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 general framework for multicharacter segmentation and its application in recognizing multilingual Asian documents

NASA Astrophysics Data System (ADS)

Wen, Di; Ding, Xiaoqing

2003-12-01

In this paper we propose a general framework for character segmentation in complex multilingual documents, which is an endeavor to combine the traditionally separated segmentation and recognition processes into a cooperative system. The framework contains three basic steps: Dissection, Local Optimization and Global Optimization, which are designed to fuse various properties of the segmentation hypotheses hierarchically into a composite evaluation to decide the final recognition results. Experimental results show that this framework is general enough to be applied in variety of documents. A sample system based on this framework to recognize Chinese, Japanese and Korean documents and experimental performance is reported finally.

Immobilization of Bacillus subtilis lipase on a Cu-BTC based hierarchically porous metal-organic framework material: a biocatalyst for esterification.

PubMed

Cao, Yu; Wu, Zhuofu; Wang, Tao; Xiao, Yu; Huo, Qisheng; Liu, Yunling

2016-04-28

Bacillus subtilis lipase (BSL2) has been successfully immobilized into a Cu-BTC based hierarchically porous metal-organic framework material for the first time. The Cu-BTC hierarchically porous MOF material with large mesopore apertures is prepared conveniently by using a template-free strategy under mild conditions. The immobilized BSL2 presents high enzymatic activity and perfect reusability during the esterification reaction. After 10 cycles, the immobilized BSL2 still exhibits 90.7% of its initial enzymatic activity and 99.6% of its initial conversion.

Gating Mechanisms of Mechanosensitive Channels of Large Conductance, I: A Continuum Mechanics-Based Hierarchical Framework

PubMed Central

Chen, Xi; Cui, Qiang; Tang, Yuye; Yoo, Jejoong; Yethiraj, Arun

2008-01-01

A hierarchical simulation framework that integrates information from molecular dynamics (MD) simulations into a continuum model is established to study the mechanical response of mechanosensitive channel of large-conductance (MscL) using the finite element method (FEM). The proposed MD-decorated FEM (MDeFEM) approach is used to explore the detailed gating mechanisms of the MscL in Escherichia coli embedded in a palmitoyloleoylphosphatidylethanolamine lipid bilayer. In Part I of this study, the framework of MDeFEM is established. The transmembrane and cytoplasmic helices are taken to be elastic rods, the loops are modeled as springs, and the lipid bilayer is approximated by a three-layer sheet. The mechanical properties of the continuum components, as well as their interactions, are derived from molecular simulations based on atomic force fields. In addition, analytical closed-form continuum model and elastic network model are established to complement the MDeFEM approach and to capture the most essential features of gating. In Part II of this study, the detailed gating mechanisms of E. coli-MscL under various types of loading are presented and compared with experiments, structural model, and all-atom simulations, as well as the analytical models established in Part I. It is envisioned that such a hierarchical multiscale framework will find great value in the study of a variety of biological processes involving complex mechanical deformations such as muscle contraction and mechanotransduction. PMID:18390626

A Unified Theoretical Framework for Cognitive Sequencing.

PubMed

Savalia, Tejas; Shukla, Anuj; Bapi, Raju S

2016-01-01

The capacity to sequence information is central to human performance. Sequencing ability forms the foundation stone for higher order cognition related to language and goal-directed planning. Information related to the order of items, their timing, chunking and hierarchical organization are important aspects in sequencing. Past research on sequencing has emphasized two distinct and independent dichotomies: implicit vs. explicit and goal-directed vs. habits. We propose a theoretical framework unifying these two streams. Our proposal relies on brain's ability to implicitly extract statistical regularities from the stream of stimuli and with attentional engagement organizing sequences explicitly and hierarchically. Similarly, sequences that need to be assembled purposively to accomplish a goal require engagement of attentional processes. With repetition, these goal-directed plans become habits with concomitant disengagement of attention. Thus, attention and awareness play a crucial role in the implicit-to-explicit transition as well as in how goal-directed plans become automatic habits. Cortico-subcortical loops basal ganglia-frontal cortex and hippocampus-frontal cortex loops mediate the transition process. We show how the computational principles of model-free and model-based learning paradigms, along with a pivotal role for attention and awareness, offer a unifying framework for these two dichotomies. Based on this framework, we make testable predictions related to the potential influence of response-to-stimulus interval (RSI) on developing awareness in implicit learning tasks.

A Unified Theoretical Framework for Cognitive Sequencing

PubMed Central

Savalia, Tejas; Shukla, Anuj; Bapi, Raju S.

2016-01-01

The capacity to sequence information is central to human performance. Sequencing ability forms the foundation stone for higher order cognition related to language and goal-directed planning. Information related to the order of items, their timing, chunking and hierarchical organization are important aspects in sequencing. Past research on sequencing has emphasized two distinct and independent dichotomies: implicit vs. explicit and goal-directed vs. habits. We propose a theoretical framework unifying these two streams. Our proposal relies on brain's ability to implicitly extract statistical regularities from the stream of stimuli and with attentional engagement organizing sequences explicitly and hierarchically. Similarly, sequences that need to be assembled purposively to accomplish a goal require engagement of attentional processes. With repetition, these goal-directed plans become habits with concomitant disengagement of attention. Thus, attention and awareness play a crucial role in the implicit-to-explicit transition as well as in how goal-directed plans become automatic habits. Cortico-subcortical loops basal ganglia-frontal cortex and hippocampus-frontal cortex loops mediate the transition process. We show how the computational principles of model-free and model-based learning paradigms, along with a pivotal role for attention and awareness, offer a unifying framework for these two dichotomies. Based on this framework, we make testable predictions related to the potential influence of response-to-stimulus interval (RSI) on developing awareness in implicit learning tasks. PMID:27917146

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.

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

Development of balanced key performance indicators for emergency departments strategic dashboards following analytic hierarchical process.

PubMed

Safdari, Reza; Ghazisaeedi, Marjan; Mirzaee, Mahboobeh; Farzi, Jebrail; Goodini, Azadeh

2014-01-01

Dynamic reporting tools, such as dashboards, should be developed to measure emergency department (ED) performance. However, choosing an effective balanced set of performance measures and key performance indicators (KPIs) is a main challenge to accomplish this. The aim of this study was to develop a balanced set of KPIs for use in ED strategic dashboards following an analytic hierarchical process. The study was carried out in 2 phases: constructing ED performance measures based on balanced scorecard perspectives and incorporating them into analytic hierarchical process framework to select the final KPIs. The respondents placed most importance on ED internal processes perspective especially on measures related to timeliness and accessibility of care in ED. Some measures from financial, customer, and learning and growth perspectives were also selected as other top KPIs. Measures of care effectiveness and care safety were placed as the next priorities too. The respondents placed least importance on disease-/condition-specific "time to" measures. The methodology can be presented as a reference model for development of KPIs in various performance related areas based on a consistent and fair approach. Dashboards that are designed based on such a balanced set of KPIs will help to establish comprehensive performance measurements and fair benchmarks and comparisons.

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

PubMed

Chen, Ying-Chih; Wen, Chih-Yu

2012-11-08

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

CHAMPION: Intelligent Hierarchical Reasoning Agents for Enhanced Decision Support

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

Hohimer, Ryan E.; Greitzer, Frank L.; Noonan, Christine F.

2011-11-15

We describe the design and development of an advanced reasoning framework employing semantic technologies, organized within a hierarchy of computational reasoning agents that interpret domain specific information. Designed based on an inspirational metaphor of the pattern recognition functions performed by the human neocortex, the CHAMPION reasoning framework represents a new computational modeling approach that derives invariant knowledge representations through memory-prediction belief propagation processes that are driven by formal ontological language specification and semantic technologies. The CHAMPION framework shows promise for enhancing complex decision making in diverse problem domains including cyber security, nonproliferation and energy consumption analysis.

Advanced techniques for the storage and use of very large, heterogeneous spatial databases. The representation of geographic knowledge: Toward a universal framework. [relations (mathematics)

NASA Technical Reports Server (NTRS)

Peuquet, Donna J.

1987-01-01

A new approach to building geographic data models that is based on the fundamental characteristics of the data is presented. An overall theoretical framework for representing geographic data is proposed. An example of utilizing this framework in a Geographic Information System (GIS) context by combining artificial intelligence techniques with recent developments in spatial data processing techniques is given. Elements of data representation discussed include hierarchical structure, separation of locational and conceptual views, and the ability to store knowledge at variable levels of completeness and precision.

A dynamic framework on travel mode choice focusing on utilitarian walking based on the integration of current knowledge.

PubMed

Yang, Yong

2016-09-01

Recently, research on utilitarian walking has gained momentum due to its benefits on both health and the environment. However, our overall understanding of how built and social environments affect travel mode choice (walking or not) is still limited, and most existing frameworks on travel mode choice lack dynamic processes. After a review of several mainstream theories and a number of frameworks, we propose an integrated framework. The basic constructs in the travel mode choice function are utilities, constraints, attitudes, and habits. With a hierarchical structure and heuristic rules, the travel mode choice function is modified by individual characteristics and travel characteristics. The framework explicitly presents several dynamic processes, including the perception process on the environment, attitude formation process, habit formation process, interactions among an individual's own behaviors, interactions among travelers, feedback from travel to the built and social environments, and feedback from other behaviors to the built and social environments. For utilitarian walking, the framework may contribute to the study design, data collection, adoption of new research methods, and provide indications for policy interventions.

A dynamic framework on travel mode choice focusing on utilitarian walking based on the integration of current knowledge

PubMed Central

Yang, Yong

2016-01-01

Recently, research on utilitarian walking has gained momentum due to its benefits on both health and the environment. However, our overall understanding of how built and social environments affect travel mode choice (walking or not) is still limited, and most existing frameworks on travel mode choice lack dynamic processes. After a review of several mainstream theories and a number of frameworks, we propose an integrated framework. The basic constructs in the travel mode choice function are utilities, constraints, attitudes, and habits. With a hierarchical structure and heuristic rules, the travel mode choice function is modified by individual characteristics and travel characteristics. The framework explicitly presents several dynamic processes, including the perception process on the environment, attitude formation process, habit formation process, interactions among an individual’s own behaviors, interactions among travelers, feedback from travel to the built and social environments, and feedback from other behaviors to the built and social environments. For utilitarian walking, the framework may contribute to the study design, data collection, adoption of new research methods, and provide indications for policy interventions. PMID:27747158

An Intrusion Detection System Based on Multi-Level Clustering for Hierarchical Wireless Sensor Networks

PubMed Central

Butun, Ismail; Ra, In-Ho; Sankar, Ravi

2015-01-01

In this work, an intrusion detection system (IDS) framework based on multi-level clustering for hierarchical wireless sensor networks is proposed. The framework employs two types of intrusion detection approaches: (1) “downward-IDS (D-IDS)” to detect the abnormal behavior (intrusion) of the subordinate (member) nodes; and (2) “upward-IDS (U-IDS)” to detect the abnormal behavior of the cluster heads. By using analytical calculations, the optimum parameters for the D-IDS (number of maximum hops) and U-IDS (monitoring group size) of the framework are evaluated and presented. PMID:26593915

Exploring factors that influence the spread and sustainability of a dysphagia innovation: an instrumental case study.

PubMed

Ilott, Irene; Gerrish, Kate; Eltringham, Sabrina A; Taylor, Carolyn; Pownall, Sue

2016-08-18

Swallowing difficulties challenge patient safety due to the increased risk of malnutrition, dehydration and aspiration pneumonia. A theoretically driven study was undertaken to examine the spread and sustainability of a locally developed innovation that involved using the Inter-Professional Dysphagia Framework to structure education for the workforce. A conceptual framework with 3 spread strategies (hierarchical control, participatory adaptation and facilitated evolution) was blended with a processual approach to sustaining organisational change. The aim was to understand the processes, mechanism and outcomes associated with the spread and sustainability of this safety initiative. An instrumental case study, prospectively tracked a dysphagia innovation for 34 months (April 2011 to January 2014) in a large health care organisation in England. A train-the-trainer intervention (as participatory adaptation) was deployed on care pathways for stroke and fractured neck of femur. Data were collected at the organisational and clinical level through interviews (n = 30) and document review. The coding frame combined the processual approach with the spread mechanisms. Pre-determined outcomes included the number of staff trained about dysphagia and impact related to changes in practice. The features and processes associated with hierarchical control and participatory adaptation were identified. Leadership, critical junctures, temporality and making the innovation routine were aspects of hierarchical control. Participatory adaptation was evident on the care pathways through stakeholder responses, workload and resource pressures. Six of the 25 ward based trainers cascaded the dysphagia training. The expected outcomes were achieved when the top-down mandate (hierarchical control) was supplemented by local engagement and support (participatory adaptation). Frameworks for spread and sustainability were combined to create a 'small theory' that described the interventions, the processes and desired outcomes a priori. This novel methodological approach confirmed what is known about spread and sustainability, highlighted the particularity of change and offered new insights into the factors associated with hierarchical control and participatory adaptation. The findings illustrate the dualities of organisational change as universal and context specific; as particular and amendable to theoretical generalisation. Appreciating these dualities may contribute to understanding why many innovations fail to become routine.

A management-oriented classification of pinyon-juniper woodlands of the Great Basin

Treesearch

Neil E. West; Robin J. Tausch; Paul T. Tueller

1998-01-01

A hierarchical framework for the classification of Great Basin pinyon-juniper woodlands was based on a systematic sample of 426 stands from a random selection of 66 of the 110 mountain ranges in the region. That is, mountain ranges were randomly selected, but stands were systematically located on mountain ranges. The National Hierarchical Framework of Ecological Units...

Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

PubMed

Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

2017-08-01

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2  = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.

Unsupervised active learning based on hierarchical graph-theoretic clustering.

PubMed

Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

2009-10-01

Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing.

PubMed

Pajak, Bozena; Fine, Alex B; Kleinschmidt, Dave F; Jaeger, T Florian

2016-12-01

We present a framework of second and additional language (L2/L n ) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/L n learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/L n acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/L n learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing

PubMed Central

Pajak, Bozena; Fine, Alex B.; Kleinschmidt, Dave F.; Jaeger, T. Florian

2015-01-01

We present a framework of second and additional language (L2/Ln) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/Ln learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/Ln acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/Ln learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa. PMID:28348442

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.

Computational Properties of the Hippocampus Increase the Efficiency of Goal-Directed Foraging through Hierarchical Reinforcement Learning

PubMed Central

Chalmers, Eric; Luczak, Artur; Gruber, Aaron J.

2016-01-01

The mammalian brain is thought to use a version of Model-based Reinforcement Learning (MBRL) to guide “goal-directed” behavior, wherein animals consider goals and make plans to acquire desired outcomes. However, conventional MBRL algorithms do not fully explain animals' ability to rapidly adapt to environmental changes, or learn multiple complex tasks. They also require extensive computation, suggesting that goal-directed behavior is cognitively expensive. We propose here that key features of processing in the hippocampus support a flexible MBRL mechanism for spatial navigation that is computationally efficient and can adapt quickly to change. We investigate this idea by implementing a computational MBRL framework that incorporates features inspired by computational properties of the hippocampus: a hierarchical representation of space, “forward sweeps” through future spatial trajectories, and context-driven remapping of place cells. We find that a hierarchical abstraction of space greatly reduces the computational load (mental effort) required for adaptation to changing environmental conditions, and allows efficient scaling to large problems. It also allows abstract knowledge gained at high levels to guide adaptation to new obstacles. Moreover, a context-driven remapping mechanism allows learning and memory of multiple tasks. Simulating dorsal or ventral hippocampal lesions in our computational framework qualitatively reproduces behavioral deficits observed in rodents with analogous lesions. The framework may thus embody key features of how the brain organizes model-based RL to efficiently solve navigation and other difficult tasks. PMID:28018203

PNNL Report on the Development of Bench-scale CFD Simulations for Gas Absorption across a Wetted Wall Column

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

Wang, Chao; Xu, Zhijie; Lai, Canhai

This report is prepared for the demonstration of hierarchical prediction of carbon capture efficiency of a solvent-based absorption column. A computational fluid dynamics (CFD) model is first developed to simulate the core phenomena of solvent-based carbon capture, i.e., the CO2 physical absorption and chemical reaction, on a simplified geometry of wetted wall column (WWC) at bench scale. Aqueous solutions of ethanolamine (MEA) are commonly selected as a CO2 stream scrubbing liquid. CO2 is captured by both physical and chemical absorption using highly CO2 soluble and reactive solvent, MEA, during the scrubbing process. In order to provide confidence bound on themore » computational predictions of this complex engineering system, a hierarchical calibration and validation framework is proposed. The overall goal of this effort is to provide a mechanism-based predictive framework with confidence bound for overall mass transfer coefficient of the wetted wall column (WWC) with statistical analyses of the corresponding WWC experiments with increasing physical complexity.« less

Hierarchical models of animal abundance and occurrence

USGS Publications Warehouse

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

2006-01-01

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

Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework

USGS Publications Warehouse

Omernik, James M.; Griffith, Glenn E.

2014-01-01

A map of ecological regions of the conterminous United States, first published in 1987, has been greatly refined and expanded into a hierarchical spatial framework in response to user needs, particularly by state resource management agencies. In collaboration with scientists and resource managers from numerous agencies and institutions in the United States, Mexico, and Canada, the framework has been expanded to cover North America, and the original ecoregions (now termed Level III) have been refined, subdivided, and aggregated to identify coarser as well as more detailed spatial units. The most generalized units (Level I) define 10 ecoregions in the conterminous U.S., while the finest-scale units (Level IV) identify 967 ecoregions. In this paper, we explain the logic underpinning the approach, discuss the evolution of the regional mapping process, and provide examples of how the ecoregions were distinguished at each hierarchical level. The variety of applications of the ecoregion framework illustrates its utility in resource assessment and management.

Ecoregions of the Conterminous United States: Evolution of a Hierarchical Spatial Framework

NASA Astrophysics Data System (ADS)

Omernik, James M.; Griffith, Glenn E.

2014-12-01

A map of ecological regions of the conterminous United States, first published in 1987, has been greatly refined and expanded into a hierarchical spatial framework in response to user needs, particularly by state resource management agencies. In collaboration with scientists and resource managers from numerous agencies and institutions in the United States, Mexico, and Canada, the framework has been expanded to cover North America, and the original ecoregions (now termed Level III) have been refined, subdivided, and aggregated to identify coarser as well as more detailed spatial units. The most generalized units (Level I) define 10 ecoregions in the conterminous U.S., while the finest-scale units (Level IV) identify 967 ecoregions. In this paper, we explain the logic underpinning the approach, discuss the evolution of the regional mapping process, and provide examples of how the ecoregions were distinguished at each hierarchical level. The variety of applications of the ecoregion framework illustrates its utility in resource assessment and management.

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.

A process-based hierarchical framework for monitoring glaciated alpine headwaters

USGS Publications Warehouse

Weekes, Anne A.; Torgersen, Christian E.; Montgomery, David R.; Woodward, Andrea; Bolton, Susan M.

2012-01-01

Recent studies have demonstrated the geomorphic complexity and wide range of hydrologic regimes found in alpine headwater channels that provide complex habitats for aquatic taxa. These geohydrologic elements are fundamental to better understand patterns in species assemblages and indicator taxa and are necessary to aquatic monitoring protocols that aim to track changes in physical conditions. Complex physical variables shape many biological and ecological traits, including life history strategies, but these mechanisms can only be understood if critical physical variables are adequately represented within the sampling framework. To better align sampling design protocols with current geohydrologic knowledge, we present a conceptual framework that incorporates regional-scale conditions, basin-scale longitudinal profiles, valley-scale glacial macroform structure, valley segment-scale (i.e., colluvial, alluvial, and bedrock), and reach-scale channel types. At the valley segment- and reach-scales, these hierarchical levels are associated with differences in streamflow and sediment regime, water source contribution and water temperature. Examples of linked physical-ecological hypotheses placed in a landscape context and a case study using the proposed framework are presented to demonstrate the usefulness of this approach for monitoring complex temporal and spatial patterns and processes in glaciated basins. This approach is meant to aid in comparisons between mountain regions on a global scale and to improve management of potentially endangered alpine species affected by climate change and other stressors.

Materials Knowledge Systems in Python - A Data Science Framework for Accelerated Development of Hierarchical Materials.

PubMed

Brough, David B; Wheeler, Daniel; Kalidindi, Surya R

2017-03-01

There is a critical need for customized analytics that take into account the stochastic nature of the internal structure of materials at multiple length scales in order to extract relevant and transferable knowledge. Data driven Process-Structure-Property (PSP) linkages provide systemic, modular and hierarchical framework for community driven curation of materials knowledge, and its transference to design and manufacturing experts. The Materials Knowledge Systems in Python project (PyMKS) is the first open source materials data science framework that can be used to create high value PSP linkages for hierarchical materials that can be leveraged by experts in materials science and engineering, manufacturing, machine learning and data science communities. This paper describes the main functions available from this repository, along with illustrations of how these can be accessed, utilized, and potentially further refined by the broader community of researchers.

Materials Knowledge Systems in Python - A Data Science Framework for Accelerated Development of Hierarchical Materials

PubMed Central

Brough, David B; Wheeler, Daniel; Kalidindi, Surya R.

2017-01-01

There is a critical need for customized analytics that take into account the stochastic nature of the internal structure of materials at multiple length scales in order to extract relevant and transferable knowledge. Data driven Process-Structure-Property (PSP) linkages provide systemic, modular and hierarchical framework for community driven curation of materials knowledge, and its transference to design and manufacturing experts. The Materials Knowledge Systems in Python project (PyMKS) is the first open source materials data science framework that can be used to create high value PSP linkages for hierarchical materials that can be leveraged by experts in materials science and engineering, manufacturing, machine learning and data science communities. This paper describes the main functions available from this repository, along with illustrations of how these can be accessed, utilized, and potentially further refined by the broader community of researchers. PMID:28690971

General overview of the disaster management framework in Cameroon.

PubMed

Bang, Henry Ngenyam

2014-07-01

Efficient and effective disaster management will prevent many hazardous events from becoming disasters. This paper constitutes the most comprehensive document on the natural disaster management framework of Cameroon. It reviews critically disaster management in Cameroon, examining the various legislative, institutional, and administrative frameworks that help to facilitate the process. Furthermore, it illuminates the vital role that disaster managers at the national, regional, and local level play to ease the process. Using empirical data, the study analyses the efficiency and effectiveness of the actions of disaster managers. Its findings reveal inadequate disaster management policies, poor coordination between disaster management institutions at the national level, the lack of trained disaster managers, a skewed disaster management system, and a top-down hierarchical structure within Cameroon's disaster management framework. By scrutinising the disaster management framework of the country, policy recommendations based on the research findings are made on the institutional and administrative frameworks. © 2014 The Author(s). Disasters © Overseas Development Institute, 2014.

Cheetah: A Framework for Scalable Hierarchical Collective Operations

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

Graham, Richard L; Gorentla Venkata, Manjunath; Ladd, Joshua S

2011-01-01

Collective communication operations, used by many scientific applications, tend to limit overall parallel application performance and scalability. Computer systems are becoming more heterogeneous with increasing node and core-per-node counts. Also, a growing number of data-access mechanisms, of varying characteristics, are supported within a single computer system. We describe a new hierarchical collective communication framework that takes advantage of hardware-specific data-access mechanisms. It is flexible, with run-time hierarchy specification, and sharing of collective communication primitives between collective algorithms. Data buffers are shared between levels in the hierarchy reducing collective communication management overhead. We have implemented several versions of the Message Passingmore » Interface (MPI) collective operations, MPI Barrier() and MPI Bcast(), and run experiments using up to 49, 152 processes on a Cray XT5, and a small InfiniBand based cluster. At 49, 152 processes our barrier implementation outperforms the optimized native implementation by 75%. 32 Byte and one Mega-Byte broadcasts outperform it by 62% and 11%, respectively, with better scalability characteristics. Improvements relative to the default Open MPI implementation are much larger.« less

Model-based hierarchical reinforcement learning and human action control

PubMed Central

Botvinick, Matthew; Weinstein, Ari

2014-01-01

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

Bayesian Hierarchical Grouping: perceptual grouping as mixture estimation

PubMed Central

Froyen, Vicky; Feldman, Jacob; Singh, Manish

2015-01-01

We propose a novel framework for perceptual grouping based on the idea of mixture models, called Bayesian Hierarchical Grouping (BHG). In BHG we assume that the configuration of image elements is generated by a mixture of distinct objects, each of which generates image elements according to some generative assumptions. Grouping, in this framework, means estimating the number and the parameters of the mixture components that generated the image, including estimating which image elements are “owned” by which objects. We present a tractable implementation of the framework, based on the hierarchical clustering approach of Heller and Ghahramani (2005). We illustrate it with examples drawn from a number of classical perceptual grouping problems, including dot clustering, contour integration, and part decomposition. Our approach yields an intuitive hierarchical representation of image elements, giving an explicit decomposition of the image into mixture components, along with estimates of the probability of various candidate decompositions. We show that BHG accounts well for a diverse range of empirical data drawn from the literature. Because BHG provides a principled quantification of the plausibility of grouping interpretations over a wide range of grouping problems, we argue that it provides an appealing unifying account of the elusive Gestalt notion of Prägnanz. PMID:26322548

Semantic Image Segmentation with Contextual Hierarchical Models.

PubMed

Seyedhosseini, Mojtaba; Tasdizen, Tolga

2016-05-01

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

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.

A hierarchical, ontology-driven Bayesian concept for ubiquitous medical environments--a case study for pulmonary diseases.

PubMed

Maragoudakis, Manolis; Lymberopoulos, Dimitrios; Fakotakis, Nikos; Spiropoulos, Kostas

2008-01-01

The present paper extends work on an existing computer-based Decision Support System (DSS) that aims to provide assistance to physicians as regards to pulmonary diseases. The extension deals with allowing for a hierarchical decomposition of the task, at different levels of domain granularity, using a novel approach, i.e. Hierarchical Bayesian Networks. The proposed framework uses data from various networking appliances such as mobile phones and wireless medical sensors to establish a ubiquitous environment for medical treatment of pulmonary diseases. Domain knowledge is encoded at the upper levels of the hierarchy, thus making the process of generalization easier to accomplish. The experimental results were carried out under the Pulmonary Department, University Regional Hospital Patras, Patras, Greece. They have supported our initial beliefs about the ability of Bayesian networks to provide an effective, yet semantically-oriented, means of prognosis and reasoning under conditions of uncertainty.

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.

A conceptual modeling framework for discrete event simulation using hierarchical control structures.

PubMed

Furian, N; O'Sullivan, M; Walker, C; Vössner, S; Neubacher, D

2015-08-01

Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM's applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models' system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example.

A conceptual modeling framework for discrete event simulation using hierarchical control structures

PubMed Central

Furian, N.; O’Sullivan, M.; Walker, C.; Vössner, S.; Neubacher, D.

2015-01-01

Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM’s applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models’ system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example. PMID:26778940

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets.

PubMed

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O; Gelfand, Alan E

2016-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online.

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets

PubMed Central

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O.; Gelfand, Alan E.

2018-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online. PMID:29720777

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.

A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Jung, Jinha; Pasolli, Edoardo; Prasad, Saurabh; Tilton, James C.; Crawford, Melba M.

2014-01-01

Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach.

American Society for Enhanced Recovery (ASER) and Perioperative Quality Initiative (POQI) joint consensus statement on measurement to maintain and improve quality of enhanced recovery pathways for elective colorectal surgery.

PubMed

Moonesinghe, S Ramani; Grocott, Michael P W; Bennett-Guerrero, Elliott; Bergamaschi, Roberto; Gottumukkala, Vijaya; Hopkins, Thomas J; McCluskey, Stuart; Gan, Tong J; Mythen, Michael Monty G; Shaw, Andrew D; Miller, Timothy E

2017-01-01

This article sets out a framework for measurement of quality of care relevant to enhanced recovery pathways (ERPs) in elective colorectal surgery. The proposed framework is based on established measurement systems and/or theories, and provides an overview of the different approaches for improving clinical monitoring, and enhancing quality improvement or research in varied settings with different levels of available resources. Using a structure-process-outcome framework, we make recommendations for three hierarchical tiers of data collection. Core, Quality Improvement, and Best Practice datasets are proposed. The suggested datasets incorporate patient data to describe case-mix, process measures to describe delivery of enhanced recovery and clinical outcomes. The fundamental importance of routine collection of data for the initiation, maintenance, and enhancement of enhanced recovery pathways is emphasized.

Automated Analysis of Stateflow Models

NASA Technical Reports Server (NTRS)

Bourbouh, Hamza; Garoche, Pierre-Loic; Garion, Christophe; Gurfinkel, Arie; Kahsaia, Temesghen; Thirioux, Xavier

2017-01-01

Stateflow is a widely used modeling framework for embedded and cyber physical systems where control software interacts with physical processes. In this work, we present a framework a fully automated safety verification technique for Stateflow models. Our approach is two-folded: (i) we faithfully compile Stateflow models into hierarchical state machines, and (ii) we use automated logic-based verification engine to decide the validity of safety properties. The starting point of our approach is a denotational semantics of State flow. We propose a compilation process using continuation-passing style (CPS) denotational semantics. Our compilation technique preserves the structural and modal behavior of the system. The overall approach is implemented as an open source toolbox that can be integrated into the existing Mathworks Simulink Stateflow modeling framework. We present preliminary experimental evaluations that illustrate the effectiveness of our approach in code generation and safety verification of industrial scale Stateflow models.

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 general science-based framework for dynamical spatio-temporal models

USGS Publications Warehouse

Wikle, C.K.; Hooten, M.B.

2010-01-01

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic nonlinearity and demonstrate that it accommodates many different classes of scientific-based parameterizations as special cases. The model is presented in a hierarchical Bayesian framework and is illustrated with examples from ecology and oceanography. ?? 2010 Sociedad de Estad??stica e Investigaci??n Operativa.

Collective Framework and Performance Optimizations to Open MPI for Cray XT Platforms

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

Ladd, Joshua S; Gorentla Venkata, Manjunath; Shamis, Pavel

2011-01-01

The performance and scalability of collective operations plays a key role in the performance and scalability of many scientific applications. Within the Open MPI code base we have developed a general purpose hierarchical collective operations framework called Cheetah, and applied it at large scale on the Oak Ridge Leadership Computing Facility's Jaguar (OLCF) platform, obtaining better performance and scalability than the native MPI implementation. This paper discuss Cheetah's design and implementation, and optimizations to the framework for Cray XT 5 platforms. Our results show that the Cheetah's Broadcast and Barrier perform better than the native MPI implementation. For medium data,more » the Cheetah's Broadcast outperforms the native MPI implementation by 93% for 49,152 processes problem size. For small and large data, it out performs the native MPI implementation by 10% and 9%, respectively, at 24,576 processes problem size. The Cheetah's Barrier performs 10% better than the native MPI implementation for 12,288 processes problem size.« less

A biologically consistent hierarchical framework for self-referencing survivalist computation

NASA Astrophysics Data System (ADS)

Cottam, Ron; Ranson, Willy; Vounckx, Roger

2000-05-01

Extensively scaled formally rational hardware and software are indirectly fallible, at the very least through temporal restrictions on the evaluation of their correctness. In addition, the apparent inability of formal rationality to successfully describe living systems as anything other than inanimate structures suggests that the development of self-referencing computational machines will require a different approach. There is currently a strong movement towards the adoption of semiotics as a descriptive medium in theoretical biology. We present a related computational semiosic construction (1, 2) consistent with evolutionary hierarchical emergence (3), which may serve as a framework for implementing anticipatory-oriented survivalist processing in real environments.

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.

Development of a Dynamically Configurable,Object-Oriented Framework for Distributed, Multi-modal Computational Aerospace Systems Simulation

NASA Technical Reports Server (NTRS)

Afjeh, Abdollah A.; Reed, John A.

2003-01-01

This research is aimed at developing a neiv and advanced simulation framework that will significantly improve the overall efficiency of aerospace systems design and development. This objective will be accomplished through an innovative integration of object-oriented and Web-based technologies ivith both new and proven simulation methodologies. The basic approach involves Ihree major areas of research: Aerospace system and component representation using a hierarchical object-oriented component model which enables the use of multimodels and enforces component interoperability. Collaborative software environment that streamlines the process of developing, sharing and integrating aerospace design and analysis models. . Development of a distributed infrastructure which enables Web-based exchange of models to simplify the collaborative design process, and to support computationally intensive aerospace design and analysis processes. Research for the first year dealt with the design of the basic architecture and supporting infrastructure, an initial implementation of that design, and a demonstration of its application to an example aircraft engine system simulation.

Incorporating ecogeomorphic feedbacks to better understand resiliency in streams: A review and directions forward

NASA Astrophysics Data System (ADS)

Atkinson, Carla L.; Allen, Daniel C.; Davis, Lisa; Nickerson, Zachary L.

2018-03-01

Decades of interdisciplinary research show river form and function depends on interactions between the living and nonliving world, but a dominant paradigm underlying ecogeomorphic work consists of a top-down, unidirectional approach with abiotic forces driving biotic systems. Stream form and location within the stream network does dictate the habitat and resources available for organisms and overall community structure. Yet this traditional hierarchal framework on its own is inadequate in communicating information regarding the influence of biological systems on fluvial geomorphology that lead to changes in channel morphology, sediment cycling, and system-scale functions (e.g., sediment yield, biogeochemical nutrient cycling). Substantial evidence that organisms influence fluvial geomorphology exists, specifically the ability of aquatic vegetation and lotic animals to modify flow velocities and sediment deposition and transport - thus challenging the traditional hierarchal framework. Researchers recognize the need for ecogeomorphic frameworks that conceptualize feedbacks between organisms, sediment transport, and geomorphic structure. Furthermore, vital ecosystem processes, such as biogeochemical nutrient cycling represent the conversations that are occurring between geomorphological and biological systems. Here we review and synthesize selected case studies highlighting the role organisms play in moderating geomorphic processes and likely interact with these processes to have an impact on an essential ecosystem process, biogeochemical nutrient recycling. We explore whether biophysical interactions can provide information essential to improving predictions of system-scale river functions, specifically sediment transport and biogeochemical cycling, and discuss tools used to study these interactions. We suggest that current conceptual frameworks should acknowledge that hydrologic, geomorphologic, and ecologic processes operate on different temporal scales, generating bidirectional feedback loops over space and time. Hydro- and geomorphologic processes, operating episodically during bankfull conditions, influence ecological processes (e.g., biogeochemical cycling) occurring over longer time periods during base-flow conditions. This ecological activity generates the antecedent conditions that influence the hydro- and geomorphologic processes occurring during the next high flow event, creating a bidirectional feedback. This feedback should enhance the resiliency of fluvial landforms and ecosystem processes, allowing physical and biological processes to pull and push against each other over time.

Beyond comorbidity: Toward a dimensional and hierarchal approach to understanding psychopathology across the lifespan

PubMed Central

Forbes, Miriam K.; Tackett, Jennifer L.; Markon, Kristian E.; Krueger, Robert F.

2016-01-01

In this review, we propose a novel developmentally informed framework to push research beyond a focus on comorbidity between discrete diagnostic categories, and to move towards research based on the well-validated dimensional and hierarchical structure of psychopathology. For example, a large body of research speaks to the validity and utility of the Internalizing and Externalizing (IE) spectra as organizing constructs for research on common forms of psychopathology. The IE spectra act as powerful explanatory variables that channel the psychopathological effects of genetic and environmental risk factors, predict adaptive functioning, and account for the likelihood of disorder-level manifestations of psychopathology. As such, our proposed theoretical framework uses the IE spectra as central constructs to guide future psychopathology research across the lifespan. The framework is particularly flexible, as any of the facets or factors from the dimensional and hierarchical structure of psychopathology can form the focus of research. We describe the utility and strengths of this framework for developmental psychopathology in particular, and explore avenues for future research. PMID:27739384

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

NASA Astrophysics Data System (ADS)

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

2016-07-01

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

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.

Construction of hierarchically porous metal–organic frameworks through linker labilization

DOE PAGES

Yuan, Shuai; Zou, Lanfang; Qin, Jun-Sheng; ...

2017-05-25

One major goal of metal–organic framework (MOF) research is the expansion of pore size and volume. Although many approaches have been attempted to increase the pore size of MOF materials, it is still a challenge to construct MOFs with precisely customized pore apertures for specific applications. W present a new method, namely linker labilization, to increase the MOF porosity and pore size, giving rise to hierarchical-pore architectures. Microporous MOFs with robust metal nodes and pro-labile linkers were initially synthesized. The mesopores were subsequently created as crystal defects through the splitting of a pro-labile-linker and the removal of the linker fragmentsmore » by acid treatment. We also demonstrate that linker labilization method can create controllable hierarchical porous structures in stable MOFs, which facilitates the diffusion and adsorption process of guest molecules to improve the performances of MOFs in adsorption and catalysis.« less

Construction of hierarchically porous metal-organic frameworks through linker labilization

NASA Astrophysics Data System (ADS)

Yuan, Shuai; Zou, Lanfang; Qin, Jun-Sheng; Li, Jialuo; Huang, Lan; Feng, Liang; Wang, Xuan; Bosch, Mathieu; Alsalme, Ali; Cagin, Tahir; Zhou, Hong-Cai

2017-05-01

A major goal of metal-organic framework (MOF) research is the expansion of pore size and volume. Although many approaches have been attempted to increase the pore size of MOF materials, it is still a challenge to construct MOFs with precisely customized pore apertures for specific applications. Herein, we present a new method, namely linker labilization, to increase the MOF porosity and pore size, giving rise to hierarchical-pore architectures. Microporous MOFs with robust metal nodes and pro-labile linkers were initially synthesized. The mesopores were subsequently created as crystal defects through the splitting of a pro-labile-linker and the removal of the linker fragments by acid treatment. We demonstrate that linker labilization method can create controllable hierarchical porous structures in stable MOFs, which facilitates the diffusion and adsorption process of guest molecules to improve the performances of MOFs in adsorption and catalysis.

Construction of hierarchically porous metal–organic frameworks through linker labilization

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

Yuan, Shuai; Zou, Lanfang; Qin, Jun-Sheng

One major goal of metal–organic framework (MOF) research is the expansion of pore size and volume. Although many approaches have been attempted to increase the pore size of MOF materials, it is still a challenge to construct MOFs with precisely customized pore apertures for specific applications. W present a new method, namely linker labilization, to increase the MOF porosity and pore size, giving rise to hierarchical-pore architectures. Microporous MOFs with robust metal nodes and pro-labile linkers were initially synthesized. The mesopores were subsequently created as crystal defects through the splitting of a pro-labile-linker and the removal of the linker fragmentsmore » by acid treatment. We also demonstrate that linker labilization method can create controllable hierarchical porous structures in stable MOFs, which facilitates the diffusion and adsorption process of guest molecules to improve the performances of MOFs in adsorption and catalysis.« less

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.

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.

Digested sludge-derived three-dimensional hierarchical porous carbon for high-performance supercapacitor electrode.

PubMed

Zhang, Jia-Jia; Fan, Hao-Xiang; Dai, Xiao-Hu; Yuan, Shi-Jie

2018-04-01

Digested sludge, as the main by-product of the sewage sludge anaerobic digestion process, still contains considerable organic compounds. In this protocol, we report a facile method for preparing digested sludge-derived self-doped porous carbon material for high-performance supercapacitor electrodes via a sustainable pyrolysis/activation process. The obtained digested sludge-derived carbon material (HPDSC) exhibits versatile O-, N-doped hierarchical porous framework, high specific surface area (2103.6 m 2  g -1 ) and partial graphitization phase, which can facilitate ion transport, provide more storage sites for electrolyte ions and enhance the conductivity of active electrode materials. The HPDSC-based supercapacitor electrodes show favourable energy storage performance, with a specific capacitance of 245 F g -1 at 1.0 A g -1 in 0.5 M Na 2 SO 4 ; outstanding cycling stability, with 98.4% capacitance retention after 2000 cycles; and good rate performance (211 F g -1 at 11 A g -1 ). This work provides a unique self-doped three-dimensional hierarchical porous carbon material with a favourable charge storage capacity and at the same time finds a high value-added and environment-friendly strategy for disposal and recycling of digested sludge.

Digested sludge-derived three-dimensional hierarchical porous carbon for high-performance supercapacitor electrode

NASA Astrophysics Data System (ADS)

Zhang, Jia-Jia; Fan, Hao-Xiang; Dai, Xiao-Hu; Yuan, Shi-Jie

2018-04-01

Digested sludge, as the main by-product of the sewage sludge anaerobic digestion process, still contains considerable organic compounds. In this protocol, we report a facile method for preparing digested sludge-derived self-doped porous carbon material for high-performance supercapacitor electrodes via a sustainable pyrolysis/activation process. The obtained digested sludge-derived carbon material (HPDSC) exhibits versatile O-, N-doped hierarchical porous framework, high specific surface area (2103.6 m2 g-1) and partial graphitization phase, which can facilitate ion transport, provide more storage sites for electrolyte ions and enhance the conductivity of active electrode materials. The HPDSC-based supercapacitor electrodes show favourable energy storage performance, with a specific capacitance of 245 F g-1 at 1.0 A g-1 in 0.5 M Na2SO4; outstanding cycling stability, with 98.4% capacitance retention after 2000 cycles; and good rate performance (211 F g-1 at 11 A g-1). This work provides a unique self-doped three-dimensional hierarchical porous carbon material with a favourable charge storage capacity and at the same time finds a high value-added and environment-friendly strategy for disposal and recycling of digested sludge.

Learning In networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1995-01-01

Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deterministic nodes. Chain graphs are defined as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simplification process or to generate learning algorithms.

Feature-Based Visual Short-Term Memory Is Widely Distributed and Hierarchically Organized.

PubMed

Dotson, Nicholas M; Hoffman, Steven J; Goodell, Baldwin; Gray, Charles M

2018-06-15

Feature-based visual short-term memory is known to engage both sensory and association cortices. However, the extent of the participating circuit and the neural mechanisms underlying memory maintenance is still a matter of vigorous debate. To address these questions, we recorded neuronal activity from 42 cortical areas in monkeys performing a feature-based visual short-term memory task and an interleaved fixation task. We find that task-dependent differences in firing rates are widely distributed throughout the cortex, while stimulus-specific changes in firing rates are more restricted and hierarchically organized. We also show that microsaccades during the memory delay encode the stimuli held in memory and that units modulated by microsaccades are more likely to exhibit stimulus specificity, suggesting that eye movements contribute to visual short-term memory processes. These results support a framework in which most cortical areas, within a modality, contribute to mnemonic representations at timescales that increase along the cortical hierarchy. Copyright © 2018 Elsevier Inc. All rights reserved.

Photothermal-enhanced catalysis in core-shell plasmonic hierarchical Cu7S4 microsphere@zeolitic imidazole framework-8.

PubMed

Wang, Feifan; Huang, Yanjie; Chai, Zhigang; Zeng, Min; Li, Qi; Wang, Yuan; Xu, Dongsheng

2016-12-01

Conventional semiconductor photocatalysis based on band-edge absorption remains inefficient due to the limited harvesting of solar irradiation and the complicated surface/interface chemistry. Herein, novel photothermal-enhanced catalysis was achieved in a core-shell hierarchical Cu 7 S 4 nano-heater@ZIF-8 heterostructures via near-infrared localized surface plasmon resonance. Our results demonstrated that both the high surface temperature of the photothermal Cu 7 S 4 core and the close-adjacency of catalytic ZIF-8 shell contributed to the extremely enhanced catalytic activity. Under laser irradiation (1450 nm, 500 mW), the cyclocondensation reaction rate increased 4.5-5.4 fold compared to that of the process at room temperature, in which the 1.6-1.8 fold enhancement was due to the localized heating effect. The simulated sunlight experiments showed a photothermal activation efficiency (PTAE) of 0.07%, further indicating the validity of photothermal catalysis based on the plasmonic semiconductor nanomaterials. More generally, this approach provides a platform to improve reaction activity with efficient utilization of solar energy, which can be readily extended to other green-chemistry processes.

Implementation of a Goal-Based Systems Engineering Process Using the Systems Modeling Language (SysML)

NASA Technical Reports Server (NTRS)

Breckenridge, Jonathan T.; Johnson, Stephen B.

2013-01-01

This paper describes the core framework used to implement a Goal-Function Tree (GFT) based systems engineering process using the Systems Modeling Language. It defines a set of principles built upon by the theoretical approach described in the InfoTech 2013 ISHM paper titled "Goal-Function Tree Modeling for Systems Engineering and Fault Management" presented by Dr. Stephen B. Johnson. Using the SysML language, the principles in this paper describe the expansion of the SysML language as a baseline in order to: hierarchically describe a system, describe that system functionally within success space, and allocate detection mechanisms to success functions for system protection.

A hierarchical framework of aquatic ecological units in North America (Nearctic Zone).

Treesearch

James R. Maxwell; Clayton J. Edwards; Mark E. Jensen; Steven J. Paustian; Harry Parrott; Donley M. Hill

1995-01-01

Proposes a framework for classifying and mapping aquatic systems at various scales using ecologically significant physical and biological criteria. Classification and mapping concepts follow tenets of hierarchical theory, pattern recognition, and driving variables. Criteria are provided for the hierarchical classification and mapping of aquatic ecological units of...

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

Treesearch

Chad Babcock; Andrew O. Finley; John B. Bradford; Randy Kolka; Richard Birdsey; Michael G. Ryan

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both...

Assessing archetypes of organizational culture based on the Competing Values Framework: the experimental use of the framework in Japanese neonatal intensive care units

PubMed Central

Sasaki, Hatoko; Yonemoto, Naohiro; Mori, Rintaro; Nishida, Toshihiko; Kusuda, Satoshi; Nakayama, Takeo

2017-01-01

Abstract Objective To assess organizational culture in neonatal intensive care units (NICUs) in Japan. Design Cross-sectional survey of organizational culture. Setting Forty NICUs across Japan. Participants Physicians and nurses who worked in NICUs (n = 2006). Main Outcome Measures The Competing Values Framework (CVF) was used to assess the organizational culture of the study population. The 20-item CVF was divided into four culture archetypes: Group, Developmental, Hierarchical and Rational. We calculated geometric means (gmean) and 95% bootstrap confidence intervals of the individual dimensions by unit and occupation. The median number of staff, beds, physicians’ work hours and work engagement were also calculated to examine the differences by culture archetypes. Results Group (gmean = 34.6) and Hierarchical (gmean = 31.7) culture archetypes were higher than Developmental (gmean = 16.3) and Rational (gmean = 17.4) among physicians as a whole. Hierarchical (gmean = 36.3) was the highest followed by Group (gmean = 25.8), Developmental (gmean = 16.3) and Rational (gmean = 21.7) among nurses as a whole. Units with dominant Hierarchical culture had a slightly higher number of physicians (median = 7) than dominant Group culture (median = 6). Units with dominant Group culture had a higher number of beds (median = 12) than dominant Hierarchical culture (median = 9) among physicians. Nurses from units with a dominant Group culture (median = 2.8) had slightly higher work engagement compared with those in units with a dominant Hierarchical culture (median = 2.6). Conclusions Our findings revealed that organizational culture in NICUs varies depending on occupation and group size. Group and Hierarchical cultures predominated in Japanese NICUs. Assessing organizational culture will provide insights into the perceptions of unit values to improve quality of care. PMID:28371865

Assessing archetypes of organizational culture based on the Competing Values Framework: the experimental use of the framework in Japanese neonatal intensive care units.

PubMed

Sasaki, Hatoko; Yonemoto, Naohiro; Mori, Rintaro; Nishida, Toshihiko; Kusuda, Satoshi; Nakayama, Takeo

2017-06-01

To assess organizational culture in neonatal intensive care units (NICUs) in Japan. Cross-sectional survey of organizational culture. Forty NICUs across Japan. Physicians and nurses who worked in NICUs (n = 2006). The Competing Values Framework (CVF) was used to assess the organizational culture of the study population. The 20-item CVF was divided into four culture archetypes: Group, Developmental, Hierarchical and Rational. We calculated geometric means (gmean) and 95% bootstrap confidence intervals of the individual dimensions by unit and occupation. The median number of staff, beds, physicians' work hours and work engagement were also calculated to examine the differences by culture archetypes. Group (gmean = 34.6) and Hierarchical (gmean = 31.7) culture archetypes were higher than Developmental (gmean = 16.3) and Rational (gmean = 17.4) among physicians as a whole. Hierarchical (gmean = 36.3) was the highest followed by Group (gmean = 25.8), Developmental (gmean = 16.3) and Rational (gmean = 21.7) among nurses as a whole. Units with dominant Hierarchical culture had a slightly higher number of physicians (median = 7) than dominant Group culture (median = 6). Units with dominant Group culture had a higher number of beds (median = 12) than dominant Hierarchical culture (median = 9) among physicians. Nurses from units with a dominant Group culture (median = 2.8) had slightly higher work engagement compared with those in units with a dominant Hierarchical culture (median = 2.6). Our findings revealed that organizational culture in NICUs varies depending on occupation and group size. Group and Hierarchical cultures predominated in Japanese NICUs. Assessing organizational culture will provide insights into the perceptions of unit values to improve quality of care. © The Author 2017. Published by Oxford University Press in association with the International Society for Quality in Health Care

Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine

NASA Astrophysics Data System (ADS)

Graham, James; Ternovskiy, Igor V.

2013-06-01

We applied a two stage unsupervised hierarchical learning system to model complex dynamic surveillance and cyber space monitoring systems using a non-commercial version of the NeoAxis visualization software. The hierarchical scene learning and recognition approach is based on hierarchical expectation maximization, and was linked to a 3D graphics engine for validation of learning and classification results and understanding the human - autonomous system relationship. Scene recognition is performed by taking synthetically generated data and feeding it to a dynamic logic algorithm. The algorithm performs hierarchical recognition of the scene by first examining the features of the objects to determine which objects are present, and then determines the scene based on the objects present. This paper presents a framework within which low level data linked to higher-level visualization can provide support to a human operator and be evaluated in a detailed and systematic way.

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.

Robust, Efficient Depth Reconstruction With Hierarchical Confidence-Based Matching.

PubMed

Sun, Li; Chen, Ke; Song, Mingli; Tao, Dacheng; Chen, Gang; Chen, Chun

2017-07-01

In recent years, taking photos and capturing videos with mobile devices have become increasingly popular. Emerging applications based on the depth reconstruction technique have been developed, such as Google lens blur. However, depth reconstruction is difficult due to occlusions, non-diffuse surfaces, repetitive patterns, and textureless surfaces, and it has become more difficult due to the unstable image quality and uncontrolled scene condition in the mobile setting. In this paper, we present a novel hierarchical framework with multi-view confidence-based matching for robust, efficient depth reconstruction in uncontrolled scenes. Particularly, the proposed framework combines local cost aggregation with global cost optimization in a complementary manner that increases efficiency and accuracy. A depth map is efficiently obtained in a coarse-to-fine manner by using an image pyramid. Moreover, confidence maps are computed to robustly fuse multi-view matching cues, and to constrain the stereo matching on a finer scale. The proposed framework has been evaluated with challenging indoor and outdoor scenes, and has achieved robust and efficient depth reconstruction.

Regional forest resource assessment in an ecological framework: the Southern United States

Treesearch

Victor A. Rudis

1998-01-01

Information about forest resources grouped by ecologically homogeneous area can be used to discern relationships between those resources and ecological processes. The author used forest resource data from 0.4-ha plots, and data on population and land area (by county), together with a global-to-local hierarchical framework of land areas with similar ecological potential...

A Framework for Teaching Social and Environmental Sustainability to Undergraduate Business Majors

ERIC Educational Resources Information Center

Brumagim, Alan L.; Cann, Cynthia W.

2012-01-01

The authors outline an undergraduate exercise to help students more fully understand the environmental and social justice aspects of business sustainability activities. A simple hierarchical framework, based on Maslow's (1943) work, was utilized to help the students understand, analyze, and judge the vast amount of corporate sustainability…

How Misapplication of the Hydrologic Unit Framework Diminishes the Meaning of Watersheds

EPA Science Inventory

Hydrologic units provide a convenient nationwide set of geographic polygons based on an arbitrary subdivision of the drainage of land surface areas at several hierarchical levels. Half or more of these units, however, are not true watersheds as the official name of the framework,...

A multiscale, hierarchical model of pulse dynamics in arid-land ecosystems

USGS Publications Warehouse

Collins, Scott L.; Belnap, Jayne; Grimm, N. B.; Rudgers, J. A.; Dahm, Clifford N.; D'Odorico, P.; Litvak, M.; Natvig, D. O.; Peters, Douglas C.; Pockman, W. T.; Sinsabaugh, R. L.; Wolf, B. O.

2014-01-01

Ecological processes in arid lands are often described by the pulse-reserve paradigm, in which rain events drive biological activity until moisture is depleted, leaving a reserve. This paradigm is frequently applied to processes stimulated by one or a few precipitation events within a growing season. Here we expand the original framework in time and space and include other pulses that interact with rainfall. This new hierarchical pulse-dynamics framework integrates space and time through pulse-driven exchanges, interactions, transitions, and transfers that occur across individual to multiple pulses extending from micro to watershed scales. Climate change will likely alter the size, frequency, and intensity of precipitation pulses in the future, and arid-land ecosystems are known to be highly sensitive to climate variability. Thus, a more comprehensive understanding of arid-land pulse dynamics is needed to determine how these ecosystems will respond to, and be shaped by, increased climate variability.

Use Hierarchical Storage and Analysis to Exploit Intrinsic Parallelism

NASA Astrophysics Data System (ADS)

Zender, C. S.; Wang, W.; Vicente, P.

2013-12-01

Big Data is an ugly name for the scientific opportunities and challenges created by the growing wealth of geoscience data. How to weave large, disparate datasets together to best reveal their underlying properties, to exploit their strengths and minimize their weaknesses, to continually aggregate more information than the world knew yesterday and less than we will learn tomorrow? Data analytics techniques (statistics, data mining, machine learning, etc.) can accelerate pattern recognition and discovery. However, often researchers must, prior to analysis, organize multiple related datasets into a coherent framework. Hierarchical organization permits entire dataset to be stored in nested groups that reflect their intrinsic relationships and similarities. Hierarchical data can be simpler and faster to analyze by coding operators to automatically parallelize processes over isomorphic storage units, i.e., groups. The newest generation of netCDF Operators (NCO) embody this hierarchical approach, while still supporting traditional analysis approaches. We will use NCO to demonstrate the trade-offs involved in processing a prototypical Big Data application (analysis of CMIP5 datasets) using hierarchical and traditional analysis approaches.

A Framework for a Decision Support System in a Hierarchical Extended Enterprise Decision Context

NASA Astrophysics Data System (ADS)

Boza, Andrés; Ortiz, Angel; Vicens, Eduardo; Poler, Raul

Decision Support System (DSS) tools provide useful information to decision makers. In an Extended Enterprise, a new goal, changes in the current objectives or small changes in the extended enterprise configuration produce a necessary adjustment in its decision system. A DSS in this context must be flexible and agile to make suitable an easy and quickly adaptation to this new context. This paper proposes to extend the Hierarchical Production Planning (HPP) structure to an Extended Enterprise decision making context. In this way, a framework for DSS in Extended Enterprise context is defined using components of HPP. Interoperability details have been reviewed to identify the impact in this framework. The proposed framework allows overcoming some interoperability barriers, identifying and organizing components for a DSS in Extended Enterprise context, and working in the definition of an architecture to be used in the design process of a flexible DSS in Extended Enterprise context which can reuse components for futures Extended Enterprise configurations.

Statistical label fusion with hierarchical performance models

PubMed Central

Asman, Andrew J.; Dagley, Alexander S.; Landman, Bennett A.

2014-01-01

Label fusion is a critical step in many image segmentation frameworks (e.g., multi-atlas segmentation) as it provides a mechanism for generalizing a collection of labeled examples into a single estimate of the underlying segmentation. In the multi-label case, typical label fusion algorithms treat all labels equally – fully neglecting the known, yet complex, anatomical relationships exhibited in the data. To address this problem, we propose a generalized statistical fusion framework using hierarchical models of rater performance. Building on the seminal work in statistical fusion, we reformulate the traditional rater performance model from a multi-tiered hierarchical perspective. This new approach provides a natural framework for leveraging known anatomical relationships and accurately modeling the types of errors that raters (or atlases) make within a hierarchically consistent formulation. Herein, we describe several contributions. First, we derive a theoretical advancement to the statistical fusion framework that enables the simultaneous estimation of multiple (hierarchical) performance models within the statistical fusion context. Second, we demonstrate that the proposed hierarchical formulation is highly amenable to the state-of-the-art advancements that have been made to the statistical fusion framework. Lastly, in an empirical whole-brain segmentation task we demonstrate substantial qualitative and significant quantitative improvement in overall segmentation accuracy. PMID:24817809

Hierarchical Higher Order Crf for the Classification of Airborne LIDAR Point Clouds in Urban Areas

NASA Astrophysics Data System (ADS)

Niemeyer, J.; Rottensteiner, F.; Soergel, U.; Heipke, C.

2016-06-01

We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the distance and the orientation of a segment with respect to the closest road. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.

Towards a hierarchical optimization framework for spatially targeting incentive policies to promote green infrastructure amidst multiple objectives and uncertainty

EPA Science Inventory

We introduce a hierarchical optimization framework for spatially targeting green infrastructure (GI) incentive policies in order to meet objectives related to cost and environmental effectiveness. The framework explicitly simulates the interaction between multiple levels of polic...

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Cultural adaptation of evidence-based practice utilizing an iterative stakeholder process and theoretical framework: problem solving therapy for Chinese older adults

PubMed Central

Chu, Joyce P.; Huynh, Loanie; Areán, Patricia

2011-01-01

Objectives Main objectives were to familiarize the reader with a theoretical framework for modifying evidence-based interventions for cultural groups, and to provide an example of one method, Formative Method for Adapting Psychotherapies (FMAP), in the adaptation of an evidence-based intervention for a cultural group notorious for refusing mental health treatment. Methods Provider and client stakeholder input combined with an iterative testing process within the FMAP framework was utilized to create the Problem Solving Therapy—Chinese Older Adult (PST-COA) manual for depression. Data from pilot-testing the intervention with a clinically depressed Chinese elderly woman are reported. Results PST-COA is categorized as a ‘culturally-adapted’ treatment, where core mediating mechanisms of PST were preserved, but cultural themes of measurement methodology, stigma, hierarchical provider-client relationship expectations, and acculturation enhanced core components to make PST more understandable and relevant for Chinese elderly. Modifications also encompassed therapeutic framework and peripheral elements affecting engagement and retention. PST-COA applied with a depressed Chinese older adult indicated remission of clinical depression and improvement in mood. Fidelity with and acceptability of the treatment was sufficient as the client completed and reported high satisfaction with PST-COA. Conclusions PST, as a non-emotion-focused, evidence-based intervention, is a good fit for depressed Chinese elderly. Through an iterative stakeholder process of cultural adaptation, several culturally-specific modifications were applied to PST to create the PST-COA manual. PST-COA preserves core therapeutic PST elements but includes cultural adaptations in therapeutic framework and key administration and content areas that ensure greater applicability and effectiveness for the Chinese elderly community. PMID:21500283

Metascalable molecular dynamics simulation of nano-mechano-chemistry

NASA Astrophysics Data System (ADS)

Shimojo, F.; Kalia, R. K.; Nakano, A.; Nomura, K.; Vashishta, P.

2008-07-01

We have developed a metascalable (or 'design once, scale on new architectures') parallel application-development framework for first-principles based simulations of nano-mechano-chemical processes on emerging petaflops architectures based on spatiotemporal data locality principles. The framework consists of (1) an embedded divide-and-conquer (EDC) algorithmic framework based on spatial locality to design linear-scaling algorithms, (2) a space-time-ensemble parallel (STEP) approach based on temporal locality to predict long-time dynamics, and (3) a tunable hierarchical cellular decomposition (HCD) parallelization framework to map these scalable algorithms onto hardware. The EDC-STEP-HCD framework exposes and expresses maximal concurrency and data locality, thereby achieving parallel efficiency as high as 0.99 for 1.59-billion-atom reactive force field molecular dynamics (MD) and 17.7-million-atom (1.56 trillion electronic degrees of freedom) quantum mechanical (QM) MD in the framework of the density functional theory (DFT) on adaptive multigrids, in addition to 201-billion-atom nonreactive MD, on 196 608 IBM BlueGene/L processors. We have also used the framework for automated execution of adaptive hybrid DFT/MD simulation on a grid of six supercomputers in the US and Japan, in which the number of processors changed dynamically on demand and tasks were migrated according to unexpected faults. The paper presents the application of the framework to the study of nanoenergetic materials: (1) combustion of an Al/Fe2O3 thermite and (2) shock initiation and reactive nanojets at a void in an energetic crystal.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights from First Language Processing

ERIC Educational Resources Information Center

Pajak, Bozena; Fine, Alex B.; Kleinschmidt, Dave F.; Jaeger, T. Florian

2016-01-01

We present a framework of second and additional language (L2/L"n") acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit…

A Hierarchical Framework for Demand-Side Frequency Control

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

Moya, Christian; Zhang, Wei; Lian, Jianming

2014-06-02

With large-scale plans to integrate renewable generation, more resources will be needed to compensate for the uncertainty associated with intermittent generation resources. Under such conditions, performing frequency control using only supply-side resources become not only prohibitively expensive but also technically difficult. It is therefore important to explore how a sufficient proportion of the loads could assume a routine role in frequency control to maintain the stability of the system at an acceptable cost. In this paper, a novel hierarchical decentralized framework for frequency based load control is proposed. The framework involves two decision layers. The top decision layer determines themore » optimal droop gain required from the aggregated load response on each bus using a robust decentralized control approach. The second layer consists of a large number of devices, which switch probabilistically during contingencies so that the aggregated power change matches the desired droop amount according to the updated gains. The proposed framework is based on the classical nonlinear multi-machine power system model, and can deal with timevarying system operating conditions while respecting the physical constraints of individual devices. Realistic simulation results based on a 68-bus system are provided to demonstrate the effectiveness of the proposed strategy.« less

Hierarchical SAPO‐34 Architectures with Tailored Acid Sites using Sustainable Sugar Templates

PubMed Central

Miletto, Ivana; Ivaldi, Chiara; Paul, Geo; Chapman, Stephanie; Marchese, Leonardo; Raja, Robert

2018-01-01

Abstract In a distinct, bottom‐up synthetic methodology, monosaccharides (fructose and glucose) and disaccharides (sucrose) have been used as mesoporogens to template hierarchical SAPO‐34 catalysts. Detailed materials characterization, which includes solid‐state magic angle spinning NMR and probe‐based FTIR, reveals that, although the mesopore dimensions are modified by the identity of the sugar template, the desirable acid characteristics of the microporous framework are retained. When the activity of the hierarchical SAPO‐34 catalysts was evaluated in the industrially relevant Beckmann rearrangement, under liquid‐phase conditions, the enhanced mass‐transport properties of sucrose‐templated hierarchical SAPO‐34 were found to deliver a superior yield of ϵ‐caprolactam. PMID:29686961

Ecological subregion codes by county, coterminous United States

Treesearch

Victor A. Rudis

1999-01-01

This publication presents the National Hierarchical Framework of Ecological Units (ECOMAP 1993) by county for the coterminous United States. Assignment of the framework to individual counties is based on the predominant area by province and section to facilitate integration of county-referenced information with areas of uniform ecological potential. Included are maps...

Multiple Object Retrieval in Image Databases Using Hierarchical Segmentation Tree

ERIC Educational Resources Information Center

Chen, Wei-Bang

2012-01-01

The purpose of this research is to develop a new visual information analysis, representation, and retrieval framework for automatic discovery of salient objects of user's interest in large-scale image databases. In particular, this dissertation describes a content-based image retrieval framework which supports multiple-object retrieval. The…

Array-based Hierarchical Mesh Generation in Parallel

DOE PAGES

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

2015-11-03

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

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

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

On Developing a Taxonomy for Multidisciplinary Design Optimization: A Decision-Based Perspective

NASA Technical Reports Server (NTRS)

Lewis, Kemper; Mistree, Farrokh

1995-01-01

In this paper, we approach MDO from a Decision-Based Design (DBD) perspective and explore classification schemes for designing complex systems and processes. Specifically, we focus on decisions, which are only a small portion of the Decision Support Problem (DSP) Technique, our implementation of DBD. We map coupled nonhierarchical and hierarchical representations from the DSP Technique into the Balling-Sobieski (B-S) framework (Balling and Sobieszczanski-Sobieski, 1994), and integrate domain-independent linguistic terms to complete our taxonomy. Application of DSPs to the design of complex, multidisciplinary systems include passenger aircraft, ships, damage tolerant structural and mechanical systems, and thermal energy systems. In this paper we show that Balling-Sobieski framework is consistent with that of the Decision Support Problem Technique through the use of linguistic entities to describe the same type of formulations. We show that the underlying linguistics of the solution approaches are the same and can be coalesced into a homogeneous framework with which to base the research, application, and technology MDO upon. We introduce, in the Balling-Sobieski framework, examples of multidisciplinary design, namely, aircraft, damage tolerant structural and mechanical systems, and thermal energy systems.

Formation Flying With Decentralized Control in Libration Point Orbits

NASA Technical Reports Server (NTRS)

Folta, David; Carpenter, J. Russell; Wagner, Christoph

2000-01-01

A decentralized control framework is investigated for applicability of formation flying control in libration orbits. The decentralized approach, being non-hierarchical, processes only direct measurement data, in parallel with the other spacecraft. Control is accomplished via linearization about a reference libration orbit with standard control using a Linear Quadratic Regulator (LQR) or the GSFC control algorithm. Both are linearized about the current state estimate as with the extended Kalman filter. Based on this preliminary work, the decentralized approach appears to be feasible for upcoming libration missions using distributed spacecraft.

Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, Robert M.

2008-01-01

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics.

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.

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

PubMed

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

2017-05-01

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

Semantic-based surveillance video retrieval.

PubMed

Hu, Weiming; Xie, Dan; Fu, Zhouyu; Zeng, Wenrong; Maybank, Steve

2007-04-01

Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene.

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.

A hierarchical instrumental decision theory of nicotine dependence.

PubMed

Hogarth, Lee; Troisi, Joseph R

2015-01-01

It is important to characterize the learning processes governing tobacco-seeking in order to understand how best to treat this behavior. Most drug learning theories have adopted a Pavlovian framework wherein the conditioned response is the main motivational process. We favor instead a hierarchical instrumental decision account, wherein expectations about the instrumental contingency between voluntary tobacco-seeking and the receipt of nicotine reward determines the probability of executing this behavior. To support this view, we review titration and nicotine discrimination research showing that internal signals for deprivation/satiation modulate expectations about the current incentive value of smoking, thereby modulating the propensity of this behavior. We also review research on cue-reactivity which has shown that external smoking cues modulate expectations about the probability of the tobacco-seeking response being effective, thereby modulating the propensity of this behavior. Economic decision theory is then considered to elucidate how expectations about the value and probability of response-nicotine contingency are integrated to form an overall utility estimate for that option for comparison with qualitatively different, nonsubstitute reinforcers, to determine response selection. As an applied test for this hierarchical instrumental decision framework, we consider how well it accounts for individual liability to smoking uptake and perseveration, pharmacotherapy, cue-extinction therapies, and plain packaging. We conclude that the hierarchical instrumental account is successful in reconciling this broad range of phenomenon precisely because it accepts that multiple diverse sources of internal and external information must be integrated to shape the decision to smoke.

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.

Biological framework for soil aggregation: Implications for ecological functions.

NASA Astrophysics Data System (ADS)

Ghezzehei, Teamrat; Or, Dani

2016-04-01

Soil aggregation is heuristically understood as agglomeration of primary particles bound together by biotic and abiotic cementing agents. The organization of aggregates is believed to be hierarchical in nature; whereby primary particles bond together to form secondary particles and subsequently merge to form larger aggregates. Soil aggregates are not permanent structures, they continuously change in response to internal and external forces and other drivers, including moisture, capillary pressure, temperature, biological activity, and human disturbances. Soil aggregation processes and the resulting functionality span multiple spatial and temporal scales. The intertwined biological and physical nature of soil aggregation, and the time scales involved precluded a universally applicable and quantifiable framework for characterizing the nature and function of soil aggregation. We introduce a biophysical framework of soil aggregation that considers the various modes and factors of the genesis, maturation and degradation of soil aggregates including wetting/drying cycles, soil mechanical processes, biological activity and the nature of primary soil particles. The framework attempts to disentangle mechanical (compaction and soil fragmentation) from in-situ biophysical aggregation and provides a consistent description of aggregate size, hierarchical organization, and life time. It also enables quantitative description of biotic and abiotic functions of soil aggregates including diffusion and storage of mass and energy as well as role of aggregates as hot spots of nutrient accumulation, biodiversity, and biogeochemical cycles.

A Hierarchical and Dynamic Seascape Framework for Scaling and Comparing Ocean Biodiversity Observations

NASA Astrophysics Data System (ADS)

Kavanaugh, M.; Muller-Karger, F. E.; Montes, E.; Santora, J. A.; Chavez, F.; Messié, M.; Doney, S. C.

2016-02-01

The pelagic ocean is a complex system in which physical, chemical and biological processes interact to shape patterns on multiple spatial and temporal scales and levels of ecological organization. Monitoring and management of marine seascapes must consider a hierarchical and dynamic mosaic, where the boundaries, extent, and location of features change with time. As part of a Marine Biodiversity Observing Network demonstration project, we conducted a multiscale classification of dynamic coastal seascapes in the northeastern Pacific and Gulf of Mexico using multivariate satellite and modeled data. Synoptic patterns were validated using mooring and ship-based observations that spanned multiple trophic levels and were collected as part of several long-term monitoring programs, including the Monterey Bay and Florida Keys National Marine Sanctuaries. Seascape extent and habitat diversity varied as a function of both seasonal and interannual forcing. We discuss the patterns of in situ observations in the context of seascape dynamics and the effect on rarefaction, spatial patchiness, and tracking and comparing ecosystems through time. A seascape framework presents an effective means to translate local biodiversity measurements to broader spatiotemporal scales, scales relevant for modeling the effects of global change and enabling whole-ecosystem management in the dynamic ocean.

Adverse Outcome Pathway (AOP) framework for embryonic vascular disruption and developmental defects (SOT)

EPA Science Inventory

Vascular development commences with de novo assembly of a primary capillary plexus (vasculogenesis) followed by its expansion (angiogenesis) and maturation (angio-adaptation) into a hierarchical system of arteries and veins. These processes are tightly regulated by genetic signal...

Managing changes in distributed biomedical ontologies using hierarchical distributed graph transformation.

PubMed

Shaban-Nejad, Arash; Haarslev, Volker

2015-01-01

The issue of ontology evolution and change management is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies and interactions with other existing ontologies have been widely neglected. In our research, after revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, Represent, Legitimate and Reproduce (RLR), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general and aids in tracking and representing the changes, particularly through the use of category theory and hierarchical graph transformation.

Hierarchical ZIF-8 toward Immobilizing Burkholderia cepacia Lipase for Application in Biodiesel Preparation.

PubMed

Adnan, Miaad; Li, Kai; Wang, Jianhua; Xu, Li; Yan, Yunjun

2018-05-10

A hierarchical mesoporous zeolitic imidazolate framework (ZIF-8) was processed based on cetyltrimethylammonium bromide (CTAB) as a morphological regulating agent and amino acid (l-histidine) as assisting template agent. Burkholderia cepacia lipase (BCL) was successfully immobilized by ZIF-8 as the carrier via an adsorption method (BCL-ZIF-8). The immobilized lipase (BCL) showed utmost activity recovery up to 1279%, a 12-fold boost in its free counterpart. BCL-ZIF-8 was used as a biocatalyst in the transesterification reaction for the production of biodiesel with 93.4% yield. There was no significant lowering of conversion yield relative to original activity for BCL-ZIF-8 when continuously reused for eight cycles. This work provides a new outlook for biotechnological importance by immobilizing lipase on the hybrid catalyst (ZIF-8) and opens the door for its uses in the industrial field.

The new model of chemical evolution of r-process elements based on the hierarchical galaxy formation. I. Ba and Eu

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

Komiya, Yutaka; Suda, Takuma; Yamada, Shimako

2014-03-10

We investigate the chemical enrichment of r-process elements in the early evolutionary stages of the Milky Way halo within the framework of hierarchical galaxy formation using a semi-analytic merger tree. In this paper, we focus on heavy r-process elements, Ba and Eu, of extremely metal-poor (EMP) stars and give constraints on their astronomical sites. Our models take into account changes of the surface abundances of EMP stars by the accretion of interstellar medium (ISM). We also consider metal-enrichment of intergalactic medium by galactic winds and the resultant pre-enrichment of proto-galaxies. The trend and scatter of the observed r-process abundances aremore » well reproduced by our hierarchical model with ∼10% of core-collapse supernovae in low-mass end (∼10 M {sub ☉}) as a dominant r-process source and the star formation efficiency of ∼10{sup –10} yr{sup –1}. For neutron star mergers as an r-process source, their coalescence timescale has to be ∼10{sup 7} yr, and the event rates ∼100 times larger than currently observed in the Galaxy. We find that the accretion of ISM is a dominant source of r-process elements for stars with [Ba/H] < –3.5. In this model, a majority of stars at [Fe/H] < –3 are formed without r-process elements, but their surfaces are polluted by the ISM accretion. The pre-enrichment affects ∼4% of proto-galaxies, and yet, is surpassed by the ISM accretion in the surface of EMP stars.« less

A Bayesian Hierarchical Modeling Scheme for Estimating Erosion Rates Under Current Climate Conditions

NASA Astrophysics Data System (ADS)

Lowman, L.; Barros, A. P.

2014-12-01

Computational modeling of surface erosion processes is inherently difficult because of the four-dimensional nature of the problem and the multiple temporal and spatial scales that govern individual mechanisms. Landscapes are modified via surface and fluvial erosion and exhumation, each of which takes place over a range of time scales. Traditional field measurements of erosion/exhumation rates are scale dependent, often valid for a single point-wise location or averaging over large aerial extents and periods with intense and mild erosion. We present a method of remotely estimating erosion rates using a Bayesian hierarchical model based upon the stream power erosion law (SPEL). A Bayesian approach allows for estimating erosion rates using the deterministic relationship given by the SPEL and data on channel slopes and precipitation at the basin and sub-basin scale. The spatial scale associated with this framework is the elevation class, where each class is characterized by distinct morphologic behavior observed through different modes in the distribution of basin outlet elevations. Interestingly, the distributions of first-order outlets are similar in shape and extent to the distribution of precipitation events (i.e. individual storms) over a 14-year period between 1998-2011. We demonstrate an application of the Bayesian hierarchical modeling framework for five basins and one intermontane basin located in the central Andes between 5S and 20S. Using remotely sensed data of current annual precipitation rates from the Tropical Rainfall Measuring Mission (TRMM) and topography from a high resolution (3 arc-seconds) digital elevation map (DEM), our erosion rate estimates are consistent with decadal-scale estimates based on landslide mapping and sediment flux observations and 1-2 orders of magnitude larger than most millennial and million year timescale estimates from thermochronology and cosmogenic nuclides.

The Frontal Lobes and Theory of Mind: Developmental Concepts from Adult Focal Lesion Research

ERIC Educational Resources Information Center

Stuss, Donald T.; Anderson, Vicki

2004-01-01

The primary objective in this paper is to present a framework to understand the structure of consciousness. We argue that consciousness has been difficult to define because there are different kinds of consciousness, hierarchically organized, which need to be differentiated. Our framework is based on evidence from adult focal lesion research. The…

MOF-derived hierarchical double-shelled NiO/ZnO hollow spheres for high-performance supercapacitors.

PubMed

Li, Guo-Chang; Liu, Peng-Fei; Liu, Rui; Liu, Minmin; Tao, Kai; Zhu, Shuai-Ru; Wu, Meng-Ke; Yi, Fei-Yan; Han, Lei

2016-09-14

Nanorods-composed yolk-shell bimetallic-organic frameworks microspheres are successfully synthesized by a one-step solvothermal method in the absence of any template or surfactant. Furthermore, hierarchical double-shelled NiO/ZnO hollow spheres are obtained by calcination of the bimetallic organic frameworks in air. The NiO/ZnO hollow spheres, as supercapacitor electrodes, exhibit high capacitance of 497 F g(-1) at the current density of 1.3 A g(-1) and present a superior cycling stability. The superior electrochemical performance is believed to come from the unique double-shelled NiO/ZnO hollow structures, which offer free space to accommodate the volume change during the ion insertion and desertion processes, as well as provide rich electroactive sites for the electrochemical reactions.

A hierarchical spatial framework for forest landscape planning.

Treesearch

Pete Bettinger; Marie Lennette; K. Norman Johnson; Thomas A. Spies

2005-01-01

A hierarchical spatial framework for large-scale, long-term forest landscape planning is presented along with example policy analyses for a 560,000 ha area of the Oregon Coast Range. The modeling framework suggests utilizing the detail provided by satellite imagery to track forest vegetation condition and for representation of fine-scale features, such as riparian...

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

DOE PAGES

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

2015-12-04

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

Decentralized hierarchical partitioning of centralized integrated controllers. [for flight propulsion in STOVLs

NASA Technical Reports Server (NTRS)

Schmidt, Phillip; Garg, Sanjay

1991-01-01

A framework for a decentralized hierarchical controller partitioning structure is developed. This structure allows for the design of separate airframe and propulsion controllers which, when assembled, will meet the overall design criterion for the integrated airframe/propulsion system. An algorithm based on parameter optimization of the state-space representation for the subsystem controllers is described. The algorithm is currently being applied to an integrated flight propulsion control design example.

Gel-based morphological design of zirconium metal–organic frameworks† †Electronic supplementary information (ESI) available: Optical microscopy, PDF TGA, Hg intrusion, nanoindentation, gels of various Zr-MOFs, oil-drop process. See DOI: 10.1039/c6sc05602d Click here for additional data file.

PubMed Central

Bueken, Bart; Van Velthoven, Niels; Willhammar, Tom; Stassin, Timothée; Stassen, Ivo; Keen, David A.; Baron, Gino V.; Denayer, Joeri F. M.; Ameloot, Rob; Bals, Sara

2017-01-01

The ability of metal–organic frameworks (MOFs) to gelate under specific synthetic conditions opens up new opportunities in the preparation and shaping of hierarchically porous MOF monoliths, which could be directly implemented for catalytic and adsorptive applications. In this work, we present the first examples of xero- or aerogel monoliths consisting solely of nanoparticles of several prototypical Zr4+-based MOFs: UiO-66-X (X = H, NH2, NO2, (OH)2), UiO-67, MOF-801, MOF-808 and NU-1000. High reactant and water concentrations during synthesis were observed to induce the formation of gels, which were converted to monolithic materials by drying in air or supercritical CO2. Electron microscopy, combined with N2 physisorption experiments, was used to show that irregular nanoparticle packing leads to pure MOF monoliths with hierarchical pore systems, featuring both intraparticle micropores and interparticle mesopores. Finally, UiO-66 gels were shaped into monolithic spheres of 600 μm diameter using an oil-drop method, creating promising candidates for packed-bed catalytic or adsorptive applications, where hierarchical pore systems can greatly mitigate mass transfer limitations. PMID:28553536

Hierarchical and hybrid energy storage devices in data centers: Architecture, control and provisioning.

PubMed

Sun, Mengshu; Xue, Yuankun; Bogdan, Paul; Tang, Jian; Wang, Yanzhi; Lin, Xue

2018-01-01

Recently, a new approach has been introduced that leverages and over-provisions energy storage devices (ESDs) in data centers for performing power capping and facilitating capex/opex reductions, without performance overhead. To fully realize the potential benefits of the hierarchical ESD structure, we propose a comprehensive design, control, and provisioning framework including (i) designing power delivery architecture supporting hierarchical ESD structure and hybrid ESDs for some levels, as well as (ii) control and provisioning of the hierarchical ESD structure including run-time ESD charging/discharging control and design-time determination of ESD types, homogeneous/hybrid options, ESD provisioning at each level. Experiments have been conducted using real Google data center workloads based on realistic data center specifications.

Hierarchical and hybrid energy storage devices in data centers: Architecture, control and provisioning

PubMed Central

Xue, Yuankun; Bogdan, Paul; Tang, Jian; Wang, Yanzhi; Lin, Xue

2018-01-01

Recently, a new approach has been introduced that leverages and over-provisions energy storage devices (ESDs) in data centers for performing power capping and facilitating capex/opex reductions, without performance overhead. To fully realize the potential benefits of the hierarchical ESD structure, we propose a comprehensive design, control, and provisioning framework including (i) designing power delivery architecture supporting hierarchical ESD structure and hybrid ESDs for some levels, as well as (ii) control and provisioning of the hierarchical ESD structure including run-time ESD charging/discharging control and design-time determination of ESD types, homogeneous/hybrid options, ESD provisioning at each level. Experiments have been conducted using real Google data center workloads based on realistic data center specifications. PMID:29351553

Collaborative enterprise and virtual prototyping (CEVP): a product-centric approach to distributed simulation

NASA Astrophysics Data System (ADS)

Saunders, Vance M.

1999-06-01

The downsizing of the Department of Defense (DoD) and the associated reduction in budgets has re-emphasized the need for commonality, reuse, and standards with respect to the way DoD does business. DoD has implemented significant changes in how it buys weapon systems. The new emphasis is on concurrent engineering with Integrated Product and Process Development and collaboration with Integrated Product Teams. The new DoD vision includes Simulation Based Acquisition (SBA), a process supported by robust, collaborative use of simulation technology that is integrated across acquisition phases and programs. This paper discusses the Air Force Research Laboratory's efforts to use Modeling and Simulation (M&S) resources within a Collaborative Enterprise Environment to support SBA and other Collaborative Enterprise and Virtual Prototyping (CEVP) applications. The paper will discuss four technology areas: (1) a Processing Ontology that defines a hierarchically nested set of collaboration contexts needed to organize and support multi-disciplinary collaboration using M&S, (2) a partial taxonomy of intelligent agents needed to manage different M&S resource contributions to advancing the state of product development, (3) an agent- based process for interfacing disparate M&S resources into a CEVP framework, and (4) a Model-View-Control based approach to defining `a new way of doing business' for users of CEVP frameworks/systems.

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.

Understanding multiple stressors in a Mediterranean basin: Combined effects of land use, water scarcity and nutrient enrichment.

PubMed

Segurado, Pedro; Almeida, Carina; Neves, Ramiro; Ferreira, Maria Teresa; Branco, Paulo

2018-05-15

River basins are extremely complex hierarchical and directional systems that are affected by a multitude of interacting stressors. This complexity hampers effective management and conservation planning to be effectively implemented, especially under climate change. The objective of this work is to provide a wide scale approach to basin management by interpreting the effect of isolated and interacting factors in several biotic elements (fish, macroinvertebrates, phytobenthos and macrophytes). For that, a case study in the Sorraia basin (Central Portugal), a Mediterranean system mainly facing water scarcity and diffuse pollution problems, was chosen. To develop the proposed framework, a combination of process-based modelling to simulate hydrological and nutrient enrichment stressors and empirical modelling to relate these stressors - along with land use and natural background - with biotic indicators, was applied. Biotic indicators based on ecological quality ratios from WFD biomonitoring data were used as response variables. Temperature, river slope, % of agriculture in the upstream catchment and total N were the variables more frequently ranked as the most relevant. Both the two significant interactions found between single hydrological and nutrient enrichment stressors indicated antagonistic effects. This study demonstrates the potentialities of coupling process-based modelling with empirical modelling within a single framework, allowing relationships among different ecosystem states to be hierarchized, interpreted and predicted at multiple spatial and temporal scales. It also demonstrates how isolated and interacting stressors can have a different impact on biotic quality. When performing conservation or management plans, the stressor hierarchy should be considered as a way of prioritizing actions in a cost-effective perspective. Copyright © 2017 Elsevier B.V. All rights reserved.

A study of microindentation hardness tests by mechanism-based strain gradient plasticity

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

Huang, Y.; Xue, Z.; Gao, H.

2000-08-01

We recently proposed a theory of mechanism-based strain gradient (MSG) plasticity to account for the size dependence of plastic deformation at micron- and submicron-length scales. The MSG plasticity theory connects micron-scale plasticity to dislocation theories via a multiscale, hierarchical framework linking Taylor's dislocation hardening model to strain gradient plasticity. Here we show that the theory of MSG plasticity, when used to study micro-indentation, indeed reproduces the linear dependence observed in experiments, thus providing an important self-consistent check of the theory. The effects of pileup, sink-in, and the radius of indenter tip have been taken into account in the indentation model.more » In accomplishing this objective, we have generalized the MSG plasticity theory to include the elastic deformation in the hierarchical framework. (c) 2000 Materials Research Society.« less

Decision-making in schizophrenia: A predictive-coding perspective.

PubMed

Sterzer, Philipp; Voss, Martin; Schlagenhauf, Florian; Heinz, Andreas

2018-05-31

Dysfunctional decision-making has been implicated in the positive and negative symptoms of schizophrenia. Decision-making can be conceptualized within the framework of hierarchical predictive coding as the result of a Bayesian inference process that uses prior beliefs to infer states of the world. According to this idea, prior beliefs encoded at higher levels in the brain are fed back as predictive signals to lower levels. Whenever these predictions are violated by the incoming sensory data, a prediction error is generated and fed forward to update beliefs encoded at higher levels. Well-documented impairments in cognitive decision-making support the view that these neural inference mechanisms are altered in schizophrenia. There is also extensive evidence relating the symptoms of schizophrenia to aberrant signaling of prediction errors, especially in the domain of reward and value-based decision-making. Moreover, the idea of altered predictive coding is supported by evidence for impaired low-level sensory mechanisms and motor processes. We review behavioral and neural findings from these research areas and provide an integrated view suggesting that schizophrenia may be related to a pervasive alteration in predictive coding at multiple hierarchical levels, including cognitive and value-based decision-making processes as well as sensory and motor systems. We relate these findings to decision-making processes and propose that varying degrees of impairment in the implicated brain areas contribute to the variety of psychotic experiences. Copyright © 2018 Elsevier Inc. All rights reserved.

A dual-process perspective on fluency-based aesthetics: the pleasure-interest model of aesthetic liking.

PubMed

Graf, Laura K M; Landwehr, Jan R

2015-11-01

In this article, we develop an account of how aesthetic preferences can be formed as a result of two hierarchical, fluency-based processes. Our model suggests that processing performed immediately upon encountering an aesthetic object is stimulus driven, and aesthetic preferences that accrue from this processing reflect aesthetic evaluations of pleasure or displeasure. When sufficient processing motivation is provided by a perceiver's need for cognitive enrichment and/or the stimulus' processing affordance, elaborate perceiver-driven processing can emerge, which gives rise to fluency-based aesthetic evaluations of interest, boredom, or confusion. Because the positive outcomes in our model are pleasure and interest, we call it the Pleasure-Interest Model of Aesthetic Liking (PIA Model). Theoretically, this model integrates a dual-process perspective and ideas from lay epistemology into processing fluency theory, and it provides a parsimonious framework to embed and unite a wealth of aesthetic phenomena, including contradictory preference patterns for easy versus difficult-to-process aesthetic stimuli. © 2015 by the Society for Personality and Social Psychology, Inc.

A Bayesian Network Based Global Sensitivity Analysis Method for Identifying Dominant Processes in a Multi-physics Model

NASA Astrophysics Data System (ADS)

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

2016-12-01

Sensitivity analysis has been an important tool in groundwater modeling to identify the influential parameters. Among various sensitivity analysis methods, the variance-based global sensitivity analysis has gained popularity for its model independence characteristic and capability of providing accurate sensitivity measurements. However, the conventional variance-based method only considers uncertainty contribution of single model parameters. In this research, we extended the variance-based method to consider more uncertainty sources and developed a new framework to allow flexible combinations of different uncertainty components. We decompose the uncertainty sources into a hierarchical three-layer structure: scenario, model and parametric. Furthermore, each layer of uncertainty source is capable of containing multiple components. An uncertainty and sensitivity analysis framework was then constructed following this three-layer structure using Bayesian network. Different uncertainty components are represented as uncertain nodes in this network. Through the framework, variance-based sensitivity analysis can be implemented with great flexibility of using different grouping strategies for uncertainty components. The variance-based sensitivity analysis thus is improved to be able to investigate the importance of an extended range of uncertainty sources: scenario, model, and other different combinations of uncertainty components which can represent certain key model system processes (e.g., groundwater recharge process, flow reactive transport process). For test and demonstration purposes, the developed methodology was implemented into a test case of real-world groundwater reactive transport modeling with various uncertainty sources. The results demonstrate that the new sensitivity analysis method is able to estimate accurate importance measurements for any uncertainty sources which were formed by different combinations of uncertainty components. The new methodology can provide useful information for environmental management and decision-makers to formulate policies and strategies.

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.

Anatomical Entity Recognition with a Hierarchical Framework Augmented by External Resources

PubMed Central

Xu, Yan; Hua, Ji; Ni, Zhaoheng; Chen, Qinlang; Fan, Yubo; Ananiadou, Sophia; Chang, Eric I-Chao; Tsujii, Junichi

2014-01-01

References to anatomical entities in medical records consist not only of explicit references to anatomical locations, but also other diverse types of expressions, such as specific diseases, clinical tests, clinical treatments, which constitute implicit references to anatomical entities. In order to identify these implicit anatomical entities, we propose a hierarchical framework, in which two layers of named entity recognizers (NERs) work in a cooperative manner. Each of the NERs is implemented using the Conditional Random Fields (CRF) model, which use a range of external resources to generate features. We constructed a dictionary of anatomical entity expressions by exploiting four existing resources, i.e., UMLS, MeSH, RadLex and BodyPart3D, and supplemented information from two external knowledge bases, i.e., Wikipedia and WordNet, to improve inference of anatomical entities from implicit expressions. Experiments conducted on 300 discharge summaries showed a micro-averaged performance of 0.8509 Precision, 0.7796 Recall and 0.8137 F1 for explicit anatomical entity recognition, and 0.8695 Precision, 0.6893 Recall and 0.7690 F1 for implicit anatomical entity recognition. The use of the hierarchical framework, which combines the recognition of named entities of various types (diseases, clinical tests, treatments) with information embedded in external knowledge bases, resulted in a 5.08% increment in F1. The resources constructed for this research will be made publicly available. PMID:25343498

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

Low temperature oxidative desulfurization with hierarchically mesoporous titaniumsilicate Ti-SBA-2 single crystals.

PubMed

Shi, Chengxiang; Wang, Wenxuan; Liu, Ni; Xu, Xueyan; Wang, Danhong; Zhang, Minghui; Sun, Pingchuan; Chen, Tiehong

2015-07-21

Hierarchically porous Ti-SBA-2 with high framework Ti content (up to 5 wt%) was firstly synthesized by employing organic mesomorphous complexes of a cationic surfactant (CTAB) and an anionic polyelectrolyte (PAA) as templates. The material exhibited excellent performance in oxidative desulfurization of diesel fuel at low temperature (40 °C or 25 °C) due to the unique hierarchically porous structure and high framework Ti content.

Hierarchical Metal-Organic Framework-Assembled Membrane Filter for Efficient Removal of Particulate Matter.

PubMed

Koo, Won-Tae; Jang, Ji-Soo; Qiao, Shaopeng; Hwang, Wontae; Jha, Gaurav; Penner, Reginald M; Kim, Il-Doo

2018-06-13

Here, we propose heterogeneous nucleation-assisted hierarchical growth of metal-organic frameworks (MOFs) for efficient particulate matter (PM) removal. The assembly of two-dimensional (2D) Zn-based zeolite imidazole frameworks (2D-ZIF-L) in deionized water over a period of time produced hierarchical ZIF-L (H-ZIF-L) on hydrophilic substrates. During the assembly, the second nucleation and growth of ZIF-L occurred on the surface of the first ZIF-L, leading to the formation of flowerlike H-ZIF-L on the substrate. The flowerlike H-ZIF-L was easily synthesized on various substrates, namely, glass, polyurethane three-dimensional foam, nylon microfibers, and nonwoven fabrics. We demonstrated H-ZIF-L-assembled polypropylene microfibers as a washable membrane filter with highly efficient PM removal property (92.5 ± 0.8% for PM 2.5 and 99.5 ± 0.2% for PM 10 ), low pressure drop (10.5 Pa at 25 L min -1 ), long-term stability, and superior recyclability. These outstanding particle filtering properties are mainly attributed to the unique structure of the 2D-shaped H-ZIF-L, which is tightly anchored on individual fibers comprising the membrane.

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

Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models

USGS Publications Warehouse

Tipton, John; Hooten, Mevin B.; Pederson, Neil; Tingley, Martin; Bishop, Daniel

2016-01-01

Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate has changed in the past and how climate might change in the future. Statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one-dimensional summary of annual growth that represents a multi-dimensional set of climatic and biotic influences. We develop a Bayesian hierarchical framework using a nonlinear, biologically motivated tree ring growth model to jointly reconstruct temperature and precipitation in the Hudson Valley, New York. Using a common growth function to describe the response of a tree to climate, we allow for species-specific parameterizations of the growth response. To enable predictive backcasts, we model the climate variables with a vector autoregressive process on an annual timescale coupled with a multivariate conditional autoregressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation on a monthly scale. Our multi-scale temporal model allows for flexibility in the climate response through time at different temporal scales and predicts reasonable climate scenarios given tree ring width data.

[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.

Rod-like hierarchical Sn/SnOx@C nanostructures with enhanced lithium storage properties

NASA Astrophysics Data System (ADS)

Yang, Juan; Chen, Sanmei; Tang, Jingjing; Tian, Hangyu; Bai, Tao; Zhou, Xiangyang

2018-03-01

Rod-like hierarchical Sn/SnOx@C nanostructures have been designed and synthesized via calcining resorcinol-formaldehyde (RF) resin coated Sn-based metal-organic frameworks. The rod-like hierarchical Sn/SnOx@C nanostructures are made of a great number of carbon-wrapped primary Sn/SnOx nanospheres of 100-200 nm in diameter. The as-prepared hierarchical Sn/SnOx@C nanocomposite manifests a high initial reversible capacity of 1177 mAh g-1 and remains 1001 mAh g-1 after 240 cycles at a current density of 200 mA g-1. It delivers outstanding high-rate performance with a reversible capacity of 823 mAh g-1 even at a high current density of 1000 mA g-1. The enhanced electrochemical performances of the Sn/SnOx@C electrode are mainly attributed to the synergistic effect of the unique hierarchical micro/nanostructures and the protective carbon layer.

Synthesis and characterization of high-surface-area millimeter-sized silica beads with hierarchical multi-modal pore structure by the addition of agar

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

Han, Yosep; Choi, Junhyun; Tong, Meiping, E-mail: tongmeiping@iee.pku.edu.cn

2014-04-01

Millimeter-sized spherical silica foams (SSFs) with hierarchical multi-modal pore structure featuring high specific surface area and ordered mesoporous frameworks were successfully prepared using aqueous agar addition, foaming and drop-in-oil processes. The pore-related properties of the prepared spherical silica (SSs) and SSFs were systematically characterized by field emission-scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), small-angle X-ray diffraction (SAXRD), Hg intrusion porosimetry, and N{sub 2} adsorption–desorption isotherm measurements. Improvements in the BET surface area and total pore volume were observed at 504 m{sup 2} g{sup −1} and 5.45 cm{sup 3} g{sup −1}, respectively, after an agar addition and foaming process. Despitemore » the increase in the BET surface area, the mesopore wall thickness and the pore size of the mesopores generated from the block copolymer with agar addition were unchanged based on the SAXRD, TEM, and BJH methods. The SSFs prepared in the present study were confirmed to have improved BET surface area and micropore volume through the agar loading, and to exhibit interconnected 3-dimensional network macropore structure leading to the enhancement of total porosity and BET surface area via the foaming process. - Highlights: • Millimeter-sized spherical silica foams (SSFs) are successfully prepared. • SSFs exhibit high BET surface area and ordered hierarchical pore structure. • Agar addition improves BET surface area and micropore volume of SSFs. • Foaming process generates interconnected 3-D network macropore structure of SSFs.« less

Working toward integrated models of alpine plant distribution.

PubMed

Carlson, Bradley Z; Randin, Christophe F; Boulangeat, Isabelle; Lavergne, Sébastien; Thuiller, Wilfried; Choler, Philippe

2013-10-01

Species distribution models (SDMs) have been frequently employed to forecast the response of alpine plants to global changes. Efforts to model alpine plant distribution have thus far been primarily based on a correlative approach, in which ecological processes are implicitly addressed through a statistical relationship between observed species occurrences and environmental predictors. Recent evidence, however, highlights the shortcomings of correlative SDMs, especially in alpine landscapes where plant species tend to be decoupled from atmospheric conditions in micro-topographic habitats and are particularly exposed to geomorphic disturbances. While alpine plants respond to the same limiting factors as plants found at lower elevations, alpine environments impose a particular set of scale-dependent and hierarchical drivers that shape the realized niche of species and that require explicit consideration in a modelling context. Several recent studies in the European Alps have successfully integrated both correlative and process-based elements into distribution models of alpine plants, but for the time being a single integrative modelling framework that includes all key drivers remains elusive. As a first step in working toward a comprehensive integrated model applicable to alpine plant communities, we propose a conceptual framework that structures the primary mechanisms affecting alpine plant distributions. We group processes into four categories, including multi-scalar abiotic drivers, gradient dependent species interactions, dispersal and spatial-temporal plant responses to disturbance. Finally, we propose a methodological framework aimed at developing an integrated model to better predict alpine plant distribution.

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems.

PubMed

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A

2017-03-01

The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems

PubMed Central

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A.

2017-01-01

Abstract The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework. PMID:28328252

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

PubMed

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

2017-04-12

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

Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales

USDA-ARS?s Scientific Manuscript database

Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organization (genes, population, species, ecological communities and ecosyst...

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).

Temperament, Personality and Achievement Goals among Chinese Adolescent Students

ERIC Educational Resources Information Center

Chen, Chen; Zhang, Li-Fang

2011-01-01

Temperament and personality have been presumed to affect achievement goals based on the hierarchical model of achievement motivation. This research investigated the relationships of temperament dimensions and the Big Five personality traits to achievement goals based on the 2 x 2 achievement goal framework among 775 Chinese adolescent students.…

Distributed Agent-Based Networks in Support of Advanced Marine Corps Command and Control Concept

DTIC Science & Technology

2012-09-01

clusters of managers and clients that form a hierarchical management framework (Figure 14). However, since it is SNMP-based, due to the size and...that are much less computationally intensive than other proposed approaches such as multivariate calculations of Pareto boundaries (Bordetsky and

Biological hierarchies and the nature of extinction.

PubMed

Congreve, Curtis R; Falk, Amanda R; Lamsdell, James C

2018-05-01

Hierarchy theory recognises that ecological and evolutionary units occur in a nested and interconnected hierarchical system, with cascading effects occurring between hierarchical levels. Different biological disciplines have routinely come into conflict over the primacy of different forcing mechanisms behind evolutionary and ecological change. These disconnects arise partly from differences in perspective (with some researchers favouring ecological forcing mechanisms while others favour developmental/historical mechanisms), as well as differences in the temporal framework in which workers operate. In particular, long-term palaeontological data often show that large-scale (macro) patterns of evolution are predominantly dictated by shifts in the abiotic environment, while short-term (micro) modern biological studies stress the importance of biotic interactions. We propose that thinking about ecological and evolutionary interactions in a hierarchical framework is a fruitful way to resolve these conflicts. Hierarchy theory suggests that changes occurring at lower hierarchical levels can have unexpected, complex effects at higher scales due to emergent interactions between simple systems. In this way, patterns occurring on short- and long-term time scales are equally valid, as changes that are driven from lower levels will manifest in different forms at higher levels. We propose that the dual hierarchy framework fits well with our current understanding of evolutionary and ecological theory. Furthermore, we describe how this framework can be used to understand major extinction events better. Multi-generational attritional loss of reproductive fitness (MALF) has recently been proposed as the primary mechanism behind extinction events, whereby extinction is explainable solely through processes that result in extirpation of populations through a shutdown of reproduction. While not necessarily explicit, the push to explain extinction through solely population-level dynamics could be used to suggest that environmentally mediated patterns of extinction or slowed speciation across geological time are largely artefacts of poor preservation or a coarse temporal scale. We demonstrate how MALF fits into a hierarchical framework, showing that MALF can be a primary forcing mechanism at lower scales that still results in differential survivorship patterns at the species and clade level which vary depending upon the initial environmental forcing mechanism. Thus, even if MALF is the primary mechanism of extinction across all mass extinction events, the primary environmental cause of these events will still affect the system and result in differential responses. Therefore, patterns at both temporal scales are relevant. © 2017 Cambridge Philosophical Society.

Activated Carbon Fibers with Hierarchical Nanostructure Derived from Waste Cotton Gloves as High-Performance Electrodes for Supercapacitors.

PubMed

Wei, Chao; Yu, Jianlin; Yang, Xiaoqing; Zhang, Guoqing

2017-12-01

One of the most challenging issues that restrict the biomass/waste-based nanocarbons in supercapacitor application is the poor structural inheritability during the activating process. Herein, we prepare a class of activated carbon fibers by carefully selecting waste cotton glove (CG) as the precursor, which mainly consists of cellulose fibers that can be transformed to carbon along with good inheritability of their fiber morphology upon activation. As prepared, the CG-based activated carbon fiber (CGACF) demonstrates a surface area of 1435 m 2  g -1 contributed by micropores of 1.3 nm and small mesopores of 2.7 nm, while the fiber morphology can be well inherited from the CG with 3D interconnected frameworks created on the fiber surface. This hierarchically porous structure and well-retained fiber-like skeleton can simultaneously minimize the diffusion/transfer resistance of the electrolyte and electron, respectively, and maximize the surface area utilization for charge accumulation. Consequently, CGACF presents a higher specific capacitance of 218 F g -1 and an excellent high-rate performance as compared to commercial activated carbon.

Using a fuzzy comprehensive evaluation method to determine product usability: A proposed theoretical framework.

PubMed

Zhou, Ronggang; Chan, Alan H S

2017-01-01

In order to compare existing usability data to ideal goals or to that for other products, usability practitioners have tried to develop a framework for deriving an integrated metric. However, most current usability methods with this aim rely heavily on human judgment about the various attributes of a product, but often fail to take into account of the inherent uncertainties in these judgments in the evaluation process. This paper presents a universal method of usability evaluation by combining the analytic hierarchical process (AHP) and the fuzzy evaluation method. By integrating multiple sources of uncertain information during product usability evaluation, the method proposed here aims to derive an index that is structured hierarchically in terms of the three usability components of effectiveness, efficiency, and user satisfaction of a product. With consideration of the theoretical basis of fuzzy evaluation, a two-layer comprehensive evaluation index was first constructed. After the membership functions were determined by an expert panel, the evaluation appraisals were computed by using the fuzzy comprehensive evaluation technique model to characterize fuzzy human judgments. Then with the use of AHP, the weights of usability components were elicited from these experts. Compared to traditional usability evaluation methods, the major strength of the fuzzy method is that it captures the fuzziness and uncertainties in human judgments and provides an integrated framework that combines the vague judgments from multiple stages of a product evaluation process.

Towards a hierarchical optimization modeling framework for ...

EPA Pesticide Factsheets

Background:Bilevel optimization has been recognized as a 2-player Stackelberg game where players are represented as leaders and followers and each pursue their own set of objectives. Hierarchical optimization problems, which are a generalization of bilevel, are especially difficult because the optimization is nested, meaning that the objectives of one level depend on solutions to the other levels. We introduce a hierarchical optimization framework for spatially targeting multiobjective green infrastructure (GI) incentive policies under uncertainties related to policy budget, compliance, and GI effectiveness. We demonstrate the utility of the framework using a hypothetical urban watershed, where the levels are characterized by multiple levels of policy makers (e.g., local, regional, national) and policy followers (e.g., landowners, communities), and objectives include minimization of policy cost, implementation cost, and risk; reduction of combined sewer overflow (CSO) events; and improvement in environmental benefits such as reduced nutrient run-off and water availability. Conclusions: While computationally expensive, this hierarchical optimization framework explicitly simulates the interaction between multiple levels of policy makers (e.g., local, regional, national) and policy followers (e.g., landowners, communities) and is especially useful for constructing and evaluating environmental and ecological policy. Using the framework with a hypothetical urba

An assembly process model based on object-oriented hierarchical time Petri Nets

NASA Astrophysics Data System (ADS)

Wang, Jiapeng; Liu, Shaoli; Liu, Jianhua; Du, Zenghui

2017-04-01

In order to improve the versatility, accuracy and integrity of the assembly process model of complex products, an assembly process model based on object-oriented hierarchical time Petri Nets is presented. A complete assembly process information model including assembly resources, assembly inspection, time, structure and flexible parts is established, and this model describes the static and dynamic data involved in the assembly process. Through the analysis of three-dimensional assembly process information, the assembly information is hierarchically divided from the whole, the local to the details and the subnet model of different levels of object-oriented Petri Nets is established. The communication problem between Petri subnets is solved by using message database, and it reduces the complexity of system modeling effectively. Finally, the modeling process is presented, and a five layer Petri Nets model is established based on the hoisting process of the engine compartment of a wheeled armored vehicle.

Small drinking water systems under spatiotemporal water quality variability: a risk-based performance benchmarking framework.

PubMed

Bereskie, Ty; Haider, Husnain; Rodriguez, Manuel J; Sadiq, Rehan

2017-08-23

Traditional approaches for benchmarking drinking water systems are binary, based solely on the compliance and/or non-compliance of one or more water quality performance indicators against defined regulatory guidelines/standards. The consequence of water quality failure is dependent on location within a water supply system as well as time of the year (i.e., season) with varying levels of water consumption. Conventional approaches used for water quality comparison purposes fail to incorporate spatiotemporal variability and degrees of compliance and/or non-compliance. This can lead to misleading or inaccurate performance assessment data used in the performance benchmarking process. In this research, a hierarchical risk-based water quality performance benchmarking framework is proposed to evaluate small drinking water systems (SDWSs) through cross-comparison amongst similar systems. The proposed framework (R WQI framework) is designed to quantify consequence associated with seasonal and location-specific water quality issues in a given drinking water supply system to facilitate more efficient decision-making for SDWSs striving for continuous performance improvement. Fuzzy rule-based modelling is used to address imprecision associated with measuring performance based on singular water quality guidelines/standards and the uncertainties present in SDWS operations and monitoring. This proposed R WQI framework has been demonstrated using data collected from 16 SDWSs in Newfoundland and Labrador and Quebec, Canada, and compared to the Canadian Council of Ministers of the Environment WQI, a traditional, guidelines/standard-based approach. The study found that the R WQI framework provides an in-depth state of water quality and benchmarks SDWSs more rationally based on the frequency of occurrence and consequence of failure events.

Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data.

PubMed

Erdoğdu, Utku; Tan, Mehmet; Alhajj, Reda; Polat, Faruk; Rokne, Jon; Demetrick, Douglas

2013-01-01

The availability of enough samples for effective analysis and knowledge discovery has been a challenge in the research community, especially in the area of gene expression data analysis. Thus, the approaches being developed for data analysis have mostly suffered from the lack of enough data to train and test the constructed models. We argue that the process of sample generation could be successfully automated by employing some sophisticated machine learning techniques. An automated sample generation framework could successfully complement the actual sample generation from real cases. This argument is validated in this paper by describing a framework that integrates multiple models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples, a highly demanding area that has not received attention. The three perspectives employed in the process are based on models that are not closely related. The independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The first model is based on the Probabilistic Boolean Network (PBN) representation of the gene regulatory network underlying the given gene expression data. The second model integrates Hierarchical Markov Model (HIMM) and the third model employs a genetic algorithm in the process. Each model learns as much as possible characteristics of the domain being analysed and tries to incorporate the learned characteristics in generating new samples. In other words, the models base their analysis on domain knowledge implicitly present in the data itself. The developed framework has been extensively tested by checking how the new samples complement the original samples. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.

Resolving the Antarctic contribution to sea-level rise: a hierarchical modelling framework.

PubMed

Zammit-Mangion, Andrew; Rougier, Jonathan; Bamber, Jonathan; Schön, Nana

2014-06-01

Determining the Antarctic contribution to sea-level rise from observational data is a complex problem. The number of physical processes involved (such as ice dynamics and surface climate) exceeds the number of observables, some of which have very poor spatial definition. This has led, in general, to solutions that utilise strong prior assumptions or physically based deterministic models to simplify the problem. Here, we present a new approach for estimating the Antarctic contribution, which only incorporates descriptive aspects of the physically based models in the analysis and in a statistical manner. By combining physical insights with modern spatial statistical modelling techniques, we are able to provide probability distributions on all processes deemed to play a role in both the observed data and the contribution to sea-level rise. Specifically, we use stochastic partial differential equations and their relation to geostatistical fields to capture our physical understanding and employ a Gaussian Markov random field approach for efficient computation. The method, an instantiation of Bayesian hierarchical modelling, naturally incorporates uncertainty in order to reveal credible intervals on all estimated quantities. The estimated sea-level rise contribution using this approach corroborates those found using a statistically independent method. © 2013 The Authors. Environmetrics Published by John Wiley & Sons, Ltd.

A bio-inspired N-doped porous carbon electrocatalyst with hierarchical superstructure for efficient oxygen reduction reaction

NASA Astrophysics Data System (ADS)

Miao, Yue-E.; Yan, Jiajie; Ouyang, Yue; Lu, Hengyi; Lai, Feili; Wu, Yue; Liu, Tianxi

2018-06-01

The bio-inspired hierarchical "grape cluster" superstructure provides an effective integration of one-dimensional carbon nanofibers (CNF) with isolated carbonaceous nanoparticles into three-dimensional (3D) conductive frameworks for efficient electron and mass transfer. Herein, a 3D N-doped porous carbon electrocatalyst consisting of carbon nanofibers with grape-like N-doped hollow carbon particles (CNF@NC) has been prepared through a simple electrospinning strategy combined with in-situ growth and carbonization processes. Such a bio-inspired hierarchically organized conductive network largely facilitates both the mass diffusion and electron transfer during the oxygen reduction reactions (ORR). Therefore, the metal-free CNF@NC catalyst demonstrates superior catalytic activity with an absolute four-electron transfer mechanism, strong methanol tolerance and good long-term stability towards ORR in alkaline media.

The BaBar Data Reconstruction Control System

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

Ceseracciu, A

2005-04-20

The BaBar experiment is characterized by extremely high luminosity and very large volume of data produced and stored, with increasing computing requirements each year. To fulfill these requirements a Control System has been designed and developed for the offline distributed data reconstruction system. The control system described in this paper provides the performance and flexibility needed to manage a large number of small computing farms, and takes full benefit of OO design. The infrastructure is well isolated from the processing layer, it is generic and flexible, based on a light framework providing message passing and cooperative multitasking. The system ismore » distributed in a hierarchical way: the top-level system is organized in farms, farms in services, and services in subservices or code modules. It provides a powerful Finite State Machine framework to describe custom processing models in a simple regular language. This paper describes the design and evolution of this control system, currently in use at SLAC and Padova on {approx}450 CPUs organized in 9 farms.« less

The BaBar Data Reconstruction Control System

NASA Astrophysics Data System (ADS)

Ceseracciu, A.; Piemontese, M.; Tehrani, F. S.; Pulliam, T. M.; Galeazzi, F.

2005-08-01

The BaBar experiment is characterized by extremely high luminosity and very large volume of data produced and stored, with increasing computing requirements each year. To fulfill these requirements a control system has been designed and developed for the offline distributed data reconstruction system. The control system described in this paper provides the performance and flexibility needed to manage a large number of small computing farms, and takes full benefit of object oriented (OO) design. The infrastructure is well isolated from the processing layer, it is generic and flexible, based on a light framework providing message passing and cooperative multitasking. The system is distributed in a hierarchical way: the top-level system is organized in farms, farms in services, and services in subservices or code modules. It provides a powerful finite state machine framework to describe custom processing models in a simple regular language. This paper describes the design and evolution of this control system, currently in use at SLAC and Padova on /spl sim/450 CPUs organized in nine farms.

Pilot study of a point-of-use decision support tool for cancer clinical trials eligibility.

PubMed

Breitfeld, P P; Weisburd, M; Overhage, J M; Sledge, G; Tierney, W M

1999-01-01

Many adults with cancer are not enrolled in clinical trials because caregivers do not have the time to match the patient's clinical findings with varying eligibility criteria associated with multiple trials for which the patient might be eligible. The authors developed a point-of-use portable decision support tool (DS-TRIEL) to automate this matching process. The support tool consists of a hand-held computer with a programmable relational database. A two-level hierarchic decision framework was used for the identification of eligible subjects for two open breast cancer clinical trials. The hand-held computer also provides protocol consent forms and schemas to further help the busy oncologist. This decision support tool and the decision framework on which it is based could be used for multiple trials and different cancer sites.

Pilot Study of a Point-of-use Decision Support Tool for Cancer Clinical Trials Eligibility

PubMed Central

Breitfeld, Philip P.; Weisburd, Marina; Overhage, J. Marc; Sledge, George; Tierney, William M.

1999-01-01

Many adults with cancer are not enrolled in clinical trials because caregivers do not have the time to match the patient's clinical findings with varying eligibility criteria associated with multiple trials for which the patient might be eligible. The authors developed a point-of-use portable decision support tool (DS-TRIEL) to automate this matching process. The support tool consists of a hand-held computer with a programmable relational database. A two-level hierarchic decision framework was used for the identification of eligible subjects for two open breast cancer clinical trials. The hand-held computer also provides protocol consent forms and schemas to further help the busy oncologist. This decision support tool and the decision framework on which it is based could be used for multiple trials and different cancer sites. PMID:10579605

[Governance of primary health-care-based health-care organization].

PubMed

Báscolo, Ernesto

2010-01-01

An analytical framework was developed for explaining the conditions for the effectiveness of different strategies promoting integrated primary health-care (PHC) service-based systems in Latin-America. Different modes of governance (clan, incentives and hierarchy) were characterised from a political economics viewpoint for representing alternative forms of regulation promoting innovation in health-service-providing organisations. The necessary conditions for guaranteeing the modes of governance's effectiveness are presented, as are their implications in terms of posts in play. The institutional construction of an integrated health system is interpreted as being a product of a social process in which different modes of governance are combined, operating with different ways of resolving normative aspects for regulating service provision (with the hierarchical mode), resource distribution (with the incentives mode) and on the social values legitimising such process (with the clan mode).

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.

Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms

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

Arampatzis, Giorgos, E-mail: garab@math.uoc.gr; Katsoulakis, Markos A., E-mail: markos@math.umass.edu; Plechac, Petr, E-mail: plechac@math.udel.edu

2012-10-01

We present a mathematical framework for constructing and analyzing parallel algorithms for lattice kinetic Monte Carlo (KMC) simulations. The resulting algorithms have the capacity to simulate a wide range of spatio-temporal scales in spatially distributed, non-equilibrium physiochemical processes with complex chemistry and transport micro-mechanisms. Rather than focusing on constructing exactly the stochastic trajectories, our approach relies on approximating the evolution of observables, such as density, coverage, correlations and so on. More specifically, we develop a spatial domain decomposition of the Markov operator (generator) that describes the evolution of all observables according to the kinetic Monte Carlo algorithm. This domain decompositionmore » corresponds to a decomposition of the Markov generator into a hierarchy of operators and can be tailored to specific hierarchical parallel architectures such as multi-core processors or clusters of Graphical Processing Units (GPUs). Based on this operator decomposition, we formulate parallel Fractional step kinetic Monte Carlo algorithms by employing the Trotter Theorem and its randomized variants; these schemes, (a) are partially asynchronous on each fractional step time-window, and (b) are characterized by their communication schedule between processors. The proposed mathematical framework allows us to rigorously justify the numerical and statistical consistency of the proposed algorithms, showing the convergence of our approximating schemes to the original serial KMC. The approach also provides a systematic evaluation of different processor communicating schedules. We carry out a detailed benchmarking of the parallel KMC schemes using available exact solutions, for example, in Ising-type systems and we demonstrate the capabilities of the method to simulate complex spatially distributed reactions at very large scales on GPUs. Finally, we discuss work load balancing between processors and propose a re-balancing scheme based on probabilistic mass transport methods.« less

Analysis of composition-based metagenomic classification.

PubMed

Higashi, Susan; Barreto, André da Motta Salles; Cantão, Maurício Egidio; de Vasconcelos, Ana Tereza Ribeiro

2012-01-01

An essential step of a metagenomic study is the taxonomic classification, that is, the identification of the taxonomic lineage of the organisms in a given sample. The taxonomic classification process involves a series of decisions. Currently, in the context of metagenomics, such decisions are usually based on empirical studies that consider one specific type of classifier. In this study we propose a general framework for analyzing the impact that several decisions can have on the classification problem. Instead of focusing on any specific classifier, we define a generic score function that provides a measure of the difficulty of the classification task. Using this framework, we analyze the impact of the following parameters on the taxonomic classification problem: (i) the length of n-mers used to encode the metagenomic sequences, (ii) the similarity measure used to compare sequences, and (iii) the type of taxonomic classification, which can be conventional or hierarchical, depending on whether the classification process occurs in a single shot or in several steps according to the taxonomic tree. We defined a score function that measures the degree of separability of the taxonomic classes under a given configuration induced by the parameters above. We conducted an extensive computational experiment and found out that reasonable values for the parameters of interest could be (i) intermediate values of n, the length of the n-mers; (ii) any similarity measure, because all of them resulted in similar scores; and (iii) the hierarchical strategy, which performed better in all of the cases. As expected, short n-mers generate lower configuration scores because they give rise to frequency vectors that represent distinct sequences in a similar way. On the other hand, large values for n result in sparse frequency vectors that represent differently metagenomic fragments that are in fact similar, also leading to low configuration scores. Regarding the similarity measure, in contrast to our expectations, the variation of the measures did not change the configuration scores significantly. Finally, the hierarchical strategy was more effective than the conventional strategy, which suggests that, instead of using a single classifier, one should adopt multiple classifiers organized as a hierarchy.

Hierarchical population monitoring of greater sage-grouse (Centrocercus urophasianus) in Nevada and California—Identifying populations for management at the appropriate spatial scale

USGS Publications Warehouse

Coates, Peter S.; Prochazka, Brian G.; Ricca, Mark A.; Wann, Gregory T.; Aldridge, Cameron L.; Hanser, Steven E.; Doherty, Kevin E.; O'Donnell, Michael S.; Edmunds, David R.; Espinosa, Shawn P.

2017-08-10

Population ecologists have long recognized the importance of ecological scale in understanding processes that guide observed demographic patterns for wildlife species. However, directly incorporating spatial and temporal scale into monitoring strategies that detect whether trajectories are driven by local or regional factors is challenging and rarely implemented. Identifying the appropriate scale is critical to the development of management actions that can attenuate or reverse population declines. We describe a novel example of a monitoring framework for estimating annual rates of population change for greater sage-grouse (Centrocercus urophasianus) within a hierarchical and spatially nested structure. Specifically, we conducted Bayesian analyses on a 17-year dataset (2000–2016) of lek counts in Nevada and northeastern California to estimate annual rates of population change, and compared trends across nested spatial scales. We identified leks and larger scale populations in immediate need of management, based on the occurrence of two criteria: (1) crossing of a destabilizing threshold designed to identify significant rates of population decline at a particular nested scale; and (2) crossing of decoupling thresholds designed to identify rates of population decline at smaller scales that decouple from rates of population change at a larger spatial scale. This approach establishes how declines affected by local disturbances can be separated from those operating at larger scales (for example, broad-scale wildfire and region-wide drought). Given the threshold output from our analysis, this adaptive management framework can be implemented readily and annually to facilitate responsive and effective actions for sage-grouse populations in the Great Basin. The rules of the framework can also be modified to identify populations responding positively to management action or demonstrating strong resilience to disturbance. Similar hierarchical approaches might be beneficial for other species occupying landscapes with heterogeneous disturbance and climatic regimes.

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.

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.

Reconciling multiple data sources to improve accuracy of large-scale prediction of forest disease incidence

USGS Publications Warehouse

Hanks, E.M.; Hooten, M.B.; Baker, F.A.

2011-01-01

Ecological spatial data often come from multiple sources, varying in extent and accuracy. We describe a general approach to reconciling such data sets through the use of the Bayesian hierarchical framework. This approach provides a way for the data sets to borrow strength from one another while allowing for inference on the underlying ecological process. We apply this approach to study the incidence of eastern spruce dwarf mistletoe (Arceuthobium pusillum) in Minnesota black spruce (Picea mariana). A Minnesota Department of Natural Resources operational inventory of black spruce stands in northern Minnesota found mistletoe in 11% of surveyed stands, while a small, specific-pest survey found mistletoe in 56% of the surveyed stands. We reconcile these two surveys within a Bayesian hierarchical framework and predict that 35-59% of black spruce stands in northern Minnesota are infested with dwarf mistletoe. ?? 2011 by the Ecological Society of America.

Quantifying loopy network architectures.

PubMed

Katifori, Eleni; Magnasco, Marcelo O

2012-01-01

Biology presents many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture containing closed loops at many different levels. Although a number of approaches have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework, the hierarchical loop decomposition, that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated graphs, such as artificial models and optimal distribution networks, as well as natural graphs extracted from digitized images of dicotyledonous leaves and vasculature of rat cerebral neocortex. We calculate various metrics based on the asymmetry, the cumulative size distribution and the Strahler bifurcation ratios of the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information (exact location of edges and nodes) from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs.

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

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.

The land-cover cascade: relationships coupling land and water

Treesearch

C.L. Burcher; H.M. Valett; E.F. Benfield

2007-01-01

We introduce the land-cover cascade (LCC) as a conceptual framework to quantify the transfer of land-cover-disturbance effects to stream biota. We hypothesize that disturbance is propagated through multivariate systems through key variables that transform a disturbance and pass a reorganized disturbance effect to the next hierarchical level where the process repeats...

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…

Bridging Literacy Acquisition and Self-Regulated Learning: Using a SRL Framework to Support Struggling Readers

ERIC Educational Resources Information Center

Holtzheuser, Sierra; McNamara, John

2014-01-01

Reading is conceptualized as a hierarchy of component skills where lower order emergent literacy skills set the foundation for higher order reading skills such as fluency and comprehension. Approximately 20% of readers struggle within this hierarchical process (Fielding, Kerr, & Rosier, 2007). Struggling readers are susceptible to the Matthew…

Doing Without Schema Hierarchies: A Recurrent Connectionist Approach to Normal and Impaired Routine Sequential Action

ERIC Educational Resources Information Center

Botvinick, Matthew; Plaut, David C.

2004-01-01

In everyday tasks, selecting actions in the proper sequence requires a continuously updated representation of temporal context. Previous models have addressed this problem by positing a hierarchy of processing units, mirroring the roughly hierarchical structure of naturalistic tasks themselves. The present study considers an alternative framework,…

Architecture for reactive planning of robot actions

NASA Astrophysics Data System (ADS)

Riekki, Jukka P.; Roening, Juha

1995-01-01

In this article, a reactive system for planning robot actions is described. The described hierarchical control system architecture consists of planning-executing-monitoring-modelling elements (PEMM elements). A PEMM element is a goal-oriented, combined processing and data element. It includes a planner, an executor, a monitor, a modeler, and a local model. The elements form a tree-like structure. An element receives tasks from its ancestor and sends subtasks to its descendants. The model knowledge is distributed into the local models, which are connected to each other. The elements can be synchronized. The PEMM architecture is strictly hierarchical. It integrated planning, sensing, and modelling into a single framework. A PEMM-based control system is reactive, as it can cope with asynchronous events and operate under time constraints. The control system is intended to be used primarily to control mobile robots and robot manipulators in dynamic and partially unknown environments. It is suitable especially for applications consisting of physically separated devices and computing resources.

Man-made objects cuing in satellite imagery

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

Skurikhin, Alexei N

2009-01-01

We present a multi-scale framework for man-made structures cuing in satellite image regions. The approach is based on a hierarchical image segmentation followed by structural analysis. A hierarchical segmentation produces an image pyramid that contains a stack of irregular image partitions, represented as polygonized pixel patches, of successively reduced levels of detail (LOOs). We are jumping off from the over-segmented image represented by polygons attributed with spectral and texture information. The image is represented as a proximity graph with vertices corresponding to the polygons and edges reflecting polygon relations. This is followed by the iterative graph contraction based on Boruvka'smore » Minimum Spanning Tree (MST) construction algorithm. The graph contractions merge the patches based on their pairwise spectral and texture differences. Concurrently with the construction of the irregular image pyramid, structural analysis is done on the agglomerated patches. Man-made object cuing is based on the analysis of shape properties of the constructed patches and their spatial relations. The presented framework can be used as pre-scanning tool for wide area monitoring to quickly guide the further analysis to regions of interest.« less

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

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

Evidence-based librarianship: an overview.

PubMed

Eldredge, J D

2000-10-01

To demonstrate how the core characteristics of both evidence-based medicine (EBM) and evidence-based health care (EBHC) can be adapted to health sciences librarianship. Narrative review essay involving development of a conceptual framework. The author describes the central features of EBM and EBHC. Following each description of a central feature, the author then suggests ways that this feature applies to health sciences librarianship. First, the decision-making processes of EBM and EBHC are compatible with health sciences librarianship. Second, the EBM and EBHC values of favoring rigorously produced scientific evidence in decision making are congruent with the core values of librarianship. Third, the hierarchical levels of evidence can be applied to librarianship with some modifications. Library researchers currently favor descriptive-survey and case-study methods over systematic reviews, randomized controlled trials, or other higher levels of evidence. The library literature nevertheless contains diverse examples of randomized controlled trials, controlled-comparison studies, and cohort studies conducted by health sciences librarians. Health sciences librarians are confronted with making many practical decisions. Evidence-based librarianship offers a decision-making framework, which integrates the best available research evidence. By employing this framework and the higher levels of research evidence it promotes, health sciences librarians can lay the foundation for more collaborative and scientific endeavors.

Evidence-based librarianship: an overview

PubMed Central

Eldredge, Jonathan D.

2000-01-01

Objective: To demonstrate how the core characteristics of both evidence-based medicine (EBM) and evidence-based health care (EBHC) can be adapted to health sciences librarianship. Method: Narrative review essay involving development of a conceptual framework. The author describes the central features of EBM and EBHC. Following each description of a central feature, the author then suggests ways that this feature applies to health sciences librarianship. Results: First, the decision-making processes of EBM and EBHC are compatible with health sciences librarianship. Second, the EBM and EBHC values of favoring rigorously produced scientific evidence in decision making are congruent with the core values of librarianship. Third, the hierarchical levels of evidence can be applied to librarianship with some modifications. Library researchers currently favor descriptive-survey and case-study methods over systematic reviews, randomized controlled trials, or other higher levels of evidence. The library literature nevertheless contains diverse examples of randomized controlled trials, controlled-comparison studies, and cohort studies conducted by health sciences librarians. Conclusions: Health sciences librarians are confronted with making many practical decisions. Evidence-based librarianship offers a decision-making framework, which integrates the best available research evidence. By employing this framework and the higher levels of research evidence it promotes, health sciences librarians can lay the foundation for more collaborative and scientific endeavors. PMID:11055296

Complex temporal topic evolution modelling using the Kullback-Leibler divergence and the Bhattacharyya distance.

PubMed

Andrei, Victor; Arandjelović, Ognjen

2016-12-01

The rapidly expanding corpus of medical research literature presents major challenges in the understanding of previous work, the extraction of maximum information from collected data, and the identification of promising research directions. We present a case for the use of advanced machine learning techniques as an aide in this task and introduce a novel methodology that is shown to be capable of extracting meaningful information from large longitudinal corpora and of tracking complex temporal changes within it. Our framework is based on (i) the discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes. More specifically, this is the first work that discusses and distinguishes between two groups of particularly challenging topic evolution phenomena: topic splitting and speciation and topic convergence and merging, in addition to the more widely recognized emergence and disappearance and gradual evolution. The proposed framework is evaluated on a public medical literature corpus.

Framework based on communicability and flow to analyze complex network dynamics

NASA Astrophysics Data System (ADS)

Gilson, M.; Kouvaris, N. E.; Deco, G.; Zamora-López, G.

2018-05-01

Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in the interactions of self-sustained oscillations (synchronization patterns) or propagationlike processes such as random walks. However, networks are often inferred from real-data-forming dynamic systems, which are different from those employed to reveal their topological characteristics. This stresses the necessity for a theoretical framework dedicated to the mutual relationship between the structure and dynamics in complex networks, as the two sides of the same coin. Here we propose a rigorous framework based on the network response over time (i.e., Green function) to study interactions between nodes across time. For this purpose we define the flow that describes the interplay between the network connectivity and external inputs. This multivariate measure relates to the concepts of graph communicability and the map equation. We illustrate our theory using the multivariate Ornstein-Uhlenbeck process, which describes stable and non-conservative dynamics, but the formalism can be adapted to other local dynamics for which the Green function is known. We provide applications to classical network examples, such as small-world ring and hierarchical networks. Our theory defines a comprehensive framework that is canonically related to directed and weighted networks, thus paving a way to revise the standards for network analysis, from the pairwise interactions between nodes to the global properties of networks including community detection.

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

Gorentla Venkata, Manjunath; Graham, Richard L; Ladd, Joshua S

This paper describes the design and implementation of InfiniBand (IB) CORE-Direct based blocking and nonblocking broadcast operations within the Cheetah collective operation framework. It describes a novel approach that fully ofFLoads collective operations and employs only user-supplied buffers. For a 64 rank communicator, the latency of CORE-Direct based hierarchical algorithm is better than production-grade Message Passing Interface (MPI) implementations, 150% better than the default Open MPI algorithm and 115% better than the shared memory optimized MVAPICH implementation for a one kilobyte (KB) message, and for eight mega-bytes (MB) it is 48% and 64% better, respectively. Flat-topology broadcast achieves 99.9% overlapmore » in a polling based communication-computation test, and 95.1% overlap for a wait based test, compared with 92.4% and 17.0%, respectively, for a similar Central Processing Unit (CPU) based implementation.« less

Metal-Organic Framework-Derived Hollow Hierarchical Co3O4 Nanocages with Tunable Size and Morphology: Ultrasensitive and Highly Selective Detection of Methylbenzenes.

PubMed

Jo, Young-Moo; Kim, Tae-Hyung; Lee, Chul-Soon; Lim, Kyeorei; Na, Chan Woong; Abdel-Hady, Faissal; Wazzan, Abdulaziz A; Lee, Jong-Heun

2018-03-14

Nearly monodisperse hollow hierarchical Co 3 O 4 nanocages of four different sizes (∼0.3, 1.0, 2.0, and 4.0 μm) consisting of nanosheets were prepared by controlled precipitation of zeolitic imidazolate framework-67 (ZIF-67) rhombic dodecahedra, followed by solvothermal synthesis of Co 3 O 4 nanocages using ZIF-67 self-sacrificial templates, and subsequent heat treatment for the development of high-performance methylbenzene sensors. The sensor based on hollow hierarchical Co 3 O 4 nanocages with the size of ∼1.0 μm exhibited not only ultrahigh responses (resistance ratios) to 5 ppm p-xylene (78.6) and toluene (43.8) but also a remarkably high selectivity to methylbenzene over the interference of ubiquitous ethanol at 225 °C. The unprecedented and high response and selectivity to methylbenzenes are attributed to the highly gas-accessible hollow hierarchical morphology with thin shells, abundant mesopores, and high surface area per unit volume as well as the high catalytic activity of Co 3 O 4 . Moreover, the size, shell thickness, mesopores, and hollow/hierarchical morphology of the nanocages, the key parameters determining the gas response and selectivity, could be well-controlled by tuning the precipitation of ZIF-67 rhombic dodecahedra and solvothermal reaction. This method can pave a new pathway for the design of high-performance methylbenzene sensors for monitoring the quality of indoor air.

High-linearity piezoresistive response of mechanically strong graphene-based elastomer

NASA Astrophysics Data System (ADS)

Yuanzheng, Luo; Buyin, Li; Xiaoqi

2017-05-01

Traditional additive-free graphene bulk materials based on mono- three dimensional(3D) graphene networks type are fragile in most cases, which is unfavorable for their potential applications. Here we present compressible graphene foams (CGF) with superior properties endowed by the hierarchical porous structure, which taking graphene sheets as an inorganic embedding material and polyurethane sponge (PUS) as a polymer open-framework. The preparation process utilized a dip-coating method associated with directional freezing followed by lyophilization. The as-synthesized CGF not only possess a combination of ultralow density and excellent electrical conductivity, but it also can withstand large strains (>99%) without permanent deformation or fracture. We believe that these sponge/graphene embeddable multifunctional nanocomposites will expand practical applications of graphene monolith in the future.

Food-web based unified model of macro- and microevolution.

PubMed

Chowdhury, Debashish; Stauffer, Dietrich

2003-10-01

We incorporate the generic hierarchical architecture of foodwebs into a "unified" model that describes both micro- and macroevolutions within a single theoretical framework. This model describes the microevolution in detail by accounting for the birth, ageing, and natural death of individual organisms as well as prey-predator interactions on a hierarchical dynamic food web. It also provides a natural description of random mutations and speciation (origination) of species as well as their extinctions. The distribution of lifetimes of species follows an approximate power law only over a limited regime.

Working toward integrated models of alpine plant distribution

PubMed Central

Carlson, Bradley Z.; Randin, Christophe F.; Boulangeat, Isabelle; Lavergne, Sébastien; Thuiller, Wilfried; Choler, Philippe

2014-01-01

Species distribution models (SDMs) have been frequently employed to forecast the response of alpine plants to global changes. Efforts to model alpine plant distribution have thus far been primarily based on a correlative approach, in which ecological processes are implicitly addressed through a statistical relationship between observed species occurrences and environmental predictors. Recent evidence, however, highlights the shortcomings of correlative SDMs, especially in alpine landscapes where plant species tend to be decoupled from atmospheric conditions in micro-topographic habitats and are particularly exposed to geomorphic disturbances. While alpine plants respond to the same limiting factors as plants found at lower elevations, alpine environments impose a particular set of scale-dependent and hierarchical drivers that shape the realized niche of species and that require explicit consideration in a modelling context. Several recent studies in the European Alps have successfully integrated both correlative and process-based elements into distribution models of alpine plants, but for the time being a single integrative modelling framework that includes all key drivers remains elusive. As a first step in working toward a comprehensive integrated model applicable to alpine plant communities, we propose a conceptual framework that structures the primary mechanisms affecting alpine plant distributions. We group processes into four categories, including multi-scalar abiotic drivers, gradient dependent species interactions, dispersal and spatial–temporal plant responses to disturbance. Finally, we propose a methodological framework aimed at developing an integrated model to better predict alpine plant distribution. PMID:24790594

Microwave-assisted preparation of flower-like cobalt phosphate and its application as a new heterogeneous Fenton-like catalyst

NASA Astrophysics Data System (ADS)

Hu, Xiaoxia; Li, Rong; Zhao, Shuyu; Xing, Yanjun

2017-02-01

A novel flower-like 3D hierarchical cobalt phosphate Co3(PO4)2·8H2O (fCoP), and a plate-like cobalt phosphate (pCoP) were successfully synthesized via a microwave-assisted method at low temperature under atmospheric pressure using hexamethylene tetramine (HMTA) or urea as a template. All CoPs were characterized using XRD, FESEM, TEM, DRS and surface photovoltage spectra (SPS). The performance of the photocatalytic degradation of Rhodamine B (RhB) via a Fenton-like process on CoPs was evaluated both in the dark and under illumination. The results showed that the morphology and composition of the CoPs affected the RhB degradation. The flower-like hierarchical fCoP favored the photo degradation of RhB. fCoP was also confirmed to have the merits of easy recycling and good stability based on successive degradation experiments. The active species trapping experiments showed that the superoxide radical (rad O2-) was the dominant active species in the Fenton-like process. The catalytic activation was confirmed to be related to both the Co(II) on the surface and the fCoP framework.

Development and application of a real-time testbed for multiagent system interoperability: A case study on hierarchical microgrid control

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

Cintuglu, Mehmet Hazar; Youssef, Tarek; Mohammed, Osama A.

This article presents the development and application of a real-time testbed for multiagent system interoperability. As utility independent private microgrids are installed constantly, standardized interoperability frameworks are required to define behavioral models of the individual agents for expandability and plug-and-play operation. In this paper, we propose a comprehensive hybrid agent framework combining the foundation for intelligent physical agents (FIPA), IEC 61850, and data distribution service (DDS) standards. The IEC 61850 logical node concept is extended using FIPA based agent communication language (ACL) with application specific attributes and deliberative behavior modeling capability. The DDS middleware is adopted to enable a real-timemore » publisher-subscriber interoperability mechanism between platforms. The proposed multi-agent framework was validated in a laboratory based testbed involving developed intelligent electronic device (IED) prototypes and actual microgrid setups. Experimental results were demonstrated for both decentralized and distributed control approaches. Secondary and tertiary control levels of a microgrid were demonstrated for decentralized hierarchical control case study. A consensus-based economic dispatch case study was demonstrated as a distributed control example. Finally, it was shown that the developed agent platform is industrially applicable for actual smart grid field deployment.« less

Development and application of a real-time testbed for multiagent system interoperability: A case study on hierarchical microgrid control

DOE PAGES

Cintuglu, Mehmet Hazar; Youssef, Tarek; Mohammed, Osama A.

2016-08-10

This article presents the development and application of a real-time testbed for multiagent system interoperability. As utility independent private microgrids are installed constantly, standardized interoperability frameworks are required to define behavioral models of the individual agents for expandability and plug-and-play operation. In this paper, we propose a comprehensive hybrid agent framework combining the foundation for intelligent physical agents (FIPA), IEC 61850, and data distribution service (DDS) standards. The IEC 61850 logical node concept is extended using FIPA based agent communication language (ACL) with application specific attributes and deliberative behavior modeling capability. The DDS middleware is adopted to enable a real-timemore » publisher-subscriber interoperability mechanism between platforms. The proposed multi-agent framework was validated in a laboratory based testbed involving developed intelligent electronic device (IED) prototypes and actual microgrid setups. Experimental results were demonstrated for both decentralized and distributed control approaches. Secondary and tertiary control levels of a microgrid were demonstrated for decentralized hierarchical control case study. A consensus-based economic dispatch case study was demonstrated as a distributed control example. Finally, it was shown that the developed agent platform is industrially applicable for actual smart grid field deployment.« less

A hierarchical competing systems model of the emergence and early development of executive function

PubMed Central

Marcovitch, Stuart; Zelazo, Philip David

2010-01-01

The hierarchical competing systems model (HCSM) provides a framework for understanding the emergence and early development of executive function – the cognitive processes underlying the conscious control of behavior – in the context of search for hidden objects. According to this model, behavior is determined by the joint influence of a developmentally invariant habit system and a conscious representational system that becomes increasingly influential as children develop. This article describes a computational formalization of the HCSM, reviews behavioral and computational research consistent with the model, and suggests directions for future research on the development of executive function. PMID:19120405

Using a fuzzy comprehensive evaluation method to determine product usability: A proposed theoretical framework

PubMed Central

Zhou, Ronggang; Chan, Alan H. S.

2016-01-01

BACKGROUND: In order to compare existing usability data to ideal goals or to that for other products, usability practitioners have tried to develop a framework for deriving an integrated metric. However, most current usability methods with this aim rely heavily on human judgment about the various attributes of a product, but often fail to take into account of the inherent uncertainties in these judgments in the evaluation process. OBJECTIVE: This paper presents a universal method of usability evaluation by combining the analytic hierarchical process (AHP) and the fuzzy evaluation method. By integrating multiple sources of uncertain information during product usability evaluation, the method proposed here aims to derive an index that is structured hierarchically in terms of the three usability components of effectiveness, efficiency, and user satisfaction of a product. METHODS: With consideration of the theoretical basis of fuzzy evaluation, a two-layer comprehensive evaluation index was first constructed. After the membership functions were determined by an expert panel, the evaluation appraisals were computed by using the fuzzy comprehensive evaluation technique model to characterize fuzzy human judgments. Then with the use of AHP, the weights of usability components were elicited from these experts. RESULTS AND CONCLUSIONS: Compared to traditional usability evaluation methods, the major strength of the fuzzy method is that it captures the fuzziness and uncertainties in human judgments and provides an integrated framework that combines the vague judgments from multiple stages of a product evaluation process. PMID:28035943

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...

How School Context and Educator Characteristics Predict Distributed Leadership: A Hierarchical Structural Equation Model with 2013 TALIS Data

ERIC Educational Resources Information Center

Liu, Yan; Bellibas, Mehmet Sukru; Printy, Susan

2018-01-01

Distributed leadership is a dynamic process and reciprocal interaction of the leader, the subordinates and the situation. This research was inspired by the theoretical framework of Spillane in order to contextualize distributed leadership and compare the variations using the Teaching and Learning International Survey 2013 data. The two-level…

Sensory evaluation based fuzzy AHP approach for material selection in customized garment design and development process

NASA Astrophysics Data System (ADS)

Hong, Y.; Curteza, A.; Zeng, X.; Bruniaux, P.; Chen, Y.

2016-06-01

Material selection is the most difficult section in the customized garment product design and development process. This study aims to create a hierarchical framework for material selection. The analytic hierarchy process and fuzzy sets theories have been applied to mindshare the diverse requirements from the customer and inherent interaction/interdependencies among these requirements. Sensory evaluation ensures a quick and effective selection without complex laboratory test such as KES and FAST, using the professional knowledge of the designers. A real empirical application for the physically disabled people is carried out to demonstrate the proposed method. Both the theoretical and practical background of this paper have indicated the fuzzy analytical network process can capture expert's knowledge existing in the form of incomplete, ambiguous and vague information for the mutual influence on attribute and criteria of the material selection.

Ubiquitous Robotic Technology for Smart Manufacturing System.

PubMed

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

2016-01-01

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

Ubiquitous Robotic Technology for Smart Manufacturing System

PubMed Central

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

2016-01-01

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

Improving removal-based estimates of abundance by sampling a population of spatially distinct subpopulations

USGS Publications Warehouse

Dorazio, R.M.; Jelks, H.L.; Jordan, F.

2005-01-01

 A statistical modeling framework is described for estimating the abundances of spatially distinct subpopulations of animals surveyed using removal sampling. To illustrate this framework, hierarchical models are developed using the Poisson and negative-binomial distributions to model variation in abundance among subpopulations and using the beta distribution to model variation in capture probabilities. These models are fitted to the removal counts observed in a survey of a federally endangered fish species. The resulting estimates of abundance have similar or better precision than those computed using the conventional approach of analyzing the removal counts of each subpopulation separately. Extension of the hierarchical models to include spatial covariates of abundance is straightforward and may be used to identify important features of an animal's habitat or to predict the abundance of animals at unsampled locations.

Hierarchical modeling of cluster size in wildlife surveys

USGS Publications Warehouse

Royle, J. Andrew

2008-01-01

Clusters or groups of individuals are the fundamental unit of observation in many wildlife sampling problems, including aerial surveys of waterfowl, marine mammals, and ungulates. Explicit accounting of cluster size in models for estimating abundance is necessary because detection of individuals within clusters is not independent and detectability of clusters is likely to increase with cluster size. This induces a cluster size bias in which the average cluster size in the sample is larger than in the population at large. Thus, failure to account for the relationship between delectability and cluster size will tend to yield a positive bias in estimates of abundance or density. I describe a hierarchical modeling framework for accounting for cluster-size bias in animal sampling. The hierarchical model consists of models for the observation process conditional on the cluster size distribution and the cluster size distribution conditional on the total number of clusters. Optionally, a spatial model can be specified that describes variation in the total number of clusters per sample unit. Parameter estimation, model selection, and criticism may be carried out using conventional likelihood-based methods. An extension of the model is described for the situation where measurable covariates at the level of the sample unit are available. Several candidate models within the proposed class are evaluated for aerial survey data on mallard ducks (Anas platyrhynchos).

Towards Stability Analysis of Jump Linear Systems with State-Dependent and Stochastic Switching

NASA Technical Reports Server (NTRS)

Tejada, Arturo; Gonzalez, Oscar R.; Gray, W. Steven

2004-01-01

This paper analyzes the stability of hierarchical jump linear systems where the supervisor is driven by a Markovian stochastic process and by the values of the supervised jump linear system s states. The stability framework for this class of systems is developed over infinite and finite time horizons. The framework is then used to derive sufficient stability conditions for a specific class of hybrid jump linear systems with performance supervision. New sufficient stochastic stability conditions for discrete-time jump linear systems are also presented.

Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes.

PubMed

Subbanna, Nagesh K; Precup, Doina; Collins, D Louis; Arbel, Tal

2013-01-01

In this paper, we present a fully automated hierarchical probabilistic framework for segmenting brain tumours from multispectral human brain magnetic resonance images (MRIs) using multiwindow Gabor filters and an adapted Markov Random Field (MRF) framework. In the first stage, a customised Gabor decomposition is developed, based on the combined-space characteristics of the two classes (tumour and non-tumour) in multispectral brain MRIs in order to optimally separate tumour (including edema) from healthy brain tissues. A Bayesian framework then provides a coarse probabilistic texture-based segmentation of tumours (including edema) whose boundaries are then refined at the voxel level through a modified MRF framework that carefully separates the edema from the main tumour. This customised MRF is not only built on the voxel intensities and class labels as in traditional MRFs, but also models the intensity differences between neighbouring voxels in the likelihood model, along with employing a prior based on local tissue class transition probabilities. The second inference stage is shown to resolve local inhomogeneities and impose a smoothing constraint, while also maintaining the appropriate boundaries as supported by the local intensity difference observations. The method was trained and tested on the publicly available MICCAI 2012 Brain Tumour Segmentation Challenge (BRATS) Database [1] on both synthetic and clinical volumes (low grade and high grade tumours). Our method performs well compared to state-of-the-art techniques, outperforming the results of the top methods in cases of clinical high grade and low grade tumour core segmentation by 40% and 45% respectively.

A Framework for Distributed Problem Solving

NASA Astrophysics Data System (ADS)

Leone, Joseph; Shin, Don G.

1989-03-01

This work explores a distributed problem solving (DPS) approach, namely the AM/AG model, to cooperative memory recall. The AM/AG model is a hierarchic social system metaphor for DPS based on the Mintzberg's model of organizations. At the core of the model are information flow mechanisms, named amplification and aggregation. Amplification is a process of expounding a given task, called an agenda, into a set of subtasks with magnified degree of specificity and distributing them to multiple processing units downward in the hierarchy. Aggregation is a process of combining the results reported from multiple processing units into a unified view, called a resolution, and promoting the conclusion upward in the hierarchy. The combination of amplification and aggregation can account for a memory recall process which primarily relies on the ability of making associations between vast amounts of related concepts, sorting out the combined results, and promoting the most plausible ones. The amplification process is discussed in detail. An implementation of the amplification process is presented. The process is illustrated by an example.

Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty: STRUCTURAL UNCERTAINTY DIAGNOSTICS

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

Moges, Edom; Demissie, Yonas; Li, Hong-Yi

2016-04-01

In most water resources applications, a single model structure might be inadequate to capture the dynamic multi-scale interactions among different hydrological processes. Calibrating single models for dynamic catchments, where multiple dominant processes exist, can result in displacement of errors from structure to parameters, which in turn leads to over-correction and biased predictions. An alternative to a single model structure is to develop local expert structures that are effective in representing the dominant components of the hydrologic process and adaptively integrate them based on an indicator variable. In this study, the Hierarchical Mixture of Experts (HME) framework is applied to integratemore » expert model structures representing the different components of the hydrologic process. Various signature diagnostic analyses are used to assess the presence of multiple dominant processes and the adequacy of a single model, as well as to identify the structures of the expert models. The approaches are applied for two distinct catchments, the Guadalupe River (Texas) and the French Broad River (North Carolina) from the Model Parameter Estimation Experiment (MOPEX), using different structures of the HBV model. The results show that the HME approach has a better performance over the single model for the Guadalupe catchment, where multiple dominant processes are witnessed through diagnostic measures. Whereas, the diagnostics and aggregated performance measures prove that French Broad has a homogeneous catchment response, making the single model adequate to capture the response.« less

Development and evaluation of a new taxonomy of mobility-related assistive technology devices.

PubMed

Shoemaker, Laura L; Lenker, James A; Fuhrer, Marcus J; Jutai, Jeffrey W; Demers, Louise; DeRuyter, Frank

2010-10-01

This article reports on the development of a new taxonomy for mobility-related assistive technology devices. A prototype taxonomy was created based on the extant literature. Five mobility device experts were engaged in a modified Delphi process to evaluate and refine the taxonomy. Multiple iterations of expert feedback and revision yielded consensual agreement on the structure and terminology of a new mobility device taxonomy. The taxonomy uses a hierarchical framework to classify ambulation aids and wheeled mobility devices, including their key features that impact mobility. Five attributes of the new taxonomy differentiate it from previous mobility-related device classifications: (1) hierarchical structure, (2) primary device categories are grouped based on their intended mobility impact, (3) comprehensive inclusion of technical features, (4) a capacity to assimilate reimbursement codes, and (5) availability of a detailed glossary. The taxonomy is intended to support assistive technology outcomes research. The taxonomy will enable researchers to capture mobility-related assistive technology device interventions with precision and provide a common terminology that will allow comparisons among studies. The prominence of technical features within the new taxonomy will hopefully promote research that helps clinicians predict how devices will perform, thus aiding clinical decision making and supporting funding recommendations.

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

PubMed Central

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

2015-01-01

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

Construction of a Hierarchical Architecture of Covalent Organic Frameworks via a Postsynthetic Approach.

PubMed

Zhang, Gen; Tsujimoto, Masahiko; Packwood, Daniel; Duong, Nghia Tuan; Nishiyama, Yusuke; Kadota, Kentaro; Kitagawa, Susumu; Horike, Satoshi

2018-02-21

Covalent organic frameworks (COFs) represent an emerging class of crystalline porous materials that are constructed by the assembly of organic building blocks linked via covalent bonds. Several strategies have been developed for the construction of new COF structures; however, a facile approach to fabricate hierarchical COF architectures with controlled domain structures remains a significant challenge, and has not yet been achieved. In this study, a dynamic covalent chemistry (DCC)-based postsynthetic approach was employed at the solid-liquid interface to construct such structures. Two-dimensional imine-bonded COFs having different aromatic groups were prepared, and a homogeneously mixed-linker structure and a heterogeneously core-shell hollow structure were fabricated by controlling the reactivity of the postsynthetic reactions. Solid-state nuclear magnetic resonance (NMR) spectroscopy and transmission electron microscopy (TEM) confirmed the structures. COFs prepared by a postsynthetic approach exhibit several functional advantages compared with their parent phases. Their Brunauer-Emmett-Teller (BET) surface areas are 2-fold greater than those of their parent phases because of the higher crystallinity. In addition, the hydrophilicity of the material and the stepwise adsorption isotherms of H 2 O vapor in the hierarchical frameworks were precisely controlled, which was feasible because of the distribution of various domains of the two COFs by controlling the postsynthetic reaction. The approach opens new routes for constructing COF architectures with functionalities that are not possible in a single phase.

Complex optimization for big computational and experimental neutron datasets

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

Bao, Feng; Oak Ridge National Lab.; Archibald, Richard

Here, we present a framework to use high performance computing to determine accurate solutions to the inverse optimization problem of big experimental data against computational models. We demonstrate how image processing, mathematical regularization, and hierarchical modeling can be used to solve complex optimization problems on big data. We also demonstrate how both model and data information can be used to further increase solution accuracy of optimization by providing confidence regions for the processing and regularization algorithms. Finally, we use the framework in conjunction with the software package SIMPHONIES to analyze results from neutron scattering experiments on silicon single crystals, andmore » refine first principles calculations to better describe the experimental data.« less

Complex optimization for big computational and experimental neutron datasets

DOE PAGES

Bao, Feng; Oak Ridge National Lab.; Archibald, Richard; ...

2016-11-07

Here, we present a framework to use high performance computing to determine accurate solutions to the inverse optimization problem of big experimental data against computational models. We demonstrate how image processing, mathematical regularization, and hierarchical modeling can be used to solve complex optimization problems on big data. We also demonstrate how both model and data information can be used to further increase solution accuracy of optimization by providing confidence regions for the processing and regularization algorithms. Finally, we use the framework in conjunction with the software package SIMPHONIES to analyze results from neutron scattering experiments on silicon single crystals, andmore » refine first principles calculations to better describe the experimental data.« less

Combining High Spatial Resolution Optical and LIDAR Data for Object-Based Image Classification

NASA Astrophysics Data System (ADS)

Li, R.; Zhang, T.; Geng, R.; Wang, L.

2018-04-01

In order to classify high spatial resolution images more accurately, in this research, a hierarchical rule-based object-based classification framework was developed based on a high-resolution image with airborne Light Detection and Ranging (LiDAR) data. The eCognition software is employed to conduct the whole process. In detail, firstly, the FBSP optimizer (Fuzzy-based Segmentation Parameter) is used to obtain the optimal scale parameters for different land cover types. Then, using the segmented regions as basic units, the classification rules for various land cover types are established according to the spectral, morphological and texture features extracted from the optical images, and the height feature from LiDAR respectively. Thirdly, the object classification results are evaluated by using the confusion matrix, overall accuracy and Kappa coefficients. As a result, a method using the combination of an aerial image and the airborne Lidar data shows higher accuracy.

Relating Memory To Functional Performance In Normal Aging to Dementia Using Hierarchical Bayesian Cognitive Processing Models

PubMed Central

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

2012-01-01

Determining how cognition affects functional abilities is important in Alzheimer’s disease and related disorders (ADRD). 280 patients (normal or ADRD) received a total of 1,514 assessments using the Functional Assessment Staging Test (FAST) procedure and the MCI Screen (MCIS). A hierarchical Bayesian cognitive processing (HBCP) model was created by embedding a signal detection theory (SDT) model of the MCIS delayed recognition memory task into a hierarchical Bayesian framework. The SDT 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 six 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. HBCP models applied to recognition memory data from clinical practice settings accurately translated a latent measure of cognition to a continuous measure of functional severity for both individuals and FAST groups. Such a translation links two levels of brain information processing, and may enable more accurate correlations with other levels, such as those characterized by biomarkers. PMID:22407225

Hierarchical pictorial structures for simultaneously localizing multiple organs in volumetric pre-scan CT

NASA Astrophysics Data System (ADS)

Montillo, Albert; Song, Qi; Das, Bipul; Yin, Zhye

2015-03-01

Parsing volumetric computed tomography (CT) into 10 or more salient organs simultaneously is a challenging task with many applications such as personalized scan planning and dose reporting. In the clinic, pre-scan data can come in the form of very low dose volumes acquired just prior to the primary scan or from an existing primary scan. To localize organs in such diverse data, we propose a new learning based framework that we call hierarchical pictorial structures (HPS) which builds multiple levels of models in a tree-like hierarchy that mirrors the natural decomposition of human anatomy from gross structures to finer structures. Each node of our hierarchical model learns (1) the local appearance and shape of structures, and (2) a generative global model that learns probabilistic, structural arrangement. Our main contribution is twofold. First we embed the pictorial structures approach in a hierarchical framework which reduces test time image interpretation and allows for the incorporation of additional geometric constraints that robustly guide model fitting in the presence of noise. Second we guide our HPS framework with the probabilistic cost maps extracted using random decision forests using volumetric 3D HOG features which makes our model fast to train and fast to apply to novel test data and posses a high degree of invariance to shape distortion and imaging artifacts. All steps require approximate 3 mins to compute and all organs are located with suitably high accuracy for our clinical applications such as personalized scan planning for radiation dose reduction. We assess our method using a database of volumetric CT scans from 81 subjects with widely varying age and pathology and with simulated ultra-low dose cadaver pre-scan data.

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.

Predicting the performance uncertainty of a 1-MW pilot-scale carbon capture system after hierarchical laboratory-scale calibration and validation

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

Xu, Zhijie; Lai, Canhai; Marcy, Peter William

2017-05-01

A challenging problem in designing pilot-scale carbon capture systems is to predict, with uncertainty, the adsorber performance and capture efficiency under various operating conditions where no direct experimental data exist. Motivated by this challenge, we previously proposed a hierarchical framework in which relevant parameters of physical models were sequentially calibrated from different laboratory-scale carbon capture unit (C2U) experiments. Specifically, three models of increasing complexity were identified based on the fundamental physical and chemical processes of the sorbent-based carbon capture technology. Results from the corresponding laboratory experiments were used to statistically calibrate the physical model parameters while quantifying some of theirmore » inherent uncertainty. The parameter distributions obtained from laboratory-scale C2U calibration runs are used in this study to facilitate prediction at a larger scale where no corresponding experimental results are available. In this paper, we first describe the multiphase reactive flow model for a sorbent-based 1-MW carbon capture system then analyze results from an ensemble of simulations with the upscaled model. The simulation results are used to quantify uncertainty regarding the design’s predicted efficiency in carbon capture. In particular, we determine the minimum gas flow rate necessary to achieve 90% capture efficiency with 95% confidence.« less

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

DOE PAGES

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

2016-11-29

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

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

PubMed

Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

PubMed Central

Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks. PMID:27806074

Best-worst scaling to assess the most important barriers and facilitators for the use of health technology assessment in Austria.

PubMed

Feig, Chiara; Cheung, Kei Long; Hiligsmann, Mickaël; Evers, Silvia M A A; Simon, Judit; Mayer, Susanne

2018-04-01

Although Health Technology Assessment (HTA) is increasingly used to support evidence-based decision-making in health care, several barriers and facilitators for the use of HTA have been identified. This best-worst scaling (BWS) study aims to assess the relative importance of selected barriers and facilitators of the uptake of HTA studies in Austria. A BWS object case survey was conducted among 37 experts in Austria to assess the relative importance of HTA barriers and facilitators. Hierarchical Bayes estimation was applied, with the best-worst count analysis as sensitivity analysis. Subgroup analyses were also performed on professional role and HTA experience. The most important barriers were 'lack of transparency in the decision-making process', 'fragmentation', 'absence of appropriate incentives', 'no explicit framework for decision-making process', and 'insufficient legal support'. The most important facilitators were 'transparency in the decision-making process', 'availability of relevant HTA research for policy makers', 'availability of explicit framework for decision-making process', 'sufficient legal support', and 'appropriate incentives'. This study suggests that HTA barriers and facilitators related to the context of decision makers, especially 'policy characteristics' and 'organization and resources' are the most important in Austria. A transparent and participatory decision-making process could improve the adoption of HTA evidence.

Ecological units of the Northern Region: Subsections

Treesearch

John A. Nesser; Gary L. Ford; C. Lee Maynard; Debbie Dumroese

1997-01-01

Ecological units are described at the subsection level of the Forest Service National Hierarchical Framework of Ecological Units. A total of 91 subsections are delineated on the 1996 map "Ecological Units of the Northern Region: Subsections," based on physical and biological criteria. This document consists of descriptions of the climate, geomorphology,...

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.

Resolving the Framework Position of Organic Structure-Directing Agents in Hierarchical Zeolites via Polarized Stimulated Raman Scattering.

PubMed

Fleury, Guillaume; Steele, Julian A; Gerber, Iann C; Jolibois, F; Puech, P; Muraoka, Koki; Keoh, Sye Hoe; Chaikittisilp, Watcharop; Okubo, Tatsuya; Roeffaers, Maarten B J

2018-04-05

The direct synthesis of hierarchically intergrown silicalite-1 can be achieved using a specific diquaternary ammonium agent. However, the location of these molecules in the zeolite framework, which is critical to understand the formation of the material, remains unclear. Where traditional characterization tools have previously failed, herein we use polarized stimulated Raman scattering (SRS) microscopy to resolve molecular organization inside few-micron-sized crystals. Through a combination of experiment and first-principles calculations, our investigation reveals the preferential location of the templating agent inside the linear pores of the MFI framework. Besides illustrating the attractiveness of SRS microscopy in the field of material science to study and spatially resolve local molecular distribution as well as orientation, these results can be exploited in the design of new templating agents for the preparation of hierarchical zeolites.

Green Fabrication of Ultrathin Co3O4 Nanosheets from Metal-Organic Framework for Robust High-Rate Supercapacitors.

PubMed

Xiao, Zhenyu; Fan, Lili; Xu, Ben; Zhang, Shanqing; Kang, Wenpei; Kang, Zixi; Lin, Huan; Liu, Xiuping; Zhang, Shiyu; Sun, Daofeng

2017-12-06

Two-dimensional cobalt oxide (Co 3 O 4 ) is a promising candidate for robust electrochemical capacitors with high performance. Herein, we use 2,3,5,6-tetramethyl-1,4-diisophthalate as a recyclable ligand to construct a Co-based metal-organic framework of UPC-9, and subsequently, we obtain ultrathin hierarchical Co 3 O 4 hexagonal nanosheets with a thickness of 3.5 nm through a hydrolysis and calcination process. A remarkable and excellent specific capacitance of 1121 F·g -1 at a current density of 1 A·g -1 and 873 F·g -1 at a current density of 25 A·g -1 were achieved for the as-prepared asymmetric supercapacitor, which can be attributed to the ultrathin 2D morphology and the rich macroporous and mesoporous structures of the ultrathin Co 3 O 4 nanosheets. This synthesis strategy is environmentally benign and economically viable due to the fact that the costly organic ligand molecules are recycled, reducing the materials cost as well as the environmental cost for the synthesis process.

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

2008-01-01

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

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 fuzzy MCDM framework based on fuzzy measure and fuzzy integral for agile supplier evaluation

NASA Astrophysics Data System (ADS)

Dursun, Mehtap

2017-06-01

Supply chains need to be agile in order to response quickly to the changes in today's competitive environment. The success of an agile supply chain depends on the firm's ability to select the most appropriate suppliers. This study proposes a multi-criteria decision making technique for conducting an analysis based on multi-level hierarchical structure and fuzzy logic for the evaluation of agile suppliers. The ideal and anti-ideal solutions are taken into consideration simultaneously in the developed approach. The proposed decision approach enables the decision-makers to use linguistic terms, and thus, reduce their cognitive burden in the evaluation process. Furthermore, a hierarchy of evaluation criteria and their related sub-criteria is employed in the presented approach in order to conduct a more effective analysis.

Auditing of SNOMED CT's Hierarchical Structure using the National Drug File - Reference Terminology.

PubMed

Zakharchenko, Aleksandr; Geller, James

2015-01-01

With the ongoing development in the field of Medical Informatics, the availability of cross-references and the consistency of coverage between terminologies become critical requirements for clinical decision support. In this paper, we examine the possibility of developing a framework that highlights and exposes hierarchical incompatibilities between different medical terminologies in order to facilitate the process of achieving a sufficient level of consistency between terminologies. For the purpose of this research, we are working with the Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT) and the National Drug File--Reference Terminology (NDF-RT)--a clinical terminology focused on drugs. For discovery of inconsistencies we built an automated tool.

[Sex/Gender, Violence and Human Rights: Conceptual Perspectives for Approaching Gender-Based Violence against Women from the Health Sector].

PubMed

Bello-Urrego, Alejandra Del Rocío

2013-03-01

Based upon the public health sector perspective, this article explores conceptual approaches to address the issue of gender violence against women. To consider the election of an analysis framework regarding the phenomenon of violence against women in the health sector, in the light of the political implications of becoming a woman in the midst of a specific social order. Expert review of scientific literature published on free-access data bases so as to identify the most commonly used interpretation frameworks with regard to the phenomenon of violence against women in order to explain its political implications according to a specific social order. Becoming woman implies participation in social aspects from an inequity stemming from structural power. This is the reason why violence against women can never be considered away from its roots. i.e., a society that assigns to women social roles that imply diminished possibilities of access to the use of power through a sex/gender system which is binary, hierarchic and exclusive. In public health areas, the selection of interpretation frameworks that do not take into account the structural origin of violence against women contribute to their invisibilization and even to perpetuate it, independently from the conscience of the researcher on the basis of the political burden arising from the use of such frameworks to the detriment of others, or the intention of objectivity regarding frameworks with a heavy political burden that contribute to the maintenance of a sex/gender binary, hierarchic and excluding structure. Copyright © 2013 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics.

PubMed

Benedek, C; Descombes, X; Zerubia, J

2012-01-01

In this paper, we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: 1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low-level change information between the time layers and object-level building description to recognize and separate changed and unaltered buildings. 2) To answer the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature-based modules. 3) To simultaneously ensure the convergence, optimality, and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel nonuniform stochastic object birth process which generates relevant objects with higher probability based on low-level image features.

Feature integration and object representations along the dorsal stream visual hierarchy

PubMed Central

Perry, Carolyn Jeane; Fallah, Mazyar

2014-01-01

The visual system is split into two processing streams: a ventral stream that receives color and form information and a dorsal stream that receives motion information. Each stream processes that information hierarchically, with each stage building upon the previous. In the ventral stream this leads to the formation of object representations that ultimately allow for object recognition regardless of changes in the surrounding environment. In the dorsal stream, this hierarchical processing has classically been thought to lead to the computation of complex motion in three dimensions. However, there is evidence to suggest that there is integration of both dorsal and ventral stream information into motion computation processes, giving rise to intermediate object representations, which facilitate object selection and decision making mechanisms in the dorsal stream. First we review the hierarchical processing of motion along the dorsal stream and the building up of object representations along the ventral stream. Then we discuss recent work on the integration of ventral and dorsal stream features that lead to intermediate object representations in the dorsal stream. Finally we propose a framework describing how and at what stage different features are integrated into dorsal visual stream object representations. Determining the integration of features along the dorsal stream is necessary to understand not only how the dorsal stream builds up an object representation but also which computations are performed on object representations instead of local features. PMID:25140147

Model Based Mission Assurance: Emerging Opportunities for Robotic Systems

NASA Technical Reports Server (NTRS)

Evans, John W.; DiVenti, Tony

2016-01-01

The emergence of Model Based Systems Engineering (MBSE) in a Model Based Engineering framework has created new opportunities to improve effectiveness and efficiencies across the assurance functions. The MBSE environment supports not only system architecture development, but provides for support of Systems Safety, Reliability and Risk Analysis concurrently in the same framework. Linking to detailed design will further improve assurance capabilities to support failures avoidance and mitigation in flight systems. This also is leading new assurance functions including model assurance and management of uncertainty in the modeling environment. Further, the assurance cases, a structured hierarchal argument or model, are emerging as a basis for supporting a comprehensive viewpoint in which to support Model Based Mission Assurance (MBMA).

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.

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.

Image Search Reranking With Hierarchical Topic Awareness.

PubMed

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

2015-10-01

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

Hierarchical Bayesian Model Averaging for Non-Uniqueness and Uncertainty Analysis of Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Fijani, E.; Chitsazan, N.; Nadiri, A.; Tsai, F. T.; Asghari Moghaddam, A.

2012-12-01

Artificial Neural Networks (ANNs) have been widely used to estimate concentration of chemicals in groundwater systems. However, estimation uncertainty is rarely discussed in the literature. Uncertainty in ANN output stems from three sources: ANN inputs, ANN parameters (weights and biases), and ANN structures. Uncertainty in ANN inputs may come from input data selection and/or input data error. ANN parameters are naturally uncertain because they are maximum-likelihood estimated. ANN structure is also uncertain because there is no unique ANN model given a specific case. Therefore, multiple plausible AI models are generally resulted for a study. One might ask why good models have to be ignored in favor of the best model in traditional estimation. What is the ANN estimation variance? How do the variances from different ANN models accumulate to the total estimation variance? To answer these questions we propose a Hierarchical Bayesian Model Averaging (HBMA) framework. Instead of choosing one ANN model (the best ANN model) for estimation, HBMA averages outputs of all plausible ANN models. The model weights are based on the evidence of data. Therefore, the HBMA avoids overconfidence on the single best ANN model. In addition, HBMA is able to analyze uncertainty propagation through aggregation of ANN models in a hierarchy framework. This method is applied for estimation of fluoride concentration in the Poldasht plain and the Bazargan plain in Iran. Unusually high fluoride concentration in the Poldasht and Bazargan plains has caused negative effects on the public health. Management of this anomaly requires estimation of fluoride concentration distribution in the area. The results show that the HBMA provides a knowledge-decision-based framework that facilitates analyzing and quantifying ANN estimation uncertainties from different sources. In addition HBMA allows comparative evaluation of the realizations for each source of uncertainty by segregating the uncertainty sources in a hierarchical framework. Fluoride concentration estimation using the HBMA method shows better agreement to the observation data in the test step because they are not based on a single model with a non-dominate weights.

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.

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.

The Fluke Security Project

DTIC Science & Technology

2000-04-01

be an extension of Utah’s nascent Quarks system, oriented to closely coupled cluster environments. However, the grant did not actually begin until... Intel x86, implemented ten virtual machine monitors and servers, including a virtual memory manager, a checkpointer, a process manager, a file server...Fluke, we developed a novel hierarchical processor scheduling frame- work called CPU inheritance scheduling [5]. This is a framework for scheduling

A Hierarchical Approach to Examine Personal and School Effect on Teacher Motivation

ERIC Educational Resources Information Center

Wei, Yi-En

2012-01-01

In order to depict a better picture of teacher motivation, the researcher developed the theoretical framework based on Deci and Ryan's (1985) self-determination theory (SDT) and examined factors affecting teachers' autonomous motivation at both the personal and school level. Several multilevel structural equation models (ML-SEM) were…

Applying Toulmin: Teaching Logical Reasoning and Argumentative Writing

ERIC Educational Resources Information Center

Rex, Lesley A.; Thomas, Ebony Elizabeth; Engel, Steven

2010-01-01

To learn to write well-reasoned persuasive arguments, students need in situ help thinking through the complexity and complications of an issue, making inferences based on evidence, and hierarchically grouping and logically sequencing ideas. They rely on teachers to make this happen. In this article, the authors explain the framework they used and…

Efficient Gas-Sensing for Formaldehyde with 3D Hierarchical Co3O4 Derived from Co5-Based MOF Microcrystals.

PubMed

Zhou, Wei; Wu, Ya-Pan; Zhao, Jun; Dong, Wen-Wen; Qiao, Xiu-Qing; Hou, Dong-Fang; Bu, Xianhui; Li, Dong-Sheng

2017-11-20

Detecting formaldehyde at low operating temperature and maintaining long-term stability are of great significance. In this work, a hierarchical Co 3 O 4 nanostructure has been fabricated by calcining Co 5 -based metal-organic framework (MOF) microcrystals. Co 3 O 4 -350 particles were used for efficient gas-sensing for the detecting of formaldehyde vapor at lower working temperature (170 °C), low detection limit of 10 ppm, and long-term stability (30 days), which not only is the optimal value among all reported pure Co 3 O 4 sensing materials for the detection of formaldehyde but also is superior to that of majority of Co 3 O 4 -based composites. Such extraordinarily efficient properties might be resulted from hierarchically structures, larger surface area and unique pore structure. This strategy is further confirmed that MOFs, especially Co-clusters MOFs, could be used as precursor to synthesize 3D nanostructure metal oxide materials with high-performance, which possess high porosity and more active sites and shorter ionic diffusion lengths.

Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package

PubMed Central

Ahn, Woo-Young; Haines, Nathaniel; Zhang, Lei

2017-01-01

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations. PMID:29601060

Multifaceted Modularity: A Key for Stepwise Building of Hierarchical Complexity in Actinide Metal–Organic Frameworks

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

Dolgopolova, Ekaterina A.; Ejegbavwo, Otega A.; Martin, Corey R.

Growing necessity for efficient nuclear waste management is a driving force for development of alternative architectures towards fundamental understanding of mechanisms involved in actinide integration inside extended structures. In this manuscript, metal-organic frameworks (MOFs) were investigated as a model system for engineering radionuclide containing materials through utilization of unprecedented MOF modularity, which cannot be replicated in any other type of materials. Through the implementation of recent synthetic advances in the MOF field, hierarchical complexity of An-materials were built stepwise, which was only feasible due to preparation of the first examples of actinide-based frameworks with “unsaturated” metal nodes. The first successfulmore » attempts of solid-state metathesis and metal node extension in An-MOFs are reported, and the results of the former approach revealed drastic differences in chemical behavior of extended structures versus molecular species. Successful utilization of MOF modularity also allowed us to structurally characterize the first example of bimetallic An-An nodes. To the best of our knowledge, through combination of solid-state metathesis, guest incorporation, and capping linker installation, we were able to achieve the highest Th wt% in mono- and bi-actinide frameworks with minimal structural density. Overall, combination of a multistep synthetic approach with homogeneous actinide distribution and moderate solvothermal conditions could make MOFs an exceptionally powerful tool to address fundamental questions responsible for chemical behavior of An-based extended structures, and therefore, shed light on possible optimization of nuclear waste administration.« less

Multifaceted Modularity: A Key for Stepwise Building of Hierarchical Complexity in Actinide Metal-Organic Frameworks.

PubMed

Dolgopolova, Ekaterina A; Ejegbavwo, Otega A; Martin, Corey R; Smith, Mark D; Setyawan, Wahyu; Karakalos, Stavros G; Henager, Charles H; Zur Loye, Hans-Conrad; Shustova, Natalia B

2017-11-22

Growing necessity for efficient nuclear waste management is a driving force for development of alternative architectures toward fundamental understanding of mechanisms involved in actinide (An) integration inside extended structures. In this manuscript, metal-organic frameworks (MOFs) were investigated as a model system for engineering radionuclide containing materials through utilization of unprecedented MOF modularity, which cannot be replicated in any other type of materials. Through the implementation of recent synthetic advances in the MOF field, hierarchical complexity of An-materials was built stepwise, which was only feasible due to preparation of the first examples of actinide-based frameworks with "unsaturated" metal nodes. The first successful attempts of solid-state metathesis and metal node extension in An-MOFs are reported, and the results of the former approach revealed drastic differences in chemical behavior of extended structures versus molecular species. Successful utilization of MOF modularity also allowed us to structurally characterize the first example of bimetallic An-An nodes. To the best of our knowledge, through combination of solid-state metathesis, guest incorporation, and capping linker installation, we were able to achieve the highest Th wt % in mono- and biactinide frameworks with minimal structural density. Overall, the combination of a multistep synthetic approach with homogeneous actinide distribution and moderate solvothermal conditions could make MOFs an exceptionally powerful tool to address fundamental questions responsible for chemical behavior of An-based extended structures and, therefore, shed light on possible optimization of nuclear waste administration.

Synthesis of hierarchical mesoporous lithium nickel cobalt manganese oxide spheres with high rate capability for lithium-ion batteries

NASA Astrophysics Data System (ADS)

Tong, Wei; Huang, Yudai; Cai, Yanjun; Guo, Yong; Wang, Xingchao; Jia, Dianzeng; Sun, Zhipeng; Pang, Weikong; Guo, Zaiping; Zong, Jun

2018-01-01

Hierarchical mesoporous LiNi1/3Co1/3Mn1/3O2 spheres have been synthesized by urea-assisted solvothermal method with adding Triton X-100. The structure and morphology of the as-prepared materials were analyzed by X-ray diffraction and electron microscope. The results show that the as-prepared samples can be indexed as hexagonal layered structure with hierarchical architecture, and the possible formation mechanism is speculated. When evaluated as cathode material, the hierarchical mesoporous LiNi1/3Co1/3Mn1/3O2 spheres show good electrochemical properties with high initial discharge capacity of 129.9 mAh g-1, and remain the discharge capacity of 95.5 mAh g-1 after 160 cycles at 10C. The excellent electrochemical performance of the as-prepared sample can be attributed to its stable hierarchical mesoporous framework in conjunction with large specific surface, low cation mixing and small particle size. They not only provide a large number of reaction sites for surface or interface reaction, but also shorten the diffusion length of Li+ ions. Meanwhile, the mesoporous spheres composed of nanoparticles can contribute to high rate ability and buffer volume changes during charge/discharge process.

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…

Create and Publish a Hierarchical Progressive Survey (HiPS)

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Impacts of forest fragmentation on species richness: a hierarchical approach to community modelling

USGS Publications Warehouse

Zipkin, Elise F.; DeWan, Amielle; Royle, J. Andrew

2009-01-01

1. Species richness is often used as a tool for prioritizing conservation action. One method for predicting richness and other summaries of community structure is to develop species-specific models of occurrence probability based on habitat or landscape characteristics. However, this approach can be challenging for rare or elusive species for which survey data are often sparse. 2. Recent developments have allowed for improved inference about community structure based on species-specific models of occurrence probability, integrated within a hierarchical modelling framework. This framework offers advantages to inference about species richness over typical approaches by accounting for both species-level effects and the aggregated effects of landscape composition on a community as a whole, thus leading to increased precision in estimates of species richness by improving occupancy estimates for all species, including those that were observed infrequently. 3. We developed a hierarchical model to assess the community response of breeding birds in the Hudson River Valley, New York, to habitat fragmentation and analysed the model using a Bayesian approach. 4. The model was designed to estimate species-specific occurrence and the effects of fragment area and edge (as measured through the perimeter and the perimeter/area ratio, P/A), while accounting for imperfect detection of species. 5. We used the fitted model to make predictions of species richness within forest fragments of variable morphology. The model revealed that species richness of the observed bird community was maximized in small forest fragments with a high P/A. However, the number of forest interior species, a subset of the community with high conservation value, was maximized in large fragments with low P/A. 6. Synthesis and applications. Our results demonstrate the importance of understanding the responses of both individual, and groups of species, to environmental heterogeneity while illustrating the utility of hierarchical models for inference about species richness for conservation. This framework can be used to investigate the impacts of land-use change and fragmentation on species or assemblage richness, and to further understand trade-offs in species-specific occupancy probabilities associated with landscape variability.

Overview of EVE - the event visualization environment of ROOT

NASA Astrophysics Data System (ADS)

Tadel, Matevž

2010-04-01

EVE is a high-level visualization library using ROOT's data-processing, GUI and OpenGL interfaces. It is designed as a framework for object management offering hierarchical data organization, object interaction and visualization via GUI and OpenGL representations. Automatic creation of 2D projected views is also supported. On the other hand, it can serve as an event visualization toolkit satisfying most HEP requirements: visualization of geometry, simulated and reconstructed data such as hits, clusters, tracks and calorimeter information. Special classes are available for visualization of raw-data. Object-interaction layer allows for easy selection and highlighting of objects and their derived representations (projections) across several views (3D, Rho-Z, R-Phi). Object-specific tooltips are provided in both GUI and GL views. The visual-configuration layer of EVE is built around a data-base of template objects that can be applied to specific instances of visualization objects to ensure consistent object presentation. The data-base can be retrieved from a file, edited during the framework operation and stored to file. EVE prototype was developed within the ALICE collaboration and has been included into ROOT in December 2007. Since then all EVE components have reached maturity. EVE is used as the base of AliEve visualization framework in ALICE, Firework physics-oriented event-display in CMS, and as the visualization engine of FairRoot in FAIR.

Ordered macro-microporous metal-organic framework single crystals

NASA Astrophysics Data System (ADS)

Shen, Kui; Zhang, Lei; Chen, Xiaodong; Liu, Lingmei; Zhang, Daliang; Han, Yu; Chen, Junying; Long, Jilan; Luque, Rafael; Li, Yingwei; Chen, Banglin

2018-01-01

We constructed highly oriented and ordered macropores within metal-organic framework (MOF) single crystals, opening up the area of three-dimensional–ordered macro-microporous materials (that is, materials containing both macro- and micropores) in single-crystalline form. Our methodology relies on the strong shaping effects of a polystyrene nanosphere monolith template and a double-solvent–induced heterogeneous nucleation approach. This process synergistically enabled the in situ growth of MOFs within ordered voids, rendering a single crystal with oriented and ordered macro-microporous structure. The improved mass diffusion properties of such hierarchical frameworks, together with their robust single-crystalline nature, endow them with superior catalytic activity and recyclability for bulky-molecule reactions, as compared with conventional, polycrystalline hollow, and disordered macroporous ZIF-8.

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.

Active vision and image/video understanding with decision structures based on the network-symbolic models

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2003-08-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolve ambiguity and uncertainty via feedback projections, and provide image understanding that is an interpretation of visual information in terms of such knowledge models. The ability of human brain to emulate knowledge structures in the form of networks-symbolic models is found. And that means an important shift of paradigm in our knowledge about brain from neural networks to "cortical software". Symbols, predicates and grammars naturally emerge in such active multilevel hierarchical networks, and logic is simply a way of restructuring such models. Brain analyzes an image as a graph-type decision structure created via multilevel hierarchical compression of visual information. Mid-level vision processes like clustering, perceptual grouping, separation of figure from ground, are special kinds of graph/network transformations. They convert low-level image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena are results of such analysis. Composition of network-symbolic models works similar to frames and agents, combines learning, classification, analogy together with higher-level model-based reasoning into a single framework. Such models do not require supercomputers. Based on such principles, and using methods of Computational intelligence, an Image Understanding system can convert images into the network-symbolic knowledge models, and effectively resolve uncertainty and ambiguity, providing unifying representation for perception and cognition. That allows creating new intelligent computer vision systems for robotic and defense industries.

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

Does the process map influence the outcome of quality improvement work? A comparison of a sequential flow diagram and a hierarchical task analysis diagram.

PubMed

Colligan, Lacey; Anderson, Janet E; Potts, Henry W W; Berman, Jonathan

2010-01-07

Many quality and safety improvement methods in healthcare rely on a complete and accurate map of the process. Process mapping in healthcare is often achieved using a sequential flow diagram, but there is little guidance available in the literature about the most effective type of process map to use. Moreover there is evidence that the organisation of information in an external representation affects reasoning and decision making. This exploratory study examined whether the type of process map - sequential or hierarchical - affects healthcare practitioners' judgments. A sequential and a hierarchical process map of a community-based anti coagulation clinic were produced based on data obtained from interviews, talk-throughs, attendance at a training session and examination of protocols and policies. Clinic practitioners were asked to specify the parts of the process that they judged to contain quality and safety concerns. The process maps were then shown to them in counter-balanced order and they were asked to circle on the diagrams the parts of the process where they had the greatest quality and safety concerns. A structured interview was then conducted, in which they were asked about various aspects of the diagrams. Quality and safety concerns cited by practitioners differed depending on whether they were or were not looking at a process map, and whether they were looking at a sequential diagram or a hierarchical diagram. More concerns were identified using the hierarchical diagram compared with the sequential diagram and more concerns were identified in relation to clinical work than administrative work. Participants' preference for the sequential or hierarchical diagram depended on the context in which they would be using it. The difficulties of determining the boundaries for the analysis and the granularity required were highlighted. The results indicated that the layout of a process map does influence perceptions of quality and safety problems in a process. In quality improvement work it is important to carefully consider the type of process map to be used and to consider using more than one map to ensure that different aspects of the process are captured.

Anisotropic constitutive model incorporating multiple damage mechanisms for multiscale simulation of dental enamel.

PubMed

Ma, Songyun; Scheider, Ingo; Bargmann, Swantje

2016-09-01

An anisotropic constitutive model is proposed in the framework of finite deformation to capture several damage mechanisms occurring in the microstructure of dental enamel, a hierarchical bio-composite. It provides the basis for a homogenization approach for an efficient multiscale (in this case: multiple hierarchy levels) investigation of the deformation and damage behavior. The influence of tension-compression asymmetry and fiber-matrix interaction on the nonlinear deformation behavior of dental enamel is studied by 3D micromechanical simulations under different loading conditions and fiber lengths. The complex deformation behavior and the characteristics and interaction of three damage mechanisms in the damage process of enamel are well captured. The proposed constitutive model incorporating anisotropic damage is applied to the first hierarchical level of dental enamel and validated by experimental results. The effect of the fiber orientation on the damage behavior and compressive strength is studied by comparing micro-pillar experiments of dental enamel at the first hierarchical level in multiple directions of fiber orientation. A very good agreement between computational and experimental results is found for the damage evolution process of dental enamel. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A multi-level simulation platform of natural gas internal reforming solid oxide fuel cell-gas turbine hybrid generation system - Part II. Balancing units model library and system simulation

NASA Astrophysics Data System (ADS)

Bao, Cheng; Cai, Ningsheng; Croiset, Eric

2011-10-01

Following our integrated hierarchical modeling framework of natural gas internal reforming solid oxide fuel cell (IRSOFC), this paper firstly introduces the model libraries of main balancing units, including some state-of-the-art achievements and our specific work. Based on gPROMS programming code, flexible configuration and modular design are fully realized by specifying graphically all unit models in each level. Via comparison with the steady-state experimental data of Siemens-Westinghouse demonstration system, the in-house multi-level SOFC-gas turbine (GT) simulation platform is validated to be more accurate than the advanced power system analysis tool (APSAT). Moreover, some units of the demonstration system are designed reversely for analysis of a typically part-load transient process. The framework of distributed and dynamic modeling in most of units is significant for the development of control strategies in the future.

Anomaly Detection of Electromyographic Signals.

PubMed

Ijaz, Ahsan; Choi, Jongeun

2018-04-01

In this paper, we provide a robust framework to detect anomalous electromyographic (EMG) signals and identify contamination types. As a first step for feature selection, optimally selected Lawton wavelets transform is applied. Robust principal component analysis (rPCA) is then performed on these wavelet coefficients to obtain features in a lower dimension. The rPCA based features are used for constructing a self-organizing map (SOM). Finally, hierarchical clustering is applied on the SOM that separates anomalous signals residing in the smaller clusters and breaks them into logical units for contamination identification. The proposed methodology is tested using synthetic and real world EMG signals. The synthetic EMG signals are generated using a heteroscedastic process mimicking desired experimental setups. A sub-part of these synthetic signals is introduced with anomalies. These results are followed with real EMG signals introduced with synthetic anomalies. Finally, a heterogeneous real world data set is used with known quality issues under an unsupervised setting. The framework provides recall of 90% (± 3.3) and precision of 99%(±0.4).

Construction of hierarchically porous monoliths from covalent organic frameworks (COFs) and their application for bisphenol A removal.

PubMed

Liu, Zhongshan; Wang, Hongwei; Ou, Junjie; Chen, Lianfang; Ye, Mingliang

2018-05-11

Subject to synthetic conditions, covalent organic frameworks (COFs) are usually in powder form. Herein, taking an azine-linked COF as an example, detailed characterizations indicated that accessible aldehyde groups and hydrazine groups (CNNH 2 , 88 μmol g -1 ) concurrently existed on its surface. Intrigued by such feature, we have developed an approach based on ring-opening polymerization to shape COF powder into monoliths. The crystallinity and micropore of COF in monoliths were well remained, meanwhile, the ring-opening polymerization remarkably generated macropores ranging from 0.43 to 3.51 μm, indicating a hierarchically porous structure. The BET surface area of resultant monoliths with different COF mass fractions of 16%, 28% and 43% ranged from 105 to 281 m 2  g -1 . Due to the π-π interaction and hydrogen bond interaction, COF-based monoliths exhibited strong retention and rapid adsorption for bisphenol A (BPA) in aqueous medium. When 29 mL BPA solution (22.8 mg L -1 ) passed through COF-based monolith (28%), the adsorption capacity was up to 61.3 mg g -1 . Furthermore, the COF-based monolith demonstrated excellent cycle use for catalyzing Suzuki-Miyaura coupling reaction after being coordinated with palladium acetate. Copyright © 2018 Elsevier B.V. All rights reserved.

Review: bioprinting: a beginning.

PubMed

Mironov, Vladimir; Reis, Nuno; Derby, Brian

2006-04-01

An increasing demand for directed assembly of biologically relevant materials, with prescribed three-dimensional hierarchical organizations, is stimulating technology developments with the ultimate goal of re-creating multicellular tissues and organs de novo. Existing techniques, mostly adapted from other applications or fields of research, are capable of independently meeting partial requirements for engineering biological or biomimetic structures, but their integration toward organ engineering is proving difficult. Inspired by recent developments in material transfer processes operating at all relevant length scales--from nano to macro--which are amenable to biological elements, a new research field of bioprinting and biopatterning has emerged. Here we present a short review regarding the framework, state of the art, and perspectives of this new field, based on the findings presented at a recent international workshop.

Hierarchy of Gambling Choices: A Framework for Examining EGM Gambling Environment Preferences.

PubMed

Thorne, Hannah Briony; Rockloff, Matthew Justus; Langham, Erika; Li, En

2016-12-01

This paper presents the Hierarchy of Gambling Choices (HGC), which is a consumer-oriented framework for understanding the key environmental and contextual features that influence peoples' selections of online and venue-based electronic gaming machines (EGMs). The HGC framework proposes that EGM gamblers make choices in selection of EGM gambling experiences utilising Tversky's (Psychol Rev 79(4):281-299, 1972). Elimination-by-Aspects model, and organise their choice in a hierarchical manner by virtue of EGMs being an "experience good" (Nelson in J Polit Econ 78(2):311-329, 1970). EGM features are divided into three levels: the platform-including, online, mobile or land-based; the provider or specific venue in which the gambling occurs; and the game or machine characteristics, such as graphical themes and bonus features. This framework will contribute to the gambling field by providing a manner in which to systematically explore the environment surrounding EGM gambling and how it affects behaviour.

Carbon with hierarchical pores from carbonized metal-organic frameworks for lithium sulphur batteries.

PubMed

Xi, Kai; Cao, Shuai; Peng, Xiaoyu; Ducati, Caterina; Kumar, R Vasant; Cheetham, Anthony K

2013-03-18

This paper presents a novel method and rationale for utilizing carbonized MOFs for sulphur loading to fabricate cathode structures for lithium-sulphur batteries. Unique carbon materials with differing hierarchical pore structures were synthesized from four types of zinc-containing metal-organic frameworks (MOFs). It is found that cathode materials made from MOFs-derived carbons with higher mesopore (2-50 nm) volumes exhibit increased initial discharge capacities, whereas carbons with higher micropore (<2 nm) volumes lead to cathode materials with better cycle stability.

Removal of Pertechnetate-Related Oxyanions from Solution Using Functionalized Hierarchical Porous Frameworks

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

Banerjee, Debasis; Elsaidi, Sameh K.; Aguila, Briana

2016-10-20

Efficient and cost-effective removal of radioactive pertechnetate anions from nuclear waste is a key challenge to mitigate long-term nuclear waste storage issues. Traditional materials such as resins and layered double hydroxides (LDHs) were evaluated for their pertechnetate or perrhenate (the non-radioactive surrogate) removal capacity, but there is room for improvement in terms of capacity, selectivity and kinetics. A series of functionalized hierarchical porous frameworks were evaluated for their perrhenate removal capacity in the presence of other competing anions.

Reconstructing the regulatory network controlling commitment and sporulation in Physarum polycephalum based on hierarchical Petri Net modelling and simulation.

PubMed

Marwan, Wolfgang; Sujatha, Arumugam; Starostzik, Christine

2005-10-21

We reconstruct the regulatory network controlling commitment and sporulation of Physarum polycephalum from experimental results using a hierarchical Petri Net-based modelling and simulation framework. The stochastic Petri Net consistently describes the structure and simulates the dynamics of the molecular network as analysed by genetic, biochemical and physiological experiments within a single coherent model. The Petri Net then is extended to simulate time-resolved somatic complementation experiments performed by mixing the cytoplasms of mutants altered in the sporulation response, to systematically explore the network structure and to probe its dynamics. This reverse engineering approach presumably can be employed to explore other molecular or genetic signalling systems where the activity of genes or their products can be experimentally controlled in a time-resolved manner.

A Hierarchical Approach to Fracture Mechanics

NASA Technical Reports Server (NTRS)

Saether, Erik; Taasan, Shlomo

2004-01-01

Recent research conducted under NASA LaRC's Creativity and Innovation Program has led to the development of an initial approach for a hierarchical fracture mechanics. This methodology unites failure mechanisms occurring at different length scales and provides a framework for a physics-based theory of fracture. At the nanoscale, parametric molecular dynamic simulations are used to compute the energy associated with atomic level failure mechanisms. This information is used in a mesoscale percolation model of defect coalescence to obtain statistics of fracture paths and energies through Monte Carlo simulations. The mathematical structure of predicted crack paths is described using concepts of fractal geometry. The non-integer fractal dimension relates geometric and energy measures between meso- and macroscales. For illustration, a fractal-based continuum strain energy release rate is derived for inter- and transgranular fracture in polycrystalline metals.

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

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.

Construction of 3D nanostructure hierarchical porous graphitic carbons by charge-induced self-assembly and nanocrystal-assisted catalytic graphitization for supercapacitors.

PubMed

Ma, Fangwei; Ma, Di; Wu, Guang; Geng, Weidan; Shao, Jinqiu; Song, Shijiao; Wan, Jiafeng; Qiu, Jieshan

2016-05-10

A smart and sustainable strategy based on charge-induced self-assembly and nanocrystal-assisted catalytic graphitization is explored for the efficient construction of 3D nanostructure hierarchical porous graphitic carbons from the pectin biopolymer. The electrostatic interaction between the negatively charged pectin chains and magnesium ions plays a crucial role in the formation of 3D architectures. The 3D HPGCs possess a three-dimensional carbon framework with a hierarchical porous structure, flake-like graphitic carbon walls and high surface area (1320 m(2) g(-1)). The 3D HPGCs show an outstanding specific capacitance of 274 F g(-1) and excellent rate capability with a high capacitance retention of 85% at a high current density of 50 A g(-1) for supercapacitor electrodes. This strategy provided a novel approach to effectively construct 3D porous carbon nanostructures from biopolymers.

Thermal Instability Induced Oriented 2D Pores for Enhanced Sodium Storage.

PubMed

Kong, Lingjun; Xie, Chen-Chao; Gu, Haichen; Wang, Chao-Peng; Zhou, Xianlong; Liu, Jian; Zhou, Zhen; Li, Zhao-Yang; Zhu, Jian; Bu, Xian-He

2018-04-19

Hierarchical porous structures are highly desired for various applications. However, it is still challenging to obtain such materials with tunable architectures. Here, this paper reports hierarchical nanomaterials with oriented 2D pores by taking advantages of thermally instable bonds in vanadium-based metal-organic frameworks (MOFs). High-temperature calcination of these MOFs accompanied by the loss of coordinated water molecules and other components enables the formation of orderly slit-like 2D pores in vanadium oxide/porous carbon nanorods (VO x /PCs). This unique combination leads to an increase of the reactive surface area. In addition, optimized VO x /PCs demonstrate high-rate capability and ultralong cycling life for sodium storage. The assembled full cells also show high capacity and cycling stability. This report provides an effective strategy for producing MOFs-derived composites with hierarchical porous architectures for energy storage. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Hierarchical Marginal Land Assessment for Land Use Planning

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

Kang, Shujiang; Post, Wilfred M; Wang, Dali

2013-01-01

Marginal land provides an alternative potential for food and bioenergy production in the face of limited land resources; however, effective assessment of marginal lands is not well addressed. Concerns over environmental risks, ecosystem services and sustainability for marginal land have been widely raised. The objective of this study was to develop a hierarchical marginal land assessment framework for land use planning and management. We first identified major land functions linking production, environment, ecosystem services and economics, and then classified land resources into four categories of marginal land using suitability and limitations associated with major management goals, including physically marginal land,more » biologically marginal land, environmental-ecological marginal land, and economically marginal land. We tested this assessment framework in south-western Michigan, USA. Our results indicated that this marginal land assessment framework can be potentially feasible on land use planning for food and bioenergy production, and balancing multiple goals of land use management. We also compared our results with marginal land assessment from the Conservation Reserve Program (CRP) and land capability classes (LCC) that are used in the US. The hierarchical assessment framework has advantages of quantitatively reflecting land functions and multiple concerns. This provides a foundation upon which focused studies can be identified in order to improve the assessment framework by quantifying high-resolution land functions associated with environment and ecosystem services as well as their criteria are needed to improve the assessment framework.« less

Model-free learning on robot kinematic chains using a nested multi-agent topology

NASA Astrophysics Data System (ADS)

Karigiannis, John N.; Tzafestas, Costas S.

2016-11-01

This paper proposes a model-free learning scheme for the developmental acquisition of robot kinematic control and dexterous manipulation skills. The approach is based on a nested-hierarchical multi-agent architecture that intuitively encapsulates the topology of robot kinematic chains, where the activity of each independent degree-of-freedom (DOF) is finally mapped onto a distinct agent. Each one of those agents progressively evolves a local kinematic control strategy in a game-theoretic sense, that is, based on a partial (local) view of the whole system topology, which is incrementally updated through a recursive communication process according to the nested-hierarchical topology. Learning is thus approached not through demonstration and training but through an autonomous self-exploration process. A fuzzy reinforcement learning scheme is employed within each agent to enable efficient exploration in a continuous state-action domain. This paper constitutes in fact a proof of concept, demonstrating that global dexterous manipulation skills can indeed evolve through such a distributed iterative learning of local agent sensorimotor mappings. The main motivation behind the development of such an incremental multi-agent topology is to enhance system modularity, to facilitate extensibility to more complex problem domains and to improve robustness with respect to structural variations including unpredictable internal failures. These attributes of the proposed system are assessed in this paper through numerical experiments in different robot manipulation task scenarios, involving both single and multi-robot kinematic chains. The generalisation capacity of the learning scheme is experimentally assessed and robustness properties of the multi-agent system are also evaluated with respect to unpredictable variations in the kinematic topology. Furthermore, these numerical experiments demonstrate the scalability properties of the proposed nested-hierarchical architecture, where new agents can be recursively added in the hierarchy to encapsulate individual active DOFs. The results presented in this paper demonstrate the feasibility of such a distributed multi-agent control framework, showing that the solutions which emerge are plausible and near-optimal. Numerical efficiency and computational cost issues are also discussed.

Synthesis of hierarchically meso-macroporous TiO2/CdS heterojunction photocatalysts with excellent visible-light photocatalytic activity.

PubMed

Zhao, Haixin; Cui, Shu; Yang, Lan; Li, Guodong; Li, Nan; Li, Xiaotian

2018-02-15

Photocatalysts with a hierarchically porous structure have attracted considerable attention owing to their wide pore size distribution and high surface area, which enhance the efficiency of transporting species to active sites. In this study, hierarchically meso-macroporous TiO 2 photocatalysts decorated with highly dispersed CdS nanoparticles were synthesized via hydrolysis, followed by a hydrothermal treatment. The textural mesopores and interconnected pore framework provided more accessible active sites and efficient mass transport for the photocatalytic process. The light collection efficiency was enhanced because of multiple scattering of incident light in the macropores. Moreover, the formation of a heterojunction between the CdS and TiO 2 nanoparticles extended the photoresponse of TiO 2 to the visible-light range and enhanced the charge separation efficiency. Therefore, the hierarchically meso-macroporous TiO 2 /CdS photocatalysts exhibited excellent photocatalytic activity for the degradation of rhodaming B under visible-light irradiation. Trapping experiments demonstrated that superoxide radicals (O 2 - ) and hydroxyl radicals (OH) were the main active species in photocatalysis. A reasonable photocatalytic mechanism of TiO 2 /CdS heterojunction photocatalysts was also presented. Copyright © 2017 Elsevier Inc. All rights reserved.

2D Metal-Organic Frameworks Derived Nanocarbon Arrays for Substrate Enhancement in Flexible Supercapacitors.

PubMed

Liu, Ximeng; Guan, Cao; Hu, Yating; Zhang, Lei; Elshahawy, Abdelnaby M; Wang, John

2017-10-27

Direct assembling of active materials on carbon cloth (CC) is a promising way to achieve flexible electrodes for energy storage. However, the overall surface area and electrical conductivity of such electrodes are usually limited. Herein, 2D metal-organic framework derived nanocarbon nanowall (MOFC) arrays are successfully developed on carbon cloth by a facile solution + carbonization process. Upon growth of the MOFC arrays, the sites for growth of the active materials are greatly increased, and the equivalent series resistance is decreased, which contribute to the enhancement of the bare CC substrate. After decorating ultrathin flakes of MnO 2 and Bi 2 O 3 on the flexible CC/MOFC substrate, the hierarchical electrode materials show an abrupt improvement of areal capacitances by around 50% and 100%, respectively, compared to those of the active materials on pristine carbon cloth. A flexible supercapacitor can be further assembled using two hierarchical electrodes, which demonstrates an energy density of 124.8 µWh cm -2 at the power density of 2.55 mW cm -2 . © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Nitrogen-doped graphitic hierarchically porous carbon nanofibers obtained via bimetallic-coordination organic framework modification and their application in supercapacitors.

PubMed

Yao, Yuechao; Liu, Peng; Li, Xiaoyan; Zeng, Shaozhong; Lan, Tongbin; Huang, Haitao; Zeng, Xierong; Zou, Jizhao

2018-05-17

Herein, N-doped graphitic hierarchically porous carbon nanofibers (NGHPCF) were prepared by electrospinning the composite of bimetallic-coordination metal-organic frameworks and polyacrylonitrile, followed by a pyrolysis and acid wash process. Control over the N content, specific surface area, and degree of graphitization of NGHPCF materials has been realized by adjusting the Co/Zn metal coordination content as well as the pyrolysis temperature. The obtained NGHPCF with a high specific surface area (623 m2 g-1) and nitrogen content (13.83 wt%) exhibit a high capacitance of 326 F g-1 at 0.5 A g-1. In addition, the capacitance of 170 F g-1 is still maintained at a high current density (40 A g-1); this indicates a high capacitance retention capability. Furthermore, a superb energy density (9.61 W h kg-1) is obtained with a high power density (62.4 W kg-1) using an organic electrolyte. These results fully illustrate that the prepared NGHPCF binder-free electrodes are promising candidates for high-performance supercapacitors.

Flexible and Hierarchical Metal-Organic Framework Composites for High-Performance Catalysis.

PubMed

Huang, Ning; Drake, Hannah; Li, Jialuo; Pang, Jiangdong; Wang, Ying; Yuan, Shuai; Wang, Qi; Cai, Peiyu; Qin, Junsheng; Zhou, Hong-Cai

2018-05-18

The development of new types of porous composite materials is of great significance owing to their potentially improved performance over those of individual components and extensive applications in separation, energy storage, and heterogeneous catalysis. In this work, we integrated mesoporous metal-organic frameworks (MOFs) with macroporous melamine foam (MF) using a one-pot process, generating a series of MOF/MF composite materials with preserved crystallinity, hierarchical porosity, and increased stability over that of melamine foam. The MOF nanocrystals were threaded by the melamine foam networks, resembling a ball-and-stick model overall. As a proof-of-concept study, the resulting MOF/MF composite materials were employed as an effective heterogeneous catalyst for the epoxidation of cholesteryl esters. Combining the advantages of interpenetrative mesoporous and macroporous structures, the MOF/melamine foam composite provided higher dispersibility and more accessibility of catalytic sites, exhibiting excellent catalytic performance. This strategy constitutes an important step forward the development of other MOF composites and exploration of their high-performance catalysis. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

NUMERICAL SIMULATION OF NANOINDENTATION AND PATCH CLAMP EXPERIMENTS ON MECHANOSENSITIVE CHANNELS OF LARGE CONDUCTANCE IN ESCHERICHIA COLI

PubMed Central

Tang, Yuye; Chen, Xi; Yoo, Jejoong; Yethiraj, Arun; Cui, Qiang

2010-01-01

A hierarchical simulation framework that integrates information from all-atom simulations into a finite element model at the continuum level is established to study the mechanical response of a mechanosensitive channel of large conductance (MscL) in bacteria Escherichia Coli (E.coli) embedded in a vesicle formed by the dipalmitoylphosphatidycholine (DPPC) lipid bilayer. Sufficient structural details of the protein are built into the continuum model, with key parameters and material properties derived from molecular mechanics simulations. The multi-scale framework is used to analyze the gating of MscL when the lipid vesicle is subjective to nanoindentation and patch clamp experiments, and the detailed structural transitions of the protein are obtained explicitly as a function of external load; it is currently impossible to derive such information based solely on all-atom simulations. The gating pathways of E.coli-MscL qualitatively agree with results from previous patch clamp experiments. The gating mechanisms under complex indentation-induced deformation are also predicted. This versatile hierarchical multi-scale framework may be further extended to study the mechanical behaviors of cells and biomolecules, as well as to guide and stimulate biomechanics experiments. PMID:21874098

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

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

PubMed

Yao, Dongren; Calhoun, Vince D; Fu, Zening; Du, Yuhui; Sui, Jing

2018-05-15

Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and healthy controls), a competition was hosted by Kaggle to invite the scientific community to apply their machine learning approaches on pre-processed sets of T1-weighted magnetic resonance images (MRI) data and the demographic information from the international Alzheimer's disease neuroimaging initiative (ADNI) database. This paper summarizes our competition results. We first proposed a hierarchical process by turning the 4-way classification into five binary classification problems. A new feature selection technology based on relative importance was also proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features, to ensure each binary classifier to achieve its highest accuracy. As a result, about 2% of the original features were selected to build a new feature space, which can achieve the final four-way classification with a 54.38% accuracy on testing data through hierarchical grouping, higher than several alternative methods in comparison. More importantly, the selected discriminative features such as hippocampal volume, parahippocampal surface area, and medial orbitofrontal thickness, etc. as well as the MMSE score, are reasonable and consistent with those reported in AD/MCI deficits. In summary, the proposed method provides a new framework for multi-way classification using hierarchical grouping and precise feature selection. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Hierarchical Decentralized Control Strategy for Demand-Side Primary Frequency Response

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

Lian, Jianming; Hansen, Jacob; Marinovici, Laurentiu D.

The Grid Friendlymore » $$^\\textrm{TM}$$ Appliance~(GFA) controller, developed at Pacific Northwest National Laboratory, was designed for the purpose of autonomously switching off appliances by detecting under-frequency events. In this paper, a new frequency responsive load~(FRL) controller is first proposed by extending the functionality of the original GFA controller. The proposed FRL controller can autonomously switch on (or off) end-use loads by detecting over-frequency (or under-frequency) events through local frequency measurement. Then, a hierarchical decentralized control framework is developed for engaging the end-use loads to provide primary frequency response with the proposed FRL controller. The developed framework has several important features that are desirable in terms of providing primary frequency control. It not only exclusively maintains the autonomous operation of the end-use loads, but also effectively overcomes the stability issue associated with high penetration of FRLs. The simulation results illustrate the effectiveness of the developed hierarchical control framework for providing primary frequency response with the proposed FRL controller.« less

Loops in hierarchical channel networks

NASA Astrophysics Data System (ADS)

Katifori, Eleni; Magnasco, Marcelo

2012-02-01

Nature provides us with many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture. Although a number of methods have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated and natural graphs extracted from digitized images of dicotyledonous leaves and animal vasculature. We calculate various metrics on the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs.

A facile strategy for enzyme immobilization with highly stable hierarchically porous metal-organic frameworks.

PubMed

Liu, Xiao; Qi, Wei; Wang, Yuefei; Su, Rongxin; He, Zhimin

2017-11-16

Metal-organic frameworks (MOFs) have drawn extensive research interest as candidates for enzyme immobilization owing to their tunable porosity, high surface area, and excellent chemical/thermal stability. Herein, we report a facile and universal strategy for enzyme immobilization using highly stable hierarchically porous metal-organic frameworks (HP-MOFs). The HP-MOFs were stable over a wide pH range (pH = 2-11 for HP-DUT-5) and met the catalysis conditions of most enzymes. The as-prepared hierarchical micro/mesoporous MOFs with mesoporous defects showed a superior adsorption capacity towards enzymes. The maximum adsorption capacity of HP-DUT-5 for glucose oxidase (GOx) and uricase was 208 mg g -1 and 225 mg g -1 , respectively. Furthermore, we constructed two multi-enzyme biosensors for glucose and uric acid (UA) by immobilizing GOx and uricase with horseradish peroxidase (HRP) on HP-DUT-5, respectively. These sensors were efficiently applied in the colorimetric detection of glucose and UA and showed good sensitivity, selectivity, and recyclability.

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.

An Efficient, Recyclable, and Stable Immobilized Biocatalyst Based on Bioinspired Microcapsules-in-Hydrogel Scaffolds.

PubMed

Zhang, Shaohua; Jiang, Zhongyi; Shi, Jiafu; Wang, Xueyan; Han, Pingping; Qian, Weilun

2016-09-28

Design and preparation of high-performance immobilized biocatalysts with exquisite structures and elucidation of their profound structure-performance relationship are highly desired for green and sustainable biotransformation processes. Learning from nature has been recognized as a shortcut to achieve such an impressive goal. Loose connective tissue, which is composed of hierarchically organized cells by extracellular matrix (ECM) and is recognized as an efficient catalytic system to ensure the ordered proceeding of metabolism, may offer an ideal prototype for preparing immobilized biocatalysts with high catalytic activity, recyclability, and stability. Inspired by the hierarchical structure of loose connective tissue, we prepared an immobilized biocatalyst enabled by microcapsules-in-hydrogel (MCH) scaffolds via biomimetic mineralization in agarose hydrogel. In brief, the in situ synthesized hybrid microcapsules encapsulated with glucose oxidase (GOD) are hierarchically organized by the fibrous framework of agarose hydrogel, where the fibers are intercalated into the capsule wall. The as-prepared immobilized biocatalyst shows structure-dependent catalytic performance. The porous hydrogel permits free diffusion of glucose molecules (diffusion coefficient: ∼6 × 10(-6) cm(2) s(-1), close to that in water) and retains the enzyme activity as much as possible after immobilization (initial reaction rate: 1.5 × 10(-2) mM min(-1)). The monolithic macroscale of agarose hydrogel facilitates the easy recycling of the immobilized biocatalyst (only by using tweezers), which contributes to the nonactivity decline during the recycling test. The fiber-intercalating structure elevates the mechanical stability of the in situ synthesized hybrid microcapsules, which inhibits the leaching and enhances the stability of the encapsulated GOD, achieving immobilization efficiency of ∼95%. This study will, therefore, provide a generic method for the hierarchical organization of (bio)active materials and the rational design of novel (bio)catalysts.

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.

Synthesis and Electric Properties of a Two-Dimensional Metal-Organic Framework Based on Phthalocyanine.

PubMed

Nagatomi, Hisanori; Yanai, Nobuhiro; Yamada, Teppei; Shiraishi, Kanji; Kimizuka, Nobuo

2018-02-06

Complexation of copper(II) 2,3,9,10,16,17,23,24-octahydroxy-29H,31H-phthalocyanine (CuPcOH) with copper(II) ions gives a two-dimensional (2D) metal-organic framework (MOF). This is the first report of a phthalocyanine-based MOF. This 2D MOF was obtained as a black powder and showed an electrical conductivity of 1.6×10 -6  S cm -1 at 80 °C. When this MOF is used as a cathode of lithium ion battery (LIB), large charge/discharge capacities of 151/128 mAh g -1 were obtained. In addition, it showed a good stability during 200 charge/discharge cycles. The obtained LIB performance mainly originates from the electrically conductive and redox-active framework of the phthalocyanine-based 2D MOF and its hierarchical microporous/mesoporous structure. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Web-based Visual Analytics for Extreme Scale Climate Science

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

Steed, Chad A; Evans, Katherine J; Harney, John F

In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, ineffi- cient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via newmore » visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.« less

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.

Thermal conductivity from hierarchical heat sinks using carbon nanotubes and graphene nanosheets.

PubMed

Hsieh, Chien-Te; Lee, Cheng-En; Chen, Yu-Fu; Chang, Jeng-Kuei; Teng, Hsi-sheng

2015-11-28

The in-plane (kip) and through-plane (ktp) thermal conductivities of heat sinks using carbon nanotubes (CNTs), graphene nanosheets (GNs), and CNT/GN composites are extracted from two experimental setups within the 323-373 K temperature range. Hierarchical three-dimensional CNT/GN frameworks display higher kip and ktp values, as compared to the CNT- and GN-based heat sinks. The kip and ktp values of the CNT/GN-based heat sink reach as high as 1991 and 76 W m(-1) K(-1) at 323 K, respectively. This improved thermal conductivity is attributed to the fact that the hierarchical heat sink offers a stereo thermal conductive network that combines point, line, and plane contact, leading to better heat transport. Furthermore, the compression treatment provided an efficient route to increase both kip and ktp values. This result reveals that the hierarchical carbon structures become denser, inducing more thermal conductive area and less thermal resistivity, i.e., a reduced possibility of phonon-boundary scattering. The correlation between thermal and electrical conductivity (ε) can be well described by two empirical equations: kip = 567 ln(ε) + 1120 and ktp = 20.6 ln(ε) + 36.1. The experimental results are obtained within the temperature range of 323-373 K, suitably complementing the thermal management of chips for consumer electronics.

Hierarchical clustering of EMD based interest points for road sign detection

NASA Astrophysics Data System (ADS)

Khan, Jesmin; Bhuiyan, Sharif; Adhami, Reza

2014-04-01

This paper presents an automatic road traffic signs detection and recognition system based on hierarchical clustering of interest points and joint transform correlation. The proposed algorithm consists of the three following stages: interest points detection, clustering of those points and similarity search. At the first stage, good discriminative, rotation and scale invariant interest points are selected from the image edges based on the 1-D empirical mode decomposition (EMD). We propose a two-step unsupervised clustering technique, which is adaptive and based on two criterion. In this context, the detected points are initially clustered based on the stable local features related to the brightness and color, which are extracted using Gabor filter. Then points belonging to each partition are reclustered depending on the dispersion of the points in the initial cluster using position feature. This two-step hierarchical clustering yields the possible candidate road signs or the region of interests (ROIs). Finally, a fringe-adjusted joint transform correlation (JTC) technique is used for matching the unknown signs with the existing known reference road signs stored in the database. The presented framework provides a novel way to detect a road sign from the natural scenes and the results demonstrate the efficacy of the proposed technique, which yields a very low false hit rate.

BioASF: a framework for automatically generating executable pathway models specified in BioPAX.

PubMed

Haydarlou, Reza; Jacobsen, Annika; Bonzanni, Nicola; Feenstra, K Anton; Abeln, Sanne; Heringa, Jaap

2016-06-15

Biological pathways play a key role in most cellular functions. To better understand these functions, diverse computational and cell biology researchers use biological pathway data for various analysis and modeling purposes. For specifying these biological pathways, a community of researchers has defined BioPAX and provided various tools for creating, validating and visualizing BioPAX models. However, a generic software framework for simulating BioPAX models is missing. Here, we attempt to fill this gap by introducing a generic simulation framework for BioPAX. The framework explicitly separates the execution model from the model structure as provided by BioPAX, with the advantage that the modelling process becomes more reproducible and intrinsically more modular; this ensures natural biological constraints are satisfied upon execution. The framework is based on the principles of discrete event systems and multi-agent systems, and is capable of automatically generating a hierarchical multi-agent system for a given BioPAX model. To demonstrate the applicability of the framework, we simulated two types of biological network models: a gene regulatory network modeling the haematopoietic stem cell regulators and a signal transduction network modeling the Wnt/β-catenin signaling pathway. We observed that the results of the simulations performed using our framework were entirely consistent with the simulation results reported by the researchers who developed the original models in a proprietary language. The framework, implemented in Java, is open source and its source code, documentation and tutorial are available at http://www.ibi.vu.nl/programs/BioASF CONTACT: j.heringa@vu.nl. © The Author 2016. Published by Oxford University Press.

Analysing task design and students' responses to context-based problems through different analytical frameworks

NASA Astrophysics Data System (ADS)

Broman, Karolina; Bernholt, Sascha; Parchmann, Ilka

2015-05-01

Background:Context-based learning approaches are used to enhance students' interest in, and knowledge about, science. According to different empirical studies, students' interest is improved by applying these more non-conventional approaches, while effects on learning outcomes are less coherent. Hence, further insights are needed into the structure of context-based problems in comparison to traditional problems, and into students' problem-solving strategies. Therefore, a suitable framework is necessary, both for the analysis of tasks and strategies. Purpose:The aim of this paper is to explore traditional and context-based tasks as well as students' responses to exemplary tasks to identify a suitable framework for future design and analyses of context-based problems. The paper discusses different established frameworks and applies the Higher-Order Cognitive Skills/Lower-Order Cognitive Skills (HOCS/LOCS) taxonomy and the Model of Hierarchical Complexity in Chemistry (MHC-C) to analyse traditional tasks and students' responses. Sample:Upper secondary students (n=236) at the Natural Science Programme, i.e. possible future scientists, are investigated to explore learning outcomes when they solve chemistry tasks, both more conventional as well as context-based chemistry problems. Design and methods:A typical chemistry examination test has been analysed, first the test items in themselves (n=36), and thereafter 236 students' responses to one representative context-based problem. Content analysis using HOCS/LOCS and MHC-C frameworks has been applied to analyse both quantitative and qualitative data, allowing us to describe different problem-solving strategies. Results:The empirical results show that both frameworks are suitable to identify students' strategies, mainly focusing on recall of memorized facts when solving chemistry test items. Almost all test items were also assessing lower order thinking. The combination of frameworks with the chemistry syllabus has been found successful to analyse both the test items as well as students' responses in a systematic way. The framework can therefore be applied in the design of new tasks, the analysis and assessment of students' responses, and as a tool for teachers to scaffold students in their problem-solving process. Conclusions:This paper gives implications for practice and for future research to both develop new context-based problems in a structured way, as well as providing analytical tools for investigating students' higher order thinking in their responses to these tasks.

A Finite Element Framework for Studying the Mechanical Response of Macromolecules: Application to the Gating of the Mechanosensitive Channel MscL

PubMed Central

Tang, Yuye; Cao, Guoxin; Chen, Xi; Yoo, Jejoong; Yethiraj, Arun; Cui, Qiang

2006-01-01

The gating pathways of mechanosensitive channels of large conductance (MscL) in two bacteria (Mycobacterium tuberculosis and Escherichia coli) are studied using the finite element method. The phenomenological model treats transmembrane helices as elastic rods and the lipid membrane as an elastic sheet of finite thickness; the model is inspired by the crystal structure of MscL. The interactions between various continuum components are derived from molecular-mechanics energy calculations using the CHARMM all-atom force field. Both bacterial MscLs open fully upon in-plane tension in the membrane and the variation of pore diameter with membrane tension is found to be essentially linear. The estimated gating tension is close to the experimental value. The structural variations along the gating pathway are consistent with previous analyses based on structural models with experimental constraints and biased atomistic molecular-dynamics simulations. Upon membrane bending, neither MscL opens substantially, although there is notable and nonmonotonic variation in the pore radius. This emphasizes that the gating behavior of MscL depends critically on the form of the mechanical perturbation and reinforces the idea that the crucial gating parameter is lateral tension in the membrane rather than the curvature of the membrane. Compared to popular all-atom-based techniques such as targeted or steered molecular-dynamics simulations, the finite element method-based continuum-mechanics framework offers a unique alternative to bridge detailed intermolecular interactions and biological processes occurring at large spatial scales and long timescales. It is envisioned that such a hierarchical multiscale framework will find great value in the study of a variety of biological processes involving complex mechanical deformations such as muscle contraction and mechanotransduction. PMID:16731564

Hierarchical Nanostructures of Metal-Organic Frameworks Applied in Gas Separating ZIF-8-on-ZIF-67 Membranes.

PubMed

Knebel, Alexander; Wulfert-Holzmann, Paul; Friebe, Sebastian; Pavel, Janet; Strauß, Ina; Mundstock, Alexander; Steinbach, Frank; Caro, Jürgen

2018-04-17

Membranes from metal-organic frameworks (MOFs) are highly interesting for industrial gas separation applications. Strongly improved performances for carbon capture and H 2 purification tasks in MOF membranes are obtained by using highly reproducable and very accuratly, hierarchically grown ZIF-8-on-ZIF-67 (ZIF-8@ZIF-67) nanostructures. To forgo hardly controllable solvothermal synthesis, particles and layers are prepared by self-assembling methods. It was possible for the first time to confirm ZIF-8-on-ZIF-67 membrane growth on rough and porous ceramic supports using the layer-by-layer deposition. Additionally, hierarchical particles are made in a fast RT synthesis with high monodispersity. Characterization of the hierarchical and epitaxial grown layers and particles is performed by SEM, TEM, EDXM and gas permeation. The system ZIF-8@ZIF-67 shows a nearly doubled H 2 /CO 2 separation factor, regardless of whether neat membrane or mixed-matrix-membrane in comparison to other MOF materials. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Programming Hierarchical Self-Assembly of Patchy Particles into Colloidal Crystals via Colloidal Molecules.

PubMed

Morphew, Daniel; Shaw, James; Avins, Christopher; Chakrabarti, Dwaipayan

2018-03-27

Colloidal self-assembly is a promising bottom-up route to a wide variety of three-dimensional structures, from clusters to crystals. Programming hierarchical self-assembly of colloidal building blocks, which can give rise to structures ordered at multiple levels to rival biological complexity, poses a multiscale design problem. Here we explore a generic design principle that exploits a hierarchy of interaction strengths and employ this design principle in computer simulations to demonstrate the hierarchical self-assembly of triblock patchy colloidal particles into two distinct colloidal crystals. We obtain cubic diamond and body-centered cubic crystals via distinct clusters of uniform size and shape, namely, tetrahedra and octahedra, respectively. Such a conceptual design framework has the potential to reliably encode hierarchical self-assembly of colloidal particles into a high level of sophistication. Moreover, the design framework underpins a bottom-up route to cubic diamond colloidal crystals, which have remained elusive despite being much sought after for their attractive photonic applications.

Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models

PubMed Central

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

2013-01-01

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

Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models.

PubMed

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

2014-01-01

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

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2016-09-09

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

Design and Tailoring of the 3D Macroporous Hydrous RuO2 Hierarchical Architectures with a Hard-Template Method for High-Performance Supercapacitors.

PubMed

Peng, Zhikun; Liu, Xu; Meng, Huan; Li, Zhongjun; Li, Baojun; Liu, Zhongyi; Liu, Shouchang

2017-02-08

In this work, RuO 2 honeycomb networks (RHCs) and hollow spherical structures (RHSs) were rationally designed and synthesized with modified-SiO 2 as a sacrificial template via two hydrothermal approaches. At a high current density of 20 A g -1 , the two hierarchical porous RuO 2 ·xH 2 O frameworks showed the specific capacitance as high as 628 and 597 F g -1 ; this is about 80% and 75% of the capacitance retention of 0.5 A g -1 for RHCs and RHSs, respectively. Even after 4000 cycles at 5 A g -1 , the RHCs and RHSs can still remain at 86% and 91% of their initial specific capacitances, respectively. These two hierarchical frameworks have a well-defined pathway that benefits for the transmission/diffusion of electrolyte and surface redox reactions. As a result, they exhibit good supercapacitor performance in both acid (H 2 SO 4 ) and alkaline (KOH) electrolytes. As compared to RuO 2 bulk structure and similar RuO 2 counterpart reported in pseudocapacitors, the two hierarchical porous RuO 2 ·xH 2 O frameworks have better energy storage capabilities, high-rate performance, and excellent cycling stability.

Selection of remedial alternatives for mine sites: a multicriteria decision analysis approach.

PubMed

Betrie, Getnet D; Sadiq, Rehan; Morin, Kevin A; Tesfamariam, Solomon

2013-04-15

The selection of remedial alternatives for mine sites is a complex task because it involves multiple criteria and often with conflicting objectives. However, an existing framework used to select remedial alternatives lacks multicriteria decision analysis (MCDA) aids and does not consider uncertainty in the selection of alternatives. The objective of this paper is to improve the existing framework by introducing deterministic and probabilistic MCDA methods. The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods have been implemented in this study. The MCDA analysis involves processing inputs to the PROMETHEE methods that are identifying the alternatives, defining the criteria, defining the criteria weights using analytical hierarchical process (AHP), defining the probability distribution of criteria weights, and conducting Monte Carlo Simulation (MCS); running the PROMETHEE methods using these inputs; and conducting a sensitivity analysis. A case study was presented to demonstrate the improved framework at a mine site. The results showed that the improved framework provides a reliable way of selecting remedial alternatives as well as quantifying the impact of different criteria on selecting alternatives. Copyright © 2013 Elsevier Ltd. All rights reserved.

Estimating the Term Structure With a Semiparametric Bayesian Hierarchical Model: An Application to Corporate Bonds.

PubMed

Cruz-Marcelo, Alejandro; Ensor, Katherine B; Rosner, Gary L

2011-06-01

The term structure of interest rates is used to price defaultable bonds and credit derivatives, as well as to infer the quality of bonds for risk management purposes. We introduce a model that jointly estimates term structures by means of a Bayesian hierarchical model with a prior probability model based on Dirichlet process mixtures. The modeling methodology borrows strength across term structures for purposes of estimation. The main advantage of our framework is its ability to produce reliable estimators at the company level even when there are only a few bonds per company. After describing the proposed model, we discuss an empirical application in which the term structure of 197 individual companies is estimated. The sample of 197 consists of 143 companies with only one or two bonds. In-sample and out-of-sample tests are used to quantify the improvement in accuracy that results from approximating the term structure of corporate bonds with estimators by company rather than by credit rating, the latter being a popular choice in the financial literature. A complete description of a Markov chain Monte Carlo (MCMC) scheme for the proposed model is available as Supplementary Material.

Estimating the Term Structure With a Semiparametric Bayesian Hierarchical Model: An Application to Corporate Bonds1

PubMed Central

Cruz-Marcelo, Alejandro; Ensor, Katherine B.; Rosner, Gary L.

2011-01-01

The term structure of interest rates is used to price defaultable bonds and credit derivatives, as well as to infer the quality of bonds for risk management purposes. We introduce a model that jointly estimates term structures by means of a Bayesian hierarchical model with a prior probability model based on Dirichlet process mixtures. The modeling methodology borrows strength across term structures for purposes of estimation. The main advantage of our framework is its ability to produce reliable estimators at the company level even when there are only a few bonds per company. After describing the proposed model, we discuss an empirical application in which the term structure of 197 individual companies is estimated. The sample of 197 consists of 143 companies with only one or two bonds. In-sample and out-of-sample tests are used to quantify the improvement in accuracy that results from approximating the term structure of corporate bonds with estimators by company rather than by credit rating, the latter being a popular choice in the financial literature. A complete description of a Markov chain Monte Carlo (MCMC) scheme for the proposed model is available as Supplementary Material. PMID:21765566

Depressive symptoms and untreated dental caries in older independently living South Brazilians.

PubMed

Hugo, F N; Hilgert, J B; de Sousa, M D L R; Cury, J A

2012-01-01

The importance of psychological reactions in modifying oral health behaviors and salivary immunity has been shown previously, but few studies assessed whether psychological reactions are associated with caries in populations. Thus, the aim of this study was to examine the association of depressive symptoms with untreated caries using a hierarchal approach. In this cross-sectional study, a random sample of 390 South Brazilians aged 60 years or more was evaluated using a structured questionnaire assessing sociodemographic, behavior, health and depressive symptoms (Geriatric Depression Scale) data. Oral examinations were carried out in order to assess: (1) dental status, using the DMFT index; (2) dental plaque, using the Visible Plaque Index, and (3) unstimulated saliva flow, using the spit method. A hierarchical model based on the framework of caries was carried out to assess whether depressive symptoms were associated with prevalent untreated dental caries (or D >0). Depressive symptoms, number of teeth and plaque accumulation were significant predictors of caries with respect to the D >0 outcome. Our findings suggest that depressive symptoms may act as determinants of caries, adding to the body of knowledge supporting the importance of psychological reactions in oral health/disease processes. Copyright © 2012 S. Karger AG, Basel.

A Hierarchical Generative Framework of Language Processing: Linking Language Perception, Interpretation, and Production Abnormalities in Schizophrenia

PubMed Central

Brown, Meredith; Kuperberg, Gina R.

2015-01-01

Language and thought dysfunction are central to the schizophrenia syndrome. They are evident in the major symptoms of psychosis itself, particularly as disorganized language output (positive thought disorder) and auditory verbal hallucinations (AVHs), and they also manifest as abnormalities in both high-level semantic and contextual processing and low-level perception. However, the literatures characterizing these abnormalities have largely been separate and have sometimes provided mutually exclusive accounts of aberrant language in schizophrenia. In this review, we propose that recent generative probabilistic frameworks of language processing can provide crucial insights that link these four lines of research. We first outline neural and cognitive evidence that real-time language comprehension and production normally involve internal generative circuits that propagate probabilistic predictions to perceptual cortices — predictions that are incrementally updated based on prediction error signals as new inputs are encountered. We then explain how disruptions to these circuits may compromise communicative abilities in schizophrenia by reducing the efficiency and robustness of both high-level language processing and low-level speech perception. We also argue that such disruptions may contribute to the phenomenology of thought-disordered speech and false perceptual inferences in the language system (i.e., AVHs). This perspective suggests a number of productive avenues for future research that may elucidate not only the mechanisms of language abnormalities in schizophrenia, but also promising directions for cognitive rehabilitation. PMID:26640435

Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems

DOE PAGES

Rangarajan, Srinivas; Maravelias, Christos T.; Mavrikakis, Manos

2017-11-09

Here, we present a general optimization-based framework for (i) ab initio and experimental data driven mechanistic modeling and (ii) optimal catalyst design of heterogeneous catalytic systems. Both cases are formulated as a nonlinear optimization problem that is subject to a mean-field microkinetic model and thermodynamic consistency requirements as constraints, for which we seek sparse solutions through a ridge (L 2 regularization) penalty. The solution procedure involves an iterative sequence of forward simulation of the differential algebraic equations pertaining to the microkinetic model using a numerical tool capable of handling stiff systems, sensitivity calculations using linear algebra, and gradient-based nonlinear optimization.more » A multistart approach is used to explore the solution space, and a hierarchical clustering procedure is implemented for statistically classifying potentially competing solutions. An example of methanol synthesis through hydrogenation of CO and CO 2 on a Cu-based catalyst is used to illustrate the framework. The framework is fast, is robust, and can be used to comprehensively explore the model solution and design space of any heterogeneous catalytic system.« less

Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems

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

Rangarajan, Srinivas; Maravelias, Christos T.; Mavrikakis, Manos

Here, we present a general optimization-based framework for (i) ab initio and experimental data driven mechanistic modeling and (ii) optimal catalyst design of heterogeneous catalytic systems. Both cases are formulated as a nonlinear optimization problem that is subject to a mean-field microkinetic model and thermodynamic consistency requirements as constraints, for which we seek sparse solutions through a ridge (L 2 regularization) penalty. The solution procedure involves an iterative sequence of forward simulation of the differential algebraic equations pertaining to the microkinetic model using a numerical tool capable of handling stiff systems, sensitivity calculations using linear algebra, and gradient-based nonlinear optimization.more » A multistart approach is used to explore the solution space, and a hierarchical clustering procedure is implemented for statistically classifying potentially competing solutions. An example of methanol synthesis through hydrogenation of CO and CO 2 on a Cu-based catalyst is used to illustrate the framework. The framework is fast, is robust, and can be used to comprehensively explore the model solution and design space of any heterogeneous catalytic system.« less

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)

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions

PubMed Central

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage. PMID:27468262

20170312 - Adverse Outcome Pathway (AOP) framework for ...

EPA Pesticide Factsheets

Vascular development commences with de novo assembly of a primary capillary plexus (vasculogenesis) followed by its expansion (angiogenesis) and maturation (angio-adaptation) into a hierarchical system of arteries and veins. These processes are tightly regulated by genetic signals and environmental factors linked to morphogenesis and microphysiology. Gestational exposure to some chemicals disrupts vascular development leading to adverse outcomes. To broadly assess consequences of gestational toxicant exposure on vascular development, an Adverse Outcome Pathway (AOP) framework was constructed that integrates data from ToxCast high-throughput screening (HTS) assays with pathway-level information from the literature and public databases. The AOP-based model resolved the ToxCast library (1065 compounds) into a matrix based on several dozen molecular functions critical for developmental angiogenesis. A sample of 38 ToxCast chemicals selected across the matrix tested model performance. Putative vascular disrupting chemical (pVDC) bioactivity was assessed by multiple laboratories utilizing diverse angiogenesis assays, including: transgenic zebrafish, complex human cell co-cultures, engineered microscale systems, and human-synthetic models. The ToxCast pVDC signature predicted vascular disruption in a manner that was chemical-specific and assay-dependent. An AOP for developmental vascular toxicity was constructed that focuses on inhibition of VEGF receptor (VEGFR2). Thi

Adverse Outcome Pathway (AOP) framework for embryonic ...

EPA Pesticide Factsheets

Vascular development commences with de novo assembly of a primary capillary plexus (vasculogenesis) followed by its expansion (angiogenesis) and maturation (angio-adaptation) into a hierarchical system of arteries and veins. These processes are tightly regulated by genetic signals and environmental factors linked to morphogenesis and microphysiology. Gestational exposure to some chemicals disrupts vascular development leading to adverse outcomes. To broadly assess consequences of gestational toxicant exposure on vascular development, an Adverse Outcome Pathway (AOP) framework was constructed that integrates data from ToxCast high-throughput screening (HTS) assays with pathway-level information from the literature and public databases. The AOP-based model resolved the ToxCast library (1065 compounds) into a matrix based on several dozen molecular functions critical for developmental angiogenesis. A sample of 38 ToxCast chemicals selected across the matrix tested model performance. Putative vascular disrupting chemical (pVDC) bioactivity was assessed by multiple laboratories utilizing diverse angiogenesis assays, including: transgenic zebrafish, complex human cell co-cultures, engineered microscale systems, and human-synthetic models. The ToxCast pVDC signature predicted vascular disruption in a manner that was chemical-specific and assay-dependent. An AOP for developmental vascular toxicity was constructed that focuses on inhibition of VEGF receptor (VEGFR2). Thi

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.

PubMed

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.

Hierarchical filters determine community assembly of urban species pools.

PubMed

Aronson, Myla F J; Nilon, Charles H; Lepczyk, Christopher A; Parker, Tommy S; Warren, Paige S; Cilliers, Sarel S; Goddard, Mark A; Hahs, Amy K; Herzog, Cecilia; Katti, Madhusudan; La Sorte, Frank A; Williams, Nicholas S G; Zipperer, Wayne

2016-11-01

The majority of humanity now lives in cities or towns, with this proportion expected to continue increasing for the foreseeable future. As novel ecosystems, urban areas offer an ideal opportunity to examine multi-scalar processes involved in community assembly as well as the role of human activities in modulating environmental drivers of biodiversity. Although ecologists have made great strides in recent decades at documenting ecological relationships in urban areas, much remains unknown, and we still need to identify the major ecological factors, aside from habitat loss, behind the persistence or extinction of species and guilds of species in cities. Given this paucity of knowledge, there is an immediate need to facilitate collaborative, interdisciplinary research on the patterns and drivers of biodiversity in cities at multiple spatial scales. In this review, we introduce a new conceptual framework for understanding the filtering processes that mold diversity of urban floras and faunas. We hypothesize that the following hierarchical series of filters influence species distributions in cities: (1) regional climatic and biogeographical factors; (2) human facilitation; (3) urban form and development history; (4) socioeconomic and cultural factors; and (5) species interactions. In addition to these filters, life history and functional traits of species are important in determining community assembly and act at multiple spatial scales. Using these filters as a conceptual framework can help frame future research needed to elucidate processes of community assembly in urban areas. Understanding how humans influence community structure and processes will aid in the management, design, and planning of our cities to best support biodiversity. © 2016 by the Ecological Society of America.

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.

A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system.

PubMed

Lim, Jongwoo; Wang, Bohyun; Lim, Joon S

2016-04-29

Finding the minimum number of appropriate biomarkers for specific targets such as a lung cancer has been a challenging issue in bioinformatics. We propose a hierarchical two-phase framework for selecting appropriate biomarkers that extracts candidate biomarkers from the cancer microarray datasets and then selects the minimum number of appropriate biomarkers from the extracted candidate biomarkers datasets with a specific neuro-fuzzy algorithm, which is called a neural network with weighted fuzzy membership function (NEWFM). In this context, as the first phase, the proposed framework is to extract candidate biomarkers by using a Bhattacharyya distance method that measures the similarity of two discrete probability distributions. Finally, the proposed framework is able to reduce the cost of finding biomarkers by not receiving medical supplements and improve the accuracy of the biomarkers in specific cancer target datasets.

A Formal Investigation of Human Spatial Control Skills: Mathematical Formalization, Skill Development, and Skill Assessment

NASA Astrophysics Data System (ADS)

Li, Bin

Spatial control behaviors account for a large proportion of human everyday activities from normal daily tasks, such as reaching for objects, to specialized tasks, such as driving, surgery, or operating equipment. These behaviors involve intensive interactions within internal processes (i.e. cognitive, perceptual, and motor control) and with the physical world. This dissertation builds on a concept of interaction pattern and a hierarchical functional model. Interaction pattern represents a type of behavior synergy that humans coordinates cognitive, perceptual, and motor control processes. It contributes to the construction of the hierarchical functional model that delineates humans spatial control behaviors as the coordination of three functional subsystems: planning, guidance, and tracking/pursuit. This dissertation formalizes and validates these two theories and extends them for the investigation of human spatial control skills encompassing development and assessment. Specifically, this dissertation first presents an overview of studies in human spatial control skills encompassing definition, characteristic, development, and assessment, to provide theoretical evidence for the concept of interaction pattern and the hierarchical functional model. The following, the human experiments for collecting motion and gaze data and techniques to register and classify gaze data, are described. This dissertation then elaborates and mathematically formalizes the hierarchical functional model and the concept of interaction pattern. These theories then enables the construction of a succinct simulation model that can reproduce a variety of human performance with a minimal set of hypotheses. This validates the hierarchical functional model as a normative framework for interpreting human spatial control behaviors. The dissertation then investigates human skill development and captures the emergence of interaction pattern. The final part of the dissertation applies the hierarchical functional model for skill assessment and introduces techniques to capture interaction patterns both from the top down using their geometric features and from the bottom up using their dynamical characteristics. The validity and generality of the skill assessment is illustrated using two the remote-control flight and laparoscopic surgical training experiments.

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

Internal cycling, not external loading, decides the nutrient limitation in eutrophic lake: A dynamic model with temporal Bayesian hierarchical inference.

PubMed

Wu, Zhen; Liu, Yong; Liang, Zhongyao; Wu, Sifeng; Guo, Huaicheng

2017-06-01

Lake eutrophication is associated with excessive anthropogenic nutrients (mainly nitrogen (N) and phosphorus (P)) and unobserved internal nutrient cycling. Despite the advances in understanding the role of external loadings, the contribution of internal nutrient cycling is still an open question. A dynamic mass-balance model was developed to simulate and measure the contributions of internal cycling and external loading. It was based on the temporal Bayesian Hierarchical Framework (BHM), where we explored the seasonal patterns in the dynamics of nutrient cycling processes and the limitation of N and P on phytoplankton growth in hyper-eutrophic Lake Dianchi, China. The dynamic patterns of the five state variables (Chla, TP, ammonia, nitrate and organic N) were simulated based on the model. Five parameters (algae growth rate, sediment exchange rate of N and P, nitrification rate and denitrification rate) were estimated based on BHM. The model provided a good fit to observations. Our model results highlighted the role of internal cycling of N and P in Lake Dianchi. The internal cycling processes contributed more than external loading to the N and P changes in the water column. Further insights into the nutrient limitation analysis indicated that the sediment exchange of P determined the P limitation. Allowing for the contribution of denitrification to N removal, N was the more limiting nutrient in most of the time, however, P was the more important nutrient for eutrophication management. For Lake Dianchi, it would not be possible to recover solely by reducing the external watershed nutrient load; the mechanisms of internal cycling should also be considered as an approach to inhibit the release of sediments and to enhance denitrification. Copyright © 2017 Elsevier Ltd. All rights reserved.

An AHP-based evaluation method for teacher training workshop on information and communication technology.

PubMed

Lucas, Rochelle Irene; Promentilla, Michael Angelo; Ubando, Aristotle; Tan, Raymond Girard; Aviso, Kathleen; Yu, Krista Danielle

2017-08-01

The emergence of information and communication technology (ICT) has created opportunities for enhancing the learning process at different educational levels. However, its potential benefits can only be fully realized if teachers are properly trained to utilize such tools. The rapid evolution of ICT also necessitates rigorous assessment of training programs by participants. Thus, this study proposes an evaluation framework based on the Analytic Hierarchy Process (AHP) to systematically evaluate such workshops designed for teachers. The evaluation model is decomposed hierarchically into four main criteria namely: (1) workshop design, (2) quality of content of the workshop, (3) quality of delivery of the content of the workshop, and the (4) relevance of the workshop. These criteria are further disaggregated into 24 sub-indicators to measure the effectiveness of the workshop as perceived by the participants based on their own expectations. This framework is applied to a case study of ICT workshops done in the Philippines. In this case, relevance of the workshop is found to be the most important main criterion identified by the participants, particularly on the new ICT knowledge that promotes teachers' professional growth and development. The workshop evaluation index (WEI) is also proposed as a metric to support decision-making by providing a mechanism for benchmarking performance, tracking improvement over time, and developing strategies for the design and improvement of training programs or workshops on ICT for teachers. Copyright © 2017 Elsevier Ltd. All rights reserved.

A hierarchical approach for simulating northern forest dynamics

Treesearch

Don C. Bragg; David W. Roberts; Thomas R. Crow

2004-01-01

Complexity in ecological systems has challenged forest simulation modelers for years, resulting in a number of approaches with varying degrees of success. Arguments in favor of hierarchical modeling are made, especially for considering a complex environmental issue like widespread eastern hemlock regeneration failure. We present the philosophy and basic framework for...

Process-based principles for restoring river ecosystems

Treesearch

Timothy J. Beechie; David A. Sear; Julian D. Olden; George R. Pess; John M. Buffington; Hamish Moir; Philip Roni; Michael M. Pollock

2010-01-01

Process-based restoration aims to reestablish normative rates and magnitudes of physical, chemical, and biological processes that sustain river and floodplain ecosystems. Ecosystem conditions at any site are governed by hierarchical regional, watershed, and reach-scale processes controlling hydrologic and sediment regimes; floodplain and aquatic habitat...

Clustering and Bayesian hierarchical modeling for the definition of informative prior distributions in hydrogeology

NASA Astrophysics Data System (ADS)

Cucchi, K.; Kawa, N.; Hesse, F.; Rubin, Y.

2017-12-01

In order to reduce uncertainty in the prediction of subsurface flow and transport processes, practitioners should use all data available. However, classic inverse modeling frameworks typically only make use of information contained in in-situ field measurements to provide estimates of hydrogeological parameters. Such hydrogeological information about an aquifer is difficult and costly to acquire. In this data-scarce context, the transfer of ex-situ information coming from previously investigated sites can be critical for improving predictions by better constraining the estimation procedure. Bayesian inverse modeling provides a coherent framework to represent such ex-situ information by virtue of the prior distribution and combine them with in-situ information from the target site. In this study, we present an innovative data-driven approach for defining such informative priors for hydrogeological parameters at the target site. Our approach consists in two steps, both relying on statistical and machine learning methods. The first step is data selection; it consists in selecting sites similar to the target site. We use clustering methods for selecting similar sites based on observable hydrogeological features. The second step is data assimilation; it consists in assimilating data from the selected similar sites into the informative prior. We use a Bayesian hierarchical model to account for inter-site variability and to allow for the assimilation of multiple types of site-specific data. We present the application and validation of the presented methods on an established database of hydrogeological parameters. Data and methods are implemented in the form of an open-source R-package and therefore facilitate easy use by other practitioners.

Temperament as a unifying basis for personality and psychopathology.

PubMed

Clark, Lee Anna

2005-11-01

Personality and psychopathology long have been viewed as related domains, but the precise nature of their relations remains unclear. Through most of the 20th century, they were studied as separate fields; within psychopathology, clinical syndromes were separated from personality disorders in 1980. This division led to the revelation of substantial overlap among disorders both within and across axes and to the joint study of normal and abnormal personality. The author reviews these literatures and proposes an integrative framework to explain personality-psychopathology relations: Three broad, innate temperament dimensions--negative affectivity, positive affectivity, and disinhibition--differentiate through both biologically and environmentally based developmental processes into a hierarchical personality trait structure and, at their extremes, are risk factors (diatheses) for psychopathology, especially given adverse life experiences (stress). Copyright (c) 2005 APA, all rights reserved.

Morphology-Controlled Synthesis and Metalation of Porphyrin Nanoparticles with Enhanced Photocatalytic Performance

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

Wang, Jiefei; Zhong, Yong; Wang, Liang

The design and engineering of the size, shape, and chemistry of photoactive building blocks enables the fabrication of functional nanoparticles for applications in light harvesting, photocatalytic synthesis, water splitting, phototherapy, and photodegradation. Here, we report the synthesis of such nanoparticles through a surfactant-assisted interfacial self-assembly process using optically active porphyrin as a functional building block. The self-assembly process relies on specific interactions such as π–π stacking and metalation (metal atoms and ligand coordination) between individual porphyrin building blocks. Depending on the kinetic conditions and type of surfactants, resulting structures exhibit well-defined one- to three-dimensional morphologies such as nanowires, nanooctahedra, andmore » hierarchically ordered internal architectures. Specifically, electron microscopy and X-ray diffraction results indicate that these nanoparticles exhibit stable single-crystalline and nanoporous frameworks. In conclusion, due to the hierarchical ordering of the porphyrins, the nanoparticles exhibit collective optical properties resulted from coupling of molecular porphyrins and photocatalytic activities such as photodegradation of methyl orange (MO) pollutants and hydrogen production.« less

Morphology-Controlled Synthesis and Metalation of Porphyrin Nanoparticles with Enhanced Photocatalytic Performance

DOE PAGES

Wang, Jiefei; Zhong, Yong; Wang, Liang; ...

2016-09-12

The design and engineering of the size, shape, and chemistry of photoactive building blocks enables the fabrication of functional nanoparticles for applications in light harvesting, photocatalytic synthesis, water splitting, phototherapy, and photodegradation. Here, we report the synthesis of such nanoparticles through a surfactant-assisted interfacial self-assembly process using optically active porphyrin as a functional building block. The self-assembly process relies on specific interactions such as π–π stacking and metalation (metal atoms and ligand coordination) between individual porphyrin building blocks. Depending on the kinetic conditions and type of surfactants, resulting structures exhibit well-defined one- to three-dimensional morphologies such as nanowires, nanooctahedra, andmore » hierarchically ordered internal architectures. Specifically, electron microscopy and X-ray diffraction results indicate that these nanoparticles exhibit stable single-crystalline and nanoporous frameworks. In conclusion, due to the hierarchical ordering of the porphyrins, the nanoparticles exhibit collective optical properties resulted from coupling of molecular porphyrins and photocatalytic activities such as photodegradation of methyl orange (MO) pollutants and hydrogen production.« less

[The gestalt problem in neurobiology].

PubMed

Sokolov, E N

1996-01-01

The paper concerns the concept of gestalt in the framework of neuronal mechanisms. Two hypotheses are discussed: 1) hierarchy of neurons (gnostic units), 2) synchronization of neurons. It is concluded that gestalt is based on hierarchical vectorial organization of neurons. The high frequency oscillations in gamma range (40-200 Hz) are of intrinsic origin. They are operating as amplifiers of synaptic inputs of neurons involved in gestalt representation.

Leading processes of patient care and treatment in hierarchical healthcare organizations in Sweden--process managers' experiences.

PubMed

Nilsson, Kerstin; Sandoff, Mette

2015-01-01

The purpose of this study is to gain better understanding of the roles and functions of process managers by describing Swedish process managers' experiences of leading processes involving patient care and treatment when working in a hierarchical health-care organization. This study is based on an explorative design. The data were gathered from interviews with 12 process managers at three Swedish hospitals. These data underwent qualitative and interpretative analysis with a modified editing style. The process managers' experiences of leading processes in a hierarchical health-care organization are described under three themes: having or not having a mandate, exposure to conflict situations and leading process development. The results indicate a need for clarity regarding process manager's responsibility and work content, which need to be communicated to all managers and staff involved in the patient care and treatment process, irrespective of department. There also needs to be an emphasis on realistic expectations and orientation of the goals that are an intrinsic part of the task of being a process manager. Generalizations from the results of the qualitative interview studies are limited, but a deeper understanding of the phenomenon was reached, which, in turn, can be transferred to similar settings. This study contributes qualitative descriptions of leading care and treatment processes in a functional, hierarchical health-care organization from process managers' experiences, a subject that has not been investigated earlier.

Hierarchical spatial models of abundance and occurrence from imperfect survey data

USGS Publications Warehouse

Royle, J. Andrew; Kery, M.; Gautier, R.; Schmid, Hans

2007-01-01

Many estimation and inference problems arising from large-scale animal surveys are focused on developing an understanding of patterns in abundance or occurrence of a species based on spatially referenced count data. One fundamental challenge, then, is that it is generally not feasible to completely enumerate ('census') all individuals present in each sample unit. This observation bias may consist of several components, including spatial coverage bias (not all individuals in the Population are exposed to sampling) and detection bias (exposed individuals may go undetected). Thus, observations are biased for the state variable (abundance, occupancy) that is the object of inference. Moreover, data are often sparse for most observation locations, requiring consideration of methods for spatially aggregating or otherwise combining sparse data among sample units. The development of methods that unify spatial statistical models with models accommodating non-detection is necessary to resolve important spatial inference problems based on animal survey data. In this paper, we develop a novel hierarchical spatial model for estimation of abundance and occurrence from survey data wherein detection is imperfect. Our application is focused on spatial inference problems in the Swiss Survey of Common Breeding Birds. The observation model for the survey data is specified conditional on the unknown quadrat population size, N(s). We augment the observation model with a spatial process model for N(s), describing the spatial variation in abundance of the species. The model includes explicit sources of variation in habitat structure (forest, elevation) and latent variation in the form of a correlated spatial process. This provides a model-based framework for combining the spatially referenced samples while at the same time yielding a unified treatment of estimation problems involving both abundance and occurrence. We provide a Bayesian framework for analysis and prediction based on the integrated likelihood, and we use the model to obtain estimates of abundance and occurrence maps for the European Jay (Garrulus glandarius), a widespread, elusive, forest bird. The naive national abundance estimate ignoring imperfect detection and incomplete quadrat coverage was 77 766 territories. Accounting for imperfect detection added approximately 18 000 territories, and adjusting for coverage bias added another 131 000 territories to yield a fully corrected estimate of the national total of about 227 000 territories. This is approximately three times as high as previous estimates that assume every territory is detected in each quadrat.

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.

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.

The Hydrolytic Stability and Degradation Mechanism of a Hierarchically Porous Metal Alkylphosphonate Framework

PubMed Central

Lv, Kai; Yang, Chu-Ting; Liu, Yi; Hu, Sheng; Wang, Xiao-Lin

2018-01-01

To aid the design of a hierarchically porous unconventional metal-phosphonate framework (HP-UMPF) for practical radioanalytical separation, a systematic investigation of the hydrolytic stability of bulk phase against acidic corrosion has been carried out for an archetypical HP-UMPF. Bulk dissolution results suggest that aqueous acidity has a more paramount effect on incongruent leaching than the temperature, and the kinetic stability reaches equilibrium by way of an accumulation of a partial leached species on the corrosion conduits. A variation of particle morphology, hierarchical porosity and backbone composition upon corrosion reveals that they are hydrolytically resilient without suffering any great degradation of porous texture, although large aggregates crack into sporadic fractures while the nucleophilic attack of inorganic layers cause the leaching of tin and phosphorus. The remaining selectivity of these HP-UMPFs is dictated by a balance between the elimination of free phosphonate and the exposure of confined phosphonates, thus allowing a real-time tailor of radionuclide sequestration. Moreover, a plausible degradation mechanism has been proposed for the triple progressive dissolution of three-level hierarchical porous structures to elucidate resultant reactivity. These HP-UMPFs are compared with benchmark metal-organic frameworks (MOFs) to obtain a rough grading of hydrolytic stability and two feasible approaches are suggested for enhancing their hydrolytic stability that are intended for real-life separation protocols. PMID:29538348

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.

A similarity learning approach to content-based image retrieval: application to digital mammography.

PubMed

El-Naqa, Issam; Yang, Yongyi; Galatsanos, Nikolas P; Nishikawa, Robert M; Wernick, Miles N

2004-10-01

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.

Hierarchical control framework for integrated coordination between distributed energy resources and demand response

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

Wu, Di; Lian, Jianming; Sun, Yannan

Demand response is representing a significant but largely untapped resource that can greatly enhance the flexibility and reliability of power systems. In this paper, a hierarchical control framework is proposed to facilitate the integrated coordination between distributed energy resources and demand response. The proposed framework consists of coordination and device layers. In the coordination layer, various resource aggregations are optimally coordinated in a distributed manner to achieve the system-level objectives. In the device layer, individual resources are controlled in real time to follow the optimal power generation or consumption dispatched from the coordination layer. For the purpose of practical applications,more » a method is presented to determine the utility functions of controllable loads by taking into account the real-time load dynamics and the preferences of individual customers. The effectiveness of the proposed framework is validated by detailed simulation studies.« less

Hierarchical quantum master equation approach to electronic-vibrational coupling in nonequilibrium transport through nanosystems

NASA Astrophysics Data System (ADS)

Schinabeck, C.; Erpenbeck, A.; Härtle, R.; Thoss, M.

2016-11-01

Within the hierarchical quantum master equation (HQME) framework, an approach is presented, which allows a numerically exact description of nonequilibrium charge transport in nanosystems with strong electronic-vibrational coupling. The method is applied to a generic model of vibrationally coupled transport considering a broad spectrum of parameters ranging from the nonadiabatic to the adiabatic regime and including both resonant and off-resonant transport. We show that nonequilibrium effects are important in all these regimes. In particular, in the off-resonant transport regime, the inelastic cotunneling signal is analyzed for a vibrational mode in full nonequilibrium, revealing a complex interplay of different transport processes and deviations from the commonly used G0/2 rule of thumb. In addition, the HQME approach is used to benchmark approximate master equation and nonequilibrium Green's function methods.

Compression-based integral curve data reuse framework for flow visualization

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

Hong, Fan; Bi, Chongke; Guo, Hanqi

Currently, by default, integral curves are repeatedly re-computed in different flow visualization applications, such as FTLE field computation, source-destination queries, etc., leading to unnecessary resource cost. We present a compression-based data reuse framework for integral curves, to greatly reduce their retrieval cost, especially in a resource-limited environment. In our design, a hierarchical and hybrid compression scheme is proposed to balance three objectives, including high compression ratio, controllable error, and low decompression cost. Specifically, we use and combine digitized curve sparse representation, floating-point data compression, and octree space partitioning to adaptively achieve the objectives. Results have shown that our data reusemore » framework could acquire tens of times acceleration in the resource-limited environment compared to on-the-fly particle tracing, and keep controllable information loss. Moreover, our method could provide fast integral curve retrieval for more complex data, such as unstructured mesh data.« less

Hierarchical Production Planning: A Two Stage System.

DTIC Science & Technology

1980-05-01

production planning issues (see [101,[121,[ 131 ). However, some elements of the hierarchical framework can also be construct- ively used to enhance an...inventories are nonzero, but the product families’ initial inventories are well balanced, i.e., if IJo = d , for 11,2,... ,I and JEJ(i)E) I ° Z j JCi ) JEJ(i

Intraclass Correlation Coefficients in Hierarchical Designs: Evaluation Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko

2011-01-01

Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…

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

PubMed

McNamara, Nicholas D; Hicks, Jason C

2015-03-11

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

A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer’s Disease Diagnosis

NASA Astrophysics Data System (ADS)

An, Le; Adeli, Ehsan; Liu, Mingxia; Zhang, Jun; Lee, Seong-Whan; Shen, Dinggang

2017-03-01

Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer’s disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.

Sensor-Web Operations Explorer

NASA Technical Reports Server (NTRS)

Meemong, Lee; Miller, Charles; Bowman, Kevin; Weidner, Richard

2008-01-01

Understanding the atmospheric state and its impact on air quality requires observations of trace gases, aerosols, clouds, and physical parameters across temporal and spatial scales that range from minutes to days and from meters to more than 10,000 kilometers. Observations include continuous local monitoring for particle formation; field campaigns for emissions, local transport, and chemistry; and periodic global measurements for continental transport and chemistry. Understanding includes global data assimilation framework capable of hierarchical coupling, dynamic integration of chemical data and atmospheric models, and feedback loops between models and observations. The objective of the sensor-web system is to observe trace gases, aerosols, clouds, and physical parameters, an integrated observation infrastructure composed of space-borne, air-borne, and in-situ sensors will be simulated based on their measurement physics properties. The objective of the sensor-web operation is to optimally plan for heterogeneous multiple sensors, the sampling strategies will be explored and science impact will be analyzed based on comprehensive modeling of atmospheric phenomena including convection, transport, and chemical process. Topics include system architecture, software architecture, hardware architecture, process flow, technology infusion, challenges, and future direction.

Predicting Motivation: Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease.

PubMed

Vassena, Eliana; Deraeve, James; Alexander, William H

2017-10-01

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response-outcome [PRO] model [Alexander, W. H., & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14, 1338-1344, 2011], hierarchical error representation [HER] model [Alexander, W. H., & Brown, J. W. Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354-2410, 2015]). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO model based on hierarchical error prediction, developed to explain MPFC-DLPFC interactions. We derive behavioral predictions that describe how effort and reward information is coded in PFC and how changing the configuration of such environmental information might affect decision-making and task performance involving motivation.

Hierarchical Object-based Image Analysis approach for classification of sub-meter multispectral imagery in Tanzania

NASA Astrophysics Data System (ADS)

Chung, C.; Nagol, J. R.; Tao, X.; Anand, A.; Dempewolf, J.

2015-12-01

Increasing agricultural production while at the same time preserving the environment has become a challenging task. There is a need for new approaches for use of multi-scale and multi-source remote sensing data as well as ground based measurements for mapping and monitoring crop and ecosystem state to support decision making by governmental and non-governmental organizations for sustainable agricultural development. High resolution sub-meter imagery plays an important role in such an integrative framework of landscape monitoring. It helps link the ground based data to more easily available coarser resolution data, facilitating calibration and validation of derived remote sensing products. Here we present a hierarchical Object Based Image Analysis (OBIA) approach to classify sub-meter imagery. The primary reason for choosing OBIA is to accommodate pixel sizes smaller than the object or class of interest. Especially in non-homogeneous savannah regions of Tanzania, this is an important concern and the traditional pixel based spectral signature approach often fails. Ortho-rectified, calibrated, pan sharpened 0.5 meter resolution data acquired from DigitalGlobe's WorldView-2 satellite sensor was used for this purpose. Multi-scale hierarchical segmentation was performed using multi-resolution segmentation approach to facilitate the use of texture, neighborhood context, and the relationship between super and sub objects for training and classification. eCognition, a commonly used OBIA software program, was used for this purpose. Both decision tree and random forest approaches for classification were tested. The Kappa index agreement for both algorithms surpassed the 85%. The results demonstrate that using hierarchical OBIA can effectively and accurately discriminate classes at even LCCS-3 legend.

3D architecture constructed via the confined growth of MoS2 nanosheets in nanoporous carbon derived from metal-organic frameworks for efficient hydrogen production.

PubMed

Liu, Yun; Zhou, Xiaoli; Ding, Tao; Wang, Chunde; Yang, Qing

2015-11-21

The design and synthesis of robust, high-performance and low-cost three-dimensional (3D) hierarchical structured materials for the electrochemical reduction of water to generate hydrogen is of great significance for practical water splitting applications. In this study, we develop an in situ space-confined method to synthesize an MoS2-based 3D hierarchical structure, in which the MoS2 nanosheets grow in the confined nanopores of metal-organic frameworks (MOFs)-derived 3D carbons as electrocatalysts for efficient hydrogen production. Benefiting from its unique structure, which has more exposed active sites and enhanced conductivity, the as-prepared MoS2/3D nanoporous carbon (3D-NPC) composite exhibits remarkable electrocatalytic activity for the hydrogen evolution reaction (HER) with a small onset overpotential of ∼0.16 V, large cathodic currents, small Tafel slope of 51 mV per decade and good durability. We anticipate that this in situ confined growth provides new insights into the construction of high performance catalysts for energy storage and conversion.

Interaction of light with hematite hierarchical structures: Experiments and simulations

NASA Astrophysics Data System (ADS)

Distaso, Monica; Zhuromskyy, Oleksander; Seemann, Benjamin; Pflug, Lukas; Mačković, Mirza; Encina, Ezequiel; Taylor, Robin Klupp; Müller, Rolf; Leugering, Günter; Spiecker, Erdmann; Peschel, Ulf; Peukert, Wolfgang

2017-03-01

Mesocrystalline particles have been recognized as a class of multifunctional materials with potential applications in different fields. However, the internal organization of nanocomposite mesocrystals and its influence on the final properties have not yet been investigated. In this paper, a novel strategy based on electrodynamic simulations is developed to shed light on how the internal structure of mesocrystals influences their optical properties. In a first instance, a unified design protocol is reported for the fabrication of hematite/PVP particles with different morphologies such as pseudo-cubes, rods-like and apple-like structures and controlled particle size distributions. The optical properties of hematite/PVP mesocrystals are effectively simulated by taking their aggregate and nanocomposite structure into consideration. The superposition T-Matrix approach accounts for the aggregate nature of mesocrystalline particles and validate the effective medium approximation used in the framework of the Mie theory and electromagnetic simulation such as Finite Element Method. The approach described in our paper provides the framework to understand and predict the optical properties of mesocrystals and more general, of hierarchical nanostructured particles.

A Literature Review and Compilation of Nuclear Waste Management System Attributes for Use in Multi-Objective System Evaluations.

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

Kalinina, Elena Arkadievna; Samsa, Michael

The purpose of this work was to compile a comprehensive initial set of potential nuclear waste management system attributes. This initial set of attributes is intended to serve as a starting point for additional consideration by system analysts and planners to facilitate the development of a waste management system multi-objective evaluation framework based on the principles and methodology of multi-attribute utility analysis. The compilation is primarily based on a review of reports issued by the Canadian Nuclear Waste Management Organization (NWMO) and the Blue Ribbon Commission on America's Nuclear Future (BRC), but also an extensive review of the available literaturemore » for similar and past efforts as well. Numerous system attributes found in different sources were combined into a single objectives-oriented hierarchical structure. This study provides a discussion of the data sources and the descriptions of the hierarchical structure. A particular focus of this study was on collecting and compiling inputs from past studies that involved the participation of various external stakeholders. However, while the important role of stakeholder input in a country's waste management decision process is recognized in the referenced sources, there are only a limited number of in-depth studies of the stakeholders' differing perspectives. Compiling a comprehensive hierarchical listing of attributes is a complex task since stakeholders have multiple and often conflicting interests. The BRC worked for two years (January 2010 to January 2012) to "ensure it has heard from as many points of view as possible." The Canadian NWMO study took four years and ample resources, involving national and regional stakeholders' dialogs, internet-based dialogs, information and discussion sessions, open houses, workshops, round tables, public attitude research, website, and topic reports. The current compilation effort benefited from the distillation of these many varied inputs conducted by the previous studies.« less

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.

Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

PubMed

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

2018-03-01

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 article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) 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 characterized 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. © 2017, The International Biometric Society.

Hierarchical and successive approximate registration of the non-rigid medical image based on thin-plate splines

NASA Astrophysics Data System (ADS)

Hu, Jinyan; Li, Li; Yang, Yunfeng

2017-06-01

The hierarchical and successive approximate registration method of non-rigid medical image based on the thin-plate splines is proposed in the paper. There are two major novelties in the proposed method. First, the hierarchical registration based on Wavelet transform is used. The approximate image of Wavelet transform is selected as the registered object. Second, the successive approximation registration method is used to accomplish the non-rigid medical images registration, i.e. the local regions of the couple images are registered roughly based on the thin-plate splines, then, the current rough registration result is selected as the object to be registered in the following registration procedure. Experiments show that the proposed method is effective in the registration process of the non-rigid medical images.

Hierarchical calibration and validation of computational fluid dynamics models for solid sorbent-based carbon capture

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

Lai, Canhai; Xu, Zhijie; Pan, Wenxiao

2016-01-01

To quantify the predictive confidence of a solid sorbent-based carbon capture design, a hierarchical validation methodology—consisting of basic unit problems with increasing physical complexity coupled with filtered model-based geometric upscaling has been developed and implemented. This paper describes the computational fluid dynamics (CFD) multi-phase reactive flow simulations and the associated data flows among different unit problems performed within the said hierarchical validation approach. The bench-top experiments used in this calibration and validation effort were carefully designed to follow the desired simple-to-complex unit problem hierarchy, with corresponding data acquisition to support model parameters calibrations at each unit problem level. A Bayesianmore » calibration procedure is employed and the posterior model parameter distributions obtained at one unit-problem level are used as prior distributions for the same parameters in the next-tier simulations. Overall, the results have demonstrated that the multiphase reactive flow models within MFIX can be used to capture the bed pressure, temperature, CO2 capture capacity, and kinetics with quantitative accuracy. The CFD modeling methodology and associated uncertainty quantification techniques presented herein offer a solid framework for estimating the predictive confidence in the virtual scale up of a larger carbon capture device.« less

Hierarchical decision processes that operate over distinct timescales underlie choice and changes in strategy

PubMed Central

Purcell, Braden A.; Kiani, Roozbeh

2016-01-01

Decision-making in a natural environment depends on a hierarchy of interacting decision processes. A high-level strategy guides ongoing choices, and the outcomes of those choices determine whether or not the strategy should change. When the right decision strategy is uncertain, as in most natural settings, feedback becomes ambiguous because negative outcomes may be due to limited information or bad strategy. Disambiguating the cause of feedback requires active inference and is key to updating the strategy. We hypothesize that the expected accuracy of a choice plays a crucial rule in this inference, and setting the strategy depends on integration of outcome and expectations across choices. We test this hypothesis with a task in which subjects report the net direction of random dot kinematograms with varying difficulty while the correct stimulus−response association undergoes invisible and unpredictable switches every few trials. We show that subjects treat negative feedback as evidence for a switch but weigh it with their expected accuracy. Subjects accumulate switch evidence (in units of log-likelihood ratio) across trials and update their response strategy when accumulated evidence reaches a bound. A computational framework based on these principles quantitatively explains all aspects of the behavior, providing a plausible neural mechanism for the implementation of hierarchical multiscale decision processes. We suggest that a similar neural computation—bounded accumulation of evidence—underlies both the choice and switches in the strategy that govern the choice, and that expected accuracy of a choice represents a key link between the levels of the decision-making hierarchy. PMID:27432960

Hierarchical decision processes that operate over distinct timescales underlie choice and changes in strategy.

PubMed

Purcell, Braden A; Kiani, Roozbeh

2016-08-02

Decision-making in a natural environment depends on a hierarchy of interacting decision processes. A high-level strategy guides ongoing choices, and the outcomes of those choices determine whether or not the strategy should change. When the right decision strategy is uncertain, as in most natural settings, feedback becomes ambiguous because negative outcomes may be due to limited information or bad strategy. Disambiguating the cause of feedback requires active inference and is key to updating the strategy. We hypothesize that the expected accuracy of a choice plays a crucial rule in this inference, and setting the strategy depends on integration of outcome and expectations across choices. We test this hypothesis with a task in which subjects report the net direction of random dot kinematograms with varying difficulty while the correct stimulus-response association undergoes invisible and unpredictable switches every few trials. We show that subjects treat negative feedback as evidence for a switch but weigh it with their expected accuracy. Subjects accumulate switch evidence (in units of log-likelihood ratio) across trials and update their response strategy when accumulated evidence reaches a bound. A computational framework based on these principles quantitatively explains all aspects of the behavior, providing a plausible neural mechanism for the implementation of hierarchical multiscale decision processes. We suggest that a similar neural computation-bounded accumulation of evidence-underlies both the choice and switches in the strategy that govern the choice, and that expected accuracy of a choice represents a key link between the levels of the decision-making hierarchy.

Hierarchical Boltzmann simulations and model error estimation

NASA Astrophysics Data System (ADS)

Torrilhon, Manuel; Sarna, Neeraj

2017-08-01

A hierarchical simulation approach for Boltzmann's equation should provide a single numerical framework in which a coarse representation can be used to compute gas flows as accurately and efficiently as in computational fluid dynamics, but a subsequent refinement allows to successively improve the result to the complete Boltzmann result. We use Hermite discretization, or moment equations, for the steady linearized Boltzmann equation for a proof-of-concept of such a framework. All representations of the hierarchy are rotationally invariant and the numerical method is formulated on fully unstructured triangular and quadrilateral meshes using a implicit discontinuous Galerkin formulation. We demonstrate the performance of the numerical method on model problems which in particular highlights the relevance of stability of boundary conditions on curved domains. The hierarchical nature of the method allows also to provide model error estimates by comparing subsequent representations. We present various model errors for a flow through a curved channel with obstacles.

Hierarchical content-based image retrieval by dynamic indexing and guided search

NASA Astrophysics Data System (ADS)

You, Jane; Cheung, King H.; Liu, James; Guo, Linong

2003-12-01

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.

Array-based, parallel hierarchical mesh refinement algorithms for unstructured meshes

DOE PAGES

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

2016-08-18

In this paper, we describe an array-based hierarchical mesh refinement capability through uniform refinement of unstructured meshes for efficient solution of PDE's using finite element methods and multigrid solvers. A multi-degree, multi-dimensional and multi-level framework is designed to generate the nested hierarchies from an initial coarse mesh that can be used for a variety of purposes such as in multigrid solvers/preconditioners, to do solution convergence and verification studies and to improve overall parallel efficiency by decreasing I/O bandwidth requirements (by loading smaller meshes and in memory refinement). We also describe a high-order boundary reconstruction capability that can be used tomore » project the new points after refinement using high-order approximations instead of linear projection in order to minimize and provide more control on geometrical errors introduced by curved boundaries.The capability is developed under the parallel unstructured mesh framework "Mesh Oriented dAtaBase" (MOAB Tautges et al. (2004)). We describe the underlying data structures and algorithms to generate such hierarchies in parallel and present numerical results for computational efficiency and effect on mesh quality. Furthermore, we also present results to demonstrate the applicability of the developed capability to study convergence properties of different point projection schemes for various mesh hierarchies and to a multigrid finite-element solver for elliptic problems.« 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.

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.

A Hierarchical Building Segmentation in Digital Surface Models for 3D Reconstruction

PubMed Central

Yan, Yiming; Gao, Fengjiao; Deng, Shupei; Su, Nan

2017-01-01

In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM), which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on completeness of the offline-constructed models of buildings, and the completeness is not easily guaranteed since in modern cities buildings can be of a variety of types. Therefore, a model-free framework using high precision DSM and texture-images buildings was introduced. There are two key problems with this framework. The first one is how to accurately extract the buildings from the DSM. Most segmentation methods are limited by either the terrain factors or the difficult choice of parameter-settings. A level-set method are employed to roughly find the building regions in the DSM, and then a recently proposed ‘occlusions of random textures model’ are used to enhance the local segmentation of the buildings. The second problem is how to generate the facades of buildings. Synergizing with the corresponding texture-images, we propose a roof-contour guided interpolation of building facades. The 3D reconstruction results achieved by airborne-like images and satellites are compared. Experiments show that the segmentation method has good performance, and 3D reconstruction is easily performed by our framework, and better visualization results can be obtained by airborne-like images, which can be further replaced by UAV images. PMID:28125018

A Hierarchical Building Segmentation in Digital Surface Models for 3D Reconstruction.

PubMed

Yan, Yiming; Gao, Fengjiao; Deng, Shupei; Su, Nan

2017-01-24

In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM), which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on completeness of the offline-constructed models of buildings, and the completeness is not easily guaranteed since in modern cities buildings can be of a variety of types. Therefore, a model-free framework using high precision DSM and texture-images buildings was introduced. There are two key problems with this framework. The first one is how to accurately extract the buildings from the DSM. Most segmentation methods are limited by either the terrain factors or the difficult choice of parameter-settings. A level-set method are employed to roughly find the building regions in the DSM, and then a recently proposed 'occlusions of random textures model' are used to enhance the local segmentation of the buildings. The second problem is how to generate the facades of buildings. Synergizing with the corresponding texture-images, we propose a roof-contour guided interpolation of building facades. The 3D reconstruction results achieved by airborne-like images and satellites are compared. Experiments show that the segmentation method has good performance, and 3D reconstruction is easily performed by our framework, and better visualization results can be obtained by airborne-like images, which can be further replaced by UAV images.

From Specific Information Extraction to Inferences: A Hierarchical Framework of Graph Comprehension

DTIC Science & Technology

2004-09-01

The skill to interpret the information displayed in graphs is so important to have, the National Council of Teachers of Mathematics has created...guidelines to ensure that students learn these skills ( NCTM : Standards for Mathematics , 2003). These guidelines are based primarily on the extraction of...graphical perception. Human Computer Interaction, 8, 353-388. NCTM : Standards for Mathematics . (2003, 2003). Peebles, D., & Cheng, P. C.-H. (2002

A Layered Approach for Robust Spatial Virtual Human Pose Reconstruction Using a Still Image

PubMed Central

Guo, Chengyu; Ruan, Songsong; Liang, Xiaohui; Zhao, Qinping

2016-01-01

Pedestrian detection and human pose estimation are instructive for reconstructing a three-dimensional scenario and for robot navigation, particularly when large amounts of vision data are captured using various data-recording techniques. Using an unrestricted capture scheme, which produces occlusions or breezing, the information describing each part of a human body and the relationship between each part or even different pedestrians must be present in a still image. Using this framework, a multi-layered, spatial, virtual, human pose reconstruction framework is presented in this study to recover any deficient information in planar images. In this framework, a hierarchical parts-based deep model is used to detect body parts by using the available restricted information in a still image and is then combined with spatial Markov random fields to re-estimate the accurate joint positions in the deep network. Then, the planar estimation results are mapped onto a virtual three-dimensional space using multiple constraints to recover any deficient spatial information. The proposed approach can be viewed as a general pre-processing method to guide the generation of continuous, three-dimensional motion data. The experiment results of this study are used to describe the effectiveness and usability of the proposed approach. PMID:26907289

Exploring the Specifications of Spatial Adjacencies and Weights in Bayesian Spatial Modeling with Intrinsic Conditional Autoregressive Priors in a Small-area Study of Fall Injuries

PubMed Central

Law, Jane

2016-01-01

Intrinsic conditional autoregressive modeling in a Bayeisan hierarchical framework has been increasingly applied in small-area ecological studies. This study explores the specifications of spatial structure in this Bayesian framework in two aspects: adjacency, i.e., the set of neighbor(s) for each area; and (spatial) weight for each pair of neighbors. Our analysis was based on a small-area study of falling injuries among people age 65 and older in Ontario, Canada, that was aimed to estimate risks and identify risk factors of such falls. In the case study, we observed incorrect adjacencies information caused by deficiencies in the digital map itself. Further, when equal weights was replaced by weights based on a variable of expected count, the range of estimated risks increased, the number of areas with probability of estimated risk greater than one at different probability thresholds increased, and model fit improved. More importantly, significance of a risk factor diminished. Further research to thoroughly investigate different methods of variable weights; quantify the influence of specifications of spatial weights; and develop strategies for better defining spatial structure of a map in small-area analysis in Bayesian hierarchical spatial modeling is recommended. PMID:29546147

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.

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.

Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development

PubMed Central

Alvarez, Stéphanie; Timler, Carl J.; Michalscheck, Mirja; Paas, Wim; Descheemaeker, Katrien; Tittonell, Pablo; Andersson, Jens A.; Groot, Jeroen C. J.

2018-01-01

Creating typologies is a way to summarize the large heterogeneity of smallholder farming systems into a few farm types. Various methods exist, commonly using statistical analysis, to create these typologies. We demonstrate that the methodological decisions on data collection, variable selection, data-reduction and clustering techniques can bear a large impact on the typology results. We illustrate the effects of analysing the diversity from different angles, using different typology objectives and different hypotheses, on typology creation by using an example from Zambia’s Eastern Province. Five separate typologies were created with principal component analysis (PCA) and hierarchical clustering analysis (HCA), based on three different expert-informed hypotheses. The greatest overlap between typologies was observed for the larger, wealthier farm types but for the remainder of the farms there were no clear overlaps between typologies. Based on these results, we argue that the typology development should be guided by a hypothesis on the local agriculture features and the drivers and mechanisms of differentiation among farming systems, such as biophysical and socio-economic conditions. That hypothesis is based both on the typology objective and on prior expert knowledge and theories of the farm diversity in the study area. We present a methodological framework that aims to integrate participatory and statistical methods for hypothesis-based typology construction. This is an iterative process whereby the results of the statistical analysis are compared with the reality of the target population as hypothesized by the local experts. Using a well-defined hypothesis and the presented methodological framework, which consolidates the hypothesis through local expert knowledge for the creation of typologies, warrants development of less subjective and more contextualized quantitative farm typologies. PMID:29763422

Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development.

PubMed

Alvarez, Stéphanie; Timler, Carl J; Michalscheck, Mirja; Paas, Wim; Descheemaeker, Katrien; Tittonell, Pablo; Andersson, Jens A; Groot, Jeroen C J

2018-01-01

Creating typologies is a way to summarize the large heterogeneity of smallholder farming systems into a few farm types. Various methods exist, commonly using statistical analysis, to create these typologies. We demonstrate that the methodological decisions on data collection, variable selection, data-reduction and clustering techniques can bear a large impact on the typology results. We illustrate the effects of analysing the diversity from different angles, using different typology objectives and different hypotheses, on typology creation by using an example from Zambia's Eastern Province. Five separate typologies were created with principal component analysis (PCA) and hierarchical clustering analysis (HCA), based on three different expert-informed hypotheses. The greatest overlap between typologies was observed for the larger, wealthier farm types but for the remainder of the farms there were no clear overlaps between typologies. Based on these results, we argue that the typology development should be guided by a hypothesis on the local agriculture features and the drivers and mechanisms of differentiation among farming systems, such as biophysical and socio-economic conditions. That hypothesis is based both on the typology objective and on prior expert knowledge and theories of the farm diversity in the study area. We present a methodological framework that aims to integrate participatory and statistical methods for hypothesis-based typology construction. This is an iterative process whereby the results of the statistical analysis are compared with the reality of the target population as hypothesized by the local experts. Using a well-defined hypothesis and the presented methodological framework, which consolidates the hypothesis through local expert knowledge for the creation of typologies, warrants development of less subjective and more contextualized quantitative farm typologies.

Hierarchical HMM based learning of navigation primitives for cooperative robotic endovascular catheterization.

PubMed

Rafii-Tari, Hedyeh; Liu, Jindong; Payne, Christopher J; Bicknell, Colin; Yang, Guang-Zhong

2014-01-01

Despite increased use of remote-controlled steerable catheter navigation systems for endovascular intervention, most current designs are based on master configurations which tend to alter natural operator tool interactions. This introduces problems to both ergonomics and shared human-robot control. This paper proposes a novel cooperative robotic catheterization system based on learning-from-demonstration. By encoding the higher-level structure of a catheterization task as a sequence of primitive motions, we demonstrate how to achieve prospective learning for complex tasks whilst incorporating subject-specific variations. A hierarchical Hidden Markov Model is used to model each movement primitive as well as their sequential relationship. This model is applied to generation of motion sequences, recognition of operator input, and prediction of future movements for the robot. The framework is validated by comparing catheter tip motions against the manual approach, showing significant improvements in the quality of catheterization. The results motivate the design of collaborative robotic systems that are intuitive to use, while reducing the cognitive workload of the operator.

Getting the Hologenome Concept Right: an Eco-Evolutionary Framework for Hosts and Their Microbiomes.

PubMed

Theis, Kevin R; Dheilly, Nolwenn M; Klassen, Jonathan L; Brucker, Robert M; Baines, John F; Bosch, Thomas C G; Cryan, John F; Gilbert, Scott F; Goodnight, Charles J; Lloyd, Elisabeth A; Sapp, Jan; Vandenkoornhuyse, Philippe; Zilber-Rosenberg, Ilana; Rosenberg, Eugene; Bordenstein, Seth R

2016-01-01

Given the complexity of host-microbiota symbioses, scientists and philosophers are asking questions at new biological levels of hierarchical organization-what is a holobiont and hologenome? When should this vocabulary be applied? Are these concepts a null hypothesis for host-microbe systems or limited to a certain spectrum of symbiotic interactions such as host-microbial coevolution? Critical discourse is necessary in this nascent area, but productive discourse requires that skeptics and proponents use the same lexicon. For instance, critiquing the hologenome concept is not synonymous with critiquing coevolution, and arguing that an entity is not a primary unit of selection dismisses the fact that the hologenome concept has always embraced multilevel selection. Holobionts and hologenomes are incontrovertible, multipartite entities that result from ecological, evolutionary, and genetic processes at various levels. They are not restricted to one special process but constitute a wider vocabulary and framework for host biology in light of the microbiome.

A Metascalable Computing Framework for Large Spatiotemporal-Scale Atomistic Simulations

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

Nomura, K; Seymour, R; Wang, W

2009-02-17

A metascalable (or 'design once, scale on new architectures') parallel computing framework has been developed for large spatiotemporal-scale atomistic simulations of materials based on spatiotemporal data locality principles, which is expected to scale on emerging multipetaflops architectures. The framework consists of: (1) an embedded divide-and-conquer (EDC) algorithmic framework based on spatial locality to design linear-scaling algorithms for high complexity problems; (2) a space-time-ensemble parallel (STEP) approach based on temporal locality to predict long-time dynamics, while introducing multiple parallelization axes; and (3) a tunable hierarchical cellular decomposition (HCD) parallelization framework to map these O(N) algorithms onto a multicore cluster based onmore » hybrid implementation combining message passing and critical section-free multithreading. The EDC-STEP-HCD framework exposes maximal concurrency and data locality, thereby achieving: (1) inter-node parallel efficiency well over 0.95 for 218 billion-atom molecular-dynamics and 1.68 trillion electronic-degrees-of-freedom quantum-mechanical simulations on 212,992 IBM BlueGene/L processors (superscalability); (2) high intra-node, multithreading parallel efficiency (nanoscalability); and (3) nearly perfect time/ensemble parallel efficiency (eon-scalability). The spatiotemporal scale covered by MD simulation on a sustained petaflops computer per day (i.e. petaflops {center_dot} day of computing) is estimated as NT = 2.14 (e.g. N = 2.14 million atoms for T = 1 microseconds).« less

Hierarchical models for informing general biomass equations with felled tree data

Treesearch

Brian J. Clough; Matthew B. Russell; Christopher W. Woodall; Grant M. Domke; Philip J. Radtke

2015-01-01

We present a hierarchical framework that uses a large multispecies felled tree database to inform a set of general models for predicting tree foliage biomass, with accompanying uncertainty, within the FIA database. Results suggest significant prediction uncertainty for individual trees and reveal higher errors when predicting foliage biomass for larger trees and for...

Hierarchical Linear Modeling (HLM): An Introduction to Key Concepts within Cross-Sectional and Growth Modeling Frameworks. Technical Report #1308

ERIC Educational Resources Information Center

Anderson, Daniel

2012-01-01

This manuscript provides an overview of hierarchical linear modeling (HLM), as part of a series of papers covering topics relevant to consumers of educational research. HLM is tremendously flexible, allowing researchers to specify relations across multiple "levels" of the educational system (e.g., students, classrooms, schools, etc.).…

High-performance non-enzymatic catalysts based on 3D hierarchical hollow porous Co3O4 nanododecahedras in situ decorated on carbon nanotubes for glucose detection and biofuel cell application.

PubMed

Wang, Shiyue; Zhang, Xiaohua; Huang, Junlin; Chen, Jinhua

2018-03-01

In this work, high-performance non-enzymatic catalysts based on 3D hierarchical hollow porous Co 3 O 4 nanododecahedras in situ decorated on carbon nanotubes (3D Co 3 O 4 -HPND/CNTs) were successfully prepared via direct carbonizing metal-organic framework-67 in situ grown on carbon nanotubes. The morphology, microstructure, and composite of 3D Co 3 O 4 -HPND/CNTs were characterized by scanning electron microscopy, transmission electron microscopy, micropore and chemisorption analyzer, and X-ray diffraction. The electrochemical characterizations indicated that 3D Co 3 O 4 -HPND/CNTs present considerably catalytic activity toward glucose oxidation and could be promising for constructing high-performance electrochemical non-enzymatic glucose sensors and glucose/O 2 biofuel cell. When used for non-enzymatic glucose detection, the 3D Co 3 O 4 -HPND/CNTs modified glassy carbon electrode (3D Co 3 O 4 -HPND/CNTs/GCE) exhibited excellent analytical performance with high sensitivity (22.21 mA mM -1  cm -2 ), low detection limit of 0.35 μM (S/N = 3), fast response (less than 5 s) and good stability. On the other hand, when the 3D Co 3 O 4 -HPND/CNTs/GCE worked as an anode of a biofuel cell, a maximum power density of 210 μW cm -2 at 0.15 V could be obtained, and the open circuit potential was 0.68 V. The attractive 3D hierarchical porous structural features, the large surface area, and the excellent conductivity based on the continuous and effective electron transport network in 3D Co 3 O 4 -HPND/CNTs endow 3D Co 3 O 4 -HPND/CNTs with the enhanced electrochemical performance and promising applications in electrochemical sensing, biofuel cell, and other energy storage and conversion devices such as supercapacitor. Graphical abstract High-performance non-enzymatic catalysts for enzymeless glucose sensing and biofuel cell based on 3D hierarchical hollow porous Co 3 O 4 nanododecahedras anchored on carbon nanotubes were successfully prepared via direct carbonizing metal-organic framework-67 in situ grown on carbon nanotubes.

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…

Framework for robot skill learning using reinforcement learning

NASA Astrophysics Data System (ADS)

Wei, Yingzi; Zhao, Mingyang

2003-09-01

Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is an on-line actor critic method for a robot to develop its skill. The reinforcement function has become the critical component for its effect of evaluating the action and guiding the learning process. We present an augmented reward function that provides a new way for RL controller to incorporate prior knowledge and experience into the RL controller. Also, the difference form of augmented reward function is considered carefully. The additional reward beyond conventional reward will provide more heuristic information for RL. In this paper, we present a strategy for the task of complex skill learning. Automatic robot shaping policy is to dissolve the complex skill into a hierarchical learning process. The new form of value function is introduced to attain smooth motion switching swiftly. We present a formal, but practical, framework for robot skill learning and also illustrate with an example the utility of method for learning skilled robot control on line.

Ultradispersed Cobalt Ferrite Nanoparticles Assembled in Graphene Aerogel for Continuous Photo-Fenton Reaction and Enhanced Lithium Storage Performance

NASA Astrophysics Data System (ADS)

Qiu, Bocheng; Deng, Yuanxin; Du, Mengmeng; Xing, Mingyang; Zhang, Jinlong

2016-07-01

The Photo-Fenton reaction is an advanced technology to eliminate organic pollutants in environmental chemistry. Moreover, the conversion rate of Fe3+/Fe2+ and utilization rate of H2O2 are significant factors in Photo-Fenton reaction. In this work, we reported three dimensional (3D) hierarchical cobalt ferrite/graphene aerogels (CoFe2O4/GAs) composites by the in situ growing CoFe2O4 crystal seeds on the graphene oxide (GO) followed by the hydrothermal process. The resulting CoFe2O4/GAs composites demonstrated 3D hierarchical pore structure with mesopores (14~18 nm), macropores (50~125 nm), and a remarkable surface area (177.8 m2 g-1). These properties endowed this hybrid with the high and recyclable Photo-Fenton activity for methyl orange pollutant degradation. More importantly, the CoFe2O4/GAs composites can keep high Photo-Fenton activity in a wide pH. Besides, the CoFe2O4/GAs composites also exhibited excellent cyclic performance and good rate capability. The 3D framework can not only effectively prevent the volume expansion and aggregation of CoFe2O4 nanoparticles during the charge/discharge processes for Lithium-ion batteries (LIBs), but also shorten lithium ions and electron diffusion length in 3D pathways. These results indicated a broaden application prospect of 3D-graphene based hybrids in wastewater treatment and energy storage.

Ultradispersed Cobalt Ferrite Nanoparticles Assembled in Graphene Aerogel for Continuous Photo-Fenton Reaction and Enhanced Lithium Storage Performance.

PubMed

Qiu, Bocheng; Deng, Yuanxin; Du, Mengmeng; Xing, Mingyang; Zhang, Jinlong

2016-07-04

The Photo-Fenton reaction is an advanced technology to eliminate organic pollutants in environmental chemistry. Moreover, the conversion rate of Fe(3+)/Fe(2+) and utilization rate of H2O2 are significant factors in Photo-Fenton reaction. In this work, we reported three dimensional (3D) hierarchical cobalt ferrite/graphene aerogels (CoFe2O4/GAs) composites by the in situ growing CoFe2O4 crystal seeds on the graphene oxide (GO) followed by the hydrothermal process. The resulting CoFe2O4/GAs composites demonstrated 3D hierarchical pore structure with mesopores (14~18 nm), macropores (50~125 nm), and a remarkable surface area (177.8 m(2 )g(-1)). These properties endowed this hybrid with the high and recyclable Photo-Fenton activity for methyl orange pollutant degradation. More importantly, the CoFe2O4/GAs composites can keep high Photo-Fenton activity in a wide pH. Besides, the CoFe2O4/GAs composites also exhibited excellent cyclic performance and good rate capability. The 3D framework can not only effectively prevent the volume expansion and aggregation of CoFe2O4 nanoparticles during the charge/discharge processes for Lithium-ion batteries (LIBs), but also shorten lithium ions and electron diffusion length in 3D pathways. These results indicated a broaden application prospect of 3D-graphene based hybrids in wastewater treatment and energy storage.

Sinkhole Susceptibility Hazard Zones Using GIS and Analytical Hierarchical Process (ahp): a Case Study of Kuala Lumpur and Ampang Jaya

NASA Astrophysics Data System (ADS)

Rosdi, M. A. H. M.; Othman, A. N.; Zubir, M. A. M.; Latif, Z. A.; Yusoff, Z. M.

2017-10-01

Sinkhole is not classified as new phenomenon in this country, especially surround Klang Valley. Since 1968, the increasing numbers of sinkhole incident have been reported in Kuala Lumpur and the vicinity areas. As the results, it poses a serious threat for human lives, assets and structure especially in the capital city of Malaysia. Therefore, a Sinkhole Hazard Model (SHM) was generated with integration of GIS framework by applying Analytical Hierarchical Process (AHP) technique in order to produced sinkhole susceptibility hazard map for the particular area. Five consecutive parameters for main criteria each categorized by five sub classes were selected for this research which is Lithology (LT), Groundwater Level Decline (WLD), Soil Type (ST), Land Use (LU) and Proximity to Groundwater Wells (PG). A set of relative weights were assigned to each inducing factor and computed through pairwise comparison matrix derived from expert judgment. Lithology and Groundwater Level Decline has been identified gives the highest impact to the sinkhole development. A sinkhole susceptibility hazard zones was classified into five prone areas namely very low, low, moderate, high and very high hazard. The results obtained were validated with thirty three (33) previous sinkhole inventory data. This evaluation shows that the model indicates 64 % and 21 % of the sinkhole events fall within high and very high hazard zones respectively. Based on this outcome, it clearly represents that AHP approach is useful to predict natural disaster such as sinkhole hazard.

Ultradispersed Cobalt Ferrite Nanoparticles Assembled in Graphene Aerogel for Continuous Photo-Fenton Reaction and Enhanced Lithium Storage Performance

PubMed Central

Qiu, Bocheng; Deng, Yuanxin; Du, Mengmeng; Xing, Mingyang; Zhang, Jinlong

2016-01-01

The Photo-Fenton reaction is an advanced technology to eliminate organic pollutants in environmental chemistry. Moreover, the conversion rate of Fe3+/Fe2+ and utilization rate of H2O2 are significant factors in Photo-Fenton reaction. In this work, we reported three dimensional (3D) hierarchical cobalt ferrite/graphene aerogels (CoFe2O4/GAs) composites by the in situ growing CoFe2O4 crystal seeds on the graphene oxide (GO) followed by the hydrothermal process. The resulting CoFe2O4/GAs composites demonstrated 3D hierarchical pore structure with mesopores (14~18 nm), macropores (50~125 nm), and a remarkable surface area (177.8 m2 g−1). These properties endowed this hybrid with the high and recyclable Photo-Fenton activity for methyl orange pollutant degradation. More importantly, the CoFe2O4/GAs composites can keep high Photo-Fenton activity in a wide pH. Besides, the CoFe2O4/GAs composites also exhibited excellent cyclic performance and good rate capability. The 3D framework can not only effectively prevent the volume expansion and aggregation of CoFe2O4 nanoparticles during the charge/discharge processes for Lithium-ion batteries (LIBs), but also shorten lithium ions and electron diffusion length in 3D pathways. These results indicated a broaden application prospect of 3D-graphene based hybrids in wastewater treatment and energy storage. PMID:27373343

Mechanosensitive Channels: Insights from Continuum-Based Simulations

PubMed Central

Tang, Yuye; Yoo, Jejoong; Yethiraj, Arun; Cui, Qiang; Chen, Xi

2009-01-01

Mechanotransduction plays an important role in regulating cell functions and it is an active topic of research in biophysics. Despite recent advances in experimental and numerical techniques, the intrinsic multiscale nature imposes tremendous challenges for revealing the working mechanisms of mechanosensitive channels. Recently, a continuum-mechanics based hierarchical modeling and simulation framework has been established and applied to study the mechanical responses and gating behaviors of a prototypical mechanosensitive channel, the mechanosensitive channel of large conductance (MscL) in bacteria Escherichia coli (E. coli), from which several putative gating mechanisms have been tested and new insights deduced. This article reviews these latest findings using the continuum mechanics framework and suggests possible improvements for future simulation studies. This computationally efficient and versatile continuum-mechanics based protocol is poised to make contributions to the study of a variety of mechanobiology problems. PMID:18787764

An Efficient Framework for Development of Task-Oriented Dialog Systems in a Smart Home Environment.

PubMed

Park, Youngmin; Kang, Sangwoo; Seo, Jungyun

2018-05-16

In recent times, with the increasing interest in conversational agents for smart homes, task-oriented dialog systems are being actively researched. However, most of these studies are focused on the individual modules of such a system, and there is an evident lack of research on a dialog framework that can integrate and manage the entire dialog system. Therefore, in this study, we propose a framework that enables the user to effectively develop an intelligent dialog system. The proposed framework ontologically expresses the knowledge required for the task-oriented dialog system's process and can build a dialog system by editing the dialog knowledge. In addition, the framework provides a module router that can indirectly run externally developed modules. Further, it enables a more intelligent conversation by providing a hierarchical argument structure (HAS) to manage the various argument representations included in natural language sentences. To verify the practicality of the framework, an experiment was conducted in which developers without any previous experience in developing a dialog system developed task-oriented dialog systems using the proposed framework. The experimental results show that even beginner dialog system developers can develop a high-level task-oriented dialog system.

An Efficient Framework for Development of Task-Oriented Dialog Systems in a Smart Home Environment

PubMed Central

Park, Youngmin; Kang, Sangwoo; Seo, Jungyun

2018-01-01

In recent times, with the increasing interest in conversational agents for smart homes, task-oriented dialog systems are being actively researched. However, most of these studies are focused on the individual modules of such a system, and there is an evident lack of research on a dialog framework that can integrate and manage the entire dialog system. Therefore, in this study, we propose a framework that enables the user to effectively develop an intelligent dialog system. The proposed framework ontologically expresses the knowledge required for the task-oriented dialog system’s process and can build a dialog system by editing the dialog knowledge. In addition, the framework provides a module router that can indirectly run externally developed modules. Further, it enables a more intelligent conversation by providing a hierarchical argument structure (HAS) to manage the various argument representations included in natural language sentences. To verify the practicality of the framework, an experiment was conducted in which developers without any previous experience in developing a dialog system developed task-oriented dialog systems using the proposed framework. The experimental results show that even beginner dialog system developers can develop a high-level task-oriented dialog system. PMID:29772668

A hierarchical model for estimating density in camera-trap studies

USGS Publications Warehouse

Royle, J. Andrew; Nichols, James D.; Karanth, K.Ullas; Gopalaswamy, Arjun M.

2009-01-01

Estimating animal density using capture–recapture data from arrays of detection devices such as camera traps has been problematic due to the movement of individuals and heterogeneity in capture probability among them induced by differential exposure to trapping.We develop a spatial capture–recapture model for estimating density from camera-trapping data which contains explicit models for the spatial point process governing the distribution of individuals and their exposure to and detection by traps.We adopt a Bayesian approach to analysis of the hierarchical model using the technique of data augmentation.The model is applied to photographic capture–recapture data on tigers Panthera tigris in Nagarahole reserve, India. Using this model, we estimate the density of tigers to be 14·3 animals per 100 km2 during 2004.Synthesis and applications. Our modelling framework largely overcomes several weaknesses in conventional approaches to the estimation of animal density from trap arrays. It effectively deals with key problems such as individual heterogeneity in capture probabilities, movement of traps, presence of potential ‘holes’ in the array and ad hoc estimation of sample area. The formulation, thus, greatly enhances flexibility in the conduct of field surveys as well as in the analysis of data, from studies that may involve physical, photographic or DNA-based ‘captures’ of individual animals.

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).

An Extensible Processing Framework for Eddy-covariance Data

NASA Astrophysics Data System (ADS)

Durden, D.; Fox, A. M.; Metzger, S.; Sturtevant, C.; Durden, N. P.; Luo, H.

2016-12-01

The evolution of large data collecting networks has not only led to an increase of available information, but also in the complexity of analyzing the observations. Timely dissemination of readily usable data products necessitates a streaming processing framework that is both automatable and flexible. Tower networks, such as ICOS, Ameriflux, and NEON, exemplify this issue by requiring large amounts of data to be processed from dispersed measurement sites. Eddy-covariance data from across the NEON network are expected to amount to 100 Gigabytes per day. The complexity of the algorithmic processing necessary to produce high-quality data products together with the continued development of new analysis techniques led to the development of a modular R-package, eddy4R. This allows algorithms provided by NEON and the larger community to be deployed in streaming processing, and to be used by community members alike. In order to control the processing environment, provide a proficient parallel processing structure, and certify dependencies are available during processing, we chose Docker as our "Development and Operations" (DevOps) platform. The Docker framework allows our processing algorithms to be developed, maintained and deployed at scale. Additionally, the eddy4R-Docker framework fosters community use and extensibility via pre-built Docker images and the Github distributed version control system. The capability to process large data sets is reliant upon efficient input and output of data, data compressibility to reduce compute resource loads, and the ability to easily package metadata. The Hierarchical Data Format (HDF5) is a file format that can meet these needs. A NEON standard HDF5 file structure and metadata attributes allow users to explore larger data sets in an intuitive "directory-like" structure adopting the NEON data product naming conventions.

Hierarchical competitions subserving multi-attribute choice

PubMed Central

Hunt, Laurence T; Dolan, Raymond J; Behrens, Timothy EJ

2015-01-01

Valuation is a key tenet of decision neuroscience, where it is generally assumed that different attributes of competing options are assimilated into unitary values. Such values are central to current neural models of choice. By contrast, psychological studies emphasize complex interactions between choice and valuation. Principles of neuronal selection also suggest competitive inhibition may occur in early valuation stages, before option selection. Here, we show behavior in multi-attribute choice is best explained by a model involving competition at multiple levels of representation. This hierarchical model also explains neural signals in human brain regions previously linked to valuation, including striatum, parietal and prefrontal cortex, where activity represents competition within-attribute, competition between attributes, and option selection. This multi-layered inhibition framework challenges the assumption that option values are computed before choice. Instead our results indicate a canonical competition mechanism throughout all stages of a processing hierarchy, not simply at a final choice stage. PMID:25306549

Hierarchical mesostructured titanium phosphonates with unusual uniform lines of macropores.

PubMed

Ma, Tian-Yi; Lin, Xiu-Zhen; Zhang, Xue-Jun; Yuan, Zhong-Yong

2011-04-01

Organic-inorganic hybrid materials of mesostructured titanium phosphonates with unusual uniform lines of macropores were synthesized by using bis(hexamethylenetriamine) penta(methylenephosphonic acid) (BHMTPMP) as the coupling molecule, through a one-pot hydrothermal process without any surfactant assistance. A wormhole-like mesostructure and many uniform parallel lines of macropores divided by solid ridges in the same direction were confirmed by N(2) sorption, SEM and TEM observations. This novel macropore architecture has never been observed in other metal phosphonate materials, which may be directly related to the structure nature of BHMTPMP with extra long alkyl chains. The structural characterization of FT-IR and MAS NMR revealed the integrity of organic groups inside the hybrid framework. The hybrid materials were also used as adsorbents for heavy metal ions and CO(2), in order to clarify the impacts of the organic contents and organic types on the physicochemical properties of the synthesized hierarchical macro-/mesoporous phosphonate materials.

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.

Knowledge Translation in Audiology

PubMed Central

Kothari, Anita; Bagatto, Marlene P.; Seewald, Richard; Miller, Linda T.; Scollie, Susan D.

2011-01-01

The impetus for evidence-based practice (EBP) has grown out of widespread concern with the quality, effectiveness (including cost-effectiveness), and efficiency of medical care received by the public. Although initially focused on medicine, EBP principles have been adopted by many of the health care professions and are often represented in practice through the development and use of clinical practice guidelines (CPGs). Audiology has been working on incorporating EBP principles into its mandate for professional practice since the mid-1990s. Despite widespread efforts to implement EBP and guidelines into audiology practice, gaps still exist between the best evidence based on research and what is being done in clinical practice. A collaborative dynamic and iterative integrated knowledge translation (KT) framework rather than a researcher-driven hierarchical approach to EBP and the development of CPGs has been shown to reduce the knowledge-to-clinical action gaps. This article provides a brief overview of EBP and CPGs, including a discussion of the barriers to implementing CPGs into clinical practice. It then offers a discussion of how an integrated KT process combined with a community of practice (CoP) might facilitate the development and dissemination of evidence for clinical audiology practice. Finally, a project that uses the knowledge-to-action (KTA) framework for the development of outcome measures in pediatric audiology is introduced. PMID:22194314

Governance of water resources in the phase of change: a case study of the implementation of the EU Water Framework Directive in Sweden.

PubMed

Hammer, Monica; Balfors, Berit; Mörtberg, Ulla; Petersson, Mona; Quin, Andrew

2011-03-01

In this article, focusing on the ongoing implementation of the EU Water Framework Directive, we analyze some of the opportunities and challenges for a sustainable governance of water resources from an ecosystem management perspective. In the face of uncertainty and change, the ecosystem approach as a holistic and integrated management framework is increasingly recognized. The ongoing implementation of the Water Framework Directive (WFD) could be viewed as a reorganization phase in the process of change in institutional arrangements and ecosystems. In this case study from the Northern Baltic Sea River Basin District, Sweden, we focus in particular on data and information management from a multi-level governance perspective from the local stakeholder to the River Basin level. We apply a document analysis, hydrological mapping, and GIS models to analyze some of the institutional framework created for the implementation of the WFD. The study underlines the importance of institutional arrangements that can handle variability of local situations and trade-offs between solutions and priorities on different hierarchical levels.

Towards a hierarchical optimization modeling framework for the evaluation and construction of spatially targeted incentive policies to promote green infrastructure (GI) amidst budgetary, compliance and GI-effectiveness uncertainties

EPA Science Inventory

Background:Bilevel optimization has been recognized as a 2-player Stackelberg game where players are represented as leaders and followers and each pursue their own set of objectives. Hierarchical optimization problems, which are a generalization of bilevel, are especially difficu...

Hierarchical subdivisions of the Columbia Plateau and Blue Mountains ecoregions, Oregon and Washington.

Treesearch

Sharon E. Clarke; Sandra A. Bryce

1997-01-01

This document presents two spatial scales of a hierarchical, ecoregional framework and provides a connection to both larger and smaller scale ecological classifications. The two spatial scales are subregions (1:250,000) and landscape-level ecoregions (1:100,000), or Level IV and Level V ecoregions. Level IV ecoregions were developed by the Environmental Protection...

In-situ observation for growth of hierarchical metal-organic frameworks and their self-sequestering mechanism for gas storage

NASA Astrophysics Data System (ADS)

Hyo Park, Jung; Min Choi, Kyung; Joon Jeon, Hyung; Jung Choi, Yoon; Ku Kang, Jeung

2015-07-01

Although structures with the single functional constructions and micropores were demonstrated to capture many different molecules such as carbon dioxide, methane, and hydrogen with high capacities at low temperatures, their feeble interactions still limit practical applications at room temperature. Herein, we report in-situ growth observation of hierarchical pores in pomegranate metal-organic frameworks (pmg-MOFs) and their self-sequestering storage mechanism, not observed for pristine MOFs. Direct observation of hierarchical pores inside the pmg-MOF was evident by in-situ growth X-ray measurements while self-sequestering storage mechanism was revealed by in-situ gas sorption X-ray analysis and molecular dynamics simulations. The results show that meso/macropores are created at the early stage of crystal growth and then enclosed by micropore crystalline shells, where hierarchical pores are networking under self-sequestering mechanism to give enhanced gas storage. This pmg-MOF gives higher CO2 (39%) and CH4 (14%) storage capacity than pristine MOF at room temperature, in addition to fast kinetics with robust capacity retention during gas sorption cycles, thus giving the clue to control dynamic behaviors of gas adsorption.

Availability-Based Importance Framework for Supplier Selection

DTIC Science & Technology

2015-04-30

IMA Journal of Management Math, 15(2), 161– 174. Chen, C . -T., Lin, C . -T., & Huang, S. -F. (2006). A fuzzy approach for supplier evaluation and...reliability modeling: Principles and applications. Hoboken, NJ: Wiley. Liao, C . -N., & Kao, H. -P. (2011). An integrated fuzzy TOPSIS and MCGP approach to...5307–5326. Wang, J. -W., Cheng, C . -H., & Huang, K.- C . (2009). Fuzzy hierarchical TOPSIS for supplier selection. Applied Soft Computing, 9(1), 377

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…

Modeling Menstrual Cycle Length and Variability at the Approach of Menopause Using Hierarchical Change Point Models

PubMed Central

Huang, Xiaobi; Elliott, Michael R.; Harlow, Siobán D.

2013-01-01

SUMMARY As women approach menopause, the patterns of their menstrual cycle lengths change. To study these changes, we need to jointly model both the mean and variability of cycle length. Our proposed model incorporates separate mean and variance change points for each woman and a hierarchical model to link them together, along with regression components to include predictors of menopausal onset such as age at menarche and parity. Additional complexity arises from the fact that the calendar data have substantial missingness due to hormone use, surgery, and failure to report. We integrate multiple imputation and time-to event modeling in a Bayesian estimation framework to deal with different forms of the missingness. Posterior predictive model checks are applied to evaluate the model fit. Our method successfully models patterns of women’s menstrual cycle trajectories throughout their late reproductive life and identifies change points for mean and variability of segment length, providing insight into the menopausal process. More generally, our model points the way toward increasing use of joint mean-variance models to predict health outcomes and better understand disease processes. PMID:24729638

Theoretical Framework for Integrating Distributed Energy Resources into Distribution Systems

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

Lian, Jianming; Wu, Di; Kalsi, Karanjit

This paper focuses on developing a novel theoretical framework for effective coordination and control of a large number of distributed energy resources in distribution systems in order to more reliably manage the future U.S. electric power grid under the high penetration of renewable generation. The proposed framework provides a systematic view of the overall structure of the future distribution systems along with the underlying information flow, functional organization, and operational procedures. It is characterized by the features of being open, flexible and interoperable with the potential to support dynamic system configuration. Under the proposed framework, the energy consumption of variousmore » DERs is coordinated and controlled in a hierarchical way by using market-based approaches. The real-time voltage control is simultaneously considered to complement the real power control in order to keep nodal voltages stable within acceptable ranges during real time. In addition, computational challenges associated with the proposed framework are also discussed with recommended practices.« less

Intelligent Integrated Health Management for a System of Systems

NASA Technical Reports Server (NTRS)

Smith, Harvey; Schmalzel, John; Figueroa, Fernando

2008-01-01

An intelligent integrated health management system (IIHMS) incorporates major improvements over prior such systems. The particular IIHMS is implemented for any system defined as a hierarchical distributed network of intelligent elements (HDNIE), comprising primarily: (1) an architecture (Figure 1), (2) intelligent elements, (3) a conceptual framework and taxonomy (Figure 2), and (4) and ontology that defines standards and protocols. Some definitions of terms are prerequisite to a further brief description of this innovation: A system-of-systems (SoS) is an engineering system that comprises multiple subsystems (e.g., a system of multiple possibly interacting flow subsystems that include pumps, valves, tanks, ducts, sensors, and the like); 'Intelligent' is used here in the sense of artificial intelligence. An intelligent element may be physical or virtual, it is network enabled, and it is able to manage data, information, and knowledge (DIaK) focused on determining its condition in the context of the entire SoS; As used here, 'health' signifies the functionality and/or structural integrity of an engineering system, subsystem, or process (leading to determination of the health of components); 'Process' can signify either a physical process in the usual sense of the word or an element into which functionally related sensors are grouped; 'Element' can signify a component (e.g., an actuator, a valve), a process, a controller, an actuator, a subsystem, or a system; The term Integrated System Health Management (ISHM) is used to describe a capability that focuses on determining the condition (health) of every element in a complex system (detect anomalies, diagnose causes, prognosis of future anomalies), and provide data, information, and knowledge (DIaK) not just data to control systems for safe and effective operation. A major novel aspect of the present development is the concept of intelligent integration. The purpose of intelligent integration, as defined and implemented in the present IIHMS, is to enable automated analysis of physical phenomena in imitation of human reasoning, including the use of qualitative methods. Intelligent integration is said to occur in a system in which all elements are intelligent and can acquire, maintain, and share knowledge and information. In the HDNIE of the present IIHMS, an SoS is represented as being operationally organized in a hierarchical-distributed format. The elements of the SoS are considered to be intelligent in that they determine their own conditions within an integrated scheme that involves consideration of data, information, knowledge bases, and methods that reside in all elements of the system. The conceptual framework of the HDNIE and the methodologies of implementing it enable the flow of information and knowledge among the elements so as to make possible the determination of the condition of each element. The necessary information and knowledge is made available to each affected element at the desired time, satisfying a need to prevent information overload while providing context-sensitive information at the proper level of detail. Provision of high-quality data is a central goal in designing this or any IIHMS. In pursuit of this goal, functionally related sensors are logically assigned to groups denoted processes. An aggregate of processes is considered to form a system. Alternatively or in addition to what has been said thus far, the HDNIE of this IIHMS can be regarded as consisting of a framework containing object models that encapsulate all elements of the system, their individual and relational knowledge bases, generic methods and procedures based on models of the applicable physics, and communication processes (Figure 2). The framework enables implementation of a paradigm inspired by how expert operators monitor the health of systems with the help of (1) DIaK from various sources, (2) software tools that assist in rapid visualization of the condition of the system, (3) analical software tools that assist in reasoning about the condition, (4) sharing of information via network communication hardware and software, and (5) software tools that aid in making decisions to remedy unacceptable conditions or improve performance.

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

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.

Biomimetic Hierarchical Assembly of Helical Supraparticles from Chiral Nanoparticles

DOE PAGES

Zhou, Yunlong; Marson, Ryan L.; van Anders, Greg; ...

2016-02-22

Chiroptical materials found in butterflies, beetles, stomatopod crustaceans, and other creatures are attributed to biocomposites with helical motifs and multiscale hierarchical organization. These structurally sophisticated materials self-assemble from primitive nanoscale building blocks, a process that is simpler and more energy efficient than many top-down methods currently used to produce similarly sized three-dimensional materials. In this paper, we report that molecular-scale chirality of a CdTe nanoparticle surface can be translated to nanoscale helical assemblies, leading to chiroptical activity in the visible electromagnetic range. Chiral CdTe nanoparticles coated with cysteine self-organize around Te cores to produce helical supraparticles. D-/L-Form of the aminomore » acid determines the dominant left/right helicity of the supraparticles. Coarse-grained molecular dynamics simulations with a helical pair-potential confirm the assembly mechanism and the origin of its enantioselectivity, providing a framework for engineering three-dimensional chiral materials by self-assembly. Finally, the helical supraparticles further self-organize into lamellar crystals with liquid crystalline order, demonstrating the possibility of hierarchical organization and with multiple structural motifs and length scales determined by molecular-scale asymmetry of nanoparticle interactions.« less

Cable deformation simulation and a hierarchical framework for Nb3Sn Rutherford cables

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

Arbelaez, D.; Prestemon, S. O.; Ferracin, P.

2009-09-13

Knowledge of the three-dimensional strain state induced in the superconducting filaments due to loads on Rutherford cables is essential to analyze the performance of Nb{sub 3}Sn magnets. Due to the large range of length scales involved, we develop a hierarchical computational scheme that includes models at both the cable and strand levels. At the Rutherford cable level, where the strands are treated as a homogeneous medium, a three-dimensional computational model is developed to determine the deformed shape of the cable that can subsequently be used to determine the strain state under specified loading conditions, which may be of thermal, magnetic,more » and mechanical origins. The results can then be transferred to the model at the strand/macro-filament level for rod restack process (RRP) strands, where the geometric details of the strand are included. This hierarchical scheme can be used to estimate the three-dimensional strain state in the conductor as well as to determine the effective properties of the strands and cables from the properties of individual components. Examples of the modeling results obtained for the orthotropic mechanical properties of the Rutherford cables are presented.« less

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

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

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.

Freestanding hierarchically porous carbon framework decorated by polyaniline as binder-free electrodes for high performance supercapacitors

NASA Astrophysics Data System (ADS)

Miao, Fujun; Shao, Changlu; Li, Xinghua; Wang, Kexin; Lu, Na; Liu, Yichun

2016-10-01

Freestanding hierarchically porous carbon electrode materials with favorable features of large surface areas, hierarchical porosity and continuous conducting pathways are very attractive for practical applications in electrochemical devices. Herein, three-dimensional freestanding hierarchically porous carbon (HPC) materials have been fabricated successfully mainly by the facile phase separation method. In order to further improve the energy storage ability, polyaniline (PANI) with high pseudocapacitance has been decorated on HPC through in situ chemical polymerization of aniline monomers. Benefiting from the synergistic effects between HPC and PANI, the resulting HPC/PANI composites as electrode materials present dramatic electrochemical performance with high specific capacitance up to 290 F g-1 at 0.5 A g-1 and good rate capability with ∼86% (248 F g-1) capacitance retention at 64 A g-1 of initial capacitance in three-electrode configuration. Moreover, the as-assembled symmetric supercapacitor based on HPC/PANI composites also demonstrates good capacitive properties with high energy density of 9.6 Wh kg-1 at 223 W kg-1 and long-term cycling stability with 78% capacitance retention after 10 000 cycles. Therefore, this work provides a new approach for designing high-performance electrodes with exceptional electrochemical performance, which are very promising for practical application in the energy storage field.

Hierarchical charge distribution controls self-assembly process of silk in vitro

NASA Astrophysics Data System (ADS)

Zhang, Yi; Zhang, Cencen; Liu, Lijie; Kaplan, David L.; Zhu, Hesun; Lu, Qiang

2015-12-01

Silk materials with different nanostructures have been developed without the understanding of the inherent transformation mechanism. Here we attempt to reveal the conversion road of the various nanostructures and determine the critical regulating factors. The regulating conversion processes influenced by a hierarchical charge distribution were investigated, showing different transformations between molecules, nanoparticles and nanofibers. Various repulsion and compressive forces existed among silk fibroin molecules and aggregates due to the exterior and interior distribution of charge, which further controlled their aggregating and deaggregating behaviors and finally formed nanofibers with different sizes. Synergistic action derived from molecular mobility and concentrations could also tune the assembly process and final nanostructures. It is suggested that the complicated silk fibroin assembly processes comply a same rule based on charge distribution, offering a promising way to develop silk-based materials with designed nanostructures.

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 ...

Abdominal multi-organ segmentation from CT images using conditional shape–location and unsupervised intensity priors

PubMed Central

Linguraru, Marius George; Hori, Masatoshi; Summers, Ronald M; Tomiyama, Noriyuki

2015-01-01

This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more accurate segmentation as well as easy adaptation to various imaging conditions in CT images, as observed in clinical practice. We propose a general framework of multi-organ segmentation which effectively incorporates interrelations among multiple organs and easily adapts to various imaging conditions without the need for supervised intensity information. The features of the framework are as follows: (1) A method for modeling conditional shape and location (shape–location) priors, which we call prediction-based priors, is developed to derive accurate priors specific to each subject, which enables the estimation of intensity priors without the need for supervised intensity information. (2) Organ correlation graph is introduced, which defines how the conditional priors are constructed and segmentation processes of multiple organs are executed. In our framework, predictor organs, whose segmentation is sufficiently accurate by using conventional single-organ segmentation methods, are pre-segmented, and the remaining organs are hierarchically segmented using conditional shape–location priors. The proposed framework was evaluated through the segmentation of eight abdominal organs (liver, spleen, left and right kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from 134 CT data from 86 patients obtained under six imaging conditions at two hospitals. The experimental results show the effectiveness of the proposed prediction-based priors and the applicability to various imaging conditions without the need for supervised intensity information. Average Dice coefficients for the liver, spleen, and kidneys were more than 92%, and were around 73% and 67% for the pancreas and gallbladder, respectively. PMID:26277022

Nature-Inspired 2D-Mosaic 3D-Gradient Mesoporous Framework: Bimetal Oxide Dual-Composite Strategy toward Ultrastable and High-Capacity Lithium Storage.

PubMed

Yu, Jia; Wang, Yanlei; Mou, Lihui; Fang, Daliang; Chen, Shimou; Zhang, Suojiang

2018-02-27

In allusion to traditional transition-metal oxide (TMO) anodes for lithium-ion batteries, which face severe volume variation and poor conductivity, herein a bimetal oxide dual-composite strategy based on two-dimensional (2D)-mosaic three-dimensional (3D)-gradient design is proposed. Inspired by natural mosaic dominance phenomena, Zn 1-x Co x O/ZnCo 2 O 4 2D-mosaic-hybrid mesoporous ultrathin nanosheets serve as building blocks to assemble into a 3D Zn-Co hierarchical framework. Moreover, a series of derivative frameworks with high evolution are controllably synthesized, based on which a facile one-pot synthesis process can be developed. From a component-composite perspective, both Zn 1-x Co x O and ZnCo 2 O 4 provide superior conductivity due to bimetal doping effect, which is verified by density functional theory calculations. From a structure-composite perspective, 2D-mosaic-hybrid mode gives rise to ladder-type buffering and electrochemical synergistic effect, thus realizing mutual stabilization and activation between the mosaic pair, especially for Zn 1-x Co x O with higher capacity yet higher expansion. Moreover, the inside-out Zn-Co concentration gradient in 3D framework and rich oxygen vacancies further greatly enhance Li storage capability and stability. As a result, a high reversible capacity (1010 mA h g -1 ) and areal capacity (1.48 mA h cm -2 ) are attained, while ultrastable cyclability is obtained during high-rate and long-term cycles, rending great potential of our 2D-mosaic 3D-gradient design together with facile synthesis.

Next generation of weather generators on web service framework

NASA Astrophysics Data System (ADS)

Chinnachodteeranun, R.; Hung, N. D.; Honda, K.; Ines, A. V. M.

2016-12-01

Weather generator is a statistical model that synthesizes possible realization of long-term historical weather in future. It generates several tens to hundreds of realizations stochastically based on statistical analysis. Realization is essential information as a crop modeling's input for simulating crop growth and yield. Moreover, they can be contributed to analyzing uncertainty of weather to crop development stage and to decision support system on e.g. water management and fertilizer management. Performing crop modeling requires multidisciplinary skills which limit the usage of weather generator only in a research group who developed it as well as a barrier for newcomers. To improve the procedures of performing weather generators as well as the methodology to acquire the realization in a standard way, we implemented a framework for providing weather generators as web services, which support service interoperability. Legacy weather generator programs were wrapped in the web service framework. The service interfaces were implemented based on an international standard that was Sensor Observation Service (SOS) defined by Open Geospatial Consortium (OGC). Clients can request realizations generated by the model through SOS Web service. Hierarchical data preparation processes required for weather generator are also implemented as web services and seamlessly wired. Analysts and applications can invoke services over a network easily. The services facilitate the development of agricultural applications and also reduce the workload of analysts on iterative data preparation and handle legacy weather generator program. This architectural design and implementation can be a prototype for constructing further services on top of interoperable sensor network system. This framework opens an opportunity for other sectors such as application developers and scientists in other fields to utilize weather generators.

Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors.

PubMed

Okada, Toshiyuki; Linguraru, Marius George; Hori, Masatoshi; Summers, Ronald M; Tomiyama, Noriyuki; Sato, Yoshinobu

2015-12-01

This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more accurate segmentation as well as easy adaptation to various imaging conditions in CT images, as observed in clinical practice. We propose a general framework of multi-organ segmentation which effectively incorporates interrelations among multiple organs and easily adapts to various imaging conditions without the need for supervised intensity information. The features of the framework are as follows: (1) A method for modeling conditional shape and location (shape-location) priors, which we call prediction-based priors, is developed to derive accurate priors specific to each subject, which enables the estimation of intensity priors without the need for supervised intensity information. (2) Organ correlation graph is introduced, which defines how the conditional priors are constructed and segmentation processes of multiple organs are executed. In our framework, predictor organs, whose segmentation is sufficiently accurate by using conventional single-organ segmentation methods, are pre-segmented, and the remaining organs are hierarchically segmented using conditional shape-location priors. The proposed framework was evaluated through the segmentation of eight abdominal organs (liver, spleen, left and right kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from 134 CT data from 86 patients obtained under six imaging conditions at two hospitals. The experimental results show the effectiveness of the proposed prediction-based priors and the applicability to various imaging conditions without the need for supervised intensity information. Average Dice coefficients for the liver, spleen, and kidneys were more than 92%, and were around 73% and 67% for the pancreas and gallbladder, respectively. Copyright © 2015 Elsevier B.V. All rights reserved.

Beyond Recall in Reading Comprehension: Five Key Planning Decisions.

ERIC Educational Resources Information Center

Sinatra, Richard; Annacone, Dominic

Over the years, teacher questions have consistently aimed at literal comprehension, indicating that teachers lack understanding of the reading-thinking-questioning hierarchy. Benjamin Bloom's "Cognitive Taxonomy" can serve as a hierarchical framework for the design of questions. Within this framework, a teacher can confront decision…

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.

A Scalable Cyberinfrastructure for Interactive Visualization of Terascale Microscopy Data

PubMed Central

Venkat, A.; Christensen, C.; Gyulassy, A.; Summa, B.; Federer, F.; Angelucci, A.; Pascucci, V.

2017-01-01

The goal of the recently emerged field of connectomics is to generate a wiring diagram of the brain at different scales. To identify brain circuitry, neuroscientists use specialized microscopes to perform multichannel imaging of labeled neurons at a very high resolution. CLARITY tissue clearing allows imaging labeled circuits through entire tissue blocks, without the need for tissue sectioning and section-to-section alignment. Imaging the large and complex non-human primate brain with sufficient resolution to identify and disambiguate between axons, in particular, produces massive data, creating great computational challenges to the study of neural circuits. Researchers require novel software capabilities for compiling, stitching, and visualizing large imagery. In this work, we detail the image acquisition process and a hierarchical streaming platform, ViSUS, that enables interactive visualization of these massive multi-volume datasets using a standard desktop computer. The ViSUS visualization framework has previously been shown to be suitable for 3D combustion simulation, climate simulation and visualization of large scale panoramic images. The platform is organized around a hierarchical cache oblivious data layout, called the IDX file format, which enables interactive visualization and exploration in ViSUS, scaling to the largest 3D images. In this paper we showcase the VISUS framework used in an interactive setting with the microscopy data. PMID:28638896

A Scalable Cyberinfrastructure for Interactive Visualization of Terascale Microscopy Data.

PubMed

Venkat, A; Christensen, C; Gyulassy, A; Summa, B; Federer, F; Angelucci, A; Pascucci, V

2016-08-01

The goal of the recently emerged field of connectomics is to generate a wiring diagram of the brain at different scales. To identify brain circuitry, neuroscientists use specialized microscopes to perform multichannel imaging of labeled neurons at a very high resolution. CLARITY tissue clearing allows imaging labeled circuits through entire tissue blocks, without the need for tissue sectioning and section-to-section alignment. Imaging the large and complex non-human primate brain with sufficient resolution to identify and disambiguate between axons, in particular, produces massive data, creating great computational challenges to the study of neural circuits. Researchers require novel software capabilities for compiling, stitching, and visualizing large imagery. In this work, we detail the image acquisition process and a hierarchical streaming platform, ViSUS, that enables interactive visualization of these massive multi-volume datasets using a standard desktop computer. The ViSUS visualization framework has previously been shown to be suitable for 3D combustion simulation, climate simulation and visualization of large scale panoramic images. The platform is organized around a hierarchical cache oblivious data layout, called the IDX file format, which enables interactive visualization and exploration in ViSUS, scaling to the largest 3D images. In this paper we showcase the VISUS framework used in an interactive setting with the microscopy data.

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

Liu, zhifeng; He, Chunyang; Zhou, Yuyu

Urbanization has transformed the world’s landscapes, resulting in a series of ecological and environmental problems. To assess urbanization impacts and improve sustainability, one of the first questions that we must address is: how much of the world’s land has been urbanized? Unfortunately, the estimates of the global urban land reported in the literature vary widely from less than 1% to 3% primarily because different definitions of urban land were used. To evade confusion, here we propose a hierarchical framework for representing and communicating the spatial extent of the world’s urbanized land at the global, regional, and more local levels. Themore » hierarchical framework consists of three spatially nested definitions: “urban area” that is delineated by administrative boundaries, “built-up area” that is dominated by artificial surfaces, and “impervious surface area” that is devoid of life. These are really three different measures of urbanization. In 2010, the global urban land was close to 3%, the global built-up area was 0.65%, and the global impervious surface area was 0.45%, of the word’s total land area (excluding Antarctica and Greenland). We argue that this hierarchy of urban land measures, in particular the ratios between them, can also facilitate better understanding the biophysical and socioeconomic processes and impacts of urbanization.« less

Fabrication of hierarchical porous hollow carbon spheres with few-layer graphene framework and high electrochemical activity for supercapacitor

NASA Astrophysics Data System (ADS)

Chen, Jing; Hong, Min; Chen, Jiafu; Hu, Tianzhao; Xu, Qun

2018-06-01

Porous amorphous carbons with large number of defects and dangling bonds indicate great potential application in energy storage due to high specific surface area and strong adsorption properties, but poor conductivity and pore connection limit their practical application. Here few-layer graphene framework with high electrical conductivity is embedded and meanwhile hierarchical porous structure is constructed in amorphous hollow carbon spheres (HCSs) by catalysis of Fe clusters of angstrom scale, which are loaded in the interior of crosslinked polystyrene via a novel method. These unique HCSs effectively integrate the inherent properties from two-dimensional sp2-hybridized carbon, porous amorphous carbon, hierarchical pore structure and thin shell, leading to high specific capacitance up to 561 F g-1 at a current density of 0.5 A g-1 as an electrode of supercapacitor with excellent recyclability, which is much higher than those of other reported porous carbon materials up to present.

Development of a robust space power system decision model

NASA Astrophysics Data System (ADS)

Chew, Gilbert; Pelaccio, Dennis G.; Jacobs, Mark; Stancati, Michael; Cataldo, Robert

2001-02-01

NASA continues to evaluate power systems to support human exploration of the Moon and Mars. The system(s) would address all power needs of surface bases and on-board power for space transfer vehicles. Prior studies have examined both solar and nuclear-based alternatives with respect to individual issues such as sizing or cost. What has not been addressed is a comprehensive look at the risks and benefits of the options that could serve as the analytical framework to support a system choice that best serves the needs of the exploration program. This paper describes the SAIC developed Space Power System Decision Model, which uses a formal Two-step Analytical Hierarchy Process (TAHP) methodology that is used in the decision-making process to clearly distinguish candidate power systems in terms of benefits, safety, and risk. TAHP is a decision making process based on the Analytical Hierarchy Process, which employs a hierarchic approach of structuring decision factors by weights, and relatively ranks system design options on a consistent basis. This decision process also includes a level of data gathering and organization that produces a consistent, well-documented assessment, from which the capability of each power system option to meet top-level goals can be prioritized. The model defined on this effort focuses on the comparative assessment candidate power system options for Mars surface application(s). This paper describes the principles of this approach, the assessment criteria and weighting procedures, and the tools to capture and assess the expert knowledge associated with space power system evaluation. .

Bayesian hierarchical model for large-scale covariance matrix estimation.

PubMed

Zhu, Dongxiao; Hero, Alfred O

2007-12-01

Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.

The Hierarchical Rater Model for Rated Test Items and Its Application to Large-Scale Educational Assessment Data.

ERIC Educational Resources Information Center

Patz, Richard J.; Junker, Brian W.; Johnson, Matthew S.; Mariano, Louis T.

2002-01-01

Discusses the hierarchical rater model (HRM) of R. Patz (1996) and shows how it can be used to scale examinees and items, model aspects of consensus among raters, and model individual rater severity and consistency effects. Also shows how the HRM fits into the generalizability theory framework. Compares the HRM to the conventional item response…

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Effects of hierarchical features on longevity of submerged superhydrophobic surfaces with parallel grooves

NASA Astrophysics Data System (ADS)

Hemeda, A. A.; Gad-el-Hak, M.; Tafreshi, H. Vahedi

2014-08-01

While the air-water interface over superhydrophobic surfaces decorated with hierarchical micro- or nanosized geometrical features have shown improved stability under elevated pressures, their underwater longevity—-the time that it takes for the surface to transition to the Wenzel state—-has not been studied. The current work is devised to study the effects of such hierarchical features on the longevity of superhydrophobic surfaces. For the sake of simplicity, our study is limited to superhydrophobic surfaces composed of parallel grooves with side fins. The effects of fins on the critical pressure—-the pressure at which the surface starts transitioning to the Wenzel state—-and longevity are predicted using a mathematical approach based on the balance of forces across the air-water interface. Our results quantitatively demonstrate that the addition of hierarchical fins significantly improves the mechanical stability of the air-water interface, due to the high advancing contact angles that can be achieved when an interface comes in contact with the fins sharp corners. For longevity on the contrary, the hierarchical fins were only effective at hydrostatic pressures below the critical pressure of the original smooth-walled groove. Our results indicate that increasing the length of the fins decreases the critical pressure of a submerged superhydrophobic groove but increases its longevity. Increasing the thickness of the fins can improve both the critical pressure and longevity of a submerged groove. The mathematical framework presented in this paper can be used to custom-design superhydrophobic surfaces for different applications.

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

A Framework for Monitoring Progress Using Summary Measures of Health.

PubMed

Madans, Jennifer H; Weeks, Julie D

2016-10-01

Initiatives designed to monitor health typically incorporate numerous specific measures of health and the health system to assess improvements, or lack thereof, for policy and program purposes. The addition of summary measures provides overarching information which is essential for determining whether the goals of such initiatives are met. Summary measures are identified that relate to the individual indicators but that also reflect movement in the various parts of the system. A hierarchical framework that is conceptually consistent and which utilizes a succinct number of summary measures incorporating indicators of functioning and participation is proposed. While a large set of individual indicators can be useful for monitoring progress, these individual indicators do not provide an overall evaluation of health, defined broadly, at the population level. A hierarchical framework consisting of summary measures is important for monitoring the success of health improvement initiatives. © The Author(s) 2016.

Bayesian Mass Estimates of the Milky Way: Including Measurement Uncertainties with Hierarchical Bayes

NASA Astrophysics Data System (ADS)

Eadie, Gwendolyn M.; Springford, Aaron; Harris, William E.

2017-02-01

We present a hierarchical Bayesian method for estimating the total mass and mass profile of the Milky Way Galaxy. The new hierarchical Bayesian approach further improves the framework presented by Eadie et al. and Eadie and Harris and builds upon the preliminary reports by Eadie et al. The method uses a distribution function f({ E },L) to model the Galaxy and kinematic data from satellite objects, such as globular clusters (GCs), to trace the Galaxy’s gravitational potential. A major advantage of the method is that it not only includes complete and incomplete data simultaneously in the analysis, but also incorporates measurement uncertainties in a coherent and meaningful way. We first test the hierarchical Bayesian framework, which includes measurement uncertainties, using the same data and power-law model assumed in Eadie and Harris and find the results are similar but more strongly constrained. Next, we take advantage of the new statistical framework and incorporate all possible GC data, finding a cumulative mass profile with Bayesian credible regions. This profile implies a mass within 125 kpc of 4.8× {10}11{M}⊙ with a 95% Bayesian credible region of (4.0{--}5.8)× {10}11{M}⊙ . Our results also provide estimates of the true specific energies of all the GCs. By comparing these estimated energies to the measured energies of GCs with complete velocity measurements, we observe that (the few) remote tracers with complete measurements may play a large role in determining a total mass estimate of the Galaxy. Thus, our study stresses the need for more remote tracers with complete velocity measurements.

Integrating data from multiple sources for insights into demographic processes: Simulation studies and proof of concept for hierarchical change-in-ratio models.

PubMed

Nilsen, Erlend B; Strand, Olav

2018-01-01

We developed a model for estimating demographic rates and population abundance based on multiple data sets revealing information about population age- and sex structure. Such models have previously been described in the literature as change-in-ratio models, but we extend the applicability of the models by i) using time series data allowing the full temporal dynamics to be modelled, by ii) casting the model in an explicit hierarchical modelling framework, and by iii) estimating parameters based on Bayesian inference. Based on sensitivity analyses we conclude that the approach developed here is able to obtain estimates of demographic rate with high precision whenever unbiased data of population structure are available. Our simulations revealed that this was true also when data on population abundance are not available or not included in the modelling framework. Nevertheless, when data on population structure are biased due to different observability of different age- and sex categories this will affect estimates of all demographic rates. Estimates of population size is particularly sensitive to such biases, whereas demographic rates can be relatively precisely estimated even with biased observation data as long as the bias is not severe. We then use the models to estimate demographic rates and population abundance for two Norwegian reindeer (Rangifer tarandus) populations where age-sex data were available for all harvested animals, and where population structure surveys were carried out in early summer (after calving) and late fall (after hunting season), and population size is counted in winter. We found that demographic rates were similar regardless whether we include population count data in the modelling, but that the estimated population size is affected by this decision. This suggest that monitoring programs that focus on population age- and sex structure will benefit from collecting additional data that allow estimation of observability for different age- and sex classes. In addition, our sensitivity analysis suggests that focusing monitoring towards changes in demographic rates might be more feasible than monitoring abundance in many situations where data on population age- and sex structure can be collected.

Bayesian Variable Selection for Hierarchical Gene-Environment and Gene-Gene Interactions

PubMed Central

Liu, Changlu; Ma, Jianzhong; Amos, Christopher I.

2014-01-01

We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions and gene by environment interactions in the same model. Our approach incorporates the natural hierarchical structure between the main effects and interaction effects into a mixture model, such that our methods tend to remove the irrelevant interaction effects more effectively, resulting in more robust and parsimonious models. We consider both strong and weak hierarchical models. For a strong hierarchical model, both of the main effects between interacting factors must be present for the interactions to be considered in the model development, while for a weak hierarchical model, only one of the two main effects is required to be present for the interaction to be evaluated. Our simulation results show that the proposed strong and weak hierarchical mixture models work well in controlling false positive rates and provide a powerful approach for identifying the predisposing effects and interactions in gene-environment interaction studies, in comparison with the naive model that does not impose this hierarchical constraint in most of the scenarios simulated. We illustrated our approach using data for lung cancer and cutaneous melanoma. PMID:25154630

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.

Hierarchical patch-based co-registration of differently stained histopathology slides

NASA Astrophysics Data System (ADS)

Yigitsoy, Mehmet; Schmidt, Günter

2017-03-01

Over the past decades, digital pathology has emerged as an alternative way of looking at the tissue at subcellular level. It enables multiplexed analysis of different cell types at micron level. Information about cell types can be extracted by staining sections of a tissue block using different markers. However, robust fusion of structural and functional information from different stains is necessary for reproducible multiplexed analysis. Such a fusion can be obtained via image co-registration by establishing spatial correspondences between tissue sections. Spatial correspondences can then be used to transfer various statistics about cell types between sections. However, the multi-modal nature of images and sparse distribution of interesting cell types pose several challenges for the registration of differently stained tissue sections. In this work, we propose a co-registration framework that efficiently addresses such challenges. We present a hierarchical patch-based registration of intensity normalized tissue sections. Preliminary experiments demonstrate the potential of the proposed technique for the fusion of multi-modal information from differently stained digital histopathology sections.

Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

USGS Publications Warehouse

Wilson, T.L.; Odei, J.B.; Hooten, M.B.; Edwards, T.C.

2010-01-01

Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.

Evaluation of the scientific underpinnings for identifying ...

EPA Pesticide Factsheets

A major challenge in chemical risk assessment is extrapolation of toxicity data from tested to untested species. Successful cross-species extrapolation involves understanding similarities and differences in toxicokinetic and toxicodynamic processes among species. Herein we consider the toxicodynamic challenge, and propose a hierarchal framework, based on the adverse outcome pathway (AOP) concept, to transparently and systematically assess cross-species conservation of biological pathways that could be perturbed by toxic chemicals. The approach features consideration of computational, in vitro and in vivo evidence to assess molecular initiating and intermediate key events of an AOP in a systematic, comparative manner. To demonstrate practical application of the framework, we consider an assessment question arising from the legislatively-mandated USEPA endocrine disruptor screening program, which involves the degree to which data generated using mammalian systems can be translated to non-mammalian species. Specifically, there is a need to define cross-species conservation of pathways controlled by activation of estrogen receptor-á (ERá), as a basis for using mammalian (primarily human) high-throughput (HTP) in vitro data to prioritize subsequent testing to assess human health and ecological risks of estrogenic chemicals. The initial phase of our analysis revealed good structural conservation the ERá across vertebrate species in terms of amino acid sequence

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.

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.

Distinct Contributions of the Magnocellular and Parvocellular Visual Streams to Perceptual Selection

PubMed Central

Denison, Rachel N.; Silver, Michael A.

2014-01-01

During binocular rivalry, conflicting images presented to the two eyes compete for perceptual dominance, but the neural basis of this competition is disputed. In interocular switch (IOS) rivalry, rival images periodically exchanged between the two eyes generate one of two types of perceptual alternation: 1) a fast, regular alternation between the images that is time-locked to the stimulus switches and has been proposed to arise from competition at lower levels of the visual processing hierarchy, or 2) a slow, irregular alternation spanning multiple stimulus switches that has been associated with higher levels of the visual system. The existence of these two types of perceptual alternation has been influential in establishing the view that rivalry may be resolved at multiple hierarchical levels of the visual system. We varied the spatial, temporal, and luminance properties of IOS rivalry gratings and found, instead, an association between fast, regular perceptual alternations and processing by the magnocellular stream and between slow, irregular alternations and processing by the parvocellular stream. The magnocellular and parvocellular streams are two early visual pathways that are specialized for the processing of motion and form, respectively. These results provide a new framework for understanding the neural substrates of binocular rivalry that emphasizes the importance of parallel visual processing streams, and not only hierarchical organization, in the perceptual resolution of ambiguities in the visual environment. PMID:21861685

Active contour-based visual tracking by integrating colors, shapes, and motions.

PubMed

Hu, Weiming; Zhou, Xue; Li, Wei; Luo, Wenhan; Zhang, Xiaoqin; Maybank, Stephen

2013-05-01

In this paper, we present a framework for active contour-based visual tracking using level sets. The main components of our framework include contour-based tracking initialization, color-based contour evolution, adaptive shape-based contour evolution for non-periodic motions, dynamic shape-based contour evolution for periodic motions, and the handling of abrupt motions. For the initialization of contour-based tracking, we develop an optical flow-based algorithm for automatically initializing contours at the first frame. For the color-based contour evolution, Markov random field theory is used to measure correlations between values of neighboring pixels for posterior probability estimation. For adaptive shape-based contour evolution, the global shape information and the local color information are combined to hierarchically evolve the contour, and a flexible shape updating model is constructed. For the dynamic shape-based contour evolution, a shape mode transition matrix is learnt to characterize the temporal correlations of object shapes. For the handling of abrupt motions, particle swarm optimization is adopted to capture the global motion which is applied to the contour in the current frame to produce an initial contour in the next frame.

multi-dice: r package for comparative population genomic inference under hierarchical co-demographic models of independent single-population size changes.

PubMed

Xue, Alexander T; Hickerson, Michael J

2017-11-01

Population genetic data from multiple taxa can address comparative phylogeographic questions about community-scale response to environmental shifts, and a useful strategy to this end is to employ hierarchical co-demographic models that directly test multi-taxa hypotheses within a single, unified analysis. This approach has been applied to classical phylogeographic data sets such as mitochondrial barcodes as well as reduced-genome polymorphism data sets that can yield 10,000s of SNPs, produced by emergent technologies such as RAD-seq and GBS. A strategy for the latter had been accomplished by adapting the site frequency spectrum to a novel summarization of population genomic data across multiple taxa called the aggregate site frequency spectrum (aSFS), which potentially can be deployed under various inferential frameworks including approximate Bayesian computation, random forest and composite likelihood optimization. Here, we introduce the r package multi-dice, a wrapper program that exploits existing simulation software for flexible execution of hierarchical model-based inference using the aSFS, which is derived from reduced genome data, as well as mitochondrial data. We validate several novel software features such as applying alternative inferential frameworks, enforcing a minimal threshold of time surrounding co-demographic pulses and specifying flexible hyperprior distributions. In sum, multi-dice provides comparative analysis within the familiar R environment while allowing a high degree of user customization, and will thus serve as a tool for comparative phylogeography and population genomics. © 2017 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

Organizational culture in an academic health center: an exploratory study using a competing values framework.

PubMed

Ovseiko, Pavel V; Buchan, Alastair M

2012-06-01

Implementing cultural change and aligning organizational cultures could enhance innovation, quality, safety, and job satisfaction. The authors conducted this mixed-methods study to assess academic physician-scientists' perceptions of the current and preferred future organizational culture at a university medical school and its partner health system. In October 2010, the authors surveyed academic physicians and scientists jointly employed by the University of Oxford and its local, major partner health system. The survey included the U.S. Veterans Affairs Administration's 14-item Competing Values Framework instrument and two extra items prompting respondents to identify their substantive employer and to provide any additional open-ended comments. Of 436 academic physicians and scientists, 170 (39%) responded. Of these, 69 (41%) provided open-ended comments. Dominant hierarchical culture, moderate rational and team cultures, and underdeveloped entrepreneurial culture characterized the health system culture profile. The university profile was more balanced, with strong rational and entrepreneurial cultures, and moderate-to-strong hierarchical and team cultures. The preferred future culture (within five years) would emphasize team and entrepreneurial cultures and-to a lesser degree-rational culture, and would deemphasize hierarchical culture. Whereas the university and the health system currently have distinct organizational cultures, academic physicians and scientists would prefer the same type of culture across the two organizations so that both could more successfully pursue the shared mission of academic medicine. Further research should explore strengthening the validity and reliability of the organizational culture instrument for academic medicine and building an evidence base of effective culture change strategies and interventions.

Molecular classification based on apomorphic amino acids (Arthropoda, Hexapoda): Integrative taxonomy in the era of phylogenomics.

PubMed

Wu, Hao-Yang; Wang, Yan-Hui; Xie, Qiang; Ke, Yun-Ling; Bu, Wen-Jun

2016-06-17

With the great development of sequencing technologies and systematic methods, our understanding of evolutionary relationships at deeper levels within the tree of life has greatly improved over the last decade. However, the current taxonomic methodology is insufficient to describe the growing levels of diversity in both a standardised and general way due to the limitations of using only morphological traits to describe clades. Herein, we propose the idea of a molecular classification based on hierarchical and discrete amino acid characters. Clades are classified based on the results of phylogenetic analyses and described using amino acids with group specificity in phylograms. Practices based on the recently published phylogenomic datasets of insects together with 15 de novo sequenced transcriptomes in this study demonstrate that such a methodology can accommodate various higher ranks of taxonomy. Such an approach has the advantage of describing organisms in a standard and discrete way within a phylogenetic framework, thereby facilitating the recognition of clades from the view of the whole lineage, as indicated by PhyloCode. By combining identification keys and phylogenies, the molecular classification based on hierarchical and discrete characters may greatly boost the progress of integrative taxonomy.

Molecular classification based on apomorphic amino acids (Arthropoda, Hexapoda): Integrative taxonomy in the era of phylogenomics

PubMed Central

Wu, Hao-Yang; Wang, Yan-Hui; Xie, Qiang; Ke, Yun-Ling; Bu, Wen-Jun

2016-01-01

With the great development of sequencing technologies and systematic methods, our understanding of evolutionary relationships at deeper levels within the tree of life has greatly improved over the last decade. However, the current taxonomic methodology is insufficient to describe the growing levels of diversity in both a standardised and general way due to the limitations of using only morphological traits to describe clades. Herein, we propose the idea of a molecular classification based on hierarchical and discrete amino acid characters. Clades are classified based on the results of phylogenetic analyses and described using amino acids with group specificity in phylograms. Practices based on the recently published phylogenomic datasets of insects together with 15 de novo sequenced transcriptomes in this study demonstrate that such a methodology can accommodate various higher ranks of taxonomy. Such an approach has the advantage of describing organisms in a standard and discrete way within a phylogenetic framework, thereby facilitating the recognition of clades from the view of the whole lineage, as indicated by PhyloCode. By combining identification keys and phylogenies, the molecular classification based on hierarchical and discrete characters may greatly boost the progress of integrative taxonomy. PMID:27312960

Hierarchically organized behavior and its neural foundations: A reinforcement-learning perspective

PubMed Central

Botvinick, Matthew M.; Niv, Yael; Barto, Andrew C.

2009-01-01

Research on human and animal behavior has long emphasized its hierarchical structure — the divisibility of ongoing behavior into discrete tasks, which are comprised of subtask sequences, which in turn are built of simple actions. The hierarchical structure of behavior has also been of enduring interest within neuroscience, where it has been widely considered to reflect prefrontal cortical functions. In this paper, we reexamine behavioral hierarchy and its neural substrates from the point of view of recent developments in computational reinforcement learning. Specifically, we consider a set of approaches known collectively as hierarchical reinforcement learning, which extend the reinforcement learning paradigm by allowing the learning agent to aggregate actions into reusable subroutines or skills. A close look at the components of hierarchical reinforcement learning suggests how they might map onto neural structures, in particular regions within the dorsolateral and orbital prefrontal cortex. It also suggests specific ways in which hierarchical reinforcement learning might provide a complement to existing psychological models of hierarchically structured behavior. A particularly important question that hierarchical reinforcement learning brings to the fore is that of how learning identifies new action routines that are likely to provide useful building blocks in solving a wide range of future problems. Here and at many other points, hierarchical reinforcement learning offers an appealing framework for investigating the computational and neural underpinnings of hierarchically structured behavior. PMID:18926527

Use of Bennett's Hierarchical Model in the Evaluation of the Extension Education Program for Cacao Farmers in the Northeast Region of the Dominican Republic. Summary of Research 54.

ERIC Educational Resources Information Center

De los Santos, Saturnino; Norland, Emmalou Van Tilburg

A study evaluated the cacao farmer training program in the Dominican Republic by testing hypothesized relationships among reactions, knowledge and skills, attitudes, aspirations, and some selected demographic characteristics of farmers who attended programs. Bennett's hierarchical model of program evaluation was used as the framework of the study.…

Metal-Organic Frameworks-Derived Hierarchical Co3O4 Structures as Efficient Sensing Materials for Acetone Detection.

PubMed

Zhang, Rui; Zhou, Tingting; Wang, Lili; Zhang, Tong

2018-03-21

Highly sensitive and stable gas sensors have attracted much attention because they are the key to innovations in the fields of environment, health, energy savings and security, etc. Sensing materials, which influence the practical sensing performance, are the crucial parts for gas sensors. Metal-organic frameworks (MOFs) are considered as alluring sensing materials for gas sensors because of the possession of high specific surface area, unique morphology, abundant metal sites, and functional linkers. Herein, four kinds of porous hierarchical Co 3 O 4 structures have been selectively controlled by optimizing the thermal decomposition (temperature, rate, and atmosphere) using ZIF-67 as precursor that was obtained from coprecipitation method with the co-assistance of cobalt salt and 2-methylimidazole in the solution of methanol. These hierarchical Co 3 O 4 structures, with controllable cross-linked channels, meso-/micropores, and adjustable surface area, are efficient catalytic materials for gas sensing. Benefits from structural advantages, core-shell, and porous core-shell Co 3 O 4 exhibit enhanced sensing performance compared to those of porous popcorn and nanoparticle Co 3 O 4 to acetone gas. These novel MOF-templated Co 3 O 4 hierarchical structures are so fantastic that they can be expected to be efficient sensing materials for development of low-temperature operating gas sensors.

MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches.

PubMed

Chowdhury, Rasheda Arman; Lina, Jean Marc; Kobayashi, Eliane; Grova, Christophe

2013-01-01

Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm(2) to 30 cm(2), whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered.

MEG Source Localization of Spatially Extended Generators of Epileptic Activity: Comparing Entropic and Hierarchical Bayesian Approaches

PubMed Central

Chowdhury, Rasheda Arman; Lina, Jean Marc; Kobayashi, Eliane; Grova, Christophe

2013-01-01

Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm2 to 30 cm2, whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered. PMID:23418485

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

PubMed

Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

2017-09-01

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

The traveling salesman problem: a hierarchical model.

PubMed

Graham, S M; Joshi, A; Pizlo, Z

2000-10-01

Our review of prior literature on spatial information processing in perception, attention, and memory indicates that these cognitive functions involve similar mechanisms based on a hierarchical architecture. The present study extends the application of hierarchical models to the area of problem solving. First, we report results of an experiment in which human subjects were tested on a Euclidean traveling salesman problem (TSP) with 6 to 30 cities. The subject's solutions were either optimal or near-optimal in length and were produced in a time that was, on average, a linear function of the number of cities. Next, the performance of the subjects is compared with that of five representative artificial intelligence and operations research algorithms, that produce approximate solutions for Euclidean problems. None of these algorithms was found to be an adequate psychological model. Finally, we present a new algorithm for solving the TSP, which is based on a hierarchical pyramid architecture. The performance of this new algorithm is quite similar to the performance of the subjects.

Hierarchical Porous Carbon Materials Derived from Sheep Manure for High-Capacity Supercapacitors.

PubMed

Zhang, Caiyun; Zhu, Xiaohong; Cao, Min; Li, Menglin; Li, Na; Lai, Liuqin; Zhu, Jiliang; Wei, Dacheng

2016-05-10

3 D capacitance: Hierarchical porous carbon-based electrode materials with a composite structure are prepared from a biomass waste by a facile carbonization and activation process without using any additional templates. Benefiting from the composite structure, the ions experience a variety of environments, which contribute significantly to the excellent electrochemical properties of supercapacitors. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Processing prosodic structure by adults with language-based learning disability.

PubMed

Bahl, Megha; Plante, Elena; Gerken, LouAnn

2009-01-01

Two experiments investigated the ability of adults with a history of language-based learning disability (hLLD) and their normal language (NL) peers to learn prosodic patterns of a novel language. Participants were exposed to stimuli from an artificial language and tested on items that required generalization of the stress patterns and the hierarchical principles of stress assignment that could be inferred from the input. In Study 1, the NL group successfully generalized the patterns of stress heard during familiarization, but failed to show generalization of the hierarchical principles. The hLLD group performed at chance for both types of generalization items. In Study 2, the intensity of stress elements was increased. The performance of the NL group improved whereas the hLLD groups' performance decreased on both types of generalization items. The results indicate that NL adults are able to successfully abstract the complex hierarchical rules of stress if the prosodic cues are made sufficiently salient, but this same task is difficult for adults with hLLD. The reader will be able to understand: (1) the difference in the ability of hLLD and NL adults to process stress assignment in an implicit learning context and (2) that typical adults can abstract complex hierarchical rules of stress assignment when provided with strong cues.

The hierarchical structure of self-reported impulsivity

PubMed Central

Kirby, Kris N.; Finch, Julia C.

2010-01-01

The hierarchical structure of 95 self-reported impulsivity items, along with delay-discount rates for money, was examined. A large sample of college students participated in the study (N = 407). Items represented every previously proposed dimension of self-reported impulsivity. Exploratory PCA yielded at least 7 interpretable components: Prepared/Careful, Impetuous, Divertible, Thrill and Risk Seeking, Happy-Go-Lucky, Impatiently Pleasure Seeking, and Reserved. Discount rates loaded on Impatiently Pleasure Seeking, and correlated with the impulsiveness and venturesomeness scales from the I7 (Eysenck, Pearson, Easting, & Allsopp, 1985). The hierarchical emergence of the components was explored, and we show how this hierarchical structure may help organize conflicting dimensions found in previous analyses. Finally, we argue that the discounting model (Ainslie, 1975) provides a qualitative framework for understanding the dimensions of impulsivity. PMID:20224803

A generalized linear factor model approach to the hierarchical framework for responses and response times.

PubMed

Molenaar, Dylan; Tuerlinckx, Francis; van der Maas, Han L J

2015-05-01

We show how the hierarchical model for responses and response times as developed by van der Linden (2007), Fox, Klein Entink, and van der Linden (2007), Klein Entink, Fox, and van der Linden (2009), and Glas and van der Linden (2010) can be simplified to a generalized linear factor model with only the mild restriction that there is no hierarchical model at the item side. This result is valuable as it enables all well-developed modelling tools and extensions that come with these methods. We show that the restriction we impose on the hierarchical model does not influence parameter recovery under realistic circumstances. In addition, we present two illustrative real data analyses to demonstrate the practical benefits of our approach. © 2014 The British Psychological Society.

Modular Autonomous Systems Technology Framework: A Distributed Solution for System Monitoring and Control

NASA Technical Reports Server (NTRS)

Badger, Julia M.; Claunch, Charles; Mathis, Frank

2017-01-01

The Modular Autonomous Systems Technology (MAST) framework is a tool for building distributed, hierarchical autonomous systems. Originally intended for the autonomous monitoring and control of spacecraft, this framework concept provides support for variable autonomy, assume-guarantee contracts, and efficient communication between subsystems and a centralized systems manager. MAST was developed at NASA's Johnson Space Center (JSC) and has been applied to an integrated spacecraft example scenario.

Analytic hierarchy process helps select site for limestone quarry expansion in Barbados.

PubMed

Dey, Prasanta Kumar; Ramcharan, Eugene K

2008-09-01

Site selection is a key activity for quarry expansion to support cement production, and is governed by factors such as resource availability, logistics, costs, and socio-economic-environmental factors. Adequate consideration of all the factors facilitates both industrial productivity and sustainable economic growth. This study illustrates the site selection process that was undertaken for the expansion of limestone quarry operations to support cement production in Barbados. First, alternate sites with adequate resources to support a 25-year development horizon were identified. Second, technical and socio-economic-environmental factors were then identified. Third, a database was developed for each site with respect to each factor. Fourth, a hierarchical model in analytic hierarchy process (AHP) framework was then developed. Fifth, the relative ranking of the alternate sites was then derived through pair wise comparison in all the levels and through subsequent synthesizing of the results across the hierarchy through computer software (Expert Choice). The study reveals that an integrated framework using the AHP can help select a site for the quarry expansion project in Barbados.

Neighbourhood-scale urban forest ecosystem classification.

PubMed

Steenberg, James W N; Millward, Andrew A; Duinker, Peter N; Nowak, David J; Robinson, Pamela J

2015-11-01

Urban forests are now recognized as essential components of sustainable cities, but there remains uncertainty concerning how to stratify and classify urban landscapes into units of ecological significance at spatial scales appropriate for management. Ecosystem classification is an approach that entails quantifying the social and ecological processes that shape ecosystem conditions into logical and relatively homogeneous management units, making the potential for ecosystem-based decision support available to urban planners. The purpose of this study is to develop and propose a framework for urban forest ecosystem classification (UFEC). The multifactor framework integrates 12 ecosystem components that characterize the biophysical landscape, built environment, and human population. This framework is then applied at the neighbourhood scale in Toronto, Canada, using hierarchical cluster analysis. The analysis used 27 spatially-explicit variables to quantify the ecosystem components in Toronto. Twelve ecosystem classes were identified in this UFEC application. Across the ecosystem classes, tree canopy cover was positively related to economic wealth, especially income. However, education levels and homeownership were occasionally inconsistent with the expected positive relationship with canopy cover. Open green space and stocking had variable relationships with economic wealth and were more closely related to population density, building intensity, and land use. The UFEC can provide ecosystem-based information for greening initiatives, tree planting, and the maintenance of the existing canopy. Moreover, its use has the potential to inform the prioritization of limited municipal resources according to ecological conditions and to concerns of social equity in the access to nature and distribution of ecosystem service supply. Copyright © 2015 Elsevier Ltd. All rights reserved.

Hierarchy of Information Processing in the Brain: A Novel 'Intrinsic Ignition' Framework.

PubMed

Deco, Gustavo; Kringelbach, Morten L

2017-06-07

A general theory of brain function has to be able to explain local and non-local network computations over space and time. We propose a new framework to capture the key principles of how local activity influences global computation, i.e., describing the propagation of information and thus the broadness of communication driven by local activity. More specifically, we consider the diversity in space (nodes or brain regions) over time using the concept of intrinsic ignition, which are naturally occurring intrinsic perturbations reflecting the capability of a given brain area to propagate neuronal activity to other regions in a given brain state. Characterizing the profile of intrinsic ignition for a given brain state provides insight into the precise nature of hierarchical information processing. Combining this data-driven method with a causal whole-brain computational model can provide novel insights into the imbalance of brain states found in neuropsychiatric disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Individual differences in attention influence perceptual decision making.

PubMed

Nunez, Michael D; Srinivasan, Ramesh; Vandekerckhove, Joachim

2015-01-01

Sequential sampling decision-making models have been successful in accounting for reaction time (RT) and accuracy data in two-alternative forced choice tasks. These models have been used to describe the behavior of populations of participants, and explanatory structures have been proposed to account for between individual variability in model parameters. In this study we show that individual differences in behavior from a novel perceptual decision making task can be attributed to (1) differences in evidence accumulation rates, (2) differences in variability of evidence accumulation within trials, and (3) differences in non-decision times across individuals. Using electroencephalography (EEG), we demonstrate that these differences in cognitive variables, in turn, can be explained by attentional differences as measured by phase-locking of steady-state visual evoked potential (SSVEP) responses to the signal and noise components of the visual stimulus. Parameters of a cognitive model (a diffusion model) were obtained from accuracy and RT distributions and related to phase-locking indices (PLIs) of SSVEPs with a single step in a hierarchical Bayesian framework. Participants who were able to suppress the SSVEP response to visual noise in high frequency bands were able to accumulate correct evidence faster and had shorter non-decision times (preprocessing or motor response times), leading to more accurate responses and faster response times. We show that the combination of cognitive modeling and neural data in a hierarchical Bayesian framework relates physiological processes to the cognitive processes of participants, and that a model with a new (out-of-sample) participant's neural data can predict that participant's behavior more accurately than models without physiological data.

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.

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.

Adding a landscape ecology perspective to conservation and management planning

Treesearch

Kathryn E. Freemark; John R. Probst; John B. Dunning; Salllie J. Hejl

1993-01-01

We briefly review concepts in landscape ecology and discuss their relevance to the conservation and management of neotropical migrant landbirds. We then integrate a landscape perspective into a spatially-hierarchical framework for conservation and management planning for neotropical migrant landbirds (and other biota). The framework outlines a comprehensive approach by...

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

Hansen, Jacob; Edgar, Thomas W.; Daily, Jeffrey A.

With an ever-evolving power grid, concerns regarding how to maintain system stability, efficiency, and reliability remain constant because of increasing uncertainties and decreasing rotating inertia. To alleviate some of these concerns, demand response represents a viable solution and is virtually an untapped resource in the current power grid. This work describes a hierarchical control framework that allows coordination between distributed energy resources and demand response. This control framework is composed of two control layers: a coordination layer that ensures aggregations of resources are coordinated to achieve system objectives and a device layer that controls individual resources to assure the predeterminedmore » power profile is tracked in real time. Large-scale simulations are executed to study the hierarchical control, requiring advancements in simulation capabilities. Technical advancements necessary to investigate and answer control interaction questions, including the Framework for Network Co-Simulation platform and Arion modeling capability, are detailed. Insights into the interdependencies of controls across a complex system and how they must be tuned, as well as validation of the effectiveness of the proposed control framework, are yielded using a large-scale integrated transmission system model coupled with multiple distribution systems.« less

The Ophidia framework: toward cloud-based data analytics for climate change

NASA Astrophysics Data System (ADS)

Fiore, Sandro; D'Anca, Alessandro; Elia, Donatello; Mancini, Marco; Mariello, Andrea; Mirto, Maria; Palazzo, Cosimo; Aloisio, Giovanni

2015-04-01

The Ophidia project is a research effort on big data analytics facing scientific data analysis challenges in the climate change domain. It provides parallel (server-side) data analysis, an internal storage model and a hierarchical data organization to manage large amount of multidimensional scientific data. The Ophidia analytics platform provides several MPI-based parallel operators to manipulate large datasets (data cubes) and array-based primitives to perform data analysis on large arrays of scientific data. The most relevant data analytics use cases implemented in national and international projects target fire danger prevention (OFIDIA), interactions between climate change and biodiversity (EUBrazilCC), climate indicators and remote data analysis (CLIP-C), sea situational awareness (TESSA), large scale data analytics on CMIP5 data in NetCDF format, Climate and Forecast (CF) convention compliant (ExArch). Two use cases regarding the EU FP7 EUBrazil Cloud Connect and the INTERREG OFIDIA projects will be presented during the talk. In the former case (EUBrazilCC) the Ophidia framework is being extended to integrate scalable VM-based solutions for the management of large volumes of scientific data (both climate and satellite data) in a cloud-based environment to study how climate change affects biodiversity. In the latter one (OFIDIA) the data analytics framework is being exploited to provide operational support regarding processing chains devoted to fire danger prevention. To tackle the project challenges, data analytics workflows consisting of about 130 operators perform, among the others, parallel data analysis, metadata management, virtual file system tasks, maps generation, rolling of datasets, import/export of datasets in NetCDF format. Finally, the entire Ophidia software stack has been deployed at CMCC on 24-nodes (16-cores/node) of the Athena HPC cluster. Moreover, a cloud-based release tested with OpenNebula is also available and running in the private cloud infrastructure of the CMCC Supercomputing Centre.

Ce-Doped NiFe-Layered Double Hydroxide Ultrathin Nanosheets/Nanocarbon Hierarchical Nanocomposite as an Efficient Oxygen Evolution Catalyst.

PubMed

Xu, Huajie; Wang, Bingkai; Shan, Changfu; Xi, Pinxian; Liu, Weisheng; Tang, Yu

2018-02-21

Developing convenient doping to build highly active oxygen evolution reaction (OER) electrocatalysts is a practical process for solving the energy crisis. Herein, a facile and low-cost in situ self-assembly strategy for preparing a Ce-doped NiFe-LDH nanosheets/nanocarbon (denoted as NiFeCe-LDH/CNT, LDH = layered double hydroxide and CNT = carbon nanotube) hierarchical nanocomposite is established for enhanced OER, in which the novel material provides its overall advantageous structural features, including high intrinsic catalytic activity, rich redox properties, high, flexible coordination number of Ce 3+ , and strongly coupled interface. Further experimental results indicate that doped Ce into NiFe-LDH/CNT nanoarrays brings about the reinforced specific surface area, electrochemical surface area, lattice defects, and the electron transport between the LDH nanolayered structure and the framework of CNTs. The effective synergy prompts the NiFeCe-LDH/CNT nanocomposite to possess superior OER electrocatalytic activity with a low onset potential (227 mV) and Tafel slope (33 mV dec -1 ), better than the most non-noble metal-based OER electrocatalysts reported. Therefore, the combination of the remarkable catalytic ability and the facile normal temperature synthesis conditions endows the Ce-doped LDH nanocomposite as a promising catalyst to expand the field of lanthanide-doped layered materials for efficient water-splitting electrocatalysis with scale-up potential.

The Role of Discrete Global Grid Systems in the Global Statistical Geospatial Framework

NASA Astrophysics Data System (ADS)

Purss, M. B. J.; Peterson, P.; Minchin, S. A.; Bermudez, L. E.

2016-12-01

The United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) has proposed the development of a Global Statistical Geospatial Framework (GSGF) as a mechanism for the establishment of common analytical systems that enable the integration of statistical and geospatial information. Conventional coordinate reference systems address the globe with a continuous field of points suitable for repeatable navigation and analytical geometry. While this continuous field is represented on a computer in a digitized and discrete fashion by tuples of fixed-precision floating point values, it is a non-trivial exercise to relate point observations spatially referenced in this way to areal coverages on the surface of the Earth. The GSGF states the need to move to gridded data delivery and the importance of using common geographies and geocoding. The challenges associated with meeting these goals are not new and there has been a significant effort within the geospatial community to develop nested gridding standards to tackle these issues over many years. These efforts have recently culminated in the development of a Discrete Global Grid Systems (DGGS) standard which has been developed under the auspices of Open Geospatial Consortium (OGC). DGGS provide a fixed areal based geospatial reference frame for the persistent location of measured Earth observations, feature interpretations, and modelled predictions. DGGS address the entire planet by partitioning it into a discrete hierarchical tessellation of progressively finer resolution cells, which are referenced by a unique index that facilitates rapid computation, query and analysis. The geometry and location of the cell is the principle aspect of a DGGS. Data integration, decomposition, and aggregation is optimised in the DGGS hierarchical structure and can be exploited for efficient multi-source data processing, storage, discovery, transmission, visualization, computation, analysis, and modelling. During the 6th Session of the UN-GGIM in August 2016 the role of DGGS in the context of the GSGF was formally acknowledged. This paper proposes to highlight the synergies and role of DGGS in the Global Statistical Geospatial Framework and to show examples of the use of DGGS to combine geospatial statistics with traditional geoscientific data.

Trade Services System Adaptation for Sustainable Development

NASA Astrophysics Data System (ADS)

Khrichenkov, A.; Shaufler, V.; Bannikova, L.

2017-11-01

Under market conditions, the trade services system in post-Soviet Russia, being one of the most important city infrastructures, loses its systematic and hierarchic consistency hence provoking the degradation of communicating transport systems and urban planning framework. This article describes the results of the research carried out to identify objects and object parameters that influence functioning of a locally significant trade services system. Based on the revealed consumer behaviour patterns, we propose methods to determine the optimal parameters of objects inside a locally significant trade services system.

Hierarchical Bayesian inference of the initial mass function in composite stellar populations

NASA Astrophysics Data System (ADS)

Dries, M.; Trager, S. C.; Koopmans, L. V. E.; Popping, G.; Somerville, R. S.

2018-03-01

The initial mass function (IMF) is a key ingredient in many studies of galaxy formation and evolution. Although the IMF is often assumed to be universal, there is continuing evidence that it is not universal. Spectroscopic studies that derive the IMF of the unresolved stellar populations of a galaxy often assume that this spectrum can be described by a single stellar population (SSP). To alleviate these limitations, in this paper we have developed a unique hierarchical Bayesian framework for modelling composite stellar populations (CSPs). Within this framework, we use a parametrized IMF prior to regulate a direct inference of the IMF. We use this new framework to determine the number of SSPs that is required to fit a set of realistic CSP mock spectra. The CSP mock spectra that we use are based on semi-analytic models and have an IMF that varies as a function of stellar velocity dispersion of the galaxy. Our results suggest that using a single SSP biases the determination of the IMF slope to a higher value than the true slope, although the trend with stellar velocity dispersion is overall recovered. If we include more SSPs in the fit, the Bayesian evidence increases significantly and the inferred IMF slopes of our mock spectra converge, within the errors, to their true values. Most of the bias is already removed by using two SSPs instead of one. We show that we can reconstruct the variable IMF of our mock spectra for signal-to-noise ratios exceeding ˜75.

Recognition of complex human behaviours using 3D imaging for intelligent surveillance applications

NASA Astrophysics Data System (ADS)

Yao, Bo; Lepley, Jason J.; Peall, Robert; Butler, Michael; Hagras, Hani

2016-10-01

We introduce a system that exploits 3-D imaging technology as an enabler for the robust recognition of the human form. We combine this with pose and feature recognition capabilities from which we can recognise high-level human behaviours. We propose a hierarchical methodology for the recognition of complex human behaviours, based on the identification of a set of atomic behaviours, individual and sequential poses (e.g. standing, sitting, walking, drinking and eating) that provides a framework from which we adopt time-based machine learning techniques to recognise complex behaviour patterns.

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.

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.

Localized lossless authentication watermark (LAW)

NASA Astrophysics Data System (ADS)

Celik, Mehmet U.; Sharma, Gaurav; Tekalp, A. Murat; Saber, Eli S.

2003-06-01

A novel framework is proposed for lossless authentication watermarking of images which allows authentication and recovery of original images without any distortions. This overcomes a significant limitation of traditional authentication watermarks that irreversibly alter image data in the process of watermarking and authenticate the watermarked image rather than the original. In particular, authenticity is verified before full reconstruction of the original image, whose integrity is inferred from the reversibility of the watermarking procedure. This reduces computational requirements in situations when either the verification step fails or the zero-distortion reconstruction is not required. A particular instantiation of the framework is implemented using a hierarchical authentication scheme and the lossless generalized-LSB data embedding mechanism. The resulting algorithm, called localized lossless authentication watermark (LAW), can localize tampered regions of the image; has a low embedding distortion, which can be removed entirely if necessary; and supports public/private key authentication and recovery options. The effectiveness of the framework and the instantiation is demonstrated through examples.

Understanding the distributed cognitive processes of intensive care patient discharge.

PubMed

Lin, Frances; Chaboyer, Wendy; Wallis, Marianne

2014-03-01

To better understand and identify vulnerabilities and risks in the ICU patient discharge process, which provides evidence for service improvement. Previous studies have identified that 'after hours' discharge and 'premature' discharge from ICU are associated with increased mortality. However, some of these studies have largely been retrospective reviews of various administrative databases, while others have focused on specific aspects of the process, which may miss crucial components of the discharge process. This is an ethnographic exploratory study. Distributed cognition and activity theory were used as theoretical frameworks. Ethnographic data collection techniques including informal interviews, direct observations and collecting existing documents were used. A total of 56 one-to-one interviews were conducted with 46 participants; 28 discharges were observed; and numerous documents were collected during a five-month period. A triangulated technique was used in both data collection and data analysis to ensure the research rigour. Under the guidance of activity theory and distributed cognition theoretical frameworks, five themes emerged: hierarchical power and authority, competing priorities, ineffective communication, failing to enact the organisational processes and working collaboratively to optimise the discharge process. Issues with teamwork, cognitive processes and team members' interaction with cognitive artefacts influenced the discharge process. Strategies to improve shared situational awareness are needed to improve teamwork, patient flow and resource efficiency. Tools need to be evaluated regularly to ensure their continuous usefulness. Health care professionals need to be aware of the impact of their competing priorities and ensure discharges occur in a timely manner. Activity theory and distributed cognition are useful theoretical frameworks to support healthcare organisational research. © 2013 John Wiley & Sons Ltd.

Hierarchic models for laminated plates. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Actis, Ricardo Luis

1991-01-01

Structural plates and shells are three-dimensional bodies, one dimension of which happens to be much smaller than the other two. Thus, the quality of a plate or shell model must be judged on the basis of how well its exact solution approximates the corresponding three-dimensional problem. Of course, the exact solution depends not only on the choice of the model but also on the topology, material properties, loading and constraints. The desired degree of approximation depends on the analyst's goals in performing the analysis. For these reasons models have to be chosen adaptively. Hierarchic sequences of models make adaptive selection of the model which is best suited for the purposes of a particular analysis possible. The principles governing the formulation of hierarchic models for laminated plates are presented. The essential features of the hierarchic models described models are: (1) the exact solutions corresponding to the hierarchic sequence of models converge to the exact solution of the corresponding problem of elasticity for a fixed laminate thickness; and (2) the exact solution of each model converges to the same limit as the exact solution of the corresponding problem of elasticity with respect to the laminate thickness approaching zero. The formulation is based on one parameter (beta) which characterizes the hierarchic sequence of models, and a set of constants whose influence was assessed by a numerical sensitivity study. The recommended selection of these constants results in the number of fields increasing by three for each increment in the power of beta. Numerical examples analyzed with the proposed sequence of models are included and good correlation with the reference solutions was found. Results were obtained for laminated strips (plates in cylindrical bending) and for square and rectangular plates with uniform loading and with homogeneous boundary conditions. Cross-ply and angle-ply laminates were evaluated and the results compared with those of MSC/PROBE. Hierarchic models make the computation of any engineering data possible to an arbitrary level of precision within the framework of the theory of elasticity.

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

Spogen, L.R.; Cleland, L.L.

An approach to the development of performance based regulations (PBR's) is described. Initially, a framework is constructed that consists of a function hierarchy and associated measures. The function at the top of the hierarchy is described in terms of societal objectives. Decomposition of this function into subordinate functions and their subsequent decompositions yield the function hierarchy. ''Bottom'' functions describe the roles of system components. When measures are identified for the performance of each function and means of aggregating performances to higher levels are established, the framework may be employed for developing PBR's. Consideration of system flexibility and performance uncertainty guidemore » in determining the hierarchical level at which regulations are formulated. Ease of testing compliance is also a factor. To show the viability of the approach, the framework developed by Lawrence Livermore Laboratory for the Nuclear Regulatory Commission for evaluation of material control systems at fixed facilities is presented.« less

Self-assembly of cobalt-centered metal organic framework and multiwalled carbon nanotubes hybrids as a highly active and corrosion-resistant bifunctional oxygen catalyst

NASA Astrophysics Data System (ADS)

Fang, Yiyun; Li, Xinzhe; Li, Feng; Lin, Xiaoqing; Tian, Min; Long, Xuefeng; An, Xingcai; Fu, Yan; Jin, Jun; Ma, Jiantai

2016-09-01

Metal organic frameworks (MOF) derived carbonaceous materials have emerged as promising bifunctional oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) catalysts for electrochemical energy conversion and storage. But previous attempts to overcome the poor electrical conductivity of MOFs hybrids involve a harsh high-template pyrolytic process to in situ form carbon, which suffer from extremely complex operation and inevitable carbon corrosion at high positive potentials when OER is operated. Herein, a self-assembly approach is presented to synthesize a non-precious metal-based, high active and strong durable Co-MOF@CNTs bifunctional catalyst for OER and ORR. CNTs not only improve the transportation of the electrons but also can sustain the harsh oxidative environment of OER without carbon corrosion. Meanwhile, the unique 3D hierarchical structure offers a large surface area and stable anchoring sites for active centers and CNTs, which enables the superior durability of hybrid. Moreover, a synergistic catalysis of Co(II), organic ligands and CNTs will enhance the bifunctional electrocatalytic performance. Impressively, the hybrid exhibits comparable OER and ORR catalytic activity to RuO2 and 20 wt% Pt/C catalysts and superior stability. This facile and versatile strategy to fabricating MOF-based hybrids may be extended to other electrode materials for fuel cell and water splitting applications.

An agent-based hydroeconomic model to evaluate water policies in Jordan

NASA Astrophysics Data System (ADS)

Yoon, J.; Gorelick, S.

2014-12-01

Modern water systems can be characterized by a complex network of institutional and private actors that represent competing sectors and interests. Identifying solutions to enhance water security in such systems calls for analysis that can adequately account for this level of complexity and interaction. Our work focuses on the development of a hierarchical, multi-agent, hydroeconomic model that attempts to realistically represent complex interactions between hydrologic and multi-faceted human systems. The model is applied to Jordan, one of the most water-poor countries in the world. In recent years, the water crisis in Jordan has escalated due to an ongoing drought and influx of refugees from regional conflicts. We adopt a modular approach in which biophysical modules simulate natural and engineering phenomena, and human modules represent behavior at multiple scales of decision making. The human modules employ agent-based modeling, in which agents act as autonomous decision makers at the transboundary, state, organizational, and user levels. A systematic nomenclature and conceptual framework is used to characterize model agents and modules. Concepts from the Unified Modeling Language (UML) are adopted to promote clear conceptualization of model classes and process sequencing, establishing a foundation for full deployment of the integrated model in a scalable object-oriented programming environment. Although the framework is applied to the Jordanian water context, it is generalizable to other regional human-natural freshwater supply systems.

Unified framework for information integration based on information geometry

PubMed Central

Oizumi, Masafumi; Amari, Shun-ichi

2016-01-01

Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major mathematical difficulties. First, overestimation occurs due to interdependence among influences if each influence is separately quantified in a part-based manner and then simply summed over. Second, it is difficult to isolate causal influences while avoiding noncausal confounding influences. To resolve these difficulties, we propose a theoretical framework based on information geometry for the quantification of multiple causal influences with a holistic approach. We derive a measure of integrated information, which is geometrically interpreted as the divergence between the actual probability distribution of a system and an approximated probability distribution where causal influences among elements are statistically disconnected. This framework provides intuitive geometric interpretations harmonizing various information theoretic measures in a unified manner, including mutual information, transfer entropy, stochastic interaction, and integrated information, each of which is characterized by how causal influences are disconnected. In addition to the mathematical assessment of consciousness, our framework should help to analyze causal relationships in complex systems in a complete and hierarchical manner. PMID:27930289

Geomechanical Modeling of Gas Hydrate Bearing Sediments

NASA Astrophysics Data System (ADS)

Sanchez, M. J.; Gai, X., Sr.

2015-12-01

This contribution focuses on an advance geomechanical model for methane hydrate-bearing soils based on concepts of elasto-plasticity for strain hardening/softening soils and incorporates bonding and damage effects. The core of the proposed model includes: a hierarchical single surface critical state framework, sub-loading concepts for modeling the plastic strains generally observed inside the yield surface and a hydrate enhancement factor to account for the cementing effects provided by the presence of hydrates in sediments. The proposed framework has been validated against recently published experiments involving both, synthetic and natural hydrate soils, as well as different sediments types (i.e., different hydrate saturations, and different hydrates morphologies) and confinement conditions. The performance of the model in these different case studies was very satisfactory.

Hierarchically-Driven Approach for Quantifying Fatigue Crack Initiation and Short Crack Growth Behavior in Aerospace Materials

DTIC Science & Technology

2016-08-31

crack initiation and SCG mechanisms (initiation and growth versus resistance). 2. Final summary Here, we present a hierarchical form of multiscale...prismatic faults in -Ti: A combined quantum mechanics /molecular mechanics study 2. Nano-indentation and slip transfer (critical in understanding crack...initiation) 3. An extended-finite element framework (XFEM) to study SCG mechanisms 4. Atomistic methods to develop a grain and twin boundaries database

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

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

PubMed

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

2017-01-01

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

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

PubMed

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

2017-07-19

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

How Misapplication of the Hydrologic Unit Framework ...

EPA Pesticide Factsheets

Hydrologic units provide a convenient nationwide set of geographic polygons based on an arbitrary subdivision of the drainage of land surface areas at several hierarchical levels. Half or more of these units, however, are not true watersheds as the official name of the framework, Watershed Boundary Dataset (WBD), implies. Hydrologic units and watersheds are commonly treated as synonymous, and this misuse and misunderstanding can have some serious consequences. We discuss some of the strengths and limitations of watersheds and hydrologic units as spatial frameworks. Using examples from the Northwest and Southeast U.S., we explain how the misuse of the hydrologic unit framework has affected the meaning of watersheds and can impair the understanding of the associations of spatial geographic phenomena relative to a potentially infinite number of points on streams due to their linear nature. Watersheds are a fundamental geographic unit used to study the effects of natural and anthropogenic characteristics on the quality and quantity of water. Most scientists and resource managers historically have been in agreement on the spatial meaning of the term ‘watershed’ – that is, the topographic area within which water drains to a specific point on a stream, river, or particular waterbody. The Hydrologic Unit Code (HUC) framework, however, has changed this understanding. Hydrologic units provide a convenient nationwide set of geographic polygons based on an arbitra

A Novel Information-Theoretic Approach for Variable Clustering and Predictive Modeling Using Dirichlet Process Mixtures

PubMed Central

Chen, Yun; Yang, Hui

2016-01-01

In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, depend on nontrivial assumptions about the data structure. Note that nonlinear interdependence among variables poses significant challenges on the traditional framework of predictive modeling. In the present work, we reformulate the problem of variable clustering from an information theoretic perspective that does not require the assumption of data structure for the identification of nonlinear interdependence among variables. Specifically, we propose the use of mutual information to characterize and measure nonlinear correlation structures among variables. Further, we develop Dirichlet process (DP) models to cluster variables based on the mutual-information measures among variables. Finally, orthonormalized variables in each cluster are integrated with group elastic-net model to improve the performance of predictive modeling. Both simulation and real-world case studies showed that the proposed methodology not only effectively reveals the nonlinear interdependence structures among variables but also outperforms traditional variable clustering algorithms such as hierarchical clustering. PMID:27966581