Temporal dynamics of 2D motion integration for ocular following in macaque monkeys.

PubMed

Barthélemy, Fréderic V; Fleuriet, Jérome; Masson, Guillaume S

2010-03-01

Several recent studies have shown that extracting pattern motion direction is a dynamical process where edge motion is first extracted and pattern-related information is encoded with a small time lag by MT neurons. A similar dynamics was found for human reflexive or voluntary tracking. Here, we bring an essential, but still missing, piece of information by documenting macaque ocular following responses to gratings, unikinetic plaids, and barber-poles. We found that ocular tracking was always initiated first in the grating motion direction with ultra-short latencies (approximately 55 ms). A second component was driven only 10-15 ms later, rotating tracking toward pattern motion direction. At the end the open-loop period, tracking direction was aligned with pattern motion direction (plaids) or the average of the line-ending motion directions (barber-poles). We characterized the dependency on contrast of each component. Both timing and direction of ocular following were quantitatively very consistent with the dynamics of neuronal responses reported by others. Overall, we found a remarkable consistency between neuronal dynamics and monkey behavior, advocating for a direct link between the neuronal solution of the aperture problem and primate perception and action.

The development of a dynamic software for the user interaction from the geographic information system environment with the database of the calibration site of the satellite remote electro-optic sensors

NASA Astrophysics Data System (ADS)

Zyelyk, Ya. I.; Semeniv, O. V.

2015-12-01

The state of the problem of the post-launch calibration of the satellite electro-optic remote sensors and its solutions in Ukraine is analyzed. The database is improved and dynamic services for user interaction with database from the environment of open geographical information system Quantum GIS for information support of calibration activities are created. A dynamic application under QGIS is developed, implementing these services in the direction of the possibility of data entering, editing and extraction from the database, using the technology of object-oriented programming and of modern complex program design patterns. The functional and algorithmic support of this dynamic software and its interface are developed.

SIDECACHE: Information access, management and dissemination framework for web services.

PubMed

Doderer, Mark S; Burkhardt, Cory; Robbins, Kay A

2011-06-14

Many bioinformatics algorithms and data sets are deployed using web services so that the results can be explored via the Internet and easily integrated into other tools and services. These services often include data from other sites that is accessed either dynamically or through file downloads. Developers of these services face several problems because of the dynamic nature of the information from the upstream services. Many publicly available repositories of bioinformatics data frequently update their information. When such an update occurs, the developers of the downstream service may also need to update. For file downloads, this process is typically performed manually followed by web service restart. Requests for information obtained by dynamic access of upstream sources is sometimes subject to rate restrictions. SideCache provides a framework for deploying web services that integrate information extracted from other databases and from web sources that are periodically updated. This situation occurs frequently in biotechnology where new information is being continuously generated and the latest information is important. SideCache provides several types of services including proxy access and rate control, local caching, and automatic web service updating. We have used the SideCache framework to automate the deployment and updating of a number of bioinformatics web services and tools that extract information from remote primary sources such as NCBI, NCIBI, and Ensembl. The SideCache framework also has been used to share research results through the use of a SideCache derived web service.

Multi-scale statistical analysis of coronal solar activity

DOE PAGES

Gamborino, Diana; del-Castillo-Negrete, Diego; Martinell, Julio J.

2016-07-08

Multi-filter images from the solar corona are used to obtain temperature maps that are analyzed using techniques based on proper orthogonal decomposition (POD) in order to extract dynamical and structural information at various scales. Exploring active regions before and after a solar flare and comparing them with quiet regions, we show that the multi-scale behavior presents distinct statistical properties for each case that can be used to characterize the level of activity in a region. Information about the nature of heat transport is also to be extracted from the analysis.

Reactive immunization on complex networks

NASA Astrophysics Data System (ADS)

Alfinito, Eleonora; Beccaria, Matteo; Fachechi, Alberto; Macorini, Guido

2017-01-01

Epidemic spreading on complex networks depends on the topological structure as well as on the dynamical properties of the infection itself. Generally speaking, highly connected individuals play the role of hubs and are crucial to channel information across the network. On the other hand, static topological quantities measuring the connectivity structure are independent of the dynamical mechanisms of the infection. A natural question is therefore how to improve the topological analysis by some kind of dynamical information that may be extracted from the ongoing infection itself. In this spirit, we propose a novel vaccination scheme that exploits information from the details of the infection pattern at the moment when the vaccination strategy is applied. Numerical simulations of the infection process show that the proposed immunization strategy is effective and robust on a wide class of complex networks.

Compressive Information Extraction: A Dynamical Systems Approach

DTIC Science & Technology

2016-01-24

sparsely encoded in very large data streams. (a) Target tracking in an urban canyon; (b) and (c) sample frames showing contextually abnormal events: onset...extraction to identify contextually abnormal se- quences (see section 2.2.3). Formally, the problem of interest can be stated as establishing whether a noisy...relaxations with optimality guarantees can be obtained using tools from semi-algebraic geometry. 2.2 Application: Detecting Contextually Abnormal Events

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.

PubMed

Chen, Xiaobo; Zhang, Han; Zhang, Lichi; Shen, Celina; Lee, Seong-Whan; Shen, Dinggang

2017-10-01

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Evaluation of human dynamic balance in Grassmann manifold

NASA Astrophysics Data System (ADS)

Michalczuk, Agnieszka; Wereszczyński, Kamil; Mucha, Romualda; Świtoński, Adam; Josiński, Henryk; Wojciechowski, Konrad

2017-07-01

The authors present an application of Grassmann manifold to the evaluation of human dynamic balance based on the time series representing movements of hip, knee and ankle joints in the sagittal, frontal and transverse planes. Time series were extracted from gait sequences which were recorded in the Human Motion Laboratory (HML) of the Polish-Japanese Academy of Information Technology in Bytom, Poland using the Vicon system.

Sequential estimation and satellite data assimilation in meteorology and oceanography

NASA Technical Reports Server (NTRS)

Ghil, M.

1986-01-01

The role of dynamics in estimating the state of the atmosphere and ocean from incomplete and noisy data is discussed and the classical applications of four-dimensional data assimilation to large-scale atmospheric dynamics are presented. It is concluded that sequential updating of a forecast model with continuously incoming conventional and remote-sensing data is the most natural way of extracting the maximum amount of information from the imperfectly known dynamics, on the one hand, and the inaccurate and incomplete observations, on the other.

Observer properties for understanding dynamical displays: Capacities, limitations, and defaults

NASA Technical Reports Server (NTRS)

Proffitt, Dennis R.; Kaiser, Mary K.

1991-01-01

People's ability to extract relevant information while viewing ongoing events is discussed in terms of human capabilities, limitations, and defaults. A taxonomy of event complexity is developed which predicts which dynamical events people can and cannot construe. This taxonomy is related to the distinction drawn in classical mechanics between particle and extended body motions. People's commonsense understandings of simple mechanical systems are impacted little by formal training, but rather reflect heuristical simplifications that focus on a single dimension of perceived dynamical relevance.

Experimental resource pulses influence social-network dynamics and the potential for information flow in tool-using crows

PubMed Central

St Clair, James J. H.; Burns, Zackory T.; Bettaney, Elaine M.; Morrissey, Michael B.; Otis, Brian; Ryder, Thomas B.; Fleischer, Robert C.; James, Richard; Rutz, Christian

2015-01-01

Social-network dynamics have profound consequences for biological processes such as information flow, but are notoriously difficult to measure in the wild. We used novel transceiver technology to chart association patterns across 19 days in a wild population of the New Caledonian crow—a tool-using species that may socially learn, and culturally accumulate, tool-related information. To examine the causes and consequences of changing network topology, we manipulated the environmental availability of the crows' preferred tool-extracted prey, and simulated, in silico, the diffusion of information across field-recorded time-ordered networks. Here we show that network structure responds quickly to environmental change and that novel information can potentially spread rapidly within multi-family communities, especially when tool-use opportunities are plentiful. At the same time, we report surprisingly limited social contact between neighbouring crow communities. Such scale dependence in information-flow dynamics is likely to influence the evolution and maintenance of material cultures. PMID:26529116

Verification of nonlinear dynamic structural test results by combined image processing and acoustic analysis

NASA Astrophysics Data System (ADS)

Tene, Yair; Tene, Noam; Tene, G.

1993-08-01

An interactive data fusion methodology of video, audio, and nonlinear structural dynamic analysis for potential application in forensic engineering is presented. The methodology was developed and successfully demonstrated in the analysis of heavy transportable bridge collapse during preparation for testing. Multiple bridge elements failures were identified after the collapse, including fracture, cracks and rupture of high performance structural materials. Videotape recording by hand held camcorder was the only source of information about the collapse sequence. The interactive data fusion methodology resulted in extracting relevant information form the videotape and from dynamic nonlinear structural analysis, leading to full account of the sequence of events during the bridge collapse.

Dynamic Features for Iris Recognition.

PubMed

da Costa, R M; Gonzaga, A

2012-08-01

The human eye is sensitive to visible light. Increasing illumination on the eye causes the pupil of the eye to contract, while decreasing illumination causes the pupil to dilate. Visible light causes specular reflections inside the iris ring. On the other hand, the human retina is less sensitive to near infra-red (NIR) radiation in the wavelength range from 800 nm to 1400 nm, but iris detail can still be imaged with NIR illumination. In order to measure the dynamic movement of the human pupil and iris while keeping the light-induced reflexes from affecting the quality of the digitalized image, this paper describes a device based on the consensual reflex. This biological phenomenon contracts and dilates the two pupils synchronously when illuminating one of the eyes by visible light. In this paper, we propose to capture images of the pupil of one eye using NIR illumination while illuminating the other eye using a visible-light pulse. This new approach extracts iris features called "dynamic features (DFs)." This innovative methodology proposes the extraction of information about the way the human eye reacts to light, and to use such information for biometric recognition purposes. The results demonstrate that these features are discriminating features, and, even using the Euclidean distance measure, an average accuracy of recognition of 99.1% was obtained. The proposed methodology has the potential to be "fraud-proof," because these DFs can only be extracted from living irises.

Novel method of extracting motion from natural movies.

PubMed

Suzuki, Wataru; Ichinohe, Noritaka; Tani, Toshiki; Hayami, Taku; Miyakawa, Naohisa; Watanabe, Satoshi; Takeichi, Hiroshige

2017-11-01

The visual system in primates can be segregated into motion and shape pathways. Interaction occurs at multiple stages along these pathways. Processing of shape-from-motion and biological motion is considered to be a higher-order integration process involving motion and shape information. However, relatively limited types of stimuli have been used in previous studies on these integration processes. We propose a new algorithm to extract object motion information from natural movies and to move random dots in accordance with the information. The object motion information is extracted by estimating the dynamics of local normal vectors of the image intensity projected onto the x-y plane of the movie. An electrophysiological experiment on two adult common marmoset monkeys (Callithrix jacchus) showed that the natural and random dot movies generated with this new algorithm yielded comparable neural responses in the middle temporal visual area. In principle, this algorithm provided random dot motion stimuli containing shape information for arbitrary natural movies. This new method is expected to expand the neurophysiological and psychophysical experimental protocols to elucidate the integration processing of motion and shape information in biological systems. The novel algorithm proposed here was effective in extracting object motion information from natural movies and provided new motion stimuli to investigate higher-order motion information processing. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.

PubMed

Huang, Xin; Zeng, Jun; Zhou, Lina; Hu, Chunxiu; Yin, Peiyuan; Lin, Xiaohui

2016-08-31

Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.

A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma

NASA Astrophysics Data System (ADS)

Huang, Xin; Zeng, Jun; Zhou, Lina; Hu, Chunxiu; Yin, Peiyuan; Lin, Xiaohui

2016-08-01

Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.

Rapid acquisition of data dense solid-state CPMG NMR spectral sets using multi-dimensional statistical analysis

DOE PAGES

Mason, H. E.; Uribe, E. C.; Shusterman, J. A.

2018-01-01

Tensor-rank decomposition methods have been applied to variable contact time 29 Si{ 1 H} CP/CPMG NMR data sets to extract NMR dynamics information and dramatically decrease conventional NMR acquisition times.

Rapid acquisition of data dense solid-state CPMG NMR spectral sets using multi-dimensional statistical analysis

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

Mason, H. E.; Uribe, E. C.; Shusterman, J. A.

Tensor-rank decomposition methods have been applied to variable contact time 29 Si{ 1 H} CP/CPMG NMR data sets to extract NMR dynamics information and dramatically decrease conventional NMR acquisition times.

PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics.

PubMed

Fernandez-Ricaud, Luciano; Kourtchenko, Olga; Zackrisson, Martin; Warringer, Jonas; Blomberg, Anders

2016-06-23

Phenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology. To automate visualization, analysis and exploration of complex and highly resolved microbial growth data as well as standardized extraction of the fitness components it contains, we developed the software PRECOG (PREsentation and Characterization Of Growth-data). PRECOG allows the user to quality control, interact with and evaluate microbial growth data with ease, speed and accuracy, also in cases of non-standard growth dynamics. Quality indices filter high- from low-quality growth experiments, reducing false positives. The pre-processing filters in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network procedures. We provide examples where data calibration, project design and feature extraction methodologies have a clear impact on the estimated growth traits, emphasising the need for proper standardization in data analysis. PRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases.

Reconstruction of dynamic structures of experimental setups based on measurable experimental data only

NASA Astrophysics Data System (ADS)

Chen, Tian-Yu; Chen, Yang; Yang, Hu-Jiang; Xiao, Jing-Hua; Hu, Gang

2018-03-01

Nowadays, massive amounts of data have been accumulated in various and wide fields, it has become today one of the central issues in interdisciplinary fields to analyze existing data and extract as much useful information as possible from data. It is often that the output data of systems are measurable while dynamic structures producing these data are hidden, and thus studies to reveal system structures by analyzing available data, i.e., reconstructions of systems become one of the most important tasks of information extractions. In the past, most of the works in this respect were based on theoretical analyses and numerical verifications. Direct analyses of experimental data are very rare. In physical science, most of the analyses of experimental setups were based on the first principles of physics laws, i.e., so-called top-down analyses. In this paper, we conducted an experiment of “Boer resonant instrument for forced vibration” (BRIFV) and inferred the dynamic structure of the experimental set purely from the analysis of the measurable experimental data, i.e., by applying the bottom-up strategy. Dynamics of the experimental set is strongly nonlinear and chaotic, and itʼs subjects to inevitable noises. We proposed to use high-order correlation computations to treat nonlinear dynamics; use two-time correlations to treat noise effects. By applying these approaches, we have successfully reconstructed the structure of the experimental setup, and the dynamic system reconstructed with the measured data reproduces good experimental results in a wide range of parameters.

Evol and ProDy for bridging protein sequence evolution and structural dynamics

PubMed Central

Mao, Wenzhi; Liu, Ying; Chennubhotla, Chakra; Lezon, Timothy R.; Bahar, Ivet

2014-01-01

Correlations between sequence evolution and structural dynamics are of utmost importance in understanding the molecular mechanisms of function and their evolution. We have integrated Evol, a new package for fast and efficient comparative analysis of evolutionary patterns and conformational dynamics, into ProDy, a computational toolbox designed for inferring protein dynamics from experimental and theoretical data. Using information-theoretic approaches, Evol coanalyzes conservation and coevolution profiles extracted from multiple sequence alignments of protein families with their inferred dynamics. Availability and implementation: ProDy and Evol are open-source and freely available under MIT License from http://prody.csb.pitt.edu/. Contact: bahar@pitt.edu PMID:24849577

Temperature Measurements in Dynamically-loaded Systems Using Neutron Resonance Spectroscopy (NRS) at LANSCE

NASA Astrophysics Data System (ADS)

Yuan, V. W.

2002-12-01

In previous attempts to determine the internal temperature in systems subjected to dynamic loading, experimenters have usually relied on surface-based optical techniques that are often hampered by insufficient information regarding the emissivity of the surfaces under study. Neutron Resonance Spectroscopy (NRS) is a technique that uses Doppler-broadened neutron resonances to measure internal temperatures in dynamically-loaded samples. NRS has developed its own target-moderator assembly to provide single pulses with an order of magnitude higher brightness than the Lujan production target. The resonance line shapes from which temperature information is extracted are also influenced by non-temperature-dependent broadening from the moderator and detector phosphorescence. Dynamic NRS experiments have been performed to measure the temperature in a silver sheet jet and behind the passage of a shock wave in molybdenum.

Photoionization of NaK molecule with a double-well potential in femtosecond pump probe pulse laser fields

NASA Astrophysics Data System (ADS)

Yu, Jie; Wang, Sen-Ming; Yuan, Kai-Jun; Cong, Shu-Lin

2006-09-01

The method of time-dependent quantum wave packet dynamics is used to calculate the femtosecond pump-probe photoelectron spectra and study the wave packet dynamic processes of the double-minimum potential state 61Σ+ of NaK in intense laser fields. The evolutions of the wave packet and the photoelectron energy spectra with time and internuclear distance are described in detail. The wave packet dynamic information of the 61Σ+ state can be extracted from the photoelectron energy spectra.

Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis.

PubMed

Zhang, Hanyuan; Tian, Xuemin; Deng, Xiaogang; Cao, Yuping

2018-05-16

As an attractive nonlinear dynamic data analysis tool, global preserving kernel slow feature analysis (GKSFA) has achieved great success in extracting the high nonlinearity and inherently time-varying dynamics of batch process. However, GKSFA is an unsupervised feature extraction method and lacks the ability to utilize batch process class label information, which may not offer the most effective means for dealing with batch process monitoring. To overcome this problem, we propose a novel batch process monitoring method based on the modified GKSFA, referred to as discriminant global preserving kernel slow feature analysis (DGKSFA), by closely integrating discriminant analysis and GKSFA. The proposed DGKSFA method can extract discriminant feature of batch process as well as preserve global and local geometrical structure information of observed data. For the purpose of fault detection, a monitoring statistic is constructed based on the distance between the optimal kernel feature vectors of test data and normal data. To tackle the challenging issue of nonlinear fault variable identification, a new nonlinear contribution plot method is also developed to help identifying the fault variable after a fault is detected, which is derived from the idea of variable pseudo-sample trajectory projection in DGKSFA nonlinear biplot. Simulation results conducted on a numerical nonlinear dynamic system and the benchmark fed-batch penicillin fermentation process demonstrate that the proposed process monitoring and fault diagnosis approach can effectively detect fault and distinguish fault variables from normal variables. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Molecular dynamics: deciphering the data.

PubMed

Dauber-Osguthorpe, P; Maunder, C M; Osguthorpe, D J

1996-06-01

The dynamic behaviour of molecules is important in determining their activity. Molecular dynamics (MD) simulations give a detailed description of motion, from small fluctuations to conformational transitions, and can include solvent effects. However, extracting useful information about conformational motion from a trajectory is not trivial. We have used digital signal-processing techniques to characterise the motion in MD simulations, including: calculating the frequency distribution, applying filtering functions, and extraction of vectors defining the characteristic motion for each frequency in an MD simulation. We describe here some typical results obtained for peptides and proteins. The nature of the low-frequency modes of motion, as obtained from MD and normal mode (NM) analysis, of Ace-(Ala)31-Nma and of a proline mutant is discussed. Low-frequency modes extracted from the MD trajectories of Rop protein and phospholipase A2 reveal characteristic motions of secondary structure elements, as well as concerned motions that are of significance to the protein's biological activity. MD simulations are also used frequently as a tool for conformational searches and for investigating protein folding/unfolding. We have developed a novel method that uses time-domain filtering to channel energy into conformational motion and thus enhance conformational transitions. The selectively enhanced molecular dynamics method is tested on the small molecule hexane.

Molecular dynamics: Deciphering the data

NASA Astrophysics Data System (ADS)

Dauber-Osguthorpe, Pnina; Maunder, Colette M.; Osguthorpe, David J.

1996-06-01

The dynamic behaviour of molecules is important in determining their activity. Molecular dynamics (MD) simulations give a detailed description of motion, from small fluctuations to conformational transitions, and can include solvent effects. However, extracting useful information about conformational motion from a trajectory is not trivial. We have used digital signal-processing techniques to characterise the motion in MD simulations, including: calculating the frequency distribution, applying filtering functions, and extraction of vectors defining the characteristic motion for each frequency in an MD simulation. We describe here some typical results obtained for peptides and proteins. The nature of the low-frequency modes of motion, as obtained from MD and normal mode (NM) analysis, of Ace-(Ala)31-Nma and of a proline mutant is discussed. Low-frequency modes extracted from the MD trajectories of Rop protein and phospholipase A2 reveal characteristic motions of secondary structure elements, as well as concerted motions that are of significance to the protein's biological activity. MD simulations are also used frequently as a tool for conformational searches and for investigating protein folding/unfolding. We have developed a novel method that uses time-domain filtering to channel energy into conformational motion and thus enhance conformational transitions. The selectively enhanced molecular dynamics method is tested on the small molecule hexane.

Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning

NASA Astrophysics Data System (ADS)

Zhao, Lei; Wang, Zengcai; Wang, Xiaojin; Qi, Yazhou; Liu, Qing; Zhang, Guoxin

2016-09-01

Human fatigue is an important cause of traffic accidents. To improve the safety of transportation, we propose, in this paper, a framework for fatigue expression recognition using image-based facial dynamic multi-information and a bimodal deep neural network. First, the landmark of face region and the texture of eye region, which complement each other in fatigue expression recognition, are extracted from facial image sequences captured by a single camera. Then, two stacked autoencoder neural networks are trained for landmark and texture, respectively. Finally, the two trained neural networks are combined by learning a joint layer on top of them to construct a bimodal deep neural network. The model can be used to extract a unified representation that fuses landmark and texture modalities together and classify fatigue expressions accurately. The proposed system is tested on a human fatigue dataset obtained from an actual driving environment. The experimental results demonstrate that the proposed method performs stably and robustly, and that the average accuracy achieves 96.2%.

Combining Experiments and Simulations of Extraction Kinetics and Thermodynamics in Advanced Separation Processes for Used Nuclear Fuel

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

Nilsson, Mikael

This 3-year project was a collaboration between University of California Irvine (UC Irvine), Pacific Northwest National Laboratory (PNNL), Idaho National Laboratory (INL), Argonne National Laboratory (ANL) and with an international collaborator at ForschungZentrum Jülich (FZJ). The project was led from UC Irvine under the direction of Profs. Mikael Nilsson and Hung Nguyen. The leads at PNNL, INL, ANL and FZJ were Dr. Liem Dang, Dr. Peter Zalupski, Dr. Nathaniel Hoyt and Dr. Giuseppe Modolo, respectively. Involved in this project at UC Irvine were three full time PhD graduate students, Tro Babikian, Ted Yoo, and Quynh Vo, and one MS student,more » Alba Font Bosch. The overall objective of this project was to study how the kinetics and thermodynamics of metal ion extraction can be described by molecular dynamic (MD) simulations and how the simulations can be validated by experimental data. Furthermore, the project includes the applied separation by testing the extraction systems in a single stage annular centrifugal contactor and coupling the experimental data with computational fluid dynamic (CFD) simulations. Specific objectives of the proposed research were: Study and establish a rigorous connection between MD simulations based on polarizable force fields and extraction thermodynamic and kinetic data. Compare and validate CFD simulations of extraction processes for An/Ln separation using different sizes (and types) of annular centrifugal contactors. Provide a theoretical/simulation and experimental base for scale-up of batch-wise extraction to continuous contactors. We approached objective 1 and 2 in parallel. For objective 1 we started by studying a well established extraction system with a relatively simple extraction mechanism, namely tributyl phosphate. What we found was that well optimized simulations can inform experiments and new information on TBP behavior was presented in this project, as well be discussed below. The second objective proved a larger challenge and most of the efforts were devoted to experimental studies.« less

Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies

PubMed Central

Zheng, Shuai; Ghasemzadeh, Nima; Hayek, Salim S; Quyyumi, Arshed A

2017-01-01

Background Extracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedbacks for improving the extraction algorithm in real time. Objective Our goal was to provide a generic information extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results. Methods A clinical information extraction system IDEAL-X has been built on top of online machine learning. It processes one document at a time, and user interactions are recorded as feedbacks to update the learning model in real time. The updated model is used to predict values for extraction in subsequent documents. Once prediction accuracy reaches a user-acceptable threshold, the remaining documents may be batch processed. A customizable controlled vocabulary may be used to support extraction. Results Three datasets were used for experiments based on report styles: 100 cardiac catheterization procedure reports, 100 coronary angiographic reports, and 100 integrated reports—each combines history and physical report, discharge summary, outpatient clinic notes, outpatient clinic letter, and inpatient discharge medication report. Data extraction was performed by 3 methods: online machine learning, controlled vocabularies, and a combination of these. The system delivers results with F1 scores greater than 95%. Conclusions IDEAL-X adopts a unique online machine learning–based approach combined with controlled vocabularies to support data extraction for clinical reports. The system can quickly learn and improve, thus it is highly adaptable. PMID:28487265

Fractal Folding and Medium Viscoelasticity Contribute Jointly to Chromosome Dynamics

NASA Astrophysics Data System (ADS)

Polovnikov, K. E.; Gherardi, M.; Cosentino-Lagomarsino, M.; Tamm, M. V.

2018-02-01

Chromosomes are key players of cell physiology, their dynamics provides valuable information about its physical organization. In both prokaryotes and eukaryotes, the short-time motion of chromosomal loci has been described with a Rouse model in a simple or viscoelastic medium. However, little emphasis has been put on the influence of the folded organization of chromosomes on the local dynamics. Clearly, stress propagation, and thus dynamics, must be affected by such organization, but a theory allowing us to extract such information from data, e.g., on two-point correlations, is lacking. Here, we describe a theoretical framework able to answer this general polymer dynamics question. We provide a scaling analysis of the stress-propagation time between two loci at a given arclength distance along the chromosomal coordinate. The results suggest a precise way to assess folding information from the dynamical coupling of chromosome segments. Additionally, we realize this framework in a specific model of a polymer whose long-range interactions are designed to make it fold in a fractal way and immersed in a medium characterized by subdiffusive fractional Langevin motion with a tunable scaling exponent. This allows us to derive explicit analytical expressions for the correlation functions.

Sequential visibility-graph motifs

NASA Astrophysics Data System (ADS)

Iacovacci, Jacopo; Lacasa, Lucas

2016-04-01

Visibility algorithms transform time series into graphs and encode dynamical information in their topology, paving the way for graph-theoretical time series analysis as well as building a bridge between nonlinear dynamics and network science. In this work we introduce and study the concept of sequential visibility-graph motifs, smaller substructures of n consecutive nodes that appear with characteristic frequencies. We develop a theory to compute in an exact way the motif profiles associated with general classes of deterministic and stochastic dynamics. We find that this simple property is indeed a highly informative and computationally efficient feature capable of distinguishing among different dynamics and robust against noise contamination. We finally confirm that it can be used in practice to perform unsupervised learning, by extracting motif profiles from experimental heart-rate series and being able, accordingly, to disentangle meditative from other relaxation states. Applications of this general theory include the automatic classification and description of physical, biological, and financial time series.

A Risk Assessment System with Automatic Extraction of Event Types

NASA Astrophysics Data System (ADS)

Capet, Philippe; Delavallade, Thomas; Nakamura, Takuya; Sandor, Agnes; Tarsitano, Cedric; Voyatzi, Stavroula

In this article we describe the joint effort of experts in linguistics, information extraction and risk assessment to integrate EventSpotter, an automatic event extraction engine, into ADAC, an automated early warning system. By detecting as early as possible weak signals of emerging risks ADAC provides a dynamic synthetic picture of situations involving risk. The ADAC system calculates risk on the basis of fuzzy logic rules operated on a template graph whose leaves are event types. EventSpotter is based on a general purpose natural language dependency parser, XIP, enhanced with domain-specific lexical resources (Lexicon-Grammar). Its role is to automatically feed the leaves with input data.

Development of Availability and Sustainability Spares Optimization Models for Aircraft Reparables

DTIC Science & Technology

2013-09-01

the integrated SAP ® Enterprise Resource Planning ( ERP ) information system of the RSAF. A more in-depth review of OPUS10 capabilities will be provided...Dynamic Multi-Echelon Technique for Recoverable Item Control EBO: Expected Backorder EOQ: Economic Order Quantity ERP : Enterprise Resource...particular, the propulsion sub-system was expanded to include SSRUs. Spares information are extracted from the RSAF ERP system and include: 22

Advanced image collection, information extraction, and change detection in support of NN-20 broad area search and analysis

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

Petrie, G.M.; Perry, E.M.; Kirkham, R.R.

1997-09-01

This report describes the work performed at the Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy`s Office of Nonproliferation and National Security, Office of Research and Development (NN-20). The work supports the NN-20 Broad Area Search and Analysis, a program initiated by NN-20 to improve the detection and classification of undeclared weapons facilities. Ongoing PNNL research activities are described in three main components: image collection, information processing, and change analysis. The Multispectral Airborne Imaging System, which was developed to collect georeferenced imagery in the visible through infrared regions of the spectrum, and flown on a light aircraftmore » platform, will supply current land use conditions. The image information extraction software (dynamic clustering and end-member extraction) uses imagery, like the multispectral data collected by the PNNL multispectral system, to efficiently generate landcover information. The advanced change detection uses a priori (benchmark) information, current landcover conditions, and user-supplied rules to rank suspect areas by probable risk of undeclared facilities or proliferation activities. These components, both separately and combined, provide important tools for improving the detection of undeclared facilities.« less

The dynamics of information-driven coordination phenomena: A transfer entropy analysis

PubMed Central

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-01-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data. PMID:27051875

The dynamics of information-driven coordination phenomena: A transfer entropy analysis.

PubMed

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-04-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

Role of core excitation in (d ,p ) transfer reactions

NASA Astrophysics Data System (ADS)

Deltuva, A.; Ross, A.; Norvaišas, E.; Nunes, F. M.

2016-10-01

Background: Recent work found that core excitations can be important in extracting structure information from (d ,p ) reactions. Purpose: Our objective is to systematically explore the role of core excitation in (d ,p ) reactions and to understand the origin of the dynamical effects. Method: Based on the particle-rotor model of n +10Be , we generate a number of models with a range of separation energies (Sn=0.1 -5.0 MeV), while maintaining a significant core excited component. We then apply the latest extension of the momentum-space-based Faddeev method, including dynamical core excitation in the reaction mechanism to all orders, to the 10Be(d ,p )11Be -like reactions, and study the excitation effects for beam energies Ed=15 -90 MeV. Results: We study the resulting angular distributions and the differences between the spectroscopic factor that would be extracted from the cross sections, when including dynamical core excitation in the reaction, and that of the original structure model. We also explore how different partial waves affect the final cross section. Conclusions: Our results show a strong beam-energy dependence of the extracted spectroscopic factors that become smaller for intermediate beam energies. This dependence increases for loosely bound systems.

Near infrared spectroscopy based monitoring of extraction processes of raw material with the help of dynamic predictive modeling

NASA Astrophysics Data System (ADS)

Wang, Haixia; Suo, Tongchuan; Wu, Xiaolin; Zhang, Yue; Wang, Chunhua; Yu, Heshui; Li, Zheng

2018-03-01

The control of batch-to-batch quality variations remains a challenging task for pharmaceutical industries, e.g., traditional Chinese medicine (TCM) manufacturing. One difficult problem is to produce pharmaceutical products with consistent quality from raw material of large quality variations. In this paper, an integrated methodology combining the near infrared spectroscopy (NIRS) and dynamic predictive modeling is developed for the monitoring and control of the batch extraction process of licorice. With the spectra data in hand, the initial state of the process is firstly estimated with a state-space model to construct a process monitoring strategy for the early detection of variations induced by the initial process inputs such as raw materials. Secondly, the quality property of the end product is predicted at the mid-course during the extraction process with a partial least squares (PLS) model. The batch-end-time (BET) is then adjusted accordingly to minimize the quality variations. In conclusion, our study shows that with the help of the dynamic predictive modeling, NIRS can offer the past and future information of the process, which enables more accurate monitoring and control of process performance and product quality.

Topological properties of flat electroencephalography's state space

NASA Astrophysics Data System (ADS)

Ken, Tan Lit; Ahmad, Tahir bin; Mohd, Mohd Sham bin; Ngien, Su Kong; Suwa, Tohru; Meng, Ong Sie

2016-02-01

Neuroinverse problem are often associated with complex neuronal activity. It involves locating problematic cell which is highly challenging. While epileptic foci localization is possible with the aid of EEG signals, it relies greatly on the ability to extract hidden information or pattern within EEG signals. Flat EEG being an enhancement of EEG is a way of viewing electroencephalograph on the real plane. In the perspective of dynamical systems, Flat EEG is equivalent to epileptic seizure hence, making it a great platform to study epileptic seizure. Throughout the years, various mathematical tools have been applied on Flat EEG to extract hidden information that is hardly noticeable by traditional visual inspection. While these tools have given worthy results, the journey towards understanding seizure process completely is yet to be succeeded. Since the underlying structure of Flat EEG is dynamic and is deemed to contain wealthy information regarding brainstorm, it would certainly be appealing to explore in depth its structures. To better understand the complex seizure process, this paper studies the event of epileptic seizure via Flat EEG in a more general framework by means of topology, particularly, on the state space where the event of Flat EEG lies.

Cueing Animations: Dynamic Signaling Aids Information Extraction and Comprehension

ERIC Educational Resources Information Center

Boucheix, Jean-Michel; Lowe, Richard K.; Putri, Dian K.; Groff, Jonathan

2013-01-01

The effectiveness of animations containing two novel forms of animation cueing that target relations between event units rather than individual entities was compared with that of animations containing conventional entity-based cueing or no cues. These relational event unit cues ("progressive path" and "local coordinated" cues) were specifically…

A graph-based approach to detect spatiotemporal dynamics in satellite image time series

NASA Astrophysics Data System (ADS)

Guttler, Fabio; Ienco, Dino; Nin, Jordi; Teisseire, Maguelonne; Poncelet, Pascal

2017-08-01

Enhancing the frequency of satellite acquisitions represents a key issue for Earth Observation community nowadays. Repeated observations are crucial for monitoring purposes, particularly when intra-annual process should be taken into account. Time series of images constitute a valuable source of information in these cases. The goal of this paper is to propose a new methodological framework to automatically detect and extract spatiotemporal information from satellite image time series (SITS). Existing methods dealing with such kind of data are usually classification-oriented and cannot provide information about evolutions and temporal behaviors. In this paper we propose a graph-based strategy that combines object-based image analysis (OBIA) with data mining techniques. Image objects computed at each individual timestamp are connected across the time series and generates a set of evolution graphs. Each evolution graph is associated to a particular area within the study site and stores information about its temporal evolution. Such information can be deeply explored at the evolution graph scale or used to compare the graphs and supply a general picture at the study site scale. We validated our framework on two study sites located in the South of France and involving different types of natural, semi-natural and agricultural areas. The results obtained from a Landsat SITS support the quality of the methodological approach and illustrate how the framework can be employed to extract and characterize spatiotemporal dynamics.

Comparison between 2 methods of solid-liquid extraction for the production of Cinchona calisaya elixir: an experimental kinetics and numerical modeling approach.

PubMed

Naviglio, Daniele; Formato, Andrea; Gallo, Monica

2014-09-01

The purpose of this study is to compare the extraction process for the production of China elixir starting from the same vegetable mixture, as performed by conventional maceration or a cyclically pressurized extraction process (rapid solid-liquid dynamic extraction) using the Naviglio Extractor. Dry residue was used as a marker for the kinetics of the extraction process because it was proportional to the amount of active principles extracted and, therefore, to their total concentration in the solution. UV spectra of the hydroalcoholic extracts allowed for the identification of the predominant chemical species in the extracts, while the organoleptic tests carried out on the final product provided an indication of the acceptance of the beverage and highlighted features that were not detectable by instrumental analytical techniques. In addition, a numerical simulation of the process has been performed, obtaining useful information about the timing of the process (time history) as well as its mathematical description. © 2014 Institute of Food Technologists®

Radiation crosslinking of highly plasticized PVC

NASA Astrophysics Data System (ADS)

Mendizabal, E.; Cruz, L.; Jasso, C. F.; Burillo, G.; Dakin, V. I.

1996-02-01

To improve the physical properties of highly plasticized PVC, the polymer was crosslinked by gamma irradiation using a dose rate of 91 kGy/h. The effect of plasticizer type was studied by using three different plasticizers, 2,2,4-trimethyl-1,3-pentanediol diisobutyrate (TXIB), di(2-ethyl hexyl) phthalate (DOP), and di(2-ethylhexyl terephthalate) (DOTP), and varying irradiation doses. Gel content was determined by soxhlet extraction, tensile measurements were made on a universal testing machine and the mechano-dynamic measurements were made in a dynamic rheometer. It was found that a considerable bonding of plasticizer molecules to macromolelcules takes place along with crosslinking, so that the use of the solvent extraction method for measuring the degree of crosslinking can give erroneous information. Radiation-chemical crosslinking yield ( Gc) and molecular weight of interjunctions chains ( Mc), were calculated for different systems studied. Addition of ethylene glycol dimethacrylate (EGDM) as a crosslinking coagent and dioctyl tin oxide (DOTO) as a stabilizer was also studied. Plasticizers extraction resistance was increased by irradiation treatment.

SS-mPMG and SS-GA: tools for finding pathways and dynamic simulation of metabolic networks.

PubMed

Katsuragi, Tetsuo; Ono, Naoaki; Yasumoto, Keiichi; Altaf-Ul-Amin, Md; Hirai, Masami Y; Sriyudthsak, Kansuporn; Sawada, Yuji; Yamashita, Yui; Chiba, Yukako; Onouchi, Hitoshi; Fujiwara, Toru; Naito, Satoshi; Shiraishi, Fumihide; Kanaya, Shigehiko

2013-05-01

Metabolomics analysis tools can provide quantitative information on the concentration of metabolites in an organism. In this paper, we propose the minimum pathway model generator tool for simulating the dynamics of metabolite concentrations (SS-mPMG) and a tool for parameter estimation by genetic algorithm (SS-GA). SS-mPMG can extract a subsystem of the metabolic network from the genome-scale pathway maps to reduce the complexity of the simulation model and automatically construct a dynamic simulator to evaluate the experimentally observed behavior of metabolites. Using this tool, we show that stochastic simulation can reproduce experimentally observed dynamics of amino acid biosynthesis in Arabidopsis thaliana. In this simulation, SS-mPMG extracts the metabolic network subsystem from published databases. The parameters needed for the simulation are determined using a genetic algorithm to fit the simulation results to the experimental data. We expect that SS-mPMG and SS-GA will help researchers to create relevant metabolic networks and carry out simulations of metabolic reactions derived from metabolomics data.

Single-molecule dynamics in nanofabricated traps

NASA Astrophysics Data System (ADS)

Cohen, Adam

2009-03-01

The Anti-Brownian Electrokinetic trap (ABEL trap) provides a means to immobilize a single fluorescent molecule in solution, without surface attachment chemistry. The ABEL trap works by tracking the Brownian motion of a single molecule, and applying feedback electric fields to induce an electrokinetic motion that approximately cancels the Brownian motion. We present a new design for the ABEL trap that allows smaller molecules to be trapped and more information to be extracted from the dynamics of a single molecule than was previously possible. In particular, we present strategies for extracting dynamically fluctuating mobilities and diffusion coefficients, as a means to probe dynamic changes in molecular charge and shape. If one trapped molecule is good, many trapped molecules are better. An array of single molecules in solution, each immobilized without surface attachment chemistry, provides an ideal test-bed for single-molecule analyses of intramolecular dynamics and intermolecular interactions. We present a technology for creating such an array, using a fused silica plate with nanofabricated dimples and a removable cover for sealing single molecules within the dimples. With this device one can watch the shape fluctuations of single molecules of DNA or study cooperative interactions in weakly associating protein complexes.

Evol and ProDy for bridging protein sequence evolution and structural dynamics.

PubMed

Bakan, Ahmet; Dutta, Anindita; Mao, Wenzhi; Liu, Ying; Chennubhotla, Chakra; Lezon, Timothy R; Bahar, Ivet

2014-09-15

Correlations between sequence evolution and structural dynamics are of utmost importance in understanding the molecular mechanisms of function and their evolution. We have integrated Evol, a new package for fast and efficient comparative analysis of evolutionary patterns and conformational dynamics, into ProDy, a computational toolbox designed for inferring protein dynamics from experimental and theoretical data. Using information-theoretic approaches, Evol coanalyzes conservation and coevolution profiles extracted from multiple sequence alignments of protein families with their inferred dynamics. ProDy and Evol are open-source and freely available under MIT License from http://prody.csb.pitt.edu/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

PubMed Central

Covino, Roberto; Coifman, Ronald R.; Gear, C. William; Georgiou, Anastasia S.; Kevrekidis, Ioannis G.

2017-01-01

We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES. PMID:28634293

Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies.

PubMed

Zheng, Shuai; Lu, James J; Ghasemzadeh, Nima; Hayek, Salim S; Quyyumi, Arshed A; Wang, Fusheng

2017-05-09

Extracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedbacks for improving the extraction algorithm in real time. Our goal was to provide a generic information extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results. A clinical information extraction system IDEAL-X has been built on top of online machine learning. It processes one document at a time, and user interactions are recorded as feedbacks to update the learning model in real time. The updated model is used to predict values for extraction in subsequent documents. Once prediction accuracy reaches a user-acceptable threshold, the remaining documents may be batch processed. A customizable controlled vocabulary may be used to support extraction. Three datasets were used for experiments based on report styles: 100 cardiac catheterization procedure reports, 100 coronary angiographic reports, and 100 integrated reports-each combines history and physical report, discharge summary, outpatient clinic notes, outpatient clinic letter, and inpatient discharge medication report. Data extraction was performed by 3 methods: online machine learning, controlled vocabularies, and a combination of these. The system delivers results with F1 scores greater than 95%. IDEAL-X adopts a unique online machine learning-based approach combined with controlled vocabularies to support data extraction for clinical reports. The system can quickly learn and improve, thus it is highly adaptable. ©Shuai Zheng, James J Lu, Nima Ghasemzadeh, Salim S Hayek, Arshed A Quyyumi, Fusheng Wang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 09.05.2017.

Extracting protein dynamics information from overlapped NMR signals using relaxation dispersion difference NMR spectroscopy.

PubMed

Konuma, Tsuyoshi; Harada, Erisa; Sugase, Kenji

2015-12-01

Protein dynamics plays important roles in many biological events, such as ligand binding and enzyme reactions. NMR is mostly used for investigating such protein dynamics in a site-specific manner. Recently, NMR has been actively applied to large proteins and intrinsically disordered proteins, which are attractive research targets. However, signal overlap, which is often observed for such proteins, hampers accurate analysis of NMR data. In this study, we have developed a new methodology called relaxation dispersion difference that can extract conformational exchange parameters from overlapped NMR signals measured using relaxation dispersion spectroscopy. In relaxation dispersion measurements, the signal intensities of fluctuating residues vary according to the Carr-Purcell-Meiboon-Gill pulsing interval, whereas those of non-fluctuating residues are constant. Therefore, subtraction of each relaxation dispersion spectrum from that with the highest signal intensities, measured at the shortest pulsing interval, leaves only the signals of the fluctuating residues. This is the principle of the relaxation dispersion difference method. This new method enabled us to extract exchange parameters from overlapped signals of heme oxygenase-1, which is a relatively large protein. The results indicate that the structural flexibility of a kink in the heme-binding site is important for efficient heme binding. Relaxation dispersion difference requires neither selectively labeled samples nor modification of pulse programs; thus it will have wide applications in protein dynamics analysis.

Querying databases of trajectories of differential equations: Data structures for trajectories

NASA Technical Reports Server (NTRS)

Grossman, Robert

1989-01-01

One approach to qualitative reasoning about dynamical systems is to extract qualitative information by searching or making queries on databases containing very large numbers of trajectories. The efficiency of such queries depends crucially upon finding an appropriate data structure for trajectories of dynamical systems. Suppose that a large number of parameterized trajectories gamma of a dynamical system evolving in R sup N are stored in a database. Let Eta is contained in set R sup N denote a parameterized path in Euclidean Space, and let the Euclidean Norm denote a norm on the space of paths. A data structure is defined to represent trajectories of dynamical systems, and an algorithm is sketched which answers queries.

Data Analysis Approaches for the Risk-Informed Safety Margins Characterization Toolkit

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

Mandelli, Diego; Alfonsi, Andrea; Maljovec, Daniel P.

2016-09-01

In the past decades, several numerical simulation codes have been employed to simulate accident dynamics (e.g., RELAP5-3D, RELAP-7, MELCOR, MAAP). In order to evaluate the impact of uncertainties into accident dynamics, several stochastic methodologies have been coupled with these codes. These stochastic methods range from classical Monte-Carlo and Latin Hypercube sampling to stochastic polynomial methods. Similar approaches have been introduced into the risk and safety community where stochastic methods (such as RAVEN, ADAPT, MCDET, ADS) have been coupled with safety analysis codes in order to evaluate the safety impact of timing and sequencing of events. These approaches are usually calledmore » Dynamic PRA or simulation-based PRA methods. These uncertainties and safety methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). The scope of this paper is to present a broad overview of methods and algorithms that can be used to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers. Some of the algorithms presented here have been developed or are under development within the RAVEN statistical framework.« less

Dynamic information processing states revealed through neurocognitive models of object semantics

PubMed Central

Clarke, Alex

2015-01-01

Recognising objects relies on highly dynamic, interactive brain networks to process multiple aspects of object information. To fully understand how different forms of information about objects are represented and processed in the brain requires a neurocognitive account of visual object recognition that combines a detailed cognitive model of semantic knowledge with a neurobiological model of visual object processing. Here we ask how specific cognitive factors are instantiated in our mental processes and how they dynamically evolve over time. We suggest that coarse semantic information, based on generic shared semantic knowledge, is rapidly extracted from visual inputs and is sufficient to drive rapid category decisions. Subsequent recurrent neural activity between the anterior temporal lobe and posterior fusiform supports the formation of object-specific semantic representations – a conjunctive process primarily driven by the perirhinal cortex. These object-specific representations require the integration of shared and distinguishing object properties and support the unique recognition of objects. We conclude that a valuable way of understanding the cognitive activity of the brain is though testing the relationship between specific cognitive measures and dynamic neural activity. This kind of approach allows us to move towards uncovering the information processing states of the brain and how they evolve over time. PMID:25745632

[Study on the dynamic model with supercritical CO2 fluid extracting the lipophilic components in Panax notoginseng].

PubMed

Duan, Xian-Chun; Wang, Yong-Zhong; Zhang, Jun-Ru; Luo, Huan; Zhang, Heng; Xia, Lun-Zhu

2011-08-01

To establish a dynamics model for extracting the lipophilic components in Panax notoginseng with supercritical carbon dioxide (CO2). Based on the theory of counter-flow mass transfer and the molecular mass transfer between the material and the supercritical CO2 fluid under differential mass-conservation equation, a dynamics model was established and computed to compare forecasting result with the experiment process. A dynamics model has been established for supercritical CO2 to extract the lipophilic components in Panax notoginseng, the computed result of this model was consistent with the experiment process basically. The supercritical fluid extract dynamics model established in this research can expound the mechanism in the extract process of which lipophilic components of Panax notoginseng dissolve the mass transfer and is tallied with the actual extract process. This provides certain instruction for the supercritical CO2 fluid extract' s industrialization enlargement.

Static and dynamic superheated water extraction of essential oil components from Thymus vulgaris L.

PubMed

Dawidowicz, Andrzej L; Rado, Ewelina; Wianowska, Dorota

2009-09-01

Superheated water extraction (SWE) performed in both static and dynamic condition (S-SWE and D-SWE, respectively) was applied for the extraction of essential oil from Thymus vulgaris L. The influence of extraction pressure, temperature, time, and flow rate on the total yield of essential oil and the influence of extraction temperature on the extraction of some chosen components are discussed in the paper. The SWE extracts are related to PLE extracts with n-hexane and essential oil obtained by steam distillation. The superheated water extraction in dynamic condition seems to be a feasible option for the extraction of essential oil components from T. vulgaris L.

Optimisation of supercritical carbon dioxide extraction of essential oil of flowers of tea (Camellia sinensis L.) plants and its antioxidative activity.

PubMed

Chen, Zhenchun; Mei, Xin; Jin, Yuxia; Kim, Eun-Hye; Yang, Ziyin; Tu, Youying

2014-01-30

To extract natural volatile compounds from tea (Camellia sinensis) flowers without thermal degradation and residue of organic solvents, supercritical fluid extraction (SFE) using carbon dioxide was employed to prepare essential oil of tea flowers in the present study. Four important parameters--pressure, temperature, static extraction time, and dynamic extraction time--were selected as independent variables in the SFE. The optimum extraction conditions were the pressure of 30 MPa, temperature of 50°C, static time of 10 min, and dynamic time of 90 min. Based on gas chromatography-mass spectrometry analysis, 59 compounds, including alkanes (45.4%), esters (10.5%), ketones (7.1%), aldehydes (3.7%), terpenes (3.7%), acids (2.1%), alcohols (1.6%), ethers (1.3%) and others (10.3%) were identified in the essential oil of tea flowers. Moreover, the essential oil of tea flowers showed relatively stronger DPPH radical scavenging activity than essential oils of geranium and peppermint, although its antioxidative activity was weaker than those of essential oil of clove, ascorbic acid, tert-butylhydroquinone, and butylated hydroxyanisole. Essential oil of tea flowers using SFE contained many types of volatile compounds and showed considerable DPPH scavenging activity. The information will contribute to the future application of tea flowers as raw materials in health-care food and food flavour industries. © 2013 Society of Chemical Industry.

Image Alignment for Multiple Camera High Dynamic Range Microscopy.

PubMed

Eastwood, Brian S; Childs, Elisabeth C

2012-01-09

This paper investigates the problem of image alignment for multiple camera high dynamic range (HDR) imaging. HDR imaging combines information from images taken with different exposure settings. Combining information from multiple cameras requires an alignment process that is robust to the intensity differences in the images. HDR applications that use a limited number of component images require an alignment technique that is robust to large exposure differences. We evaluate the suitability for HDR alignment of three exposure-robust techniques. We conclude that image alignment based on matching feature descriptors extracted from radiant power images from calibrated cameras yields the most accurate and robust solution. We demonstrate the use of this alignment technique in a high dynamic range video microscope that enables live specimen imaging with a greater level of detail than can be captured with a single camera.

Image Alignment for Multiple Camera High Dynamic Range Microscopy

PubMed Central

Eastwood, Brian S.; Childs, Elisabeth C.

2012-01-01

This paper investigates the problem of image alignment for multiple camera high dynamic range (HDR) imaging. HDR imaging combines information from images taken with different exposure settings. Combining information from multiple cameras requires an alignment process that is robust to the intensity differences in the images. HDR applications that use a limited number of component images require an alignment technique that is robust to large exposure differences. We evaluate the suitability for HDR alignment of three exposure-robust techniques. We conclude that image alignment based on matching feature descriptors extracted from radiant power images from calibrated cameras yields the most accurate and robust solution. We demonstrate the use of this alignment technique in a high dynamic range video microscope that enables live specimen imaging with a greater level of detail than can be captured with a single camera. PMID:22545028

Force and Stress along Simulated Dissociation Pathways of Cucurbituril-Guest Systems.

PubMed

Velez-Vega, Camilo; Gilson, Michael K

2012-03-13

The field of host-guest chemistry provides computationally tractable yet informative model systems for biomolecular recognition. We applied molecular dynamics simulations to study the forces and mechanical stresses associated with forced dissociation of aqueous cucurbituril-guest complexes with high binding affinities. First, the unbinding transitions were modeled with constant velocity pulling (steered dynamics) and a soft spring constant, to model atomic force microscopy (AFM) experiments. The computed length-force profiles yield rupture forces in good agreement with available measurements. We also used steered dynamics with high spring constants to generate paths characterized by a tight control over the specified pulling distance; these paths were then equilibrated via umbrella sampling simulations and used to compute time-averaged mechanical stresses along the dissociation pathways. The stress calculations proved to be informative regarding the key interactions determining the length-force profiles and rupture forces. In particular, the unbinding transition of one complex is found to be a stepwise process, which is initially dominated by electrostatic interactions between the guest's ammoniums and the host's carbonyl groups, and subsequently limited by the extraction of the guest's bulky bicyclooctane moiety; the latter step requires some bond stretching at the cucurbituril's extraction portal. Conversely, the dissociation of a second complex with a more slender guest is mainly driven by successive electrostatic interactions between the different guest's ammoniums and the host's carbonyl groups. The calculations also provide information on the origins of thermodynamic irreversibilities in these forced dissociation processes.

Inferring Small Scale Dynamics from Aircraft Measurements of Tracers

NASA Technical Reports Server (NTRS)

Sparling, L. C.; Einaudi, Franco (Technical Monitor)

2000-01-01

The millions of ER-2 and DC-8 aircraft measurements of long-lived tracers in the Upper Troposphere/Lower Stratosphere (UT/LS) hold enormous potential as a source of statistical information about subgrid scale dynamics. Extracting this information however can be extremely difficult because the measurements are made along a 1-D transect through fields that are highly anisotropic in all three dimensions. Some of the challenges and limitations posed by both the instrumentation and platform are illustrated within the context of the problem of using the data to obtain an estimate of the dissipation scale. This presentation will also include some tutorial remarks about the conditional and two-point statistics used in the analysis.

On the use of orientation filters for 3D reconstruction in event-driven stereo vision

PubMed Central

Camuñas-Mesa, Luis A.; Serrano-Gotarredona, Teresa; Ieng, Sio H.; Benosman, Ryad B.; Linares-Barranco, Bernabe

2014-01-01

The recently developed Dynamic Vision Sensors (DVS) sense visual information asynchronously and code it into trains of events with sub-micro second temporal resolution. This high temporal precision makes the output of these sensors especially suited for dynamic 3D visual reconstruction, by matching corresponding events generated by two different sensors in a stereo setup. This paper explores the use of Gabor filters to extract information about the orientation of the object edges that produce the events, therefore increasing the number of constraints applied to the matching algorithm. This strategy provides more reliably matched pairs of events, improving the final 3D reconstruction. PMID:24744694

Hdr Imaging for Feature Detection on Detailed Architectural Scenes

NASA Astrophysics Data System (ADS)

Kontogianni, G.; Stathopoulou, E. K.; Georgopoulos, A.; Doulamis, A.

2015-02-01

3D reconstruction relies on accurate detection, extraction, description and matching of image features. This is even truer for complex architectural scenes that pose needs for 3D models of high quality, without any loss of detail in geometry or color. Illumination conditions influence the radiometric quality of images, as standard sensors cannot depict properly a wide range of intensities in the same scene. Indeed, overexposed or underexposed pixels cause irreplaceable information loss and degrade digital representation. Images taken under extreme lighting environments may be thus prohibitive for feature detection/extraction and consequently for matching and 3D reconstruction. High Dynamic Range (HDR) images could be helpful for these operators because they broaden the limits of illumination range that Standard or Low Dynamic Range (SDR/LDR) images can capture and increase in this way the amount of details contained in the image. Experimental results of this study prove this assumption as they examine state of the art feature detectors applied both on standard dynamic range and HDR images.

Preliminary experiments on pharmacokinetic diffuse fluorescence tomography of CT-scanning mode

NASA Astrophysics Data System (ADS)

Zhang, Yanqi; Wang, Xin; Yin, Guoyan; Li, Jiao; Zhou, Zhongxing; Zhao, Huijuan; Gao, Feng; Zhang, Limin

2016-10-01

In vivo tomographic imaging of the fluorescence pharmacokinetic parameters in tissues can provide additional specific and quantitative physiological and pathological information to that of fluorescence concentration. This modality normally requires a highly-sensitive diffuse fluorescence tomography (DFT) working in dynamic way to finally extract the pharmacokinetic parameters from the measured pharmacokinetics-associated temporally-varying boundary intensity. This paper is devoted to preliminary experimental validation of our proposed direct reconstruction scheme of instantaneous sampling based pharmacokinetic-DFT: A highly-sensitive DFT system of CT-scanning mode working with parallel four photomultiplier-tube photon-counting channels is developed to generate an instantaneous sampling dataset; A direct reconstruction scheme then extracts images of the pharmacokinetic parameters using the adaptive-EKF strategy. We design a dynamic phantom that can simulate the agent metabolism in living tissue. The results of the dynamic phantom experiments verify the validity of the experiment system and reconstruction algorithms, and demonstrate that system provides good resolution, high sensitivity and quantitativeness at different pump speed.

Seeing is believing: on the use of image databases for visually exploring plant organelle dynamics.

PubMed

Mano, Shoji; Miwa, Tomoki; Nishikawa, Shuh-ichi; Mimura, Tetsuro; Nishimura, Mikio

2009-12-01

Organelle dynamics vary dramatically depending on cell type, developmental stage and environmental stimuli, so that various parameters, such as size, number and behavior, are required for the description of the dynamics of each organelle. Imaging techniques are superior to other techniques for describing organelle dynamics because these parameters are visually exhibited. Therefore, as the results can be seen immediately, investigators can more easily grasp organelle dynamics. At present, imaging techniques are emerging as fundamental tools in plant organelle research, and the development of new methodologies to visualize organelles and the improvement of analytical tools and equipment have allowed the large-scale generation of image and movie data. Accordingly, image databases that accumulate information on organelle dynamics are an increasingly indispensable part of modern plant organelle research. In addition, image databases are potentially rich data sources for computational analyses, as image and movie data reposited in the databases contain valuable and significant information, such as size, number, length and velocity. Computational analytical tools support image-based data mining, such as segmentation, quantification and statistical analyses, to extract biologically meaningful information from each database and combine them to construct models. In this review, we outline the image databases that are dedicated to plant organelle research and present their potential as resources for image-based computational analyses.

The economic value of remote sensing of earth resources from space: An ERTS overview and the value of continuity of service. Volume 7: Nonreplenishable natural resources: Minerals, fossil fuels and geothermal energy sources

NASA Technical Reports Server (NTRS)

Lietzke, K. R.

1974-01-01

The application of remotely-sensed information to the mineral, fossil fuel, and geothermal energy extraction industry is investigated. Public and private cost savings are documented in geologic mapping activities. Benefits and capabilities accruing to the ERS system are assessed. It is shown that remote sensing aids in resource extraction, as well as the monitoring of several dynamic phenomena, including disturbed lands, reclamation, erosion, glaciation, and volcanic and seismic activity.

Future Directions of Nonlinear Dynamics in Physical and Biological Systems. (Physica D Nonlinear. Volume 68, Number 1)

DTIC Science & Technology

1993-09-15

and structure of the equations. The Lagrangian for- c and we can extract information for any speed of mulation gives us an extremum principle for the...Dueholm and N.F. Pedersen, J. Appi. [261 For references on this see e.g. N.F. Pedersen, in: Phys. 60 (1986) 1447. SQUID 80, eds. H. Hahlbohm and H...obtained for arbitrary initial conditions, and a number of physical How do we augment the DNLSE (4) to treat features have been extracted [121. The

Feature extraction inspired by V1 in visual cortex

NASA Astrophysics Data System (ADS)

Lv, Chao; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Xin, Peng; Zhu, Mingning; Ma, Hongqiang

2018-04-01

Target feature extraction plays an important role in pattern recognition. It is the most complicated activity in the brain mechanism of biological vision. Inspired by high properties of primary visual cortex (V1) in extracting dynamic and static features, a visual perception model was raised. Firstly, 28 spatial-temporal filters with different orientations, half-squaring operation and divisive normalization were adopted to obtain the responses of V1 simple cells; then, an adjustable parameter was added to the output weight so that the response of complex cells was got. Experimental results indicate that the proposed V1 model can perceive motion information well. Besides, it has a good edge detection capability. The model inspired by V1 has good performance in feature extraction and effectively combines brain-inspired intelligence with computer vision.

A novel speech-processing strategy incorporating tonal information for cochlear implants.

PubMed

Lan, N; Nie, K B; Gao, S K; Zeng, F G

2004-05-01

Good performance in cochlear implant users depends in large part on the ability of a speech processor to effectively decompose speech signals into multiple channels of narrow-band electrical pulses for stimulation of the auditory nerve. Speech processors that extract only envelopes of the narrow-band signals (e.g., the continuous interleaved sampling (CIS) processor) may not provide sufficient information to encode the tonal cues in languages such as Chinese. To improve the performance in cochlear implant users who speak tonal language, we proposed and developed a novel speech-processing strategy, which extracted both the envelopes of the narrow-band signals and the fundamental frequency (F0) of the speech signal, and used them to modulate both the amplitude and the frequency of the electrical pulses delivered to stimulation electrodes. We developed an algorithm to extract the fundatmental frequency and identified the general patterns of pitch variations of four typical tones in Chinese speech. The effectiveness of the extraction algorithm was verified with an artificial neural network that recognized the tonal patterns from the extracted F0 information. We then compared the novel strategy with the envelope-extraction CIS strategy in human subjects with normal hearing. The novel strategy produced significant improvement in perception of Chinese tones, phrases, and sentences. This novel processor with dynamic modulation of both frequency and amplitude is encouraging for the design of a cochlear implant device for sensorineurally deaf patients who speak tonal languages.

Speed Limits: Orientation and Semantic Context Interactions Constrain Natural Scene Discrimination Dynamics

ERIC Educational Resources Information Center

Rieger, Jochem W.; Kochy, Nick; Schalk, Franziska; Gruschow, Marcus; Heinze, Hans-Jochen

2008-01-01

The visual system rapidly extracts information about objects from the cluttered natural environment. In 5 experiments, the authors quantified the influence of orientation and semantics on the classification speed of objects in natural scenes, particularly with regard to object-context interactions. Natural scene photographs were presented in an…

Fault detection method for railway wheel flat using an adaptive multiscale morphological filter

NASA Astrophysics Data System (ADS)

Li, Yifan; Zuo, Ming J.; Lin, Jianhui; Liu, Jianxin

2017-02-01

This study explores the capacity of the morphology analysis for railway wheel flat fault detection. A dynamic model of vehicle systems with 56 degrees of freedom was set up along with a wheel flat model to calculate the dynamic responses of axle box. The vehicle axle box vibration signal is complicated because it not only contains the information of wheel defect, but also includes track condition information. Thus, how to extract the influential features of wheels from strong background noise effectively is a typical key issue for railway wheel fault detection. In this paper, an algorithm for adaptive multiscale morphological filtering (AMMF) was proposed, and its effect was evaluated by a simulated signal. And then this algorithm was employed to study the axle box vibration caused by wheel flats, as well as the influence of track irregularity and vehicle running speed on diagnosis results. Finally, the effectiveness of the proposed method was verified by bench testing. Research results demonstrate that the AMMF extracts the influential characteristic of axle box vibration signals effectively and can diagnose wheel flat faults in real time.

Dynamic analysis and pattern visualization of forest fires.

PubMed

Lopes, António M; Tenreiro Machado, J A

2014-01-01

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

Dynamic Analysis and Pattern Visualization of Forest Fires

PubMed Central

Lopes, António M.; Tenreiro Machado, J. A.

2014-01-01

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns. PMID:25137393

Experimental Rectification of Entropy Production by Maxwell's Demon in a Quantum System

NASA Astrophysics Data System (ADS)

Camati, Patrice A.; Peterson, John P. S.; Batalhão, Tiago B.; Micadei, Kaonan; Souza, Alexandre M.; Sarthour, Roberto S.; Oliveira, Ivan S.; Serra, Roberto M.

2016-12-01

Maxwell's demon explores the role of information in physical processes. Employing information about microscopic degrees of freedom, this "intelligent observer" is capable of compensating entropy production (or extracting work), apparently challenging the second law of thermodynamics. In a modern standpoint, it is regarded as a feedback control mechanism and the limits of thermodynamics are recast incorporating information-to-energy conversion. We derive a trade-off relation between information-theoretic quantities empowering the design of an efficient Maxwell's demon in a quantum system. The demon is experimentally implemented as a spin-1 /2 quantum memory that acquires information, and employs it to control the dynamics of another spin-1 /2 system, through a natural interaction. Noise and imperfections in this protocol are investigated by the assessment of its effectiveness. This realization provides experimental evidence that the irreversibility in a nonequilibrium dynamics can be mitigated by assessing microscopic information and applying a feed-forward strategy at the quantum scale.

Experimental Rectification of Entropy Production by Maxwell's Demon in a Quantum System.

PubMed

Camati, Patrice A; Peterson, John P S; Batalhão, Tiago B; Micadei, Kaonan; Souza, Alexandre M; Sarthour, Roberto S; Oliveira, Ivan S; Serra, Roberto M

2016-12-09

Maxwell's demon explores the role of information in physical processes. Employing information about microscopic degrees of freedom, this "intelligent observer" is capable of compensating entropy production (or extracting work), apparently challenging the second law of thermodynamics. In a modern standpoint, it is regarded as a feedback control mechanism and the limits of thermodynamics are recast incorporating information-to-energy conversion. We derive a trade-off relation between information-theoretic quantities empowering the design of an efficient Maxwell's demon in a quantum system. The demon is experimentally implemented as a spin-1/2 quantum memory that acquires information, and employs it to control the dynamics of another spin-1/2 system, through a natural interaction. Noise and imperfections in this protocol are investigated by the assessment of its effectiveness. This realization provides experimental evidence that the irreversibility in a nonequilibrium dynamics can be mitigated by assessing microscopic information and applying a feed-forward strategy at the quantum scale.

HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition.

PubMed

Lagorce, Xavier; Orchard, Garrick; Galluppi, Francesco; Shi, Bertram E; Benosman, Ryad B

2017-07-01

This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.

Biomass Increases Go under Cover: Woody Vegetation Dynamics in South African Rangelands

PubMed Central

Mograbi, Penelope J.; Knapp, David E.; Martin, Roberta E.; Main, Russell

2015-01-01

Woody biomass dynamics are an expression of ecosystem function, yet biomass estimates do not provide information on the spatial distribution of woody vegetation within the vertical vegetation subcanopy. We demonstrate the ability of airborne light detection and ranging (LiDAR) to measure aboveground biomass and subcanopy structure, as an explanatory tool to unravel vegetation dynamics in structurally heterogeneous landscapes. We sampled three communal rangelands in Bushbuckridge, South Africa, utilised by rural communities for fuelwood harvesting. Woody biomass estimates ranged between 9 Mg ha-1 on gabbro geology sites to 27 Mg ha-1 on granitic geology sites. Despite predictions of woodland depletion due to unsustainable fuelwood extraction in previous studies, biomass in all the communal rangelands increased between 2008 and 2012. Annual biomass productivity estimates (10–14% p.a.) were higher than previous estimates of 4% and likely a significant contributor to the previous underestimations of modelled biomass supply. We show that biomass increases are attributable to growth of vegetation <5 m in height, and that, in the high wood extraction rangeland, 79% of the changes in the vertical vegetation subcanopy are gains in the 1-3m height class. The higher the wood extraction pressure on the rangelands, the greater the biomass increases in the low height classes within the subcanopy, likely a strong resprouting response to intensive harvesting. Yet, fuelwood shortages are still occurring, as evidenced by the losses in the tall tree height class in the high extraction rangeland. Loss of large trees and gain in subcanopy shrubs could result in a structurally simple landscape with reduced functional capacity. This research demonstrates that intensive harvesting can, paradoxically, increase biomass and this has implications for the sustainability of ecosystem service provision. The structural implications of biomass increases in communal rangelands could be misinterpreted as woodland recovery in the absence of three-dimensional, subcanopy information. PMID:25969985

Validating clustering of molecular dynamics simulations using polymer models.

PubMed

Phillips, Joshua L; Colvin, Michael E; Newsam, Shawn

2011-11-14

Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers.

Validating clustering of molecular dynamics simulations using polymer models

PubMed Central

2011-01-01

Background Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. Results We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. Conclusions We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers. PMID:22082218

Prediction and validation of protein intermediate states from structurally rich ensembles and coarse-grained simulations

NASA Astrophysics Data System (ADS)

Orellana, Laura; Yoluk, Ozge; Carrillo, Oliver; Orozco, Modesto; Lindahl, Erik

2016-08-01

Protein conformational changes are at the heart of cell functions, from signalling to ion transport. However, the transient nature of the intermediates along transition pathways hampers their experimental detection, making the underlying mechanisms elusive. Here we retrieve dynamic information on the actual transition routes from principal component analysis (PCA) of structurally-rich ensembles and, in combination with coarse-grained simulations, explore the conformational landscapes of five well-studied proteins. Modelling them as elastic networks in a hybrid elastic-network Brownian dynamics simulation (eBDIMS), we generate trajectories connecting stable end-states that spontaneously sample the crystallographic motions, predicting the structures of known intermediates along the paths. We also show that the explored non-linear routes can delimit the lowest energy passages between end-states sampled by atomistic molecular dynamics. The integrative methodology presented here provides a powerful framework to extract and expand dynamic pathway information from the Protein Data Bank, as well as to validate sampling methods in general.

Role of core excitation in ( d , p ) transfer reactions

DOE PAGES

Deltuva, A.; Ross, A.; Norvaišas, E.; ...

2016-10-24

In our recent work we found that core excitations can be important in extracting structure information from (d,p) reactions. Our objective is to systematically explore the role of core excitation in (d,p) reactions and to understand the origin of the dynamical effects. Based on the particle-rotor model of n+Be 10, we generate a number of models with a range of separation energies (S n=0.1–5.0 MeV), while maintaining a significant core excited component. We then apply the latest extension of the momentum-space-based Faddeev method, including dynamical core excitation in the reaction mechanism to all orders, to the Be 10(d,p)Be 11-like reactions,more » and study the excitation effects for beam energies E d=15–90 MeV. We study the resulting angular distributions and the differences between the spectroscopic factor that would be extracted from the cross sections, when including dynamical core excitation in the reaction, and that of the original structure model. We also explore how different partial waves affect the final cross section. Our results show a strong beam-energy dependence of the extracted spectroscopic factors that become smaller for intermediate beam energies. Finally, this dependence increases for loosely bound systems.« less

The potential of satellite data to study individual wildfire events

NASA Astrophysics Data System (ADS)

Benali, Akli; López-Saldana, Gerardo; Russo, Ana; Sá, Ana C. L.; Pinto, Renata M. S.; Nikos, Koutsias; Owen, Price; Pereira, Jose M. C.

2014-05-01

Large wildfires have important social, economic and environmental impacts. In order to minimize their impacts, understand their main drivers and study their dynamics, different approaches have been used. The reconstruction of individual wildfire events is usually done by collection of field data, interviews and by implementing fire spread simulations. All these methods have clear limitations in terms of spatial and temporal coverage, accuracy, subjectivity of the collected information and lack of objective independent validation information. In this sense, remote sensing is a promising tool with the potential to provide relevant information for stakeholders and the research community, by complementing or filling gaps in existing information and providing independent accurate quantitative information. In this work we show the potential of satellite data to provide relevant information regarding the dynamics of individual large wildfire events, filling an important gap in wildfire research. We show how MODIS active-fire data, acquired up to four times per day, and satellite-derived burnt perimeters can be combined to extract relevant information wildfire events by describing the methods involved and presenting results for four regions of the world: Portugal, Greece, SE Australia and California. The information that can be retrieved encompasses the start and end date of a wildfire event and its ignition area. We perform an evaluation of the information retrieved by comparing the satellite-derived parameters with national databases, highlighting the strengths and weaknesses of both and showing how the former can complement the latter leading to more complete and accurate datasets. We also show how the spatio-temporal distribution of wildfire spread dynamics can be reconstructed using satellite-derived active-fires and how relevant descriptors can be extracted. Applying graph theory to satellite active-fire data, we define the major fire spread paths that yield information about the major spatial corridors through which fires spread, and their relative importance in the full fire event. These major fire paths are then used to extract relevant descriptors, such as the distribution of fire spread direction, rate of spread and fire intensity (i.e. energy emitted). The reconstruction of the fire spread is shown for some case studies for Portugal and is also compared with fire progressions obtained by air-borne sensors for SE Australia. The approach shows solid results, providing a valuable tool for the reconstruction of individual fire events, understand their complex spread patterns and their main drivers of fire propagation. The major fire pathsand the spatio-temporal distribution of active fires are being currently combined with fire spread simulations within the scope oftheFIRE-MODSATproject, to provideuseful information to support and improve fire suppression strategies.

Motion Estimation and Compensation Strategies in Dynamic Computerized Tomography

NASA Astrophysics Data System (ADS)

Hahn, Bernadette N.

2017-12-01

A main challenge in computerized tomography consists in imaging moving objects. Temporal changes during the measuring process lead to inconsistent data sets, and applying standard reconstruction techniques causes motion artefacts which can severely impose a reliable diagnostics. Therefore, novel reconstruction techniques are required which compensate for the dynamic behavior. This article builds on recent results from a microlocal analysis of the dynamic setting, which enable us to formulate efficient analytic motion compensation algorithms for contour extraction. Since these methods require information about the dynamic behavior, we further introduce a motion estimation approach which determines parameters of affine and certain non-affine deformations directly from measured motion-corrupted Radon-data. Our methods are illustrated with numerical examples for both types of motion.

Solving of the coefficient inverse problems for a nonlinear singularly perturbed reaction-diffusion-advection equation with the final time data

NASA Astrophysics Data System (ADS)

Lukyanenko, D. V.; Shishlenin, M. A.; Volkov, V. T.

2018-01-01

We propose the numerical method for solving coefficient inverse problem for a nonlinear singularly perturbed reaction-diffusion-advection equation with the final time observation data based on the asymptotic analysis and the gradient method. Asymptotic analysis allows us to extract a priory information about interior layer (moving front), which appears in the direct problem, and boundary layers, which appear in the conjugate problem. We describe and implement the method of constructing a dynamically adapted mesh based on this a priory information. The dynamically adapted mesh significantly reduces the complexity of the numerical calculations and improve the numerical stability in comparison with the usual approaches. Numerical example shows the effectiveness of the proposed method.

Malware analysis using visualized image matrices.

PubMed

Han, KyoungSoo; Kang, BooJoong; Im, Eul Gyu

2014-01-01

This paper proposes a novel malware visual analysis method that contains not only a visualization method to convert binary files into images, but also a similarity calculation method between these images. The proposed method generates RGB-colored pixels on image matrices using the opcode sequences extracted from malware samples and calculates the similarities for the image matrices. Particularly, our proposed methods are available for packed malware samples by applying them to the execution traces extracted through dynamic analysis. When the images are generated, we can reduce the overheads by extracting the opcode sequences only from the blocks that include the instructions related to staple behaviors such as functions and application programming interface (API) calls. In addition, we propose a technique that generates a representative image for each malware family in order to reduce the number of comparisons for the classification of unknown samples and the colored pixel information in the image matrices is used to calculate the similarities between the images. Our experimental results show that the image matrices of malware can effectively be used to classify malware families both statically and dynamically with accuracy of 0.9896 and 0.9732, respectively.

Putting Humpty together and pulling him apart: accessing and unbinding the hippocampal item-context engram.

PubMed

Sadeh, Talya; Maril, Anat; Bitan, Tali; Goshen-Gottstein, Yonatan

2012-03-01

A remarkable act of memory entails binding different forms of information. We focus on the timeless question of how the bound engram is accessed such that its component features-item and context-are extracted. To shed light on this question, we investigate the dynamics between brain structures that together mediate the binding and extraction of item and context. Converging evidence has implicated the Parahippocampal cortex (PHc) in contextual processing, the Perirhinal cortex (PRc) in item processing, and the hippocampus in item-context binding. Effective connectivity analysis was conducted on fMRI data gathered during retrieval on tests that differ with regard to the to-be-extracted information. Results revealed that recall is initiated by context-related PHc activity, followed by hippocampal item-context engram activation, and completed with retrieval of the study-item by the PRc. The reverse path was found for recognition. We thus provide novel evidence for dissociative patterns of item-context unbinding during retrieval. Copyright © 2011 Elsevier Inc. All rights reserved.

Identification of degradation products in loxoprofen sodium adhesive tapes by liquid chromatography-mass spectrometry and dynamic pressurized liquid extraction-solid-phase extraction coupled to liquid chromatography-nuclear magnetic resonance spectroscopy.

PubMed

Murakami, Tomonori; Kawasaki, Takao; Takemura, Akira; Fukutsu, Naoto; Kishi, Naoyuki; Kusu, Fumiyo

2008-10-24

Rapid and unambiguous identification of three degradation products (DP-1, DP-2 and DP-3) found in heat-stressed loxoprofen sodium adhesive tapes (Loxonin tapes) was achieved by LC-MS and dynamic pressurized liquid extraction (PLE)-solid-phase extraction (SPE) coupled to LC-NMR without complicated isolation or purification processes. The molecular formulae of the degradation products were determined by accurate mass measurements and product ion analyses and on-line hydrogen/deuterium (H/D) exchange experiments provided information about changes in the degradation of loxoprofen. To compensate for the low sensitivity of NMR, on-line dynamic PLE-SPE was employed and higher concentrations of degradation products trapped on the SPE column were afforded in a shorter time than they would be in such time-consuming sample preparations as pre-concentration after extraction. The loop-storage procedure was used in the LC-NMR analysis to allow the acquisition of the (1)H spectra of the three degradation products in one chromatographic run without affecting the peak separation and to avoid the carry-over of previously eluted DP-1 of high concentration by washing the NMR detection cell prior to the measurement of the DP-2 spectrum. Based on the resulting (1)H NMR spectra in combination with the MS results, DP-1 was successfully identified as an oxidation product having an oxodicarboxylic acid structure formed by the cleavage of the cyclopentanone ring of loxoprofen, DP-2 as a cyclopentanone ring-hydroxylated loxoprofen and DP-3 as a loxoprofen l-menthol ester.

Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity

PubMed Central

Yeh, Hsiang J.; Guindani, Michele; Vannucci, Marina; Haneef, Zulfi; Stern, John M.

2018-01-01

Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation. To computationally handle this increased complexity, a limited set of dFC properties, primarily the mean and variance, have generally been considered. Additionally, it remains unclear how to integrate the increased information from dFC into pattern recognition techniques for subject-level prediction. In this study, we propose an approach to address these two issues based on a large number of previously unexplored temporal and spectral features of dynamic functional connectivity. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to estimate time-varying patterns of functional connectivity between resting-state networks. Time-frequency analysis is then performed on dFC estimates, and a large number of previously unexplored temporal and spectral features drawn from signal processing literature are extracted for dFC estimates. We apply the investigated features to two neurologic populations of interest, healthy controls and patients with temporal lobe epilepsy, and show that the proposed approach leads to substantial increases in predictive performance compared to both traditional estimates of static connectivity as well as current approaches to dFC. Variable importance is assessed and shows that there are several quantities that can be extracted from dFC signal which are more informative than the traditional mean or variance of dFC. This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function. PMID:29320526

A signal processing framework for simultaneous detection of multiple environmental contaminants

NASA Astrophysics Data System (ADS)

Chakraborty, Subhadeep; Manahan, Michael P.; Mench, Matthew M.

2013-11-01

The possibility of large-scale attacks using chemical warfare agents (CWAs) has exposed the critical need for fundamental research enabling the reliable, unambiguous and early detection of trace CWAs and toxic industrial chemicals. This paper presents a unique approach for the identification and classification of simultaneously present multiple environmental contaminants by perturbing an electrochemical (EC) sensor with an oscillating potential for the extraction of statistically rich information from the current response. The dynamic response, being a function of the degree and mechanism of contamination, is then processed with a symbolic dynamic filter for the extraction of representative patterns, which are then classified using a trained neural network. The approach presented in this paper promises to extend the sensing power and sensitivity of these EC sensors by augmenting and complementing sensor technology with state-of-the-art embedded real-time signal processing capabilities.

Transition Characteristic Analysis of Traffic Evolution Process for Urban Traffic Network

PubMed Central

Chen, Hong; Li, Yang

2014-01-01

The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights into traffic dynamics in the temporal domain, this paper explored temporal characteristics and distinct regularity in the traffic evolution process of urban traffic network. We defined traffic state pattern through clustering multidimensional traffic time series using self-organizing maps and construct a pattern transition network model that is appropriate for representing and analyzing the evolution progress. The methodology is illustrated by an application to data flow rate of multiple road sections from Network of Shenzhen's Nanshan District, China. Analysis and numerical results demonstrated that the methodology permits extracting many useful traffic transition characteristics including stability, preference, activity, and attractiveness. In addition, more information about the relationships between these characteristics was extracted, which should be helpful in understanding the complex behavior of the temporal evolution features of traffic patterns. PMID:24982969

Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force.

PubMed

Potluri, Chandrasekhar; Anugolu, Madhavi; Chiu, Steve; Urfer, Alex; Schoen, Marco P; Naidu, D Subbaram

2012-01-01

In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.

Sparse learning of stochastic dynamical equations

NASA Astrophysics Data System (ADS)

Boninsegna, Lorenzo; Nüske, Feliks; Clementi, Cecilia

2018-06-01

With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Nonlinear Dynamics (SINDy) has been introduced to identify the governing equations of dynamical systems from simulation data. In this study, we extend SINDy to stochastic dynamical systems which are frequently used to model biophysical processes. We prove the asymptotic correctness of stochastic SINDy in the infinite data limit, both in the original and projected variables. We discuss algorithms to solve the sparse regression problem arising from the practical implementation of SINDy and show that cross validation is an essential tool to determine the right level of sparsity. We demonstrate the proposed methodology on two test systems, namely, the diffusion in a one-dimensional potential and the projected dynamics of a two-dimensional diffusion process.

Interaction of spatially separated oscillating solitons in biased two-photon photorefractive materials

NASA Astrophysics Data System (ADS)

Asif, Noushin; Biswas, Anjan; Jovanoski, Z.; Konar, S.

2015-01-01

This paper presents the dynamics of two spatially separated optical solitons in two-photon photorefractive materials. The variational formalism has been employed to derive evolution equations of different parameters which characterize the dynamics of two interacting solitons. This approach yields a system of coupled ordinary differential equations for evolution of different parameters characterizing solitons such as amplitude, spatial width, chirp, center of gravity, etc., which have been subsequently solved adopting numerical method to extract information on their dynamics. Depending on their initial separation and power, solitons are shown to either disperse or compresses individually and attract each other. Dragging and trapping of a probe soliton by another pump have been discussed.

Phenomenological analysis of medical time series with regular and stochastic components

NASA Astrophysics Data System (ADS)

Timashev, Serge F.; Polyakov, Yuriy S.

2007-06-01

Flicker-Noise Spectroscopy (FNS), a general approach to the extraction and parameterization of resonant and stochastic components contained in medical time series, is presented. The basic idea of FNS is to treat the correlation links present in sequences of different irregularities, such as spikes, "jumps", and discontinuities in derivatives of different orders, on all levels of the spatiotemporal hierarchy of the system under study as main information carriers. The tools to extract and analyze the information are power spectra and difference moments (structural functions), which complement the information of each other. The structural function stochastic component is formed exclusively by "jumps" of the dynamic variable while the power spectrum stochastic component is formed by both spikes and "jumps" on every level of the hierarchy. The information "passport" characteristics that are determined by fitting the derived expressions to the experimental variations for the stochastic components of power spectra and structural functions are interpreted as the correlation times and parameters that describe the rate of "memory loss" on these correlation time intervals for different irregularities. The number of the extracted parameters is determined by the requirements of the problem under study. Application of this approach to the analysis of tremor velocity signals for a Parkinsonian patient is discussed.

Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy

NASA Astrophysics Data System (ADS)

Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee

2016-04-01

Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.

Learning of spatio-temporal codes in a coupled oscillator system.

PubMed

Orosz, Gábor; Ashwin, Peter; Townley, Stuart

2009-07-01

In this paper, we consider a learning strategy that allows one to transmit information between two coupled phase oscillator systems (called teaching and learning systems) via frequency adaptation. The dynamics of these systems can be modeled with reference to a number of partially synchronized cluster states and transitions between them. Forcing the teaching system by steady but spatially nonhomogeneous inputs produces cyclic sequences of transitions between the cluster states, that is, information about inputs is encoded via a "winnerless competition" process into spatio-temporal codes. The large variety of codes can be learned by the learning system that adapts its frequencies to those of the teaching system. We visualize the dynamics using "weighted order parameters (WOPs)" that are analogous to "local field potentials" in neural systems. Since spatio-temporal coding is a mechanism that appears in olfactory systems, the developed learning rules may help to extract information from these neural ensembles.

Advanced spectral methods for climatic time series

USGS Publications Warehouse

Ghil, M.; Allen, M.R.; Dettinger, M.D.; Ide, K.; Kondrashov, D.; Mann, M.E.; Robertson, A.W.; Saunders, A.; Tian, Y.; Varadi, F.; Yiou, P.

2002-01-01

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss signal- to-noise enhancement, and present some of the novel methods for spectral analysis. The various steps, as well as the advantages and disadvantages of these methods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate variability and is used extensively in its prediction. Regional and global sea surface temperature data sets are used to illustrate multivariate spectral methods. Open questions and further prospects conclude the review.

Handling of subpixel structures in the application of satellite derived irradiance data for solar energy system analysis - a review

NASA Astrophysics Data System (ADS)

Beyer, Hans Georg

2016-04-01

With the increasing availability of satellite derived irradiance information, this type of data set is more and more in use for the design and operation of solar energy systems, most notably PV- and CSP-systems. By this, the need for data measured on-site is reduced. However, due to basic limitations of the satellite-derived data, several requirements put by the intended application cannot be coped with this data type directly. Traw satellite information has to be enhanced in both space and time resolution by additional information to be fully applicable for all aspects of the modelling od solar energy systems. To cope with this problem, several individual and collaborative projects had been performed in the recent years or are ongoing. Approaches are on one hand based on pasting synthesized high-resolution data into the low-resolution original sets. Pre-requite is an appropriate model, validated against real world data. For the case of irradiance data, these models can be extracted either directly from ground measured data sets or from data referring to the cloud situation as gained from the images of sky cameras or from monte -carlo initialized physical models. The current models refer to the spatial structure of the cloud fields. Dynamics are imposed by moving the cloud structures according to a large scale cloud motion vector, either extracted from the dynamics interfered from consecutive satellite images or taken from a meso-scale meteorological model. Dynamic irradiance information is then derived from the cloud field structure and the cloud motion vector. This contribution, which is linked to subtask A - Solar Resource Applications for High Penetration of Solar Technologies - of IEA SHC task 46, will present the different approaches and discuss examples in view of validation, need for auxiliary information and respective general applicability.

Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research.

PubMed

Marshall, Deborah A; Burgos-Liz, Lina; Pasupathy, Kalyan S; Padula, William V; IJzerman, Maarten J; Wong, Peter K; Higashi, Mitchell K; Engbers, Jordan; Wiebe, Samuel; Crown, William; Osgood, Nathaniel D

2016-02-01

In the era of the Information Age and personalized medicine, healthcare delivery systems need to be efficient and patient-centred. The health system must be responsive to individual patient choices and preferences about their care, while considering the system consequences. While dynamic simulation modelling (DSM) and big data share characteristics, they present distinct and complementary value in healthcare. Big data and DSM are synergistic-big data offer support to enhance the application of dynamic models, but DSM also can greatly enhance the value conferred by big data. Big data can inform patient-centred care with its high velocity, volume, and variety (the three Vs) over traditional data analytics; however, big data are not sufficient to extract meaningful insights to inform approaches to improve healthcare delivery. DSM can serve as a natural bridge between the wealth of evidence offered by big data and informed decision making as a means of faster, deeper, more consistent learning from that evidence. We discuss the synergies between big data and DSM, practical considerations and challenges, and how integrating big data and DSM can be useful to decision makers to address complex, systemic health economics and outcomes questions and to transform healthcare delivery.

Research and technology, fiscal year 1985

NASA Technical Reports Server (NTRS)

1985-01-01

Capabilities in spacecraft subsystems, sensors, space communications and navigation, the acquisition of data from space missions and the extraction of information from that data were reviewed. The use of satellite data in the study of the Earth's atmosphere and climate, the dynamics of its crust and the monitoring of land and water resources were examined. A review of NASA flight projects for 1985 was presented.

The Rapidity Density Distributions and Longitudinal Expansion Dynamics of Identified Pions from the STAR Beam Energy Scan

NASA Astrophysics Data System (ADS)

Flores, Christopher E.

2016-12-01

The Beam Energy Scan (BES) at the Relativistic Heavy-Ion Collider was proposed to characterize the properties of the medium produced in heavy-ion interactions over a broad range of baryon chemical potential. The aptitude of the STAR detector for mid-rapidity measurements has previously been leveraged to measure identified particle yields and spectra to extract bulk properties for the BES energies for | y | ≤ 0.1. However, to extract information on expansion dynamics and full phase space particle production, it is necessary to study identified particle rapidity density distributions. We present the first rapidity density distributions of identified pions from Au+Au collisions at √{sNN} = 7.7 , 11.5, and 19.6 GeV from the BES program as measured by the STAR detector. We use these distributions to obtain the full phase space yields of the pions to provide additional information of the system's chemistry. Further, we report the width of the rapidity density distributions compared to the width expected from Landau hydrodynamics. Finally, we interpret the results as a function of collision energy and discuss them in the context of previous energy scans done at the AGS and SPS.

Price dynamics in political prediction markets

PubMed Central

Majumder, Saikat Ray; Diermeier, Daniel; Rietz, Thomas A.; Amaral, Luís A. Nunes

2009-01-01

Prediction markets, in which contract prices are used to forecast future events, are increasingly applied to various domains ranging from political contests to scientific breakthroughs. However, the dynamics of such markets are not well understood. Here, we study the return dynamics of the oldest, most data-rich prediction markets, the Iowa Electronic Presidential Election “winner-takes-all” markets. As with other financial markets, we find uncorrelated returns, power-law decaying volatility correlations, and, usually, power-law decaying distributions of returns. However, unlike other financial markets, we find conditional diverging volatilities as the contract settlement date approaches. We propose a dynamic binary option model that captures all features of the empirical data and can potentially provide a tool with which one may extract true information events from a price time series. PMID:19155442

Collisionless Dynamics and the Cosmic Web

NASA Astrophysics Data System (ADS)

Hahn, Oliver

2016-10-01

I review the nature of three-dimensional collapse in the Zeldovich approximation, how it relates to the underlying nature of the three-dimensional Lagrangian manifold and naturally gives rise to a hierarchical structure formation scenario that progresses through collapse from voids to pancakes, filaments and then halos. I then discuss how variations of the Zeldovich approximation (based on the gravitational or the velocity potential) have been used to define classifications of the cosmic large-scale structure into dynamically distinct parts. Finally, I turn to recent efforts to devise new approaches relying on tessellations of the Lagrangian manifold to follow the fine-grained dynamics of the dark matter fluid into the highly non-linear regime and both extract the maximum amount of information from existing simulations as well as devise new simulation techniques for cold collisionless dynamics.

Mode extraction on wind turbine blades via phase-based video motion estimation

NASA Astrophysics Data System (ADS)

Sarrafi, Aral; Poozesh, Peyman; Niezrecki, Christopher; Mao, Zhu

2017-04-01

In recent years, image processing techniques are being applied more often for structural dynamics identification, characterization, and structural health monitoring. Although as a non-contact and full-field measurement method, image processing still has a long way to go to outperform other conventional sensing instruments (i.e. accelerometers, strain gauges, laser vibrometers, etc.,). However, the technologies associated with image processing are developing rapidly and gaining more attention in a variety of engineering applications including structural dynamics identification and modal analysis. Among numerous motion estimation and image-processing methods, phase-based video motion estimation is considered as one of the most efficient methods regarding computation consumption and noise robustness. In this paper, phase-based video motion estimation is adopted for structural dynamics characterization on a 2.3-meter long Skystream wind turbine blade, and the modal parameters (natural frequencies, operating deflection shapes) are extracted. Phase-based video processing adopted in this paper provides reliable full-field 2-D motion information, which is beneficial for manufacturing certification and model updating at the design stage. The phase-based video motion estimation approach is demonstrated through processing data on a full-scale commercial structure (i.e. a wind turbine blade) with complex geometry and properties, and the results obtained have a good correlation with the modal parameters extracted from accelerometer measurements, especially for the first four bending modes, which have significant importance in blade characterization.

Manipulating the content of dynamic natural scenes to characterize response in human MT/MST.

PubMed

Durant, Szonya; Wall, Matthew B; Zanker, Johannes M

2011-09-09

Optic flow is one of the most important sources of information for enabling human navigation through the world. A striking finding from single-cell studies in monkeys is the rapid saturation of response of MT/MST areas with the density of optic flow type motion information. These results are reflected psychophysically in human perception in the saturation of motion aftereffects. We began by comparing responses to natural optic flow scenes in human visual brain areas to responses to the same scenes with inverted contrast (photo negative). This changes scene familiarity while preserving local motion signals. This manipulation had no effect; however, the response was only correlated with the density of local motion (calculated by a motion correlation model) in V1, not in MT/MST. To further investigate this, we manipulated the visible proportion of natural dynamic scenes and found that areas MT and MST did not increase in response over a 16-fold increase in the amount of information presented, i.e., response had saturated. This makes sense in light of the sparseness of motion information in natural scenes, suggesting that the human brain is well adapted to exploit a small amount of dynamic signal and extract information important for survival.

Cooperative dynamics in auditory brain response

NASA Astrophysics Data System (ADS)

Kwapień, J.; DrożdŻ, S.; Liu, L. C.; Ioannides, A. A.

1998-11-01

Simultaneous estimates of activity in the left and right auditory cortex of five normal human subjects were extracted from multichannel magnetoencephalography recordings. Left, right, and binaural stimulations were used, in separate runs, for each subject. The resulting time series of left and right auditory cortex activity were analyzed using the concept of mutual information. The analysis constitutes an objective method to address the nature of interhemispheric correlations in response to auditory stimulations. The results provide clear evidence of the occurrence of such correlations mediated by a direct information transport, with clear laterality effects: as a rule, the contralateral hemisphere leads by 10-20 ms, as can be seen in the average signal. The strength of the interhemispheric coupling, which cannot be extracted from the average data, is found to be highly variable from subject to subject, but remarkably stable for each subject.

Evaluation of aerodynamic derivatives from a magnetic balance system

NASA Technical Reports Server (NTRS)

Raghunath, B. S.; Parker, H. M.

1972-01-01

The dynamic testing of a model in the University of Virginia cold magnetic balance wind-tunnel facility is expected to consist of measurements of the balance forces and moments, and the observation of the essentially six degree of freedom motion of the model. The aerodynamic derivatives of the model are to be evaluated from these observations. The basic feasibility of extracting aerodynamic information from the observation of a model which is executing transient, complex, multi-degree of freedom motion is demonstrated. It is considered significant that, though the problem treated here involves only linear aerodynamics, the methods used are capable of handling a very large class of aerodynamic nonlinearities. The basic considerations include the effect of noise in the data on the accuracy of the extracted information. Relationships between noise level and the accuracy of the evaluated aerodynamic derivatives are presented.

Probing the holographic principle using dynamical gauge effects from open spin-orbit coupling

NASA Astrophysics Data System (ADS)

Zhao, Jianshi; Price, Craig; Liu, Qi; Gemelke, Nathan

2016-05-01

Dynamical gauge fields result from locally defined symmetries and an effective over-labeling of quantum states. Coupling atoms weakly to a reservoir of laser modes can create an effective dynamical gauge field purely due to the disregard of information in the optical states. Here we report measurements revealing effects of open spin-orbit coupling in a system where an effective model can be formed from a non-abelian SU(2) × U(1) field theory following the Yang-Mills construct. Forming a close analogy to dynamical gauge effects in quantum chromodynamics, we extract a measure of atomic motion which reveals the analog of a closing mass gap for the relevant gauge boson, shedding insight on long standing open problems in gauge-fixing scale anomalies. Using arguments following the holographic principle, we measure scaling relations which can be understood by quantifying information present in the local potential. New prospects using these techniques for developing fractionalization of multi-particle and macroscopic systems using dissipative and non-abelian gauge fields will also be discussed. We acknowledge support from NSF Award No. 1068570, and the Charles E. Kaufman Foundation.

Gesture-Controlled Interfaces for Self-Service Machines

NASA Technical Reports Server (NTRS)

Cohen, Charles J.; Beach, Glenn

2006-01-01

Gesture-controlled interfaces are software- driven systems that facilitate device control by translating visual hand and body signals into commands. Such interfaces could be especially attractive for controlling self-service machines (SSMs) for example, public information kiosks, ticket dispensers, gasoline pumps, and automated teller machines (see figure). A gesture-controlled interface would include a vision subsystem comprising one or more charge-coupled-device video cameras (at least two would be needed to acquire three-dimensional images of gestures). The output of the vision system would be processed by a pure software gesture-recognition subsystem. Then a translator subsystem would convert a sequence of recognized gestures into commands for the SSM to be controlled; these could include, for example, a command to display requested information, change control settings, or actuate a ticket- or cash-dispensing mechanism. Depending on the design and operational requirements of the SSM to be controlled, the gesture-controlled interface could be designed to respond to specific static gestures, dynamic gestures, or both. Static and dynamic gestures can include stationary or moving hand signals, arm poses or motions, and/or whole-body postures or motions. Static gestures would be recognized on the basis of their shapes; dynamic gestures would be recognized on the basis of both their shapes and their motions. Because dynamic gestures include temporal as well as spatial content, this gesture- controlled interface can extract more information from dynamic than it can from static gestures.

Automated Fluid Feature Extraction from Transient Simulations

NASA Technical Reports Server (NTRS)

Haimes, Robert; Lovely, David

1999-01-01

In the past, feature extraction and identification were interesting concepts, but not required to understand the underlying physics of a steady flow field. This is because the results of the more traditional tools like iso-surfaces, cuts and streamlines were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of much interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one "snap-shot" of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for co-processing environments like pV3). And methods must be developed to abstract the feature and display it in a manner that physically makes sense. The following is a list of the important physical phenomena found in transient (and steady-state) fluid flow: (1) Shocks, (2) Vortex cores, (3) Regions of recirculation, (4) Boundary layers, (5) Wakes. Three papers and an initial specification for the (The Fluid eXtraction tool kit) FX Programmer's guide were included. The papers, submitted to the AIAA Computational Fluid Dynamics Conference, are entitled : (1) Using Residence Time for the Extraction of Recirculation Regions, (2) Shock Detection from Computational Fluid Dynamics results and (3) On the Velocity Gradient Tensor and Fluid Feature Extraction.

Using Medical Text Extraction, Reasoning and Mapping System (MTERMS) to Process Medication Information in Outpatient Clinical Notes

PubMed Central

Zhou, Li; Plasek, Joseph M; Mahoney, Lisa M; Karipineni, Neelima; Chang, Frank; Yan, Xuemin; Chang, Fenny; Dimaggio, Dana; Goldman, Debora S.; Rocha, Roberto A.

2011-01-01

Clinical information is often coded using different terminologies, and therefore is not interoperable. Our goal is to develop a general natural language processing (NLP) system, called Medical Text Extraction, Reasoning and Mapping System (MTERMS), which encodes clinical text using different terminologies and simultaneously establishes dynamic mappings between them. MTERMS applies a modular, pipeline approach flowing from a preprocessor, semantic tagger, terminology mapper, context analyzer, and parser to structure inputted clinical notes. Evaluators manually reviewed 30 free-text and 10 structured outpatient clinical notes compared to MTERMS output. MTERMS achieved an overall F-measure of 90.6 and 94.0 for free-text and structured notes respectively for medication and temporal information. The local medication terminology had 83.0% coverage compared to RxNorm’s 98.0% coverage for free-text notes. 61.6% of mappings between the terminologies are exact match. Capture of duration was significantly improved (91.7% vs. 52.5%) from systems in the third i2b2 challenge. PMID:22195230

3D imaging of translucent media with a plenoptic sensor based on phase space optics

NASA Astrophysics Data System (ADS)

Zhang, Xuanzhe; Shu, Bohong; Du, Shaojun

2015-05-01

Traditional stereo imaging technology is not working for dynamical translucent media, because there are no obvious characteristic patterns on it and it's not allowed using multi-cameras in most cases, while phase space optics can solve the problem, extracting depth information directly from "space-spatial frequency" distribution of the target obtained by plenoptic sensor with single lens. This paper discussed the presentation of depth information in phase space data, and calculating algorithms with different transparency. A 3D imaging example of waterfall was given at last.

A Novel Dynamic Update Framework for Epileptic Seizure Prediction

PubMed Central

Wang, Minghui; Hong, Xiaojun; Han, Jie

2014-01-01

Epileptic seizure prediction is a difficult problem in clinical applications, and it has the potential to significantly improve the patients' daily lives whose seizures cannot be controlled by either drugs or surgery. However, most current studies of epileptic seizure prediction focus on high sensitivity and low false-positive rate only and lack the flexibility for a variety of epileptic seizures and patients' physical conditions. Therefore, a novel dynamic update framework for epileptic seizure prediction is proposed in this paper. In this framework, two basic sample pools are constructed and updated dynamically. Furthermore, the prediction model can be updated to be the most appropriate one for the prediction of seizures' arrival. Mahalanobis distance is introduced in this part to solve the problem of side information, measuring the distance between two data sets. In addition, a multichannel feature extraction method based on Hilbert-Huang transform and extreme learning machine is utilized to extract the features of a patient's preseizure state against the normal state. At last, a dynamic update epileptic seizure prediction system is built up. Simulations on Freiburg database show that the proposed system has a better performance than the one without update. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices. PMID:25050381

A novel dynamic update framework for epileptic seizure prediction.

PubMed

Han, Min; Ge, Sunan; Wang, Minghui; Hong, Xiaojun; Han, Jie

2014-01-01

Epileptic seizure prediction is a difficult problem in clinical applications, and it has the potential to significantly improve the patients' daily lives whose seizures cannot be controlled by either drugs or surgery. However, most current studies of epileptic seizure prediction focus on high sensitivity and low false-positive rate only and lack the flexibility for a variety of epileptic seizures and patients' physical conditions. Therefore, a novel dynamic update framework for epileptic seizure prediction is proposed in this paper. In this framework, two basic sample pools are constructed and updated dynamically. Furthermore, the prediction model can be updated to be the most appropriate one for the prediction of seizures' arrival. Mahalanobis distance is introduced in this part to solve the problem of side information, measuring the distance between two data sets. In addition, a multichannel feature extraction method based on Hilbert-Huang transform and extreme learning machine is utilized to extract the features of a patient's preseizure state against the normal state. At last, a dynamic update epileptic seizure prediction system is built up. Simulations on Freiburg database show that the proposed system has a better performance than the one without update. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

The optimization of essential oils supercritical CO2 extraction from Lavandula hybrida through static-dynamic steps procedure and semi-continuous technique using response surface method

PubMed Central

Kamali, Hossein; Aminimoghadamfarouj, Noushin; Golmakani, Ebrahim; Nematollahi, Alireza

2015-01-01

Aim: The aim of this study was to examine and evaluate crucial variables in essential oils extraction process from Lavandula hybrida through static-dynamic and semi-continuous techniques using response surface method. Materials and Methods: Essential oil components were extracted from Lavandula hybrida (Lavandin) flowers using supercritical carbon dioxide via static-dynamic steps (SDS) procedure, and semi-continuous (SC) technique. Results: Using response surface method the optimum extraction yield (4.768%) was obtained via SDS at 108.7 bar, 48.5°C, 120 min (static: 8×15), 24 min (dynamic: 8×3 min) in contrast to the 4.620% extraction yield for the SC at 111.6 bar, 49.2°C, 14 min (static), 121.1 min (dynamic). Conclusion: The results indicated that a substantial reduction (81.56%) solvent usage (kg CO2/g oil) is observed in the SDS method versus the conventional SC method. PMID:25598636

Remote sensing monitoring and driving force analysis to forest and greenbelt in Zhuhai

NASA Astrophysics Data System (ADS)

Yuliang Qiao, Pro.

As an important city in the southern part of Chu Chiang Delta, Zhuhai is one of the four special economic zones which are opening up to the outside at the earliest in China. With pure and fresh air and trees shading the street, Zhuhai is a famous beach port city which is near the mountain and by the sea. On the basis of Garden City, the government of Zhuhai decides to build National Forest City in 2011, which firstly should understand the situation of greenbelt in Zhuhai in short term. Traditional methods of greenbelt investigation adopt the combination of field surveying and statistics, whose efficiency is low and results are not much objective because of artificial influence. With the adventure of the information technology such as remote sensing to earth observation, especially the launch of many remote sensing satellites with high resolution for the past few years, kinds of urban greenbelt information extraction can be carried out by using remote sensing technology; and dynamic monitoring to spatial pattern evolvement of forest and greenbelt in Zhuhai can be achieved by the combination of remote sensing and GIS technology. Taking Landsat5 TM data in 1995, Landsat7 ETM+ data in 2002, CCD and HR data of CBERS-02B in 2009 as main information source, this research firstly makes remote sensing monitoring to dynamic change of forest and greenbelt in Zhuhai by using the combination of vegetation coverage index and three different information extraction methods, then does a driving force analysis to the dynamic change results in 3 months. The results show: the forest area in Zhuhai shows decreasing tendency from 1995 to 2002, increasing tendency from 2002 to 2009; overall, the forest area show a small diminution tendency from 1995 to 2009. Through the comparison to natural and artificial driving force, the artificial driving force is the leading factor to the change of forest and greenbelt in Zhuhai. The research results provide a timely and reliable scientific basis for the Zhuhai Government in building National Forest City. Keywords: forest and greenbelt; remote sensing; dynamic monitoring; driving force; vegetation coverage

Study on environment detection and appraisement of mining area with RS

NASA Astrophysics Data System (ADS)

Yang, Fengjie; Hou, Peng; Zhou, Guangzhu; Li, Qingting; Wang, Jie; Cheng, Jianguang

2006-12-01

In this paper, the big coal mining area Yanzhou is selected as the typical research area. According to the special dynamic change characteristic of the environment in the mining area, the environmental dynamic changes are timely monitored with the remote sensing detection technology. Environmental special factors, such as vegetation, water, air, land-over, are extracted by the professional remote sensing image processing software, then the spatial information is managed and analyzed in the geographical information system (GIS) software. As the result, the dynamic monitor and query for change information is achieved, and the special environmental factor dynamic change maps are protracted. On the base of the data coming from the remote sensing image, GIS and the traditional environment monitoring, the environmental quality is appraised with the method of indistinct matrix analysis, the multi-index and the analytical hierarchy process. At last, those provide the credible science foundation for the local environment appraised and the sustained development. In addition, this paper apply the hyper spectrum graphs by the FieldSpec Pro spectroradiometer, together with the analytical data from environmental chemical, to study the growth of vegetation which were seed in the land-over consisting of gangue, which is a new method to study the impact to vegetation that are growing in the soil.

Intermodulation Atomic Force Microscopy and Spectroscopy

NASA Astrophysics Data System (ADS)

Hutter, Carsten; Platz, Daniel; Tholen, Erik; Haviland, David; Hansson, Hans

2009-03-01

We present a powerful new method of dynamic AFM, which allows to gain far more information about the tip-surface interaction than standard amplitude or phase imaging, while scanning at comparable speed. Our method, called intermodulation atomic force microscopy (ImAFM), employs the manifestly nonlinear phenomenon of intermodulation to extract information about tip-surface forces. ImAFM uses one eigenmode of a mechanical resonator, the latter driven at two frequencies to produce many spectral peaks near its resonace, where sensitivity is highest [1]. We furthermore present a protocol for decoding the combined information encoded in the spectrum of intermodulation peaks. Our theoretical framework suggests methods to enhance the gained information by using a different parameter regime as compared to Ref. [1]. We also discuss strategies for solving the inverse problem, i.e., for extracting the nonlinear tip-surface interaction from the response, also naming limitations of our theoretical analysis. We will further report on latest progress to experimentally employ our new protocol.[3pt] [1] D. Platz, E. A. Tholen, D. Pesen, and D. B. Haviland, Appl. Phys. Lett. 92, 153106 (2008).

A hybrid system identification methodology for wireless structural health monitoring systems based on dynamic substructuring

NASA Astrophysics Data System (ADS)

Dragos, Kosmas; Smarsly, Kay

2016-04-01

System identification has been employed in numerous structural health monitoring (SHM) applications. Traditional system identification methods usually rely on centralized processing of structural response data to extract information on structural parameters. However, in wireless SHM systems the centralized processing of structural response data introduces a significant communication bottleneck. Exploiting the merits of decentralization and on-board processing power of wireless SHM systems, many system identification methods have been successfully implemented in wireless sensor networks. While several system identification approaches for wireless SHM systems have been proposed, little attention has been paid to obtaining information on the physical parameters (e.g. stiffness, damping) of the monitored structure. This paper presents a hybrid system identification methodology suitable for wireless sensor networks based on the principles of component mode synthesis (dynamic substructuring). A numerical model of the monitored structure is embedded into the wireless sensor nodes in a distributed manner, i.e. the entire model is segmented into sub-models, each embedded into one sensor node corresponding to the substructure the sensor node is assigned to. The parameters of each sub-model are estimated by extracting local mode shapes and by applying the equations of the Craig-Bampton method on dynamic substructuring. The proposed methodology is validated in a laboratory test conducted on a four-story frame structure to demonstrate the ability of the methodology to yield accurate estimates of stiffness parameters. Finally, the test results are discussed and an outlook on future research directions is provided.

Image contrast mechanisms in dynamic friction force microscopy: Antimony particles on graphite

NASA Astrophysics Data System (ADS)

Mertens, Felix; Göddenhenrich, Thomas; Dietzel, Dirk; Schirmeisen, Andre

2017-01-01

Dynamic Friction Force Microscopy (DFFM) is a technique based on Atomic Force Microscopy (AFM) where resonance oscillations of the cantilever are excited by lateral actuation of the sample. During this process, the AFM tip in contact with the sample undergoes a complex movement which consists of alternating periods of sticking and sliding. Therefore, DFFM can give access to dynamic transition effects in friction that are not accessible by alternative techniques. Using antimony nanoparticles on graphite as a model system, we analyzed how combined influences of friction and topography can effect different experimental configurations of DFFM. Based on the experimental results, for example, contrast inversion between fractional resonance and band excitation imaging strategies to extract reliable tribological information from DFFM images are devised.

Malware Analysis Using Visualized Image Matrices

PubMed Central

Im, Eul Gyu

2014-01-01

This paper proposes a novel malware visual analysis method that contains not only a visualization method to convert binary files into images, but also a similarity calculation method between these images. The proposed method generates RGB-colored pixels on image matrices using the opcode sequences extracted from malware samples and calculates the similarities for the image matrices. Particularly, our proposed methods are available for packed malware samples by applying them to the execution traces extracted through dynamic analysis. When the images are generated, we can reduce the overheads by extracting the opcode sequences only from the blocks that include the instructions related to staple behaviors such as functions and application programming interface (API) calls. In addition, we propose a technique that generates a representative image for each malware family in order to reduce the number of comparisons for the classification of unknown samples and the colored pixel information in the image matrices is used to calculate the similarities between the images. Our experimental results show that the image matrices of malware can effectively be used to classify malware families both statically and dynamically with accuracy of 0.9896 and 0.9732, respectively. PMID:25133202

Tracking transcriptional activities with high-content epifluorescent imaging

NASA Astrophysics Data System (ADS)

Hua, Jianping; Sima, Chao; Cypert, Milana; Gooden, Gerald C.; Shack, Sonsoles; Alla, Lalitamba; Smith, Edward A.; Trent, Jeffrey M.; Dougherty, Edward R.; Bittner, Michael L.

2012-04-01

High-content cell imaging based on fluorescent protein reporters has recently been used to track the transcriptional activities of multiple genes under different external stimuli for extended periods. This technology enhances our ability to discover treatment-induced regulatory mechanisms, temporally order their onsets and recognize their relationships. To fully realize these possibilities and explore their potential in biological and pharmaceutical applications, we introduce a new data processing procedure to extract information about the dynamics of cell processes based on this technology. The proposed procedure contains two parts: (1) image processing, where the fluorescent images are processed to identify individual cells and allow their transcriptional activity levels to be quantified; and (2) data representation, where the extracted time course data are summarized and represented in a way that facilitates efficient evaluation. Experiments show that the proposed procedure achieves fast and robust image segmentation with sufficient accuracy. The extracted cellular dynamics are highly reproducible and sensitive enough to detect subtle activity differences and identify mechanisms responding to selected perturbations. This method should be able to help biologists identify the alterations of cellular mechanisms that allow drug candidates to change cell behavior and thereby improve the efficiency of drug discovery and treatment design.

Road boundary detection

NASA Technical Reports Server (NTRS)

Sowers, J.; Mehrotra, R.; Sethi, I. K.

1989-01-01

A method for extracting road boundaries using the monochrome image of a visual road scene is presented. The statistical information regarding the intensity levels present in the image along with some geometrical constraints concerning the road are the basics of this approach. Results and advantages of this technique compared to others are discussed. The major advantages of this technique, when compared to others, are its ability to process the image in only one pass, to limit the area searched in the image using only knowledge concerning the road geometry and previous boundary information, and dynamically adjust for inconsistencies in the located boundary information, all of which helps to increase the efficacy of this technique.

Theoretical Foundations of Remote Sensing for Glacier Assessment and Mapping

NASA Technical Reports Server (NTRS)

Bishop, Michael P.; Bush, Andrew B. G.; Furfaro, Roberto; Gillespie, Alan R.; Hall, Dorothy K.; Haritashya, Umesh K.; Shroder, John F., Jr.

2014-01-01

The international scientific community is actively engaged in assessing ice sheet and alpine glacier fluctuations at a variety of scales. The availability of stereoscopic, multitemporal, and multispectral satellite imagery from the optical wavelength regions of the electromagnetic spectrum has greatly increased our ability to assess glaciological conditions and map the cryosphere. There are, however, important issues and limitations associated with accurate satellite information extraction and mapping, as well as new opportunities for assessment and mapping that are all rooted in understanding the fundamentals of the radiation transfer cascade. We address the primary radiation transfer components, relate them to glacier dynamics and mapping, and summarize the analytical approaches that permit transformation of spectral variation into thematic and quantitative parameters. We also discuss the integration of satellite-derived information into numerical modeling approaches to facilitate understandings of glacier dynamics and causal mechanisms.

Zone analysis in biology articles as a basis for information extraction.

PubMed

Mizuta, Yoko; Korhonen, Anna; Mullen, Tony; Collier, Nigel

2006-06-01

In the field of biomedicine, an overwhelming amount of experimental data has become available as a result of the high throughput of research in this domain. The amount of results reported has now grown beyond the limits of what can be managed by manual means. This makes it increasingly difficult for the researchers in this area to keep up with the latest developments. Information extraction (IE) in the biological domain aims to provide an effective automatic means to dynamically manage the information contained in archived journal articles and abstract collections and thus help researchers in their work. However, while considerable advances have been made in certain areas of IE, pinpointing and organizing factual information (such as experimental results) remains a challenge. In this paper we propose tackling this task by incorporating into IE information about rhetorical zones, i.e. classification of spans of text in terms of argumentation and intellectual attribution. As the first step towards this goal, we introduce a scheme for annotating biological texts for rhetorical zones and provide a qualitative and quantitative analysis of the data annotated according to this scheme. We also discuss our preliminary research on automatic zone analysis, and its incorporation into our IE framework.

Extracting duration information in a picture category decoding task using hidden Markov Models

NASA Astrophysics Data System (ADS)

Pfeiffer, Tim; Heinze, Nicolai; Frysch, Robert; Deouell, Leon Y.; Schoenfeld, Mircea A.; Knight, Robert T.; Rose, Georg

2016-04-01

Objective. Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain-computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. Approach. Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. Main results. Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. Significance. The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.

Task-dependent recurrent dynamics in visual cortex

PubMed Central

Tajima, Satohiro; Koida, Kowa; Tajima, Chihiro I; Suzuki, Hideyuki; Aihara, Kazuyuki; Komatsu, Hidehiko

2017-01-01

The capacity for flexible sensory-action association in animals has been related to context-dependent attractor dynamics outside the sensory cortices. Here, we report a line of evidence that flexibly modulated attractor dynamics during task switching are already present in the higher visual cortex in macaque monkeys. With a nonlinear decoding approach, we can extract the particular aspect of the neural population response that reflects the task-induced emergence of bistable attractor dynamics in a neural population, which could be obscured by standard unsupervised dimensionality reductions such as PCA. The dynamical modulation selectively increases the information relevant to task demands, indicating that such modulation is beneficial for perceptual decisions. A computational model that features nonlinear recurrent interaction among neurons with a task-dependent background input replicates the key properties observed in the experimental data. These results suggest that the context-dependent attractor dynamics involving the sensory cortex can underlie flexible perceptual abilities. DOI: http://dx.doi.org/10.7554/eLife.26868.001 PMID:28737487

Enhancing the sensitivity of fluorescence correlation spectroscopy by using time-correlated single photon counting.

PubMed

Lamb, D C; Müller, B K; Bräuchle, C

2005-10-01

Fluorescence correlation spectroscopy (FCS) and fluorescence cross-correlation spectroscopy (FCCS) are methods that extract information about a sample from the influence of thermodynamic equilibrium fluctuations on the fluorescence intensity. This method allows dynamic information to be obtained from steady state equilibrium measurements and its popularity has dramatically increased in the last 10 years due to the development of high sensitivity detectors and its combination with confocal microscopy. Using time-correlated single-photon counting (TCSPC) detection and pulsed excitation, information over the duration of the excited state can be extracted and incorporated in the analysis. In this short review, we discuss new methodologies that have recently emerged which incorporated fluorescence lifetime information or TCSPC data in the FCS and FCCS analysis. Time-gated FCS discriminates between which photons are to be incorporated in the analysis dependent upon their arrival time after excitation. This allows for accurate FCS measurements in the presence of fluorescent background, determination of sample homogeneity, and the ability to distinguish between static and dynamic heterogeneities. A similar method, time-resolved FCS can be used to resolve the individual correlation functions from multiple fluorophores through the different fluorescence lifetimes. Pulsed interleaved excitation (PIE) encodes the excitation source into the TCSPC data. PIE can be used to perform dual-channel FCCS with a single detector and allows elimination of spectral cross-talk with dual-channel detection. For samples that undergo fluorescence resonance energy transfer (FRET), quantitative FCCS measurements can be performed in spite of the FRET and the static FRET efficiency can be determined.

4D motion modeling of the coronary arteries from CT images for robotic assisted minimally invasive surgery

NASA Astrophysics Data System (ADS)

Zhang, Dong Ping; Edwards, Eddie; Mei, Lin; Rueckert, Daniel

2009-02-01

In this paper, we present a novel approach for coronary artery motion modeling from cardiac Computed Tomography( CT) images. The aim of this work is to develop a 4D motion model of the coronaries for image guidance in robotic-assisted totally endoscopic coronary artery bypass (TECAB) surgery. To utilize the pre-operative cardiac images to guide the minimally invasive surgery, it is essential to have a 4D cardiac motion model to be registered with the stereo endoscopic images acquired intraoperatively using the da Vinci robotic system. In this paper, we are investigating the extraction of the coronary arteries and the modelling of their motion from a dynamic sequence of cardiac CT. We use a multi-scale vesselness filter to enhance vessels in the cardiac CT images. The centerlines of the arteries are extracted using a ridge traversal algorithm. Using this method the coronaries can be extracted in near real-time as only local information is used in vessel tracking. To compute the deformation of the coronaries due to cardiac motion, the motion is extracted from a dynamic sequence of cardiac CT. Each timeframe in this sequence is registered to the end-diastole timeframe of the sequence using a non-rigid registration algorithm based on free-form deformations. Once the images have been registered a dynamic motion model of the coronaries can be obtained by applying the computed free-form deformations to the extracted coronary arteries. To validate the accuracy of the motion model we compare the actual position of the coronaries in each time frame with the predicted position of the coronaries as estimated from the non-rigid registration. We expect that this motion model of coronaries can facilitate the planning of TECAB surgery, and through the registration with real-time endoscopic video images it can reduce the conversion rate from TECAB to conventional procedures.

Super-pixel extraction based on multi-channel pulse coupled neural network

NASA Astrophysics Data System (ADS)

Xu, GuangZhu; Hu, Song; Zhang, Liu; Zhao, JingJing; Fu, YunXia; Lei, BangJun

2018-04-01

Super-pixel extraction techniques group pixels to form over-segmented image blocks according to the similarity among pixels. Compared with the traditional pixel-based methods, the image descripting method based on super-pixel has advantages of less calculation, being easy to perceive, and has been widely used in image processing and computer vision applications. Pulse coupled neural network (PCNN) is a biologically inspired model, which stems from the phenomenon of synchronous pulse release in the visual cortex of cats. Each PCNN neuron can correspond to a pixel of an input image, and the dynamic firing pattern of each neuron contains both the pixel feature information and its context spatial structural information. In this paper, a new color super-pixel extraction algorithm based on multi-channel pulse coupled neural network (MPCNN) was proposed. The algorithm adopted the block dividing idea of SLIC algorithm, and the image was divided into blocks with same size first. Then, for each image block, the adjacent pixels of each seed with similar color were classified as a group, named a super-pixel. At last, post-processing was adopted for those pixels or pixel blocks which had not been grouped. Experiments show that the proposed method can adjust the number of superpixel and segmentation precision by setting parameters, and has good potential for super-pixel extraction.

Development of Markup Language for Medical Record Charting: A Charting Language.

PubMed

Jung, Won-Mo; Chae, Younbyoung; Jang, Bo-Hyoung

2015-01-01

Nowadays a lot of trials for collecting electronic medical records (EMRs) exist. However, structuring data format for EMR is an especially labour-intensive task for practitioners. Here we propose a new mark-up language for medical record charting (called Charting Language), which borrows useful properties from programming languages. Thus, with Charting Language, the text data described in dynamic situation can be easily used to extract information.

Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots.

PubMed

Tošić, Tamara; Sellers, Kristin K; Fröhlich, Flavio; Fedotenkova, Mariia; Beim Graben, Peter; Hutt, Axel

2015-01-01

For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain.

Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots

PubMed Central

Tošić, Tamara; Sellers, Kristin K.; Fröhlich, Flavio; Fedotenkova, Mariia; beim Graben, Peter; Hutt, Axel

2016-01-01

For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain. PMID:26834580

Ad Hoc Information Extraction for Clinical Data Warehouses.

PubMed

Dietrich, Georg; Krebs, Jonathan; Fette, Georg; Ertl, Maximilian; Kaspar, Mathias; Störk, Stefan; Puppe, Frank

2018-05-01

Clinical Data Warehouses (CDW) reuse Electronic health records (EHR) to make their data retrievable for research purposes or patient recruitment for clinical trials. However, much information are hidden in unstructured data like discharge letters. They can be preprocessed and converted to structured data via information extraction (IE), which is unfortunately a laborious task and therefore usually not available for most of the text data in CDW. The goal of our work is to provide an ad hoc IE service that allows users to query text data ad hoc in a manner similar to querying structured data in a CDW. While search engines just return text snippets, our systems also returns frequencies (e.g. how many patients exist with "heart failure" including textual synonyms or how many patients have an LVEF < 45) based on the content of discharge letters or textual reports for special investigations like heart echo. Three subtasks are addressed: (1) To recognize and to exclude negations and their scopes, (2) to extract concepts, i.e. Boolean values and (3) to extract numerical values. We implemented an extended version of the NegEx-algorithm for German texts that detects negations and determines their scope. Furthermore, our document oriented CDW PaDaWaN was extended with query functions, e.g. context sensitive queries and regex queries, and an extraction mode for computing the frequencies for Boolean and numerical values. Evaluations in chest X-ray reports and in discharge letters showed high F1-scores for the three subtasks: Detection of negated concepts in chest X-ray reports with an F1-score of 0.99 and in discharge letters with 0.97; of Boolean values in chest X-ray reports about 0.99, and of numerical values in chest X-ray reports and discharge letters also around 0.99 with the exception of the concept age. The advantages of an ad hoc IE over a standard IE are the low development effort (just entering the concept with its variants), the promptness of the results and the adaptability by the user to his or her particular question. Disadvantage are usually lower accuracy and confidence.This ad hoc information extraction approach is novel and exceeds existing systems: Roogle [1] extracts predefined concepts from texts at preprocessing and makes them retrievable at runtime. Dr. Warehouse [2] applies negation detection and indexes the produced subtexts which include affirmed findings. Our approach combines negation detection and the extraction of concepts. But the extraction does not take place during preprocessing, but at runtime. That provides an ad hoc, dynamic, interactive and adjustable information extraction of random concepts and even their values on the fly at runtime. We developed an ad hoc information extraction query feature for Boolean and numerical values within a CDW with high recall and precision based on a pipeline that detects and removes negations and their scope in clinical texts. Schattauer GmbH.

Deep coupling of star tracker and MEMS-gyro data under highly dynamic and long exposure conditions

NASA Astrophysics Data System (ADS)

Sun, Ting; Xing, Fei; You, Zheng; Wang, Xiaochu; Li, Bin

2014-08-01

Star trackers and gyroscopes are the two most widely used attitude measurement devices in spacecrafts. The star tracker is supposed to have the highest accuracy in stable conditions among different types of attitude measurement devices. In general, to detect faint stars and reduce the size of the star tracker, a method with long exposure time method is usually used. Thus, under dynamic conditions, smearing of the star image may appear and result in decreased accuracy or even failed extraction of the star spot. This may cause inaccuracies in attitude measurement. Gyros have relatively good dynamic performance and are usually used in combination with star trackers. However, current combination methods focus mainly on the data fusion of the output attitude data levels, which are inadequate for utilizing and processing internal blurred star image information. A method for tracking deep coupling stars and MEMS-gyro data is proposed in this work. The method achieves deep fusion at the star image level. First, dynamic star image processing is performed based on the angular velocity information of the MEMS-gyro. Signal-to-noise ratio (SNR) of the star spot could be improved, and extraction is achieved more effectively. Then, a prediction model for optimal estimation of the star spot position is obtained through the MEMS-gyro, and an extended Kalman filter is introduced. Meanwhile, the MEMS-gyro drift can be estimated and compensated though the proposed method. These enable the star tracker to achieve high star centroid determination accuracy under dynamic conditions. The MEMS-gyro drift can be corrected even when attitude data of the star tracker are unable to be solved and only one navigation star is captured in the field of view. Laboratory experiments were performed to verify the effectiveness of the proposed method and the whole system.

Common-path biodynamic imaging for dynamic fluctuation spectroscopy of 3D living tissue

NASA Astrophysics Data System (ADS)

Li, Zhe; Turek, John; Nolte, David D.

2017-03-01

Biodynamic imaging is a novel 3D optical imaging technology based on short-coherence digital holography that measures intracellular motions of cells inside their natural microenvironments. Here both common-path and Mach-Zehnder designs are presented. Biological tissues such as tumor spheroids and ex vivo biopsies are used as targets, and backscattered light is collected as signal. Drugs are applied to samples, and their effects are evaluated by identifying biomarkers that capture intracellular dynamics from the reconstructed holograms. Through digital holography and coherence gating, information from different depths of the samples can be extracted, enabling the deep-tissue measurement of the responses to drugs.

Performance enhancement for audio-visual speaker identification using dynamic facial muscle model.

PubMed

Asadpour, Vahid; Towhidkhah, Farzad; Homayounpour, Mohammad Mehdi

2006-10-01

Science of human identification using physiological characteristics or biometry has been of great concern in security systems. However, robust multimodal identification systems based on audio-visual information has not been thoroughly investigated yet. Therefore, the aim of this work to propose a model-based feature extraction method which employs physiological characteristics of facial muscles producing lip movements. This approach adopts the intrinsic properties of muscles such as viscosity, elasticity, and mass which are extracted from the dynamic lip model. These parameters are exclusively dependent on the neuro-muscular properties of speaker; consequently, imitation of valid speakers could be reduced to a large extent. These parameters are applied to a hidden Markov model (HMM) audio-visual identification system. In this work, a combination of audio and video features has been employed by adopting a multistream pseudo-synchronized HMM training method. Noise robust audio features such as Mel-frequency cepstral coefficients (MFCC), spectral subtraction (SS), and relative spectra perceptual linear prediction (J-RASTA-PLP) have been used to evaluate the performance of the multimodal system once efficient audio feature extraction methods have been utilized. The superior performance of the proposed system is demonstrated on a large multispeaker database of continuously spoken digits, along with a sentence that is phonetically rich. To evaluate the robustness of algorithms, some experiments were performed on genetically identical twins. Furthermore, changes in speaker voice were simulated with drug inhalation tests. In 3 dB signal to noise ratio (SNR), the dynamic muscle model improved the identification rate of the audio-visual system from 91 to 98%. Results on identical twins revealed that there was an apparent improvement on the performance for the dynamic muscle model-based system, in which the identification rate of the audio-visual system was enhanced from 87 to 96%.

Toward On-Demand Deep Brain Stimulation Using Online Parkinson's Disease Prediction Driven by Dynamic Detection.

PubMed

Mohammed, Ameer; Zamani, Majid; Bayford, Richard; Demosthenous, Andreas

2017-12-01

In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

The inverse problem of using the information of historical data to estimate model errors is one of the science frontier research topics. In this study, we investigate such a problem using the classic Lorenz (1963) equation as a prediction model.

NASA Astrophysics Data System (ADS)

Wan, S.; He, W.

2016-12-01

The inverse problem of using the information of historical data to estimate model errors is one of the science frontier research topics. In this study, we investigate such a problem using the classic Lorenz (1963) equation as a prediction model and the Lorenz equation with a periodic evolutionary function as an accurate representation of reality to generate "observational data." On the basis of the intelligent features of evolutionary modeling (EM), including self-organization, self-adaptive and self-learning, the dynamic information contained in the historical data can be identified and extracted by computer automatically. Thereby, a new approach is proposed to estimate model errors based on EM in the present paper. Numerical tests demonstrate the ability of the new approach to correct model structural errors. In fact, it can actualize the combination of the statistics and dynamics to certain extent.

Insight into the time-resolved extraction of aroma compounds during espresso coffee preparation: online monitoring by PTR-ToF-MS.

PubMed

Sánchez-López, José A; Zimmermann, Ralf; Yeretzian, Chahan

2014-12-02

Using proton-transfer-reaction time-of-flight mass-spectrometry (PTR-ToF-MS), we investigated the extraction dynamic of 95 ion traces in real time (time resolution = 1 s) during espresso coffee preparation. Fifty-two of these ions were tentatively identified. This was achieved by online sampling of the volatile organic compounds (VOCs) in close vicinity to the coffee flow, at the exit of the extraction hose of the espresso machine (single serve capsules). Ten replicates of six different single serve coffee types were extracted to a final weight between 20-120 g, according to the recommended cup size of the respective coffee capsule (Ristretto, Espresso, and Lungo), and analyzed. The results revealed considerable differences in the extraction kinetics between compounds, which led to a fast evolution of the volatile profiles in the extract flow and consequently to an evolution of the final aroma balance in the cup. Besides exploring the time-resolved extraction dynamics of VOCs, the dynamic data also allowed the coffees types (capsules) to be distinguished from one another. Both hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed full separation between the coffees types. The methodology developed provides a fast and simple means of studying the extraction dynamics of VOCs and differentiating between different coffee types.

Dynamic area telethermometry and its clinical applications

NASA Astrophysics Data System (ADS)

Anbar, Michael

1995-03-01

Dynamic area telethermometry (DAT) is a recent development in thermology, the science of biological heat generation and dissipation. DAT is based on monitoring changes in infrared emission, deriving from them information on the kinetics and mechanisms of biological thermoregulation. Remotely monitoring infrared emission is the most reliable technique to study bioenergetics, because it minimally perturbs the investigated system. Area monitoring of heat dissipating surfaces is needed because temporal changes in the spatial distribution of temperature conveys information on mechanisms of thermoregulation. DAT can be applied to biological systems ranging from single cells (microtelecalorimetry) to large areas of human skin (clinical thermology). DAT requires the accumulation of many (hundreds to thousands) thermal images followed by analysis of the thermokinetics of each pixel or group of pixels. In clinical thermology this analysis uses FFT to extract systemic, regional and local thermoregulatory frequencies (TRFs). DAT also extracts information on local thermoregulation from the temporal behavior of homogeneity of skin temperature (HST). Analysis of the relative contributions (FFT amplitudes) of the different frequencies allows distinction between vascular, neurological, and immunological thermoregulatory dysfunctions. This analysis, which can reveal the mechanism of the dysfunction, can be very useful in the diagnosis and staging of various disorders, ranging from diabetes mellitus and liver cirrhosis to breast cancer and malignant melanoma. From the engineering standpoint DAT requires highly stable imaging systems and effective display of the spatial distribution of TRFs to allow identification of thermoregulatory pathways and their dysfunction.

Study on the extraction method of tidal flat area in northern Jiangsu Province based on remote sensing waterlines

NASA Astrophysics Data System (ADS)

Zhang, Yuanyuan; Gao, Zhiqiang; Liu, Xiangyang; Xu, Ning; Liu, Chaoshun; Gao, Wei

2016-09-01

Reclamation caused a significant dynamic change in the coastal zone, the tidal flat zone is an unstable reserve land resource, it has important significance for its research. In order to realize the efficient extraction of the tidal flat area information, this paper takes Rudong County in Jiangsu Province as the research area, using the HJ1A/1B images as the data source, on the basis of previous research experience and literature review, the paper chooses the method of object-oriented classification as a semi-automatic extraction method to generate waterlines. Then waterlines are analyzed by DSAS software to obtain tide points, automatic extraction of outer boundary points are followed under the use of Python to determine the extent of tidal flats in 2014 of Rudong County, the extraction area was 55182hm2, the confusion matrix is used to verify the accuracy and the result shows that the kappa coefficient is 0.945. The method could improve deficiencies of previous studies and its available free nature on the Internet makes a generalization.

Avian Semen Collection by Cloacal Massage and Isolation of DNA from Sperm.

PubMed

Kucera, Aurelia C; Heidinger, Britt J

2018-02-05

Collection of semen may be useful for a wide range of applications including studies involving sperm quality, sperm telomere dynamics, and epigenetics. Birds are widely used subjects in biological research and are ideal for studies involving repeated sperm samples. However, few resources are currently available for those wishing to learn how to collect and extract DNA from avian sperm. Here we describe cloacal massage, a gentle, non-invasive manual technique for collecting avian sperm. Although this technique is established in the literature, it can be difficult to learn from the available descriptions. We also provide information for extracting DNA from avian semen using a commercial extraction kit with modifications. Cloacal massage can be easily used on any small- to medium-sized male bird in reproductive condition. Following collection, the semen can be used immediately for motility assays, or frozen for DNA extraction following the protocol described herein. This extraction protocol was refined for avian sperm and has been successfully used on samples collected from several passerine species (Passer domesticus, Spizella passerina, Haemorhous mexicanus, and Turdus migratorius) and one columbid (Columba livia).

De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets

NASA Astrophysics Data System (ADS)

Hemati, Maziar S.; Rowley, Clarence W.; Deem, Eric A.; Cattafesta, Louis N.

2017-08-01

The dynamic mode decomposition (DMD)—a popular method for performing data-driven Koopman spectral analysis—has gained increased popularity for extracting dynamically meaningful spatiotemporal descriptions of fluid flows from snapshot measurements. Often times, DMD descriptions can be used for predictive purposes as well, which enables informed decision-making based on DMD model forecasts. Despite its widespread use and utility, DMD can fail to yield accurate dynamical descriptions when the measured snapshot data are imprecise due to, e.g., sensor noise. Here, we express DMD as a two-stage algorithm in order to isolate a source of systematic error. We show that DMD's first stage, a subspace projection step, systematically introduces bias errors by processing snapshots asymmetrically. To remove this systematic error, we propose utilizing an augmented snapshot matrix in a subspace projection step, as in problems of total least-squares, in order to account for the error present in all snapshots. The resulting unbiased and noise-aware total DMD (TDMD) formulation reduces to standard DMD in the absence of snapshot errors, while the two-stage perspective generalizes the de-biasing framework to other related methods as well. TDMD's performance is demonstrated in numerical and experimental fluids examples. In particular, in the analysis of time-resolved particle image velocimetry data for a separated flow, TDMD outperforms standard DMD by providing dynamical interpretations that are consistent with alternative analysis techniques. Further, TDMD extracts modes that reveal detailed spatial structures missed by standard DMD.

The Temporal Dynamics of Regularity Extraction in Non-Human Primates

ERIC Educational Resources Information Center

Minier, Laure; Fagot, Joël; Rey, Arnaud

2016-01-01

Extracting the regularities of our environment is one of our core cognitive abilities. To study the fine-grained dynamics of the extraction of embedded regularities, a method combining the advantages of the artificial language paradigm (Saffran, Aslin, & Newport, [Saffran, J. R., 1996]) and the serial response time task (Nissen & Bullemer,…

Effects of Neutron-Star Dynamic Tides on Gravitational Waveforms within the Effective-One-Body Approach

NASA Astrophysics Data System (ADS)

Hinderer, Tanja; Taracchini, Andrea; Foucart, Francois; Buonanno, Alessandra; Steinhoff, Jan; Duez, Matthew; Kidder, Lawrence E.; Pfeiffer, Harald P.; Scheel, Mark A.; Szilagyi, Bela; Hotokezaka, Kenta; Kyutoku, Koutarou; Shibata, Masaru; Carpenter, Cory W.

2016-05-01

Extracting the unique information on ultradense nuclear matter from the gravitational waves emitted by merging neutron-star binaries requires robust theoretical models of the signal. We develop a novel effective-one-body waveform model that includes, for the first time, dynamic (instead of only adiabatic) tides of the neutron star as well as the merger signal for neutron-star-black-hole binaries. We demonstrate the importance of the dynamic tides by comparing our model against new numerical-relativity simulations of nonspinning neutron-star-black-hole binaries spanning more than 24 gravitational-wave cycles, and to other existing numerical simulations for double neutron-star systems. Furthermore, we derive an effective description that makes explicit the dependence of matter effects on two key parameters: tidal deformability and fundamental oscillation frequency.

MDANSE: An Interactive Analysis Environment for Molecular Dynamics Simulations.

PubMed

Goret, G; Aoun, B; Pellegrini, E

2017-01-23

The MDANSE software-Molecular Dynamics Analysis of Neutron Scattering Experiments-is presented. It is an interactive application for postprocessing molecular dynamics (MD) simulations. Given the widespread use of MD simulations in material and biomolecular sciences to get a better insight for experimental techniques such as thermal neutron scattering (TNS), the development of MDANSE has focused on providing a user-friendly, interactive, graphical user interface for analyzing many trajectories in the same session and running several analyses simultaneously independently of the interface. This first version of MDANSE already proposes a broad range of analyses, and the application has been designed to facilitate the introduction of new analyses in the framework. All this makes MDANSE a valuable tool for extracting useful information from trajectories resulting from a wide range of MD codes.

Effects of Neutron-Star Dynamic Tides on Gravitational Waveforms within the Effective-One-Body Approach.

PubMed

Hinderer, Tanja; Taracchini, Andrea; Foucart, Francois; Buonanno, Alessandra; Steinhoff, Jan; Duez, Matthew; Kidder, Lawrence E; Pfeiffer, Harald P; Scheel, Mark A; Szilagyi, Bela; Hotokezaka, Kenta; Kyutoku, Koutarou; Shibata, Masaru; Carpenter, Cory W

2016-05-06

Extracting the unique information on ultradense nuclear matter from the gravitational waves emitted by merging neutron-star binaries requires robust theoretical models of the signal. We develop a novel effective-one-body waveform model that includes, for the first time, dynamic (instead of only adiabatic) tides of the neutron star as well as the merger signal for neutron-star-black-hole binaries. We demonstrate the importance of the dynamic tides by comparing our model against new numerical-relativity simulations of nonspinning neutron-star-black-hole binaries spanning more than 24 gravitational-wave cycles, and to other existing numerical simulations for double neutron-star systems. Furthermore, we derive an effective description that makes explicit the dependence of matter effects on two key parameters: tidal deformability and fundamental oscillation frequency.

Covariant kaon dynamics and kaon flow in heavy ion collisions

NASA Astrophysics Data System (ADS)

Zheng, Yu-Ming; Fuchs, C.; Faessler, Amand; Shekhter, K.; Yan, Yu-Peng; Kobdaj, Chinorat

2004-03-01

The influence of the chiral mean field on the K+ transverse flow in heavy ion collisions at SIS energy is investigated within covariant kaon dynamics. For the kaon mesons inside the nuclear medium a quasiparticle picture including scalar and vector fields is adopted and compared to the standard treatment with a static potential. It is confirmed that a Lorentz force from spatial component of the vector field provides an important contribution to the in-medium kaon dynamics and strongly counterbalances the influence of the vector potential on the K+ in-plane flow. The FOPI data can be reasonably described using in-medium kaon potentials based on effective chiral models. The information on the in-medium K+ potential extracted from kaon flow is consistent with the knowledge from other sources.

A combinatorial framework to quantify peak/pit asymmetries in complex dynamics.

PubMed

Hasson, Uri; Iacovacci, Jacopo; Davis, Ben; Flanagan, Ryan; Tagliazucchi, Enzo; Laufs, Helmut; Lacasa, Lucas

2018-02-23

We explore a combinatorial framework which efficiently quantifies the asymmetries between minima and maxima in local fluctuations of time series. We first showcase its performance by applying it to a battery of synthetic cases. We find rigorous results on some canonical dynamical models (stochastic processes with and without correlations, chaotic processes) complemented by extensive numerical simulations for a range of processes which indicate that the methodology correctly distinguishes different complex dynamics and outperforms state of the art metrics in several cases. Subsequently, we apply this methodology to real-world problems emerging across several disciplines including cases in neurobiology, finance and climate science. We conclude that differences between the statistics of local maxima and local minima in time series are highly informative of the complex underlying dynamics and a graph-theoretic extraction procedure allows to use these features for statistical learning purposes.

Dark-field-based observation of single-nanoparticle dynamics on a supported lipid bilayer for in situ analysis of interacting molecules and nanoparticles.

PubMed

Lee, Young Kwang; Kim, Sungi; Nam, Jwa-Min

2015-01-12

Observation of single plasmonic nanoparticles in reconstituted biological systems allows us to obtain snapshots of dynamic processes between molecules and nanoparticles with unprecedented spatiotemporal resolution and single-molecule/single-particle-level data acquisition. This Concept is intended to introduce nanoparticle-tethered supported lipid bilayer platforms that allow for the dynamic confinement of nanoparticles on a two-dimensional fluidic surface. The dark-field-based long-term, stable, real-time observation of freely diffusing plasmonic nanoparticles on a lipid bilayer enables one to extract a broad range of information about interparticle and molecular interactions throughout the entire reaction period. Herein, we highlight important developments in this context to provide ideas on how molecular interactions can be interpreted by monitoring dynamic behaviors and optical signals of laterally mobile nanoparticles. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Combining facial dynamics with appearance for age estimation.

PubMed

Dibeklioglu, Hamdi; Alnajar, Fares; Ali Salah, Albert; Gevers, Theo

2015-06-01

Estimating the age of a human from the captured images of his/her face is a challenging problem. In general, the existing approaches to this problem use appearance features only. In this paper, we show that in addition to appearance information, facial dynamics can be leveraged in age estimation. We propose a method to extract and use dynamic features for age estimation, using a person's smile. Our approach is tested on a large, gender-balanced database with 400 subjects, with an age range between 8 and 76. In addition, we introduce a new database on posed disgust expressions with 324 subjects in the same age range, and evaluate the reliability of the proposed approach when used with another expression. State-of-the-art appearance-based age estimation methods from the literature are implemented as baseline. We demonstrate that for each of these methods, the addition of the proposed dynamic features results in statistically significant improvement. We further propose a novel hierarchical age estimation architecture based on adaptive age grouping. We test our approach extensively, including an exploration of spontaneous versus posed smile dynamics, and gender-specific age estimation. We show that using spontaneity information reduces the mean absolute error by up to 21%, advancing the state of the art for facial age estimation.

A two-level cache for distributed information retrieval in search engines.

PubMed

Zhang, Weizhe; He, Hui; Ye, Jianwei

2013-01-01

To improve the performance of distributed information retrieval in search engines, we propose a two-level cache structure based on the queries of the users' logs. We extract the highest rank queries of users from the static cache, in which the queries are the most popular. We adopt the dynamic cache as an auxiliary to optimize the distribution of the cache data. We propose a distribution strategy of the cache data. The experiments prove that the hit rate, the efficiency, and the time consumption of the two-level cache have advantages compared with other structures of cache.

A Two-Level Cache for Distributed Information Retrieval in Search Engines

PubMed Central

Zhang, Weizhe; He, Hui; Ye, Jianwei

2013-01-01

To improve the performance of distributed information retrieval in search engines, we propose a two-level cache structure based on the queries of the users' logs. We extract the highest rank queries of users from the static cache, in which the queries are the most popular. We adopt the dynamic cache as an auxiliary to optimize the distribution of the cache data. We propose a distribution strategy of the cache data. The experiments prove that the hit rate, the efficiency, and the time consumption of the two-level cache have advantages compared with other structures of cache. PMID:24363621

Data-Flow Based Model Analysis

NASA Technical Reports Server (NTRS)

Saad, Christian; Bauer, Bernhard

2010-01-01

The concept of (meta) modeling combines an intuitive way of formalizing the structure of an application domain with a high expressiveness that makes it suitable for a wide variety of use cases and has therefore become an integral part of many areas in computer science. While the definition of modeling languages through the use of meta models, e.g. in Unified Modeling Language (UML), is a well-understood process, their validation and the extraction of behavioral information is still a challenge. In this paper we present a novel approach for dynamic model analysis along with several fields of application. Examining the propagation of information along the edges and nodes of the model graph allows to extend and simplify the definition of semantic constraints in comparison to the capabilities offered by e.g. the Object Constraint Language. Performing a flow-based analysis also enables the simulation of dynamic behavior, thus providing an "abstract interpretation"-like analysis method for the modeling domain.

The dynamic and steady state behavior of a PEM fuel cell as an electric energy source

NASA Astrophysics Data System (ADS)

Costa, R. A.; Camacho, J. R.

The main objective of this work is to extract information on the internal behavior of three small polymer electrolyte membrane fuel cells under static and dynamic load conditions. A computational model was developed using Scilab [SCILAB 4, Scilab-a free scientific software package, http://www.scilab.org/, INRIA, France, December, 2005] to simulate the static and dynamic performance [J.M. Correa, A.F. Farret, L.N. Canha, An analysis of the dynamic performance of proton exchange membrane fuel cells using an electrochemical model, in: 27th Annual Conference of IEEE Industrial Electronics Society, 2001, pp. 141-146] of this particular type of fuel cell. This dynamic model is based on electrochemical equations and takes into consideration most of the chemical and physical characteristics of the device in order to generate electric power. The model takes into consideration the operating, design parameters and physical material properties. The results show the internal losses and concentration effects behavior, which are of interest for power engineers and researchers.

Principal component analysis of indocyanine green fluorescence dynamics for diagnosis of vascular diseases

NASA Astrophysics Data System (ADS)

Seo, Jihye; An, Yuri; Lee, Jungsul; Choi, Chulhee

2015-03-01

Indocyanine green (ICG), a near-infrared fluorophore, has been used in visualization of vascular structure and non-invasive diagnosis of vascular disease. Although many imaging techniques have been developed, there are still limitations in diagnosis of vascular diseases. We have recently developed a minimally invasive diagnostics system based on ICG fluorescence imaging for sensitive detection of vascular insufficiency. In this study, we used principal component analysis (PCA) to examine ICG spatiotemporal profile and to obtain pathophysiological information from ICG dynamics. Here we demonstrated that principal components of ICG dynamics in both feet showed significant differences between normal control and diabetic patients with vascula complications. We extracted the PCA time courses of the first three components and found distinct pattern in diabetic patient. We propose that PCA of ICG dynamics reveal better classification performance compared to fluorescence intensity analysis. We anticipate that specific feature of spatiotemporal ICG dynamics can be useful in diagnosis of various vascular diseases.

Dynamic Policy Evaluation for Containing Network Attacks (DEFCN)

DTIC Science & Technology

2005-03-01

API reads policy information from the target users ".ssh" directory and applies those policies to determine whether remote login is allowed to a...types of events that can be controlled by the threshold detectors and reported by the GAA-API include the number of failed login attempts within a given...other uses of the system. Emerald architecture [2] includes a data- collection module integrated with Apache Web server. The module extracts the request

Numerical modelling of distributed vibration sensor based on phase-sensitive OTDR

NASA Astrophysics Data System (ADS)

Masoudi, A.; Newson, T. P.

2017-04-01

A Distributed Vibration Sensor Based on Phase-Sensitive OTDR is numerically modeled. The advantage of modeling the building blocks of the sensor individually and combining the blocks to analyse the behavior of the sensing system is discussed. It is shown that the numerical model can accurately imitate the response of the experimental setup to dynamic perturbations a signal processing procedure similar to that used to extract the phase information from sensing setup.

Dynamic fabric phase sorptive extraction for a group of pharmaceuticals and personal care products from environmental waters.

PubMed

Lakade, Sameer S; Borrull, Francesc; Furton, Kenneth G; Kabir, Abuzar; Marcé, Rosa Maria; Fontanals, Núria

2016-07-22

This paper describes for the first time the use of a new extraction technique, based on fabric phase sorptive extraction (FPSE). This new mode proposes the extraction of the analytes in dynamic mode in order to reduce the extraction time. Dynamic fabric phase sorptive extraction (DFPSE) followed by liquid chromatography-tandem mass spectrometry was evaluated for the extraction of a group of pharmaceuticals and personal care products (PPCPs) from environmental water samples. Different parameters affecting the extraction were optimized and best conditions were achieved when 50mL of sample at pH 3 was passed through 3 disks and analytes retained were eluted with 10mL of ethyl acetate. The recoveries were higher than 60% for most of compounds with the exception of the most polar ones (between 8% and 38%). The analytical method was validated with environmental samples such as river water and effluent and influent wastewater, and good performance was obtained. The analysis of samples revealed the presence of some PPCPs at low ngL(-1) concentrations. Copyright © 2016 Elsevier B.V. All rights reserved.

Assessment of the Reconstructed Aerodynamics of the Mars Science Laboratory Entry Vehicle

NASA Technical Reports Server (NTRS)

Schoenenberger, Mark; Van Norman, John W.; Dyakonov, Artem A.; Karlgaard, Christopher D.; Way, David W.; Kutty, Prasad

2013-01-01

On August 5, 2012, the Mars Science Laboratory entry vehicle successfully entered Mars atmosphere, flying a guided entry until parachute deploy. The Curiosity rover landed safely in Gale crater upon completion of the Entry Descent and Landing sequence. This paper compares the aerodynamics of the entry capsule extracted from onboard flight data, including Inertial Measurement Unit (IMU) accelerometer and rate gyro information, and heatshield surface pressure measurements. From the onboard data, static force and moment data has been extracted. This data is compared to preflight predictions. The information collected by MSL represents the most complete set of information collected during Mars entry to date. It allows the separation of aerodynamic performance from atmospheric conditions. The comparisons show the MSL aerodynamic characteristics have been identified and resolved to an accuracy better than the aerodynamic database uncertainties used in preflight simulations. A number of small anomalies have been identified and are discussed. This data will help revise aerodynamic databases for future missions and will guide computational fluid dynamics (CFD) development to improved prediction codes.

Magnetic field sensing with nitrogen-vacancy color centers in diamond

NASA Astrophysics Data System (ADS)

Pham, Linh My

In recent years, the nitrogen-vacancy (NV) center has emerged as a promising magnetic sensor capable of measuring magnetic fields with high sensitivity and spatial resolution under ambient conditions. This combination of characteristics allows NV magnetometers to probe magnetic structures and systems that were previously inaccessible with alternative magnetic sensing technologies This dissertation presents and discusses a number of the initial efforts to demonstrate and improve NV magnetometry. In particular, a wide-field CCD based NV magnetic field imager capable of micron-scale spatial resolution is demonstrated; and magnetic field alignment, preferential NV orientation, and multipulse dynamical decoupling techniques are explored for enhancing magnetic sensitivity. The further application of dynamical decoupling control sequences as a spectral probe to extract information about the dynamics of the NV spin environment is also discussed; such information may be useful for determining optimal diamond sample parameters for different applications. Finally, several proposed and recently demonstrated applications which take advantage of NV magnetometers' sensitivity and spatial resolution at room temperature are presented, with particular focus on bio-magnetic field imaging.

Some anticipated contributions to core fluid dynamics from the GRM

NASA Technical Reports Server (NTRS)

Vanvorhies, C.

1985-01-01

It is broadly maintained that the secular variation (SV) of the large scale geomagnetic field contains information on the fluid dynamics of Earth's electrically conducting outer core. The electromagnetic theory appropriate to a simple Earth model has recently been combined with reduced geomagnetic data in order to extract some of this information and ascertain its significance. The simple Earth model consists of a rigid, electrically insulating mantle surrounding a spherical, inviscid, and perfectly conducting liquid outer core. This model was tested against seismology by using truncated spherical harmonic models of the observed geomagnetic field to locate Earth's core-mantle boundary, CMB. Further electromagnetic theory has been developed and applied to the problem of estimating the horizontal fluid motion just beneath CMB. Of particular geophysical interest are the hypotheses that these motions: (1) include appreciable surface divergence indicative of vertical motion at depth, and (2) are steady for time intervals of a decade or more. In addition to the extended testing of the basic Earth model, the proposed GRM provides a unique opportunity to test these dynamical hypotheses.

Angular Declination and the Dynamic Perception of Egocentric Distance

PubMed Central

Gajewski, Daniel A.; Philbeck, John W.; Wirtz, Philip W.; Chichka, David

2014-01-01

The extraction of the distance between an object and an observer is fast when angular declination is informative, as it is with targets placed on the ground. To what extent does angular declination drive performance when viewing time is limited? Participants judged target distances in a real-world environment with viewing durations ranging from 36–220 ms. An important role for angular declination was supported by experiments showing that the cue provides information about egocentric distance even on the very first glimpse, and that it supports a sensitive response to distance in the absence of other useful cues. Performance was better at 220 ms viewing durations than for briefer glimpses, suggesting that the perception of distance is dynamic even within the time frame of a typical eye fixation. Critically, performance in limited viewing trials was better when preceded by a 15 second preview of the room without a designated target. The results indicate that the perception of distance is powerfully shaped by memory from prior visual experience with the scene. A theoretical framework for the dynamic perception of distance is presented. PMID:24099588

Extracting Temporal and Spatial Distributions Information about Algal Glooms Based on Multitemporal Modis

NASA Astrophysics Data System (ADS)

Chunguang, L.; Qingjiu, T.

2012-07-01

Based on MODIS remote sensing data, method and technology to extraction the time and space distribution information of algae bloom is studied and established. The dynamic feature of time and space in Taihu Lake from 2009 to 2011 can be obtained by extracted method. Variation of cyanobacterial bloom in the Taihu Lake is analyzed and discussed. The algae bloom frequency index (AFI) and algae bloom sustainability index (ASI) is important criterion which can show the interannual and inter-monthly variation in the whole area or the subregion of Taihu Lake. Utilizing the AFI and ASI from 2009 to 2011, it found some phenomena that: the booming frequency decreased from the north and west to the East and South of Taihu Lake. The annual month algae bloom variation of AFI reflect the booming existing twin peaks in the high shock level and lag trend in general. In the subregion statistics, the IBD and ASI in 2011 show the abnormal condition in the border between the Gongshan Bay and Central Lake. The date is obvious earlier than that on the same subregion in previous years and that on others subregion in the same year.

Informatics in radiology: automated Web-based graphical dashboard for radiology operational business intelligence.

PubMed

Nagy, Paul G; Warnock, Max J; Daly, Mark; Toland, Christopher; Meenan, Christopher D; Mezrich, Reuben S

2009-11-01

Radiology departments today are faced with many challenges to improve operational efficiency, performance, and quality. Many organizations rely on antiquated, paper-based methods to review their historical performance and understand their operations. With increased workloads, geographically dispersed image acquisition and reading sites, and rapidly changing technologies, this approach is increasingly untenable. A Web-based dashboard was constructed to automate the extraction, processing, and display of indicators and thereby provide useful and current data for twice-monthly departmental operational meetings. The feasibility of extracting specific metrics from clinical information systems was evaluated as part of a longer-term effort to build a radiology business intelligence architecture. Operational data were extracted from clinical information systems and stored in a centralized data warehouse. Higher-level analytics were performed on the centralized data, a process that generated indicators in a dynamic Web-based graphical environment that proved valuable in discussion and root cause analysis. Results aggregated over a 24-month period since implementation suggest that this operational business intelligence reporting system has provided significant data for driving more effective management decisions to improve productivity, performance, and quality of service in the department.

Complete wetting of graphene by biological lipids

NASA Astrophysics Data System (ADS)

Luan, Binquan; Huynh, Tien; Zhou, Ruhong

2016-03-01

Graphene nanosheets have been demonstrated to extract large amounts of lipid molecules directly out of the cell membrane of bacteria and thus cause serious damage to the cell's integrity. This interesting phenomenon, however, is so far not well understood theoretically. Here through extensive molecular dynamics simulations and theoretical analyses, we show that this phenomenon can be categorized as a complete wetting of graphene by membrane lipids in water. A wetting-based theory was utilized to associate the free energy change during the microscopic extraction of a lipid with the spreading parameter for the macroscopic wetting. With a customized thermodynamic cycle for detailed energetics, we show that the dispersive adhesion between graphene and lipids plays a dominant role during this extraction as manifested by the curved graphene. Our simulation results suggest that biological lipids can completely wet the concave, flat or even convex (with a small curvature) surface of a graphene sheet.Graphene nanosheets have been demonstrated to extract large amounts of lipid molecules directly out of the cell membrane of bacteria and thus cause serious damage to the cell's integrity. This interesting phenomenon, however, is so far not well understood theoretically. Here through extensive molecular dynamics simulations and theoretical analyses, we show that this phenomenon can be categorized as a complete wetting of graphene by membrane lipids in water. A wetting-based theory was utilized to associate the free energy change during the microscopic extraction of a lipid with the spreading parameter for the macroscopic wetting. With a customized thermodynamic cycle for detailed energetics, we show that the dispersive adhesion between graphene and lipids plays a dominant role during this extraction as manifested by the curved graphene. Our simulation results suggest that biological lipids can completely wet the concave, flat or even convex (with a small curvature) surface of a graphene sheet. Electronic supplementary information (ESI) available: The movie showing the simulation trajectory for the extraction of lipids from the membrane. See DOI: 10.1039/C6NR00202A

Time dependent calibration of a sediment extraction scheme.

PubMed

Roychoudhury, Alakendra N

2006-04-01

Sediment extraction methods to quantify metal concentration in aquatic sediments usually present limitations in accuracy and reproducibility because metal concentration in the supernatant is controlled to a large extent by the physico-chemical properties of the sediment that result in a complex interplay between the solid and the solution phase. It is suggested here that standardization of sediment extraction methods using pure mineral phases or reference material is futile and instead the extraction processes should be calibrated using site-specific sediments before their application. For calibration, time dependent release of metals should be observed for each leachate to ascertain the appropriate time for a given extraction step. Although such an approach is tedious and time consuming, using iron extraction as an example, it is shown here that apart from quantitative data such an approach provides additional information on factors that play an intricate role in metal dynamics in the environment. Single step ascorbate, HCl, oxalate and dithionite extractions were used for targeting specific iron phases from saltmarsh sediments and their response was observed over time in order to calibrate the extraction times for each extractant later to be used in a sequential extraction. For surficial sediments, an extraction time of 24 h, 1 h, 2 h and 3 h was ascertained for ascorbate, HCl, oxalate and dithionite extractions, respectively. Fluctuations in iron concentration in the supernatant over time were ubiquitous. The adsorption-desorption behavior is possibly controlled by the sediment organic matter, formation or consumption of active exchange sites during extraction and the crystallinity of iron mineral phase present in the sediments.

Near ground level sensing for spatial analysis of vegetation

NASA Technical Reports Server (NTRS)

Sauer, Tom; Rasure, John; Gage, Charlie

1991-01-01

Measured changes in vegetation indicate the dynamics of ecological processes and can identify the impacts from disturbances. Traditional methods of vegetation analysis tend to be slow because they are labor intensive; as a result, these methods are often confined to small local area measurements. Scientists need new algorithms and instruments that will allow them to efficiently study environmental dynamics across a range of different spatial scales. A new methodology that addresses this problem is presented. This methodology includes the acquisition, processing, and presentation of near ground level image data and its corresponding spatial characteristics. The systematic approach taken encompasses a feature extraction process, a supervised and unsupervised classification process, and a region labeling process yielding spatial information.

Visualizing and quantifying the in vivo structure and dynamics of the Arabidopsis cortical cytoskeleton using CLSM and VAEM.

PubMed

Rosero, Amparo; Zárský, Viktor; Cvrčková, Fatima

2014-01-01

The cortical microtubules, and to some extent also the actin meshwork, play a central role in the shaping of plant cells. Transgenic plants expressing fluorescent protein markers specifically tagging the two main cytoskeletal systems are available, allowing noninvasive in vivo studies. Advanced microscopy techniques, in particular confocal laser scanning microscopy (CLSM) and variable angle epifluorescence microscopy (VAEM), can be nowadays used for imaging the cortical cytoskeleton of living cells with unprecedented spatial and temporal resolution. With the aid of suitable computing techniques, quantitative information can be extracted from microscopic images and video sequences, providing insight into both architecture and dynamics of the cortical cytoskeleton.

Andreev Bound States Formation and Quasiparticle Trapping in Quench Dynamics Revealed by Time-Dependent Counting Statistics.

PubMed

Souto, R Seoane; Martín-Rodero, A; Yeyati, A Levy

2016-12-23

We analyze the quantum quench dynamics in the formation of a phase-biased superconducting nanojunction. We find that in the absence of an external relaxation mechanism and for very general conditions the system gets trapped in a metastable state, corresponding to a nonequilibrium population of the Andreev bound states. The use of the time-dependent full counting statistics analysis allows us to extract information on the asymptotic population of even and odd many-body states, demonstrating that a universal behavior, dependent only on the Andreev state energy, is reached in the quantum point contact limit. These results shed light on recent experimental observations on quasiparticle trapping in superconducting atomic contacts.

Comprehensive cosmographic analysis by Markov chain method

NASA Astrophysics Data System (ADS)

Capozziello, S.; Lazkoz, R.; Salzano, V.

2011-12-01

We study the possibility of extracting model independent information about the dynamics of the Universe by using cosmography. We intend to explore it systematically, to learn about its limitations and its real possibilities. Here we are sticking to the series expansion approach on which cosmography is based. We apply it to different data sets: Supernovae type Ia (SNeIa), Hubble parameter extracted from differential galaxy ages, gamma ray bursts, and the baryon acoustic oscillations data. We go beyond past results in the literature extending the series expansion up to the fourth order in the scale factor, which implies the analysis of the deceleration q0, the jerk j0, and the snap s0. We use the Markov chain Monte Carlo method (MCMC) to analyze the data statistically. We also try to relate direct results from cosmography to dark energy (DE) dynamical models parametrized by the Chevallier-Polarski-Linder model, extracting clues about the matter content and the dark energy parameters. The main results are: (a) even if relying on a mathematical approximate assumption such as the scale factor series expansion in terms of time, cosmography can be extremely useful in assessing dynamical properties of the Universe; (b) the deceleration parameter clearly confirms the present acceleration phase; (c) the MCMC method can help giving narrower constraints in parameter estimation, in particular for higher order cosmographic parameters (the jerk and the snap), with respect to the literature; and (d) both the estimation of the jerk and the DE parameters reflect the possibility of a deviation from the ΛCDM cosmological model.

Nonlinear analysis and synthesis of video images using deep dynamic bottleneck neural networks for face recognition.

PubMed

Moghadam, Saeed Montazeri; Seyyedsalehi, Seyyed Ali

2018-05-31

Nonlinear components extracted from deep structures of bottleneck neural networks exhibit a great ability to express input space in a low-dimensional manifold. Sharing and combining the components boost the capability of the neural networks to synthesize and interpolate new and imaginary data. This synthesis is possibly a simple model of imaginations in human brain where the components are expressed in a nonlinear low dimensional manifold. The current paper introduces a novel Dynamic Deep Bottleneck Neural Network to analyze and extract three main features of videos regarding the expression of emotions on the face. These main features are identity, emotion and expression intensity that are laid in three different sub-manifolds of one nonlinear general manifold. The proposed model enjoying the advantages of recurrent networks was used to analyze the sequence and dynamics of information in videos. It is noteworthy to mention that this model also has also the potential to synthesize new videos showing variations of one specific emotion on the face of unknown subjects. Experiments on discrimination and recognition ability of extracted components showed that the proposed model has an average of 97.77% accuracy in recognition of six prominent emotions (Fear, Surprise, Sadness, Anger, Disgust, and Happiness), and 78.17% accuracy in the recognition of intensity. The produced videos revealed variations from neutral to the apex of an emotion on the face of the unfamiliar test subject which is on average 0.8 similar to reference videos in the scale of the SSIM method. Copyright © 2018 Elsevier Ltd. All rights reserved.

Coherent Wave Measurement Buoy Arrays to Support Wave Energy Extraction

NASA Astrophysics Data System (ADS)

Spada, F.; Chang, G.; Jones, C.; Janssen, T. T.; Barney, P.; Roberts, J.

2016-02-01

Wave energy is the most abundant form of hydrokinetic energy in the United States and wave energy converters (WECs) are being developed to extract the maximum possible power from the prevailing wave climate. However, maximum wave energy capture is currently limited by the narrow banded frequency response of WECs as well as extended protective shutdown requirements during periods of large waves. These limitations must be overcome in order to maximize energy extraction, thus significantly decreasing the cost of wave energy and making it a viable energy source. Techno-economic studies of several WEC devices have shown significant potential to improve wave energy capture efficiency through operational control strategies that incorporate real-time information about local surface wave motions. Integral Consulting Inc., with ARPA-E support, is partnering with Sandia National Laboratories and Spoondrift LLC to develop a coherent array of wave-measuring devices to relay and enable the prediction of wave-resolved surface dynamics at a WEC location ahead of real time. This capability will provide necessary information to optimize power production of WECs through control strategies, thereby allowing for a single WEC design to perform more effectively across a wide range of wave environments. The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000514.

A statistical framework for multiparameter analysis at the single-cell level.

PubMed

Torres-García, Wandaliz; Ashili, Shashanka; Kelbauskas, Laimonas; Johnson, Roger H; Zhang, Weiwen; Runger, George C; Meldrum, Deirdre R

2012-03-01

Phenotypic characterization of individual cells provides crucial insights into intercellular heterogeneity and enables access to information that is unavailable from ensemble averaged, bulk cell analyses. Single-cell studies have attracted significant interest in recent years and spurred the development of a variety of commercially available and research-grade technologies. To quantify cell-to-cell variability of cell populations, we have developed an experimental platform for real-time measurements of oxygen consumption (OC) kinetics at the single-cell level. Unique challenges inherent to these single-cell measurements arise, and no existing data analysis methodology is available to address them. Here we present a data processing and analysis method that addresses challenges encountered with this unique type of data in order to extract biologically relevant information. We applied the method to analyze OC profiles obtained with single cells of two different cell lines derived from metaplastic and dysplastic human Barrett's esophageal epithelium. In terms of method development, three main challenges were considered for this heterogeneous dynamic system: (i) high levels of noise, (ii) the lack of a priori knowledge of single-cell dynamics, and (iii) the role of intercellular variability within and across cell types. Several strategies and solutions to address each of these three challenges are presented. The features such as slopes, intercepts, breakpoint or change-point were extracted for every OC profile and compared across individual cells and cell types. The results demonstrated that the extracted features facilitated exposition of subtle differences between individual cells and their responses to cell-cell interactions. With minor modifications, this method can be used to process and analyze data from other acquisition and experimental modalities at the single-cell level, providing a valuable statistical framework for single-cell analysis.

Quantum Quench Dynamics

NASA Astrophysics Data System (ADS)

Mitra, Aditi

2018-03-01

Quench dynamics is an active area of study encompassing condensed matter physics and quantum information, with applications to cold-atomic gases and pump-probe spectroscopy of materials. Recent theoretical progress in studying quantum quenches is reviewed. Quenches in interacting one-dimensional systems as well as systems in higher spatial dimensions are covered. The appearance of nontrivial steady states following a quench in exactly solvable models is discussed, and the stability of these states to perturbations is described. Proper conserving approximations needed to capture the onset of thermalization at long times are outlined. The appearance of universal scaling for quenches near critical points and the role of the renormalization group in capturing the transient regime are reviewed. Finally, the effect of quenches near critical points on the dynamics of entanglement entropy and entanglement statistics is discussed. The extraction of critical exponents from the entanglement statistics is outlined.

Monitoring and characterizing natural hazards with satellite InSAR imagery

USGS Publications Warehouse

Lu, Zhong; Zhang, Jixian; Zhang, Yonghong; Dzurisin, Daniel

2010-01-01

Interferometric synthetic aperture radar (InSAR) provides an all-weather imaging capability for measuring ground-surface deformation and inferring changes in land surface characteristics. InSAR enables scientists to monitor and characterize hazards posed by volcanic, seismic, and hydrogeologic processes, by landslides and wildfires, and by human activities such as mining and fluid extraction or injection. Measuring how a volcano’s surface deforms before, during, and after eruptions provides essential information about magma dynamics and a basis for mitigating volcanic hazards. Measuring spatial and temporal patterns of surface deformation in seismically active regions is extraordinarily useful for understanding rupture dynamics and estimating seismic risks. Measuring how landslides develop and activate is a prerequisite to minimizing associated hazards. Mapping surface subsidence or uplift related to extraction or injection of fluids during exploitation of groundwater aquifers or petroleum reservoirs provides fundamental data on aquifer or reservoir properties and improves our ability to mitigate undesired consequences. Monitoring dynamic water-level changes in wetlands improves hydrological modeling predictions and the assessment of future flood impacts. In addition, InSAR imagery can provide near-real-time estimates of fire scar extents and fire severity for wildfire management and control. All-weather satellite radar imagery is critical for studying various natural processes and is playing an increasingly important role in understanding and forecasting natural hazards.

Determination of differential cross sections and kinetic energy release of co-products from central sliced images in photo-initiated dynamic processes.

PubMed

Chen, Kuo-mei; Chen, Yu-wei

2011-04-07

For photo-initiated inelastic and reactive collisions, dynamic information can be extracted from central sliced images of state-selected Newton spheres of product species. An analysis framework has been established to determine differential cross sections and the kinetic energy release of co-products from experimental images. When one of the reactants exhibits a high recoil speed in a photo-initiated dynamic process, the present theory can be employed to analyze central sliced images from ion imaging or three-dimensional sliced fluorescence imaging experiments. It is demonstrated that the differential cross section of a scattering process can be determined from the central sliced image by a double Legendre moment analysis, for either a fixed or continuously distributed recoil speeds in the center-of-mass reference frame. Simultaneous equations which lead to the determination of the kinetic energy release of co-products can be established from the second-order Legendre moment of the experimental image, as soon as the differential cross section is extracted. The intensity distribution of the central sliced image, along with its outer and inner ring sizes, provide all the clues to decipher the differential cross section and the kinetic energy release of co-products.

Quantitative, simultaneous, and collinear eye-tracked, high dynamic range optical coherence tomography at 850 and 1060 nm

NASA Astrophysics Data System (ADS)

Mooser, Matthias; Burri, Christian; Stoller, Markus; Luggen, David; Peyer, Michael; Arnold, Patrik; Meier, Christoph; Považay, Boris

2017-07-01

Ocular optical coherence tomography at the wavelengths ranges of 850 and 1060 nm have been integrated with a confocal scanning laser ophthalmoscope eye-tracker as a clinical commercial-class system. Collinear optics enables an exact overlap of the different channels to produce precisely overlapping depth-scans for evaluating the similarities and differences between the wavelengths to extract additional physiologic information. A reliable segmentation algorithm utilizing Graphcuts has been implemented and applied to automatically extract retinal and choroidal shape in cross-sections and volumes. The device has been tested in normals and pathologies including a cross-sectional and longitudinal study of myopia progress and control with a duplicate instrument in Asian children.

Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks

PubMed Central

Mori, Hiroki; Okuyama, Yuji; Asada, Minoru

2017-01-01

Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the “information networks” different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed. PMID:28796797

Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks.

PubMed

Park, Jihoon; Mori, Hiroki; Okuyama, Yuji; Asada, Minoru

2017-01-01

Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the "information networks" different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed.

Numerical Modelling of Tsunami Generated by Deformable Submarine Slides: Parameterisation of Slide Dynamics for Coupling to Tsunami Propagation Model

NASA Astrophysics Data System (ADS)

Smith, R. C.; Collins, G. S.; Hill, J.; Piggott, M. D.; Mouradian, S. L.

2015-12-01

Numerical modelling informs risk assessment of tsunami generated by submarine slides; however, for large-scale slides modelling can be complex and computationally challenging. Many previous numerical studies have approximated slides as rigid blocks that moved according to prescribed motion. However, wave characteristics are strongly dependent on the motion of the slide and previous work has recommended that more accurate representation of slide dynamics is needed. We have used the finite-element, adaptive-mesh CFD model Fluidity, to perform multi-material simulations of deformable submarine slide-generated waves at real world scales for a 2D scenario in the Gulf of Mexico. Our high-resolution approach represents slide dynamics with good accuracy, compared to other numerical simulations of this scenario, but precludes tracking of wave propagation over large distances. To enable efficient modelling of further propagation of the waves, we investigate an approach to extract information about the slide evolution from our multi-material simulations in order to drive a single-layer wave propagation model, also using Fluidity, which is much less computationally expensive. The extracted submarine slide geometry and position as a function of time are parameterised using simple polynomial functions. The polynomial functions are used to inform a prescribed velocity boundary condition in a single-layer simulation, mimicking the effect the submarine slide motion has on the water column. The approach is verified by successful comparison of wave generation in the single-layer model with that recorded in the multi-material, multi-layer simulations. We then extend this approach to 3D for further validation of this methodology (using the Gulf of Mexico scenario proposed by Horrillo et al., 2013) and to consider the effect of lateral spreading. This methodology is then used to simulate a series of hypothetical submarine slide events in the Arctic Ocean (based on evidence of historic slides) and examine the hazard posed to the UK coast.

Chromatin dynamics during interphase explored by single-particle tracking.

PubMed

Levi, Valeria; Gratton, Enrico

2008-01-01

Our view of the structure and function of the interphase nucleus has changed drastically in recent years. It is now widely accepted that the nucleus is a well organized and highly compartmentalized organelle and that this organization is intimately related to nuclear function. In this context, chromatin-initially considered a randomly entangled polymer-has also been shown to be structurally organized in interphase and its organization was found to be very important to gene regulation. Relevant and not completely answered questions are how chromatin organization is achieved and what mechanisms are responsible for changes in the positions of chromatin loci in the nucleus. A significant advance in the field resulted from tagging chromosome sites with bacterial operator sequences, and visualizing these tags using green fluorescent protein fused with the appropriate repressor protein. Simultaneously, fluorescence imaging techniques evolved significantly during recent years, allowing observation of the time evolution of processes in living specimens. In this context, the motion of the tagged locus was observed and analyzed to extract quantitative information regarding its dynamics. This review focuses on recent advances in our understanding of chromatin dynamics in interphase with the emphasis placed on the information obtained from single-particle tracking (SPT) experiments. We introduce the basis of SPT methods and trajectory analysis, and summarize what has been learnt by using this new technology in the context of chromatin dynamics. Finally, we briefly describe a method of SPT in a two-photon excitation microscope that has several advantages over methods based on conventional microscopy and review the information obtained using this novel approach to study chromatin dynamics.

Chromatin dynamics during interphase explored by single particle tracking

PubMed Central

Levi, Valeria; Gratton, Enrico

2009-01-01

Our view of the structure and function of the interphase nucleus has drastically changed in the last years. It is now widely accepted that the nucleus is a well organized and highly compartmentalized organelle and that this organization is intimately related to nuclear function. In this context, chromatin -initially considered a randomly entangled polymer- has also been shown to be structurally organized in interphase and its organization was found to be very important to gene regulation. Relevant and not completely answered questions are how chromatin organization is achieved and what mechanisms are responsible for changes in the positions of chromatin loci in the nucleus. A significant advance in the field resulted from tagging chromosome sites with bacterial operator sequences, and visualizing these tags using green fluorescent protein fused with the appropriate repressor protein. Simultaneously, fluorescence imaging techniques significantly evolved during the last years allowing the observation of the time evolution of processes in living specimens. In this context, the motion of the tagged locus was observed and analyzed to extract quantitative information regarding its dynamics. This review focuses on recent advances in our understanding of chromatin dynamics in interphase with the emphasis placed on the information obtained from single particle tracking (SPT) experiments. We introduce the basis of SPT methods and trajectories analysis, and summarize what has been learnt by using this new technology in the context of chromatin dynamics. Finally, we briefly describe a method of SPT in a two-photon excitation microscope that has several advantages over methods based on conventional microscopy and review the information obtained by using this novel approach to study chromatin dynamics. PMID:18461483

Temporal dynamics underlying the modulation of social status on social attention.

PubMed

Dalmaso, Mario; Galfano, Giovanni; Coricelli, Carol; Castelli, Luigi

2014-01-01

Fixating someone suddenly moving the eyes is known to trigger a corresponding shift of attention in the observer. This phenomenon, known as gaze-cueing effect, can be modulated as a function of the social status of the individual depicted in the cueing face. Here, in two experiments, we investigated the temporal dynamics underlying this modulation. To this end, a gaze-cueing paradigm was implemented in which centrally-placed faces depicting high- and low-status individuals suddenly shifted the eyes towards a location either spatially congruent or incongruent with that occupied by a subsequent target stimulus. Social status was manipulated by presenting fictive Curriculum Vitae before the experimental phase. In Experiment 1, in which two temporal intervals (50 ms vs. 900 ms) occurred between the direct-gaze face and the averted-gaze face onsets, a stronger gaze-cueing effect in response to high-status faces than low-status faces was observed, irrespective of the time participants were allowed for extracting social information. In Experiment 2, in which two temporal intervals (200 ms vs. 1000 ms) occurred between the averted-gaze face and target onset, a stronger gaze cueing for high-status faces was observed at the shorter interval only. Taken together, these results suggest that information regarding social status is extracted from faces rapidly (Experiment 1), and that the tendency to selectively attend to the locations gazed by high-status individuals may decay with time (Experiment 2).

Biomimetic measurement of allelochemical dynamics in the rhizosphere.

PubMed

Weidenhamer, Jeffrey D

2005-02-01

Polydimethylsiloxane (PDMS) materials were used to quantify levels of the photosynthesis inhibitor sorgoleone in the undisturbed rhizosphere of sorghum plants. The materials used included stir bars coated with PDMS (stir bar sorptive extraction), technical grade optical fiber coated with a thin film of PDMS (matrix-solid phase microextraction), and PDMS tubing. PDMS tubing retained the most sorgoleone. As analyzed by high performance liquid chromatography, amounts of sorgoleone retained on the PDMS materials increased with time. Other materials tested (polyurethane foam plugs, C18 and Tenax disks, and resin capsules) proved less suitable, as they were subject to sometimes extensive penetration by fine root hairs. These results demonstrate the potential for PDMS-based materials to monitor the release of allelochemicals in the undisturbed rhizosphere of allelopathic plants. Unlike extraction procedures that recover all available compounds present in the soil, PDMS functions in a manner more analogous to plant roots in sorbing compounds from soil solution or root exudates. Information on chemical dynamics in the rhizosphere is crucial for evaluating specific hypotheses of allelopathic effects, understanding allelopathic mechanisms, and assessing the importance of allelopathic processes in plant communities.

OBSERVATIONAL DETECTION OF DRIFT VELOCITY BETWEEN IONIZED AND NEUTRAL SPECIES IN SOLAR PROMINENCES

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

Khomenko, Elena; Collados, Manuel; Díaz, Antonio J., E-mail: khomenko@iac.es, E-mail: mcv@iac.es, E-mail: aj.diaz@uib.es

2016-06-01

We report the detection of differences in the ion and neutral velocities in prominences using high-resolution spectral data obtained in 2012 September at the German Vacuum Tower Telescope (Observatorio del Teide, Tenerife). A time series of scans of a small portion of a solar prominence was obtained simultaneously with high cadence using the lines of two elements with different ionization states, namely, Ca ii 8542 Å and He i 10830 Å. The displacements, widths, and amplitudes of both lines were carefully compared to extract dynamical information about the plasma. Many dynamical features are detected, such as counterstreaming flows, jets, andmore » propagating waves. In all of the cases, we find a very strong correlation between the parameters extracted from the lines of both elements, confirming that both lines trace the same plasma. Nevertheless, we also find short-lived transients where this correlation is lost. These transients are associated with ion-neutral drift velocities of the order of several hundred m s{sup −1}. The patches of non-zero drift velocity show coherence in time–distance diagrams.« less

Neural computing for numeric-to-symbolic conversion in control systems

NASA Technical Reports Server (NTRS)

Passino, Kevin M.; Sartori, Michael A.; Antsaklis, Panos J.

1989-01-01

A type of neural network, the multilayer perceptron, is used to classify numeric data and assign appropriate symbols to various classes. This numeric-to-symbolic conversion results in a type of information extraction, which is similar to what is called data reduction in pattern recognition. The use of the neural network as a numeric-to-symbolic converter is introduced, its application in autonomous control is discussed, and several applications are studied. The perceptron is used as a numeric-to-symbolic converter for a discrete-event system controller supervising a continuous variable dynamic system. It is also shown how the perceptron can implement fault trees, which provide useful information (alarms) in a biological system and information for failure diagnosis and control purposes in an aircraft example.

Collaborative human-machine analysis to disambiguate entities in unstructured text and structured datasets

NASA Astrophysics Data System (ADS)

Davenport, Jack H.

2016-05-01

Intelligence analysts demand rapid information fusion capabilities to develop and maintain accurate situational awareness and understanding of dynamic enemy threats in asymmetric military operations. The ability to extract relationships between people, groups, and locations from a variety of text datasets is critical to proactive decision making. The derived network of entities must be automatically created and presented to analysts to assist in decision making. DECISIVE ANALYTICS Corporation (DAC) provides capabilities to automatically extract entities, relationships between entities, semantic concepts about entities, and network models of entities from text and multi-source datasets. DAC's Natural Language Processing (NLP) Entity Analytics model entities as complex systems of attributes and interrelationships which are extracted from unstructured text via NLP algorithms. The extracted entities are automatically disambiguated via machine learning algorithms, and resolution recommendations are presented to the analyst for validation; the analyst's expertise is leveraged in this hybrid human/computer collaborative model. Military capability is enhanced by these NLP Entity Analytics because analysts can now create/update an entity profile with intelligence automatically extracted from unstructured text, thereby fusing entity knowledge from structured and unstructured data sources. Operational and sustainment costs are reduced since analysts do not have to manually tag and resolve entities.

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

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

2017-01-01

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

Extracting information from S-curves of language change

PubMed Central

Ghanbarnejad, Fakhteh; Gerlach, Martin; Miotto, José M.; Altmann, Eduardo G.

2014-01-01

It is well accepted that adoption of innovations are described by S-curves (slow start, accelerating period and slow end). In this paper, we analyse how much information on the dynamics of innovation spreading can be obtained from a quantitative description of S-curves. We focus on the adoption of linguistic innovations for which detailed databases of written texts from the last 200 years allow for an unprecedented statistical precision. Combining data analysis with simulations of simple models (e.g. the Bass dynamics on complex networks), we identify signatures of endogenous and exogenous factors in the S-curves of adoption. We propose a measure to quantify the strength of these factors and three different methods to estimate it from S-curves. We obtain cases in which the exogenous factors are dominant (in the adoption of German orthographic reforms and of one irregular verb) and cases in which endogenous factors are dominant (in the adoption of conventions for romanization of Russian names and in the regularization of most studied verbs). These results show that the shape of S-curve is not universal and contains information on the adoption mechanism. PMID:25339692

Analysis of musical expression in audio signals

NASA Astrophysics Data System (ADS)

Dixon, Simon

2003-01-01

In western art music, composers communicate their work to performers via a standard notation which specificies the musical pitches and relative timings of notes. This notation may also include some higher level information such as variations in the dynamics, tempo and timing. Famous performers are characterised by their expressive interpretation, the ability to convey structural and emotive information within the given framework. The majority of work on audio content analysis focusses on retrieving score-level information; this paper reports on the extraction of parameters describing the performance, a task which requires a much higher degree of accuracy. Two systems are presented: BeatRoot, an off-line beat tracking system which finds the times of musical beats and tracks changes in tempo throughout a performance, and the Performance Worm, a system which provides a real-time visualisation of the two most important expressive dimensions, tempo and dynamics. Both of these systems are being used to process data for a large-scale study of musical expression in classical and romantic piano performance, which uses artificial intelligence (machine learning) techniques to discover fundamental patterns or principles governing expressive performance.

A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan W.

2014-01-01

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan Walker

2015-01-01

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

Dynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits.

PubMed

Gámez Serna, Citlalli; Ruichek, Yassine

2017-06-14

A critical concern of autonomous vehicles is safety. Different approaches have tried to enhance driving safety to reduce the number of fatal crashes and severe injuries. As an example, Intelligent Speed Adaptation (ISA) systems warn the driver when the vehicle exceeds the recommended speed limit. However, these systems only take into account fixed speed limits without considering factors like road geometry. In this paper, we consider road curvature with speed limits to automatically adjust vehicle's speed with the ideal one through our proposed Dynamic Speed Adaptation (DSA) method. Furthermore, 'curve analysis extraction' and 'speed limits database creation' are also part of our contribution. An algorithm that analyzes GPS information off-line identifies high curvature segments and estimates the speed for each curve. The speed limit database contains information about the different speed limit zones for each traveled path. Our DSA senses speed limits and curves of the road using GPS information and ensures smooth speed transitions between current and ideal speeds. Through experimental simulations with different control algorithms on real and simulated datasets, we prove that our method is able to significantly reduce lateral errors on sharp curves, to respect speed limits and consequently increase safety and comfort for the passenger.

Development of emergent processing loops as a system of systems concept

NASA Astrophysics Data System (ADS)

Gainey, James C., Jr.; Blasch, Erik P.

1999-03-01

This paper describes an engineering approach toward implementing the current neuroscientific understanding of how the primate brain fuses, or integrates, 'information' in the decision-making process. We describe a System of Systems (SoS) design for improving the overall performance, capabilities, operational robustness, and user confidence in Identification (ID) systems and show how it could be applied to biometrics security. We use the Physio-associative temporal sensor integration algorithm (PATSIA) which is motivated by observed functions and interactions of the thalamus, hippocampus, and cortical structures in the brain. PATSIA utilizes signal theory mathematics to model how the human efficiently perceives and uses information from the environment. The hybrid architecture implements a possible SoS-level description of the Joint Directors of US Laboratories for Fusion Working Group's functional description involving 5 levels of fusion and their associated definitions. This SoS architecture propose dynamic sensor and knowledge-source integration by implementing multiple Emergent Processing Loops for predicting, feature extracting, matching, and Searching both static and dynamic database like MSTAR's PEMS loops. Biologically, this effort demonstrates these objectives by modeling similar processes from the eyes, ears, and somatosensory channels, through the thalamus, and to the cortices as appropriate while using the hippocampus for short-term memory search and storage as necessary. The particular approach demonstrated incorporates commercially available speaker verification and face recognition software and hardware to collect data and extract features to the PATSIA. The PATSIA maximizes the confidence levels for target identification or verification in dynamic situations using a belief filter. The proof of concept described here is easily adaptable and scaleable to other military and nonmilitary sensor fusion applications.

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

Kim, Sang Beom; Dsilva, Carmeline J.; Debenedetti, Pablo G., E-mail: pdebene@princeton.edu

Understanding the mechanisms by which proteins fold from disordered amino-acid chains to spatially ordered structures remains an area of active inquiry. Molecular simulations can provide atomistic details of the folding dynamics which complement experimental findings. Conventional order parameters, such as root-mean-square deviation and radius of gyration, provide structural information but fail to capture the underlying dynamics of the protein folding process. It is therefore advantageous to adopt a method that can systematically analyze simulation data to extract relevant structural as well as dynamical information. The nonlinear dimensionality reduction technique known as diffusion maps automatically embeds the high-dimensional folding trajectories inmore » a lower-dimensional space from which one can more easily visualize folding pathways, assuming the data lie approximately on a lower-dimensional manifold. The eigenvectors that parametrize the low-dimensional space, furthermore, are determined systematically, rather than chosen heuristically, as is done with phenomenological order parameters. We demonstrate that diffusion maps can effectively characterize the folding process of a Trp-cage miniprotein. By embedding molecular dynamics simulation trajectories of Trp-cage folding in diffusion maps space, we identify two folding pathways and intermediate structures that are consistent with the previous studies, demonstrating that this technique can be employed as an effective way of analyzing and constructing protein folding pathways from molecular simulations.« less

Correlation between hedonic liking and facial expression measurement using dynamic affective response representation.

PubMed

Zhi, Ruicong; Wan, Jingwei; Zhang, Dezheng; Li, Weiping

2018-06-01

Emotional reactions towards products play an essential role in consumers' decision making, and are more important than rational evaluation of sensory attributes. It is crucial to understand consumers' emotion, and the relationship between sensory properties, human liking and choice. There are many inconsistencies between Asian and Western consumers in the usage of hedonic scale, as well as the intensity of facial reactions, due to different culture and consuming habits. However, very few studies discussed the facial responses characteristics of Asian consumers during food consumption. In this paper, explicit liking measurement (hedonic scale) and implicit emotional measurement (facial expressions) were evaluated to judge the consumers' emotions elicited by five types of juices. The contributions of this study included: (1) Constructed the relationship model between hedonic liking and facial expressions analyzed by face reading technology. Negative emotions "sadness", "anger", and "disgust" showed noticeable high negative correlation tendency to hedonic scores. The "liking" hedonic scores could be characterized by positive emotion "happiness". (2) Several emotional intensity based parameters, especially dynamic parameter, were extracted to describe the facial characteristic in sensory evaluation procedure. Both amplitude information and frequency information were involved in the dynamic parameters to remain more information of the emotional responses signals. From the comparison of four types of emotional descriptive parameters, the maximum parameter and dynamic parameter were suggested to be utilized for representing emotional state and intensities. Copyright © 2018 Elsevier Ltd. All rights reserved.

Extraction-Separation Performance and Dynamic Modeling of Orion Test Vehicles with Adams Simulation: 2nd Edition

NASA Technical Reports Server (NTRS)

Fraire, Usbaldo, Jr.; Anderson, Keith; Varela, Jose G.; Bernatovich, Michael A.

2015-01-01

NASA's Orion Capsule Parachute Assembly System (CPAS) project has advanced into the third generation of its parachute test campaign and requires technically comprehensive modeling capabilities to simulate multi-body dynamics (MBD) of test articles released from a C-17. Safely extracting a 30,000 lbm mated test article from a C-17 and performing stable mid-air separation maneuvers requires an understanding of the interaction between elements in the test configuration and how they are influenced by extraction parachute performance, aircraft dynamics, aerodynamics, separation dynamics, and kinetic energy experienced by the system. During the real-time extraction and deployment sequences, these influences can be highly unsteady and difficult to bound. An avionics logic window based on time, pitch, and pitch rate is used to account for these effects and target a favorable separation state in real time. The Adams simulation has been employed to fine-tune this window, as well as predict and reconstruct the coupled dynamics of the Parachute Test Vehicle (PTV) and Cradle Platform Separation System (CPSS) from aircraft extraction through the mid-air separation event. The test-technique for the extraction of CPAS test articles has evolved with increased complexity and requires new modeling concepts to ensure the test article is delivered to a stable test condition for the programmer phase. Prompted by unexpected dynamics and hardware malfunctions in drop tests, these modeling improvements provide a more accurate loads prediction by incorporating a spring-damper line-model derived from the material properties. The qualification phase of CPAS testing is on the horizon and modeling increasingly complex test-techniques with Adams is vital to successfully qualify the Orion parachute system for human spaceflight.

Assessing pharmacokinetics of indocyanine green-loaded nanoparticle in tumor with a dynamic diffuse fluorescence tomography system

NASA Astrophysics Data System (ADS)

Zhang, Yanqi; Yin, Guoyan; Zhao, Huijuan; Ma, Wenjuan; Gao, Feng; Zhang, Limin

2018-02-01

Real-time and continuous monitoring of drug release in vivo is an important task in pharmaceutical development. Here, we devoted to explore a real-time continuous study of the pharmacokinetics of free indocyanine green (ICG) and ICG loaded in the shell-sheddable nanoparticles in tumor based on a dynamic diffuse fluorescence tomography (DFT) system: A highly-sensitive dynamic DFT system of CT-scanning mode generates informative and instantaneous sampling datasets; An analysis procedure extracts the pharmacokinetic parameters from the reconstructed time curves of the mean ICG concentration in tumor, using the Gauss-Newton scheme based on two-compartment model. Compared with the pharmacokinetic parameters of free ICG in tumor, the ICG loaded in the shell-sheddable nanoparticles shows efficient accumulation in tumor. The results demonstrate our proposed dynamic-DFT can provide an integrated and continuous view of the drug delivery of the injected agents in different formulations, which is helpful for the development of diagnosis and therapy for tumors.

Phase correlation imaging of unlabeled cell dynamics

NASA Astrophysics Data System (ADS)

Ma, Lihong; Rajshekhar, Gannavarpu; Wang, Ru; Bhaduri, Basanta; Sridharan, Shamira; Mir, Mustafa; Chakraborty, Arindam; Iyer, Rajashekar; Prasanth, Supriya; Millet, Larry; Gillette, Martha U.; Popescu, Gabriel

2016-09-01

We present phase correlation imaging (PCI) as a novel approach to study cell dynamics in a spatially-resolved manner. PCI relies on quantitative phase imaging time-lapse data and, as such, functions in label-free mode, without the limitations associated with exogenous markers. The correlation time map outputted in PCI informs on the dynamics of the intracellular mass transport. Specifically, we show that PCI can extract quantitatively the diffusion coefficient map associated with live cells, as well as standard Brownian particles. Due to its high sensitivity to mass transport, PCI can be applied to studying the integrity of actin polymerization dynamics. Our results indicate that the cyto-D treatment blocking the actin polymerization has a dominant effect at the large spatial scales, in the region surrounding the cell. We found that PCI can distinguish between senescent and quiescent cells, which is extremely difficult without using specific markers currently. We anticipate that PCI will be used alongside established, fluorescence-based techniques to enable valuable new studies of cell function.

α - synuclein under the magnifying glass. Insights from atomistic and coarse-grain simulations

NASA Astrophysics Data System (ADS)

Ilie, Ioana M.; Nayar, Divya; den Otter, Wouter K.; van der Vegt, Nico F. A.; Briels, Wim J.; University of Twente Collaboration; University of Darmstadt Collaboration

Neurodegenerative diseases are linked to the accumulation of misfolded intrinsically disordered proteins in the brain. Here, we use both all-atom and coarse-grain simulations to explore the intricate dynamics and the aggregation of α-synuclein, the protein implicated in Parkinson's disease. We explore the free energy landscapes of α-synuclein by using Molecular Dynamics simulations and extract information on the structure of the protein as well as on its binding affinities. Next, to study the aggregation, we proceed with representing α-synuclein as a chain of deformable particles that can adapt their geometry, binding affinities and can rearrange into different disordered and ordered structures. We use Brownian Dynamics to simulate the translational and rotational motions of the particles, as well as their interaction properties. The simulations show valuable insight into the internal dynamics of α-synuclein and the formation of ordered and disordered aggregates. In addition, the study is extended to investigate the attachment and folding of a protein to a fiber.

Self-similarity in nature

NASA Astrophysics Data System (ADS)

Timashev, S. F.

2000-02-01

A general phenomenological approach to the analysis of experimental temporal, spatial and energetic series for extracting truly physical non-model parameters ("passport data") is presented, which may be used to characterize and distinguish the evolution as well as the spatial and energetic structure of any open nonlinear dissipative system. This methodology is based on a postulate concerning the crucial information contained in the sequences of non-regularities of the measured dynamic variable (temporal, spatial, energetic). In accordance with this approach, multi-parametric formulas for dynamic variable power spectra as well as for structural functions of different orders are identical for every spatial-temporal-energetic level of the system under consideration. In effect, this entails the introduction of a new kind of self-similarity in Nature. An algorithm has been developed for obtaining as many "passport data" as are necessary for the characterization of a dynamic system. Applications of this approach in the analysis of various experimental series (temporal, spatial, energetic) demonstrate its potential for defining adequate phenomenological parameters of different dynamic processes and structures.

Imaging transcription factors dynamics with advanced fluorescence microscopy methods.

PubMed

Verneri, Paula; Romero, Juan José; De Rossi, María Cecilia; Alvarez, Yanina; Oses, Camila; Guberman, Alejandra; Levi, Valeria

2018-05-10

Pluripotent stem cells (PSCs) are capable of self-renewing and producing all cell types derived from the three germ layers in response to developmental cues, constituting an important promise for regenerative medicine. Pluripotency depends on specific transcription factors (TFs) that induce genes required to preserve the undifferentiated state and repress other genes related to differentiation. The transcription machinery and regulatory components such as TFs are recruited dynamically on their target genes making it essential exploring their dynamics in living cells to understand the transcriptional output. Non-invasive and very sensitive fluorescence microscopy methods are making it possible visualizing the dynamics of TFs in living specimens, complementing the information extracted from studies in fixed specimens and bulk assays. In this work, we briefly describe the basis of these microscopy methods and review how they contributed to our knowledge of the function of TFs relevant to embryo development and cell differentiation in a variety of systems ranging from single cells to whole organisms. Copyright © 2017. Published by Elsevier B.V.

Modeling behavior dynamics using computational psychometrics within virtual worlds.

PubMed

Cipresso, Pietro

2015-01-01

In case of fire in a building, how will people behave in the crowd? The behavior of each individual affects the behavior of others and, conversely, each one behaves considering the crowd as a whole and the individual others. In this article, I propose a three-step method to explore a brand new way to study behavior dynamics. The first step relies on the creation of specific situations with standard techniques (such as mental imagery, text, video, and audio) and an advanced technique [Virtual Reality (VR)] to manipulate experimental settings. The second step concerns the measurement of behavior in one, two, or many individuals focusing on parameters extractions to provide information about the behavior dynamics. Finally, the third step, which uses the parameters collected and measured in the previous two steps in order to simulate possible scenarios to forecast through computational models, understand, and explain behavior dynamics at the social level. An experimental study was also included to demonstrate the three-step method and a possible scenario.

Three-Dimensional Grain Shape-Fabric from Unconsolidated Pyroclastic Density Current Deposits: Implications for Extracting Flow Direction and Insights on Rheology

NASA Astrophysics Data System (ADS)

Hawkins, T. T.; Brand, B. D.; Sarrochi, D.; Pollock, N.

2016-12-01

One of the greatest challenges volcanologists face is the ability to extrapolate information about eruption dynamics and emplacement conditions from deposits. Pyroclastic density current (PDC) deposits are particularly challenging given the wide range of initial current conditions, (e.g., granular, fluidized, concentrated, dilute), and rapid flow transformations due to interaction with evolving topography. Analysis of particle shape-fabric can be used to determine flow direction, and may help to understand the rheological characteristics of the flows. However, extracting shape-fabric information from outcrop (2D) apparent fabric is limited, especially when outcrop exposure is incomplete or lacks context. To better understand and quantify the complex flow dynamics reflected in PDC deposits, we study the complete shape-fabric data in 3D using oriented samples. In the field, the prospective sample is carved from the unconsolidated deposit in blocks, the dimensions of which depend on the average clast size in the sample. The sample is saturated in situ with a water-based sodium silicate solution, then wrapped in plaster-soaked gauze to form a protective cast. The orientation of the sample is recorded on the block faces. The samples dry for five days and are then extracted in intact blocks. In the lab, the sample is vacuum impregnated with sodium silicate and cured in an oven. The fully lithified sample is first cut along the plan view to identify orientations of the long axes of the grains (flow direction), and then cut in the two plains perpendicular to grain elongation. 3D fabric analysis is performed using high resolution images of the cut-faces using computer assisted image analysis software devoted to shape-fabric analysis. Here we present the results of samples taken from the 18 May 1980 PDC deposit facies, including massive, diffuse-stratified and cross-stratified lapilli tuff. We show a relationship between the strength of iso-orientation of the elongated particles and different facies architectures, which is used to interpret rheological conditions of the flow. We chose the 18 May PDC deposits because their well-exposed and well-studied outcrops provide context, which allow us to test the method and extract information useful for interpreting ancient deposits that lack context.

Extracting temporal and spatial information from remotely sensed data for mapping wildlife habitat: Tucson

USGS Publications Warehouse

Wallace, Cynthia S.A.; Advised by Marsh, Stuart E.

2002-01-01

The research accomplished in this dissertation used both mathematical and statistical techniques to extract and evaluate measures of landscape temporal dynamics and spatial structure from remotely sensed data for the purpose of mapping wildlife habitat. By coupling the landscape measures gleaned from the remotely sensed data with various sets of animal sightings and population data, effective models of habitat preference were created.Measures of temporal dynamics of vegetation greenness as measured by National Oceanographic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (AVHRR) satellite were used to effectively characterize and map season specific habitat of the Sonoran pronghorn antelope, as well as produce preliminary models of potential yellow-billed cuckoo habitat in Arizona. Various measures that capture different aspects of the temporal dynamics of the landscape were derived from AVHRR Normalized Difference Vegetation Index composite data using three main classes of calculations: basic statistics, standardized principal components analysis, and Fourier analysis. Pronghorn habitat models based on the AVHRR measures correspond visually and statistically to GIS-based models produced using data that represent detailed knowledge of ground-condition.Measures of temporal dynamics also revealed statistically significant correlations with annual estimates of elk population in selected Arizona Game Management Units, suggesting elk respond to regional environmental changes that can be measured using satellite data. Such relationships, once verified and established, can be used to help indirectly monitor the population.Measures of landscape spatial structure derived from IKONOS high spatial resolution (1-m) satellite data using geostatistics effectively map details of Sonoran pronghorn antelope habitat. Local estimates of the nugget, sill, and range variogram parameters calculated within 25 x 25-meter image windows describe the spatial autocorrelation of the image, permitting classification of all pixels into coherent units whose signature graphs exhibit a classic variogram shape. The variogram parameters captured in these signatures have been shown in previous studies to discriminate between different species-specific vegetation associations.The synoptic view of the landscape provided by satellite data can inform resource management efforts. The ability to characterize the spatial structure and temporal dynamics of habitat using repeatable remote sensing data allows closer monitoring of the relationship between a species and its landscape.

Dynamic Decision-Making in Multi-Task Environments: Theory and Experimental Results.

DTIC Science & Technology

1981-03-15

The operator’s primary responsibility in this new role is to extract information from his environment, and to integrate it for’ action selection and its...of the human operator from one of a controller to one of a supervisory decision-maker. The operator’s primary responsibility in this new role is to...troller to that of a monitor of multiple tasks, or a supervisor of sev- ~ I eral semi-automated subsystems. The operator’s primary task in these

A study of the formation and dynamics of the Earth's plasma sheet using ion composition data

NASA Technical Reports Server (NTRS)

Lennartsson, O. W.

1994-01-01

Over two years of data from the Lockheed Plasma Composition Experiment on the ISEE 1 spacecraft, covering ion energies between 100 eV/e and about 16 keV/e, have been analyzed in an attempt to extract new information about three geophysical issues: (1) solar wind penetration of the Earth's magnetic tail; (2) relationship between plasma sheet and tail lobe ion composition; and (3) possible effects of heavy terrestrial ions on plasma sheet stability.

Two-Dimensional Spectroscopy Is Being Used to Address Core Scientific Questions in Biology and Materials Science.

PubMed

Petti, Megan K; Lomont, Justin P; Maj, Michał; Zanni, Martin T

2018-02-15

Two-dimensional spectroscopy is a powerful tool for extracting structural and dynamic information from a wide range of chemical systems. We provide a brief overview of the ways in which two-dimensional visible and infrared spectroscopies are being applied to elucidate fundamental details of important processes in biological and materials science. The topics covered include amyloid proteins, photosynthetic complexes, ion channels, photovoltaics, batteries, as well as a variety of promising new methods in two-dimensional spectroscopy.

Extraction of the respiratory signal from small-animal CT projections for a retrospective gating method

NASA Astrophysics Data System (ADS)

Chavarrías, C.; Vaquero, J. J.; Sisniega, A.; Rodríguez-Ruano, A.; Soto-Montenegro, M. L.; García-Barreno, P.; Desco, M.

2008-09-01

We propose a retrospective respiratory gating algorithm to generate dynamic CT studies. To this end, we compared three different methods of extracting the respiratory signal from the projections of small-animal cone-beam computed tomography (CBCT) scanners. Given a set of frames acquired from a certain axial angle, subtraction of their average image from each individual frame produces a set of difference images. Pixels in these images have positive or negative values (according to the respiratory phase) in those areas where there is lung movement. The respiratory signals were extracted by analysing the shape of the histogram of these difference images: we calculated the first four central and non-central moments. However, only odd-order moments produced the desired breathing signal, as the even-order moments lacked information about the phase. Each of these curves was compared to a reference signal recorded by means of a pneumatic pillow. Given the similar correlation coefficients yielded by all of them, we selected the mean to implement our retrospective protocol. Respiratory phase bins were separated, reconstructed independently and included in a dynamic sequence, suitable for cine playback. We validated our method in five adult rat studies by comparing profiles drawn across the diaphragm dome, with and without retrospective respiratory gating. Results showed a sharper transition in the gated reconstruction, with an average slope improvement of 60.7%.

Leveraging Ensemble Dynamical Properties to Prioritize Exoplanet Follow-Up Observations

NASA Astrophysics Data System (ADS)

Ballard, Sarah

2017-01-01

The number of transiting exoplanets now exceeds several thousand, enabling ensemble studies of the dynamical properties of exoplanetary systems. We require a mixture model of dynamical conditions (whether frozen in from formation or sculpted by planet-planet interactions) to recover Kepler's yield of transiting planets. Around M dwarfs, which will be predominate sites of exoplanet follow-up atmospheric study in the next decade, even a modest orbital eccentricity can sterilize a planet. I will describe efforts to link cheap observables, such as number of transiting planets and presence of transit timing variations, to eccentricity and mutual inclination in exoplanet systems. The addition of a second transiting planet, for example, halves the expected orbital eccentricity. For the vast majority of TESS targets, the light curve alone will furnish the sum total of data about the exoplanet. Extracting information about orbital properties from these light curves will help prioritize precious follow-up resources.

Inferring Biological Structures from Super-Resolution Single Molecule Images Using Generative Models

PubMed Central

Maji, Suvrajit; Bruchez, Marcel P.

2012-01-01

Localization-based super resolution imaging is presently limited by sampling requirements for dynamic measurements of biological structures. Generating an image requires serial acquisition of individual molecular positions at sufficient density to define a biological structure, increasing the acquisition time. Efficient analysis of biological structures from sparse localization data could substantially improve the dynamic imaging capabilities of these methods. Using a feature extraction technique called the Hough Transform simple biological structures are identified from both simulated and real localization data. We demonstrate that these generative models can efficiently infer biological structures in the data from far fewer localizations than are required for complete spatial sampling. Analysis at partial data densities revealed efficient recovery of clathrin vesicle size distributions and microtubule orientation angles with as little as 10% of the localization data. This approach significantly increases the temporal resolution for dynamic imaging and provides quantitatively useful biological information. PMID:22629348

High order magnetic optics for high dynamic range proton radiography at a kinetic energy of 800 MeV

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

Sjue, S. K. L., E-mail: sjue@lanl.gov; Mariam, F. G.; Merrill, F. E.

2016-01-15

Flash radiography with 800 MeV kinetic energy protons at Los Alamos National Laboratory is an important experimental tool for investigations of dynamic material behavior driven by high explosives or pulsed power. The extraction of quantitative information about density fields in a dynamic experiment from proton generated images requires a high fidelity model of the proton imaging process. It is shown that accurate calculations of the transmission through the magnetic lens system require terms beyond second order for protons far from the tune energy. The approach used integrates the correlated multiple Coulomb scattering distribution simultaneously over the collimator and the imagemore » plane. Comparison with a series of static calibration images demonstrates the model’s accurate reproduction of both the transmission and blur over a wide range of tune energies in an inverse identity lens that consists of four quadrupole electromagnets.« less

High order magnetic optics for high dynamic range proton radiography at a kinetic energy 800 MeV

DOE PAGES

Sjue, Sky K. L.; Morris, Christopher L.; Merrill, Frank Edward; ...

2016-01-14

Flash radiography with 800 MeV kinetic energy protons at Los Alamos National Laboratory is an important experimental tool for investigations of dynamic material behavior driven by high explosives or pulsed power. The extraction of quantitative information about density fields in a dynamic experiment from proton generated images requires a high fidelity model of the protonimaging process. It is shown that accurate calculations of the transmission through the magnetic lens system require terms beyond second order for protons far from the tune energy. The approach used integrates the correlated multiple Coulomb scattering distribution simultaneously over the collimator and the image plane.more » Furthermore, comparison with a series of static calibrationimages demonstrates the model’s accurate reproduction of both the transmission and blur over a wide range of tune energies in an inverse identity lens that consists of four quadrupole electromagnets.« less

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

Wang, Shaobu; Lu, Shuai; Zhou, Ning

In interconnected power systems, dynamic model reduction can be applied on generators outside the area of interest to mitigate the computational cost with transient stability studies. This paper presents an approach of deriving the reduced dynamic model of the external area based on dynamic response measurements, which comprises of three steps, dynamic-feature extraction, attribution and reconstruction (DEAR). In the DEAR approach, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highestmore » similarity, forming a suboptimal ‘basis’ of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original external system. Network model is un-changed in the DEAR method. Tests on several IEEE standard systems show that the proposed method gets better reduction ratio and response errors than the traditional coherency aggregation methods.« less

Validation for Vegetation Green-up Date Extracted from GIMMS NDVI and NDVI3g Using Variety of Methods

NASA Astrophysics Data System (ADS)

Chang, Q.; Jiao, W.

2017-12-01

Phenology is a sensitive and critical feature of vegetation change that has regarded as a good indicator in climate change studies. So far, variety of remote sensing data sources and phenology extraction methods from satellite datasets have been developed to study the spatial-temporal dynamics of vegetation phenology. However, the differences between vegetation phenology results caused by the varies satellite datasets and phenology extraction methods are not clear, and the reliability for different phenology results extracted from remote sensing datasets is not verified and compared using the ground observation data. Based on three most popular remote sensing phenology extraction methods, this research calculated the Start of the growing season (SOS) for each pixels in the Northern Hemisphere for two kinds of long time series satellite datasets: GIMMS NDVIg (SOSg) and GIMMS NDVI3g (SOS3g). The three methods used in this research are: maximum increase method, dynamic threshold method and midpoint method. Then, this study used SOS calculated from NEE datasets (SOS_NEE) monitored by 48 eddy flux tower sites in global flux website to validate the reliability of six phenology results calculated from remote sensing datasets. Results showed that both SOSg and SOS3g extracted by maximum increase method are not correlated with ground observed phenology metrics. SOSg and SOS3g extracted by the dynamic threshold method and midpoint method are both correlated with SOS_NEE significantly. Compared with SOSg extracted by the dynamic threshold method, SOSg extracted by the midpoint method have a stronger correlation with SOS_NEE. And, the same to SOS3g. Additionally, SOSg showed stronger correlation with SOS_NEE than SOS3g extracted by the same method. SOS extracted by the midpoint method from GIMMS NDVIg datasets seemed to be the most reliable results when validated with SOS_NEE. These results can be used as reference for data and method selection in future's phenology study.

Videomicroscopic extraction of specific information on cell proliferation and migration in vitro

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

Debeir, Olivier; Megalizzi, Veronique; Warzee, Nadine

2008-10-01

In vitro cell imaging is a useful exploratory tool for cell behavior monitoring with a wide range of applications in cell biology and pharmacology. Combined with appropriate image analysis techniques, this approach has been shown to provide useful information on the detection and dynamic analysis of cell events. In this context, numerous efforts have been focused on cell migration analysis. In contrast, the cell division process has been the subject of fewer investigations. The present work focuses on this latter aspect and shows that, in complement to cell migration data, interesting information related to cell division can be extracted frommore » phase-contrast time-lapse image series, in particular cell division duration, which is not provided by standard cell assays using endpoint analyses. We illustrate our approach by analyzing the effects induced by two sigma-1 receptor ligands (haloperidol and 4-IBP) on the behavior of two glioma cell lines using two in vitro cell models, i.e., the low-density individual cell model and the high-density scratch wound model. This illustration also shows that the data provided by our approach are suggestive as to the mechanism of action of compounds, and are thus capable of informing the appropriate selection of further time-consuming and more expensive biological evaluations required to elucidate a mechanism.« less

Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures

PubMed Central

Pi, Yiming

2017-01-01

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar. PMID:29267249

Dynamic feature analysis of vector-based images for neuropsychological testing

NASA Astrophysics Data System (ADS)

Smith, Stephen L.; Cervantes, Basilio R.

1998-07-01

The dynamic properties of human motor activities, such as those observed in the course of drawing simple geometric shapes, are considerably more complex and often more informative than the goal to be achieved; in this case a static line drawing. This paper demonstrates how these dynamic properties may be used to provide a means of assessing a patient's visuo-spatial ability -- an important component of neuropsychological testing. The work described here provides a quantitative assessment of visuo-spatial ability, whilst preserving the conventional test environment. Results will be presented for a clinical population of long-term haemodialysis patients and test population comprises three groups of children (1) 7-8 years, (2) 9-10 years and (3) 11-12 years, all of which have no known neurological dysfunction. Ten new dynamic measurements extracted from patient responses in conjunction with one static feature deduced from earlier work describe a patient's visuo-spatial ability in a quantitative manner with sensitivity not previously attainable. The dynamic feature measurements in isolation provide a unique means of tracking a patient's approach to motor activities and could prove useful in monitoring a child' visuo-motor development.

Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures.

PubMed

Zhou, Zhi; Cao, Zongjie; Pi, Yiming

2017-12-21

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar.

Cumulants and correlation functions versus the QCD phase diagram

DOE PAGES

Bzdak, Adam; Koch, Volker; Strodthoff, Nils

2017-05-12

Here, we discuss the relation of particle number cumulants and correlation functions. It is argued that measuring couplings of the genuine multiparticle correlation functions could provide cleaner information on possible nontrivial dynamics in heavy-ion collisions. We also extract integrated multiproton correlation functions from the presently available experimental data on proton cumulants. We find that the STAR data contain significant four-proton correlations, at least at the lower energies, with indication of changing dynamics in central collisions. We also find that these correlations are rather long ranged in rapidity. Finally, using the Ising model, we demonstrate how the signs of the multiprotonmore » correlation functions may be used to exclude certain regions of the phase diagram close to the critical point.« less

Association in ethylammonium nitrate-dimethyl sulfoxide mixtures: First structural and dynamical evidences

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

Russina, Olga; Macchiagodena, Marina; Kirchner, Barbara

2015-01-01

Here we report the first structural and dynamic investigation on ethylammonium nitrate, a representative protic Ionic liquid, and dimethylsulfoxide. By using joined x/ray and neutron diffraction, we exploit the EPSR approach to extract structural information at atomistic level. EAN/DMSO turns out to be homogeneous at microscopic scales and indications for the existence of a structural leit motiv with stoichiometric composition 2DMSO:1EAN are found. Dielectric spectroscopy is used to access the relaxation map of the DMSO:EAN = 60:40 mixture. No crystallisation is detected and three relaxation processes could be characterised. Overall this study provides new indications of strict analogies between watermore » and ethylammonium nitrate. (c) 2014 Elsevier B.V. All rights reserved.« less

Multiscale analysis of heart rate dynamics: entropy and time irreversibility measures.

PubMed

Costa, Madalena D; Peng, Chung-Kang; Goldberger, Ary L

2008-06-01

Cardiovascular signals are largely analyzed using traditional time and frequency domain measures. However, such measures fail to account for important properties related to multiscale organization and non-equilibrium dynamics. The complementary role of conventional signal analysis methods and emerging multiscale techniques, is, therefore, an important frontier area of investigation. The key finding of this presentation is that two recently developed multiscale computational tools--multiscale entropy and multiscale time irreversibility--are able to extract information from cardiac interbeat interval time series not contained in traditional methods based on mean, variance or Fourier spectrum (two-point correlation) techniques. These new methods, with careful attention to their limitations, may be useful in diagnostics, risk stratification and detection of toxicity of cardiac drugs.

Cumulants and correlation functions versus the QCD phase diagram

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

Bzdak, Adam; Koch, Volker; Strodthoff, Nils

Here, we discuss the relation of particle number cumulants and correlation functions. It is argued that measuring couplings of the genuine multiparticle correlation functions could provide cleaner information on possible nontrivial dynamics in heavy-ion collisions. We also extract integrated multiproton correlation functions from the presently available experimental data on proton cumulants. We find that the STAR data contain significant four-proton correlations, at least at the lower energies, with indication of changing dynamics in central collisions. We also find that these correlations are rather long ranged in rapidity. Finally, using the Ising model, we demonstrate how the signs of the multiprotonmore » correlation functions may be used to exclude certain regions of the phase diagram close to the critical point.« less

Gender-specific automatic valence recognition of affective olfactory stimulation through the analysis of the electrodermal activity.

PubMed

Greco, Alberto; Lanata, Antonio; Valenza, Gaetano; Di Francesco, Fabio; Scilingo, Enzo Pasquale

2016-08-01

This study reports on the development of a gender-specific classification system able to discern between two valence levels of smell, through information gathered from electrodermal activity (EDA) dynamics. Specifically, two odorants were administered to 32 healthy volunteers (16 males) while monitoring EDA. CvxEDA model was used to process the EDA signal and extract features from both tonic and phasic components. The feature set was used as input to a K-NN classifier implementing a leave-one-subject-out procedure. Results show strong differences in the accuracy of valence recognition between men (62.5%) and women (78%). We can conclude that affective olfactory stimulation significantly affect EDA dynamics with a highly specific gender dependency.

Deep visual-semantic for crowded video understanding

NASA Astrophysics Data System (ADS)

Deng, Chunhua; Zhang, Junwen

2018-03-01

Visual-semantic features play a vital role for crowded video understanding. Convolutional Neural Networks (CNNs) have experienced a significant breakthrough in learning representations from images. However, the learning of visualsemantic features, and how it can be effectively extracted for video analysis, still remains a challenging task. In this study, we propose a novel visual-semantic method to capture both appearance and dynamic representations. In particular, we propose a spatial context method, based on the fractional Fisher vector (FV) encoding on CNN features, which can be regarded as our main contribution. In addition, to capture temporal context information, we also applied fractional encoding method on dynamic images. Experimental results on the WWW crowed video dataset demonstrate that the proposed method outperform the state of the art.

Experimental analysis of bruises in human volunteers using radiometric depth profiling and diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Vidovič, Luka; Milanič, Matija; Majaron, Boris

2015-07-01

We combine pulsed photothermal radiometry (PPTR) depth profiling with diffuse reflectance spectroscopy (DRS) measurements for a comprehensive analysis of bruise evolution in vivo. While PPTR enables extraction of detailed depth distribution and concentration profiles of selected absorbers (e.g. melanin, hemoglobin), DRS provides information in a wide range of visible wavelengths and thus offers an additional insight into dynamics of the hemoglobin degradation products. Combining the two approaches enables us to quantitatively characterize bruise evolution dynamics. Our results indicate temporal variations of the bruise evolution parameters in the course of bruise self-healing process. The obtained parameter values and trends represent a basis for a future development of an objective technique for bruise age determination.

Multiscale Analysis of Heart Rate Dynamics: Entropy and Time Irreversibility Measures

PubMed Central

Peng, Chung-Kang; Goldberger, Ary L.

2016-01-01

Cardiovascular signals are largely analyzed using traditional time and frequency domain measures. However, such measures fail to account for important properties related to multiscale organization and nonequilibrium dynamics. The complementary role of conventional signal analysis methods and emerging multiscale techniques, is, therefore, an important frontier area of investigation. The key finding of this presentation is that two recently developed multiscale computational tools— multiscale entropy and multiscale time irreversibility—are able to extract information from cardiac interbeat interval time series not contained in traditional methods based on mean, variance or Fourier spectrum (two-point correlation) techniques. These new methods, with careful attention to their limitations, may be useful in diagnostics, risk stratification and detection of toxicity of cardiac drugs. PMID:18172763

Extraction of stability and control derivatives from orbiter flight data

NASA Technical Reports Server (NTRS)

Iliff, Kenneth W.; Shafer, Mary F.

1993-01-01

The Space Shuttle Orbiter has provided unique and important information on aircraft flight dynamics. This information has provided the opportunity to assess the flight-derived stability and control derivatives for maneuvering flight in the hypersonic regime. In the case of the Space Shuttle Orbiter, these derivatives are required to determine if certain configuration placards (limitations on the flight envelope) can be modified. These placards were determined on the basis of preflight predictions and the associated uncertainties. As flight-determined derivatives are obtained, the placards are reassessed, and some of them are removed or modified. Extraction of the stability and control derivatives was justified by operational considerations and not by research considerations. Using flight results to update the predicted database of the orbiter is one of the most completely documented processes for a flight vehicle. This process followed from the requirement for analysis of flight data for control system updates and for expansion of the operational flight envelope. These results show significant changes in many important stability and control derivatives from the preflight database. This paper presents some of the stability and control derivative results obtained from Space Shuttle flights. Some of the limitations of this information are also examined.

The Determination of Metals in Sediment Pore Waters and in 1N HCl-Extracted Sediments by ICP-MS

USGS Publications Warehouse

May, T.W.; Wiedmeyer, Ray H.; Brumbaugh, W.G.; Schmitt, C.J.

1997-01-01

Concentrations of metals in sediment interstitial water (pore water) and those extractable from sediment with weak acids can provide important information about the bioavailability and toxicological effects of such contaminants. The highly variable nature of metal concentrations in these matrices requires instrumentation with the detection limit capability of graphite furnace atomic absorption and the wide dynamic linear range capability of ICP-OES. These criteria are satisfied with ICP-MS instrumentation. We investigated the performance of ICP-MS in the determination of certain metals from these matrices. The results for three metals were compared to those determined by graphite furnace atomic absorption spectroscopy. It was concluded that ICP-MS was an excellent instrumental approach for the determination of metals in these matrices.

Analysis of spike-wave discharges in rats using discrete wavelet transform.

PubMed

Ubeyli, Elif Derya; Ilbay, Gül; Sahin, Deniz; Ateş, Nurbay

2009-03-01

A feature is a distinctive or characteristic measurement, transform, structural component extracted from a segment of a pattern. Features are used to represent patterns with the goal of minimizing the loss of important information. The discrete wavelet transform (DWT) as a feature extraction method was used in representing the spike-wave discharges (SWDs) records of Wistar Albino Glaxo/Rijswijk (WAG/Rij) rats. The SWD records of WAG/Rij rats were decomposed into time-frequency representations using the DWT and the statistical features were calculated to depict their distribution. The obtained wavelet coefficients were used to identify characteristics of the signal that were not apparent from the original time domain signal. The present study demonstrates that the wavelet coefficients are useful in determining the dynamics in the time-frequency domain of SWD records.

Comparison of supercritical fluid extraction and ultrasound-assisted extraction of fatty acids from quince (Cydonia oblonga Miller) seed using response surface methodology and central composite design.

PubMed

Daneshvand, Behnaz; Ara, Katayoun Mahdavi; Raofie, Farhad

2012-08-24

Fatty acids of Cydonia oblonga Miller cultivated in Iran were obtained by supercritical (carbon dioxide) extraction and ultrasound-assisted extraction methods. The oils were analyzed by capillary gas chromatography using mass spectrometric detections. The compounds were identified according to their retention indices and mass spectra (EI, 70eV). The experimental parameters of SFE such as pressure, temperature, modifier volume, static and dynamic extraction time were optimized using a Central Composite Design (CCD) after a 2(5) factorial design. Pressure and dynamic extraction time had significant effect on the extraction yield, while the other factors (temperature, static extraction time and modifier volume) were not identified as significant factors under the selected conditions. The results of chemometrics analysis showed the highest yield for SFE (24.32%), which was obtained at a pressure of 353bar, temperature of 35°C, modifier (methanol) volume of 150μL, and static and dynamic extraction times of 10 and 60min, respectively. Ultrasound-assisted extraction (UAE) of Fatty acids from C. oblonga Miller was optimized, using a rotatable central composite design. The optimum conditions were as follows: solvent (n-hexane) volume, 22mL; extraction time, 30min; and extraction temperature, 55°C. This resulted in a maximum oil recovery of 19.5%. The extracts with higher yield from both methods were subjected to transesterification and GC-MS analysis. The results show that the oil obtained by SFE with the optimal operating conditions allowed a fatty acid composition similar to the oil obtained by UAE in optimum condition and no significant differences were found. The major components of oil extract were Linoleic, Palmitic, Oleic, Stearic and Eicosanoic acids. Copyright © 2012 Elsevier B.V. All rights reserved.

Automated smoother for the numerical decoupling of dynamics models.

PubMed

Vilela, Marco; Borges, Carlos C H; Vinga, Susana; Vasconcelos, Ana Tereza R; Santos, Helena; Voit, Eberhard O; Almeida, Jonas S

2007-08-21

Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittaker's smoother and demonstrate its role within a robust, fully automated structure identification procedure. In this report we propose a robust, fully automated solution for signal extraction from time series, which is the prerequisite for the efficient reverse engineering of biological systems models. The Whittaker's smoother is reformulated within the context of information theory and extended by the development of adaptive signal segmentation to account for heterogeneous noise structures. The resulting procedure can be used on arbitrary time series with a nonstationary noise process; it is illustrated here with metabolic profiles obtained from in-vivo NMR experiments. The smoothed solution that is free of parametric bias permits differentiation, which is crucial for the numerical decoupling of systems of differential equations. The method is applicable in signal extraction from time series with nonstationary noise structure and can be applied in the numerical decoupling of system of differential equations into algebraic equations, and thus constitutes a rather general tool for the reverse engineering of mechanistic model descriptions from multivariate experimental time series.

Emerging Concepts of Data Integration in Pathogen Phylodynamics.

PubMed

Baele, Guy; Suchard, Marc A; Rambaut, Andrew; Lemey, Philippe

2017-01-01

Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics.

Emerging Concepts of Data Integration in Pathogen Phylodynamics

PubMed Central

Baele, Guy; Suchard, Marc A.; Rambaut, Andrew; Lemey, Philippe

2017-01-01

Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics. PMID:28173504

Computational Amide I Spectroscopy for Refinement of Disordered Peptide Ensembles: Maximum Entropy and Related Approaches

NASA Astrophysics Data System (ADS)

Reppert, Michael; Tokmakoff, Andrei

The structural characterization of intrinsically disordered peptides (IDPs) presents a challenging biophysical problem. Extreme heterogeneity and rapid conformational interconversion make traditional methods difficult to interpret. Due to its ultrafast (ps) shutter speed, Amide I vibrational spectroscopy has received considerable interest as a novel technique to probe IDP structure and dynamics. Historically, Amide I spectroscopy has been limited to delivering global secondary structural information. More recently, however, the method has been adapted to study structure at the local level through incorporation of isotope labels into the protein backbone at specific amide bonds. Thanks to the acute sensitivity of Amide I frequencies to local electrostatic interactions-particularly hydrogen bonds-spectroscopic data on isotope labeled residues directly reports on local peptide conformation. Quantitative information can be extracted using electrostatic frequency maps which translate molecular dynamics trajectories into Amide I spectra for comparison with experiment. Here we present our recent efforts in the development of a rigorous approach to incorporating Amide I spectroscopic restraints into refined molecular dynamics structural ensembles using maximum entropy and related approaches. By combining force field predictions with experimental spectroscopic data, we construct refined structural ensembles for a family of short, strongly disordered, elastin-like peptides in aqueous solution.

A cortical framework for invariant object categorization and recognition.

PubMed

Rodrigues, João; Hans du Buf, J M

2009-08-01

In this paper we present a new model for invariant object categorization and recognition. It is based on explicit multi-scale features: lines, edges and keypoints are extracted from responses of simple, complex and end-stopped cells in cortical area V1, and keypoints are used to construct saliency maps for Focus-of-Attention. The model is a functional but dichotomous one, because keypoints are employed to model the "where" data stream, with dynamic routing of features from V1 to higher areas to obtain translation, rotation and size invariance, whereas lines and edges are employed in the "what" stream for object categorization and recognition. Furthermore, both the "where" and "what" pathways are dynamic in that information at coarse scales is employed first, after which information at progressively finer scales is added in order to refine the processes, i.e., both the dynamic feature routing and the categorization level. The construction of group and object templates, which are thought to be available in the prefrontal cortex with "what" and "where" components in PF46d and PF46v, is also illustrated. The model was tested in the framework of an integrated and biologically plausible architecture.

Angular declination and the dynamic perception of egocentric distance.

PubMed

Gajewski, Daniel A; Philbeck, John W; Wirtz, Philip W; Chichka, David

2014-02-01

The extraction of the distance between an object and an observer is fast when angular declination is informative, as it is with targets placed on the ground. To what extent does angular declination drive performance when viewing time is limited? Participants judged target distances in a real-world environment with viewing durations ranging from 36-220 ms. An important role for angular declination was supported by experiments showing that the cue provides information about egocentric distance even on the very first glimpse, and that it supports a sensitive response to distance in the absence of other useful cues. Performance was better at 220-ms viewing durations than for briefer glimpses, suggesting that the perception of distance is dynamic even within the time frame of a typical eye fixation. Critically, performance in limited viewing trials was better when preceded by a 15-s preview of the room without a designated target. The results indicate that the perception of distance is powerfully shaped by memory from prior visual experience with the scene. A theoretical framework for the dynamic perception of distance is presented. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Dynamic replanning of 3D automated reconstruction using situation graph trees and illumination adjustment

NASA Astrophysics Data System (ADS)

Kohler, Sophie; Far, Aïcha Beya; Hirsch, Ernest

2007-01-01

This paper presents an original approach for the optimal 3D reconstruction of manufactured workpieces based on a priori planification of the task, enhanced on-line through dynamic adjustment of the lighting conditions, and built around a cognitive intelligent sensory system using so-called Situation Graph Trees. The system takes explicitely structural knowledge related to image acquisition conditions, type of illumination sources, contents of the scene (e. g., CAD models and tolerance information), etc. into account. The principle of the approach relies on two steps. First, a socalled initialization phase, leading to the a priori task plan, collects this structural knowledge. This knowledge is conveniently encoded, as a sub-part, in the Situation Graph Tree building the backbone of the planning system specifying exhaustively the behavior of the application. Second, the image is iteratively evaluated under the control of this Situation Graph Tree. The information describing the quality of the piece to analyze is thus extracted and further exploited for, e. g., inspection tasks. Lastly, the approach enables dynamic adjustment of the Situation Graph Tree, enabling the system to adjust itself to the actual application run-time conditions, thus providing the system with a self-learning capability.

Development of a Refined Space Vehicle Rollout Forcing Function

NASA Technical Reports Server (NTRS)

James, George; Tucker, Jon-Michael; Valle, Gerard; Grady, Robert; Schliesing, John; Fahling, James; Emory, Benjamin; Armand, Sasan

2016-01-01

For several decades, American manned spaceflight vehicles and the associated launch platforms have been transported from final assembly to the launch pad via a pre-launch phase called rollout. The rollout environment is rich with forced harmonics and higher order effects can be used for extracting structural dynamics information. To enable this utilization, processing tools are needed to move from measured and analytical data to dynamic metrics such as transfer functions, mode shapes, modal frequencies, and damping. This paper covers the range of systems and tests that are available to estimate rollout forcing functions for the Space Launch System (SLS). The specific information covered in this paper includes: the different definitions of rollout forcing functions; the operational and developmental data sets that are available; the suite of analytical processes that are currently in-place or in-development; and the plans and future work underway to solve two immediate problems related to rollout forcing functions. Problem 1 involves estimating enforced accelerations to drive finite element models for developing design requirements for the SLS class of launch vehicles. Problem 2 involves processing rollout measured data in near real time to understand structural dynamics properties of a specific vehicle and the class to which it belongs.

A harmonic linear dynamical system for prominent ECG feature extraction.

PubMed

Thi, Ngoc Anh Nguyen; Yang, Hyung-Jeong; Kim, SunHee; Do, Luu Ngoc

2014-01-01

Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.

Drilling informatics: data-driven challenges of scientific drilling

NASA Astrophysics Data System (ADS)

Yamada, Yasuhiro; Kyaw, Moe; Saito, Sanny

2017-04-01

The primary aim of scientific drilling is to precisely understand the dynamic nature of the Earth. This is the reason why we investigate the subsurface materials (rock and fluid including microbial community) existing under particular environmental conditions. This requires sample collection and analytical data production from the samples, and in-situ data measurement at boreholes. Current available data comes from cores, cuttings, mud logging, geophysical logging, and exploration geophysics, but these datasets are difficult to be integrated because of their different kinds and scales. Now we are producing more useful datasets to fill the gap between the exiting data and extracting more information from such datasets and finally integrating the information. In particular, drilling parameters are very useful datasets as geomechanical properties. We believe such approach, 'drilling informatics', would be the most appropriate to obtain the comprehensive and dynamic picture of our scientific target, such as the seismogenic fault zone and the Moho discontinuity surface. This presentation introduces our initiative and current achievements of drilling informatics.

High speed digital holographic interferometry for hypersonic flow visualization

NASA Astrophysics Data System (ADS)

Hegde, G. M.; Jagdeesh, G.; Reddy, K. P. J.

2013-06-01

Optical imaging techniques have played a major role in understanding the flow dynamics of varieties of fluid flows, particularly in the study of hypersonic flows. Schlieren and shadowgraph techniques have been the flow diagnostic tools for the investigation of compressible flows since more than a century. However these techniques provide only the qualitative information about the flow field. Other optical techniques such as holographic interferometry and laser induced fluorescence (LIF) have been used extensively for extracting quantitative information about the high speed flows. In this paper we present the application of digital holographic interferometry (DHI) technique integrated with short duration hypersonic shock tunnel facility having 1 ms test time, for quantitative flow visualization. Dynamics of the flow fields in hypersonic/supersonic speeds around different test models is visualized with DHI using a high-speed digital camera (0.2 million fps). These visualization results are compared with schlieren visualization and CFD simulation results. Fringe analysis is carried out to estimate the density of the flow field.

Analysis of Instantaneous Linear, Nonlinear and Complex Cardiovascular Dynamics from Videophotoplethysmography.

PubMed

Valenza, Gaetano; Iozzia, Luca; Cerina, Luca; Mainardi, Luca; Barbieri, Riccardo

2018-05-01

There is a fast growing interest in the use of non-contact devices for health and performance assessment in humans. In particular, the use of non-contact videophotoplethysmography (vPPG) has been recently demonstrated as a feasible way to extract cardiovascular information. Nevertheless, proper validation of vPPG-derived heartbeat dynamics is still missing. We aim to an in-depth validation of time-varying, linear and nonlinear/complex dynamics of the pulse rate variability extracted from vPPG. We apply inhomogeneous pointprocess nonlinear models to assess instantaneous measures defined in the time, frequency, and bispectral domains as estimated through vPPG and standard ECG. Instantaneous complexity measures, such as the instantaneous Lyapunov exponents and the recently defined inhomogeneous point-process approximate and sample entropy, were estimated as well. Video recordings were processed using our recently proposed method based on zerophase principal component analysis. Experimental data were gathered from 60 young healthy subjects (age: 24±3 years) undergoing postural changes (rest-to-stand maneuver). Group averaged results show that there is an overall agreement between linear and nonlinear/complexity indices computed from ECG and vPPG during resting state conditions. However, important differences are found, particularly in the bispectral and complexity domains, in recordings where the subjects has been instructed to stand up. Although significant differences exist between cardiovascular estimates from vPPG and ECG, it is very promising that instantaneous sympathovagal changes, as well as time-varying complex dynamics, were correctly identified, especially during resting state. In addition to a further improvement of the video signal quality, more research is advocated towards a more precise estimation of cardiovascular dynamics by a comprehensive nonlinear/complex paradigm specifically tailored to the non-contact quantification. Schattauer GmbH.

[Application of regular expression in extracting key information from Chinese medicine literatures about re-evaluation of post-marketing surveillance].

PubMed

Wang, Zhifei; Xie, Yanming; Wang, Yongyan

2011-10-01

Computerizing extracting information from Chinese medicine literature seems more convenient than hand searching, which could simplify searching process and improve the accuracy. However, many computerized auto-extracting methods are increasingly used, regular expression is so special that could be efficient for extracting useful information in research. This article focused on regular expression applying in extracting information from Chinese medicine literature. Two practical examples were reported in this article about regular expression to extract "case number (non-terminology)" and "efficacy rate (subgroups for related information identification)", which explored how to extract information in Chinese medicine literature by means of some special research method.

Automating Network Node Behavior Characterization by Mining Communication Patterns

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

Carroll, Thomas E.; Chikkagoudar, Satish; Arthur-Durett, Kristine M.

Enterprise networks of scale are complex, dynamic computing environments that respond to evolv- ing business objectives and requirements. Characteriz- ing system behaviors in these environments is essential for network management and cyber security operations. Characterization of system’s communication is typical and is supported using network flow information (NetFlow). Related work has characterized behavior using theoretical graph metrics; results are often difficult to interpret by enterprise staff. We propose a different approach, where flow information is mapped to sets of tags that contextualize the data in terms of network principals and enterprise concepts. Frequent patterns are then extracted and are expressedmore » as behaviors. Behaviors can be com- pared, identifying systems expressing similar behaviors. We evaluate the approach using flow information collected by a third party.« less

Collective Intelligence Generation from User Contributed Content

NASA Astrophysics Data System (ADS)

Solachidis, Vassilios; Mylonas, Phivos; Geyer-Schulz, Andreas; Hoser, Bettina; Chapman, Sam; Ciravegna, Fabio; Lanfranchi, Vita; Scherp, Ansgar; Staab, Steffen; Contopoulos, Costis; Gkika, Ioanna; Bakaimis, Byron; Smrz, Pavel; Kompatsiaris, Yiannis; Avrithis, Yannis

In this paper we provide a foundation for a new generation of services and tools. We define new ways of capturing, sharing and reusing information and intelligence provided by single users and communities, as well as organizations by enabling the extraction, generation, interpretation and management of Collective Intelligence from user generated digital multimedia content. Different layers of intelligence are generated, which together constitute the notion of Collective Intelligence. The automatic generation of Collective Intelligence constitutes a departure from traditional methods for information sharing, since information from both the multimedia content and social aspects will be merged, while at the same time the social dynamics will be taken into account. In the context of this work, we present two case studies: an Emergency Response and a Consumers Social Group case study.

Possibility of the real-time dynamic strain field monitoring deduced from GNSS data: case study of the 2016 Kumamoto earthquake sequence

NASA Astrophysics Data System (ADS)

Ohta, Y.; Ohzono, M.; Takahashi, H.; Kawamoto, S.; Hino, R.

2017-12-01

A large and destructive earthquake (Mjma 7.3) occurred on April 15, 2016 in Kumamoto region, southwestern Japan. This earthquake was accompanied approximately 32 s later by an M 6 earthquake in central Oita region, which hypocenter located 80 km northeast from the hypocenter of the mainshock of the Kumamoto earthquake. This triggered earthquake also had the many aftershocks in and around the Oita region. It is important to understand how to occur such chain-reacted earthquake sequences. We used the 1Hz dual-frequency phase and range data from GEONET in Kyushu island. The data were processed using GIPSY-OASIS (version 6.4). We adopoted kinematic PPP strategy for the coordinate estimation. The reference GPS satellite orbit and 5 s clock information were obtained using the CODE product. We also applied simple sidereal filter technique for the estimated time series. Based on the obtained 1Hz GNSS time series, we estimated the areal strain and principle strain field using the method of the Shen et al. (1996). For the assessment of the dynamic strain, firstly we calculated the averaged absolute value of areal strain field between 60-85s after the origin time of the mainshock of the Kumamoto earthquake which was used as the "reference" static strain field. Secondly, we estimated the absolute value of areal strain in each time step. Finally, we calculated the strain ratio in each time step relative to the "reference". Based on this procedure, we can extract the spatial and temporal characteristic of the dynamic strain in each time step. Extracted strain ratio clearly shows the spatial and temporal dynamic strain characteristic. When an attention is paid to a region of triggered Oita earthquake, the timing of maximum dynamic strain ratio in the epicenter just corresponds to the origin time of the triggered event. It strongly suggested that the large dynamic strain may trigger the Oita event. The epicenter of the triggered earthquake located within the geothermal region. In the geothermal region, the crustal materials are more sensitive to stress perturbations, and the earthquakes are more easily triggered compared with other typical regions. Our result also suggested that the real-time strain field monitoring may be useful information for the understanding of the possibility of the remotely triggered earthquake in the future.

Dynamics of processing invisible faces in the brain: automatic neural encoding of facial expression information.

PubMed

Jiang, Yi; Shannon, Robert W; Vizueta, Nathalie; Bernat, Edward M; Patrick, Christopher J; He, Sheng

2009-02-01

The fusiform face area (FFA) and the superior temporal sulcus (STS) are suggested to process facial identity and facial expression information respectively. We recently demonstrated a functional dissociation between the FFA and the STS as well as correlated sensitivity of the STS and the amygdala to facial expressions using an interocular suppression paradigm [Jiang, Y., He, S., 2006. Cortical responses to invisible faces: dissociating subsystems for facial-information processing. Curr. Biol. 16, 2023-2029.]. In the current event-related brain potential (ERP) study, we investigated the temporal dynamics of facial information processing. Observers viewed neutral, fearful, and scrambled face stimuli, either visibly or rendered invisible through interocular suppression. Relative to scrambled face stimuli, intact visible faces elicited larger positive P1 (110-130 ms) and larger negative N1 or N170 (160-180 ms) potentials at posterior occipital and bilateral occipito-temporal regions respectively, with the N170 amplitude significantly greater for fearful than neutral faces. Invisible intact faces generated a stronger signal than scrambled faces at 140-200 ms over posterior occipital areas whereas invisible fearful faces (compared to neutral and scrambled faces) elicited a significantly larger negative deflection starting at 220 ms along the STS. These results provide further evidence for cortical processing of facial information without awareness and elucidate the temporal sequence of automatic facial expression information extraction.

Probing nanocrystalline grain dynamics in nanodevices

PubMed Central

Yeh, Sheng-Shiuan; Chang, Wen-Yao; Lin, Juhn-Jong

2017-01-01

Dynamical structural defects exist naturally in a wide variety of solids. They fluctuate temporally and hence can deteriorate the performance of many electronic devices. Thus far, the entities of these dynamic objects have been identified to be individual atoms. On the other hand, it is a long-standing question whether a nanocrystalline grain constituted of a large number of atoms can switch, as a whole, reversibly like a dynamical atomic defect (that is, a two-level system). This is an emergent issue considering the current development of nanodevices with ultralow electrical noise, qubits with long quantum coherence time, and nanoelectromechanical system sensors with ultrahigh resolution. We demonstrate experimental observations of dynamic nanocrystalline grains that repeatedly switch between two or more metastable coordinate states. We study temporal resistance fluctuations in thin ruthenium dioxide (RuO2) metal nanowires and extract microscopic parameters, including relaxation time scales, mobile grain sizes, and the bonding strengths of nanograin boundaries. These material parameters are not obtainable by other experimental approaches. When combined with previous in situ high-resolution transmission electron microscopy, our electrical method can be used to infer rich information about the structural dynamics of a wide variety of nanodevices and new two-dimensional materials. PMID:28691094

Dynamic Visualization of Co-expression in Systems Genetics Data

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

New, Joshua Ryan; Huang, Jian; Chesler, Elissa J

2008-01-01

Biologists hope to address grand scientific challenges by exploring the abundance of data made available through modern microarray technology and other high-throughput techniques. The impact of this data, however, is limited unless researchers can effectively assimilate such complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression require novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These tools should allow biologists to develop an intuitive understanding of the structure of biologicalmore » networks and discover genes which reside in critical positions in networks and pathways. By using a graph as a universal data representation of correlation in gene expression data, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene co-expression data integrates techniques such as: graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized b-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using a real-world workflow from a large-scale, systems genetics study of mammalian gene co-expression.« less

Quantitative Analysis of Bioactive Compounds from Aromatic Plants by Means of Dynamic Headspace Extraction and Multiple Headspace Extraction-Gas Chromatography-Mass Spectrometry.

PubMed

Omar, Jone; Olivares, Maitane; Alonso, Ibone; Vallejo, Asier; Aizpurua-Olaizola, Oier; Etxebarria, Nestor

2016-04-01

Seven monoterpenes in 4 aromatic plants (sage, cardamom, lavender, and rosemary) were quantified in liquid extracts and directly in solid samples by means of dynamic headspace-gas chromatography-mass spectrometry (DHS-GC-MS) and multiple headspace extraction-gas chromatography-mass spectrometry (MHSE), respectively. The monoterpenes were 1st extracted by means of supercritical fluid extraction (SFE) and analyzed by an optimized DHS-GC-MS. The optimization of the dynamic extraction step and the desorption/cryo-focusing step were tackled independently by experimental design assays. The best working conditions were set at 30 °C for the incubation temperature, 5 min of incubation time, and 40 mL of purge volume for the dynamic extraction step of these bioactive molecules. The conditions of the desorption/cryo-trapping step from the Tenax TA trap were set at follows: the temperature was increased from 30 to 300 °C at 150 °C/min, although the cryo-trapping was maintained at -70 °C. In order to estimate the efficiency of the SFE process, the analysis of monoterpenes in the 4 aromatic plants was directly carried out by means of MHSE because it did not require any sample preparation. Good linearity (r2) > 0.99) and reproducibility (relative standard deviation % <12) was obtained for solid and liquid quantification approaches, in the ranges of 0.5 to 200 ng and 10 to 500 ng/mL, respectively. The developed methods were applied to analyze the concentration of 7 monoterpenes in aromatic plants obtaining concentrations in the range of 2 to 6000 ng/g and 0.25 to 110 μg/mg, respectively. © 2016 Institute of Food Technologists®

Automated prostate cancer localization without the need for peripheral zone extraction using multiparametric MRI.

PubMed

Liu, Xin; Yetik, Imam Samil

2011-06-01

Multiparametric magnetic resonance imaging (MRI) has been shown to have higher localization accuracy than transrectal ultrasound (TRUS) for prostate cancer. Therefore, automated cancer segmentation using multiparametric MRI is receiving a growing interest, since MRI can provide both morphological and functional images for tissue of interest. However, all automated methods to this date are applicable to a single zone of the prostate, and the peripheral zone (PZ) of the prostate needs to be extracted manually, which is a tedious and time-consuming job. In this paper, our goal is to remove the need of PZ extraction by incorporating the spatial and geometric information of prostate tumors with multiparametric MRI derived from T2-weighted MRI, diffusion-weighted imaging (DWI) and dynamic contrast enhanced MRI (DCE-MRI). In order to remove the need of PZ extraction, the authors propose a new method to incorporate the spatial information of the cancer. This is done by introducing a new feature called location map. This new feature is constructed by applying a nonlinear transformation to the spatial position coordinates of each pixel, so that the location map implicitly represents the geometric position of each pixel with respect to the prostate region. Then, this new feature is combined with multiparametric MR images to perform tumor localization. The proposed algorithm is applied to multiparametric prostate MRI data obtained from 20 patients with biopsy-confirmed prostate cancer. The proposed method which does not need the masks of PZ was found to have prostate cancer detection specificity of 0.84, sensitivity of 0.80 and dice coefficient value of 0.42. The authors have found that fusing the spatial information allows us to obtain tumor outline without the need of PZ extraction with a considerable success (better or similar performance to methods that require manual PZ extraction). Our experimental results quantitatively demonstrate the effectiveness of the proposed method, depicting that the proposed method has a slightly better or similar localization performance compared to methods which require the masks of PZ.

Challenges in Managing Information Extraction

ERIC Educational Resources Information Center

Shen, Warren H.

2009-01-01

This dissertation studies information extraction (IE), the problem of extracting structured information from unstructured data. Example IE tasks include extracting person names from news articles, product information from e-commerce Web pages, street addresses from emails, and names of emerging music bands from blogs. IE is all increasingly…

A Pipeline To Enhance Ligand Virtual Screening: Integrating Molecular Dynamics and Fingerprints for Ligand and Proteins.

PubMed

Spyrakis, Francesca; Benedetti, Paolo; Decherchi, Sergio; Rocchia, Walter; Cavalli, Andrea; Alcaro, Stefano; Ortuso, Francesco; Baroni, Massimo; Cruciani, Gabriele

2015-10-26

The importance of taking into account protein flexibility in drug design and virtual ligand screening (VS) has been widely debated in the literature, and molecular dynamics (MD) has been recognized as one of the most powerful tools for investigating intrinsic protein dynamics. Nevertheless, deciphering the amount of information hidden in MD simulations and recognizing a significant minimal set of states to be used in virtual screening experiments can be quite complicated. Here we present an integrated MD-FLAP (molecular dynamics-fingerprints for ligand and proteins) approach, comprising a pipeline of molecular dynamics, clustering and linear discriminant analysis, for enhancing accuracy and efficacy in VS campaigns. We first extracted a limited number of representative structures from tens of nanoseconds of MD trajectories by means of the k-medoids clustering algorithm as implemented in the BiKi Life Science Suite ( http://www.bikitech.com [accessed July 21, 2015]). Then, instead of applying arbitrary selection criteria, that is, RMSD, pharmacophore properties, or enrichment performances, we allowed the linear discriminant analysis algorithm implemented in FLAP ( http://www.moldiscovery.com [accessed July 21, 2015]) to automatically choose the best performing conformational states among medoids and X-ray structures. Retrospective virtual screenings confirmed that ensemble receptor protocols outperform single rigid receptor approaches, proved that computationally generated conformations comprise the same quantity/quality of information included in X-ray structures, and pointed to the MD-FLAP approach as a valuable tool for improving VS performances.

Oscillations during observations: Dynamic oscillatory networks serving visuospatial attention.

PubMed

Wiesman, Alex I; Heinrichs-Graham, Elizabeth; Proskovec, Amy L; McDermott, Timothy J; Wilson, Tony W

2017-10-01

The dynamic allocation of neural resources to discrete features within a visual scene enables us to react quickly and accurately to salient environmental circumstances. A network of bilateral cortical regions is known to subserve such visuospatial attention functions; however the oscillatory and functional connectivity dynamics of information coding within this network are not fully understood. Particularly, the coding of information within prototypical attention-network hubs and the subsecond functional connections formed between these hubs have not been adequately characterized. Herein, we use the precise temporal resolution of magnetoencephalography (MEG) to define spectrally specific functional nodes and connections that underlie the deployment of attention in visual space. Twenty-three healthy young adults completed a visuospatial discrimination task designed to elicit multispectral activity in visual cortex during MEG, and the resulting data were preprocessed and reconstructed in the time-frequency domain. Oscillatory responses were projected to the cortical surface using a beamformer, and time series were extracted from peak voxels to examine their temporal evolution. Dynamic functional connectivity was then computed between nodes within each frequency band of interest. We find that visual attention network nodes are defined functionally by oscillatory frequency, that the allocation of attention to the visual space dynamically modulates functional connectivity between these regions on a millisecond timescale, and that these modulations significantly correlate with performance on a spatial discrimination task. We conclude that functional hubs underlying visuospatial attention are segregated not only anatomically but also by oscillatory frequency, and importantly that these oscillatory signatures promote dynamic communication between these hubs. Hum Brain Mapp 38:5128-5140, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Modulating RNA Alignment Using Directional Dynamic Kinks: Application in Determining an Atomic-Resolution Ensemble for a Hairpin using NMR Residual Dipolar Couplings.

PubMed

Salmon, Loïc; Giambaşu, George M; Nikolova, Evgenia N; Petzold, Katja; Bhattacharya, Akash; Case, David A; Al-Hashimi, Hashim M

2015-10-14

Approaches that combine experimental data and computational molecular dynamics (MD) to determine atomic resolution ensembles of biomolecules require the measurement of abundant experimental data. NMR residual dipolar couplings (RDCs) carry rich dynamics information, however, difficulties in modulating overall alignment of nucleic acids have limited the ability to fully extract this information. We present a strategy for modulating RNA alignment that is based on introducing variable dynamic kinks in terminal helices. With this strategy, we measured seven sets of RDCs in a cUUCGg apical loop and used this rich data set to test the accuracy of an 0.8 μs MD simulation computed using the Amber ff10 force field as well as to determine an atomic resolution ensemble. The MD-generated ensemble quantitatively reproduces the measured RDCs, but selection of a sub-ensemble was required to satisfy the RDCs within error. The largest discrepancies between the RDC-selected and MD-generated ensembles are observed for the most flexible loop residues and backbone angles connecting the loop to the helix, with the RDC-selected ensemble resulting in more uniform dynamics. Comparison of the RDC-selected ensemble with NMR spin relaxation data suggests that the dynamics occurs on the ps-ns time scales as verified by measurements of R(1ρ) relaxation-dispersion data. The RDC-satisfying ensemble samples many conformations adopted by the hairpin in crystal structures indicating that intrinsic plasticity may play important roles in conformational adaptation. The approach presented here can be applied to test nucleic acid force fields and to characterize dynamics in diverse RNA motifs at atomic resolution.

a Probability-Based Statistical Method to Extract Water Body of TM Images with Missing Information

NASA Astrophysics Data System (ADS)

Lian, Shizhong; Chen, Jiangping; Luo, Minghai

2016-06-01

Water information cannot be accurately extracted using TM images because true information is lost in some images because of blocking clouds and missing data stripes, thereby water information cannot be accurately extracted. Water is continuously distributed in natural conditions; thus, this paper proposed a new method of water body extraction based on probability statistics to improve the accuracy of water information extraction of TM images with missing information. Different disturbing information of clouds and missing data stripes are simulated. Water information is extracted using global histogram matching, local histogram matching, and the probability-based statistical method in the simulated images. Experiments show that smaller Areal Error and higher Boundary Recall can be obtained using this method compared with the conventional methods.

State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.

PubMed

De Feo, Vito; Boi, Fabio; Safaai, Houman; Onken, Arno; Panzeri, Stefano; Vato, Alessandro

2017-01-01

Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state. Here we tested this idea by using a bidirectional BMI to investigate the gain in performance arising from using a state-dependent decoding algorithm. This BMI, implemented in anesthetized rats, controlled the movement of a dynamical system using neural activity decoded from motor cortex and fed back to the brain the dynamical system's position by electrically microstimulating somatosensory cortex. We found that using state-dependent algorithms that tracked the dynamics of ongoing activity led to an increase in the amount of information extracted form neural activity by 22%, with a consequently increase in all of the indices measuring the BMI's performance in controlling the dynamical system. This suggests that state-dependent decoding algorithms may be used to enhance BMIs at moderate computational cost.

Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states.

PubMed

Dao Duc, Khanh; Parutto, Pierre; Chen, Xiaowei; Epsztein, Jérôme; Konnerth, Arthur; Holcman, David

2015-01-01

The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-field model that the residence time of the neuronal membrane potential in cortical Up states does not follow a Poissonian law, but presents several peaks. Furthermore, the present modeling approach allows extracting some information about the neuronal network connectivity from the time distribution histogram. Based on a synaptic-depression model, we find that these peaks, that can be observed in histograms of patch-clamp recordings are not artifacts of electrophysiological measurements, but rather are an inherent property of the network dynamics. Analysis of the equations reveals a stable focus located close to the unstable limit cycle, delimiting a region that defines the Up state. The model further shows that the peaks observed in the Up state time distribution are due to winding around the focus before escaping from the basin of attraction. Finally, we use in vivo recordings of intracellular membrane potential and we recover from the peak distribution, some information about the network connectivity. We conclude that it is possible to recover the network connectivity from the distribution of times that the neuronal membrane voltage spends in Up states.

[Mass Transfer Kinetics Model of Ultrasonic Extraction of Pomegranate Peel Polyphenols].

PubMed

Wang, Zhan-yi; Zhang, Li-hua; Wang, Yu-hai; Zhang, Yuan-hu; Ma, Li; Zheng, Dan-dan

2015-05-01

The dynamic mathematical model of ultrasonic extraction of polyphenols from pomegranate peel was constructed with the Fick's second law as the theoretical basis. The spherical model was selected, with mass concentrations of pomegranate peel polyphenols as the index, 50% ethanol as the extraction solvent and ultrasonic extraction as the extraction method. In different test conditions including the liquid ratio, extraction temperature and extraction time, a series of kinetic parameters were solved, such as the extraction process (k), relative raffinate rate, surface diffusion coefficient(D(S)), half life (t½) and the apparent activation energy (E(a)). With the extraction temperature increasing, k and D(S) were gradually increased with t½ decreasing,which indicated that the elevated temperature was favorable to the extraction of pomegranate peel polyphenols. The exponential equation of relative raffinate rate showed that the established numerical dynamics model fitted the extraction of pomegranate peel polyphenols, and the relationship between the reaction conditions and pomegranate peel polyphenols concentration was well reflected by the model. Based on the experimental results, a feasible and reliable kinetic model for ultrasonic extraction of polyphenols from pomegranate peel is established, which can be used for the optimization control of engineering magnifying production.

Information Gain Based Dimensionality Selection for Classifying Text Documents

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

Dumidu Wijayasekara; Milos Manic; Miles McQueen

2013-06-01

Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexitymore » is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.« less

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Polarization Observables T and F in the yp -> pi p Reaction

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

Jiang, Hao

The theory that describes the interaction of quarks is Quantum Chromodynamics (QCD), but how quarks are bound inside a nucleon is not yet well understood. Pion photoproduction experiments reveal important information about the nucleon excited states and the dynamics of the quarks within it and thus provide a useful tool to study QCD. Detailed information about this reaction can be obtained in experiments that utilize polarized photon beams and polarized targets. Pion photoproduction in the γρ -> π0ρ reaction has been measured in the FROST experiment at the Thomas Jefferson National Accelerator Facility. In this experiment circularly polarized photons withmore » electron-beam energies up to 3.082 GeV impinged on a transversely polarized frozen-spin target. Final-state protons were detected in the CEBAF Large Acceptance Spectrometer. Results of the polarization observables T and F have been extracted. The data generally agree with predictions of present partial wave analyses, but also show marked differences. The data will constrain further partial wave analyses and improve the extraction of proton resonance properties.« less

Extracting conformational structure information of benzene molecules via laser-induced electron diffraction

DOE PAGES

Ito, Yuta; Wang, Chuncheng; Le, Anh-Thu; ...

2016-05-01

Here, we have measured the angular distributions of high energy photoelectrons of benzene molecules generated by intense infrared femtosecond laser pulses. These electrons arise from the elastic collisions between the benzene ions with the previously tunnel-ionized electrons that have been driven back by the laser field. Theory shows that laser-free elastic differential cross sections (DCSs) can be extracted from these photoelectrons, and the DCS can be used to retrieve the bond lengths of gas-phase molecules similar to the conventional electron diffraction method. From our experimental results, we have obtained the C-C and C-H bond lengths of benzene with a spatialmore » resolution of about 10 pm. Our results demonstrate that laser induced electron diffraction (LIED) experiments can be carried out with the present-day ultrafast intense lasers already. Looking ahead, with aligned or oriented molecules, more complete spatial information of the molecule can be obtained from LIED, and applying LIED to probe photo-excited molecules, a “molecular movie” of the dynamic system may be created with sub-A°ngstrom spatial and few-ten femtosecond temporal resolutions.« less

Non-invasive assessment of the liver using imaging

NASA Astrophysics Data System (ADS)

Thorling Thompson, Camilla; Wang, Haolu; Liu, Xin; Liang, Xiaowen; Crawford, Darrell H.; Roberts, Michael S.

2016-12-01

Chronic liver disease causes 2,000 deaths in Australia per year and early diagnosis is crucial to avoid progression to cirrhosis and end stage liver disease. There is no ideal method to evaluate liver function. Blood tests and liver biopsies provide spot examinations and are unable to track changes in function quickly. Therefore better techniques are needed. Non-invasive imaging has the potential to extract increased information over a large sampling area, continuously tracking dynamic changes in liver function. This project aimed to study the ability of three imaging techniques, multiphoton and fluorescence lifetime imaging microscopy, infrared thermography and photoacoustic imaging, in measuring liver function. Collagen deposition was obvious in multiphoton and fluorescence lifetime imaging in fibrosis and cirrhosis and comparable to conventional histology. Infrared thermography revealed a significantly increased liver temperature in hepatocellular carcinoma. In addition, multiphoton and fluorescence lifetime imaging and photoacoustic imaging could both track uptake and excretion of indocyanine green in rat liver. These results prove that non-invasive imaging can extract crucial information about the liver continuously over time and has the potential to be translated into clinic in the assessment of liver disease.

Multiunit Activity-Based Real-Time Limb-State Estimation from Dorsal Root Ganglion Recordings

PubMed Central

Han, Sungmin; Chu, Jun-Uk; Kim, Hyungmin; Park, Jong Woong; Youn, Inchan

2017-01-01

Proprioceptive afferent activities could be useful for providing sensory feedback signals for closed-loop control during functional electrical stimulation (FES). However, most previous studies have used the single-unit activity of individual neurons to extract sensory information from proprioceptive afferents. This study proposes a new decoding method to estimate ankle and knee joint angles using multiunit activity data. Proprioceptive afferent signals were recorded from a dorsal root ganglion with a single-shank microelectrode during passive movements of the ankle and knee joints, and joint angles were measured as kinematic data. The mean absolute value (MAV) was extracted from the multiunit activity data, and a dynamically driven recurrent neural network (DDRNN) was used to estimate ankle and knee joint angles. The multiunit activity-based MAV feature was sufficiently informative to estimate limb states, and the DDRNN showed a better decoding performance than conventional linear estimators. In addition, processing time delay satisfied real-time constraints. These results demonstrated that the proposed method could be applicable for providing real-time sensory feedback signals in closed-loop FES systems. PMID:28276474

Information extraction system

DOEpatents

Lemmond, Tracy D; Hanley, William G; Guensche, Joseph Wendell; Perry, Nathan C; Nitao, John J; Kidwell, Paul Brandon; Boakye, Kofi Agyeman; Glaser, Ron E; Prenger, Ryan James

2014-05-13

An information extraction system and methods of operating the system are provided. In particular, an information extraction system for performing meta-extraction of named entities of people, organizations, and locations as well as relationships and events from text documents are described herein.

Information transfer and information modification to identify the structure of cardiovascular and cardiorespiratory networks.

PubMed

Faes, Luca; Nollo, Giandomenico; Krohova, Jana; Czippelova, Barbora; Turianikova, Zuzana; Javorka, Michal

2017-07-01

To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting state and during postural stress. Computations are performed in the framework of multivariate linear regression, using bootstrap techniques to assess on a single-subject basis the statistical significance of each measure and of its transitions across conditions. We find patterns of information transfer and modification which are related to specific cardiovascular and cardiorespiratory mechanisms in resting conditions and to their modification induced by the orthostatic stress.

A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images.

PubMed

Zheng, Qiang; Warner, Steven; Tasian, Gregory; Fan, Yong

2018-02-12

Automatic segmentation of kidneys in ultrasound (US) images remains a challenging task because of high speckle noise, low contrast, and large appearance variations of kidneys in US images. Because texture features may improve the US image segmentation performance, we propose a novel graph cuts method to segment kidney in US images by integrating image intensity information and texture feature maps. We develop a new graph cuts-based method to segment kidney US images by integrating original image intensity information and texture feature maps extracted using Gabor filters. To handle large appearance variation within kidney images and improve computational efficiency, we build a graph of image pixels close to kidney boundary instead of building a graph of the whole image. To make the kidney segmentation robust to weak boundaries, we adopt localized regional information to measure similarity between image pixels for computing edge weights to build the graph of image pixels. The localized graph is dynamically updated and the graph cuts-based segmentation iteratively progresses until convergence. Our method has been evaluated based on kidney US images of 85 subjects. The imaging data of 20 randomly selected subjects were used as training data to tune parameters of the image segmentation method, and the remaining data were used as testing data for validation. Experiment results demonstrated that the proposed method obtained promising segmentation results for bilateral kidneys (average Dice index = 0.9446, average mean distance = 2.2551, average specificity = 0.9971, average accuracy = 0.9919), better than other methods under comparison (P < .05, paired Wilcoxon rank sum tests). The proposed method achieved promising performance for segmenting kidneys in two-dimensional US images, better than segmentation methods built on any single channel of image information. This method will facilitate extraction of kidney characteristics that may predict important clinical outcomes such as progression of chronic kidney disease. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

A neural coding scheme reproducing foraging trajectories

NASA Astrophysics Data System (ADS)

Gutiérrez, Esther D.; Cabrera, Juan Luis

2015-12-01

The movement of many animals may follow Lévy patterns. The underlying generating neuronal dynamics of such a behavior is unknown. In this paper we show that a novel discovery of multifractality in winnerless competition (WLC) systems reveals a potential encoding mechanism that is translatable into two dimensional superdiffusive Lévy movements. The validity of our approach is tested on a conductance based neuronal model showing WLC and through the extraction of Lévy flights inducing fractals from recordings of rat hippocampus during open field foraging. Further insights are gained analyzing mice motor cortex neurons and non motor cell signals. The proposed mechanism provides a plausible explanation for the neuro-dynamical fundamentals of spatial searching patterns observed in animals (including humans) and illustrates an until now unknown way to encode information in neuronal temporal series.

Extracting information from S-curves of language change.

PubMed

Ghanbarnejad, Fakhteh; Gerlach, Martin; Miotto, José M; Altmann, Eduardo G

2014-12-06

It is well accepted that adoption of innovations are described by S-curves (slow start, accelerating period and slow end). In this paper, we analyse how much information on the dynamics of innovation spreading can be obtained from a quantitative description of S-curves. We focus on the adoption of linguistic innovations for which detailed databases of written texts from the last 200 years allow for an unprecedented statistical precision. Combining data analysis with simulations of simple models (e.g. the Bass dynamics on complex networks), we identify signatures of endogenous and exogenous factors in the S-curves of adoption. We propose a measure to quantify the strength of these factors and three different methods to estimate it from S-curves. We obtain cases in which the exogenous factors are dominant (in the adoption of German orthographic reforms and of one irregular verb) and cases in which endogenous factors are dominant (in the adoption of conventions for romanization of Russian names and in the regularization of most studied verbs). These results show that the shape of S-curve is not universal and contains information on the adoption mechanism. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Extraction of phase information in daily stock prices

NASA Astrophysics Data System (ADS)

Fujiwara, Yoshi; Maekawa, Satoshi

2000-06-01

It is known that, in an intermediate time-scale such as days, stock market fluctuations possess several statistical properties that are common to different markets. Namely, logarithmic returns of an asset price have (i) truncated Pareto-Lévy distribution, (ii) vanishing linear correlation, (iii) volatility clustering and its power-law autocorrelation. The fact (ii) is a consequence of nonexistence of arbitragers with simple strategies, but this does not mean statistical independence of market fluctuations. Little attention has been paid to temporal structure of higher-order statistics, although it contains some important information on market dynamics. We applied a signal separation technique, called Independent Component Analysis (ICA), to actual data of daily stock prices in Tokyo and New York Stock Exchange (TSE/NYSE). ICA does a linear transformation of lag vectors from time-series to find independent components by a nonlinear algorithm. We obtained a similar impulse response for these dataset. If it were a Martingale process, it can be shown that impulse response should be a delta-function under a few conditions that could be numerically checked and as was verified by surrogate data. This result would provide information on the market dynamics including speculative bubbles and arbitrating processes. .

Dynamic fractionation of trace metals in soil and sediment samples using rotating coiled column extraction and sequential injection microcolumn extraction: a comparative study.

PubMed

Rosende, Maria; Savonina, Elena Yu; Fedotov, Petr S; Miró, Manuel; Cerdà, Víctor; Wennrich, Rainer

2009-09-15

Dynamic fractionation has been recognized as an appealing alternative to conventional equilibrium-based sequential extraction procedures (SEPs) for partitioning of trace elements (TE) in environmental solid samples. This paper reports the first attempt for harmonization of flow-through dynamic fractionation using two novel methods, the so-called sequential injection microcolumn (SIMC) extraction and rotating coiled column (RCC) extraction. In SIMC extraction, a column packed with the solid sample is clustered in a sequential injection system, while in RCC, the particulate matter is retained under the action of centrifugal forces. In both methods, the leachants are continuously pumped through the solid substrates by the use of either peristaltic or syringe pumps. A five-step SEP was selected for partitioning of Cu, Pb and Zn in water soluble/exchangeable, acid-soluble, easily reducible, easily oxidizable and moderately reducible fractions from 0.2 to 0.5 g samples at an extractant flow rate of 1.0 mL min(-1) prior to leachate analysis by inductively coupled plasma-atomic emission spectrometry. Similarities and discrepancies between both dynamic approaches were ascertained by fractionation of TE in certified reference materials, namely, SRM 2711 Montana Soil and GBW 07311 sediment, and two real soil samples as well. Notwithstanding the different extraction conditions set by both methods, similar trends of metal distribution were in generally found. The most critical parameters for reliable assessment of mobilizable pools of TE in worse-case scenarios are the size-distribution of sample particles, the density of particles, the content of organic matter and the concentration of major elements. For reference materials and a soil rich in organic matter, the extraction in RCC results in slightly higher recoveries of environmentally relevant fractions of TE, whereas SIMC leaching is more effective for calcareous soils.

Single ion dynamics in molten sodium bromide

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

Alcaraz, O.; Trullas, J.; Demmel, F.

We present a study on the single ion dynamics in the molten alkali halide NaBr. Quasielastic neutron scattering was employed to extract the self-diffusion coefficient of the sodium ions at three temperatures. Molecular dynamics simulations using rigid and polarizable ion models have been performed in parallel to extract the sodium and bromide single dynamics and ionic conductivities. Two methods have been employed to derive the ion diffusion, calculating the mean squared displacements and the velocity autocorrelation functions, as well as analysing the increase of the line widths of the self-dynamic structure factors. The sodium diffusion coefficients show a remarkable goodmore » agreement between experiment and simulation utilising the polarisable potential.« less

Nonlinear techniques for forecasting solar activity directly from its time series

NASA Technical Reports Server (NTRS)

Ashrafi, S.; Roszman, L.; Cooley, J.

1992-01-01

Numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series are presented. This approach makes it possible to extract dynamical invariants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), given a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

Nonlinear techniques for forecasting solar activity directly from its time series

NASA Technical Reports Server (NTRS)

Ashrafi, S.; Roszman, L.; Cooley, J.

1993-01-01

This paper presents numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series. This approach makes it possible to extract dynamical in variants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), give a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

Contributions to the understanding of large-scale coherent structures in developing free turbulent shear flows

NASA Technical Reports Server (NTRS)

Liu, J. T. C.

1986-01-01

Advances in the mechanics of boundary layer flow are reported. The physical problems of large scale coherent structures in real, developing free turbulent shear flows, from the nonlinear aspects of hydrodynamic stability are addressed. The presence of fine grained turbulence in the problem, and its absence, lacks a small parameter. The problem is presented on the basis of conservation principles, which are the dynamics of the problem directed towards extracting the most physical information, however, it is emphasized that it must also involve approximations.

Advanced ballistic range technology

NASA Technical Reports Server (NTRS)

Yates, Leslie A.

1994-01-01

The research conducted supported two facilities at NASA Ames Research Center: the Hypervelocity Free-Flight Aerodynamic Facility and the 16-Inch Shock Tunnel. During the grant period, a computerized film-reading system was developed, and five- and six-degree-of-freedom parameter-identification routines were written and successfully implemented. Studies of flow separation were conducted, and methods to extract phase shift information from finite-fringe interferograms were developed. Methods for constructing optical images from Computational Fluid Dynamics solutions were also developed, and these methods were used for one-to-one comparisons of experiment and computations.

Neural Methods for Imagery, GMTI, and Information Fusion

DTIC Science & Technology

2006-03-15

Dynamic Range Contrast Enhanced Length & Power Normalized JV\\ 0.7 11 2500. No0 00 0.6 :a -30 - i 0.5 1 •4e ,3 ta I. a* 20 4B 60 Be° .0 120 140 160 s e...conditioned profile after length and power normalization. 2 Neural-Based Feature Extraction 2.2 Contrast enhancement and adaptive Rather than directly... power of the signal comprised of HRR range profiles synthesized from the is normalized. This ensures that very weak or very strong MSTAR SAR imagery

Lattice field theory applications in high energy physics

NASA Astrophysics Data System (ADS)

Gottlieb, Steven

2016-10-01

Lattice gauge theory was formulated by Kenneth Wilson in 1974. In the ensuing decades, improvements in actions, algorithms, and computers have enabled tremendous progress in QCD, to the point where lattice calculations can yield sub-percent level precision for some quantities. Beyond QCD, lattice methods are being used to explore possible beyond the standard model (BSM) theories of dynamical symmetry breaking and supersymmetry. We survey progress in extracting information about the parameters of the standard model by confronting lattice calculations with experimental results and searching for evidence of BSM effects.

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

The four-dimensional scattering function S(Q,w) obtained by inelastic neutron scattering measurements provides unique "dynamical fingerprints" of the spin state and interactions present in complex magnetic materials. Extracting this information however is currently a slow and complex process that may take an expert -depending on the complexity of the system- up to several weeks of painstaking work to complete. Spin Wave Genie was created to abstract and automate this process. It strives to both reduce the time to complete this analysis and make these calculations more accessible to a broader group of scientists and engineers.

Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals.

PubMed

Vanrell, Sebastian Rodrigo; Milone, Diego Humberto; Rufiner, Hugo Leonardo

2017-07-03

Unobtrusive activity monitoring can provide valuable information for medical and sports applications. In recent years, human activity recognition has moved to wearable sensors to deal with unconstrained scenarios. Accelerometers are the preferred sensors due to their simplicity and availability. Previous studies have examined several \\azul{classic} techniques for extracting features from acceleration signals, including time-domain, time-frequency, frequency-domain, and other heuristic features. Spectral and temporal features are the preferred ones and they are generally computed from acceleration components, leaving the acceleration magnitude potential unexplored. In this study, based on homomorphic analysis, a new type of feature extraction stage is proposed in order to exploit discriminative activity information present in acceleration signals. Homomorphic analysis can isolate the information about whole body dynamics and translate it into a compact representation, called cepstral coefficients. Experiments have explored several configurations of the proposed features, including size of representation, signals to be used, and fusion with other features. Cepstral features computed from acceleration magnitude obtained one of the highest recognition rates. In addition, a beneficial contribution was found when time-domain and moving pace information was included in the feature vector. Overall, the proposed system achieved a recognition rate of 91.21% on the publicly available SCUT-NAA dataset. To the best of our knowledge, this is the highest recognition rate on this dataset.

Real-time high-resolution heterodyne-based measurements of spectral dynamics in fibre lasers

PubMed Central

Sugavanam, Srikanth; Fabbri, Simon; Le, Son Thai; Lobach, Ivan; Kablukov, Sergey; Khorev, Serge; Churkin, Dmitry

2016-01-01

Conventional tools for measurement of laser spectra (e.g. optical spectrum analysers) capture data averaged over a considerable time period. However, the generation spectrum of many laser types may involve spectral dynamics whose relatively fast time scale is determined by their cavity round trip period, calling for instrumentation featuring both high temporal and spectral resolution. Such real-time spectral characterisation becomes particularly challenging if the laser pulses are long, or they have continuous or quasi-continuous wave radiation components. Here we combine optical heterodyning with a technique of spatio-temporal intensity measurements that allows the characterisation of such complex sources. Fast, round-trip-resolved spectral dynamics of cavity-based systems in real-time are obtained, with temporal resolution of one cavity round trip and frequency resolution defined by its inverse (85 ns and 24 MHz respectively are demonstrated). We also show how under certain conditions for quasi-continuous wave sources, the spectral resolution could be further increased by a factor of 100 by direct extraction of phase information from the heterodyned dynamics or by using double time scales within the spectrogram approach. PMID:26984634

Harvesting liquid from unsaturated vapor - nanoflows induced by capillary condensation

NASA Astrophysics Data System (ADS)

Vincent, Olivier; Marguet, Bastien; Stroock, Abraham

2016-11-01

A vapor, even subsaturated, can spontaneously form liquid in nanoscale spaces. This process, known as capillary condensation, plays a fundamental role in various contexts, such as the formation of clouds or the dynamics of hydrocarbons in the geological subsurface. However, large uncertainties remain on the thermodynamics and fluid mechanics of the phenomenon, due to experimental challenges as well as outstanding questions about the validity of macroscale physics at the nanometer scale. We studied experimentally the spatio-temporal dynamics of water condensation in a model nanoporous medium (pore radius 2 nm), taking advantage of the color change of the material upon hydration. We found that at low relative humidities (< 60 % RH), capillary condensation progressed in a diffusive fashion, while it occurred through a well-defined capillary-viscous imbibition front at > 60 % RH, driven by a balance between the pore capillary pressure and the condensation stress given by Kelvin equation. Further analyzing the imbibition dynamics as a function of saturation allowed us to extract detailed information about the physics of nano-confined fluids. Our results suggest excellent extension of macroscale fluid dynamics and thermodynamics even in pores 10 molecules in diameter.

Highly excited and exotic meson spectrum from dynamical lattice QCD.

PubMed

Dudek, Jozef J; Edwards, Robert G; Peardon, Michael J; Richards, David G; Thomas, Christopher E

2009-12-31

Using a new quark-field construction algorithm and a large variational basis of operators, we extract a highly excited isovector meson spectrum on dynamical anisotropic lattices. We show how carefully constructed operators can be used to reliably identify the continuum spin of extracted states, overcoming the reduced cubic symmetry of the lattice. Using this method we extract, with confidence, excited states, states with exotic quantum numbers (0+-, 1-+, and 2+-), and states of high spin, including, for the first time in lattice QCD, spin-four states.

Droplet-based microfluidics and the dynamics of emulsions

NASA Astrophysics Data System (ADS)

Baret, Jean-Christophe; Brosseau, Quentin; Semin, Benoit; Qu, Xiaopeng

2012-02-01

Emulsions are complex fluids already involved for a long time in a wide-range of industrial processes, such as, for example, food, cosmetics or materials synthesis [1]. More recently, applications of emulsions have been extended to new fields like biotechnology or biochemistry where the compartmentalization of compounds in emulsion droplets is used to parallelise (bio-) chemical reactions [2]. Interestingly, these applications pinpoint to fundamental questions dealing with surfactant dynamics, dynamic surface tension, hydrodynamic interactions and electrohydrodynamics. Droplet-based microfluidics is a very powerful tool to quantitatively study the dynamics of emulsions at the single droplet level or even at the single interface level: well-controlled emulsions are produced and manipulated using hydrodynamics, electrical forces, optical actuation and combination of these effects. We will describe here how droplet-based microfluidics is used to extract quantitative informations on the physical-chemistry of emulsions for a better understanding and control of the dynamics of these systems [3].[4pt] [1] J. Bibette et al. Rep. Prog. Phys., 62, 969-1033 (1999)[0pt] [2] A. Theberge et al., Angewandte Chemie Int. Ed. 49, 5846 (2010)[0pt] [3] J.-C. Baret et al., Langmuir, 25, 6088 (2009)

Information extraction from multi-institutional radiology reports.

PubMed

Hassanpour, Saeed; Langlotz, Curtis P

2016-01-01

The radiology report is the most important source of clinical imaging information. It documents critical information about the patient's health and the radiologist's interpretation of medical findings. It also communicates information to the referring physicians and records that information for future clinical and research use. Although efforts to structure some radiology report information through predefined templates are beginning to bear fruit, a large portion of radiology report information is entered in free text. The free text format is a major obstacle for rapid extraction and subsequent use of information by clinicians, researchers, and healthcare information systems. This difficulty is due to the ambiguity and subtlety of natural language, complexity of described images, and variations among different radiologists and healthcare organizations. As a result, radiology reports are used only once by the clinician who ordered the study and rarely are used again for research and data mining. In this work, machine learning techniques and a large multi-institutional radiology report repository are used to extract the semantics of the radiology report and overcome the barriers to the re-use of radiology report information in clinical research and other healthcare applications. We describe a machine learning system to annotate radiology reports and extract report contents according to an information model. This information model covers the majority of clinically significant contents in radiology reports and is applicable to a wide variety of radiology study types. Our automated approach uses discriminative sequence classifiers for named-entity recognition to extract and organize clinically significant terms and phrases consistent with the information model. We evaluated our information extraction system on 150 radiology reports from three major healthcare organizations and compared its results to a commonly used non-machine learning information extraction method. We also evaluated the generalizability of our approach across different organizations by training and testing our system on data from different organizations. Our results show the efficacy of our machine learning approach in extracting the information model's elements (10-fold cross-validation average performance: precision: 87%, recall: 84%, F1 score: 85%) and its superiority and generalizability compared to the common non-machine learning approach (p-value<0.05). Our machine learning information extraction approach provides an effective automatic method to annotate and extract clinically significant information from a large collection of free text radiology reports. This information extraction system can help clinicians better understand the radiology reports and prioritize their review process. In addition, the extracted information can be used by researchers to link radiology reports to information from other data sources such as electronic health records and the patient's genome. Extracted information also can facilitate disease surveillance, real-time clinical decision support for the radiologist, and content-based image retrieval. Copyright © 2015 Elsevier B.V. All rights reserved.

Review of Extracting Information From the Social Web for Health Personalization

PubMed Central

Karlsen, Randi; Bonander, Jason

2011-01-01

In recent years the Web has come into its own as a social platform where health consumers are actively creating and consuming Web content. Moreover, as the Web matures, consumers are gaining access to personalized applications adapted to their health needs and interests. The creation of personalized Web applications relies on extracted information about the users and the content to personalize. The Social Web itself provides many sources of information that can be used to extract information for personalization apart from traditional Web forms and questionnaires. This paper provides a review of different approaches for extracting information from the Social Web for health personalization. We reviewed research literature across different fields addressing the disclosure of health information in the Social Web, techniques to extract that information, and examples of personalized health applications. In addition, the paper includes a discussion of technical and socioethical challenges related to the extraction of information for health personalization. PMID:21278049

Dynamic microwave assisted extraction coupled with dispersive micro-solid-phase extraction of herbicides in soybeans.

PubMed

Li, Na; Wu, Lijie; Nian, Li; Song, Ying; Lei, Lei; Yang, Xiao; Wang, Kun; Wang, Zhibing; Zhang, Liyuan; Zhang, Hanqi; Yu, Aimin; Zhang, Ziwei

2015-09-01

Non-polar solvent dynamic microwave assisted extraction was firstly applied to the treatment of high-fat soybean samples. In the dispersive micro-solid-phase extraction (D-µ-SPE), the herbicides in the high-fat extract were directly adsorbed on metal-organic frameworks MIL-101(Cr). The effects of several experimental parameters, including extraction solvent, microwave absorption medium, microwave power, volume and flow rate of extraction solvent, amount of MIL-101(Cr), and D-µ-SPE time, were investigated. At the optimal conditions, the limits of detection for the herbicides ranged from 1.56 to 2.00 μg kg(-1). The relative recoveries of the herbicides were in the range of 91.1-106.7%, and relative standard deviations were equal to or lower than 6.7%. The present method was simple, rapid and effective. A large amount of fat was also removed. This method was demonstrated to be suitable for treatment of high-fat samples. Copyright © 2015 Elsevier B.V. All rights reserved.

Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

NASA Astrophysics Data System (ADS)

Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.

2016-11-01

The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.

Visual words for lip-reading

NASA Astrophysics Data System (ADS)

Hassanat, Ahmad B. A.; Jassim, Sabah

2010-04-01

In this paper, the automatic lip reading problem is investigated, and an innovative approach to providing solutions to this problem has been proposed. This new VSR approach is dependent on the signature of the word itself, which is obtained from a hybrid feature extraction method dependent on geometric, appearance, and image transform features. The proposed VSR approach is termed "visual words". The visual words approach consists of two main parts, 1) Feature extraction/selection, and 2) Visual speech feature recognition. After localizing face and lips, several visual features for the lips where extracted. Such as the height and width of the mouth, mutual information and the quality measurement between the DWT of the current ROI and the DWT of the previous ROI, the ratio of vertical to horizontal features taken from DWT of ROI, The ratio of vertical edges to horizontal edges of ROI, the appearance of the tongue and the appearance of teeth. Each spoken word is represented by 8 signals, one of each feature. Those signals maintain the dynamic of the spoken word, which contains a good portion of information. The system is then trained on these features using the KNN and DTW. This approach has been evaluated using a large database for different people, and large experiment sets. The evaluation has proved the visual words efficiency, and shown that the VSR is a speaker dependent problem.

In Vitro Anti/Pro-oxidant Activities of R. ferruginea Extract and Its Effect on Glioma Cell Viability: Correlation with Phenolic Compound Content and Effects on Membrane Dynamics.

PubMed

Dos Santos, Desirée Magalhães; Rocha, Camila Valesca Jardim; da Silveira, Elita Ferreira; Marinho, Marcelo Augusto Germani; Rodrigues, Marisa Raquel; Silva, Nichole Osti; da Silva Ferreira, Ailton; de Moura, Neusa Fernandes; Darelli, Gabriel Jorge Sagrera; Braganhol, Elizandra; Horn, Ana Paula; de Lima, Vânia Rodrigues

2018-04-01

Rapanea ferruginea antioxidant and antitumoral properties were not explored before in literature. This study aimed to investigate these biological activities for the R. ferruginea leaf extract and correlate them with its phenolic content and influence in biological membrane dynamics. Thus, in this study, anti/pro-oxidative properties of R. ferruginea leaf extract by in vitro DPPH and TBARS assays, with respect to the free radical reducing potential and to its activity regarding membrane free radical-induced peroxidation, respectively. Furthermore, preliminary tests related to the extract effect on in vitro glioma cell viability were also performed. In parallel, the phenolic content was detected by HPLC-DAD and included syringic and trans-cinnamic acids, quercetrin, catechin, quercetin, and gallic acid. In an attempt to correlate the biological activity of R. ferruginea extract and its effect on membrane dynamics, the molecular interaction between the extract and a liposomal model with natural-sourced phospholipids was investigated. Location and changes in vibrational, rotational, and translational lipid motions, as well as in the phase state of liposomes, induced by R. ferruginea extract, were monitored by Fourier-transform infrared spectroscopy, nuclear magnetic resonance, differential scanning calorimetry, and UV-visible spectroscopy. In its free form, the extract showed promising in vitro antioxidant properties. Free-form extract (at 1000µ g/mL) exposure reduced glioma cell in vitro viability in 40%, as evidenced by MTT tests. Pro-oxidant behavior was observed when the extract was loaded into liposomes. A 70.8% cell viability reduction was achieved with 500 µg/mL of liposome-loaded extract. The compounds of R. ferruginea extract ordered liposome interface and disorder edits a polar region. Phenolic content, as well as membrane interaction and modulation may have an important role in the oxidative and antitumoral activities of the R. ferruginea leaf extract.

Millimeter wave scattering characteristics and radar cross section measurements of common roadway objects

NASA Astrophysics Data System (ADS)

Zoratti, Paul K.; Gilbert, R. Kent; Majewski, Ronald; Ference, Jack

1995-12-01

Development of automotive collision warning systems has progressed rapidly over the past several years. A key enabling technology for these systems is millimeter-wave radar. This paper addresses a very critical millimeter-wave radar sensing issue for automotive radar, namely the scattering characteristics of common roadway objects such as vehicles, roadsigns, and bridge overpass structures. The data presented in this paper were collected on ERIM's Fine Resolution Radar Imaging Rotary Platform Facility and processed with ERIM's image processing tools. The value of this approach is that it provides system developers with a 2D radar image from which information about individual point scatterers `within a single target' can be extracted. This information on scattering characteristics will be utilized to refine threat assessment processing algorithms and automotive radar hardware configurations. (1) By evaluating the scattering characteristics identified in the radar image, radar signatures as a function of aspect angle for common roadway objects can be established. These signatures will aid in the refinement of threat assessment processing algorithms. (2) Utilizing ERIM's image manipulation tools, total RCS and RCS as a function of range and azimuth can be extracted from the radar image data. This RCS information will be essential in defining the operational envelope (e.g. dynamic range) within which any radar sensor hardware must be designed.

Pediatric Brain Extraction Using Learning-based Meta-algorithm

PubMed Central

Shi, Feng; Wang, Li; Dai, Yakang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

2012-01-01

Magnetic resonance imaging of pediatric brain provides valuable information for early brain development studies. Automated brain extraction is challenging due to the small brain size and dynamic change of tissue contrast in the developing brains. In this paper, we propose a novel Learning Algorithm for Brain Extraction and Labeling (LABEL) specially for the pediatric MR brain images. The idea is to perform multiple complementary brain extractions on a given testing image by using a meta-algorithm, including BET and BSE, where the parameters of each run of the meta-algorithm are effectively learned from the training data. Also, the representative subjects are selected as exemplars and used to guide brain extraction of new subjects in different age groups. We further develop a level-set based fusion method to combine multiple brain extractions together with a closed smooth surface for obtaining the final extraction. The proposed method has been extensively evaluated in subjects of three representative age groups, such as neonate (less than 2 months), infant (1–2 years), and child (5–18 years). Experimental results show that, with 45 subjects for training (15 neonates, 15 infant, and 15 children), the proposed method can produce more accurate brain extraction results on 246 testing subjects (75 neonates, 126 infants, and 45 children), i.e., at average Jaccard Index of 0.953, compared to those by BET (0.918), BSE (0.902), ROBEX (0.901), GCUT (0.856), and other fusion methods such as Majority Voting (0.919) and STAPLE (0.941). Along with the largely-improved computational efficiency, the proposed method demonstrates its ability of automated brain extraction for pediatric MR images in a large age range. PMID:22634859

Experimental Determination of the Dynamic Hydraulic Transfer Function for the J-2X Oxidizer Turbopump. Part One; Methodology

NASA Technical Reports Server (NTRS)

Zoladz, Tom; Patel, Sandeep; Lee, Erik; Karon, Dave

2011-01-01

An advanced methodology for extracting the hydraulic dynamic pump transfer matrix (Yp) for a cavitating liquid rocket engine turbopump inducer+impeller has been developed. The transfer function is required for integrated vehicle pogo stability analysis as well as optimization of local inducer pumping stability. Laboratory pulsed subscale waterflow test of the J-2X oxygen turbo pump is introduced and our new extraction method applied to the data collected. From accurate measures of pump inlet and discharge perturbational mass flows and pressures, and one-dimensional flow models that represents complete waterflow loop physics, we are able to derive Yp and hence extract the characteristic pump parameters: compliance, pump gain, impedance, mass flow gain. Detailed modeling is necessary to accurately translate instrument plane measurements to the pump inlet and discharge and extract Yp. We present the MSFC Dynamic Lump Parameter Fluid Model Framework and describe critical dynamic component details. We report on fit minimization techniques, cost (fitness) function derivation, and resulting model fits to our experimental data are presented. Comparisons are made to alternate techniques for spatially translating measurement stations to actual pump inlet and discharge.

Research of information classification and strategy intelligence extract algorithm based on military strategy hall

NASA Astrophysics Data System (ADS)

Chen, Lei; Li, Dehua; Yang, Jie

2007-12-01

Constructing virtual international strategy environment needs many kinds of information, such as economy, politic, military, diploma, culture, science, etc. So it is very important to build an information auto-extract, classification, recombination and analysis management system with high efficiency as the foundation and component of military strategy hall. This paper firstly use improved Boost algorithm to classify obtained initial information, then use a strategy intelligence extract algorithm to extract strategy intelligence from initial information to help strategist to analysis information.

Map as a Service: A Framework for Visualising and Maximising Information Return from Multi-Modal Wireless Sensor Networks

PubMed Central

Hammoudeh, Mohammad; Newman, Robert; Dennett, Christopher; Mount, Sarah; Aldabbas, Omar

2015-01-01

This paper presents a distributed information extraction and visualisation service, called the mapping service, for maximising information return from large-scale wireless sensor networks. Such a service would greatly simplify the production of higher-level, information-rich, representations suitable for informing other network services and the delivery of field information visualisations. The mapping service utilises a blend of inductive and deductive models to map sense data accurately using externally available knowledge. It utilises the special characteristics of the application domain to render visualisations in a map format that are a precise reflection of the concrete reality. This service is suitable for visualising an arbitrary number of sense modalities. It is capable of visualising from multiple independent types of the sense data to overcome the limitations of generating visualisations from a single type of sense modality. Furthermore, the mapping service responds dynamically to changes in the environmental conditions, which may affect the visualisation performance by continuously updating the application domain model in a distributed manner. Finally, a distributed self-adaptation function is proposed with the goal of saving more power and generating more accurate data visualisation. We conduct comprehensive experimentation to evaluate the performance of our mapping service and show that it achieves low communication overhead, produces maps of high fidelity, and further minimises the mapping predictive error dynamically through integrating the application domain model in the mapping service. PMID:26378539

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

NASA Astrophysics Data System (ADS)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations.

PubMed

Serçinoglu, Onur; Ozbek, Pemra

2018-05-25

Atomistic molecular dynamics (MD) simulations generate a wealth of information related to the dynamics of proteins. If properly analyzed, this information can lead to new insights regarding protein function and assist wet-lab experiments. Aiming to identify interactions between individual amino acid residues and the role played by each in the context of MD simulations, we present a stand-alone software called gRINN (get Residue Interaction eNergies and Networks). gRINN features graphical user interfaces (GUIs) and a command-line interface for generating and analyzing pairwise residue interaction energies and energy correlations from protein MD simulation trajectories. gRINN utilizes the features of NAMD or GROMACS MD simulation packages and automatizes the steps necessary to extract residue-residue interaction energies from user-supplied simulation trajectories, greatly simplifying the analysis for the end-user. A GUI, including an embedded molecular viewer, is provided for visualization of interaction energy time-series, distributions, an interaction energy matrix, interaction energy correlations and a residue correlation matrix. gRINN additionally offers construction and analysis of Protein Energy Networks, providing residue-based metrics such as degrees, betweenness-centralities, closeness centralities as well as shortest path analysis. gRINN is free and open to all users without login requirement at http://grinn.readthedocs.io.

Facial expression movement enhances the measurement of temporal dynamics of attentional bias in the dot-probe task.

PubMed

Caudek, Corrado; Ceccarini, Francesco; Sica, Claudio

2017-08-01

The facial dot-probe task is one of the most common experimental paradigms used to assess attentional bias toward emotional information. In recent years, however, the psychometric properties of this paradigm have been questioned. In the present study, attentional bias to emotional face stimuli was measured with dynamic and static images of realistic human faces in 97 college students (63 women) who underwent either a positive or a negative mood-induction prior to the experiment. We controlled the bottom-up salience of the stimuli in order to dissociate the top-down orienting of attention from the effects of the bottom-up physical properties of the stimuli. A Bayesian analysis of our results indicates that 1) the traditional global attentional bias index shows a low reliability, 2) reliability increases dramatically when biased attention is analyzed by extracting a series of bias estimations from trial-to-trial (Zvielli, Bernstein, & Koster, 2015), 3) dynamic expression of emotions strengthens biased attention to emotional information, and 4) mood-congruency facilitates the measurement of biased attention to emotional stimuli. These results highlight the importance of using ecologically valid stimuli in attentional bias research, together with the importance of estimating biased attention at the trial level. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dynamic Bayesian wavelet transform: New methodology for extraction of repetitive transients

NASA Astrophysics Data System (ADS)

Wang, Dong; Tsui, Kwok-Leung

2017-05-01

Thanks to some recent research works, dynamic Bayesian wavelet transform as new methodology for extraction of repetitive transients is proposed in this short communication to reveal fault signatures hidden in rotating machine. The main idea of the dynamic Bayesian wavelet transform is to iteratively estimate posterior parameters of wavelet transform via artificial observations and dynamic Bayesian inference. First, a prior wavelet parameter distribution can be established by one of many fast detection algorithms, such as the fast kurtogram, the improved kurtogram, the enhanced kurtogram, the sparsogram, the infogram, continuous wavelet transform, discrete wavelet transform, wavelet packets, multiwavelets, empirical wavelet transform, empirical mode decomposition, local mean decomposition, etc.. Second, artificial observations can be constructed based on one of many metrics, such as kurtosis, the sparsity measurement, entropy, approximate entropy, the smoothness index, a synthesized criterion, etc., which are able to quantify repetitive transients. Finally, given artificial observations, the prior wavelet parameter distribution can be posteriorly updated over iterations by using dynamic Bayesian inference. More importantly, the proposed new methodology can be extended to establish the optimal parameters required by many other signal processing methods for extraction of repetitive transients.

Extracting motor synergies from random movements for low-dimensional task-space control of musculoskeletal robots.

PubMed

Fu, Kin Chung Denny; Dalla Libera, Fabio; Ishiguro, Hiroshi

2015-10-08

In the field of human motor control, the motor synergy hypothesis explains how humans simplify body control dimensionality by coordinating groups of muscles, called motor synergies, instead of controlling muscles independently. In most applications of motor synergies to low-dimensional control in robotics, motor synergies are extracted from given optimal control signals. In this paper, we address the problems of how to extract motor synergies without optimal data given, and how to apply motor synergies to achieve low-dimensional task-space tracking control of a human-like robotic arm actuated by redundant muscles, without prior knowledge of the robot. We propose to extract motor synergies from a subset of randomly generated reaching-like movement data. The essence is to first approximate the corresponding optimal control signals, using estimations of the robot's forward dynamics, and to extract the motor synergies subsequently. In order to avoid modeling difficulties, a learning-based control approach is adopted such that control is accomplished via estimations of the robot's inverse dynamics. We present a kernel-based regression formulation to estimate the forward and the inverse dynamics, and a sliding controller in order to cope with estimation error. Numerical evaluations show that the proposed method enables extraction of motor synergies for low-dimensional task-space control.

Possible signals of vacuum dynamics in the Universe

NASA Astrophysics Data System (ADS)

Peracaula, Joan Solà; de Cruz Pérez, Javier; Gómez-Valent, Adrià

2018-05-01

We study a generic class of time-evolving vacuum models which can provide a better phenomenological account of the overall cosmological observations as compared to the ΛCDM. Among these models, the running vacuum model (RVM) appears to be the most motivated and favored one, at a confidence level of ˜3σ. We further support these results by computing the Akaike and Bayesian information criteria. Our analysis also shows that we can extract fair signals of dynamical dark energy (DDE) by confronting the same set of data to the generic XCDM and CPL parametrizations. In all cases we confirm that the combined triad of modern observations on Baryonic Acoustic Oscillations, Large Scale Structure formation, and the Cosmic Microwave Background, provide the bulk of the signal sustaining a possible vacuum dynamics. In the absence of any of these three crucial data sources, the DDE signal can not be perceived at a significant confidence level. Its possible existence could be a cure for some of the tensions existing in the ΛCDM when confronted to observations.

Quantitative analysis of backbone dynamics in a crystalline protein from nitrogen-15 spin-lattice relaxation.

PubMed

Giraud, Nicolas; Blackledge, Martin; Goldman, Maurice; Böckmann, Anja; Lesage, Anne; Penin, François; Emsley, Lyndon

2005-12-28

A detailed analysis of nitrogen-15 longitudinal relaxation times in microcrystalline proteins is presented. A theoretical model to quantitatively interpret relaxation times is developed in terms of motional amplitude and characteristic time scale. Different averaging schemes are examined in order to propose an analysis of relaxation curves that takes into account the specificity of MAS experiments. In particular, it is shown that magic angle spinning averages the relaxation rate experienced by a single spin over one rotor period, resulting in individual relaxation curves that are dependent on the orientation of their corresponding carousel with respect to the rotor axis. Powder averaging thus leads to a nonexponential behavior in the observed decay curves. We extract dynamic information from experimental decay curves, using a diffusion in a cone model. We apply this study to the analysis of spin-lattice relaxation rates of the microcrystalline protein Crh at two different fields and determine differential dynamic parameters for several residues in the protein.

Robust variable selection method for nonparametric differential equation models with application to nonlinear dynamic gene regulatory network analysis.

PubMed

Lu, Tao

2016-01-01

The gene regulation network (GRN) evaluates the interactions between genes and look for models to describe the gene expression behavior. These models have many applications; for instance, by characterizing the gene expression mechanisms that cause certain disorders, it would be possible to target those genes to block the progress of the disease. Many biological processes are driven by nonlinear dynamic GRN. In this article, we propose a nonparametric differential equation (ODE) to model the nonlinear dynamic GRN. Specially, we address following questions simultaneously: (i) extract information from noisy time course gene expression data; (ii) model the nonlinear ODE through a nonparametric smoothing function; (iii) identify the important regulatory gene(s) through a group smoothly clipped absolute deviation (SCAD) approach; (iv) test the robustness of the model against possible shortening of experimental duration. We illustrate the usefulness of the model and associated statistical methods through a simulation and a real application examples.

Learning to classify wakes from local sensory information

NASA Astrophysics Data System (ADS)

Alsalman, Mohamad; Colvert, Brendan; Kanso, Eva; Kanso Team

2017-11-01

Aquatic organisms exhibit remarkable abilities to sense local flow signals contained in their fluid environment and to surmise the origins of these flows. For example, fish can discern the information contained in various flow structures and utilize this information for obstacle avoidance and prey tracking. Flow structures created by flapping and swimming bodies are well characterized in the fluid dynamics literature; however, such characterization relies on classical methods that use an external observer to reconstruct global flow fields. The reconstructed flows, or wakes, are then classified according to the unsteady vortex patterns. Here, we propose a new approach for wake identification: we classify the wakes resulting from a flapping airfoil by applying machine learning algorithms to local flow information. In particular, we simulate the wakes of an oscillating airfoil in an incoming flow, extract the downstream vorticity information, and train a classifier to learn the different flow structures and classify new ones. This data-driven approach provides a promising framework for underwater navigation and detection in application to autonomous bio-inspired vehicles.

Using articulated scene models for dynamic 3d scene analysis in vista spaces

NASA Astrophysics Data System (ADS)

Beuter, Niklas; Swadzba, Agnes; Kummert, Franz; Wachsmuth, Sven

2010-09-01

In this paper we describe an efficient but detailed new approach to analyze complex dynamic scenes directly in 3D. The arising information is important for mobile robots to solve tasks in the area of household robotics. In our work a mobile robot builds an articulated scene model by observing the environment in the visual field or rather in the so-called vista space. The articulated scene model consists of essential knowledge about the static background, about autonomously moving entities like humans or robots and finally, in contrast to existing approaches, information about articulated parts. These parts describe movable objects like chairs, doors or other tangible entities, which could be moved by an agent. The combination of the static scene, the self-moving entities and the movable objects in one articulated scene model enhances the calculation of each single part. The reconstruction process for parts of the static scene benefits from removal of the dynamic parts and in turn, the moving parts can be extracted more easily through the knowledge about the background. In our experiments we show, that the system delivers simultaneously an accurate static background model, moving persons and movable objects. This information of the articulated scene model enables a mobile robot to detect and keep track of interaction partners, to navigate safely through the environment and finally, to strengthen the interaction with the user through the knowledge about the 3D articulated objects and 3D scene analysis. [Figure not available: see fulltext.

Molecular Dynamics Simulation of Mahkota Dewa (Phaleria Macrocarpa) Extract in Subcritical Water Extraction Process

NASA Astrophysics Data System (ADS)

Hashim, N. A.; Mudalip, S. K. Abdul; Harun, N.; Che Man, R.; Sulaiman, S. Z.; Arshad, Z. I. M.; Shaarani, S. M.

2018-05-01

Mahkota Dewa (Phaleria Macrocarpa), a good source of saponin, flavanoid, polyphenol, alkaloid, and mangiferin has an extensive range of medicinal effects. The intermolecular interactions between solute and solvents such as hydrogen bonding considered as an important factor that affect the extraction of bioactive compounds. In this work, molecular dynamics simulation was performed to elucidate the hydrogen bonding exists between Mahkota Dewa extracts and water during subcritical extraction process. A bioactive compound in the Mahkota Dewa extract, namely mangiferin was selected as a model compound. The simulation was performed at 373 K and 4.0 MPa using COMPASS force field and Ewald summation method available in Material Studio 7.0 simulation package. The radial distribution functions (RDF) between mangiferin and water signify the presence of hydrogen bonding in the extraction process. The simulation of the binary mixture of mangiferin:water shows that strong hydrogen bonding was formed. It is suggested that, the intermolecular interaction between OH2O••HMR4(OH1) has been identified to be responsible for the mangiferin extraction process.

Flow-through dynamic microextraction system for automatic in vitro assessment of chyme bioaccessibility in food commodities.

PubMed

Souza, Lais A; Rosende, María; Korn, Maria Graças A; Miró, Manuel

2018-10-05

An automatic flow-through dynamic extraction method is proposed for the first time for in vitro exploration, with high temporal resolution, of the transit of the chyme from the gastric to the duodenal compartment using the Versantvoort's fed-state physiologically relevant extraction test. The flow manifold was coupled on-line to an inductively coupled plasma optical emission spectrometer (ICP OES) for real-time elucidation of the bioaccessible elemental fraction of micronutrients (viz., Cu, Fe and Mn) in food commodities across the gastrointestinal tract. The simulated intestinal and bile biofluid (added to the gastric phase) was successively pumped at 1.0 mL min -1 through a large-bore column (maintained at 37.0 ± 2.0 °C) initially loaded with a weighed amount of linseed (250 mg) using a PVDF filter membrane (5.0 μm pore size) for retaining of the solid sample and in-line filtration of the extracts. The lack of bias (trueness) of the on-line gastrointestinal extraction method coupled to ICP OES was confirmed using mass balance validation following microwave assisted digestion of the residual (non-bioaccessible) elemental fraction. Mass balance validation yielded absolute recoveries spanning from 79 to 121% for the overall analytes and samples. On-line dynamic extraction was critically appraised against batch counterparts for both gastric and gastrointestinal compartments. Due to the lack of consensus in the literature regarding the agitation method for batch oral bioaccessibility testing, several extraction approaches (viz., magnetic stirring, end-over-end rotation and orbital shaking) were evaluated. Improved gastric extractability of Fe along with bioaccessible data comparable to the dynamic counterpart based on the continuous displacement of the extraction equilibrium was obtained with batchwise magnetic stirring, which is deemed most appropriate for ascertaining worst-case/maximum bioaccessibility scenarios. Copyright © 2018 Elsevier B.V. All rights reserved.

Update: Advancement of Contact Dynamics Modeling for Human Spaceflight Simulation Applications

NASA Technical Reports Server (NTRS)

Brain, Thomas A.; Kovel, Erik B.; MacLean, John R.; Quiocho, Leslie J.

2017-01-01

Pong is a new software tool developed at the NASA Johnson Space Center that advances interference-based geometric contact dynamics based on 3D graphics models. The Pong software consists of three parts: a set of scripts to extract geometric data from 3D graphics models, a contact dynamics engine that provides collision detection and force calculations based on the extracted geometric data, and a set of scripts for visualizing the dynamics response with the 3D graphics models. The contact dynamics engine can be linked with an external multibody dynamics engine to provide an integrated multibody contact dynamics simulation. This paper provides a detailed overview of Pong including the overall approach and modeling capabilities, which encompasses force generation from contact primitives and friction to computational performance. Two specific Pong-based examples of International Space Station applications are discussed, and the related verification and validation using this new tool are also addressed.

A synergic simulation-optimization approach for analyzing biomolecular dynamics in living organisms.

PubMed

Sadegh Zadeh, Kouroush

2011-01-01

A synergic duo simulation-optimization approach was developed and implemented to study protein-substrate dynamics and binding kinetics in living organisms. The forward problem is a system of several coupled nonlinear partial differential equations which, with a given set of kinetics and diffusion parameters, can provide not only the commonly used bleached area-averaged time series in fluorescence microscopy experiments but more informative full biomolecular/drug space-time series and can be successfully used to study dynamics of both Dirac and Gaussian fluorescence-labeled biomacromolecules in vivo. The incomplete Cholesky preconditioner was coupled with the finite difference discretization scheme and an adaptive time-stepping strategy to solve the forward problem. The proposed approach was validated with analytical as well as reference solutions and used to simulate dynamics of GFP-tagged glucocorticoid receptor (GFP-GR) in mouse cancer cell during a fluorescence recovery after photobleaching experiment. Model analysis indicates that the commonly practiced bleach spot-averaged time series is not an efficient approach to extract physiological information from the fluorescence microscopy protocols. It was recommended that experimental biophysicists should use full space-time series, resulting from experimental protocols, to study dynamics of biomacromolecules and drugs in living organisms. It was also concluded that in parameterization of biological mass transfer processes, setting the norm of the gradient of the penalty function at the solution to zero is not an efficient stopping rule to end the inverse algorithm. Theoreticians should use multi-criteria stopping rules to quantify model parameters by optimization. Copyright © 2010 Elsevier Ltd. All rights reserved.

Perspective: Differential dynamic microscopy extracts multi-scale activity in complex fluids and biological systems

NASA Astrophysics Data System (ADS)

Cerbino, Roberto; Cicuta, Pietro

2017-09-01

Differential dynamic microscopy (DDM) is a technique that exploits optical microscopy to obtain local, multi-scale quantitative information about dynamic samples, in most cases without user intervention. It is proving extremely useful in understanding dynamics in liquid suspensions, soft materials, cells, and tissues. In DDM, image sequences are analyzed via a combination of image differences and spatial Fourier transforms to obtain information equivalent to that obtained by means of light scattering techniques. Compared to light scattering, DDM offers obvious advantages, principally (a) simplicity of the setup; (b) possibility of removing static contributions along the optical path; (c) power of simultaneous different microscopy contrast mechanisms; and (d) flexibility of choosing an analysis region, analogous to a scattering volume. For many questions, DDM has also advantages compared to segmentation/tracking approaches and to correlation techniques like particle image velocimetry. The very straightforward DDM approach, originally demonstrated with bright field microscopy of aqueous colloids, has lately been used to probe a variety of other complex fluids and biological systems with many different imaging methods, including dark-field, differential interference contrast, wide-field, light-sheet, and confocal microscopy. The number of adopting groups is rapidly increasing and so are the applications. Here, we briefly recall the working principles of DDM, we highlight its advantages and limitations, we outline recent experimental breakthroughs, and we provide a perspective on future challenges and directions. DDM can become a standard primary tool in every laboratory equipped with a microscope, at the very least as a first bias-free automated evaluation of the dynamics in a system.

Extracting relevant information for cancer diagnosis from dynamic full field OCT through image processing and learning

NASA Astrophysics Data System (ADS)

Apelian, Clément; Gastaud, Clément; Boccara, A. Claude

2017-02-01

For a large number of cancer surgeries, the lack of reliable intraoperative diagnosis leads to reoperations or bad outcomes for the patients. To deliver better diagnosis, we developed Dynamic Full Field OCT (D-FFOCT) as a complement to FFOCT. FFOCT already presents interesting results for cancer diagnosis e.g. Mohs surgery and reaching 96% accuracy on prostate cancer. D-FFOCT accesses the dynamic processes of metabolism and gives new tools to diagnose the state of a tissue at the cellular level to complement FFOCT contrast. We developed a processing framework that intends to maximize the information provided by the FFOCT technology as well as D-FFOCT and synthetize this as a meaningful image. We use different time processing to generate metrics (standard deviation of time signals, decorrelation times and more) and spatial processing to sort out structures and the corresponding imaging modality, which is the most appropriate. Sorting was achieved through quadratic discriminant analysis in a N-dimension parametric space corresponding to our metrics. Combining the best imaging modalities for each structure leads to a rich morphology image. This image displaying the morphology is then colored to represent the dynamic behavior of these structures (slow or fast) and to be quickly analyzed by doctors. Therefore, we achieved a micron resolved image, rich of both FFOCT ability of imaging fixed and highly backscattering structures as well as D-FFOCT ability of imaging low level scattering cellular level details. We believe that this morphological contrast close to histology and the dynamic behavior contrast will push forward the limits of intraoperative diagnosis further on.

How do you assign persistent identifiers to extracts from large, complex, dynamic data sets that underpin scholarly publications?

NASA Astrophysics Data System (ADS)

Wyborn, Lesley; Car, Nicholas; Evans, Benjamin; Klump, Jens

2016-04-01

Persistent identifiers in the form of a Digital Object Identifier (DOI) are becoming more mainstream, assigned at both the collection and dataset level. For static datasets, this is a relatively straight-forward matter. However, many new data collections are dynamic, with new data being appended, models and derivative products being revised with new data, or the data itself revised as processing methods are improved. Further, because data collections are becoming accessible as services, researchers can log in and dynamically create user-defined subsets for specific research projects: they also can easily mix and match data from multiple collections, each of which can have a complex history. Inevitably extracts from such dynamic data sets underpin scholarly publications, and this presents new challenges. The National Computational Infrastructure (NCI) has been experiencing and making progress towards addressing these issues. The NCI is large node of the Research Data Services initiative (RDS) of the Australian Government's research infrastructure, which currently makes available over 10 PBytes of priority research collections, ranging from geosciences, geophysics, environment, and climate, through to astronomy, bioinformatics, and social sciences. Data are replicated to, or are produced at, NCI and then processed there to higher-level data products or directly analysed. Individual datasets range from multi-petabyte computational models and large volume raster arrays, down to gigabyte size, ultra-high resolution datasets. To facilitate access, maximise reuse and enable integration across the disciplines, datasets have been organized on a platform called the National Environmental Research Data Interoperability Platform (NERDIP). Combined, the NERDIP data collections form a rich and diverse asset for researchers: their co-location and standardization optimises the value of existing data, and forms a new resource to underpin data-intensive Science. New publication procedures require that a persistent identifier (DOI) be provided for the dataset that underpins the publication. Being able to produce these for data extracts from the NCI data node using only DOIs is proving difficult: preserving a copy of each data extract is not possible due to data scale. A proposal is for researchers to use workflows that capture the provenance of each data extraction, including metadata (e.g., version of the dataset used, the query and time of extraction). In parallel, NCI is now working with the NERDIP dataset providers to ensure that the provenance of data publication is also captured in provenance systems including references to previous versions and a history of data appended or modified. This proposed solution would require an enhancement to new scholarly publication procedures whereby the reference to underlying dataset to a scholarly publication would be the persistent identifier of the provenance workflow that created the data extract. In turn, the provenance workflow would itself link to a series of persistent identifiers that, at a minimum, provide complete dataset production transparency and, if required, would facilitate reconstruction of the dataset. Such a solution will require strict adherence to design patterns for provenance representation to ensure that the provenance representation of the workflow does indeed contain information required to deliver dataset generation transparency and a pathway to reconstruction.

Dynamic fiber Bragg gratings based health monitoring system of composite aerospace structures

NASA Astrophysics Data System (ADS)

Panopoulou, A.; Loutas, T.; Roulias, D.; Fransen, S.; Kostopoulos, V.

2011-09-01

The main purpose of the current work is to develop a new system for structural health monitoring of composite aerospace structures based on real-time dynamic measurements, in order to identify the structural state condition. Long-gauge Fibre Bragg Grating (FBG) optical sensors were used for monitoring the dynamic response of the composite structure. The algorithm that was developed for structural damage detection utilizes the collected dynamic response data, analyzes them in various ways and through an artificial neural network identifies the damage state and its location. Damage was simulated by slightly varying locally the mass of the structure (by adding a known mass) at different zones of the structure. Lumped masses in different locations upon the structure alter the eigen-frequencies in a way similar to actual damage. The structural dynamic behaviour has been numerically simulated and experimentally verified by means of modal testing on two different composite aerospace structures. Advanced digital signal processing techniques, e.g. the wavelet transform (WT), were used for the analysis of the dynamic response for feature extraction. WT's capability of separating the different frequency components in the time domain without loosing frequency information makes it a versatile tool for demanding signal processing applications. The use of WT is also suggested by the no-stationary nature of dynamic response signals and the opportunity of evaluating the temporal evolution of their frequency contents. Feature extraction is the first step of the procedure. The extracted features are effective indices of damage size and location. The classification step comprises of a feed-forward back propagation network, whose output determines the simulated damage location. Finally, dedicated training and validation activities were carried out by means of numerical simulations and experimental procedures. Experimental validation was performed initially on a flat stiffened panel, representing a section of a typical aeronautical structure, manufactured and tested in the lab and, as a second step, on a scaled up space oriented structure, which is a composite honeycomb plate, used as a deployment base for antenna arrays. An integrated FBG sensor network, based on the advantage of multiplexing, was mounted on both structures and different excitation positions and boundary conditions were used. The analysis of operational dynamic responses was employed to identify both the damage and its position. The system that was designed and tested initially on the thin composite panel, was successfully validated on the larger honeycomb structure. Numerical simulation of both structures was used as a support tool at all the steps of the work providing among others the location of the optical sensors used. The proposed work will be the base for the whole system qualification and validation on an antenna reflector in future work.

Extracting, Tracking, and Visualizing Magnetic Flux Vortices in 3D Complex-Valued Superconductor Simulation Data.

PubMed

Guo, Hanqi; Phillips, Carolyn L; Peterka, Tom; Karpeyev, Dmitry; Glatz, Andreas

2016-01-01

We propose a method for the vortex extraction and tracking of superconducting magnetic flux vortices for both structured and unstructured mesh data. In the Ginzburg-Landau theory, magnetic flux vortices are well-defined features in a complex-valued order parameter field, and their dynamics determine electromagnetic properties in type-II superconductors. Our method represents each vortex line (a 1D curve embedded in 3D space) as a connected graph extracted from the discretized field in both space and time. For a time-varying discrete dataset, our vortex extraction and tracking method is as accurate as the data discretization. We then apply 3D visualization and 2D event diagrams to the extraction and tracking results to help scientists understand vortex dynamics and macroscale superconductor behavior in greater detail than previously possible.

A tool for NDVI time series extraction from wide-swath remotely sensed images

NASA Astrophysics Data System (ADS)

Li, Zhishan; Shi, Runhe; Zhou, Cong

2015-09-01

Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.

Salient contour extraction from complex natural scene in night vision image

NASA Astrophysics Data System (ADS)

Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lian-fa

2014-03-01

The theory of center-surround interaction in non-classical receptive field can be applied in night vision information processing. In this work, an optimized compound receptive field modulation method is proposed to extract salient contour from complex natural scene in low-light-level (LLL) and infrared images. The kernel idea is that multi-feature analysis can recognize the inhomogeneity in modulatory coverage more accurately and that center and surround with the grouping structure satisfying Gestalt rule deserves high connection-probability. Computationally, a multi-feature contrast weighted inhibition model is presented to suppress background and lower mutual inhibition among contour elements; a fuzzy connection facilitation model is proposed to achieve the enhancement of contour response, the connection of discontinuous contour and the further elimination of randomly distributed noise and texture; a multi-scale iterative attention method is designed to accomplish dynamic modulation process and extract contours of targets in multi-size. This work provides a series of biologically motivated computational visual models with high-performance for contour detection from cluttered scene in night vision images.

Sparse networks of directly coupled, polymorphic, and functional side chains in allosteric proteins.

PubMed

Soltan Ghoraie, Laleh; Burkowski, Forbes; Zhu, Mu

2015-03-01

Recent studies have highlighted the role of coupled side-chain fluctuations alone in the allosteric behavior of proteins. Moreover, examination of X-ray crystallography data has recently revealed new information about the prevalence of alternate side-chain conformations (conformational polymorphism), and attempts have been made to uncover the hidden alternate conformations from X-ray data. Hence, new computational approaches are required that consider the polymorphic nature of the side chains, and incorporate the effects of this phenomenon in the study of information transmission and functional interactions of residues in a molecule. These studies can provide a more accurate understanding of the allosteric behavior. In this article, we first present a novel approach to generate an ensemble of conformations and an efficient computational method to extract direct couplings of side chains in allosteric proteins, and provide sparse network representations of the couplings. We take the side-chain conformational polymorphism into account, and show that by studying the intrinsic dynamics of an inactive structure, we are able to construct a network of functionally crucial residues. Second, we show that the proposed method is capable of providing a magnified view of the coupled and conformationally polymorphic residues. This model reveals couplings between the alternate conformations of a coupled residue pair. To the best of our knowledge, this is the first computational method for extracting networks of side chains' alternate conformations. Such networks help in providing a detailed image of side-chain dynamics in functionally important and conformationally polymorphic sites, such as binding and/or allosteric sites. © 2014 Wiley Periodicals, Inc.

Nonlinear Dynamics of River Runoff Elucidated by Horizontal Visibility Graphs

NASA Astrophysics Data System (ADS)

Lange, Holger; Rosso, Osvaldo A.

2017-04-01

We investigate a set of long-term river runoff time series at daily resolution from Brazil, monitored by the Agencia Nacional de Aguas. A total of 150 time series was obtained, with an average length of 65 years. Both long-term trends and human influence (water management, e.g. for power production) on the dynamical behaviour are analyzed. We use Horizontal Visibility Graphs (HVGs) to determine the individual temporal networks for the time series, and extract their degree and their distance (shortest path length) distributions. Statistical and information-theoretic properties of these distributions are calculated: robust estimators of skewness and kurtosis, the maximum degree occurring in the time series, the Shannon entropy, permutation complexity and Fisher Information. For the latter, we also compare the information measures obtained from the degree distributions to those using the original time series directly, to investigate the impact of graph construction on the dynamical properties as reflected in these measures. Focus is on one hand on universal properties of the HVG, common to all runoff series, and on site-specific aspects on the other. Results demonstrate that the assumption of power law behaviour for the degree distribtion does not generally hold, and that management has a significant impact on this distribution. We also show that a specific pretreatment of the time series conventional in hydrology, the elimination of seasonality by a separate z-transformation for each calendar day, is highly detrimental to the nonlinear behaviour. It changes long-term correlations and the overall dynamics towards more random behaviour. Analysis based on the transformed data easily leads to spurious results, and bear a high risk of misinterpretation.

Reconstructing ancient river dynamics from the stratigraphic record: can lessons from the past inform our future?

NASA Astrophysics Data System (ADS)

Hajek, E. A.; Chamberlin, E.; Baisden, T.

2014-12-01

The richness of the deep-time record and its potential for revealing important characteristics of ancient fluvial landscapes has been demonstrated time and again, including compelling examples of rivers altering their behavior in response to changes in vegetation patterns or abrupt shifts in water and sediment discharge. At present, reconstructions of ancient river and floodplain dynamics are commonly qualitative, and when quantitative metrics are used, it is often for comparison among ancient deposits. Without being able to reconstruct, more comprehensively, important aspects of ancient river and floodplains dynamics, this information has only anecdotal relevance for evaluating and managing present-day landscapes. While methods for reconstructing hydrodynamic and morphodynamic aspects of ancient rivers and floodplains are useful, uncertainties associated with these snapshots complicate the ability to translate observations from geologic to engineering scales, thereby limiting the utility of insight from Earth's past in decision-making and development of sustainable landscape-management practices for modern fluvial landscapes. Here, we explore the degree to which paleomorphodynamic reconstructions from ancient channel and floodplain deposits can be used to make specific, quantitative inferences about ancient river dynamics. We compare a suite of paleoenvironmental measurements from a variety of ancient fluvial deposits (including reconstructions of paleoflow depth, paleoslope, paleo-channel mobility, the caliber of paleo-sediment load, and paleo-floodplain heterogeneity) in an effort to evaluate sampling and empirical uncertainties associated with these methods and identify promising avenues for developing more detailed landscape reconstructions. This work is aimed at helping to develop strategies for extracting practicable information from the stratigraphic record that is relevant for sustainably managing and predicting changes in today's environments.

Dimension reduction and multiscaling law through source extraction

NASA Astrophysics Data System (ADS)

Capobianco, Enrico

2003-04-01

Through the empirical analysis of financial return generating processes one may find features that are common to other research fields, such as internet data from network traffic, physiological studies about human heart beat, speech and sleep recorded time series, geophysics signals, just to mention well-known cases of study. In particular, long range dependence, intermittency, heteroscedasticity are clearly appearing, and consequently power laws and multi-scaling behavior result typical signatures of either the spectral or the time correlation diagnostics. We study these features and the dynamics underlying financial volatility, which can respectively be detected and inferred from high frequency realizations of stock index returns, and show that they vary according to the resolution levels used for both the analysis and the synthesis of the available information. Discovering whether the volatility dynamics are subject to changes in scaling regimes requires the consideration of a model embedding scale-dependent information packets, thus accounting for possible heterogeneous activity occurring in financial markets. Independent component analysis result to be an important tool for reducing the dimension of the problem and calibrating greedy approximation techniques aimed to learn the structure of the underlying volatility.

Interface Metaphors for Interactive Machine Learning

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

Jasper, Robert J.; Blaha, Leslie M.

To promote more interactive and dynamic machine learn- ing, we revisit the notion of user-interface metaphors. User-interface metaphors provide intuitive constructs for supporting user needs through interface design elements. A user-interface metaphor provides a visual or action pattern that leverages a user’s knowledge of another domain. Metaphors suggest both the visual representations that should be used in a display as well as the interactions that should be afforded to the user. We argue that user-interface metaphors can also offer a method of extracting interaction-based user feedback for use in machine learning. Metaphors offer indirect, context-based information that can be usedmore » in addition to explicit user inputs, such as user-provided labels. Implicit information from user interactions with metaphors can augment explicit user input for active learning paradigms. Or it might be leveraged in systems where explicit user inputs are more challenging to obtain. Each interaction with the metaphor provides an opportunity to gather data and learn. We argue this approach is especially important in streaming applications, where we desire machine learning systems that can adapt to dynamic, changing data.« less

Damage-mitigating control of aerospace systems for high performance and extended life

NASA Technical Reports Server (NTRS)

Ray, Asok; Wu, Min-Kuang; Carpino, Marc; Lorenzo, Carl F.; Merrill, Walter C.

1992-01-01

The concept of damage-mitigating control is to minimize fatigue (as well as creep and corrosion) damage of critical components of mechanical structures while simultaneously maximizing the system dynamic performance. Given a dynamic model of the plant and the specifications for performance and stability robustness, the task is to synthesize a control law that would meet the system requirements and, at the same time, satisfy the constraints that are imposed by the material and structural properties of the critical components. The authors present the concept of damage-mitigating control systems design with the following objectives: (1) to achieve high performance with a prolonged life span; and (2) to systematically update the controller as the new technology of advanced materials evolves. The major challenge is to extract the information from the material properties and then utilize this information in a mathematical form so that it can be directly applied to robust control synthesis for mechanical systems. The basic concept of damage-mitigating control is illustrated using a relatively simplified model of a space shuttle main engine.

Potential for wind extraction from 4D-Var assimilation of aerosols and moisture

NASA Astrophysics Data System (ADS)

Zaplotnik, Žiga; Žagar, Nedjeljka

2017-04-01

We discuss the potential of the four-dimensional variational data assimilation (4D-Var) to retrieve the unobserved wind field from observations of atmospheric tracers and the mass field through internal model dynamics and the multivariate relationships in the background-error term for 4D-Var. The presence of non-linear moist dynamics makes the wind retrieval from tracers very difficult. On the other hand, it has been shown that moisture observations strongly influence both tropical and mid-latitude wind field in 4D-Var. We present an intermediate complexity model that describes nonlinear interactions between the wind, temperature, aerosols and moisture including their sinks and sources in the framework of the so-called first baroclinic mode atmosphere envisaged by A. Gill. Aerosol physical processes, which are included in the model, are the non-linear advection, diffusion and sources and sinks that exist as dry and wet deposition and diffusion. Precipitation is parametrized according to the Betts-Miller scheme. The control vector for 4D-Var includes aerosols, moisture and the three dynamical variables. The former is analysed univariately whereas wind field and mass field are analysed in a multivariate fashion taking into account quasi-geostrophic and unbalanced dynamics. The OSSE type of studies are performed for the tropical region to assess the ability of 4D-Var to extract wind-field information from the time series of observations of tracers as a function of the flow nonlinearity, the observations density and the length of the assimilation window (12 hours and 24 hours), in dry and moist environment. Results show that the 4D-Var assimilation of aerosols and temperature data is beneficial for the wind analysis with analysis errors strongly dependent on the moist processes and reliable background-error covariances.

Untangling the proximate causes and underlying drivers of deforestation and forest degradation in Myanmar.

PubMed

Lim, Cheng Ling; Prescott, Graham W; De Alban, Jose Don T; Ziegler, Alan D; Webb, Edward L

2017-12-01

Political transitions often trigger substantial environmental changes. In particular, deforestation can result from the complex interplay among the components of a system-actors, institutions, and existing policies-adapting to new opportunities. A dynamic conceptual map of system components is particularly useful for systems in which multiple actors, each with different worldviews and motivations, may be simultaneously trying to alter different facets of the system, unaware of the impacts on other components. In Myanmar, a global biodiversity hotspot with the largest forest area in mainland Southeast Asia, ongoing political and economic reforms are likely to change the dynamics of deforestation drivers. A fundamental conceptual map of these dynamics is therefore a prerequisite for interventions to reduce deforestation. We used a system-dynamics approach and causal-network analysis to determine the proximate causes and underlying drivers of forest loss and degradation in Myanmar from 1995 to 2016 and to articulate the linkages among them. Proximate causes included infrastructure development, timber extraction, and agricultural expansion. These were stimulated primarily by formal agricultural, logging, mining, and hydropower concessions and economic investment and social issues relating to civil war and land tenure. Reform of land laws, the link between natural resource extraction and civil war, and the allocation of agricultural concessions will influence the extent of future forest loss and degradation in Myanmar. The causal-network analysis identified priority areas for policy interventions, for example, creating a public registry of land-concession holders to deter corruption in concession allocation. We recommend application of this analytical approach to other countries, particularly those undergoing political transition, to inform policy interventions to reduce forest loss and degradation. © 2017 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.

Quantification of Diaphragm Mechanics in Pompe Disease Using Dynamic 3D MRI

PubMed Central

Mogalle, Katja; Perez-Rovira, Adria; Ciet, Pierluigi; Wens, Stephan C. A.; van Doorn, Pieter A.; Tiddens, Harm A. W. M.; van der Ploeg, Ans T.; de Bruijne, Marleen

2016-01-01

Background Diaphragm weakness is the main reason for respiratory dysfunction in patients with Pompe disease, a progressive metabolic myopathy affecting respiratory and limb-girdle muscles. Since respiratory failure is the major cause of death among adult patients, early identification of respiratory muscle involvement is necessary to initiate treatment in time and possibly prevent irreversible damage. In this paper we investigate the suitability of dynamic MR imaging in combination with state-of-the-art image analysis methods to assess respiratory muscle weakness. Methods The proposed methodology relies on image registration and lung surface extraction to quantify lung kinematics during breathing. This allows for the extraction of geometry and motion features of the lung that characterize the independent contribution of the diaphragm and the thoracic muscles to the respiratory cycle. Results Results in 16 3D+t MRI scans (10 Pompe patients and 6 controls) of a slow expiratory maneuver show that kinematic analysis from dynamic 3D images reveals important additional information about diaphragm mechanics and respiratory muscle involvement when compared to conventional pulmonary function tests. Pompe patients with severely reduced pulmonary function showed severe diaphragm weakness presented by minimal motion of the diaphragm. In patients with moderately reduced pulmonary function, cranial displacement of posterior diaphragm parts was reduced and the diaphragm dome was oriented more horizontally at full inspiration compared to healthy controls. Conclusion Dynamic 3D MRI provides data for analyzing the contribution of both diaphragm and thoracic muscles independently. The proposed image analysis method has the potential to detect less severe diaphragm weakness and could thus be used to determine the optimal start of treatment in adult patients with Pompe disease in prospect of increased treatment response. PMID:27391236

A rapid extraction of landslide disaster information research based on GF-1 image

NASA Astrophysics Data System (ADS)

Wang, Sai; Xu, Suning; Peng, Ling; Wang, Zhiyi; Wang, Na

2015-08-01

In recent years, the landslide disasters occurred frequently because of the seismic activity. It brings great harm to people's life. It has caused high attention of the state and the extensive concern of society. In the field of geological disaster, landslide information extraction based on remote sensing has been controversial, but high resolution remote sensing image can improve the accuracy of information extraction effectively with its rich texture and geometry information. Therefore, it is feasible to extract the information of earthquake- triggered landslides with serious surface damage and large scale. Taking the Wenchuan county as the study area, this paper uses multi-scale segmentation method to extract the landslide image object through domestic GF-1 images and DEM data, which uses the estimation of scale parameter tool to determine the optimal segmentation scale; After analyzing the characteristics of landslide high-resolution image comprehensively and selecting spectrum feature, texture feature, geometric features and landform characteristics of the image, we can establish the extracting rules to extract landslide disaster information. The extraction results show that there are 20 landslide whose total area is 521279.31 .Compared with visual interpretation results, the extraction accuracy is 72.22%. This study indicates its efficient and feasible to extract earthquake landslide disaster information based on high resolution remote sensing and it provides important technical support for post-disaster emergency investigation and disaster assessment.

Multi-Filter String Matching and Human-Centric Entity Matching for Information Extraction

ERIC Educational Resources Information Center

Sun, Chong

2012-01-01

More and more information is being generated in text documents, such as Web pages, emails and blogs. To effectively manage this unstructured information, one broadly used approach includes locating relevant content in documents, extracting structured information and integrating the extracted information for querying, mining or further analysis. In…

A microfluidic cigarette smoke collecting platform for simultaneous sample extraction and multiplex analysis.

PubMed

Hu, Shan-Wen; Xu, Bi-Yi; Qiao, Shu; Zhao, Ge; Xu, Jing-Juan; Chen, Hong-Yuan; Xie, Fu-Wei

2016-04-01

In this work, we report a novel microfluidic gas collecting platform aiming at simultaneous sample extraction and multiplex mass spectrometry (MS) analysis. An alveolar-mimicking elastic polydimethylsiloxane (PDMS) structures was designed to move dynamically driven by external pressure. The movement was well tuned both by its amplitude and rhythm following the natural process of human respiration. By integrating the alveolar units into arrays and assembling them to gas channels, a cyclic contraction/expansion system for gas inhale and exhale was successfully constructed. Upon equipping this system with a droplet array on the alveolar array surface, we were able to get information of inhaled smoke in a new strategy. Here, with cigarette smoke as an example, analysis of accumulation for target molecules during passive smoking is taken. Relationships between the breathing times, distances away from smokers and inhaled content of nicotine are clarified. Further, by applying different types of extraction solvent droplets on different locations of the droplet array, simultaneous extraction of nicotine, formaldehyde and caproic acid in sidestream smoke (SS) are realized. Since the extract droplets are spatially separated, they can be directly analyzed by MS which is fast and can rid us of all complex sample separation and purification steps. Combining all these merits, this small, cheap and portable platform might find wide application in inhaled air pollutant analysis both in and outdoors. Copyright © 2015 Elsevier B.V. All rights reserved.

A High-Speed Vision-Based Sensor for Dynamic Vibration Analysis Using Fast Motion Extraction Algorithms.

PubMed

Zhang, Dashan; Guo, Jie; Lei, Xiujun; Zhu, Changan

2016-04-22

The development of image sensor and optics enables the application of vision-based techniques to the non-contact dynamic vibration analysis of large-scale structures. As an emerging technology, a vision-based approach allows for remote measuring and does not bring any additional mass to the measuring object compared with traditional contact measurements. In this study, a high-speed vision-based sensor system is developed to extract structure vibration signals in real time. A fast motion extraction algorithm is required for this system because the maximum sampling frequency of the charge-coupled device (CCD) sensor can reach up to 1000 Hz. Two efficient subpixel level motion extraction algorithms, namely the modified Taylor approximation refinement algorithm and the localization refinement algorithm, are integrated into the proposed vision sensor. Quantitative analysis shows that both of the two modified algorithms are at least five times faster than conventional upsampled cross-correlation approaches and achieve satisfactory error performance. The practicability of the developed sensor is evaluated by an experiment in a laboratory environment and a field test. Experimental results indicate that the developed high-speed vision-based sensor system can extract accurate dynamic structure vibration signals by tracking either artificial targets or natural features.

The importance of cantilever dynamics in the interpretation of Kelvin probe force microscopy.

PubMed

Satzinger, Kevin J; Brown, Keith A; Westervelt, Robert M

2012-09-15

A realistic interpretation of the measured contact potential difference (CPD) in Kelvin probe force microscopy (KPFM) is crucial in order to extract meaningful information about the sample. Central to this interpretation is a method to include contributions from the macroscopic cantilever arm, as well as the cone and sharp tip of a KPFM probe. Here, three models of the electrostatic interaction between a KPFM probe and a sample are tested through an electrostatic simulation and compared with experiment. In contrast with previous studies that treat the KPFM cantilever as a rigid object, we allow the cantilever to bend and rotate; accounting for cantilever bending provides the closest agreement between theory and experiment. We demonstrate that cantilever dynamics play a major role in CPD measurements and provide a simulation technique to explore this phenomenon.

Modal decomposition of turbulent supersonic cavity

NASA Astrophysics Data System (ADS)

Soni, R. K.; Arya, N.; De, A.

2018-06-01

Self-sustained oscillations in a Mach 3 supersonic cavity with a length-to-depth ratio of three are investigated using wall-modeled large eddy simulation methodology for ReD = 3.39× 105 . The unsteady data obtained through computation are utilized to investigate the spatial and temporal evolution of the flow field, especially the second invariant of the velocity tensor, while the phase-averaged data are analyzed over a feedback cycle to study the spatial structures. This analysis is accompanied by the proper orthogonal decomposition (POD) data, which reveals the presence of discrete vortices along the shear layer. The POD analysis is performed in both the spanwise and streamwise planes to extract the coherence in flow structures. Finally, dynamic mode decomposition is performed on the data sequence to obtain the dynamic information and deeper insight into the self-sustained mechanism.

A neural coding scheme reproducing foraging trajectories

PubMed Central

Gutiérrez, Esther D.; Cabrera, Juan Luis

2015-01-01

The movement of many animals may follow Lévy patterns. The underlying generating neuronal dynamics of such a behavior is unknown. In this paper we show that a novel discovery of multifractality in winnerless competition (WLC) systems reveals a potential encoding mechanism that is translatable into two dimensional superdiffusive Lévy movements. The validity of our approach is tested on a conductance based neuronal model showing WLC and through the extraction of Lévy flights inducing fractals from recordings of rat hippocampus during open field foraging. Further insights are gained analyzing mice motor cortex neurons and non motor cell signals. The proposed mechanism provides a plausible explanation for the neuro-dynamical fundamentals of spatial searching patterns observed in animals (including humans) and illustrates an until now unknown way to encode information in neuronal temporal series. PMID:26648311

Interstate vibronic coupling constants between electronic excited states for complex molecules

NASA Astrophysics Data System (ADS)

Fumanal, Maria; Plasser, Felix; Mai, Sebastian; Daniel, Chantal; Gindensperger, Etienne

2018-03-01

In the construction of diabatic vibronic Hamiltonians for quantum dynamics in the excited-state manifold of molecules, the coupling constants are often extracted solely from information on the excited-state energies. Here, a new protocol is applied to get access to the interstate vibronic coupling constants at the time-dependent density functional theory level through the overlap integrals between excited-state adiabatic auxiliary wavefunctions. We discuss the advantages of such method and its potential for future applications to address complex systems, in particular, those where multiple electronic states are energetically closely lying and interact. We apply the protocol to the study of prototype rhenium carbonyl complexes [Re(CO)3(N,N)(L)]n+ for which non-adiabatic quantum dynamics within the linear vibronic coupling model and including spin-orbit coupling have been reported recently.

Research highlights: microfluidics meets big data.

PubMed

Tseng, Peter; Weaver, Westbrook M; Masaeli, Mahdokht; Owsley, Keegan; Di Carlo, Dino

2014-03-07

In this issue we highlight a collection of recent work in which microfluidic parallelization and automation have been employed to address the increasing need for large amounts of quantitative data concerning cellular function--from correlating microRNA levels to protein expression, increasing the throughput and reducing the noise when studying protein dynamics in single-cells, and understanding how signal dynamics encodes information. The painstaking dissection of cellular pathways one protein at a time appears to be coming to an end, leading to more rapid discoveries which will inevitably translate to better cellular control--in producing useful gene products and treating disease at the individual cell level. From these studies it is also clear that development of large scale mutant or fusion libraries, automation of microscopy, image analysis, and data extraction will be key components as microfluidics contributes its strengths to aid systems biology moving forward.

Extracting Useful Semantic Information from Large Scale Corpora of Text

ERIC Educational Resources Information Center

Mendoza, Ray Padilla, Jr.

2012-01-01

Extracting and representing semantic information from large scale corpora is at the crux of computer-assisted knowledge generation. Semantic information depends on collocation extraction methods, mathematical models used to represent distributional information, and weighting functions which transform the space. This dissertation provides a…

Combined non-parametric and parametric approach for identification of time-variant systems

NASA Astrophysics Data System (ADS)

Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz

2018-03-01

Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.

Capturing Revolute Motion and Revolute Joint Parameters with Optical Tracking

NASA Astrophysics Data System (ADS)

Antonya, C.

2017-12-01

Optical tracking of users and various technical systems are becoming more and more popular. It consists of analysing sequence of recorded images using video capturing devices and image processing algorithms. The returned data contains mainly point-clouds, coordinates of markers or coordinates of point of interest. These data can be used for retrieving information related to the geometry of the objects, but also to extract parameters for the analytical model of the system useful in a variety of computer aided engineering simulations. The parameter identification of joints deals with extraction of physical parameters (mainly geometric parameters) for the purpose of constructing accurate kinematic and dynamic models. The input data are the time-series of the marker’s position. The least square method was used for fitting the data into different geometrical shapes (ellipse, circle, plane) and for obtaining the position and orientation of revolute joins.

Performance of a high resolution cavity beam position monitor system

NASA Astrophysics Data System (ADS)

Walston, Sean; Boogert, Stewart; Chung, Carl; Fitsos, Pete; Frisch, Joe; Gronberg, Jeff; Hayano, Hitoshi; Honda, Yosuke; Kolomensky, Yury; Lyapin, Alexey; Malton, Stephen; May, Justin; McCormick, Douglas; Meller, Robert; Miller, David; Orimoto, Toyoko; Ross, Marc; Slater, Mark; Smith, Steve; Smith, Tonee; Terunuma, Nobuhiro; Thomson, Mark; Urakawa, Junji; Vogel, Vladimir; Ward, David; White, Glen

2007-07-01

It has been estimated that an RF cavity Beam Position Monitor (BPM) could provide a position measurement resolution of less than 1 nm. We have developed a high resolution cavity BPM and associated electronics. A triplet comprised of these BPMs was installed in the extraction line of the Accelerator Test Facility (ATF) at the High Energy Accelerator Research Organization (KEK) for testing with its ultra-low emittance beam. The three BPMs were each rigidly mounted inside an alignment frame on six variable-length struts which could be used to move the BPMs in position and angle. We have developed novel methods for extracting the position and tilt information from the BPM signals including a robust calibration algorithm which is immune to beam jitter. To date, we have demonstrated a position resolution of 15.6 nm and a tilt resolution of 2.1 μrad over a dynamic range of approximately ±20 μm.

Dynamics of acoustic-convective drying of sunflower cake

NASA Astrophysics Data System (ADS)

Zhilin, A. A.

2017-10-01

The dynamics of drying sunflower cake by a new acoustic-convective method has been studied. Unlike the conventional (thermal-convective) method, the proposed method allows moisture to be extracted from porous materials without applying heat to the sample to be dried. Kinetic curves of drying by the thermal-convective and acoustic-convective methods were obtained and analyzed. The advantages of the acoustic-convective extraction of moisture over the thermal-convective method are discussed. The relaxation times of drying were determined for both drying methods. An intermittent drying mode which improves the efficiency of acoustic-convective extraction of moisture is considered.

Model and Data Reduction for Control, Identification and Compressed Sensing

NASA Astrophysics Data System (ADS)

Kramer, Boris

This dissertation focuses on problems in design, optimization and control of complex, large-scale dynamical systems from different viewpoints. The goal is to develop new algorithms and methods, that solve real problems more efficiently, together with providing mathematical insight into the success of those methods. There are three main contributions in this dissertation. In Chapter 3, we provide a new method to solve large-scale algebraic Riccati equations, which arise in optimal control, filtering and model reduction. We present a projection based algorithm utilizing proper orthogonal decomposition, which is demonstrated to produce highly accurate solutions at low rank. The method is parallelizable, easy to implement for practitioners, and is a first step towards a matrix free approach to solve AREs. Numerical examples for n ≥ 106 unknowns are presented. In Chapter 4, we develop a system identification method which is motivated by tangential interpolation. This addresses the challenge of fitting linear time invariant systems to input-output responses of complex dynamics, where the number of inputs and outputs is relatively large. The method reduces the computational burden imposed by a full singular value decomposition, by carefully choosing directions on which to project the impulse response prior to assembly of the Hankel matrix. The identification and model reduction step follows from the eigensystem realization algorithm. We present three numerical examples, a mass spring damper system, a heat transfer problem, and a fluid dynamics system. We obtain error bounds and stability results for this method. Chapter 5 deals with control and observation design for parameter dependent dynamical systems. We address this by using local parametric reduced order models, which can be used online. Data available from simulations of the system at various configurations (parameters, boundary conditions) is used to extract a sparse basis to represent the dynamics (via dynamic mode decomposition). Subsequently, a new, compressed sensing based classification algorithm is developed which incorporates the extracted dynamic information into the sensing basis. We show that this augmented classification basis makes the method more robust to noise, and results in superior identification of the correct parameter. Numerical examples consist of a Navier-Stokes, as well as a Boussinesq flow application.

Cosmology from group field theory formalism for quantum gravity.

PubMed

Gielen, Steffen; Oriti, Daniele; Sindoni, Lorenzo

2013-07-19

We identify a class of condensate states in the group field theory (GFT) formulation of quantum gravity that can be interpreted as macroscopic homogeneous spatial geometries. We then extract the dynamics of such condensate states directly from the fundamental quantum GFT dynamics, following the procedure used in ordinary quantum fluids. The effective dynamics is a nonlinear and nonlocal extension of quantum cosmology. We also show that any GFT model with a kinetic term of Laplacian type gives rise, in a semiclassical (WKB) approximation and in the isotropic case, to a modified Friedmann equation. This is the first concrete, general procedure for extracting an effective cosmological dynamics directly from a fundamental theory of quantum geometry.

Modelling dental implant extraction by pullout and torque procedures.

PubMed

Rittel, D; Dorogoy, A; Shemtov-Yona, K

2017-07-01

Dental implants extraction, achieved either by applying torque or pullout force, is used to estimate the bone-implant interfacial strength. A detailed description of the mechanical and physical aspects of the extraction process in the literature is still missing. This paper presents 3D nonlinear dynamic finite element simulations of a commercial implant extraction process from the mandible bone. Emphasis is put on the typical load-displacement and torque-angle relationships for various types of cortical and trabecular bone strengths. The simulations also study of the influence of the osseointegration level on those relationships. This is done by simulating implant extraction right after insertion when interfacial frictional contact exists between the implant and bone, and long after insertion, assuming that the implant is fully bonded to the bone. The model does not include a separate representation and model of the interfacial layer for which available data is limited. The obtained relationships show that the higher the strength of the trabecular bone the higher the peak extraction force, while for application of torque, it is the cortical bone which might dictate the peak torque value. Information on the relative strength contrast of the cortical and trabecular components, as well as the progressive nature of the damage evolution, can be revealed from the obtained relations. It is shown that full osseointegration might multiply the peak and average load values by a factor 3-12 although the calculated work of extraction varies only by a factor of 1.5. From a quantitative point of view, it is suggested that, as an alternative to reporting peak load or torque values, an average value derived from the extraction work be used to better characterize the bone-implant interfacial strength. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computational consciousness: building a self-preserving organism.

PubMed

Barros, Allan Kardec

2010-01-01

Consciousness has been a subject of crescent interest among the neuroscience community. However, building machine models of it is quite challenging, as it involves many characteristics and properties of the human brain which are poorly defined or are very abstract. Here I propose to use information theory (IT) to give a mathematical framework to understand consciousness. For this reason, I used the term "computational". This work is grounded on some recent results on the use of IT to understand how the cortex codes information, where redundancy reduction plays a fundamental role. Basically, I propose a system, here called "organism", whose strategy is to extract the maximal amount of information from the environment in order to survive. To highlight the proposed framework, I show a simple organism composed of a single neuron which adapts itself to the outside dynamics by taking into account its internal state, whose perception is understood here to be related to "feelings".

Probing hydrogen positions in hydrous compounds: information from parametric neutron powder diffraction studies.

PubMed

Ting, Valeska P; Henry, Paul F; Schmidtmann, Marc; Wilson, Chick C; Weller, Mark T

2012-05-21

We demonstrate the extent to which modern detector technology, coupled with a high flux constant wavelength neutron source, can be used to obtain high quality diffraction data from short data collections, allowing the refinement of the full structures (including hydrogen positions) of hydrous compounds from in situ neutron powder diffraction measurements. The in situ thermodiffractometry and controlled humidity studies reported here reveal that important information on the reorientations of structural water molecules with changing conditions can be easily extracted, providing insight into the effects of hydrogen bonding on bulk physical properties. Using crystalline BaCl2·2H2O as an example system, we analyse the structural changes in the compound and its dehydration intermediates with changing temperature and humidity levels to demonstrate the quality of the dynamic structural information on the hydrogen atoms and associated hydrogen bonding that can be obtained without resorting to sample deuteration.

Comparative Analysis of Volcanic Inflation—Deflation Cycles

NASA Astrophysics Data System (ADS)

Walwer, D.; Ghil, M.; Calais, E.

2016-12-01

GPS geodetic data together with INSAR images are often used to formulate kinematic models of the sources of volcanic deformations. The increasing amount of data now available allows one to produce time series that are several years long and thus capture continuously the history of volcanic deformations, in particular their nonlinear behavior. This information is highly valuable in helping understand the dynamics of volcanic systems.Nonlinear deformation signals are, however, difficult to extract from the background noise inherent in the GPS time series. It is also arduous to unravel the signal of interest from other nonlinear signals, such as the seasonal oscillations associated with mass variations in the atmosphere, the ocean, and the hydrological reservoirs. Here we use Multichannel Singular Spectrum Analysis (M-SSA) — an advanced, data-adaptive method for time series analysis that exploits simultaneously the temporal and spatial correlations of geophysical fields — to extract such deformation signals.We apply M-SSA to GPS data sets from four volcanoes: Akutan, Alaska; Okmok, Alaska; Westdahl, Alaska; and Piton de la Fournaise, La Reunion. Our analyses show that all four volcanoes share similar features in their deformation history, suggesting similarities in the dynamics that generate the inflation-deflation cycles. In particular, all four volcanic systems exhibit sawtooth-shaped oscillations with slow inflations followed by slower deflations, with time scales that vary from 6 months to 4 years. This relation of dynamical similarity is further highlighted by the phase portrait reconstruction of the four systems in the plane of deformation vs. rate-of-deformation, as obtained from the deformation signals extracted from the GPS time series using M-SSA.The inflating phase of these oscillations is followed by eruptions at Okmok volcano and at Piton de la Fournaise. These analysis results suggest that these volcanic inflation—deflation cycles are associated with the destabilization of a volcanic system and may lead to the identification of premonitory signals for an eruptive regime.

a Web-Based Platform for Visualizing Spatiotemporal Dynamics of Big Taxi Data

NASA Astrophysics Data System (ADS)

Xiong, H.; Chen, L.; Gui, Z.

2017-09-01

With more and more vehicles equipped with Global Positioning System (GPS), access to large-scale taxi trajectory data has become increasingly easy. Taxis are valuable sensors and information associated with taxi trajectory can provide unprecedented insight into many aspects of city life. But analysing these data presents many challenges. Visualization of taxi data is an efficient way to represent its distributions and structures and reveal hidden patterns in the data. However, Most of the existing visualization systems have some shortcomings. On the one hand, the passenger loading status and speed information cannot be expressed. On the other hand, mono-visualization form limits the information presentation. In view of these problems, this paper designs and implements a visualization system in which we use colour and shape to indicate passenger loading status and speed information and integrate various forms of taxi visualization. The main work as follows: 1. Pre-processing and storing the taxi data into MongoDB database. 2. Visualization of hotspots for taxi pickup points. Through DBSCAN clustering algorithm, we cluster the extracted taxi passenger's pickup locations to produce passenger hotspots. 3. Visualizing the dynamic of taxi moving trajectory using interactive animation. We use a thinning algorithm to reduce the amount of data and design a preloading strategyto load the data smoothly. Colour and shape are used to visualize the taxi trajectory data.

On the interpretation of continuous wave electron spin resonance spectra of tempo-palmitate in 5-cyanobiphenyl.

PubMed

Zerbetto, Mirco; Polimeno, Antonino; Cimino, Paola; Barone, Vincenzo

2008-01-14

Electron spin resonance (ESR) measurements are highly informative on the dynamic behavior of molecules, which is of fundamental importance to understand their stability, biological functions and activities, and catalytic action. The wealth of dynamic information which can be extracted from a continuous wave electron spin resonance (cw-ESR) spectrum can be inferred by a basic theoretical approach defined within the stochastic Liouville equation formalism, i.e., the direct inclusion of motional dynamics in the form of stochastic (Fokker-Planck/diffusive) operators in the super Hamiltonian H governing the time evolution of the system. Modeling requires the characterization of magnetic parameters (e.g., hyperfine and Zeeman tensors) and the calculation of ESR observables in terms of spectral densities. The magnetic observables can be pursued by the employment of density functional theory which is apt, provided that hybrid functionals are employed, for the accurate computation of structural properties of molecular systems. Recently, an ab initio integrated computational approach to the in silico interpretation of cw-ESR spectra of multilabeled systems in isotropic fluids has been discussed. In this work we present the extension to the case of nematic liquid crystalline environments by performing simulations of the ESR spectra of the prototypical nitroxide probe 4-(hexadecanoyloxy)-2,2,6,6-tetramethylpiperidine-1-oxy in isotropic and nematic phases of 5-cyanobiphenyl. We first discuss the basic ingredients of the integrated approach, i.e., (1) determination of geometric and local magnetic parameters by quantum-mechanical calculations, taking into account the solvent and, when needed, the vibrational averaging contributions; (2) numerical solution of a stochastic Liouville equation in the presence of diffusive rotational dynamics, based on (3) parameterization of diffusion rotational tensor provided by a hydrodynamic model. Next we present simulated spectra with minimal resorting to fitting procedures, proving that the combination of sensitive ESR spectroscopy and sophisticated modeling can be highly helpful in providing three-dimensional structural and dynamic information on molecular systems in anisotropic environments.

Sol-gel titania-coated needles for solid phase dynamic extraction-GC/MS analysis of desomorphine and desocodeine.

PubMed

Su, Chi-Ju; Srimurugan, Sankarewaran; Chen, Chinpiao; Shu, Hun-Chi

2011-01-01

Novel sol-gel titania film coated needles for solid-phase dynamic extraction (SPDE)-GC/MS analysis of desomorphine and desocodeine are described. The high thermal stability of titania film permits efficient extraction and analysis of poorly volatile opiate drugs. The influences of sol-gel reaction time, coating layer, extraction and desorption time and temperature on the SPDE needle performance were investigated. The deuterium labeled internal standard was introduced either during the extraction of analyte or directly injected to GC after the extraction process. The latter method was shown to be more sensitive for the analysis of water and urine samples containing opiate drugs. The proposed conditions provided a wide linear range (from 5-5000 ppb), and satisfactory linearity, with R(2) values from 0.9958 to 0.9999, and prominent sensitivity, LOQs (1.0-5.0 ng/g). The sol-gel titania film coated needle with SPDE-GC/MS will be a promising technique for desomorphine and desocodeine analysis in urine.

Extraction of Blebs in Human Embryonic Stem Cell Videos.

PubMed

Guan, Benjamin X; Bhanu, Bir; Talbot, Prue; Weng, Nikki Jo-Hao

2016-01-01

Blebbing is an important biological indicator in determining the health of human embryonic stem cells (hESC). Especially, areas of a bleb sequence in a video are often used to distinguish two cell blebbing behaviors in hESC: dynamic and apoptotic blebbings. This paper analyzes various segmentation methods for bleb extraction in hESC videos and introduces a bio-inspired score function to improve the performance in bleb extraction. Full bleb formation consists of bleb expansion and retraction. Blebs change their size and image properties dynamically in both processes and between frames. Therefore, adaptive parameters are needed for each segmentation method. A score function derived from the change of bleb area and orientation between consecutive frames is proposed which provides adaptive parameters for bleb extraction in videos. In comparison to manual analysis, the proposed method provides an automated fast and accurate approach for bleb sequence extraction.

Multiaperture ion beam extraction from gas-dynamic electron cyclotron resonance source of multicharged ions

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

Sidorov, A.; Dorf, M.; Zorin, V.

2008-02-15

Electron cyclotron resonance ion source with quasi-gas-dynamic regime of plasma confinement (ReGIS), constructed at the Institute of Applied Physics, Russia, provides opportunities for extracting intense and high-brightness multicharged ion beams. Despite the short plasma lifetime in a magnetic trap of a ReGIS, the degree of multiple ionization may be significantly enhanced by the increase in power and frequency of the applied microwave radiation. The present work is focused on studying the intense beam quality of this source by the pepper-pot method. A single beamlet emittance measured by the pepper-pot method was found to be {approx}70 {pi} mm mrad, and themore » total extracted beam current obtained at 14 kV extraction voltage was {approx}25 mA. The results of the numerical simulations of ion beam extraction are found to be in good agreement with experimental data.« less

Method for Face-Emotion Retrieval Using A Cartoon Emotional Expression Approach

NASA Astrophysics Data System (ADS)

Kostov, Vlaho; Yanagisawa, Hideyoshi; Johansson, Martin; Fukuda, Shuichi

A simple method for extracting emotion from a human face, as a form of non-verbal communication, was developed to cope with and optimize mobile communication in a globalized and diversified society. A cartoon face based model was developed and used to evaluate emotional content of real faces. After a pilot survey, basic rules were defined and student subjects were asked to express emotion using the cartoon face. Their face samples were then analyzed using principal component analysis and the Mahalanobis distance method. Feature parameters considered as having relations with emotions were extracted and new cartoon faces (based on these parameters) were generated. The subjects evaluated emotion of these cartoon faces again and we confirmed these parameters were suitable. To confirm how these parameters could be applied to real faces, we asked subjects to express the same emotions which were then captured electronically. Simple image processing techniques were also developed to extract these features from real faces and we then compared them with the cartoon face parameters. It is demonstrated via the cartoon face that we are able to express the emotions from very small amounts of information. As a result, real and cartoon faces correspond to each other. It is also shown that emotion could be extracted from still and dynamic real face images using these cartoon-based features.

A extract method of mountainous area settlement place information from GF-1 high resolution optical remote sensing image under semantic constraints

NASA Astrophysics Data System (ADS)

Guo, H., II

2016-12-01

Spatial distribution information of mountainous area settlement place is of great significance to the earthquake emergency work because most of the key earthquake hazardous areas of china are located in the mountainous area. Remote sensing has the advantages of large coverage and low cost, it is an important way to obtain the spatial distribution information of mountainous area settlement place. At present, fully considering the geometric information, spectral information and texture information, most studies have applied object-oriented methods to extract settlement place information, In this article, semantic constraints is to be added on the basis of object-oriented methods. The experimental data is one scene remote sensing image of domestic high resolution satellite (simply as GF-1), with a resolution of 2 meters. The main processing consists of 3 steps, the first is pretreatment, including ortho rectification and image fusion, the second is Object oriented information extraction, including Image segmentation and information extraction, the last step is removing the error elements under semantic constraints, in order to formulate these semantic constraints, the distribution characteristics of mountainous area settlement place must be analyzed and the spatial logic relation between settlement place and other objects must be considered. The extraction accuracy calculation result shows that the extraction accuracy of object oriented method is 49% and rise up to 86% after the use of semantic constraints. As can be seen from the extraction accuracy, the extract method under semantic constraints can effectively improve the accuracy of mountainous area settlement place information extraction. The result shows that it is feasible to extract mountainous area settlement place information form GF-1 image, so the article proves that it has a certain practicality to use domestic high resolution optical remote sensing image in earthquake emergency preparedness.

Automatic flow-through dynamic extraction: A fast tool to evaluate char-based remediation of multi-element contaminated mine soils.

PubMed

Rosende, María; Beesley, Luke; Moreno-Jimenez, Eduardo; Miró, Manuel

2016-02-01

An automatic in-vitro bioaccessibility test based upon dynamic microcolumn extraction in a programmable flow setup is herein proposed as a screening tool to evaluate bio-char based remediation of mine soils contaminated with trace elements as a compelling alternative to conventional phyto-availability tests. The feasibility of the proposed system was evaluated by extracting the readily bioaccessible pools of As, Pb and Zn in two contaminated mine soils before and after the addition of two biochars (9% (w:w)) of diverse source origin (pine and olive). Bioaccessible fractions under worst-case scenarios were measured using 0.001 mol L(-1) CaCl2 as extractant for mimicking plant uptake, and analysis of the extracts by inductively coupled optical emission spectrometry. The t-test of comparison of means revealed an efficient metal (mostly Pb and Zn) immobilization by the action of olive pruning-based biochar against the bare (control) soil at the 0.05 significance level. In-vitro flow-through bioaccessibility tests are compared for the first time with in-vivo phyto-toxicity assays in a microcosm soil study. By assessing seed germination and shoot elongation of Lolium perenne in contaminated soils with and without biochar amendments the dynamic flow-based bioaccessibility data proved to be in good agreement with the phyto-availability tests. Experimental results indicate that the dynamic extraction method is a viable and economical in-vitro tool in risk assessment explorations to evaluate the feasibility of a given biochar amendment for revegetation and remediation of metal contaminated soils in a mere 10 min against 4 days in case of phyto-toxicity assays. Copyright © 2015 Elsevier B.V. All rights reserved.

Hierarchic spatio-temporal dynamics in glycolysis

NASA Astrophysics Data System (ADS)

Shinjyo, Takahiro; Nakagawa, Yoshiyuki; Ueda, Tetsuo

Yeast extracts exhibit oscillations when the glycolytic system is far away from equilibrium. Spatio-temporal dynamics in this system was studied in the newly developed gel as well as in the solution. Small regions (about 10 um) with very complex shape with high or low concentrations of NADH appeared, and upon these small structures large-scale dynamics were superimposed. Concentration waves propagated, and the source of wave was induced by contact with high ADP. Sink of waves was generated by contacting the reaction gel to two small gels rich in ADP. Upon these spatio-temporal dynamics were superimposed much slower global oscillations throughout the system with a period of about 40 min. Similar dynamics was seen in a solution of yeast extract, but the size of domains was about ten times larger than that in the gel. In this way, the multi-enzyme system of glycolysis exhibits self-organization of hierarchy in spatio-temporal dynamics.

Storage Thresholds for Relative Sea Level Signals in the Stratigraphic Record

NASA Astrophysics Data System (ADS)

Li, Q.; Yu, L.; Straub, K. M.

2015-12-01

Many argue that the tug of Relative Sea Level (RSL) change represents the most important allogenic forcing affecting deltas and is the primary control on stratigraphic architecture of deltas. However, the range of amplitudes and periodicities of RSL cycles stored in stratigraphy remains unknown. Here we use a suite of physical experiments to show that RSL cycles with magnitudes and periodicities less than the spatial and temporal scales of deltaic internal (autogenic) dynamics cannot confidently be extracted from the physical stratigraphic record. Additional analysis of deltaic morphodynamics also suggest no significant differences between an experiment with constant boundary conditions (control experiment) and an experiment with small magnitude and short periodicity RSL cycles, relative to the autogenic dynamics. Significant differences in the aspect ratio of channel bodies and deposit sand fractions do exists between our control experiment and those experiments with either large magnitudes or long periodicities RSL cycles. Using a compilation of data from major river delta systems, we show that our predicted thresholds for RSL signal storage often overlap with the magnitudes and periodicities of commonly discussed drivers of global sea level. This theory defines quantitative limits on the range of paleo-RSL information that can be extracted from the stratigraphic record, which could aid stratigraphic prediction and the inversion of stratigraphy for paleo- deltaic response to climate change.

Automatic Human Movement Assessment With Switching Linear Dynamic System: Motion Segmentation and Motor Performance.

PubMed

de Souza Baptista, Roberto; Bo, Antonio P L; Hayashibe, Mitsuhiro

2017-06-01

Performance assessment of human movement is critical in diagnosis and motor-control rehabilitation. Recent developments in portable sensor technology enable clinicians to measure spatiotemporal aspects to aid in the neurological assessment. However, the extraction of quantitative information from such measurements is usually done manually through visual inspection. This paper presents a novel framework for automatic human movement assessment that executes segmentation and motor performance parameter extraction in time-series of measurements from a sequence of human movements. We use the elements of a Switching Linear Dynamic System model as building blocks to translate formal definitions and procedures from human movement analysis. Our approach provides a method for users with no expertise in signal processing to create models for movements using labeled dataset and later use it for automatic assessment. We validated our framework on preliminary tests involving six healthy adult subjects that executed common movements in functional tests and rehabilitation exercise sessions, such as sit-to-stand and lateral elevation of the arms and five elderly subjects, two of which with limited mobility, that executed the sit-to-stand movement. The proposed method worked on random motion sequences for the dual purpose of movement segmentation (accuracy of 72%-100%) and motor performance assessment (mean error of 0%-12%).

Diffusing-wave spectroscopy in a standard dynamic light scattering setup

NASA Astrophysics Data System (ADS)

Fahimi, Zahra; Aangenendt, Frank J.; Voudouris, Panayiotis; Mattsson, Johan; Wyss, Hans M.

2017-12-01

Diffusing-wave spectroscopy (DWS) extends dynamic light scattering measurements to samples with strong multiple scattering. DWS treats the transport of photons through turbid samples as a diffusion process, thereby making it possible to extract the dynamics of scatterers from measured correlation functions. The analysis of DWS data requires knowledge of the path length distribution of photons traveling through the sample. While for flat sample cells this path length distribution can be readily calculated and expressed in analytical form; no such expression is available for cylindrical sample cells. DWS measurements have therefore typically relied on dedicated setups that use flat sample cells. Here we show how DWS measurements, in particular DWS-based microrheology measurements, can be performed in standard dynamic light scattering setups that use cylindrical sample cells. To do so we perform simple random-walk simulations that yield numerical predictions of the path length distribution as a function of both the transport mean free path and the detection angle. This information is used in experiments to extract the mean-square displacement of tracer particles in the material, as well as the corresponding frequency-dependent viscoelastic response. An important advantage of our approach is that by performing measurements at different detection angles, the average path length through the sample can be varied. For measurements performed on a single sample cell, this gives access to a wider range of length and time scales than obtained in a conventional DWS setup. Such angle-dependent measurements also offer an important consistency check, as for all detection angles the DWS analysis should yield the same tracer dynamics, even though the respective path length distributions are very different. We validate our approach by performing measurements both on aqueous suspensions of tracer particles and on solidlike gelatin samples, for which we find our DWS-based microrheology data to be in good agreement with rheological measurements performed on the same samples.

[Study on condition for extraction of arctiin from fruits of Arctium lappa using supercritical fluid extraction].

PubMed

Dong, Wen-hong; Liu, Ben

2006-08-01

To study the feasibility of supercritical fluid extraction (SFE) for arctiin from the fruits of Arctium lappa. The extracts were analyzed by HPLC, optimum extraction conditions were studied by orthogonal tests. The optimal extraction conditions were: pressure 40 MPa, temperature 70 degrees C, using methanol as modifier carrier at the rate of 0.55 mL x min(-1), static extraction time 5 min, dynamic extraction 30 min, flow rate of CO2 2 L x min(-1). SFE has the superiority of adjustable polarity, and has the ability of extracting arctiin.

Supported Lipid Bilayer/Carbon Nanotube Hybrids

NASA Astrophysics Data System (ADS)

Zhou, Xinjian; Moran-Mirabal, Jose; Craighead, Harold; McEuen, Paul

2007-03-01

We form supported lipid bilayers on single-walled carbon nanotubes and use this hybrid structure to probe the properties of lipid membranes and their functional constituents. We first demonstrate membrane continuity and lipid diffusion over the nanotube. A membrane-bound tetanus toxin protein, on the other hand, sees the nanotube as a diffusion barrier whose strength depends on the diameter of the nanotube. Finally, we present results on the electrical detection of specific binding of streptavidin to biotinylated lipids with nanotube field effect transistors. Possible techniques to extract dynamic information about the protein binding events will also be discussed.

Joint principal trend analysis for longitudinal high-dimensional data.

PubMed

Zhang, Yuping; Ouyang, Zhengqing

2018-06-01

We consider a research scenario motivated by integrating multiple sources of information for better knowledge discovery in diverse dynamic biological processes. Given two longitudinal high-dimensional datasets for a group of subjects, we want to extract shared latent trends and identify relevant features. To solve this problem, we present a new statistical method named as joint principal trend analysis (JPTA). We demonstrate the utility of JPTA through simulations and applications to gene expression data of the mammalian cell cycle and longitudinal transcriptional profiling data in response to influenza viral infections. © 2017, The International Biometric Society.

Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information

DOEpatents

Hively, Lee M.

2014-09-16

Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By monitoring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (unstructured data) into discrete-phase-space states, and hence into a graph (structured data) for extraction of condition change.

Non-invasive estimation of dissipation from non-equilibrium fluctuations in chemical reactions.

PubMed

Muy, S; Kundu, A; Lacoste, D

2013-09-28

We show how to extract an estimate of the entropy production from a sufficiently long time series of stationary fluctuations of chemical reactions. This method, which is based on recent work on fluctuation theorems, is direct, non-invasive, does not require any knowledge about the underlying dynamics and is applicable even when only partial information is available. We apply it to simple stochastic models of chemical reactions involving a finite number of states, and for this case, we study how the estimate of dissipation is affected by the degree of coarse-graining present in the input data.

Parameter as a Switch Between Dynamical States of a Network in Population Decoding.

PubMed

Yu, Jiali; Mao, Hua; Yi, Zhang

2017-04-01

Population coding is a method to represent stimuli using the collective activities of a number of neurons. Nevertheless, it is difficult to extract information from these population codes with the noise inherent in neuronal responses. Moreover, it is a challenge to identify the right parameter of the decoding model, which plays a key role for convergence. To address the problem, a population decoding model is proposed for parameter selection. Our method successfully identified the key conditions for a nonzero continuous attractor. Both the theoretical analysis and the application studies demonstrate the correctness and effectiveness of this strategy.

Automated video-based assessment of surgical skills for training and evaluation in medical schools.

PubMed

Zia, Aneeq; Sharma, Yachna; Bettadapura, Vinay; Sarin, Eric L; Ploetz, Thomas; Clements, Mark A; Essa, Irfan

2016-09-01

Routine evaluation of basic surgical skills in medical schools requires considerable time and effort from supervising faculty. For each surgical trainee, a supervisor has to observe the trainees in person. Alternatively, supervisors may use training videos, which reduces some of the logistical overhead. All these approaches however are still incredibly time consuming and involve human bias. In this paper, we present an automated system for surgical skills assessment by analyzing video data of surgical activities. We compare different techniques for video-based surgical skill evaluation. We use techniques that capture the motion information at a coarser granularity using symbols or words, extract motion dynamics using textural patterns in a frame kernel matrix, and analyze fine-grained motion information using frequency analysis. We were successfully able to classify surgeons into different skill levels with high accuracy. Our results indicate that fine-grained analysis of motion dynamics via frequency analysis is most effective in capturing the skill relevant information in surgical videos. Our evaluations show that frequency features perform better than motion texture features, which in-turn perform better than symbol-/word-based features. Put succinctly, skill classification accuracy is positively correlated with motion granularity as demonstrated by our results on two challenging video datasets.

Kinetic quantitation of cerebral PET-FDG studies without concurrent blood sampling: statistical recovery of the arterial input function.

PubMed

O'Sullivan, F; Kirrane, J; Muzi, M; O'Sullivan, J N; Spence, A M; Mankoff, D A; Krohn, K A

2010-03-01

Kinetic quantitation of dynamic positron emission tomography (PET) studies via compartmental modeling usually requires the time-course of the radio-tracer concentration in the arterial blood as an arterial input function (AIF). For human and animal imaging applications, significant practical difficulties are associated with direct arterial sampling and as a result there is substantial interest in alternative methods that require no blood sampling at the time of the study. A fixed population template input function derived from prior experience with directly sampled arterial curves is one possibility. Image-based extraction, including requisite adjustment for spillover and recovery, is another approach. The present work considers a hybrid statistical approach based on a penalty formulation in which the information derived from a priori studies is combined in a Bayesian manner with information contained in the sampled image data in order to obtain an input function estimate. The absolute scaling of the input is achieved by an empirical calibration equation involving the injected dose together with the subject's weight, height and gender. The technique is illustrated in the context of (18)F -Fluorodeoxyglucose (FDG) PET studies in humans. A collection of 79 arterially sampled FDG blood curves are used as a basis for a priori characterization of input function variability, including scaling characteristics. Data from a series of 12 dynamic cerebral FDG PET studies in normal subjects are used to evaluate the performance of the penalty-based AIF estimation technique. The focus of evaluations is on quantitation of FDG kinetics over a set of 10 regional brain structures. As well as the new method, a fixed population template AIF and a direct AIF estimate based on segmentation are also considered. Kinetics analyses resulting from these three AIFs are compared with those resulting from radially sampled AIFs. The proposed penalty-based AIF extraction method is found to achieve significant improvements over the fixed template and the segmentation methods. As well as achieving acceptable kinetic parameter accuracy, the quality of fit of the region of interest (ROI) time-course data based on the extracted AIF, matches results based on arterially sampled AIFs. In comparison, significant deviation in the estimation of FDG flux and degradation in ROI data fit are found with the template and segmentation methods. The proposed AIF extraction method is recommended for practical use.

Autism, Context/Noncontext Information Processing, and Atypical Development

PubMed Central

Skoyles, John R.

2011-01-01

Autism has been attributed to a deficit in contextual information processing. Attempts to understand autism in terms of such a defect, however, do not include more recent computational work upon context. This work has identified that context information processing depends upon the extraction and use of the information hidden in higher-order (or indirect) associations. Higher-order associations underlie the cognition of context rather than that of situations. This paper starts by examining the differences between higher-order and first-order (or direct) associations. Higher-order associations link entities not directly (as with first-order ones) but indirectly through all the connections they have via other entities. Extracting this information requires the processing of past episodes as a totality. As a result, this extraction depends upon specialised extraction processes separate from cognition. This information is then consolidated. Due to this difference, the extraction/consolidation of higher-order information can be impaired whilst cognition remains intact. Although not directly impaired, cognition will be indirectly impaired by knock on effects such as cognition compensating for absent higher-order information with information extracted from first-order associations. This paper discusses the implications of this for the inflexible, literal/immediate, and inappropriate information processing of autistic individuals. PMID:22937255

Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation

PubMed Central

2015-01-01

This paper presents a data-driven multiscale entropy measure to reveal the scale dependent information quantity of electroencephalogram (EEG) recordings. This work is motivated by the previous observations on the nonlinear and nonstationary nature of EEG over multiple time scales. Here, a new framework of entropy measures considering changing dynamics over multiple oscillatory scales is presented. First, to deal with nonstationarity over multiple scales, EEG recording is decomposed by applying the empirical mode decomposition (EMD) which is known to be effective for extracting the constituent narrowband components without a predetermined basis. Following calculation of Renyi entropy of the probability distributions of the intrinsic mode functions extracted by EMD leads to a data-driven multiscale Renyi entropy. To validate the performance of the proposed entropy measure, actual EEG recordings from rats (n = 9) experiencing 7 min cardiac arrest followed by resuscitation were analyzed. Simulation and experimental results demonstrate that the use of the multiscale Renyi entropy leads to better discriminative capability of the injury levels and improved correlations with the neurological deficit evaluation after 72 hours after cardiac arrest, thus suggesting an effective diagnostic and prognostic tool. PMID:26380297

Analyzing research trends on drug safety using topic modeling.

PubMed

Zou, Chen

2018-06-01

Published drug safety data has evolved in the past decade due to scientific and technological advances in the relevant research fields. Considering that a vast amount of scientific literature has been published in this area, it is not easy to identify the key information. Topic modeling has emerged as a powerful tool to extract meaningful information from a large volume of unstructured texts. Areas covered: We analyzed the titles and abstracts of 4347 articles in four journals dedicated to drug safety from 2007 to 2016. We applied Latent Dirichlet allocation (LDA) model to extract 50 main topics, and conducted trend analysis to explore the temporal popularity of these topics over years. Expert Opinion/Commentary: We found that 'benefit-risk assessment and communication', 'diabetes' and 'biologic therapy for autoimmune diseases' are the top 3 most published topics. The topics relevant to the use of electronic health records/observational data for safety surveillance are becoming increasingly popular over time. Meanwhile, there is a slight decrease in research on signal detection based on spontaneous reporting, although spontaneous reporting still plays an important role in benefit-risk assessment. The topics related to medical conditions and treatment showed highly dynamic patterns over time.

Defining photon channels in strong-field physics: the photon-phase Fourier representation

NASA Astrophysics Data System (ADS)

Zeng, Shuo; Zohrabi, Mohammad; Berry, Ben; Ablikim, Utuq; Kling, Nora; Severt, Travis; Jochim, Bethany; Carnes, Kevin; Ben-Itzhak, Itzik; Esry, Brett

2014-05-01

In strong-field physics, complex atomic and molecular dynamics can be steered by the carrier-envelope phase (CEP). The general theory formulated in Refs., provides a rigorous foundation upon which this understanding might be built. By recognizing the underlying periodicity of the time-dependent Schrödinger equation--and thus its solutions--in the CEP, all CEP effects can be understood as the interference of different photon channels. We will show that this understanding can be turned around to extract information on the photon channel by examining the CEP dependence. In particular, by taking the Fourier transform with respect to the CEP, photon channel information can be extracted from both theory and experiment. Through several examples, we will also show that this technique can be applied to any system and provides knowledge of the net numbers of photons absorbed--even in few-cycle pulses--that is not available in any other way. This work was supported by the Chemical Sciences, Geosciences, and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U.S. Department of Energy under Grant No. DE-FG02-86ER13491. The PULSAR laser was provided by Grant No. DE-FG02-09.

Extraction-Separation Performance and Dynamic Modeling of Orion Test Vehicles with Adams Simulation: 3rd Edition

NASA Technical Reports Server (NTRS)

Varela, Jose G.; Reddy, Satish; Moeller, Enrique; Anderson, Keith

2017-01-01

NASA's Orion Capsule Parachute Assembly System (CPAS) Project is now in the qualification phase of testing, and the Adams simulation has continued to evolve to model the complex dynamics experienced during the test article extraction and separation phases of flight. The ability to initiate tests near the upper altitude limit of the Orion parachute deployment envelope requires extractions from the aircraft at 35,000 ft-MSL. Engineering development phase testing of the Parachute Test Vehicle (PTV) carried by the Carriage Platform Separation System (CPSS) at altitude resulted in test support equipment hardware failures due to increased energy caused by higher true airspeeds. As a result, hardware modifications became a necessity requiring ground static testing of the textile components to be conducted and a new ground dynamic test of the extraction system to be devised. Force-displacement curves from static tests were incorporated into the Adams simulations, allowing prediction of loads, velocities and margins encountered during both flight and ground dynamic tests. The Adams simulation was then further refined by fine tuning the damping terms to match the peak loads recorded in the ground dynamic tests. The failure observed in flight testing was successfully replicated in ground testing and true safety margins of the textile components were revealed. A multi-loop energy modulator was then incorporated into the system level Adams simulation model and the effect on improving test margins be properly evaluated leading to high confidence ground verification testing of the final design solution.

Reconstructing biochemical pathways from time course data.

PubMed

Srividhya, Jeyaraman; Crampin, Edmund J; McSharry, Patrick E; Schnell, Santiago

2007-03-01

Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.

What Can Interfacial Water Molecules Tell Us About Solute Structure?

NASA Astrophysics Data System (ADS)

Willard, Adam

The molecular structure of bulk liquid water reflects a molecular tendency to engage in tetrahedrally coordinated hydrogen bonding. At a solute interface waters preferred three-dimensional hydrogen bonding network must conform to a locally anisotropy interfacial environment. Interfacial water molecules adopt configurations that balance water-solute and water-water interactions. The arrangements of interfacial water molecules, therefore encode information about the effective solute-water interactions. This solute-specific information is difficult to extract, however, because interfacial structure also reflects waters collective response to an anisotropic hydrogen bonding environment. Here I present a methodology for characterizing the molecular-level structure of liquid water interface from simulation data. This method can be used to explore waters static and/or dynamic response to a wide range of chemically and topologically heterogeneous solutes such as proteins.

Adaptive Correlation Model for Visual Tracking Using Keypoints Matching and Deep Convolutional Feature.

PubMed

Li, Yuankun; Xu, Tingfa; Deng, Honggao; Shi, Guokai; Guo, Jie

2018-02-23

Although correlation filter (CF)-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved. When the target object goes through long-term occlusions or scale variation, the correlation model used in existing CF-based algorithms will inevitably learn some non-target information or partial-target information. In order to avoid model contamination and enhance the adaptability of model updating, we introduce the keypoints matching strategy and adjust the model learning rate dynamically according to the matching score. Moreover, the proposed approach extracts convolutional features from a deep convolutional neural network (DCNN) to accurately estimate the position and scale of the target. Experimental results demonstrate that the proposed tracker has achieved satisfactory performance in a wide range of challenging tracking scenarios.

Identifying the Critical Time Period for Information Extraction when Recognizing Sequences of Play

ERIC Educational Resources Information Center

North, Jamie S.; Williams, A. Mark

2008-01-01

The authors attempted to determine the critical time period for information extraction when recognizing play sequences in soccer. Although efforts have been made to identify the perceptual information underpinning such decisions, no researchers have attempted to determine "when" this information may be extracted from the display. The authors…

Can we replace curation with information extraction software?

PubMed

Karp, Peter D

2016-01-01

Can we use programs for automated or semi-automated information extraction from scientific texts as practical alternatives to professional curation? I show that error rates of current information extraction programs are too high to replace professional curation today. Furthermore, current IEP programs extract single narrow slivers of information, such as individual protein interactions; they cannot extract the large breadth of information extracted by professional curators for databases such as EcoCyc. They also cannot arbitrate among conflicting statements in the literature as curators can. Therefore, funding agencies should not hobble the curation efforts of existing databases on the assumption that a problem that has stymied Artificial Intelligence researchers for more than 60 years will be solved tomorrow. Semi-automated extraction techniques appear to have significantly more potential based on a review of recent tools that enhance curator productivity. But a full cost-benefit analysis for these tools is lacking. Without such analysis it is possible to expend significant effort developing information-extraction tools that automate small parts of the overall curation workflow without achieving a significant decrease in curation costs.Database URL. © The Author(s) 2016. Published by Oxford University Press.

GIS-based analysis and modelling with empirical and remotely-sensed data on coastline advance and retreat

NASA Astrophysics Data System (ADS)

Ahmad, Sajid Rashid

With the understanding that far more research remains to be done on the development and use of innovative and functional geospatial techniques and procedures to investigate coastline changes this thesis focussed on the integration of remote sensing, geographical information systems (GIS) and modelling techniques to provide meaningful insights on the spatial and temporal dynamics of coastline changes. One of the unique strengths of this research was the parameterization of the GIS with long-term empirical and remote sensing data. Annual empirical data from 1941--2007 were analyzed by the GIS, and then modelled with statistical techniques. Data were also extracted from Landsat TM and ETM+ images. The band ratio method was used to extract the coastlines. Topographic maps were also used to extract digital map data. All data incorporated into ArcGIS 9.2 were analyzed with various modules, including Spatial Analyst, 3D Analyst, and Triangulated Irregular Networks. The Digital Shoreline Analysis System was used to analyze and predict rates of coastline change. GIS results showed the spatial locations along the coast that will either advance or retreat over time. The linear regression results highlighted temporal changes which are likely to occur along the coastline. Box-Jenkins modelling procedures were utilized to determine statistical models which best described the time series (1941--2007) of coastline change data. After several iterations and goodness-of-fit tests, second-order spatial cyclic autoregressive models, first-order autoregressive models and autoregressive moving average models were identified as being appropriate for describing the deterministic and random processes operating in Guyana's coastal system. The models highlighted not only cyclical patterns in advance and retreat of the coastline, but also the existence of short and long-term memory processes. Long-term memory processes could be associated with mudshoal propagation and stabilization while short-term memory processes were indicative of transitory hydrodynamic and other processes. An innovative framework for a spatio-temporal information-based system (STIBS) was developed. STIBS incorporated diverse datasets within a GIS, dynamic computer-based simulation models, and a spatial information query and graphical subsystem. Tests of the STIBS proved that it could be used to simulate and visualize temporal variability in shifting morphological states of the coastline.

Informing child welfare policy and practice: using knowledge discovery and data mining technology via a dynamic Web site.

PubMed

Duncan, Dean F; Kum, Hye-Chung; Weigensberg, Elizabeth Caplick; Flair, Kimberly A; Stewart, C Joy

2008-11-01

Proper management and implementation of an effective child welfare agency requires the constant use of information about the experiences and outcomes of children involved in the system, emphasizing the need for comprehensive, timely, and accurate data. In the past 20 years, there have been many advances in technology that can maximize the potential of administrative data to promote better evaluation and management in the field of child welfare. Specifically, this article discusses the use of knowledge discovery and data mining (KDD), which makes it possible to create longitudinal data files from administrative data sources, extract valuable knowledge, and make the information available via a user-friendly public Web site. This article demonstrates a successful project in North Carolina where knowledge discovery and data mining technology was used to develop a comprehensive set of child welfare outcomes available through a public Web site to facilitate information sharing of child welfare data to improve policy and practice.

Iterative filtering decomposition based on local spectral evolution kernel

PubMed Central

Wang, Yang; Wei, Guo-Wei; Yang, Siyang

2011-01-01

The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dynamic systems, etc. The utility, robustness and usefulness of the proposed LESK based IFD are demonstrated via a large number of applications, such as the analysis of stock market data, the decomposition of ocean wave magnitudes, the understanding of physiologic signals and information recovery from noisy images. The performance of the proposed method is compared with that of existing methods in the literature. Our results indicate that the LSEK based IFD improves both the efficiency and the stability of conventional EMD algorithms. PMID:22350559

Timing and Mode of Landscape Response to Glacial-Interglacial Climate Forcing From Fluvial Fill Terrace Sediments: Humahuaca Basin, E Cordillera, NW Argentina

NASA Astrophysics Data System (ADS)

Schildgen, T. F.; Robinson, R. A. J.; Savi, S.; Bookhagen, B.; Tofelde, S.; Strecker, M. R.

2014-12-01

Numerical modelling informs risk assessment of tsunami generated by submarine slides; however, for large-scale slides modelling can be complex and computationally challenging. Many previous numerical studies have approximated slides as rigid blocks that moved according to prescribed motion. However, wave characteristics are strongly dependent on the motion of the slide and previous work has recommended that more accurate representation of slide dynamics is needed. We have used the finite-element, adaptive-mesh CFD model Fluidity, to perform multi-material simulations of deformable submarine slide-generated waves at real world scales for a 2D scenario in the Gulf of Mexico. Our high-resolution approach represents slide dynamics with good accuracy, compared to other numerical simulations of this scenario, but precludes tracking of wave propagation over large distances. To enable efficient modelling of further propagation of the waves, we investigate an approach to extract information about the slide evolution from our multi-material simulations in order to drive a single-layer wave propagation model, also using Fluidity, which is much less computationally expensive. The extracted submarine slide geometry and position as a function of time are parameterised using simple polynomial functions. The polynomial functions are used to inform a prescribed velocity boundary condition in a single-layer simulation, mimicking the effect the submarine slide motion has on the water column. The approach is verified by successful comparison of wave generation in the single-layer model with that recorded in the multi-material, multi-layer simulations. We then extend this approach to 3D for further validation of this methodology (using the Gulf of Mexico scenario proposed by Horrillo et al., 2013) and to consider the effect of lateral spreading. This methodology is then used to simulate a series of hypothetical submarine slide events in the Arctic Ocean (based on evidence of historic slides) and examine the hazard posed to the UK coast.

Supercritical carbon dioxide extraction of seed oil from winter melon (Benincasa hispida) and its antioxidant activity and fatty acid composition.

PubMed

Bimakr, Mandana; Rahman, Russly Abdul; Taip, Farah Saleena; Adzahan, Noranizan Mohd; Sarker, Md Zaidul Islam; Ganjloo, Ali

2013-01-15

In the present study, supercritical carbon dioxide (SC-CO(2)) extraction of seed oil from winter melon (Benincasa hispida) was investigated. The effects of process variables namely pressure (150-300 bar), temperature (40-50 °C) and dynamic extraction time (60-120 min) on crude extraction yield (CEY) were studied through response surface methodology (RSM). The SC-CO(2) extraction process was modified using ethanol (99.9%) as co-solvent. Perturbation plot revealed the significant effect of all process variables on the CEY. A central composite design (CCD) was used to optimize the process conditions to achieve maximum CEY. The optimum conditions were 244 bar pressure, 46 °C temperature and 97 min dynamic extraction time. Under these optimal conditions, the CEY was predicted to be 176.30 mg-extract/g-dried sample. The validation experiment results agreed with the predicted value. The antioxidant activity and fatty acid composition of crude oil obtained under optimized conditions were determined and compared with published results using Soxhlet extraction (SE) and ultrasound assisted extraction (UAE). It was found that the antioxidant activity of the extract obtained by SC-CO(2) extraction was strongly higher than those obtained by SE and UAE. Identification of fatty acid composition using gas chromatography (GC) showed that all the extracts were rich in unsaturated fatty acids with the most being linoleic acid. In contrast, the amount of saturated fatty acids extracted by SE was higher than that extracted under optimized SC-CO(2) extraction conditions.

Extraction of CT dose information from DICOM metadata: automated Matlab-based approach.

PubMed

Dave, Jaydev K; Gingold, Eric L

2013-01-01

The purpose of this study was to extract exposure parameters and dose-relevant indexes of CT examinations from information embedded in DICOM metadata. DICOM dose report files were identified and retrieved from a PACS. An automated software program was used to extract from these files information from the structured elements in the DICOM metadata relevant to exposure. Extracting information from DICOM metadata eliminated potential errors inherent in techniques based on optical character recognition, yielding 100% accuracy.

Dynamic detection-rate-based bit allocation with genuine interval concealment for binary biometric representation.

PubMed

Lim, Meng-Hui; Teoh, Andrew Beng Jin; Toh, Kar-Ann

2013-06-01

Biometric discretization is a key component in biometric cryptographic key generation. It converts an extracted biometric feature vector into a binary string via typical steps such as segmentation of each feature element into a number of labeled intervals, mapping of each interval-captured feature element onto a binary space, and concatenation of the resulted binary output of all feature elements into a binary string. Currently, the detection rate optimized bit allocation (DROBA) scheme is one of the most effective biometric discretization schemes in terms of its capability to assign binary bits dynamically to user-specific features with respect to their discriminability. However, we learn that DROBA suffers from potential discriminative feature misdetection and underdiscretization in its bit allocation process. This paper highlights such drawbacks and improves upon DROBA based on a novel two-stage algorithm: 1) a dynamic search method to efficiently recapture such misdetected features and to optimize the bit allocation of underdiscretized features and 2) a genuine interval concealment technique to alleviate crucial information leakage resulted from the dynamic search. Improvements in classification accuracy on two popular face data sets vindicate the feasibility of our approach compared with DROBA.

Reduced quantum dynamics with arbitrary bath spectral densities: hierarchical equations of motion based on several different bath decomposition schemes.

PubMed

Liu, Hao; Zhu, Lili; Bai, Shuming; Shi, Qiang

2014-04-07

We investigated applications of the hierarchical equation of motion (HEOM) method to perform high order perturbation calculations of reduced quantum dynamics for a harmonic bath with arbitrary spectral densities. Three different schemes are used to decompose the bath spectral density into analytical forms that are suitable to the HEOM treatment: (1) The multiple Lorentzian mode model that can be obtained by numerically fitting the model spectral density. (2) The combined Debye and oscillatory Debye modes model that can be constructed by fitting the corresponding classical bath correlation function. (3) A new method that uses undamped harmonic oscillator modes explicitly in the HEOM formalism. Methods to extract system-bath correlations were investigated for the above bath decomposition schemes. We also show that HEOM in the undamped harmonic oscillator modes can give detailed information on the partial Wigner transform of the total density operator. Theoretical analysis and numerical simulations of the spin-Boson dynamics and the absorption line shape of molecular dimers show that the HEOM formalism for high order perturbations can serve as an important tool in studying the quantum dissipative dynamics in the intermediate coupling regime.

Effects of cockpit lateral stick characteristics on handling qualities and pilot dynamics

NASA Technical Reports Server (NTRS)

Mitchell, David G.; Aponso, Bimal L.; Klyde, David H.

1992-01-01

This report presents the results of analysis of cockpit lateral control feel-system studies. Variations in feel-system natural frequency, damping, and command sensing reference (force and position) were investigated, in combination with variations in the aircraft response characteristics. The primary data for the report were obtained from a flight investigation conducted with a variable-stability airplane, with additional information taken from other flight experiments and ground-based simulations for both airplanes and helicopters . The study consisted of analysis of handling qualities ratings and extraction of open-loop, pilot-vehicle describing functions from sum-of-sines tracking data, including, for a limited subset of these data, the development of pilot models. The study confirms the findings of other investigators that the effects on pilot opinion of cockpit feel-system dynamics are not equivalent to a comparable level of added time delay, and until a more comprehensive set of criteria are developed, it is recommended that feel-system dynamics be considered a delay-inducing element in the aircraft response. The best correlation with time-delay requirements was found when the feel-system dynamics were included in the delay measurements, regardless of the command reference. This is a radical departure from past approaches.

Reduced quantum dynamics with arbitrary bath spectral densities: Hierarchical equations of motion based on several different bath decomposition schemes

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

Liu, Hao; Zhu, Lili; Bai, Shuming

2014-04-07

We investigated applications of the hierarchical equation of motion (HEOM) method to perform high order perturbation calculations of reduced quantum dynamics for a harmonic bath with arbitrary spectral densities. Three different schemes are used to decompose the bath spectral density into analytical forms that are suitable to the HEOM treatment: (1) The multiple Lorentzian mode model that can be obtained by numerically fitting the model spectral density. (2) The combined Debye and oscillatory Debye modes model that can be constructed by fitting the corresponding classical bath correlation function. (3) A new method that uses undamped harmonic oscillator modes explicitly inmore » the HEOM formalism. Methods to extract system-bath correlations were investigated for the above bath decomposition schemes. We also show that HEOM in the undamped harmonic oscillator modes can give detailed information on the partial Wigner transform of the total density operator. Theoretical analysis and numerical simulations of the spin-Boson dynamics and the absorption line shape of molecular dimers show that the HEOM formalism for high order perturbations can serve as an important tool in studying the quantum dissipative dynamics in the intermediate coupling regime.« less

Real-time fusion of endoscopic views with dynamic 3-D cardiac images: a phantom study.

PubMed

Szpala, Stanislaw; Wierzbicki, Marcin; Guiraudon, Gerard; Peters, Terry M

2005-09-01

Minimally invasive robotically assisted cardiac surgical systems currently do not routinely employ 3-D image guidance. However, preoperative magnetic resonance and computed tomography (CT) images have the potential to be used in this role, if appropriately registered with the patient anatomy and animated synchronously with the motion of the actual heart. This paper discusses the fusion of optical images of a beating heart phantom obtained from an optically tracked endoscope, with volumetric images of the phantom created from a dynamic CT dataset. High quality preoperative dynamic CT images are created by first extracting the motion parameters of the heart from the series of temporal frames, and then applying this information to animate a high-quality heart image acquired at end systole. Temporal synchronization of the endoscopic and CT model is achieved by selecting the appropriate CT image from the dynamic set, based on an electrocardiographic trigger signal. The spatial error between the optical and virtual images is 1.4 +/- 1.1 mm, while the time discrepancy is typically 50-100 ms. Index Terms-Image guidance, image warping, minimally invasive cardiac surgery, virtual endoscopy, virtual reality.

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

NASA Astrophysics Data System (ADS)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

Local and global aspects of biological motion perception in children born at very low birth weight

PubMed Central

Williamson, K. E.; Jakobson, L. S.; Saunders, D. R.; Troje, N. F.

2015-01-01

Biological motion perception can be assessed using a variety of tasks. In the present study, 8- to 11-year-old children born prematurely at very low birth weight (<1500 g) and matched, full-term controls completed tasks that required the extraction of local motion cues, the ability to perceptually group these cues to extract information about body structure, and the ability to carry out higher order processes required for action recognition and person identification. Preterm children exhibited difficulties in all 4 aspects of biological motion perception. However, intercorrelations between test scores were weak in both full-term and preterm children—a finding that supports the view that these processes are relatively independent. Preterm children also displayed more autistic-like traits than full-term peers. In preterm (but not full-term) children, these traits were negatively correlated with performance in the task requiring structure-from-motion processing, r(30) = −.36, p < .05), but positively correlated with the ability to extract identity, r(30) = .45, p < .05). These findings extend previous reports of vulnerability in systems involved in processing dynamic cues in preterm children and suggest that a core deficit in social perception/cognition may contribute to the development of the social and behavioral difficulties even in members of this population who are functioning within the normal range intellectually. The results could inform the development of screening, diagnostic, and intervention tools. PMID:25103588

Repetitive Regeneration of Media #1 in a Dynamic Column Extraction using Brine #1

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

Gary Garland

This data is from a regeneration study from a dynamic column extraction experiment where we ran a solution of REE's through a column of media #1 then stripped the REE's off the media using 2M HNO3 solution. We then re-equilibrated the media and repeated the process of running a REE solution through the column and stripping the REE's off the media and comparing the two runs.

Microalgae fractionation using steam explosion, dynamic and tangential cross-flow membrane filtration.

PubMed

Lorente, E; Hapońska, M; Clavero, E; Torras, C; Salvadó, J

2017-08-01

In this study, the microalga Nannochloropsis gaditana was subjected to acid catalysed steam explosion treatment and the resulting exploded material was subsequently fractionated to separate the different fractions (lipids, sugars and solids). Conventional and vibrational membrane setups were used with several polymeric commercial membranes. Two different routes were followed: 1) filtration+lipid solvent extraction and 2) lipid solvent extraction+filtration. Route 1 revealed to be much better since the used membrane for filtration was able to permeate the sugar aqueous phase and retained the fraction containing lipids; after this, an extraction required a much lower amount of solvent and a better recovering yield. Filtration allowed complete lipid rejection. Dynamic filtration improved permeability compared to the tangential cross-flow filtration. Best membrane performance was achieved using a 5000Da membrane with the dynamic system, obtaining a permeability of 6L/h/m 2 /bar. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multi-Component Profiling of Trace Volatiles in Blood by Gas Chromatography/Mass Spectrometry with Dynamic Headspace Extraction

PubMed Central

Kakuta, Shoji; Yamashita, Toshiyuki; Nishiumi, Shin; Yoshida, Masaru; Fukusaki, Eiichiro; Bamba, Takeshi

2015-01-01

A dynamic headspace extraction method (DHS) with high-pressure injection is described. This dynamic extraction method has superior sensitivity to solid phase micro extraction, SPME and is capable of extracting the entire gas phase by purging the headspace of a vial. Optimization of the DHS parameters resulted in a highly sensitive volatile profiling system with the ability to detect various volatile components including alcohols at nanogram levels. The average LOD for a standard volatile mixture was 0.50 ng mL−1, and the average LOD for alcohols was 0.66 ng mL−1. This method was used for the analysis of volatile components from biological samples and compared with acute and chronic inflammation models. The method permitted the identification of volatiles with the same profile pattern as in vitro oxidized lipid-derived volatiles. In addition, the concentration of alcohols and aldehydes from the acute inflammation model samples were significantly higher than that for the chronic inflammation model samples. The different profiles between these samples could also be identified by this method. Finally, it was possible to analyze alcohols and low-molecular-weight volatiles that are difficult to analyze by SPME in high sensitivity and to show volatile profiling based on multi-volatile simultaneous analysis. PMID:26819905

Multi-Component Profiling of Trace Volatiles in Blood by Gas Chromatography/Mass Spectrometry with Dynamic Headspace Extraction.

PubMed

Kakuta, Shoji; Yamashita, Toshiyuki; Nishiumi, Shin; Yoshida, Masaru; Fukusaki, Eiichiro; Bamba, Takeshi

2015-01-01

A dynamic headspace extraction method (DHS) with high-pressure injection is described. This dynamic extraction method has superior sensitivity to solid phase micro extraction, SPME and is capable of extracting the entire gas phase by purging the headspace of a vial. Optimization of the DHS parameters resulted in a highly sensitive volatile profiling system with the ability to detect various volatile components including alcohols at nanogram levels. The average LOD for a standard volatile mixture was 0.50 ng mL(-1), and the average LOD for alcohols was 0.66 ng mL(-1). This method was used for the analysis of volatile components from biological samples and compared with acute and chronic inflammation models. The method permitted the identification of volatiles with the same profile pattern as in vitro oxidized lipid-derived volatiles. In addition, the concentration of alcohols and aldehydes from the acute inflammation model samples were significantly higher than that for the chronic inflammation model samples. The different profiles between these samples could also be identified by this method. Finally, it was possible to analyze alcohols and low-molecular-weight volatiles that are difficult to analyze by SPME in high sensitivity and to show volatile profiling based on multi-volatile simultaneous analysis.

Robust X-ray angular correlations for the study of meso-structures

DOE PAGES

Lhermitte, Julien R.; Tian, Cheng; Stein, Aaron; ...

2017-05-08

As self-assembling nanomaterials become more sophisticated, it is becoming increasingly important to measure the structural order of finite-sized assemblies of nano-objects. These mesoscale clusters represent an acute challenge to conventional structural probes, owing to the range of implicated size scales (10 nm to several micrometres), the weak scattering signal and the dynamic nature of meso-clusters in native solution environments. The high X-ray flux and coherence of modern synchrotrons present an opportunity to extract structural information from these challenging systems, but conventional ensemble X-ray scattering averages out crucial information about local particle configurations. Conversely, a single meso-cluster scatters too weakly tomore » recover the full diffraction pattern. Using X-ray angular cross-correlation analysis, it is possible to combine multiple noisy measurements to obtain robust structural information. This paper explores the key theoretical limits and experimental challenges that constrain the application of these methods to probing structural order in real nanomaterials. A metric is presented to quantify the signal-to-noise ratio of angular correlations, and it is used to identify several experimental artifacts that arise. In particular, it is found that background scattering, data masking and inter-cluster interference profoundly affect the quality of correlation analyses. A robust workflow is demonstrated for mitigating these effects and extracting reliable angular correlations from realistic experimental data.« less

Monitoring evolving urban cluster systems using DMSP/OLS nighttime light data: a case study of the Yangtze River Delta region, China

NASA Astrophysics Data System (ADS)

Wang, Zhao; Yang, Shan; Wang, Shuguang; Shen, Yan

2017-10-01

The assessment of the dynamic urban structure has been affected by lack of timely and accurate spatial information for a long period, which has hindered the measurements of structural continuity at the macroscale. Defense meteorological satellite program's operational linescan system (DMSP/OLS) nighttime light (NTL) data provide an ideal source for urban information detection with a long-time span, short-time interval, and wide coverage. In this study, we extracted the physical boundaries of urban clusters from corrected NTL images and quantitatively analyzed the structure of the urban cluster system based on rank-size distribution, spatial metrics, and Mann-Kendall trend test. Two levels of urban cluster systems in the Yangtze River Delta region (YRDR) were examined. We found that (1) in the entire YRDR, the urban cluster system showed a periodic process, with a significant trend of even distribution before 2007 but an unequal growth pattern after 2007, and (2) at the metropolitan level, vast disparities exist in four metropolitan areas for the fluctuations of Pareto's exponent, the speed of cluster expansion, and the dominance of core cluster. The results suggest that the extracted urban cluster information from NTL data effectively reflect the evolving nature of regional urbanization, which in turn can aid in the planning of cities and help achieve more sustainable regional development.

Fusion of Remote Sensing Methods, UAV Photogrammetry and LiDAR Scanning products for monitoring fluvial dynamics

NASA Astrophysics Data System (ADS)

Lendzioch, Theodora; Langhammer, Jakub; Hartvich, Filip

2015-04-01

Fusion of remote sensing data is a common and rapidly developing discipline, which combines data from multiple sources with different spatial and spectral resolution, from satellite sensors, aircraft and ground platforms. Fusion data contains more detailed information than each of the source and enhances the interpretation performance and accuracy of the source data and produces a high-quality visualisation of the final data. Especially, in fluvial geomorphology it is essential to get valuable images in sub-meter resolution to obtain high quality 2D and 3D information for a detailed identification, extraction and description of channel features of different river regimes and to perform a rapid mapping of changes in river topography. In order to design, test and evaluate a new approach for detection of river morphology, we combine different research techniques from remote sensing products to drone-based photogrammetry and LiDAR products (aerial LiDAR Scanner and TLS). Topographic information (e.g. changes in river channel morphology, surface roughness, evaluation of floodplain inundation, mapping gravel bars and slope characteristics) will be extracted either from one single layer or from combined layers in accordance to detect fluvial topographic changes before and after flood events. Besides statistical approaches for predictive geomorphological mapping and the determination of errors and uncertainties of the data, we will also provide 3D modelling of small fluvial features.

Analysis of different image-based biofeedback models for improving cycling performances

NASA Astrophysics Data System (ADS)

Bibbo, D.; Conforto, S.; Bernabucci, I.; Carli, M.; Schmid, M.; D'Alessio, T.

2012-03-01

Sport practice can take advantage from the quantitative assessment of task execution, which is strictly connected to the implementation of optimized training procedures. To this aim, it is interesting to explore the effectiveness of biofeedback training techniques. This implies a complete chain for information extraction containing instrumented devices, processing algorithms and graphical user interfaces (GUIs) to extract valuable information (i.e. kinematics, dynamics, and electrophysiology) to be presented in real-time to the athlete. In cycling, performance indexes displayed in a simple and perceivable way can help the cyclist optimize the pedaling. To this purpose, in this study four different GUIs have been designed and used in order to understand if and how a graphical biofeedback can influence the cycling performance. In particular, information related to the mechanical efficiency of pedaling is represented in each of the designed interfaces and then displayed to the user. This index is real-time calculated on the basis of the force signals exerted on the pedals during cycling. Instrumented pedals for bikes, already designed and implemented in our laboratory, have been used to measure those force components. A group of subjects underwent an experimental protocol and pedaled with (the interfaces have been used in a randomized order) and without graphical biofeedback. Preliminary results show how the effective perception of the biofeedback influences the motor performance.

Rotorcraft Diagnostics

NASA Technical Reports Server (NTRS)

Haste, Deepak; Azam, Mohammad; Ghoshal, Sudipto; Monte, James

2012-01-01

Health management (HM) in any engineering systems requires adequate understanding about the system s functioning; a sufficient amount of monitored data; the capability to extract, analyze, and collate information; and the capability to combine understanding and information for HM-related estimation and decision-making. Rotorcraft systems are, in general, highly complex. Obtaining adequate understanding about functioning of such systems is quite difficult, because of the proprietary (restricted access) nature of their designs and dynamic models. Development of an EIM (exact inverse map) solution for rotorcraft requires a process that can overcome the abovementioned difficulties and maximally utilize monitored information for HM facilitation via employing advanced analytic techniques. The goal was to develop a versatile HM solution for rotorcraft for facilitation of the Condition Based Maintenance Plus (CBM+) capabilities. The effort was geared towards developing analytic and reasoning techniques, and proving the ability to embed the required capabilities on a rotorcraft platform, paving the way for implementing the solution on an aircraft-level system for consolidation and reporting. The solution for rotorcraft can he used offboard or embedded directly onto a rotorcraft system. The envisioned solution utilizes available monitored and archived data for real-time fault detection and identification, failure precursor identification, and offline fault detection and diagnostics, health condition forecasting, optimal guided troubleshooting, and maintenance decision support. A variant of the onboard version is a self-contained hardware and software (HW+SW) package that can be embedded on rotorcraft systems. The HM solution comprises components that gather/ingest data and information, perform information/feature extraction, analyze information in conjunction with the dependency/diagnostic model of the target system, facilitate optimal guided troubleshooting, and offer decision support for optimal maintenance.

The Agent of extracting Internet Information with Lead Order

NASA Astrophysics Data System (ADS)

Mo, Zan; Huang, Chuliang; Liu, Aijun

In order to carry out e-commerce better, advanced technologies to access business information are in need urgently. An agent is described to deal with the problems of extracting internet information that caused by the non-standard and skimble-scamble structure of Chinese websites. The agent designed includes three modules which respond to the process of extracting information separately. A method of HTTP tree and a kind of Lead algorithm is proposed to generate a lead order, with which the required web can be retrieved easily. How to transform the extracted information structuralized with natural language is also discussed.

Emotion Analysis of Telephone Complaints from Customer Based on Affective Computing.

PubMed

Gong, Shuangping; Dai, Yonghui; Ji, Jun; Wang, Jinzhao; Sun, Hai

2015-01-01

Customer complaint has been the important feedback for modern enterprises to improve their product and service quality as well as the customer's loyalty. As one of the commonly used manners in customer complaint, telephone communication carries rich emotional information of speeches, which provides valuable resources for perceiving the customer's satisfaction and studying the complaint handling skills. This paper studies the characteristics of telephone complaint speeches and proposes an analysis method based on affective computing technology, which can recognize the dynamic changes of customer emotions from the conversations between the service staff and the customer. The recognition process includes speaker recognition, emotional feature parameter extraction, and dynamic emotion recognition. Experimental results show that this method is effective and can reach high recognition rates of happy and angry states. It has been successfully applied to the operation quality and service administration in telecom and Internet service company.

Single-Molecule Sensing with Nanopore Confinement: From Chemical Reactions to Biological Interactions.

PubMed

Lin, Yao; Ying, Yi-Lun; Gao, Rui; Long, Yi-Tao

2018-03-25

The nanopore can generate an electrochemical confinement for single-molecule sensing that help understand the fundamental chemical principle in nanoscale dimensions. By observing the generated ionic current, individual bond-making and bond-breaking steps, single biomolecule dynamic conformational changes and electron transfer processes that occur within pore can be monitored with high temporal and current resolution. These single-molecule studies in nanopore confinement are revealing information about the fundamental chemical and biological processes that cannot be extracted from ensemble measurements. In this Concept article, we introduce and discuss the electrochemical confinement effects on single-molecule covalent reactions, conformational dynamics of individual molecules and host-guest interactions in protein nanopores. Then, we extend the concept of nanopore confinement effects to confine electrochemical redox reactions in solid-state nanopores for developing new sensing mechanisms. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Experimental study of non-stationary operation of a dual-wavelength passively mode-locked fibre ring laser

NASA Astrophysics Data System (ADS)

Ibarra Villalón, H. E.; Pottiez, O.; Bracamontes Rodriguez, Y. E.; Lauterio-Cruz, J. P.; Gomez Vieyra, A.

2018-06-01

In this paper, we report an experimental study of different dynamics taking place in a 20 m long passively mode-locked fibre ring laser in dual-wavelength operation, at 1531 nm and 1558 nm. For different polarization adjustments, self-starting mode locking is obtained, yielding different types of emission: bunches of solitons in quasi-stationary regime, a compact bunch of solitons coexisting with loose bunches of solitons, a noise-like pulse coexisting with bunches of solitons and a noise-like pulse displaying quasi-periodic fluctuations. In each regime, we extract information on the pulse dynamics from measurements of the temporal profile evolution using a 16 GHz real-time oscilloscope and, at the same time, we propose a phase-space diagram representation of the intensity versus the energy of the temporal profile of the pulses; the latter allows evidencing patterns that could not be identified using conventional measurement techniques.

Pseudo-polar drive patterns for brain electrical impedance tomography.

PubMed

Shi, Xuetao; Dong, Xiuzhen; Shuai, Wanjun; You, Fusheng; Fu, Feng; Liu, Ruigang

2006-11-01

Brain electrical impedance tomography (EIT) is a difficult task as brain tissues are enclosed by the skull of high resistance and cerebrospinal fluid (CSF) of low resistance, which makes internal resistivity information more difficult to extract. In order to seek a single source drive pattern that is more suitable for brain EIT, we built a more realistic experimental setting that simulates a head with the resistivity of the scalp, skull, CSF and brain, and compared the performance of adjacent, cross, polar and pseudo-polar drive patterns in terms of the boundary voltage dynamic range, independent measurement number, total boundary voltage changes and anti-noise performance based on it. The results demonstrate that the pseudo-polar drive pattern is optimal in all the aspects except for the dynamic range. The polar and cross drive patterns come next, and the adjacent drive pattern is the worst. Therefore, the pseudo-polar drive pattern should be chosen for brain EIT.

Two dimensional laser induced fluorescence in the gas phase: a spectroscopic tool for studying molecular spectroscopy and dynamics

NASA Astrophysics Data System (ADS)

Gascooke, Jason R.; Lawrance, Warren D.

2017-11-01

Two dimensional laser induced fluorescence (2D-LIF) extends the usual laser induced fluorescence technique by adding a second dimension, the wavelength at which excited states emit, thereby significantly enhancing the information that can be extracted. It allows overlapping absorption features, whether they arise from within the same molecule or from different molecules in a mixture, to be associated with their appropriate "parent" state and/or molecule. While the first gas phase version of the technique was published a decade ago, the technique is in its infancy, having been exploited by only a few groups to date. However, its potential in gas phase spectroscopy and dynamics is significant. In this article we provide an overview of the technique and illustrate its potential with examples, with a focus on those utilising high resolution in the dispersed fluorescence dimension.

Quantifying the cross-sectional relationship between online sentiment and the skewness of stock returns

NASA Astrophysics Data System (ADS)

Shen, Dehua; Liu, Lanbiao; Zhang, Yongjie

2018-01-01

The constantly increasing utilization of social media as the alternative information channel, e.g., Twitter, provides us a unique opportunity to investigate the dynamics of the financial market. In this paper, we employ the daily happiness sentiment extracted from Twitter as the proxy for the online sentiment dynamics and investigate its association with the skewness of stock returns of 26 international stock market index returns. The empirical results show that: (1) by dividing the daily happiness sentiment into quintiles from the least to the most happiness days, the skewness of the Most-happiness subgroup is significantly larger than that of the Least-happiness subgroup. Besides, there exist significant differences in any pair of subgroups; (2) in an event study methodology, we further show that the skewness around the highest happiness days is significantly larger than the skewness around the lowest happiness days.

Nonparametric variational optimization of reaction coordinates

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

Banushkina, Polina V.; Krivov, Sergei V., E-mail: s.krivov@leeds.ac.uk

State of the art realistic simulations of complex atomic processes commonly produce trajectories of large size, making the development of automated analysis tools very important. A popular approach aimed at extracting dynamical information consists of projecting these trajectories into optimally selected reaction coordinates or collective variables. For equilibrium dynamics between any two boundary states, the committor function also known as the folding probability in protein folding studies is often considered as the optimal coordinate. To determine it, one selects a functional form with many parameters and trains it on the trajectories using various criteria. A major problem with such anmore » approach is that a poor initial choice of the functional form may lead to sub-optimal results. Here, we describe an approach which allows one to optimize the reaction coordinate without selecting its functional form and thus avoiding this source of error.« less

Emotion Analysis of Telephone Complaints from Customer Based on Affective Computing

PubMed Central

Gong, Shuangping; Ji, Jun; Wang, Jinzhao; Sun, Hai

2015-01-01

Customer complaint has been the important feedback for modern enterprises to improve their product and service quality as well as the customer's loyalty. As one of the commonly used manners in customer complaint, telephone communication carries rich emotional information of speeches, which provides valuable resources for perceiving the customer's satisfaction and studying the complaint handling skills. This paper studies the characteristics of telephone complaint speeches and proposes an analysis method based on affective computing technology, which can recognize the dynamic changes of customer emotions from the conversations between the service staff and the customer. The recognition process includes speaker recognition, emotional feature parameter extraction, and dynamic emotion recognition. Experimental results show that this method is effective and can reach high recognition rates of happy and angry states. It has been successfully applied to the operation quality and service administration in telecom and Internet service company. PMID:26633967

Global dynamics of selective attention and its lapses in primary auditory cortex.

PubMed

Lakatos, Peter; Barczak, Annamaria; Neymotin, Samuel A; McGinnis, Tammy; Ross, Deborah; Javitt, Daniel C; O'Connell, Monica Noelle

2016-12-01

Previous research demonstrated that while selectively attending to relevant aspects of the external world, the brain extracts pertinent information by aligning its neuronal oscillations to key time points of stimuli or their sampling by sensory organs. This alignment mechanism is termed oscillatory entrainment. We investigated the global, long-timescale dynamics of this mechanism in the primary auditory cortex of nonhuman primates, and hypothesized that lapses of entrainment would correspond to lapses of attention. By examining electrophysiological and behavioral measures, we observed that besides the lack of entrainment by external stimuli, attentional lapses were also characterized by high-amplitude alpha oscillations, with alpha frequency structuring of neuronal ensemble and single-unit operations. Entrainment and alpha-oscillation-dominated periods were strongly anticorrelated and fluctuated rhythmically at an ultra-slow rate. Our results indicate that these two distinct brain states represent externally versus internally oriented computational resources engaged by large-scale task-positive and task-negative functional networks.

An Overview of Advanced SILAC-Labeling Strategies for Quantitative Proteomics.

PubMed

Terzi, F; Cambridge, S

2017-01-01

Comparative, quantitative mass spectrometry of proteins provides great insight to protein abundance and function, but some molecular characteristics related to protein dynamics are not so easily obtained. Because the metabolic incorporation of stable amino acid isotopes allows the extraction of distinct temporal and spatial aspects of protein dynamics, the SILAC methodology is uniquely suited to be adapted for advanced labeling strategies. New SILAC strategies have emerged that allow deeper foraging into the complexity of cellular proteomes. Here, we review a few advanced SILAC-labeling strategies that have been published during last the years. Among them, different subsaturating-labeling as well as dual-labeling schemes are most prominent for a range of analyses including those of neuronal proteomes, secretion, or cell-cell-induced stimulations. These recent developments suggest that much more information can be gained from proteomic analyses if the labeling strategies are specifically tailored toward the experimental design. © 2017 Elsevier Inc. All rights reserved.

Laboratory Production of Lemon Liqueur (Limoncello) by Conventional Maceration and a Two-Syringe System to Illustrate Rapid Solid-Liquid Dynamic Extraction

ERIC Educational Resources Information Center

Naviglio, Daniele; Montesano, Domenico; Gallo, Monica

2015-01-01

Two experimental techniques of solid-liquid extraction are compared relating to the lab-scale production of lemon liqueur, most commonly named "limoncello"; the first is the official method of maceration for the solid-liquid extraction of analytes and is widely used to extract active ingredients from a great variety of natural products;…

Angular motion estimation using dynamic models in a gyro-free inertial measurement unit.

PubMed

Edwan, Ezzaldeen; Knedlik, Stefan; Loffeld, Otmar

2012-01-01

In this paper, we summarize the results of using dynamic models borrowed from tracking theory in describing the time evolution of the state vector to have an estimate of the angular motion in a gyro-free inertial measurement unit (GF-IMU). The GF-IMU is a special type inertial measurement unit (IMU) that uses only a set of accelerometers in inferring the angular motion. Using distributed accelerometers, we get an angular information vector (AIV) composed of angular acceleration and quadratic angular velocity terms. We use a Kalman filter approach to estimate the angular velocity vector since it is not expressed explicitly within the AIV. The bias parameters inherent in the accelerometers measurements' produce a biased AIV and hence the AIV bias parameters are estimated within an augmented state vector. Using dynamic models, the appended bias parameters of the AIV become observable and hence we can have unbiased angular motion estimate. Moreover, a good model is required to extract the maximum amount of information from the observation. Observability analysis is done to determine the conditions for having an observable state space model. For higher grades of accelerometers and under relatively higher sampling frequency, the error of accelerometer measurements is dominated by the noise error. Consequently, simulations are conducted on two models, one has bias parameters appended in the state space model and the other is a reduced model without bias parameters.

Angular Motion Estimation Using Dynamic Models in a Gyro-Free Inertial Measurement Unit

PubMed Central

Edwan, Ezzaldeen; Knedlik, Stefan; Loffeld, Otmar

2012-01-01

In this paper, we summarize the results of using dynamic models borrowed from tracking theory in describing the time evolution of the state vector to have an estimate of the angular motion in a gyro-free inertial measurement unit (GF-IMU). The GF-IMU is a special type inertial measurement unit (IMU) that uses only a set of accelerometers in inferring the angular motion. Using distributed accelerometers, we get an angular information vector (AIV) composed of angular acceleration and quadratic angular velocity terms. We use a Kalman filter approach to estimate the angular velocity vector since it is not expressed explicitly within the AIV. The bias parameters inherent in the accelerometers measurements' produce a biased AIV and hence the AIV bias parameters are estimated within an augmented state vector. Using dynamic models, the appended bias parameters of the AIV become observable and hence we can have unbiased angular motion estimate. Moreover, a good model is required to extract the maximum amount of information from the observation. Observability analysis is done to determine the conditions for having an observable state space model. For higher grades of accelerometers and under relatively higher sampling frequency, the error of accelerometer measurements is dominated by the noise error. Consequently, simulations are conducted on two models, one has bias parameters appended in the state space model and the other is a reduced model without bias parameters. PMID:22778586

FY10 Report on Multi-scale Simulation of Solvent Extraction Processes: Molecular-scale and Continuum-scale Studies

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

Wardle, Kent E.; Frey, Kurt; Pereira, Candido

2014-02-02

This task is aimed at predictive modeling of solvent extraction processes in typical extraction equipment through multiple simulation methods at various scales of resolution. We have conducted detailed continuum fluid dynamics simulation on the process unit level as well as simulations of the molecular-level physical interactions which govern extraction chemistry. Through combination of information gained through simulations at each of these two tiers along with advanced techniques such as the Lattice Boltzmann Method (LBM) which can bridge these two scales, we can develop the tools to work towards predictive simulation for solvent extraction on the equipment scale (Figure 1). Themore » goal of such a tool-along with enabling optimized design and operation of extraction units-would be to allow prediction of stage extraction effrciency under specified conditions. Simulation efforts on each of the two scales will be described below. As the initial application of FELBM in the work performed during FYl0 has been on annular mixing it will be discussed in context of the continuum-scale. In the future, however, it is anticipated that the real value of FELBM will be in its use as a tool for sub-grid model development through highly refined DNS-like multiphase simulations facilitating exploration and development of droplet models including breakup and coalescence which will be needed for the large-scale simulations where droplet level physics cannot be resolved. In this area, it can have a significant advantage over traditional CFD methods as its high computational efficiency allows exploration of significantly greater physical detail especially as computational resources increase in the future.« less

Integrated Computational System for Aerodynamic Steering and Visualization

NASA Technical Reports Server (NTRS)

Hesselink, Lambertus

1999-01-01

In February of 1994, an effort from the Fluid Dynamics and Information Sciences Divisions at NASA Ames Research Center with McDonnel Douglas Aerospace Company and Stanford University was initiated to develop, demonstrate, validate and disseminate automated software for numerical aerodynamic simulation. The goal of the initiative was to develop a tri-discipline approach encompassing CFD, Intelligent Systems, and Automated Flow Feature Recognition to improve the utility of CFD in the design cycle. This approach would then be represented through an intelligent computational system which could accept an engineer's definition of a problem and construct an optimal and reliable CFD solution. Stanford University's role focused on developing technologies that advance visualization capabilities for analysis of CFD data, extract specific flow features useful for the design process, and compare CFD data with experimental data. During the years 1995-1997, Stanford University focused on developing techniques in the area of tensor visualization and flow feature extraction. Software libraries were created enabling feature extraction and exploration of tensor fields. As a proof of concept, a prototype system called the Integrated Computational System (ICS) was developed to demonstrate CFD design cycle. The current research effort focuses on finding a quantitative comparison of general vector fields based on topological features. Since the method relies on topological information, grid matching and vector alignment is not needed in the comparison. This is often a problem with many data comparison techniques. In addition, since only topology based information is stored and compared for each field, there is a significant compression of information that enables large databases to be quickly searched. This report will (1) briefly review the technologies developed during 1995-1997 (2) describe current technologies in the area of comparison techniques, (4) describe the theory of our new method researched during the grant year (5) summarize a few of the results and finally (6) discuss work within the last 6 months that are direct extensions from the grant.

Longitudinal Analysis of New Information Types in Clinical Notes

PubMed Central

Zhang, Rui; Pakhomov, Serguei; Melton, Genevieve B.

2014-01-01

It is increasingly recognized that redundant information in clinical notes within electronic health record (EHR) systems is ubiquitous, significant, and may negatively impact the secondary use of these notes for research and patient care. We investigated several automated methods to identify redundant versus relevant new information in clinical reports. These methods may provide a valuable approach to extract clinically pertinent information and further improve the accuracy of clinical information extraction systems. In this study, we used UMLS semantic types to extract several types of new information, including problems, medications, and laboratory information. Automatically identified new information highly correlated with manual reference standard annotations. Methods to identify different types of new information can potentially help to build up more robust information extraction systems for clinical researchers as well as aid clinicians and researchers in navigating clinical notes more effectively and quickly identify information pertaining to changes in health states. PMID:25717418

Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis*

PubMed Central

Gillet, Ludovic C.; Navarro, Pedro; Tate, Stephen; Röst, Hannes; Selevsek, Nathalie; Reiter, Lukas; Bonner, Ron; Aebersold, Ruedi

2012-01-01

Most proteomic studies use liquid chromatography coupled to tandem mass spectrometry to identify and quantify the peptides generated by the proteolysis of a biological sample. However, with the current methods it remains challenging to rapidly, consistently, reproducibly, accurately, and sensitively detect and quantify large fractions of proteomes across multiple samples. Here we present a new strategy that systematically queries sample sets for the presence and quantity of essentially any protein of interest. It consists of using the information available in fragment ion spectral libraries to mine the complete fragment ion maps generated using a data-independent acquisition method. For this study, the data were acquired on a fast, high resolution quadrupole-quadrupole time-of-flight (TOF) instrument by repeatedly cycling through 32 consecutive 25-Da precursor isolation windows (swaths). This SWATH MS acquisition setup generates, in a single sample injection, time-resolved fragment ion spectra for all the analytes detectable within the 400–1200 m/z precursor range and the user-defined retention time window. We show that suitable combinations of fragment ions extracted from these data sets are sufficiently specific to confidently identify query peptides over a dynamic range of 4 orders of magnitude, even if the precursors of the queried peptides are not detectable in the survey scans. We also show that queried peptides are quantified with a consistency and accuracy comparable with that of selected reaction monitoring, the gold standard proteomic quantification method. Moreover, targeted data extraction enables ad libitum quantification refinement and dynamic extension of protein probing by iterative re-mining of the once-and-forever acquired data sets. This combination of unbiased, broad range precursor ion fragmentation and targeted data extraction alleviates most constraints of present proteomic methods and should be equally applicable to the comprehensive analysis of other classes of analytes, beyond proteomics. PMID:22261725

Overview of image processing tools to extract physical information from JET videos

NASA Astrophysics Data System (ADS)

Craciunescu, T.; Murari, A.; Gelfusa, M.; Tiseanu, I.; Zoita, V.; EFDA Contributors, JET

2014-11-01

In magnetic confinement nuclear fusion devices such as JET, the last few years have witnessed a significant increase in the use of digital imagery, not only for the surveying and control of experiments, but also for the physical interpretation of results. More than 25 cameras are routinely used for imaging on JET in the infrared (IR) and visible spectral regions. These cameras can produce up to tens of Gbytes per shot and their information content can be very different, depending on the experimental conditions. However, the relevant information about the underlying physical processes is generally of much reduced dimensionality compared to the recorded data. The extraction of this information, which allows full exploitation of these diagnostics, is a challenging task. The image analysis consists, in most cases, of inverse problems which are typically ill-posed mathematically. The typology of objects to be analysed is very wide, and usually the images are affected by noise, low levels of contrast, low grey-level in-depth resolution, reshaping of moving objects, etc. Moreover, the plasma events have time constants of ms or tens of ms, which imposes tough conditions for real-time applications. On JET, in the last few years new tools and methods have been developed for physical information retrieval. The methodology of optical flow has allowed, under certain assumptions, the derivation of information about the dynamics of video objects associated with different physical phenomena, such as instabilities, pellets and filaments. The approach has been extended in order to approximate the optical flow within the MPEG compressed domain, allowing the manipulation of the large JET video databases and, in specific cases, even real-time data processing. The fast visible camera may provide new information that is potentially useful for disruption prediction. A set of methods, based on the extraction of structural information from the visual scene, have been developed for the automatic detection of MARFE (multifaceted asymmetric radiation from the edge) occurrences, which precede disruptions in density limit discharges. An original spot detection method has been developed for large surveys of videos in JET, and for the assessment of the long term trends in their evolution. The analysis of JET IR videos, recorded during JET operation with the ITER-like wall, allows the retrieval of data and hence correlation of the evolution of spots properties with macroscopic events, in particular series of intentional disruptions.

Optimal Information Extraction of Laser Scanning Dataset by Scale-Adaptive Reduction

NASA Astrophysics Data System (ADS)

Zang, Y.; Yang, B.

2018-04-01

3D laser technology is widely used to collocate the surface information of object. For various applications, we need to extract a good perceptual quality point cloud from the scanned points. To solve the problem, most of existing methods extract important points based on a fixed scale. However, geometric features of 3D object come from various geometric scales. We propose a multi-scale construction method based on radial basis function. For each scale, important points are extracted from the point cloud based on their importance. We apply a perception metric Just-Noticeable-Difference to measure degradation of each geometric scale. Finally, scale-adaptive optimal information extraction is realized. Experiments are undertaken to evaluate the effective of the proposed method, suggesting a reliable solution for optimal information extraction of object.

Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images.

PubMed

Kimori, Yoshitaka; Baba, Norio; Morone, Nobuhiro

2010-07-08

A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods. A new method to extract closely located, small target spots from biological images is proposed. This method starts with a simple but practical operation based on the extended morphological top-hat transformation to subtract an uneven background. The core of our novel approach is the following: first, the original image is rotated in an arbitrary direction and each rotated image is opened with a single straight line-segment structuring element. Second, the opened images are unified and then subtracted from the original image. To evaluate these procedures, model images of simulated spots with closely located targets were created and the efficacy of our method was compared to that of conventional morphological filtering methods. The results showed the better performance of our method. The spots of real microscope images can be quantified to confirm that the method is applicable in a given practice. Our method achieved effective spot extraction under various image conditions, including aggregated target spots, poor signal-to-noise ratio, and large variations in the background intensity. Furthermore, it has no restrictions with respect to the shape of the extracted spots. The features of our method allow its broad application in biological and biomedical image information analysis.

Three-dimensional tracking for efficient fire fighting in complex situations

NASA Astrophysics Data System (ADS)

Akhloufi, Moulay; Rossi, Lucile

2009-05-01

Each year, hundred millions hectares of forests burn causing human and economic losses. For efficient fire fighting, the personnel in the ground need tools permitting the prediction of fire front propagation. In this work, we present a new technique for automatically tracking fire spread in three-dimensional space. The proposed approach uses a stereo system to extract a 3D shape from fire images. A new segmentation technique is proposed and permits the extraction of fire regions in complex unstructured scenes. It works in the visible spectrum and combines information extracted from YUV and RGB color spaces. Unlike other techniques, our algorithm does not require previous knowledge about the scene. The resulting fire regions are classified into different homogenous zones using clustering techniques. Contours are then extracted and a feature detection algorithm is used to detect interest points like local maxima and corners. Extracted points from stereo images are then used to compute the 3D shape of the fire front. The resulting data permits to build the fire volume. The final model is used to compute important spatial and temporal fire characteristics like: spread dynamics, local orientation, heading direction, etc. Tests conducted on the ground show the efficiency of the proposed scheme. This scheme is being integrated with a fire spread mathematical model in order to predict and anticipate the fire behaviour during fire fighting. Also of interest to fire-fighters, is the proposed automatic segmentation technique that can be used in early detection of fire in complex scenes.

Methodology for extracting local constants from petroleum cracking flows

DOEpatents

Chang, Shen-Lin; Lottes, Steven A.; Zhou, Chenn Q.

2000-01-01

A methodology provides for the extraction of local chemical kinetic model constants for use in a reacting flow computational fluid dynamics (CFD) computer code with chemical kinetic computations to optimize the operating conditions or design of the system, including retrofit design improvements to existing systems. The coupled CFD and kinetic computer code are used in combination with data obtained from a matrix of experimental tests to extract the kinetic constants. Local fluid dynamic effects are implicitly included in the extracted local kinetic constants for each particular application system to which the methodology is applied. The extracted local kinetic model constants work well over a fairly broad range of operating conditions for specific and complex reaction sets in specific and complex reactor systems. While disclosed in terms of use in a Fluid Catalytic Cracking (FCC) riser, the inventive methodology has application in virtually any reaction set to extract constants for any particular application and reaction set formulation. The methodology includes the step of: (1) selecting the test data sets for various conditions; (2) establishing the general trend of the parametric effect on the measured product yields; (3) calculating product yields for the selected test conditions using coupled computational fluid dynamics and chemical kinetics; (4) adjusting the local kinetic constants to match calculated product yields with experimental data; and (5) validating the determined set of local kinetic constants by comparing the calculated results with experimental data from additional test runs at different operating conditions.

Chromatography, solid-phase extraction, and capillary electrochromatography with MIPs.

PubMed

Tóth, Blanka; Horvai, George

2012-01-01

Most analytical applications of molecularly imprinted polymers are based on their selective adsorption properties towards the template or its analogs. In chromatography, solid phase extraction and electrochromatography this adsorption is a dynamic process. The dynamic process combined with the nonlinear adsorption isotherm of the polymers and other factors results in complications which have limited the success of imprinted polymers. This chapter explains these problems and shows many examples of successful applications overcoming or avoiding the problems.

Optimization of dynamic-microwave assisted enzymatic hydrolysis extraction of total ginsenosides from stems and leaves of panax ginseng by response surface methodology.

PubMed

Wang, Xiao-Yan; Ren, Hui

2018-03-21

Ginseng stems and leaves (GSAL) are abundant in ginsenosides compounds. For efficient utilization of GSAL and the enhancement of total ginsenosides (TG) compound yields in GSAL, TG from GSAL were extracted, using dynamic-microwave assisted extraction coupled with enzymatic hydrolysis (DMAE-EH) method. The extraction process has been simulated and its main influencing factors such as ethanol concentration, microwave temperature, microwave time and pump flow rate have been optimized by response surface methodology coupled with a Box-Behnken design(BBD). The experimental results indicated that optimal extraction conditions of TG from GSAL were as follows: ethanol concentration of 75%, microwave temperature of 60°C, microwave time of 20 min and pump flow rate of 38 r/min. After experimental verification, the experimental yields of TG was 60.62 ± 0.85 mg g -1 , which were well agreement with the predicted by the model. In general, the present results demonstrated that DMAE-EH method was successfully used to extract total ginsenosides in GSAL.

The community dynamics of major bioleaching microorganisms during chalcopyrite leaching under the effect of organics.

PubMed

Li, Qihou; Tian, Ye; Fu, Xian; Yin, Huaqun; Zhou, Zhijun; Liang, Yiting; Qiu, Guanzhou; Liu, Jie; Liu, Hongwei; Liang, Yili; Shen, Li; Cong, Jing; Liu, Xueduan

2011-08-01

To determine the effect of organics (yeast extract) on microbial community during chalcopyrite bioleaching at different temperature, real-time polymerase chain reaction (PCR) was employed to analyze community dynamics of major bacteria applied in bioleaching. The results showed that yeast extract exerted great impact on microbial community, and therefore influencing bioleaching rate. To be specific, yeast extract was adverse to this bioleaching process at 30°C due to decreased proportion of important chemolithotrophs such as Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans. However, yeast extract could promote bioleaching rate at 40°C on account of the increased number and enhanced work of Ferroplasma thermophilum, a kind of facultative bacteria. Similarly, bioleaching rate was enhanced under the effect of yeast extract at 50°C owing to the work of Acidianus brierleyi. At 60°C, bioleaching rate was close to 100% and temperature was the dominant factor determining bioleaching rate. Interestingly, the existence of yeast extract greatly enhanced the relative competitiveness of Ferroplasma thermophilum in this complex bioleaching microbial community.

Dynamic leaching and fractionation of trace elements from environmental solids exploiting a novel circulating-flow platform.

PubMed

Mori, Masanobu; Nakano, Koji; Sasaki, Masaya; Shinozaki, Haruka; Suzuki, Shiho; Okawara, Chitose; Miró, Manuel; Itabashi, Hideyuki

2016-02-01

A dynamic flow-through microcolumn extraction system based on extractant re-circulation is herein proposed as a novel analytical approach for simplification of bioaccessibility tests of trace elements in sediments. On-line metal leaching is undertaken in the format of all injection (AI) analysis, which is a sequel of flow injection analysis, but involving extraction under steady-state conditions. The minimum circulation times and flow rates required to determine the maximum bioaccessible pools of target metals (viz., Cu, Zn, Cd, and Pb) from lake and river sediment samples were estimated using Tessier's sequential extraction scheme and an acid single extraction test. The on-line AIA method was successfully validated by mass balance studies of CRM and real sediment samples. Tessier's test in on-line AI format demonstrated to be carried out by one third of extraction time (6h against more than 17 h by the conventional method), with better analytical precision (<9.2% against >15% by the conventional method) and significant decrease in blank readouts as compared with the manual batch counterpart. Copyright © 2015 Elsevier B.V. All rights reserved.

Effects of pyrite and sphalerite on population compositions, dynamics and copper extraction efficiency in chalcopyrite bioleaching process.

PubMed

Xiao, Yunhua; Liu, Xueduan; Dong, Weiling; Liang, Yili; Niu, Jiaojiao; Gu, Yabing; Ma, Liyuan; Hao, Xiaodong; Zhang, Xian; Xu, Zhen; Yin, Huaqun

2017-07-01

This study used an artificial microbial community with four known moderately thermophilic acidophiles (three bacteria including Acidithiobacillus caldus S1, Sulfobacillus thermosulfidooxidans ST and Leptospirillum ferriphilum YSK, and one archaea, Ferroplasma thermophilum L1) to explore the variation of microbial community structure, composition, dynamics and function (e.g., copper extraction efficiency) in chalcopyrite bioleaching (C) systems with additions of pyrite (CP) or sphalerite (CS). The community compositions and dynamics in the solution and on the ore surface were investigated by real-time quantitative PCR (qPCR). The results showed that the addition of pyrite or sphalerite changed the microbial community composition and dynamics dramatically during the chalcopyrite bioleaching process. For example, A. caldus (above 60%) was the dominant species at the initial stage in three groups, and at the middle stage, still dominated C group (above 70%), but it was replaced by L. ferriphilum (above 60%) in CP and CS groups; at the final stage, L. ferriphilum dominated C group, while F. thermophilum dominated CP group on the ore surface. Furthermore, the additions of pyrite or sphalerite both made the increase of redox potential (ORP) and the concentrations of Fe 3+ and H + , which would affect the microbial community compositions and copper extraction efficiency. Additionally, pyrite could enhance copper extraction efficiency (e.g., improving around 13.2% on day 6) during chalcopyrite bioleaching; on the contrary, sphalerite restrained it.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

PubMed Central

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

Information extraction during simultaneous motion processing.

PubMed

Rideaux, Reuben; Edwards, Mark

2014-02-01

When confronted with multiple moving objects the visual system can process them in two stages: an initial stage in which a limited number of signals are processed in parallel (i.e. simultaneously) followed by a sequential stage. We previously demonstrated that during the simultaneous stage, observers could discriminate between presentations containing up to 5 vs. 6 spatially localized motion signals (Edwards & Rideaux, 2013). Here we investigate what information is actually extracted during the simultaneous stage and whether the simultaneous limit varies with the detail of information extracted. This was achieved by measuring the ability of observers to extract varied information from low detail, i.e. the number of signals presented, to high detail, i.e. the actual directions present and the direction of a specific element, during the simultaneous stage. The results indicate that the resolution of simultaneous processing varies as a function of the information which is extracted, i.e. as the information extraction becomes more detailed, from the number of moving elements to the direction of a specific element, the capacity to process multiple signals is reduced. Thus, when assigning a capacity to simultaneous motion processing, this must be qualified by designating the degree of information extraction. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

PubMed

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.

CAMERRA: An analysis tool for the computation of conformational dynamics by evaluating residue-residue associations.

PubMed

Johnson, Quentin R; Lindsay, Richard J; Shen, Tongye

2018-02-21

A computational method which extracts the dominant motions from an ensemble of biomolecular conformations via a correlation analysis of residue-residue contacts is presented. The algorithm first renders the structural information into contact matrices, then constructs the collective modes based on the correlated dynamics of a selected set of dynamic contacts. Associated programs can bridge the results for further visualization using graphics software. The aim of this method is to provide an analysis of conformations of biopolymers from the contact viewpoint. It may assist a systematical uncovering of conformational switching mechanisms existing in proteins and biopolymer systems in general by statistical analysis of simulation snapshots. In contrast to conventional correlation analyses of Cartesian coordinates (such as distance covariance analysis and Cartesian principal component analysis), this program also provides an alternative way to locate essential collective motions in general. Herein, we detail the algorithm in a stepwise manner and comment on the importance of the method as applied to decoding allosteric mechanisms. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

A comparative analysis of alternative approaches for quantifying nonlinear dynamics in cardiovascular system.

PubMed

Chen, Yun; Yang, Hui

2013-01-01

Heart rate variability (HRV) analysis has emerged as an important research topic to evaluate autonomic cardiac function. However, traditional time and frequency-domain analysis characterizes and quantify only linear and stationary phenomena. In the present investigation, we made a comparative analysis of three alternative approaches (i.e., wavelet multifractal analysis, Lyapunov exponents and multiscale entropy analysis) for quantifying nonlinear dynamics in heart rate time series. Note that these extracted nonlinear features provide information about nonlinear scaling behaviors and the complexity of cardiac systems. To evaluate the performance, we used 24-hour HRV recordings from 54 healthy subjects and 29 heart failure patients, available in PhysioNet. Three nonlinear methods are evaluated not only individually but also in combination using three classification algorithms, i.e., linear discriminate analysis, quadratic discriminate analysis and k-nearest neighbors. Experimental results show that three nonlinear methods capture nonlinear dynamics from different perspectives and the combined feature set achieves the best performance, i.e., sensitivity 97.7% and specificity 91.5%. Collectively, nonlinear HRV features are shown to have the promise to identify the disorders in autonomic cardiovascular function.

An augmented magnetic navigation system for Transcatheter Aortic Valve Implantation.

PubMed

Luo, Zhe; Cai, Junfeng; Nie, Yuanyuan; Wang, Guotai; Gu, Lixu

2013-01-01

This research proposes an augmented magnetic navigation system for Transcatheter Aortic Valve Implantation (TAVI) employing a magnetic tracking system (MTS) combined with a dynamic aortic model and intra-operative ultrasound (US) images. The dynamic 3D aortic model is constructed based on the preoperative 4D computed tomography (CT), which is animated according to the real time electrocardiograph (ECG) input of patient. And a preoperative planning is performed to determine the target position of the aortic valve prosthesis. The temporal alignment is performed to synchronize the ECG signals, intra-operative US image and tracking information. Afterwards, with the assistance of synchronized ECG signals, the contour of aortic root automatic extracted from short axis US image is registered to the dynamic aortic model by a feature based registration intra-operatively. Then the augmented MTS guides the interventionist to confidently position and deploy the aortic valve prosthesis to target. The system was validated by animal studies on three porcine subjects, the deployment and tilting errors of which are 3.17 ± 0.91 mm and 7.40 ± 2.89° respectively.

Motion-Blurred Particle Image Restoration for On-Line Wear Monitoring

PubMed Central

Peng, Yeping; Wu, Tonghai; Wang, Shuo; Kwok, Ngaiming; Peng, Zhongxiao

2015-01-01

On-line images of wear debris contain important information for real-time condition monitoring, and a dynamic imaging technique can eliminate particle overlaps commonly found in static images, for instance, acquired using ferrography. However, dynamic wear debris images captured in a running machine are unavoidably blurred because the particles in lubricant are in motion. Hence, it is difficult to acquire reliable images of wear debris with an adequate resolution for particle feature extraction. In order to obtain sharp wear particle images, an image processing approach is proposed. Blurred particles were firstly separated from the static background by utilizing a background subtraction method. Second, the point spread function was estimated using power cepstrum to determine the blur direction and length. Then, the Wiener filter algorithm was adopted to perform image restoration to improve the image quality. Finally, experiments were conducted with a large number of dynamic particle images to validate the effectiveness of the proposed method and the performance of the approach was also evaluated. This study provides a new practical approach to acquire clear images for on-line wear monitoring. PMID:25856328

What Tumor Dynamics Modeling Can Teach us About Exploiting the Stem-Cell View for Better Cancer Treatment

PubMed Central

Day, Roger S

2015-01-01

The cancer stem cell hypothesis is that in human solid cancers, only a small proportion of the cells, the cancer stem cells (CSCs), are self-renewing; the vast majority of the cancer cells are unable to sustain tumor growth indefinitely on their own. In recent years, discoveries have led to the concentration, if not isolation, of putative CSCs. The evidence has mounted that CSCs do exist and are important. This knowledge may promote better understanding of treatment resistance, create opportunities to test agents against CSCs, and open up promise for a fresh approach to cancer treatment. The first clinical trials of new anti-CSC agents are completed, and many others follow. Excitement is mounting that this knowledge will lead to major improvements, even breakthroughs, in treating cancer. However, exploitation of this phenomenon may be more successful if informed by insights into the population dynamics of tumor development. We revive some ideas in tumor dynamics modeling to extract some guidance in designing anti-CSC treatment regimens and the clinical trials that test them. PMID:25780337

Modelling Nonlinear Dynamic Textures using Hybrid DWT-DCT and Kernel PCA with GPU

NASA Astrophysics Data System (ADS)

Ghadekar, Premanand Pralhad; Chopade, Nilkanth Bhikaji

2016-12-01

Most of the real-world dynamic textures are nonlinear, non-stationary, and irregular. Nonlinear motion also has some repetition of motion, but it exhibits high variation, stochasticity, and randomness. Hybrid DWT-DCT and Kernel Principal Component Analysis (KPCA) with YCbCr/YIQ colour coding using the Dynamic Texture Unit (DTU) approach is proposed to model a nonlinear dynamic texture, which provides better results than state-of-art methods in terms of PSNR, compression ratio, model coefficients, and model size. Dynamic texture is decomposed into DTUs as they help to extract temporal self-similarity. Hybrid DWT-DCT is used to extract spatial redundancy. YCbCr/YIQ colour encoding is performed to capture chromatic correlation. KPCA is applied to capture nonlinear motion. Further, the proposed algorithm is implemented on Graphics Processing Unit (GPU), which comprise of hundreds of small processors to decrease time complexity and to achieve parallelism.

Initiation of DNA replication requires actin dynamics and formin activity.

PubMed

Parisis, Nikolaos; Krasinska, Liliana; Harker, Bethany; Urbach, Serge; Rossignol, Michel; Camasses, Alain; Dewar, James; Morin, Nathalie; Fisher, Daniel

2017-11-02

Nuclear actin regulates transcriptional programmes in a manner dependent on its levels and polymerisation state. This dynamics is determined by the balance of nucleocytoplasmic shuttling, formin- and redox-dependent filament polymerisation. Here, using Xenopus egg extracts and human somatic cells, we show that actin dynamics and formins are essential for DNA replication. In proliferating cells, formin inhibition abolishes nuclear transport and initiation of DNA replication, as well as general transcription. In replicating nuclei from transcriptionally silent Xenopus egg extracts, we identified numerous actin regulators, and disruption of actin dynamics abrogates nuclear transport, preventing NLS (nuclear localisation signal)-cargo release from RanGTP-importin complexes. Nuclear formin activity is further required to promote loading of cyclin-dependent kinase (CDK) and proliferating cell nuclear antigen (PCNA) onto chromatin, as well as initiation and elongation of DNA replication. Therefore, actin dynamics and formins control DNA replication by multiple direct and indirect mechanisms. © 2017 The Authors.

[Advances in studies on multi-stage countercurrent extraction technology in traditional Chinese medicine].

PubMed

Xie, Zhi-Peng; Liu, Xue-Song; Chen, Yong; Cai, Ming; Qu, Hai-Bin; Cheng, Yi-Yu

2007-05-01

Multi-stage countercurrent extraction technology, integrating solvent extraction, repercolation with dynamic and countercurrent extraction, is a novel extraction technology for the traditional Chinese medicine. This solvent-saving, energy-saving and high-extraction-efficiency technology can at the most drive active compounds to diffuse from the herbal materials into the solvent stage by stage by creating concentration differences between the herbal materials and the solvents. This paper reviewed the basic principle, the influence factors and the research progress and trends of the equipments and the application of the multi-stage countercurrent extraction.

Combining EPR spectroscopy and X-ray crystallography to elucidate the structure and dynamics of conformationally constrained spin labels in T4 lysozyme single crystals.

PubMed

Consentius, Philipp; Gohlke, Ulrich; Loll, Bernhard; Alings, Claudia; Heinemann, Udo; Wahl, Markus C; Risse, Thomas

2017-08-09

Electron paramagnetic resonance (EPR) spectroscopy in combination with site-directed spin labeling is used to investigate the structure and dynamics of conformationally constrained spin labels in T4 lysozyme single crystals. Within a single crystal, the oriented ensemble of spin bearing moieties results in a strong angle dependence of the EPR spectra. A quantitative description of the EPR spectra requires the determination of the unit cell orientation with respect to the sample tube and the orientation of the spin bearing moieties within the crystal lattice. Angle dependent EPR spectra were analyzed by line shape simulations using the stochastic Liouville equation approach developed by Freed and co-workers and an effective Hamiltonian approach. The gain in spectral information obtained from the EPR spectra of single crystalline samples taken at different frequencies, namely the X-band and Q-band, allows us to discriminate between motional models describing the spectra of isotropic solutions similarly well. In addition, it is shown that the angle dependent single crystal spectra allow us to identify two spin label rotamers with very similar side chain dynamics. These results demonstrate the utility of single crystal EPR spectroscopy in combination with spectral line shape simulation techniques to extract valuable dynamic information not readily available from the analysis of isotropic systems. In addition, it will be shown that the loss of electron density in high resolution diffraction experiments at room temperature does not allow us to conclude that there is significant structural disorder in the system.

Cortical responses to dynamic emotional facial expressions generalize across stimuli, and are sensitive to task-relevance, in adults with and without Autism.

PubMed

Kliemann, Dorit; Richardson, Hilary; Anzellotti, Stefano; Ayyash, Dima; Haskins, Amanda J; Gabrieli, John D E; Saxe, Rebecca R

2018-06-01

Individuals with Autism Spectrum Disorders (ASD) report difficulties extracting meaningful information from dynamic and complex social cues, like facial expressions. The nature and mechanisms of these difficulties remain unclear. Here we tested whether that difficulty can be traced to the pattern of activity in "social brain" regions, when viewing dynamic facial expressions. In two studies, adult participants (male and female) watched brief videos of a range of positive and negative facial expressions, while undergoing functional magnetic resonance imaging (Study 1: ASD n = 16, control n = 21; Study 2: ASD n = 22, control n = 30). Patterns of hemodynamic activity differentiated among facial emotional expressions in left and right superior temporal sulcus, fusiform gyrus, and parts of medial prefrontal cortex. In both control participants and high-functioning individuals with ASD, we observed (i) similar responses to emotional valence that generalized across facial expressions and animated social events; (ii) similar flexibility of responses to emotional valence, when manipulating the task-relevance of perceived emotions; and (iii) similar responses to a range of emotions within valence. Altogether, the data indicate that there was little or no group difference in cortical responses to isolated dynamic emotional facial expressions, as measured with fMRI. Difficulties with real-world social communication and social interaction in ASD may instead reflect differences in initiating and maintaining contingent interactions, or in integrating social information over time or context. Copyright © 2018 Elsevier Ltd. All rights reserved.

Research on Optimal Observation Scale for Damaged Buildings after Earthquake Based on Optimal Feature Space

NASA Astrophysics Data System (ADS)

Chen, J.; Chen, W.; Dou, A.; Li, W.; Sun, Y.

2018-04-01

A new information extraction method of damaged buildings rooted in optimal feature space is put forward on the basis of the traditional object-oriented method. In this new method, ESP (estimate of scale parameter) tool is used to optimize the segmentation of image. Then the distance matrix and minimum separation distance of all kinds of surface features are calculated through sample selection to find the optimal feature space, which is finally applied to extract the image of damaged buildings after earthquake. The overall extraction accuracy reaches 83.1 %, the kappa coefficient 0.813. The new information extraction method greatly improves the extraction accuracy and efficiency, compared with the traditional object-oriented method, and owns a good promotional value in the information extraction of damaged buildings. In addition, the new method can be used for the information extraction of different-resolution images of damaged buildings after earthquake, then to seek the optimal observation scale of damaged buildings through accuracy evaluation. It is supposed that the optimal observation scale of damaged buildings is between 1 m and 1.2 m, which provides a reference for future information extraction of damaged buildings.

Laser Rayleigh and Raman Diagnostics for Small Hydrogen/oxygen Rockets

NASA Technical Reports Server (NTRS)

Degroot, Wilhelmus A.; Zupanc, Frank J.

1993-01-01

Localized velocity, temperature, and species concentration measurements in rocket flow fields are needed to evaluate predictive computational fluid dynamics (CFD) codes and identify causes of poor rocket performance. Velocity, temperature, and total number density information have been successfully extracted from spectrally resolved Rayleigh scattering in the plume of small hydrogen/oxygen rockets. Light from a narrow band laser is scattered from the moving molecules with a Doppler shifted frequency. Two components of the velocity can be extracted by observing the scattered light from two directions. Thermal broadening of the scattered light provides a measure of the temperature, while the integrated scattering intensity is proportional to the number density. Spontaneous Raman scattering has been used to measure temperature and species concentration in similar plumes. Light from a dye laser is scattered by molecules in the rocket plume. Raman spectra scattered from major species are resolved by observing the inelastically scattered light with linear array mounted to a spectrometer. Temperature and oxygen concentrations have been extracted by fitting a model function to the measured Raman spectrum. Results of measurements on small rockets mounted inside a high altitude chamber using both diagnostic techniques are reported.

Analysis of Technique to Extract Data from the Web for Improved Performance

NASA Astrophysics Data System (ADS)

Gupta, Neena; Singh, Manish

2010-11-01

The World Wide Web rapidly guides the world into a newly amazing electronic world, where everyone can publish anything in electronic form and extract almost all the information. Extraction of information from semi structured or unstructured documents, such as web pages, is a useful yet complex task. Data extraction, which is important for many applications, extracts the records from the HTML files automatically. Ontologies can achieve a high degree of accuracy in data extraction. We analyze method for data extraction OBDE (Ontology-Based Data Extraction), which automatically extracts the query result records from the web with the help of agents. OBDE first constructs an ontology for a domain according to information matching between the query interfaces and query result pages from different web sites within the same domain. Then, the constructed domain ontology is used during data extraction to identify the query result section in a query result page and to align and label the data values in the extracted records. The ontology-assisted data extraction method is fully automatic and overcomes many of the deficiencies of current automatic data extraction methods.

Contextual information and perceptual-cognitive expertise in a dynamic, temporally-constrained task.

PubMed

Murphy, Colm P; Jackson, Robin C; Cooke, Karl; Roca, André; Benguigui, Nicolas; Williams, A Mark

2016-12-01

Skilled performers extract and process postural information from an opponent during anticipation more effectively than their less-skilled counterparts. In contrast, the role and importance of contextual information in anticipation has received only minimal attention. We evaluate the importance of contextual information in anticipation and examine the underlying perceptual-cognitive processes. We present skilled and less-skilled tennis players with normal video or animated footage of the same rallies. In the animated condition, sequences were created using player movement and ball trajectory data, and postural information from the players was removed, constraining participants to anticipate based on contextual information alone. Participants judged ball bounce location of the opponent's final occluded shot. The 2 groups were more accurate than chance in both display conditions with skilled being more accurate than less-skilled (Exp. 1) participants. When anticipating based on contextual information alone, skilled participants employed different gaze behaviors to less-skilled counterparts and provided verbal reports of thoughts which were indicative of more thorough evaluation of contextual information (Exp. 2). Findings highlight the importance of both postural and contextual information in anticipation and indicate that perceptual-cognitive expertise is underpinned by processes that facilitate more effective processing of contextual information, in the absence of postural information. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Bayesian inversion analysis of nonlinear dynamics in surface heterogeneous reactions.

PubMed

Omori, Toshiaki; Kuwatani, Tatsu; Okamoto, Atsushi; Hukushima, Koji

2016-09-01

It is essential to extract nonlinear dynamics from time-series data as an inverse problem in natural sciences. We propose a Bayesian statistical framework for extracting nonlinear dynamics of surface heterogeneous reactions from sparse and noisy observable data. Surface heterogeneous reactions are chemical reactions with conjugation of multiple phases, and they have the intrinsic nonlinearity of their dynamics caused by the effect of surface-area between different phases. We adapt a belief propagation method and an expectation-maximization (EM) algorithm to partial observation problem, in order to simultaneously estimate the time course of hidden variables and the kinetic parameters underlying dynamics. The proposed belief propagation method is performed by using sequential Monte Carlo algorithm in order to estimate nonlinear dynamical system. Using our proposed method, we show that the rate constants of dissolution and precipitation reactions, which are typical examples of surface heterogeneous reactions, as well as the temporal changes of solid reactants and products, were successfully estimated only from the observable temporal changes in the concentration of the dissolved intermediate product.

A randomized control trial comparing the visual and verbal communication methods for reducing fear and anxiety during tooth extraction.

PubMed

Gazal, Giath; Tola, Ahmed W; Fareed, Wamiq M; Alnazzawi, Ahmad A; Zafar, Muhammad S

2016-04-01

To evaluate the value of using the visual information for reducing the level of dental fear and anxiety in patients undergoing teeth extraction under LA. A total of 64 patients were indiscriminately allotted to solitary of the study groups following reading the information sheet and signing the formal consent. If patient was in the control group, only verbal information and routine warnings were provided. If patient was in the study group, tooth extraction video was showed. The level of dental fear and anxiety was detailed by the patients on customary 100 mm visual analog scales (VAS), with "no dental fear and anxiety" (0 mm) and "severe dental distress and unease" (100 mm). Evaluation of dental apprehension and fretfulness was made pre-operatively, following visual/verbal information and post-extraction. There was a substantial variance among the mean dental fear and anxiety scores for both groups post-extraction (p-value < 0.05). Patients in tooth extraction video group were more comfortable after dental extraction than verbal information and routine warning group. For tooth extraction video group there were major decreases in dental distress and anxiety scores between the pre-operative and either post video information scores or postoperative scores (p-values < 0.05). Younger patients recorded higher dental fear and anxiety scores than older ones (P < 0.05). Dental fear and anxiety associated with dental extractions under local anesthesia can be reduced by showing a tooth extraction video to the patients preoperatively.

The study on the parallel processing based time series correlation analysis of RBC membrane flickering in quantitative phase imaging

NASA Astrophysics Data System (ADS)

Lee, Minsuk; Won, Youngjae; Park, Byungjun; Lee, Seungrag

2017-02-01

Not only static characteristics but also dynamic characteristics of the red blood cell (RBC) contains useful information for the blood diagnosis. Quantitative phase imaging (QPI) can capture sample images with subnanometer scale depth resolution and millisecond scale temporal resolution. Various researches have been used QPI for the RBC diagnosis, and recently many researches has been developed to decrease the process time of RBC information extraction using QPI by the parallel computing algorithm, however previous studies are interested in the static parameters such as morphology of the cells or simple dynamic parameters such as root mean square (RMS) of the membrane fluctuations. Previously, we presented a practical blood test method using the time series correlation analysis of RBC membrane flickering with QPI. However, this method has shown that there is a limit to the clinical application because of the long computation time. In this study, we present an accelerated time series correlation analysis of RBC membrane flickering using the parallel computing algorithm. This method showed consistent fractal scaling exponent results of the surrounding medium and the normal RBC with our previous research.

Spectral gap optimization of order parameters for sampling complex molecular systems

PubMed Central

Tiwary, Pratyush; Berne, B. J.

2016-01-01

In modern-day simulations of many-body systems, much of the computational complexity is shifted to the identification of slowly changing molecular order parameters called collective variables (CVs) or reaction coordinates. A vast array of enhanced-sampling methods are based on the identification and biasing of these low-dimensional order parameters, whose fluctuations are important in driving rare events of interest. Here, we describe a new algorithm for finding optimal low-dimensional CVs for use in enhanced-sampling biasing methods like umbrella sampling, metadynamics, and related methods, when limited prior static and dynamic information is known about the system, and a much larger set of candidate CVs is specified. The algorithm involves estimating the best combination of these candidate CVs, as quantified by a maximum path entropy estimate of the spectral gap for dynamics viewed as a function of that CV. The algorithm is called spectral gap optimization of order parameters (SGOOP). Through multiple practical examples, we show how this postprocessing procedure can lead to optimization of CV and several orders of magnitude improvement in the convergence of the free energy calculated through metadynamics, essentially giving the ability to extract useful information even from unsuccessful metadynamics runs. PMID:26929365

Unraveling nonadiabatic ionization and Coulomb potential effect in strong-field photoelectron holography.

PubMed

Song, Xiaohong; Lin, Cheng; Sheng, Zhihao; Liu, Peng; Chen, Zhangjin; Yang, Weifeng; Hu, Shilin; Lin, C D; Chen, Jing

2016-06-22

Strong field photoelectron holography has been proposed as a means for interrogating the spatial and temporal information of electrons and ions in a dynamic system. After ionization, part of the electron wave packet may directly go to the detector (the reference wave), while another part may be driven back and scatters off the ion(the signal wave). The interference hologram of the two waves may be used to extract target information embedded in the collision process. Unlike conventional optical holography, however, propagation of the electron wave packet is affected by the Coulomb potential as well as by the laser field. In addition, electrons are emitted over the whole laser pulse duration, thus multiple interferences may occur. In this work, we used a generalized quantum-trajectory Monte Carlo method to investigate the effect of Coulomb potential and the nonadiabatic subcycle ionization on the photoelectron hologram. We showed that photoelectron hologram can be well described only when the effect of nonadiabatic ionization is accounted for, and Coulomb potential can be neglected only in the tunnel ionization regime. Our results help paving the way for establishing photoelectron holography for probing spatial and dynamic properties of atoms and molecules.

Unraveling nonadiabatic ionization and Coulomb potential effect in strong-field photoelectron holography

PubMed Central

Song, Xiaohong; Lin, Cheng; Sheng, Zhihao; Liu, Peng; Chen, Zhangjin; Yang, Weifeng; Hu, Shilin; Lin, C. D.; Chen, Jing

2016-01-01

Strong field photoelectron holography has been proposed as a means for interrogating the spatial and temporal information of electrons and ions in a dynamic system. After ionization, part of the electron wave packet may directly go to the detector (the reference wave), while another part may be driven back and scatters off the ion(the signal wave). The interference hologram of the two waves may be used to extract target information embedded in the collision process. Unlike conventional optical holography, however, propagation of the electron wave packet is affected by the Coulomb potential as well as by the laser field. In addition, electrons are emitted over the whole laser pulse duration, thus multiple interferences may occur. In this work, we used a generalized quantum-trajectory Monte Carlo method to investigate the effect of Coulomb potential and the nonadiabatic subcycle ionization on the photoelectron hologram. We showed that photoelectron hologram can be well described only when the effect of nonadiabatic ionization is accounted for, and Coulomb potential can be neglected only in the tunnel ionization regime. Our results help paving the way for establishing photoelectron holography for probing spatial and dynamic properties of atoms and molecules. PMID:27329071

Unraveling nonadiabatic ionization and Coulomb potential effect in strong-field photoelectron holography

DOE PAGES

Song, Xiaohong; Lin, Cheng; Sheng, Zhihao; ...

2016-06-22

Strong field photoelectron holography has been proposed as a means for interrogating the spatial and temporal information of electrons and ions in a dynamic system. After ionization, part of the electron wave packet may directly go to the detector (the reference wave), while another part may be driven back and scatters off the ion(the signal wave). The interference hologram of the two waves may be used to extract target information embedded in the collision process. Unlike conventional optical holography, however, propagation of the electron wave packet is affected by the Coulomb potential as well as by the laser field. Inmore » addition, electrons are emitted over the whole laser pulse duration, thus multiple interferences may occur. In this work, we used a generalized quantum-trajectory Monte Carlo method to investigate the effect of Coulomb potential and the nonadiabatic subcycle ionization on the photoelectron hologram. Here, we showed that photoelectron hologram can be well described only when the effect of nonadiabatic ionization is accounted for, and Coulomb potential can be neglected only in the tunnel ionization regime. Our results help paving the way for establishing photoelectron holography for probing spatial and dynamic properties of atoms and molecules.« less

Manifold angles, the concept of self-similarity, and angle-enhanced bifurcation diagrams

PubMed Central

Beims, Marcus W.; Gallas, Jason A. C.

2016-01-01

Chaos and regularity are routinely discriminated by using Lyapunov exponents distilled from the norm of orthogonalized Lyapunov vectors, propagated during the temporal evolution of the dynamics. Such exponents are mean-field-like averages that, for each degree of freedom, squeeze the whole temporal evolution complexity into just a single number. However, Lyapunov vectors also contain a step-by-step record of what exactly happens with the angles between stable and unstable manifolds during the whole evolution, a big-data information permanently erased by repeated orthogonalizations. Here, we study changes of angles between invariant subspaces as observed during temporal evolution of Hénon’s system. Such angles are calculated numerically and analytically and used to characterize self-similarity of a chaotic attractor. In addition, we show how standard tools of dynamical systems may be angle-enhanced by dressing them with informations not difficult to extract. Such angle-enhanced tools reveal unexpected and practical facts that are described in detail. For instance, we present a video showing an angle-enhanced bifurcation diagram that exposes from several perspectives the complex geometrical features underlying the attractors. We believe such findings to be generic for extended classes of systems. PMID:26732416

Manifold angles, the concept of self-similarity, and angle-enhanced bifurcation diagrams

NASA Astrophysics Data System (ADS)

Beims, Marcus W.; Gallas, Jason A. C.

2016-01-01

Chaos and regularity are routinely discriminated by using Lyapunov exponents distilled from the norm of orthogonalized Lyapunov vectors, propagated during the temporal evolution of the dynamics. Such exponents are mean-field-like averages that, for each degree of freedom, squeeze the whole temporal evolution complexity into just a single number. However, Lyapunov vectors also contain a step-by-step record of what exactly happens with the angles between stable and unstable manifolds during the whole evolution, a big-data information permanently erased by repeated orthogonalizations. Here, we study changes of angles between invariant subspaces as observed during temporal evolution of Hénon’s system. Such angles are calculated numerically and analytically and used to characterize self-similarity of a chaotic attractor. In addition, we show how standard tools of dynamical systems may be angle-enhanced by dressing them with informations not difficult to extract. Such angle-enhanced tools reveal unexpected and practical facts that are described in detail. For instance, we present a video showing an angle-enhanced bifurcation diagram that exposes from several perspectives the complex geometrical features underlying the attractors. We believe such findings to be generic for extended classes of systems.

Model learning for robot control: a survey.

PubMed

Nguyen-Tuong, Duy; Peters, Jan

2011-11-01

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

Information Extraction from Unstructured Text for the Biodefense Knowledge Center

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

Samatova, N F; Park, B; Krishnamurthy, R

2005-04-29

The Bio-Encyclopedia at the Biodefense Knowledge Center (BKC) is being constructed to allow an early detection of emerging biological threats to homeland security. It requires highly structured information extracted from variety of data sources. However, the quantity of new and vital information available from every day sources cannot be assimilated by hand, and therefore reliable high-throughput information extraction techniques are much anticipated. In support of the BKC, Lawrence Livermore National Laboratory and Oak Ridge National Laboratory, together with the University of Utah, are developing an information extraction system built around the bioterrorism domain. This paper reports two important pieces ofmore » our effort integrated in the system: key phrase extraction and semantic tagging. Whereas two key phrase extraction technologies developed during the course of project help identify relevant texts, our state-of-the-art semantic tagging system can pinpoint phrases related to emerging biological threats. Also we are enhancing and tailoring the Bio-Encyclopedia by augmenting semantic dictionaries and extracting details of important events, such as suspected disease outbreaks. Some of these technologies have already been applied to large corpora of free text sources vital to the BKC mission, including ProMED-mail, PubMed abstracts, and the DHS's Information Analysis and Infrastructure Protection (IAIP) news clippings. In order to address the challenges involved in incorporating such large amounts of unstructured text, the overall system is focused on precise extraction of the most relevant information for inclusion in the BKC.« less

An Effective Approach to Biomedical Information Extraction with Limited Training Data

ERIC Educational Resources Information Center

Jonnalagadda, Siddhartha

2011-01-01

In the current millennium, extensive use of computers and the internet caused an exponential increase in information. Few research areas are as important as information extraction, which primarily involves extracting concepts and the relations between them from free text. Limitations in the size of training data, lack of lexicons and lack of…

Tagline: Information Extraction for Semi-Structured Text Elements in Medical Progress Notes

ERIC Educational Resources Information Center

Finch, Dezon Kile

2012-01-01

Text analysis has become an important research activity in the Department of Veterans Affairs (VA). Statistical text mining and natural language processing have been shown to be very effective for extracting useful information from medical documents. However, neither of these techniques is effective at extracting the information stored in…

Rare tradition of the folk medicinal use of Aconitum spp. is kept alive in Solčavsko, Slovenia.

PubMed

Povšnar, Marija; Koželj, Gordana; Kreft, Samo; Lumpert, Mateja

2017-08-08

Aconitum species are poisonous plants that have been used in Western medicine for centuries. In the nineteenth century, these plants were part of official and folk medicine in the Slovenian territory. According to current ethnobotanical studies, folk use of Aconitum species is rarely reported in Europe. The purpose of this study was to research the folk medicinal use of Aconitum species in Solčavsko, Slovenia; to collect recipes for the preparation of Aconitum spp., indications for use, and dosing; and to investigate whether the folk use of aconite was connected to poisoning incidents. In Solčavsko, a remote alpine area in northern Slovenia, we performed semi-structured interviews with 19 informants in Solčavsko, 3 informants in Luče, and two retired physicians who worked in that area. Three samples of homemade ethanolic extracts were obtained from informants, and the concentration of aconitine was measured. In addition, four extracts were prepared according to reported recipes. All 22 informants knew of Aconitum spp. and their therapeutic use, and 5 of them provided a detailed description of the preparation and use of "voukuc", an ethanolic extract made from aconite roots. Seven informants were unable to describe the preparation in detail, since they knew of the extract only from the narration of others or they remembered it from childhood. Most likely, the roots of Aconitum tauricum and Aconitum napellus were used for the preparation of the extract, and the solvent was homemade spirits. Four informants kept the extract at home; two extracts were prepared recently (1998 and 2015). Three extracts were analyzed, and 2 contained aconitine. Informants reported many indications for the use of the extract; it was used internally and, in some cases, externally as well. The extract was also used in animals. The extract was measured in drops, but the number of drops differed among the informants. The informants reported nine poisonings with Aconitum spp., but none of them occurred as a result of medicinal use of the extract. In this study, we determined that folk knowledge of the medicinal use of Aconitum spp. is still present in Solčavsko, but Aconitum preparations are used only infrequently.

New Method for Knowledge Management Focused on Communication Pattern in Product Development

NASA Astrophysics Data System (ADS)

Noguchi, Takashi; Shiba, Hajime

In the field of manufacturing, the importance of utilizing knowledge and know-how has been growing. To meet this background, there is a need for new methods to efficiently accumulate and extract effective knowledge and know-how. To facilitate the extraction of knowledge and know-how needed by engineers, we first defined business process information which includes schedule/progress information, document data, information about communication among parties concerned, and information which corresponds to these three types of information. Based on our definitions, we proposed an IT system (FlexPIM: Flexible and collaborative Process Information Management) to register and accumulate business process information with the least effort. In order to efficiently extract effective information from huge volumes of accumulated business process information, focusing attention on “actions” and communication patterns, we propose a new extraction method using communication patterns. And the validity of this method has been verified for some communication patterns.

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

Islam, Md. Shafiqul, E-mail: shafique@eng.ukm.my; Hannan, M.A., E-mail: hannan@eng.ukm.my; Basri, Hassan

Highlights: • Solid waste bin level detection using Dynamic Time Warping (DTW). • Gabor wavelet filter is used to extract the solid waste image features. • Multi-Layer Perceptron classifier network is used for bin image classification. • The classification performance evaluated by ROC curve analysis. - Abstract: The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensormore » intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.« less

Dynamic pH junction high-speed counter-current chromatography coupled with microwave-assisted extraction for online separation and purification of alkaloids from Stephania cepharantha.

PubMed

Yuan, Zhiquan; Xiao, Xiaohua; Li, Gongke

2013-11-22

A simple and efficient dynamic pH junction high-speed counter-current chromatography method was developed and further applied to the online extraction, separation and purification of alkaloids from Stephania cepharantha by coupling with microwave-assisted extraction. Mineral acid and organic base were added into the mobile phase and the sample solution, respectively, leading to the formation of a dynamic pH junction in the column and causing focus of alkaloids. Selective focus of analytes can be achieved on the basis of velocity changes of the pH junction through appropriate selection of solvent systems and optimization of additive concentrations. The extract can be directly introduced into the HSCCC for the online extraction, separation and purification of alkaloids from S. cepharantha. Continuous separation can be easily achieved with the same solvent system. Under the optimum conditions, 6.0 g original sample was extracted with 60 mL of the upper phase of hexane-ethyl acetate-methanol-water (1:1:1:1, v/v/v/v) containing 10% triethylamine under 50 °C and 400 W irradiation power for 10 min, the extracts were directly separated and purified by high-speed counter-current chromatography. A total of 5.7 mg sinomenine, 8.3mg 6,7-di-O-acetylsinococuline, 17.9 mg berbamine, 12.7 mg isotetrandrine and 14.6 mg cepharanthine were obtained with purities of 96.7%, 93.7%, 98.7%, 97.3% and 99.3%, respectively. The online method provides good selectivity to ionizable compounds and improves the separation and purification efficiency of the high-speed counter-current chromatography technique. It has good potential for separation and purification of effective compounds from natural products. Copyright © 2013 Elsevier B.V. All rights reserved.

Perpetual extraction of work from a nonequilibrium dynamical system under Markovian feedback control

NASA Astrophysics Data System (ADS)

Kosugi, Taichi

2013-09-01

By treating both control parameters and dynamical variables as probabilistic variables, we develop a succinct theory of perpetual extraction of work from a generic classical nonequilibrium system subject to a heat bath via repeated measurements under a Markovian feedback control. It is demonstrated that a problem for perpetual extraction of work in a nonequilibrium system is reduced to a problem of Markov chain in the higher-dimensional phase space. We derive a version of the detailed fluctuation theorem, which was originally derived for classical nonequilibrium systems by Horowitz and Vaikuntanathan [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.82.061120 82, 061120 (2010)], in a form suitable for the analyses of perpetual extraction of work. Since our theory is formulated for generic dynamics of probability distribution function in phase space, its application to a physical system is straightforward. As simple applications of the theory, two exactly solvable models are analyzed. The one is a nonequilibrium two-state system and the other is a particle confined to a one-dimensional harmonic potential in thermal equilibrium. For the former example, it is demonstrated that the observer on the transitory steps to the stationary state can lose energy and that work larger than that achieved in the stationary state can be extracted. For the latter example, it is demonstrated that the optimal protocol for the extraction of work via repeated measurements can differ from that via a single measurement. The validity of our version of the detailed fluctuation theorem, which determines the upper bound of the expected work in the stationary state, is also confirmed for both examples. These observations provide useful insights into exploration for realistic modeling of a machine that extracts work from its environment.

Symbolic dynamic filtering and language measure for behavior identification of mobile robots.

PubMed

Mallapragada, Goutham; Ray, Asok; Jin, Xin

2012-06-01

This paper presents a procedure for behavior identification of mobile robots, which requires limited or no domain knowledge of the underlying process. While the features of robot behavior are extracted by symbolic dynamic filtering of the observed time series, the behavior patterns are classified based on language measure theory. The behavior identification procedure has been experimentally validated on a networked robotic test bed by comparison with commonly used tools, namely, principal component analysis for feature extraction and Bayesian risk analysis for pattern classification.

Segmentation of Unstructured Datasets

NASA Technical Reports Server (NTRS)

Bhat, Smitha

1996-01-01

Datasets generated by computer simulations and experiments in Computational Fluid Dynamics tend to be extremely large and complex. It is difficult to visualize these datasets using standard techniques like Volume Rendering and Ray Casting. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This thesis explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and from Finite Element Analysis.

Electromagnetic {\\varvec{N}}^{\\varvec{*}} Transition Form Factors in the ANL-Osaka Dynamical Coupled-Channels Approach

NASA Astrophysics Data System (ADS)

Kamano, Hiroyuki

2018-05-01

We give an overview of our recent efforts to extract electromagnetic transition form factors for N^* and Δ^* baryon resonances through a global analysis of the single-pion electroproductions off the proton within the ANL-Osaka dynamical coupled-channels approach. Preliminary results for the extracted form factors associated with Δ(1232)3/2^+ and the Roper resonance are presented, with emphasis on the complex-valued nature of the transition form factors defined by poles.

An introduction to chaotic and random time series analysis

NASA Technical Reports Server (NTRS)

Scargle, Jeffrey D.

1989-01-01

The origin of chaotic behavior and the relation of chaos to randomness are explained. Two mathematical results are described: (1) a representation theorem guarantees the existence of a specific time-domain model for chaos and addresses the relation between chaotic, random, and strictly deterministic processes; (2) a theorem assures that information on the behavior of a physical system in its complete state space can be extracted from time-series data on a single observable. Focus is placed on an important connection between the dynamical state space and an observable time series. These two results lead to a practical deconvolution technique combining standard random process modeling methods with new embedded techniques.

Single molecule optical measurements of orientation and rotations of biological macromolecules.

PubMed

Shroder, Deborah Y; Lippert, Lisa G; Goldman, Yale E

2016-11-22

Subdomains of macromolecules often undergo large orientation changes during their catalytic cycles that are essential for their activity. Tracking these rearrangements in real time opens a powerful window into the link between protein structure and functional output. Site-specific labeling of individual molecules with polarized optical probes and measurement of their spatial orientation can give insight into the crucial conformational changes, dynamics, and fluctuations of macromolecules. Here we describe the range of single molecule optical technologies that can extract orientation information from these probes, review the relevant types of probes and labeling techniques, and highlight the advantages and disadvantages of these technologies for addressing specific inquiries.

Bio-inspired sensing and control for disturbance rejection and stabilization

NASA Astrophysics Data System (ADS)

Gremillion, Gregory; Humbert, James S.

2015-05-01

The successful operation of small unmanned aircraft systems (sUAS) in dynamic environments demands robust stability in the presence of exogenous disturbances. Flying insects are sensor-rich platforms, with highly redundant arrays of sensors distributed across the insect body that are integrated to extract rich information with diminished noise. This work presents a novel sensing framework in which measurements from an array of accelerometers distributed across a simulated flight vehicle are linearly combined to directly estimate the applied forces and torques with improvements in SNR. In simulation, the estimation performance is quantified as a function of sensor noise level, position estimate error, and sensor quantity.

A New Adaptive Structural Signature for Symbol Recognition by Using a Galois Lattice as a Classifier.

PubMed

Coustaty, M; Bertet, K; Visani, M; Ogier, J

2011-08-01

In this paper, we propose a new approach for symbol recognition using structural signatures and a Galois lattice as a classifier. The structural signatures are based on topological graphs computed from segments which are extracted from the symbol images by using an adapted Hough transform. These structural signatures-that can be seen as dynamic paths which carry high-level information-are robust toward various transformations. They are classified by using a Galois lattice as a classifier. The performance of the proposed approach is evaluated based on the GREC'03 symbol database, and the experimental results we obtain are encouraging.

Introduction to the Application of the Dynalist Computer Program to the Analysis of Rail Systems Dynamics

DOT National Transportation Integrated Search

1974-08-01

DYNALIST, a computer program that extracts complex eigenvalues and eigenvectors for dynamic systems described in terms of matrix equations of motion, has been acquired and made operational at TSC. In this report, simple dynamic systems are used to de...

Information Extraction Using Controlled English to Support Knowledge-Sharing and Decision-Making

DTIC Science & Technology

2012-06-01

or language variants. CE-based information extraction will greatly facilitate the processes in the cognitive and social domains that enable forces...terminology or language variants. CE-based information extraction will greatly facilitate the processes in the cognitive and social domains that...processor is run to turn the atomic CE into a more “ stylistically felicitous” CE, using techniques such as: aggregating all information about an entity

Real-Time Information Extraction from Big Data

DTIC Science & Technology

2015-10-01

I N S T I T U T E F O R D E F E N S E A N A L Y S E S Real-Time Information Extraction from Big Data Robert M. Rolfe...Information Extraction from Big Data Jagdeep Shah Robert M. Rolfe Francisco L. Loaiza-Lemos October 7, 2015 I N S T I T U T E F O R D E F E N S E...AN A LY S E S Abstract We are drowning under the 3 Vs (volume, velocity and variety) of big data . Real-time information extraction from big

Fundamental limits on dynamic inference from single-cell snapshots

PubMed Central

Weinreb, Caleb; Tusi, Betsabeh K.; Socolovsky, Merav

2018-01-01

Single-cell expression profiling reveals the molecular states of individual cells with unprecedented detail. Because these methods destroy cells in the process of analysis, they cannot measure how gene expression changes over time. However, some information on dynamics is present in the data: the continuum of molecular states in the population can reflect the trajectory of a typical cell. Many methods for extracting single-cell dynamics from population data have been proposed. However, all such attempts face a common limitation: for any measured distribution of cell states, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we describe the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone and identify assumptions necessary to constrain a unique solution for cell dynamics from static snapshots. We translate these constraints into a practical algorithmic approach, population balance analysis (PBA), which makes use of a method from spectral graph theory to solve a class of high-dimensional differential equations. We use simulations to show the strengths and limitations of PBA, and then apply it to single-cell profiles of hematopoietic progenitor cells (HPCs). Cell state predictions from this analysis agree with HPC fate assays reported in several papers over the past two decades. By highlighting the fundamental limits on dynamic inference faced by any method, our framework provides a rigorous basis for dynamic interpretation of a gene expression continuum and clarifies best experimental designs for trajectory reconstruction from static snapshot measurements. PMID:29463712

Detecting peatland drains with Object Based Image Analysis and Geoeye-1 imagery.

PubMed

Connolly, J; Holden, N M

2017-12-01

Peatlands play an important role in the global carbon cycle. They provide important ecosystem services including carbon sequestration and storage. Drainage disturbs peatland ecosystem services. Mapping drains is difficult and expensive and their spatial extent is, in many cases, unknown. An object based image analysis (OBIA) was performed on a very high resolution satellite image (Geoeye-1) to extract information about drain location and extent on a blanket peatland in Ireland. Two accuracy assessment methods: Error matrix and the completeness, correctness and quality (CCQ) were used to assess the extracted data across the peatland and at several sub sites. The cost of the OBIA method was compared with manual digitisation and field survey. The drain maps were also used to assess the costs relating to blocking drains vs. a business-as-usual scenario and estimating the impact of each on carbon fluxes at the study site. The OBIA method performed well at almost all sites. Almost 500 km of drains were detected within the peatland. In the error matrix method, overall accuracy (OA) of detecting the drains was 94% and the kappa statistic was 0.66. The OA for all sub-areas, except one, was 95-97%. The CCQ was 85%, 85% and 71% respectively. The OBIA method was the most cost effective way to map peatland drains and was at least 55% cheaper than either field survey or manual digitisation, respectively. The extracted drain maps were used constrain the study area CO 2 flux which was 19% smaller than the prescribed Peatland Code value for drained peatlands. The OBIA method used in this study showed that it is possible to accurately extract maps of fine scale peatland drains over large areas in a cost effective manner. The development of methods to map the spatial extent of drains is important as they play a critical role in peatland carbon dynamics. The objective of this study was to extract data on the spatial extent of drains on a blanket bog in the west of Ireland. The results show that information on drain extent and location can be extracted from high resolution imagery and mapped with a high degree of accuracy. Under Article 3.4 of the Kyoto Protocol Annex 1 parties can account for greenhouse gas emission by sources and removals by sinks resulting from "wetlands drainage and rewetting". The ability to map the spatial extent, density and location of peatlands drains means that Annex 1 parties can develop strategies for drain blocking to aid reduction of CO 2 emissions, DOC runoff and water discoloration. This paper highlights some uncertainty around using one-size-fits-all emission factors for GHG in drained peatlands and re-wetting scenarios. However, the OBIA method is robust and accurate and could be used to assess the extent of drains in peatlands across the globe aiding the refinement of peatland carbon dynamics .

Detecting peatland drains with Object Based Image Analysis and Geoeye-1 imagery.

PubMed

Connolly, J; Holden, N M

2017-12-01

Peatlands play an important role in the global carbon cycle. They provide important ecosystem services including carbon sequestration and storage. Drainage disturbs peatland ecosystem services. Mapping drains is difficult and expensive and their spatial extent is, in many cases, unknown. An object based image analysis (OBIA) was performed on a very high resolution satellite image (Geoeye-1) to extract information about drain location and extent on a blanket peatland in Ireland. Two accuracy assessment methods: Error matrix and the completeness, correctness and quality ( CCQ ) were used to assess the extracted data across the peatland and at several sub sites. The cost of the OBIA method was compared with manual digitisation and field survey. The drain maps were also used to assess the costs relating to blocking drains vs. a business-as-usual scenario and estimating the impact of each on carbon fluxes at the study site. The OBIA method performed well at almost all sites. Almost 500 km of drains were detected within the peatland. In the error matrix method, overall accuracy (OA) of detecting the drains was 94% and the kappa statistic was 0.66. The OA for all sub-areas, except one, was 95-97%. The CCQ was 85%, 85% and 71% respectively. The OBIA method was the most cost effective way to map peatland drains and was at least 55% cheaper than either field survey or manual digitisation, respectively. The extracted drain maps were used constrain the study area CO 2 flux which was 19% smaller than the prescribed Peatland Code value for drained peatlands. The OBIA method used in this study showed that it is possible to accurately extract maps of fine scale peatland drains over large areas in a cost effective manner. The development of methods to map the spatial extent of drains is important as they play a critical role in peatland carbon dynamics. The objective of this study was to extract data on the spatial extent of drains on a blanket bog in the west of Ireland. The results show that information on drain extent and location can be extracted from high resolution imagery and mapped with a high degree of accuracy. Under Article 3.4 of the Kyoto Protocol Annex 1 parties can account for greenhouse gas emission by sources and removals by sinks resulting from "wetlands drainage and rewetting". The ability to map the spatial extent, density and location of peatlands drains means that Annex 1 parties can develop strategies for drain blocking to aid reduction of CO 2 emissions, DOC runoff and water discoloration. This paper highlights some uncertainty around using one-size-fits-all emission factors for GHG in drained peatlands and re-wetting scenarios. However, the OBIA method is robust and accurate and could be used to assess the extent of drains in peatlands across the globe aiding the refinement of peatland carbon dynamics .

Extraction of Data from a Hospital Information System to Perform Process Mining.

PubMed

Neira, Ricardo Alfredo Quintano; de Vries, Gert-Jan; Caffarel, Jennifer; Stretton, Erin

2017-01-01

The aim of this work is to share our experience in relevant data extraction from a hospital information system in preparation for a research study using process mining techniques. The steps performed were: research definition, mapping the normative processes, identification of tables and fields names of the database, and extraction of data. We then offer lessons learned during data extraction phase. Any errors made in the extraction phase will propagate and have implications on subsequent analyses. Thus, it is essential to take the time needed and devote sufficient attention to detail to perform all activities with the goal of ensuring high quality of the extracted data. We hope this work will be informative for other researchers to plan and execute extraction of data for process mining research studies.

Extracting Damping Ratio from Dynamic Data and Numerical Solutions

NASA Technical Reports Server (NTRS)

Casiano, M. J.

2016-01-01

There are many ways to extract damping parameters from data or models. This Technical Memorandum provides a quick reference for some of the more common approaches used in dynamics analysis. Described are six methods of extracting damping from data: the half-power method, logarithmic decrement (decay rate) method, an autocorrelation/power spectral density fitting method, a frequency response fitting method, a random decrement fitting method, and a newly developed half-quadratic gain method. Additionally, state-space models and finite element method modeling tools, such as COMSOL Multiphysics (COMSOL), provide a theoretical damping via complex frequency. Each method has its advantages which are briefly noted. There are also likely many other advanced techniques in extracting damping within the operational modal analysis discipline, where an input excitation is unknown; however, these approaches discussed here are objective, direct, and can be implemented in a consistent manner.

[Study on dynamic accumulation of index components from Bupleurum chinense in various collecting periods].

PubMed

Wang, Qi-shuai; Li, Xiao-kun; Yang, Yun; Xiao, Gong-sheng; Feng, Wei-sheng

2010-08-01

To study the dynamic change law of volatile oil, saikosaponin a, d and alcohol-extract from Bupleurum chinense at Songxian region in Henan province, and to explore the optimal harvest period of Bupleurum chinense. With the contents of saikosaponin a and d, absorbance of volatile oil and percentage of alcohol-extract as indexes, HPLC-ELSD and ultraviolet spectrophotometry were successively used to analyze them. There are obvious differences among the contents of volatile oil, saikosaponin a, d and alcohol-extract in various collecting periods sample, the absorption of volatile oil in distillation was the highest in October, the content of saikosaponin a was the highest in September, the saikosaponin d in December and the percentage of alcohol-extract in October. The optimal harvest period of Bupleurum chinense at Songxian region in Henan is identified, which can provide scientific basis for crude drug production and processing.

Place in Perspective: Extracting Online Information about Points of Interest

NASA Astrophysics Data System (ADS)

Alves, Ana O.; Pereira, Francisco C.; Rodrigues, Filipe; Oliveirinha, João

During the last few years, the amount of online descriptive information about places has reached reasonable dimensions for many cities in the world. Being such information mostly in Natural Language text, Information Extraction techniques are needed for obtaining the meaning of places that underlies these massive amounts of commonsense and user made sources. In this article, we show how we automatically label places using Information Extraction techniques applied to online resources such as Wikipedia, Yellow Pages and Yahoo!.