic zones and characterize their associated processes. One way to approach the study of the hyporheic zone is to define facies, which are elements of a (hydrobio) geologic classification scheme that groups components of a complex system with high variability into a manageable set of discrete classes. In this study, we try to classify the hyporheic zone based on the geology, geochemistry, microbiology, and understand their interactive influences on the integrated biogeochemical distributions and processes. A number of measurements have been taken for 21 freeze core samples along the Columbia River bank in the Hanford 300 Area, and unique datasets have been obtained on biomass, pH, number of microbial taxa, percentage of N/C/H/S, microbial activity parameters, as well as microbial community attributes/modules. In order to gain a complete understanding of the geological control on these variables and processes, the explanatory variables are set to include quantitative gravel/sand/mud/silt/clay percentages, statistical moments of grain size distributions, as well as geological (e.g., Folk-Wentworth) and statistical (e.g., hierarchical) clusters. The dominant factors for major microbial and geochemical variables are identified and summarized using exploratory data analysis approaches (e.g., principal component analysis, hierarchical clustering, factor analysis, multivariate analysis of variance). The feasibility of extending the facies definition and its control of microbial and geochemical properties to larger scales is discussed.

Microbial facies distribution and its geological and geochemical controls at the Hanford 300 area

NASA Astrophysics Data System (ADS)

Hou, Z.; Nelson, W.; Stegen, J.; Murray, C. J.; Arntzen, E.

2015-12-01

Efforts have been made by various scientific disciplines to study hyporheic zones and characterize their associated processes. One way to approach the study of the hyporheic zone is to define facies, which are elements of a (hydrobio) geologic classification scheme that groups components of a complex system with high variability into a manageable set of discrete classes. In this study, we try to classify the hyporheic zone based on the geology, geochemistry, microbiology, and understand their interactive influences on the integrated biogeochemical distributions and processes. A number of measurements have been taken for 21 freeze core samples along the Columbia River bank in the Hanford 300 Area, and unique datasets have been obtained on biomass, pH, number of microbial taxa, percentage of N/C/H/S, microbial activity parameters, as well as microbial community attributes/modules. In order to gain a complete understanding of the geological control on these variables and processes, the explanatory variables are set to include quantitative gravel/sand/mud/silt/clay percentages, statistical moments of grain size distributions, as well as geological (e.g., Folk-Wentworth) and statistical (e.g., hierarchical) clusters. The dominant factors for major microbial and geochemical variables are identified and summarized using exploratory data analysis approaches (e.g., principal component analysis, hierarchical clustering, factor analysis, multivariate analysis of variance). The feasibility of extending the facies definition and its control of microbial and geochemical properties to larger scales is discussed.

Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service

PubMed Central

Bao, Shunxing; Plassard, Andrew J.; Landman, Bennett A.; Gokhale, Aniruddha

2017-01-01

Traditional in-house, laboratory-based medical imaging studies use hierarchical data structures (e.g., NFS file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance from these approaches is, however, impeded by standard network switches since they can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. To that end, a cloud-based “medical image processing-as-a-service” offers promise in utilizing the ecosystem of Apache Hadoop, which is a flexible framework providing distributed, scalable, fault tolerant storage and parallel computational modules, and HBase, which is a NoSQL database built atop Hadoop’s distributed file system. Despite this promise, HBase’s load distribution strategy of region split and merge is detrimental to the hierarchical organization of imaging data (e.g., project, subject, session, scan, slice). This paper makes two contributions to address these concerns by describing key cloud engineering principles and technology enhancements we made to the Apache Hadoop ecosystem for medical imaging applications. First, we propose a row-key design for HBase, which is a necessary step that is driven by the hierarchical organization of imaging data. Second, we propose a novel data allocation policy within HBase to strongly enforce collocation of hierarchically related imaging data. The proposed enhancements accelerate data processing by minimizing network usage and localizing processing to machines where the data already exist. Moreover, our approach is amenable to the traditional scan, subject, and project-level analysis procedures, and is compatible with standard command line/scriptable image processing software. Experimental results for an illustrative sample of imaging data reveals that our new HBase policy results in a three-fold time improvement in conversion of classic DICOM to NiFTI file formats when compared with the default HBase region split policy, and nearly a six-fold improvement over a commonly available network file system (NFS) approach even for relatively small file sets. Moreover, file access latency is lower than network attached storage. PMID:28884169

Formal Multilevel Hierarchical Verification of Synchronous MOS VLSI Circuits.

DTIC Science & Technology

1987-06-01

166 12.4 Capacitance Coupling............................. 166 12.5 Multiple Abstraction Fuctions ....................... 168...depend on whether it is performing flat verification or hierarchical verification. The primary operations of Silica Pithecus when performing flat...signals never arise. The primary operation of Silica Pithecus when performing hierarchical verification is processing constraints to show they hold

Syntactic Structure and Artificial Grammar Learning: The Learnability of Embedded Hierarchical Structures

ERIC Educational Resources Information Center

de Vries, Meinou H.; Monaghan, Padraic; Knecht, Stefan; Zwitserlood, Pienie

2008-01-01

Embedded hierarchical structures, such as "the rat the cat ate was brown", constitute a core generative property of a natural language theory. Several recent studies have reported learning of hierarchical embeddings in artificial grammar learning (AGL) tasks, and described the functional specificity of Broca's area for processing such structures.…

Hierarchical process memory: memory as an integral component of information processing

PubMed Central

Hasson, Uri; Chen, Janice; Honey, Christopher J.

2015-01-01

Models of working memory commonly focus on how information is encoded into and retrieved from storage at specific moments. However, in the majority of real-life processes, past information is used continuously to process incoming information across multiple timescales. Considering single unit, electrocorticography, and functional imaging data, we argue that (i) virtually all cortical circuits can accumulate information over time, and (ii) the timescales of accumulation vary hierarchically, from early sensory areas with short processing timescales (tens to hundreds of milliseconds) to higher-order areas with long processing timescales (many seconds to minutes). In this hierarchical systems perspective, memory is not restricted to a few localized stores, but is intrinsic to information processing that unfolds throughout the brain on multiple timescales. “The present contains nothing more than the past, and what is found in the effect was already in the cause.”Henri L Bergson PMID:25980649

Hierarchical Forms Processing in Adults and Children

ERIC Educational Resources Information Center

Harrison, Tamara B.; Stiles, Joan

2009-01-01

Two experiments examined child and adult processing of hierarchical stimuli composed of geometric forms. Adults (ages 18-23 years) and children (ages 7-10 years) performed a forced-choice task gauging similarity between visual stimuli consisting of large geometric objects (global level) composed of small geometric objects (local level). The…

Leadership styles across hierarchical levels in nursing departments.

PubMed

Stordeur, S; Vandenberghe, C; D'hoore, W

2000-01-01

Some researchers have reported on the cascading effect of transformational leadership across hierarchical levels. One study examined this effect in nursing, but it was limited to a single hospital. To examine the cascading effect of leadership styles across hierarchical levels in a sample of nursing departments and to investigate the effect of hierarchical level on the relationships between leadership styles and various work outcomes. Based on a sample of eight hospitals, the cascading effect was tested using correlation analysis. The main sources of variation among leadership scores were determined with analyses of variance (ANOVA), and the interaction effect of hierarchical level and leadership styles on criterion variables was tested with moderated regression analysis. No support was found for a cascading effect of leadership across hierarchical levels. Rather, the variation of leadership scores was explained primarily by the organizational context. Transformational leadership had a stronger impact on criterion variables than transactional leadership. Interaction effects between leadership styles and hierarchical level were observed only for perceived unit effectiveness. The hospital's structure and culture are major determinants of leadership styles.

The influence of visual and phonological features on the hemispheric processing of hierarchical Navon letters.

PubMed

Aiello, Marilena; Merola, Sheila; Lasaponara, Stefano; Pinto, Mario; Tomaiuolo, Francesco; Doricchi, Fabrizio

2018-01-31

The possibility of allocating attentional resources to the "global" shape or to the "local" details of pictorial stimuli helps visual processing. Investigations with hierarchical Navon letters, that are large "global" letters made up of small "local" ones, consistently demonstrate a right hemisphere advantage for global processing and a left hemisphere advantage for local processing. Here we investigated how the visual and phonological features of the global and local components of Navon letters influence these hemispheric advantages. In a first study in healthy participants, we contrasted the hemispheric processing of hierarchical letters with global and local items competing for response selection, to the processing of hierarchical letters in which a letter, a false-letter conveying no phonological information or a geometrical shape presented at the unattended level did not compete for response selection. In a second study, we investigated the hemispheric processing of hierarchical stimuli in which global and local letters were both visually and phonologically congruent (e.g. large uppercase G made of smaller uppercase G), visually incongruent and phonologically congruent (e.g. large uppercase G made of small lowercase g) or visually incongruent and phonologically incongruent (e.g. large uppercase G made of small lowercase or uppercase M). In a third study, we administered the same tasks to a right brain damaged patient with a lesion involving pre-striate areas engaged by global processing. The results of the first two experiments showed that the global abilities of the left hemisphere are limited because of its strong susceptibility to interference from local letters even when these are irrelevant to the task. Phonological features played a crucial role in this interference because the interference was entirely maintained also when letters at the global and local level were presented in different uppercase vs. lowercase formats. In contrast, when local features conveyed no phonological information, the left hemisphere showed preserved global processing abilities. These findings were supported by the study of the right brain damaged patient. These results offer a new look at the hemispheric dominance in the attentional processing of the global and local levels of hierarchical stimuli. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integration Framework of Process Planning based on Resource Independent Operation Summary to Support Collaborative Manufacturing

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

Kulvatunyou, Boonserm; Wysk, Richard A.; Cho, Hyunbo

2004-06-01

In today's global manufacturing environment, manufacturing functions are distributed as never before. Design, engineering, fabrication, and assembly of new products are done routinely in many different enterprises scattered around the world. Successful business transactions require the sharing of design and engineering data on an unprecedented scale. This paper describes a framework that facilitates the collaboration of engineering tasks, particularly process planning and analysis, to support such globalized manufacturing activities. The information models of data and the software components that integrate those information models are described. The integration framework uses an Integrated Product and Process Data (IPPD) representation called a Resourcemore » Independent Operation Summary (RIOS) to facilitate the communication of business and manufacturing requirements. Hierarchical process modeling, process planning decomposition and an augmented AND/OR directed graph are used in this representation. The Resource Specific Process Planning (RSPP) module assigns required equipment and tools, selects process parameters, and determines manufacturing costs based on two-level hierarchical RIOS data. The shop floor knowledge (resource and process knowledge) and a hybrid approach (heuristic and linear programming) to linearize the AND/OR graph provide the basis for the planning. Finally, a prototype system is developed and demonstrated with an exemplary part. Java and XML (Extensible Markup Language) are used to ensure software and information portability.« less

WE-G-BRA-06: Application of Systems and Control Theory-Based Hazard Analysis to Radiotherapy

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

Pawlicki, T; Samost, A; Leveson, N

Purpose: The process of delivering radiation occurs in a complex socio-technical system heavily reliant on human operators. Furthermore, both humans and software are notoriously challenging to account for in traditional hazard analysis models. High reliability industries such as aviation have approached this problem through using hazard analysis techniques grounded in systems and control theory. The purpose of this work is to apply the Systems Theoretic Accident Model Processes (STAMP) hazard model to radiotherapy. In particular, the System-Theoretic Process Analysis (STPA) approach is used to perform a hazard analysis of a proposed on-line adaptive cranial radiosurgery procedure that omits the CTmore » Simulation step and uses only CBCT for planning, localization, and treatment. Methods: The STPA procedure first requires the definition of high-level accidents and hazards leading to those accidents. From there, hierarchical control structures were created followed by the identification and description of control actions for each control structure. Utilizing these control structures, unsafe states of each control action were created. Scenarios contributing to unsafe control action states were then identified and translated into system requirements to constrain process behavior within safe boundaries. Results: Ten control structures were created for this new CBCT-only process which covered the areas of hospital and department management, treatment design and delivery, and vendor service. Twenty three control actions were identified that contributed to over 80 unsafe states of those control actions resulting in over 220 failure scenarios. Conclusion: The interaction of people, hardware, and software are highlighted through the STPA approach. STPA provides a hierarchical model for understanding the role of management decisions in impacting system safety so that a process design requirement can be traced back to the hazard and accident that it is intended to mitigate. Varian Medical Systems, Inc.« less

Hierarchical clustering using correlation metric and spatial continuity constraint

DOEpatents

Stork, Christopher L.; Brewer, Luke N.

2012-10-02

Large data sets are analyzed by hierarchical clustering using correlation as a similarity measure. This provides results that are superior to those obtained using a Euclidean distance similarity measure. A spatial continuity constraint may be applied in hierarchical clustering analysis of images.

Delineation, peer review, and refinement of subregions of the conterminous United States

Treesearch

J.E. Keys; D.T. Cleland; W.H. McNab

2007-01-01

This paper briefly describes the background of the U.S. Department of Agriculture (USDA) Forest Service National Hierarchical Framework of Ecological Units and the methods used for delineating map units at the subregion planning and analysis scale. The process for scientific review and continuous refinement of ecological units and associated data of subregions is also...

Examining the Antecedents of ICT Adoption in Education Using an Extended Technology Acceptance Model (TAM)

ERIC Educational Resources Information Center

Teeroovengadum, Viraiyan; Heeraman, Nabeel; Jugurnath, Bhavish

2017-01-01

This study assesses the determinants of ICT adoption by educators in the teaching and learning process in the context of a developing country, Mauritius. A hierarchical regression analysis is used, to firstly determine the incremental effects of factors from the technology acceptance model (TAM) while controlling for demographic variables such as…

A Hierarchical Analysis of Bridge Decision Makers ... The Role of New Technology Adoption in the Timber Bridge Market: Special Project

Treesearch

Robert L. Smith; Robert J. Bush; Daniel L. Schmoldt

1995-01-01

Bridge design engineers and local highway officials make bridge replacement decisions across the United States. The Analytical Hierarchy Process was used to characterize the bridge material selection decision of these individuals. State Department of Transportation engineers, private consulting engineers, and local highway officials were personally interviewed in...

Use of NARCCAP data to characterize regional climate uncertainty in the impact of global climate change on large river fish population: Missouri River sturgeon example

NASA Astrophysics Data System (ADS)

Anderson, C. J.; Wildhaber, M. L.; Wikle, C. K.; Moran, E. H.; Franz, K. J.; Dey, R.

2012-12-01

Climate change operates over a broad range of spatial and temporal scales. Understanding the effects of change on ecosystems requires accounting for the propagation of information and uncertainty across these scales. For example, to understand potential climate change effects on fish populations in riverine ecosystems, climate conditions predicted by course-resolution atmosphere-ocean global climate models must first be translated to the regional climate scale. In turn, this regional information is used to force watershed models, which are used to force river condition models, which impact the population response. A critical challenge in such a multiscale modeling environment is to quantify sources of uncertainty given the highly nonlinear nature of interactions between climate variables and the individual organism. We use a hierarchical modeling approach for accommodating uncertainty in multiscale ecological impact studies. This framework allows for uncertainty due to system models, model parameter settings, and stochastic parameterizations. This approach is a hybrid between physical (deterministic) downscaling and statistical downscaling, recognizing that there is uncertainty in both. We use NARCCAP data to determine confidence the capability of climate models to simulate relevant processes and to quantify regional climate variability within the context of the hierarchical model of uncertainty quantification. By confidence, we mean the ability of the regional climate model to replicate observed mechanisms. We use the NCEP-driven simulations for this analysis. This provides a base from which regional change can be categorized as either a modification of previously observed mechanisms or emergence of new processes. The management implications for these categories of change are significantly different in that procedures to address impacts from existing processes may already be known and need adjustment; whereas, an emergent processes may require new management strategies. The results from hierarchical analysis of uncertainty are used to study the relative change in weights of the endangered Missouri River pallid sturgeon (Scaphirhynchus albus) under a 21st century climate scenario.

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

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.

Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a triaxial hierarchical model.

PubMed

Xiao, Fuyuan; Aritsugi, Masayoshi; Wang, Qing; Zhang, Rong

2016-09-01

For efficient and sophisticated analysis of complex event patterns that appear in streams of big data from health care information systems and support for decision-making, a triaxial hierarchical model is proposed in this paper. Our triaxial hierarchical model is developed by focusing on hierarchies among nested event pattern queries with an event concept hierarchy, thereby allowing us to identify the relationships among the expressions and sub-expressions of the queries extensively. We devise a cost-based heuristic by means of the triaxial hierarchical model to find an optimised query execution plan in terms of the costs of both the operators and the communications between them. According to the triaxial hierarchical model, we can also calculate how to reuse the results of the common sub-expressions in multiple queries. By integrating the optimised query execution plan with the reuse schemes, a multi-query optimisation strategy is developed to accomplish efficient processing of multiple nested event pattern queries. We present empirical studies in which the performance of multi-query optimisation strategy was examined under various stream input rates and workloads. Specifically, the workloads of pattern queries can be used for supporting monitoring patients' conditions. On the other hand, experiments with varying input rates of streams can correspond to changes of the numbers of patients that a system should manage, whereas burst input rates can correspond to changes of rushes of patients to be taken care of. The experimental results have shown that, in Workload 1, our proposal can improve about 4 and 2 times throughput comparing with the relative works, respectively; in Workload 2, our proposal can improve about 3 and 2 times throughput comparing with the relative works, respectively; in Workload 3, our proposal can improve about 6 times throughput comparing with the relative work. The experimental results demonstrated that our proposal was able to process complex queries efficiently which can support health information systems and further decision-making. Copyright © 2016 Elsevier B.V. All rights reserved.

Nanoporous Tin with a Granular Hierarchical Ligament Morphology as a Highly Stable Li-Ion Battery Anode

DOE PAGES

Cook, John B.; Detsi, Eric; Liu, Yijin; ...

2016-12-07

Next generation Li-ion batteries will require negative electrode materials with energy densities many-fold higher than that found in the graphitic carbon currently used in commercial Li-ion batteries. While various nanostructured alloying-type anode materials may satisfy that requirement, such materials do not always exhibit long cycle lifetimes and/or their processing routes are not always suitable for large-scale synthesis. Here, we report on a high-performance anode material for next generation Li-ion batteries made of nanoporous Sn powders with hierarchical ligament morphology. This material system combines both long cycle lifetimes (more than 72% capacity retention after 350 cycles), high capacity (693 mAh/g, nearlymore » twice that of commercial graphitic carbon), good charging/discharging capabilities (545 mAh/g at 1 A/g, 1.5C), and a scalable processing route that involves selective alloy corrosion. The good cycling performance of this system is attributed to its nanoporous architecture and its unique hierarchical ligament morphology, which accommodates the large volume changes taking place during lithiation, as confirmed by synchrotron-based ex-situ X-ray 3D tomography analysis. In conclusion, our findings are an important step for the development of high-performance Li-ion batteries.« less

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.

Active Interaction Mapping as a tool to elucidate hierarchical functions of biological processes.

PubMed

Farré, Jean-Claude; Kramer, Michael; Ideker, Trey; Subramani, Suresh

2017-07-03

Increasingly, various 'omics data are contributing significantly to our understanding of novel biological processes, but it has not been possible to iteratively elucidate hierarchical functions in complex phenomena. We describe a general systems biology approach called Active Interaction Mapping (AI-MAP), which elucidates the hierarchy of functions for any biological process. Existing and new 'omics data sets can be iteratively added to create and improve hierarchical models which enhance our understanding of particular biological processes. The best datatypes to further improve an AI-MAP model are predicted computationally. We applied this approach to our understanding of general and selective autophagy, which are conserved in most eukaryotes, setting the stage for the broader application to other cellular processes of interest. In the particular application to autophagy-related processes, we uncovered and validated new autophagy and autophagy-related processes, expanded known autophagy processes with new components, integrated known non-autophagic processes with autophagy and predict other unexplored connections.

Hierarchical MoO3/SnS2 core-shell nanowires with enhanced electrochemical performance for lithium-ion batteries.

PubMed

Hu, Chenli; Shu, Haibo; Shen, Zihong; Zhao, Tianfeng; Liang, Pei; Chen, Xiaoshuang

2018-06-27

Two-dimensional (2D) tin disulfide (SnS2) is a promising anode material for lithium-ion batteries (LIBs) because of its high theoretical capacity. The main challenges associated with the SnS2 electrodes are the poor cycling stability and low rate capability due to structural degradation in the discharge/charge process. Here, a facile two-step synthesis method is developed to fabricate hierarchical MoO3/SnS2 core-shell nanowires, where ultrathin SnS2 nanosheets are vertically anchored on MoO3 nanobelts to induce a heterointerface. Benefiting from the unique structural and compositional characteristics, the hierarchical MoO3/SnS2 core-shell nanowires exhibit excellent electrochemical performance and deliver a high reversible capacity of 504 mA h g-1 after 100 stable cycles at a current density of 100 mA g-1, which is far superior to the MoO3 and SnS2 electrodes. An analysis of lithiation dynamics based on ab initio molecular dynamics simulations demonstrates that the formation of a hierarchical MoO3/SnS2 core-shell heterostructure can effectively suppress the rapid dissociation of shell-layer SnS2 nanosheets via the interfacial coupling effect and the central MoO3 backbone can trap and support the polysulfide in the discharge/charge process. The results are responsible for the high storage capacity and rate capability of MoO3/SnS2 electrode materials. This work provides a novel design strategy for constructing high-performance electrodes for LIBs.

Hierarchical Task Analysis and Training Decisions.

ERIC Educational Resources Information Center

Shepherd, A.

1985-01-01

Hierarchical task analysis (HTA), which requires description of a task in terms of a hierarchy of operations and plans, is reviewed and examined as a basis for making training decisions. Benefits of HTA in terms of economy of analysis and as a means of accounting for complex performance are outlined. (Author/MBR)

High surface area electrodes by template-free self-assembled hierarchical porous gold architecture.

PubMed

Morag, Ahiud; Golub, Tatiana; Becker, James; Jelinek, Raz

2016-06-15

The electrode active surface area is a crucial determinant in many electrochemical applications and devices. Porous metal substrates have been employed in electrode design, however construction of such materials generally involves multistep processes, generating in many instances electrodes exhibiting incomplete access to internal pore surfaces. Here we describe fabrication of electrodes comprising hierarchical, nano-to-microscale porous gold matrix, synthesized through spontaneous crystallization of gold thiocyanate in water. Cyclic voltammetry analysis revealed that the specific surface area of the conductive nanoporous Au microwires was very high and depended only upon the amount of gold used, not electrode areas or geometries. Application of the electrode in a pseudo-capacitor device is presented. Copyright © 2016 Elsevier Inc. All rights reserved.

A Hierarchical multi-input and output Bi-GRU Model for Sentiment Analysis on Customer Reviews

NASA Astrophysics Data System (ADS)

Zhang, Liujie; Zhou, Yanquan; Duan, Xiuyu; Chen, Ruiqi

2018-03-01

Multi-label sentiment classification on customer reviews is a practical challenging task in Natural Language Processing. In this paper, we propose a hierarchical multi-input and output model based bi-directional recurrent neural network, which both considers the semantic and lexical information of emotional expression. Our model applies two independent Bi-GRU layer to generate part of speech and sentence representation. Then the lexical information is considered via attention over output of softmax activation on part of speech representation. In addition, we combine probability of auxiliary labels as feature with hidden layer to capturing crucial correlation between output labels. The experimental result shows that our model is computationally efficient and achieves breakthrough improvements on customer reviews dataset.

Spatio-temporal hierarchical modeling of rates and variability of Holocene sea-level changes in the western North Atlantic and the Caribbean

NASA Astrophysics Data System (ADS)

Ashe, E.; Kopp, R. E.; Khan, N.; Horton, B.; Engelhart, S. E.

2016-12-01

Sea level varies over of both space and time. Prior to the instrumental period, the sea-level record depends upon geological reconstructions that contain vertical and temporal uncertainty. Spatio-temporal statistical models enable the interpretation of RSL and rates of change as well as the reconstruction of the entire sea-level field from such noisy data. Hierarchical models explicitly distinguish between a process level, which characterizes the spatio-temporal field, and a data level, by which sparse proxy data and its noise is recorded. A hyperparameter level depicts prior expectations about the structure of variability in the spatio-temporal field. Spatio-temporal hierarchical models are amenable to several analysis approaches, with tradeoffs regarding computational efficiency and comprehensiveness of uncertainty characterization. A fully-Bayesian hierarchical model (BHM), which places prior probability distributions upon the hyperparameters, is more computationally intensive than an empirical hierarchical model (EHM), which uses point estimates of hyperparameters, derived from the data [1]. Here, we assess the sensitivity of posterior estimates of relative sea level (RSL) and rates to different statistical approaches by varying prior assumptions about the spatial and temporal structure of sea-level variability and applying multiple analytical approaches to Holocene sea-level proxies along the Atlantic coast of North American and the Caribbean [2]. References: 1. N Cressie, Wikle CK (2011) Statistics for spatio-temporal data (John Wiley & Sons). 2. Kahn N et al. (2016). Quaternary Science Reviews (in revision).

Segregating the core computational faculty of human language from working memory.

PubMed

Makuuchi, Michiru; Bahlmann, Jörg; Anwander, Alfred; Friederici, Angela D

2009-05-19

In contrast to simple structures in animal vocal behavior, hierarchical structures such as center-embedded sentences manifest the core computational faculty of human language. Previous artificial grammar learning studies found that the left pars opercularis (LPO) subserves the processing of hierarchical structures. However, it is not clear whether this area is activated by the structural complexity per se or by the increased memory load entailed in processing hierarchical structures. To dissociate the effect of structural complexity from the effect of memory cost, we conducted a functional magnetic resonance imaging study of German sentence processing with a 2-way factorial design tapping structural complexity (with/without hierarchical structure, i.e., center-embedding of clauses) and working memory load (long/short distance between syntactically dependent elements; i.e., subject nouns and their respective verbs). Functional imaging data revealed that the processes for structure and memory operate separately but co-operatively in the left inferior frontal gyrus; activities in the LPO increased as a function of structural complexity, whereas activities in the left inferior frontal sulcus (LIFS) were modulated by the distance over which the syntactic information had to be transferred. Diffusion tensor imaging showed that these 2 regions were interconnected through white matter fibers. Moreover, functional coupling between the 2 regions was found to increase during the processing of complex, hierarchically structured sentences. These results suggest a neuroanatomical segregation of syntax-related aspects represented in the LPO from memory-related aspects reflected in the LIFS, which are, however, highly interconnected functionally and anatomically.

Analysis of dengue fever risk using geostatistics model in bone regency

NASA Astrophysics Data System (ADS)

Amran, Stang, Mallongi, Anwar

2017-03-01

This research aim is to analysis of dengue fever risk based on Geostatistics model in Bone Regency. Risk levels of dengue fever are denoted by parameter of Binomial distribution. Effect of temperature, rainfalls, elevation, and larvae abundance are investigated through Geostatistics model. Bayesian hierarchical method is used in estimation process. Using dengue fever data in eleven locations this research shows that temperature and rainfall have significant effect of dengue fever risk in Bone regency.

A hierarchical fuzzy rule-based approach to aphasia diagnosis.

PubMed

Akbarzadeh-T, Mohammad-R; Moshtagh-Khorasani, Majid

2007-10-01

Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy.

Stability and structural properties of gene regulation networks with coregulation rules.

PubMed

Warrell, Jonathan; Mhlanga, Musa

2017-05-07

Coregulation of the expression of groups of genes has been extensively demonstrated empirically in bacterial and eukaryotic systems. Such coregulation can arise through the use of shared regulatory motifs, which allow the coordinated expression of modules (and module groups) of functionally related genes across the genome. Coregulation can also arise through the physical association of multi-gene complexes through chromosomal looping, which are then transcribed together. We present a general formalism for modeling coregulation rules in the framework of Random Boolean Networks (RBN), and develop specific models for transcription factor networks with modular structure (including module groups, and multi-input modules (MIM) with autoregulation) and multi-gene complexes (including hierarchical differentiation between multi-gene complex members). We develop a mean-field approach to analyse the dynamical stability of large networks incorporating coregulation, and show that autoregulated MIM and hierarchical gene-complex models can achieve greater stability than networks without coregulation whose rules have matching activation frequency. We provide further analysis of the stability of small networks of both kinds through simulations. We also characterize several general properties of the transients and attractors in the hierarchical coregulation model, and show using simulations that the steady-state distribution factorizes hierarchically as a Bayesian network in a Markov Jump Process analogue of the RBN model. Copyright © 2017. Published by Elsevier Ltd.

Fabrication of hierarchical polymer surfaces with superhydrophobicity by injection molding from nature and function-oriented design

NASA Astrophysics Data System (ADS)

Weng, Can; Wang, Fei; Zhou, Mingyong; Yang, Dongjiao; Jiang, Bingyan

2018-04-01

A comparison of processes and wettability characteristics was presented for injection molded superhydrophobic polypropylene surfaces from two fabricating strategies. One is the biomimetic replication of patterns from indocalamus leaf in nature. The contact angle of water sitting on this PP surface was measured as 152 ± 2°, with comparable wetting behavior to natural indocalamus leaf surface. The other strategy is the fabrication of superhydrophobic structure by combining methods that produce structures at different length scales. Regarding both the machinability of mold inserts and function-oriented design, three micro-quadrangular arrays and one hierarchical micro-nano cylinder array were designed with the goal of superhydrophobicity. Particularly, a simple approach to the fabrication of hierarchical structures was proposed by combining the anodized plate and the punching plate. The function-oriented design targets as superhydrophobicity were all reached for the designed four structures. The measured contact angles of droplet for these structures were almost consistent with the calculated equilibrium contact angles from thermodynamic analysis. Among them, the contact angle of droplet on the surface of designed hierarchical structure reached about 163° with the sliding angle of 5°, resulting in self-cleaning characteristic. The superhydrophobicity of function-oriented designed polymer surfaces could be modified and controlled, which is exactly the limitation of replicating from natural organisms.

Hierarchical 3-dimensional nickel-iron nanosheet arrays on carbon fiber paper as a novel electrode for non-enzymatic glucose sensing.

PubMed

Kannan, Palanisamy; Maiyalagan, Thandavarayan; Marsili, Enrico; Ghosh, Srabanti; Niedziolka-Jönsson, Joanna; Jönsson-Niedziolka, Martin

2016-01-14

Three-dimensional nickel-iron (3-D/Ni-Fe) nanostructures are exciting candidates for various applications because they produce more reaction-active sites than 1-D and 2-D nanostructured materials and exhibit attractive optical, electrical and catalytic properties. In this work, freestanding 3-D/Ni-Fe interconnected hierarchical nanosheets, hierarchical nanospheres, and porous nanospheres are directly grown on a flexible carbon fiber paper (CFP) substrate by a single-step hydrothermal process. Among the nanostructures, 3-D/Ni-Fe interconnected hierarchical nanosheets show excellent electrochemical properties because of its high conductivity, large specific active surface area, and mesopores on its walls (vide infra). The 3-D/Ni-Fe hierarchical nanosheet array modified CFP substrate is further explored as a novel electrode for electrochemical non-enzymatic glucose sensor application. The 3-D/Ni-Fe hierarchical nanosheet arrays exhibit significant catalytic activity towards the electrochemical oxidation of glucose, as compared to the 3-D/Ni-Fe hierarchical nanospheres, and porous nanospheres. The 3-D/Ni-Fe hierarchical nanosheet arrays can access a large amount of glucose molecules on their surface (mesopore walls) for an efficient electrocatalytic oxidation process. Moreover, 3-D/Ni-Fe hierarchical nanosheet arrays showed higher sensitivity (7.90 μA μM(-1) cm(-2)) with wide linear glucose concentration ranging from 0.05 μM to 0.2 mM, and the low detection limit (LOD) of 0.031 μM (S/N = 3) is achieved by the amperometry method. Further, the 3-D/Ni-Fe hierarchical nanosheet array modified CFP electrode can be demonstrated to have excellent selectivity towards the detection of glucose in the presence of 500-fold excess of major important interferents. All these results indicate that 3-D/Ni-Fe hierarchical nanosheet arrays are promising candidates for non-enzymatic glucose sensing.

[The system-oriented model of psychosocial rehabilitation].

PubMed

Iastrebov V S; Mitikhin, V G; Solokhina, T A; Mikhaĭlova, I I

2008-01-01

A model of psychosocial rehabilitation based on the system approach that allows taking into account both the patient-centered approach of the rehabilitation service, the development of its resource basis, the effectiveness of this care system in whole and its patterns as well has been worked out. In the framework of this model, the authors suggest to single out three basic stages of the psychosocial rehabilitation process: evaluation and planning, rehabilitation interventions per se, achievement of the result. In author's opinion, the most successful way for constructing a modern model of psychosocial rehabilitation is a method of hierarchic modeling which can reveal a complex chain of interactions between all participants of the rehabilitation process and factors involved in this process and at the same time specify the multi-level hierarchic character of these interactions and factors. An important advantage of this method is the possibility of obtaining as static as well dynamic evaluations of the rehabilitation service activity that may be used on the following levels: 1) patient; 2) his/her close environment; 3) macrosocial level. The obvious merits of the system-oriented model appear to be the possibility of application of its principles in the organization of specialized care for psychiatric patients on the local, regional and federal levels. The authors emphasize that hierarchic models have universal character and can be implemented in the elaboration of information-analytical systems aimed at solving the problems of monitoring and analysis of social-medical service activity in order to increase its effectiveness.

Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography.

PubMed

Phillips, Holly N; Blenkmann, Alejandro; Hughes, Laura E; Kochen, Silvia; Bekinschtein, Tristan A; Cam-Can; Rowe, James B

2016-09-01

We propose that sensory inputs are processed in terms of optimised predictions and prediction error signals within hierarchical neurocognitive models. The combination of non-invasive brain imaging and generative network models has provided support for hierarchical frontotemporal interactions in oddball tasks, including recent identification of a temporal expectancy signal acting on prefrontal cortex. However, these studies are limited by the need to invert magnetoencephalographic or electroencephalographic sensor signals to localise activity from cortical 'nodes' in the network, or to infer neural responses from indirect measures such as the fMRI BOLD signal. To overcome this limitation, we examined frontotemporal interactions estimated from direct cortical recordings from two human participants with cortical electrode grids (electrocorticography - ECoG). Their frontotemporal network dynamics were compared to those identified by magnetoencephalography (MEG) in forty healthy adults. All participants performed the same auditory oddball task with standard tones interspersed with five deviant tone types. We normalised post-operative electrode locations to standardised anatomic space, to compare across modalities, and inverted the MEG to cortical sources using the estimated lead field from subject-specific head models. A mismatch negativity signal in frontal and temporal cortex was identified in all subjects. Generative models of the electrocorticographic and magnetoencephalographic data were separately compared using the free-energy estimate of the model evidence. Model comparison confirmed the same critical features of hierarchical frontotemporal networks in each patient as in the group-wise MEG analysis. These features included bilateral, feedforward and feedback frontotemporal modulated connectivity, in addition to an asymmetric expectancy driving input on left frontal cortex. The invasive ECoG provides an important step in construct validation of the use of neural generative models of MEG, which in turn enables generalisation to larger populations. Together, they give convergent evidence for the hierarchical interactions in frontotemporal networks for expectation and processing of sensory inputs. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

Hierarchical Bi2Te3 Nanostrings: Green Synthesis and Their Thermoelectric Properties.

PubMed

Song, Shuyan; Liu, Yu; Wang, Qishun; Pan, Jing; Sun, Yabin; Zhang, Lingling

2018-05-20

Bi2Te3 hierarchical nanostrings have been synthesized through a solvothermal approach with the assistance of sucrose. The hierarchical Bi2Te3 was supposed to be fabricated through a self-assembly process. Te nanorods first emerge with the reduction of TeO32- followed by heterogeneous nucleation of Bi2Te3 nanoplates on the surface and tips of Te nanorods. Te nanorods further transform into Bi2Te3 nanorods simultaneously with the nanoplates' growth leading to a hierarchical structure. Through controlling the reaction kinetics by adding different amount of ethylene glycol, the length of nanorods and the number of nanoplates could be tailored. The use of sucrose is vital to the formation of hierarchical structure because it not only serves as a template for the well-defined growth of Te nanorods but also promotes the heterogeneous nucleation of Bi2Te3 in the self-assembly process. The Bi2Te3 nanomaterial shows a moderate thermoelectric performance because of its hierarchical structure. This study shows a promising way to synthesize Bi2Te3-based nanostructures through environmental friendly approach. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Local and Global Processing in Blind and Sighted Children in a Naming and Drawing Task

ERIC Educational Resources Information Center

Puspitawati, Ira; Jebrane, Ahmed; Vinter, Annie

2014-01-01

This study investigated the spatial analysis of tactile hierarchical patterns in 110 early-blind children aged 6-8 to 16-18 years, as compared to 90 blindfolded sighted children, in a naming and haptic drawing task. The results revealed that regardless of visual status, young children predominantly produced local responses in both tasks, whereas…

Resource analysis and land use planning with space and high altitude photography

NASA Technical Reports Server (NTRS)

Schrumpf, B. J.

1972-01-01

Photographic scales providing resource data for decision making processes of land use and a legend system for barren lands, water resources, natural vegetation, agricultural, urban, and industrial lands in hierarchical framework are applied to various remote sensing techniques. Two natural vegetation resource and land use maps for a major portion of Maricopa County, Arizona are also produced.

The Constraint Method for Solid Finite Elements.

DTIC Science & Technology

1980-09-30

9. ’Hierarchical Approximation in Finite Element Analysis", by I. Norman Katz, International Symposium on Innovative Numerical Analysis In Applied ... Engineering Science, Versailles, France, May 23-27, 1977. 10. "Efficient Generation of Hierarchal Finite Elamnts Through the Use of Precomputed Arrays

Ecogenomics: Ensemble Analysis of Gene Expression in Microbial Communities

NASA Technical Reports Server (NTRS)

Sogin, Mitchell; DesMarais, David J.; Stahl, D. A.; Pace, Norman R.

2001-01-01

The hierarchical organization of microbial ecosystems determines process rates that shape Earth's environment, create the biomarker sedimentary and atmospheric signatures of life, and define the stage upon which major evolutionary events occurred. In order to understand how microorganisms have shaped the global environment of Earth and, potentially, other worlds, we must develop an experimental paradigm that links biogeochemical processes with ever-changing temporal and spatial distributions of microbial populations and their metabolic properties. Additional information is contained in the original extended abstract.

Developmental Changes in the Processing of Hierarchical Shapes Continue into Adolescence.

ERIC Educational Resources Information Center

Mondloch, Catherine J.; Geldart, Sybil; Maurer, Daphne; de Schonen, Scania

2003-01-01

Three experiments obtained same-different judgments from children and adults to trace normal development of local and global processing of hierarchical visual forms. Findings indicated that reaction time was faster on global trials than local trials; bias was stronger in children and diminished to adult levels between ages 10 and 14. Reaction time…

Relative Contributions of Goal Representation and Kinematic Information to Self-Monitoring by Chimpanzees and Humans

ERIC Educational Resources Information Center

Kaneko, Takaaki; Tomonaga, Masaki

2012-01-01

It is important to monitor feedback related to the intended result of an action while executing that action. This monitoring process occurs hierarchically; that is, sensorimotor processing occurs at a lower level, and conceptual representation of action goals occurs at a higher level. Although the hierarchical nature of self-monitoring may derive…

Silk-regulated hierarchical hollow magnetite/carbon nanocomposite spheroids for lithium-ion battery anodes.

PubMed

Sheng, Weiqin; Zhu, Guobin; Kaplan, David L; Cao, Chuanbao; Zhu, Hesun; Lu, Qiang

2015-03-20

Hierarchical olive-like structured carbon-Fe3O4 nanocomposite particles composed of a hollow interior and a carbon coated surface are prepared by a facile, silk protein-assisted hydrothermal method. Silk nanofibers as templates and carbon precursors first regulate the formation of hollow Fe2O3 microspheres and then they are converted into carbon by a reduction process into Fe3O4. This process significantly simplifies the fabrication and carbon coating processes to form complex hollow structures. When tested as anode materials for lithium-ion batteries, these hollow carbon-coated particles exhibit high capacity (900 mAh g(-1)), excellent cycle stability (180 cycles) and rate performance due to their unique hierarchical hollow structure and carbon coating.

Laser synthesized super-hydrophobic conducting carbon with broccoli-type morphology as a counter-electrode for dye sensitized solar cells

NASA Astrophysics Data System (ADS)

Gokhale, Rohan; Agarkar, Shruti; Debgupta, Joyashish; Shinde, Deodatta; Lefez, Benoit; Banerjee, Abhik; Jog, Jyoti; More, Mahendra; Hannoyer, Beatrice; Ogale, Satishchandra

2012-10-01

A laser photochemical process is introduced to realize superhydrophobic conducting carbon coatings with broccoli-type hierarchical morphology for use as a metal-free counter electrode in a dye sensitized solar cell. The process involves pulsed excimer laser irradiation of a thin layer of liquid haloaromatic organic solvent o-dichlorobenzene (DCB). The coating reflects a carbon nanoparticle-self assembled and process-controlled morphology that yields solar to electric power conversion efficiency of 5.1% as opposed to 6.2% obtained with the conventional Pt-based electrode.A laser photochemical process is introduced to realize superhydrophobic conducting carbon coatings with broccoli-type hierarchical morphology for use as a metal-free counter electrode in a dye sensitized solar cell. The process involves pulsed excimer laser irradiation of a thin layer of liquid haloaromatic organic solvent o-dichlorobenzene (DCB). The coating reflects a carbon nanoparticle-self assembled and process-controlled morphology that yields solar to electric power conversion efficiency of 5.1% as opposed to 6.2% obtained with the conventional Pt-based electrode. Electronic supplementary information (ESI) available: Materials and equipment details, solar cell fabrication protocol, electrolyte spreading time measurement details, XPS spectra, electronic study, film adhesion test detailed analysis and field emission results. See DOI: 10.1039/c2nr32082g

The awareness and want matrix with adoption gap ratio analysis for e-service diffusion effect.

PubMed

Liang, Te-Hsin

2011-03-01

Since the hierarchical stages of a customer purchasing decision or innovation adoption process are interrelated, an analysis of all their stages, including awareness, want, and adoption, in relation to product or service diffusion, is urgently needed. Therefore, this study proposes the use of an awareness and want matrix, together with an adoption gap ratio analysis, to assess the effectiveness of innovation and technology diffusion for e-services. This study also conducts an empirical test on the promotion performance evaluation of 12 e-services promoted by the Taiwanese government.

Inter-subject Functional Correlation Reveal a Hierarchical Organization of Extrinsic and Intrinsic Systems in the Brain.

PubMed

Ren, Yudan; Nguyen, Vinh Thai; Guo, Lei; Guo, Christine Cong

2017-09-07

The brain is constantly monitoring and integrating both cues from the external world and signals generated intrinsically. These extrinsically and intrinsically-driven neural processes are thought to engage anatomically distinct regions, which are thought to constitute the extrinsic and intrinsic systems of the brain. While the specialization of extrinsic and intrinsic system is evident in primary and secondary sensory cortices, a systematic mapping of the whole brain remains elusive. Here, we characterized the extrinsic and intrinsic functional activities in the brain during naturalistic movie-viewing. Using a novel inter-subject functional correlation (ISFC) analysis, we found that the strength of ISFC shifts along the hierarchical organization of the brain. Primary sensory cortices appear to have strong inter-subject functional correlation, consistent with their role in processing exogenous information, while heteromodal regions that attend to endogenous processes have low inter-subject functional correlation. Those brain systems with higher intrinsic tendency show greater inter-individual variability, likely reflecting the aspects of brain connectivity architecture unique to individuals. Our study presents a novel framework for dissecting extrinsically- and intrinsically-driven processes, as well as examining individual differences in brain function during naturalistic stimulation.

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

The Plastic Surgery Match: A Quantitative Analysis of Applicant Impressions From the Interview Visit.

PubMed

Frojo, Gianfranco; Tadisina, Kashyap Komarraju; Pressman, Zachary; Chibnall, John T; Lin, Alexander Y; Kraemer, Bruce A

2016-12-01

The integrated plastic surgery match is a competitive process not only for applicants but also for programs vying for highly qualified candidates. Interactions between applicants and program constituents are limited to a single interview visit. The authors aimed to identify components of the interview visit that influence applicant decision making when determining a final program rank list. Thirty-six applicants who were interviewed (100% response) completed the survey. Applicants rated the importance of 20 elements of the interview visit regarding future ranking of the program on a 1 to 5 Likert scale. Data were analyzed using descriptive statistics, hierarchical cluster analysis, analysis of variance, and Pearson correlations. A literature review was performed regarding the plastic surgery integrated residency interview process. Survey questions were categorized into four groups based on mean survey responses:1. Interactions with faculty and residents (mean response > 4),2. Information about the program (3.5-4),3. Ancillaries (food, amenities, stipends) (3-3.5),4. Hospital tour, hotel (<3).Hierarchical item cluster analysis and analysis of variance testing validated these groupings. Average summary scores were calculated for the items representing Interactions, Information, and Ancillaries. Correlation analysis between clusters yielded no significant correlations. A review of the literature yielded a paucity of data on analysis of the interview visit. The interview visit consists of a discrete hierarchy of perceived importance by applicants. The strongest independent factor in determining future program ranking is the quality of interactions between applicants and program constituents on the interview visit. This calls for further investigation and optimization of the interview visit experience.

Utilizing Hierarchical Segmentation to Generate Water and Snow Masks to Facilitate Monitoring Change with Remotely Sensed Image Data

NASA Technical Reports Server (NTRS)

Tilton, James C.; Lawrence, William T.; Plaza, Antonio J.

2006-01-01

The hierarchical segmentation (HSEG) algorithm is a hybrid of hierarchical step-wise optimization and constrained spectral clustering that produces a hierarchical set of image segmentations. This segmentation hierarchy organizes image data in a manner that makes the image's information content more accessible for analysis by enabling region-based analysis. This paper discusses data analysis with HSEG and describes several measures of region characteristics that may be useful analyzing segmentation hierarchies for various applications. Segmentation hierarchy analysis for generating landwater and snow/ice masks from MODIS (Moderate Resolution Imaging Spectroradiometer) data was demonstrated and compared with the corresponding MODIS standard products. The masks based on HSEG segmentation hierarchies compare very favorably to the MODIS standard products. Further, the HSEG based landwater mask was specifically tailored to the MODIS data and the HSEG snow/ice mask did not require the setting of a critical threshold as required in the production of the corresponding MODIS standard product.

Dynamic anticipatory processing of hierarchical sequential events: a common role for Broca's area and ventral premotor cortex across domains?

PubMed

Fiebach, Christian J; Schubotz, Ricarda I

2006-05-01

This paper proposes a domain-general model for the functional contribution of ventral premotor cortex (PMv) and adjacent Broca's area to perceptual, cognitive, and motor processing. We propose to understand this frontal region as a highly flexible sequence processor, with the PMv mapping sequential events onto stored structural templates and Broca's Area involved in more complex, hierarchical or hypersequential processing. This proposal is supported by reference to previous functional neuroimaging studies investigating abstract sequence processing and syntactic processing.

A Comparison of Reasoning Processes in a Collaborative Modelling Environment: Learning about genetics problems using virtual chat

NASA Astrophysics Data System (ADS)

Pata, Kai; Sarapuu, Tago

2006-09-01

This study investigated the possible activation of different types of model-based reasoning processes in two learning settings, and the influence of various terms of reasoning on the learners’ problem representation development. Changes in 53 students’ problem representations about genetic issue were analysed while they worked with different modelling tools in a synchronous network-based environment. The discussion log-files were used for the “microgenetic” analysis of reasoning types. For studying the stages of students’ problem representation development, individual pre-essays and post-essays and their utterances during two reasoning phases were used. An approach for mapping problem representations was developed. Characterizing the elements of mental models and their reasoning level enabled the description of five hierarchical categories of problem representations. Learning in exploratory and experimental settings was registered as the shift towards more complex stages of problem representations in genetics. The effect of different types of reasoning could be observed as the divergent development of problem representations within hierarchical categories.

Stress reaction process-based hierarchical recognition algorithm for continuous intrusion events in optical fiber prewarning system

NASA Astrophysics Data System (ADS)

Qu, Hongquan; Yuan, Shijiao; Wang, Yanping; Yang, Dan

2018-04-01

To improve the recognition performance of optical fiber prewarning system (OFPS), this study proposed a hierarchical recognition algorithm (HRA). Compared with traditional methods, which employ only a complex algorithm that includes multiple extracted features and complex classifiers to increase the recognition rate with a considerable decrease in recognition speed, HRA takes advantage of the continuity of intrusion events, thereby creating a staged recognition flow inspired by stress reaction. HRA is expected to achieve high-level recognition accuracy with less time consumption. First, this work analyzed the continuity of intrusion events and then presented the algorithm based on the mechanism of stress reaction. Finally, it verified the time consumption through theoretical analysis and experiments, and the recognition accuracy was obtained through experiments. Experiment results show that the processing speed of HRA is 3.3 times faster than that of a traditional complicated algorithm and has a similar recognition rate of 98%. The study is of great significance to fast intrusion event recognition in OFPS.

Targeted and untargeted-metabolite profiling to track the compositional integrity of ginger during processing using digitally-enhanced HPTLC pattern recognition analysis.

PubMed

Ibrahim, Reham S; Fathy, Hoda

2018-03-30

Tracking the impact of commonly applied post-harvesting and industrial processing practices on the compositional integrity of ginger rhizome was implemented in this work. Untargeted metabolite profiling was performed using digitally-enhanced HPTLC method where the chromatographic fingerprints were extracted using ImageJ software then analysed with multivariate Principal Component Analysis (PCA) for pattern recognition. A targeted approach was applied using a new, validated, simple and fast HPTLC image analysis method for simultaneous quantification of the officially recognized markers 6-, 8-, 10-gingerol and 6-shogaol in conjunction with chemometric Hierarchical Clustering Analysis (HCA). The results of both targeted and untargeted metabolite profiling revealed that peeling, drying in addition to storage employed during processing have a great influence on ginger chemo-profile, the different forms of processed ginger shouldn't be used interchangeably. Moreover, it deemed necessary to consider the holistic metabolic profile for comprehensive evaluation of ginger during processing. Copyright © 2018. Published by Elsevier B.V.

Study on Desorption Process of n-Heptane and Methyl Cyclohexane Using UiO-66 with Hierarchical Pores.

PubMed

Chen, Sijia; Zhang, Lin; Zhang, Zhao; Qian, Gang; Liu, Zongjian; Cui, Qun; Wang, Haiyan

2018-06-06

UiO-66 (UiO for University of Oslo), is a zirconium-based MOF with reverse shape selectivity, gives an alternative way to produce high purity n-heptane used for the manufacture of high-purity pharmaceuticals. Couple of studies have shown that UiO-66 gives a high selectivity on the separation of n-/iso-alkanes. However, the microporous structure of UiO-66 causes poor mass transport during the desorption process. In this work, hierarchical-pore UiO-66 (H-UiO-66) was synthesized and utilized as an adsorbent of n-heptane (nHEP) and methyl cyclohexane (MCH) for systematically studying the desorption process of n/iso-alkanes. A suite of physical methods, including XRD patterns verified the UiO-66 structures and HRTEM showed the existence of hierarchical pores. N2 adsorption-desorption isotherms further confirmed the size distribution of hierarchical pores in H-UiO-66. Of particular note, the MCH/nHEP selectivity of H-UiO-66 is similar with UiO-66 in the same adsorption conditions, the desorption process of nHEP/MCH from H-UiO-66 is dramatically enhanced, viz, the desorption rates for nHEP/MCH from H-UiO-66 is enhanced by 30%/23% as comparing to UiO-66 at most. Moreover, desorption activation energy (Ed) derived from temperature-programmed desorption (TPD) experiments indicate that the Ed for nHEP/MCH is lower on H-UiO-66, i.e., the Ed of MCH on H-UiO-66 is ~37% lower than that on UiO-66 at most, leading to a milder condition for the desorption process. The introduction of hierarchical structures will be applicable for the optimization of desorption process during separation on porous materials.

Segregating the core computational faculty of human language from working memory

PubMed Central

Makuuchi, Michiru; Bahlmann, Jörg; Anwander, Alfred; Friederici, Angela D.

2009-01-01

In contrast to simple structures in animal vocal behavior, hierarchical structures such as center-embedded sentences manifest the core computational faculty of human language. Previous artificial grammar learning studies found that the left pars opercularis (LPO) subserves the processing of hierarchical structures. However, it is not clear whether this area is activated by the structural complexity per se or by the increased memory load entailed in processing hierarchical structures. To dissociate the effect of structural complexity from the effect of memory cost, we conducted a functional magnetic resonance imaging study of German sentence processing with a 2-way factorial design tapping structural complexity (with/without hierarchical structure, i.e., center-embedding of clauses) and working memory load (long/short distance between syntactically dependent elements; i.e., subject nouns and their respective verbs). Functional imaging data revealed that the processes for structure and memory operate separately but co-operatively in the left inferior frontal gyrus; activities in the LPO increased as a function of structural complexity, whereas activities in the left inferior frontal sulcus (LIFS) were modulated by the distance over which the syntactic information had to be transferred. Diffusion tensor imaging showed that these 2 regions were interconnected through white matter fibers. Moreover, functional coupling between the 2 regions was found to increase during the processing of complex, hierarchically structured sentences. These results suggest a neuroanatomical segregation of syntax-related aspects represented in the LPO from memory-related aspects reflected in the LIFS, which are, however, highly interconnected functionally and anatomically. PMID:19416819

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.

Hierarchical Model for the Analysis of Scattering Data of Complex Materials

DOE PAGES

Oyedele, Akinola; Mcnutt, Nicholas W.; Rios, Orlando; ...

2016-05-16

Interpreting the results of scattering data for complex materials with a hierarchical structure in which at least one phase is amorphous presents a significant challenge. Often the interpretation relies on the use of large-scale molecular dynamics (MD) simulations, in which a structure is hypothesized and from which a radial distribution function (RDF) can be extracted and directly compared against an experimental RDF. This computationally intensive approach presents a bottleneck in the efficient characterization of the atomic structure of new materials. Here, we propose and demonstrate an approach for a hierarchical decomposition of the RDF in which MD simulations are replacedmore » by a combination of tractable models and theory at the atomic scale and the mesoscale, which when combined yield the RDF. We apply the procedure to a carbon composite, in which graphitic nanocrystallites are distributed in an amorphous domain. We compare the model with the RDF from both MD simulation and neutron scattering data. Ultimately, this procedure is applicable for understanding the fundamental processing-structure-property relationships in complex magnetic materials.« less

How hierarchical is language use?

PubMed Central

Frank, Stefan L.; Bod, Rens; Christiansen, Morten H.

2012-01-01

It is generally assumed that hierarchical phrase structure plays a central role in human language. However, considerations of simplicity and evolutionary continuity suggest that hierarchical structure should not be invoked too hastily. Indeed, recent neurophysiological, behavioural and computational studies show that sequential sentence structure has considerable explanatory power and that hierarchical processing is often not involved. In this paper, we review evidence from the recent literature supporting the hypothesis that sequential structure may be fundamental to the comprehension, production and acquisition of human language. Moreover, we provide a preliminary sketch outlining a non-hierarchical model of language use and discuss its implications and testable predictions. If linguistic phenomena can be explained by sequential rather than hierarchical structure, this will have considerable impact in a wide range of fields, such as linguistics, ethology, cognitive neuroscience, psychology and computer science. PMID:22977157

Hierarchical drivers of reef-fish metacommunity structure.

PubMed

MacNeil, M Aaron; Graham, Nicholas A J; Polunin, Nicholas V C; Kulbicki, Michel; Galzin, René; Harmelin-Vivien, Mireille; Rushton, Steven P

2009-01-01

Coral reefs are highly complex ecological systems, where multiple processes interact across scales in space and time to create assemblages of exceptionally high biodiversity. Despite the increasing frequency of hierarchically structured sampling programs used in coral-reef science, little progress has been made in quantifying the relative importance of processes operating across multiple scales. The vast majority of reef studies are conducted, or at least analyzed, at a single spatial scale, ignoring the implicitly hierarchical structure of the overall system in favor of small-scale experiments or large-scale observations. Here we demonstrate how alpha (mean local number of species), beta diversity (degree of species dissimilarity among local sites), and gamma diversity (overall species richness) vary with spatial scale, and using a hierarchical, information-theoretic approach, we evaluate the relative importance of site-, reef-, and atoll-level processes driving the fish metacommunity structure among 10 atolls in French Polynesia. Process-based models, representing well-established hypotheses about drivers of reef-fish community structure, were assembled into a candidate set of 12 hierarchical linear models. Variation in fish abundance, biomass, and species richness were unevenly distributed among transect, reef, and atoll levels, establishing the relative contribution of variation at these spatial scales to the structure of the metacommunity. Reef-fish biomass, species richness, and the abundance of most functional-groups corresponded primarily with transect-level habitat diversity and atoll-lagoon size, whereas detritivore and grazer abundances were largely correlated with potential covariates of larval dispersal. Our findings show that (1) within-transect and among-atoll factors primarily drive the relationship between alpha and gamma diversity in this reef-fish metacommunity; (2) habitat is the primary correlate with reef-fish metacommunity structure at multiple spatial scales; and (3) inter-atoll connectedness was poorly correlated with the nonrandom clustering of reef-fish species. These results demonstrate the importance of modeling hierarchical data and processes in understanding reef-fish metacommunity structure.

Visual Attention to Global and Local Stimulus Properties in 6-Month-Old Infants: Individual Differences and Event-Related Potentials

ERIC Educational Resources Information Center

Guy, Maggie W.; Reynolds, Greg D.; Zhang, Dantong

2013-01-01

Event-related potentials (ERPs) were utilized in an investigation of 21 six-month-olds' attention to and processing of global and local properties of hierarchical patterns. Overall, infants demonstrated an advantage for processing the overall configuration (i.e., global properties) of local features of hierarchical patterns; however,…

Modeling place field activity with hierarchical slow feature analysis

PubMed Central

Schönfeld, Fabian; Wiskott, Laurenz

2015-01-01

What are the computational laws of hippocampal activity? In this paper we argue for the slowness principle as a fundamental processing paradigm behind hippocampal place cell firing. We present six different studies from the experimental literature, performed with real-life rats, that we replicated in computer simulations. Each of the chosen studies allows rodents to develop stable place fields and then examines a distinct property of the established spatial encoding: adaptation to cue relocation and removal; directional dependent firing in the linear track and open field; and morphing and scaling the environment itself. Simulations are based on a hierarchical Slow Feature Analysis (SFA) network topped by a principal component analysis (ICA) output layer. The slowness principle is shown to account for the main findings of the presented experimental studies. The SFA network generates its responses using raw visual input only, which adds to its biological plausibility but requires experiments performed in light conditions. Future iterations of the model will thus have to incorporate additional information, such as path integration and grid cell activity, in order to be able to also replicate studies that take place during darkness. PMID:26052279

Relating memory to functional performance in normal aging to dementia using hierarchical Bayesian cognitive processing models.

PubMed

Shankle, William R; Pooley, James P; Steyvers, Mark; Hara, Junko; Mangrola, Tushar; Reisberg, Barry; Lee, Michael D

2013-01-01

Determining how cognition affects functional abilities is important in Alzheimer disease and related disorders. A total of 280 patients (normal or Alzheimer disease and related disorders) received a total of 1514 assessments using the functional assessment staging test (FAST) procedure and the MCI Screen. A hierarchical Bayesian cognitive processing model was created by embedding a signal detection theory model of the MCI Screen-delayed recognition memory task into a hierarchical Bayesian framework. The signal detection theory model used latent parameters of discriminability (memory process) and response bias (executive function) to predict, simultaneously, recognition memory performance for each patient and each FAST severity group. The observed recognition memory data did not distinguish the 6 FAST severity stages, but the latent parameters completely separated them. The latent parameters were also used successfully to transform the ordinal FAST measure into a continuous measure reflecting the underlying continuum of functional severity. Hierarchical Bayesian cognitive processing models applied to recognition memory data from clinical practice settings accurately translated a latent measure of cognition into a continuous measure of functional severity for both individuals and FAST groups. Such a translation links 2 levels of brain information processing and may enable more accurate correlations with other levels, such as those characterized by biomarkers.

Understanding the mind of a worm: hierarchical network structure underlying nervous system function in C. elegans.

PubMed

Chatterjee, Nivedita; Sinha, Sitabhra

2008-01-01

The nervous system of the nematode C. elegans provides a unique opportunity to understand how behavior ('mind') emerges from activity in the nervous system ('brain') of an organism. The hermaphrodite worm has only 302 neurons, all of whose connections (synaptic and gap junctional) are known. Recently, many of the functional circuits that make up its behavioral repertoire have begun to be identified. In this paper, we investigate the hierarchical structure of the nervous system through k-core decomposition and find it to be intimately related to the set of all known functional circuits. Our analysis also suggests a vital role for the lateral ganglion in processing information, providing an essential connection between the sensory and motor components of the C. elegans nervous system.

Operating room scheduling using hybrid clustering priority rule and genetic algorithm

NASA Astrophysics Data System (ADS)

Santoso, Linda Wahyuni; Sinawan, Aisyah Ashrinawati; Wijaya, Andi Rahadiyan; Sudiarso, Andi; Masruroh, Nur Aini; Herliansyah, Muhammad Kusumawan

2017-11-01

Operating room is a bottleneck resource in most hospitals so that operating room scheduling system will influence the whole performance of the hospitals. This research develops a mathematical model of operating room scheduling for elective patients which considers patient priority with limit number of surgeons, operating rooms, and nurse team. Clustering analysis was conducted to the data of surgery durations using hierarchical and non-hierarchical methods. The priority rule of each resulting cluster was determined using Shortest Processing Time method. Genetic Algorithm was used to generate daily operating room schedule which resulted in the lowest values of patient waiting time and nurse overtime. The computational results show that this proposed model reduced patient waiting time by approximately 32.22% and nurse overtime by approximately 32.74% when compared to actual schedule.

A hierarchical cluster analysis of normal-tension glaucoma using spectral-domain optical coherence tomography parameters.

PubMed

Bae, Hyoung Won; Ji, Yongwoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

2015-01-01

Normal-tension glaucoma (NTG) is a heterogenous disease, and there is still controversy about subclassifications of this disorder. On the basis of spectral-domain optical coherence tomography (SD-OCT), we subdivided NTG with hierarchical cluster analysis using optic nerve head (ONH) parameters and retinal nerve fiber layer (RNFL) thicknesses. A total of 200 eyes of 200 NTG patients between March 2011 and June 2012 underwent SD-OCT scans to measure ONH parameters and RNFL thicknesses. We classified NTG into homogenous subgroups based on these variables using a hierarchical cluster analysis, and compared clusters to evaluate diverse NTG characteristics. Three clusters were found after hierarchical cluster analysis. Cluster 1 (62 eyes) had the thickest RNFL and widest rim area, and showed early glaucoma features. Cluster 2 (60 eyes) was characterized by the largest cup/disc ratio and cup volume, and showed advanced glaucomatous damage. Cluster 3 (78 eyes) had small disc areas in SD-OCT and were comprised of patients with significantly younger age, longer axial length, and greater myopia than the other 2 groups. A hierarchical cluster analysis of SD-OCT scans divided NTG patients into 3 groups based upon ONH parameters and RNFL thicknesses. It is anticipated that the small disc area group comprised of younger and more myopic patients may show unique features unlike the other 2 groups.

A Hierarchical Visualization Analysis Model of Power Big Data

NASA Astrophysics Data System (ADS)

Li, Yongjie; Wang, Zheng; Hao, Yang

2018-01-01

Based on the conception of integrating VR scene and power big data analysis, a hierarchical visualization analysis model of power big data is proposed, in which levels are designed, targeting at different abstract modules like transaction, engine, computation, control and store. The regularly departed modules of power data storing, data mining and analysis, data visualization are integrated into one platform by this model. It provides a visual analysis solution for the power big data.

3D Printing of Hierarchical Silk Fibroin Structures.

PubMed

Sommer, Marianne R; Schaffner, Manuel; Carnelli, Davide; Studart, André R

2016-12-21

Like many other natural materials, silk is hierarchically structured from the amino acid level up to the cocoon or spider web macroscopic structures. Despite being used industrially in a number of applications, hierarchically structured silk fibroin objects with a similar degree of architectural control as in natural structures have not been produced yet due to limitations in fabrication processes. In a combined top-down and bottom-up approach, we exploit the freedom in macroscopic design offered by 3D printing and the template-guided assembly of ink building blocks at the meso- and nanolevel to fabricate hierarchical silk porous materials with unprecedented structural control. Pores with tunable sizes in the range 40-350 μm are generated by adding sacrificial organic microparticles as templates to a silk fibroin-based ink. Commercially available wax particles or monodisperse polycaprolactone made by microfluidics can be used as microparticle templates. Since closed pores are generated after template removal, an ultrasonication treatment can optionally be used to achieve open porosity. Such pore templating particles can be further modified with nanoparticles to create a hierarchical template that results in porous structures with a defined nanotopography on the pore walls. The hierarchically porous silk structures obtained with this processing technique can potentially be utilized in various application fields from structural materials to thermal insulation to tissue engineering scaffolds.

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 approach to separating the levels of hierarchical structure building in language and mathematics.

PubMed

Makuuchi, Michiru; Bahlmann, Jörg; Friederici, Angela D

2012-07-19

We aimed to dissociate two levels of hierarchical structure building in language and mathematics, namely 'first-level' (the build-up of hierarchical structure with externally given elements) and 'second-level' (the build-up of hierarchical structure with internally represented elements produced by first-level processes). Using functional magnetic resonance imaging, we investigated these processes in three domains: sentence comprehension, arithmetic calculation (using Reverse Polish notation, which gives two operands followed by an operator) and a working memory control task. All tasks required the build-up of hierarchical structures at the first- and second-level, resulting in a similar computational hierarchy across language and mathematics, as well as in a working memory control task. Using a novel method that estimates the difference in the integration cost for conditions of different trial durations, we found an anterior-to-posterior functional organization in the prefrontal cortex, according to the level of hierarchy. Common to all domains, the ventral premotor cortex (PMv) supports first-level hierarchy building, while the dorsal pars opercularis (POd) subserves second-level hierarchy building, with lower activation for language compared with the other two tasks. These results suggest that the POd and the PMv support domain-general mechanisms for hierarchical structure building, with the POd being uniquely efficient for language.

Shared neural coding for social hierarchy and reward value in primate amygdala.

PubMed

Munuera, Jérôme; Rigotti, Mattia; Salzman, C Daniel

2018-03-01

The social brain hypothesis posits that dedicated neural systems process social information. In support of this, neurophysiological data have shown that some brain regions are specialized for representing faces. It remains unknown, however, whether distinct anatomical substrates also represent more complex social variables, such as the hierarchical rank of individuals within a social group. Here we show that the primate amygdala encodes the hierarchical rank of individuals in the same neuronal ensembles that encode the rewards associated with nonsocial stimuli. By contrast, orbitofrontal and anterior cingulate cortices lack strong representations of hierarchical rank while still representing reward values. These results challenge the conventional view that dedicated neural systems process social information. Instead, information about hierarchical rank-which contributes to the assessment of the social value of individuals within a group-is linked in the amygdala to representations of rewards associated with nonsocial stimuli.

Taking a closer look: disentangling effects of functional diversity on ecosystem functions with a trait-based model across hierarchy and time

PubMed Central

Holzwarth, Frédéric; Rüger, Nadja; Wirth, Christian

2015-01-01

Biodiversity and ecosystem functioning (BEF) research has progressed from the detection of relationships to elucidating their drivers and underlying mechanisms. In this context, replacing taxonomic predictors by trait-based measures of functional composition (FC)—bridging functions of species and of ecosystems—is a widely used approach. The inherent challenge of trait-based approaches is the multi-faceted, dynamic and hierarchical nature of trait influence: (i) traits may act via different facets of their distribution in a community, (ii) their influence may change over time and (iii) traits may influence processes at different levels of the natural hierarchy of organization. Here, we made use of the forest ecosystem model ‘LPJ-GUESS’ parametrized with empirical trait data, which creates output of individual performance, community assembly, stand-level states and processes. To address the three challenges, we resolved the dynamics of the top-level ecosystem function ‘annual biomass change’ hierarchically into its various component processes (growth, leaf and root turnover, recruitment and mortality) and states (stand structures, water stress) and traced the influence of different facets of FC along this hierarchy in a path analysis. We found an independent influence of functional richness, dissimilarity and identity on ecosystem states and processes and hence biomass change. Biodiversity effects were only positive during early succession and later turned negative. Unexpectedly, resource acquisition (growth, recruitment) and conservation (mortality, turnover) played an equally important role throughout the succession. These results add to a mechanistic understanding of biodiversity effects and place a caveat on simplistic approaches omitting hierarchical levels when analysing BEF relationships. They support the view that BEF relationships experience dramatic shifts over successional time that should be acknowledged in mechanistic theories. PMID:26064620

Taking a closer look: disentangling effects of functional diversity on ecosystem functions with a trait-based model across hierarchy and time.

PubMed

Holzwarth, Frédéric; Rüger, Nadja; Wirth, Christian

2015-03-01

Biodiversity and ecosystem functioning (BEF) research has progressed from the detection of relationships to elucidating their drivers and underlying mechanisms. In this context, replacing taxonomic predictors by trait-based measures of functional composition (FC)-bridging functions of species and of ecosystems-is a widely used approach. The inherent challenge of trait-based approaches is the multi-faceted, dynamic and hierarchical nature of trait influence: (i) traits may act via different facets of their distribution in a community, (ii) their influence may change over time and (iii) traits may influence processes at different levels of the natural hierarchy of organization. Here, we made use of the forest ecosystem model 'LPJ-GUESS' parametrized with empirical trait data, which creates output of individual performance, community assembly, stand-level states and processes. To address the three challenges, we resolved the dynamics of the top-level ecosystem function 'annual biomass change' hierarchically into its various component processes (growth, leaf and root turnover, recruitment and mortality) and states (stand structures, water stress) and traced the influence of different facets of FC along this hierarchy in a path analysis. We found an independent influence of functional richness, dissimilarity and identity on ecosystem states and processes and hence biomass change. Biodiversity effects were only positive during early succession and later turned negative. Unexpectedly, resource acquisition (growth, recruitment) and conservation (mortality, turnover) played an equally important role throughout the succession. These results add to a mechanistic understanding of biodiversity effects and place a caveat on simplistic approaches omitting hierarchical levels when analysing BEF relationships. They support the view that BEF relationships experience dramatic shifts over successional time that should be acknowledged in mechanistic theories.

New Bio-Ceramization Processes Applied to Vegetable Hierarchical Structures for Bone Regeneration: An Experimental Model in Sheep

PubMed Central

Filardo, Giuseppe; Tampieri, Anna; Cabezas-Rodríguez, Rafael; Di Martino, Alessandro; Fini, Milena; Giavaresi, Gianluca; Lelli, Marco; Martínez-Fernández, Julian; Martini, Lucia; Ramírez-Rico, Joaquin; Salamanna, Francesca; Sandri, Monica; Sprio, Simone; Marcacci, Maurilio

2014-01-01

Bone loss is still a major problem in orthopedics. The purpose of this experimental study is to evaluate the safety and regenerative potential of a new scaffold based on a bio-ceramization process for bone regeneration in long diaphyseal defects in a sheep model. The scaffold was obtained by transformation of wood pieces into porous biomorphic silicon carbide (BioSiC®). The process enabled the maintenance of the original wood microstructure, thus exhibiting hierarchically organized porosity and high mechanical strength. To improve cell adhesion and osseointegration, the external surface of the hollow cylinder was made more bioactive by electrodeposition of a uniform layer of collagen fibers that were mineralized with biomimetic hydroxyapatite, whereas the internal part was filled with a bio-hybrid HA/collagen composite. The final scaffold was then implanted in the metatarsus of 15 crossbred (Merinos-Sarda) adult sheep, divided into 3 groups: scaffold alone, scaffold with platelet-rich plasma (PRP) augmentation, and scaffold with bone marrow stromal cells (BMSCs) added during implantation. Radiological analysis was performed at 4, 8, 12 weeks, and 4 months, when animals were sacrificed for the final radiological, histological, and histomorphometric evaluation. In all tested treatments, these analyses highlighted the presence of newly formed bone at the bone scaffolds' interface. Although a lack of substantial effect of PRP was demonstrated, the scaffold+BMSC augmentation showed the highest value of bone-to-implant contact and new bone growth inside the scaffold. The findings of this study suggest the potential of bio-ceramization processes applied to vegetable hierarchical structures for the production of wood-derived bone scaffolds, and document a suitable augmentation procedure in enhancing bone regeneration, particularly when combined with BMSCs. PMID:24099033

Hierarchial mark-recapture models: a framework for inference about demographic processes

USGS Publications Warehouse

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

2004-01-01

The development of sophisticated mark-recapture models over the last four decades has provided fundamental tools for the study of wildlife populations, allowing reliable inference about population sizes and demographic rates based on clearly formulated models for the sampling processes. Mark-recapture models are now routinely described by large numbers of parameters. These large models provide the next challenge to wildlife modelers: the extraction of signal from noise in large collections of parameters. Pattern among parameters can be described by strong, deterministic relations (as in ultrastructural models) but is more flexibly and credibly modeled using weaker, stochastic relations. Trend in survival rates is not likely to be manifest by a sequence of values falling precisely on a given parametric curve; rather, if we could somehow know the true values, we might anticipate a regression relation between parameters and explanatory variables, in which true value equals signal plus noise. Hierarchical models provide a useful framework for inference about collections of related parameters. Instead of regarding parameters as fixed but unknown quantities, we regard them as realizations of stochastic processes governed by hyperparameters. Inference about demographic processes is based on investigation of these hyperparameters. We advocate the Bayesian paradigm as a natural, mathematically and scientifically sound basis for inference about hierarchical models. We describe analysis of capture-recapture data from an open population based on hierarchical extensions of the Cormack-Jolly-Seber model. In addition to recaptures of marked animals, we model first captures of animals and losses on capture, and are thus able to estimate survival probabilities w (i.e., the complement of death or permanent emigration) and per capita growth rates f (i.e., the sum of recruitment and immigration rates). Covariation in these rates, a feature of demographic interest, is explicitly described in the model.

On selecting a prior for the precision parameter of Dirichlet process mixture models

USGS Publications Warehouse

Dorazio, R.M.

2009-01-01

In hierarchical mixture models the Dirichlet process is used to specify latent patterns of heterogeneity, particularly when the distribution of latent parameters is thought to be clustered (multimodal). The parameters of a Dirichlet process include a precision parameter ?? and a base probability measure G0. In problems where ?? is unknown and must be estimated, inferences about the level of clustering can be sensitive to the choice of prior assumed for ??. In this paper an approach is developed for computing a prior for the precision parameter ?? that can be used in the presence or absence of prior information about the level of clustering. This approach is illustrated in an analysis of counts of stream fishes. The results of this fully Bayesian analysis are compared with an empirical Bayes analysis of the same data and with a Bayesian analysis based on an alternative commonly used prior.

In situ analysis of the organic framework in the prismatic layer of mollusc shell.

PubMed

Tong, Hua; Hu, Jiming; Ma, Wentao; Zhong, Guirong; Yao, Songnian; Cao, Nianxing

2002-06-01

A novel in situ analytic approach was constructed by means of ion sputtering, decalcification and deprotein techniques combining with scanning electron microscopy (SEM) and transmission electron microscope (TEM) ultrastructural analysis. The method was employed to determine the spatial distribution of the organic framework outside and the inner crystal and organic/inorganic interface spatial geometrical relationship in the prismatic layer of cristaris plicate (leach). The results show that there is a substructure of organic matrix in the intracrystalline region. The prismatic layer forms according to strict hierarchical configuration of regular pattern. Each unit of organic template of prismatic layer can uniquely determine the column crystal growth direction, spatial orientation and size. Cavity templates are responsible for supporting. limiting size and shape and determining the crystal growth spatial orientation, while the intracrystal organic matrix is responsible for providing nucleation point and inducing the nucleation process of calcite. The stereo hierarchical fabrication of prismatic layer was elucidated for the first time.

A self-similar hierarchy of the Korean stock market

NASA Astrophysics Data System (ADS)

Lim, Gyuchang; Min, Seungsik; Yoo, Kun-Woo

2013-01-01

A scaling analysis is performed on market values of stocks listed on Korean stock exchanges such as the KOSPI and the KOSDAQ. Different from previous studies on price fluctuations, market capitalizations are dealt with in this work. First, we show that the sum of the two stock exchanges shows a clear rank-size distribution, i.e., the Zipf's law, just as each separate one does. Second, by abstracting Zipf's law as a γ-sequence, we define a self-similar hierarchy consisting of many levels, with the numbers of firms at each level forming a geometric sequence. We also use two exponential functions to describe the hierarchy and derive a scaling law from them. Lastly, we propose a self-similar hierarchical process and perform an empirical analysis on our data set. Based on our findings, we argue that all money invested in the stock market is distributed in a hierarchical way and that a slight difference exists between the two exchanges.

Predicting the decision to pursue mediation in civil disputes: a hierarchical classes analysis.

PubMed

Reich, Warren A; Kressel, Kenneth; Scanlon, Kathleen M; Weiner, Gary A

2007-11-01

Clients (N = 185) involved in civil court cases completed the CPR Institute's Mediation Screen, which is designed to assist in making a decision about pursuing mediation. The authors modeled data using hierarchical classes analysis (HICLAS), a clustering algorithm that places clients into 1 set of classes and CPRMS items into another set of classes. HICLAS then links the sets of classes so that any class of clients can be identified in terms of the classes of items they endorsed. HICLAS-derived item classes reflected 2 underlying themes: (a) suitability of the dispute for a problem-solving process and (b) potential benefits of mediation. All clients who perceived that mediation would be beneficial also believed that the context of their conflict was favorable to mediation; however, not all clients who saw a favorable context believed they would benefit from mediation. The majority of clients who agreed to pursue mediation endorsed items reflecting both contextual suitability and perceived benefits of mediation.

Hierarchical species distribution models

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.

2016-01-01

Determining the distribution pattern of a species is important to increase scientific knowledge, inform management decisions, and conserve biodiversity. To infer spatial and temporal patterns, species distribution models have been developed for use with many sampling designs and types of data. Recently, it has been shown that count, presence-absence, and presence-only data can be conceptualized as arising from a point process distribution. Therefore, it is important to understand properties of the point process distribution. We examine how the hierarchical species distribution modeling framework has been used to incorporate a wide array of regression and theory-based components while accounting for the data collection process and making use of auxiliary information. The hierarchical modeling framework allows us to demonstrate how several commonly used species distribution models can be derived from the point process distribution, highlight areas of potential overlap between different models, and suggest areas where further research is needed.

Analyzing Multilevel Data: An Empirical Comparison of Parameter Estimates of Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2011-01-01

Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random…

New Directions in the Digital Signal Processing of Image Data.

DTIC Science & Technology

1987-05-01

and identify by block number) FIELD GROUP SUB-GROUP Object detection and idLntification 12 01 restoration of photon noise limited imagery 15 04 image...from incomplete information, restoration of blurred images in additive and multiplicative noise , motion analysis with fast hierarchical algorithms...different resolutions. As is well known, the solution to the matched filter problem under additive white noise conditions is the correlation receiver

European Scientific Notes. Volume 38, Number 6.

DTIC Science & Technology

1984-06-01

powerful information-processing theories and + * eye-movement analysis provides .i path toward integration in cognitive psychology. The Neo-Piayetian...differences. This work has in turn led to While confirming the hierarchical aspect similar studies of adult cognitive of Piagetian theory in part, the results...Convection Phenomena, and Experiments in languages of the conference are French Solid State Physics Relevant to Litho- and English , with simultaneous

The Equivalence of Three Statistical Packages for Performing Hierarchical Cluster Analysis

ERIC Educational Resources Information Center

Blashfield, Roger

1977-01-01

Three different software programs which contain hierarchical agglomerative cluster analysis procedures were shown to generate different solutions on the same data set using apparently the same options. The basis for the differences in the solutions was the formulae used to calculate Euclidean distance. (Author/JKS)

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.

Instability of Hierarchical Cluster Analysis Due to Input Order of the Data: The PermuCLUSTER Solution

ERIC Educational Resources Information Center

van der Kloot, Willem A.; Spaans, Alexander M. J.; Heiser, Willem J.

2005-01-01

Hierarchical agglomerative cluster analysis (HACA) may yield different solutions under permutations of the input order of the data. This instability is caused by ties, either in the initial proximity matrix or arising during agglomeration. The authors recommend to repeat the analysis on a large number of random permutations of the rows and columns…

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.

Bayesian state space models for dynamic genetic network construction across multiple tissues.

PubMed

Liang, Yulan; Kelemen, Arpad

2016-08-01

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

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.

Differential gene expression profiles of peripheral blood mononuclear cells in childhood asthma.

PubMed

Kong, Qian; Li, Wen-Jing; Huang, Hua-Rong; Zhong, Ying-Qiang; Fang, Jian-Pei

2015-05-01

Asthma is a common childhood disease with strong genetic components. This study compared whole-genome expression differences between asthmatic young children and healthy controls to identify gene signatures of childhood asthma. Total RNA extracted from peripheral blood mononuclear cells (PBMC) was subjected to microarray analysis. QRT-PCR was performed to verify the microarray results. Classification and functional characterization of differential genes were illustrated by hierarchical clustering and gene ontology analysis. Multiple logistic regression (MLR) analysis, receiver operating characteristic (ROC) curve analysis, and discriminate power were used to scan asthma-specific diagnostic markers. For fold-change>2 and p < 0.05, there were 758 named differential genes. The results of QRT-PCR confirmed successfully the array data. Hierarchical clustering divided 29 highly possible genes into seven categories and the genes in the same cluster were likely to possess similar expression patterns or functions. Gene ontology analysis presented that differential genes primarily enriched in immune response, response to stress or stimulus, and regulation of apoptosis in biological process. MLR and ROC curve analysis revealed that the combination of ADAM33, Smad7, and LIGHT possessed excellent discriminating power. The combination of ADAM33, Smad7, and LIGHT would be a reliable and useful childhood asthma model for prediction and diagnosis.

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

Comparison of a species distribution model and a process model from a hierarchical perspective to quantify effects of projected climate change on tree species

Treesearch

Jeffrey E. Schneiderman; Hong S. He; Frank R. Thompson; William D. Dijak; Jacob S. Fraser

2015-01-01

Tree species distribution and abundance are affected by forces operating across a hierarchy of ecological scales. Process and species distribution models have been developed emphasizing forces at different scales. Understanding model agreement across hierarchical scales provides perspective on prediction uncertainty and ultimately enables policy makers and managers to...

Synthesis of hollow silica spheres with hierarchical shell structure by the dual action of liquid indium microbeads in vapor-liquid-solid growth.

PubMed

Wang, Jian-Tao; Wang, Hui; Ou, Xue-Mei; Lee, Chun-Sing; Zhang, Xiao-Hong

2011-07-05

Geometry-based adhesion arising from hierarchical surface structure enables microspheres to adhere to cells strongly, which is essential for inorganic microcapsules that function as drug delivery or diagnostic imaging agents. However, constructing a hierarchical structure on the outer shell of the products via the current microcapsule synthesis method is difficult. This work presents a novel approach to fabricating hollow microspheres with a hierarchical shell structure through the vapor-liquid-solid (VLS) process in which liquid indium droplets act as both templates for the formation of silica capsules and catalysts for the growth of hierarchical shell structure. This hierarchical shell structure offers the hollow microsphere an enhanced geometry-based adhesion. The results provide a facile method for fabricating hollow spheres and enriching their function through tailoring the geometry of their outer shells. © 2011 American Chemical Society

Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus-plasma separation and cell classification using a hierarchical tree model of hematopoesis

NASA Astrophysics Data System (ADS)

Krappe, Sebastian; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2016-03-01

The morphological differentiation of bone marrow is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually under the use of bright field microscopy. This is a time-consuming, subjective, tedious and error-prone process. Furthermore, repeated examinations of a slide may yield intra- and inter-observer variances. For that reason a computer assisted diagnosis system for bone marrow differentiation is pursued. In this work we focus (a) on a new method for the separation of nucleus and plasma parts and (b) on a knowledge-based hierarchical tree classifier for the differentiation of bone marrow cells in 16 different classes. Classification trees are easily interpretable and understandable and provide a classification together with an explanation. Using classification trees, expert knowledge (i.e. knowledge about similar classes and cell lines in the tree model of hematopoiesis) is integrated in the structure of the tree. The proposed segmentation method is evaluated with more than 10,000 manually segmented cells. For the evaluation of the proposed hierarchical classifier more than 140,000 automatically segmented bone marrow cells are used. Future automated solutions for the morphological analysis of bone marrow smears could potentially apply such an approach for the pre-classification of bone marrow cells and thereby shortening the examination time.

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…

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,…

Principles Supporting the Perceptional Teaching of Physics: A ``Practical Teaching Philosophy''

NASA Astrophysics Data System (ADS)

Kurki-Suonio, Kaarle

2011-03-01

This article sketches a framework of ideas developed in the context of decades of physics teacher-education that was entitled the "perceptional approach". Individual learning and the scientific enterprise are interpreted as different manifestations of the same process aimed at understanding the natural and social worlds. The process is understood to possess the basic nature of perception, where empirical meanings are first born and then conceptualised. The accumulation of perceived gestalts in the "structure of the mind" leads to structural perception and the generation of conceptual hierarchies, which form a general principle for the expansion of our understanding. The process undergoes hierarchical development from early sensory perception to individual learning and finally to science. The process is discussed in terms of a three-process dynamic. Scientific and technological processes are driven by the interaction of the mind and nature. They are embedded in the social process due to the interaction of individual minds. These sub-processes are defined by their aims: The scientific process affects the mind and aims at understanding; the technological process affects nature and aims at human well-being; and the social process aims at mutual agreement and cooperation. In hierarchical development the interaction of nature and the mind gets structured into a "methodical cycle" by procedures involving conscious activities. Its intuitive nature is preserved due to subordination of the procedures to empirical meanings. In physics, two dimensions of hierarchical development are distinguished: Unification development gives rise to a generalisation hierarchy of concepts; Quantification development transfers the empirical meanings to quantities, laws and theories representing successive hierarchical levels of quantitative concepts. Consequences for physics teaching are discussed in principle, and in the light of examples and experiences from physics teacher education.

Unveiling the mechanisms of dressed-photon-phonon etching based on hierarchical surface roughness measure

NASA Astrophysics Data System (ADS)

Naruse, Makoto; Yatsui, Takashi; Nomura, Wataru; Kawazoe, Tadashi; Aida, Masaki; Ohtsu, Motoichi

2013-02-01

Dressed-photon-phonon (DPP) etching is a disruptive technology in planarizing material surfaces because it completely eliminates mechanical contact processes. However, adequate metrics for evaluating the surface roughness and the underlying physical mechanisms are still not well understood. Here, we propose a two-dimensional hierarchical surface roughness measure, inspired by the Allan variance, that represents the effectiveness of DPP etching while conserving the original two-dimensional surface topology. Also, we build a simple physical model of DPP etching that agrees well with the experimental observations, which clearly shows the involvement of the intrinsic hierarchical properties of dressed photons, or optical near-fields, in the surface processing.

"Sentido de Pertenencia": A Hierarchical Analysis Predicting Sense of Belonging among Latino College Students

ERIC Educational Resources Information Center

Strayhorn, Terrell Lamont

2008-01-01

The present study estimated the influence of academic and social collegiate experiences on Latino students' sense of belonging, controlling for background differences, using hierarchical analysis techniques with a nested design. In addition, results were compared between Latino students and their White counterparts. Findings reveal that grades,…

Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

ERIC Educational Resources Information Center

Luo, Wen; Azen, Razia

2013-01-01

Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…

Novel MoSe2 hierarchical microspheres for applications in visible-light-driven advanced oxidation processes.

PubMed

Dai, Chu; Qing, Enping; Li, Yong; Zhou, Zhaoxin; Yang, Chao; Tian, Xike; Wang, Yanxin

2015-12-21

Advanced oxidation processes as a green technology have been adopted by combining the semiconductor catalyst MoSe2 with H2O2 under visible radiation. And novel three-dimensional self-assembled molybdenum diselenide (MoSe2) hierarchical microspheres from nanosheets were produced by using organic, selenium cyanoacetic acid sodium (NCSeCH2COONa) as the source of Se. The obtained products possess good crystallinity and present hierarchical structures with the average diameter of 1 μm. The band gap of MoSe2 microspheres is 1.68 eV and they present excellent photocatalytic activity under visible light irradiation in the MoSe2-H2O2 system. This effective photocatalytic mechanism was investigated in this study and can be attributed to visible-light-driven advanced oxidation processes.

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.

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.

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.

Groundwater Quality: Analysis of Its Temporal and Spatial Variability in a Karst Aquifer.

PubMed

Pacheco Castro, Roger; Pacheco Ávila, Julia; Ye, Ming; Cabrera Sansores, Armando

2018-01-01

This study develops an approach based on hierarchical cluster analysis for investigating the spatial and temporal variation of water quality governing processes. The water quality data used in this study were collected in the karst aquifer of Yucatan, Mexico, the only source of drinking water for a population of nearly two million people. Hierarchical cluster analysis was applied to the quality data of all the sampling periods lumped together. This was motivated by the observation that, if water quality does not vary significantly in time, two samples from the same sampling site will belong to the same cluster. The resulting distribution maps of clusters and box-plots of the major chemical components reveal the spatial and temporal variability of groundwater quality. Principal component analysis was used to verify the results of cluster analysis and to derive the variables that explained most of the variation of the groundwater quality data. Results of this work increase the knowledge about how precipitation and human contamination impact groundwater quality in Yucatan. Spatial variability of groundwater quality in the study area is caused by: a) seawater intrusion and groundwater rich in sulfates at the west and in the coast, b) water rock interactions and the average annual precipitation at the middle and east zones respectively, and c) human contamination present in two localized zones. Changes in the amount and distribution of precipitation cause temporal variation by diluting groundwater in the aquifer. This approach allows to analyze the variation of groundwater quality controlling processes efficiently and simultaneously. © 2017, National Ground Water Association.

Hierarchically macro–mesoporous TiO{sub 2} film via self-assembled strategy for enhanced efficiency of dye sensitized solar cells

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

Wang, Ping; Wang, Jin; Yu, Huogen, E-mail: yuhuogen@whut.edu.cn

2016-02-15

Highlights: • A new hierarchically macro–mesoporous TiO{sub 2} film is fabricated via TiF{sub 4} hydrolysis. • TiF{sub 4} hydrolysis is accompanied with self-assembled process of TiO{sub 2} nanoparticles. • The hierarchically porous TiO{sub 2} films show higher performance than nonporous film. - Abstract: The hierarchically porous structure of TiO{sub 2} film plays an important role on improved photoelectric conversion efficiency in dye-sensitized solar cells (DSSCs). It is highly required to develop a facile strategy to prepare the hierarchical porous photoelectrode. In this study, a novel hierarchically macro–mesoporous TiO{sub 2} film as photoelectrode of DSSCs is fabricated by a self-assembled processmore » of TiO{sub 2} nanoparticles via TiF{sub 4} hydrolysis. The hydrolysis of TiF{sub 4} is accompanied with self-assembled process of TiO{sub 2} nanoparticles on the surface of electrophoretic-deposited titanate nanotube film which provides effective active sites for the deposition of TiO{sub 2} nanoparticles owing to a large amount of hydroxyl groups, resulting in the formation of hierarchically porous structures. The hierarchically porous TiO{sub 2} film is mainly composed of mesopores with a size of 2–50 nm and macropores with a wide range of 0.5–5 μm, which contribute to an obviously higher conversion performance (6.70%) than nonporous P25-TiO{sub 2} film (4.01%). The main reasons for enhanced conversion efficiency of hierarchically porous TiO{sub 2} film can be attributed to adsorption of more dye molecules, rapid diffusion and efficient transport of electrolyte, and longer electron lifetime. This work may provide new insights into preparing porous structure of TiO{sub 2} films in DSSCs for modification of photoelectric conversion efficiency.« less

A hierarchical network-based algorithm for multi-scale watershed delineation

NASA Astrophysics Data System (ADS)

Castronova, Anthony M.; Goodall, Jonathan L.

2014-11-01

Watershed delineation is a process for defining a land area that contributes surface water flow to a single outlet point. It is a commonly used in water resources analysis to define the domain in which hydrologic process calculations are applied. There has been a growing effort over the past decade to improve surface elevation measurements in the U.S., which has had a significant impact on the accuracy of hydrologic calculations. Traditional watershed processing on these elevation rasters, however, becomes more burdensome as data resolution increases. As a result, processing of these datasets can be troublesome on standard desktop computers. This challenge has resulted in numerous works that aim to provide high performance computing solutions to large data, high resolution data, or both. This work proposes an efficient watershed delineation algorithm for use in desktop computing environments that leverages existing data, U.S. Geological Survey (USGS) National Hydrography Dataset Plus (NHD+), and open source software tools to construct watershed boundaries. This approach makes use of U.S. national-level hydrography data that has been precomputed using raster processing algorithms coupled with quality control routines. Our approach uses carefully arranged data and mathematical graph theory to traverse river networks and identify catchment boundaries. We demonstrate this new watershed delineation technique, compare its accuracy with traditional algorithms that derive watershed solely from digital elevation models, and then extend our approach to address subwatershed delineation. Our findings suggest that the open-source hierarchical network-based delineation procedure presented in the work is a promising approach to watershed delineation that can be used summarize publicly available datasets for hydrologic model input pre-processing. Through our analysis, we explore the benefits of reusing the NHD+ datasets for watershed delineation, and find that the our technique offers greater flexibility and extendability than traditional raster algorithms.

A hierarchical model for estimating change in American Woodcock populations

USGS Publications Warehouse

Sauer, J.R.; Link, W.A.; Kendall, W.L.; Kelley, J.R.; Niven, D.K.

2008-01-01

The Singing-Ground Survey (SGS) is a primary source of information on population change for American woodcock (Scolopax minor). We analyzed the SGS using a hierarchical log-linear model and compared the estimates of change and annual indices of abundance to a route regression analysis of SGS data. We also grouped SGS routes into Bird Conservation Regions (BCRs) and estimated population change and annual indices using BCRs within states and provinces as strata. Based on the hierarchical model?based estimates, we concluded that woodcock populations were declining in North America between 1968 and 2006 (trend = -0.9%/yr, 95% credible interval: -1.2, -0.5). Singing-Ground Survey results are generally similar between analytical approaches, but the hierarchical model has several important advantages over the route regression. Hierarchical models better accommodate changes in survey efficiency over time and space by treating strata, years, and observers as random effects in the context of a log-linear model, providing trend estimates that are derived directly from the annual indices. We also conducted a hierarchical model analysis of woodcock data from the Christmas Bird Count and the North American Breeding Bird Survey. All surveys showed general consistency in patterns of population change, but the SGS had the shortest credible intervals. We suggest that population management and conservation planning for woodcock involving interpretation of the SGS use estimates provided by the hierarchical model.

A new application of hierarchical cluster analysis to investigate organic peaks in bulk mass spectra obtained with an Aerodyne Aerosol Mass Spectrometer

NASA Astrophysics Data System (ADS)

Middlebrook, A. M.; Marcolli, C.; Canagaratna, M. R.; Worsnop, D. R.; Bahreini, R.; de Gouw, J. A.; Warneke, C.; Goldan, P. D.; Kuster, W. C.; Williams, E. J.; Lerner, B. M.; Roberts, J. M.; Meagher, J. F.; Fehsenfeld, F. C.; Marchewka, M. L.; Bertman, S. B.

2006-12-01

We applied hierarchical cluster analysis to an Aerodyne aerosol mass spectrometer (AMS) bulk mass spectral dataset collected aboard the NOAA research vessel Ronald H. Brown during the 2002 New England Air Quality Study off the east coast of the United States. Emphasizing the organic peaks, the cluster analysis yielded a series of categories that are distinguishable with respect to their mass spectra and their occurrence as a function of time. The differences between the categories mainly arise from relative intensity changes rather than from the presence or absence of specific peaks. The most frequent category exhibits a strong signal at m/z 44 and represents oxidized organic matter probably originating from both anthropogenic as well as biogenic sources. On the basis of spectral and trace gas correlations, the second most common category with strong signals at m/z 29, 43, and 44 contains contributions from isoprene oxidation products. The third through the fifth most common categories have peak patterns characteristic of monoterpene oxidation products and were most frequently observed when air masses from monoterpene rich regions were sampled. Taken together, the second through the fifth most common categories represent on average 17% of the total organic mass that stems likely from biogenic sources during the ship's cruise. These numbers have to be viewed as lower limits since the most common category was attributed to anthropogenic sources for this calculation. The cluster analysis was also very effective in identifying a few contaminated mass spectra that were not removed during pre-processing. This study demonstrates that hierarchical clustering is a useful tool to analyze the complex patterns of the organic peaks in bulk aerosol mass spectra from a field study.

Hierarchical Self Assembly of Patterns from the Robinson Tilings: DNA Tile Design in an Enhanced Tile Assembly Model

PubMed Central

Padilla, Jennifer E.; Liu, Wenyan; Seeman, Nadrian C.

2012-01-01

We introduce a hierarchical self assembly algorithm that produces the quasiperiodic patterns found in the Robinson tilings and suggest a practical implementation of this algorithm using DNA origami tiles. We modify the abstract Tile Assembly Model, (aTAM), to include active signaling and glue activation in response to signals to coordinate the hierarchical assembly of Robinson patterns of arbitrary size from a small set of tiles according to the tile substitution algorithm that generates them. Enabling coordinated hierarchical assembly in the aTAM makes possible the efficient encoding of the recursive process of tile substitution. PMID:23226722

Hierarchical Self Assembly of Patterns from the Robinson Tilings: DNA Tile Design in an Enhanced Tile Assembly Model.

PubMed

Padilla, Jennifer E; Liu, Wenyan; Seeman, Nadrian C

2012-06-01

We introduce a hierarchical self assembly algorithm that produces the quasiperiodic patterns found in the Robinson tilings and suggest a practical implementation of this algorithm using DNA origami tiles. We modify the abstract Tile Assembly Model, (aTAM), to include active signaling and glue activation in response to signals to coordinate the hierarchical assembly of Robinson patterns of arbitrary size from a small set of tiles according to the tile substitution algorithm that generates them. Enabling coordinated hierarchical assembly in the aTAM makes possible the efficient encoding of the recursive process of tile substitution.

The Relationship between Students' Perceptions of the Teaching and Learning Process and Academic Performance in Science of Seventh and Eighth Graders Enrolled in Seventh-Day Adventist Schools

ERIC Educational Resources Information Center

Burton, Larry D.; Kijai, Jimmy; Sargeant, Marcel A.

2005-01-01

This study investigated student perceptions of the 7th and 8th grade Adventist science education program and their relationship to achievement in science as defined by performance on the Iowa Test of Basic Skills. Findings revealed that students held generally poor perceptions of science education. Hierarchical regression analysis revealed 8…

Hierarchy concepts: classification and preparation strategies for zeolite containing materials with hierarchical porosity.

PubMed

Schwieger, Wilhelm; Machoke, Albert Gonche; Weissenberger, Tobias; Inayat, Amer; Selvam, Thangaraj; Klumpp, Michael; Inayat, Alexandra

2016-06-13

'Hierarchy' is a property which can be attributed to a manifold of different immaterial systems, such as ideas, items and organisations or material ones like biological systems within living organisms or artificial, man-made constructions. The property 'hierarchy' is mainly characterised by a certain ordering of individual elements relative to each other, often in combination with a certain degree of branching. Especially mass-flow related systems in the natural environment feature special hierarchically branched patterns. This review is a survey into the world of hierarchical systems with special focus on hierarchically porous zeolite materials. A classification of hierarchical porosity is proposed based on the flow distribution pattern within the respective pore systems. In addition, this review might serve as a toolbox providing several synthetic and post-synthetic strategies to prepare zeolitic or zeolite containing material with tailored hierarchical porosity. Very often, such strategies with their underlying principles were developed for improving the performance of the final materials in different technical applications like adsorptive or catalytic processes. In the present review, besides on the hierarchically porous all-zeolite material, special focus is laid on the preparation of zeolitic composite materials with hierarchical porosity capable to face the demands of industrial application.

Transforming Hierarchical Relationships in Student Conduct Administration

ERIC Educational Resources Information Center

Jacobson, Kelly A.

2013-01-01

Conflict transformation theory provided a philosophical lens for this critical cultural, constructivist study, wherein four student conduct administrators who engage in leveling hierarchical relationships with students in conduct processes shared ways they make meaning of their professional practice. Through informal, unstructured interviews, a…

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.

Bayesian Computation for Log-Gaussian Cox Processes: A Comparative Analysis of Methods

PubMed Central

Teng, Ming; Nathoo, Farouk S.; Johnson, Timothy D.

2017-01-01

The Log-Gaussian Cox Process is a commonly used model for the analysis of spatial point pattern data. Fitting this model is difficult because of its doubly-stochastic property, i.e., it is an hierarchical combination of a Poisson process at the first level and a Gaussian Process at the second level. Various methods have been proposed to estimate such a process, including traditional likelihood-based approaches as well as Bayesian methods. We focus here on Bayesian methods and several approaches that have been considered for model fitting within this framework, including Hamiltonian Monte Carlo, the Integrated nested Laplace approximation, and Variational Bayes. We consider these approaches and make comparisons with respect to statistical and computational efficiency. These comparisons are made through several simulation studies as well as through two applications, the first examining ecological data and the second involving neuroimaging data. PMID:29200537

Information Theory Broadens the Spectrum of Molecular Ecology and Evolution.

PubMed

Sherwin, W B; Chao, A; Jost, L; Smouse, P E

2017-12-01

Information or entropy analysis of diversity is used extensively in community ecology, and has recently been exploited for prediction and analysis in molecular ecology and evolution. Information measures belong to a spectrum (or q profile) of measures whose contrasting properties provide a rich summary of diversity, including allelic richness (q=0), Shannon information (q=1), and heterozygosity (q=2). We present the merits of information measures for describing and forecasting molecular variation within and among groups, comparing forecasts with data, and evaluating underlying processes such as dispersal. Importantly, information measures directly link causal processes and divergence outcomes, have straightforward relationship to allele frequency differences (including monotonicity that q=2 lacks), and show additivity across hierarchical layers such as ecology, behaviour, cellular processes, and nongenetic inheritance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Models for Rational Decision Making. Analysis of Literature and Selected Bibliography. Analysis and Bibliography Series, No. 6.

ERIC Educational Resources Information Center

Hall, John S.

This review analyzes the trend in educational decision making to replace hierarchical authority structures with more rational models for decision making drawn from management science. Emphasis is also placed on alternatives to a hierarchical decision-making model, including governing models, union models, and influence models. A 54-item…

Changes to Serum Sample Tube and Processing Methodology Does Not Cause Inter-Individual Variation in Automated Whole Serum N-Glycan Profiling in Health and Disease

PubMed Central

Shubhakar, Archana; Kalla, Rahul; Nimmo, Elaine R.; Fernandes, Daryl L.; Satsangi, Jack; Spencer, Daniel I. R.

2015-01-01

Introduction Serum N-glycans have been identified as putative biomarkers for numerous diseases. The impact of different serum sample tubes and processing methods on N-glycan analysis has received relatively little attention. This study aimed to determine the effect of different sample tubes and processing methods on the whole serum N-glycan profile in both health and disease. A secondary objective was to describe a robot automated N-glycan release, labeling and cleanup process for use in a biomarker discovery system. Methods 25 patients with active and quiescent inflammatory bowel disease and controls had three different serum sample tubes taken at the same draw. Two different processing methods were used for three types of tube (with and without gel-separation medium). Samples were randomised and processed in a blinded fashion. Whole serum N-glycan release, 2-aminobenzamide labeling and cleanup was automated using a Hamilton Microlab STARlet Liquid Handling robot. Samples were analysed using a hydrophilic interaction liquid chromatography/ethylene bridged hybrid(BEH) column on an ultra-high performance liquid chromatography instrument. Data were analysed quantitatively by pairwise correlation and hierarchical clustering using the area under each chromatogram peak. Qualitatively, a blinded assessor attempted to match chromatograms to each individual. Results There was small intra-individual variation in serum N-glycan profiles from samples collected using different sample processing methods. Intra-individual correlation coefficients were between 0.99 and 1. Unsupervised hierarchical clustering and principal coordinate analyses accurately matched samples from the same individual. Qualitative analysis demonstrated good chromatogram overlay and a blinded assessor was able to accurately match individuals based on chromatogram profile, regardless of disease status. Conclusions The three different serum sample tubes processed using the described methods cause minimal inter-individual variation in serum whole N-glycan profile when processed using an automated workstream. This has important implications for N-glycan biomarker discovery studies using different serum processing standard operating procedures. PMID:25831126

Changes to serum sample tube and processing methodology does not cause Intra-Individual [corrected] variation in automated whole serum N-glycan profiling in health and disease.

PubMed

Ventham, Nicholas T; Gardner, Richard A; Kennedy, Nicholas A; Shubhakar, Archana; Kalla, Rahul; Nimmo, Elaine R; Fernandes, Daryl L; Satsangi, Jack; Spencer, Daniel I R

2015-01-01

Serum N-glycans have been identified as putative biomarkers for numerous diseases. The impact of different serum sample tubes and processing methods on N-glycan analysis has received relatively little attention. This study aimed to determine the effect of different sample tubes and processing methods on the whole serum N-glycan profile in both health and disease. A secondary objective was to describe a robot automated N-glycan release, labeling and cleanup process for use in a biomarker discovery system. 25 patients with active and quiescent inflammatory bowel disease and controls had three different serum sample tubes taken at the same draw. Two different processing methods were used for three types of tube (with and without gel-separation medium). Samples were randomised and processed in a blinded fashion. Whole serum N-glycan release, 2-aminobenzamide labeling and cleanup was automated using a Hamilton Microlab STARlet Liquid Handling robot. Samples were analysed using a hydrophilic interaction liquid chromatography/ethylene bridged hybrid(BEH) column on an ultra-high performance liquid chromatography instrument. Data were analysed quantitatively by pairwise correlation and hierarchical clustering using the area under each chromatogram peak. Qualitatively, a blinded assessor attempted to match chromatograms to each individual. There was small intra-individual variation in serum N-glycan profiles from samples collected using different sample processing methods. Intra-individual correlation coefficients were between 0.99 and 1. Unsupervised hierarchical clustering and principal coordinate analyses accurately matched samples from the same individual. Qualitative analysis demonstrated good chromatogram overlay and a blinded assessor was able to accurately match individuals based on chromatogram profile, regardless of disease status. The three different serum sample tubes processed using the described methods cause minimal inter-individual variation in serum whole N-glycan profile when processed using an automated workstream. This has important implications for N-glycan biomarker discovery studies using different serum processing standard operating procedures.

Directed assembly of bio-inspired hierarchical materials with controlled nanofibrillar architectures

NASA Astrophysics Data System (ADS)

Tseng, Peter; Napier, Bradley; Zhao, Siwei; Mitropoulos, Alexander N.; Applegate, Matthew B.; Marelli, Benedetto; Kaplan, David L.; Omenetto, Fiorenzo G.

2017-05-01

In natural systems, directed self-assembly of structural proteins produces complex, hierarchical materials that exhibit a unique combination of mechanical, chemical and transport properties. This controlled process covers dimensions ranging from the nano- to the macroscale. Such materials are desirable to synthesize integrated and adaptive materials and systems. We describe a bio-inspired process to generate hierarchically defined structures with multiscale morphology by using regenerated silk fibroin. The combination of protein self-assembly and microscale mechanical constraints is used to form oriented, porous nanofibrillar networks within predesigned macroscopic structures. This approach allows us to predefine the mechanical and physical properties of these materials, achieved by the definition of gradients in nano- to macroscale order. We fabricate centimetre-scale material geometries including anchors, cables, lattices and webs, as well as functional materials with structure-dependent strength and anisotropic thermal transport. Finally, multiple three-dimensional geometries and doped nanofibrillar constructs are presented to illustrate the facile integration of synthetic and natural additives to form functional, interactive, hierarchical networks.

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.

A neural model of hierarchical reinforcement learning.

PubMed

Rasmussen, Daniel; Voelker, Aaron; Eliasmith, Chris

2017-01-01

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain's general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model's behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions.

A three-dimensional hierarchical collagen scaffold fabricated by a combined solid freeform fabrication (SFF) and electrospinning process to enhance mesenchymal stem cell (MSC) proliferation

NASA Astrophysics Data System (ADS)

Ahn, SeungHyun; Koh, Young Ho; Kim, GeunHyung

2010-06-01

Collagen has the advantage of being very similar to macromolecular substances that can be recognized and metabolized in the biological environment. Although the natural material has superior property for this purpose, its use to fabricate reproducible and pore-structure-controlled 3D structures, which are designed to allow the entry of sufficient cells and the easy diffusion of nutrients, has been limited due to its low processability. Here, we propose a hybrid technology that combines a cryogenic plotting system with an electrospinning process. Using this technique, an easily pore-size-controllable hierarchical 3D scaffold consisting of micro-sized highly porous collagen strands and micro/nano-sized collagen fibers was fabricated. The pore structure of the collagen scaffold was controlled by the collagen micro/nanofibers, which were layered in the scaffold. The hierarchical scaffolds were characterized with respect to initial cell attachment and proliferation of bone marrow-derived mesenchymal stem cells within the scaffolds. The hierarchical scaffold exhibited incredibly enhanced initial cell attachment and cell compactness between pores of the plotted scaffold relative to the normally designed 3D collagen scaffold.

Hierarchical optimal control of large-scale nonlinear chemical processes.

PubMed

Ramezani, Mohammad Hossein; Sadati, Nasser

2009-01-01

In this paper, a new approach is presented for optimal control of large-scale chemical processes. In this approach, the chemical process is decomposed into smaller sub-systems at the first level, and a coordinator at the second level, for which a two-level hierarchical control strategy is designed. For this purpose, each sub-system in the first level can be solved separately, by using any conventional optimization algorithm. In the second level, the solutions obtained from the first level are coordinated using a new gradient-type strategy, which is updated by the error of the coordination vector. The proposed algorithm is used to solve the optimal control problem of a complex nonlinear chemical stirred tank reactor (CSTR), where its solution is also compared with the ones obtained using the centralized approach. The simulation results show the efficiency and the capability of the proposed hierarchical approach, in finding the optimal solution, over the centralized method.

ZnO/CuO/M (M = Ag, Au) Hierarchical Nanostructure by Successive Photoreduction Process for Solar Hydrogen Generation.

PubMed

Kwon, Jinhyeong; Cho, Hyunmin; Jung, Jinwook; Lee, Habeom; Hong, Sukjoon; Yeo, Junyeob; Han, Seungyong; Ko, Seung Hwan

2018-05-12

To date, solar energy generation devices have been widely studied to meet a clean and sustainable energy source. Among them, water splitting photoelectrochemical cell is regarded as a promising energy generation way for splitting water molecules and generating hydrogen by sunlight. While many nanostructured metal oxides are considered as a candidate, most of them have an improper bandgap structure lowering energy transition efficiency. Herein, we introduce a novel wet-based, successive photoreduction process that can improve charge transfer efficiency by surface plasmon effect for a solar-driven water splitting device. The proposed process enables to fabricate ZnO/CuO/Ag or ZnO/CuO/Au hierarchical nanostructure, having an enhanced electrical, optical, photoelectrochemical property. The fabricated hierarchical nanostructures are demonstrated as a photocathode in the photoelectrochemical cell and characterized by using various analytic tools.

ZnO/CuO/M (M = Ag, Au) Hierarchical Nanostructure by Successive Photoreduction Process for Solar Hydrogen Generation

PubMed Central

Kwon, Jinhyeong; Cho, Hyunmin; Jung, Jinwook; Lee, Habeom; Han, Seungyong

2018-01-01

To date, solar energy generation devices have been widely studied to meet a clean and sustainable energy source. Among them, water splitting photoelectrochemical cell is regarded as a promising energy generation way for splitting water molecules and generating hydrogen by sunlight. While many nanostructured metal oxides are considered as a candidate, most of them have an improper bandgap structure lowering energy transition efficiency. Herein, we introduce a novel wet-based, successive photoreduction process that can improve charge transfer efficiency by surface plasmon effect for a solar-driven water splitting device. The proposed process enables to fabricate ZnO/CuO/Ag or ZnO/CuO/Au hierarchical nanostructure, having an enhanced electrical, optical, photoelectrochemical property. The fabricated hierarchical nanostructures are demonstrated as a photocathode in the photoelectrochemical cell and characterized by using various analytic tools. PMID:29757225

Deciphering the Interdependence between Ecological and Evolutionary Networks.

PubMed

Melián, Carlos J; Matthews, Blake; de Andreazzi, Cecilia S; Rodríguez, Jorge P; Harmon, Luke J; Fortuna, Miguel A

2018-05-24

Biological systems consist of elements that interact within and across hierarchical levels. For example, interactions among genes determine traits of individuals, competitive and cooperative interactions among individuals influence population dynamics, and interactions among species affect the dynamics of communities and ecosystem processes. Such systems can be represented as hierarchical networks, but can have complex dynamics when interdependencies among levels of the hierarchy occur. We propose integrating ecological and evolutionary processes in hierarchical networks to explore interdependencies in biological systems. We connect gene networks underlying predator-prey trait distributions to food webs. Our approach addresses longstanding questions about how complex traits and intraspecific trait variation affect the interdependencies among biological levels and the stability of meta-ecosystems. Copyright © 2018 Elsevier Ltd. All rights reserved.

Synthesis of multi-hierarchical structured yttria-stabilized zirconia powders and their enhanced thermophysical properties

NASA Astrophysics Data System (ADS)

Cao, Fengmei; Gao, Yanfeng; Chen, Hongfei; Liu, Xinling; Tang, Xiaoping; Luo, Hongjie

2013-06-01

Multi-hierarchical structured yttria-stabilized zirconia (YSZ) powders were successfully synthesized by a hydrothermal-calcination process. The morphology, crystallinity, and microstructure of the products were characterized by SEM, XRD, TEM, and BET. A possible formation mechanism of the unique structure formed during hydrothermal processing was also investigated. The measured thermophysical results indicated that the prepared YSZ powders had a low thermal conductivity (0.63-1.27 W m-1 K-1), good short-term high-temperature stability up to 1300 °C. The influence of the morphology and microstructure on their thermophysical properties was briefly discussed. The unique multi-hierarchical structure makes the prepared YSZ powders candidates for use in enhanced applications involving thermal barrier coatings.

On the application of multilevel modeling in environmental and ecological studies

USGS Publications Warehouse

Qian, Song S.; Cuffney, Thomas F.; Alameddine, Ibrahim; McMahon, Gerard; Reckhow, Kenneth H.

2010-01-01

This paper illustrates the advantages of a multilevel/hierarchical approach for predictive modeling, including flexibility of model formulation, explicitly accounting for hierarchical structure in the data, and the ability to predict the outcome of new cases. As a generalization of the classical approach, the multilevel modeling approach explicitly models the hierarchical structure in the data by considering both the within- and between-group variances leading to a partial pooling of data across all levels in the hierarchy. The modeling framework provides means for incorporating variables at different spatiotemporal scales. The examples used in this paper illustrate the iterative process of model fitting and evaluation, a process that can lead to improved understanding of the system being studied.

Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis.

PubMed

Fernández-Arjona, María Del Mar; Grondona, Jesús M; Granados-Durán, Pablo; Fernández-Llebrez, Pedro; López-Ávalos, María D

2017-01-01

It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed. Main points Microglia undergo a quantifiable morphological change upon neuraminidase induced inflammation.Hierarchical cluster and principal components analysis allow morphological classification of microglia.Brain location of microglia is a relevant factor.

Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis

PubMed Central

Fernández-Arjona, María del Mar; Grondona, Jesús M.; Granados-Durán, Pablo; Fernández-Llebrez, Pedro; López-Ávalos, María D.

2017-01-01

It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed. Main points Microglia undergo a quantifiable morphological change upon neuraminidase induced inflammation.Hierarchical cluster and principal components analysis allow morphological classification of microglia.Brain location of microglia is a relevant factor. PMID:28848398

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).

A dynamic model of reasoning and memory.

PubMed

Hawkins, Guy E; Hayes, Brett K; Heit, Evan

2016-02-01

Previous models of category-based induction have neglected how the process of induction unfolds over time. We conceive of induction as a dynamic process and provide the first fine-grained examination of the distribution of response times observed in inductive reasoning. We used these data to develop and empirically test the first major quantitative modeling scheme that simultaneously accounts for inductive decisions and their time course. The model assumes that knowledge of similarity relations among novel test probes and items stored in memory drive an accumulation-to-bound sequential sampling process: Test probes with high similarity to studied exemplars are more likely to trigger a generalization response, and more rapidly, than items with low exemplar similarity. We contrast data and model predictions for inductive decisions with a recognition memory task using a common stimulus set. Hierarchical Bayesian analyses across 2 experiments demonstrated that inductive reasoning and recognition memory primarily differ in the threshold to trigger a decision: Observers required less evidence to make a property generalization judgment (induction) than an identity statement about a previously studied item (recognition). Experiment 1 and a condition emphasizing decision speed in Experiment 2 also found evidence that inductive decisions use lower quality similarity-based information than recognition. The findings suggest that induction might represent a less cautious form of recognition. We conclude that sequential sampling models grounded in exemplar-based similarity, combined with hierarchical Bayesian analysis, provide a more fine-grained and informative analysis of the processes involved in inductive reasoning than is possible solely through examination of choice data. PsycINFO Database Record (c) 2016 APA, all rights reserved.

A novel algorithm for delineating wetland depressions and ...

EPA Pesticide Factsheets

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features that are seldom fully filled with water. For instance, wetland depressions in the Prairie Pothole Region (PPR) are seasonally to permanently flooded wetlands characterized by nested hierarchical structures with dynamic filling- spilling-merging surface-water hydrological processes. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution LiDAR data and aerial imagery. We proposed a novel algorithm delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost path algorithm. The resulting flow network delineated putative temporary or seasonal flow paths connecting wetland depressions to each other or to the river network at scales finer than available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow modeling and hydrologic connectivity analysis. Presentation at AWRA Spring Specialty Conference in Sn

Fabrication of hierarchical flower-like porous ZnO nanostructures from layered ZnC2O4·3Zn(OH)2 and gas sensing properties

NASA Astrophysics Data System (ADS)

Cui, Jiashan; Sun, Jianbo; Liu, Xin; Li, Jinwei; Ma, Xinzhi; Chen, Tingting

2014-07-01

ZnO materials with porous and hierarchical flower-like structure were synthesized through mild hydrothermal and simple calcination approach, in which the flower-like layered zinc oxalate hydroxide (ZnC2O4·3Zn(OH)2) precursor was first synthesized and then calcined at 600 °C. The obtained products were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopic (TEM), Brunauer-Emmett-Teller (BET) and thermogravimetric (TG) analysis. We proposed the possible growth mechanism of the material via studying the time evolution experiment results. In the process of reaction, oxalic acid as a structure-directing agent hydrolyzed and then formed primarily sheets-like intermediate ZnC2O4·2H2O. Hexamethylenetetramine (HMT) as surfactant, with directional adsorption, leads to the formation of layered zinc oxalate hydroxide precursor. Furthermore, the gas sensitivity also can be characterized, whose results indicated that the synthesized materials had a preferable selectivity to ethanol gas. The fast response rate and reversible performance can be attributed to the produced greater specific surface area produced, which was caused by the porous and hierarchical flower-like structure.

Decision Making Processes and Outcomes

PubMed Central

Hicks Patrick, Julie; Steele, Jenessa C.; Spencer, S. Melinda

2013-01-01

The primary aim of this study was to examine the contributions of individual characteristics and strategic processing to the prediction of decision quality. Data were provided by 176 adults, ages 18 to 93 years, who completed computerized decision-making vignettes and a battery of demographic and cognitive measures. We examined the relations among age, domain-specific experience, working memory, and three measures of strategic information search to the prediction of solution quality using a 4-step hierarchical linear regression analysis. Working memory and two measures of strategic processing uniquely contributed to the variance explained. Results are discussed in terms of potential advances to both theory and intervention efforts. PMID:24282638

Mexican American family processes: nurturing, support, and socialization.

PubMed

Niska, K J

1999-04-01

The purpose of this ethnographic study with Mexican American families was to document characteristics of Mexican American family processes of nurturing, support, and socialization. Audiotaped conversations with participants were transcribed verbatim in Spanish or English. Content analysis was used to derive characteristics of family processes. Family nurturing was characterized by being kin-based and intimate in nature. Family support was kin-based, with material support oriented toward household needs; with emotional support grounded in shared stories, problem solving, and prayer; and with informational support offered in consejos (wisdom sayings and words of advice), stories, and guidance. Family socialization was kin-based, hierarchical, and ritualistic.

CoMn2O4 Spinel Hierarchical Microspheres Assembled with Porous Nanosheets as Stable Anodes for Lithium-ion Batteries

PubMed Central

Hu, Lin; Zhong, Hao; Zheng, Xinrui; Huang, Yimin; Zhang, Ping; Chen, Qianwang

2012-01-01

Herein, we report the feasibility to enhance the capacity and stability of CoMn2O4 anode materials by fabricating hierarchical mesoporous structure. The open space between neighboring nanosheets allows for easy diffusion of the electrolyte. The hierarchical microspheres assembled with nanosheets can ensure that every nanosheet participates in the electrochemical reaction, because every nanosheet is contacted with the electrolyte solution. The hierarchical structure and well interconnected pores on the surface of nanosheets will enhance the CoMn2O4/electrolyte contact area, shorten the Li+ ion diffusion length in the nanosheets, and accommodate the strain induced by the volume change during the electrochemical reaction. The last, hierarchical architecture with spherical morphology possesses relatively low surface energy, which results in less extent of self-aggregation during charge/discharge process. As a result, CoMn2O4 hierarchical microspheres can achieve a good cycle ability and high rate capability. PMID:23248749

GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms.

PubMed

Jani, Saurin D; Argraves, Gary L; Barth, Jeremy L; Argraves, W Scott

2010-04-01

An important objective of DNA microarray-based gene expression experimentation is determining inter-relationships that exist between differentially expressed genes and biological processes, molecular functions, cellular components, signaling pathways, physiologic processes and diseases. Here we describe GeneMesh, a web-based program that facilitates analysis of DNA microarray gene expression data. GeneMesh relates genes in a query set to categories available in the Medical Subject Headings (MeSH) hierarchical index. The interface enables hypothesis driven relational analysis to a specific MeSH subcategory (e.g., Cardiovascular System, Genetic Processes, Immune System Diseases etc.) or unbiased relational analysis to broader MeSH categories (e.g., Anatomy, Biological Sciences, Disease etc.). Genes found associated with a given MeSH category are dynamically linked to facilitate tabular and graphical depiction of Entrez Gene information, Gene Ontology information, KEGG metabolic pathway diagrams and intermolecular interaction information. Expression intensity values of groups of genes that cluster in relation to a given MeSH category, gene ontology or pathway can be displayed as heat maps of Z score-normalized values. GeneMesh operates on gene expression data derived from a number of commercial microarray platforms including Affymetrix, Agilent and Illumina. GeneMesh is a versatile web-based tool for testing and developing new hypotheses through relating genes in a query set (e.g., differentially expressed genes from a DNA microarray experiment) to descriptors making up the hierarchical structure of the National Library of Medicine controlled vocabulary thesaurus, MeSH. The system further enhances the discovery process by providing links between sets of genes associated with a given MeSH category to a rich set of html linked tabular and graphic information including Entrez Gene summaries, gene ontologies, intermolecular interactions, overlays of genes onto KEGG pathway diagrams and heatmaps of expression intensity values. GeneMesh is freely available online at http://proteogenomics.musc.edu/genemesh/.

Anterior insula coordinates hierarchical processing of tactile mismatch responses

PubMed Central

Allen, Micah; Fardo, Francesca; Dietz, Martin J.; Hillebrandt, Hauke; Friston, Karl J.; Rees, Geraint; Roepstorff, Andreas

2016-01-01

The body underlies our sense of self, emotion, and agency. Signals arising from the skin convey warmth, social touch, and the physical characteristics of external stimuli. Surprising or unexpected tactile sensations can herald events of motivational salience, including imminent threats (e.g., an insect bite) and hedonic rewards (e.g., a caressing touch). Awareness of such events is thought to depend upon the hierarchical integration of body-related mismatch responses by the anterior insula. To investigate this possibility, we measured brain activity using functional magnetic resonance imaging, while healthy participants performed a roving tactile oddball task. Mass-univariate analysis demonstrated robust activations in limbic, somatosensory, and prefrontal cortical areas previously implicated in tactile deviancy, body awareness, and cognitive control. Dynamic Causal Modelling revealed that unexpected stimuli increased the strength of forward connections along a caudal to rostral hierarchy—projecting from thalamic and somatosensory regions towards insula, cingulate and prefrontal cortices. Within this ascending flow of sensory information, the AIC was the only region to show increased backwards connectivity to the somatosensory cortex, augmenting a reciprocal exchange of neuronal signals. Further, participants who rated stimulus changes as easier to detect showed stronger modulation of descending PFC to AIC connections by deviance. These results suggest that the AIC coordinates hierarchical processing of tactile prediction error. They are interpreted in support of an embodied predictive coding model where AIC mediated body awareness is involved in anchoring a global neuronal workspace. PMID:26584870

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.

Simple and Hierarchical Models for Stochastic Test Misgrading.

ERIC Educational Resources Information Center

Wang, Jianjun

1993-01-01

Test misgrading is treated as a stochastic process. The expected number of misgradings, inter-occurrence time of misgradings, and waiting time for the "n"th misgrading are discussed based on a simple Poisson model and a hierarchical Beta-Poisson model. Examples of model construction are given. (SLD)

Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes.

PubMed

Yang, Jingjing; Cox, Dennis D; Lee, Jong Soo; Ren, Peng; Choi, Taeryon

2017-12-01

Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected on discretized grids with measurement errors. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo methods. Compared to the standard Bayesian inference that suffers serious computational burden and instability in analyzing high-dimensional functional data, our method greatly improves the computational scalability and stability, while inheriting the advantage of simultaneously smoothing raw observations and estimating the mean-covariance functions in a nonparametric way. In addition, our method can naturally handle functional data observed on random or uncommon grids. Simulation and real studies demonstrate that our method produces similar results to those obtainable by the standard Bayesian inference with low-dimensional common grids, while efficiently smoothing and estimating functional data with random and high-dimensional observation grids when the standard Bayesian inference fails. In conclusion, our method can efficiently smooth and estimate high-dimensional functional data, providing one way to resolve the curse of dimensionality for Bayesian functional data analysis with Gaussian-Wishart processes. © 2017, The International Biometric Society.

Hierarchical representation of shapes in visual cortex—from localized features to figural shape segregation

PubMed Central

Tschechne, Stephan; Neumann, Heiko

2014-01-01

Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1–V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy. PMID:25157228

Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation.

PubMed

Tschechne, Stephan; Neumann, Heiko

2014-01-01

Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

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.

Ranging through Gabor logons-a consistent, hierarchical approach.

PubMed

Chang, C; Chatterjee, S

1993-01-01

In this work, the correspondence problem in stereo vision is handled by matching two sets of dense feature vectors. Inspired by biological evidence, these feature vectors are generated by a correlation between a bank of Gabor sensors and the intensity image. The sensors consist of two-dimensional Gabor filters at various scales (spatial frequencies) and orientations, which bear close resemblance to the receptive field profiles of simple V1 cells in visual cortex. A hierarchical, stochastic relaxation method is then used to obtain the dense stereo disparities. Unlike traditional hierarchical methods for stereo, feature based hierarchical processing yields consistent disparities. To avoid false matchings due to static occlusion, a dual matching, based on the imaging geometry, is used.

Sleep Disrupts High-Level Speech Parsing Despite Significant Basic Auditory Processing.

PubMed

Makov, Shiri; Sharon, Omer; Ding, Nai; Ben-Shachar, Michal; Nir, Yuval; Zion Golumbic, Elana

2017-08-09

The extent to which the sleeping brain processes sensory information remains unclear. This is particularly true for continuous and complex stimuli such as speech, in which information is organized into hierarchically embedded structures. Recently, novel metrics for assessing the neural representation of continuous speech have been developed using noninvasive brain recordings that have thus far only been tested during wakefulness. Here we investigated, for the first time, the sleeping brain's capacity to process continuous speech at different hierarchical levels using a newly developed Concurrent Hierarchical Tracking (CHT) approach that allows monitoring the neural representation and processing-depth of continuous speech online. Speech sequences were compiled with syllables, words, phrases, and sentences occurring at fixed time intervals such that different linguistic levels correspond to distinct frequencies. This enabled us to distinguish their neural signatures in brain activity. We compared the neural tracking of intelligible versus unintelligible (scrambled and foreign) speech across states of wakefulness and sleep using high-density EEG in humans. We found that neural tracking of stimulus acoustics was comparable across wakefulness and sleep and similar across all conditions regardless of speech intelligibility. In contrast, neural tracking of higher-order linguistic constructs (words, phrases, and sentences) was only observed for intelligible speech during wakefulness and could not be detected at all during nonrapid eye movement or rapid eye movement sleep. These results suggest that, whereas low-level auditory processing is relatively preserved during sleep, higher-level hierarchical linguistic parsing is severely disrupted, thereby revealing the capacity and limits of language processing during sleep. SIGNIFICANCE STATEMENT Despite the persistence of some sensory processing during sleep, it is unclear whether high-level cognitive processes such as speech parsing are also preserved. We used a novel approach for studying the depth of speech processing across wakefulness and sleep while tracking neuronal activity with EEG. We found that responses to the auditory sound stream remained intact; however, the sleeping brain did not show signs of hierarchical parsing of the continuous stream of syllables into words, phrases, and sentences. The results suggest that sleep imposes a functional barrier between basic sensory processing and high-level cognitive processing. This paradigm also holds promise for studying residual cognitive abilities in a wide array of unresponsive states. Copyright © 2017 the authors 0270-6474/17/377772-10$15.00/0.

Factor Analysis of Computer-Based Multidimensional Aptitude Battery-Second Edition Intelligence Testing from Rated U.S. Air Force Pilots

DTIC Science & Technology

2012-09-01

intelligence continues to evolve as attention to cognitive processes and mechanisms, a deeper understanding of related issues, and new theories ...hierarchical models that describe specific abilities arranged according to increasing specificity and developmental complexity [6-8]. Theories have also...persistence) not tapped directly by existing measures of intellectual ability. Wechsler’s theory of intelligence is central to the development of the mostly

Heuristic decomposition for non-hierarchic systems

NASA Technical Reports Server (NTRS)

Bloebaum, Christina L.; Hajela, P.

1991-01-01

Design and optimization is substantially more complex in multidisciplinary and large-scale engineering applications due to the existing inherently coupled interactions. The paper introduces a quasi-procedural methodology for multidisciplinary optimization that is applicable for nonhierarchic systems. The necessary decision-making support for the design process is provided by means of an embedded expert systems capability. The method employs a decomposition approach whose modularity allows for implementation of specialized methods for analysis and optimization within disciplines.

Hierarchical Processing of Auditory Objects in Humans

PubMed Central

Kumar, Sukhbinder; Stephan, Klaas E; Warren, Jason D; Friston, Karl J; Griffiths, Timothy D

2007-01-01

This work examines the computational architecture used by the brain during the analysis of the spectral envelope of sounds, an important acoustic feature for defining auditory objects. Dynamic causal modelling and Bayesian model selection were used to evaluate a family of 16 network models explaining functional magnetic resonance imaging responses in the right temporal lobe during spectral envelope analysis. The models encode different hypotheses about the effective connectivity between Heschl's Gyrus (HG), containing the primary auditory cortex, planum temporale (PT), and superior temporal sulcus (STS), and the modulation of that coupling during spectral envelope analysis. In particular, we aimed to determine whether information processing during spectral envelope analysis takes place in a serial or parallel fashion. The analysis provides strong support for a serial architecture with connections from HG to PT and from PT to STS and an increase of the HG to PT connection during spectral envelope analysis. The work supports a computational model of auditory object processing, based on the abstraction of spectro-temporal “templates” in the PT before further analysis of the abstracted form in anterior temporal lobe areas. PMID:17542641

Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

NASA Astrophysics Data System (ADS)

Hong, Liang

2013-10-01

The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

Hierarchical MnO2 nanosheets synthesized via electrodeposition-hydrothermal method for supercapacitor electrodes

NASA Astrophysics Data System (ADS)

Zheng, Dongdong; Qiang, Yujie; Xu, Shenying; Li, Wenpo; Yu, Shanshan; Zhang, Shengtao

2017-02-01

Metal oxides have emerged as one kind of important supercapacitor electrode materials. Herein, we report hierarchical MnO2 nanosheets prepared of indium tin oxide (ITO) coated glass substrates via a hybrid two-step protocol, including a cathodic electrodeposition technique and a hydrothermal process. The samples are characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscope (SEM) with energy dispersive X-ray spectroscopy (EDX), and transmission electron microscope (TEM). SEM and TEM images show that the as-synthesized MnO2 nanosheets are hierarchical and porous, which could increase the active surface and short paths for fast ion diffusion. The results of nitrogen adsorption-desorption analysis indicate that the BET surface area of the MnO2 nanosheets is 53.031 m2 g-1. Furthermore, the electrochemical properties of the MnO2 are elucidated by cyclic voltammograms (CV), galvanostatic charge-discharge (GCD) tests, and electrochemical impedance spectroscopy (EIS) in 0.1 M Na2SO4 electrolyte. The electrochemical results demonstrate that the as-grown MnO2 nanosheet exhibits an excellent specific capacitance of 335 F g-1 at 0.5 A g-1 when it is applied as a potential electrode material for an electrochemical supercapacitor. Additionally, the MnO2 nanosheet electrode also presents high rate capability and good cycling stability with 91.8% retention after 1000 cycles. These excellent properties indicate that the hierarchical MnO2 nanosheets are a potential electrode material for electrochemical supercapacitors.

Phrase Mining of Textual Data to Analyze Extracellular Matrix Protein Patterns Across Cardiovascular Disease.

PubMed

Liem, David Alexandre; Murali, Sanjana; Sigdel, Dibakar; Shi, Yu; Wang, Xuan; Shen, Jiaming; Choi, Howard; Caufield, J Harry; Wang, Wei; Ping, Peipei; Han, Jiawei

2018-05-18

Extracellular matrix (ECM) proteins have been shown to play important roles regulating multiple biological processes in an array of organ systems, including the cardiovascular system. By using a novel bioinformatics text-mining tool, we studied six categories of cardiovascular disease (CVD), namely ischemic heart disease (IHD), cardiomyopathies (CM), cerebrovascular accident (CVA), congenital heart disease (CHD), arrhythmias (ARR), and valve disease (VD), anticipating novel ECM protein-disease and protein-protein relationships hidden within vast quantities of textual data. We conducted a phrase-mining analysis, delineating the relationships of 709 ECM proteins with the six groups of CVDs reported in 1,099,254 abstracts. The technology pipeline known as Context-aware Semantic Online Analytical Processing (CaseOLAP) was applied to semantically rank the association of proteins to each and all six CVDs, performing analyses to quantify each protein-disease relationship. We performed principal component analysis and hierarchical clustering of the data, where each protein is visualized as a six dimensional vector. We found that ECM proteins display variable degrees of association with the six CVDs; certain CVDs share groups of associated proteins whereas others have divergent protein associations. We identified 82 ECM proteins sharing associations with all six CVDs. Our bioinformatics analysis ascribed distinct ECM pathways (via Reactome) from this subset of proteins, namely insulin-like growth factor regulation and interleukin-4 and interleukin-13 signaling, suggesting their contribution to the pathogenesis of all six CVDs. Finally, we performed hierarchical clustering analysis and identified protein clusters associated with a targeted CVD; analyses revealed unexpected insights underlying ECM-pathogenesis of CVDs.

Slow feature analysis: unsupervised learning of invariances.

PubMed

Wiskott, Laurenz; Sejnowski, Terrence J

2002-04-01

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

Hierarchical Spatiotemporal Dynamics of Speech Rhythm and Articulation

ERIC Educational Resources Information Center

Tilsen, Samuel Edward

2009-01-01

Hierarchy is one of the most important concepts in the scientific study of language. This dissertation aims to understand why we observe hierarchical structures in speech by investigating the cognitive processes from which they emerge. To that end, the dissertation explores how articulatory, rhythmic, and prosodic patterns of speech interact.…

Anisotropic and Hierarchical Porosity in Multifunctional Ceramics

NASA Astrophysics Data System (ADS)

Lichtner, Aaron Zev

The performance of multifunctional porous ceramics is often hindered by the seemingly contradictory effects of porosity on both mechanical and non-structural properties and yet a sufficient body of knowledge linking microstructure to these properties does not exist. Using a combination of tailored anisotropic and hierarchical materials, these disparate effects may be reconciled. In this project, a systematic investigation of the processing, characterization and properties of anisotropic and isotropic hierarchically porous ceramics was conducted. The system chosen was a composite ceramic intended as the cathode for a solid oxide fuel cell (SOFC). Comprehensive processing investigations led to the development of approaches to make hierarchical, anisotropic porous microstructures using directional freeze-casting of well dispersed slurries. The effect of all the important processing parameters was investigated. This resulted in an ability to tailor and control the important microstructural features including the scale of the microstructure, the macropore size and total porosity. Comparable isotropic porous ceramics were also processed using fugitive pore formers. A suite of characterization techniques including x-ray tomography and 3-D sectional scanning electron micrographs (FIB-SEM) was used to characterize and quantify the green and partially sintered microstructures. The effect of sintering temperature on the microstructure was quantified and discrete element simulations (DEM) were used to explain the experimental observations. Finally, the comprehensive mechanical properties, at room temperature, were investigated, experimentally and using DEM, for the different microstructures.

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.

Overlapping communities detection based on spectral analysis of line graphs

NASA Astrophysics Data System (ADS)

Gui, Chun; Zhang, Ruisheng; Hu, Rongjing; Huang, Guoming; Wei, Jiaxuan

2018-05-01

Community in networks are often overlapping where one vertex belongs to several clusters. Meanwhile, many networks show hierarchical structure such that community is recursively grouped into hierarchical organization. In order to obtain overlapping communities from a global hierarchy of vertices, a new algorithm (named SAoLG) is proposed to build the hierarchical organization along with detecting the overlap of community structure. SAoLG applies the spectral analysis into line graphs to unify the overlap and hierarchical structure of the communities. In order to avoid the limitation of absolute distance such as Euclidean distance, SAoLG employs Angular distance to compute the similarity between vertices. Furthermore, we make a micro-improvement partition density to evaluate the quality of community structure and use it to obtain the more reasonable and sensible community numbers. The proposed SAoLG algorithm achieves a balance between overlap and hierarchy by applying spectral analysis to edge community detection. The experimental results on one standard network and six real-world networks show that the SAoLG algorithm achieves higher modularity and reasonable community number values than those generated by Ahn's algorithm, the classical CPM and GN ones.

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.

Addressing the Big-Earth-Data Variety Challenge with the Hierarchical Triangular Mesh

NASA Technical Reports Server (NTRS)

Rilee, Michael L.; Kuo, Kwo-Sen; Clune, Thomas; Oloso, Amidu; Brown, Paul G.; Yu, Honfeng

2016-01-01

We have implemented an updated Hierarchical Triangular Mesh (HTM) as the basis for a unified data model and an indexing scheme for geoscience data to address the variety challenge of Big Earth Data. We observe that, in the absence of variety, the volume challenge of Big Data is relatively easily addressable with parallel processing. The more important challenge in achieving optimal value with a Big Data solution for Earth Science (ES) data analysis, however, is being able to achieve good scalability with variety. With HTM unifying at least the three popular data models, i.e. Grid, Swath, and Point, used by current ES data products, data preparation time for integrative analysis of diverse datasets can be drastically reduced and better variety scaling can be achieved. In addition, since HTM is also an indexing scheme, when it is used to index all ES datasets, data placement alignment (or co-location) on the shared nothing architecture, which most Big Data systems are based on, is guaranteed and better performance is ensured. Moreover, our updated HTM encoding turns most geospatial set operations into integer interval operations, gaining further performance advantages.

Yeast Mitoribosome Large Subunit Assembly Proceeds by Hierarchical Incorporation of Protein Clusters and Modules on the Inner Membrane.

PubMed

Zeng, Rui; Smith, Erin; Barrientos, Antoni

2018-03-06

Mitoribosomes are specialized for the synthesis of hydrophobic membrane proteins encoded by mtDNA, all essential for oxidative phosphorylation. Despite their linkage to human mitochondrial diseases and the recent cryoelectron microscopy reconstruction of yeast and mammalian mitoribosomes, how they are assembled remains obscure. Here, we dissected the yeast mitoribosome large subunit (mtLSU) assembly process by systematic genomic deletion of 44 mtLSU proteins (MRPs). Analysis of the strain collection unveiled 37 proteins essential for functional mtLSU assembly, three of which are critical for mtLSU 21S rRNA stability. Hierarchical cluster analysis of mtLSU subassemblies accumulated in mutant strains revealed co-operative assembly of protein sets forming structural clusters and preassembled modules. It also indicated crucial roles for mitochondrion-specific membrane-binding MRPs in anchoring newly transcribed 21S rRNA to the inner membrane, where assembly proceeds. Our results define the yeast mtLSU assembly landscape in vivo and provide a foundation for studies of mitoribosome assembly across evolution. Copyright © 2018 Elsevier Inc. All rights reserved.

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

Hierarchical FeTiO3-TiO2 hollow spheres for efficient simulated sunlight-driven water oxidation.

PubMed

Han, Taoran; Chen, Yajie; Tian, Guohui; Wang, Jian-Qiang; Ren, Zhiyu; Zhou, Wei; Fu, Honggang

2015-10-14

Oxygen generation is the key step for the photocatalytic overall water splitting and considered to be kinetically more challenging than hydrogen generation. Here, an effective water oxidation catalyst of hierarchical FeTiO3-TiO2 hollow spheres are prepared via a two-step sequential solvothermal processes and followed by thermal treatment. The existence of an effective heterointerface and built-in electric field in the surface space charge region in FeTiO3-TiO2 hollow spheres plays a positive role in promoting the separation of photoinduced electron-hole pairs. Surface photovoltage, transient-state photovoltage, fluorescence and electrochemical characterization are used to investigate the transfer process of photoinduced charge carriers. The photogenerated charge carriers in the hierarchical FeTiO3-TiO2 hollow spheres with a proper molar ratio display much higher separation efficiency and longer lifetime than those in the FeTiO3 alone. Moreover, it is suggested that the hierarchical porous hollow structure can contribute to the enhancement of light utilization, surface active sites and material transportation through the framework walls. This specific synergy significantly contributes to the remarkable improvement of the photocatalytic water oxidation activity of the hierarchical FeTiO3-TiO2 hollow spheres under simulated sunlight (AM1.5).

Hierarchical Nanocomposites of Polyaniline Nanowire Arrays on Reduced Graphene Oxide Sheets for Supercapacitors

PubMed Central

Wang, Li; Ye, Yinjian; Lu, Xingping; Wen, Zhubiao; Li, Zhuang; Hou, Haoqing; Song, Yonghai

2013-01-01

Here we reported a novel route to synthesize a hierarchical nanocomposite (PANI-frGO) of polyaniline (PANI) nanowire arrays covalently bonded on reduced graphene oxide (rGO). In this strategy, nitrophenyl groups were initially grafted on rGO via C-C bond, and then reduced to aminophenyl to act as anchor sites for the growth of PANI arrays on rGO. The functionalized process was confirmed by atomic force microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy and thermogravimetric analysis. The electrochemical properties of the PANI-frGO as supercapacitor materials were investigated. The PANI-frGO nanocomposites showed high capacitance of 590 F g−1 at 0.1 A g−1, and had no loss of capacitance after 200 cycles at 2 A g−1. The improved electrochemical performance suggests promising application of the PANI-frGO nanocomposites in high-performance supercapacitors. PMID:24356535

Noise induced escape from a nonhyperbolic chaotic attractor of a periodically driven nonlinear oscillator

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

Chen, Zhen, E-mail: czkillua@icloud.com, E-mail: xbliu@nuaa.edu.cn; Li, Yang; Liu, Xianbin, E-mail: czkillua@icloud.com, E-mail: xbliu@nuaa.edu.cn

2016-06-15

Noise induced escape from the domain of attraction of a nonhyperbolic chaotic attractor in a periodically excited nonlinear oscillator is investigated. The general mechanism of the escape in the weak noise limit is studied in the continuous case, and the fluctuational path is obtained by statistical analysis. Selecting the primary homoclinic tangency as the initial condition, the action plot is presented by parametrizing the set of escape trajectories and the global minimum gives rise to the optimal path. Results of both methods show good agreements. The entire process of escape is discussed in detail step by step using the fluctuationalmore » force. A structure of hierarchical heteroclinic crossings of stable and unstable manifolds of saddle cycles is found, and the escape is observed to take place through successive jumps through this deterministic hierarchical structure.« less

Hierarchical Nanocomposites of Polyaniline Nanowire Arrays on Reduced Graphene Oxide Sheets for Supercapacitors

NASA Astrophysics Data System (ADS)

Wang, Li; Ye, Yinjian; Lu, Xingping; Wen, Zhubiao; Li, Zhuang; Hou, Haoqing; Song, Yonghai

2013-12-01

Here we reported a novel route to synthesize a hierarchical nanocomposite (PANI-frGO) of polyaniline (PANI) nanowire arrays covalently bonded on reduced graphene oxide (rGO). In this strategy, nitrophenyl groups were initially grafted on rGO via C-C bond, and then reduced to aminophenyl to act as anchor sites for the growth of PANI arrays on rGO. The functionalized process was confirmed by atomic force microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy and thermogravimetric analysis. The electrochemical properties of the PANI-frGO as supercapacitor materials were investigated. The PANI-frGO nanocomposites showed high capacitance of 590 F g-1 at 0.1 A g-1, and had no loss of capacitance after 200 cycles at 2 A g-1. The improved electrochemical performance suggests promising application of the PANI-frGO nanocomposites in high-performance supercapacitors.

Hierarchical nanocomposites of polyaniline nanowire arrays on reduced graphene oxide sheets for supercapacitors.

PubMed

Wang, Li; Ye, Yinjian; Lu, Xingping; Wen, Zhubiao; Li, Zhuang; Hou, Haoqing; Song, Yonghai

2013-12-20

Here we reported a novel route to synthesize a hierarchical nanocomposite (PANI-frGO) of polyaniline (PANI) nanowire arrays covalently bonded on reduced graphene oxide (rGO). In this strategy, nitrophenyl groups were initially grafted on rGO via C-C bond, and then reduced to aminophenyl to act as anchor sites for the growth of PANI arrays on rGO. The functionalized process was confirmed by atomic force microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy and thermogravimetric analysis. The electrochemical properties of the PANI-frGO as supercapacitor materials were investigated. The PANI-frGO nanocomposites showed high capacitance of 590 F g(-1) at 0.1 A g(-1), and had no loss of capacitance after 200 cycles at 2 A g(-1). The improved electrochemical performance suggests promising application of the PANI-frGO nanocomposites in high-performance supercapacitors.

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.

Hierarchical effects on target detection and conflict monitoring

PubMed Central

Cao, Bihua; Gao, Feng; Ren, Maofang; Li, Fuhong

2016-01-01

Previous neuroimaging studies have demonstrated a hierarchical functional structure of the frontal cortices of the human brain, but the temporal course and the electrophysiological signature of the hierarchical representation remains unaddressed. In the present study, twenty-one volunteers were asked to perform a nested cue-target task, while their scalp potentials were recorded. The results showed that: (1) in comparison with the lower-level hierarchical targets, the higher-level targets elicited a larger N2 component (220–350 ms) at the frontal sites, and a smaller P3 component (350–500 ms) across the frontal and parietal sites; (2) conflict-related negativity (non-target minus target) was greater for the lower-level hierarchy than the higher-level, reflecting a more intensive process of conflict monitoring at the final step of target detection. These results imply that decision making, context updating, and conflict monitoring differ among different hierarchical levels of abstraction. PMID:27561989

Perception of hierarchical boundaries in music and its modulation by expertise.

PubMed

Zhang, Jingjing; Jiang, Cunmei; Zhou, Linshu; Yang, Yufang

2016-10-01

Hierarchical structure with units of different timescales is a key feature of music. For the perception of such structures, the detection of each boundary is crucial. Here, using electroencephalography (EEG), we explore the perception of hierarchical boundaries in music, and test whether musical expertise modifies such processing. Musicians and non-musicians were presented with musical excerpts containing boundaries at three hierarchical levels, including section, phrase and period boundaries. Non-boundary was chosen as a baseline condition. Recordings from musicians showed CPS (closure positive shift) was evoked at all the three boundaries, and their amplitude increased as the hierarchical level became higher, which suggest that musicians could represent music events at different timescales in a hierarchical way. For non-musicians, the CPS was only elicited at the period boundary and undistinguishable negativities were induced at all the three boundaries. The results indicate that a different and less clear way was used by non-musicians in boundary perception. Our findings reveal, for the first time, an ERP correlate of perceiving hierarchical boundaries in music, and show that the phrasing ability could be enhanced by musical expertise. Copyright © 2016 Elsevier Ltd. All rights reserved.

Using a Systematic Approach in the Analysis of the Factors That Influence On a Form Formation of Buildings of Higher Educational Establishments

NASA Astrophysics Data System (ADS)

Martyniv, Oleksandra; Kinasz, Roman

2017-10-01

This material covers the row of basic factors that influence on architectonically-spatial solution formation of building of Higher educational establishments (hereinafter universities). For this purpose, the systematization process of factors that influence on the university architecture was conducted and presented. The conclusion of this article was the proposed concept of considering universities as a hierarchical system, elements of which act as factors of influence, which in the process of alternating influence lead to the main goal, namely the formation of a new university building.

One wouldn't expect an expert bowler to hit only two pins: Hierarchical predictive processing of agent-caused events.

PubMed

Heil, Lieke; Kwisthout, Johan; van Pelt, Stan; van Rooij, Iris; Bekkering, Harold

2018-01-01

Evidence is accumulating that our brains process incoming information using top-down predictions. If lower level representations are correctly predicted by higher level representations, this enhances processing. However, if they are incorrectly predicted, additional processing is required at higher levels to "explain away" prediction errors. Here, we explored the potential nature of the models generating such predictions. More specifically, we investigated whether a predictive processing model with a hierarchical structure and causal relations between its levels is able to account for the processing of agent-caused events. In Experiment 1, participants watched animated movies of "experienced" and "novice" bowlers. The results are in line with the idea that prediction errors at a lower level of the hierarchy (i.e., the outcome of how many pins fell down) slow down reporting of information at a higher level (i.e., which agent was throwing the ball). Experiments 2 and 3 suggest that this effect is specific to situations in which the predictor is causally related to the outcome. Overall, the study supports the idea that a hierarchical predictive processing model can account for the processing of observed action outcomes and that the predictions involved are specific to cases where action outcomes can be predicted based on causal knowledge.

Empirical links between instruction with teaching tools and the hierarchical model of intrinsic and extrinsic motivation in a Korean college tennis class.

PubMed

Shin, Myoungjin; Kwon, Sungho

2015-04-01

The objective of this study was to demonstrate the sequential process (i.e., social factors→mediators→motivation→consequences) underlying the Hierarchical Model of Intrinsic and Extrinsic Motivation at the contextual level in instruction using three teaching tools, modified balls, a high net, and colored balls and cones in a college-level tennis class in South Korea. 126 students enrolled in a 15-week tennis class participated in the study. The results indicate that the three teaching tools positively affected students' perceived competence, with perceived competence's beta on intrinsic motivation equal to 0.45. Intrinsic motivation was found to reduce negative affect further by -0.33, thereby demonstrating the sequential process of the Hierarchical Model of Intrinsic and Extrinsic Motivation.

Predictive Ability of Pender's Health Promotion Model for Physical Activity and Exercise in People with Spinal Cord Injuries: A Hierarchical Regression Analysis

ERIC Educational Resources Information Center

Keegan, John P.; Chan, Fong; Ditchman, Nicole; Chiu, Chung-Yi

2012-01-01

The main objective of this study was to validate Pender's Health Promotion Model (HPM) as a motivational model for exercise/physical activity self-management for people with spinal cord injuries (SCIs). Quantitative descriptive research design using hierarchical regression analysis (HRA) was used. A total of 126 individuals with SCI were recruited…

A Comprehensive Meta-Analysis of Triple P-Positive Parenting Program Using Hierarchical Linear Modeling: Effectiveness and Moderating Variables

ERIC Educational Resources Information Center

Nowak, Christoph; Heinrichs, Nina

2008-01-01

A meta-analysis encompassing all studies evaluating the impact of the Triple P-Positive Parenting Program on parent and child outcome measures was conducted in an effort to identify variables that moderate the program's effectiveness. Hierarchical linear models (HLM) with three levels of data were employed to analyze effect sizes. The results (N =…

Hierarchical Traces for Reduced NSM Memory Requirements

NASA Astrophysics Data System (ADS)

Dahl, Torbjørn S.

This paper presents work on using hierarchical long term memory to reduce the memory requirements of nearest sequence memory (NSM) learning, a previously published, instance-based reinforcement learning algorithm. A hierarchical memory representation reduces the memory requirements by allowing traces to share common sub-sequences. We present moderated mechanisms for estimating discounted future rewards and for dealing with hidden state using hierarchical memory. We also present an experimental analysis of how the sub-sequence length affects the memory compression achieved and show that the reduced memory requirements do not effect the speed of learning. Finally, we analyse and discuss the persistence of the sub-sequences independent of specific trace instances.

Emergence of the interplay between hierarchy and contact splitting in biological adhesion highlighted through a hierarchical shear lag model.

PubMed

Brely, Lucas; Bosia, Federico; Pugno, Nicola M

2018-06-20

Contact unit size reduction is a widely studied mechanism as a means to improve adhesion in natural fibrillar systems, such as those observed in beetles or geckos. However, these animals also display complex structural features in the way the contact is subdivided in a hierarchical manner. Here, we study the influence of hierarchical fibrillar architectures on the load distribution over the contact elements of the adhesive system, and the corresponding delamination behaviour. We present an analytical model to derive the load distribution in a fibrillar system loaded in shear, including hierarchical splitting of contacts, i.e. a "hierarchical shear-lag" model that generalizes the well-known shear-lag model used in mechanics. The influence on the detachment process is investigated introducing a numerical procedure that allows the derivation of the maximum delamination force as a function of the considered geometry, including statistical variability of local adhesive energy. Our study suggests that contact splitting generates improved adhesion only in the ideal case of extremely compliant contacts. In real cases, to produce efficient adhesive performance, contact splitting needs to be coupled with hierarchical architectures to counterbalance high load concentrations resulting from contact unit size reduction, generating multiple delamination fronts and helping to avoid detrimental non-uniform load distributions. We show that these results can be summarized in a generalized adhesion scaling scheme for hierarchical structures, proving the beneficial effect of multiple hierarchical levels. The model can thus be used to predict the adhesive performance of hierarchical adhesive structures, as well as the mechanical behaviour of composite materials with hierarchical reinforcements.

Cryptanalysis of Chatterjee-Sarkar Hierarchical Identity-Based Encryption Scheme at PKC 06

NASA Astrophysics Data System (ADS)

Park, Jong Hwan; Lee, Dong Hoon

In 2006, Chatterjee and Sarkar proposed a hierarchical identity-based encryption (HIBE) scheme which can support an unbounded number of identity levels. This property is particularly useful in providing forward secrecy by embedding time components within hierarchical identities. In this paper we show that their scheme does not provide the claimed property. Our analysis shows that if the number of identity levels becomes larger than the value of a fixed public parameter, an unintended receiver can reconstruct a new valid ciphertext and decrypt the ciphertext using his or her own private key. The analysis is similarly applied to a multi-receiver identity-based encryption scheme presented as an application of Chatterjee and Sarkar's HIBE scheme.

Hierarchical Cluster Analysis of Three-Dimensional Reconstructions of Unbiased Sampled Microglia Shows not Continuous Morphological Changes from Stage 1 to 2 after Multiple Dengue Infections in Callithrix penicillata

PubMed Central

Diniz, Daniel G.; Silva, Geane O.; Naves, Thaís B.; Fernandes, Taiany N.; Araújo, Sanderson C.; Diniz, José A. P.; de Farias, Luis H. S.; Sosthenes, Marcia C. K.; Diniz, Cristovam G.; Anthony, Daniel C.; da Costa Vasconcelos, Pedro F.; Picanço Diniz, Cristovam W.

2016-01-01

It is known that microglial morphology and function are related, but few studies have explored the subtleties of microglial morphological changes in response to specific pathogens. In the present report we quantitated microglia morphological changes in a monkey model of dengue disease with virus CNS invasion. To mimic multiple infections that usually occur in endemic areas, where higher dengue infection incidence and abundant mosquito vectors carrying different serotypes coexist, subjects received once a week subcutaneous injections of DENV3 (genotype III)-infected culture supernatant followed 24 h later by an injection of anti-DENV2 antibody. Control animals received either weekly anti-DENV2 antibodies, or no injections. Brain sections were immunolabeled for DENV3 antigens and IBA-1. Random and systematic microglial samples were taken from the polymorphic layer of dentate gyrus for 3-D reconstructions, where we found intense immunostaining for TNFα and DENV3 virus antigens. We submitted all bi- or multimodal morphological parameters of microglia to hierarchical cluster analysis and found two major morphological phenotypes designated types I and II. Compared to type I (stage 1), type II microglia were more complex; displaying higher number of nodes, processes and trees and larger surface area and volumes (stage 2). Type II microglia were found only in infected monkeys, whereas type I microglia was found in both control and infected subjects. Hierarchical cluster analysis of morphological parameters of 3-D reconstructions of random and systematic selected samples in control and ADE dengue infected monkeys suggests that microglia morphological changes from stage 1 to stage 2 may not be continuous. PMID:27047345

Hierarchical Cluster Analysis of Three-Dimensional Reconstructions of Unbiased Sampled Microglia Shows not Continuous Morphological Changes from Stage 1 to 2 after Multiple Dengue Infections in Callithrix penicillata.

PubMed

Diniz, Daniel G; Silva, Geane O; Naves, Thaís B; Fernandes, Taiany N; Araújo, Sanderson C; Diniz, José A P; de Farias, Luis H S; Sosthenes, Marcia C K; Diniz, Cristovam G; Anthony, Daniel C; da Costa Vasconcelos, Pedro F; Picanço Diniz, Cristovam W

2016-01-01

It is known that microglial morphology and function are related, but few studies have explored the subtleties of microglial morphological changes in response to specific pathogens. In the present report we quantitated microglia morphological changes in a monkey model of dengue disease with virus CNS invasion. To mimic multiple infections that usually occur in endemic areas, where higher dengue infection incidence and abundant mosquito vectors carrying different serotypes coexist, subjects received once a week subcutaneous injections of DENV3 (genotype III)-infected culture supernatant followed 24 h later by an injection of anti-DENV2 antibody. Control animals received either weekly anti-DENV2 antibodies, or no injections. Brain sections were immunolabeled for DENV3 antigens and IBA-1. Random and systematic microglial samples were taken from the polymorphic layer of dentate gyrus for 3-D reconstructions, where we found intense immunostaining for TNFα and DENV3 virus antigens. We submitted all bi- or multimodal morphological parameters of microglia to hierarchical cluster analysis and found two major morphological phenotypes designated types I and II. Compared to type I (stage 1), type II microglia were more complex; displaying higher number of nodes, processes and trees and larger surface area and volumes (stage 2). Type II microglia were found only in infected monkeys, whereas type I microglia was found in both control and infected subjects. Hierarchical cluster analysis of morphological parameters of 3-D reconstructions of random and systematic selected samples in control and ADE dengue infected monkeys suggests that microglia morphological changes from stage 1 to stage 2 may not be continuous.

Correlation between Hierarchical Structure and Processing Control of Large-area Spray-coated Polymer Solar Cells toward High Performance

PubMed Central

Huang, Yu-Ching; Tsao, Cheng-Si; Cha, Hou-Chin; Chuang, Chih-Min; Su, Chun-Jen; Jeng, U-Ser; Chen, Charn-Ying

2016-01-01

The formation mechanism of a spray-coated film is different from that of a spin-coated film. This study employs grazing incidence small- and wide-angle X-ray Scattering (GISAXS and GIWAXS, respectively) quantitatively and systematically to investigate the hierarchical structure and phase-separated behavior of a spray-deposited blend film. The formation of PCBM clusters involves mutual interactions with both the P3HT crystal domains and droplet boundary. The processing control and the formed hierarchical structure of the active layer in the spray-coated polymer/fullerene blend film are compared to those in the spin-coated film. How the different post-treatments, such as thermal and solvent vapor annealing, tailor the hierarchical structure of the spray-coated films is quantitatively studied. Finally, the relationship between the processing control and tailored BHJ structures and the performance of polymer solar cell devices is established here, taking into account the evolution of the device area from 1 × 0.3 and 1 × 1 cm2. The formation and control of the special networks formed by the PCBM cluster and P3HT crystallites, respectively, are related to the droplet boundary. These structures are favorable for the transverse transport of electrons and holes. PMID:26817585

Spatial Intensity Duration Frequency Relationships Using Hierarchical Bayesian Analysis for Urban Areas

NASA Astrophysics Data System (ADS)

Rupa, Chandra; Mujumdar, Pradeep

2016-04-01

In urban areas, quantification of extreme precipitation is important in the design of storm water drains and other infrastructure. Intensity Duration Frequency (IDF) relationships are generally used to obtain design return level for a given duration and return period. Due to lack of availability of extreme precipitation data for sufficiently large number of years, estimating the probability of extreme events is difficult. Typically, a single station data is used to obtain the design return levels for various durations and return periods, which are used in the design of urban infrastructure for the entire city. In an urban setting, the spatial variation of precipitation can be high; the precipitation amounts and patterns often vary within short distances of less than 5 km. Therefore it is crucial to study the uncertainties in the spatial variation of return levels for various durations. In this work, the extreme precipitation is modeled spatially using the Bayesian hierarchical analysis and the spatial variation of return levels is studied. The analysis is carried out with Block Maxima approach for defining the extreme precipitation, using Generalized Extreme Value (GEV) distribution for Bangalore city, Karnataka state, India. Daily data for nineteen stations in and around Bangalore city is considered in the study. The analysis is carried out for summer maxima (March - May), monsoon maxima (June - September) and the annual maxima rainfall. In the hierarchical analysis, the statistical model is specified in three layers. The data layer models the block maxima, pooling the extreme precipitation from all the stations. In the process layer, the latent spatial process characterized by geographical and climatological covariates (lat-lon, elevation, mean temperature etc.) which drives the extreme precipitation is modeled and in the prior level, the prior distributions that govern the latent process are modeled. Markov Chain Monte Carlo (MCMC) algorithm (Metropolis Hastings algorithm within a Gibbs sampler) is used to obtain the samples of parameters from the posterior distribution of parameters. The spatial maps of return levels for specified return periods, along with the associated uncertainties, are obtained for the summer, monsoon and annual maxima rainfall. Considering various covariates, the best fit model is selected using Deviance Information Criteria. It is observed that the geographical covariates outweigh the climatological covariates for the monsoon maxima rainfall (latitude and longitude). The best covariates for summer maxima and annual maxima rainfall are mean summer precipitation and mean monsoon precipitation respectively, including elevation for both the cases. The scale invariance theory, which states that statistical properties of a process observed at various scales are governed by the same relationship, is used to disaggregate the daily rainfall to hourly scales. The spatial maps of the scale are obtained for the study area. The spatial maps of IDF relationships thus generated are useful in storm water designs, adequacy analysis and identifying the vulnerable flooding areas.

Formulation and Application of the Hierarchical Generalized Random-Situation Random-Weight MIRID

ERIC Educational Resources Information Center

Hung, Lai-Fa

2011-01-01

The process-component approach has become quite popular for examining many psychological concepts. A typical example is the model with internal restrictions on item difficulty (MIRID) described by Butter (1994) and Butter, De Boeck, and Verhelst (1998). This study proposes a hierarchical generalized random-situation random-weight MIRID. The…

People, Policy and Process in College-Level Academic Management

ERIC Educational Resources Information Center

Nguyen, Thang N.

2016-01-01

Academic institution structure is both hierarchical and committee-based. It is hierarchical in the Administration including staff, similar to business corporations. It is committee-based for the Faculty body in a fashion similar to US Congress. It can exploit the best of both models for better governance and rightfully democratic decisions. The…

Hierarchical semi-numeric method for pairwise fuzzy group decision making.

PubMed

Marimin, M; Umano, M; Hatono, I; Tamura, H

2002-01-01

Gradual improvements to a single-level semi-numeric method, i.e., linguistic labels preference representation by fuzzy sets computation for pairwise fuzzy group decision making are summarized. The method is extended to solve multiple criteria hierarchical structure pairwise fuzzy group decision-making problems. The problems are hierarchically structured into focus, criteria, and alternatives. Decision makers express their evaluations of criteria and alternatives based on each criterion by using linguistic labels. The labels are converted into and processed in triangular fuzzy numbers (TFNs). Evaluations of criteria yield relative criteria weights. Evaluations of the alternatives, based on each criterion, yield a degree of preference for each alternative or a degree of satisfaction for each preference value. By using a neat ordered weighted average (OWA) or a fuzzy weighted average operator, solutions obtained based on each criterion are aggregated into final solutions. The hierarchical semi-numeric method is suitable for solving a larger and more complex pairwise fuzzy group decision-making problem. The proposed method has been verified and applied to solve some real cases and is compared to Saaty's (1996) analytic hierarchy process (AHP) method.

Pushing typists back on the learning curve: revealing chunking in skilled typewriting.

PubMed

Yamaguchi, Motonori; Logan, Gordon D

2014-04-01

Theories of skilled performance propose that highly trained skills involve hierarchically structured control processes. The present study examined and demonstrated hierarchical control at several levels of processing in skilled typewriting. In the first two experiments, we scrambled the order of letters in words to prevent skilled typists from chunking letters, and compared typing words and scrambled words. Experiment 1 manipulated stimulus quality to reveal chunking in perception, and Experiment 2 manipulated concurrent memory load to reveal chunking in short-term memory (STM). Both experiments manipulated the number of letters in words and nonwords to reveal chunking in motor planning. In the next two experiments, we degraded typing skill by altering the usual haptic feedback by using a laser-projection keyboard, so that typists had to monitor keystrokes. Neither the number of motor chunks (Experiment 3) nor the number of STM items (Experiment 4) was influenced by the manipulation. The results indicate that the utilization of hierarchical control depends on whether the input allows chunking but not on whether the output is generated automatically. We consider the role of automaticity in hierarchical control of skilled performance.

A neural model of hierarchical reinforcement learning

PubMed Central

Rasmussen, Daniel; Eliasmith, Chris

2017-01-01

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain’s general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model’s behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions. PMID:28683111

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.

NOA: A Scalable Multi-Parent Clustering Hierarchy for WSNs

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

Cree, Johnathan V.; Delgado-Frias, Jose; Hughes, Michael A.

2012-08-10

NOA is a multi-parent, N-tiered, hierarchical clustering algorithm that provides a scalable, robust and reliable solution to autonomous configuration of large-scale wireless sensor networks. The novel clustering hierarchy's inherent benefits can be utilized by in-network data processing techniques to provide equally robust, reliable and scalable in-network data processing solutions capable of reducing the amount of data sent to sinks. Utilizing a multi-parent framework, NOA reduces the cost of network setup when compared to hierarchical beaconing solutions by removing the expense of r-hop broadcasting (r is the radius of the cluster) needed to build the network and instead passes network topologymore » information among shared children. NOA2, a two-parent clustering hierarchy solution, and NOA3, the three-parent variant, saw up to an 83% and 72% reduction in overhead, respectively, when compared to performing one round of a one-parent hierarchical beaconing, as well as 92% and 88% less overhead when compared to one round of two- and three-parent hierarchical beaconing hierarchy.« less

Robust Real-Time Music Transcription with a Compositional Hierarchical Model.

PubMed

Pesek, Matevž; Leonardis, Aleš; Marolt, Matija

2017-01-01

The paper presents a new compositional hierarchical model for robust music transcription. Its main features are unsupervised learning of a hierarchical representation of input data, transparency, which enables insights into the learned representation, as well as robustness and speed which make it suitable for real-world and real-time use. The model consists of multiple layers, each composed of a number of parts. The hierarchical nature of the model corresponds well to hierarchical structures in music. The parts in lower layers correspond to low-level concepts (e.g. tone partials), while the parts in higher layers combine lower-level representations into more complex concepts (tones, chords). The layers are learned in an unsupervised manner from music signals. Parts in each layer are compositions of parts from previous layers based on statistical co-occurrences as the driving force of the learning process. In the paper, we present the model's structure and compare it to other hierarchical approaches in the field of music information retrieval. We evaluate the model's performance for the multiple fundamental frequency estimation. Finally, we elaborate on extensions of the model towards other music information retrieval tasks.

Advancements in Automated Circuit Grouping for Intellectual Property Trust Analysis

DTIC Science & Technology

2017-03-20

operation What had often taken weeks of manual effort has now been reduced to an overnight process or just a matter of hours . This new starting...between the flops and the major macros is added to that hierarchy Rule 4 . Next any flops between hierarchies, or boundary flops, are assigned to a...COMB. LOGIC 4 . Next assign any combinatorial logic between hierarchical blocks, or boundary logic, to a hierarchy using the rule: If-and-only-if

Being BOLD: The neural dynamics of face perception.

PubMed

Gentile, Francesco; Ales, Justin; Rossion, Bruno

2017-01-01

According to a non-hierarchical view of human cortical face processing, selective responses to faces may emerge in a higher-order area of the hierarchy, in the lateral part of the middle fusiform gyrus (fusiform face area [FFA]) independently from face-selective responses in the lateral inferior occipital gyrus (occipital face area [OFA]), a lower order area. Here we provide a stringent test of this hypothesis by gradually revealing segmented face stimuli throughout strict linear descrambling of phase information [Ales et al., 2012]. Using a short sampling rate (500 ms) of fMRI acquisition and single subject statistical analysis, we show a face-selective responses emerging earlier, that is, at a lower level of structural (i.e., phase) information, in the FFA compared with the OFA. In both regions, a face detection response emerging at a lower level of structural information for upright than inverted faces, both in the FFA and OFA, in line with behavioral responses and with previous findings of delayed responses to inverted faces with direct recordings of neural activity were also reported. Overall, these results support the non-hierarchical view of human cortical face processing and open new perspectives for time-resolved analysis at the single subject level of fMRI data obtained during continuously evolving visual stimulation. Hum Brain Mapp 38:120-139, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Nanoclusters first: a hierarchical phase transformation in a novel Mg alloy

NASA Astrophysics Data System (ADS)

Okuda, Hiroshi; Yamasaki, Michiaki; Kawamura, Yoshihito; Tabuchi, Masao; Kimizuka, Hajime

2015-09-01

The Mg-Y-Zn ternary alloy system contains a series of novel structures known as long-period stacking ordered (LPSO) structures. The formation process and its key concept from a viewpoint of phase transition are not yet clear. The current study reveals that the phase transformation process is not a traditional spinodal decomposition or structural transformation but, rather a novel hierarchical phase transformation. In this transformation, clustering occurs first, and the spatial rearrangement of the clusters induce a secondary phase transformation that eventually lead to two-dimensional ordering of the clusters. The formation process was examined using in situ synchrotron radiation small-angle X-ray scattering (SAXS). Rapid quenching from liquid alloy into thin ribbons yielded strongly supersaturated amorphous samples. The samples were heated at a constant rate of 10 K/min. and the scattering patterns were acquired. The SAXS analysis indicated that small clusters grew to sizes of 0.2 nm after they crystallized. The clusters distributed randomly in space grew and eventually transformed into a microstructure with two well-defined cluster-cluster distances, one for the segregation periodicity of LPSO and the other for the in-plane ordering in segregated layer. This transformation into the LPSO structure concomitantly introduces the periodical stacking fault required for the 18R structures.

Process metallurgy simulation for metal drawing process optimization by using two-scale finite element method

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

Nakamachi, Eiji; Yoshida, Takashi; Yamaguchi, Toshihiko

2014-10-06

We developed two-scale FE analysis procedure based on the crystallographic homogenization method by considering the hierarchical structure of poly-crystal aluminium alloy metal. It can be characterized as the combination of two-scale structure, such as the microscopic polycrystal structure and the macroscopic elastic plastic continuum. Micro polycrystal structure can be modeled as a three dimensional representative volume element (RVE). RVE is featured as by 3×3×3 eight-nodes solid finite elements, which has 216 crystal orientations. This FE analysis code can predict the deformation, strain and stress evolutions in the wire drawing processes in the macro- scales, and further the crystal texture andmore » hardening evolutions in the micro-scale. In this study, we analyzed the texture evolution in the wire drawing processes by our two-scale FE analysis code under conditions of various drawing angles of dice. We evaluates the texture evolution in the surface and center regions of the wire cross section, and to clarify the effects of processing conditions on the texture evolution.« less

Process metallurgy simulation for metal drawing process optimization by using two-scale finite element method

NASA Astrophysics Data System (ADS)

Nakamachi, Eiji; Yoshida, Takashi; Kuramae, Hiroyuki; Morimoto, Hideo; Yamaguchi, Toshihiko; Morita, Yusuke

2014-10-01

We developed two-scale FE analysis procedure based on the crystallographic homogenization method by considering the hierarchical structure of poly-crystal aluminium alloy metal. It can be characterized as the combination of two-scale structure, such as the microscopic polycrystal structure and the macroscopic elastic plastic continuum. Micro polycrystal structure can be modeled as a three dimensional representative volume element (RVE). RVE is featured as by 3×3×3 eight-nodes solid finite elements, which has 216 crystal orientations. This FE analysis code can predict the deformation, strain and stress evolutions in the wire drawing processes in the macro- scales, and further the crystal texture and hardening evolutions in the micro-scale. In this study, we analyzed the texture evolution in the wire drawing processes by our two-scale FE analysis code under conditions of various drawing angles of dice. We evaluates the texture evolution in the surface and center regions of the wire cross section, and to clarify the effects of processing conditions on the texture evolution.

Hierarchical hollow microsphere and flower-like indium oxide: Controllable synthesis and application as H{sub 2}S cataluminescence sensing materials

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

Cai, Pingyang, E-mail: cpyxx@163.com; Bai, Wei, E-mail: weibaiscu@gmail.com; Zhang, Lichun, E-mail: lichun0203@yahoo.cn

Graphical abstract: Hierarchical hollow microsphere and flower-like In{sub 2}O{sub 3} were controllable fabricated through a novel and simple hydrothermal process, and the former showed superior cataluminescence sensing performance to H{sub 2}S. Highlights: ► In{sub 2}O{sub 3} hierarchical hollow sphere were prepared via a hydrothermal route. ► The growth process of In{sub 2}O{sub 3} hierarchical hollow sphere has been investigated. ► The sensor based on prepared In{sub 2}O{sub 3} shows good sensing performance to H{sub 2}S. -- Abstract: In the present work, In{sub 2}O{sub 3} hierarchical hollow microsphere and flower-like microstructure were achieved controllably by a hydrothermal process in the sodiummore » dodecyl sulfate (SDS)-N,N-dimethyl-formamide (DMF) system. XRD, SEM, HRTEM and N{sub 2} adsorption measurements were used to characterize the as-prepared indium oxide materials and the possible mechanism for the microstructures formation was briefly discussed. The cataluminescence gas sensor based on the as-prepared In{sub 2}O{sub 3} was utilized to detect H{sub 2}S concentrations in flowing air. Comparative gas sensing results revealed that the sensor based on hierarchical hollow microsphere exhibited much higher sensing sensitivity in detecting H{sub 2}S gas than the sensor based on flower-like microstructure. The present gas sensor had a fast response time of 5 s and a recovery time of less than 25 s, furthermore, the cataluminescence intensity vs. H{sub 2}S concentration was linear in range of 2–20 μg mL{sup −1} with a detection limit of 0.5 μg mL{sup −1}. The present highly sensitive, fast-responding, and low-cost In{sub 2}O{sub 3}-based gas sensor for H{sub 2}S would have many practical applications.« less

Performance Analysis of Hierarchical Group Key Management Integrated with Adaptive Intrusion Detection in Mobile ad hoc Networks

DTIC Science & Technology

2016-04-05

applications in wireless networks such as military battlefields, emergency response, mobile commerce , online gaming, and collaborative work are based on the...www.elsevier.com/locate/peva Performance analysis of hierarchical group key management integrated with adaptive intrusion detection in mobile ad hoc...Accepted 19 September 2010 Available online 26 September 2010 Keywords: Mobile ad hoc networks Intrusion detection Group communication systems Group

Periorbital melasma: Hierarchical cluster analysis of clinical features in Asian patients.

PubMed

Jung, Y S; Bae, J M; Kim, B J; Kang, J-S; Cho, S B

2017-11-01

Studies have shown melasma lesions to be distributed across the face in centrofacial, malar, and mandibular patterns. Meanwhile, however, melasma lesions of the periorbital area have yet to be thoroughly described. We analyzed normal and ultraviolet light-exposed photographs of patients with melasma. The periorbital melasma lesions were measured according to anatomical reference points and a hierarchical cluster analysis was performed. The periorbital melasma lesions showed clinical features of fine and homogenous melasma pigmentation, involving both the upper and lower eyelids that extended to other anatomical sites with a darker and coarser appearance. The hierarchical cluster analysis indicated that patients with periorbital melasma can be categorized into two clusters according to the surface anatomy of the face. Significant differences between cluster 1 and cluster 2 were found in lateral distance and inferolateral distance, but not in medial distance and superior distance. Comparing the two clusters, patients in cluster 2 were found to be significantly older and more commonly accompanied by melasma lesions of the temple and medial cheek. Our hierarchical cluster analysis of periorbital melasma lesions demonstrated that Asian patients with periorbital melasma can be categorized into two clusters according to the surface anatomy of the face. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Activating the Microscale Edge Effect in a Hierarchical Surface for Frosting Suppression and Defrosting Promotion

PubMed Central

Chen, Xuemei; Ma, Ruiyuan; Zhou, Hongbo; Zhou, Xiaofeng; Che, Lufeng; Yao, Shuhuai; Wang, Zuankai

2013-01-01

Despite extensive progress, current icephobic materials are limited by the breakdown of their icephobicity in the condensation frosting environment. In particular, the frost formation over the entire surface is inevitable as a result of undesired inter-droplet freezing wave propagation initiated by the sample edges. Moreover, the frost formation directly results in an increased frost adhesion, posing severe challenges for the subsequent defrosting process. Here, we report a hierarchical surface which allows for interdroplet freezing wave propagation suppression and efficient frost removal. The enhanced performances are mainly owing to the activation of the microscale edge effect in the hierarchical surface, which increases the energy barrier for ice bridging as well as engendering the liquid lubrication during the defrosting process. We believe the concept of harnessing the surface morphology to achieve superior performances in two opposite phase transition processes might shed new light on the development of novel materials for various applications. PMID:23981909

The default-mode, ego-functions and free-energy: a neurobiological account of Freudian ideas

PubMed Central

Friston, K. J.

2010-01-01

This article explores the notion that Freudian constructs may have neurobiological substrates. Specifically, we propose that Freud’s descriptions of the primary and secondary processes are consistent with self-organized activity in hierarchical cortical systems and that his descriptions of the ego are consistent with the functions of the default-mode and its reciprocal exchanges with subordinate brain systems. This neurobiological account rests on a view of the brain as a hierarchical inference or Helmholtz machine. In this view, large-scale intrinsic networks occupy supraordinate levels of hierarchical brain systems that try to optimize their representation of the sensorium. This optimization has been formulated as minimizing a free-energy; a process that is formally similar to the treatment of energy in Freudian formulations. We substantiate this synthesis by showing that Freud’s descriptions of the primary process are consistent with the phenomenology and neurophysiology of rapid eye movement sleep, the early and acute psychotic state, the aura of temporal lobe epilepsy and hallucinogenic drug states. PMID:20194141

A framework for performance measurement in university using extended network data envelopment analysis (DEA) structures

NASA Astrophysics Data System (ADS)

Kashim, Rosmaini; Kasim, Maznah Mat; Rahman, Rosshairy Abd

2015-12-01

Measuring university performance is essential for efficient allocation and utilization of educational resources. In most of the previous studies, performance measurement in universities emphasized the operational efficiency and resource utilization without investigating the university's ability to fulfill the needs of its stakeholders and society. Therefore, assessment of the performance of university should be separated into two stages namely efficiency and effectiveness. In conventional DEA analysis, a decision making unit (DMU) or in this context, a university is generally treated as a black-box which ignores the operation and interdependence of the internal processes. When this happens, the results obtained would be misleading. Thus, this paper suggest an alternative framework for measuring the overall performance of a university by incorporating both efficiency and effectiveness and applies network DEA model. The network DEA models are recommended because this approach takes into account the interrelationship between the processes of efficiency and effectiveness in the system. This framework also focuses on the university structure which is expanded from the hierarchical to form a series of horizontal relationship between subordinate units by assuming both intermediate unit and its subordinate units can generate output(s). Three conceptual models are proposed to evaluate the performance of a university. An efficiency model is developed at the first stage by using hierarchical network model. It is followed by an effectiveness model which take output(s) from the hierarchical structure at the first stage as a input(s) at the second stage. As a result, a new overall performance model is proposed by combining both efficiency and effectiveness models. Thus, once this overall model is realized and utilized, the university's top management can determine the overall performance of each unit more accurately and systematically. Besides that, the result from the network DEA model can give a superior benchmarking power over the conventional models.

Self-cleaning poly(dimethylsiloxane) film with functional micro/nano hierarchical structures.

PubMed

Zhang, Xiao-Sheng; Zhu, Fu-Yun; Han, Meng-Di; Sun, Xu-Ming; Peng, Xu-Hua; Zhang, Hai-Xia

2013-08-27

This paper reports a novel single-step wafer-level fabrication of superhydrophobic micro/nano dual-scale (MNDS) poly(dimethylsiloxane) (PDMS) films. The MNDS PDMS films were replicated directly from an ultralow-surface-energy silicon substrate at high temperature without any surfactant coating, achieving high precision. An improved deep reactive ion etching (DRIE) process with enhanced passivation steps was proposed to easily realize the ultralow-surface-energy MNDS silicon substrate and also utilized as a post-treatment process to strengthen the hydrophobicity of the MNDS PDMS film. The chemical modification of this enhanced passivation step to the surface energy has been studied by density functional theory, which is also the first investigation of C4F8 plasma treatment at molecular level by using first-principle calculations. From the results of a systematic study on the effect of key process parameters (i.e., baking temperature and time) on PDMS replication, insight into the interaction of hierarchical multiscale structures of polymeric materials during the micro/nano integrated fabrication process is experimentally obtained for the first time. Finite element simulation has been employed to illustrate this new phenomenon. Additionally, hierarchical PDMS pyramid arrays and V-shaped grooves have been developed and are intended for applications as functional structures for a light-absorption coating layer and directional transport of liquid droplets, respectively. This stable, self-cleaning PDMS film with functional micro/nano hierarchical structures, which is fabricated through a wafer-level single-step fabrication process using a reusable silicon mold, shows attractive potential for future applications in micro/nanodevices, especially in micro/nanofluidics.

Mesoporous titanium dioxide (TiO2) with hierarchically 3D dendrimeric architectures: formation mechanism and highly enhanced photocatalytic activity.

PubMed

Li, Xiao-Yun; Chen, Li-Hua; Rooke, Joanna Claire; Deng, Zhao; Hu, Zhi-Yi; Wang, Shao-Zhuan; Wang, Li; Li, Yu; Krief, Alain; Su, Bao-Lian

2013-03-15

Mesoporous TiO(2) with a hierarchically 3D dendrimeric nanostructure comprised of nanoribbon building units has been synthesized via a spontaneous self-formation process from various titanium alkoxides. These hierarchically 3D dendrimeric architectures can be obtained by a very facile, template-free method, by simply dropping a titanium butoxide precursor into methanol solution. The novel configuration of the mesoporous TiO(2) nanostructure in nanoribbon building units yields a high surface area. The calcined samples show significantly enhanced photocatalytic activity and degradation rates owing to the mesoporosity and their improved crystallinity after calcination. Furthermore, the 3D dendrimeric architectures can be preserved after phase transformation from amorphous TiO(2) to anatase or rutile, which occurs during calcination. In addition, the spontaneous self-formation process of mesoporous TiO(2) with hierarchically 3D dendrimeric architectures from the hydrolysis and condensation reaction of titanium butoxide in methanol has been followed by in situ optical microscopy (OM), revealing the secret on the formation of hierarchically 3D dendrimeric nanostructures. Moreover, mesoporous TiO(2) nanostructures with similar hierarchically 3D dendrimeric architectures can also be obtained using other titanium alkoxides. The porosities and nanostructures of the resultant products were characterized by SEM, TEM, XRD, and N(2) adsorption-desorption measurements. The present work provides a facile and reproducible method for the synthesis of novel mesoporous TiO(2) nanoarchitectures, which in turn could herald the fabrication of more efficient photocatalysts. Copyright © 2012 Elsevier Inc. All rights reserved.

A highly efficient electrocatalyst for oxygen reduction reaction: phosphorus and nitrogen co-doped hierarchically ordered porous carbon derived from an iron-functionalized polymer

NASA Astrophysics Data System (ADS)

Deng, Chengwei; Zhong, Hexiang; Li, Xianfeng; Yao, Lan; Zhang, Huamin

2016-01-01

Heteroatom-doped carbon materials have shown respectable activity for the oxygen reduction reaction (ORR) in alkaline media. However, the performances of these materials are not satisfactory for energy conversion devices, such as fuel cells. Here, we demonstrate a new type of phosphorus and nitrogen co-doped hierarchically ordered porous carbon (PNHOPC) derived from an iron-functionalized mesoporous polymer through an evaporation-induced self-assembly process that simultaneously combines the carbonization and nitrogen doping processes. The soft template and the nitrogen doping process facilitate the formation of the hierarchically ordered structure for the PNHOPC. The catalyst possesses a large surface area (1118 cm2 g-1) and a pore volume of 1.14 cm3 g-1. Notably, it exhibits excellent ORR catalytic performance, superior stability and methanol tolerance in acidic electrolytes, thus making the catalyst promising for fuel cells. The correlations between the unique pore structure and the nitrogen and phosphorus configuration of the catalysts with high catalytic activity are thoroughly investigated.Heteroatom-doped carbon materials have shown respectable activity for the oxygen reduction reaction (ORR) in alkaline media. However, the performances of these materials are not satisfactory for energy conversion devices, such as fuel cells. Here, we demonstrate a new type of phosphorus and nitrogen co-doped hierarchically ordered porous carbon (PNHOPC) derived from an iron-functionalized mesoporous polymer through an evaporation-induced self-assembly process that simultaneously combines the carbonization and nitrogen doping processes. The soft template and the nitrogen doping process facilitate the formation of the hierarchically ordered structure for the PNHOPC. The catalyst possesses a large surface area (1118 cm2 g-1) and a pore volume of 1.14 cm3 g-1. Notably, it exhibits excellent ORR catalytic performance, superior stability and methanol tolerance in acidic electrolytes, thus making the catalyst promising for fuel cells. The correlations between the unique pore structure and the nitrogen and phosphorus configuration of the catalysts with high catalytic activity are thoroughly investigated. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr06749a

Cognitive Diagnostic Analysis Using Hierarchically Structured Skills

ERIC Educational Resources Information Center

Su, Yu-Lan

2013-01-01

This dissertation proposes two modified cognitive diagnostic models (CDMs), the deterministic, inputs, noisy, "and" gate with hierarchy (DINA-H) model and the deterministic, inputs, noisy, "or" gate with hierarchy (DINO-H) model. Both models incorporate the hierarchical structures of the cognitive skills in the model estimation…

Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

PubMed Central

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.

2017-01-01

Summary Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. PMID:28498564

The fluency of social hierarchy: the ease with which hierarchical relationships are seen, remembered, learned, and liked.

PubMed

Zitek, Emily M; Tiedens, Larissa Z

2012-01-01

We tested the hypothesis that social hierarchies are fluent social stimuli; that is, they are processed more easily and therefore liked better than less hierarchical stimuli. In Study 1, pairs of people in a hierarchy based on facial dominance were identified faster than pairs of people equal in their facial dominance. In Study 2, a diagram representing hierarchy was memorized more quickly than a diagram representing equality or a comparison diagram. This faster processing led the hierarchy diagram to be liked more than the equality diagram. In Study 3, participants were best able to learn a set of relationships that represented hierarchy (asymmetry of power)--compared to relationships in which there was asymmetry of friendliness, or compared to relationships in which there was symmetry--and this processing ease led them to like the hierarchy the most. In Study 4, participants found it easier to make decisions about a company that was more hierarchical and thus thought the hierarchical organization had more positive qualities. In Study 5, familiarity as a basis for the fluency of hierarchy was demonstrated by showing greater fluency for male than female hierarchies. This study also showed that when social relationships are difficult to learn, people's preference for hierarchy increases. Taken together, these results suggest one reason people might like hierarchies--hierarchies are easy to process. This fluency for social hierarchies might contribute to the construction and maintenance of hierarchies.

Quality assessment of crude and processed Arecae semen based on colorimeter and HPLC combined with chemometrics methods.

PubMed

Sun, Meng; Yan, Donghui; Yang, Xiaolu; Xue, Xingyang; Zhou, Sujuan; Liang, Shengwang; Wang, Shumei; Meng, Jiang

2017-05-01

Raw Arecae Semen, the seed of Areca catechu L., as well as Arecae Semen Tostum and Arecae semen carbonisata are traditionally processed by stir-baking for subsequent use in a variety of clinical applications. These three Arecae semen types, important Chinese herbal drugs, have been used in China and other Asian countries for thousands of years. In this study, the sensory technologies of a colorimeter and sensitive validated high-performance liquid chromatography with diode array detection were employed to discriminate raw Arecae semen and its processed drugs. The color parameters of the samples were determined by a colorimeter instrument CR-410. Moreover, the fingerprints of the four alkaloids of arecaidine, guvacine, arecoline and guvacoline were surveyed by high-performance liquid chromatography. Subsequently, Student's t test, the analysis of variance, fingerprint similarity analysis, hierarchical cluster analysis, principal component analysis, factor analysis and Pearson's correlation test were performed for final data analysis. The results obtained demonstrated a significant color change characteristic for components in raw Arecae semen and its processed drugs. Crude and processed Arecae semen could be determined based on colorimetry and high-performance liquid chromatography with a diode array detector coupled with chemometrics methods for a comprehensive quality evaluation. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Synthesis of hierarchical three-dimensional copper oxide nanostructures through a biomineralization-inspired approach

NASA Astrophysics Data System (ADS)

Fei, Xiang; Shao, Zhengzhong; Chen, Xin

2013-08-01

Three-dimensional (3D) copper oxide (CuO) nanostructures were synthesized in a regenerated Bombyx mori silk fibroin aqueous solution at room temperature. In the synthesis process, silk fibroin served as the template and helped to form the hierarchical CuO nanostructures by self-assembly. Cu(OH)2 nanowires were formed initially, and then they transformed into almond-like CuO nanostructures with branched edges and a compact middle. The size of the final CuO nanostructures can be tuned by varying the concentration of silk fibroin in the reaction system. A possible mechanism has been proposed based on various characterization techniques, such as scanning and transmission electron microscopy, X-ray diffraction, and thermogravimetric analysis. The synthesized CuO nanostructured material has been evaluated as an anode material for lithium ion batteries, and the result showed that they had a good electrochemical performance. The straightforward energy-saving method developed in this research may provide a useful preparation strategy for other functional inorganic materials through an environmentally friendly process.Three-dimensional (3D) copper oxide (CuO) nanostructures were synthesized in a regenerated Bombyx mori silk fibroin aqueous solution at room temperature. In the synthesis process, silk fibroin served as the template and helped to form the hierarchical CuO nanostructures by self-assembly. Cu(OH)2 nanowires were formed initially, and then they transformed into almond-like CuO nanostructures with branched edges and a compact middle. The size of the final CuO nanostructures can be tuned by varying the concentration of silk fibroin in the reaction system. A possible mechanism has been proposed based on various characterization techniques, such as scanning and transmission electron microscopy, X-ray diffraction, and thermogravimetric analysis. The synthesized CuO nanostructured material has been evaluated as an anode material for lithium ion batteries, and the result showed that they had a good electrochemical performance. The straightforward energy-saving method developed in this research may provide a useful preparation strategy for other functional inorganic materials through an environmentally friendly process. Electronic supplementary information (ESI) available: SEM image of the synthesized CuO without silk fibroin addition, and TEM images of the synthesized CuO with different silk fibroin concentrations. See DOI: 10.1039/c3nr01872e

From synthetic montroseite VOOH to topochemical paramontroseite VO2 and their applications in aqueous lithium ion batteries.

PubMed

Xu, Yang; Zheng, Lei; Xie, Yi

2010-11-28

Synthetic montroseite VOOH has been successfully prepared via a simple template-free hydrothermal route on a large scale for the first time-after sixty years of delay. The as-obtained sample shows a hierarchical morphology of urchin-like nanoarchitecture with hollow interiors consisting of well-crystalline nanorods standing vertically on the shell surface. Time-dependent experiments illustrated that these hierarchical hollow nanourchins were formed through the hydrolysis-driven Kirkendall effect coupled with a new-phased vanadium oxyhydroxide V(10)O(14)(OH)(2) precursor templated approach. Meanwhile, the as-obtained VOOH hollow nanourchins could convert topochemically to paramontroseite VO(2) without altering the size and original appearance during the annealing process due to the extreme structural similarity revealed by crystal structure analysis. Furthermore, the improved electrochemical performance of both montroseite VOOH and paramontroseite VO(2) hierarchical hollow structures toward Li uptake and release verifies their potential applications as anode materials in aqueous lithium ion batteries. These improved electrochemical properties could be ascribed to the synergetic effect of the microscopic tunneled crystal structure and macroscopic hollow morphological features, which provide the easy infiltration of electrolyte, short diffusion lengths for lithium ions and electron transport as well as sufficient void space to buffer the volume change.

Mace-like gold hollow hierarchical micro/nanostructures fabricated by co-effect of catalytic etching and electrodeposition and their SERS performance

NASA Astrophysics Data System (ADS)

Zhang, Haibao; Wang, Jingjing; Wang, Hua; Tian, Xingyou

2017-09-01

In this paper, we presented the fabrication of mace-like gold hollow hierarchical micro/nanostructures (HMNs) grafted on ZnO nanorods array by using an electrochemical deposition in chloroauric acid solution on gold layer pre-coated ZnO nanorods array. Different from general electrochemical deposition process, the catalytic etching to ZnO and electrodeposition of gold are co-existed in our case, which lead to an inner hollow structure and an outer gold shell. Due to the appropriate electrodeposition conditions, the outer gold shell was built of many wimble-like nanoparticles, and the hierarchical micro/nanostructures were thus formed. In addition, because of the deposition rate is decreased gradually away from the top of ZnO nanorods, the final structures show mace-like appearance. The surface-enhanced Raman scattering (SERS) effect of the as-prepared gold hollow HMNs was further studied by using rhodamine 6G as probe molecule. It is demonstrated that these structures show ultrahigh SERS activity, and the detecting low limit of R6G solution can be to 10-10 M on single mace-like gold HMNs, which is quite important for their potential application in SERS-based surface analysis and sensors.

Setting Directions: Anisotropy in Hierarchically Organized Porous Silica

PubMed Central

2017-01-01

Structural hierarchy, porosity, and isotropy/anisotropy are highly relevant factors for mechanical properties and thereby the functionality of porous materials. However, even though anisotropic and hierarchically organized, porous materials are well known in nature, such as bone or wood, producing the synthetic counterparts in the laboratory is difficult. We report for the first time a straightforward combination of sol–gel processing and shear-induced alignment to create hierarchical silica monoliths exhibiting anisotropy on the levels of both, meso- and macropores. The resulting material consists of an anisotropic macroporous network of struts comprising 2D hexagonally organized cylindrical mesopores. While the anisotropy of the mesopores is an inherent feature of the pores formed by liquid crystal templating, the anisotropy of the macropores is induced by shearing of the network. Scanning electron microscopy and small-angle X-ray scattering show that the majority of network forming struts is oriented towards the shearing direction; a quantitative analysis of scattering data confirms that roughly 40% of the strut volume exhibits a preferred orientation. The anisotropy of the material’s macroporosity is also reflected in its mechanical properties; i.e., the Young’s modulus differs by nearly a factor of 2 between the directions of shear application and perpendicular to it. Unexpectedly, the adsorption-induced strain of the material exhibits little to no anisotropy. PMID:28989232

Preparation of multifunctional Al-Mg alloy surface with hierarchical micro/nanostructures by selective chemical etching processes

NASA Astrophysics Data System (ADS)

Shi, Tian; Kong, Jianyi; Wang, Xingdong; Li, Xuewu

2016-12-01

A superamphiphobic aluminum magnesium alloy surface with enhanced anticorrosion behavior has been prepared in this work via a simple and low-cost method. By successively polishing, etching and boiling treatments, the multifunctional hierarchical binary structures composed of the labyrinth-like concave-convex microstructures and twisty nanoflakes have been prepared. Results indicate that a superhydrophobic contact angle of 160.5° and superoleophobic contact angle larger than 150° as well as low adhesive property to liquids are achieved after such structures being modified with fluoroalkyl-silane. Furthermore, the anticorrosion behaviors in seawater of as-prepared samples are characterized by electrochemical tests including the impedance spectroscopies, equivalent circuits fittings and polarization curves. It is found that the hierarchical micro/nanostructures accompanying with the modified coating are proved to possess the maximal coating coverage rate of 90.0% larger than microstructures of 85.9%, nanostructures of 83.8% and bare polished surface of 67.1% suggesting the optimal anticorrosion. Finally, a great potential application in concentrators for surface-enhanced Raman scattering (SERS) analysis of toxic and pollutive ions on the superamphiphobic surface is also confirmed. This work has wider significance in extending further applications of alloys in engineering and environmental detecting fields.

A Feasible One-Step Synthesis of Hierarchical Zeolite Beta with Uniform Nanocrystals via CTAB

PubMed Central

Zhang, Weimin; Hu, Sufang; Qin, Bo; Li, Ruifeng

2018-01-01

A hierarchical zeolite Beta has been prepared by a feasible one-pot and one-step method, which is suitable for application in industrial production. The synthesis is a simple hydrothermal process with low-cost raw materials, without adding alcohol or adding seeds, and without aging, recrystallization, and other complex steps. The hierarchical zeolite Beta is a uniform nanocrystal (20–50 nm) aggregation with high external surface area (300 m2/g) and mesoporous volume (0.50 cm3/g), with the mesoporous structure composed of intercrystal and intracrystal pores. As an acid catalyst in benzylation of naphthalene with benzyl chloride, the hierarchical zeolite Beta has shown high activity in the bulky molecule reaction due to its introduction of mesostructure. PMID:29695044

Asymmetric block copolymer membranes with ultrahigh porosity and hierarchical pore structure by plain solvent evaporation.

PubMed

Yu, H; Qiu, X; Behzad, A R; Musteata, V; Smilgies, D-M; Nunes, S P; Peinemann, K-V

2016-10-04

Membranes with a hierarchical porous structure could be manufactured from a block copolymer blend by pure solvent evaporation. Uniform pores in a 30 nm thin skin layer supported by a macroporous structure were formed. This new process is attractive for membrane production because of its simplicity and the lack of liquid waste.

A hierarchical linear model for tree height prediction.

Treesearch

Vicente J. Monleon

2003-01-01

Measuring tree height is a time-consuming process. Often, tree diameter is measured and height is estimated from a published regression model. Trees used to develop these models are clustered into stands, but this structure is ignored and independence is assumed. In this study, hierarchical linear models that account explicitly for the clustered structure of the data...

Hierarchical Control and Skilled Typing: Evidence for Word-Level Control over the Execution of Individual Keystrokes

ERIC Educational Resources Information Center

Crump, Matthew J. C.; Logan, Gordon D.

2010-01-01

Routine actions are commonly assumed to be controlled by hierarchically organized processes and representations. In the domain of typing theories, word-level information is assumed to activate the constituent keystrokes required to type each letter in a word. We tested this assumption directly using a novel single-letter probe technique. Subjects…

Covariates of the Rating Process in Hierarchical Models for Multiple Ratings of Test Items

ERIC Educational Resources Information Center

Mariano, Louis T.; Junker, Brian W.

2007-01-01

When constructed response test items are scored by more than one rater, the repeated ratings allow for the consideration of individual rater bias and variability in estimating student proficiency. Several hierarchical models based on item response theory have been introduced to model such effects. In this article, the authors demonstrate how these…

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/

Enhanced photocatalytic performance and degradation pathway of Rhodamine B over hierarchical double-shelled zinc nickel oxide hollow sphere heterojunction

NASA Astrophysics Data System (ADS)

Zhang, Ying; Zhou, Jiabin; Cai, Weiquan; Zhou, Jun; Li, Zhen

2018-02-01

In this study, hierarchical double-shelled NiO/ZnO hollow spheres heterojunction were prepared by calcination of the metallic organic frameworks (MOFs) as a sacrificial template in air via a one-step solvothermal method. Additionally, the photocatalytic activity of the as-prepared samples for the degradation of Rhodamine B (RhB) under UV-vis light irradiation were also investigated. NiO/ZnO microsphere comprised a core and a shell with unique hierarchically porous structure. The photocatalytic results showed that NiO/ZnO hollow spheres exhibited excellent catalytic activity for RhB degradation, causing complete decomposition of RhB (200 mL of 10 g/L) under UV-vis light irradiation within 3 h. Furthermore, the degradation pathway was proposed on the basis of the intermediates during the photodegradation process using liquid chromatography analysis coupled with mass spectroscopy (LC-MS). The improvement in photocatalytic performance could be attributed to the p-n heterojunction in the NiO/ZnO hollow spheres with hierarchically porous structure and the strong double-shell binding interaction, which enhances adsorption of the dye molecules on the catalyst surface and facilitates the electron/hole transfer within the framework. The degradation mechanism of pollutant is ascribed to the hydroxyl radicals (rad OH), which is the main oxidative species for the photocatalytic degradation of RhB. This work provides a facile and effective approach for the fabrication of porous metal oxides heterojunction with high photocatalytic activity and thus can be potentially used in the environmental purification.

Vanishing point: Scale independence in geomorphological hierarchies

NASA Astrophysics Data System (ADS)

Phillips, Jonathan D.

2016-08-01

Scale linkage problems in geosciences are often associated with a hierarchy of components. Both dynamical systems perspectives and intuition suggest that processes or relationships operating at fundamentally different scales are independent with respect to influences on system dynamics. But how far apart is ;fundamentally different;-that is, what is the ;vanishing point; at which scales are no longer interdependent? And how do we reconcile that with the idea (again, supported by both theory and intuition) that we can work our way along scale hierarchies from microscale to planetary (and vice-versa)? Graph and network theory are employed here to address these questions. Analysis of two archetypal hierarchical networks shows low algebraic connectivity, indicating low levels of inferential synchronization. This explains the apparent paradox between scale independence and hierarchical linkages. Incorporating more hierarchical levels results in an increase in complexity or entropy of the network as a whole, but at a nonlinear rate. Complexity increases as a power α of the number of levels in the hierarchy, with α < 1 and usually ≤ 0.6. However, algebraic connectivity decreases at a more rapid rate. Thus, the ability to infer one part of the hierarchical network from other level decays rapidly as more levels are added. Relatedness among system components decreases with differences in scale or resolution, analogous to distance decay in the spatial domain. These findings suggest a strategy of identifying and focusing on the most important or interesting scale levels, rather than attempting to identify the smallest or largest scale levels and work top-down or bottom-up from there. Examples are given from soil geomorphology and karst flow networks.

Hierarchical star formation across the grand-design spiral NGC 1566

NASA Astrophysics Data System (ADS)

Gouliermis, Dimitrios A.; Elmegreen, Bruce G.; Elmegreen, Debra M.; Calzetti, Daniela; Cignoni, Michele; Gallagher, John S., III; Kennicutt, Robert C.; Klessen, Ralf S.; Sabbi, Elena; Thilker, David; Ubeda, Leonardo; Aloisi, Alessandra; Adamo, Angela; Cook, David O.; Dale, Daniel; Grasha, Kathryn; Grebel, Eva K.; Johnson, Kelsey E.; Sacchi, Elena; Shabani, Fayezeh; Smith, Linda J.; Wofford, Aida

2017-06-01

We investigate how star formation is spatially organized in the grand-design spiral NGC 1566 from deep Hubble Space Telescope photometry with the Legacy ExtraGalactic UV Survey. Our contour-based clustering analysis reveals 890 distinct stellar conglomerations at various levels of significance. These star-forming complexes are organized in a hierarchical fashion with the larger congregations consisting of smaller structures, which themselves fragment into even smaller and more compact stellar groupings. Their size distribution, covering a wide range in length-scales, shows a power law as expected from scale-free processes. We explain this shape with a simple 'fragmentation and enrichment' model. The hierarchical morphology of the complexes is confirmed by their mass-size relation that can be represented by a power law with a fractional exponent, analogous to that determined for fractal molecular clouds. The surface stellar density distribution of the complexes shows a lognormal shape similar to that for supersonic non-gravitating turbulent gas. Between 50 and 65 per cent of the recently formed stars, as well as about 90 per cent of the young star clusters, are found inside the stellar complexes, located along the spiral arms. We find an age difference between young stars inside the complexes and those in their direct vicinity in the arms of at least 10 Myr. This time-scale may relate to the minimum time for stellar evaporation, although we cannot exclude the in situ formation of stars. As expected, star formation preferentially occurs in spiral arms. Our findings reveal turbulent-driven hierarchical star formation along the arms of a grand-design galaxy.

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

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.

Hierarchical cluster analysis of progression patterns in open-angle glaucoma patients with medical treatment.

PubMed

Bae, Hyoung Won; Rho, Seungsoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

2014-04-29

To classify medically treated open-angle glaucoma (OAG) by the pattern of progression using hierarchical cluster analysis, and to determine OAG progression characteristics by comparing clusters. Ninety-five eyes of 95 OAG patients who received medical treatment, and who had undergone visual field (VF) testing at least once per year for 5 or more years. OAG was classified into subgroups using hierarchical cluster analysis based on the following five variables: baseline mean deviation (MD), baseline visual field index (VFI), MD slope, VFI slope, and Glaucoma Progression Analysis (GPA) printout. After that, other parameters were compared between clusters. Two clusters were made after a hierarchical cluster analysis. Cluster 1 showed -4.06 ± 2.43 dB baseline MD, 92.58% ± 6.27% baseline VFI, -0.28 ± 0.38 dB per year MD slope, -0.52% ± 0.81% per year VFI slope, and all "no progression" cases in GPA printout, whereas cluster 2 showed -8.68 ± 3.81 baseline MD, 77.54 ± 12.98 baseline VFI, -0.72 ± 0.55 MD slope, -2.22 ± 1.89 VFI slope, and seven "possible" and four "likely" progression cases in GPA printout. There were no significant differences in age, sex, mean IOP, central corneal thickness, and axial length between clusters. However, cluster 2 included more high-tension glaucoma patients and used a greater number of antiglaucoma eye drops significantly compared with cluster 1. Hierarchical cluster analysis of progression patterns divided OAG into slow and fast progression groups, evidenced by assessing the parameters of glaucomatous progression in VF testing. In the fast progression group, the prevalence of high-tension glaucoma was greater and the number of antiglaucoma medications administered was increased versus the slow progression group. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

Hierarchically Superstructured Prussian Blue Analogues: Spontaneous Assembly Synthesis and Applications as Pseudocapacitive Materials

DOE PAGES

Yue, Yanfeng; Zhang, Zhiyong; Binder, Andrew J.; ...

2014-11-10

Hierarchically superstructured Prussian blue analogues (hexa- conventional hybrid graphene/MnO 2 nanostructured textiles. cyanoferrate, M = Ni II, Co II and Cu II) are synthesized through Because sodium or potassium ions are involved in energy stor- a spontaneous assembly technique. In sharp contrast to mac- age processes, more environmentally neutral electrolytes can roporous-only Prussian blue analogues, the hierarchically su- be utilized, making the superstructured porous Prussian blue perstructured porous Prussian blue materials are demonstrated analogues a great contender for applications as high-per- to possess a high capacitance, which is similar to those of the formance pseudocapacitors.

Self-organized emergence of multilayer structure and chimera states in dynamical networks with adaptive couplings

NASA Astrophysics Data System (ADS)

Kasatkin, D. V.; Yanchuk, S.; Schöll, E.; Nekorkin, V. I.

2017-12-01

We report the phenomenon of self-organized emergence of hierarchical multilayered structures and chimera states in dynamical networks with adaptive couplings. This process is characterized by a sequential formation of subnetworks (layers) of densely coupled elements, the size of which is ordered in a hierarchical way, and which are weakly coupled between each other. We show that the hierarchical structure causes the decoupling of the subnetworks. Each layer can exhibit either a two-cluster state, a periodic traveling wave, or an incoherent state, and these states can coexist on different scales of subnetwork sizes.

Macro-fingerprint analysis-through-separation of licorice based on FT-IR and 2DCOS-IR

NASA Astrophysics Data System (ADS)

Wang, Yang; Wang, Ping; Xu, Changhua; Yang, Yan; Li, Jin; Chen, Tao; Li, Zheng; Cui, Weili; Zhou, Qun; Sun, Suqin; Li, Huifen

2014-07-01

In this paper, a step-by-step analysis-through-separation method under the navigation of multi-step IR macro-fingerprint (FT-IR integrated with second derivative IR (SD-IR) and 2DCOS-IR) was developed for comprehensively characterizing the hierarchical chemical fingerprints of licorice from entirety to single active components. Subsequently, the chemical profile variation rules of three parts (flavonoids, saponins and saccharides) in the separation process were holistically revealed and the number of matching peaks and correlation coefficients with standards of pure compounds was increasing along the extracting directions. The findings were supported by UPLC results and a verification experiment of aqueous separation process. It has been demonstrated that the developed multi-step IR macro-fingerprint analysis-through-separation approach could be a rapid, effective and integrated method not only for objectively providing comprehensive chemical characterization of licorice and all its separated parts, but also for rapidly revealing the global enrichment trend of the active components in licorice separation process.

Hierarchical Bayes approach for subgroup analysis.

PubMed

Hsu, Yu-Yi; Zalkikar, Jyoti; Tiwari, Ram C

2017-01-01

In clinical data analysis, both treatment effect estimation and consistency assessment are important for a better understanding of the drug efficacy for the benefit of subjects in individual subgroups. The linear mixed-effects model has been used for subgroup analysis to describe treatment differences among subgroups with great flexibility. The hierarchical Bayes approach has been applied to linear mixed-effects model to derive the posterior distributions of overall and subgroup treatment effects. In this article, we discuss the prior selection for variance components in hierarchical Bayes, estimation and decision making of the overall treatment effect, as well as consistency assessment of the treatment effects across the subgroups based on the posterior predictive p-value. Decision procedures are suggested using either the posterior probability or the Bayes factor. These decision procedures and their properties are illustrated using a simulated example with normally distributed response and repeated measurements.

Simultaneous formation of multiscale hierarchical surface morphologies through sequential wrinkling and folding

NASA Astrophysics Data System (ADS)

Wang, Yu; Sun, Qingyang; Xiao, Jianliang

2018-02-01

Highly organized hierarchical surface morphologies possess various intriguing properties that could find important potential applications. In this paper, we demonstrate a facile approach to simultaneously form multiscale hierarchical surface morphologies through sequential wrinkling. This method combines surface wrinkling induced by thermal expansion and mechanical strain on a three-layer structure composed of an aluminum film, a hard Polydimethylsiloxane (PDMS) film, and a soft PDMS substrate. Deposition of the aluminum film on hard PDMS induces biaxial wrinkling due to thermal expansion mismatch, and recovering the prestrain in the soft PDMS substrate leads to wrinkling of the hard PDMS film. In total, three orders of wrinkling patterns form in this process, with wavelength and amplitude spanning 3 orders of magnitude in length scale. By increasing the prestrain in the soft PDMS substrate, a hierarchical wrinkling-folding structure was also obtained. This approach can be easily extended to other thin films for fabrication of multiscale hierarchical surface morphologies with potential applications in different areas.

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

Fabrication of hierarchical porous ZnO/NiO hollow microspheres for adsorptive removal of Congo red

NASA Astrophysics Data System (ADS)

Lei, Chunsheng; Pi, Meng; Cheng, Bei; Jiang, Chuanjia; Qin, Jiaqian

2018-03-01

Hierarchical porous zinc oxide (ZnO)/nickel(II) oxide (NiO) hollow microspheres were fabricated by a facile hydrothermal approach and subsequent calcination process. The synthesized samples were used as adsorbent for removing Congo red (CR), a commercial azo dye. The synthesized hierarchical porous ZnO/NiO composites exhibit a superior adsorption capacity for CR (518 mg/g), compared with pure NiO (397 mg/g) and ZnO (304 mg/g). The high CR adsorption capacity of ZnO/NiO composites was associated with its hierarchical porous hollow structures and large specific surface area (130 m2/g), which provide a large quantity of active sites for CR molecules. The adsorption kinetics data were perfectly fitted to a pseudo-second-order model. The isotherms were accurately described by the Langmuir model. The results suggest that the as-prepared hierarchical porous ZnO/NiO composites are a highly efficient adsorbent for treating organic dye-impacted wastewater.

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

A hierarchical model for regional analysis of population change using Christmas Bird Count data, with application to the American Black Duck

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.; Niven, D.K.

2006-01-01

Analysis of Christmas Bird Count (CBC) data is complicated by the need to account for variation in effort on counts and to provide summaries over large geographic regions. We describe a hierarchical model for analysis of population change using CBC data that addresses these needs. The effect of effort is modeled parametrically, with parameter values varying among strata as identically distributed random effects. Year and site effects are modeled hierarchically, accommodating large regional variation in number of samples and precision of estimates. The resulting model is complex, but a Bayesian analysis can be conducted using Markov chain Monte Carlo techniques. We analyze CBC data for American Black Ducks (Anas rubripes), a species of considerable management interest that has historically been monitored using winter surveys. Over the interval 1966-2003, Black Duck populations showed distinct regional patterns of population change. The patterns shown by CBC data are similar to those shown by the Midwinter Waterfowl Inventory for the United States.

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.

Conceptual Chemical Process Design for Sustainability.

EPA Science Inventory

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 analyse...

The efficiency of average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling in identifying homogeneous precipitation catchments

NASA Astrophysics Data System (ADS)

Chuan, Zun Liang; Ismail, Noriszura; Shinyie, Wendy Ling; Lit Ken, Tan; Fam, Soo-Fen; Senawi, Azlyna; Yusoff, Wan Nur Syahidah Wan

2018-04-01

Due to the limited of historical precipitation records, agglomerative hierarchical clustering algorithms widely used to extrapolate information from gauged to ungauged precipitation catchments in yielding a more reliable projection of extreme hydro-meteorological events such as extreme precipitation events. However, identifying the optimum number of homogeneous precipitation catchments accurately based on the dendrogram resulted using agglomerative hierarchical algorithms are very subjective. The main objective of this study is to propose an efficient regionalized algorithm to identify the homogeneous precipitation catchments for non-stationary precipitation time series. The homogeneous precipitation catchments are identified using average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling, while uncentered correlation coefficient as the similarity measure. The regionalized homogeneous precipitation is consolidated using K-sample Anderson Darling non-parametric test. The analysis result shows the proposed regionalized algorithm performed more better compared to the proposed agglomerative hierarchical clustering algorithm in previous studies.

Analysis hierarchical model for discrete event systems

NASA Astrophysics Data System (ADS)

Ciortea, E. M.

2015-11-01

The This paper presents the hierarchical model based on discrete event network for robotic systems. Based on the hierarchical approach, Petri network is analysed as a network of the highest conceptual level and the lowest level of local control. For modelling and control of complex robotic systems using extended Petri nets. Such a system is structured, controlled and analysed in this paper by using Visual Object Net ++ package that is relatively simple and easy to use, and the results are shown as representations easy to interpret. The hierarchical structure of the robotic system is implemented on computers analysed using specialized programs. Implementation of hierarchical model discrete event systems, as a real-time operating system on a computer network connected via a serial bus is possible, where each computer is dedicated to local and Petri model of a subsystem global robotic system. Since Petri models are simplified to apply general computers, analysis, modelling, complex manufacturing systems control can be achieved using Petri nets. Discrete event systems is a pragmatic tool for modelling industrial systems. For system modelling using Petri nets because we have our system where discrete event. To highlight the auxiliary time Petri model using transport stream divided into hierarchical levels and sections are analysed successively. Proposed robotic system simulation using timed Petri, offers the opportunity to view the robotic time. Application of goods or robotic and transmission times obtained by measuring spot is obtained graphics showing the average time for transport activity, using the parameters sets of finished products. individually.

Application research of 3D additive manufacturing technology in the nail shell

NASA Astrophysics Data System (ADS)

Xiao, Shanhua; Yan, Ruiqiang; Song, Ning

2018-04-01

Based on the analysis of hierarchical slicing algorithm, 3D scanning of enterprise product nailing handle case file is carried out, point cloud data processing is performed on the source file, and the surface modeling and innovative design of nail handling handle case are completed. Using MakerBot Replicator2X-based 3D printer for layered 3D print samples, for the new nail product development to provide reverse modeling and rapid prototyping technical support.

Data processing of high-rate low-voltage distribution grid recordings for smart grid monitoring and analysis

NASA Astrophysics Data System (ADS)

Maaß, Heiko; Cakmak, Hüseyin Kemal; Bach, Felix; Mikut, Ralf; Harrabi, Aymen; Süß, Wolfgang; Jakob, Wilfried; Stucky, Karl-Uwe; Kühnapfel, Uwe G.; Hagenmeyer, Veit

2015-12-01

Power networks will change from a rigid hierarchic architecture to dynamic interconnected smart grids. In traditional power grids, the frequency is the controlled quantity to maintain supply and load power balance. Thereby, high rotating mass inertia ensures for stability. In the future, system stability will have to rely more on real-time measurements and sophisticated control, especially when integrating fluctuating renewable power sources or high-load consumers like electrical vehicles to the low-voltage distribution grid.

Neurocomputing

NASA Technical Reports Server (NTRS)

Hecht-Nielsen, Robert

1990-01-01

The present work is intended to give technologists, research scientists, and mathematicians a graduate-level overview of the field of neurocomputing. After exploring the relationship of this field to general neuroscience, attention is given to neural network building blocks, the self-adaptation equations of learning laws, the data-transformation structures of associative networks, and the multilayer data-transformation structures of mapping networks. Also treated are the neurocomputing frontiers of spatiotemporal, stochastic, and hierarchical networks, 'neurosoftware', the creation of neural network-based computers, and neurocomputing applications in sensor processing, control, and data analysis.

Fault-Tree Compiler Program

NASA Technical Reports Server (NTRS)

Butler, Ricky W.; Martensen, Anna L.

1992-01-01

FTC, Fault-Tree Compiler program, is reliability-analysis software tool used to calculate probability of top event of fault tree. Five different types of gates allowed in fault tree: AND, OR, EXCLUSIVE OR, INVERT, and M OF N. High-level input language of FTC easy to understand and use. Program supports hierarchical fault-tree-definition feature simplifying process of description of tree and reduces execution time. Solution technique implemented in FORTRAN, and user interface in Pascal. Written to run on DEC VAX computer operating under VMS operating system.

Hierarchical recruitment of ribosomal proteins and assembly factors remodels nucleolar pre-60S ribosomes.

PubMed

Biedka, Stephanie; Micic, Jelena; Wilson, Daniel; Brown, Hailey; Diorio-Toth, Luke; Woolford, John L

2018-04-24

Ribosome biogenesis involves numerous preribosomal RNA (pre-rRNA) processing events to remove internal and external transcribed spacer sequences, ultimately yielding three mature rRNAs. Removal of the internal transcribed spacer 2 spacer RNA is the final step in large subunit pre-rRNA processing and begins with endonucleolytic cleavage at the C 2 site of 27SB pre-rRNA. C 2 cleavage requires the hierarchical recruitment of 11 ribosomal proteins and 14 ribosome assembly factors. However, the function of these proteins in C 2 cleavage remained unclear. In this study, we have performed a detailed analysis of the effects of depleting proteins required for C 2 cleavage and interpreted these results using cryo-electron microscopy structures of assembling 60S subunits. This work revealed that these proteins are required for remodeling of several neighborhoods, including two major functional centers of the 60S subunit, suggesting that these remodeling events form a checkpoint leading to C 2 cleavage. Interestingly, when C 2 cleavage is directly blocked by depleting or inactivating the C 2 endonuclease, assembly progresses through all other subsequent steps. © 2018 Biedka et al.

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

Cook, John B.; Detsi, Eric; Liu, Yijin

Next generation Li-ion batteries will require negative electrode materials with energy densities many-fold higher than that found in the graphitic carbon currently used in commercial Li-ion batteries. While various nanostructured alloying-type anode materials may satisfy that requirement, such materials do not always exhibit long cycle lifetimes and/or their processing routes are not always suitable for large-scale synthesis. Here, we report on a high-performance anode material for next generation Li-ion batteries made of nanoporous Sn powders with hierarchical ligament morphology. This material system combines both long cycle lifetimes (more than 72% capacity retention after 350 cycles), high capacity (693 mAh/g, nearlymore » twice that of commercial graphitic carbon), good charging/discharging capabilities (545 mAh/g at 1 A/g, 1.5C), and a scalable processing route that involves selective alloy corrosion. The good cycling performance of this system is attributed to its nanoporous architecture and its unique hierarchical ligament morphology, which accommodates the large volume changes taking place during lithiation, as confirmed by synchrotron-based ex-situ X-ray 3D tomography analysis. In conclusion, our findings are an important step for the development of high-performance Li-ion batteries.« less

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

Clinical time series prediction: Toward a hierarchical dynamical system framework.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Large-Area Direct Laser-Shock Imprinting of a 3D Biomimic Hierarchical Metal Surface for Triboelectric Nanogenerators.

PubMed

Jin, Shengyu; Wang, Yixiu; Motlag, Maithilee; Gao, Shengjie; Xu, Jin; Nian, Qiong; Wu, Wenzhuo; Cheng, Gary J

2018-03-01

Ongoing efforts in triboelectric nanogenerators (TENGs) focus on enhancing power generation, but obstacles concerning the economical and cost-effective production of TENGs continue to prevail. Micro-/nanostructure engineering of polymer surfaces has been dominantly utilized for boosting the contact triboelectrification, with deposited metal electrodes for collecting the scavenged energy. Nevertheless, this state-of-the-art approach is limited by the vague potential for producing 3D hierarchical surface structures with conformable coverage of high-quality metal. Laser-shock imprinting (LSI) is emerging as a potentially scalable approach for directly surface patterning of a wide range of metals with 3D nanoscale structures by design, benefiting from the ultrahigh-strain-rate forming process. Here, a TENG device is demonstrated with LSI-processed biomimetic hierarchically structured metal electrodes for efficient harvesting of water-drop energy in the environment. Mimicking and transferring hierarchical microstructures from natural templates, such as leaves, into these water-TENG devices is effective regarding repelling water drops from the device surface, since surface hydrophobicity from these biomicrostructures maximizes the TENG output. Among various leaves' microstructures, hierarchical microstructures from dried bamboo leaves are preferable regarding maximizing power output, which is attributed to their unique structures, containing both dense nanostructures and microscale features, compared with other types of leaves. Also, the triboelectric output is significantly improved by closely mimicking the hydrophobic nature of the leaves in the LSI-processed metal surface after functionalizing it with low-surface-energy self-assembled-monolayers. The approach opens doors to new manufacturable TENG technologies for economically feasible and ecologically friendly production of functional devices with directly patterned 3D biomimic metallic surfaces in energy, electronics, and sensor applications. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A distributed, hierarchical and recurrent framework for reward-based choice

PubMed Central

Hunt, Laurence T.; Hayden, Benjamin Y.

2017-01-01

Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organisation of timescales for information processing across the cortex. This account also suggests that certain correlates of value may be emergent rather than represented explicitly in the brain. PMID:28209978

TU-FG-209-12: Treatment Site and View Recognition in X-Ray Images with Hierarchical Multiclass Recognition Models

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

Chang, X; Mazur, T; Yang, D

Purpose: To investigate an approach of automatically recognizing anatomical sites and imaging views (the orientation of the image acquisition) in 2D X-ray images. Methods: A hierarchical (binary tree) multiclass recognition model was developed to recognize the treatment sites and views in x-ray images. From top to bottom of the tree, the treatment sites are grouped hierarchically from more general to more specific. Each node in the hierarchical model was designed to assign images to one of two categories of anatomical sites. The binary image classification function of each node in the hierarchical model is implemented by using a PCA transformationmore » and a support vector machine (SVM) model. The optimal PCA transformation matrices and SVM models are obtained by learning from a set of sample images. Alternatives of the hierarchical model were developed to support three scenarios of site recognition that may happen in radiotherapy clinics, including two or one X-ray images with or without view information. The performance of the approach was tested with images of 120 patients from six treatment sites – brain, head-neck, breast, lung, abdomen and pelvis – with 20 patients per site and two views (AP and RT) per patient. Results: Given two images in known orthogonal views (AP and RT), the hierarchical model achieved a 99% average F1 score to recognize the six sites. Site specific view recognition models have 100 percent accuracy. The computation time to process a new patient case (preprocessing, site and view recognition) is 0.02 seconds. Conclusion: The proposed hierarchical model of site and view recognition is effective and computationally efficient. It could be useful to automatically and independently confirm the treatment sites and views in daily setup x-ray 2D images. It could also be applied to guide subsequent image processing tasks, e.g. site and view dependent contrast enhancement and image registration. The senior author received research grants from ViewRay Inc. and Varian Medical System.« less

Constructing accountability in inter-organisational collaboration: the implications of a narrow performance-based focus.

PubMed

Andersson, Johanna; Wikström, Ewa

2014-01-01

The purpose of this paper is to analyse how accounts of collaboration practice were made and used to construct accountability in the empirical context of coordination associations, a Swedish form of collaboration between four authorities in health and social care. They feature pooled budgets, joint leadership and joint reporting systems, intended to facilitate both collaboration and (shared) accountability. Empirical data were collected in field observations in local, regional and national settings. In addition, the study is based on analysis of local association documents such as evaluations and annual reports, and analysis of national agency reports. Accountability is constructed hierarchically with a narrow focus on performance, and horizontal (shared) accountability as well as outcomes are de-emphasised. Through this narrow construction of accountability the coordination associations are re-created as hierarchical and accountability is delegated rather than shared. Features such as pooled budgets, joint leadership and joint reporting systems can support collaboration but do not necessarily translate into shared accountability if accountability is interpreted and constructed hierarchically. When practice conforms to what is counted and accounted for, using the hierarchical and narrow construction of accountability, the result may be that the associations become an additional authority. That would increase rather than decrease fragmentation in the field. This research derives from first-hand observations of actor-to-actor episodes complemented with the analysis of documents and reports. It provides critical analysis of the construction and evaluation of accounts and accountability related to practice and performance in collaboration. The main contribution is the finding that despite the conditions intended to facilitate inter-organisational collaboration and horizontal accountability, the hierarchical accountability persisted.

Hierarchical Network Models for Education Research: Hierarchical Latent Space Models

ERIC Educational Resources Information Center

Sweet, Tracy M.; Thomas, Andrew C.; Junker, Brian W.

2013-01-01

Intervention studies in school systems are sometimes aimed not at changing curriculum or classroom technique, but rather at changing the way that teachers, teaching coaches, and administrators in schools work with one another--in short, changing the professional social networks of educators. Current methods of social network analysis are…

Hierarchical analysis of forest bird species-environment relationships in the Oregon Coast Range

Treesearch

Samuel A. Cushman; Kevin McGarigal

2004-01-01

Species in biological communities respond to environmental variation simultaneously across a range of organizational levels. Accordingly, it is important to quantify the effects of environmental factors at multiple levels on species distribution and abundance. Hierarchical methods that explicitly separate the independent and confounded influences of environmental...

Multilevel Analysis of Structural Equation Models via the EM Algorithm.

ERIC Educational Resources Information Center

Jo, See-Heyon

The question of how to analyze unbalanced hierarchical data generated from structural equation models has been a common problem for researchers and analysts. Among difficulties plaguing statistical modeling are estimation bias due to measurement error and the estimation of the effects of the individual's hierarchical social milieu. This paper…

Hierarchical Recursive Organization and the Free Energy Principle: From Biological Self-Organization to the Psychoanalytic Mind

PubMed Central

Connolly, Patrick; van Deventer, Vasi

2017-01-01

The present paper argues that a systems theory epistemology (and particularly the notion of hierarchical recursive organization) provides the critical theoretical context within which the significance of Friston's (2010a) Free Energy Principle (FEP) for both evolution and psychoanalysis is best understood. Within this perspective, the FEP occupies a particular level of the hierarchical organization of the organism, which is the level of biological self-organization. This form of biological self-organization is in turn understood as foundational and pervasive to the higher levels of organization of the human organism that are of interest to both neuroscience as well as psychoanalysis. Consequently, central psychoanalytic claims should be restated, in order to be located in their proper place within a hierarchical recursive organization of the (situated) organism. In light of the FEP the realization of the psychoanalytic mind by the brain should be seen in terms of the evolution of different levels of systematic organization where the concepts of psychoanalysis describe a level of hierarchical recursive organization superordinate to that of biological self-organization and the FEP. The implication of this formulation is that while “psychoanalytic” mental processes are fundamentally subject to the FEP, they nonetheless also add their own principles of process over and above that of the FEP. A model found in Grobbelaar (1989) offers a recursive bottom-up description of the self-organization of the psychoanalytic ego as dependent on the organization of language (and affect), which is itself founded upon the tendency toward autopoiesis (self-making) within the organism, which is in turn described as formally similar to the FEP. Meaningful consilience between Grobbelaar's model and the hierarchical recursive description available in Friston's (2010a) theory is described. The paper concludes that the valuable contribution of the FEP to psychoanalysis underscores the necessity of reengagement with the core concepts of psychoanalytic theory, and the usefulness that a systems theory epistemology—particularly hierarchical recursive description—can have for this goal. PMID:29038652

Hierarchical Recursive Organization and the Free Energy Principle: From Biological Self-Organization to the Psychoanalytic Mind.

PubMed

Connolly, Patrick; van Deventer, Vasi

2017-01-01

The present paper argues that a systems theory epistemology (and particularly the notion of hierarchical recursive organization) provides the critical theoretical context within which the significance of Friston's (2010a) Free Energy Principle (FEP) for both evolution and psychoanalysis is best understood. Within this perspective, the FEP occupies a particular level of the hierarchical organization of the organism, which is the level of biological self-organization. This form of biological self-organization is in turn understood as foundational and pervasive to the higher levels of organization of the human organism that are of interest to both neuroscience as well as psychoanalysis. Consequently, central psychoanalytic claims should be restated, in order to be located in their proper place within a hierarchical recursive organization of the (situated) organism. In light of the FEP the realization of the psychoanalytic mind by the brain should be seen in terms of the evolution of different levels of systematic organization where the concepts of psychoanalysis describe a level of hierarchical recursive organization superordinate to that of biological self-organization and the FEP. The implication of this formulation is that while "psychoanalytic" mental processes are fundamentally subject to the FEP, they nonetheless also add their own principles of process over and above that of the FEP. A model found in Grobbelaar (1989) offers a recursive bottom-up description of the self-organization of the psychoanalytic ego as dependent on the organization of language (and affect), which is itself founded upon the tendency toward autopoiesis (self-making) within the organism, which is in turn described as formally similar to the FEP. Meaningful consilience between Grobbelaar's model and the hierarchical recursive description available in Friston's (2010a) theory is described. The paper concludes that the valuable contribution of the FEP to psychoanalysis underscores the necessity of reengagement with the core concepts of psychoanalytic theory, and the usefulness that a systems theory epistemology-particularly hierarchical recursive description-can have for this goal.

PEDOT-PSS coated ZnO/C hierarchical porous nanorods as ultralong-life anode material for lithium ion batteries

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

Xu, Gui-Liang; Li, Yan; Ma, Tianyuan

2015-11-01

ZnO/C hierarchical porous nanorods were synthesized through one-pot wet-chemical reaction followed by thermal calcination. It was found that ZnO/C porous nanorods are composed of numerous nanograins, exhibiting a hierarchical micro/nanostructure. In-situ synchrotron high energy X-ray diffraction study revealed that ZnO/C hierarchical porous nanorods involve a two-step reversible lithiation mechanism during charge/discharge; and part of ZnO and Zn remains at the end of the first discharge and charge process, respectively, leading to a low coulombic efficiency in the initial few cycles. The electrochemical test demonstrated that the reversible capacity and the rate performance of ZnO/C hierarchical porous nanorods anode have beenmore » greatly improved by PEDOT-PSS coating, which could maintain a reversible capacity of 623.94 mA h g(-1) after 1500 cycles at 1 C. Its excellent high rate capability and long cycle stability were attributed to the high electronic conductivity of PEDOT-PSS coating layer and the hierarchical structures of ZnO/C porous nanorods. (C) 2015 Elsevier Ltd. All rights reserved.« less

The hierarchical expert tuning of PID controllers using tools of soft computing.

PubMed

Karray, F; Gueaieb, W; Al-Sharhan, S

2002-01-01

We present soft computing-based results pertaining to the hierarchical tuning process of PID controllers located within the control loop of a class of nonlinear systems. The results are compared with PID controllers implemented either in a stand alone scheme or as a part of conventional gain scheduling structure. This work is motivated by the increasing need in the industry to design highly reliable and efficient controllers for dealing with regulation and tracking capabilities of complex processes characterized by nonlinearities and possibly time varying parameters. The soft computing-based controllers proposed are hybrid in nature in that they integrate within a well-defined hierarchical structure the benefits of hard algorithmic controllers with those having supervisory capabilities. The controllers proposed also have the distinct features of learning and auto-tuning without the need for tedious and computationally extensive online systems identification schemes.

Hierarchically structured photonic crystals for integrated chemical separation and colorimetric detection.

PubMed

Fu, Qianqian; Zhu, Biting; Ge, Jianping

2017-02-16

A SiO 2 colloidal photonic crystal film with a hierarchical porous structure is fabricated to demonstrate an integrated separation and colorimetric detection of chemical species for the first time. This new photonic crystal based thin layer chromatography process requires no dyeing, developing and UV irradiation compared to the traditional TLC. The assembling of mesoporous SiO 2 particles via a supersaturation-induced-precipitation process forms uniform and hierarchical photonic crystals with micron-scale cracks and mesopores, which accelerate the diffusion of developers and intensify the adsorption/desorption between the analytes and silica for efficient separation. Meanwhile, the chemical substances infiltrated to the voids of photonic crystals cause an increase of the refractive index and a large contrast of structural colors towards the unloaded part, so that the sample spots can be directly recognized with the naked eye before and after separation.

Monitoring Farmland Loss Caused by Urbanization in Beijing from Modis Time Series Using Hierarchical Hidden Markov Model

NASA Astrophysics Data System (ADS)

Yuan, Y.; Meng, Y.; Chen, Y. X.; Jiang, C.; Yue, A. Z.

2018-04-01

In this study, we proposed a method to map urban encroachment onto farmland using satellite image time series (SITS) based on the hierarchical hidden Markov model (HHMM). In this method, the farmland change process is decomposed into three hierarchical levels, i.e., the land cover level, the vegetation phenology level, and the SITS level. Then a three-level HHMM is constructed to model the multi-level semantic structure of farmland change process. Once the HHMM is established, a change from farmland to built-up could be detected by inferring the underlying state sequence that is most likely to generate the input time series. The performance of the method is evaluated on MODIS time series in Beijing. Results on both simulated and real datasets demonstrate that our method improves the change detection accuracy compared with the HMM-based method.

Broca's area processes the hierarchical organization of observed action

PubMed Central

Wakita, Masumi

2014-01-01

Broca's area has been suggested as the area responsible for the domain-general hierarchical processing of language and music. Although meaningful action shares a common hierarchical structure with language and music, the role of Broca's area in this domain remains controversial. To address the involvement of Broca's area in the processing action hierarchy, the activation of Broca's area was measured using near-infrared spectroscopy. Measurements were taken while participants watched silent movies that featured hand movements playing familiar and unfamiliar melodies. The unfamiliar melodies were reversed versions of the familiar melodies. Additionally, to investigate the effect of a motor experience on the activation of Broca's area, the participants were divided into well-trained and less-trained groups. The results showed that Broca's area in the well-trained participants demonstrated a significantly larger activation in response to the hand motion when an unfamiliar melody was played than when a familiar melody was played. However, Broca's area in the less-trained participants did not show a contrast between conditions despite identical abilities of the two participant groups to identify the melodies by watching key pressing actions. These results are consistent with previous findings that Broca's area exhibits increased activation in response to grammatically violated sentences and musically deviated chord progressions as well as the finding that this region does not represent the processing of grammatical structure in less-proficient foreign language speakers. Thus, the current study suggests that Broca's area represents action hierarchy and that sufficiently long motor training is necessary for it to become sensitive to motor syntax. Therefore, the notion that hierarchical processing in Broca's area is a common function shared between language and music may help to explain the role of Broca's area in action perception. PMID:24478668

Au functionalized ZnO rose-like hierarchical structures and their enhanced NO2 sensing performance

NASA Astrophysics Data System (ADS)

Shingange, K.; Swart, H. C.; Mhlongo, G. H.

2018-04-01

Herein, we present ZnO rose-like hierarchical nanostructures employed as support to Au nanoparticles to produce Au functionalized three dimensional (3D) ZnO hierarchical nanostructures (Au/ZnO) for NO2 detection using a microwave-assisted method. Comparative analysis of NO2 sensing performance between the pristine ZnO and Au/ZnO rose-like structures at 300 °C revealed improved NO2 response and rapid response-recovery times with Au incorporation owing to a combination of high surface accessibility induced by hierarchical nanostructure design and catalytic activity of the small Au nanoparticles. Structural and optical analyses acquired from X-ray diffraction, scanning electron microscopy, transmission electron microscope and photoluminescence spectroscopy were also performed.

The Role of Prototype Learning in Hierarchical Models of Vision

ERIC Educational Resources Information Center

Thomure, Michael David

2014-01-01

I conduct a study of learning in HMAX-like models, which are hierarchical models of visual processing in biological vision systems. Such models compute a new representation for an image based on the similarity of image sub-parts to a number of specific patterns, called prototypes. Despite being a central piece of the overall model, the issue of…

Hierarchical 3D ordered meso-/macroporous metal-organic framework produced through a facile template-free self-assembly

NASA Astrophysics Data System (ADS)

Yang, Xiaoli; Wu, Suilan; Wang, Panhao; Yang, Lin

2018-02-01

The synthesis of well-ordered hierarchical metal-organic frameworks (MOFs) in an efficient manner is a great challenge. Here, a 3D regular ordered meso-/macroporous MOF of Cu-TATAB (referred to as MM-MOF) was synthesized through a facile template-free self-assembly process with pore sizes of 31 nm and 119 nm.

A unique patterned diamond stamp for a periodically hierarchical nanoarray structure.

PubMed

Wang, Yi; Shen, Yanting; Xu, Weiqing; Xu, Shuping; Li, Hongdong

2016-09-23

A diamond stamp with a hierarchical pattern was designed for the direct preparation of a periodic nanoarray structure, which was prepared by the reactive ion etching technique with a hierarchical ultrathin alumina membrane (HUTAM) as a mask. The optimal etching conditions for fabricating the diamond stamp were discussed in order to realize a vertical nanopore structure, avoiding structural damage from lateral etching. By using this diamond stamp, a polymer film with the desired hierarchical nanorod array structure can be obtained easily via the simple stamping process, which greatly simplifies the processing procedure. More importantly, the stamp is reusable because of its super-hardness, which ensures the reproducibility of the nanorod array pattern. Another merit is that the smooth surface of the etched diamond can avoid the use of a release agent. Our results prove that this hard stamp can be used for quick preparation of an elaborate periodic nanoarray structure. This study is significant in that it solves the problems of high cost and easy damage of stamps in nanoimprint lithography, and it might inspire more sophisticated applications of such an ordered structure in nanoplasmonics, biochemical sensing and nanophotonic devices.

Emotional intelligence is a second-stratum factor of intelligence: evidence from hierarchical and bifactor models.

PubMed

MacCann, Carolyn; Joseph, Dana L; Newman, Daniel A; Roberts, Richard D

2014-04-01

This article examines the status of emotional intelligence (EI) within the structure of human cognitive abilities. To evaluate whether EI is a 2nd-stratum factor of intelligence, data were fit to a series of structural models involving 3 indicators each for fluid intelligence, crystallized intelligence, quantitative reasoning, visual processing, and broad retrieval ability, as well as 2 indicators each for emotion perception, emotion understanding, and emotion management. Unidimensional, multidimensional, hierarchical, and bifactor solutions were estimated in a sample of 688 college and community college students. Results suggest adequate fit for 2 models: (a) an oblique 8-factor model (with 5 traditional cognitive ability factors and 3 EI factors) and (b) a hierarchical solution (with cognitive g at the highest level and EI representing a 2nd-stratum factor that loads onto g at λ = .80). The acceptable relative fit of the hierarchical model confirms the notion that EI is a group factor of cognitive ability, marking the expression of intelligence in the emotion domain. The discussion proposes a possible expansion of Cattell-Horn-Carroll theory to include EI as a 2nd-stratum factor of similar standing to factors such as fluid intelligence and visual processing.

Alkali-corrosion synthesis and excellent DSSC performance of novel jujube-like hierarchical TiO2 microspheres

NASA Astrophysics Data System (ADS)

Xiao, Jiajia; Li, Po; Wen, Xiaogang

2018-04-01

Novel jujube-like hierarchical TiO2 microspheres (HTMs) were synthesized by an alkali-corrosion process of titanium phosphate (Ti2O3(H2PO4)2 · 2H2O) microspheres. The hierarchical titanium phosphate microsphere (HTPM) intermediates consisting of nanoflakes with a thickness of 20 nm were firstly prepared by a facile hydrothermal method. After reacting with diluted NaOH at low temperature and atmospheric pressure, followed by subsequent acid washing and a calcination process, the HTPM intermediates were transformed to TiO2 with the microsphere morphology well retained, while the nanoflakes became porous, and some new nanowires were formed between the nanoflakes. Finally, HTMs consisting of porous nanoflakes and nanowires were obtained. The possible growth mechanisms of HTPMs and HTMs are discussed. The HTMs demonstrate high specific surface area and excellent light-scattering ability. The performance of the dye sensitized solar cells (DSSCs) of the HTMs synthesized under different conditions is studied, and a total conversion efficiency of up to 8.93% was obtained. The improved DSSC performance was attributed to the enhanced dye loading, light-scattering, and charge transporting ability of the HTMs with a unique hierarchical nanostructure.

Alkali-corrosion synthesis and excellent DSSC performance of novel jujube-like hierarchical TiO2 microspheres.

PubMed

Xiao, Jiajia; Li, Po; Wen, Xiaogang

2018-04-27

Novel jujube-like hierarchical TiO 2 microspheres (HTMs) were synthesized by an alkali-corrosion process of titanium phosphate (Ti 2 O 3 (H 2 PO 4 ) 2  · 2H 2 O) microspheres. The hierarchical titanium phosphate microsphere (HTPM) intermediates consisting of nanoflakes with a thickness of 20 nm were firstly prepared by a facile hydrothermal method. After reacting with diluted NaOH at low temperature and atmospheric pressure, followed by subsequent acid washing and a calcination process, the HTPM intermediates were transformed to TiO 2 with the microsphere morphology well retained, while the nanoflakes became porous, and some new nanowires were formed between the nanoflakes. Finally, HTMs consisting of porous nanoflakes and nanowires were obtained. The possible growth mechanisms of HTPMs and HTMs are discussed. The HTMs demonstrate high specific surface area and excellent light-scattering ability. The performance of the dye sensitized solar cells (DSSCs) of the HTMs synthesized under different conditions is studied, and a total conversion efficiency of up to 8.93% was obtained. The improved DSSC performance was attributed to the enhanced dye loading, light-scattering, and charge transporting ability of the HTMs with a unique hierarchical nanostructure.

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.

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.

Geographical Characterization of Tunisian Olive Tree Leaves (cv. Chemlali) Using HPLC-ESI-TOF and IT/MS Fingerprinting with Hierarchical Cluster Analysis

PubMed Central

Arráez Román, David; Gómez Caravaca, Ana María; Zarrouk, Mokhtar

2018-01-01

The olive plant has been extensively studied for its nutritional value, whereas its leaves have been specifically recognized as a processing by-product. Leaves are considered by-products of olive farming, representing a significant material arriving to the olive mill. They have been considered for centuries as an important herbal remedy in Mediterranean countries. Their beneficial properties are generally attributed to the presence of a range of phytochemicals such as secoiridoids, triterpenes, lignans, and flavonoids. With the aim to study the impact of geographical location on the phenolic compounds, Olea europaea leaves were handpicked from the Tunisian cultivar “Chemlali” from nine regions in the north, center, and south of Tunisia. The ground leaves were then extracted with methanol : water 80% (v/v) and analyzed by using high-performance liquid chromatography coupled to electrospray time of flight and ion trap mass spectrometry analyzers. A total of 38 compounds could be identified. Their contents showed significant variation among samples from different regions. Hierarchical cluster analysis was applied to highlight similarities in the phytochemical composition observed between the samples of different regions. PMID:29725553

Improving phylogenetic analyses by incorporating additional information from genetic sequence databases.

PubMed

Liang, Li-Jung; Weiss, Robert E; Redelings, Benjamin; Suchard, Marc A

2009-10-01

Statistical analyses of phylogenetic data culminate in uncertain estimates of underlying model parameters. Lack of additional data hinders the ability to reduce this uncertainty, as the original phylogenetic dataset is often complete, containing the entire gene or genome information available for the given set of taxa. Informative priors in a Bayesian analysis can reduce posterior uncertainty; however, publicly available phylogenetic software specifies vague priors for model parameters by default. We build objective and informative priors using hierarchical random effect models that combine additional datasets whose parameters are not of direct interest but are similar to the analysis of interest. We propose principled statistical methods that permit more precise parameter estimates in phylogenetic analyses by creating informative priors for parameters of interest. Using additional sequence datasets from our lab or public databases, we construct a fully Bayesian semiparametric hierarchical model to combine datasets. A dynamic iteratively reweighted Markov chain Monte Carlo algorithm conveniently recycles posterior samples from the individual analyses. We demonstrate the value of our approach by examining the insertion-deletion (indel) process in the enolase gene across the Tree of Life using the phylogenetic software BALI-PHY; we incorporate prior information about indels from 82 curated alignments downloaded from the BAliBASE database.

New insight in magnetic saturation behavior of nickel hierarchical structures

NASA Astrophysics Data System (ADS)

Ma, Ji; Zhang, Jianxing; Liu, Chunting; Chen, Kezheng

2017-09-01

It is unanimously accepted that non-ferromagnetic inclusions in a ferromagnetic system will lower down total saturation magnetization in unit of emu/g. In this study, ;lattice strain; was found to be another key factor to have critical impact on magnetic saturation behavior of the system. The lattice strain determined assembling patterns of primary nanoparticles in hierarchical structures and was intimately related with the formation process of these architectures. Therefore, flower-necklace-like and cauliflower-like nickel hierarchical structures were used as prototype systems to evidence the relationship between assembling patterns of primary nanoparticles and magnetic saturation behaviors of these architectures. It was found that the influence of lattice strain on saturation magnetization outperformed that of non-ferromagnetic inclusions in these hierarchical structures. This will enable new insights into fundamental understanding of related magnetic effects.

Fabrication of malachite with a hierarchical sphere-like architecture.

PubMed

Xu, Jiasheng; Xue, Dongfeng

2005-09-15

Malachite (Cu2(OH)2CO3) with a hierarchical sphere-like architecture has been successfully synthesized via a simple and mild hydrothermal route in the absence of any external inorganic additives or organic structure-directing templates. Powder X-ray diffraction, scanning electron microscopy, and Fourier transmission infrared spectrometry are used to characterize various properties of the obtained malachite samples. The hierarchical malachite particles are uniform spheres with a diameter of 10-20 microm, which are comprised of numerous two-dimensional microplatelets paralleling the sphere surface. The initial concentration of reagents, the hydrothermal reaction time, and temperature are important factors which dominantly affect the evolution of crystal morphologies. The growth of the hierarchical architecture is believed to be a layer-by-layer growth process. Further, copper oxide with the similar morphology can be easily obtained from the as-prepared malachite.

Hierarchical Bayesian Modeling of Fluid-Induced Seismicity

NASA Astrophysics Data System (ADS)

Broccardo, M.; Mignan, A.; Wiemer, S.; Stojadinovic, B.; Giardini, D.

2017-11-01

In this study, we present a Bayesian hierarchical framework to model fluid-induced seismicity. The framework is based on a nonhomogeneous Poisson process with a fluid-induced seismicity rate proportional to the rate of injected fluid. The fluid-induced seismicity rate model depends upon a set of physically meaningful parameters and has been validated for six fluid-induced case studies. In line with the vision of hierarchical Bayesian modeling, the rate parameters are considered as random variables. We develop both the Bayesian inference and updating rules, which are used to develop a probabilistic forecasting model. We tested the Basel 2006 fluid-induced seismic case study to prove that the hierarchical Bayesian model offers a suitable framework to coherently encode both epistemic uncertainty and aleatory variability. Moreover, it provides a robust and consistent short-term seismic forecasting model suitable for online risk quantification and mitigation.

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses.

PubMed

Montenegro-Burke, J Rafael; Phommavongsay, Thiery; Aisporna, Aries E; Huan, Tao; Rinehart, Duane; Forsberg, Erica; Poole, Farris L; Thorgersen, Michael P; Adams, Michael W W; Krantz, Gregory; Fields, Matthew W; Northen, Trent R; Robbins, Paul D; Niedernhofer, Laura J; Lairson, Luke; Benton, H Paul; Siuzdak, Gary

2016-10-04

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses

PubMed Central

2016-01-01

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism. PMID:27560777

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses

DOE PAGES

Montenegro-Burke, J. Rafael; Phommavongsay, Thiery; Aisporna, Aries E.; ...

2016-08-25

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process.more » Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.« less

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses

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

Montenegro-Burke, J. Rafael; Phommavongsay, Thiery; Aisporna, Aries E.

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process.more » Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.« less

The right inferior frontal gyrus processes nested non-local dependencies in music.

PubMed

Cheung, Vincent K M; Meyer, Lars; Friederici, Angela D; Koelsch, Stefan

2018-02-28

Complex auditory sequences known as music have often been described as hierarchically structured. This permits the existence of non-local dependencies, which relate elements of a sequence beyond their temporal sequential order. Previous studies in music have reported differential activity in the inferior frontal gyrus (IFG) when comparing regular and irregular chord-transitions based on theories in Western tonal harmony. However, it is unclear if the observed activity reflects the interpretation of hierarchical structure as the effects are confounded by local irregularity. Using functional magnetic resonance imaging (fMRI), we found that violations to non-local dependencies in nested sequences of three-tone musical motifs in musicians elicited increased activity in the right IFG. This is in contrast to similar studies in language which typically report the left IFG in processing grammatical syntax. Effects of increasing auditory working demands are moreover reflected by distributed activity in frontal and parietal regions. Our study therefore demonstrates the role of the right IFG in processing non-local dependencies in music, and suggests that hierarchical processing in different cognitive domains relies on similar mechanisms that are subserved by domain-selective neuronal subpopulations.

Architecture of the parallel hierarchical network for fast image recognition

NASA Astrophysics Data System (ADS)

Timchenko, Leonid; Wójcik, Waldemar; Kokriatskaia, Natalia; Kutaev, Yuriy; Ivasyuk, Igor; Kotyra, Andrzej; Smailova, Saule

2016-09-01

Multistage integration of visual information in the brain allows humans to respond quickly to most significant stimuli while maintaining their ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing includes main types of cortical multistage convergence. The input images are mapped into a flexible hierarchy that reflects complexity of image data. Procedures of the temporal image decomposition and hierarchy formation are described in mathematical expressions. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image that encapsulates a structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a quick response of the system. The result is presented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match. With regard to the forecasting method, its idea lies in the following. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the parallel-hierarchical network.

Neural basis of hierarchical visual form processing of Japanese Kanji characters.

PubMed

Higuchi, Hiroki; Moriguchi, Yoshiya; Murakami, Hiroki; Katsunuma, Ruri; Mishima, Kazuo; Uno, Akira

2015-12-01

We investigated the neural processing of reading Japanese Kanji characters, which involves unique hierarchical visual processing, including the recognition of visual components specific to Kanji, such as "radicals." We performed functional MRI to measure brain activity in response to hierarchical visual stimuli containing (1) real Kanji characters (complete structure with semantic information), (2) pseudo Kanji characters (subcomponents without complete character structure), (3) artificial characters (character fragments), and (4) checkerboard (simple photic stimuli). As we expected, the peaks of the activation in response to different stimulus types were aligned within the left occipitotemporal visual region along the posterior-anterior axis in order of the structural complexity of the stimuli, from fragments (3) to complete characters (1). Moreover, only the real Kanji characters produced functional connectivity between the left inferotemporal area and the language area (left inferior frontal triangularis), while pseudo Kanji characters induced connectivity between the left inferotemporal area and the bilateral cerebellum and left putamen. Visual processing of Japanese Kanji takes place in the left occipitotemporal cortex, with a clear hierarchy within the region such that the neural activation differentiates the elements in Kanji characters' fragments, subcomponents, and semantics, with different patterns of connectivity to remote regions among the elements.

Logistic Stick-Breaking Process

PubMed Central

Ren, Lu; Du, Lan; Carin, Lawrence; Dunson, David B.

2013-01-01

A logistic stick-breaking process (LSBP) is proposed for non-parametric clustering of general spatially- or temporally-dependent data, imposing the belief that proximate data are more likely to be clustered together. The sticks in the LSBP are realized via multiple logistic regression functions, with shrinkage priors employed to favor contiguous and spatially localized segments. The LSBP is also extended for the simultaneous processing of multiple data sets, yielding a hierarchical logistic stick-breaking process (H-LSBP). The model parameters (atoms) within the H-LSBP are shared across the multiple learning tasks. Efficient variational Bayesian inference is derived, and comparisons are made to related techniques in the literature. Experimental analysis is performed for audio waveforms and images, and it is demonstrated that for segmentation applications the LSBP yields generally homogeneous segments with sharp boundaries. PMID:25258593

Wavelets and molecular structure

NASA Astrophysics Data System (ADS)

Carson, Mike

1996-08-01

The wavelet method offers possibilities for display, editing, and topological comparison of proteins at a user-specified level of detail. Wavelets are a mathematical tool that first found application in signal processing. The multiresolution analysis of a signal via wavelets provides a hierarchical series of `best' lower-resolution approximations. B-spline ribbons model the protein fold, with one control point per residue. Wavelet analysis sets limits on the information required to define the winding of the backbone through space, suggesting a recognizable fold is generated from a number of points equal to 1/4 or less the number of residues. Wavelets applied to surfaces and volumes show promise in structure-based drug design.

Investigation of variety resources and quantitative analysis on Heyin pomegranate in Xingyang City

NASA Astrophysics Data System (ADS)

Li, Wenzeng; Wang, Zhihong

2018-04-01

Various factors that should be considered in variety breeding of Heyin pomegranate, the hierarchical analysis is carried out through analytic hierarchy process (AHP) and its analytic result can be used to help fruit farmers make scientific decision on the variety breeding of pomegranate. In the six main Heyin pomegranate varieties, the ranking weight value of Tunisian soft-seeded pomegranate is 0.3105, which is No.1 in all pomegranate varieties and is obviously better than other varieties in comprehensive feature. It shows that, in the cultivation of pomegranate in Xingyang, the Tunisian soft-seeded pomegranate is the preferred variety for fruit farmers.

HPLC-PDA Combined with Chemometrics for Quantitation of Active Components and Quality Assessment of Raw and Processed Fruits of Xanthium strumarium L.

PubMed

Jiang, Hai; Yang, Liu; Xing, Xudong; Yan, Meiling; Guo, Xinyue; Yang, Bingyou; Wang, Qiuhong; Kuang, Haixue

2018-01-25

As a valuable herbal medicine, the fruits of Xanthium strumarium L. (Xanthii Fructus) have been widely used in raw and processed forms to achieve different therapeutic effects in practice. In this study, a comprehensive strategy was proposed for evaluating the active components in 30 batches of raw and processed Xanthii Fructus (RXF and PXF) samples, based on high-performance liquid chromatography coupled with photodiode array detection (HPLC-PDA). Twelve common peaks were detected and eight compounds of caffeoylquinic acids were simultaneously quantified in RXF and PXF. All the analytes were detected with satisfactory linearity (R² > 0.9991) over wide concentration ranges. Simultaneously, the chemically latent information was revealed by hierarchical cluster analysis (HCA) and principal component analysis (PCA). The results suggest that there were significant differences between RXF and PXF from different regions in terms of the content of eight caffeoylquinic acids. Potential chemical markers for XF were found during processing by chemometrics.

Towards health care process description framework: an XML DTD design.

PubMed Central

Staccini, P.; Joubert, M.; Quaranta, J. F.; Aymard, S.; Fieschi, D.; Fieschi, M.

2001-01-01

The development of health care and hospital information systems has to meet users needs as well as requirements such as the tracking of all care activities and the support of quality improvement. The use of process-oriented analysis is of-value to provide analysts with: (i) a systematic description of activities; (ii) the elicitation of the useful data to perform and record care tasks; (iii) the selection of relevant decision-making support. But paper-based tools are not a very suitable way to manage and share the documentation produced during this step. The purpose of this work is to propose a method to implement the results of process analysis according to XML techniques (eXtensible Markup Language). It is based on the IDEF0 activity modeling language (Integration DEfinition for Function modeling). A hierarchical description of a process and its components has been defined through a flat XML file with a grammar of proper metadata tags. Perspectives of this method are discussed. PMID:11825265

Distributed Scene Analysis For Autonomous Road Vehicle Guidance

NASA Astrophysics Data System (ADS)

Mysliwetz, Birger D.; Dickmanns, E. D.

1987-01-01

An efficient distributed processing scheme has been developed for visual road boundary tracking by 'VaMoRs', a testbed vehicle for autonomous mobility and computer vision. Ongoing work described here is directed to improving the robustness of the road boundary detection process in the presence of shadows, ill-defined edges and other disturbing real world effects. The system structure and the techniques applied for real-time scene analysis are presented along with experimental results. All subfunctions of road boundary detection for vehicle guidance, such as edge extraction, feature aggregation and camera pointing control, are executed in parallel by an onboard multiprocessor system. On the image processing level local oriented edge extraction is performed in multiple 'windows', tighly controlled from a hierarchically higher, modelbased level. The interpretation process involving a geometric road model and the observer's relative position to the road boundaries is capable of coping with ambiguity in measurement data. By using only selected measurements to update the model parameters even high noise levels can be dealt with and misleading edges be rejected.

An effective hierarchical model for the biomolecular covalent bond: an approach integrating artificial chemistry and an actual terrestrial life system.

PubMed

Oohashi, Tsutomu; Ueno, Osamu; Maekawa, Tadao; Kawai, Norie; Nishina, Emi; Honda, Manabu

2009-01-01

Under the AChem paradigm and the programmed self-decomposition (PSD) model, we propose a hierarchical model for the biomolecular covalent bond (HBCB model). This model assumes that terrestrial organisms arrange their biomolecules in a hierarchical structure according to the energy strength of their covalent bonds. It also assumes that they have evolutionarily selected the PSD mechanism of turning biological polymers (BPs) into biological monomers (BMs) as an efficient biomolecular recycling strategy We have examined the validity and effectiveness of the HBCB model by coordinating two complementary approaches: biological experiments using existent terrestrial life, and simulation experiments using an AChem system. Biological experiments have shown that terrestrial life possesses a PSD mechanism as an endergonic, genetically regulated process and that hydrolysis, which decomposes a BP into BMs, is one of the main processes of such a mechanism. In simulation experiments, we compared different virtual self-decomposition processes. The virtual species in which the self-decomposition process mainly involved covalent bond cleavage from a BP to BMs showed evolutionary superiority over other species in which the self-decomposition process involved cleavage from BP to classes lower than BM. These converging findings strongly support the existence of PSD and the validity and effectiveness of the HBCB model.

An exactly solvable model of hierarchical self-assembly

NASA Astrophysics Data System (ADS)

Dudowicz, Jacek; Douglas, Jack F.; Freed, Karl F.

2009-06-01

Many living and nonliving structures in the natural world form by hierarchical organization, but physical theories that describe this type of organization are scarce. To address this problem, a model of equilibrium self-assembly is formulated in which dynamically associating species organize into hierarchical structures that preserve their shape at each stage of assembly. In particular, we consider symmetric m-gons that associate at their vertices into Sierpinski gasket structures involving the hierarchical association of triangles, squares, hexagons, etc., at their corner vertices, thereby leading to fractal structures after many generations of assembly. This rather idealized model of hierarchical assembly yields an infinite sequence of self-assembly transitions as the morphology progressively organizes to higher levels of the hierarchy, and these structures coexists at dynamic equilibrium, as found in real hierarchically self-assembling systems such as amyloid fiber forming proteins. Moreover, the transition sharpness progressively grows with increasing m, corresponding to larger and larger loops in the assembled structures. Calculations are provided for several basic thermodynamic properties (including the order parameters for assembly for each stage of the hierarchy, average mass of clusters, specific heat, transition sharpness, etc.) that are required for characterizing the interaction parameters governing this type of self-assembly and for elucidating other basic qualitative aspects of these systems. Our idealized model of hierarchical assembly gives many insights into this ubiquitous type of self-organization process.

The Hierarchical Cortical Organization of Human Speech Processing

PubMed Central

de Heer, Wendy A.; Huth, Alexander G.; Griffiths, Thomas L.

2017-01-01

Speech comprehension requires that the brain extract semantic meaning from the spectral features represented at the cochlea. To investigate this process, we performed an fMRI experiment in which five men and two women passively listened to several hours of natural narrative speech. We then used voxelwise modeling to predict BOLD responses based on three different feature spaces that represent the spectral, articulatory, and semantic properties of speech. The amount of variance explained by each feature space was then assessed using a separate validation dataset. Because some responses might be explained equally well by more than one feature space, we used a variance partitioning analysis to determine the fraction of the variance that was uniquely explained by each feature space. Consistent with previous studies, we found that speech comprehension involves hierarchical representations starting in primary auditory areas and moving laterally on the temporal lobe: spectral features are found in the core of A1, mixtures of spectral and articulatory in STG, mixtures of articulatory and semantic in STS, and semantic in STS and beyond. Our data also show that both hemispheres are equally and actively involved in speech perception and interpretation. Further, responses as early in the auditory hierarchy as in STS are more correlated with semantic than spectral representations. These results illustrate the importance of using natural speech in neurolinguistic research. Our methodology also provides an efficient way to simultaneously test multiple specific hypotheses about the representations of speech without using block designs and segmented or synthetic speech. SIGNIFICANCE STATEMENT To investigate the processing steps performed by the human brain to transform natural speech sound into meaningful language, we used models based on a hierarchical set of speech features to predict BOLD responses of individual voxels recorded in an fMRI experiment while subjects listened to natural speech. Both cerebral hemispheres were actively involved in speech processing in large and equal amounts. Also, the transformation from spectral features to semantic elements occurs early in the cortical speech-processing stream. Our experimental and analytical approaches are important alternatives and complements to standard approaches that use segmented speech and block designs, which report more laterality in speech processing and associated semantic processing to higher levels of cortex than reported here. PMID:28588065

Towards a multi-level approach to the emergence of meaning processes in living systems.

PubMed

Queiroz, João; El-Hani, Charbel Niño

2006-09-01

Any description of the emergence and evolution of different types of meaning processes (semiosis, sensu C.S.Peirce) in living systems must be supported by a theoretical framework which makes it possible to understand the nature and dynamics of such processes. Here we propose that the emergence of semiosis of different kinds can be understood as resulting from fundamental interactions in a triadically-organized hierarchical process. To grasp these interactions, we develop a model grounded on Stanley Salthe's hierarchical structuralism. This model can be applied to establish, in a general sense, a set of theoretical constraints for explaining the instantiation of different kinds of meaning processes (iconic, indexical, symbolic) in semiotic systems. We use it to model a semiotic process in the immune system, namely, B-cell activation, in order to offer insights into the heuristic role it can play in the development of explanations for specific semiotic processes.

A hierarchical model of the evolution of human brain specializations

PubMed Central

Barrett, H. Clark

2012-01-01

The study of information-processing adaptations in the brain is controversial, in part because of disputes about the form such adaptations might take. Many psychologists assume that adaptations come in two kinds, specialized and general-purpose. Specialized mechanisms are typically thought of as innate, domain-specific, and isolated from other brain systems, whereas generalized mechanisms are developmentally plastic, domain-general, and interactive. However, if brain mechanisms evolve through processes of descent with modification, they are likely to be heterogeneous, rather than coming in just two kinds. They are likely to be hierarchically organized, with some design features widely shared across brain systems and others specific to particular processes. Also, they are likely to be largely developmentally plastic and interactive with other brain systems, rather than canalized and isolated. This article presents a hierarchical model of brain specialization, reviewing evidence for the model from evolutionary developmental biology, genetics, brain mapping, and comparative studies. Implications for the search for uniquely human traits are discussed, along with ways in which conventional views of modularity in psychology may need to be revised. PMID:22723350

A simplified computational memory model from information processing.

PubMed

Zhang, Lanhua; Zhang, Dongsheng; Deng, Yuqin; Ding, Xiaoqian; Wang, Yan; Tang, Yiyuan; Sun, Baoliang

2016-11-23

This paper is intended to propose a computational model for memory from the view of information processing. The model, called simplified memory information retrieval network (SMIRN), is a bi-modular hierarchical functional memory network by abstracting memory function and simulating memory information processing. At first meta-memory is defined to express the neuron or brain cortices based on the biology and graph theories, and we develop an intra-modular network with the modeling algorithm by mapping the node and edge, and then the bi-modular network is delineated with intra-modular and inter-modular. At last a polynomial retrieval algorithm is introduced. In this paper we simulate the memory phenomena and functions of memorization and strengthening by information processing algorithms. The theoretical analysis and the simulation results show that the model is in accordance with the memory phenomena from information processing view.

Hydrothermal synthesis and photoluminescent properties of hierarchical GdPO4·H2O:Ln3+ (Ln3+ = Eu3+, Ce3+, Tb3+) flower-like clusters

NASA Astrophysics Data System (ADS)

Amurisana, Bao.; Zhiqiang, Song.; Haschaolu, O.; Yi, Chen; Tegus, O.

2018-02-01

3D hierarchical GdPO4·H2O:Ln3+ (Ln3+ = Eu3+, Ce3+, Tb3+) flower clusters were successfully prepared on glass slide substrate by a simple, economical hydrothermal process with the assistance of disodium ethylenediaminetetraacetic acid (Na2H2L, where L4- = (CH2COO)2N(CH2)2N(CH2COO)24-). In this process, Na2H2L was used as both a chelating agent and a structure-director. The hierarchical flower clusters have an average diameter of 7-12 μm and are composed of well-aligned microrods. The influence of the molar ratio of Na2H2L/Gd3+ and reaction time on the morphology was systematically studied. A possible crystal growth and formation mechanism of hierarchical flower clusters is proposed based on the evolution of morphology as a function of reaction time. The self-assembled GdPO4·H2O:Ln3+ superstructures exhibit strong orange-red (Eu3+, 5D0 → 7F1), green (Tb3+, 5D4 → 7F5) and near ultraviolet emissions (Ce3+, 5d → 7F5/2) under ultraviolet excitation, respectively. This study may provide a new channel for building hierarchically superstructued oxide micro/nanomaterials with optical and new properties.

Novel hierarchical tantalum oxide-PDMS hybrid coating for medical implants: One pot synthesis, characterization and modulation of fibroblast proliferation.

PubMed

Tran, Phong A; Fox, Kate; Tran, Nhiem

2017-01-01

Surface properties such as morphology, roughness and charge density have a strong influence on the interaction of biomaterials and cells. Hierarchical materials with a combination of micron/submicron and nanoscale features for coating of medical implants could therefore have significant potential to modulate cellular responses and eventually improve the performance of the implants. In this study, we report a simple, one pot wet chemistry preparation of a hybrid coating system with hierarchical surface structures consisting of polydimethylsiloxane (PDMS) and tantalum oxide. Medical grade, amine functional PDMS was mixed with tantalum ethoxide which subsequently formed Ta 2 O 5 in situ through hydrolysis and condensation during coating process. The coatings were characterized by SEM, EDS, XPS, confocal scanning microscopy, contact angle measurement and in vitro cell culture. Varying PDMS and tantalum ethoxide ratios resulted in coatings of different surface textures ranging from smooth to submicro- and nano-structured. Strikingly, hierarchical surfaces containing both microscale (1-1.5μm) and nanoscale (86-163nm) particles were found on coatings synthesized with 20% and 40% (v/v) tantalum ethoxide. The coatings were similar in term of hydrophobicity but showed different surface roughness and chemical composition. Importantly, higher cell proliferation was observed on hybrid surface with hierarchical structures compared to pure PDMS or pure tantalum oxide. The coating process is simple, versatile, carried out under ambient condition and requires no special equipment. Copyright © 2016 Elsevier Inc. All rights reserved.

Social Influence on Information Technology Adoption and Sustained Use in Healthcare: A Hierarchical Bayesian Learning Method Analysis

ERIC Educational Resources Information Center

Hao, Haijing

2013-01-01

Information technology adoption and diffusion is currently a significant challenge in the healthcare delivery setting. This thesis includes three papers that explore social influence on information technology adoption and sustained use in the healthcare delivery environment using conventional regression models and novel hierarchical Bayesian…

A Comparison of Techniques for Handling and Assessing the Influence of Mobility on Student Achievement

ERIC Educational Resources Information Center

Smith, Lindsey J. Wolff; Beretvas, S. Natasha

2017-01-01

Conventional multilevel modeling works well with purely hierarchical data; however, pure hierarchies rarely exist in real datasets. Applied researchers employ ad hoc procedures to create purely hierarchical data. For example, applied educational researchers either delete mobile participants' data from the analysis or identify the student only with…

An Analysis of Prospective Teachers' Knowledge for Constructing Concept Maps

ERIC Educational Resources Information Center

Subramaniam, Karthigeyan; Esprívalo Harrell, Pamela

2015-01-01

Background: Literature contends that a teacher's knowledge of concept map-based tasks influence how their students perceive the task and execute the creation of acceptable concept maps. Teachers who are skilled concept mappers are able to (1) understand and apply the operational terms to construct a hierarchical/non-hierarchical concept map; (2)…

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...

Hierarchical Multiple Regression in Counseling Research: Common Problems and Possible Remedies.

ERIC Educational Resources Information Center

Petrocelli, John V.

2003-01-01

A brief content analysis was conducted on the use of hierarchical regression in counseling research published in the "Journal of Counseling Psychology" and the "Journal of Counseling & Development" during the years 1997-2001. Common problems are cited and possible remedies are described. (Contains 43 references and 3 tables.) (Author)

Hierarchical organization of functional connectivity in the mouse brain: a complex network approach.

PubMed

Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

2016-08-18

This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.

Hierarchical organization of functional connectivity in the mouse brain: a complex network approach

NASA Astrophysics Data System (ADS)

Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

2016-08-01

This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.

Fuzzy logic control and optimization system

DOEpatents

Lou, Xinsheng [West Hartford, CT

2012-04-17

A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

Glycol-modified silanes: novel possibilities for the synthesis of hierarchically organized (hybrid) porous materials.

PubMed

Hartmann, Sarah; Brandhuber, Doris; Hüsing, Nicola

2007-09-01

The preparation of porous hierarchical architectures that have structural features spanning from the nanometer to micrometer and even larger dimensions and that exhibit certain functionalities is one of the new challenging frontiers in materials chemistry. The sol-gel process is one of the most promising synthesis routes toward such materials because it not only offers the possibility to incorporate organic functions into the porous host but also offers the possibility to deliberately tailor the pore structure. In this Account, the opportunities given by the application of novel diol-modified silanes are discussed for the synthesis of hierarchically organized inorganic and also inorganic-organic porous monoliths.

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.

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).

Hierarchical Kohonenen net for anomaly detection in network security.

PubMed

Sarasamma, Suseela T; Zhu, Qiuming A; Huff, Julie

2005-04-01

A novel multilevel hierarchical Kohonen Net (K-Map) for an intrusion detection system is presented. Each level of the hierarchical map is modeled as a simple winner-take-all K-Map. One significant advantage of this multilevel hierarchical K-Map is its computational efficiency. Unlike other statistical anomaly detection methods such as nearest neighbor approach, K-means clustering or probabilistic analysis that employ distance computation in the feature space to identify the outliers, our approach does not involve costly point-to-point computation in organizing the data into clusters. Another advantage is the reduced network size. We use the classification capability of the K-Map on selected dimensions of data set in detecting anomalies. Randomly selected subsets that contain both attacks and normal records from the KDD Cup 1999 benchmark data are used to train the hierarchical net. We use a confidence measure to label the clusters. Then we use the test set from the same KDD Cup 1999 benchmark to test the hierarchical net. We show that a hierarchical K-Map in which each layer operates on a small subset of the feature space is superior to a single-layer K-Map operating on the whole feature space in detecting a variety of attacks in terms of detection rate as well as false positive rate.

The Topic Analysis of Hospice Care Research Using Co-word Analysis and GHSOM

NASA Astrophysics Data System (ADS)

Yang, Yu-Hsiang; Bhikshu, Huimin; Tsaih, Rua-Huan

The purpose of this study was to propose a multi-layer topic map analysis of palliative care research using co-word analysis of informetrics with Growing Hierarchical Self-Organizing Map (GHSOM). The topic map illustrated the delicate intertwining of subject areas and provided a more explicit illustration of the concepts within each subject area. We applied GHSOM, a text-mining Neural Networks tool, to obtain a hierarchical topic map. The result of the topic map may indicate that the subject area of health care science and service played an importance role in multidiscipline within the research related to palliative care.

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/ .

Understanding the mental lexicon through neglect dyslexia: a study on compound noun reading.

PubMed

Marelli, Marco; Aggujaro, Silvia; Molteni, Franco; Luzzatti, Claudio

2013-04-01

The present study employs neglect dyslexia (ND) as an experimental model to study compound-word processing; in particular, it investigates whether compound constituents are hierarchically organized at mental level and addresses the possibility of whole-word representation. Seven Italian-speaking patients suffering from ND participated in a word naming task. Both left-headed (pescespada, swordfish) and right-headed (astronave, spaceship) Italian compound nouns were used as stimuli. Non-existent compounds, which were generated by substituting the leftmost constituent of a compound with an orthographically similar word (e.g., *pestespada, *plaguesword), were also employed. A significant headedness effect emerged in the group analysis: patients read left-headed compounds better than right-headed compounds. A significant lexicality effect was also found: the participants read real compounds better than their non-existent compound pairs. Moreover, logit mixed-effects analyses indicated a left-hand constituent frequency effect. Results are discussed in terms of hierarchical representation of compounds and direct access to compound lemma nodes.

Moving template analysis of crack growth. 1: Procedure development

NASA Astrophysics Data System (ADS)

Padovan, Joe; Guo, Y. H.

1994-06-01

Based on a moving template procedure, this two part series will develop a method to follow the crack tip physics in a self-adaptive manner which provides a uniformly accurate prediction of crack growth. For multiple crack environments, this is achieved by attaching a moving template to each crack tip. The templates are each individually oriented to follow the associated growth orientation and rate. In this part, the essentials of the procedure are derived for application to fatigue crack environments. Overall the scheme derived possesses several hierarchical levels, i.e. the global model, the interpolatively tied moving template, and a multilevel element death option to simulate the crack wake. To speed up computation, the hierarchical polytree scheme is used to reorganize the global stiffness inversion process. In addition to developing the various features of the scheme, the accuracy of predictions for various crack lengths is also benchmarked. Part 2 extends the scheme to multiple crack problems. Extensive benchmarking is also presented to verify the scheme.

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.

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.

Hierarchical Process Control of Chemical Vapor Infiltration.

DTIC Science & Technology

1995-05-31

convergence artificial neural network and used it to discover improved regions of the CVI processing parameter space; also, the Technology Assessment...identify in situ process sensors of considerable promise and as artificial neural network training pairs.

Drifting through Basic Subprocesses of Reading: A Hierarchical Diffusion Model Analysis of Age Effects on Visual Word Recognition

PubMed Central

Froehlich, Eva; Liebig, Johanna; Ziegler, Johannes C.; Braun, Mario; Lindenberger, Ulman; Heekeren, Hauke R.; Jacobs, Arthur M.

2016-01-01

Reading is one of the most popular leisure activities and it is routinely performed by most individuals even in old age. Successful reading enables older people to master and actively participate in everyday life and maintain functional independence. Yet, reading comprises a multitude of subprocesses and it is undoubtedly one of the most complex accomplishments of the human brain. Not surprisingly, findings of age-related effects on word recognition and reading have been partly contradictory and are often confined to only one of four central reading subprocesses, i.e., sublexical, orthographic, phonological and lexico-semantic processing. The aim of the present study was therefore to systematically investigate the impact of age on each of these subprocesses. A total of 1,807 participants (young, N = 384; old, N = 1,423) performed four decision tasks specifically designed to tap one of the subprocesses. To account for the behavioral heterogeneity in older adults, this subsample was split into high and low performing readers. Data were analyzed using a hierarchical diffusion modeling approach, which provides more information than standard response time/accuracy analyses. Taking into account incorrect and correct response times, their distributions and accuracy data, hierarchical diffusion modeling allowed us to differentiate between age-related changes in decision threshold, non-decision time and the speed of information uptake. We observed longer non-decision times for older adults and a more conservative decision threshold. More importantly, high-performing older readers outperformed younger adults at the speed of information uptake in orthographic and lexico-semantic processing, whereas a general age-disadvantage was observed at the sublexical and phonological levels. Low-performing older readers were slowest in information uptake in all four subprocesses. Discussing these results in terms of computational models of word recognition, we propose age-related disadvantages for older readers to be caused by inefficiencies in temporal sampling and activation and/or inhibition processes. PMID:27933029

Drifting through Basic Subprocesses of Reading: A Hierarchical Diffusion Model Analysis of Age Effects on Visual Word Recognition.

PubMed

Froehlich, Eva; Liebig, Johanna; Ziegler, Johannes C; Braun, Mario; Lindenberger, Ulman; Heekeren, Hauke R; Jacobs, Arthur M

2016-01-01

Reading is one of the most popular leisure activities and it is routinely performed by most individuals even in old age. Successful reading enables older people to master and actively participate in everyday life and maintain functional independence. Yet, reading comprises a multitude of subprocesses and it is undoubtedly one of the most complex accomplishments of the human brain. Not surprisingly, findings of age-related effects on word recognition and reading have been partly contradictory and are often confined to only one of four central reading subprocesses, i.e., sublexical, orthographic, phonological and lexico-semantic processing. The aim of the present study was therefore to systematically investigate the impact of age on each of these subprocesses. A total of 1,807 participants (young, N = 384; old, N = 1,423) performed four decision tasks specifically designed to tap one of the subprocesses. To account for the behavioral heterogeneity in older adults, this subsample was split into high and low performing readers. Data were analyzed using a hierarchical diffusion modeling approach, which provides more information than standard response time/accuracy analyses. Taking into account incorrect and correct response times, their distributions and accuracy data, hierarchical diffusion modeling allowed us to differentiate between age-related changes in decision threshold, non-decision time and the speed of information uptake. We observed longer non-decision times for older adults and a more conservative decision threshold. More importantly, high-performing older readers outperformed younger adults at the speed of information uptake in orthographic and lexico-semantic processing, whereas a general age-disadvantage was observed at the sublexical and phonological levels. Low-performing older readers were slowest in information uptake in all four subprocesses. Discussing these results in terms of computational models of word recognition, we propose age-related disadvantages for older readers to be caused by inefficiencies in temporal sampling and activation and/or inhibition processes.

Automation process for morphometric analysis of volumetric CT data from pulmonary vasculature in rats.

PubMed

Shingrani, Rahul; Krenz, Gary; Molthen, Robert

2010-01-01

With advances in medical imaging scanners, it has become commonplace to generate large multidimensional datasets. These datasets require tools for a rapid, thorough analysis. To address this need, we have developed an automated algorithm for morphometric analysis incorporating A Visualization Workshop computational and image processing libraries for three-dimensional segmentation, vascular tree generation and structural hierarchical ordering with a two-stage numeric optimization procedure for estimating vessel diameters. We combine this new technique with our mathematical models of pulmonary vascular morphology to quantify structural and functional attributes of lung arterial trees. Our physiological studies require repeated measurements of vascular structure to determine differences in vessel biomechanical properties between animal models of pulmonary disease. Automation provides many advantages including significantly improved speed and minimized operator interaction and biasing. The results are validated by comparison with previously published rat pulmonary arterial micro-CT data analysis techniques, in which vessels were manually mapped and measured using intense operator intervention. Published by Elsevier Ireland Ltd.

Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

PubMed

Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

2010-12-01

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies conservation planning. Journal compilation © 2010 Society for Conservation Biology. No claim to original US government works.

A holistic framework for design of cost-effective minimum water utilization network.

PubMed

Wan Alwi, S R; Manan, Z A; Samingin, M H; Misran, N

2008-07-01

Water pinch analysis (WPA) is a well-established tool for the design of a maximum water recovery (MWR) network. MWR, which is primarily concerned with water recovery and regeneration, only partly addresses water minimization problem. Strictly speaking, WPA can only lead to maximum water recovery targets as opposed to the minimum water targets as widely claimed by researchers over the years. The minimum water targets can be achieved when all water minimization options including elimination, reduction, reuse/recycling, outsourcing and regeneration have been holistically applied. Even though WPA has been well established for synthesis of MWR network, research towards holistic water minimization has lagged behind. This paper describes a new holistic framework for designing a cost-effective minimum water network (CEMWN) for industry and urban systems. The framework consists of five key steps, i.e. (1) Specify the limiting water data, (2) Determine MWR targets, (3) Screen process changes using water management hierarchy (WMH), (4) Apply Systematic Hierarchical Approach for Resilient Process Screening (SHARPS) strategy, and (5) Design water network. Three key contributions have emerged from this work. First is a hierarchical approach for systematic screening of process changes guided by the WMH. Second is a set of four new heuristics for implementing process changes that considers the interactions among process changes options as well as among equipment and the implications of applying each process change on utility targets. Third is the SHARPS cost-screening technique to customize process changes and ultimately generate a minimum water utilization network that is cost-effective and affordable. The CEMWN holistic framework has been successfully implemented on semiconductor and mosque case studies and yielded results within the designer payback period criterion.

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.

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.

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 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.

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.

An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data.

PubMed

Hsu, Arthur L; Tang, Sen-Lin; Halgamuge, Saman K

2003-11-01

Current Self-Organizing Maps (SOMs) approaches to gene expression pattern clustering require the user to predefine the number of clusters likely to be expected. Hierarchical clustering methods used in this area do not provide unique partitioning of data. We describe an unsupervised dynamic hierarchical self-organizing approach, which suggests an appropriate number of clusters, to perform class discovery and marker gene identification in microarray data. In the process of class discovery, the proposed algorithm identifies corresponding sets of predictor genes that best distinguish one class from other classes. The approach integrates merits of hierarchical clustering with robustness against noise known from self-organizing approaches. The proposed algorithm applied to DNA microarray data sets of two types of cancers has demonstrated its ability to produce the most suitable number of clusters. Further, the corresponding marker genes identified through the unsupervised algorithm also have a strong biological relationship to the specific cancer class. The algorithm tested on leukemia microarray data, which contains three leukemia types, was able to determine three major and one minor cluster. Prediction models built for the four clusters indicate that the prediction strength for the smaller cluster is generally low, therefore labelled as uncertain cluster. Further analysis shows that the uncertain cluster can be subdivided further, and the subdivisions are related to two of the original clusters. Another test performed using colon cancer microarray data has automatically derived two clusters, which is consistent with the number of classes in data (cancerous and normal). JAVA software of dynamic SOM tree algorithm is available upon request for academic use. A comparison of rectangular and hexagonal topologies for GSOM is available from http://www.mame.mu.oz.au/mechatronics/journalinfo/Hsu2003supp.pdf

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.

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.

Fabrication and condensation characteristics of metallic superhydrophobic surface with hierarchical micro-nano structures

NASA Astrophysics Data System (ADS)

Chu, Fuqiang; Wu, Xiaomin

2016-05-01

Metallic superhydrophobic surfaces have various applications in aerospace, refrigeration and other engineering fields due to their excellent water repellent characteristics. This study considers a simple but widely applicable fabrication method using a two simultaneous chemical reactions method to prepare the acid-salt mixed solutions to process the metal surfaces with surface deposition and surface etching to construct hierarchical micro-nano structures on the surface and then modify the surface with low surface-energy materials. Al-based and Cu-based superhydrophobic surfaces were fabricated using this method. The Al-based superhydrophobic surface had a water contact angle of 164° with hierarchical micro-nano structures similar to the lotus leaves. The Cu-based surface had a water contact angle of 157° with moss-like hierarchical micro-nano structures. Droplet condensation experiments were also performed on these two superhydrophobic surfaces to investigate their condensation characteristics. The results show that the Al-based superhydrophobic surface has lower droplet density, higher droplet jumping probability, slower droplet growth rate and lower surface coverage due to the more structured hierarchical structures.

Growth of hierarchical GaN nanowires for optoelectronic device applications

NASA Astrophysics Data System (ADS)

Raj, Rishabh; Vignesh, Veeramuthu; Ra, Yong-Ho; Nirmala, Rajkumar; Lee, Cheul-Ro; Navamathavan, Rangaswamy

2017-01-01

Gallium nitride nanostructures have been receiving considerable attention as building blocks for nanophotonic technologies due to their unique high aspect ratios, promising the realization of photonic and biological nanodevices such as blue light emitting diodes (LEDs), short-wavelength ultraviolet nanolasers, and nanofluidic biochemical sensors. We report on the growth of hierarchical GaN nanowires (NWs) by dynamically adjusting the growth parameters using the pulsed flow metal-organic chemical vapor deposition technique. We carried out two step growth processes to grow hierarchical GaN NWs. In the first step, the GaN NWs were grown at 950°C, and in the second, we suitably decreased the growth temperature to 630°C and 710°C to grow the hierarchical structures. The surface morphology and optical characterization of the grown GaN NWs were studied by field-emission scanning electron microscopy, high-resolution transmission electron microscopy, photoluminescence, and cathodoluminescence measurements. These kinds of hierarchical GaN NWs are promising for allowing flat band quantum structures that are shown to improve the efficiency of LEDs.

Selective hierarchical patterning of silicon nanostructures via soft nanostencil lithography

NASA Astrophysics Data System (ADS)

Du, Ke; Ding, Junjun; Wathuthanthri, Ishan; Choi, Chang-Hwan

2017-11-01

It is challenging to hierarchically pattern high-aspect-ratio nanostructures on microstructures using conventional lithographic techniques, where photoresist (PR) film is not able to uniformly cover on the microstructures as the aspect ratio increases. Such non-uniformity causes poor definition of nanopatterns over the microstructures. Nanostencil lithography can provide an alternative means to hierarchically construct nanostructures on microstructures via direct deposition or plasma etching through a free-standing nanoporous membrane. In this work, we demonstrate the multiscale hierarchical fabrication of high-aspect-ratio nanostructures on microstructures of silicon using a free-standing nanostencil, which is a nanoporous membrane consisting of metal (Cr), PR, and anti-reflective coating. The nanostencil membrane is used as a deposition mask to define Cr nanodot patterns on the predefined silicon microstructures. Then, deep reactive ion etching is used to hierarchically create nanostructures on the microstructures using the Cr nanodots as an etch mask. With simple modification of the main fabrication processes, high-aspect-ratio nanopillars are selectively defined only on top of the microstructures, on bottom, or on both top and bottom.

Chemical grafting of the superhydrophobic surface on copper with hierarchical microstructure and its formation mechanism

NASA Astrophysics Data System (ADS)

Cai, Junyan; Wang, Shuhui; Zhang, Junhong; Liu, Yang; Hang, Tao; Ling, Huiqin; Li, Ming

2018-04-01

In this paper, a superhydrophobic surface with hierarchical structure was fabricated by chemical deposition of Cu micro-cones array, followed by chemical grafting of poly(methyl methacrylate) (PMMA). Water contact measurements give contact angle of 131.0° on these surfaces after PMMA grafting of 2 min and 165.2° after 6 min. The superhydrophobicity results from two factors: (1) the hierarchical structure due to Cu micro-cones array and the second level structure caused by intergranular corrosion during grafting of PMMA (confirmed by the scanning electron microscopy) and (2) the chemical modification of a low surface energy PMMA layer (confirmed by Fourier transform infrared spectrometer and X-ray photoelectron spectroscopy). In the chemical grafting process, the spontaneous reduction of nitrobenzene diazonium (NBD) tetrafluoroborate not only causes the corrosion of the Cu surface that leads to a hierarchical structure, but also initiates the polymerization of methyl methacrylate (MMA) monomers and thus the low free energy surface. Such a robust approach to fabricate the hierarchical structured surface with superhydrophobicity is expected to have practical application in anti-corrosion industry.

Selective hierarchical patterning of silicon nanostructures via soft nanostencil lithography.

PubMed

Du, Ke; Ding, Junjun; Wathuthanthri, Ishan; Choi, Chang-Hwan

2017-11-17

It is challenging to hierarchically pattern high-aspect-ratio nanostructures on microstructures using conventional lithographic techniques, where photoresist (PR) film is not able to uniformly cover on the microstructures as the aspect ratio increases. Such non-uniformity causes poor definition of nanopatterns over the microstructures. Nanostencil lithography can provide an alternative means to hierarchically construct nanostructures on microstructures via direct deposition or plasma etching through a free-standing nanoporous membrane. In this work, we demonstrate the multiscale hierarchical fabrication of high-aspect-ratio nanostructures on microstructures of silicon using a free-standing nanostencil, which is a nanoporous membrane consisting of metal (Cr), PR, and anti-reflective coating. The nanostencil membrane is used as a deposition mask to define Cr nanodot patterns on the predefined silicon microstructures. Then, deep reactive ion etching is used to hierarchically create nanostructures on the microstructures using the Cr nanodots as an etch mask. With simple modification of the main fabrication processes, high-aspect-ratio nanopillars are selectively defined only on top of the microstructures, on bottom, or on both top and bottom.

Application of Bayesian inference to the study of hierarchical organization in self-organized complex adaptive systems

NASA Astrophysics Data System (ADS)

Knuth, K. H.

2001-05-01

We consider the application of Bayesian inference to the study of self-organized structures in complex adaptive systems. In particular, we examine the distribution of elements, agents, or processes in systems dominated by hierarchical structure. We demonstrate that results obtained by Caianiello [1] on Hierarchical Modular Systems (HMS) can be found by applying Jaynes' Principle of Group Invariance [2] to a few key assumptions about our knowledge of hierarchical organization. Subsequent application of the Principle of Maximum Entropy allows inferences to be made about specific systems. The utility of the Bayesian method is considered by examining both successes and failures of the hierarchical model. We discuss how Caianiello's original statements suffer from the Mind Projection Fallacy [3] and we restate his assumptions thus widening the applicability of the HMS model. The relationship between inference and statistical physics, described by Jaynes [4], is reiterated with the expectation that this realization will aid the field of complex systems research by moving away from often inappropriate direct application of statistical mechanics to a more encompassing inferential methodology.

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

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

Hierarchical Mesoporous Zinc-Nickel-Cobalt Ternary Oxide Nanowire Arrays on Nickel Foam as High-Performance Electrodes for Supercapacitors.

PubMed

Wu, Chun; Cai, Junjie; Zhang, Qiaobao; Zhou, Xiang; Zhu, Ying; Shen, Pei Kang; Zhang, Kaili

2015-12-09

Nickel foam supported hierarchical mesoporous Zn-Ni-Co ternary oxide (ZNCO) nanowire arrays are synthesized by a simple two-step approach including a hydrothermal method and subsequent calcination process and directly utilized for supercapacitive investigation for the first time. The nickel foam supported hierarchical mesoporous ZNCO nanowire arrays possess an ultrahigh specific capacitance value of 2481.8 F g(-1) at 1 A g(-1) and excellent rate capability of about 91.9% capacitance retention at 5 A g(-1). More importantly, an asymmetric supercapacitor with a high energy density (35.6 Wh kg(-1)) and remarkable cycle stability performance (94% capacitance retention over 3000 cycles) is assembled successfully by employing the ZNCO electrode as positive electrode and activated carbon as negative electrode. The remarkable electrochemical behaviors demonstrate that the nickel foam supported hierarchical mesoporous ZNCO nanowire array electrodes are highly desirable for application as advanced supercapacitor electrodes.

Microfluidic Droplet-Facilitated Hierarchical Assembly for Dual Cargo Loading and Synergistic Delivery.

PubMed

Yu, Ziyi; Zheng, Yu; Parker, Richard M; Lan, Yang; Wu, Yuchao; Coulston, Roger J; Zhang, Jing; Scherman, Oren A; Abell, Chris

2016-04-06

Bottom-up hierarchical assembly has emerged as an elaborate and energy-efficient strategy for the fabrication of smart materials. Herein, we present a hierarchical assembly process, whereby linear amphiphilic block copolymers are self-assembled into micelles, which in turn are accommodated at the interface of microfluidic droplets via cucurbit[8]uril-mediated host-guest chemistry to form supramolecular microcapsules. The monodisperse microcapsules can be used for simultaneous carriage of both organic (Nile Red) and aqueous-soluble (fluorescein isothiocyanate-dextran) cargo. Furthermore, the well-defined compartmentalized structure benefits from the dynamic nature of the supramolecular interaction and offers synergistic delivery of cargos with triggered release or through photocontrolled porosity. This demonstration of premeditated hierarchical assembly, where interactions from the molecular to microscale are designed, illustrates the power of this route toward accessing the next generation of functional materials and encapsulation strategies.

Leading virtual teams: hierarchical leadership, structural supports, and shared team leadership.

PubMed

Hoch, Julia E; Kozlowski, Steve W J

2014-05-01

Using a field sample of 101 virtual teams, this research empirically evaluates the impact of traditional hierarchical leadership, structural supports, and shared team leadership on team performance. Building on Bell and Kozlowski's (2002) work, we expected structural supports and shared team leadership to be more, and hierarchical leadership to be less, strongly related to team performance when teams were more virtual in nature. As predicted, results from moderation analyses indicated that the extent to which teams were more virtual attenuated relations between hierarchical leadership and team performance but strengthened relations for structural supports and team performance. However, shared team leadership was significantly related to team performance regardless of the degree of virtuality. Results are discussed in terms of needed research extensions for understanding leadership processes in virtual teams and practical implications for leading virtual teams. (c) 2014 APA, all rights reserved.

Hierarchical MFMO Circuit Modules for an Energy-Efficient SDR DBF

NASA Astrophysics Data System (ADS)

Mar, Jeich; Kuo, Chi-Cheng; Wu, Shin-Ru; Lin, You-Rong

The hierarchical multi-function matrix operation (MFMO) circuit modules are designed using coordinate rotations digital computer (CORDIC) algorithm for realizing the intensive computation of matrix operations. The paper emphasizes that the designed hierarchical MFMO circuit modules can be used to develop a power-efficient software-defined radio (SDR) digital beamformer (DBF). The formulas of the processing time for the scalable MFMO circuit modules implemented in field programmable gate array (FPGA) are derived to allocate the proper logic resources for the hardware reconfiguration. The hierarchical MFMO circuit modules are scalable to the changing number of array branches employed for the SDR DBF to achieve the purpose of power saving. The efficient reuse of the common MFMO circuit modules in the SDR DBF can also lead to energy reduction. Finally, the power dissipation and reconfiguration function in the different modes of the SDR DBF are observed from the experiment results.

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.

Metal hierarchical patterning by direct nanoimprint lithography

PubMed Central

Radha, Boya; Lim, Su Hui; Saifullah, Mohammad S. M.; Kulkarni, Giridhar U.

2013-01-01

Three-dimensional hierarchical patterning of metals is of paramount importance in diverse fields involving photonics, controlling surface wettability and wearable electronics. Conventionally, this type of structuring is tedious and usually involves layer-by-layer lithographic patterning. Here, we describe a simple process of direct nanoimprint lithography using palladium benzylthiolate, a versatile metal-organic ink, which not only leads to the formation of hierarchical patterns but also is amenable to layer-by-layer stacking of the metal over large areas. The key to achieving such multi-faceted patterning is hysteretic melting of ink, enabling its shaping. It undergoes transformation to metallic palladium under gentle thermal conditions without affecting the integrity of the hierarchical patterns on micro- as well as nanoscale. A metallic rice leaf structure showing anisotropic wetting behavior and woodpile-like structures were thus fabricated. Furthermore, this method is extendable for transferring imprinted structures to a flexible substrate to make them robust enough to sustain numerous bending cycles. PMID:23446801

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.

Epidemic spreading in a hierarchical social network.

PubMed

Grabowski, A; Kosiński, R A

2004-09-01

A model of epidemic spreading in a population with a hierarchical structure of interpersonal interactions is described and investigated numerically. The structure of interpersonal connections is based on a scale-free network. Spatial localization of individuals belonging to different social groups, and the mobility of a contemporary community, as well as the effectiveness of different interpersonal interactions, are taken into account. Typical relations characterizing the spreading process, like a range of epidemic and epidemic curves, are discussed. The influence of preventive vaccinations on the spreading process is investigated. The critical value of preventively vaccinated individuals that is sufficient for the suppression of an epidemic is calculated. Our results are compared with solutions of the master equation for the spreading process and good agreement of the character of this process is found.

Aerial surveillance based on hierarchical object classification for ground target detection

NASA Astrophysics Data System (ADS)

Vázquez-Cervantes, Alberto; García-Huerta, Juan-Manuel; Hernández-Díaz, Teresa; Soto-Cajiga, J. A.; Jiménez-Hernández, Hugo

2015-03-01

Unmanned aerial vehicles have turned important in surveillance application due to the flexibility and ability to inspect and displace in different regions of interest. The instrumentation and autonomy of these vehicles have been increased; i.e. the camera sensor is now integrated. Mounted cameras allow flexibility to monitor several regions of interest, displacing and changing the camera view. A well common task performed by this kind of vehicles correspond to object localization and tracking. This work presents a hierarchical novel algorithm to detect and locate objects. The algorithm is based on a detection-by-example approach; this is, the target evidence is provided at the beginning of the vehicle's route. Afterwards, the vehicle inspects the scenario, detecting all similar objects through UTM-GPS coordinate references. Detection process consists on a sampling information process of the target object. Sampling process encode in a hierarchical tree with different sampling's densities. Coding space correspond to a huge binary space dimension. Properties such as independence and associative operators are defined in this space to construct a relation between the target object and a set of selected features. Different densities of sampling are used to discriminate from general to particular features that correspond to the target. The hierarchy is used as a way to adapt the complexity of the algorithm due to optimized battery duty cycle of the aerial device. Finally, this approach is tested in several outdoors scenarios, proving that the hierarchical algorithm works efficiently under several conditions.

Epidemic spreading on hierarchical geographical networks with mobile agents

NASA Astrophysics Data System (ADS)

Han, Xiao-Pu; Zhao, Zhi-Dan; Hadzibeganovic, Tarik; Wang, Bing-Hong

2014-05-01

Hierarchical geographical traffic networks are critical for our understanding of scaling laws in human trajectories. Here, we investigate the susceptible-infected epidemic process evolving on hierarchical networks in which agents randomly walk along the edges and establish contacts in network nodes. We employ a metapopulation modeling framework that allows us to explore the contagion spread patterns in relation to multi-scale mobility behaviors. A series of computer simulations revealed that a shifted power-law-like negative relationship between the peak timing of epidemics τ0 and population density, and a logarithmic positive relationship between τ0 and the network size, can both be explained by the gradual enlargement of fluctuations in the spreading process. We employ a semi-analytical method to better understand the nature of these relationships and the role of pertinent demographic factors. Additionally, we provide a quantitative discussion of the efficiency of a border screening procedure in delaying epidemic outbreaks on hierarchical networks, yielding a rather limited feasibility of this mitigation strategy but also its non-trivial dependence on population density, infector detectability, and the diversity of the susceptible region. Our results suggest that the interplay between the human spatial dynamics, network topology, and demographic factors can have important consequences for the global spreading and control of infectious diseases. These findings provide novel insights into the combined effects of human mobility and the organization of geographical networks on spreading processes, with important implications for both epidemiological research and health policy.

Oscillatory Critical Amplitudes in Hierarchical Models and the Harris Function of Branching Processes

NASA Astrophysics Data System (ADS)

Costin, Ovidiu; Giacomin, Giambattista

2013-02-01

Oscillatory critical amplitudes have been repeatedly observed in hierarchical models and, in the cases that have been taken into consideration, these oscillations are so small to be hardly detectable. Hierarchical models are tightly related to iteration of maps and, in fact, very similar phenomena have been repeatedly reported in many fields of mathematics, like combinatorial evaluations and discrete branching processes. It is precisely in the context of branching processes with bounded off-spring that T. Harris, in 1948, first set forth the possibility that the logarithm of the moment generating function of the rescaled population size, in the super-critical regime, does not grow near infinity as a power, but it has an oscillatory prefactor (the Harris function). These oscillations have been observed numerically only much later and, while the origin is clearly tied to the discrete character of the iteration, the amplitude size is not so well understood. The purpose of this note is to reconsider the issue for hierarchical models and in what is arguably the most elementary setting—the pinning model—that actually just boils down to iteration of polynomial maps (and, notably, quadratic maps). In this note we show that the oscillatory critical amplitude for pinning models and the Harris function coincide. Moreover we make explicit the link between these oscillatory functions and the geometry of the Julia set of the map, making thus rigorous and quantitative some ideas set forth in Derrida et al. (Commun. Math. Phys. 94:115-132, 1984).

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

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.

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.

Hierarchical and Well-Ordered Porous Copper for Liquid Transport Properties Control.

PubMed

Pham, Quang N; Shao, Bowen; Kim, Yongsung; Won, Yoonjin

2018-05-09

Liquid delivery through interconnected pore network is essential for various interfacial transport applications ranging from energy storage to evaporative cooling. The liquid transport performance in porous media can be significantly improved through the use of hierarchical morphology that leverages transport phenomena at different length scales. Traditional surface engineering techniques using chemical or thermal reactions often show nonuniform surface nanostructuring within three-dimensional pore network due to uncontrollable diffusion and reactivity in geometrically complex porous structures. Here, we demonstrate hierarchical architectures on the basis of crystalline copper inverse opals using an electrochemistry approach, which offers volumetric controllability of structural and surface properties within the complex porous metal. The electrochemical process sequentially combines subtractive and additive steps-electrochemical polishing and electrochemical oxidation-to improve surface wetting properties without sacrificing structural permeability. We report the transport performance of the hierarchical inverse opals by measuring the capillary-driven liquid rise. The capillary performance parameter of hierarchically engineered inverse opal ( K/ R eff = ∼5 × 10 -3 μm) is shown to be higher than that of a typical crystalline inverse opal ( K/ R eff = ∼1 × 10 -3 μm) owing to the enhancement in fluid permeable and hydrophilic pathways. The new surface engineering method presented in this work provides a rational approach in designing hierarchical porous copper for transport performance enhancements.