SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria.

PubMed

Chevrette, Marc G; Aicheler, Fabian; Kohlbacher, Oliver; Currie, Cameron R; Medema, Marnix H

2017-10-15

Nonribosomally synthesized peptides (NRPs) are natural products with widespread applications in medicine and biotechnology. Many algorithms have been developed to predict the substrate specificities of nonribosomal peptide synthetase adenylation (A) domains from DNA sequences, which enables prioritization and dereplication, and integration with other data types in discovery efforts. However, insufficient training data and a lack of clarity regarding prediction quality have impeded optimal use. Here, we introduce prediCAT, a new phylogenetics-inspired algorithm, which quantitatively estimates the degree of predictability of each A-domain. We then systematically benchmarked all algorithms on a newly gathered, independent test set of 434 A-domain sequences, showing that active-site-motif-based algorithms outperform whole-domain-based methods. Subsequently, we developed SANDPUMA, a powerful ensemble algorithm, based on newly trained versions of all high-performing algorithms, which significantly outperforms individual methods. Finally, we deployed SANDPUMA in a systematic investigation of 7635 Actinobacteria genomes, suggesting that NRP chemical diversity is much higher than previously estimated. SANDPUMA has been integrated into the widely used antiSMASH biosynthetic gene cluster analysis pipeline and is also available as an open-source, standalone tool. SANDPUMA is freely available at https://bitbucket.org/chevrm/sandpuma and as a docker image at https://hub.docker.com/r/chevrm/sandpuma/ under the GNU Public License 3 (GPL3). chevrette@wisc.edu or marnix.medema@wur.nl. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Extraction and classification of 3D objects from volumetric CT data

NASA Astrophysics Data System (ADS)

Song, Samuel M.; Kwon, Junghyun; Ely, Austin; Enyeart, John; Johnson, Chad; Lee, Jongkyu; Kim, Namho; Boyd, Douglas P.

2016-05-01

We propose an Automatic Threat Detection (ATD) algorithm for Explosive Detection System (EDS) using our multistage Segmentation Carving (SC) followed by Support Vector Machine (SVM) classifier. The multi-stage Segmentation and Carving (SC) step extracts all suspect 3-D objects. The feature vector is then constructed for all extracted objects and the feature vector is classified by the Support Vector Machine (SVM) previously learned using a set of ground truth threat and benign objects. The learned SVM classifier has shown to be effective in classification of different types of threat materials. The proposed ATD algorithm robustly deals with CT data that are prone to artifacts due to scatter, beam hardening as well as other systematic idiosyncrasies of the CT data. Furthermore, the proposed ATD algorithm is amenable for including newly emerging threat materials as well as for accommodating data from newly developing sensor technologies. Efficacy of the proposed ATD algorithm with the SVM classifier is demonstrated by the Receiver Operating Characteristics (ROC) curve that relates Probability of Detection (PD) as a function of Probability of False Alarm (PFA). The tests performed using CT data of passenger bags shows excellent performance characteristics.

Polarizable Molecular Dynamics in a Polarizable Continuum Solvent

PubMed Central

Lipparini, Filippo; Lagardère, Louis; Raynaud, Christophe; Stamm, Benjamin; Cancès, Eric; Mennucci, Benedetta; Schnieders, Michael; Ren, Pengyu; Maday, Yvon; Piquemal, Jean-Philip

2015-01-01

We present for the first time scalable polarizable molecular dynamics (MD) simulations within a polarizable continuum solvent with molecular shape cavities and exact solution of the mutual polarization. The key ingredients are a very efficient algorithm for solving the equations associated with the polarizable continuum, in particular, the domain decomposition Conductor-like Screening Model (ddCOSMO), a rigorous coupling of the continuum with the polarizable force field achieved through a robust variational formulation and an effective strategy to solve the coupled equations. The coupling of ddCOSMO with non variational force fields, including AMOEBA, is also addressed. The MD simulations are feasible, for real life systems, on standard cluster nodes; a scalable parallel implementation allows for further speed up in the context of a newly developed module in Tinker, named Tinker-HP. NVE simulations are stable and long term energy conservation can be achieved. This paper is focused on the methodological developments, on the analysis of the algorithm and on the stability of the simulations; a proof-of-concept application is also presented to attest the possibilities of this newly developed technique. PMID:26516318

The Chandra Source Catalog: Algorithms

NASA Astrophysics Data System (ADS)

McDowell, Jonathan; Evans, I. N.; Primini, F. A.; Glotfelty, K. J.; McCollough, M. L.; Houck, J. C.; Nowak, M. A.; Karovska, M.; Davis, J. E.; Rots, A. H.; Siemiginowska, A. L.; Hain, R.; Evans, J. D.; Anderson, C. S.; Bonaventura, N. R.; Chen, J. C.; Doe, S. M.; Fabbiano, G.; Galle, E. C.; Gibbs, D. G., II; Grier, J. D.; Hall, D. M.; Harbo, P. N.; He, X.; Lauer, J.; Miller, J. B.; Mitschang, A. W.; Morgan, D. L.; Nichols, J. S.; Plummer, D. A.; Refsdal, B. L.; Sundheim, B. A.; Tibbetts, M. S.; van Stone, D. W.; Winkelman, S. L.; Zografou, P.

2009-09-01

Creation of the Chandra Source Catalog (CSC) required adjustment of existing pipeline processing, adaptation of existing interactive analysis software for automated use, and development of entirely new algorithms. Data calibration was based on the existing pipeline, but more rigorous data cleaning was applied and the latest calibration data products were used. For source detection, a local background map was created including the effects of ACIS source readout streaks. The existing wavelet source detection algorithm was modified and a set of post-processing scripts used to correct the results. To analyse the source properties we ran the SAO Traceray trace code for each source to generate a model point spread function, allowing us to find encircled energy correction factors and estimate source extent. Further algorithms were developed to characterize the spectral, spatial and temporal properties of the sources and to estimate the confidence intervals on count rates and fluxes. Finally, sources detected in multiple observations were matched, and best estimates of their merged properties derived. In this paper we present an overview of the algorithms used, with more detailed treatment of some of the newly developed algorithms presented in companion papers.

Development of adaptive noise reduction filter algorithm for pediatric body images in a multi-detector CT

NASA Astrophysics Data System (ADS)

Nishimaru, Eiji; Ichikawa, Katsuhiro; Okita, Izumi; Ninomiya, Yuuji; Tomoshige, Yukihiro; Kurokawa, Takehiro; Ono, Yutaka; Nakamura, Yuko; Suzuki, Masayuki

2008-03-01

Recently, several kinds of post-processing image filters which reduce the noise of computed tomography (CT) images have been proposed. However, these image filters are mostly for adults. Because these are not very effective in small (< 20 cm) display fields of view (FOV), we cannot use them for pediatric body images (e.g., premature babies and infant children). We have developed a new noise reduction filter algorithm for pediatric body CT images. This algorithm is based on a 3D post-processing in which the output pixel values are calculated by nonlinear interpolation in z-directions on original volumetric-data-sets. This algorithm does not need the in-plane (axial plane) processing, so the spatial resolution does not change. From the phantom studies, our algorithm could reduce SD up to 40% without affecting the spatial resolution of x-y plane and z-axis, and improved the CNR up to 30%. This newly developed filter algorithm will be useful for the diagnosis and radiation dose reduction of the pediatric body CT images.

Investigation of cloud/water vapor motion winds from geostationary satellite

NASA Technical Reports Server (NTRS)

Nieman, Steve; Velden, Chris; Hayden, Kit; Menzel, Paul

1993-01-01

Work has been primarily focussed on three tasks: (1) comparison of wind fields produced at MSFC with the CO2 autowind/autoeditor system newly installed in NESDIS operations; (2) evaluation of techniques for improved tracer selection through use of cloud classification predictors; and (3) development of height assignment algorithm with water vapor channel radiances. The contract goal is to improve the CIMSS wind system by developing new techniques and assimilating better existing techniques. The work reported here was done in collaboration with the NESDIS scientists working on the operational winds software, so that NASA funded research can benefit NESDIS operational algorithms.

Physical environment virtualization for human activities recognition

NASA Astrophysics Data System (ADS)

Poshtkar, Azin; Elangovan, Vinayak; Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

2015-05-01

Human activity recognition research relies heavily on extensive datasets to verify and validate performance of activity recognition algorithms. However, obtaining real datasets are expensive and highly time consuming. A physics-based virtual simulation can accelerate the development of context based human activity recognition algorithms and techniques by generating relevant training and testing videos simulating diverse operational scenarios. In this paper, we discuss in detail the requisite capabilities of a virtual environment to aid as a test bed for evaluating and enhancing activity recognition algorithms. To demonstrate the numerous advantages of virtual environment development, a newly developed virtual environment simulation modeling (VESM) environment is presented here to generate calibrated multisource imagery datasets suitable for development and testing of recognition algorithms for context-based human activities. The VESM environment serves as a versatile test bed to generate a vast amount of realistic data for training and testing of sensor processing algorithms. To demonstrate the effectiveness of VESM environment, we present various simulated scenarios and processed results to infer proper semantic annotations from the high fidelity imagery data for human-vehicle activity recognition under different operational contexts.

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

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

Design development of a neural network-based telemetry monitor

NASA Technical Reports Server (NTRS)

Lembeck, Michael F.

1992-01-01

This paper identifies the requirements and describes an architectural framework for an artificial neural network-based system that is capable of fulfilling monitoring and control requirements of future aerospace missions. Incorporated into this framework are a newly developed training algorithm and the concept of cooperative network architectures. The feasibility of such an approach is demonstrated for its ability to identify faults in low frequency waveforms.

Colour gamut mapping between small and large colour gamuts: Part I. gamut compression.

PubMed

Xu, Lihao; Zhao, Baiyue; Luo, M R

2018-04-30

This paper describes an investigation into the performance of different gamut compression algorithms (GCAs) in different uniform colour spaces (UCSs) between small and large colour gamuts. Gamut mapping is a key component in a colour management system and has drawn much attention in the last two decades. Two new GCAs, i.e. vividness-preserved (VP) and depth-preserved (DP), based on the concepts of 'vividness' and 'depth' are proposed and compared with the other commonly used GCAs with the exception of spatial GCAs since the goal of this study was to develop an algorithm that could be implemented in real time for mobile phone applications. In addition, UCSs including CIELAB, CAM02-UCS, and a newly developed UCS, J z a z b z , were tested to verify how they affect the performance of the GCAs. A psychophysical experiment was conducted and the results showed that one of the newly proposed GCAs, VP, gave the best performance among different GCAs and the J z a z b z is a promising UCS for gamut mapping.

Automatic selection of optimal Savitzky-Golay filter parameters for Coronary Wave Intensity Analysis.

PubMed

Rivolo, Simone; Nagel, Eike; Smith, Nicolas P; Lee, Jack

2014-01-01

Coronary Wave Intensity Analysis (cWIA) is a technique capable of separating the effects of proximal arterial haemodynamics from cardiac mechanics. The cWIA ability to establish a mechanistic link between coronary haemodynamics measurements and the underlying pathophysiology has been widely demonstrated. Moreover, the prognostic value of a cWIA-derived metric has been recently proved. However, the clinical application of cWIA has been hindered due to the strong dependence on the practitioners, mainly ascribable to the cWIA-derived indices sensitivity to the pre-processing parameters. Specifically, as recently demonstrated, the cWIA-derived metrics are strongly sensitive to the Savitzky-Golay (S-G) filter, typically used to smooth the acquired traces. This is mainly due to the inability of the S-G filter to deal with the different timescale features present in the measured waveforms. Therefore, we propose to apply an adaptive S-G algorithm that automatically selects pointwise the optimal filter parameters. The newly proposed algorithm accuracy is assessed against a cWIA gold standard, provided by a newly developed in-silico cWIA modelling framework, when physiological noise is added to the simulated traces. The adaptive S-G algorithm, when used to automatically select the polynomial degree of the S-G filter, provides satisfactory results with ≤ 10% error for all the metrics through all the levels of noise tested. Therefore, the newly proposed method makes cWIA fully automatic and independent from the practitioners, opening the possibility to multi-centre trials.

Computing border bases using mutant strategies

NASA Astrophysics Data System (ADS)

Ullah, E.; Abbas Khan, S.

2014-01-01

Border bases, a generalization of Gröbner bases, have actively been addressed during recent years due to their applicability to industrial problems. In cryptography and coding theory a useful application of border based is to solve zero-dimensional systems of polynomial equations over finite fields, which motivates us for developing optimizations of the algorithms that compute border bases. In 2006, Kehrein and Kreuzer formulated the Border Basis Algorithm (BBA), an algorithm which allows the computation of border bases that relate to a degree compatible term ordering. In 2007, J. Ding et al. introduced mutant strategies bases on finding special lower degree polynomials in the ideal. The mutant strategies aim to distinguish special lower degree polynomials (mutants) from the other polynomials and give them priority in the process of generating new polynomials in the ideal. In this paper we develop hybrid algorithms that use the ideas of J. Ding et al. involving the concept of mutants to optimize the Border Basis Algorithm for solving systems of polynomial equations over finite fields. In particular, we recall a version of the Border Basis Algorithm which is actually called the Improved Border Basis Algorithm and propose two hybrid algorithms, called MBBA and IMBBA. The new mutants variants provide us space efficiency as well as time efficiency. The efficiency of these newly developed hybrid algorithms is discussed using standard cryptographic examples.

Video-rate nanoscopy enabled by sCMOS camera-specific single-molecule localization algorithms

PubMed Central

Huang, Fang; Hartwich, Tobias M. P.; Rivera-Molina, Felix E.; Lin, Yu; Duim, Whitney C.; Long, Jane J.; Uchil, Pradeep D.; Myers, Jordan R.; Baird, Michelle A.; Mothes, Walther; Davidson, Michael W.; Toomre, Derek; Bewersdorf, Joerg

2013-01-01

Newly developed scientific complementary metal–oxide–semiconductor (sCMOS) cameras have the potential to dramatically accelerate data acquisition in single-molecule switching nanoscopy (SMSN) while simultaneously increasing the effective quantum efficiency. However, sCMOS-intrinsic pixel-dependent readout noise substantially reduces the localization precision and introduces localization artifacts. Here we present algorithms that overcome these limitations and provide unbiased, precise localization of single molecules at the theoretical limit. In combination with a multi-emitter fitting algorithm, we demonstrate single-molecule localization super-resolution imaging at up to 32 reconstructed images/second (recorded at 1,600–3,200 camera frames/second) in both fixed and living cells. PMID:23708387

Global Bathymetry: Machine Learning for Data Editing

NASA Astrophysics Data System (ADS)

Sandwell, D. T.; Tea, B.; Freund, Y.

2017-12-01

The accuracy of global bathymetry depends primarily on the coverage and accuracy of the sounding data and secondarily on the depth predicted from gravity. A main focus of our research is to add newly-available data to the global compilation. Most data sources have 1-12% of erroneous soundings caused by a wide array of blunders and measurement errors. Over the years we have hand-edited this data using undergraduate employees at UCSD (440 million soundings at 500 m resolution). We are developing a machine learning approach to refine the flagging of the older soundings and provide automated editing of newly-acquired soundings. The approach has three main steps: 1) Combine the sounding data with additional information that may inform the machine learning algorithm. The additional parameters include: depth predicted from gravity; distance to the nearest sounding from other cruises; seafloor age; spreading rate; sediment thickness; and vertical gravity gradient. 2) Use available edit decisions as training data sets for a boosted tree algorithm with a binary logistic objective function and L2 regularization. Initial results with poor quality single beam soundings show that the automated algorithm matches the hand-edited data 89% of the time. The results show that most of the information for detecting outliers comes from predicted depth with secondary contributions from distance to the nearest sounding and longitude. A similar analysis using very high quality multibeam data shows that the automated algorithm matches the hand-edited data 93% of the time. Again, most of the information for detecting outliers comes from predicted depth secondary contributions from distance to the nearest sounding and longitude. 3) The third step in the process is to use the machine learning parameters, derived from the training data, to edit 12 million newly acquired single beam sounding data provided by the National Geospatial-Intelligence Agency. The output of the learning algorithm will be confidence ratedindicating which edits the algorithm is confident on and which it is not confident. We expect the majority ( 90%) of edits to be confident and not require human intervention. Human intervention will be required only on the 10% unconfident decisions, thus reducing the amount of human work by a factor of 10 or more.

Advanced time integration algorithms for dislocation dynamics simulations of work hardening

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

Sills, Ryan B.; Aghaei, Amin; Cai, Wei

Efficient time integration is a necessity for dislocation dynamics simulations of work hardening to achieve experimentally relevant strains. In this work, an efficient time integration scheme using a high order explicit method with time step subcycling and a newly-developed collision detection algorithm are evaluated. First, time integrator performance is examined for an annihilating Frank–Read source, showing the effects of dislocation line collision. The integrator with subcycling is found to significantly out-perform other integration schemes. The performance of the time integration and collision detection algorithms is then tested in a work hardening simulation. The new algorithms show a 100-fold speed-up relativemore » to traditional schemes. As a result, subcycling is shown to improve efficiency significantly while maintaining an accurate solution, and the new collision algorithm allows an arbitrarily large time step size without missing collisions.« less

Advanced time integration algorithms for dislocation dynamics simulations of work hardening

DOE PAGES

Sills, Ryan B.; Aghaei, Amin; Cai, Wei

2016-04-25

Efficient time integration is a necessity for dislocation dynamics simulations of work hardening to achieve experimentally relevant strains. In this work, an efficient time integration scheme using a high order explicit method with time step subcycling and a newly-developed collision detection algorithm are evaluated. First, time integrator performance is examined for an annihilating Frank–Read source, showing the effects of dislocation line collision. The integrator with subcycling is found to significantly out-perform other integration schemes. The performance of the time integration and collision detection algorithms is then tested in a work hardening simulation. The new algorithms show a 100-fold speed-up relativemore » to traditional schemes. As a result, subcycling is shown to improve efficiency significantly while maintaining an accurate solution, and the new collision algorithm allows an arbitrarily large time step size without missing collisions.« less

Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach

PubMed Central

Gu, Yuhua; Kumar, Virendra; Hall, Lawrence O; Goldgof, Dmitry B; Li, Ching-Yen; Korn, René; Bendtsen, Claus; Velazquez, Emmanuel Rios; Dekker, Andre; Aerts, Hugo; Lambin, Philippe; Li, Xiuli; Tian, Jie; Gatenby, Robert A; Gillies, Robert J

2012-01-01

A single click ensemble segmentation (SCES) approach based on an existing “Click&Grow” algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76% respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated. PMID:23459617

Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions.

PubMed

Chen, Ke; Wang, Shihai

2011-01-01

Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster, and manifold assumptions, together into account during boosting learning. In this paper, we propose a novel cost functional consisting of the margin cost on labeled data and the regularization penalty on unlabeled data based on three fundamental semi-supervised assumptions. Thus, minimizing our proposed cost functional with a greedy yet stagewise functional optimization procedure leads to a generic boosting framework for semi-supervised learning. Extensive experiments demonstrate that our algorithm yields favorite results for benchmark and real-world classification tasks in comparison to state-of-the-art semi-supervised learning algorithms, including newly developed boosting algorithms. Finally, we discuss relevant issues and relate our algorithm to the previous work.

Decoupled Modulation Control

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

Wang, Shaobu; Huang, Renke; Huang, Zhenyu

The objective of this research work is to develop decoupled modulation control methods for damping inter-area oscillations with low frequencies, so the damping control can be more effective and easier to design with less interference among different oscillation modes in the power system. A signal-decoupling algorithm was developed that can enable separation of multiple oscillation frequency contents and extraction of a “pure” oscillation frequency mode that are fed into Power System Stabilizers (PSSs) as the modulation input signals. As a result, instead of introducing interferences between different oscillation modes from the traditional approaches, the output of the new PSS modulationmore » control signal mainly affects only one oscillation mode of interest. The new decoupled modulation damping control algorithm has been successfully developed and tested on the standard IEEE 4-machine 2-area test system and a minniWECC system. The results are compared against traditional modulation controls, which demonstrates the validity and effectiveness of the newly-developed decoupled modulation damping control algorithm.« less

A novel data-driven learning method for radar target detection in nonstationary environments

DOE PAGES

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

2016-04-12

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

Deducing chemical structure from crystallographically determined atomic coordinates

PubMed Central

Bruno, Ian J.; Shields, Gregory P.; Taylor, Robin

2011-01-01

An improved algorithm has been developed for assigning chemical structures to incoming entries to the Cambridge Structural Database, using only the information available in the deposited CIF. Steps in the algorithm include detection of bonds, selection of polymer unit, resolution of disorder, and assignment of bond types and formal charges. The chief difficulty is posed by the large number of metallo-organic crystal structures that must be processed, given our aspiration that assigned chemical structures should accurately reflect properties such as the oxidation states of metals and redox-active ligands, metal coordination numbers and hapticities, and the aromaticity or otherwise of metal ligands. Other complications arise from disorder, especially when it is symmetry imposed or modelled with the SQUEEZE algorithm. Each assigned structure is accompanied by an estimate of reliability and, where necessary, diagnostic information indicating probable points of error. Although the algorithm was written to aid building of the Cambridge Structural Database, it has the potential to develop into a general-purpose tool for adding chemical information to newly determined crystal structures. PMID:21775812

A new memetic algorithm for mitigating tandem automated guided vehicle system partitioning problem

NASA Astrophysics Data System (ADS)

Pourrahimian, Parinaz

2017-11-01

Automated Guided Vehicle System (AGVS) provides the flexibility and automation demanded by Flexible Manufacturing System (FMS). However, with the growing concern on responsible management of resource use, it is crucial to manage these vehicles in an efficient way in order reduces travel time and controls conflicts and congestions. This paper presents the development process of a new Memetic Algorithm (MA) for optimizing partitioning problem of tandem AGVS. MAs employ a Genetic Algorithm (GA), as a global search, and apply a local search to bring the solutions to a local optimum point. A new Tabu Search (TS) has been developed and combined with a GA to refine the newly generated individuals by GA. The aim of the proposed algorithm is to minimize the maximum workload of the system. After all, the performance of the proposed algorithm is evaluated using Matlab. This study also compared the objective function of the proposed MA with GA. The results showed that the TS, as a local search, significantly improves the objective function of the GA for different system sizes with large and small numbers of zone by 1.26 in average.

Algorithmic tools for interpreting vital signs.

PubMed

Rathbun, Melina C; Ruth-Sahd, Lisa A

2009-07-01

Today's complex world of nursing practice challenges nurse educators to develop teaching methods that promote critical thinking skills and foster quick problem solving in the novice nurse. Traditional pedagogies previously used in the classroom and clinical setting are no longer adequate to prepare nursing students for entry into practice. In addition, educators have expressed frustration when encouraging students to apply newly learned theoretical content to direct the care of assigned patients in the clinical setting. This article presents algorithms as an innovative teaching strategy to guide novice student nurses in the interpretation and decision making related to vital sign assessment in an acute care setting.

Redundant and fault-tolerant algorithms for real-time measurement and control systems for weapon equipment.

PubMed

Li, Dan; Hu, Xiaoguang

2017-03-01

Because of the high availability requirements from weapon equipment, an in-depth study has been conducted on the real-time fault-tolerance of the widely applied Compact PCI (CPCI) bus measurement and control system. A redundancy design method that uses heartbeat detection to connect the primary and alternate devices has been developed. To address the low successful execution rate and relatively large waste of time slices in the primary version of the task software, an improved algorithm for real-time fault-tolerant scheduling is proposed based on the Basic Checking available time Elimination idle time (BCE) algorithm, applying a single-neuron self-adaptive proportion sum differential (PSD) controller. The experimental validation results indicate that this system has excellent redundancy and fault-tolerance, and the newly developed method can effectively improve the system availability. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Thermodynamic Analysis of Coherently Grown GaAsN/Ge: Effects of Different Gaseous Sources

NASA Astrophysics Data System (ADS)

Kawano, Jun; Kangawa, Yoshihiro; Yayama, Tomoe; Kakimoto, Koichi; Koukitu, Akinori

2013-04-01

Thermodynamic analysis of coherently grown GaAs1-xNx on Ge with low N content was performed to determine the relationship between solid composition and growth conditions. In this study, a new algorithm for the simulation code, which is applicable to wider combinations of gaseous sources than the traditional algorithm, was developed to determine the influence of different gaseous sources on N incorporation. Using this code, here we successfully compared two cases: one is a system using trimethylgallium (TMG), AsH3, and NH3, and the other uses dimethylhydrazine (DMHy) instead of NH3. It was found that the optimal N/As ratio of input gas in the system using DMHy was much lower than that using NH3. This shows that the newly developed algorithm could be a useful tool for analyzing the N incorporation during the vapor growth of GaAs1-xNx.

Validation of Special Sensor Ultraviolet Limb Imager (SSULI) Ionospheric Tomography using ALTAIR Incoherent Scatter Radar Measurements

NASA Astrophysics Data System (ADS)

Dymond, K.; Nicholas, A. C.; Budzien, S. A.; Stephan, A. W.; Coker, C.; Hei, M. A.; Groves, K. M.

2015-12-01

The Special Sensor Ultraviolet Limb Imager (SSULI) instruments are ultraviolet limb scanning sensors flying on the Defense Meteorological Satellite Program (DMSP) satellites. The SSULIs observe the 80-170 nanometer wavelength range covering emissions at 91 and 136 nm, which are produced by radiative recombination of the ionosphere. We invert these emissions tomographically using newly developed algorithms that include optical depth effects due to pure absorption and resonant scattering. We present the details of our approach including how the optimal altitude and along-track sampling were determined and the newly developed approach we are using for regularizing the SSULI tomographic inversions. Finally, we conclude with validations of the SSULI inversions against ALTAIR incoherent scatter radar measurements and demonstrate excellent agreement between the measurements.

Replication and Comparison of the Newly Proposed ADOS-2, Module 4 Algorithm in ASD without ID: A Multi-Site Study

ERIC Educational Resources Information Center

Pugliese, Cara E.; Kenworthy, Lauren; Bal, Vanessa Hus; Wallace, Gregory L.; Yerys, Benjamin E.; Maddox, Brenna B.; White, Susan W.; Popal, Haroon; Armour, Anna Chelsea; Miller, Judith; Herrington, John D.; Schultz, Robert T.; Martin, Alex; Anthony, Laura Gutermuth

2015-01-01

Recent updates have been proposed to the Autism Diagnostic Observation Schedule-2 Module 4 diagnostic algorithm. This new algorithm, however, has not yet been validated in an independent sample without intellectual disability (ID). This multi-site study compared the original and revised algorithms in individuals with ASD without ID. The revised…

Use of microwave satellite data to study variations in rainfall over the Indian Ocean

NASA Technical Reports Server (NTRS)

Hinton, Barry B.; Martin, David W.; Auvine, Brian; Olson, William S.

1990-01-01

The University of Wisconsin Space Science and Engineering Center mapped rainfall over the Indian Ocean using a newly developed Scanning Multichannel Microwave Radiometer (SMMR) rain-retrieval algorithm. The short-range objective was to characterize the distribution and variability of Indian Ocean rainfall on seasonal and annual scales. In the long-range, the objective is to clarify differences between land and marine regimes of monsoon rain. Researchers developed a semi-empirical algorithm for retrieving Indian Ocean rainfall. Tools for this development have come from radiative transfer and cloud liquid water models. Where possible, ground truth information from available radars was used in development and testing. SMMR rainfalls were also compared with Indian Ocean gauge rainfalls. Final Indian Ocean maps were produced for months, seasons, and years and interpreted in terms of historical analysis over the sub-continent.

Semimechanistic Bone Marrow Exhaustion Pharmacokinetic/Pharmacodynamic Model for Chemotherapy-Induced Cumulative Neutropenia.

PubMed

Henrich, Andrea; Joerger, Markus; Kraff, Stefanie; Jaehde, Ulrich; Huisinga, Wilhelm; Kloft, Charlotte; Parra-Guillen, Zinnia Patricia

2017-08-01

Paclitaxel is a commonly used cytotoxic anticancer drug with potentially life-threatening toxicity at therapeutic doses and high interindividual pharmacokinetic variability. Thus, drug and effect monitoring is indicated to control dose-limiting neutropenia. Joerger et al. (2016) developed a dose individualization algorithm based on a pharmacokinetic (PK)/pharmacodynamic (PD) model describing paclitaxel and neutrophil concentrations. Furthermore, the algorithm was prospectively compared in a clinical trial against standard dosing (Central European Society for Anticancer Drug Research Study of Paclitaxel Therapeutic Drug Monitoring; 365 patients, 720 cycles) but did not substantially improve neutropenia. This might be caused by misspecifications in the PK/PD model underlying the algorithm, especially without consideration of the observed cumulative pattern of neutropenia or the platinum-based combination therapy, both impacting neutropenia. This work aimed to externally evaluate the original PK/PD model for potential misspecifications and to refine the PK/PD model while considering the cumulative neutropenia pattern and the combination therapy. An underprediction was observed for the PK (658 samples), the PK parameters, and these parameters were re-estimated using the original estimates as prior information. Neutrophil concentrations (3274 samples) were overpredicted by the PK/PD model, especially for later treatment cycles when the cumulative pattern aggravated neutropenia. Three different modeling approaches (two from the literature and one newly developed) were investigated. The newly developed model, which implemented the bone marrow hypothesis semiphysiologically, was superior. This model further included an additive effect for toxicity of carboplatin combination therapy. Overall, a physiologically plausible PK/PD model was developed that can be used for dose adaptation simulations and prospective studies to further improve paclitaxel/carboplatin combination therapy. Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.

Motion Cueing Algorithm Development: Piloted Performance Testing of the Cueing Algorithms

NASA Technical Reports Server (NTRS)

Houck, Jacob A. (Technical Monitor); Telban, Robert J.; Cardullo, Frank M.; Kelly, Lon C.

2005-01-01

The relative effectiveness in simulating aircraft maneuvers with both current and newly developed motion cueing algorithms was assessed with an eleven-subject piloted performance evaluation conducted on the NASA Langley Visual Motion Simulator (VMS). In addition to the current NASA adaptive algorithm, two new cueing algorithms were evaluated: the optimal algorithm and the nonlinear algorithm. The test maneuvers included a straight-in approach with a rotating wind vector, an offset approach with severe turbulence and an on/off lateral gust that occurs as the aircraft approaches the runway threshold, and a takeoff both with and without engine failure after liftoff. The maneuvers were executed with each cueing algorithm with added visual display delay conditions ranging from zero to 200 msec. Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. Piloted performance parameters for the approach maneuvers, the vertical velocity upon touchdown and the runway touchdown position, were also analyzed but did not show any noticeable difference among the cueing algorithms. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input analysis shows pilot-induced oscillations on a straight-in approach were less prevalent compared to the optimal algorithm. The augmented turbulence cues increased workload on an offset approach that the pilots deemed more realistic compared to the NASA adaptive algorithm. The takeoff with engine failure showed the least roll activity for the nonlinear algorithm, with the least rudder pedal activity for the optimal algorithm.

Reduction from cost-sensitive ordinal ranking to weighted binary classification.

PubMed

Lin, Hsuan-Tien; Li, Ling

2012-05-01

We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary classifier upper-bounds the mislabeling cost of the ranker, both error-wise and regret-wise. Our framework allows not only the design of good ordinal ranking algorithms based on well-tuned binary classification approaches, but also the derivation of new generalization bounds for ordinal ranking from known bounds for binary classification. In addition, our framework unifies many existing ordinal ranking algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms. In addition, the newly designed algorithms lead to better cost-sensitive ordinal ranking performance, as well as improved listwise ranking performance.

Content-based histopathology image retrieval using CometCloud.

PubMed

Qi, Xin; Wang, Daihou; Rodero, Ivan; Diaz-Montes, Javier; Gensure, Rebekah H; Xing, Fuyong; Zhong, Hua; Goodell, Lauri; Parashar, Manish; Foran, David J; Yang, Lin

2014-08-26

The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance. The CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers. In this paper, we present a set of newly developed CBIR algorithms and validate them using two different pathology applications, which are regularly evaluated in the practice of pathology. Comparative experimental results demonstrate excellent performance throughout the course of a set of systematic studies. Additionally, we present and evaluate a framework to enable the execution of these algorithms across distributed resources. We show how parallel searching of content-wise similar images in the dataset significantly reduces the overall computational time to ensure the practical utility of the proposed CBIR algorithms.

Lower bound on the time complexity of local adiabatic evolution

NASA Astrophysics Data System (ADS)

Chen, Zhenghao; Koh, Pang Wei; Zhao, Yan

2006-11-01

The adiabatic theorem of quantum physics has been, in recent times, utilized in the design of local search quantum algorithms, and has been proven to be equivalent to standard quantum computation, that is, the use of unitary operators [D. Aharonov in Proceedings of the 45th Annual Symposium on the Foundations of Computer Science, 2004, Rome, Italy (IEEE Computer Society Press, New York, 2004), pp. 42-51]. Hence, the study of the time complexity of adiabatic evolution algorithms gives insight into the computational power of quantum algorithms. In this paper, we present two different approaches of evaluating the time complexity for local adiabatic evolution using time-independent parameters, thus providing effective tests (not requiring the evaluation of the entire time-dependent gap function) for the time complexity of newly developed algorithms. We further illustrate our tests by displaying results from the numerical simulation of some problems, viz. specially modified instances of the Hamming weight problem.

A landscape model for predicting potential natural vegetation of the Olympic Peninsula USA using boundary equations and newly developed environmental variables.

Treesearch

Jan A. Henderson; Robin D. Lesher; David H. Peter; Chris D. Ringo

2011-01-01

A gradient-analysis-based model and grid-based map are presented that use the potential vegetation zone as the object of the model. Several new variables are presented that describe the environmental gradients of the landscape at different scales. Boundary algorithms are conceptualized, and then defined, that describe the environmental boundaries between vegetation...

Development of visual peak selection system based on multi-ISs normalization algorithm to apply to methamphetamine impurity profiling.

PubMed

Lee, Hun Joo; Han, Eunyoung; Lee, Jaesin; Chung, Heesun; Min, Sung-Gi

2016-11-01

The aim of this study is to improve resolution of impurity peaks using a newly devised normalization algorithm for multi-internal standards (ISs) and to describe a visual peak selection system (VPSS) for efficient support of impurity profiling. Drug trafficking routes, location of manufacture, or synthetic route can be identified from impurities in seized drugs. In the analysis of impurities, different chromatogram profiles are obtained from gas chromatography and used to examine similarities between drug samples. The data processing method using relative retention time (RRT) calculated by a single internal standard is not preferred when many internal standards are used and many chromatographic peaks present because of the risk of overlapping between peaks and difficulty in classifying impurities. In this study, impurities in methamphetamine (MA) were extracted by liquid-liquid extraction (LLE) method using ethylacetate containing 4 internal standards and analyzed by gas chromatography-flame ionization detection (GC-FID). The newly developed VPSS consists of an input module, a conversion module, and a detection module. The input module imports chromatograms collected from GC and performs preprocessing, which is converted with a normalization algorithm in the conversion module, and finally the detection module detects the impurities in MA samples using a visualized zoning user interface. The normalization algorithm in the conversion module was used to convert the raw data from GC-FID. The VPSS with the built-in normalization algorithm can effectively detect different impurities in samples even in complex matrices and has high resolution keeping the time sequence of chromatographic peaks the same as that of the RRT method. The system can widen a full range of chromatograms so that the peaks of impurities were better aligned for easy separation and classification. The resolution, accuracy, and speed of impurity profiling showed remarkable improvement. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Real-time PM10 concentration monitoring on Penang Bridge by using traffic monitoring CCTV

NASA Astrophysics Data System (ADS)

Low, K. L.; Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Wong, C. J.

2007-04-01

For this study, an algorithm was developed to determine concentration of particles less than 10μm (PM10) from still images captured by a CCTV camera on the Penang Bridge. The objective of this study is to remotely monitor the PM10 concentrations on the Penang Bridge through the internet. So, an algorithm was developed based on the relationship between the atmospheric reflectance and the corresponding air quality. By doing this, the still images were separated into three bands namely red, green and blue and their digital number values were determined. A special transformation was then performed to the data. Ground PM10 measurements were taken by using DustTrak TM meter. The algorithm was calibrated using a regression analysis. The proposed algorithm produced a high correlation coefficient (R) and low root-mean-square error (RMS) between the measured and produced PM10. Later, a program was written by using Microsoft Visual Basic 6.0 to download still images from the camera over the internet and implement the newly developed algorithm. Meanwhile, the program is running in real time and the public will know the air pollution index from time to time. This indicates that the technique using the CCTV camera images can provide a useful tool for air quality studies.

Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images.

PubMed

Banzato, T; Cherubini, G B; Atzori, M; Zotti, A

2018-05-01

An established deep neural network (DNN) based on transfer learning and a newly designed DNN were tested to predict the grade of meningiomas from magnetic resonance (MR) images in dogs and to determine the accuracy of classification of using pre- and post-contrast T1-weighted (T1W), and T2-weighted (T2W) MR images. The images were randomly assigned to a training set, a validation set and a test set, comprising 60%, 10% and 30% of images, respectively. The combination of DNN and MR sequence displaying the highest discriminating accuracy was used to develop an image classifier to predict the grading of new cases. The algorithm based on transfer learning using the established DNN did not provide satisfactory results, whereas the newly designed DNN had high classification accuracy. On the basis of classification accuracy, an image classifier built on the newly designed DNN using post-contrast T1W images was developed. This image classifier correctly predicted the grading of 8 out of 10 images not included in the data set. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Mind the Gaps: Controversies about Algorithms, Learning and Trendy Knowledge

ERIC Educational Resources Information Center

Argenton, Gerald

2017-01-01

This article critically explores the ways by which the Web could become a more learning-oriented medium in the age of, but also in spite of, the newly bred algorithmic cultures. The social dimension of algorithms is reported in literature as being a socio-technological entanglement that has a powerful influence on users' practices and their lived…

Developments in the application of the geometrical theory of diffraction and computer graphics to aircraft inter-antenna coupling analysis

NASA Astrophysics Data System (ADS)

Bogusz, Michael

1993-01-01

The need for a systematic methodology for the analysis of aircraft electromagnetic compatibility (EMC) problems is examined. The available computer aids used in aircraft EMC analysis are assessed and a theoretical basis is established for the complex algorithms which identify and quantify electromagnetic interactions. An overview is presented of one particularly well established aircraft antenna to antenna EMC analysis code, the Aircraft Inter-Antenna Propagation with Graphics (AAPG) Version 07 software. The specific new algorithms created to compute cone geodesics and their associated path losses and to graph the physical coupling path are discussed. These algorithms are validated against basic principles. Loss computations apply the uniform geometrical theory of diffraction and are subsequently compared to measurement data. The increased modelling and analysis capabilities of the newly developed AAPG Version 09 are compared to those of Version 07. Several models of real aircraft, namely the Electronic Systems Trainer Challenger, are generated and provided as a basis for this preliminary comparative assessment. Issues such as software reliability, algorithm stability, and quality of hardcopy output are also discussed.

Development of an adjoint sensitivity field-based treatment-planning technique for the use of newly designed directional LDR sources in brachytherapy.

PubMed

Chaswal, V; Thomadsen, B R; Henderson, D L

2012-02-21

The development and application of an automated 3D greedy heuristic (GH) optimization algorithm utilizing the adjoint sensitivity fields for treatment planning to assess the advantage of directional interstitial prostate brachytherapy is presented. Directional and isotropic dose kernels generated using Monte Carlo simulations based on Best Industries model 2301 I-125 source are utilized for treatment planning. The newly developed GH algorithm is employed for optimization of the treatment plans for seven interstitial prostate brachytherapy cases using mixed sources (directional brachytherapy) and using only isotropic sources (conventional brachytherapy). All treatment plans resulted in V100 > 98% and D90 > 45 Gy for the target prostate region. For the urethra region, the D10(Ur), D90(Ur) and V150(Ur) and for the rectum region the V100cc, D2cc, D90(Re) and V90(Re) all are reduced significantly when mixed sources brachytherapy is used employing directional sources. The simulations demonstrated that the use of directional sources in the low dose-rate (LDR) brachytherapy of the prostate clearly benefits in sparing the urethra and the rectum sensitive structures from overdose. The time taken for a conventional treatment plan is less than three seconds, while the time taken for a mixed source treatment plan is less than nine seconds, as tested on an Intel Core2 Duo 2.2 GHz processor with 1GB RAM. The new 3D GH algorithm is successful in generating a feasible LDR brachytherapy treatment planning solution with an extra degree of freedom, i.e. directionality in very little time.

Development of an adjoint sensitivity field-based treatment-planning technique for the use of newly designed directional LDR sources in brachytherapy

NASA Astrophysics Data System (ADS)

Chaswal, V.; Thomadsen, B. R.; Henderson, D. L.

2012-02-01

The development and application of an automated 3D greedy heuristic (GH) optimization algorithm utilizing the adjoint sensitivity fields for treatment planning to assess the advantage of directional interstitial prostate brachytherapy is presented. Directional and isotropic dose kernels generated using Monte Carlo simulations based on Best Industries model 2301 I-125 source are utilized for treatment planning. The newly developed GH algorithm is employed for optimization of the treatment plans for seven interstitial prostate brachytherapy cases using mixed sources (directional brachytherapy) and using only isotropic sources (conventional brachytherapy). All treatment plans resulted in V100 > 98% and D90 > 45 Gy for the target prostate region. For the urethra region, the D10Ur, D90Ur and V150Ur and for the rectum region the V100cc, D2cc, D90Re and V90Re all are reduced significantly when mixed sources brachytherapy is used employing directional sources. The simulations demonstrated that the use of directional sources in the low dose-rate (LDR) brachytherapy of the prostate clearly benefits in sparing the urethra and the rectum sensitive structures from overdose. The time taken for a conventional treatment plan is less than three seconds, while the time taken for a mixed source treatment plan is less than nine seconds, as tested on an Intel Core2 Duo 2.2 GHz processor with 1GB RAM. The new 3D GH algorithm is successful in generating a feasible LDR brachytherapy treatment planning solution with an extra degree of freedom, i.e. directionality in very little time.

A Test Suite for 3D Radiative Hydrodynamics Simulations of Protoplanetary Disks

NASA Astrophysics Data System (ADS)

Boley, Aaron C.; Durisen, R. H.; Nordlund, A.; Lord, J.

2006-12-01

Radiative hydrodynamics simulations of protoplanetary disks with different treatments for radiative cooling demonstrate disparate evolutions (see Durisen et al. 2006, PPV chapter). Some of these differences include the effects of convection and metallicity on disk cooling and the susceptibility of the disk to fragmentation. Because a principal reason for these differences may be the treatment of radiative cooling, the accuracy of cooling algorithms must be evaluated. In this paper we describe a radiative transport test suite, and we challenge all researchers who use radiative hydrodynamics to study protoplanetary disk evolution to evaluate their algorithms with these tests. The test suite can be used to demonstrate an algorithm's accuracy in transporting the correct flux through an atmosphere and in reaching the correct temperature structure, to test the algorithm's dependence on resolution, and to determine whether the algorithm permits of inhibits convection when expected. In addition, we use this test suite to demonstrate the accuracy of a newly developed radiative cooling algorithm that combines vertical rays with flux-limited diffusion. This research was supported in part by a Graduate Student Researchers Program fellowship.

Genome-Wide Comparative Gene Family Classification

PubMed Central

Frech, Christian; Chen, Nansheng

2010-01-01

Correct classification of genes into gene families is important for understanding gene function and evolution. Although gene families of many species have been resolved both computationally and experimentally with high accuracy, gene family classification in most newly sequenced genomes has not been done with the same high standard. This project has been designed to develop a strategy to effectively and accurately classify gene families across genomes. We first examine and compare the performance of computer programs developed for automated gene family classification. We demonstrate that some programs, including the hierarchical average-linkage clustering algorithm MC-UPGMA and the popular Markov clustering algorithm TRIBE-MCL, can reconstruct manual curation of gene families accurately. However, their performance is highly sensitive to parameter setting, i.e. different gene families require different program parameters for correct resolution. To circumvent the problem of parameterization, we have developed a comparative strategy for gene family classification. This strategy takes advantage of existing curated gene families of reference species to find suitable parameters for classifying genes in related genomes. To demonstrate the effectiveness of this novel strategy, we use TRIBE-MCL to classify chemosensory and ABC transporter gene families in C. elegans and its four sister species. We conclude that fully automated programs can establish biologically accurate gene families if parameterized accordingly. Comparative gene family classification finds optimal parameters automatically, thus allowing rapid insights into gene families of newly sequenced species. PMID:20976221

Method and Excel VBA Algorithm for Modeling Master Recession Curve Using Trigonometry Approach.

PubMed

Posavec, Kristijan; Giacopetti, Marco; Materazzi, Marco; Birk, Steffen

2017-11-01

A new method was developed and implemented into an Excel Visual Basic for Applications (VBAs) algorithm utilizing trigonometry laws in an innovative way to overlap recession segments of time series and create master recession curves (MRCs). Based on a trigonometry approach, the algorithm horizontally translates succeeding recession segments of time series, placing their vertex, that is, the highest recorded value of each recession segment, directly onto the appropriate connection line defined by measurement points of a preceding recession segment. The new method and algorithm continues the development of methods and algorithms for the generation of MRC, where the first published method was based on a multiple linear/nonlinear regression model approach (Posavec et al. 2006). The newly developed trigonometry-based method was tested on real case study examples and compared with the previously published multiple linear/nonlinear regression model-based method. The results show that in some cases, that is, for some time series, the trigonometry-based method creates narrower overlaps of the recession segments, resulting in higher coefficients of determination R 2 , while in other cases the multiple linear/nonlinear regression model-based method remains superior. The Excel VBA algorithm for modeling MRC using the trigonometry approach is implemented into a spreadsheet tool (MRCTools v3.0 written by and available from Kristijan Posavec, Zagreb, Croatia) containing the previously published VBA algorithms for MRC generation and separation. All algorithms within the MRCTools v3.0 are open access and available free of charge, supporting the idea of running science on available, open, and free of charge software. © 2017, National Ground Water Association.

Variational and symplectic integrators for satellite relative orbit propagation including drag

NASA Astrophysics Data System (ADS)

Palacios, Leonel; Gurfil, Pini

2018-04-01

Orbit propagation algorithms for satellite relative motion relying on Runge-Kutta integrators are non-symplectic—a situation that leads to incorrect global behavior and degraded accuracy. Thus, attempts have been made to apply symplectic methods to integrate satellite relative motion. However, so far all these symplectic propagation schemes have not taken into account the effect of atmospheric drag. In this paper, drag-generalized symplectic and variational algorithms for satellite relative orbit propagation are developed in different reference frames, and numerical simulations with and without the effect of atmospheric drag are presented. It is also shown that high-order versions of the newly-developed variational and symplectic propagators are more accurate and are significantly faster than Runge-Kutta-based integrators, even in the presence of atmospheric drag.

A Time Series of Sea Surface Nitrate and Nitrate based New Production in the Global Oceans

NASA Astrophysics Data System (ADS)

Goes, J. I.; Fargion, G. S.; Gomes, H. R.; Franz, B. A.

2014-12-01

With support from NASA's MEaSUREs program, we are developing algorithms for two innovative satellite-based Earth Science Data Records (ESDRs), one Sea Surface Nitrate (SSN) and the other, Nitrate based new Production (NnP). Newly developed algorithms will be applied to mature ESDRs of Chlorophyll a and SST available from NASA, to generate maps of SSN and NnP. Our proposed ESDRs offer the potential of greatly improving our understanding of the role of the oceans in global carbon cycling, earth system processes and climate change, especially for regions and seasons which are inaccessible to traditional shipboard studies. They also provide an innovative means for validating and improving coupled ecosystem models that currently rely on global maps of nitrate generated from multi-year data sets. To aid in our algorithm development efforts and to ensure that our ESDRs are truly global in nature, we are currently in the process of assembling a large database of nutrients from oceanographic institutions all over the world. Once our products are developed and our algorithms are fine-tuned, large-scale data production will be undertaken in collaboration with NASA's Ocean Biology Processing Group (OPBG), who will make the data publicly available first as evaluation products and then as mature ESDRs.

Implementation and analysis of list mode algorithm using tubes of response on a dedicated brain and breast PET

NASA Astrophysics Data System (ADS)

Moliner, L.; Correcher, C.; González, A. J.; Conde, P.; Hernández, L.; Orero, A.; Rodríguez-Álvarez, M. J.; Sánchez, F.; Soriano, A.; Vidal, L. F.; Benlloch, J. M.

2013-02-01

In this work we present an innovative algorithm for the reconstruction of PET images based on the List-Mode (LM) technique which improves their spatial resolution compared to results obtained with current MLEM algorithms. This study appears as a part of a large project with the aim of improving diagnosis in early Alzheimer disease stages by means of a newly developed hybrid PET-MR insert. At the present, Alzheimer is the most relevant neurodegenerative disease and the best way to apply an effective treatment is its early diagnosis. The PET device will consist of several monolithic LYSO crystals coupled to SiPM detectors. Monolithic crystals can reduce scanner costs with the advantage to enable implementation of very small virtual pixels in their geometry. This is especially useful for LM reconstruction algorithms, since they do not need a pre-calculated system matrix. We have developed an LM algorithm which has been initially tested with a large aperture (186 mm) breast PET system. Such an algorithm instead of using the common lines of response, incorporates a novel calculation of tubes of response. The new approach improves the volumetric spatial resolution about a factor 2 at the border of the field of view when compared with traditionally used MLEM algorithm. Moreover, it has also shown to decrease the image noise, thus increasing the image quality.

Exploring the protein folding free energy landscape: coupling replica exchange method with P3ME/RESPA algorithm.

PubMed

Zhou, Ruhong

2004-05-01

A highly parallel replica exchange method (REM) that couples with a newly developed molecular dynamics algorithm particle-particle particle-mesh Ewald (P3ME)/RESPA has been proposed for efficient sampling of protein folding free energy landscape. The algorithm is then applied to two separate protein systems, beta-hairpin and a designed protein Trp-cage. The all-atom OPLSAA force field with an explicit solvent model is used for both protein folding simulations. Up to 64 replicas of solvated protein systems are simulated in parallel over a wide range of temperatures. The combined trajectories in temperature and configurational space allow a replica to overcome free energy barriers present at low temperatures. These large scale simulations reveal detailed results on folding mechanisms, intermediate state structures, thermodynamic properties and the temperature dependences for both protein systems.

MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm.

PubMed

Wu, Wei-Sheng; Jhou, Meng-Jhun

2017-01-13

Missing value imputation is important for microarray data analyses because microarray data with missing values would significantly degrade the performance of the downstream analyses. Although many microarray missing value imputation algorithms have been developed, an objective and comprehensive performance comparison framework is still lacking. To solve this problem, we previously proposed a framework which can perform a comprehensive performance comparison of different existing algorithms. Also the performance of a new algorithm can be evaluated by our performance comparison framework. However, constructing our framework is not an easy task for the interested researchers. To save researchers' time and efforts, here we present an easy-to-use web tool named MVIAeval (Missing Value Imputation Algorithm evaluator) which implements our performance comparison framework. MVIAeval provides a user-friendly interface allowing users to upload the R code of their new algorithm and select (i) the test datasets among 20 benchmark microarray (time series and non-time series) datasets, (ii) the compared algorithms among 12 existing algorithms, (iii) the performance indices from three existing ones, (iv) the comprehensive performance scores from two possible choices, and (v) the number of simulation runs. The comprehensive performance comparison results are then generated and shown as both figures and tables. MVIAeval is a useful tool for researchers to easily conduct a comprehensive and objective performance evaluation of their newly developed missing value imputation algorithm for microarray data or any data which can be represented as a matrix form (e.g. NGS data or proteomics data). Thus, MVIAeval will greatly expedite the progress in the research of missing value imputation algorithms.

Dissolved Organic Carbon along the Louisiana coast from MODIS and MERIS satellite data

NASA Astrophysics Data System (ADS)

Chaichi Tehrani, N.; D'Sa, E. J.

2012-12-01

Dissolved organic carbon (DOC) plays a critical role in the coastal and ocean carbon cycle. Hence, it is important to monitor and investigate its the distribution and fate in coastal waters. Since DOC cannot be measured directly through satellite remote sensors, chromophoric dissolved organic matter (CDOM) as an optically active fraction of DOC can be used as an alternative proxy to trace DOC concentrations. Here, satellite ocean color data from MODIS, MERIS, and field measurements of CDOM and DOC were used to develop and assess CDOM and DOC ocean color algorithms for coastal waters. To develop a CDOM retrieval algorithm, empirical relationships between CDOM absorption coefficient at 412 nm (aCDOM(412)) and reflectance ratios Rrs(488)/Rrs(555) for MODIS and Rrs(510)/Rrs(560) for MERIS were established. The performance of two CDOM empirical algorithms were evaluated for retrieval of (aCDOM(412)) from MODIS and MERIS in the northern Gulf of Mexico. Further, empirical algorithms were developed to estimate DOC concentration using the relationship between in situ aCDOM(412) and DOC, as well as using the newly developed CDOM empirical algorithms. Accordingly, our results revealed that DOC concentration was strongly correlated to aCDOM (412) for summer and spring-winter periods (r2 = 0.9 for both periods). Then, using the aCDOM(412)-Rrs and the aCDOM(412)-DOC relationships derived from field measurements, a relationship between DOC-Rrs was established for MODIS and MERIS data. The DOC empirical algorithms performed well as indicated by match-up comparisons between satellite estimates and field data (R2=0.52 and 0.58 for MODIS and MERIS for summer period, respectively). These algorithms were then used to examine DOC distribution along the Louisiana coast.

Development and validation of a novel algorithm based on the ECG magnet response for rapid identification of any unknown pacemaker.

PubMed

Squara, Fabien; Chik, William W; Benhayon, Daniel; Maeda, Shingo; Latcu, Decebal Gabriel; Lacaze-Gadonneix, Jonathan; Tibi, Thierry; Thomas, Olivier; Cooper, Joshua M; Duthoit, Guillaume

2014-08-01

Pacemaker (PM) interrogation requires correct manufacturer identification. However, an unidentified PM is a frequent occurrence, requiring time-consuming steps to identify the device. The purpose of this study was to develop and validate a novel algorithm for PM manufacturer identification, using the ECG response to magnet application. Data on the magnet responses of all recent PM models (≤15 years) from the 5 major manufacturers were collected. An algorithm based on the ECG response to magnet application to identify the PM manufacturer was subsequently developed. Patients undergoing ECG during magnet application in various clinical situations were prospectively recruited in 7 centers. The algorithm was applied in the analysis of every ECG by a cardiologist blinded to PM information. A second blinded cardiologist analyzed a sample of randomly selected ECGs in order to assess the reproducibility of the results. A total of 250 ECGs were analyzed during magnet application. The algorithm led to the correct single manufacturer choice in 242 ECGs (96.8%), whereas 7 (2.8%) could only be narrowed to either 1 of 2 manufacturer possibilities. Only 2 (0.4%) incorrect manufacturer identifications occurred. The algorithm identified Medtronic and Sorin Group PMs with 100% sensitivity and specificity, Biotronik PMs with 100% sensitivity and 99.5% specificity, and St. Jude and Boston Scientific PMs with 92% sensitivity and 100% specificity. The results were reproducible between the 2 blinded cardiologists with 92% concordant findings. Unknown PM manufacturers can be accurately identified by analyzing the ECG magnet response using this newly developed algorithm. Copyright © 2014 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

The Quest for Pi

NASA Technical Reports Server (NTRS)

Bailey, David H.; Borwein, Jonathan M.; Borwein, Peter B.; Plouffe, Simon

1996-01-01

This article gives a brief history of the analysis and computation of the mathematical constant Pi=3.14159 ..., including a number of the formulas that have been used to compute Pi through the ages. Recent developments in this area are then discussed in some detail, including the recent computation of Pi to over six billion decimal digits using high-order convergent algorithms, and a newly discovered scheme that permits arbitrary individual hexadecimal digits of Pi to be computed.

An Image Analysis Algorithm for Malaria Parasite Stage Classification and Viability Quantification

PubMed Central

Moon, Seunghyun; Lee, Sukjun; Kim, Heechang; Freitas-Junior, Lucio H.; Kang, Myungjoo; Ayong, Lawrence; Hansen, Michael A. E.

2013-01-01

With more than 40% of the world’s population at risk, 200–300 million infections each year, and an estimated 1.2 million deaths annually, malaria remains one of the most important public health problems of mankind today. With the propensity of malaria parasites to rapidly develop resistance to newly developed therapies, and the recent failures of artemisinin-based drugs in Southeast Asia, there is an urgent need for new antimalarial compounds with novel mechanisms of action to be developed against multidrug resistant malaria. We present here a novel image analysis algorithm for the quantitative detection and classification of Plasmodium lifecycle stages in culture as well as discriminating between viable and dead parasites in drug-treated samples. This new algorithm reliably estimates the number of red blood cells (isolated or clustered) per fluorescence image field, and accurately identifies parasitized erythrocytes on the basis of high intensity DAPI-stained parasite nuclei spots and Mitotracker-stained mitochondrial in viable parasites. We validated the performance of the algorithm by manual counting of the infected and non-infected red blood cells in multiple image fields, and the quantitative analyses of the different parasite stages (early rings, rings, trophozoites, schizonts) at various time-point post-merozoite invasion, in tightly synchronized cultures. Additionally, the developed algorithm provided parasitological effective concentration 50 (EC50) values for both chloroquine and artemisinin, that were similar to known growth inhibitory EC50 values for these compounds as determined using conventional SYBR Green I and lactate dehydrogenase-based assays. PMID:23626733

Efficient Parallel Kernel Solvers for Computational Fluid Dynamics Applications

NASA Technical Reports Server (NTRS)

Sun, Xian-He

1997-01-01

Distributed-memory parallel computers dominate today's parallel computing arena. These machines, such as Intel Paragon, IBM SP2, and Cray Origin2OO, have successfully delivered high performance computing power for solving some of the so-called "grand-challenge" problems. Despite initial success, parallel machines have not been widely accepted in production engineering environments due to the complexity of parallel programming. On a parallel computing system, a task has to be partitioned and distributed appropriately among processors to reduce communication cost and to attain load balance. More importantly, even with careful partitioning and mapping, the performance of an algorithm may still be unsatisfactory, since conventional sequential algorithms may be serial in nature and may not be implemented efficiently on parallel machines. In many cases, new algorithms have to be introduced to increase parallel performance. In order to achieve optimal performance, in addition to partitioning and mapping, a careful performance study should be conducted for a given application to find a good algorithm-machine combination. This process, however, is usually painful and elusive. The goal of this project is to design and develop efficient parallel algorithms for highly accurate Computational Fluid Dynamics (CFD) simulations and other engineering applications. The work plan is 1) developing highly accurate parallel numerical algorithms, 2) conduct preliminary testing to verify the effectiveness and potential of these algorithms, 3) incorporate newly developed algorithms into actual simulation packages. The work plan has well achieved. Two highly accurate, efficient Poisson solvers have been developed and tested based on two different approaches: (1) Adopting a mathematical geometry which has a better capacity to describe the fluid, (2) Using compact scheme to gain high order accuracy in numerical discretization. The previously developed Parallel Diagonal Dominant (PDD) algorithm and Reduced Parallel Diagonal Dominant (RPDD) algorithm have been carefully studied on different parallel platforms for different applications, and a NASA simulation code developed by Man M. Rai and his colleagues has been parallelized and implemented based on data dependency analysis. These achievements are addressed in detail in the paper.

Recognition of Protein-coding Genes Based on Z-curve Algorithms

PubMed Central

-Biao Guo, Feng; Lin, Yan; -Ling Chen, Ling

2014-01-01

Recognition of protein-coding genes, a classical bioinformatics issue, is an absolutely needed step for annotating newly sequenced genomes. The Z-curve algorithm, as one of the most effective methods on this issue, has been successfully applied in annotating or re-annotating many genomes, including those of bacteria, archaea and viruses. Two Z-curve based ab initio gene-finding programs have been developed: ZCURVE (for bacteria and archaea) and ZCURVE_V (for viruses and phages). ZCURVE_C (for 57 bacteria) and Zfisher (for any bacterium) are web servers for re-annotation of bacterial and archaeal genomes. The above four tools can be used for genome annotation or re-annotation, either independently or combined with the other gene-finding programs. In addition to recognizing protein-coding genes and exons, Z-curve algorithms are also effective in recognizing promoters and translation start sites. Here, we summarize the applications of Z-curve algorithms in gene finding and genome annotation. PMID:24822027

A hybrid-domain approach for modeling climate data time series

NASA Astrophysics Data System (ADS)

Wen, Qiuzi H.; Wang, Xiaolan L.; Wong, Augustine

2011-09-01

In order to model climate data time series that often contain periodic variations, trends, and sudden changes in mean (mean shifts, mostly artificial), this study proposes a hybrid-domain (HD) algorithm, which incorporates a time domain test and a newly developed frequency domain test through an iterative procedure that is analogue to the well known backfitting algorithm. A two-phase competition procedure is developed to address the confounding issue between modeling periodic variations and mean shifts. A variety of distinctive features of climate data time series, including trends, periodic variations, mean shifts, and a dependent noise structure, can be modeled in tandem using the HD algorithm. This is particularly important for homogenization of climate data from a low density observing network in which reference series are not available to help preserve climatic trends and long-term periodic variations, preventing them from being mistaken as artificial shifts. The HD algorithm is also powerful in estimating trend and periodicity in a homogeneous data time series (i.e., in the absence of any mean shift). The performance of the HD algorithm (in terms of false alarm rate and hit rate in detecting shifts/cycles, and estimation accuracy) is assessed via a simulation study. Its power is further illustrated through its application to a few climate data time series.

Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images

PubMed Central

Cooper, Robert F.; Lombardo, Marco; Carroll, Joseph; Sloan, Kenneth R.; Lombardo, Giuseppe

2016-01-01

The ability to non-invasively image the cone photoreceptor mosaic holds significant potential as a diagnostic for retinal disease. Central to the realization of this potential is the development of sensitive metrics for characterizing the organization of the mosaic. Here we evaluated previously-described (Pum et al., 1990) and newly-developed (Fourier- and Radon-based) methods of measuring cone orientation in both simulated and real images of the parafoveal cone mosaic. The proposed algorithms correlated well across both simulated and real mosaics, suggesting that each algorithm would provide an accurate description of individual photoreceptor orientation. Despite the high agreement between algorithms, each performed differently in response to image intensity variation and cone coordinate jitter. The integration property of the Fourier transform allowed the Fourier-based method to be resistant to cone coordinate jitter and perform the most robustly of all three algorithms. Conversely, when there is good image quality but unreliable cone identification, the Radon algorithm performed best. Finally, in cases where both the image and cone coordinate reliability was excellent, the method of Pum et al. (1990) performed best. These descriptors are complementary to conventional descriptive metrics of the cone mosaic, such as cell density and spacing, and have the potential to aid in the detection of photoreceptor pathology. PMID:27484961

A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem.

PubMed

Jiang, Zi-Bin; Yang, Qiong

2016-01-01

The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. The continuous variant version of FOA has been proven to be a powerful evolutionary approach to determining the optima of a numerical function on a continuous definition domain. In this study, a discrete FOA (DFOA) is developed and applied to the traveling salesman problem (TSP), a common combinatorial problem. In the DFOA, the TSP tour is represented by an ordering of city indices, and the bio-inspired meta-heuristic search processes are executed with two elaborately designed main procedures: the smelling and tasting processes. In the smelling process, an effective crossover operator is used by the fruit fly group to search for the neighbors of the best-known swarm location. During the tasting process, an edge intersection elimination (EXE) operator is designed to improve the neighbors of the non-optimum food location in order to enhance the exploration performance of the DFOA. In addition, benchmark instances from the TSPLIB are classified in order to test the searching ability of the proposed algorithm. Furthermore, the effectiveness of the proposed DFOA is compared to that of other meta-heuristic algorithms. The results indicate that the proposed DFOA can be effectively used to solve TSPs, especially large-scale problems.

A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem

PubMed Central

Jiang, Zi-bin; Yang, Qiong

2016-01-01

The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. The continuous variant version of FOA has been proven to be a powerful evolutionary approach to determining the optima of a numerical function on a continuous definition domain. In this study, a discrete FOA (DFOA) is developed and applied to the traveling salesman problem (TSP), a common combinatorial problem. In the DFOA, the TSP tour is represented by an ordering of city indices, and the bio-inspired meta-heuristic search processes are executed with two elaborately designed main procedures: the smelling and tasting processes. In the smelling process, an effective crossover operator is used by the fruit fly group to search for the neighbors of the best-known swarm location. During the tasting process, an edge intersection elimination (EXE) operator is designed to improve the neighbors of the non-optimum food location in order to enhance the exploration performance of the DFOA. In addition, benchmark instances from the TSPLIB are classified in order to test the searching ability of the proposed algorithm. Furthermore, the effectiveness of the proposed DFOA is compared to that of other meta-heuristic algorithms. The results indicate that the proposed DFOA can be effectively used to solve TSPs, especially large-scale problems. PMID:27812175

A deep learning method for lincRNA detection using auto-encoder algorithm.

PubMed

Yu, Ning; Yu, Zeng; Pan, Yi

2017-12-06

RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly annotated lincRNA data, deep learning methods based on auto-encoder algorithm can exert their capability in knowledge learning in order to capture the useful features and the information correlation along DNA genome sequences for lincRNA detection. As our knowledge, this is the first application to adopt the deep learning techniques for identifying lincRNA transcription sequences.

ePMV embeds molecular modeling into professional animation software environments.

PubMed

Johnson, Graham T; Autin, Ludovic; Goodsell, David S; Sanner, Michel F; Olson, Arthur J

2011-03-09

Increasingly complex research has made it more difficult to prepare data for publication, education, and outreach. Many scientists must also wade through black-box code to interface computational algorithms from diverse sources to supplement their bench work. To reduce these barriers we have developed an open-source plug-in, embedded Python Molecular Viewer (ePMV), that runs molecular modeling software directly inside of professional 3D animation applications (hosts) to provide simultaneous access to the capabilities of these newly connected systems. Uniting host and scientific algorithms into a single interface allows users from varied backgrounds to assemble professional quality visuals and to perform computational experiments with relative ease. By enabling easy exchange of algorithms, ePMV can facilitate interdisciplinary research, smooth communication between broadly diverse specialties, and provide a common platform to frame and visualize the increasingly detailed intersection(s) of cellular and molecular biology. Copyright © 2011 Elsevier Ltd. All rights reserved.

Automated contact angle estimation for three-dimensional X-ray microtomography data

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

Klise, Katherine A.; Moriarty, Dylan; Yoon, Hongkyu

2015-11-10

Multiphase flow in capillary regimes is a fundamental process in a number of geoscience applications. The ability to accurately define wetting characteristics of porous media can have a large impact on numerical models. In this paper, a newly developed automated three-dimensional contact angle algorithm is described and applied to high-resolution X-ray microtomography data from multiphase bead pack experiments with varying wettability characteristics. The algorithm calculates the contact angle by finding the angle between planes fit to each solid/fluid and fluid/fluid interface in the region surrounding each solid/fluid/fluid contact point. Results show that the algorithm is able to reliably compute contactmore » angles using the experimental data. The in situ contact angles are typically larger than flat surface laboratory measurements using the same material. Furthermore, wetting characteristics in mixed-wet systems also change significantly after displacement cycles.« less

Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine.

PubMed

Wang, Shuihua; Chen, Mengmeng; Li, Yang; Shao, Ying; Zhang, Yudong; Du, Sidan; Wu, Jane

2016-01-01

Dendritic spines are described as neuronal protrusions. The morphology of dendritic spines and dendrites has a strong relationship to its function, as well as playing an important role in understanding brain function. Quantitative analysis of dendrites and dendritic spines is essential to an understanding of the formation and function of the nervous system. However, highly efficient tools for the quantitative analysis of dendrites and dendritic spines are currently undeveloped. In this paper we propose a novel three-step cascaded algorithm-RTSVM- which is composed of ridge detection as the curvature structure identifier for backbone extraction, boundary location based on differences in density, the Hu moment as features and Twin Support Vector Machine (TSVM) classifiers for spine classification. Our data demonstrates that this newly developed algorithm has performed better than other available techniques used to detect accuracy and false alarm rates. This algorithm will be used effectively in neuroscience research.

ePMV Embeds Molecular Modeling into Professional Animation Software Environments

PubMed Central

Johnson, Graham T.; Autin, Ludovic; Goodsell, David S.; Sanner, Michel F.; Olson, Arthur J.

2011-01-01

SUMMARY Increasingly complex research has made it more difficult to prepare data for publication, education, and outreach. Many scientists must also wade through black-box code to interface computational algorithms from diverse sources to supplement their bench work. To reduce these barriers, we have developed an open-source plug-in, embedded Python Molecular Viewer (ePMV), that runs molecular modeling software directly inside of professional 3D animation applications (hosts) to provide simultaneous access to the capabilities of these newly connected systems. Uniting host and scientific algorithms into a single interface allows users from varied backgrounds to assemble professional quality visuals and to perform computational experiments with relative ease. By enabling easy exchange of algorithms, ePMV can facilitate interdisciplinary research, smooth communication between broadly diverse specialties and provide a common platform to frame and visualize the increasingly detailed intersection(s) of cellular and molecular biology. PMID:21397181

Cb-LIKE - Thunderstorm forecasts up to six hours with fuzzy logic

NASA Astrophysics Data System (ADS)

Köhler, Martin; Tafferner, Arnold

2016-04-01

Thunderstorms with their accompanying effects like heavy rain, hail, or downdrafts cause delays and flight cancellations and therefore high additional cost for airlines and airport operators. A reliable thunderstorm forecast up to several hours could provide more time for decision makers in air traffic for an appropriate reaction on possible storm cells and initiation of adequate counteractions. To provide the required forecasts Cb-LIKE (Cumulonimbus-LIKElihood) has been developed at the DLR (Deutsches Zentrum für Luft- und Raumfahrt) Institute of Atmospheric Physics. The new algorithm is an automated system which designates areas with possible thunderstorm development by using model data of the COSMO-DE weather model, which is driven by the German Meteorological Service (DWD). A newly developed "Best-Member- Selection" method allows the automatic selection of that particular model run of a time-lagged COSMO- DE model ensemble, which matches best the current thunderstorm situation. Thereby the application of the best available data basis for the calculation of the thunderstorm forecasts by Cb-LIKE is ensured. Altogether there are four different modes for the selection of the best member. Four atmospheric parameters (CAPE, vertical wind velocity, radar reflectivity and cloud top temperature) of the model output are used within the algorithm. A newly developed fuzzy logic system enables the subsequent combination of the model parameters and the calculation of a thunderstorm indicator within a value range of 12 up to 88 for each grid point of the model domain for the following six hours in one hour intervals. The higher the indicator value the more the model parameters imply the development of thunderstorms. The quality of the Cb-LIKE thunderstorm forecasts was evaluated by a substantial verification using a neighborhood verification approach and multi-event contingency tables. The verification was performed for the whole summer period of 2012. On the basis of a deterministic object comparison with heavy precipitation cells observed by the radar-based thunderstorm tracking algorithm Rad-TRAM, several verification scores like BIAS, POD, FAR and CSI were calculated to identify possible advantages of the new algorithm. The presentation illustrates in detail the concept of the Cb-LIKE algorithm with regard to the fuzzy logic system and the Best-Member-Selection. Additionally some case studies and the most important results of the verification will be shown. The implementation of the forecasts into the DLR WxFUSION system, an user oriented forecasting system for air traffic, will also be included.

Synthetic aperture radar image formation for the moving-target and near-field bistatic cases

NASA Astrophysics Data System (ADS)

Ding, Yu

This dissertation addresses topics in two areas of synthetic aperture radar (SAR) image formation: time-frequency based SAR imaging of moving targets and a fast backprojection (BP) algorithm for near-field bistatic SAR imaging. SAR imaging of a moving target is a challenging task due to unknown motion of the target. We approach this problem in a theoretical way, by analyzing the Wigner-Ville distribution (WVD) based SAR imaging technique. We derive approximate closed-form expressions for the point-target response of the SAR imaging system, which quantify the image resolution, and show how the blurring in conventional SAR imaging can be eliminated, while the target shift still remains. Our analyses lead to accurate prediction of the target position in the reconstructed images. The derived expressions also enable us to further study additional aspects of WVD-based SAR imaging. Bistatic SAR imaging is more involved than the monostatic SAR case, because of the separation of the transmitter and the receiver, and possibly the changing bistatic geometry. For near-field bistatic SAR imaging, we develop a novel fast BP algorithm, motivated by a newly proposed fast BP algorithm in computer tomography. First we show that the BP algorithm is the spatial-domain counterpart of the benchmark o -- k algorithm in bistatic SAR imaging, yet it avoids the frequency-domain interpolation in the o -- k algorithm, which may cause artifacts in the reconstructed image. We then derive the band-limited property for BP methods in both monostatic and bistatic SAR imaging, which is the basis for developing the fast BP algorithm. We compare our algorithm with other frequency-domain based algorithms, and show that it achieves better reconstructed image quality, while having the same computational complexity as that of the frequency-domain based algorithms.

Low cost MATLAB-based pulse oximeter for deployment in research and development applications.

PubMed

Shokouhian, M; Morling, R C S; Kale, I

2013-01-01

Problems such as motion artifact and effects of ambient lights have forced developers to design different signal processing techniques and algorithms to increase the reliability and accuracy of the conventional pulse oximeter device. To evaluate the robustness of these techniques, they are applied either to recorded data or are implemented on chip to be applied to real-time data. Recorded data is the most common method of evaluating however it is not as reliable as real-time measurements. On the other hand, hardware implementation can be both expensive and time consuming. This paper presents a low cost MATLAB-based pulse oximeter that can be used for rapid evaluation of newly developed signal processing techniques and algorithms. Flexibility to apply different signal processing techniques, providing both processed and unprocessed data along with low implementation cost are the important features of this design which makes it ideal for research and development purposes, as well as commercial, hospital and healthcare application.

Electronic polarization-division demultiplexing based on digital signal processing in intensity-modulation direct-detection optical communication systems.

PubMed

Kikuchi, Kazuro

2014-01-27

We propose a novel configuration of optical receivers for intensity-modulation direct-detection (IM · DD) systems, which can cope with dual-polarization (DP) optical signals electrically. Using a Stokes analyzer and a newly-developed digital signal-processing (DSP) algorithm, we can achieve polarization tracking and demultiplexing in the digital domain after direct detection. Simulation results show that the power penalty stemming from digital polarization manipulations is negligibly small.

Measuring radiation dose in computed tomography using elliptic phantom and free-in-air, and evaluating iterative metal artifact reduction algorithm

NASA Astrophysics Data System (ADS)

Morgan, Ashraf

The need for an accurate and reliable way for measuring patient dose in multi-row detector computed tomography (MDCT) has increased significantly. This research was focusing on the possibility of measuring CT dose in air to estimate Computed Tomography Dose Index (CTDI) for routine quality control purposes. New elliptic CTDI phantom that better represent human geometry was manufactured for investigating the effect of the subject shape on measured CTDI. Monte Carlo simulation was utilized in order to determine the dose distribution in comparison to the traditional cylindrical CTDI phantom. This research also investigated the effect of Siemens health care newly developed iMAR (iterative metal artifact reduction) algorithm, arthroplasty phantom was designed and manufactured that purpose. The design of new phantoms was part of the research as they mimic the human geometry more than the existing CTDI phantom. The standard CTDI phantom is a right cylinder that does not adequately represent the geometry of the majority of the patient population. Any dose reduction algorithm that is used during patient scan will not be utilized when scanning the CTDI phantom, so a better-designed phantom will allow the use of dose reduction algorithms when measuring dose, which leads to better dose estimation and/or better understanding of dose delivery. Doses from a standard CTDI phantom and the newly-designed phantoms were compared to doses measured in air. Iterative reconstruction is a promising technique in MDCT dose reduction and artifacts correction. Iterative reconstruction algorithms have been developed to address specific imaging tasks as is the case with Iterative Metal Artifact Reduction or iMAR which was developed by Siemens and is to be in use with the companys future computed tomography platform. The goal of iMAR is to reduce metal artifact when imaging patients with metal implants and recover CT number of tissues adjacent to the implant. This research evaluated iMAR capability of recovering CT numbers and reducing noise. Also, the use of iMAR should allow using lower tube voltage instead of 140 KVp which is used frequently to image patients with shoulder implants. The evaluations of image quality and dose reduction were carried out using an arthroplasty phantom.

Intellectual Dummies

NASA Technical Reports Server (NTRS)

2002-01-01

Goddard Space Flight Center and Triangle Research & Development Corporation collaborated to create "Smart Eyes," a charge coupled device camera that, for the first time, could read and measure bar codes without the use of lasers. The camera operated in conjunction with software and algorithms created by Goddard and Triangle R&D that could track bar code position and direction with speed and precision, as well as with software that could control robotic actions based on vision system input. This accomplishment was intended for robotic assembly of the International Space Station, helping NASA to increase production while using less manpower. After successfully completing the two- phase SBIR project with Goddard, Triangle R&D was awarded a separate contract from the U.S. Department of Transportation (DOT), which was interested in using the newly developed NASA camera technology to heighten automotive safety standards. In 1990, Triangle R&D and the DOT developed a mask made from a synthetic, plastic skin covering to measure facial lacerations resulting from automobile accidents. By pairing NASA's camera technology with Triangle R&D's and the DOT's newly developed mask, a system that could provide repeatable, computerized evaluations of laceration injury was born.

Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia

NASA Astrophysics Data System (ADS)

Liu, Zhiquan; Liu, Quanhua; Lin, Hui-Chuan; Schwartz, Craig S.; Lee, Yen-Huei; Wang, Tijian

2011-12-01

Assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) total aerosol optical depth (AOD) retrieval products (at 550 nm wavelength) from both Terra and Aqua satellites have been developed within the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVAR) data assimilation system. This newly developed algorithm allows, in a one-step procedure, the analysis of 3-D mass concentration of 14 aerosol variables from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) module. The Community Radiative Transfer Model (CRTM) was extended to calculate AOD using GOCART aerosol variables as input. Both the AOD forward model and corresponding Jacobian model were developed within the CRTM and used in the 3DVAR minimization algorithm to compute the AOD cost function and its gradient with respect to 3-D aerosol mass concentration. The impact of MODIS AOD data assimilation was demonstrated by application to a dust storm from 17 to 24 March 2010 over East Asia. The aerosol analyses initialized Weather Research and Forecasting/Chemistry (WRF/Chem) model forecasts. Results indicate that assimilating MODIS AOD substantially improves aerosol analyses and subsequent forecasts when compared to MODIS AOD, independent AOD observations from the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, and surface PM10 (particulate matter with diameters less than 10 μm) observations. The newly developed AOD data assimilation system can serve as a tool to improve simulations of dust storms and general air quality analyses and forecasts.

Collective translational and rotational Monte Carlo cluster move for general pairwise interaction

NASA Astrophysics Data System (ADS)

Růžička, Štěpán; Allen, Michael P.

2014-09-01

Virtual move Monte Carlo is a cluster algorithm which was originally developed for strongly attractive colloidal, molecular, or atomistic systems in order to both approximate the collective dynamics and avoid sampling of unphysical kinetic traps. In this paper, we present the algorithm in the form, which selects the moving cluster through a wider class of virtual states and which is applicable to general pairwise interactions, including hard-core repulsion. The newly proposed way of selecting the cluster increases the acceptance probability by up to several orders of magnitude, especially for rotational moves. The results have their applications in simulations of systems interacting via anisotropic potentials both to enhance the sampling of the phase space and to approximate the dynamics.

A compression scheme for radio data in high performance computing

NASA Astrophysics Data System (ADS)

Masui, K.; Amiri, M.; Connor, L.; Deng, M.; Fandino, M.; Höfer, C.; Halpern, M.; Hanna, D.; Hincks, A. D.; Hinshaw, G.; Parra, J. M.; Newburgh, L. B.; Shaw, J. R.; Vanderlinde, K.

2015-09-01

We present a procedure for efficiently compressing astronomical radio data for high performance applications. Integrated, post-correlation data are first passed through a nearly lossless rounding step which compares the precision of the data to a generalized and calibration-independent form of the radiometer equation. This allows the precision of the data to be reduced in a way that has an insignificant impact on the data. The newly developed Bitshuffle lossless compression algorithm is subsequently applied. When the algorithm is used in conjunction with the HDF5 library and data format, data produced by the CHIME Pathfinder telescope is compressed to 28% of its original size and decompression throughputs in excess of 1 GB/s are obtained on a single core.

URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries.

PubMed

Drawert, Brian; Engblom, Stefan; Hellander, Andreas

2012-06-22

Experiments in silico using stochastic reaction-diffusion models have emerged as an important tool in molecular systems biology. Designing computational software for such applications poses several challenges. Firstly, realistic lattice-based modeling for biological applications requires a consistent way of handling complex geometries, including curved inner- and outer boundaries. Secondly, spatiotemporal stochastic simulations are computationally expensive due to the fast time scales of individual reaction- and diffusion events when compared to the biological phenomena of actual interest. We therefore argue that simulation software needs to be both computationally efficient, employing sophisticated algorithms, yet in the same time flexible in order to meet present and future needs of increasingly complex biological modeling. We have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. URDME uses Unstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the Reaction-Diffusion Master Equation formalism to model the processes under study. An interface to a mature geometry and mesh handling external software (Comsol Multiphysics) provides for a stable and interactive environment for model construction. The core simulation routines are logically separated from the model building interface and written in a low-level language for computational efficiency. The connection to the geometry handling software is realized via a Matlab interface which facilitates script computing, data management, and post-processing. For practitioners, the software therefore behaves much as an interactive Matlab toolbox. At the same time, it is possible to modify and extend URDME with newly developed simulation routines. Since the overall design effectively hides the complexity of managing the geometry and meshes, this means that newly developed methods may be tested in a realistic setting already at an early stage of development. In this paper we demonstrate, in a series of examples with high relevance to the molecular systems biology community, that the proposed software framework is a useful tool for both practitioners and developers of spatial stochastic simulation algorithms. Through the combined efforts of algorithm development and improved modeling accuracy, increasingly complex biological models become feasible to study through computational methods. URDME is freely available at http://www.urdme.org.

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

Deka, Deepjyoti; Backhaus, Scott N.; Chertkov, Michael

Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as those related to demand response, outage detection and management, and improved load-monitoring. In this two part paper, inspired by proliferation of the metering technology, we discuss estimation problems in structurally loopy but operationally radial distribution grids from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. In Part I, the objective is to learn the operational layout of the grid. Part II of this paper presentsmore » algorithms that estimate load statistics or line parameters in addition to learning the grid structure. Further, Part II discusses the problem of structure estimation for systems with incomplete measurement sets. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time– which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.« less

Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images.

PubMed

Mane, Vijay Mahadeo; Jadhav, D V

2017-05-24

Diabetic retinopathy (DR) is the most common diabetic eye disease. Doctors are using various test methods to detect DR. But, the availability of test methods and requirements of domain experts pose a new challenge in the automatic detection of DR. In order to fulfill this objective, a variety of algorithms has been developed in the literature. In this paper, we propose a system consisting of a novel sparking process and a holoentropy-based decision tree for automatic classification of DR images to further improve the effectiveness. The sparking process algorithm is developed for automatic segmentation of blood vessels through the estimation of optimal threshold. The holoentropy enabled decision tree is newly developed for automatic classification of retinal images into normal or abnormal using hybrid features which preserve the disease-level patterns even more than the signal level of the feature. The effectiveness of the proposed system is analyzed using standard fundus image databases DIARETDB0 and DIARETDB1 for sensitivity, specificity and accuracy. The proposed system yields sensitivity, specificity and accuracy values of 96.72%, 97.01% and 96.45%, respectively. The experimental result reveals that the proposed technique outperforms the existing algorithms.

Deformation Estimation In Non-Urban Areas Exploiting High Resolution SAR Data

NASA Astrophysics Data System (ADS)

Goel, Kanika; Adam, Nico

2012-01-01

Advanced techniques such as the Small Baseline Subset Algorithm (SBAS) have been developed for terrain motion mapping in non-urban areas with a focus on extracting information from distributed scatterers (DSs). SBAS uses small baseline differential interferograms (to limit the effects of geometric decorrelation) and these are typically multilooked to reduce phase noise, resulting in loss of resolution. Various error sources e.g. phase unwrapping errors, topographic errors, temporal decorrelation and atmospheric effects also affect the interferometric phase. The aim of our work is an improved deformation monitoring in non-urban areas exploiting high resolution SAR data. The paper provides technical details and a processing example of a newly developed technique which incorporates an adaptive spatial phase filtering algorithm for an accurate high resolution differential interferometric stacking, followed by deformation retrieval via the SBAS approach where we perform the phase inversion using a more robust L1 norm minimization.

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

DOE PAGES

Wang, Liming; Huang, Jiaji; Yuan, Xin; ...

2015-09-17

The measurement matrix employed in compressive sensing typically cannot be known precisely a priori and must be estimated via calibration. One may take multiple compressive measurements, from which the measurement matrix and underlying signals may be estimated jointly. This is of interest as well when the measurement matrix may change as a function of the details of what is measured. This problem has been considered recently for Gaussian measurement noise, and here we develop this idea with application to Poisson systems. A collaborative maximum likelihood algorithm and alternating proximal gradient algorithm are proposed, and associated theoretical performance guarantees are establishedmore » based on newly derived concentration-of-measure results. A Bayesian model is then introduced, to improve flexibility and generality. Connections between the maximum likelihood methods and the Bayesian model are developed, and example results are presented for a real compressive X-ray imaging system.« less

Developing a Shuffled Complex-Self Adaptive Hybrid Evolution (SC-SAHEL) Framework for Water Resources Management and Water-Energy System Optimization

NASA Astrophysics Data System (ADS)

Rahnamay Naeini, M.; Sadegh, M.; AghaKouchak, A.; Hsu, K. L.; Sorooshian, S.; Yang, T.

2017-12-01

Meta-Heuristic optimization algorithms have gained a great deal of attention in a wide variety of fields. Simplicity and flexibility of these algorithms, along with their robustness, make them attractive tools for solving optimization problems. Different optimization methods, however, hold algorithm-specific strengths and limitations. Performance of each individual algorithm obeys the "No-Free-Lunch" theorem, which means a single algorithm cannot consistently outperform all possible optimization problems over a variety of problems. From users' perspective, it is a tedious process to compare, validate, and select the best-performing algorithm for a specific problem or a set of test cases. In this study, we introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme, and allows users to select the most suitable algorithm tailored to the problem at hand. The concept of SC-SAHEL is to execute different EAs as separate parallel search cores, and let all participating EAs to compete during the course of the search. The newly developed SC-SAHEL algorithm is designed to automatically select, the best performing algorithm for the given optimization problem. This algorithm is rigorously effective in finding the global optimum for several strenuous benchmark test functions, and computationally efficient as compared to individual EAs. We benchmark the proposed SC-SAHEL algorithm over 29 conceptual test functions, and two real-world case studies - one hydropower reservoir model and one hydrological model (SAC-SMA). Results show that the proposed framework outperforms individual EAs in an absolute majority of the test problems, and can provide competitive results to the fittest EA algorithm with more comprehensive information during the search. The proposed framework is also flexible for merging additional EAs, boundary-handling techniques, and sampling schemes, and has good potential to be used in Water-Energy system optimal operation and management.

CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes.

PubMed

White, Clarence; Ismail, Hamid D; Saigo, Hiroto; Kc, Dukka B

2017-12-28

The β-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computational tool to classify the newly identified BL enzymes into one of its classes. There are two types of classification of BL enzymes: Molecular Classification and Functional Classification. Existing computational methods only address Molecular Classification and the performance of these existing methods is unsatisfactory. We addressed the unsatisfactory performance of the existing methods by implementing a Deep Learning approach called Convolutional Neural Network (CNN). We developed CNN-BLPred, an approach for the classification of BL proteins. The CNN-BLPred uses Gradient Boosted Feature Selection (GBFS) in order to select the ideal feature set for each BL classification. Based on the rigorous benchmarking of CCN-BLPred using both leave-one-out cross-validation and independent test sets, CCN-BLPred performed better than the other existing algorithms. Compared with other architectures of CNN, Recurrent Neural Network, and Random Forest, the simple CNN architecture with only one convolutional layer performs the best. After feature extraction, we were able to remove ~95% of the 10,912 features using Gradient Boosted Trees. During 10-fold cross validation, we increased the accuracy of the classic BL predictions by 7%. We also increased the accuracy of Class A, Class B, Class C, and Class D performance by an average of 25.64%. The independent test results followed a similar trend. We implemented a deep learning algorithm known as Convolutional Neural Network (CNN) to develop a classifier for BL classification. Combined with feature selection on an exhaustive feature set and using balancing method such as Random Oversampling (ROS), Random Undersampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE), CNN-BLPred performs significantly better than existing algorithms for BL classification.

Aerosol optical properties retrieved from the future space lidar mission ADM-aeolus

NASA Astrophysics Data System (ADS)

Martinet, Pauline; Flament, Thomas; Dabas, Alain

2018-04-01

The ADM-Aeolus mission, to be launched by end of 2017, will enable the retrieval of aerosol optical properties (extinction and backscatter coefficients essentially) for different atmospheric conditions. A newly developed feature finder (FF) algorithm enabling the detection of aerosol and cloud targets in the atmospheric scene has been implemented. Retrievals of aerosol properties at a better horizontal resolution based on the feature finder groups have shown an improvement mainly on the backscatter coefficient compared to the common 90 km product.

An order (n) algorithm for the dynamics simulation of robotic systems

NASA Technical Reports Server (NTRS)

Chun, H. M.; Turner, J. D.; Frisch, Harold P.

1989-01-01

The formulation of an Order (n) algorithm for DISCOS (Dynamics Interaction Simulation of Controls and Structures), which is an industry-standard software package for simulation and analysis of flexible multibody systems is presented. For systems involving many bodies, the new Order (n) version of DISCOS is much faster than the current version. Results of the experimental validation of the dynamics software are also presented. The experiment is carried out on a seven-joint robot arm at NASA's Goddard Space Flight Center. The algorithm used in the current version of DISCOS requires the inverse of a matrix whose dimension is equal to the number of constraints in the system. Generally, the number of constraints in a system is roughly proportional to the number of bodies in the system, and matrix inversion requires O(p exp 3) operations, where p is the dimension of the matrix. The current version of DISCOS is therefore considered an Order (n exp 3) algorithm. In contrast, the Order (n) algorithm requires inversion of matrices which are small, and the number of matrices to be inverted increases only linearly with the number of bodies. The newly-developed Order (n) DISCOS is currently capable of handling chain and tree topologies as well as multiple closed loops. Continuing development will extend the capability of the software to deal with typical robotics applications such as put-and-place, multi-arm hand-off and surface sliding.

Innovative signal processing for Johnson Noise thermometry

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

Ezell, N. Dianne Bull; Britton, Jr, Charles L.; Roberts, Michael

This report summarizes the newly developed algorithm that subtracted the Electromagnetic Interference (EMI). The EMI performance is very important to this measurement because any interference in the form on pickup from external signal sources from such as fluorescent lighting ballasts, motors, etc. can skew the measurement. Two methods of removing EMI were developed and tested at various locations. This report also summarizes the testing performed at different facilities outside Oak Ridge National Laboratory using both EMI removal techniques. The first EMI removal technique reviewed in previous milestone reports and therefore this report will detail the second method.

Recent developments in learning control and system identification for robots and structures

NASA Technical Reports Server (NTRS)

Phan, M.; Juang, J.-N.; Longman, R. W.

1990-01-01

This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

A High-Order Direct Solver for Helmholtz Equations with Neumann Boundary Conditions

NASA Technical Reports Server (NTRS)

Sun, Xian-He; Zhuang, Yu

1997-01-01

In this study, a compact finite-difference discretization is first developed for Helmholtz equations on rectangular domains. Special treatments are then introduced for Neumann and Neumann-Dirichlet boundary conditions to achieve accuracy and separability. Finally, a Fast Fourier Transform (FFT) based technique is used to yield a fast direct solver. Analytical and experimental results show this newly proposed solver is comparable to the conventional second-order elliptic solver when accuracy is not a primary concern, and is significantly faster than that of the conventional solver if a highly accurate solution is required. In addition, this newly proposed fourth order Helmholtz solver is parallel in nature. It is readily available for parallel and distributed computers. The compact scheme introduced in this study is likely extendible for sixth-order accurate algorithms and for more general elliptic equations.

State of the art techniques for preservation and reuse of hard copy electrocardiograms.

PubMed

Lobodzinski, Suave M; Teppner, Ulrich; Laks, Michael

2003-01-01

Baseline examinations and periodic reexaminations in longitudinal population studies, together with ongoing surveillance for morbidity and mortality, provide unique opportunities for seeking ways to enhance the value of electrocardiography (ECG) as an inexpensive and noninvasive tool for prognosis and diagnosis. We used newly developed optical ECG waveform recognition (OEWR) technique capable of extracting raw waveform data from legacy hard copy ECG recording. Hardcopy ECG recordings were scanned and processed by the OEWR algorithm. The extracted ECG datasets were formatted into a newly proposed, vendor-neutral, ECG XML data format. Oracle database was used as a repository for ECG records in XML format. The proposed technique for XML encapsulation of OEWR processed hard copy records resulted in an efficient method for inclusion of paper ECG records into research databases, thus providing their preservation, reuse and accession.

Boundary layer height determination from Lidar for improving air pollution episode modelling: development of new algorithm and evaluation

NASA Astrophysics Data System (ADS)

Yang, T.; Wang, Z.; Zhang, W.; Gbaguidi, A.; Sugimoto, N.; Matsui, I.; Wang, X.; Yele, S.

2017-12-01

Predicting air pollution events in low atmosphere over megacities requires thorough understanding of the tropospheric dynamic and chemical processes, involving notably, continuous and accurate determination of the boundary layer height (BLH). Through intensive observations experimented over Beijing (China), and an exhaustive evaluation existing algorithms applied to the BLH determination, persistent critical limitations are noticed, in particular over polluted episodes. Basically, under weak thermal convection with high aerosol loading, none of the retrieval algorithms is able to fully capture the diurnal cycle of the BLH due to pollutant insufficient vertical mixing in the boundary layer associated with the impact of gravity waves on the tropospheric structure. Subsequently, a new approach based on gravity wave theory (the cubic root gradient method: CRGM), is developed to overcome such weakness and accurately reproduce the fluctuations of the BLH under various atmospheric pollution conditions. Comprehensive evaluation of CRGM highlights its high performance in determining BLH from Lidar. In comparison with the existing retrieval algorithms, the CRGM potentially reduces related computational uncertainties and errors from BLH determination (strong increase of correlation coefficient from 0.44 to 0.91 and significant decreases of the root mean square error from 643 m to 142 m). Such newly developed technique is undoubtedly expected to contribute to improve the accuracy of air quality modelling and forecasting systems.

A spatial multi-objective optimization model for sustainable urban wastewater system layout planning.

PubMed

Dong, X; Zeng, S; Chen, J

2012-01-01

Design of a sustainable city has changed the traditional centralized urban wastewater system towards a decentralized or clustering one. Note that there is considerable spatial variability of the factors that affect urban drainage performance including urban catchment characteristics. The potential options are numerous for planning the layout of an urban wastewater system, which are associated with different costs and local environmental impacts. There is thus a need to develop an approach to find the optimal spatial layout for collecting, treating, reusing and discharging the municipal wastewater of a city. In this study, a spatial multi-objective optimization model, called Urban wastewateR system Layout model (URL), was developed. It is solved by a genetic algorithm embedding Monte Carlo sampling and a series of graph algorithms. This model was illustrated by a case study in a newly developing urban area in Beijing, China. Five optimized system layouts were recommended to the local municipality for further detailed design.

Implementation of real-time digital signal processing systems

NASA Technical Reports Server (NTRS)

Narasimha, M.; Peterson, A.; Narayan, S.

1978-01-01

Special purpose hardware implementation of DFT Computers and digital filters is considered in the light of newly introduced algorithms and IC devices. Recent work by Winograd on high-speed convolution techniques for computing short length DFT's, has motivated the development of more efficient algorithms, compared to the FFT, for evaluating the transform of longer sequences. Among these, prime factor algorithms appear suitable for special purpose hardware implementations. Architectural considerations in designing DFT computers based on these algorithms are discussed. With the availability of monolithic multiplier-accumulators, a direct implementation of IIR and FIR filters, using random access memories in place of shift registers, appears attractive. The memory addressing scheme involved in such implementations is discussed. A simple counter set-up to address the data memory in the realization of FIR filters is also described. The combination of a set of simple filters (weighting network) and a DFT computer is shown to realize a bank of uniform bandpass filters. The usefulness of this concept in arriving at a modular design for a million channel spectrum analyzer, based on microprocessors, is discussed.

Investigation into adamantane-based M2 inhibitors with FB-QSAR.

PubMed

Wei, Hang; Wang, Cheng-Hua; Du, Qi-Shi; Meng, Jianzong; Chou, Kuo-Chen

2009-07-01

Because of their high resistance rate to the existing drugs, influenza A viruses have become a threat to human beings. It is known that the replication of influenza A viruses needs a pH-gated proton channel, the so-called M2 channel. Therefore, to develop effective drugs against influenza A, the most logic strategy is to inhibit the M2 channel. Recently, the atomic structure of the M2 channel was determined by NMR spectroscopy (Schnell, J.R. and Chou, J.J., Nature, 2008, 451, 591-595). The high-resolution NMR structure has provided a solid basis for structure-based drug design approaches. In this study, a benchmark dataset has been constructed that contains 34 newly-developed adamantane-based M2 inhibitors and covers considerable structural diversities and wide range of bioactivities. Based on these compounds, an in-depth analysis was performed with the newly developed fragment-based quantitative structure-activity relationship (FB-QSAR) algorithm. The results thus obtained provide useful insights for dealing with the drug-resistant problem and designing effective adamantane-based antiflu drugs.

Prediction in complex systems: The case of the international trade network

NASA Astrophysics Data System (ADS)

Vidmer, Alexandre; Zeng, An; Medo, Matúš; Zhang, Yi-Cheng

2015-10-01

Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the products that they export. Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future. These baseline predictions are improved using a recent metric of country fitness and product similarity. The overall best results are achieved with a newly developed metric of product similarity which takes advantage of causality in the network evolution.

2D-RBUC for efficient parallel compression of residuals

NASA Astrophysics Data System (ADS)

Đurđević, Đorđe M.; Tartalja, Igor I.

2018-02-01

In this paper, we present a method for lossless compression of residuals with an efficient SIMD parallel decompression. The residuals originate from lossy or near lossless compression of height fields, which are commonly used to represent models of terrains. The algorithm is founded on the existing RBUC method for compression of non-uniform data sources. We have adapted the method to capture 2D spatial locality of height fields, and developed the data decompression algorithm for modern GPU architectures already present even in home computers. In combination with the point-level SIMD-parallel lossless/lossy high field compression method HFPaC, characterized by fast progressive decompression and seamlessly reconstructed surface, the newly proposed method trades off small efficiency degradation for a non negligible compression ratio (measured up to 91%) benefit.

Nonlinear inversion of resistivity sounding data for 1-D earth models using the Neighbourhood Algorithm

NASA Astrophysics Data System (ADS)

Ojo, A. O.; Xie, Jun; Olorunfemi, M. O.

2018-01-01

To reduce ambiguity related to nonlinearities in the resistivity model-data relationships, an efficient direct-search scheme employing the Neighbourhood Algorithm (NA) was implemented to solve the 1-D resistivity problem. In addition to finding a range of best-fit models which are more likely to be global minimums, this method investigates the entire multi-dimensional model space and provides additional information about the posterior model covariance matrix, marginal probability density function and an ensemble of acceptable models. This provides new insights into how well the model parameters are constrained and make assessing trade-offs between them possible, thus avoiding some common interpretation pitfalls. The efficacy of the newly developed program is tested by inverting both synthetic (noisy and noise-free) data and field data from other authors employing different inversion methods so as to provide a good base for comparative performance. In all cases, the inverted model parameters were in good agreement with the true and recovered model parameters from other methods and remarkably correlate with the available borehole litho-log and known geology for the field dataset. The NA method has proven to be useful whilst a good starting model is not available and the reduced number of unknowns in the 1-D resistivity inverse problem makes it an attractive alternative to the linearized methods. Hence, it is concluded that the newly developed program offers an excellent complementary tool for the global inversion of the layered resistivity structure.

In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses.

PubMed

Schmier, Sonja; Mostafa, Ahmed; Haarmann, Thomas; Bannert, Norbert; Ziebuhr, John; Veljkovic, Veljko; Dietrich, Ursula; Pleschka, Stephan

2015-06-19

Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.

In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses

NASA Astrophysics Data System (ADS)

Schmier, Sonja; Mostafa, Ahmed; Haarmann, Thomas; Bannert, Norbert; Ziebuhr, John; Veljkovic, Veljko; Dietrich, Ursula; Pleschka, Stephan

2015-06-01

Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

NASA Technical Reports Server (NTRS)

Solarna, David; Moser, Gabriele; Le Moigne-Stewart, Jacqueline; Serpico, Sebastiano B.

2017-01-01

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Automated Video-Based Analysis of Contractility and Calcium Flux in Human-Induced Pluripotent Stem Cell-Derived Cardiomyocytes Cultured over Different Spatial Scales.

PubMed

Huebsch, Nathaniel; Loskill, Peter; Mandegar, Mohammad A; Marks, Natalie C; Sheehan, Alice S; Ma, Zhen; Mathur, Anurag; Nguyen, Trieu N; Yoo, Jennie C; Judge, Luke M; Spencer, C Ian; Chukka, Anand C; Russell, Caitlin R; So, Po-Lin; Conklin, Bruce R; Healy, Kevin E

2015-05-01

Contractile motion is the simplest metric of cardiomyocyte health in vitro, but unbiased quantification is challenging. We describe a rapid automated method, requiring only standard video microscopy, to analyze the contractility of human-induced pluripotent stem cell-derived cardiomyocytes (iPS-CM). New algorithms for generating and filtering motion vectors combined with a newly developed isogenic iPSC line harboring genetically encoded calcium indicator, GCaMP6f, allow simultaneous user-independent measurement and analysis of the coupling between calcium flux and contractility. The relative performance of these algorithms, in terms of improving signal to noise, was tested. Applying these algorithms allowed analysis of contractility in iPS-CM cultured over multiple spatial scales from single cells to three-dimensional constructs. This open source software was validated with analysis of isoproterenol response in these cells, and can be applied in future studies comparing the drug responsiveness of iPS-CM cultured in different microenvironments in the context of tissue engineering.

Extended-Kalman-filter-based regenerative and friction blended braking control for electric vehicle equipped with axle motor considering damping and elastic properties of electric powertrain

NASA Astrophysics Data System (ADS)

Lv, Chen; Zhang, Junzhi; Li, Yutong

2014-11-01

Because of the damping and elastic properties of an electrified powertrain, the regenerative brake of an electric vehicle (EV) is very different from a conventional friction brake with respect to the system dynamics. The flexibility of an electric drivetrain would have a negative effect on the blended brake control performance. In this study, models of the powertrain system of an electric car equipped with an axle motor are developed. Based on these models, the transfer characteristics of the motor torque in the driveline and its effect on blended braking control performance are analysed. To further enhance a vehicle's brake performance and energy efficiency, blended braking control algorithms with compensation for the powertrain flexibility are proposed using an extended Kalman filter. These algorithms are simulated under normal deceleration braking. The results show that the brake performance and blended braking control accuracy of the vehicle are significantly enhanced by the newly proposed algorithms.

Optimizing high performance computing workflow for protein functional annotation.

PubMed

Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene

2014-09-10

Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data.

Optimizing high performance computing workflow for protein functional annotation

PubMed Central

Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene

2014-01-01

Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data. PMID:25313296

Simulink based behavioural modelling of a pulse oximeter for deployment in rapid development, prototyping and verification.

PubMed

Shokouhian, M; Morling, R C S; Kale, I

2012-01-01

The pulse oximeter is a well-known device for measuring the level of oxygen in blood. Since their invention, pulse oximeters have been under constant development in both aspects of hardware and software; however there are still unsolved problems that limit their performance [6], [7]. Many fresh algorithms and new design techniques are being suggested every year by industry and academic researchers which claim that they can improve accuracy of measurements [8], [9]. With the lack of an accurate computer-based behavioural model for pulse oximeters, the only way for evaluation of these newly developed systems and algorithms is through hardware implementation which can be both expensive and time consuming. This paper presents an accurate Simulink based behavioural model for a pulse oximeter that can be used by industry and academia alike working in this area, as an exploration as well as productivity enhancement tool during their research and development process. The aim of this paper is to introduce a new computer-based behavioural model which provides a simulation environment from which new ideas can be rapidly evaluated long before the real implementation.

Development of embedded real-time and high-speed vision platform

NASA Astrophysics Data System (ADS)

Ouyang, Zhenxing; Dong, Yimin; Yang, Hua

2015-12-01

Currently, high-speed vision platforms are widely used in many applications, such as robotics and automation industry. However, a personal computer (PC) whose over-large size is not suitable and applicable in compact systems is an indispensable component for human-computer interaction in traditional high-speed vision platforms. Therefore, this paper develops an embedded real-time and high-speed vision platform, ER-HVP Vision which is able to work completely out of PC. In this new platform, an embedded CPU-based board is designed as substitution for PC and a DSP and FPGA board is developed for implementing image parallel algorithms in FPGA and image sequential algorithms in DSP. Hence, the capability of ER-HVP Vision with size of 320mm x 250mm x 87mm can be presented in more compact condition. Experimental results are also given to indicate that the real-time detection and counting of the moving target at a frame rate of 200 fps at 512 x 512 pixels under the operation of this newly developed vision platform are feasible.

Screening for Human Immunodeficiency Virus, Hepatitis B Virus, Hepatitis C Virus, and Treponema pallidum by Blood Testing Using a Bio-Flash Technology-Based Algorithm before Gastrointestinal Endoscopy

PubMed Central

Zhen, Chen; QuiuLi, Zhang; YuanQi, An; Casado, Verónica Vocero; Fan, Yuan

2016-01-01

Currently, conventional enzyme immunoassays which use manual gold immunoassays and colloidal tests (GICTs) are used as screening tools to detect Treponema pallidum (syphilis), hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus type 1 (HIV-1), and HIV-2 in patients undergoing surgery. The present observational, cross-sectional study compared the sensitivity, specificity, and work flow characteristics of the conventional algorithm with manual GICTs with those of a newly proposed algorithm that uses the automated Bio-Flash technology as a screening tool in patients undergoing gastrointestinal (GI) endoscopy. A total of 956 patients were examined for the presence of serological markers of infection with HIV-1/2, HCV, HBV, and T. pallidum. The proposed algorithm with the Bio-Flash technology was superior for the detection of all markers (100.0% sensitivity and specificity for detection of anti-HIV and anti-HCV antibodies, HBV surface antigen [HBsAg], and T. pallidum) compared with the conventional algorithm based on the manual method (80.0% sensitivity and 98.6% specificity for the detection of anti-HIV, 75.0% sensitivity for the detection of anti-HCV, 94.7% sensitivity for the detection of HBsAg, and 100% specificity for the detection of anti-HCV and HBsAg) in these patients. The automated Bio-Flash technology-based screening algorithm also reduced the operation time by 85.0% (205 min) per day, saving up to 24 h/week. In conclusion, the use of the newly proposed screening algorithm based on the automated Bio-Flash technology can provide an advantage over the use of conventional algorithms based on manual methods for screening for HIV, HBV, HCV, and syphilis before GI endoscopy. PMID:27707942

Screening for Human Immunodeficiency Virus, Hepatitis B Virus, Hepatitis C Virus, and Treponema pallidum by Blood Testing Using a Bio-Flash Technology-Based Algorithm before Gastrointestinal Endoscopy.

PubMed

Jun, Zhou; Zhen, Chen; QuiuLi, Zhang; YuanQi, An; Casado, Verónica Vocero; Fan, Yuan

2016-12-01

Currently, conventional enzyme immunoassays which use manual gold immunoassays and colloidal tests (GICTs) are used as screening tools to detect Treponema pallidum (syphilis), hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus type 1 (HIV-1), and HIV-2 in patients undergoing surgery. The present observational, cross-sectional study compared the sensitivity, specificity, and work flow characteristics of the conventional algorithm with manual GICTs with those of a newly proposed algorithm that uses the automated Bio-Flash technology as a screening tool in patients undergoing gastrointestinal (GI) endoscopy. A total of 956 patients were examined for the presence of serological markers of infection with HIV-1/2, HCV, HBV, and T. pallidum The proposed algorithm with the Bio-Flash technology was superior for the detection of all markers (100.0% sensitivity and specificity for detection of anti-HIV and anti-HCV antibodies, HBV surface antigen [HBsAg], and T. pallidum) compared with the conventional algorithm based on the manual method (80.0% sensitivity and 98.6% specificity for the detection of anti-HIV, 75.0% sensitivity for the detection of anti-HCV, 94.7% sensitivity for the detection of HBsAg, and 100% specificity for the detection of anti-HCV and HBsAg) in these patients. The automated Bio-Flash technology-based screening algorithm also reduced the operation time by 85.0% (205 min) per day, saving up to 24 h/week. In conclusion, the use of the newly proposed screening algorithm based on the automated Bio-Flash technology can provide an advantage over the use of conventional algorithms based on manual methods for screening for HIV, HBV, HCV, and syphilis before GI endoscopy. Copyright © 2016 Jun et al.

Supercooled Liquid Water Content Instrument Analysis and Winter 2014 Data with Comparisons to the NASA Icing Remote Sensing System and Pilot Reports

NASA Technical Reports Server (NTRS)

King, Michael C.

2016-01-01

The National Aeronautics and Space Administration (NASA) has developed a system for remotely detecting the hazardous conditions leading to aircraft icing in flight, the NASA Icing Remote Sensing System (NIRSS). Newly developed, weather balloon-borne instruments have been used to obtain in-situ measurements of supercooled liquid water during March 2014 to validate the algorithms used in the NIRSS. A mathematical model and a processing method were developed to analyze the data obtained from the weather balloon soundings. The data from soundings obtained in March 2014 were analyzed and compared to the output from the NIRSS and pilot reports.

Optimal design of a smart post-buckled beam actuator using bat algorithm: simulations and experiments

NASA Astrophysics Data System (ADS)

Mallick, Rajnish; Ganguli, Ranjan; Kumar, Ravi

2017-05-01

The optimized design of a smart post-buckled beam actuator (PBA) is performed in this study. A smart material based piezoceramic stack actuator is used as a prime-mover to drive the buckled beam actuator. Piezoceramic actuators are high force, small displacement devices; they possess high energy density and have high bandwidth. In this study, bench top experiments are conducted to investigate the angular tip deflections due to the PBA. A new design of a linear-to-linear motion amplification device (LX-4) is developed to circumvent the small displacement handicap of piezoceramic stack actuators. LX-4 enhances the piezoceramic actuator mechanical leverage by a factor of four. The PBA model is based on dynamic elastic stability and is analyzed using the Mathieu-Hill equation. A formal optimization is carried out using a newly developed meta-heuristic nature inspired algorithm, named as the bat algorithm (BA). The BA utilizes the echolocation capability of bats. An optimized PBA in conjunction with LX-4 generates end rotations of the order of 15° at the output end. The optimized PBA design incurs less weight and induces large end rotations, which will be useful in development of various mechanical and aerospace devices, such as helicopter trailing edge flaps, micro and nano aerial vehicles and other robotic systems.

On the use of adaptive multiresolution method with time-varying tolerance for compressible fluid flows

NASA Astrophysics Data System (ADS)

Soni, V.; Hadjadj, A.; Roussel, O.

2017-12-01

In this paper, a fully adaptive multiresolution (MR) finite difference scheme with a time-varying tolerance is developed to study compressible fluid flows containing shock waves in interaction with solid obstacles. To ensure adequate resolution near rigid bodies, the MR algorithm is combined with an immersed boundary method based on a direct-forcing approach in which the solid object is represented by a continuous solid-volume fraction. The resulting algorithm forms an efficient tool capable of solving linear and nonlinear waves on arbitrary geometries. Through a one-dimensional scalar wave equation, the accuracy of the MR computation is, as expected, seen to decrease in time when using a constant MR tolerance considering the accumulation of error. To overcome this problem, a variable tolerance formulation is proposed, which is assessed through a new quality criterion, to ensure a time-convergence solution for a suitable quality resolution. The newly developed algorithm coupled with high-resolution spatial and temporal approximations is successfully applied to shock-bluff body and shock-diffraction problems solving Euler and Navier-Stokes equations. Results show excellent agreement with the available numerical and experimental data, thereby demonstrating the efficiency and the performance of the proposed method.

Expediting topology data gathering for the TOPDB database.

PubMed

Dobson, László; Langó, Tamás; Reményi, István; Tusnády, Gábor E

2015-01-01

The Topology Data Bank of Transmembrane Proteins (TOPDB, http://topdb.enzim.ttk.mta.hu) contains experimentally determined topology data of transmembrane proteins. Recently, we have updated TOPDB from several sources and utilized a newly developed topology prediction algorithm to determine the most reliable topology using the results of experiments as constraints. In addition to collecting the experimentally determined topology data published in the last couple of years, we gathered topographies defined by the TMDET algorithm using 3D structures from the PDBTM. Results of global topology analysis of various organisms as well as topology data generated by high throughput techniques, like the sequential positions of N- or O-glycosylations were incorporated into the TOPDB database. Moreover, a new algorithm was developed to integrate scattered topology data from various publicly available databases and a new method was introduced to measure the reliability of predicted topologies. We show that reliability values highly correlate with the per protein topology accuracy of the utilized prediction method. Altogether, more than 52,000 new topology data and more than 2600 new transmembrane proteins have been collected since the last public release of the TOPDB database. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Deploy Nalu/Kokkos algorithmic infrastructure with performance benchmarking.

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

Domino, Stefan P.; Ananthan, Shreyas; Knaus, Robert C.

The former Nalu interior heterogeneous algorithm design, which was originally designed to manage matrix assembly operations over all elemental topology types, has been modified to operate over homogeneous collections of mesh entities. This newly templated kernel design allows for removal of workset variable resize operations that were formerly required at each loop over a Sierra ToolKit (STK) bucket (nominally, 512 entities in size). Extensive usage of the Standard Template Library (STL) std::vector has been removed in favor of intrinsic Kokkos memory views. In this milestone effort, the transition to Kokkos as the underlying infrastructure to support performance and portability onmore » many-core architectures has been deployed for key matrix algorithmic kernels. A unit-test driven design effort has developed a homogeneous entity algorithm that employs a team-based thread parallelism construct. The STK Single Instruction Multiple Data (SIMD) infrastructure is used to interleave data for improved vectorization. The collective algorithm design, which allows for concurrent threading and SIMD management, has been deployed for the core low-Mach element- based algorithm. Several tests to ascertain SIMD performance on Intel KNL and Haswell architectures have been carried out. The performance test matrix includes evaluation of both low- and higher-order methods. The higher-order low-Mach methodology builds on polynomial promotion of the core low-order control volume nite element method (CVFEM). Performance testing of the Kokkos-view/SIMD design indicates low-order matrix assembly kernel speed-up ranging between two and four times depending on mesh loading and node count. Better speedups are observed for higher-order meshes (currently only P=2 has been tested) especially on KNL. The increased workload per element on higher-order meshes bene ts from the wide SIMD width on KNL machines. Combining multiple threads with SIMD on KNL achieves a 4.6x speedup over the baseline, with assembly timings faster than that observed on Haswell architecture. The computational workload of higher-order meshes, therefore, seems ideally suited for the many-core architecture and justi es further exploration of higher-order on NGP platforms. A Trilinos/Tpetra-based multi-threaded GMRES preconditioned by symmetric Gauss Seidel (SGS) represents the core solver infrastructure for the low-Mach advection/diffusion implicit solves. The threaded solver stack has been tested on small problems on NREL's Peregrine system using the newly developed and deployed Kokkos-view/SIMD kernels. fforts are underway to deploy the Tpetra-based solver stack on NERSC Cori system to benchmark its performance at scale on KNL machines.« less

Characterizing Arctic Sea Ice Topography Using High-Resolution IceBridge Data

NASA Technical Reports Server (NTRS)

Petty, Alek; Tsamados, Michel; Kurtz, Nathan; Farrell, Sinead; Newman, Thomas; Harbeck, Jeremy; Feltham, Daniel; Richter-Menge, Jackie

2016-01-01

We present an analysis of Arctic sea ice topography using high resolution, three-dimensional, surface elevation data from the Airborne Topographic Mapper, flown as part of NASA's Operation IceBridge mission. Surface features in the sea ice cover are detected using a newly developed surface feature picking algorithm. We derive information regarding the height, volume and geometry of surface features from 2009-2014 within the Beaufort/Chukchi and Central Arctic regions. The results are delineated by ice type to estimate the topographic variability across first-year and multi-year ice regimes.

Far-field DOA estimation and source localization for different scenarios in a distributed sensor network

NASA Astrophysics Data System (ADS)

Asgari, Shadnaz

Recent developments in the integrated circuits and wireless communications not only open up many possibilities but also introduce challenging issues for the collaborative processing of signals for source localization and beamforming in an energy-constrained distributed sensor network. In signal processing, various sensor array processing algorithms and concepts have been adopted, but must be further tailored to match the communication and computational constraints. Sometimes the constraints are such that none of the existing algorithms would be an efficient option for the defined problem and as the result; the necessity of developing a new algorithm becomes undeniable. In this dissertation, we present the theoretical and the practical issues of Direction-Of-Arrival (DOA) estimation and source localization using the Approximate-Maximum-Likelihood (AML) algorithm for different scenarios. We first investigate a robust algorithm design for coherent source DOA estimation in a limited reverberant environment. Then, we provide a least-square (LS) solution for source localization based on our newly proposed virtual array model. In another scenario, we consider the determination of the location of a disturbance source which emits both wideband acoustic and seismic signals. We devise an enhanced AML algorithm to process the data collected at the acoustic sensors. For processing the seismic signals, two distinct algorithms are investigated to determine the DOAs. Then, we consider a basic algorithm for fusion of the results yielded by the acoustic and seismic arrays. We also investigate the theoretical and practical issues of DOA estimation in a three-dimensional (3D) scenario. We show that the performance of the proposed 3D AML algorithm converges to the Cramer-Rao Bound. We use the concept of an isotropic array to reduce the complexity of the proposed algorithm by advocating a decoupled 3D version. We also explore a modified version of the decoupled 3D AML algorithm which can be used for DOA estimation with non-isotropic arrays. In this dissertation, for each scenario, efficient numerical implementations of the corresponding AML algorithm are derived and applied into a real-time sensor network testbed. Extensive simulations as well as experimental results are presented to verify the effectiveness of the proposed algorithms.

High performance transcription factor-DNA docking with GPU computing

PubMed Central

2012-01-01

Background Protein-DNA docking is a very challenging problem in structural bioinformatics and has important implications in a number of applications, such as structure-based prediction of transcription factor binding sites and rational drug design. Protein-DNA docking is very computational demanding due to the high cost of energy calculation and the statistical nature of conformational sampling algorithms. More importantly, experiments show that the docking quality depends on the coverage of the conformational sampling space. It is therefore desirable to accelerate the computation of the docking algorithm, not only to reduce computing time, but also to improve docking quality. Methods In an attempt to accelerate the sampling process and to improve the docking performance, we developed a graphics processing unit (GPU)-based protein-DNA docking algorithm. The algorithm employs a potential-based energy function to describe the binding affinity of a protein-DNA pair, and integrates Monte-Carlo simulation and a simulated annealing method to search through the conformational space. Algorithmic techniques were developed to improve the computation efficiency and scalability on GPU-based high performance computing systems. Results The effectiveness of our approach is tested on a non-redundant set of 75 TF-DNA complexes and a newly developed TF-DNA docking benchmark. We demonstrated that the GPU-based docking algorithm can significantly accelerate the simulation process and thereby improving the chance of finding near-native TF-DNA complex structures. This study also suggests that further improvement in protein-DNA docking research would require efforts from two integral aspects: improvement in computation efficiency and energy function design. Conclusions We present a high performance computing approach for improving the prediction accuracy of protein-DNA docking. The GPU-based docking algorithm accelerates the search of the conformational space and thus increases the chance of finding more near-native structures. To the best of our knowledge, this is the first ad hoc effort of applying GPU or GPU clusters to the protein-DNA docking problem. PMID:22759575

cisTEM, user-friendly software for single-particle image processing.

PubMed

Grant, Timothy; Rohou, Alexis; Grigorieff, Nikolaus

2018-03-07

We have developed new open-source software called cis TEM (computational imaging system for transmission electron microscopy) for the processing of data for high-resolution electron cryo-microscopy and single-particle averaging. cis TEM features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full processing pipeline including movie processing, image defocus determination, automatic particle picking, 2D classification, ab-initio 3D map generation from random parameters, 3D classification, and high-resolution refinement and reconstruction. Some of these steps implement newly-developed algorithms; others were adapted from previously published algorithms. The software is optimized to enable processing of typical datasets (2000 micrographs, 200 k - 300 k particles) on a high-end, CPU-based workstation in half a day or less, comparable to GPU-accelerated processing. Jobs can also be scheduled on large computer clusters using flexible run profiles that can be adapted for most computing environments. cis TEM is available for download from cistem.org. © 2018, Grant et al.

cisTEM, user-friendly software for single-particle image processing

PubMed Central

2018-01-01

We have developed new open-source software called cisTEM (computational imaging system for transmission electron microscopy) for the processing of data for high-resolution electron cryo-microscopy and single-particle averaging. cisTEM features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full processing pipeline including movie processing, image defocus determination, automatic particle picking, 2D classification, ab-initio 3D map generation from random parameters, 3D classification, and high-resolution refinement and reconstruction. Some of these steps implement newly-developed algorithms; others were adapted from previously published algorithms. The software is optimized to enable processing of typical datasets (2000 micrographs, 200 k – 300 k particles) on a high-end, CPU-based workstation in half a day or less, comparable to GPU-accelerated processing. Jobs can also be scheduled on large computer clusters using flexible run profiles that can be adapted for most computing environments. cisTEM is available for download from cistem.org. PMID:29513216

A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme

NASA Astrophysics Data System (ADS)

Ghoman, Satyajit S.

The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness. The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M 3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M 3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE. The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M 3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a nonconventional supersonic tailless air vehicle wing shape optimization problem.

ibex: An open infrastructure software platform to facilitate collaborative work in radiomics

PubMed Central

Zhang, Lifei; Fried, David V.; Fave, Xenia J.; Hunter, Luke A.; Court, Laurence E.

2015-01-01

Purpose: Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (ibex), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions. Methods: The ibex software package was developed using the matlab and c/c++ programming languages. The software architecture deploys the modern model-view-controller, unit testing, and function handle programming concepts to isolate each quantitative imaging analysis task, to validate if their relevant data and algorithms are fit for use, and to plug in new modules. On one hand, ibex is self-contained and ready to use: it has implemented common data importers, common image filters, and common feature extraction algorithms. On the other hand, ibex provides an integrated development environment on top of matlab and c/c++, so users are not limited to its built-in functions. In the ibex developer studio, users can plug in, debug, and test new algorithms, extending ibex’s functionality. ibex also supports quality assurance for data and feature algorithms: image data, regions of interest, and feature algorithm-related data can be reviewed, validated, and/or modified. More importantly, two key elements in collaborative workflows, the consistency of data sharing and the reproducibility of calculation result, are embedded in the ibex workflow: image data, feature algorithms, and model validation including newly developed ones from different users can be easily and consistently shared so that results can be more easily reproduced between institutions. Results: Researchers with a variety of technical skill levels, including radiation oncologists, physicists, and computer scientists, have found the ibex software to be intuitive, powerful, and easy to use. ibex can be run at any computer with the windows operating system and 1GB RAM. The authors fully validated the implementation of all importers, preprocessing algorithms, and feature extraction algorithms. Windows version 1.0 beta of stand-alone ibex and ibex’s source code can be downloaded. Conclusions: The authors successfully implemented ibex, an open infrastructure software platform that streamlines common radiomics workflow tasks. Its transparency, flexibility, and portability can greatly accelerate the pace of radiomics research and pave the way toward successful clinical translation. PMID:25735289

IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics.

PubMed

Zhang, Lifei; Fried, David V; Fave, Xenia J; Hunter, Luke A; Yang, Jinzhong; Court, Laurence E

2015-03-01

Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (IBEX), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions. The IBEX software package was developed using the MATLAB and c/c++ programming languages. The software architecture deploys the modern model-view-controller, unit testing, and function handle programming concepts to isolate each quantitative imaging analysis task, to validate if their relevant data and algorithms are fit for use, and to plug in new modules. On one hand, IBEX is self-contained and ready to use: it has implemented common data importers, common image filters, and common feature extraction algorithms. On the other hand, IBEX provides an integrated development environment on top of MATLAB and c/c++, so users are not limited to its built-in functions. In the IBEX developer studio, users can plug in, debug, and test new algorithms, extending IBEX's functionality. IBEX also supports quality assurance for data and feature algorithms: image data, regions of interest, and feature algorithm-related data can be reviewed, validated, and/or modified. More importantly, two key elements in collaborative workflows, the consistency of data sharing and the reproducibility of calculation result, are embedded in the IBEX workflow: image data, feature algorithms, and model validation including newly developed ones from different users can be easily and consistently shared so that results can be more easily reproduced between institutions. Researchers with a variety of technical skill levels, including radiation oncologists, physicists, and computer scientists, have found the IBEX software to be intuitive, powerful, and easy to use. IBEX can be run at any computer with the windows operating system and 1GB RAM. The authors fully validated the implementation of all importers, preprocessing algorithms, and feature extraction algorithms. Windows version 1.0 beta of stand-alone IBEX and IBEX's source code can be downloaded. The authors successfully implemented IBEX, an open infrastructure software platform that streamlines common radiomics workflow tasks. Its transparency, flexibility, and portability can greatly accelerate the pace of radiomics research and pave the way toward successful clinical translation.

Changes in prescribed doses for the Seattle neutron therapy system

NASA Astrophysics Data System (ADS)

Popescu, A.

2008-06-01

From the beginning of the neutron therapy program at the University of Washington Medical Center, the neutron dose distribution in tissue has been calculated using an in-house treatment planning system called PRISM. In order to increase the accuracy of the absorbed dose calculations, two main improvements were made to the PRISM treatment planning system: (a) the algorithm was changed by the addition of an analytical expression of the central axis wedge factor dependence with field size and depth developed at UWMC. Older versions of the treatment-planning algorithm used a constant central axis wedge factor; (b) a complete newly commissioned set of measured data was introduced in the latest version of PRISM. The new version of the PRISM algorithm allowed for the use of the wedge profiles measured at different depths instead of one wedge profile measured at one depth. The comparison of the absorbed dose calculations using the old and the improved algorithm showed discrepancies mainly due to the missing central axis wedge factor dependence with field size and depth and due to the absence of the wedge profiles at depths different from 10 cm. This study concludes that the previously reported prescribed doses for neutron therapy should be changed.

A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

PubMed Central

Glusman, Gustavo; Qin, Shizhen; El-Gewely, M. Raafat; Siegel, Andrew F; Roach, Jared C; Hood, Leroy; Smit, Arian F. A

2006-01-01

The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent “genomic deserts.” PMID:16543943

Ethnicity and Sex Affect Diabetes Incidence and Outcomes

PubMed Central

Khan, Nadia A.; Wang, Hong; Anand, Sonia; Jin, Yan; Campbell, Norman R. C.; Pilote, Louise; Quan, Hude

2011-01-01

OBJECTIVE Diabetes guidelines recommend aggressive screening for type 2 diabetes in Asian patients because they are considered to have a higher risk of developing diabetes and potentially worse prognosis. We determined incidence of diabetes and risk of death or macrovascular complications by sex among major Asian subgroups, South Asian and Chinese, and white patients with newly diagnosed diabetes. RESEARCH DESIGN AND METHODS Using population-based administrative data from British Columbia and Alberta, Canada (1997–1998 to 2006–2007), we identified patients with newly diagnosed diabetes aged ≥35 years and followed them for up to 10 years for death, acute myocardial infarction, stroke, or hospitalization for heart failure. Ethnicity was determined using validated surname algorithms. RESULTS There were 15,066 South Asian, 17,754 Chinese, and 244,017 white patients with newly diagnosed diabetes. Chinese women and men had the lowest incidence of diabetes relative to that of white or South Asian patients, who had the highest incidence. Mortality in those with newly diagnosed diabetes was lower in South Asian (hazard ratio 0.69 [95% CI 0.62–0.76], P < 0.001) and Chinese patients (0.69 [0.63–0.74], P < 0.001) then in white patients. Risk of acute myocardial infarction, stroke, or heart failure was similar or lower in the ethnic groups relative to that of white patients and varied by sex. CONCLUSIONS The incidence of diagnosed diabetes varies significantly among ethnic groups. Mortality was substantially lower in South Asian and Chinese patients with newly diagnosed diabetes than in white patients. PMID:20978094

Development and Validation of a New Method to Measure Walking Speed in Free-Living Environments Using the Actibelt® Platform

PubMed Central

Schimpl, Michaela; Lederer, Christian; Daumer, Martin

2011-01-01

Walking speed is a fundamental indicator for human well-being. In a clinical setting, walking speed is typically measured by means of walking tests using different protocols. However, walking speed obtained in this way is unlikely to be representative of the conditions in a free-living environment. Recently, mobile accelerometry has opened up the possibility to extract walking speed from long-time observations in free-living individuals, but the validity of these measurements needs to be determined. In this investigation, we have developed algorithms for walking speed prediction based on 3D accelerometry data (actibelt®) and created a framework using a standardized data set with gold standard annotations to facilitate the validation and comparison of these algorithms. For this purpose 17 healthy subjects operated a newly developed mobile gold standard while walking/running on an indoor track. Subsequently, the validity of 12 candidate algorithms for walking speed prediction ranging from well-known simple approaches like combining step length with frequency to more sophisticated algorithms such as linear and non-linear models was assessed using statistical measures. As a result, a novel algorithm employing support vector regression was found to perform best with a concordance correlation coefficient of 0.93 (95%CI 0.92–0.94) and a coverage probability CP1 of 0.46 (95%CI 0.12–0.70) for a deviation of 0.1 m/s (CP2 0.78, CP3 0.94) when compared to the mobile gold standard while walking indoors. A smaller outdoor experiment confirmed those results with even better coverage probability. We conclude that walking speed thus obtained has the potential to help establish walking speed in free-living environments as a patient-oriented outcome measure. PMID:21850254

Development and improvement of the operating diagnostics systems of NPO CKTI works for turbine of thermal and nuclear power plants

NASA Astrophysics Data System (ADS)

Kovalev, I. A.; Rakovskii, V. G.; Isakov, N. Yu.; Sandovskii, A. V.

2016-03-01

The work results on the development and improvement of the techniques, algorithms, and software-hardware of continuous operating diagnostics systems of rotating units and parts of turbine equipment state are presented. In particular, to ensure the full remote service of monitored turbine equipment using web technologies, the web version of the software of the automated systems of vibration-based diagnostics (ASVD VIDAS) was developed. The experience in the automated analysis of data obtained by ASVD VIDAS form the basis of the new algorithm of early detection of such dangerous defects as rotor deflection, crack in the rotor, and strong misalignment of supports. The program-technical complex of monitoring and measuring the deflection of medium pressure rotor (PTC) realizing this algorithm will alert the electric power plant staff during a deflection and indicate its value. This will give the opportunity to take timely measures to prevent the further extension of the defect. Repeatedly, recorded cases of full or partial destruction of shrouded shelves of rotor blades of the last stages of low-pressure cylinders of steam turbines defined the need to develop a version of the automated system of blade diagnostics (ASBD SKALA) for shrouded stages. The processing, analysis, presentation, and backup of data characterizing the mechanical state of blade device are carried out with a newly developed controller of the diagnostics system. As a result of the implementation of the works, the diagnosed parameters determining the operation security of rotating elements of equipment was expanded and the new tasks on monitoring the state of units and parts of turbines were solved. All algorithmic solutions and hardware-software implementations mentioned in the article were tested on the test benches and applied at some power plants.

Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms.

PubMed

Gaur, Pallavi; Chaturvedi, Anoop

2017-07-22

The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes. Along with obtaining clustering pattern and candidate motifs in exosomal miRNAs, this work also elaborates the usefulness of the machine learning algorithms that can be efficiently used and executed on various programming languages/platforms. Data were clustered and sequence candidate motifs were detected successfully. The results were compared and validated with some available web tools such as 'BLASTN' and 'MEME suite'. The machine learning algorithms for aforementioned objectives were applied successfully. This work elaborated utility of machine learning algorithms and language platforms to achieve the tasks of clustering and candidate motif detection in exosomal miRNAs. With the information on mentioned objectives, deeper insight would be gained for analyses of newly discovered miRNAs in exosomes which are considered to be circulating biomarkers. In addition, the execution of machine learning algorithms on various language platforms gives more flexibility to users to try multiple iterations according to their requirements. This approach can be applied to other biological data-mining tasks as well.

Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms.

PubMed

Tang, Jie; Nett, Brian E; Chen, Guang-Hong

2009-10-07

Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.

Chemical reacting flows

NASA Astrophysics Data System (ADS)

Lezberg, Erwin A.; Mularz, Edward J.; Liou, Meng-Sing

1991-03-01

The objectives and accomplishments of research in chemical reacting flows, including both experimental and computational problems are described. The experimental research emphasizes the acquisition of reliable reacting-flow data for code validation, the development of chemical kinetics mechanisms, and the understanding of two-phase flow dynamics. Typical results from two nonreacting spray studies are presented. The computational fluid dynamics (CFD) research emphasizes the development of efficient and accurate algorithms and codes, as well as validation of methods and modeling (turbulence and kinetics) for reacting flows. Major developments of the RPLUS code and its application to mixing concepts, the General Electric combustor, and the Government baseline engine for the National Aerospace Plane are detailed. Finally, the turbulence research in the newly established Center for Modeling of Turbulence and Transition (CMOTT) is described.

Loading relativistic Maxwell distributions in particle simulations

NASA Astrophysics Data System (ADS)

Zenitani, S.

2015-12-01

In order to study energetic plasma phenomena by using particle-in-cell (PIC) and Monte-Carlo simulations, we need to deal with relativistic velocity distributions in these simulations. However, numerical algorithms to deal with relativistic distributions are not well known. In this contribution, we overview basic algorithms to load relativistic Maxwell distributions in PIC and Monte-Carlo simulations. For stationary relativistic Maxwellian, the inverse transform method and the Sobol algorithm are reviewed. To boost particles to obtain relativistic shifted-Maxwellian, two rejection methods are newly proposed in a physically transparent manner. Their acceptance efficiencies are 􏰅50% for generic cases and 100% for symmetric distributions. They can be combined with arbitrary base algorithms.

STRV RADMON: An integrated high-energy particle detector

NASA Technical Reports Server (NTRS)

Buehler, Martin; Soli, George; Blaes, Brent; Tardio, Gemma

1993-01-01

The RADMON (Radiation Monitor) was developed as a compact device with a 4-kbit SRAM particle detector and two p-FET total dose monitors. Thus it can be used as a spacecraft radiation alarm and in situ total dose monitor. This paper discusses the design and calibration of the SRAM for proton, alpha, and heavy ion detection. Upset rates for the RADMON, based on a newly developed space particle flux algorithm, are shown to vary over eight orders of magnitude. On the STRV (Space Technology Research Vehicle) the RADMON's SRAM will be used to detect trapped protons, solar flares, and cosmic rays and to evaluate our ability to predict space results from ground tests.

KIRMES: kernel-based identification of regulatory modules in euchromatic sequences.

PubMed

Schultheiss, Sebastian J; Busch, Wolfgang; Lohmann, Jan U; Kohlbacher, Oliver; Rätsch, Gunnar

2009-08-15

Understanding transcriptional regulation is one of the main challenges in computational biology. An important problem is the identification of transcription factor (TF) binding sites in promoter regions of potential TF target genes. It is typically approached by position weight matrix-based motif identification algorithms using Gibbs sampling, or heuristics to extend seed oligos. Such algorithms succeed in identifying single, relatively well-conserved binding sites, but tend to fail when it comes to the identification of combinations of several degenerate binding sites, as those often found in cis-regulatory modules. We propose a new algorithm that combines the benefits of existing motif finding with the ones of support vector machines (SVMs) to find degenerate motifs in order to improve the modeling of regulatory modules. In experiments on microarray data from Arabidopsis thaliana, we were able to show that the newly developed strategy significantly improves the recognition of TF targets. The python source code (open source-licensed under GPL), the data for the experiments and a Galaxy-based web service are available at http://www.fml.mpg.de/raetsch/suppl/kirmes/.

Automated Video-Based Analysis of Contractility and Calcium Flux in Human-Induced Pluripotent Stem Cell-Derived Cardiomyocytes Cultured over Different Spatial Scales

PubMed Central

Huebsch, Nathaniel; Loskill, Peter; Mandegar, Mohammad A.; Marks, Natalie C.; Sheehan, Alice S.; Ma, Zhen; Mathur, Anurag; Nguyen, Trieu N.; Yoo, Jennie C.; Judge, Luke M.; Spencer, C. Ian; Chukka, Anand C.; Russell, Caitlin R.; So, Po-Lin

2015-01-01

Contractile motion is the simplest metric of cardiomyocyte health in vitro, but unbiased quantification is challenging. We describe a rapid automated method, requiring only standard video microscopy, to analyze the contractility of human-induced pluripotent stem cell-derived cardiomyocytes (iPS-CM). New algorithms for generating and filtering motion vectors combined with a newly developed isogenic iPSC line harboring genetically encoded calcium indicator, GCaMP6f, allow simultaneous user-independent measurement and analysis of the coupling between calcium flux and contractility. The relative performance of these algorithms, in terms of improving signal to noise, was tested. Applying these algorithms allowed analysis of contractility in iPS-CM cultured over multiple spatial scales from single cells to three-dimensional constructs. This open source software was validated with analysis of isoproterenol response in these cells, and can be applied in future studies comparing the drug responsiveness of iPS-CM cultured in different microenvironments in the context of tissue engineering. PMID:25333967

Replication and Comparison of the Newly Proposed ADOS-2, Module 4 Algorithm in ASD Without ID: A Multi-site Study.

PubMed

Pugliese, Cara E; Kenworthy, Lauren; Bal, Vanessa Hus; Wallace, Gregory L; Yerys, Benjamin E; Maddox, Brenna B; White, Susan W; Popal, Haroon; Armour, Anna Chelsea; Miller, Judith; Herrington, John D; Schultz, Robert T; Martin, Alex; Anthony, Laura Gutermuth

2015-12-01

Recent updates have been proposed to the Autism Diagnostic Observation Schedule-2 Module 4 diagnostic algorithm. This new algorithm, however, has not yet been validated in an independent sample without intellectual disability (ID). This multi-site study compared the original and revised algorithms in individuals with ASD without ID. The revised algorithm demonstrated increased sensitivity, but lower specificity in the overall sample. Estimates were highest for females, individuals with a verbal IQ below 85 or above 115, and ages 16 and older. Best practice diagnostic procedures should include the Module 4 in conjunction with other assessment tools. Balancing needs for sensitivity and specificity depending on the purpose of assessment (e.g., clinical vs. research) and demographic characteristics mentioned above will enhance its utility.

Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies.

PubMed

Kumberg, Timo; Schindelhauer, Christian; Reindl, Leonhard

2017-07-26

Wake-up receivers are the natural choice for wireless sensor networks because of their ultra-low power consumption and their ability to provide communications on demand. A downside of ultra-low power wake-up receivers is their low sensitivity caused by the passive demodulation of the carrier signal. In this article, we present a novel communication scheme by exploiting purposefully-interfering out-of-tune signals of two or more wireless sensor nodes, which produce the wake-up signal as the beat frequency of superposed carriers. Additionally, we introduce a communication algorithm and a flooding protocol based on this approach. Our experiments show that our approach increases the received signal strength up to 3 dB, improving communication robustness and reliability. Furthermore, we demonstrate the feasibility of our newly-developed protocols by means of an outdoor experiment and an indoor setup consisting of several nodes. The flooding algorithm achieves almost a 100% wake-up rate in less than 20 ms.

An Improved Neutron Transport Algorithm for HZETRN

NASA Technical Reports Server (NTRS)

Slaba, Tony C.; Blattnig, Steve R.; Clowdsley, Martha S.; Walker, Steven A.; Badavi, Francis F.

2010-01-01

Long term human presence in space requires the inclusion of radiation constraints in mission planning and the design of shielding materials, structures, and vehicles. In this paper, the numerical error associated with energy discretization in HZETRN is addressed. An inadequate numerical integration scheme in the transport algorithm is shown to produce large errors in the low energy portion of the neutron and light ion fluence spectra. It is further shown that the errors result from the narrow energy domain of the neutron elastic cross section spectral distributions, and that an extremely fine energy grid is required to resolve the problem under the current formulation. Two numerical methods are developed to provide adequate resolution in the energy domain and more accurately resolve the neutron elastic interactions. Convergence testing is completed by running the code for various environments and shielding materials with various energy grids to ensure stability of the newly implemented method.

Robust neural network with applications to credit portfolio data analysis.

PubMed

Feng, Yijia; Li, Runze; Sudjianto, Agus; Zhang, Yiyun

2010-01-01

In this article, we study nonparametric conditional quantile estimation via neural network structure. We proposed an estimation method that combines quantile regression and neural network (robust neural network, RNN). It provides good smoothing performance in the presence of outliers and can be used to construct prediction bands. A Majorization-Minimization (MM) algorithm was developed for optimization. Monte Carlo simulation study is conducted to assess the performance of RNN. Comparison with other nonparametric regression methods (e.g., local linear regression and regression splines) in real data application demonstrate the advantage of the newly proposed procedure.

Capacity of non-invasive hepatic fibrosis algorithms to replace transient elastography to exclude cirrhosis in people with hepatitis C virus infection: A multi-centre observational study

PubMed Central

Riordan, Stephen M.; Bopage, Rohan; Lloyd, Andrew R.

2018-01-01

Introduction Achievement of the 2030 World Health Organisation (WHO) global hepatitis C virus (HCV) elimination targets will be underpinned by scale-up of testing and use of direct-acting antiviral treatments. In Australia, despite publically-funded testing and treatment, less than 15% of patients were treated in the first year of treatment access, highlighting the need for greater efficiency of health service delivery. To this end, non-invasive fibrosis algorithms were examined to reduce reliance on transient elastography (TE) which is currently utilised for the assessment of cirrhosis in most Australian clinical settings. Materials and methods This retrospective and prospective study, with derivation and validation cohorts, examined consecutive patients in a tertiary referral centre, a sexual health clinic, and a prison-based hepatitis program. The negative predictive value (NPV) of seven non-invasive algorithms were measured using published and newly derived cut-offs. The number of TEs avoided for each algorithm, or combination of algorithms, was determined. Results The 850 patients included 780 (92%) with HCV mono-infection, and 70 (8%) co-infected with HIV or hepatitis B. The mono-infected cohort included 612 men (79%), with an overall prevalence of cirrhosis of 16% (125/780). An ‘APRI’ algorithm cut-off of 1.0 had a 94% NPV (95%CI: 91–96%). Newly derived cut-offs of ‘APRI’ (0.49), ‘FIB-4’ (0.93) and ‘GUCI’ (0.5) algorithms each had NPVs of 99% (95%CI: 97–100%), allowing avoidance of TE in 40% (315/780), 40% (310/780) and 40% (298/749) respectively. When used in combination, NPV was retained and TE avoidance reached 54% (405/749), regardless of gender or co-infection. Conclusions Non-invasive algorithms can reliably exclude cirrhosis in many patients, allowing improved efficiency of HCV assessment services in Australia and worldwide. PMID:29438397

Capacity of non-invasive hepatic fibrosis algorithms to replace transient elastography to exclude cirrhosis in people with hepatitis C virus infection: A multi-centre observational study.

PubMed

Kelly, Melissa Louise; Riordan, Stephen M; Bopage, Rohan; Lloyd, Andrew R; Post, Jeffrey John

2018-01-01

Achievement of the 2030 World Health Organisation (WHO) global hepatitis C virus (HCV) elimination targets will be underpinned by scale-up of testing and use of direct-acting antiviral treatments. In Australia, despite publically-funded testing and treatment, less than 15% of patients were treated in the first year of treatment access, highlighting the need for greater efficiency of health service delivery. To this end, non-invasive fibrosis algorithms were examined to reduce reliance on transient elastography (TE) which is currently utilised for the assessment of cirrhosis in most Australian clinical settings. This retrospective and prospective study, with derivation and validation cohorts, examined consecutive patients in a tertiary referral centre, a sexual health clinic, and a prison-based hepatitis program. The negative predictive value (NPV) of seven non-invasive algorithms were measured using published and newly derived cut-offs. The number of TEs avoided for each algorithm, or combination of algorithms, was determined. The 850 patients included 780 (92%) with HCV mono-infection, and 70 (8%) co-infected with HIV or hepatitis B. The mono-infected cohort included 612 men (79%), with an overall prevalence of cirrhosis of 16% (125/780). An 'APRI' algorithm cut-off of 1.0 had a 94% NPV (95%CI: 91-96%). Newly derived cut-offs of 'APRI' (0.49), 'FIB-4' (0.93) and 'GUCI' (0.5) algorithms each had NPVs of 99% (95%CI: 97-100%), allowing avoidance of TE in 40% (315/780), 40% (310/780) and 40% (298/749) respectively. When used in combination, NPV was retained and TE avoidance reached 54% (405/749), regardless of gender or co-infection. Non-invasive algorithms can reliably exclude cirrhosis in many patients, allowing improved efficiency of HCV assessment services in Australia and worldwide.

4D inversion of time-lapse magnetotelluric data sets for monitoring geothermal reservoir

NASA Astrophysics Data System (ADS)

Nam, Myung Jin; Song, Yoonho; Jang, Hannuree; Kim, Bitnarae

2017-06-01

The productivity of a geothermal reservoir, which is a function of the pore-space and fluid-flow path of the reservoir, varies since the properties of the reservoir changes with geothermal reservoir production. Because the variation in the reservoir properties causes changes in electrical resistivity, time-lapse (TL) three-dimensional (3D) magnetotelluric (MT) methods can be applied to monitor the productivity variation of a geothermal reservoir thanks to not only its sensitivity to the electrical resistivity but also its deep depth of survey penetration. For an accurate interpretation of TL MT-data sets, a four-dimensional (4D) MT inversion algorithm has been developed to simultaneously invert all vintage data considering time-coupling between vintages. However, the changes in electrical resistivity of deep geothermal reservoirs are usually small generating minimum variation in TL MT responses. Maximizing the sensitivity of inversion to the changes in resistivity is critical in the success of 4D MT inversion. Thus, we further developed a focused 4D MT inversion method by considering not only the location of a reservoir but also the distribution of newly-generated fractures during the production. For the evaluation of the 4D MT algorithm, we tested our 4D inversion algorithms using synthetic TL MT-data sets.

Large-scale gravity wave perturbations in the mesopause region above Northern Hemisphere midlatitudes during autumnal equinox: a joint study by the USU Na lidar and Whole Atmosphere Community Climate Model

NASA Astrophysics Data System (ADS)

Cai, X.

2017-12-01

To investigate gravity wave (GW) perturbations in the midlatitude mesopause region during boreal equinox, 433 h of continuous Na lidar full diurnal cycle temperature measurements in September between 2011 and 2015 are utilized to derive the monthly profiles of GW-induced temperature variance, T'^2, and the potential energy density (PED). Operating at Utah State University (42° N, 112° W), these lidar measurements reveal severe GW dissipation near 90 km, where both parameters drop to their minima (˜ 20 K^2 and ˜ 50 m^2/ s^2, respectively). The study also shows that GWs with periods of 3-5 h dominate the midlatitude mesopause region during the summer-winter transition. To derive the precise temperature perturbations a new tide removal algorithm suitable for all ground-based observations is developed to de-trend the lidar temperature measurements and to isolate GW-induced perturbations. It removes the tidal perturbations completely and provides the most accurate GW perturbations for the ground-based observations. This algorithm is validated by comparing the true GW perturbations in the latest mesoscale-resolving Whole Atmosphere Community Climate Model (WACCM) with those derived from the WACCM local outputs by applying this newly developed tidal removal algorithm.

Two New Tools for Glycopeptide Analysis Researchers: A Glycopeptide Decoy Generator and a Large Data Set of Assigned CID Spectra of Glycopeptides.

PubMed

Lakbub, Jude C; Su, Xiaomeng; Zhu, Zhikai; Patabandige, Milani W; Hua, David; Go, Eden P; Desaire, Heather

2017-08-04

The glycopeptide analysis field is tightly constrained by a lack of effective tools that translate mass spectrometry data into meaningful chemical information, and perhaps the most challenging aspect of building effective glycopeptide analysis software is designing an accurate scoring algorithm for MS/MS data. We provide the glycoproteomics community with two tools to address this challenge. The first tool, a curated set of 100 expert-assigned CID spectra of glycopeptides, contains a diverse set of spectra from a variety of glycan types; the second tool, Glycopeptide Decoy Generator, is a new software application that generates glycopeptide decoys de novo. We developed these tools so that emerging methods of assigning glycopeptides' CID spectra could be rigorously tested. Software developers or those interested in developing skills in expert (manual) analysis can use these tools to facilitate their work. We demonstrate the tools' utility in assessing the quality of one particular glycopeptide software package, GlycoPep Grader, which assigns glycopeptides to CID spectra. We first acquired the set of 100 expert assigned CID spectra; then, we used the Decoy Generator (described herein) to generate 20 decoys per target glycopeptide. The assigned spectra and decoys were used to test the accuracy of GlycoPep Grader's scoring algorithm; new strengths and weaknesses were identified in the algorithm using this approach. Both newly developed tools are freely available. The software can be downloaded at http://glycopro.chem.ku.edu/GPJ.jar.

Classification-based quantitative analysis of stable isotope labeling by amino acids in cell culture (SILAC) data.

PubMed

Kim, Seongho; Carruthers, Nicholas; Lee, Joohyoung; Chinni, Sreenivasa; Stemmer, Paul

2016-12-01

Stable isotope labeling by amino acids in cell culture (SILAC) is a practical and powerful approach for quantitative proteomic analysis. A key advantage of SILAC is the ability to simultaneously detect the isotopically labeled peptides in a single instrument run and so guarantee relative quantitation for a large number of peptides without introducing any variation caused by separate experiment. However, there are a few approaches available to assessing protein ratios and none of the existing algorithms pays considerable attention to the proteins having only one peptide hit. We introduce new quantitative approaches to dealing with SILAC protein-level summary using classification-based methodologies, such as Gaussian mixture models with EM algorithms and its Bayesian approach as well as K-means clustering. In addition, a new approach is developed using Gaussian mixture model and a stochastic, metaheuristic global optimization algorithm, particle swarm optimization (PSO), to avoid either a premature convergence or being stuck in a local optimum. Our simulation studies show that the newly developed PSO-based method performs the best among others in terms of F1 score and the proposed methods further demonstrate the ability of detecting potential markers through real SILAC experimental data. No matter how many peptide hits the protein has, the developed approach can be applicable, rescuing many proteins doomed to removal. Furthermore, no additional correction for multiple comparisons is necessary for the developed methods, enabling direct interpretation of the analysis outcomes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks

NASA Astrophysics Data System (ADS)

Sun, Wei; Chang, K. C.

2005-05-01

Probabilistic inference for Bayesian networks is in general NP-hard using either exact algorithms or approximate methods. However, for very complex networks, only the approximate methods such as stochastic sampling could be used to provide a solution given any time constraint. There are several simulation methods currently available. They include logic sampling (the first proposed stochastic method for Bayesian networks, the likelihood weighting algorithm) the most commonly used simulation method because of its simplicity and efficiency, the Markov blanket scoring method, and the importance sampling algorithm. In this paper, we first briefly review and compare these available simulation methods, then we propose an improved importance sampling algorithm called linear Gaussian importance sampling algorithm for general hybrid model (LGIS). LGIS is aimed for hybrid Bayesian networks consisting of both discrete and continuous random variables with arbitrary distributions. It uses linear function and Gaussian additive noise to approximate the true conditional probability distribution for continuous variable given both its parents and evidence in a Bayesian network. One of the most important features of the newly developed method is that it can adaptively learn the optimal important function from the previous samples. We test the inference performance of LGIS using a 16-node linear Gaussian model and a 6-node general hybrid model. The performance comparison with other well-known methods such as Junction tree (JT) and likelihood weighting (LW) shows that LGIS-GHM is very promising.

New Enhanced Artificial Bee Colony (JA-ABC5) Algorithm with Application for Reactive Power Optimization

PubMed Central

2015-01-01

The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes. The performance of JA-ABC5 has been analyzed on 27 commonly used benchmark functions and tested to optimize the reactive power optimization problem. The performance results have clearly shown that the newly proposed algorithm has outperformed other compared algorithms in terms of convergence speed and global optimum achievement. PMID:25879054

New enhanced artificial bee colony (JA-ABC5) algorithm with application for reactive power optimization.

PubMed

Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani

2015-01-01

The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes. The performance of JA-ABC5 has been analyzed on 27 commonly used benchmark functions and tested to optimize the reactive power optimization problem. The performance results have clearly shown that the newly proposed algorithm has outperformed other compared algorithms in terms of convergence speed and global optimum achievement.

Testing a polarimetric cloud imager aboard research vessel Polarstern: comparison of color-based and polarimetric cloud detection algorithms.

PubMed

Barta, András; Horváth, Gábor; Horváth, Ákos; Egri, Ádám; Blahó, Miklós; Barta, Pál; Bumke, Karl; Macke, Andreas

2015-02-10

Cloud cover estimation is an important part of routine meteorological observations. Cloudiness measurements are used in climate model evaluation, nowcasting solar radiation, parameterizing the fluctuations of sea surface insolation, and building energy transfer models of the atmosphere. Currently, the most widespread ground-based method to measure cloudiness is based on analyzing the unpolarized intensity and color distribution of the sky obtained by digital cameras. As a new approach, we propose that cloud detection can be aided by the additional use of skylight polarization measured by 180° field-of-view imaging polarimetry. In the fall of 2010, we tested such a novel polarimetric cloud detector aboard the research vessel Polarstern during expedition ANT-XXVII/1. One of our goals was to test the durability of the measurement hardware under the extreme conditions of a trans-Atlantic cruise. Here, we describe the instrument and compare the results of several different cloud detection algorithms, some conventional and some newly developed. We also discuss the weaknesses of our design and its possible improvements. The comparison with cloud detection algorithms developed for traditional nonpolarimetric full-sky imagers allowed us to evaluate the added value of polarimetric quantities. We found that (1) neural-network-based algorithms perform the best among the investigated schemes and (2) global information (the mean and variance of intensity), nonoptical information (e.g., sun-view geometry), and polarimetric information (e.g., the degree of polarization) improve the accuracy of cloud detection, albeit slightly.

A new statistical PCA-ICA algorithm for location of R-peaks in ECG.

PubMed

Chawla, M P S; Verma, H K; Kumar, Vinod

2008-09-16

The success of ICA to separate the independent components from the mixture depends on the properties of the electrocardiogram (ECG) recordings. This paper discusses some of the conditions of independent component analysis (ICA) that could affect the reliability of the separation and evaluation of issues related to the properties of the signals and number of sources. Principal component analysis (PCA) scatter plots are plotted to indicate the diagnostic features in the presence and absence of base-line wander in interpreting the ECG signals. In this analysis, a newly developed statistical algorithm by authors, based on the use of combined PCA-ICA for two correlated channels of 12-channel ECG data is proposed. ICA technique has been successfully implemented in identifying and removal of noise and artifacts from ECG signals. Cleaned ECG signals are obtained using statistical measures like kurtosis and variance of variance after ICA processing. This analysis also paper deals with the detection of QRS complexes in electrocardiograms using combined PCA-ICA algorithm. The efficacy of the combined PCA-ICA algorithm lies in the fact that the location of the R-peaks is bounded from above and below by the location of the cross-over points, hence none of the peaks are ignored or missed.

Replication and Comparison of the Newly Proposed ADOS-2, Module 4 Algorithm in ASD without ID: A Multi-site Study

PubMed Central

Pugliese, Cara E.; Kenworthy, Lauren; Bal, Vanessa Hus; Wallace, Gregory L; Yerys, Benjamin E; Maddox, Brenna B.; White, Susan W.; Popal, Haroon; Armour, Anna Chelsea; Miller, Judith; Herrington, John D.; Schultz, Robert T.; Martin, Alex; Anthony, Laura Gutermuth

2015-01-01

Recent updates have been proposed to the Autism Diagnostic Observation Schedule-2 Module 4 diagnostic algorithm. This new algorithm, however, has not yet been validated in an independent sample without intellectual disability (ID). This multi-site study compared the original and revised algorithms in individuals with ASD without ID. The revised algorithm demonstrated increased sensitivity, but lower specificity in the overall sample. Estimates were highest for females, individuals with a verbal IQ below 85 or above 115, and ages 16 and older. Best practice diagnostic procedures should include the Module 4 in conjunction with other assessment tools. Balancing needs for sensitivity and specificity depending on the purpose of assessment (e.g., clinical vs. research) and demographic characteristics mentioned above will enhance its utility. PMID:26385796

[Evaluation of the benefit of different complementary exams in the search for a TB diagnosis algorithm for HIV patients put on ART in Niamey, Niger].

PubMed

Ouedraogo, E; Lurton, G; Mohamadou, S; Dillé, I; Diallo, I; Mamadou, S; Adehossi, E; Hanki, Y; Tchousso, O; Arzika, M; Gazeré, O; Amadou, F; Illo, N; Abdourahmane, Y; Idé, M; Alhousseini, Z; Lamontagne, F; Deze, C; D'Ortenzio, E; Diallo, S

2016-12-01

In Niger, the tuberculosis (TB) screening among people living with human immunodeficiency virus (HIV) (PLHIV) is nonsystematic and the use of additional tests is very often limited. The objective of this research is to evaluate the performance and the cost-effectiveness of various paraclinical testing strategies of TB among adult patients with HIV, using available tests in routine for patients cared in Niamey. This is a multicentric prospective intervention study performed in Niamey between 2010 and 2013. TB screening has been sought in newly diagnosed PLHIV, before ART treatment, performing consistently: a sputum examination by MZN (Ziehl-Nielsen staining) and microscopy fluorescence (MIF), chest radiography (CR), and abdominal ultrasound. The performance of these different tests was calculated using sputum culture as a gold standard. The various examinations were then combined in different algorithms. The cost-effectiveness of different algorithms was assessed by calculating the money needed to prevent a patient, put on ART, dying of TB. Between November 2010 and November 2012, 509 PLHIV were included. TB was diagnosed in 78 patients (15.3%), including 35 pulmonary forms, 24 ganglion, and 19 multifocal. The sensitivity of the evaluated algorithms varied between 0.35 and 0.85. The specificity ranged from 0.85 to 0.97. The most costeffective algorithm was the one involving MIF and CR. We recommend implementing a systematic and free direct examination of sputum by MIF and a CR for the detection of TB among newly diagnosed PLHIV in Niger.

The role of imaging based prostate biopsy morphology in a data fusion paradigm for transducing prognostic predictions

NASA Astrophysics Data System (ADS)

Khan, Faisal M.; Kulikowski, Casimir A.

2016-03-01

A major focus area for precision medicine is in managing the treatment of newly diagnosed prostate cancer patients. For patients with a positive biopsy, clinicians aim to develop an individualized treatment plan based on a mechanistic understanding of the disease factors unique to each patient. Recently, there has been a movement towards a multi-modal view of the cancer through the fusion of quantitative information from multiple sources, imaging and otherwise. Simultaneously, there have been significant advances in machine learning methods for medical prognostics which integrate a multitude of predictive factors to develop an individualized risk assessment and prognosis for patients. An emerging area of research is in semi-supervised approaches which transduce the appropriate survival time for censored patients. In this work, we apply a novel semi-supervised approach for support vector regression to predict the prognosis for newly diagnosed prostate cancer patients. We integrate clinical characteristics of a patient's disease with imaging derived metrics for biomarker expression as well as glandular and nuclear morphology. In particular, our goal was to explore the performance of nuclear and glandular architecture within the transduction algorithm and assess their predictive power when compared with the Gleason score manually assigned by a pathologist. Our analysis in a multi-institutional cohort of 1027 patients indicates that not only do glandular and morphometric characteristics improve the predictive power of the semi-supervised transduction algorithm; they perform better when the pathological Gleason is absent. This work represents one of the first assessments of quantitative prostate biopsy architecture versus the Gleason grade in the context of a data fusion paradigm which leverages a semi-supervised approach for risk prognosis.

Formularity: Software for Automated Formula Assignment of Natural and Other Organic Matter from Ultrahigh-Resolution Mass Spectra.

PubMed

Tolić, Nikola; Liu, Yina; Liyu, Andrey; Shen, Yufeng; Tfaily, Malak M; Kujawinski, Elizabeth B; Longnecker, Krista; Kuo, Li-Jung; Robinson, Errol W; Paša-Tolić, Ljiljana; Hess, Nancy J

2017-12-05

Ultrahigh resolution mass spectrometry, such as Fourier transform ion cyclotron resonance mass spectrometry (FT ICR MS), can resolve thousands of molecular ions in complex organic matrices. A Compound Identification Algorithm (CIA) was previously developed for automated elemental formula assignment for natural organic matter (NOM). In this work, we describe software Formularity with a user-friendly interface for CIA function and newly developed search function Isotopic Pattern Algorithm (IPA). While CIA assigns elemental formulas for compounds containing C, H, O, N, S, and P, IPA is capable of assigning formulas for compounds containing other elements. We used halogenated organic compounds (HOC), a chemical class that is ubiquitous in nature as well as anthropogenic systems, as an example to demonstrate the capability of Formularity with IPA. A HOC standard mix was used to evaluate the identification confidence of IPA. Tap water and HOC spike in Suwannee River NOM were used to assess HOC identification in complex environmental samples. Strategies for reconciliation of CIA and IPA assignments were discussed. Software and sample databases with documentation are freely available.

Modeling NIF experimental designs with adaptive mesh refinement and Lagrangian hydrodynamics

NASA Astrophysics Data System (ADS)

Koniges, A. E.; Anderson, R. W.; Wang, P.; Gunney, B. T. N.; Becker, R.; Eder, D. C.; MacGowan, B. J.; Schneider, M. B.

2006-06-01

Incorporation of adaptive mesh refinement (AMR) into Lagrangian hydrodynamics algorithms allows for the creation of a highly powerful simulation tool effective for complex target designs with three-dimensional structure. We are developing an advanced modeling tool that includes AMR and traditional arbitrary Lagrangian-Eulerian (ALE) techniques. Our goal is the accurate prediction of vaporization, disintegration and fragmentation in National Ignition Facility (NIF) experimental target elements. Although our focus is on minimizing the generation of shrapnel in target designs and protecting the optics, the general techniques are applicable to modern advanced targets that include three-dimensional effects such as those associated with capsule fill tubes. Several essential computations in ordinary radiation hydrodynamics need to be redesigned in order to allow for AMR to work well with ALE, including algorithms associated with radiation transport. Additionally, for our goal of predicting fragmentation, we include elastic/plastic flow into our computations. We discuss the integration of these effects into a new ALE-AMR simulation code. Applications of this newly developed modeling tool as well as traditional ALE simulations in two and three dimensions are applied to NIF early-light target designs.

Temporal Planning for Compilation of Quantum Approximate Optimization Algorithm Circuits

NASA Technical Reports Server (NTRS)

Venturelli, Davide; Do, Minh Binh; Rieffel, Eleanor Gilbert; Frank, Jeremy David

2017-01-01

We investigate the application of temporal planners to the problem of compiling quantum circuits to newly emerging quantum hardware. While our approach is general, we focus our initial experiments on Quantum Approximate Optimization Algorithm (QAOA) circuits that have few ordering constraints and allow highly parallel plans. We report on experiments using several temporal planners to compile circuits of various sizes to a realistic hardware. This early empirical evaluation suggests that temporal planning is a viable approach to quantum circuit compilation.

On the numerical computation of nonlinear force-free magnetic fields. [from solar photosphere

NASA Technical Reports Server (NTRS)

Wu, S. T.; Sun, M. T.; Chang, H. M.; Hagyard, M. J.; Gary, G. A.

1990-01-01

An algorithm has been developed to extrapolate nonlinear force-free magnetic fields from the photosphere, given the proper boundary conditions. This paper presents the results of this work, describing the mathematical formalism that was developed, the numerical techniques employed, and comments on the stability criteria and accuracy developed for these numerical schemes. An analytical solution is used for a benchmark test; the results show that the computational accuracy for the case of a nonlinear force-free magnetic field was on the order of a few percent (less than 5 percent). This newly developed scheme was applied to analyze a solar vector magnetogram, and the results were compared with the results deduced from the classical potential field method. The comparison shows that additional physical features of the vector magnetogram were revealed in the nonlinear force-free case.

Energy Models for One-Carrier Transport in Semiconductor Devices

NASA Technical Reports Server (NTRS)

Jerome, Joseph W.; Shu, Chi-Wang

1991-01-01

Moment models of carrier transport, derived from the Boltzmann equation, made possible the simulation of certain key effects through such realistic assumptions as energy dependent mobility functions. This type of global dependence permits the observation of velocity overshoot in the vicinity of device junctions, not discerned via classical drift-diffusion models, which are primarily local in nature. It was found that a critical role is played in the hydrodynamic model by the heat conduction term. When ignored, the overshoot is inappropriately damped. When the standard choice of the Wiedemann-Franz law is made for the conductivity, spurious overshoot is observed. Agreement with Monte-Carlo simulation in this regime required empirical modification of this law, or nonstandard choices. Simulations of the hydrodynamic model in one and two dimensions, as well as simulations of a newly developed energy model, the RT model, are presented. The RT model, intermediate between the hydrodynamic and drift-diffusion model, was developed to eliminate the parabolic energy band and Maxwellian distribution assumptions, and to reduce the spurious overshoot with physically consistent assumptions. The algorithms employed for both models are the essentially non-oscillatory shock capturing algorithms. Some mathematical results are presented and contrasted with the highly developed state of the drift-diffusion model.

Comparison study of noise reduction algorithms in dual energy chest digital tomosynthesis

NASA Astrophysics Data System (ADS)

Lee, D.; Kim, Y.-S.; Choi, S.; Lee, H.; Choi, S.; Kim, H.-J.

2018-04-01

Dual energy chest digital tomosynthesis (CDT) is a recently developed medical technique that takes advantage of both tomosynthesis and dual energy X-ray images. However, quantum noise, which occurs in dual energy X-ray images, strongly interferes with diagnosis in various clinical situations. Therefore, noise reduction is necessary in dual energy CDT. In this study, noise-compensating algorithms, including a simple smoothing of high-energy images (SSH) and anti-correlated noise reduction (ACNR), were evaluated in a CDT system. We used a newly developed prototype CDT system and anthropomorphic chest phantom for experimental studies. The resulting images demonstrated that dual energy CDT can selectively image anatomical structures, such as bone and soft tissue. Among the resulting images, those acquired with ACNR showed the best image quality. Both coefficient of variation and contrast to noise ratio (CNR) were the highest in ACNR among the three different dual energy techniques, and the CNR of bone was significantly improved compared to the reconstructed images acquired at a single energy. This study demonstrated the clinical value of dual energy CDT and quantitatively showed that ACNR is the most suitable among the three developed dual energy techniques, including standard log subtraction, SSH, and ACNR.

Development and validation of P-MODTRAN7 and P-MCScene, 1D and 3D polarimetric radiative transfer models

NASA Astrophysics Data System (ADS)

Hawes, Frederick T.; Berk, Alexander; Richtsmeier, Steven C.

2016-05-01

A validated, polarimetric 3-dimensional simulation capability, P-MCScene, is being developed by generalizing Spectral Sciences' Monte Carlo-based synthetic scene simulation model, MCScene, to include calculation of all 4 Stokes components. P-MCScene polarimetric optical databases will be generated by a new version (MODTRAN7) of the government-standard MODTRAN radiative transfer algorithm. The conversion of MODTRAN6 to a polarimetric model is being accomplished by (1) introducing polarimetric data, by (2) vectorizing the MODTRAN radiation calculations and by (3) integrating the newly revised and validated vector discrete ordinate model VDISORT3. Early results, presented here, demonstrate a clear pathway to the long-term goal of fully validated polarimetric models.

2MASS Extended Source Catalog: Overview and Algorithms

NASA Technical Reports Server (NTRS)

Jarrett, T.; Chester, T.; Cutri, R.; Schneider, S.; Skrutskie, M.; Huchra, J.

1999-01-01

The 2 Micron All-Sky Survey (2MASS)will observe over one-million galaxies and extended Galactic sources covering the entire sky at wavelenghts between 1 and 2 m. Most of these galaxies, from 70 to 80%, will be newly catalogued objetcs.

Marine Boundary Layer Cloud Property Retrievals from High-Resolution ASTER Observations: Case Studies and Comparison with Terra MODIS

NASA Technical Reports Server (NTRS)

Werner, Frank; Wind, Galina; Zhang, Zhibo; Platnick, Steven; Di Girolamo, Larry; Zhao, Guangyu; Amarasinghe, Nandana; Meyer, Kerry

2016-01-01

A research-level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. It is based on the operational MODIS algorithm. This paper documents the technical details of this algorithm and evaluates the retrievals for selected marine boundary layer cloud scenes through comparisons with the operational MODIS Data Collection 6 (C6) cloud product. The newly developed, ASTERspecific cloud masking algorithm is evaluated through comparison with an independent algorithm reported in Zhao and Di Girolamo (2006). To validate and evaluate the cloud optical thickness (tau) and cloud effective radius (r(sub eff)) from ASTER, the high-spatial-resolution ASTER observations are first aggregated to the same 1000m resolution as MODIS. Subsequently, tau(sub aA) and r(sub eff, aA) retrieved from the aggregated ASTER radiances are compared with the collocated MODIS retrievals. For overcast pixels, the two data sets agree very well with Pearson's product-moment correlation coefficients of R greater than 0.970. However, for partially cloudy pixels there are significant differences between r(sub eff, aA) and the MODIS results which can exceed 10 micrometers. Moreover, it is shown that the numerous delicate cloud structures in the example marine boundary layer scenes, resolved by the high-resolution ASTER retrievals, are smoothed by the MODIS observations. The overall good agreement between the research-level ASTER results and the operational MODIS C6 products proves the feasibility of MODIS-like retrievals from ASTER reflectance measurements and provides the basis for future studies concerning the scale dependency of satellite observations and three-dimensional radiative effects.

Marine boundary layer cloud property retrievals from high-resolution ASTER observations: case studies and comparison with Terra MODIS

NASA Astrophysics Data System (ADS)

Werner, Frank; Wind, Galina; Zhang, Zhibo; Platnick, Steven; Di Girolamo, Larry; Zhao, Guangyu; Amarasinghe, Nandana; Meyer, Kerry

2016-12-01

A research-level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. It is based on the operational MODIS algorithm. This paper documents the technical details of this algorithm and evaluates the retrievals for selected marine boundary layer cloud scenes through comparisons with the operational MODIS Data Collection 6 (C6) cloud product. The newly developed, ASTER-specific cloud masking algorithm is evaluated through comparison with an independent algorithm reported in [Zhao and Di Girolamo(2006)]. To validate and evaluate the cloud optical thickness (τ) and cloud effective radius (reff) from ASTER, the high-spatial-resolution ASTER observations are first aggregated to the same 1000 m resolution as MODIS. Subsequently, τaA and reff, aA retrieved from the aggregated ASTER radiances are compared with the collocated MODIS retrievals. For overcast pixels, the two data sets agree very well with Pearson's product-moment correlation coefficients of R > 0.970. However, for partially cloudy pixels there are significant differences between reff, aA and the MODIS results which can exceed 10 µm. Moreover, it is shown that the numerous delicate cloud structures in the example marine boundary layer scenes, resolved by the high-resolution ASTER retrievals, are smoothed by the MODIS observations. The overall good agreement between the research-level ASTER results and the operational MODIS C6 products proves the feasibility of MODIS-like retrievals from ASTER reflectance measurements and provides the basis for future studies concerning the scale dependency of satellite observations and three-dimensional radiative effects.

Ambient occlusion - A powerful algorithm to segment shell and skeletal intrapores in computed tomography data

NASA Astrophysics Data System (ADS)

Titschack, J.; Baum, D.; Matsuyama, K.; Boos, K.; Färber, C.; Kahl, W.-A.; Ehrig, K.; Meinel, D.; Soriano, C.; Stock, S. R.

2018-06-01

During the last decades, X-ray (micro-)computed tomography has gained increasing attention for the description of porous skeletal and shell structures of various organism groups. However, their quantitative analysis is often hampered by the difficulty to discriminate cavities and pores within the object from the surrounding region. Herein, we test the ambient occlusion (AO) algorithm and newly implemented optimisations for the segmentation of cavities (implemented in the software Amira). The segmentation accuracy is evaluated as a function of (i) changes in the ray length input variable, and (ii) the usage of AO (scalar) field and other AO-derived (scalar) fields. The results clearly indicate that the AO field itself outperforms all other AO-derived fields in terms of segmentation accuracy and robustness against variations in the ray length input variable. The newly implemented optimisations improved the AO field-based segmentation only slightly, while the segmentations based on the AO-derived fields improved considerably. Additionally, we evaluated the potential of the AO field and AO-derived fields for the separation and classification of cavities as well as skeletal structures by comparing them with commonly used distance-map-based segmentations. For this, we tested the zooid separation within a bryozoan colony, the stereom classification of an ophiuroid tooth, the separation of bioerosion traces within a marble block and the calice (central cavity)-pore separation within a dendrophyllid coral. The obtained results clearly indicate that the ideal input field depends on the three-dimensional morphology of the object of interest. The segmentations based on the AO-derived fields often provided cavity separations and skeleton classifications that were superior to or impossible to obtain with commonly used distance-map-based segmentations. The combined usage of various AO-derived fields by supervised or unsupervised segmentation algorithms might provide a promising target for future research to further improve the results for this kind of high-end data segmentation and classification. Furthermore, the application of the developed segmentation algorithm is not restricted to X-ray (micro-)computed tomographic data but may potentially be useful for the segmentation of 3D volume data from other sources.

Large scale GW calculations

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

Govoni, Marco; Galli, Giulia

We present GW calculations of molecules, ordered and disordered solids and interfaces, which employ an efficient contour deformation technique for frequency integration and do not require the explicit evaluation of virtual electronic states nor the inversion of dielectric matrices. We also present a parallel implementation of the algorithm, which takes advantage of separable expressions of both the single particle Green’s function and the screened Coulomb interaction. The method can be used starting from density functional theory calculations performed with semilocal or hybrid functionals. The newly developed technique was applied to GW calculations of systems of unprecedented size, including water/semiconductor interfacesmore » with thousands of electrons.« less

Large Scale GW Calculations

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

Govoni, Marco; Galli, Giulia

We present GW calculations of molecules, ordered and disordered solids and interfaces, which employ an efficient contour deformation technique for frequency integration and do not require the explicit evaluation of virtual electronic states nor the inversion of dielectric matrices. We also present a parallel implementation of the algorithm which takes advantage of separable expressions of both the single particle Green's function and the screened Coulomb interaction. The method can be used starting from density functional theory calculations performed with semilocal or hybrid functionals. We applied the newly developed technique to GW calculations of systems of unprecedented size, including water/semiconductor interfacesmore » with thousands of electrons.« less

Large scale GW calculations

DOE PAGES

Govoni, Marco; Galli, Giulia

2015-01-12

We present GW calculations of molecules, ordered and disordered solids and interfaces, which employ an efficient contour deformation technique for frequency integration and do not require the explicit evaluation of virtual electronic states nor the inversion of dielectric matrices. We also present a parallel implementation of the algorithm, which takes advantage of separable expressions of both the single particle Green’s function and the screened Coulomb interaction. The method can be used starting from density functional theory calculations performed with semilocal or hybrid functionals. The newly developed technique was applied to GW calculations of systems of unprecedented size, including water/semiconductor interfacesmore » with thousands of electrons.« less

Evaluation of a New Backtrack Free Path Planning Algorithm for Manipulators

NASA Astrophysics Data System (ADS)

Islam, Md. Nazrul; Tamura, Shinsuke; Murata, Tomonari; Yanase, Tatsuro

This paper evaluates a newly proposed backtrack free path planning algorithm (BFA) for manipulators. BFA is an exact algorithm, i.e. it is resolution complete. Different from existing resolution complete algorithms, its computation time and memory space are proportional to the number of arms. Therefore paths can be calculated within practical and predetermined time even for manipulators with many arms, and it becomes possible to plan complicated motions of multi-arm manipulators in fully automated environments. The performance of BFA is evaluated for 2-dimensional environments while changing the number of arms and obstacle placements. Its performance under locus and attitude constraints is also evaluated. Evaluation results show that the computation volume of the algorithm is almost the same as the theoretical one, i.e. it increases linearly with the number of arms even in complicated environments. Moreover BFA achieves the constant performance independent of environments.

An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments.

PubMed

Yang, Yifei; Tan, Minjia; Dai, Yuewei

2017-01-01

A ship power equipments' fault monitoring signal usually provides few samples and the data's feature is non-linear in practical situation. This paper adopts the method of the least squares support vector machine (LSSVM) to deal with the problem of fault pattern identification in the case of small sample data. Meanwhile, in order to avoid involving a local extremum and poor convergence precision which are induced by optimizing the kernel function parameter and penalty factor of LSSVM, an improved Cuckoo Search (CS) algorithm is proposed for the purpose of parameter optimization. Based on the dynamic adaptive strategy, the newly proposed algorithm improves the recognition probability and the searching step length, which can effectively solve the problems of slow searching speed and low calculation accuracy of the CS algorithm. A benchmark example demonstrates that the CS-LSSVM algorithm can accurately and effectively identify the fault pattern types of ship power equipments.

Comparative advantages of novel algorithms using MSR threshold and MSR difference threshold for biclustering gene expression data.

PubMed

Das, Shyama; Idicula, Sumam Mary

2011-01-01

The goal of biclustering in gene expression data matrix is to find a submatrix such that the genes in the submatrix show highly correlated activities across all conditions in the submatrix. A measure called mean squared residue (MSR) is used to simultaneously evaluate the coherence of rows and columns within the submatrix. MSR difference is the incremental increase in MSR when a gene or condition is added to the bicluster. In this chapter, three biclustering algorithms using MSR threshold (MSRT) and MSR difference threshold (MSRDT) are experimented and compared. All these methods use seeds generated from K-Means clustering algorithm. Then these seeds are enlarged by adding more genes and conditions. The first algorithm makes use of MSRT alone. Both the second and third algorithms make use of MSRT and the newly introduced concept of MSRDT. Highly coherent biclusters are obtained using this concept. In the third algorithm, a different method is used to calculate the MSRDT. The results obtained on bench mark datasets prove that these algorithms are better than many of the metaheuristic algorithms.

Verification of Minimum Detectable Activity for Radiological Threat Source Search

NASA Astrophysics Data System (ADS)

Gardiner, Hannah; Myjak, Mitchell; Baciak, James; Detwiler, Rebecca; Seifert, Carolyn

2015-10-01

The Department of Homeland Security's Domestic Nuclear Detection Office is working to develop advanced technologies that will improve the ability to detect, localize, and identify radiological and nuclear sources from airborne platforms. The Airborne Radiological Enhanced-sensor System (ARES) program is developing advanced data fusion algorithms for analyzing data from a helicopter-mounted radiation detector. This detector platform provides a rapid, wide-area assessment of radiological conditions at ground level. The NSCRAD (Nuisance-rejection Spectral Comparison Ratios for Anomaly Detection) algorithm was developed to distinguish low-count sources of interest from benign naturally occurring radiation and irrelevant nuisance sources. It uses a number of broad, overlapping regions of interest to statistically compare each newly measured spectrum with the current estimate for the background to identify anomalies. We recently developed a method to estimate the minimum detectable activity (MDA) of NSCRAD in real time. We present this method here and report on the MDA verification using both laboratory measurements and simulated injects on measured backgrounds at or near the detection limits. This work is supported by the US Department of Homeland Security, Domestic Nuclear Detection Office, under competitively awarded contract/IAA HSHQDC-12-X-00376. This support does not constitute an express or implied endorsement on the part of the Gov't.

Different realizations of Cooper-Frye sampling with conservation laws

NASA Astrophysics Data System (ADS)

Schwarz, C.; Oliinychenko, D.; Pang, L.-G.; Ryu, S.; Petersen, H.

2018-01-01

Approaches based on viscous hydrodynamics for the hot and dense stage and hadronic transport for the final dilute rescattering stage are successfully applied to the dynamic description of heavy ion reactions at high beam energies. One crucial step in such hybrid approaches is the so-called particlization, which is the transition between the hydrodynamic description and the microscopic degrees of freedom. For this purpose, individual particles are sampled on the Cooper-Frye hypersurface. In this work, four different realizations of the sampling algorithms are compared, with three of them incorporating the global conservation laws of quantum numbers in each event. The algorithms are compared within two types of scenarios: a simple ‘box’ hypersurface consisting of only one static cell and a typical particlization hypersurface for Au+Au collisions at \\sqrt{{s}{NN}}=200 {GeV}. For all algorithms the mean multiplicities (or particle spectra) remain unaffected by global conservation laws in the case of large volumes. In contrast, the fluctuations of the particle numbers are affected considerably. The fluctuations of the newly developed SPREW algorithm based on the exponential weight, and the recently suggested SER algorithm based on ensemble rejection, are smaller than those without conservation laws and agree with the expectation from the canonical ensemble. The previously applied mode sampling algorithm produces dramatically larger fluctuations than expected in the corresponding microcanonical ensemble, and therefore should be avoided in fluctuation studies. This study might be of interest for the investigation of particle fluctuations and correlations, e.g. the suggested signatures for a phase transition or a critical endpoint, in hybrid approaches that are affected by global conservation laws.

CellAnimation: an open source MATLAB framework for microscopy assays.

PubMed

Georgescu, Walter; Wikswo, John P; Quaranta, Vito

2012-01-01

Advances in microscopy technology have led to the creation of high-throughput microscopes that are capable of generating several hundred gigabytes of images in a few days. Analyzing such wealth of data manually is nearly impossible and requires an automated approach. There are at present a number of open-source and commercial software packages that allow the user to apply algorithms of different degrees of sophistication to the images and extract desired metrics. However, the types of metrics that can be extracted are severely limited by the specific image processing algorithms that the application implements, and by the expertise of the user. In most commercial software, code unavailability prevents implementation by the end user of newly developed algorithms better suited for a particular type of imaging assay. While it is possible to implement new algorithms in open-source software, rewiring an image processing application requires a high degree of expertise. To obviate these limitations, we have developed an open-source high-throughput application that allows implementation of different biological assays such as cell tracking or ancestry recording, through the use of small, relatively simple image processing modules connected into sophisticated imaging pipelines. By connecting modules, non-expert users can apply the particular combination of well-established and novel algorithms developed by us and others that are best suited for each individual assay type. In addition, our data exploration and visualization modules make it easy to discover or select specific cell phenotypes from a heterogeneous population. CellAnimation is distributed under the Creative Commons Attribution-NonCommercial 3.0 Unported license (http://creativecommons.org/licenses/by-nc/3.0/). CellAnimationsource code and documentation may be downloaded from www.vanderbilt.edu/viibre/software/documents/CellAnimation.zip. Sample data are available at www.vanderbilt.edu/viibre/software/documents/movies.zip. walter.georgescu@vanderbilt.edu Supplementary data available at Bioinformatics online.

Determination of thiamine HCl and pyridoxine HCl in pharmaceutical preparations using UV-visible spectrophotometry and genetic algorithm based multivariate calibration methods.

PubMed

Ozdemir, Durmus; Dinc, Erdal

2004-07-01

Simultaneous determination of binary mixtures pyridoxine hydrochloride and thiamine hydrochloride in a vitamin combination using UV-visible spectrophotometry and classical least squares (CLS) and three newly developed genetic algorithm (GA) based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The sample data set contains the UV-visible spectra of 30 synthetic mixtures (8 to 40 microg/ml) of these vitamins and 10 tablets containing 250 mg from each vitamin. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the two components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of <0.01 and 0.43 microg/ml for all the four methods. The SEP values for the tablets were in the range of 2.91 and 11.51 mg/tablets. A comparison of genetic algorithm selected wavelengths for each component using GR method was also included.

Adaptive sleep-wake discrimination for wearable devices.

PubMed

Karlen, Walter; Floreano, Dario

2011-04-01

Sleep/wake classification systems that rely on physiological signals suffer from intersubject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of intersubject variability, we suggest a novel online adaptation technique that updates the sleep/wake classifier in real time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed ECG and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subject-independent classifier algorithm, the SleePic device was only able to correctly classify 74.94 ± 6.76% of the human-rated sleep/wake data. By using the suggested automatic adaptation method, the mean classification accuracy could be significantly improved to 92.98 ± 3.19%. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44 ± 3.57%. We demonstrated that subject-independent models used for online sleep-wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.

Maximum likelihood positioning and energy correction for scintillation detectors

NASA Astrophysics Data System (ADS)

Lerche, Christoph W.; Salomon, André; Goldschmidt, Benjamin; Lodomez, Sarah; Weissler, Björn; Solf, Torsten

2016-02-01

An algorithm for determining the crystal pixel and the gamma ray energy with scintillation detectors for PET is presented. The algorithm uses Likelihood Maximisation (ML) and therefore is inherently robust to missing data caused by defect or paralysed photo detector pixels. We tested the algorithm on a highly integrated MRI compatible small animal PET insert. The scintillation detector blocks of the PET gantry were built with the newly developed digital Silicon Photomultiplier (SiPM) technology from Philips Digital Photon Counting and LYSO pixel arrays with a pitch of 1 mm and length of 12 mm. Light sharing was used to readout the scintillation light from the 30× 30 scintillator pixel array with an 8× 8 SiPM array. For the performance evaluation of the proposed algorithm, we measured the scanner’s spatial resolution, energy resolution, singles and prompt count rate performance, and image noise. These values were compared to corresponding values obtained with Center of Gravity (CoG) based positioning methods for different scintillation light trigger thresholds and also for different energy windows. While all positioning algorithms showed similar spatial resolution, a clear advantage for the ML method was observed when comparing the PET scanner’s overall single and prompt detection efficiency, image noise, and energy resolution to the CoG based methods. Further, ML positioning reduces the dependence of image quality on scanner configuration parameters and was the only method that allowed achieving highest energy resolution, count rate performance and spatial resolution at the same time.

Model-based sensor-less wavefront aberration correction in optical coherence tomography.

PubMed

Verstraete, Hans R G W; Wahls, Sander; Kalkman, Jeroen; Verhaegen, Michel

2015-12-15

Several sensor-less wavefront aberration correction methods that correct nonlinear wavefront aberrations by maximizing the optical coherence tomography (OCT) signal are tested on an OCT setup. A conventional coordinate search method is compared to two model-based optimization methods. The first model-based method takes advantage of the well-known optimization algorithm (NEWUOA) and utilizes a quadratic model. The second model-based method (DONE) is new and utilizes a random multidimensional Fourier-basis expansion. The model-based algorithms achieve lower wavefront errors with up to ten times fewer measurements. Furthermore, the newly proposed DONE method outperforms the NEWUOA method significantly. The DONE algorithm is tested on OCT images and shows a significantly improved image quality.

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

PubMed Central

Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima

2013-01-01

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm. PMID:23737718

Improving Vector Evaluated Particle Swarm Optimisation by incorporating nondominated solutions.

PubMed

Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima

2013-01-01

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

The performance of the new enhanced-resolution satellite passive microwave dataset applied for snow water equivalent estimation

NASA Astrophysics Data System (ADS)

Pan, J.; Durand, M. T.; Jiang, L.; Liu, D.

2017-12-01

The newly-processed NASA MEaSures Calibrated Enhanced-Resolution Brightness Temperature (CETB) reconstructed using antenna measurement response function (MRF) is considered to have significantly improved fine-resolution measurements with better georegistration for time-series observations and equivalent field of view (FOV) for frequencies with the same monomial spatial resolution. We are looking forward to its potential for the global snow observing purposes, and therefore aim to test its performance for characterizing snow properties, especially the snow water equivalent (SWE) in large areas. In this research, two candidate SWE algorithms will be tested in China for the years between 2005 to 2010 using the reprocessed TB from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), with the results to be evaluated using the daily snow depth measurements at over 700 national synoptic stations. One of the algorithms is the SWE retrieval algorithm used for the FengYun (FY) - 3 Microwave Radiation Imager. This algorithm uses the multi-channel TB to calculate SWE for three major snow regions in China, with the coefficients adapted for different land cover types. The second algorithm is the newly-established Bayesian Algorithm for SWE Estimation with Passive Microwave measurements (BASE-PM). This algorithm uses the physically-based snow radiative transfer model to find the histogram of most-likely snow property that matches the multi-frequency TB from 10.65 to 90 GHz. It provides a rough estimation of snow depth and grain size at the same time and showed a 30 mm SWE RMS error using the ground radiometer measurements at Sodankyla. This study will be the first attempt to test it spatially for satellite. The use of this algorithm benefits from the high resolution and the spatial consistency between frequencies embedded in the new dataset. This research will answer three questions. First, to what extent can CETB increase the heterogeneity in the mapped SWE? Second, will the SWE estimation error statistics be improved using this high-resolution dataset? Third, how will the SWE retrieval accuracy be improved using CETB and the new SWE retrieval techniques?

A physics-based earthquake simulator and its application to seismic hazard assessment in Calabria (Southern Italy) region

USGS Publications Warehouse

Console, Rodolfo; Nardi, Anna; Carluccio, Roberto; Murru, Maura; Falcone, Giuseppe; Parsons, Thomas E.

2017-01-01

The use of a newly developed earthquake simulator has allowed the production of catalogs lasting 100 kyr and containing more than 100,000 events of magnitudes ≥4.5. The model of the fault system upon which we applied the simulator code was obtained from the DISS 3.2.0 database, selecting all the faults that are recognized on the Calabria region, for a total of 22 fault segments. The application of our simulation algorithm provides typical features in time, space and magnitude behavior of the seismicity, which can be compared with those of the real observations. The results of the physics-based simulator algorithm were compared with those obtained by an alternative method using a slip-rate balanced technique. Finally, as an example of a possible use of synthetic catalogs, an attenuation law has been applied to all the events reported in the synthetic catalog for the production of maps showing the exceedance probability of given values of PGA on the territory under investigation.

Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies

PubMed Central

2017-01-01

Wake-up receivers are the natural choice for wireless sensor networks because of their ultra-low power consumption and their ability to provide communications on demand. A downside of ultra-low power wake-up receivers is their low sensitivity caused by the passive demodulation of the carrier signal. In this article, we present a novel communication scheme by exploiting purposefully-interfering out-of-tune signals of two or more wireless sensor nodes, which produce the wake-up signal as the beat frequency of superposed carriers. Additionally, we introduce a communication algorithm and a flooding protocol based on this approach. Our experiments show that our approach increases the received signal strength up to 3 dB, improving communication robustness and reliability. Furthermore, we demonstrate the feasibility of our newly-developed protocols by means of an outdoor experiment and an indoor setup consisting of several nodes. The flooding algorithm achieves almost a 100% wake-up rate in less than 20 ms. PMID:28933749

Graph theory data for topological quantum chemistry.

PubMed

Vergniory, M G; Elcoro, L; Wang, Zhijun; Cano, Jennifer; Felser, C; Aroyo, M I; Bernevig, B Andrei; Bradlyn, Barry

2017-08-01

Topological phases of noninteracting particles are distinguished by the global properties of their band structure and eigenfunctions in momentum space. On the other hand, group theory as conventionally applied to solid-state physics focuses only on properties that are local (at high-symmetry points, lines, and planes) in the Brillouin zone. To bridge this gap, we have previously [Bradlyn et al., Nature (London) 547, 298 (2017)NATUAS0028-083610.1038/nature23268] mapped the problem of constructing global band structures out of local data to a graph construction problem. In this paper, we provide the explicit data and formulate the necessary algorithms to produce all topologically distinct graphs. Furthermore, we show how to apply these algorithms to certain "elementary" band structures highlighted in the aforementioned reference, and thus we identified and tabulated all orbital types and lattices that can give rise to topologically disconnected band structures. Finally, we show how to use the newly developed bandrep program on the Bilbao Crystallographic Server to access the results of our computation.

Time-reversal and Bayesian inversion

NASA Astrophysics Data System (ADS)

Debski, Wojciech

2017-04-01

Probabilistic inversion technique is superior to the classical optimization-based approach in all but one aspects. It requires quite exhaustive computations which prohibit its use in huge size inverse problems like global seismic tomography or waveform inversion to name a few. The advantages of the approach are, however, so appealing that there is an ongoing continuous afford to make the large inverse task as mentioned above manageable with the probabilistic inverse approach. One of the perspective possibility to achieve this goal relays on exploring the internal symmetry of the seismological modeling problems in hand - a time reversal and reciprocity invariance. This two basic properties of the elastic wave equation when incorporating into the probabilistic inversion schemata open a new horizons for Bayesian inversion. In this presentation we discuss the time reversal symmetry property, its mathematical aspects and propose how to combine it with the probabilistic inverse theory into a compact, fast inversion algorithm. We illustrate the proposed idea with the newly developed location algorithm TRMLOC and discuss its efficiency when applied to mining induced seismic data.

Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography.

PubMed

Jeong, Jeong-Won; Shin, Dae C; Do, Synho; Marmarelis, Vasilis Z

2006-08-01

This paper presents a novel segmentation methodology for automated classification and differentiation of soft tissues using multiband data obtained with the newly developed system of high-resolution ultrasonic transmission tomography (HUTT) for imaging biological organs. This methodology extends and combines two existing approaches: the L-level set active contour (AC) segmentation approach and the agglomerative hierarchical kappa-means approach for unsupervised clustering (UC). To prevent the trapping of the current iterative minimization AC algorithm in a local minimum, we introduce a multiresolution approach that applies the level set functions at successively increasing resolutions of the image data. The resulting AC clusters are subsequently rearranged by the UC algorithm that seeks the optimal set of clusters yielding the minimum within-cluster distances in the feature space. The presented results from Monte Carlo simulations and experimental animal-tissue data demonstrate that the proposed methodology outperforms other existing methods without depending on heuristic parameters and provides a reliable means for soft tissue differentiation in HUTT images.

A comparative study of history-based versus vectorized Monte Carlo methods in the GPU/CUDA environment for a simple neutron eigenvalue problem

NASA Astrophysics Data System (ADS)

Liu, Tianyu; Du, Xining; Ji, Wei; Xu, X. George; Brown, Forrest B.

2014-06-01

For nuclear reactor analysis such as the neutron eigenvalue calculations, the time consuming Monte Carlo (MC) simulations can be accelerated by using graphics processing units (GPUs). However, traditional MC methods are often history-based, and their performance on GPUs is affected significantly by the thread divergence problem. In this paper we describe the development of a newly designed event-based vectorized MC algorithm for solving the neutron eigenvalue problem. The code was implemented using NVIDIA's Compute Unified Device Architecture (CUDA), and tested on a NVIDIA Tesla M2090 GPU card. We found that although the vectorized MC algorithm greatly reduces the occurrence of thread divergence thus enhancing the warp execution efficiency, the overall simulation speed is roughly ten times slower than the history-based MC code on GPUs. Profiling results suggest that the slow speed is probably due to the memory access latency caused by the large amount of global memory transactions. Possible solutions to improve the code efficiency are discussed.

A novel iterative scheme and its application to differential equations.

PubMed

Khan, Yasir; Naeem, F; Šmarda, Zdeněk

2014-01-01

The purpose of this paper is to employ an alternative approach to reconstruct the standard variational iteration algorithm II proposed by He, including Lagrange multiplier, and to give a simpler formulation of Adomian decomposition and modified Adomian decomposition method in terms of newly proposed variational iteration method-II (VIM). Through careful investigation of the earlier variational iteration algorithm and Adomian decomposition method, we find unnecessary calculations for Lagrange multiplier and also repeated calculations involved in each iteration, respectively. Several examples are given to verify the reliability and efficiency of the method.

Fully automated muscle quality assessment by Gabor filtering of second harmonic generation images

NASA Astrophysics Data System (ADS)

Paesen, Rik; Smolders, Sophie; Vega, José Manolo de Hoyos; Eijnde, Bert O.; Hansen, Dominique; Ameloot, Marcel

2016-02-01

Although structural changes on the sarcomere level of skeletal muscle are known to occur due to various pathologies, rigorous studies of the reduced sarcomere quality remain scarce. This can possibly be explained by the lack of an objective tool for analyzing and comparing sarcomere images across biological conditions. Recent developments in second harmonic generation (SHG) microscopy and increasing insight into the interpretation of sarcomere SHG intensity profiles have made SHG microscopy a valuable tool to study microstructural properties of sarcomeres. Typically, sarcomere integrity is analyzed by fitting a set of manually selected, one-dimensional SHG intensity profiles with a supramolecular SHG model. To circumvent this tedious manual selection step, we developed a fully automated image analysis procedure to map the sarcomere disorder for the entire image at once. The algorithm relies on a single-frequency wavelet-based Gabor approach and includes a newly developed normalization procedure allowing for unambiguous data interpretation. The method was validated by showing the correlation between the sarcomere disorder, quantified by the M-band size obtained from manually selected profiles, and the normalized Gabor value ranging from 0 to 1 for decreasing disorder. Finally, to elucidate the applicability of our newly developed protocol, Gabor analysis was used to study the effect of experimental autoimmune encephalomyelitis on the sarcomere regularity. We believe that the technique developed in this work holds great promise for high-throughput, unbiased, and automated image analysis to study sarcomere integrity by SHG microscopy.

The Design of Ocean Turbulence Measurement with a Free Fall Vertical Profiler

NASA Astrophysics Data System (ADS)

Luan, Xin; Xin, Jia; Zhu, Tieyi; Yang, Hua; Teng, Yuru; Song, Dalei

2018-03-01

The newly designed instrument Free Fall Vertical Profiler (FFVP) developed by Ocean University of China (OUC) had been deployed in the Western Pacific in March 08, 2017 and succeed to collect turbulence signals about 350-m-deep water. According to the requirements of turbulence measurement, the mechanical design was developed for turbulence platform to achieve stability and good flow tracking. By analysing the Heading, Pitch and Roll, the results suggested that the platform satisfies the requirements of stability. The power spectrum of the cleaned shear signals using the noise correction algorithm match well with the theoretical Nasmyth spectrum and the rate of turbulence dissipation are approximately 10-8 W/kg. In general, the FFVP was rationally designed and provided a good measurement platform for turbulence observation.

New developments in optical coherence tomography

PubMed Central

Kostanyan, Tigran; Wollstein, Gadi; Schuman, Joel S.

2017-01-01

Purpose of review Optical coherence tomography (OCT) has become the cornerstone technology for clinical ocular imaging in the past few years. The technology is still rapidly evolving with newly developed applications. This manuscript reviews recent innovative OCT applications for glaucoma diagnosis and management. Recent findings The improvements made in the technology have resulted in increased scanning speed, axial and transverse resolution, and more effective use of the OCT technology as a component of multimodal imaging tools. At the same time, the parallel evolution in novel algorithms makes it possible to efficiently analyze the increased volume of acquired data. Summary The innovative iterations of OCT technology have the potential to further improve the performance of the technology in evaluating ocular structural and functional characteristics and longitudinal changes in glaucoma. PMID:25594766

Using OpenMP vs. Threading Building Blocks for Medical Imaging on Multi-cores

NASA Astrophysics Data System (ADS)

Kegel, Philipp; Schellmann, Maraike; Gorlatch, Sergei

We compare two parallel programming approaches for multi-core systems: the well-known OpenMP and the recently introduced Threading Building Blocks (TBB) library by Intel®. The comparison is made using the parallelization of a real-world numerical algorithm for medical imaging. We develop several parallel implementations, and compare them w.r.t. programming effort, programming style and abstraction, and runtime performance. We show that TBB requires a considerable program re-design, whereas with OpenMP simple compiler directives are sufficient. While TBB appears to be less appropriate for parallelizing existing implementations, it fosters a good programming style and higher abstraction level for newly developed parallel programs. Our experimental measurements on a dual quad-core system demonstrate that OpenMP slightly outperforms TBB in our implementation.

Accuracy assessment of pharmacogenetically predictive warfarin dosing algorithms in patients of an academic medical center anticoagulation clinic.

PubMed

Shaw, Paul B; Donovan, Jennifer L; Tran, Maichi T; Lemon, Stephenie C; Burgwinkle, Pamela; Gore, Joel

2010-08-01

The objectives of this retrospective cohort study are to evaluate the accuracy of pharmacogenetic warfarin dosing algorithms in predicting therapeutic dose and to determine if this degree of accuracy warrants the routine use of genotyping to prospectively dose patients newly started on warfarin. Seventy-one patients of an outpatient anticoagulation clinic at an academic medical center who were age 18 years or older on a stable, therapeutic warfarin dose with international normalized ratio (INR) goal between 2.0 and 3.0, and cytochrome P450 isoenzyme 2C9 (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1) genotypes available between January 1, 2007 and September 30, 2008 were included. Six pharmacogenetic warfarin dosing algorithms were identified from the medical literature. Additionally, a 5 mg fixed dose approach was evaluated. Three algorithms, Zhu et al. (Clin Chem 53:1199-1205, 2007), Gage et al. (J Clin Ther 84:326-331, 2008), and International Warfarin Pharmacogenetic Consortium (IWPC) (N Engl J Med 360:753-764, 2009) were similar in the primary accuracy endpoints with mean absolute error (MAE) ranging from 1.7 to 1.8 mg/day and coefficient of determination R (2) from 0.61 to 0.66. However, the Zhu et al. algorithm severely over-predicted dose (defined as >or=2x or >or=2 mg/day more than actual dose) in twice as many (14 vs. 7%) patients as Gage et al. 2008 and IWPC 2009. In conclusion, the algorithms published by Gage et al. 2008 and the IWPC 2009 were the two most accurate pharmacogenetically based equations available in the medical literature in predicting therapeutic warfarin dose in our study population. However, the degree of accuracy demonstrated does not support the routine use of genotyping to prospectively dose all patients newly started on warfarin.

Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

PubMed Central

Faye, Ibrahima; Samir, Brahim Belhaouari; Md Said, Abas

2014-01-01

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. PMID:25045727

A New Algorithm to Diagnose Atrial Ectopic Origin from Multi Lead ECG Systems - Insights from 3D Virtual Human Atria and Torso

PubMed Central

Alday, Erick A. Perez; Colman, Michael A.; Langley, Philip; Butters, Timothy D.; Higham, Jonathan; Workman, Antony J.; Hancox, Jules C.; Zhang, Henggui

2015-01-01

Rapid atrial arrhythmias such as atrial fibrillation (AF) predispose to ventricular arrhythmias, sudden cardiac death and stroke. Identifying the origin of atrial ectopic activity from the electrocardiogram (ECG) can help to diagnose the early onset of AF in a cost-effective manner. The complex and rapid atrial electrical activity during AF makes it difficult to obtain detailed information on atrial activation using the standard 12-lead ECG alone. Compared to conventional 12-lead ECG, more detailed ECG lead configurations may provide further information about spatio-temporal dynamics of the body surface potential (BSP) during atrial excitation. We apply a recently developed 3D human atrial model to simulate electrical activity during normal sinus rhythm and ectopic pacing. The atrial model is placed into a newly developed torso model which considers the presence of the lungs, liver and spinal cord. A boundary element method is used to compute the BSP resulting from atrial excitation. Elements of the torso mesh corresponding to the locations of the placement of the electrodes in the standard 12-lead and a more detailed 64-lead ECG configuration were selected. The ectopic focal activity was simulated at various origins across all the different regions of the atria. Simulated BSP maps during normal atrial excitation (i.e. sinoatrial node excitation) were compared to those observed experimentally (obtained from the 64-lead ECG system), showing a strong agreement between the evolution in time of the simulated and experimental data in the P-wave morphology of the ECG and dipole evolution. An algorithm to obtain the location of the stimulus from a 64-lead ECG system was developed. The algorithm presented had a success rate of 93%, meaning that it correctly identified the origin of atrial focus in 75/80 simulations, and involved a general approach relevant to any multi-lead ECG system. This represents a significant improvement over previously developed algorithms. PMID:25611350

Advanced Reactors-Intermediate Heat Exchanger (IHX) Coupling: Theoretical Modeling and Experimental Validation

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

Utgikar, Vivek; Sun, Xiaodong; Christensen, Richard

2016-12-29

The overall goal of the research project was to model the behavior of the advanced reactorintermediate heat exchange system and to develop advanced control techniques for off-normal conditions. The specific objectives defined for the project were: 1. To develop the steady-state thermal hydraulic design of the intermediate heat exchanger (IHX); 2. To develop mathematical models to describe the advanced nuclear reactor-IHX-chemical process/power generation coupling during normal and off-normal operations, and to simulate models using multiphysics software; 3. To develop control strategies using genetic algorithm or neural network techniques and couple these techniques with the multiphysics software; 4. To validate themore » models experimentally The project objectives were accomplished by defining and executing four different tasks corresponding to these specific objectives. The first task involved selection of IHX candidates and developing steady state designs for those. The second task involved modeling of the transient and offnormal operation of the reactor-IHX system. The subsequent task dealt with the development of control strategies and involved algorithm development and simulation. The last task involved experimental validation of the thermal hydraulic performances of the two prototype heat exchangers designed and fabricated for the project at steady state and transient conditions to simulate the coupling of the reactor- IHX-process plant system. The experimental work utilized the two test facilities at The Ohio State University (OSU) including one existing High-Temperature Helium Test Facility (HTHF) and the newly developed high-temperature molten salt facility.« less

Ionospheric-thermospheric UV tomography: 2. Comparison with incoherent scatter radar measurements

NASA Astrophysics Data System (ADS)

Dymond, K. F.; Nicholas, A. C.; Budzien, S. A.; Stephan, A. W.; Coker, C.; Hei, M. A.; Groves, K. M.

2017-03-01

The Special Sensor Ultraviolet Limb Imager (SSULI) instruments are ultraviolet limb scanning sensors that fly on the Defense Meteorological Satellite Program F16-F19 satellites. The SSULIs cover the 80-170 nm wavelength range which contains emissions at 91 and 136 nm, which are produced by radiative recombination of the ionosphere. We invert the 91.1 nm emission tomographically using a newly developed algorithm that includes optical depth effects due to pure absorption and resonant scattering. We present the details of our approach including how the optimal altitude and along-track sampling were determined and the newly developed approach we are using for regularizing the SSULI tomographic inversions. Finally, we conclude with validations of the SSULI inversions against Advanced Research Project Agency Long-range Tracking and Identification Radar (ALTAIR) incoherent scatter radar measurements and demonstrate excellent agreement between the measurements. As part of this study, we include the effects of pure absorption by O2, N2, and O in the inversions and find that best agreement between the ALTAIR and SSULI measurements is obtained when only O2 and O are included, but the agreement degrades when N2 absorption is included. This suggests that the absorption cross section of N2 needs to be reinvestigated near 91.1 nm wavelengths.

Development of an algorithm for monitoring pattern fidelity on photomasks for 0.2-μm technology and beyond based on light optical CD metrology tools

NASA Astrophysics Data System (ADS)

Schaetz, Thomas; Hay, Bernd; Walden, Lars; Ziegler, Wolfram

1999-04-01

With the ongoing shrinking of design rules, the complexity of photomasks does increase continuously. Features are getting smaller and denser, their characterization requires sophisticated procedures. Looking for the deviation from their target value and their linewidth variation is not sufficient any more. In addition, measurements of corner rounding and line end shortening are necessary to define the pattern fidelity on the mask. Otherwise printing results will not be satisfying. Contacts and small features are suffering mainly from imaging inaccuracies. The size of the contacts as an example may come out too small on the photomask and therefore reduces the process window in lithography. In order to meet customer requirements for pattern fidelity, a measurement algorithm and a measurement procedure needs to be introduced and specifications to be defined. In this paper different approaches are compared, allowing an automatic qualification of photomask by optical light microscopy based on a MueTec CD-metrology system, the newly developed MueTec 2030UV, provided with a 365 nm light source. The i-line illumination allows to resolve features down to 0.2 micrometers size with good repeatability.

Optimal Superpositioning of Flexible Molecule Ensembles

PubMed Central

Gapsys, Vytautas; de Groot, Bert L.

2013-01-01

Analysis of the internal dynamics of a biological molecule requires the successful removal of overall translation and rotation. Particularly for flexible or intrinsically disordered peptides, this is a challenging task due to the absence of a well-defined reference structure that could be used for superpositioning. In this work, we started the analysis with a widely known formulation of an objective for the problem of superimposing a set of multiple molecules as variance minimization over an ensemble. A negative effect of this superpositioning method is the introduction of ambiguous rotations, where different rotation matrices may be applied to structurally similar molecules. We developed two algorithms to resolve the suboptimal rotations. The first approach minimizes the variance together with the distance of a structure to a preceding molecule in the ensemble. The second algorithm seeks for minimal variance together with the distance to the nearest neighbors of each structure. The newly developed methods were applied to molecular-dynamics trajectories and normal-mode ensembles of the Aβ peptide, RS peptide, and lysozyme. These new (to our knowledge) superpositioning methods combine the benefits of variance and distance between nearest-neighbor(s) minimization, providing a solution for the analysis of intrinsic motions of flexible molecules and resolving ambiguous rotations. PMID:23332072

State-Dependent Pseudo-Linear Filter for Spacecraft Attitude and Rate Estimation

NASA Technical Reports Server (NTRS)

Bar-Itzhack, Itzhack Y.; Harman, Richard R.

2001-01-01

This paper presents the development and performance of a special algorithm for estimating the attitude and angular rate of a spacecraft. The algorithm is a pseudo-linear Kalman filter, which is an ordinary linear Kalman filter that operates on a linear model whose matrices are current state estimate dependent. The nonlinear rotational dynamics equation of the spacecraft is presented in the state space as a state-dependent linear system. Two types of measurements are considered. One type is a measurement of the quaternion of rotation, which is obtained from a newly introduced star tracker based apparatus. The other type of measurement is that of vectors, which permits the use of a variety of vector measuring sensors like sun sensors and magnetometers. While quaternion measurements are related linearly to the state vector, vector measurements constitute a nonlinear function of the state vector. Therefore, in this paper, a state-dependent linear measurement equation is developed for the vector measurement case. The state-dependent pseudo linear filter is applied to simulated spacecraft rotations and adequate estimates of the spacecraft attitude and rate are obtained for the case of quaternion measurements as well as of vector measurements.

Genetic Algorithm Design of a 3D Printed Heat Sink

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

Wu, Tong; Ozpineci, Burak; Ayers, Curtis William

2016-01-01

In this paper, a genetic algorithm- (GA-) based approach is discussed for designing heat sinks based on total heat generation and dissipation for a pre-specified size andshape. This approach combines random iteration processesand genetic algorithms with finite element analysis (FEA) to design the optimized heat sink. With an approach that prefers survival of the fittest , a more powerful heat sink can bedesigned which can cool power electronics more efficiently. Some of the resulting designs can only be 3D printed due totheir complexity. In addition to describing the methodology, this paper also includes comparisons of different cases to evaluate themore » performance of the newly designed heat sinkcompared to commercially available heat sinks.« less

Novel probabilistic neuroclassifier

NASA Astrophysics Data System (ADS)

Hong, Jiang; Serpen, Gursel

2003-09-01

A novel probabilistic potential function neural network classifier algorithm to deal with classes which are multi-modally distributed and formed from sets of disjoint pattern clusters is proposed in this paper. The proposed classifier has a number of desirable properties which distinguish it from other neural network classifiers. A complete description of the algorithm in terms of its architecture and the pseudocode is presented. Simulation analysis of the newly proposed neuro-classifier algorithm on a set of benchmark problems is presented. Benchmark problems tested include IRIS, Sonar, Vowel Recognition, Two-Spiral, Wisconsin Breast Cancer, Cleveland Heart Disease and Thyroid Gland Disease. Simulation results indicate that the proposed neuro-classifier performs consistently better for a subset of problems for which other neural classifiers perform relatively poorly.

Explicit and implicit springback simulation in sheet metal forming using fully coupled ductile damage and distortional hardening model

NASA Astrophysics Data System (ADS)

Yetna n'jock, M.; Houssem, B.; Labergere, C.; Saanouni, K.; Zhenming, Y.

2018-05-01

The springback is an important phenomenon which accompanies the forming of metallic sheets especially for high strength materials. A quantitative prediction of springback becomes very important for newly developed material with high mechanical characteristics. In this work, a numerical methodology is developed to quantify this undesirable phenomenon. This methodoly is based on the use of both explicit and implicit finite element solvers of Abaqus®. The most important ingredient of this methodology consists on the use of highly predictive mechanical model. A thermodynamically-consistent, non-associative and fully anisotropic elastoplastic constitutive model strongly coupled with isotropic ductile damage and accounting for distortional hardening is then used. An algorithm for local integration of the complete set of the constitutive equations is developed. This algorithm considers the rotated frame formulation (RFF) to ensure the incremental objectivity of the model in the framework of finite strains. This algorithm is implemented in both explicit (Abaqus/Explicit®) and implicit (Abaqus/Standard®) solvers of Abaqus® through the users routine VUMAT and UMAT respectively. The implicit solver of Abaqus® has been used to study spingback as it is generally a quasi-static unloading. In order to compare the methods `efficiency, the explicit method (Dynamic Relaxation Method) proposed by Rayleigh has been also used for springback prediction. The results obtained within U draw/bending benchmark are studied, discussed and compared with experimental results as reference. Finally, the purpose of this work is to evaluate the reliability of different methods predict efficiently springback in sheet metal forming.

Simulated annealing with restart strategy for the blood pickup routing problem

NASA Astrophysics Data System (ADS)

Yu, V. F.; Iswari, T.; Normasari, N. M. E.; Asih, A. M. S.; Ting, H.

2018-04-01

This study develops a simulated annealing heuristic with restart strategy (SA_RS) for solving the blood pickup routing problem (BPRP). BPRP minimizes the total length of the routes for blood bag collection between a blood bank and a set of donation sites, each associated with a time window constraint that must be observed. The proposed SA_RS is implemented in C++ and tested on benchmark instances of the vehicle routing problem with time windows to verify its performance. The algorithm is then tested on some newly generated BPRP instances and the results are compared with those obtained by CPLEX. Experimental results show that the proposed SA_RS heuristic effectively solves BPRP.

An Approach to Self-Assembling Swarm Robots Using Multitree Genetic Programming

PubMed Central

An, Jinung

2013-01-01

In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach. PMID:23861655

Hyperspectral data compression using a Wiener filter predictor

NASA Astrophysics Data System (ADS)

Villeneuve, Pierre V.; Beaven, Scott G.; Stocker, Alan D.

2013-09-01

The application of compression to hyperspectral image data is a significant technical challenge. A primary bottleneck in disseminating data products to the tactical user community is the limited communication bandwidth between the airborne sensor and the ground station receiver. This report summarizes the newly-developed "Z-Chrome" algorithm for lossless compression of hyperspectral image data. A Wiener filter prediction framework is used as a basis for modeling new image bands from already-encoded bands. The resulting residual errors are then compressed using available state-of-the-art lossless image compression functions. Compression performance is demonstrated using a large number of test data collected over a wide variety of scene content from six different airborne and spaceborne sensors .

Determination of optimal parameters for dual-layer cathode of polymer electrolyte fuel cell using computational intelligence-aided design.

PubMed

Chen, Yi; Huang, Weina; Peng, Bei

2014-01-01

Because of the demands for sustainable and renewable energy, fuel cells have become increasingly popular, particularly the polymer electrolyte fuel cell (PEFC). Among the various components, the cathode plays a key role in the operation of a PEFC. In this study, a quantitative dual-layer cathode model was proposed for determining the optimal parameters that minimize the over-potential difference η and improve the efficiency using a newly developed bat swarm algorithm with a variable population embedded in the computational intelligence-aided design. The simulation results were in agreement with previously reported results, suggesting that the proposed technique has potential applications for automating and optimizing the design of PEFCs.

An approach to self-assembling swarm robots using multitree genetic programming.

PubMed

Lee, Jong-Hyun; Ahn, Chang Wook; An, Jinung

2013-01-01

In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach.

New numerical approximation of fractional derivative with non-local and non-singular kernel: Application to chaotic models

NASA Astrophysics Data System (ADS)

Toufik, Mekkaoui; Atangana, Abdon

2017-10-01

Recently a new concept of fractional differentiation with non-local and non-singular kernel was introduced in order to extend the limitations of the conventional Riemann-Liouville and Caputo fractional derivatives. A new numerical scheme has been developed, in this paper, for the newly established fractional differentiation. We present in general the error analysis. The new numerical scheme was applied to solve linear and non-linear fractional differential equations. We do not need a predictor-corrector to have an efficient algorithm, in this method. The comparison of approximate and exact solutions leaves no doubt believing that, the new numerical scheme is very efficient and converges toward exact solution very rapidly.

BayesMotif: de novo protein sorting motif discovery from impure datasets.

PubMed

Hu, Jianjun; Zhang, Fan

2010-01-18

Protein sorting is the process that newly synthesized proteins are transported to their target locations within or outside of the cell. This process is precisely regulated by protein sorting signals in different forms. A major category of sorting signals are amino acid sub-sequences usually located at the N-terminals or C-terminals of protein sequences. Genome-wide experimental identification of protein sorting signals is extremely time-consuming and costly. Effective computational algorithms for de novo discovery of protein sorting signals is needed to improve the understanding of protein sorting mechanisms. We formulated the protein sorting motif discovery problem as a classification problem and proposed a Bayesian classifier based algorithm (BayesMotif) for de novo identification of a common type of protein sorting motifs in which a highly conserved anchor is present along with a less conserved motif regions. A false positive removal procedure is developed to iteratively remove sequences that are unlikely to contain true motifs so that the algorithm can identify motifs from impure input sequences. Experiments on both implanted motif datasets and real-world datasets showed that the enhanced BayesMotif algorithm can identify anchored sorting motifs from pure or impure protein sequence dataset. It also shows that the false positive removal procedure can help to identify true motifs even when there is only 20% of the input sequences containing true motif instances. We proposed BayesMotif, a novel Bayesian classification based algorithm for de novo discovery of a special category of anchored protein sorting motifs from impure datasets. Compared to conventional motif discovery algorithms such as MEME, our algorithm can find less-conserved motifs with short highly conserved anchors. Our algorithm also has the advantage of easy incorporation of additional meta-sequence features such as hydrophobicity or charge of the motifs which may help to overcome the limitations of PWM (position weight matrix) motif model.

Computationally efficient algorithm for high sampling-frequency operation of active noise control

NASA Astrophysics Data System (ADS)

Rout, Nirmal Kumar; Das, Debi Prasad; Panda, Ganapati

2015-05-01

In high sampling-frequency operation of active noise control (ANC) system the length of the secondary path estimate and the ANC filter are very long. This increases the computational complexity of the conventional filtered-x least mean square (FXLMS) algorithm. To reduce the computational complexity of long order ANC system using FXLMS algorithm, frequency domain block ANC algorithms have been proposed in past. These full block frequency domain ANC algorithms are associated with some disadvantages such as large block delay, quantization error due to computation of large size transforms and implementation difficulties in existing low-end DSP hardware. To overcome these shortcomings, the partitioned block ANC algorithm is newly proposed where the long length filters in ANC are divided into a number of equal partitions and suitably assembled to perform the FXLMS algorithm in the frequency domain. The complexity of this proposed frequency domain partitioned block FXLMS (FPBFXLMS) algorithm is quite reduced compared to the conventional FXLMS algorithm. It is further reduced by merging one fast Fourier transform (FFT)-inverse fast Fourier transform (IFFT) combination to derive the reduced structure FPBFXLMS (RFPBFXLMS) algorithm. Computational complexity analysis for different orders of filter and partition size are presented. Systematic computer simulations are carried out for both the proposed partitioned block ANC algorithms to show its accuracy compared to the time domain FXLMS algorithm.

Equation Discovery for Model Identification in Respiratory Mechanics of the Mechanically Ventilated Human Lung

NASA Astrophysics Data System (ADS)

Ganzert, Steven; Guttmann, Josef; Steinmann, Daniel; Kramer, Stefan

Lung protective ventilation strategies reduce the risk of ventilator associated lung injury. To develop such strategies, knowledge about mechanical properties of the mechanically ventilated human lung is essential. This study was designed to develop an equation discovery system to identify mathematical models of the respiratory system in time-series data obtained from mechanically ventilated patients. Two techniques were combined: (i) the usage of declarative bias to reduce search space complexity and inherently providing the processing of background knowledge. (ii) A newly developed heuristic for traversing the hypothesis space with a greedy, randomized strategy analogical to the GSAT algorithm. In 96.8% of all runs the applied equation discovery system was capable to detect the well-established equation of motion model of the respiratory system in the provided data. We see the potential of this semi-automatic approach to detect more complex mathematical descriptions of the respiratory system from respiratory data.

Expanding the use of administrative claims databases in conducting clinical real-world evidence studies in multiple sclerosis.

PubMed

Capkun, Gorana; Lahoz, Raquel; Verdun, Elisabetta; Song, Xue; Chen, Weston; Korn, Jonathan R; Dahlke, Frank; Freitas, Rita; Fraeman, Kathy; Simeone, Jason; Johnson, Barbara H; Nordstrom, Beth

2015-05-01

Administrative claims databases provide a wealth of data for assessing the effect of treatments in clinical practice. Our aim was to propose methodology for real-world studies in multiple sclerosis (MS) using these databases. In three large US administrative claims databases: MarketScan, PharMetrics Plus and Department of Defense (DoD), patients with MS were selected using an algorithm identified in the published literature and refined for accuracy. Algorithms for detecting newly diagnosed ('incident') MS cases were also refined and tested. Methodology based on resource and treatment use was developed to differentiate between relapses with and without hospitalization. When various patient selection criteria were applied to the MarketScan database, an algorithm requiring two MS diagnoses at least 30 days apart was identified as the preferred method of selecting patient cohorts. Attempts to detect incident MS cases were confounded by the limited continuous enrollment of patients in these databases. Relapse detection algorithms identified similar proportions of patients in the MarketScan and PharMetrics Plus databases experiencing relapses with (2% in both databases) and without (15-20%) hospitalization in the 1 year follow-up period, providing findings in the range of those in the published literature. Additional validation of the algorithms proposed here would increase their credibility. The methods suggested in this study offer a good foundation for performing real-world research in MS using administrative claims databases, potentially allowing evidence from different studies to be compared and combined more systematically than in current research practice.

Arterial cannula shape optimization by means of the rotational firefly algorithm

NASA Astrophysics Data System (ADS)

Tesch, K.; Kaczorowska, K.

2016-03-01

This article presents global optimization results of arterial cannula shapes by means of the newly modified firefly algorithm. The search for the optimal arterial cannula shape is necessary in order to minimize losses and prepare the flow that leaves the circulatory support system of a ventricle (i.e. blood pump) before it reaches the heart. A modification of the standard firefly algorithm, the so-called rotational firefly algorithm, is introduced. It is shown that the rotational firefly algorithm allows for better exploration of search spaces which results in faster convergence and better solutions in comparison with its standard version. This is particularly pronounced for smaller population sizes. Furthermore, it maintains greater diversity of populations for a longer time. A small population size and a low number of iterations are necessary to keep to a minimum the computational cost of the objective function of the problem, which comes from numerical solution of the nonlinear partial differential equations. Moreover, both versions of the firefly algorithm are compared to the state of the art, namely the differential evolution and covariance matrix adaptation evolution strategies.

Methodology of automated ionosphere front velocity estimation for ground-based augmentation of GNSS

NASA Astrophysics Data System (ADS)

Bang, Eugene; Lee, Jiyun

2013-11-01

ionospheric anomalies occurring during severe ionospheric storms can pose integrity threats to Global Navigation Satellite System (GNSS) Ground-Based Augmentation Systems (GBAS). Ionospheric anomaly threat models for each region of operation need to be developed to analyze the potential impact of these anomalies on GBAS users and develop mitigation strategies. Along with the magnitude of ionospheric gradients, the speed of the ionosphere "fronts" in which these gradients are embedded is an important parameter for simulation-based GBAS integrity analysis. This paper presents a methodology for automated ionosphere front velocity estimation which will be used to analyze a vast amount of ionospheric data, build ionospheric anomaly threat models for different regions, and monitor ionospheric anomalies continuously going forward. This procedure automatically selects stations that show a similar trend of ionospheric delays, computes the orientation of detected fronts using a three-station-based trigonometric method, and estimates speeds for the front using a two-station-based method. It also includes fine-tuning methods to improve the estimation to be robust against faulty measurements and modeling errors. It demonstrates the performance of the algorithm by comparing the results of automated speed estimation to those manually computed previously. All speed estimates from the automated algorithm fall within error bars of ± 30% of the manually computed speeds. In addition, this algorithm is used to populate the current threat space with newly generated threat points. A larger number of velocity estimates helps us to better understand the behavior of ionospheric gradients under geomagnetic storm conditions.

TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction

PubMed Central

Gunasekara, Chathura; Zhang, Kui; Deng, Wenping; Brown, Laura

2018-01-01

Abstract Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene block. The TGMI calculated the MIM for each triple gene block and then examined its statistical significance using bootstrap. Finally, the frequencies of all TFs present in all significantly interacted triple gene blocks were calculated and ranked. We showed that the TFs with higher frequencies were usually genuine pathway regulators upon evaluating multiple pathways in plants, animals and yeast. Comparison of TGMI with several other algorithms demonstrated its higher accuracy. Therefore, TGMI will be a valuable tool that can help biologists to identify regulators of metabolic pathways and biological processes from the exploded high-throughput gene expression data in public repositories. PMID:29579312

Primary chromatic aberration elimination via optimization work with genetic algorithm

NASA Astrophysics Data System (ADS)

Wu, Bo-Wen; Liu, Tung-Kuan; Fang, Yi-Chin; Chou, Jyh-Horng; Tsai, Hsien-Lin; Chang, En-Hao

2008-09-01

Chromatic Aberration plays a part in modern optical systems, especially in digitalized and smart optical systems. Much effort has been devoted to eliminating specific chromatic aberration in order to match the demand for advanced digitalized optical products. Basically, the elimination of axial chromatic and lateral color aberration of an optical lens and system depends on the selection of optical glass. According to reports from glass companies all over the world, the number of various newly developed optical glasses in the market exceeds three hundred. However, due to the complexity of a practical optical system, optical designers have so far had difficulty in finding the right solution to eliminate small axial and lateral chromatic aberration except by the Damped Least Squares (DLS) method, which is limited in so far as the DLS method has not yet managed to find a better optical system configuration. In the present research, genetic algorithms are used to replace traditional DLS so as to eliminate axial and lateral chromatic, by combining the theories of geometric optics in Tessar type lenses and a technique involving Binary/Real Encoding, Multiple Dynamic Crossover and Random Gene Mutation to find a much better configuration for optical glasses. By implementing the algorithms outlined in this paper, satisfactory results can be achieved in eliminating axial and lateral color aberration.

[Computational chemistry in structure-based drug design].

PubMed

Cao, Ran; Li, Wei; Sun, Han-Zi; Zhou, Yu; Huang, Niu

2013-07-01

Today, the understanding of the sequence and structure of biologically relevant targets is growing rapidly and researchers from many disciplines, physics and computational science in particular, are making significant contributions to modern biology and drug discovery. However, it remains challenging to rationally design small molecular ligands with desired biological characteristics based on the structural information of the drug targets, which demands more accurate calculation of ligand binding free-energy. With the rapid advances in computer power and extensive efforts in algorithm development, physics-based computational chemistry approaches have played more important roles in structure-based drug design. Here we reviewed the newly developed computational chemistry methods in structure-based drug design as well as the elegant applications, including binding-site druggability assessment, large scale virtual screening of chemical database, and lead compound optimization. Importantly, here we address the current bottlenecks and propose practical solutions.

DNA-binding specificity prediction with FoldX.

PubMed

Nadra, Alejandro D; Serrano, Luis; Alibés, Andreu

2011-01-01

With the advent of Synthetic Biology, a field between basic science and applied engineering, new computational tools are needed to help scientists reach their goal, their design, optimizing resources. In this chapter, we present a simple and powerful method to either know the DNA specificity of a wild-type protein or design new specificities by using the protein design algorithm FoldX. The only basic requirement is having a good resolution structure of the complex. Protein-DNA interaction design may aid the development of new parts designed to be orthogonal, decoupled, and precise in its target. Further, it could help to fine-tune the systems in terms of specificity, discrimination, and binding constants. In the age of newly developed devices and invented systems, computer-aided engineering promises to be an invaluable tool. Copyright © 2011 Elsevier Inc. All rights reserved.

Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents

PubMed Central

Guo, Pi; Zeng, Fangfang; Hu, Xiaomin; Zhang, Dingmei; Zhu, Shuming; Deng, Yu; Hao, Yuantao

2015-01-01

Objectives In epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability. Material and methods Two improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents. Results The proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures. Conclusions The newly proposed procedures improve the identification of significant variables and enable us to derive a new insight into epidemiological association analysis. PMID:26214802

Machine Learning for Flood Prediction in Google Earth Engine

NASA Astrophysics Data System (ADS)

Kuhn, C.; Tellman, B.; Max, S. A.; Schwarz, B.

2015-12-01

With the increasing availability of high-resolution satellite imagery, dynamic flood mapping in near real time is becoming a reachable goal for decision-makers. This talk describes a newly developed framework for predicting biophysical flood vulnerability using public data, cloud computing and machine learning. Our objective is to define an approach to flood inundation modeling using statistical learning methods deployed in a cloud-based computing platform. Traditionally, static flood extent maps grounded in physically based hydrologic models can require hours of human expertise to construct at significant financial cost. In addition, desktop modeling software and limited local server storage can impose restraints on the size and resolution of input datasets. Data-driven, cloud-based processing holds promise for predictive watershed modeling at a wide range of spatio-temporal scales. However, these benefits come with constraints. In particular, parallel computing limits a modeler's ability to simulate the flow of water across a landscape, rendering traditional routing algorithms unusable in this platform. Our project pushes these limits by testing the performance of two machine learning algorithms, Support Vector Machine (SVM) and Random Forests, at predicting flood extent. Constructed in Google Earth Engine, the model mines a suite of publicly available satellite imagery layers to use as algorithm inputs. Results are cross-validated using MODIS-based flood maps created using the Dartmouth Flood Observatory detection algorithm. Model uncertainty highlights the difficulty of deploying unbalanced training data sets based on rare extreme events.

BiPACE 2D--graph-based multiple alignment for comprehensive 2D gas chromatography-mass spectrometry.

PubMed

Hoffmann, Nils; Wilhelm, Mathias; Doebbe, Anja; Niehaus, Karsten; Stoye, Jens

2014-04-01

Comprehensive 2D gas chromatography-mass spectrometry is an established method for the analysis of complex mixtures in analytical chemistry and metabolomics. It produces large amounts of data that require semiautomatic, but preferably automatic handling. This involves the location of significant signals (peaks) and their matching and alignment across different measurements. To date, there exist only a few openly available algorithms for the retention time alignment of peaks originating from such experiments that scale well with increasing sample and peak numbers, while providing reliable alignment results. We describe BiPACE 2D, an automated algorithm for retention time alignment of peaks from 2D gas chromatography-mass spectrometry experiments and evaluate it on three previously published datasets against the mSPA, SWPA and Guineu algorithms. We also provide a fourth dataset from an experiment studying the H2 production of two different strains of Chlamydomonas reinhardtii that is available from the MetaboLights database together with the experimental protocol, peak-detection results and manually curated multiple peak alignment for future comparability with newly developed algorithms. BiPACE 2D is contained in the freely available Maltcms framework, version 1.3, hosted at http://maltcms.sf.net, under the terms of the L-GPL v3 or Eclipse Open Source licenses. The software used for the evaluation along with the underlying datasets is available at the same location. The C.reinhardtii dataset is freely available at http://www.ebi.ac.uk/metabolights/MTBLS37.

Improved neural network based scene-adaptive nonuniformity correction method for infrared focal plane arrays.

PubMed

Lai, Rui; Yang, Yin-tang; Zhou, Duan; Li, Yue-jin

2008-08-20

An improved scene-adaptive nonuniformity correction (NUC) algorithm for infrared focal plane arrays (IRFPAs) is proposed. This method simultaneously estimates the infrared detectors' parameters and eliminates the nonuniformity causing fixed pattern noise (FPN) by using a neural network (NN) approach. In the learning process of neuron parameter estimation, the traditional LMS algorithm is substituted with the newly presented variable step size (VSS) normalized least-mean square (NLMS) based adaptive filtering algorithm, which yields faster convergence, smaller misadjustment, and lower computational cost. In addition, a new NN structure is designed to estimate the desired target value, which promotes the calibration precision considerably. The proposed NUC method reaches high correction performance, which is validated by the experimental results quantitatively tested with a simulative testing sequence and a real infrared image sequence.

An algorithm for hyperspectral remote sensing of aerosols: 1. Development of theoretical framework

NASA Astrophysics Data System (ADS)

Hou, Weizhen; Wang, Jun; Xu, Xiaoguang; Reid, Jeffrey S.; Han, Dong

2016-07-01

This paper describes the first part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from a newly developed hyperspectral instrument, the GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO), by taking full advantage of available hyperspectral measurement information in the visible bands. We describe the theoretical framework of an inversion algorithm for the hyperspectral remote sensing of the aerosol optical properties, in which major principal components (PCs) for surface reflectance is assumed known, and the spectrally dependent aerosol refractive indices are assumed to follow a power-law approximation with four unknown parameters (two for real and two for imaginary part of refractive index). New capabilities for computing the Jacobians of four Stokes parameters of reflected solar radiation at the top of the atmosphere with respect to these unknown aerosol parameters and the weighting coefficients for each PC of surface reflectance are added into the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM), which in turn facilitates the optimization in the inversion process. Theoretical derivations of the formulas for these new capabilities are provided, and the analytical solutions of Jacobians are validated against the finite-difference calculations with relative error less than 0.2%. Finally, self-consistency check of the inversion algorithm is conducted for the idealized green-vegetation and rangeland surfaces that were spectrally characterized by the U.S. Geological Survey digital spectral library. It shows that the first six PCs can yield the reconstruction of spectral surface reflectance with errors less than 1%. Assuming that aerosol properties can be accurately characterized, the inversion yields a retrieval of hyperspectral surface reflectance with an uncertainty of 2% (and root-mean-square error of less than 0.003), which suggests self-consistency in the inversion framework. The next step of using this framework to study the aerosol information content in GEO-TASO measurements is also discussed.

CRIMEtoYHU: a new web tool to develop yeast-based functional assays for characterizing cancer-associated missense variants.

PubMed

Mercatanti, Alberto; Lodovichi, Samuele; Cervelli, Tiziana; Galli, Alvaro

2017-12-01

Evaluation of the functional impact of cancer-associated missense variants is more difficult than for protein-truncating mutations and consequently standard guidelines for the interpretation of sequence variants have been recently proposed. A number of algorithms and software products were developed to predict the impact of cancer-associated missense mutations on protein structure and function. Importantly, direct assessment of the variants using high-throughput functional assays using simple genetic systems can help in speeding up the functional evaluation of newly identified cancer-associated variants. We developed the web tool CRIMEtoYHU (CTY) to help geneticists in the evaluation of the functional impact of cancer-associated missense variants. Humans and the yeast Saccharomyces cerevisiae share thousands of protein-coding genes although they have diverged for a billion years. Therefore, yeast humanization can be helpful in deciphering the functional consequences of human genetic variants found in cancer and give information on the pathogenicity of missense variants. To humanize specific positions within yeast genes, human and yeast genes have to share functional homology. If a mutation in a specific residue is associated with a particular phenotype in humans, a similar substitution in the yeast counterpart may reveal its effect at the organism level. CTY simultaneously finds yeast homologous genes, identifies the corresponding variants and determines the transferability of human variants to yeast counterparts by assigning a reliability score (RS) that may be predictive for the validity of a functional assay. CTY analyzes newly identified mutations or retrieves mutations reported in the COSMIC database, provides information about the functional conservation between yeast and human and shows the mutation distribution in human genes. CTY analyzes also newly found mutations and aborts when no yeast homologue is found. Then, on the basis of the protein domain localization and functional conservation between yeast and human, the selected variants are ranked by the RS. The RS is assigned by an algorithm that computes functional data, type of mutation, chemistry of amino acid substitution and the degree of mutation transferability between human and yeast protein. Mutations giving a positive RS are highly transferable to yeast and, therefore, yeast functional assays will be more predictable. To validate the web application, we have analyzed 8078 cancer-associated variants located in 31 genes that have a yeast homologue. More than 50% of variants are transferable to yeast. Incidentally, 88% of all transferable mutations have a reliability score >0. Moreover, we analyzed by CTY 72 functionally validated missense variants located in yeast genes at positions corresponding to the human cancer-associated variants. All these variants gave a positive RS. To further validate CTY, we analyzed 3949 protein variants (with positive RS) by the predictive algorithm PROVEAN. This analysis shows that yeast-based functional assays will be more predictable for the variants with positive RS. We believe that CTY could be an important resource for the cancer research community by providing information concerning the functional impact of specific mutations, as well as for the design of functional assays useful for decision support in precision medicine. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Human face detection using motion and color information

NASA Astrophysics Data System (ADS)

Kim, Yang-Gyun; Bang, Man-Won; Park, Soon-Young; Choi, Kyoung-Ho; Hwang, Jeong-Hyun

2008-02-01

In this paper, we present a hardware implementation of a face detector for surveillance applications. To come up with a computationally cheap and fast algorithm with minimal memory requirement, motion and skin color information are fused successfully. More specifically, a newly appeared object is extracted first by comparing average Hue and Saturation values of background image and a current image. Then, the result of skin color filtering of the current image is combined with the result of a newly appeared object. Finally, labeling is performed to locate a true face region. The proposed system is implemented on Altera Cyclone2 using Quartus II 6.1 and ModelSim 6.1. For hardware description language (HDL), Verilog-HDL is used.

Using the ATL HDI 1000 to collect demodulated RF data for monitoring HIFU lesion formation

NASA Astrophysics Data System (ADS)

Anand, Ajay; Kaczkowski, Peter J.; Daigle, Ron E.; Huang, Lingyun; Paun, Marla; Beach, Kirk W.; Crum, Lawrence A.

2003-05-01

The ability to accurately track and monitor the progress of lesion formation during HIFU (High Intensity Focused Ultrasound) therapy is important for the success of HIFU-based treatment protocols. To aid in the development of algorithms for accurately targeting and monitoring formation of HIFU induced lesions, we have developed a software system to perform RF data acquisition during HIFU therapy using a commercially available clinical ultrasound scanner (ATL HDI 1000, Philips Medical Systems, Bothell, WA). The HDI 1000 scanner functions on a software dominant architecture, permitting straightforward external control of its operation and relatively easy access to quadrature demodulated RF data. A PC running a custom developed program sends control signals to the HIFU module via GPIB and to the HDI 1000 via Telnet, alternately interleaving HIFU exposures and RF frame acquisitions. The system was tested during experiments in which HIFU lesions were created in excised animal tissue. No crosstalk between the HIFU beam and the ultrasound imager was detected, thus demonstrating synchronization. Newly developed acquisition modes allow greater user control in setting the image geometry and scanline density, and enables high frame rate acquisition. This system facilitates rapid development of signal-processing based HIFU therapy monitoring algorithms and their implementation in image-guided thermal therapy systems. In addition, the HDI 1000 system can be easily customized for use with other emerging imaging modalities that require access to the RF data such as elastographic methods and new Doppler-based imaging and tissue characterization techniques.

View generation for 3D-TV using image reconstruction from irregularly spaced samples

NASA Astrophysics Data System (ADS)

Vázquez, Carlos

2007-02-01

Three-dimensional television (3D-TV) will become the next big step in the development of advanced TV systems. One of the major challenges for the deployment of 3D-TV systems is the diversity of display technologies and the high cost of capturing multi-view content. Depth image-based rendering (DIBR) has been identified as a key technology for the generation of new views for stereoscopic and multi-view displays from a small number of views captured and transmitted. We propose a disparity compensation method for DIBR that does not require spatial interpolation of the disparity map. We use a forward-mapping disparity compensation with real precision. The proposed method deals with the irregularly sampled image resulting from this disparity compensation process by applying a re-sampling algorithm based on a bi-cubic spline function space that produces smooth images. The fact that no approximation is made on the position of the samples implies that geometrical distortions in the final images due to approximations in sample positions are minimized. We also paid attention to the occlusion problem. Our algorithm detects the occluded regions in the newly generated images and uses simple depth-aware inpainting techniques to fill the gaps created by newly exposed areas. We tested the proposed method in the context of generation of views needed for viewing on SynthaGram TM auto-stereoscopic displays. We used as input either a 2D image plus a depth map or a stereoscopic pair with the associated disparity map. Our results show that this technique provides high quality images to be viewed on different display technologies such as stereoscopic viewing with shutter glasses (two views) and lenticular auto-stereoscopic displays (nine views).

Vibrational algorithms for quantitative crystallographic analyses of hydroxyapatite-based biomaterials: II, application to decayed human teeth.

PubMed

Adachi, Tetsuya; Pezzotti, Giuseppe; Yamamoto, Toshiro; Ichioka, Hiroaki; Boffelli, Marco; Zhu, Wenliang; Kanamura, Narisato

2015-05-01

A systematic investigation, based on highly spectrally resolved Raman spectroscopy, was undertaken to research the efficacy of vibrational assessments in locating chemical and crystallographic fingerprints for the characterization of dental caries and the early detection of non-cavitated carious lesions. Raman results published by other authors have indicated possible approaches for this method. However, they conspicuously lacked physical insight at the molecular scale and, thus, the rigor necessary to prove the efficacy of this spectroscopy method. After solving basic physical challenges in a companion paper, we apply them here in the form of newly developed Raman algorithms for practical dental research. Relevant differences in mineral crystallite (average) orientation and texture distribution were revealed for diseased enamel at different stages compared with healthy mineralized enamel. Clear spectroscopy features could be directly translated in terms of a rigorous and quantitative classification of crystallography and chemical characteristics of diseased enamel structures. The Raman procedure enabled us to trace back otherwise invisible characteristics in early caries, in the translucent zone (i.e., the advancing front of the disease) and in the body of lesion of cavitated caries.

A computer code for multiphase all-speed transient flows in complex geometries. MAST version 1.0

NASA Technical Reports Server (NTRS)

Chen, C. P.; Jiang, Y.; Kim, Y. M.; Shang, H. M.

1991-01-01

The operation of the MAST code, which computes transient solutions to the multiphase flow equations applicable to all-speed flows, is described. Two-phase flows are formulated based on the Eulerian-Lagrange scheme in which the continuous phase is described by the Navier-Stokes equation (or Reynolds equations for turbulent flows). Dispersed phase is formulated by a Lagrangian tracking scheme. The numerical solution algorithms utilized for fluid flows is a newly developed pressure-implicit algorithm based on the operator-splitting technique in generalized nonorthogonal coordinates. This operator split allows separate operation on each of the variable fields to handle pressure-velocity coupling. The obtained pressure correction equation has the hyperbolic nature and is effective for Mach numbers ranging from the incompressible limit to supersonic flow regimes. The present code adopts a nonstaggered grid arrangement; thus, the velocity components and other dependent variables are collocated at the same grid. A sequence of benchmark-quality problems, including incompressible, subsonic, transonic, supersonic, gas-droplet two-phase flows, as well as spray-combustion problems, were performed to demonstrate the robustness and accuracy of the present code.

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

Yu, Kuang; Libisch, Florian; Carter, Emily A., E-mail: eac@princeton.edu

We report a new implementation of the density functional embedding theory (DFET) in the VASP code, using the projector-augmented-wave (PAW) formalism. Newly developed algorithms allow us to efficiently perform optimized effective potential optimizations within PAW. The new algorithm generates robust and physically correct embedding potentials, as we verified using several test systems including a covalently bound molecule, a metal surface, and bulk semiconductors. We show that with the resulting embedding potential, embedded cluster models can reproduce the electronic structure of point defects in bulk semiconductors, thereby demonstrating the validity of DFET in semiconductors for the first time. Compared to ourmore » previous version, the new implementation of DFET within VASP affords use of all features of VASP (e.g., a systematic PAW library, a wide selection of functionals, a more flexible choice of U correction formalisms, and faster computational speed) with DFET. Furthermore, our results are fairly robust with respect to both plane-wave and Gaussian type orbital basis sets in the embedded cluster calculations. This suggests that the density functional embedding method is potentially an accurate and efficient way to study properties of isolated defects in semiconductors.« less

A positional misalignment correction method for Fourier ptychographic microscopy based on simulated annealing

NASA Astrophysics Data System (ADS)

Sun, Jiasong; Zhang, Yuzhen; Chen, Qian; Zuo, Chao

2017-02-01

Fourier ptychographic microscopy (FPM) is a newly developed super-resolution technique, which employs angularly varying illuminations and a phase retrieval algorithm to surpass the diffraction limit of a low numerical aperture (NA) objective lens. In current FPM imaging platforms, accurate knowledge of LED matrix's position is critical to achieve good recovery quality. Furthermore, considering such a wide field-of-view (FOV) in FPM, different regions in the FOV have different sensitivity of LED positional misalignment. In this work, we introduce an iterative method to correct position errors based on the simulated annealing (SA) algorithm. To improve the efficiency of this correcting process, large number of iterations for several images with low illumination NAs are firstly implemented to estimate the initial values of the global positional misalignment model through non-linear regression. Simulation and experimental results are presented to evaluate the performance of the proposed method and it is demonstrated that this method can both improve the quality of the recovered object image and relax the LED elements' position accuracy requirement while aligning the FPM imaging platforms.

Improved non-local electron thermal transport model for two-dimensional radiation hydrodynamics simulations

NASA Astrophysics Data System (ADS)

Cao, Duc; Moses, Gregory; Delettrez, Jacques

2015-08-01

An implicit, non-local thermal conduction algorithm based on the algorithm developed by Schurtz, Nicolai, and Busquet (SNB) [Schurtz et al., Phys. Plasmas 7, 4238 (2000)] for non-local electron transport is presented and has been implemented in the radiation-hydrodynamics code DRACO. To study the model's effect on DRACO's predictive capability, simulations of shot 60 303 from OMEGA are completed using the iSNB model, and the computed shock speed vs. time is compared to experiment. Temperature outputs from the iSNB model are compared with the non-local transport model of Goncharov et al. [Phys. Plasmas 13, 012702 (2006)]. Effects on adiabat are also examined in a polar drive surrogate simulation. Results show that the iSNB model is not only capable of flux-limitation but also preheat prediction while remaining numerically robust and sacrificing little computational speed. Additionally, the results provide strong incentive to further modify key parameters within the SNB theory, namely, the newly introduced non-local mean free path. This research was supported by the Laboratory for Laser Energetics of the University of Rochester.

Enhancement and Validation of an Arab Surname Database

PubMed Central

Schwartz, Kendra; Beebani, Ganj; Sedki, Mai; Tahhan, Mamon; Ruterbusch, Julie J.

2015-01-01

Objectives Arab Americans constitute a large, heterogeneous, and quickly growing subpopulation in the United States. Health statistics for this group are difficult to find because US governmental offices do not recognize Arab as separate from white. The development and validation of an Arab- and Chaldean-American name database will enhance research efforts in this population subgroup. Methods A previously validated name database was supplemented with newly identified names gathered primarily from vital statistic records and then evaluated using a multistep process. This process included 1) review by 4 Arabic- and Chaldean-speaking reviewers, 2) ethnicity assessment by social media searches, and 3) self-report of ancestry obtained from a telephone survey. Results Our Arab- and Chaldean-American name algorithm has a positive predictive value of 91% and a negative predictive value of 100%. Conclusions This enhanced name database and algorithm can be used to identify Arab Americans in health statistics data, such as cancer and hospital registries, where they are often coded as white, to determine the extent of health disparities in this population. PMID:24625771

Model parameter estimation approach based on incremental analysis for lithium-ion batteries without using open circuit voltage

NASA Astrophysics Data System (ADS)

Wu, Hongjie; Yuan, Shifei; Zhang, Xi; Yin, Chengliang; Ma, Xuerui

2015-08-01

To improve the suitability of lithium-ion battery model under varying scenarios, such as fluctuating temperature and SoC variation, dynamic model with parameters updated realtime should be developed. In this paper, an incremental analysis-based auto regressive exogenous (I-ARX) modeling method is proposed to eliminate the modeling error caused by the OCV effect and improve the accuracy of parameter estimation. Then, its numerical stability, modeling error, and parametric sensitivity are analyzed at different sampling rates (0.02, 0.1, 0.5 and 1 s). To identify the model parameters recursively, a bias-correction recursive least squares (CRLS) algorithm is applied. Finally, the pseudo random binary sequence (PRBS) and urban dynamic driving sequences (UDDSs) profiles are performed to verify the realtime performance and robustness of the newly proposed model and algorithm. Different sampling rates (1 Hz and 10 Hz) and multiple temperature points (5, 25, and 45 °C) are covered in our experiments. The experimental and simulation results indicate that the proposed I-ARX model can present high accuracy and suitability for parameter identification without using open circuit voltage.

Stochastic modelling of turbulent combustion for design optimization of gas turbine combustors

NASA Astrophysics Data System (ADS)

Mehanna Ismail, Mohammed Ali

The present work covers the development and the implementation of an efficient algorithm for the design optimization of gas turbine combustors. The purpose is to explore the possibilities and indicate constructive suggestions for optimization techniques as alternative methods for designing gas turbine combustors. The algorithm is general to the extent that no constraints are imposed on the combustion phenomena or on the combustor configuration. The optimization problem is broken down into two elementary problems: the first is the optimum search algorithm, and the second is the turbulent combustion model used to determine the combustor performance parameters. These performance parameters constitute the objective and physical constraints in the optimization problem formulation. The examination of both turbulent combustion phenomena and the gas turbine design process suggests that the turbulent combustion model represents a crucial part of the optimization algorithm. The basic requirements needed for a turbulent combustion model to be successfully used in a practical optimization algorithm are discussed. In principle, the combustion model should comply with the conflicting requirements of high fidelity, robustness and computational efficiency. To that end, the problem of turbulent combustion is discussed and the current state of the art of turbulent combustion modelling is reviewed. According to this review, turbulent combustion models based on the composition PDF transport equation are found to be good candidates for application in the present context. However, these models are computationally expensive. To overcome this difficulty, two different models based on the composition PDF transport equation were developed: an improved Lagrangian Monte Carlo composition PDF algorithm and the generalized stochastic reactor model. Improvements in the Lagrangian Monte Carlo composition PDF model performance and its computational efficiency were achieved through the implementation of time splitting, variable stochastic fluid particle mass control, and a second order time accurate (predictor-corrector) scheme used for solving the stochastic differential equations governing the particles evolution. The model compared well against experimental data found in the literature for two different configurations: bluff body and swirl stabilized combustors. The generalized stochastic reactor is a newly developed model. This model relies on the generalization of the concept of the classical stochastic reactor theory in the sense that it accounts for both finite micro- and macro-mixing processes. (Abstract shortened by UMI.)

Explore GPM IMERG and Other Global Precipitation Products with GES DISC GIOVANNI

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, Dana M.; Vollmer, Bruce; MacRitchie, Kyle; Kempler, Steven

2015-01-01

New features and capabilities in the newly released GIOVANNI allow exploring GPM IMERG (Integrated Multi-satelliE Retrievals for GPM) Early, Late and Final Run global half-hourly and monthly precipitation products as well as other precipitation products distributed by the GES DISC such as TRMM Multi-Satellite Precipitation Analysis (TMPA), MERRA (Modern Era Retrospective-Analysis for Research and Applications), NLDAS (North American Land Data Assimilation Systems), GLDAS (Global Land Data Assimilation Systems), etc. GIOVANNI is a web-based tool developed by the GES DISC (Goddard Earth Sciences and Data Information Services Center) to visualize and analyze Earth science data without having to download data and software. The new interface in GIOVANNI allows searching and filtering precipitation products from different NASA missions and projects and expands the capabilities to inter-compare different precipitation products in one interface. Knowing differences in precipitation products is important to identify issues in retrieval algorithms, biases, uncertainties, etc. Due to different formats, data structures, units and so on, it is not easy to inter-compare precipitation products. Newly added features and capabilities (unit conversion, regridding, etc.) in GIOVANNI make inter-comparisons possible. In this presentation, we will describe these new features and capabilities along with examples.

Numerical studies of various Néel-VBS transitions in SU(N) anti-ferromagnets

NASA Astrophysics Data System (ADS)

Kaul, Ribhu K.; Block, Matthew S.

2015-09-01

In this manuscript we review recent developments in the numerical simulations of bipartite SU(N) spin models by quantum Monte Carlo (QMC) methods. We provide an account of a large family of newly discovered sign-problem free spin models which can be simulated in their ground states on large lattices, containing O(105) spins, using the stochastic series expansion method with efficient loop algorithms. One of the most important applications so far of these Hamiltonians are to unbiased studies of quantum criticality between Neel and valence bond phases in two dimensions - a summary of this body of work is provided. The article concludes with an overview of the current status of and outlook for future studies of the “designer” Hamiltonians.

Determination of Optimal Parameters for Dual-Layer Cathode of Polymer Electrolyte Fuel Cell Using Computational Intelligence-Aided Design

PubMed Central

Chen, Yi; Huang, Weina; Peng, Bei

2014-01-01

Because of the demands for sustainable and renewable energy, fuel cells have become increasingly popular, particularly the polymer electrolyte fuel cell (PEFC). Among the various components, the cathode plays a key role in the operation of a PEFC. In this study, a quantitative dual-layer cathode model was proposed for determining the optimal parameters that minimize the over-potential difference and improve the efficiency using a newly developed bat swarm algorithm with a variable population embedded in the computational intelligence-aided design. The simulation results were in agreement with previously reported results, suggesting that the proposed technique has potential applications for automating and optimizing the design of PEFCs. PMID:25490761

Reverberant acoustic energy in auditoria that comprise systems of coupled rooms

NASA Astrophysics Data System (ADS)

Summers, Jason Erik

A frequency-dependent model for levels and decay rates of reverberant energy in systems of coupled rooms is developed and compared with measurements conducted in a 1:10 scale model and in Bass Hall, Fort Worth, TX. Schroeder frequencies of subrooms, fSch, characteristic size of coupling apertures, a, relative to wavelength lambda, and characteristic size of room surfaces, l, relative to lambda define the frequency regions. At high frequencies [HF (f >> f Sch, a >> lambda, l >> lambda)], this work improves upon prior statistical-acoustics (SA) coupled-ODE models by incorporating geometrical-acoustics (GA) corrections for the model of decay within subrooms and the model of energy transfer between subrooms. Previous researchers developed prediction algorithms based on computational GA. Comparisons of predictions derived from beam-axis tracing with scale-model measurements indicate that systematic errors for coupled rooms result from earlier tail-correction procedures that assume constant quadratic growth of reflection density. A new algorithm is developed that uses ray tracing rather than tail correction in the late part and is shown to correct this error. At midfrequencies [MF (f >> f Sch, a ˜ lambda)], HF models are modified to account for wave effects at coupling apertures by including analytically or heuristically derived power transmission coefficients tau. This work improves upon prior SA models of this type by developing more accurate estimates of random-incidence tau. While the accuracy of the MF models is difficult to verify, scale-model measurements evidence the expected behavior. The Biot-Tolstoy-Medwin-Svensson (BTMS) time-domain edge-diffraction model is newly adapted to study transmission through apertures. Multiple-order BTMS scattering is theoretically and experimentally shown to be inaccurate due to the neglect of slope diffraction. At low frequencies (f ˜ f Sch), scale-model measurements have been qualitatively explained by application of previously developed perturbation models. Measurements newly confirm that coupling strength between three-dimensional rooms is related to unperturbed pressure distribution on the coupling surface. In Bass Hall, measurements are conducted to determine the acoustical effects of the coupled stage house on stage and in the audience area. The high-frequency predictions of statistical- and geometrical-acoustics models agree well with measured results. Predictions of the transmission coefficients of the coupling apertures agree, at least qualitatively, with the observed behavior.

Solar Field Optical Characterization at Stillwater Geothermal/Solar Hybrid Plant

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

Zhu, Guangdong; Turchi, Craig

Concentrating solar power (CSP) can provide additional thermal energy to boost geothermal plant power generation. For a newly constructed solar field at a geothermal power plant site, it is critical to properly characterize its performance so that the prediction of thermal power generation can be derived to develop an optimum operating strategy for a hybrid system. In the past, laboratory characterization of a solar collector has often extended into the solar field performance model and has been used to predict the actual solar field performance, disregarding realistic impacting factors. In this work, an extensive measurement on mirror slope error andmore » receiver position error has been performed in the field by using the optical characterization tool called Distant Observer (DO). Combining a solar reflectance sampling procedure, a newly developed solar characterization program called FirstOPTIC and public software for annual performance modeling called System Advisor Model (SAM), a comprehensive solar field optical characterization has been conducted, thus allowing for an informed prediction of solar field annual performance. The paper illustrates this detailed solar field optical characterization procedure and demonstrates how the results help to quantify an appropriate tracking-correction strategy to improve solar field performance. In particular, it is found that an appropriate tracking-offset algorithm can improve the solar field performance by about 15%. The work here provides a valuable reference for the growing CSP industry.« less

Solar Field Optical Characterization at Stillwater Geothermal/Solar Hybrid Plant

DOE PAGES

Zhu, Guangdong; Turchi, Craig

2017-01-27

Concentrating solar power (CSP) can provide additional thermal energy to boost geothermal plant power generation. For a newly constructed solar field at a geothermal power plant site, it is critical to properly characterize its performance so that the prediction of thermal power generation can be derived to develop an optimum operating strategy for a hybrid system. In the past, laboratory characterization of a solar collector has often extended into the solar field performance model and has been used to predict the actual solar field performance, disregarding realistic impacting factors. In this work, an extensive measurement on mirror slope error andmore » receiver position error has been performed in the field by using the optical characterization tool called Distant Observer (DO). Combining a solar reflectance sampling procedure, a newly developed solar characterization program called FirstOPTIC and public software for annual performance modeling called System Advisor Model (SAM), a comprehensive solar field optical characterization has been conducted, thus allowing for an informed prediction of solar field annual performance. The paper illustrates this detailed solar field optical characterization procedure and demonstrates how the results help to quantify an appropriate tracking-correction strategy to improve solar field performance. In particular, it is found that an appropriate tracking-offset algorithm can improve the solar field performance by about 15%. The work here provides a valuable reference for the growing CSP industry.« less

Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

PubMed

Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

2014-12-01

Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). I. Automatic search for galaxy-scale strong lenses

NASA Astrophysics Data System (ADS)

Sonnenfeld, Alessandro; Chan, James H. H.; Shu, Yiping; More, Anupreeta; Oguri, Masamune; Suyu, Sherry H.; Wong, Kenneth C.; Lee, Chien-Hsiu; Coupon, Jean; Yonehara, Atsunori; Bolton, Adam S.; Jaelani, Anton T.; Tanaka, Masayuki; Miyazaki, Satoshi; Komiyama, Yutaka

2018-01-01

The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) is an excellent survey for the search for strong lenses, thanks to its area, image quality, and depth. We use three different methods to look for lenses among 43000 luminous red galaxies from the Baryon Oscillation Spectroscopic Survey (BOSS) sample with photometry from the S16A internal data release of the HSC-SSP. The first method is a newly developed algorithm, named YATTALENS, which looks for arc-like features around massive galaxies and then estimates the likelihood of an object being a lens by performing a lens model fit. The second method, CHITAH, is a modeling-based algorithm originally developed to look for lensed quasars. The third method makes use of spectroscopic data to look for emission lines from objects at a different redshift from that of the main galaxy. We find 15 definite lenses, 36 highly probable lenses, and 282 possible lenses. Among the three methods, YATTALENS, which was developed specifically for this study, performs best in terms of both completeness and purity. Nevertheless, five highly probable lenses were missed by YATTALENS but found by the other two methods, indicating that the three methods are highly complementary. Based on these numbers, we expect to find ˜300 definite or probable lenses by the end of the HSC-SSP.

Evaluation of Machine Learning Algorithms for Classification of Primary Biological Aerosol using a new UV-LIF spectrometer

NASA Astrophysics Data System (ADS)

Ruske, S. T.; Topping, D. O.; Foot, V. E.; Kaye, P. H.; Stanley, W. R.; Morse, A. P.; Crawford, I.; Gallagher, M. W.

2016-12-01

Characterisation of bio-aerosols has important implications within Environment and Public Health sectors. Recent developments in Ultra-Violet Light Induced Fluorescence (UV-LIF) detectors such as the Wideband Integrated bio-aerosol Spectrometer (WIBS) and the newly introduced Multiparameter bio-aerosol Spectrometer (MBS) has allowed for the real time collection of fluorescence, size and morphology measurements for the purpose of discriminating between bacteria, fungal Spores and pollen. This new generation of instruments has enabled ever-larger data sets to be compiled with the aim of studying more complex environments, yet the algorithms used for specie classification remain largely invalidated. It is therefore imperative that we validate the performance of different algorithms that can be used for the task of classification, which is the focus of this study. For unsupervised learning we test Hierarchical Agglomerative Clustering with various different linkages. For supervised learning, ten methods were tested; including decision trees, ensemble methods: Random Forests, Gradient Boosting and AdaBoost; two implementations for support vector machines: libsvm and liblinear; Gaussian methods: Gaussian naïve Bayesian, quadratic and linear discriminant analysis and finally the k-nearest neighbours algorithm. The methods were applied to two different data sets measured using a new Multiparameter bio-aerosol Spectrometer. We find that clustering, in general, performs slightly worse than the supervised learning methods correctly classifying, at best, only 72.7 and 91.1 percent for the two data sets. For supervised learning the gradient boosting algorithm was found to be the most effective, on average correctly classifying 88.1 and 97.8 percent of the testing data respectively across the two data sets. We discuss the wider relevance of these results with regards to challenging existing classification in real-world environments.

Comparing classification methods for diffuse reflectance spectra to improve tissue specific laser surgery.

PubMed

Engelhardt, Alexander; Kanawade, Rajesh; Knipfer, Christian; Schmid, Matthias; Stelzle, Florian; Adler, Werner

2014-07-16

In the field of oral and maxillofacial surgery, newly developed laser scalpels have multiple advantages over traditional metal scalpels. However, they lack haptic feedback. This is dangerous near e.g. nerve tissue, which has to be preserved during surgery. One solution to this problem is to train an algorithm that analyzes the reflected light spectra during surgery and can classify these spectra into different tissue types, in order to ultimately send a warning or temporarily switch off the laser when critical tissue is about to be ablated. Various machine learning algorithms are available for this task, but a detailed analysis is needed to assess the most appropriate algorithm. In this study, a small data set is used to simulate many larger data sets according to a multivariate Gaussian distribution. Various machine learning algorithms are then trained and evaluated on these data sets. The algorithms' performance is subsequently evaluated and compared by averaged confusion matrices and ultimately by boxplots of misclassification rates. The results are validated on the smaller, experimental data set. Most classifiers have a median misclassification rate below 0.25 in the simulated data. The most notable performance was observed for the Penalized Discriminant Analysis, with a misclassifiaction rate of 0.00 in the simulated data, and an average misclassification rate of 0.02 in a 10-fold cross validation on the original data. The results suggest a Penalized Discriminant Analysis is the most promising approach, most probably because it considers the functional, correlated nature of the reflectance spectra.The results of this study improve the accuracy of real-time tissue discrimination and are an essential step towards improving the safety of oral laser surgery.

Continuous Change Detection and Classification (CCDC) of Land Cover Using All Available Landsat Data

NASA Astrophysics Data System (ADS)

Zhu, Z.; Woodcock, C. E.

2012-12-01

A new algorithm for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data is developed. This new algorithm is capable of detecting many kinds of land cover change as new images are collected and at the same time provide land cover maps for any given time. To better identify land cover change, a two step cloud, cloud shadow, and snow masking algorithm is used for eliminating "noisy" observations. Next, a time series model that has components of seasonality, trend, and break estimates the surface reflectance and temperature. The time series model is updated continuously with newly acquired observations. Due to the high variability in spectral response for different kinds of land cover change, the CCDC algorithm uses a data-driven threshold derived from all seven Landsat bands. When the difference between observed and predicted exceeds the thresholds three consecutive times, a pixel is identified as land cover change. Land cover classification is done after change detection. Coefficients from the time series models and the Root Mean Square Error (RMSE) from model fitting are used as classification inputs for the Random Forest Classifier (RFC). We applied this new algorithm for one Landsat scene (Path 12 Row 31) that includes all of Rhode Island as well as much of Eastern Massachusetts and parts of Connecticut. A total of 532 Landsat images acquired between 1982 and 2011 were processed. During this period, 619,924 pixels were detected to change once (91% of total changed pixels) and 60,199 pixels were detected to change twice (8% of total changed pixels). The most frequent land cover change category is from mixed forest to low density residential which occupies more than 8% of total land cover change pixels.

Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms.

PubMed

Ozcift, Akin; Gulten, Arif

2011-12-01

Improving accuracies of machine learning algorithms is vital in designing high performance computer-aided diagnosis (CADx) systems. Researches have shown that a base classifier performance might be enhanced by ensemble classification strategies. In this study, we construct rotation forest (RF) ensemble classifiers of 30 machine learning algorithms to evaluate their classification performances using Parkinson's, diabetes and heart diseases from literature. While making experiments, first the feature dimension of three datasets is reduced using correlation based feature selection (CFS) algorithm. Second, classification performances of 30 machine learning algorithms are calculated for three datasets. Third, 30 classifier ensembles are constructed based on RF algorithm to assess performances of respective classifiers with the same disease data. All the experiments are carried out with leave-one-out validation strategy and the performances of the 60 algorithms are evaluated using three metrics; classification accuracy (ACC), kappa error (KE) and area under the receiver operating characteristic (ROC) curve (AUC). Base classifiers succeeded 72.15%, 77.52% and 84.43% average accuracies for diabetes, heart and Parkinson's datasets, respectively. As for RF classifier ensembles, they produced average accuracies of 74.47%, 80.49% and 87.13% for respective diseases. RF, a newly proposed classifier ensemble algorithm, might be used to improve accuracy of miscellaneous machine learning algorithms to design advanced CADx systems. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

PENTACLE: Parallelized particle-particle particle-tree code for planet formation

NASA Astrophysics Data System (ADS)

Iwasawa, Masaki; Oshino, Shoichi; Fujii, Michiko S.; Hori, Yasunori

2017-10-01

We have newly developed a parallelized particle-particle particle-tree code for planet formation, PENTACLE, which is a parallelized hybrid N-body integrator executed on a CPU-based (super)computer. PENTACLE uses a fourth-order Hermite algorithm to calculate gravitational interactions between particles within a cut-off radius and a Barnes-Hut tree method for gravity from particles beyond. It also implements an open-source library designed for full automatic parallelization of particle simulations, FDPS (Framework for Developing Particle Simulator), to parallelize a Barnes-Hut tree algorithm for a memory-distributed supercomputer. These allow us to handle 1-10 million particles in a high-resolution N-body simulation on CPU clusters for collisional dynamics, including physical collisions in a planetesimal disc. In this paper, we show the performance and the accuracy of PENTACLE in terms of \\tilde{R}_cut and a time-step Δt. It turns out that the accuracy of a hybrid N-body simulation is controlled through Δ t / \\tilde{R}_cut and Δ t / \\tilde{R}_cut ˜ 0.1 is necessary to simulate accurately the accretion process of a planet for ≥106 yr. For all those interested in large-scale particle simulations, PENTACLE, customized for planet formation, will be freely available from https://github.com/PENTACLE-Team/PENTACLE under the MIT licence.

Synchronization Algorithms for Co-Simulation of Power Grid and Communication Networks

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

Ciraci, Selim; Daily, Jeffrey A.; Agarwal, Khushbu

2014-09-11

The ongoing modernization of power grids consists of integrating them with communication networks in order to achieve robust and resilient control of grid operations. To understand the operation of the new smart grid, one approach is to use simulation software. Unfortunately, current power grid simulators at best utilize inadequate approximations to simulate communication networks, if at all. Cooperative simulation of specialized power grid and communication network simulators promises to more accurately reproduce the interactions of real smart grid deployments. However, co-simulation is a challenging problem. A co-simulation must manage the exchange of informa- tion, including the synchronization of simulator clocks,more » between all simulators while maintaining adequate computational perfor- mance. This paper describes two new conservative algorithms for reducing the overhead of time synchronization, namely Active Set Conservative and Reactive Conservative. We provide a detailed analysis of their performance characteristics with respect to the current state of the art including both conservative and optimistic synchronization algorithms. In addition, we provide guidelines for selecting the appropriate synchronization algorithm based on the requirements of the co-simulation. The newly proposed algorithms are shown to achieve as much as 14% and 63% im- provement, respectively, over the existing conservative algorithm.« less

Development of the VS-50 as an Intermediate Step Towards LM-1

NASA Astrophysics Data System (ADS)

Ettl, J.; Kirchhartz, R.; Hrbud, I.; Basken, R.; Raith, G.; Hecht, M.; de Almeide, F. A.; Roda, E. D.

2015-09-01

The VS-50 launch vehicle is the designated intermediate development step of the VLM-1. The VLM-1 launch system is a joint venture between the research center for space DCTAIIAE in Brazil and the German Aerospace Center (DLR) in Germany. Development highlights are application of carbon fiber technologies for the S50 motor case and interstage adaptor, use of fiberglass for the fairing, newly developed thrust vector assembly (TVA) consisting of commercial components, unique navigation system encompassing two IMUs, a GPS receiver, and adaptive control algorithms guiding the vehicle. The VS-50 is a two-stage vehicle using S50 and S44 motors. The development of the VS-50 serves two major purposes: First, VS-SO represents a technological development stage in the VLM-1 development roadmap, and second, it serves as a carrier for scientific payloads. Potential payloads are aerodynamic probes for yielding scientific aero-dynamic and thermo-dynamic data sets at regimes up to 18 Mach. Further, the VS-50 could be used for re-entry research and investigation of re-usable flight objectives.

Solid, liquid, and interfacial properties of TiAl alloys: parameterization of a new modified embedded atom method model

NASA Astrophysics Data System (ADS)

Sun, Shoutian; Ramu Ramachandran, Bala; Wick, Collin D.

2018-02-01

New interatomic potentials for pure Ti and Al, and binary TiAl were developed utilizing the second nearest neighbour modified embedded-atom method (MEAM) formalism. The potentials were parameterized to reproduce multiple properties spanning bulk solids, solid surfaces, solid/liquid phase changes, and liquid interfacial properties. This was carried out using a newly developed optimization procedure that combined the simple minimization of a fitness function with a genetic algorithm to efficiently span the parameter space. The resulting MEAM potentials gave good agreement with experimental and DFT solid and liquid properties, and reproduced the melting points for Ti, Al, and TiAl. However, the surface tensions from the model consistently underestimated experimental values. Liquid TiAl’s surface was found to be mostly covered with Al atoms, showing that Al has a significant propensity for the liquid/air interface.

Solid, liquid, and interfacial properties of TiAl alloys: parameterization of a new modified embedded atom method model.

PubMed

Sun, Shoutian; Ramachandran, Bala Ramu; Wick, Collin D

2018-02-21

New interatomic potentials for pure Ti and Al, and binary TiAl were developed utilizing the second nearest neighbour modified embedded-atom method (MEAM) formalism. The potentials were parameterized to reproduce multiple properties spanning bulk solids, solid surfaces, solid/liquid phase changes, and liquid interfacial properties. This was carried out using a newly developed optimization procedure that combined the simple minimization of a fitness function with a genetic algorithm to efficiently span the parameter space. The resulting MEAM potentials gave good agreement with experimental and DFT solid and liquid properties, and reproduced the melting points for Ti, Al, and TiAl. However, the surface tensions from the model consistently underestimated experimental values. Liquid TiAl's surface was found to be mostly covered with Al atoms, showing that Al has a significant propensity for the liquid/air interface.

The role of adhesins in bacteria motility modification

NASA Astrophysics Data System (ADS)

Conrad, Jacinta; Gibiansky, Maxsim; Jin, Fan; Gordon, Vernita; Motto, Dominick; Shrout, Joshua; Parsek, Matthew; Wong, Gerard

2010-03-01

Bacterial biofilms are multicellular communities responsible for a broad range of infections. To investigate the early-stage formation of biofilms, we have developed high-throughput techniques to quantify the motility of surface-associated bacteria. We translate microscopy movies of bacteria into a searchable database of trajectories using tracking algorithms adapted from colloidal physics. By analyzing the motion of both wild-type (WT) and isogenic knockout mutants, we have previously characterized fundamental motility mechanisms in P. aeruginosa. Here, we develop biometric routines to recognize signatures of adhesion and trapping. We find that newly attached bacteria move faster than previously adherent bacteria, and are more likely to be oriented out-of-plane. Motility appendages influence the bacterium's ability to become trapped: WT bacteria exhibit two types of trapped trajectories, whereas flagella-deficient bacteria rarely become trapped. These results suggest that flagella play a key role in adhesion.

A partial Hamiltonian approach for current value Hamiltonian systems

NASA Astrophysics Data System (ADS)

Naz, R.; Mahomed, F. M.; Chaudhry, Azam

2014-10-01

We develop a partial Hamiltonian framework to obtain reductions and closed-form solutions via first integrals of current value Hamiltonian systems of ordinary differential equations (ODEs). The approach is algorithmic and applies to many state and costate variables of the current value Hamiltonian. However, we apply the method to models with one control, one state and one costate variable to illustrate its effectiveness. The current value Hamiltonian systems arise in economic growth theory and other economic models. We explain our approach with the help of a simple illustrative example and then apply it to two widely used economic growth models: the Ramsey model with a constant relative risk aversion (CRRA) utility function and Cobb Douglas technology and a one-sector AK model of endogenous growth are considered. We show that our newly developed systematic approach can be used to deduce results given in the literature and also to find new solutions.

FPGA Coprocessor for Accelerated Classification of Images

NASA Technical Reports Server (NTRS)

Pingree, Paula J.; Scharenbroich, Lucas J.; Werne, Thomas A.

2008-01-01

An effort related to that described in the preceding article focuses on developing a spaceborne processing platform for fast and accurate onboard classification of image data, a critical part of modern satellite image processing. The approach again has been to exploit the versatility of recently developed hybrid Virtex-4FX field-programmable gate array (FPGA) to run diverse science applications on embedded processors while taking advantage of the reconfigurable hardware resources of the FPGAs. In this case, the FPGA serves as a coprocessor that implements legacy C-language support-vector-machine (SVM) image-classification algorithms to detect and identify natural phenomena such as flooding, volcanic eruptions, and sea-ice break-up. The FPGA provides hardware acceleration for increased onboard processing capability than previously demonstrated in software. The original C-language program demonstrated on an imaging instrument aboard the Earth Observing-1 (EO-1) satellite implements a linear-kernel SVM algorithm for classifying parts of the images as snow, water, ice, land, or cloud or unclassified. Current onboard processors, such as on EO-1, have limited computing power, extremely limited active storage capability and are no longer considered state-of-the-art. Using commercially available software that translates C-language programs into hardware description language (HDL) files, the legacy C-language program, and two newly formulated programs for a more capable expanded-linear-kernel and a more accurate polynomial-kernel SVM algorithm, have been implemented in the Virtex-4FX FPGA. In tests, the FPGA implementations have exhibited significant speedups over conventional software implementations running on general-purpose hardware.

Classification and assessment tools for structural motif discovery algorithms.

PubMed

Badr, Ghada; Al-Turaiki, Isra; Mathkour, Hassan

2013-01-01

Motif discovery is the problem of finding recurring patterns in biological data. Patterns can be sequential, mainly when discovered in DNA sequences. They can also be structural (e.g. when discovering RNA motifs). Finding common structural patterns helps to gain a better understanding of the mechanism of action (e.g. post-transcriptional regulation). Unlike DNA motifs, which are sequentially conserved, RNA motifs exhibit conservation in structure, which may be common even if the sequences are different. Over the past few years, hundreds of algorithms have been developed to solve the sequential motif discovery problem, while less work has been done for the structural case. In this paper, we survey, classify, and compare different algorithms that solve the structural motif discovery problem, where the underlying sequences may be different. We highlight their strengths and weaknesses. We start by proposing a benchmark dataset and a measurement tool that can be used to evaluate different motif discovery approaches. Then, we proceed by proposing our experimental setup. Finally, results are obtained using the proposed benchmark to compare available tools. To the best of our knowledge, this is the first attempt to compare tools solely designed for structural motif discovery. Results show that the accuracy of discovered motifs is relatively low. The results also suggest a complementary behavior among tools where some tools perform well on simple structures, while other tools are better for complex structures. We have classified and evaluated the performance of available structural motif discovery tools. In addition, we have proposed a benchmark dataset with tools that can be used to evaluate newly developed tools.

Active structural acoustic control of helicopter interior multifrequency noise using input-output-based hybrid control

NASA Astrophysics Data System (ADS)

Ma, Xunjun; Lu, Yang; Wang, Fengjiao

2017-09-01

This paper presents the recent advances in reduction of multifrequency noise inside helicopter cabin using an active structural acoustic control system, which is based on active gearbox struts technical approach. To attenuate the multifrequency gearbox vibrations and resulting noise, a new scheme of discrete model predictive sliding mode control has been proposed based on controlled auto-regressive moving average model. Its implementation only needs input/output data, hence a broader frequency range of controlled system is modelled and the burden on the state observer design is released. Furthermore, a new iteration form of the algorithm is designed, improving the developing efficiency and run speed. To verify the algorithm's effectiveness and self-adaptability, experiments of real-time active control are performed on a newly developed helicopter model system. The helicopter model can generate gear meshing vibration/noise similar to a real helicopter with specially designed gearbox and active struts. The algorithm's control abilities are sufficiently checked by single-input single-output and multiple-input multiple-output experiments via different feedback strategies progressively: (1) control gear meshing noise through attenuating vibrations at the key points on the transmission path, (2) directly control the gear meshing noise in the cabin using the actuators. Results confirm that the active control system is practical for cancelling multifrequency helicopter interior noise, which also weakens the frequency-modulation of the tones. For many cases, the attenuations of the measured noise exceed the level of 15 dB, with maximum reduction reaching 31 dB. Also, the control process is demonstrated to be smoother and faster.

A universal algorithm for an improved finite element mesh generation Mesh quality assessment in comparison to former automated mesh-generators and an analytic model.

PubMed

Kaminsky, Jan; Rodt, Thomas; Gharabaghi, Alireza; Forster, Jan; Brand, Gerd; Samii, Madjid

2005-06-01

The FE-modeling of complex anatomical structures is not solved satisfyingly so far. Voxel-based as opposed to contour-based algorithms allow an automated mesh generation based on the image data. Nonetheless their geometric precision is limited. We developed an automated mesh-generator that combines the advantages of voxel-based generation with improved representation of the geometry by displacement of nodes on the object-surface. Models of an artificial 3D-pipe-section and a skullbase were generated with different mesh-densities using the newly developed geometric, unsmoothed and smoothed voxel generators. Compared to the analytic calculation of the 3D-pipe-section model the normalized RMS error of the surface stress was 0.173-0.647 for the unsmoothed voxel models, 0.111-0.616 for the smoothed voxel models with small volume error and 0.126-0.273 for the geometric models. The highest element-energy error as a criterion for the mesh quality was 2.61x10(-2) N mm, 2.46x10(-2) N mm and 1.81x10(-2) N mm for unsmoothed, smoothed and geometric voxel models, respectively. The geometric model of the 3D-skullbase resulted in the lowest element-energy error and volume error. This algorithm also allowed the best representation of anatomical details. The presented geometric mesh-generator is universally applicable and allows an automated and accurate modeling by combining the advantages of the voxel-technique and of improved surface-modeling.

Development and evaluation of thermal model reduction algorithms for spacecraft

NASA Astrophysics Data System (ADS)

Deiml, Michael; Suderland, Martin; Reiss, Philipp; Czupalla, Markus

2015-05-01

This paper is concerned with the topic of the reduction of thermal models of spacecraft. The work presented here has been conducted in cooperation with the company OHB AG, formerly Kayser-Threde GmbH, and the Institute of Astronautics at Technische Universität München with the goal to shorten and automatize the time-consuming and manual process of thermal model reduction. The reduction of thermal models can be divided into the simplification of the geometry model for calculation of external heat flows and radiative couplings and into the reduction of the underlying mathematical model. For simplification a method has been developed which approximates the reduced geometry model with the help of an optimization algorithm. Different linear and nonlinear model reduction techniques have been evaluated for their applicability in reduction of the mathematical model. Thereby the compatibility with the thermal analysis tool ESATAN-TMS is of major concern, which restricts the useful application of these methods. Additional model reduction methods have been developed, which account to these constraints. The Matrix Reduction method allows the approximation of the differential equation to reference values exactly expect for numerical errors. The summation method enables a useful, applicable reduction of thermal models that can be used in industry. In this work a framework for model reduction of thermal models has been created, which can be used together with a newly developed graphical user interface for the reduction of thermal models in industry.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

PubMed Central

Kim, Eun-Youn; Kim, Seon-Young; Ashlock, Daniel; Nam, Dougu

2009-01-01

Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. Results We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. Conclusion The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors. PMID:19698124

Tree-based solvers for adaptive mesh refinement code FLASH - I: gravity and optical depths

NASA Astrophysics Data System (ADS)

Wünsch, R.; Walch, S.; Dinnbier, F.; Whitworth, A.

2018-04-01

We describe an OctTree algorithm for the MPI parallel, adaptive mesh refinement code FLASH, which can be used to calculate the gas self-gravity, and also the angle-averaged local optical depth, for treating ambient diffuse radiation. The algorithm communicates to the different processors only those parts of the tree that are needed to perform the tree-walk locally. The advantage of this approach is a relatively low memory requirement, important in particular for the optical depth calculation, which needs to process information from many different directions. This feature also enables a general tree-based radiation transport algorithm that will be described in a subsequent paper, and delivers excellent scaling up to at least 1500 cores. Boundary conditions for gravity can be either isolated or periodic, and they can be specified in each direction independently, using a newly developed generalization of the Ewald method. The gravity calculation can be accelerated with the adaptive block update technique by partially re-using the solution from the previous time-step. Comparison with the FLASH internal multigrid gravity solver shows that tree-based methods provide a competitive alternative, particularly for problems with isolated or mixed boundary conditions. We evaluate several multipole acceptance criteria (MACs) and identify a relatively simple approximate partial error MAC which provides high accuracy at low computational cost. The optical depth estimates are found to agree very well with those of the RADMC-3D radiation transport code, with the tree-solver being much faster. Our algorithm is available in the standard release of the FLASH code in version 4.0 and later.

3D landmarking in multiexpression face analysis: a preliminary study on eyebrows and mouth.

PubMed

Vezzetti, Enrico; Marcolin, Federica

2014-08-01

The application of three-dimensional (3D) facial analysis and landmarking algorithms in the field of maxillofacial surgery and other medical applications, such as diagnosis of diseases by facial anomalies and dysmorphism, has gained a lot of attention. In a previous work, we used a geometric approach to automatically extract some 3D facial key points, called landmarks, working in the differential geometry domain, through the coefficients of fundamental forms, principal curvatures, mean and Gaussian curvatures, derivatives, shape and curvedness indexes, and tangent map. In this article we describe the extension of our previous landmarking algorithm, which is now able to extract eyebrows and mouth landmarks using both old and new meshes. The algorithm has been tested on our face database and on the public Bosphorus 3D database. We chose to work on the mouth and eyebrows as a separate study because of the role that these parts play in facial expressions. In fact, since the mouth is the part of the face that moves the most and affects mainly facial expressions, extracting mouth landmarks from various facial poses means that the newly developed algorithm is pose-independent. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266 .

Adaptive Particle Swarm Optimizer with Varying Acceleration Coefficients for Finding the Most Stable Conformer of Small Molecules.

PubMed

Agrawal, Shikha; Silakari, Sanjay; Agrawal, Jitendra

2015-11-01

A novel parameter automation strategy for Particle Swarm Optimization called APSO (Adaptive PSO) is proposed. The algorithm is designed to efficiently control the local search and convergence to the global optimum solution. Parameters c1 controls the impact of the cognitive component on the particle trajectory and c2 controls the impact of the social component. Instead of fixing the value of c1 and c2 , this paper updates the value of these acceleration coefficients by considering time variation of evaluation function along with varying inertia weight factor in PSO. Here the maximum and minimum value of evaluation function is use to gradually decrease and increase the value of c1 and c2 respectively. Molecular energy minimization is one of the most challenging unsolved problems and it can be formulated as a global optimization problem. The aim of the present paper is to investigate the effect of newly developed APSO on the highly complex molecular potential energy function and to check the efficiency of the proposed algorithm to find the global minimum of the function under consideration. The proposed algorithm APSO is therefore applied in two cases: Firstly, for the minimization of a potential energy of small molecules with up to 100 degrees of freedom and finally for finding the global minimum energy conformation of 1,2,3-trichloro-1-flouro-propane molecule based on a realistic potential energy function. The computational results of all the cases show that the proposed method performs significantly better than the other algorithms. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An adaptive algorithm for the detection of microcalcifications in simulated low-dose mammography.

PubMed

Treiber, O; Wanninger, F; Führ, H; Panzer, W; Regulla, D; Winkler, G

2003-02-21

This paper uses the task of microcalcification detection as a benchmark problem to assess the potential for dose reduction in x-ray mammography. We present the results of a newly developed algorithm for detection of microcalcifications as a case study for a typical commercial film-screen system (Kodak Min-R 2000/2190). The first part of the paper deals with the simulation of dose reduction for film-screen mammography based on a physical model of the imaging process. Use of a more sensitive film-screen system is expected to result in additional smoothing of the image. We introduce two different models of that behaviour, called moderate and strong smoothing. We then present an adaptive, model-based microcalcification detection algorithm. Comparing detection results with ground-truth images obtained under the supervision of an expert radiologist allows us to establish the soundness of the detection algorithm. We measure the performance on the dose-reduced images in order to assess the loss of information due to dose reduction. It turns out that the smoothing behaviour has a strong influence on detection rates. For moderate smoothing. a dose reduction by 25% has no serious influence on the detection results. whereas a dose reduction by 50% already entails a marked deterioration of the performance. Strong smoothing generally leads to an unacceptable loss of image quality. The test results emphasize the impact of the more sensitive film-screen system and its characteristics on the problem of assessing the potential for dose reduction in film-screen mammography. The general approach presented in the paper can be adapted to fully digital mammography.

An adaptive algorithm for the detection of microcalcifications in simulated low-dose mammography

NASA Astrophysics Data System (ADS)

Treiber, O.; Wanninger, F.; Führ, H.; Panzer, W.; Regulla, D.; Winkler, G.

2003-02-01

This paper uses the task of microcalcification detection as a benchmark problem to assess the potential for dose reduction in x-ray mammography. We present the results of a newly developed algorithm for detection of microcalcifications as a case study for a typical commercial film-screen system (Kodak Min-R 2000/2190). The first part of the paper deals with the simulation of dose reduction for film-screen mammography based on a physical model of the imaging process. Use of a more sensitive film-screen system is expected to result in additional smoothing of the image. We introduce two different models of that behaviour, called moderate and strong smoothing. We then present an adaptive, model-based microcalcification detection algorithm. Comparing detection results with ground-truth images obtained under the supervision of an expert radiologist allows us to establish the soundness of the detection algorithm. We measure the performance on the dose-reduced images in order to assess the loss of information due to dose reduction. It turns out that the smoothing behaviour has a strong influence on detection rates. For moderate smoothing, a dose reduction by 25% has no serious influence on the detection results, whereas a dose reduction by 50% already entails a marked deterioration of the performance. Strong smoothing generally leads to an unacceptable loss of image quality. The test results emphasize the impact of the more sensitive film-screen system and its characteristics on the problem of assessing the potential for dose reduction in film-screen mammography. The general approach presented in the paper can be adapted to fully digital mammography.

Improved tissue assignment using dual-energy computed tomography in low-dose rate prostate brachytherapy for Monte Carlo dose calculation

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

Côté, Nicolas; Bedwani, Stéphane; Carrier, Jean-François, E-mail: jean-francois.carrier.chum@ssss.gouv.qc.ca

Purpose: An improvement in tissue assignment for low-dose rate brachytherapy (LDRB) patients using more accurate Monte Carlo (MC) dose calculation was accomplished with a metallic artifact reduction (MAR) method specific to dual-energy computed tomography (DECT). Methods: The proposed MAR algorithm followed a four-step procedure. The first step involved applying a weighted blend of both DECT scans (I {sub H/L}) to generate a new image (I {sub Mix}). This action minimized Hounsfield unit (HU) variations surrounding the brachytherapy seeds. In the second step, the mean HU of the prostate in I {sub Mix} was calculated and shifted toward the mean HUmore » of the two original DECT images (I {sub H/L}). The third step involved smoothing the newly shifted I {sub Mix} and the two original I {sub H/L}, followed by a subtraction of both, generating an image that represented the metallic artifact (I {sub A,(H/L)}) of reduced noise levels. The final step consisted of subtracting the original I {sub H/L} from the newly generated I {sub A,(H/L)} and obtaining a final image corrected for metallic artifacts. Following the completion of the algorithm, a DECT stoichiometric method was used to extract the relative electronic density (ρ{sub e}) and effective atomic number (Z {sub eff}) at each voxel of the corrected scans. Tissue assignment could then be determined with these two newly acquired physical parameters. Each voxel was assigned the tissue bearing the closest resemblance in terms of ρ{sub e} and Z {sub eff}, comparing with values from the ICRU 42 database. A MC study was then performed to compare the dosimetric impacts of alternative MAR algorithms. Results: An improvement in tissue assignment was observed with the DECT MAR algorithm, compared to the single-energy computed tomography (SECT) approach. In a phantom study, tissue misassignment was found to reach 0.05% of voxels using the DECT approach, compared with 0.40% using the SECT method. Comparison of the DECT and SECT D {sub 90} dose parameter (volume receiving 90% of the dose) indicated that D {sub 90} could be underestimated by up to 2.3% using the SECT method. Conclusions: The DECT MAR approach is a simple alternative to reduce metallic artifacts found in LDRB patient scans. Images can be processed quickly and do not require the determination of x-ray spectra. Substantial information on density and atomic number can also be obtained. Furthermore, calcifications within the prostate are detected by the tissue assignment algorithm. This enables more accurate, patient-specific MC dose calculations.« less

MRMAide: a mixed resolution modeling aide

NASA Astrophysics Data System (ADS)

Treshansky, Allyn; McGraw, Robert M.

2002-07-01

The Mixed Resolution Modeling Aide (MRMAide) technology is an effort to semi-automate the implementation of Mixed Resolution Modeling (MRM). MRMAide suggests ways of resolving differences in fidelity and resolution across diverse modeling paradigms. The goal of MRMAide is to provide a technology that will allow developers to incorporate model components into scenarios other than those for which they were designed. Currently, MRM is implemented by hand. This is a tedious, error-prone, and non-portable process. MRMAide, in contrast, will automatically suggest to a developer where and how to connect different components and/or simulations. MRMAide has three phases of operation: pre-processing, data abstraction, and validation. During pre-processing the components to be linked together are evaluated in order to identify appropriate mapping points. During data abstraction those mapping points are linked via data abstraction algorithms. During validation developers receive feedback regarding their newly created models relative to existing baselined models. The current work presents an overview of the various problems encountered during MRM and the various technologies utilized by MRMAide to overcome those problems.

Grand canonical validation of the bipartite international trade network.

PubMed

Straka, Mika J; Caldarelli, Guido; Saracco, Fabio

2017-08-01

Devising strategies for economic development in a globally competitive landscape requires a solid and unbiased understanding of countries' technological advancements and similarities among export products. Both can be addressed through the bipartite representation of the International Trade Network. In this paper, we apply the recently proposed grand canonical projection algorithm to uncover country and product communities. Contrary to past endeavors, our methodology, based on information theory, creates monopartite projections in an unbiased and analytically tractable way. Single links between countries or products represent statistically significant signals, which are not accounted for by null models such as the bipartite configuration model. We find stable country communities reflecting the socioeconomic distinction in developed, newly industrialized, and developing countries. Furthermore, we observe product clusters based on the aforementioned country groups. Our analysis reveals the existence of a complicated structure in the bipartite International Trade Network: apart from the diversification of export baskets from the most basic to the most exclusive products, we observe a statistically significant signal of an export specialization mechanism towards more sophisticated products.

Grand canonical validation of the bipartite international trade network

NASA Astrophysics Data System (ADS)

Straka, Mika J.; Caldarelli, Guido; Saracco, Fabio

2017-08-01

Devising strategies for economic development in a globally competitive landscape requires a solid and unbiased understanding of countries' technological advancements and similarities among export products. Both can be addressed through the bipartite representation of the International Trade Network. In this paper, we apply the recently proposed grand canonical projection algorithm to uncover country and product communities. Contrary to past endeavors, our methodology, based on information theory, creates monopartite projections in an unbiased and analytically tractable way. Single links between countries or products represent statistically significant signals, which are not accounted for by null models such as the bipartite configuration model. We find stable country communities reflecting the socioeconomic distinction in developed, newly industrialized, and developing countries. Furthermore, we observe product clusters based on the aforementioned country groups. Our analysis reveals the existence of a complicated structure in the bipartite International Trade Network: apart from the diversification of export baskets from the most basic to the most exclusive products, we observe a statistically significant signal of an export specialization mechanism towards more sophisticated products.

Optimal Wastewater Loading under Conflicting Goals and Technology Limitations in a Riverine System.

PubMed

Rafiee, Mojtaba; Lyon, Steve W; Zahraie, Banafsheh; Destouni, Georgia; Jaafarzadeh, Nemat

2017-03-01

  This paper investigates a novel simulation-optimization (S-O) framework for identifying optimal treatment levels and treatment processes for multiple wastewater dischargers to rivers. A commonly used water quality simulation model, Qual2K, was linked to a Genetic Algorithm optimization model for exploration of relevant fuzzy objective-function formulations for addressing imprecision and conflicting goals of pollution control agencies and various dischargers. Results showed a dynamic flow dependence of optimal wastewater loading with good convergence to near global optimum. Explicit considerations of real-world technological limitations, which were developed here in a new S-O framework, led to better compromise solutions between conflicting goals than those identified within traditional S-O frameworks. The newly developed framework, in addition to being more technologically realistic, is also less complicated and converges on solutions more rapidly than traditional frameworks. This technique marks a significant step forward for development of holistic, riverscape-based approaches that balance the conflicting needs of the stakeholders.

A new method for recognizing quadric surfaces from range data and its application to telerobotics and automation, final phase

NASA Technical Reports Server (NTRS)

Mielke, Roland; Dcunha, Ivan; Alvertos, Nicolas

1994-01-01

In the final phase of the proposed research a complete top to down three dimensional object recognition scheme has been proposed. The various three dimensional objects included spheres, cones, cylinders, ellipsoids, paraboloids, and hyperboloids. Utilizing a newly developed blob determination technique, a given range scene with several non-cluttered quadric surfaces is segmented. Next, using the earlier (phase 1) developed alignment scheme, each of the segmented objects are then aligned in a desired coordinate system. For each of the quadric surfaces based upon their intersections with certain pre-determined planes, a set of distinct features (curves) are obtained. A database with entities such as the equations of the planes and angular bounds of these planes has been created for each of the quadric surfaces. Real range data of spheres, cones, cylinders, and parallelpipeds have been utilized for the recognition process. The developed algorithm gave excellent results for the real data as well as for several sets of simulated range data.

An ensemble-based algorithm for optimizing the configuration of an in situ soil moisture monitoring network

NASA Astrophysics Data System (ADS)

De Vleeschouwer, Niels; Verhoest, Niko E. C.; Gobeyn, Sacha; De Baets, Bernard; Verwaeren, Jan; Pauwels, Valentijn R. N.

2015-04-01

The continuous monitoring of soil moisture in a permanent network can yield an interesting data product for use in hydrological modeling. Major advantages of in situ observations compared to remote sensing products are the potential vertical extent of the measurements, the smaller temporal resolution of the observation time series, the smaller impact of land cover variability on the observation bias, etc. However, two major disadvantages are the typically small integration volume of in situ measurements, and the often large spacing between monitoring locations. This causes only a small part of the modeling domain to be directly observed. Furthermore, the spatial configuration of the monitoring network is typically non-dynamic in time. Generally, e.g. when applying data assimilation, maximizing the observed information under given circumstances will lead to a better qualitative and quantitative insight of the hydrological system. It is therefore advisable to perform a prior analysis in order to select those monitoring locations which are most predictive for the unobserved modeling domain. This research focuses on optimizing the configuration of a soil moisture monitoring network in the catchment of the Bellebeek, situated in Belgium. A recursive algorithm, strongly linked to the equations of the Ensemble Kalman Filter, has been developed to select the most predictive locations in the catchment. The basic idea behind the algorithm is twofold. On the one hand a minimization of the modeled soil moisture ensemble error covariance between the different monitoring locations is intended. This causes the monitoring locations to be as independent as possible regarding the modeled soil moisture dynamics. On the other hand, the modeled soil moisture ensemble error covariance between the monitoring locations and the unobserved modeling domain is maximized. The latter causes a selection of monitoring locations which are more predictive towards unobserved locations. The main factors that will influence the outcome of the algorithm are the following: the choice of the hydrological model, the uncertainty model applied for ensemble generation, the general wetness of the catchment during which the error covariance is computed, etc. In this research the influence of the latter two is examined more in-depth. Furthermore, the optimal network configuration resulting from the newly developed algorithm is compared to network configurations obtained by two other algorithms. The first algorithm is based on a temporal stability analysis of the modeled soil moisture in order to identify catchment representative monitoring locations with regard to average conditions. The second algorithm involves the clustering of available spatially distributed data (e.g. land cover and soil maps) that is not obtained by hydrological modeling.

Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds

PubMed Central

Virshup, Aaron M.; Contreras-García, Julia; Wipf, Peter; Yang, Weitao; Beratan, David N.

2013-01-01

The “small molecule universe” (SMU), the set of all synthetically feasible organic molecules of 500 Daltons molecular weight or less, is estimated to contain over 1060 structures, making exhaustive searches for structures of interest impractical. Here, we describe the construction of a “representative universal library” spanning the SMU that samples the full extent of feasible small molecule chemistries. This library was generated using the newly developed Algorithm for Chemical Space Exploration with Stochastic Search (ACSESS). ACSESS makes two important contributions to chemical space exploration: it allows the systematic search of the unexplored regions of the small molecule universe, and it facilitates the mining of chemical libraries that do not yet exist, providing a near-infinite source of diverse novel compounds. PMID:23548177

A novel ECG data compression method based on adaptive Fourier decomposition

NASA Astrophysics Data System (ADS)

Tan, Chunyu; Zhang, Liming

2017-12-01

This paper presents a novel electrocardiogram (ECG) compression method based on adaptive Fourier decomposition (AFD). AFD is a newly developed signal decomposition approach, which can decompose a signal with fast convergence, and hence reconstruct ECG signals with high fidelity. Unlike most of the high performance algorithms, our method does not make use of any preprocessing operation before compression. Huffman coding is employed for further compression. Validated with 48 ECG recordings of MIT-BIH arrhythmia database, the proposed method achieves the compression ratio (CR) of 35.53 and the percentage root mean square difference (PRD) of 1.47% on average with N = 8 decomposition times and a robust PRD-CR relationship. The results demonstrate that the proposed method has a good performance compared with the state-of-the-art ECG compressors.

The endpoint detection technique for deep submicrometer plasma etching

NASA Astrophysics Data System (ADS)

Wang, Wei; Du, Zhi-yun; Zeng, Yong; Lan, Zhong-went

2009-07-01

The availability of reliable optical sensor technology provides opportunities to better characterize and control plasma etching processes in real time, they could play a important role in endpoint detection, fault diagnostics and processes feedback control and so on. The optical emission spectroscopy (OES) method becomes deficient in the case of deep submicrometer gate etching. In the newly developed high density inductively coupled plasma (HD-ICP) etching system, Interferometry endpoint (IEP) is introduced to get the EPD. The IEP fringe count algorithm is investigated to predict the end point, and then its signal is used to control etching rate and to call end point with OES signal in over etching (OE) processes step. The experiment results show that IEP together with OES provide extra process control margin for advanced device with thinner gate oxide.

Detecting Shielded Special Nuclear Materials Using Multi-Dimensional Neutron Source and Detector Geometries

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Molecular genetics and genomics progress in urothelial bladder cancer.

PubMed

Netto, George J

2013-11-01

The clinical management of solid tumor patients has recently undergone a paradigm shift as the result of the accelerated advances in cancer genetics and genomics. Molecular diagnostics is now an integral part of routine clinical management in lung, colon, and breast cancer patients. In a disappointing contrast, molecular biomarkers remain largely excluded from current management algorithms of urologic malignancies. The need for new treatment alternatives and validated prognostic molecular biomarkers that can help clinicians identify patients in need of early aggressive management is pressing. Identifying robust predictive biomarkers that can stratify response to newly introduced targeted therapeutics is another crucially needed development. The following is a brief discussion of some promising candidate biomarkers that may soon become a part of clinical management of bladder cancers. © 2013 Published by Elsevier Inc.

Preliminary Study on Prevalence and Associated Factors with Sarcopenia in a Geriatric Hospitalized Rehabilitation Setting.

PubMed

Pongpipatpaiboon, K; Kondo, I; Onogi, K; Mori, S; Ozaki, K; Osawa, A; Matsuo, H; Itoh, N; Tanimoto, M

2018-01-01

The reported prevalence of sarcopenia has shown a wide range, crucially based on the diagnostic criteria and setting. This cross-sectional study evaluated the prevalence of sarcopenia and sought to identify factors associated with sarcopenia on admission in a specialized geriatric rehabilitation setting based on the newly developed the Asian Working Group for Sarcopenia algorithm. Among 87 participants (mean age, 76.05 ± 7.57 years), 35 (40.2%) were classified as showing sarcopenia on admission. Prevalence was high, particularly among participants ≥80 years old, with tendencies toward lower body mass index, smoking habit, lower cognitive function, and greater functional impairment compared with the non-sarcopenic group. Identification of sarcopenia in elderly patients before rehabilitation and consideration of risk factors may prove helpful in achieving rehabilitation outcomes.

Gemtuzumab ozogamicin for the treatment of acute myeloid leukemia.

PubMed

Baron, Jeffrey; Wang, Eunice S

2018-06-11

Gemtuzumab ozogamicin (GO) is an antibody-drug conjugate consisting of a monoclonal antibody targeting CD33 linked to a cytotoxic derivative of calicheamicin. Despite the known clinical efficacy in relapsed/refractory acute myeloid leukemia (AML), GO was withdrawn from the market in 2010 due to increased early deaths witnessed in newly diagnosed AML patients receiving GO + intensive chemotherapy. In 2017, new data on the clinical efficacy and safety of GO administered on a fractionated-dosing schedule led to re-approval for newly diagnosed and relapsed/refractory AML. Areas covered: Addition of fractionated GO to chemotherapy significantly improved event-free survival of newly diagnosed AML patients with favorable and intermediate cytogenetic-risk disease. GO monotherapy also prolonged survival in newly diagnosed unfit patients and relapse-free survival in relapsed/refractory AML. This new dosing schedule was associated with decreased incidence of hepatotoxicity, veno-occlusive disease, and early mortality. Expert commentary: GO represents the first drug-antibody conjugate approved (twice) in the United States for AML. Its re-emergence adds a valuable agent back into the armamentarium for AML. The approval of GO as well as three other agents for AML in 2017 highlights the need for rapid cytogenetic and molecular characterization of AML and incorporation into new treatment algorithms.

Scoring best-worst data in unbalanced many-item designs, with applications to crowdsourcing semantic judgments.

PubMed

Hollis, Geoff

2018-04-01

Best-worst scaling is a judgment format in which participants are presented with a set of items and have to choose the superior and inferior items in the set. Best-worst scaling generates a large quantity of information per judgment because each judgment allows for inferences about the rank value of all unjudged items. This property of best-worst scaling makes it a promising judgment format for research in psychology and natural language processing concerned with estimating the semantic properties of tens of thousands of words. A variety of different scoring algorithms have been devised in the previous literature on best-worst scaling. However, due to problems of computational efficiency, these scoring algorithms cannot be applied efficiently to cases in which thousands of items need to be scored. New algorithms are presented here for converting responses from best-worst scaling into item scores for thousands of items (many-item scoring problems). These scoring algorithms are validated through simulation and empirical experiments, and considerations related to noise, the underlying distribution of true values, and trial design are identified that can affect the relative quality of the derived item scores. The newly introduced scoring algorithms consistently outperformed scoring algorithms used in the previous literature on scoring many-item best-worst data.

Identification of Disease Critical Genes Using Collective Meta-heuristic Approaches: An Application to Preeclampsia.

PubMed

Biswas, Surama; Dutta, Subarna; Acharyya, Sriyankar

2017-12-01

Identifying a small subset of disease critical genes out of a large size of microarray gene expression data is a challenge in computational life sciences. This paper has applied four meta-heuristic algorithms, namely, honey bee mating optimization (HBMO), harmony search (HS), differential evolution (DE) and genetic algorithm (basic version GA) to find disease critical genes of preeclampsia which affects women during gestation. Two hybrid algorithms, namely, HBMO-kNN and HS-kNN have been newly proposed here where kNN (k nearest neighbor classifier) is used for sample classification. Performances of these new approaches have been compared with other two hybrid algorithms, namely, DE-kNN and SGA-kNN. Three datasets of different sizes have been used. In a dataset, the set of genes found common in the output of each algorithm is considered here as disease critical genes. In different datasets, the percentage of classification or classification accuracy of meta-heuristic algorithms varied between 92.46 and 100%. HBMO-kNN has the best performance (99.64-100%) in almost all data sets. DE-kNN secures the second position (99.42-100%). Disease critical genes obtained here match with clinically revealed preeclampsia genes to a large extent.

Systemic regulation of leaf anatomical structure, photosynthetic performance, and high-light tolerance in sorghum.

PubMed

Jiang, Chuang-Dao; Wang, Xin; Gao, Hui-Yuan; Shi, Lei; Chow, Wah Soon

2011-03-01

Leaf anatomy of C3 plants is mainly regulated by a systemic irradiance signal. Since the anatomical features of C4 plants are different from that of C3 plants, we investigated whether the systemic irradiance signal regulates leaf anatomical structure and photosynthetic performance in sorghum (Sorghum bicolor), a C4 plant. Compared with growth under ambient conditions (A), no significant changes in anatomical structure were observed in newly developed leaves by shading young leaves alone (YS). Shading mature leaves (MS) or whole plants (S), on the other hand, caused shade-leaf anatomy in newly developed leaves. By contrast, chloroplast ultrastructure in developing leaves depended only on their local light conditions. Functionally, shading young leaves alone had little effect on their net photosynthetic capacity and stomatal conductance, but shading mature leaves or whole plants significantly decreased these two parameters in newly developed leaves. Specifically, the net photosynthetic rate in newly developed leaves exhibited a positive linear correlation with that of mature leaves, as did stomatal conductance. In MS and S treatments, newly developed leaves exhibited severe photoinhibition under high light. By contrast, newly developed leaves in A and YS treatments were more resistant to high light relative to those in MS- and S-treated seedlings. We suggest that (1) leaf anatomical structure, photosynthetic capacity, and high-light tolerance in newly developed sorghum leaves were regulated by a systemic irradiance signal from mature leaves; and (2) chloroplast ultrastructure only weakly influenced the development of photosynthetic capacity and high-light tolerance. The potential significance of the regulation by a systemic irradiance signal is discussed.

Electronic health record use to classify patients with newly diagnosed versus preexisting type 2 diabetes: infrastructure for comparative effectiveness research and population health management.

PubMed

Kudyakov, Rustam; Bowen, James; Ewen, Edward; West, Suzanne L; Daoud, Yahya; Fleming, Neil; Masica, Andrew

2012-02-01

Use of electronic health record (EHR) content for comparative effectiveness research (CER) and population health management requires significant data configuration. A retrospective cohort study was conducted using patients with diabetes followed longitudinally (N=36,353) in the EHR deployed at outpatient practice networks of 2 health care systems. A data extraction and classification algorithm targeting identification of patients with a new diagnosis of type 2 diabetes mellitus (T2DM) was applied, with the main criterion being a minimum 30-day window between the first visit documented in the EHR and the entry of T2DM on the EHR problem list. Chart reviews (N=144) validated the performance of refining this EHR classification algorithm with external administrative data. Extraction using EHR data alone designated 3205 patients as newly diagnosed with T2DM with classification accuracy of 70.1%. Use of external administrative data on that preselected population improved classification accuracy of cases identified as new T2DM diagnosis (positive predictive value was 91.9% with that step). Laboratory and medication data did not help case classification. The final cohort using this 2-stage classification process comprised 1972 patients with a new diagnosis of T2DM. Data use from current EHR systems for CER and disease management mandates substantial tailoring. Quality between EHR clinical data generated in daily care and that required for population health research varies. As evidenced by this process for classification of newly diagnosed T2DM cases, validation of EHR data with external sources can be a valuable step.

Comparing, optimizing, and benchmarking quantum-control algorithms in a unifying programming framework

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

Machnes, S.; Institute for Theoretical Physics, University of Ulm, D-89069 Ulm; Sander, U.

2011-08-15

For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions aremore » pointed out. Moreover, we introduce a unifying algorithmic framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.« less

An improved molecular dynamics algorithm to study thermodiffusion in binary hydrocarbon mixtures

NASA Astrophysics Data System (ADS)

Antoun, Sylvie; Saghir, M. Ziad; Srinivasan, Seshasai

2018-03-01

In multicomponent liquid mixtures, the diffusion flow of chemical species can be induced by temperature gradients, which leads to a separation of the constituent components. This cross effect between temperature and concentration is known as thermodiffusion or the Ludwig-Soret effect. The performance of boundary driven non-equilibrium molecular dynamics along with the enhanced heat exchange (eHEX) algorithm was studied by assessing the thermodiffusion process in n-pentane/n-decane (nC5-nC10) binary mixtures. The eHEX algorithm consists of an extended version of the HEX algorithm with an improved energy conservation property. In addition to this, the transferable potentials for phase equilibria-united atom force field were employed in all molecular dynamics (MD) simulations to precisely model the molecular interactions in the fluid. The Soret coefficients of the n-pentane/n-decane (nC5-nC10) mixture for three different compositions (at 300.15 K and 0.1 MPa) were calculated and compared with the experimental data and other MD results available in the literature. Results of our newly employed MD algorithm showed great agreement with experimental data and a better accuracy compared to other MD procedures.

A graphical simulation software for instruction in cardiovascular mechanics physiology.

PubMed

Wildhaber, Reto A; Verrey, François; Wenger, Roland H

2011-01-25

Computer supported, interactive e-learning systems are widely used in the teaching of physiology. However, the currently available complimentary software tools in the field of the physiology of cardiovascular mechanics have not yet been adapted to the latest systems software. Therefore, a simple-to-use replacement for undergraduate and graduate students' education was needed, including an up-to-date graphical software that is validated and field-tested. Software compatible to Windows, based on modified versions of existing mathematical algorithms, has been newly developed. Testing was performed during a full term of physiological lecturing to medical and biology students. The newly developed CLabUZH software models a reduced human cardiovascular loop containing all basic compartments: an isolated heart including an artificial electrical stimulator, main vessels and the peripheral resistive components. Students can alter several physiological parameters interactively. The resulting output variables are printed in x-y diagrams and in addition shown in an animated, graphical model. CLabUZH offers insight into the relations of volume, pressure and time dependency in the circulation and their correlation to the electrocardiogram (ECG). Established mechanisms such as the Frank-Starling Law or the Windkessel Effect are considered in this model. The CLabUZH software is self-contained with no extra installation required and runs on most of today's personal computer systems. CLabUZH is a user-friendly interactive computer programme that has proved to be useful in teaching the basic physiological principles of heart mechanics.

Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.

PubMed

Wacker, Soren; Noskov, Sergei Yu

2018-05-01

Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC 50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC 50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC 50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost displays excellent performance with a coefficient of determination of up to R 2 ~0.8 for pIC 50 values in evaluation datasets, surpassing other metrics and approaches available in literature. Ultimately, the ML-based platform developed in our work is a scalable framework with automation potential to interact with other developing technologies in cardiotoxicity field, including high-throughput electrophysiology measurements delivering large datasets of profiled drugs, rapid synthesis and drug development via progress in synthetic biology.

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response.

PubMed

Djuričić, Goran J; Radulovic, Marko; Sopta, Jelena P; Nikitović, Marina; Milošević, Nebojša T

2017-01-01

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, Λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0-0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images.

CO 2 water-alternating-gas injection for enhanced oil recovery: Optimal well controls and half-cycle lengths

DOE PAGES

Chen, Bailian; Reynolds, Albert C.

2018-03-11

We report that CO 2 water-alternating-gas (WAG) injection is an enhanced oil recovery method designed to improve sweep efficiency during CO 2 injection with the injected water to control the mobility of CO 2 and to stabilize the gas front. Optimization of CO 2 -WAG injection is widely regarded as a viable technique for controlling the CO 2 and oil miscible process. Poor recovery from CO 2 -WAG injection can be caused by inappropriately designed WAG parameters. In previous study (Chen and Reynolds, 2016), we proposed an algorithm to optimize the well controls which maximize the life-cycle net-present-value (NPV). However,more » the effect of injection half-cycle lengths for each injector on oil recovery or NPV has not been well investigated. In this paper, an optimization framework based on augmented Lagrangian method and the newly developed stochastic-simplex-approximate-gradient (StoSAG) algorithm is proposed to explore the possibility of simultaneous optimization of the WAG half-cycle lengths together with the well controls. Finally, the proposed framework is demonstrated with three reservoir examples.« less

LiCABEDS II. Modeling of ligand selectivity for G-protein-coupled cannabinoid receptors.

PubMed

Ma, Chao; Wang, Lirong; Yang, Peng; Myint, Kyaw Z; Xie, Xiang-Qun

2013-01-28

The cannabinoid receptor subtype 2 (CB2) is a promising therapeutic target for blood cancer, pain relief, osteoporosis, and immune system disease. The recent withdrawal of Rimonabant, which targets another closely related cannabinoid receptor (CB1), accentuates the importance of selectivity for the development of CB2 ligands in order to minimize their effects on the CB1 receptor. In our previous study, LiCABEDS (Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps) was reported as a generic ligand classification algorithm for the prediction of categorical molecular properties. Here, we report extension of the application of LiCABEDS to the modeling of cannabinoid ligand selectivity with molecular fingerprints as descriptors. The performance of LiCABEDS was systematically compared with another popular classification algorithm, support vector machine (SVM), according to prediction precision and recall rate. In addition, the examination of LiCABEDS models revealed the difference in structure diversity of CB1 and CB2 selective ligands. The structure determination from data mining could be useful for the design of novel cannabinoid lead compounds. More importantly, the potential of LiCABEDS was demonstrated through successful identification of newly synthesized CB2 selective compounds.

Identification of GATC- and CCGG- recognizing Type II REases and their putative specificity-determining positions using Scan2S—a novel motif scan algorithm with optional secondary structure constraints

PubMed Central

Niv, Masha Y.; Skrabanek, Lucy; Roberts, Richard J.; Scheraga, Harold A.; Weinstein, Harel

2008-01-01

Restriction endonucleases (REases) are DNA-cleaving enzymes that have become indispensable tools in molecular biology. Type II REases are highly divergent in sequence despite their common structural core, function and, in some cases, common specificities towards DNA sequences. This makes it difficult to identify and classify them functionally based on sequence, and has hampered the efforts of specificity-engineering. Here, we define novel REase sequence motifs, which extend beyond the PD-(D/E)XK hallmark, and incorporate secondary structure information. The automated search using these motifs is carried out with a newly developed fast regular expression matching algorithm that accommodates long patterns with optional secondary structure constraints. Using this new tool, named Scan2S, motifs derived from REases with specificity towards GATC- and CGGG-containing DNA sequences successfully identify REases of the same specificity. Notably, some of these sequences are not identified by standard sequence detection tools. The new motifs highlight potential specificity-determining positions that do not fully overlap for the GATC- and the CCGG-recognizing REases and are candidates for specificity re-engineering. PMID:17972284

TESS: a geometric hashing algorithm for deriving 3D coordinate templates for searching structural databases. Application to enzyme active sites.

PubMed Central

Wallace, A. C.; Borkakoti, N.; Thornton, J. M.

1997-01-01

It is well established that sequence templates such as those in the PROSITE and PRINTS databases are powerful tools for predicting the biological function and tertiary structure for newly derived protein sequences. The number of X-ray and NMR protein structures is increasing rapidly and it is apparent that a 3D equivalent of the sequence templates is needed. Here, we describe an algorithm called TESS that automatically derives 3D templates from structures deposited in the Brookhaven Protein Data Bank. While a new sequence can be searched for sequence patterns, a new structure can be scanned against these 3D templates to identify functional sites. As examples, 3D templates are derived for enzymes with an O-His-O "catalytic triad" and for the ribonucleases and lysozymes. When these 3D templates are applied to a large data set of nonidentical proteins, several interesting hits are located. This suggests that the development of a 3D template database may help to identify the function of new protein structures, if unknown, as well as to design proteins with specific functions. PMID:9385633

An Ultrasonic Multi-Beam Concentration Meter with a Neuro-Fuzzy Algorithm for Water Treatment Plants

PubMed Central

Lee, Ho-Hyun; Jang, Sang-Bok; Shin, Gang-Wook; Hong, Sung-Taek; Lee, Dae-Jong; Chun, Myung Geun

2015-01-01

Ultrasonic concentration meters have widely been used at water purification, sewage treatment and waste water treatment plants to sort and transfer high concentration sludges and to control the amount of chemical dosage. When an unusual substance is contained in the sludge, however, the attenuation of ultrasonic waves could be increased or not be transmitted to the receiver. In this case, the value measured by a concentration meter is higher than the actual density value or vibration. As well, it is difficult to automate the residuals treatment process according to the various problems such as sludge attachment or sensor failure. An ultrasonic multi-beam concentration sensor was considered to solve these problems, but an abnormal concentration value of a specific ultrasonic beam degrades the accuracy of the entire measurement in case of using a conventional arithmetic mean for all measurement values, so this paper proposes a method to improve the accuracy of the sludge concentration determination by choosing reliable sensor values and applying a neuro-fuzzy learning algorithm. The newly developed meter is proven to render useful results from a variety of experiments on a real water treatment plant. PMID:26512666

An Ultrasonic Multi-Beam Concentration Meter with a Neuro-Fuzzy Algorithm for Water Treatment Plants.

PubMed

Lee, Ho-Hyun; Jang, Sang-Bok; Shin, Gang-Wook; Hong, Sung-Taek; Lee, Dae-Jong; Chun, Myung Geun

2015-10-23

Ultrasonic concentration meters have widely been used at water purification, sewage treatment and waste water treatment plants to sort and transfer high concentration sludges and to control the amount of chemical dosage. When an unusual substance is contained in the sludge, however, the attenuation of ultrasonic waves could be increased or not be transmitted to the receiver. In this case, the value measured by a concentration meter is higher than the actual density value or vibration. As well, it is difficult to automate the residuals treatment process according to the various problems such as sludge attachment or sensor failure. An ultrasonic multi-beam concentration sensor was considered to solve these problems, but an abnormal concentration value of a specific ultrasonic beam degrades the accuracy of the entire measurement in case of using a conventional arithmetic mean for all measurement values, so this paper proposes a method to improve the accuracy of the sludge concentration determination by choosing reliable sensor values and applying a neuro-fuzzy learning algorithm. The newly developed meter is proven to render useful results from a variety of experiments on a real water treatment plant.

LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

PubMed

Zhang, Tao; Chen, Wanzhong

2017-08-01

Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

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

McKemmish, Laura K., E-mail: laura.mckemmish@gmail.com; Research School of Chemistry, Australian National University, Canberra

Algorithms for the efficient calculation of two-electron integrals in the newly developed mixed ramp-Gaussian basis sets are presented, alongside a Fortran90 implementation of these algorithms, RAMPITUP. These new basis sets have significant potential to (1) give some speed-up (estimated at up to 20% for large molecules in fully optimised code) to general-purpose Hartree-Fock (HF) and density functional theory quantum chemistry calculations, replacing all-Gaussian basis sets, and (2) give very large speed-ups for calculations of core-dependent properties, such as electron density at the nucleus, NMR parameters, relativistic corrections, and total energies, replacing the current use of Slater basis functions or verymore » large specialised all-Gaussian basis sets for these purposes. This initial implementation already demonstrates roughly 10% speed-ups in HF/R-31G calculations compared to HF/6-31G calculations for large linear molecules, demonstrating the promise of this methodology, particularly for the second application. As well as the reduction in the total primitive number in R-31G compared to 6-31G, this timing advantage can be attributed to the significant reduction in the number of mathematically complex intermediate integrals after modelling each ramp-Gaussian basis-function-pair as a sum of ramps on a single atomic centre.« less

Unmanned Aerial Vehicles for Alien Plant Species Detection and Monitoring

NASA Astrophysics Data System (ADS)

Dvořák, P.; Müllerová, J.; Bartaloš, T.; Brůna, J.

2015-08-01

Invasive species spread rapidly and their eradication is difficult. New methods enabling fast and efficient monitoring are urgently needed for their successful control. Remote sensing can improve early detection of invading plants and make their management more efficient and less expensive. In an ongoing project in the Czech Republic, we aim at developing innovative methods of mapping invasive plant species (semi-automatic detection algorithms) by using purposely designed unmanned aircraft (UAV). We examine possibilities for detection of two tree and two herb invasive species. Our aim is to establish fast, repeatable and efficient computer-assisted method of timely monitoring, reducing the costs of extensive field campaigns. For finding the best detection algorithm we test various classification approaches (object-, pixel-based and hybrid). Thanks to its flexibility and low cost, UAV enables assessing the effect of phenological stage and spatial resolution, and is most suitable for monitoring the efficiency of eradication efforts. However, several challenges exist in UAV application, such as geometrical and radiometric distortions, high amount of data to be processed and legal constrains for the UAV flight missions over urban areas (often highly invaded). The newly proposed UAV approach shall serve invasive species researchers, management practitioners and policy makers.

Identification of GATC- and CCGG-recognizing Type II REases and their putative specificity-determining positions using Scan2S--a novel motif scan algorithm with optional secondary structure constraints.

PubMed

Niv, Masha Y; Skrabanek, Lucy; Roberts, Richard J; Scheraga, Harold A; Weinstein, Harel

2008-05-01

Restriction endonucleases (REases) are DNA-cleaving enzymes that have become indispensable tools in molecular biology. Type II REases are highly divergent in sequence despite their common structural core, function and, in some cases, common specificities towards DNA sequences. This makes it difficult to identify and classify them functionally based on sequence, and has hampered the efforts of specificity-engineering. Here, we define novel REase sequence motifs, which extend beyond the PD-(D/E)XK hallmark, and incorporate secondary structure information. The automated search using these motifs is carried out with a newly developed fast regular expression matching algorithm that accommodates long patterns with optional secondary structure constraints. Using this new tool, named Scan2S, motifs derived from REases with specificity towards GATC- and CGGG-containing DNA sequences successfully identify REases of the same specificity. Notably, some of these sequences are not identified by standard sequence detection tools. The new motifs highlight potential specificity-determining positions that do not fully overlap for the GATC- and the CCGG-recognizing REases and are candidates for specificity re-engineering.

Learning in stochastic neural networks for constraint satisfaction problems

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Adorf, Hans-Martin

1989-01-01

Researchers describe a newly-developed artificial neural network algorithm for solving constraint satisfaction problems (CSPs) which includes a learning component that can significantly improve the performance of the network from run to run. The network, referred to as the Guarded Discrete Stochastic (GDS) network, is based on the discrete Hopfield network but differs from it primarily in that auxiliary networks (guards) are asymmetrically coupled to the main network to enforce certain types of constraints. Although the presence of asymmetric connections implies that the network may not converge, it was found that, for certain classes of problems, the network often quickly converges to find satisfactory solutions when they exist. The network can run efficiently on serial machines and can find solutions to very large problems (e.g., N-queens for N as large as 1024). One advantage of the network architecture is that network connection strengths need not be instantiated when the network is established: they are needed only when a participating neural element transitions from off to on. They have exploited this feature to devise a learning algorithm, based on consistency techniques for discrete CSPs, that updates the network biases and connection strengths and thus improves the network performance.

CO 2 water-alternating-gas injection for enhanced oil recovery: Optimal well controls and half-cycle lengths

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

Chen, Bailian; Reynolds, Albert C.

We report that CO 2 water-alternating-gas (WAG) injection is an enhanced oil recovery method designed to improve sweep efficiency during CO 2 injection with the injected water to control the mobility of CO 2 and to stabilize the gas front. Optimization of CO 2 -WAG injection is widely regarded as a viable technique for controlling the CO 2 and oil miscible process. Poor recovery from CO 2 -WAG injection can be caused by inappropriately designed WAG parameters. In previous study (Chen and Reynolds, 2016), we proposed an algorithm to optimize the well controls which maximize the life-cycle net-present-value (NPV). However,more » the effect of injection half-cycle lengths for each injector on oil recovery or NPV has not been well investigated. In this paper, an optimization framework based on augmented Lagrangian method and the newly developed stochastic-simplex-approximate-gradient (StoSAG) algorithm is proposed to explore the possibility of simultaneous optimization of the WAG half-cycle lengths together with the well controls. Finally, the proposed framework is demonstrated with three reservoir examples.« less

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function prediction in the context of PPI networks, the explicit consideration of network autocorrelation increases the predictive performance of the learned models. Overall, we found that this holds for different gene features/ descriptions, functional annotation schemes, and PPI networks: Best results are achieved when the PPI network is dense and contains a large proportion of function-relevant interactions.

Designing an algorithm to preserve privacy for medical record linkage with error-prone data.

PubMed

Pal, Doyel; Chen, Tingting; Zhong, Sheng; Khethavath, Praveen

2014-01-20

Linking medical records across different medical service providers is important to the enhancement of health care quality and public health surveillance. In records linkage, protecting the patients' privacy is a primary requirement. In real-world health care databases, records may well contain errors due to various reasons such as typos. Linking the error-prone data and preserving data privacy at the same time are very difficult. Existing privacy preserving solutions for this problem are only restricted to textual data. To enable different medical service providers to link their error-prone data in a private way, our aim was to provide a holistic solution by designing and developing a medical record linkage system for medical service providers. To initiate a record linkage, one provider selects one of its collaborators in the Connection Management Module, chooses some attributes of the database to be matched, and establishes the connection with the collaborator after the negotiation. In the Data Matching Module, for error-free data, our solution offered two different choices for cryptographic schemes. For error-prone numerical data, we proposed a newly designed privacy preserving linking algorithm named the Error-Tolerant Linking Algorithm, that allows the error-prone data to be correctly matched if the distance between the two records is below a threshold. We designed and developed a comprehensive and user-friendly software system that provides privacy preserving record linkage functions for medical service providers, which meets the regulation of Health Insurance Portability and Accountability Act. It does not require a third party and it is secure in that neither entity can learn the records in the other's database. Moreover, our novel Error-Tolerant Linking Algorithm implemented in this software can work well with error-prone numerical data. We theoretically proved the correctness and security of our Error-Tolerant Linking Algorithm. We have also fully implemented the software. The experimental results showed that it is reliable and efficient. The design of our software is open so that the existing textual matching methods can be easily integrated into the system. Designing algorithms to enable medical records linkage for error-prone numerical data and protect data privacy at the same time is difficult. Our proposed solution does not need a trusted third party and is secure in that in the linking process, neither entity can learn the records in the other's database.

Temporal Subtraction of Serial CT Images with Large Deformation Diffeomorphic Metric Mapping in the Identification of Bone Metastases.

PubMed

Sakamoto, Ryo; Yakami, Masahiro; Fujimoto, Koji; Nakagomi, Keita; Kubo, Takeshi; Emoto, Yutaka; Akasaka, Thai; Aoyama, Gakuto; Yamamoto, Hiroyuki; Miller, Michael I; Mori, Susumu; Togashi, Kaori

2017-11-01

Purpose To determine the improvement of radiologist efficiency and performance in the detection of bone metastases at serial follow-up computed tomography (CT) by using a temporal subtraction (TS) technique based on an advanced nonrigid image registration algorithm. Materials and Methods This retrospective study was approved by the institutional review board, and informed consent was waived. CT image pairs (previous and current scans of the torso) in 60 patients with cancer (primary lesion location: prostate, n = 14; breast, n = 16; lung, n = 20; liver, n = 10) were included. These consisted of 30 positive cases with a total of 65 bone metastases depicted only on current images and confirmed by two radiologists who had access to additional imaging examinations and clinical courses and 30 matched negative control cases (no bone metastases). Previous CT images were semiautomatically registered to current CT images by the algorithm, and TS images were created. Seven radiologists independently interpreted CT image pairs to identify newly developed bone metastases without and with TS images with an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Reading time was recorded, and usefulness was evaluated with subjective scores of 1-5, with 5 being extremely useful and 1 being useless. Significance of these values was tested with the Wilcoxon signed-rank test. Results The subtraction images depicted various types of bone metastases (osteolytic, n = 28; osteoblastic, n = 26; mixed osteolytic and blastic, n = 11) as temporal changes. The average reading time was significantly reduced (384.3 vs 286.8 seconds; Wilcoxon signed rank test, P = .028). The average figure-of-merit value increased from 0.758 to 0.835; however, this difference was not significant (JAFROC analysis, P = .092). The subjective usefulness survey response showed a median score of 5 for use of the technique (range, 3-5). Conclusion TS images obtained from serial CT scans using nonrigid registration successfully depicted newly developed bone metastases and showed promise for their efficient detection. © RSNA, 2017 Online supplemental material is available for this article.

Decomposed multidimensional control grid interpolation for common consumer electronic image processing applications

NASA Astrophysics Data System (ADS)

Zwart, Christine M.; Venkatesan, Ragav; Frakes, David H.

2012-10-01

Interpolation is an essential and broadly employed function of signal processing. Accordingly, considerable development has focused on advancing interpolation algorithms toward optimal accuracy. Such development has motivated a clear shift in the state-of-the art from classical interpolation to more intelligent and resourceful approaches, registration-based interpolation for example. As a natural result, many of the most accurate current algorithms are highly complex, specific, and computationally demanding. However, the diverse hardware destinations for interpolation algorithms present unique constraints that often preclude use of the most accurate available options. For example, while computationally demanding interpolators may be suitable for highly equipped image processing platforms (e.g., computer workstations and clusters), only more efficient interpolators may be practical for less well equipped platforms (e.g., smartphones and tablet computers). The latter examples of consumer electronics present a design tradeoff in this regard: high accuracy interpolation benefits the consumer experience but computing capabilities are limited. It follows that interpolators with favorable combinations of accuracy and efficiency are of great practical value to the consumer electronics industry. We address multidimensional interpolation-based image processing problems that are common to consumer electronic devices through a decomposition approach. The multidimensional problems are first broken down into multiple, independent, one-dimensional (1-D) interpolation steps that are then executed with a newly modified registration-based one-dimensional control grid interpolator. The proposed approach, decomposed multidimensional control grid interpolation (DMCGI), combines the accuracy of registration-based interpolation with the simplicity, flexibility, and computational efficiency of a 1-D interpolation framework. Results demonstrate that DMCGI provides improved interpolation accuracy (and other benefits) in image resizing, color sample demosaicing, and video deinterlacing applications, at a computational cost that is manageable or reduced in comparison to popular alternatives.

Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network.

PubMed

Duraisamy, Baskar; Shanmugam, Jayanthi Venkatraman; Annamalai, Jayanthi

2018-02-19

An early intervention of Alzheimer's disease (AD) is highly essential due to the fact that this neuro degenerative disease generates major life-threatening issues, especially memory loss among patients in society. Moreover, categorizing NC (Normal Control), MCI (Mild Cognitive Impairment) and AD early in course allows the patients to experience benefits from new treatments. Therefore, it is important to construct a reliable classification technique to discriminate the patients with or without AD from the bio medical imaging modality. Hence, we developed a novel FCM based Weighted Probabilistic Neural Network (FWPNN) classification algorithm and analyzed the brain images related to structural MRI modality for better discrimination of class labels. Initially our proposed framework begins with brain image normalization stage. In this stage, ROI regions related to Hippo-Campus (HC) and Posterior Cingulate Cortex (PCC) from the brain images are extracted using Automated Anatomical Labeling (AAL) method. Subsequently, nineteen highly relevant AD related features are selected through Multiple-criterion feature selection method. At last, our novel FWPNN classification algorithm is imposed to remove suspicious samples from the training data with an end goal to enhance the classification performance. This newly developed classification algorithm combines both the goodness of supervised and unsupervised learning techniques. The experimental validation is carried out with the ADNI subset and then to the Bordex-3 city dataset. Our proposed classification approach achieves an accuracy of about 98.63%, 95.4%, 96.4% in terms of classification with AD vs NC, MCI vs NC and AD vs MCI. The experimental results suggest that the removal of noisy samples from the training data can enhance the decision generation process of the expert systems.

Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques.

PubMed

Wang, Qian; Song, Enmin; Jin, Renchao; Han, Ping; Wang, Xiaotong; Zhou, Yanying; Zeng, Jianchao

2009-06-01

The aim of this study was to develop a novel algorithm for segmenting lung nodules on three-dimensional (3D) computed tomographic images to improve the performance of computer-aided diagnosis (CAD) systems. The database used in this study consists of two data sets obtained from the Lung Imaging Database Consortium. The first data set, containing 23 nodules (22% irregular nodules, 13% nonsolid nodules, 17% nodules attached to other structures), was used for training. The second data set, containing 64 nodules (37% irregular nodules, 40% nonsolid nodules, 62% nodules attached to other structures), was used for testing. Two key techniques were developed in the segmentation algorithm: (1) a 3D extended dynamic programming model, with a newly defined internal cost function based on the information between adjacent slices, allowing parameters to be adapted to each slice, and (2) a multidirection fusion technique, which makes use of the complementary relationships among different directions to improve the final segmentation accuracy. The performance of this approach was evaluated by the overlap criterion, complemented by the true-positive fraction and the false-positive fraction criteria. The mean values of the overlap, true-positive fraction, and false-positive fraction for the first data set achieved using the segmentation scheme were 66%, 75%, and 15%, respectively, and the corresponding values for the second data set were 58%, 71%, and 22%, respectively. The experimental results indicate that this segmentation scheme can achieve better performance for nodule segmentation than two existing algorithms reported in the literature. The proposed 3D extended dynamic programming model is an effective way to segment sequential images of lung nodules. The proposed multidirection fusion technique is capable of reducing segmentation errors especially for no-nodule and near-end slices, thus resulting in better overall performance.

Scalable Methods for Uncertainty Quantification, Data Assimilation and Target Accuracy Assessment for Multi-Physics Advanced Simulation of Light Water Reactors

NASA Astrophysics Data System (ADS)

Khuwaileh, Bassam

High fidelity simulation of nuclear reactors entails large scale applications characterized with high dimensionality and tremendous complexity where various physics models are integrated in the form of coupled models (e.g. neutronic with thermal-hydraulic feedback). Each of the coupled modules represents a high fidelity formulation of the first principles governing the physics of interest. Therefore, new developments in high fidelity multi-physics simulation and the corresponding sensitivity/uncertainty quantification analysis are paramount to the development and competitiveness of reactors achieved through enhanced understanding of the design and safety margins. Accordingly, this dissertation introduces efficient and scalable algorithms for performing efficient Uncertainty Quantification (UQ), Data Assimilation (DA) and Target Accuracy Assessment (TAA) for large scale, multi-physics reactor design and safety problems. This dissertation builds upon previous efforts for adaptive core simulation and reduced order modeling algorithms and extends these efforts towards coupled multi-physics models with feedback. The core idea is to recast the reactor physics analysis in terms of reduced order models. This can be achieved via identifying the important/influential degrees of freedom (DoF) via the subspace analysis, such that the required analysis can be recast by considering the important DoF only. In this dissertation, efficient algorithms for lower dimensional subspace construction have been developed for single physics and multi-physics applications with feedback. Then the reduced subspace is used to solve realistic, large scale forward (UQ) and inverse problems (DA and TAA). Once the elite set of DoF is determined, the uncertainty/sensitivity/target accuracy assessment and data assimilation analysis can be performed accurately and efficiently for large scale, high dimensional multi-physics nuclear engineering applications. Hence, in this work a Karhunen-Loeve (KL) based algorithm previously developed to quantify the uncertainty for single physics models is extended for large scale multi-physics coupled problems with feedback effect. Moreover, a non-linear surrogate based UQ approach is developed, used and compared to performance of the KL approach and brute force Monte Carlo (MC) approach. On the other hand, an efficient Data Assimilation (DA) algorithm is developed to assess information about model's parameters: nuclear data cross-sections and thermal-hydraulics parameters. Two improvements are introduced in order to perform DA on the high dimensional problems. First, a goal-oriented surrogate model can be used to replace the original models in the depletion sequence (MPACT -- COBRA-TF - ORIGEN). Second, approximating the complex and high dimensional solution space with a lower dimensional subspace makes the sampling process necessary for DA possible for high dimensional problems. Moreover, safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. Accordingly, an inverse problem can be defined and solved to assess the contributions from sources of uncertainty; and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this dissertation a subspace-based gradient-free and nonlinear algorithm for inverse uncertainty quantification namely the Target Accuracy Assessment (TAA) has been developed and tested. The ideas proposed in this dissertation were first validated using lattice physics applications simulated using SCALE6.1 package (Pressurized Water Reactor (PWR) and Boiling Water Reactor (BWR) lattice models). Ultimately, the algorithms proposed her were applied to perform UQ and DA for assembly level (CASL progression problem number 6) and core wide problems representing Watts Bar Nuclear 1 (WBN1) for cycle 1 of depletion (CASL Progression Problem Number 9) modeled via simulated using VERA-CS which consists of several multi-physics coupled models. The analysis and algorithms developed in this dissertation were encoded and implemented in a newly developed tool kit algorithms for Reduced Order Modeling based Uncertainty/Sensitivity Estimator (ROMUSE).

Combination of uniform design with artificial neural network coupling genetic algorithm: an effective way to obtain high yield of biomass and algicidal compound of a novel HABs control actinomycete

PubMed Central

2014-01-01

Controlling harmful algae blooms (HABs) using microbial algicides is cheap, efficient and environmental-friendly. However, obtaining high yield of algicidal microbes to meet the need of field test is still a big challenge since qualitative and quantitative analysis of algicidal compounds is difficult. In this study, we developed a protocol to increase the yield of both biomass and algicidal compound present in a novel algicidal actinomycete Streptomyces alboflavus RPS, which kills Phaeocystis globosa. To overcome the problem in algicidal compound quantification, we chose algicidal ratio as the index and used artificial neural network to fit the data, which was appropriate for this nonlinear situation. In this protocol, we firstly determined five main influencing factors through single factor experiments and generated the multifactorial experimental groups with a U15(155) uniform-design-table. Then, we used the traditional quadratic polynomial stepwise regression model and an accurate, fully optimized BP-neural network to simulate the fermentation. Optimized with genetic algorithm and verified using experiments, we successfully increased the algicidal ratio of the fermentation broth by 16.90% and the dry mycelial weight by 69.27%. These results suggested that this newly developed approach is a viable and easy way to optimize the fermentation conditions for algicidal microorganisms. PMID:24886410

Combination of uniform design with artificial neural network coupling genetic algorithm: an effective way to obtain high yield of biomass and algicidal compound of a novel HABs control actinomycete.

PubMed

Cai, Guanjing; Zheng, Wei; Yang, Xujun; Zhang, Bangzhou; Zheng, Tianling

2014-05-24

Controlling harmful algae blooms (HABs) using microbial algicides is cheap, efficient and environmental-friendly. However, obtaining high yield of algicidal microbes to meet the need of field test is still a big challenge since qualitative and quantitative analysis of algicidal compounds is difficult. In this study, we developed a protocol to increase the yield of both biomass and algicidal compound present in a novel algicidal actinomycete Streptomyces alboflavus RPS, which kills Phaeocystis globosa. To overcome the problem in algicidal compound quantification, we chose algicidal ratio as the index and used artificial neural network to fit the data, which was appropriate for this nonlinear situation. In this protocol, we firstly determined five main influencing factors through single factor experiments and generated the multifactorial experimental groups with a U15(155) uniform-design-table. Then, we used the traditional quadratic polynomial stepwise regression model and an accurate, fully optimized BP-neural network to simulate the fermentation. Optimized with genetic algorithm and verified using experiments, we successfully increased the algicidal ratio of the fermentation broth by 16.90% and the dry mycelial weight by 69.27%. These results suggested that this newly developed approach is a viable and easy way to optimize the fermentation conditions for algicidal microorganisms.

Cerebral correlates of muscle tone fluctuations in restless legs syndrome: a pilot study with combined functional magnetic resonance imaging and anterior tibial muscle electromyography.

PubMed

Spiegelhalder, Kai; Feige, Bernd; Paul, Dominik; Riemann, Dieter; van Elst, Ludger Tebartz; Seifritz, Erich; Hennig, Jürgen; Hornyak, Magdolna

2008-01-01

The pathology of restless legs syndrome (RLS) is still not understood. To investigate the pathomechanism of the disorder further we recorded a surface electromyogram (EMG) of the anterior tibial muscle during functional magnetic resonance imaging (fMRI) in patients with idiopathic RLS. Seven subjects with moderate to severe RLS were investigated in the present pilot study. Patients were lying supine in the scanner for over 50 min and were instructed not to move voluntarily. Sensory leg discomfort (SLD) was evaluated on a 10-point Likert scale. For brain image analysis, an algorithm for the calculation of tonic EMG values was developed. We found a negative correlation of tonic EMG and SLD (p <0.01). This finding provides evidence for the clinical experience that RLS-related subjective leg discomfort increases during muscle relaxation at rest. In the fMRI analysis, the tonic EMG was associated with activation in motor and somatosensory pathways and also in some regions that are not primarily related to motor or somatosensory functions. By using a newly developed algorithm for the investigation of muscle tone-related changes in cerebral activity, we identified structures that are potentially involved in RLS pathology. Our method, with some modification, may also be suitable for the investigation of phasic muscle activity that occurs during periodic leg movements.

Boomerang: A method for recursive reclassification.

PubMed

Devlin, Sean M; Ostrovnaya, Irina; Gönen, Mithat

2016-09-01

While there are many validated prognostic classifiers used in practice, often their accuracy is modest and heterogeneity in clinical outcomes exists in one or more risk subgroups. Newly available markers, such as genomic mutations, may be used to improve the accuracy of an existing classifier by reclassifying patients from a heterogenous group into a higher or lower risk category. The statistical tools typically applied to develop the initial classifiers are not easily adapted toward this reclassification goal. In this article, we develop a new method designed to refine an existing prognostic classifier by incorporating new markers. The two-stage algorithm called Boomerang first searches for modifications of the existing classifier that increase the overall predictive accuracy and then merges to a prespecified number of risk groups. Resampling techniques are proposed to assess the improvement in predictive accuracy when an independent validation data set is not available. The performance of the algorithm is assessed under various simulation scenarios where the marker frequency, degree of censoring, and total sample size are varied. The results suggest that the method selects few false positive markers and is able to improve the predictive accuracy of the classifier in many settings. Lastly, the method is illustrated on an acute myeloid leukemia data set where a new refined classifier incorporates four new mutations into the existing three category classifier and is validated on an independent data set. © 2016, The International Biometric Society.

Boomerang: A Method for Recursive Reclassification

PubMed Central

Devlin, Sean M.; Ostrovnaya, Irina; Gönen, Mithat

2016-01-01

Summary While there are many validated prognostic classifiers used in practice, often their accuracy is modest and heterogeneity in clinical outcomes exists in one or more risk subgroups. Newly available markers, such as genomic mutations, may be used to improve the accuracy of an existing classifier by reclassifying patients from a heterogenous group into a higher or lower risk category. The statistical tools typically applied to develop the initial classifiers are not easily adapted towards this reclassification goal. In this paper, we develop a new method designed to refine an existing prognostic classifier by incorporating new markers. The two-stage algorithm called Boomerang first searches for modifications of the existing classifier that increase the overall predictive accuracy and then merges to a pre-specified number of risk groups. Resampling techniques are proposed to assess the improvement in predictive accuracy when an independent validation data set is not available. The performance of the algorithm is assessed under various simulation scenarios where the marker frequency, degree of censoring, and total sample size are varied. The results suggest that the method selects few false positive markers and is able to improve the predictive accuracy of the classifier in many settings. Lastly, the method is illustrated on an acute myeloid leukemia dataset where a new refined classifier incorporates four new mutations into the existing three category classifier and is validated on an independent dataset. PMID:26754051

Automatic detection of blurred images in UAV image sets

NASA Astrophysics Data System (ADS)

Sieberth, Till; Wackrow, Rene; Chandler, Jim H.

2016-12-01

Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This paper describes the development of an automatic filtering process, which is based upon the quantification of blur in an image. Images with known blur are processed digitally to determine a quantifiable measure of image blur. The algorithm is required to process UAV images fast and reliably to relieve the operator from detecting blurred images manually. The newly developed method makes it possible to detect blur caused by linear camera displacement and is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of additional images. However, the calculated blur value named SIEDS (saturation image edge difference standard-deviation) on its own does not provide an absolute number to judge if an image is blurred or not. To achieve a reliable judgement of image sharpness the SIEDS value has to be compared to other SIEDS values from the same dataset. The speed and reliability of the method was tested using a range of different UAV datasets. Two datasets will be presented in this paper to demonstrate the effectiveness of the algorithm. The algorithm proves to be fast and the returned values are optically correct, making the algorithm applicable for UAV datasets. Additionally, a close range dataset was processed to determine whether the method is also useful for close range applications. The results show that the method is also reliable for close range images, which significantly extends the field of application for the algorithm.

Targeted Acoustic Data Processing for Ocean Ecological Studies

NASA Astrophysics Data System (ADS)

Sidorovskaia, N.; Li, K.; Tiemann, C.; Ackleh, A. S.; Tang, T.; Ioup, G. E.; Ioup, J. W.

2015-12-01

The Gulf of Mexico is home to many species of deep diving marine mammals. In recent years several ecological studies have collected large volumes of Passive Acoustic Monitoring (PAM) data to investigate the effects of anthropogenic activities on protected and endangered marine mammal species. To utilize these data to their fullest potential for abundance estimates and habitat preference studies, automated detection and classification algorithms are needed to extract species acoustic encounters from a continuous stream of data. The species which phonate in overlapping frequency bands represent a particular challenge. This paper analyzes the performance of a newly developed automated detector for the classification of beaked whale clicks in the Northern Gulf of Mexico. Current used beaked whale classification algorithms rely heavily on experienced human operator involvement in manually associating potential events with a particular species of beaked whales. Our detection algorithm is two-stage: the detector is triggered when the species-representative phonation band energy exceeds the baseline detection threshold. Then multiple event attributes (temporal click duration, central frequency, frequency band, frequency sweep rate, Choi-Williams distribution shape indices) are measured. An attribute vector is then used to discriminate among different species of beaked whales present in the Gulf of Mexico and Risso's dolphins which were recognized to mask the detections of beaked whales in the case of widely used energy-band detectors. The detector is applied to the PAM data collected by the Littoral Acoustic Demonstration Center to estimate abundance trends of beaked whales in the vicinity of the 2010 oil spill before and after the disaster. This algorithm will allow automated processing with minimal operator involvement for new and archival PAM data. [The research is supported by a BP/GOMRI 2015-2017 consortium grant.

Flexible trigger menu implementation on the Global Trigger for the CMS Level-1 trigger upgrade

NASA Astrophysics Data System (ADS)

MATSUSHITA, Takashi; CMS Collaboration

2017-10-01

The CMS experiment at the Large Hadron Collider (LHC) has continued to explore physics at the high-energy frontier in 2016. The integrated luminosity delivered by the LHC in 2016 was 41 fb-1 with a peak luminosity of 1.5 × 1034 cm-2s-1 and peak mean pile-up of about 50, all exceeding the initial estimations for 2016. The CMS experiment has upgraded its hardware-based Level-1 trigger system to maintain its performance for new physics searches and precision measurements at high luminosities. The Global Trigger is the final step of the CMS Level-1 trigger and implements a trigger menu, a set of selection requirements applied to the final list of objects from calorimeter and muon triggers, for reducing the 40 MHz collision rate to 100 kHz. The Global Trigger has been upgraded with state-of-the-art FPGA processors on Advanced Mezzanine Cards with optical links running at 10 GHz in a MicroTCA crate. The powerful processing resources of the upgraded system enable implementation of more algorithms at a time than previously possible, allowing CMS to be more flexible in how it handles the available trigger bandwidth. Algorithms for a trigger menu, including topological requirements on multi-objects, can be realised in the Global Trigger using the newly developed trigger menu specification grammar. Analysis-like trigger algorithms can be represented in an intuitive manner and the algorithms are translated to corresponding VHDL code blocks to build a firmware. The grammar can be extended in future as the needs arise. The experience of implementing trigger menus on the upgraded Global Trigger system will be presented.

Creep force modelling for rail traction vehicles based on the Fastsim algorithm

NASA Astrophysics Data System (ADS)

Spiryagin, Maksym; Polach, Oldrich; Cole, Colin

2013-11-01

The evaluation of creep forces is a complex task and their calculation is a time-consuming process for multibody simulation (MBS). A methodology of creep forces modelling at large traction creepages has been proposed by Polach [Creep forces in simulations of traction vehicles running on adhesion limit. Wear. 2005;258:992-1000; Influence of locomotive tractive effort on the forces between wheel and rail. Veh Syst Dyn. 2001(Suppl);35:7-22] adapting his previously published algorithm [Polach O. A fast wheel-rail forces calculation computer code. Veh Syst Dyn. 1999(Suppl);33:728-739]. The most common method for creep force modelling used by software packages for MBS of running dynamics is the Fastsim algorithm by Kalker [A fast algorithm for the simplified theory of rolling contact. Veh Syst Dyn. 1982;11:1-13]. However, the Fastsim code has some limitations which do not allow modelling the creep force - creep characteristic in agreement with measurements for locomotives and other high-power traction vehicles, mainly for large traction creep at low-adhesion conditions. This paper describes a newly developed methodology based on a variable contact flexibility increasing with the ratio of the slip area to the area of adhesion. This variable contact flexibility is introduced in a modification of Kalker's code Fastsim by replacing the constant Kalker's reduction factor, widely used in MBS, by a variable reduction factor together with a slip-velocity-dependent friction coefficient decreasing with increasing global creepage. The proposed methodology is presented in this work and compared with measurements for different locomotives. The modification allows use of the well recognised Fastsim code for simulation of creep forces at large creepages in agreement with measurements without modifying the proven modelling methodology at small creepages.

Feasibility of a Smartphone-Based Exercise Program for Office Workers With Neck Pain: An Individualized Approach Using a Self-Classification Algorithm.

PubMed

Lee, Minyoung; Lee, Sang Heon; Kim, TaeYeong; Yoo, Hyun-Joon; Kim, Sung Hoon; Suh, Dong-Won; Son, Jaebum; Yoon, BumChul

2017-01-01

To explore the feasibility of a newly developed smartphone-based exercise program with an embedded self-classification algorithm for office workers with neck pain, by examining its effect on the pain intensity, functional disability, quality of life, fear avoidance, and cervical range of motion (ROM). Single-group, repeated-measures design. The laboratory and participants' home and work environments. Offices workers with neck pain (N=23; mean age ± SD, 28.13±2.97y; 13 men). Participants were classified as having 1 of 4 types of neck pain through a self-classification algorithm implemented as a smartphone application, and conducted corresponding exercise programs for 10 to 12min/d, 3d/wk, for 8 weeks. The visual analog scale (VAS), Neck Disability Index (NDI), Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), Fear-Avoidance Beliefs Questionnaire (FABQ), and cervical ROM were measured at baseline and postintervention. The VAS (P<.001) and NDI score (P<.001) indicated significant improvements in pain intensity and functional disability. Quality of life showed significant improvements in the physical functioning (P=.007), bodily pain (P=.018), general health (P=.022), vitality (P=.046), and physical component scores (P=.002) of the SF-36. The FABQ, cervical ROM, and mental component score of the SF-36 showed no significant improvements. The smartphone-based exercise program with an embedded self-classification algorithm improves the pain intensity and perceived physical health of office workers with neck pain, although not enough to affect their mental and emotional states. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

A Newly Global Drought Index Product Basing on Remotely Sensed Leaf Area Index Percentile Using Severity-Area-Duration Algorithm

NASA Astrophysics Data System (ADS)

Li, Xinlu; Lu, Hui; Lyu, Haobo

2017-04-01

Drought is one of the typical natural disasters around the world, and it has also been an important climatic event particular under the climate change. Assess and monitor drought accurately is crucial for addressing climate change and formulating corresponding policies. Several drought indices have been developed and widely used in regional and global scale to present and monitor drought, which integrate datasets such as precipitation, soil moisture, snowpack, streamflow, evapotranspiration that deprived from land surface models or remotely sensed datasets. Vegetation is a prominent component of ecosystem that modulates the water and energy flux between land surface and atmosphere, and thus can be regarded as one of the drought indicators especially for agricultural drought. Leaf area index (LAI), as an important parameter that quantifying the terrestrial vegetation conditions, can provide a new way for drought monitoring. Drought characteristics can be described as severity, area and duration. Andreadis et al. has constructed a severity-area-duration (SAD) algorithm to reflect the spatial patterns of droughts and their dynamics over time, which is a progress of drought analysis. In our study, a newly drought index product was developed using the LAI percentile (LAIpct) SAD algorithm. The remotely sensed global GLASS (Global LAnd Surface Satellite) LAI ranging from 2001-2011 has been used as the basic data. Data was normalized for each time phase to eliminate the phenology effect, and then the percentile of the normalized data was calculated as the SAD input. 20% was set as the drought threshold, and a clustering algorithm was used to identify individual drought events for each time step. Actual drought events were identified when considering multiple clusters merge to form a larger drought or a drought event breaks up into multiple small droughts according to the distance of drought centers and the overlapping drought area. Severity, duration and area were recorded for each actual drought event. Finally, we utilized the existing DSI drought index product for comparison. LAIpct drought index can detect both short-term and long-term drought events. In the last decades, most of the droughts at global scale are short-term that less than 1 year, and the longest drought event lasts for 3 year. The LAIpct drought area percentage consist well with DSI, and according to the drought severity classification of United States Drought Monitor system, we found the 20% LAIpct corresponds to moderate drought, 15% LAIpct corresponds to severe drought, and 10% LAIpct corresponds to extreme drought. For some typical drought event, we found the LAIpct drought spatial patterns agree well with DSI, and from the aspect of temporal consistency, LAIpct seems smoother and fitter to the reality than DSI product. Although the short period LAIpct drought index product hinders the analysis of global climate change to some extent, it provides a new way to better monitor the agricultural drought.

Applications and development of new algorithms for displacement analysis using InSAR time series

NASA Astrophysics Data System (ADS)

Osmanoglu, Batuhan

Time series analysis of Synthetic Aperture Radar Interferometry (InSAR) data has become an important scientific tool for monitoring and measuring the displacement of Earth's surface due to a wide range of phenomena, including earthquakes, volcanoes, landslides, changes in ground water levels, and wetlands. Time series analysis is a product of interferometric phase measurements, which become ambiguous when the observed motion is larger than half of the radar wavelength. Thus, phase observations must first be unwrapped in order to obtain physically meaningful results. Persistent Scatterer Interferometry (PSI), Stanford Method for Persistent Scatterers (StaMPS), Short Baselines Interferometry (SBAS) and Small Temporal Baseline Subset (STBAS) algorithms solve for this ambiguity using a series of spatio-temporal unwrapping algorithms and filters. In this dissertation, I improve upon current phase unwrapping algorithms, and apply the PSI method to study subsidence in Mexico City. PSI was used to obtain unwrapped deformation rates in Mexico City (Chapter 3),where ground water withdrawal in excess of natural recharge causes subsurface, clay-rich sediments to compact. This study is based on 23 satellite SAR scenes acquired between January 2004 and July 2006. Time series analysis of the data reveals a maximum line-of-sight subsidence rate of 300mm/yr at a high enough resolution that individual subsidence rates for large buildings can be determined. Differential motion and related structural damage along an elevated metro rail was evident from the results. Comparison of PSI subsidence rates with data from permanent GPS stations indicate root mean square (RMS) agreement of 6.9 mm/yr, about the level expected based on joint data uncertainty. The Mexico City results suggest negligible recharge, implying continuing degradation and loss of the aquifer in the third largest metropolitan area in the world. Chapters 4 and 5 illustrate the link between time series analysis and three-dimensional (3-D) phase unwrapping. Chapter 4 focuses on the unwrapping path. Unwrapping algorithms can be divided into two groups, path-dependent and path-independent algorithms. Path-dependent algorithms use local unwrapping functions applied pixel-by-pixel to the dataset. In contrast, path-independent algorithms use global optimization methods such as least squares, and return a unique solution. However, when aliasing and noise are present, path-independent algorithms can underestimate the signal in some areas due to global fitting criteria. Path-dependent algorithms do not underestimate the signal, but, as the name implies, the unwrapping path can affect the result. Comparison between existing path algorithms and a newly developed algorithm based on Fisher information theory was conducted. Results indicate that Fisher information theory does indeed produce lower misfit results for most tested cases. Chapter 5 presents a new time series analysis method based on 3-D unwrapping of SAR data using extended Kalman filters. Existing methods for time series generation using InSAR data employ special filters to combine two-dimensional (2-D) spatial unwrapping with one-dimensional (1-D) temporal unwrapping results. The new method, however, combines observations in azimuth, range and time for repeat pass interferometry. Due to the pixel-by-pixel characteristic of the filter, the unwrapping path is selected based on a quality map. This unwrapping algorithm is the first application of extended Kalman filters to the 3-D unwrapping problem. Time series analyses of InSAR data are used in a variety of applications with different characteristics. Consequently, it is difficult to develop a single algorithm that can provide optimal results in all cases, given that different algorithms possess a unique set of strengths and weaknesses. Nonetheless, filter-based unwrapping algorithms such as the one presented in this dissertation have the capability of joining multiple observations into a uniform solution, which is becoming an important feature with continuously growing datasets.

An advanced algorithm for deformation estimation in non-urban areas

NASA Astrophysics Data System (ADS)

Goel, Kanika; Adam, Nico

2012-09-01

This paper presents an advanced differential SAR interferometry stacking algorithm for high resolution deformation monitoring in non-urban areas with a focus on distributed scatterers (DSs). Techniques such as the Small Baseline Subset Algorithm (SBAS) have been proposed for processing DSs. SBAS makes use of small baseline differential interferogram subsets. Singular value decomposition (SVD), i.e. L2 norm minimization is applied to link independent subsets separated by large baselines. However, the interferograms used in SBAS are multilooked using a rectangular window to reduce phase noise caused for instance by temporal decorrelation, resulting in a loss of resolution and the superposition of topography and deformation signals from different objects. Moreover, these have to be individually phase unwrapped and this can be especially difficult in natural terrains. An improved deformation estimation technique is presented here which exploits high resolution SAR data and is suitable for rural areas. The implemented method makes use of small baseline differential interferograms and incorporates an object adaptive spatial phase filtering and residual topography removal for an accurate phase and coherence estimation, while preserving the high resolution provided by modern satellites. This is followed by retrieval of deformation via the SBAS approach, wherein, the phase inversion is performed using an L1 norm minimization which is more robust to the typical phase unwrapping errors encountered in non-urban areas. Meter resolution TerraSAR-X data of an underground gas storage reservoir in Germany is used for demonstrating the effectiveness of this newly developed technique in rural areas.

Automatic detection of oesophageal intubation based on ventilation pressure waveforms shows high sensitivity and specificity in patients with pulmonary disease.

PubMed

Kalmar, Alain F; Absalom, Anthony; Rombouts, Pieter; Roets, Jelle; Dewaele, Frank; Verdonck, Pascal; Stemerdink, Arjanne; Zijlstra, Jan G; Monsieurs, Koenraad G

2016-08-01

Unrecognised endotracheal tube misplacement in emergency intubations has a reported incidence of up to 17%. Current detection methods have many limitations restricting their reliability and availability in these circumstances. There is therefore a clinical need for a device that is small enough to be practical in emergency situations and that can detect oesophageal intubation within seconds. In a first reported evaluation, we demonstrated an algorithm based on pressure waveform analysis, able to determine tube location with high reliability in healthy patients. The aim of this study was to validate the specificity of the algorithm in patients with abnormal pulmonary compliance, and to demonstrate the reliability of a newly developed small device that incorporates the technology. Intubated patients with mild to moderate lung injury, admitted to intensive care were included in the study. The device was connected to the endotracheal tube, and three test ventilations were performed in each patient. All diagnostic data were recorded on PC for subsequent specificity/sensitivity analysis. A total of 105 ventilations in 35 patients with lung injury were analysed. With the threshold D-value of 0.1, the system showed a 100% sensitivity and specificity to diagnose tube location. The algorithm retained its specificity in patients with decreased pulmonary compliance. We also demonstrated the feasibility to integrate sensors and diagnostic hardware in a small, portable hand-held device for convenient use in emergency situations. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Evaluation and integration of functional annotation pipelines for newly sequenced organisms: the potato genome as a test case.

PubMed

Amar, David; Frades, Itziar; Danek, Agnieszka; Goldberg, Tatyana; Sharma, Sanjeev K; Hedley, Pete E; Proux-Wera, Estelle; Andreasson, Erik; Shamir, Ron; Tzfadia, Oren; Alexandersson, Erik

2014-12-05

For most organisms, even if their genome sequence is available, little functional information about individual genes or proteins exists. Several annotation pipelines have been developed for functional analysis based on sequence, 'omics', and literature data. However, researchers encounter little guidance on how well they perform. Here, we used the recently sequenced potato genome as a case study. The potato genome was selected since its genome is newly sequenced and it is a non-model plant even if there is relatively ample information on individual potato genes, and multiple gene expression profiles are available. We show that the automatic gene annotations of potato have low accuracy when compared to a "gold standard" based on experimentally validated potato genes. Furthermore, we evaluate six state-of-the-art annotation pipelines and show that their predictions are markedly dissimilar (Jaccard similarity coefficient of 0.27 between pipelines on average). To overcome this discrepancy, we introduce a simple GO structure-based algorithm that reconciles the predictions of the different pipelines. We show that the integrated annotation covers more genes, increases by over 50% the number of highly co-expressed GO processes, and obtains much higher agreement with the gold standard. We find that different annotation pipelines produce different results, and show how to integrate them into a unified annotation that is of higher quality than each single pipeline. We offer an improved functional annotation of both PGSC and ITAG potato gene models, as well as tools that can be applied to additional pipelines and improve annotation in other organisms. This will greatly aid future functional analysis of '-omics' datasets from potato and other organisms with newly sequenced genomes. The new potato annotations are available with this paper.

Artificial Neural Identification and LMI Transformation for Model Reduction-Based Control of the Buck Switch-Mode Regulator

NASA Astrophysics Data System (ADS)

Al-Rabadi, Anas N.

2009-10-01

This research introduces a new method of intelligent control for the control of the Buck converter using newly developed small signal model of the pulse width modulation (PWM) switch. The new method uses supervised neural network to estimate certain parameters of the transformed system matrix [Ã]. Then, a numerical algorithm used in robust control called linear matrix inequality (LMI) optimization technique is used to determine the permutation matrix [P] so that a complete system transformation {[B˜], [C˜], [Ẽ]} is possible. The transformed model is then reduced using the method of singular perturbation, and state feedback control is applied to enhance system performance. The experimental results show that the new control methodology simplifies the model in the Buck converter and thus uses a simpler controller that produces the desired system response for performance enhancement.

Identification of Upward-going Muons for Dark Matter Searches at the NOvA Experiment

NASA Astrophysics Data System (ADS)

Xiao, Liting

2014-03-01

We search for energetic neutrinos that could originate from dark matter particles annihilating in the core of the Sun using the newly built NOvA Far Detector at Fermilab. Only upward-going muons produced via charged-current interactions are selected as signal in order to eliminate backgrounds from cosmic ray muons, which dominate the downward-going flux. We investigate several algorithms so as to develop an effective way of reconstructing the directionality of cosmic tracks at the trigger level. These studies are a crucial part of understanding how NOvA may compete with other experiments that are performing similar searches. In order to be competitive NOvA must be capable of rejecting backgrounds from downward-going cosmic rays with very high efficiency while accepting most upward-going muons. Acknowledgements: The Jefferson Trust, Fermilab, UVA Department of Physics.

Improving Photometric Calibration of Meteor Video Camera Systems

NASA Technical Reports Server (NTRS)

Ehlert, Steven; Kingery, Aaron; Cooke, William

2016-01-01

Current optical observations of meteors are commonly limited by systematic uncertainties in photometric calibration at the level of approximately 0.5 mag or higher. Future improvements to meteor ablation models, luminous efficiency models, or emission spectra will hinge on new camera systems and techniques that significantly reduce calibration uncertainties and can reliably perform absolute photometric measurements of meteors. In this talk we discuss the algorithms and tests that NASA's Meteoroid Environment Office (MEO) has developed to better calibrate photometric measurements for the existing All-Sky and Wide-Field video camera networks as well as for a newly deployed four-camera system for measuring meteor colors in Johnson-Cousins BV RI filters. In particular we will emphasize how the MEO has been able to address two long-standing concerns with the traditional procedure, discussed in more detail below.

A multi-parametric particle-pairing algorithm for particle tracking in single and multiphase flows

NASA Astrophysics Data System (ADS)

Cardwell, Nicholas D.; Vlachos, Pavlos P.; Thole, Karen A.

2011-10-01

Multiphase flows (MPFs) offer a rich area of fundamental study with many practical applications. Examples of such flows range from the ingestion of foreign particulates in gas turbines to transport of particles within the human body. Experimental investigation of MPFs, however, is challenging, and requires techniques that simultaneously resolve both the carrier and discrete phases present in the flowfield. This paper presents a new multi-parametric particle-pairing algorithm for particle tracking velocimetry (MP3-PTV) in MPFs. MP3-PTV improves upon previous particle tracking algorithms by employing a novel variable pair-matching algorithm which utilizes displacement preconditioning in combination with estimated particle size and intensity to more effectively and accurately match particle pairs between successive images. To improve the method's efficiency, a new particle identification and segmentation routine was also developed. Validation of the new method was initially performed on two artificial data sets: a traditional single-phase flow published by the Visualization Society of Japan (VSJ) and an in-house generated MPF data set having a bi-modal distribution of particles diameters. Metrics of the measurement yield, reliability and overall tracking efficiency were used for method comparison. On the VSJ data set, the newly presented segmentation routine delivered a twofold improvement in identifying particles when compared to other published methods. For the simulated MPF data set, measurement efficiency of the carrier phases improved from 9% to 41% for MP3-PTV as compared to a traditional hybrid PTV. When employed on experimental data of a gas-solid flow, the MP3-PTV effectively identified the two particle populations and reported a vector efficiency and velocity measurement error comparable to measurements for the single-phase flow images. Simultaneous measurement of the dispersed particle and the carrier flowfield velocities allowed for the calculation of instantaneous particle slip velocities, illustrating the algorithm's strength to robustly and accurately resolve polydispersed MPFs.

Quadratic trigonometric B-spline for image interpolation using GA

PubMed Central

Abbas, Samreen; Irshad, Misbah

2017-01-01

In this article, a new quadratic trigonometric B-spline with control parameters is constructed to address the problems related to two dimensional digital image interpolation. The newly constructed spline is then used to design an image interpolation scheme together with one of the soft computing techniques named as Genetic Algorithm (GA). The idea of GA has been formed to optimize the control parameters in the description of newly constructed spline. The Feature SIMilarity (FSIM), Structure SIMilarity (SSIM) and Multi-Scale Structure SIMilarity (MS-SSIM) indices along with traditional Peak Signal-to-Noise Ratio (PSNR) are employed as image quality metrics to analyze and compare the outcomes of approach offered in this work, with three of the present digital image interpolation schemes. The upshots show that the proposed scheme is better choice to deal with the problems associated to image interpolation. PMID:28640906

Quadratic trigonometric B-spline for image interpolation using GA.

PubMed

Hussain, Malik Zawwar; Abbas, Samreen; Irshad, Misbah

2017-01-01

In this article, a new quadratic trigonometric B-spline with control parameters is constructed to address the problems related to two dimensional digital image interpolation. The newly constructed spline is then used to design an image interpolation scheme together with one of the soft computing techniques named as Genetic Algorithm (GA). The idea of GA has been formed to optimize the control parameters in the description of newly constructed spline. The Feature SIMilarity (FSIM), Structure SIMilarity (SSIM) and Multi-Scale Structure SIMilarity (MS-SSIM) indices along with traditional Peak Signal-to-Noise Ratio (PSNR) are employed as image quality metrics to analyze and compare the outcomes of approach offered in this work, with three of the present digital image interpolation schemes. The upshots show that the proposed scheme is better choice to deal with the problems associated to image interpolation.

Evaluation of a newly developed media-supported 4-step approach for basic life support training

PubMed Central

2012-01-01

Objective The quality of external chest compressions (ECC) is of primary importance within basic life support (BLS). Recent guidelines delineate the so-called 4“-step approach” for teaching practical skills within resuscitation training guided by a certified instructor. The objective of this study was to evaluate whether a “media-supported 4-step approach” for BLS training leads to equal practical performance compared to the standard 4-step approach. Materials and methods After baseline testing, 220 laypersons were either trained using the widely accepted method for resuscitation training (4-step approach) or using a newly created “media-supported 4-step approach”, both of equal duration. In this approach, steps 1 and 2 were ensured via a standardised self-produced podcast, which included all of the information regarding the BLS algorithm and resuscitation skills. Participants were tested on manikins in the same mock cardiac arrest single-rescuer scenario prior to intervention, after one week and after six months with respect to ECC-performance, and participants were surveyed about the approach. Results Participants (age 23 ± 11, 69% female) reached comparable practical ECC performances in both groups, with no statistical difference. Even after six months, there was no difference detected in the quality of the initial assessment algorithm or delay concerning initiation of CPR. Overall, at least 99% of the intervention group (n = 99; mean 1.5 ± 0.8; 6-point Likert scale: 1 = completely agree, 6 = completely disagree) agreed that the video provided an adequate introduction to BLS skills. Conclusions The “media-supported 4-step approach” leads to comparable practical ECC-performance compared to standard teaching, even with respect to retention of skills. Therefore, this approach could be useful in special educational settings where, for example, instructors’ resources are sparse or large-group sessions have to be prepared. PMID:22647148

Passive Microwave Remote Sensing of Colorado Watersheds Using Calibrated, Enhanced-Resolution Brightness Temperatures (CETB) from AMSR-E and SSM/I for Estimation of Snowmelt Timing

NASA Astrophysics Data System (ADS)

Johnson, M.; Ramage, J. M.; Troy, T. J.; Brodzik, M. J.

2017-12-01

Understanding the timing of snowmelt is critical for water resources management in snow-dominated watersheds. Passive microwave remote sensing has been used to estimate melt-refreeze events through brightness temperature satellite observations taken with sensors like the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). Previous studies were limited to lower resolution ( 25 km) datasets, making it difficult to quantify the snowpack in heterogeneous, high-relief areas. This study investigates the use of newly available passive microwave calibrated, enhanced-resolution brightness temperatures (CETB) produced at the National Snow and Ice Data Center to estimate melt timing at much higher spatial resolution ( 3-6 km). CETB datasets generated from SSM/I and AMSR-E records will be used to examine three mountainous basins in Colorado. The CETB datasets retain twice-daily (day/night) observations of brightness temperatures. Therefore, we employ the diurnal amplitude variation (DAV) method to detect melt onset and melt occurrences to determine if algorithms developed for legacy data are valid with the improved CETB dataset. We compare melt variability with nearby stream discharge records to determine an optimum melt onset algorithm using the newly reprocessed data. This study investigates the effectiveness of the CETB product for several locations in Colorado (North Park, Rabbit Ears, Fraser) that were the sites of previous ground/airborne surveys during the NASA Cold Land Processes Field Experiment (CLPX 2002-2003). In summary, this work lays the foundation for the utilization of higher resolution reprocessed CETB data for snow evolution more broadly in a range of environments. Consequently, the new processing methods and improved spatial resolution will enable hydrologists to better analyze trends in snow-dominated mountainous watersheds for more effective water resources management.

Propeller performance analysis and multidisciplinary optimization using a genetic algorithm

NASA Astrophysics Data System (ADS)

Burger, Christoph

A propeller performance analysis program has been developed and integrated into a Genetic Algorithm for design optimization. The design tool will produce optimal propeller geometries for a given goal, which includes performance and/or acoustic signature. A vortex lattice model is used for the propeller performance analysis and a subsonic compact source model is used for the acoustic signature determination. Compressibility effects are taken into account with the implementation of Prandtl-Glauert domain stretching. Viscous effects are considered with a simple Reynolds number based model to account for the effects of viscosity in the spanwise direction. An empirical flow separation model developed from experimental lift and drag coefficient data of a NACA 0012 airfoil is included. The propeller geometry is generated using a recently introduced Class/Shape function methodology to allow for efficient use of a wide design space. Optimizing the angle of attack, the chord, the sweep and the local airfoil sections, produced blades with favorable tradeoffs between single and multiple point optimizations of propeller performance and acoustic noise signatures. Optimizations using a binary encoded IMPROVE(c) Genetic Algorithm (GA) and a real encoded GA were obtained after optimization runs with some premature convergence. The newly developed real encoded GA was used to obtain the majority of the results which produced generally better convergence characteristics when compared to the binary encoded GA. The optimization trade-offs show that single point optimized propellers have favorable performance, but circulation distributions were less smooth when compared to dual point or multiobjective optimizations. Some of the single point optimizations generated propellers with proplets which show a loading shift to the blade tip region. When noise is included into the objective functions some propellers indicate a circulation shift to the inboard sections of the propeller as well as a reduction in propeller diameter. In addition the propeller number was increased in some optimizations to reduce the acoustic blade signature.

Recognizing flu-like symptoms from videos.

PubMed

Thi, Tuan Hue; Wang, Li; Ye, Ning; Zhang, Jian; Maurer-Stroh, Sebastian; Cheng, Li

2014-09-12

Vision-based surveillance and monitoring is a potential alternative for early detection of respiratory disease outbreaks in urban areas complementing molecular diagnostics and hospital and doctor visit-based alert systems. Visible actions representing typical flu-like symptoms include sneeze and cough that are associated with changing patterns of hand to head distances, among others. The technical difficulties lie in the high complexity and large variation of those actions as well as numerous similar background actions such as scratching head, cell phone use, eating, drinking and so on. In this paper, we make a first attempt at the challenging problem of recognizing flu-like symptoms from videos. Since there was no related dataset available, we created a new public health dataset for action recognition that includes two major flu-like symptom related actions (sneeze and cough) and a number of background actions. We also developed a suitable novel algorithm by introducing two types of Action Matching Kernels, where both types aim to integrate two aspects of local features, namely the space-time layout and the Bag-of-Words representations. In particular, we show that the Pyramid Match Kernel and Spatial Pyramid Matching are both special cases of our proposed kernels. Besides experimenting on standard testbed, the proposed algorithm is evaluated also on the new sneeze and cough set. Empirically, we observe that our approach achieves competitive performance compared to the state-of-the-arts, while recognition on the new public health dataset is shown to be a non-trivial task even with simple single person unobstructed view. Our sneeze and cough video dataset and newly developed action recognition algorithm is the first of its kind and aims to kick-start the field of action recognition of flu-like symptoms from videos. It will be challenging but necessary in future developments to consider more complex real-life scenario of detecting these actions simultaneously from multiple persons in possibly crowded environments.

Miss-distance indicator for tank main guns

NASA Astrophysics Data System (ADS)

Bornstein, Jonathan A.; Hillis, David B.

1996-06-01

Tank main gun systems must possess extremely high levels of accuracy to perform successfully in battle. Under some circumstances, the first round fired in an engagement may miss the intended target, and it becomes necessary to rapidly correct fire. A breadboard automatic miss-distance indicator system was previously developed to assist in this process. The system, which would be mounted on a 'wingman' tank, consists of a charged-coupled device (CCD) camera and computer-based image-processing system, coupled with a separate infrared sensor to detect muzzle flash. For the system to be successfully employed with current generation tanks, it must be reliable, be relatively low cost, and respond rapidly maintaining current firing rates. Recently, the original indicator system was developed further in an effort to assist in achieving these goals. Efforts have focused primarily upon enhanced image-processing algorithms, both to improve system reliability and to reduce processing requirements. Intelligent application of newly refined trajectory models has permitted examination of reduced areas of interest and enhanced rejection of false alarms, significantly improving system performance.

Jointly reconstructing ground motion and resistivity for ERT-based slope stability monitoring

NASA Astrophysics Data System (ADS)

Boyle, Alistair; Wilkinson, Paul B.; Chambers, Jonathan E.; Meldrum, Philip I.; Uhlemann, Sebastian; Adler, Andy

2018-02-01

Electrical resistivity tomography (ERT) is increasingly being used to investigate unstable slopes and monitor the hydrogeological processes within. But movement of electrodes or incorrect placement of electrodes with respect to an assumed model can introduce significant resistivity artefacts into the reconstruction. In this work, we demonstrate a joint resistivity and electrode movement reconstruction algorithm within an iterative Gauss-Newton framework. We apply this to ERT monitoring data from an active slow-moving landslide in the UK. Results show fewer resistivity artefacts and suggest that electrode movement and resistivity can be reconstructed at the same time under certain conditions. A new 2.5-D formulation for the electrode position Jacobian is developed and is shown to give accurate numerical solutions when compared to the adjoint method on 3-D models. On large finite element meshes, the calculation time of the newly developed approach was also proven to be orders of magnitude faster than the 3-D adjoint method and addressed modelling errors in the 2-D perturbation and adjoint electrode position Jacobian.

A proposed Applications Information System - Concept, implementation, and growth

NASA Technical Reports Server (NTRS)

Mcconnell, Dudley G.; Hood, Carroll A.; Butera, M. Kristine

1987-01-01

This paper describes a newly developed concept within NASA for an Applications Information System (AIS). The AIS would provide the opportunity to the public and private sectors of shared participation in a remote sensing research program directed to a particular set of land-use or environmental problems. Towards this end, the AIS would offer the technological framework and information system resources to overcome many of the deficiencies that end-users have faced over the years such as limited access to data, delay in data delivery, and a limited access to data reduction algorithms and models to convert data to geophysical measurements. In addition, the AIS will take advantage of NASA developments in networking among information systems and use of state of the art technology, such as CD Roms and optical disks for the purpose of increasing the scientific benefits of applied environmental research. The rationale for the establishment of an AIS, a methodology for a step-wise, modular implementation, and the relationship of the AIS to other NASA information systems are discussed.

An Object-Oriented Serial DSMC Simulation Package

NASA Astrophysics Data System (ADS)

Liu, Hongli; Cai, Chunpei

2011-05-01

A newly developed three-dimensional direct simulation Monte Carlo (DSMC) simulation package, named GRASP ("Generalized Rarefied gAs Simulation Package"), is reported in this paper. This package utilizes the concept of simulation engine, many C++ features and software design patterns. The package has an open architecture which can benefit further development and maintenance of the code. In order to reduce the engineering time for three-dimensional models, a hybrid grid scheme, combined with a flexible data structure compiled by C++ language, are implemented in this package. This scheme utilizes a local data structure based on the computational cell to achieve high performance on workstation processors. This data structure allows the DSMC algorithm to be very efficiently parallelized with domain decomposition and it provides much flexibility in terms of grid types. This package can utilize traditional structured, unstructured or hybrid grids within the framework of a single code to model arbitrarily complex geometries and to simulate rarefied gas flows. Benchmark test cases indicate that this package has satisfactory accuracy for complex rarefied gas flows.

Composition/Structure/Dynamics of comet and planetary satellite atmospheres

NASA Technical Reports Server (NTRS)

Combi, Michael R. (Principal Investigator)

1995-01-01

This research program addresses two cases of tenuous planetary atmospheres: comets and Io. The comet atmospheric research seeks to analyze a set of spatial profiles of CN in comet Halley taken in a 7.4-day period in April 1986; to apply a new dust coma model to various observations; and to analyze observations of the inner hydrogen coma, which can be optically thick to the resonance scattering of Lyman-alpha radiation, with the newly developed approach that combines a spherical radiative transfer model with our Monte Carlo H coma model. The Io research seeks to understand the atmospheric escape from Io with a hybrid-kinetic model for neutral gases and plasma given methods and algorithms developed for the study of neutral gas cometary atmospheres and the earth's polar wind and plasmasphere. Progress is reported on cometary Hydrogen Lyman-alpha studies; time-series analysis of cometary spatial profiles; model analysis of the dust comae of comets; and a global kinetic atmospheric model of Io.

[Computer-assisted operational planning for pediatric abdominal surgery. 3D-visualized MRI with volume rendering].

PubMed

Günther, P; Tröger, J; Holland-Cunz, S; Waag, K L; Schenk, J P

2006-08-01

Exact surgical planning is necessary for complex operations of pathological changes in anatomical structures of the pediatric abdomen. 3D visualization and computer-assisted operational planning based on CT data are being increasingly used for difficult operations in adults. To minimize radiation exposure and for better soft tissue contrast, sonography and MRI are the preferred diagnostic methods in pediatric patients. Because of manifold difficulties 3D visualization of these MRI data has not been realized so far, even though the field of embryonal malformations and tumors could benefit from this.A newly developed and modified raycasting-based powerful 3D volume rendering software (VG Studio Max 1.2) for the planning of pediatric abdominal surgery is presented. With the help of specifically developed algorithms, a useful surgical planning system is demonstrated. Thanks to the easy handling and high-quality visualization with enormous gain of information, the presented system is now an established part of routine surgical planning.

Observation of Structure of Surfaces and Interfaces by Synchrotron X-ray Diffraction: Atomic-Scale Imaging and Time-Resolved Measurements

NASA Astrophysics Data System (ADS)

Wakabayashi, Yusuke; Shirasawa, Tetsuroh; Voegeli, Wolfgang; Takahashi, Toshio

2018-06-01

The recent developments in synchrotron optics, X-ray detectors, and data analysis algorithms have enhanced the capability of the surface X-ray diffraction technique. This technique has been used to clarify the atomic arrangement around surfaces in a non-contact and nondestructive manner. An overview of surface X-ray diffraction, from the historical development to recent topics, is presented. In the early stage of this technique, surface reconstructions of simple semiconductors or metals were studied. Currently, the surface or interface structures of complicated functional materials are examined with sub-Å resolution. As examples, the surface structure determination of organic semiconductors and of a one-dimensional structure on silicon are presented. A new frontier is time-resolved interfacial structure analysis. A recent observation of the structure and dynamics of the electric double layer of ionic liquids, and an investigation of the structural evolution in the wettability transition on a TiO2 surface that utilizes a newly designed time-resolved surface diffractometer, are presented.

Tomographic image reconstruction using x-ray phase information

NASA Astrophysics Data System (ADS)

Momose, Atsushi; Takeda, Tohoru; Itai, Yuji; Hirano, Keiichi

1996-04-01

We have been developing phase-contrast x-ray computed tomography (CT) to make possible the observation of biological soft tissues without contrast enhancement. Phase-contrast x-ray CT requires for its input data the x-ray phase-shift distributions or phase-mapping images caused by an object. These were measured with newly developed fringe-scanning x-ray interferometry. Phase-mapping images at different projection directions were obtained by rotating the object in an x-ray interferometer, and were processed with a standard CT algorithm. A phase-contrast x-ray CT image of a nonstained cancerous tissue was obtained using 17.7 keV synchrotron x rays with 12 micrometer voxel size, although the size of the observation area was at most 5 mm. The cancerous lesions were readily distinguishable from normal tissues. Moreover, fine structures corresponding to cancerous degeneration and fibrous tissues were clearly depicted. It is estimated that the present system is sensitive down to a density deviation of 4 mg/cm3.

Genetic evolutionary taboo search for optimal marker placement in infrared patient setup

NASA Astrophysics Data System (ADS)

Riboldi, M.; Baroni, G.; Spadea, M. F.; Tagaste, B.; Garibaldi, C.; Cambria, R.; Orecchia, R.; Pedotti, A.

2007-09-01

In infrared patient setup adequate selection of the external fiducial configuration is required for compensating inner target displacements (target registration error, TRE). Genetic algorithms (GA) and taboo search (TS) were applied in a newly designed approach to optimal marker placement: the genetic evolutionary taboo search (GETS) algorithm. In the GETS paradigm, multiple solutions are simultaneously tested in a stochastic evolutionary scheme, where taboo-based decision making and adaptive memory guide the optimization process. The GETS algorithm was tested on a group of ten prostate patients, to be compared to standard optimization and to randomly selected configurations. The changes in the optimal marker configuration, when TRE is minimized for OARs, were specifically examined. Optimal GETS configurations ensured a 26.5% mean decrease in the TRE value, versus 19.4% for conventional quasi-Newton optimization. Common features in GETS marker configurations were highlighted in the dataset of ten patients, even when multiple runs of the stochastic algorithm were performed. Including OARs in TRE minimization did not considerably affect the spatial distribution of GETS marker configurations. In conclusion, the GETS algorithm proved to be highly effective in solving the optimal marker placement problem. Further work is needed to embed site-specific deformation models in the optimization process.

MR fingerprinting reconstruction with Kalman filter.

PubMed

Zhang, Xiaodi; Zhou, Zechen; Chen, Shiyang; Chen, Shuo; Li, Rui; Hu, Xiaoping

2017-09-01

Magnetic resonance fingerprinting (MR fingerprinting or MRF) is a newly introduced quantitative magnetic resonance imaging technique, which enables simultaneous multi-parameter mapping in a single acquisition with improved time efficiency. The current MRF reconstruction method is based on dictionary matching, which may be limited by the discrete and finite nature of the dictionary and the computational cost associated with dictionary construction, storage and matching. In this paper, we describe a reconstruction method based on Kalman filter for MRF, which avoids the use of dictionary to obtain continuous MR parameter measurements. With this Kalman filter framework, the Bloch equation of inversion-recovery balanced steady state free-precession (IR-bSSFP) MRF sequence was derived to predict signal evolution, and acquired signal was entered to update the prediction. The algorithm can gradually estimate the accurate MR parameters during the recursive calculation. Single pixel and numeric brain phantom simulation were implemented with Kalman filter and the results were compared with those from dictionary matching reconstruction algorithm to demonstrate the feasibility and assess the performance of Kalman filter algorithm. The results demonstrated that Kalman filter algorithm is applicable for MRF reconstruction, eliminating the need for a pre-define dictionary and obtaining continuous MR parameter in contrast to the dictionary matching algorithm. Copyright © 2017 Elsevier Inc. All rights reserved.

Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous).

PubMed

Hemmateenejad, Bahram; Akhond, Morteza; Miri, Ramin; Shamsipur, Mojtaba

2003-01-01

A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus.

PubMed

Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok

2013-02-01

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm-based neural network IAQ models outperformed the traditional ANN methods of the back-propagation and the radial basis function networks. The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also.

Development of Yellow Sand Image Products Using Infrared Brightness Temperature Difference Method

NASA Astrophysics Data System (ADS)

Ha, J.; Kim, J.; Kwak, M.; Ha, K.

2007-12-01

A technique for detection of airborne yellow sand dust using meteorological satellite has been developed from various bands from ultraviolet to infrared channels. Among them, Infrared (IR) channels have an advantage of detecting aerosols over high reflecting surface as well as during nighttime. There had been suggestion of using brightness temperature difference (BTD) between 11 and 12¥ìm. We have found that the technique is highly depends on surface temperature, emissivity, and zenith angle, which results in changing the threshold of BTD. In order to overcome these problems, we have constructed the background brightness temperature threshold of BTD and then aerosol index (AI) has been determined from subtracting the background threshold from BTD of our interested scene. Along with this, we utilized high temporal coverage of geostationary satellite, MTSAT, to improve the reliability of the determined AI signal. The products have been evaluated by comparing the forecasted wind field with the movement fiend of AI. The statistical score test illustrates that this newly developed algorithm produces a promising result for detecting mineral dust by reducing the errors with respect to the current BTD method.

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging.

PubMed

Zhang, Shuanghui; Liu, Yongxiang; Li, Xiang; Bi, Guoan

2016-04-28

This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression.

Dehazed Image Quality Assessment by Haze-Line Theory

NASA Astrophysics Data System (ADS)

Song, Yingchao; Luo, Haibo; Lu, Rongrong; Ma, Junkai

2017-06-01

Images captured in bad weather suffer from low contrast and faint color. Recently, plenty of dehazing algorithms have been proposed to enhance visibility and restore color. However, there is a lack of evaluation metrics to assess the performance of these algorithms or rate them. In this paper, an indicator of contrast enhancement is proposed basing on the newly proposed haze-line theory. The theory assumes that colors of a haze-free image are well approximated by a few hundred distinct colors, which form tight clusters in RGB space. The presence of haze makes each color cluster forms a line, which is named haze-line. By using these haze-lines, we assess performance of dehazing algorithms designed to enhance the contrast by measuring the inter-cluster deviations between different colors of dehazed image. Experimental results demonstrated that the proposed Color Contrast (CC) index correlates well with human judgments of image contrast taken in a subjective test on various scene of dehazed images and performs better than state-of-the-art metrics.

RNACompress: Grammar-based compression and informational complexity measurement of RNA secondary structure.

PubMed

Liu, Qi; Yang, Yu; Chen, Chun; Bu, Jiajun; Zhang, Yin; Ye, Xiuzi

2008-03-31

With the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression. RNACompress employs an efficient grammar-based model to compress RNA sequences and their secondary structures. The main goals of this algorithm are two fold: (1) present a robust and effective way for RNA structural data compression; (2) design a suitable model to represent RNA secondary structure as well as derive the informational complexity of the structural data based on compression. Our extensive tests have shown that RNACompress achieves a universally better compression ratio compared with other sequence-specific or common text-specific compression algorithms, such as Gencompress, winrar and gzip. Moreover, a test of the activities of distinct GTP-binding RNAs (aptamers) compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity. These results lead to an objective means of comparing the functional properties of heteropolymers from the information perspective. A universal algorithm for the compression of RNA secondary structure as well as the evaluation of its informational complexity is discussed in this paper. We have developed RNACompress, as a useful tool for academic users. Extensive tests have shown that RNACompress is a universally efficient algorithm for the compression of RNA sequences with their secondary structures. RNACompress also serves as a good measurement of the informational complexity of RNA secondary structure, which can be used to study the functional activities of RNA molecules.

RNACompress: Grammar-based compression and informational complexity measurement of RNA secondary structure

PubMed Central

Liu, Qi; Yang, Yu; Chen, Chun; Bu, Jiajun; Zhang, Yin; Ye, Xiuzi

2008-01-01

Background With the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression. Results RNACompress employs an efficient grammar-based model to compress RNA sequences and their secondary structures. The main goals of this algorithm are two fold: (1) present a robust and effective way for RNA structural data compression; (2) design a suitable model to represent RNA secondary structure as well as derive the informational complexity of the structural data based on compression. Our extensive tests have shown that RNACompress achieves a universally better compression ratio compared with other sequence-specific or common text-specific compression algorithms, such as Gencompress, winrar and gzip. Moreover, a test of the activities of distinct GTP-binding RNAs (aptamers) compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity. These results lead to an objective means of comparing the functional properties of heteropolymers from the information perspective. Conclusion A universal algorithm for the compression of RNA secondary structure as well as the evaluation of its informational complexity is discussed in this paper. We have developed RNACompress, as a useful tool for academic users. Extensive tests have shown that RNACompress is a universally efficient algorithm for the compression of RNA sequences with their secondary structures. RNACompress also serves as a good measurement of the informational complexity of RNA secondary structure, which can be used to study the functional activities of RNA molecules. PMID:18373878

Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

NASA Technical Reports Server (NTRS)

Farhadi, Leila; Reichle, Rolf H.; DeLannoy, Gabrielle J. M.; Kimball, John S.

2014-01-01

The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission.

SU-F-T-20: Novel Catheter Lumen Recognition Algorithm for Rapid Digitization

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

Dise, J; McDonald, D; Ashenafi, M

Purpose: Manual catheter recognition remains a time-consuming aspect of high-dose-rate brachytherapy (HDR) treatment planning. In this work, a novel catheter lumen recognition algorithm was created for accurate and rapid digitization. Methods: MatLab v8.5 was used to create the catheter recognition algorithm. Initially, the algorithm searches the patient CT dataset using an intensity based k-means filter designed to locate catheters. Once the catheters have been located, seed points are manually selected to initialize digitization of each catheter. From each seed point, the algorithm searches locally in order to automatically digitize the remaining catheter. This digitization is accomplished by finding pixels withmore » similar image curvature and divergence parameters compared to the seed pixel. Newly digitized pixels are treated as new seed positions, and hessian image analysis is used to direct the algorithm toward neighboring catheter pixels, and to make the algorithm insensitive to adjacent catheters that are unresolvable on CT, air pockets, and high Z artifacts. The algorithm was tested using 11 HDR treatment plans, including the Syed template, tandem and ovoid applicator, and multi-catheter lung brachytherapy. Digitization error was calculated by comparing manually determined catheter positions to those determined by the algorithm. Results: he digitization error was 0.23 mm ± 0.14 mm axially and 0.62 mm ± 0.13 mm longitudinally at the tip. The time of digitization, following initial seed placement was less than 1 second per catheter. The maximum total time required to digitize all tested applicators was 4 minutes (Syed template with 15 needles). Conclusion: This algorithm successfully digitizes HDR catheters for a variety of applicators with or without CT markers. The minimal axial error demonstrates the accuracy of the algorithm, and its insensitivity to image artifacts and challenging catheter positioning. Future work to automatically place initial seed positions would improve the algorithm speed.« less

Short- and Long-Term Earthquake Forecasts Based on Statistical Models

NASA Astrophysics Data System (ADS)

Console, Rodolfo; Taroni, Matteo; Murru, Maura; Falcone, Giuseppe; Marzocchi, Warner

2017-04-01

The epidemic-type aftershock sequences (ETAS) models have been experimentally used to forecast the space-time earthquake occurrence rate during the sequence that followed the 2009 L'Aquila earthquake and for the 2012 Emilia earthquake sequence. These forecasts represented the two first pioneering attempts to check the feasibility of providing operational earthquake forecasting (OEF) in Italy. After the 2009 L'Aquila earthquake the Italian Department of Civil Protection nominated an International Commission on Earthquake Forecasting (ICEF) for the development of the first official OEF in Italy that was implemented for testing purposes by the newly established "Centro di Pericolosità Sismica" (CPS, the seismic Hazard Center) at the Istituto Nazionale di Geofisica e Vulcanologia (INGV). According to the ICEF guidelines, the system is open, transparent, reproducible and testable. The scientific information delivered by OEF-Italy is shaped in different formats according to the interested stakeholders, such as scientists, national and regional authorities, and the general public. The communication to people is certainly the most challenging issue, and careful pilot tests are necessary to check the effectiveness of the communication strategy, before opening the information to the public. With regard to long-term time-dependent earthquake forecast, the application of a newly developed simulation algorithm to Calabria region provided typical features in time, space and magnitude behaviour of the seismicity, which can be compared with those of the real observations. These features include long-term pseudo-periodicity and clustering of strong earthquakes, and a realistic earthquake magnitude distribution departing from the Gutenberg-Richter distribution in the moderate and higher magnitude range.

Radiation Hardened Low Power Digital Signal Processor

DTIC Science & Technology

2005-04-15

Image Figure 53.0 Point Spread Function PSF Figure 54.0 Restored Image and Restored PSF Figure 55.0 Newly Created Array Figure 56.0 Deblurred Image and... noise and interference rejection. WOA’s of 32-taps and greater are easily managed by the TCSP. An architecture that could efficiently perform filter...to quickly calculate a Remez filter impulse response to be used in place of the window function. Using the Remez exchange algorithm to calculate the

Consistent Estimates of Very Low HIV Incidence Among People Who Inject Drugs: New York City, 2005–2014

PubMed Central

Arasteh, Kamyar; McKnight, Courtney; Feelemyer, Jonathan; Campbell, Aimée N. C.; Tross, Susan; Smith, Lou; Cooper, Hannah L. F.; Hagan, Holly; Perlman, David

2016-01-01

Objectives. To compare methods for estimating low HIV incidence among persons who inject drugs. Methods. We examined 4 methods in New York City, 2005 to 2014: (1) HIV seroconversions among repeat participants, (2) increase of HIV prevalence by additional years of injection among new injectors, (3) the New York State and Centers for Disease Control and Prevention stratified extrapolation algorithm, and (4) newly diagnosed HIV cases reported to the New York City Department of Health and Mental Hygiene. Results. The 4 estimates were consistent: (1) repeat participants: 0.37 per 100 person-years (PY; 95% confidence interval [CI] = 0.05/100 PY, 1.33/100 PY); (2) regression of prevalence by years injecting: 0.61 per 100 PY (95% CI = 0.36/100 PY, 0.87/100 PY); (3) stratified extrapolation algorithm: 0.32 per 100 PY (95% CI = 0.18/100 PY, 0.46/100 PY); and (4) newly diagnosed cases of HIV: 0.14 per 100 PY (95% CI = 0.11/100 PY, 0.16/100 PY). Conclusions. All methods appear to capture the same phenomenon of very low and decreasing HIV transmission among persons who inject drugs. Public Health Implications. If resources are available, the use of multiple methods would provide better information for public health purposes. PMID:26794160

Automatic contact in DYNA3D for vehicle crashworthiness

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

Whirley, R.G.; Engelmann, B.E.

1993-07-15

This paper presents a new formulation for the automatic definition and treatment of mechanical contact in explicit nonlinear finite element analysis. Automatic contact offers the benefits of significantly reduced model construction time and fewer opportunities for user error, but faces significant challenges in reliability and computational costs. This paper discusses in detail a new four-step automatic contact algorithm. Key aspects of the proposed method include automatic identification of adjacent and opposite surfaces in the global search phase, and the use of a smoothly varying surface normal which allows a consistent treatment of shell intersection and corner contact conditions without ad-hocmore » rules. The paper concludes with three examples which illustrate the performance of the newly proposed algorithm in the public DYNA3D code.« less

Viewing-zone control of integral imaging display using a directional projection and elemental image resizing method.

PubMed

Alam, Md Ashraful; Piao, Mei-Lan; Bang, Le Thanh; Kim, Nam

2013-10-01

Viewing-zone control of integral imaging (II) displays using a directional projection and elemental image (EI) resizing method is proposed. Directional projection of EIs with the same size of microlens pitch causes an EI mismatch at the EI plane. In this method, EIs are generated computationally using a newly introduced algorithm: the directional elemental image generation and resizing algorithm considering the directional projection geometry of each pixel as well as an EI resizing method to prevent the EI mismatch. Generated EIs are projected as a collimated projection beam with a predefined directional angle, either horizontally or vertically. The proposed II display system allows reconstruction of a 3D image within a predefined viewing zone that is determined by the directional projection angle.

Adaptive Trajectory Tracking of Nonholonomic Mobile Robots Using Vision-Based Position and Velocity Estimation.

PubMed

Li, Luyang; Liu, Yun-Hui; Jiang, Tianjiao; Wang, Kai; Fang, Mu

2018-02-01

Despite tremendous efforts made for years, trajectory tracking control (TC) of a nonholonomic mobile robot (NMR) without global positioning system remains an open problem. The major reason is the difficulty to localize the robot by using its onboard sensors only. In this paper, a newly designed adaptive trajectory TC method is proposed for the NMR without its position, orientation, and velocity measurements. The controller is designed on the basis of a novel algorithm to estimate position and velocity of the robot online from visual feedback of an omnidirectional camera. It is theoretically proved that the proposed algorithm yields the TC errors to asymptotically converge to zero. Real-world experiments are conducted on a wheeled NMR to validate the feasibility of the control system.

Even Shallower Exploration with Airborne Electromagnetics

NASA Astrophysics Data System (ADS)

Auken, E.; Christiansen, A. V.; Kirkegaard, C.; Nyboe, N. S.; Sørensen, K.

2015-12-01

Airborne electromagnetics (EM) is in many ways undergoing the same type rapid technological development as seen in the telecommunication industry. These developments are driven by a steadily increasing demand for exploration of minerals, groundwater and geotechnical targets. The latter two areas demand shallow and accurate resolution of the near surface geology in terms of both resistivity and spatial delineation of the sedimentary layers. Airborne EM systems measure the grounds electromagnetic response when subject to either a continuous discrete sinusoidal transmitter signal (frequency domain) or by measuring the decay of currents induced in the ground by rapid transmission of transient pulses (time domain). In the last decade almost all new developments of both instrument hardware and data processing techniques has focused around time domain systems. Here we present a concept for measuring the time domain response even before the transient transmitter current has been turned off. Our approach relies on a combination of new instrument hardware and novel modeling algorithms. The newly developed hardware allows for measuring the instruments complete transfer function which is convolved with the synthetic earth response in the inversion algorithm. The effect is that earth response data measured while the transmitter current is turned off can be included in the inversion, significantly increasing the amount of available information. We demonstrate the technique using both synthetic and field data. The synthetic examples provide insight on the physics during the turn off process and the field examples document the robustness of the method. Geological near surface structures can now be resolved to a degree that is unprecedented to the best of our knowledge, making airborne EM even more attractive and cost-effective for exploration of water and minerals that are crucial for the function of our societies.

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

PubMed Central

2011-01-01

Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task. PMID:21867520

Computer-Automated Evolution of Spacecraft X-Band Antennas

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Homby, Gregory S.; Linden, Derek S.

2010-01-01

A document discusses the use of computer- aided evolution in arriving at a design for X-band communication antennas for NASA s three Space Technology 5 (ST5) satellites, which were launched on March 22, 2006. Two evolutionary algorithms, incorporating different representations of the antenna design and different fitness functions, were used to automatically design and optimize an X-band antenna design. A set of antenna designs satisfying initial ST5 mission requirements was evolved by use these algorithms. The two best antennas - one from each evolutionary algorithm - were built. During flight-qualification testing of these antennas, the mission requirements were changed. After minimal changes in the evolutionary algorithms - mostly in the fitness functions - new antenna designs satisfying the changed mission requirements were evolved and within one month of this change, two new antennas were designed and prototypes of the antennas were built and tested. One of these newly evolved antennas was approved for deployment on the ST5 mission, and flight-qualified versions of this design were built and installed on the spacecraft. At the time of writing the document, these antennas were the first computer-evolved hardware in outer space.

MADM-based smart parking guidance algorithm

PubMed Central

Li, Bo; Pei, Yijian; Wu, Hao; Huang, Dijiang

2017-01-01

In smart parking environments, how to choose suitable parking facilities with various attributes to satisfy certain criteria is an important decision issue. Based on the multiple attributes decision making (MADM) theory, this study proposed a smart parking guidance algorithm by considering three representative decision factors (i.e., walk duration, parking fee, and the number of vacant parking spaces) and various preferences of drivers. In this paper, the expected number of vacant parking spaces is regarded as an important attribute to reflect the difficulty degree of finding available parking spaces, and a queueing theory-based theoretical method was proposed to estimate this expected number for candidate parking facilities with different capacities, arrival rates, and service rates. The effectiveness of the MADM-based parking guidance algorithm was investigated and compared with a blind search-based approach in comprehensive scenarios with various distributions of parking facilities, traffic intensities, and user preferences. Experimental results show that the proposed MADM-based algorithm is effective to choose suitable parking resources to satisfy users’ preferences. Furthermore, it has also been observed that this newly proposed Markov Chain-based availability attribute is more effective to represent the availability of parking spaces than the arrival rate-based availability attribute proposed in existing research. PMID:29236698

The underlying dimensionality of PTSD in the diagnostic and statistical manual of mental disorders: where are we going?

PubMed

Armour, Cherie

2015-01-01

There has been a substantial body of literature devoted to answering one question: Which latent model of posttraumatic stress disorder (PTSD) best represents PTSD's underlying dimensionality? This research summary will, therefore, focus on the literature pertaining to PTSD's latent structure as represented in the fourth (DSM-IV, 1994) to the fifth (DSM-5, 2013) edition of the DSM. This article will begin by providing a clear rationale as to why this is a pertinent research area, then the body of literature pertaining to the DSM-IV and DSM-IV-TR will be summarised, and this will be followed by a summary of the literature pertaining to the recently published DSM-5. To conclude, there will be a discussion with recommendations for future research directions, namely that researchers must investigate the applicability of the new DSM-5 criteria and the newly created DSM-5 symptom sets to trauma survivors. In addition, researchers must continue to endeavour to identify the "correct" constellations of symptoms within symptom sets to ensure that diagnostic algorithms are appropriate and aid in the development of targeted treatment approaches and interventions. In particular, the newly proposed DSM-5 anhedonia model, externalising behaviours model, and hybrid models must be further investigated. It is also important that researchers follow up on the idea that a more parsimonious latent structure of PTSD may exist.

Dust Storm over the Middle East: Retrieval Approach, Source Identification, and Trend Analysis

NASA Astrophysics Data System (ADS)

Moridnejad, A.; Karimi, N.; Ariya, P. A.

2014-12-01

The Middle East region has been considered to be responsible for approximately 25% of the Earth's global emissions of dust particles. By developing Middle East Dust Index (MEDI) and applying to 70 dust storms characterized on MODIS images and occurred during the period between 2001 and 2012, we herein present a new high resolution mapping of major atmospheric dust source points participating in this region. To assist environmental managers and decision maker in taking proper and prioritized measures, we then categorize identified sources in terms of intensity based on extracted indices for Deep Blue algorithm and also utilize frequency of occurrence approach to find the sensitive sources. In next step, by implementing the spectral mixture analysis on the Landsat TM images (1984 and 2012), a novel desertification map will be presented. The aim is to understand how human perturbations and land-use change have influenced the dust storm points in the region. Preliminary results of this study indicate for the first time that c.a., 39 % of all detected source points are located in this newly anthropogenically desertified area. A large number of low frequency sources are located within or close to the newly desertified areas. These severely desertified regions require immediate concern at a global scale. During next 6 months, further research will be performed to confirm these preliminary results.

The underlying dimensionality of PTSD in the diagnostic and statistical manual of mental disorders: where are we going?

PubMed Central

Armour, Cherie

2015-01-01

There has been a substantial body of literature devoted to answering one question: Which latent model of posttraumatic stress disorder (PTSD) best represents PTSD's underlying dimensionality? This research summary will, therefore, focus on the literature pertaining to PTSD's latent structure as represented in the fourth (DSM-IV, 1994) to the fifth (DSM-5, 2013) edition of the DSM. This article will begin by providing a clear rationale as to why this is a pertinent research area, then the body of literature pertaining to the DSM-IV and DSM-IV-TR will be summarised, and this will be followed by a summary of the literature pertaining to the recently published DSM-5. To conclude, there will be a discussion with recommendations for future research directions, namely that researchers must investigate the applicability of the new DSM-5 criteria and the newly created DSM-5 symptom sets to trauma survivors. In addition, researchers must continue to endeavour to identify the “correct” constellations of symptoms within symptom sets to ensure that diagnostic algorithms are appropriate and aid in the development of targeted treatment approaches and interventions. In particular, the newly proposed DSM-5 anhedonia model, externalising behaviours model, and hybrid models must be further investigated. It is also important that researchers follow up on the idea that a more parsimonious latent structure of PTSD may exist. PMID:25994027

Remote sensing estimation of terrestrially derived colored dissolved organic matterinput to the Arctic Ocean

NASA Astrophysics Data System (ADS)

Li, J.; Yu, Q.; Tian, Y. Q.

2017-12-01

The DOC flux from land to the Arctic Ocean has remarkable implication on the carbon cycle, biogeochemical & ecological processes in the Arctic. This lateral carbon flux is required to be monitored with high spatial & temporal resolution. However, the current studies in the Arctic regions were obstructed by the factors of the low spatial coverages. The remote sensing could provide an alternative bio-optical approach to field sampling for DOC dynamics monitoring through the observation of the colored dissolved organic matter (CDOM). The DOC and CDOM were found highly correlated based on the analysis of the field sampling data from the Arctic-GRO. These provide the solid foundation of the remote sensing observation. In this study, six major Arctic Rivers (Yukon, Kolyma, Lena, Mackenzie, Ob', Yenisey) were selected to derive the CDOM dynamics along four years. Our newly developed SBOP algorithm was applied to the large Landsat-8 OLI image data (nearly 100 images) for getting the high spatial resolution results. The SBOP algorithm is the first approach developing for the Shallow Water Bio-optical properties estimation. The CDOM absorption derived from the satellite images were verified with the field sampling results with high accuracy (R2 = 0.87). The distinct CDOM dynamics were found in different Rivers. The CDOM absorptions were found highly related to the hydrological activities and the terrestrially environmental dynamics. Our study helps to build the reliable system for studying the carbon cycle at Arctic regions.

Augmenting epidemiological models with point-of-care diagnostics data

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

Pullum, Laura L.; Ramanathan, Arvind; Nutaro, James J.

Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnosticsmore » data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. As a result, calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions.« less

High Resolution Deformation Time Series Estimation for Distributed Scatterers Using Terrasar-X Data

NASA Astrophysics Data System (ADS)

Goel, K.; Adam, N.

2012-07-01

In recent years, several SAR satellites such as TerraSAR-X, COSMO-SkyMed and Radarsat-2 have been launched. These satellites provide high resolution data suitable for sophisticated interferometric applications. With shorter repeat cycles, smaller orbital tubes and higher bandwidth of the satellites; deformation time series analysis of distributed scatterers (DSs) is now supported by a practical data basis. Techniques for exploiting DSs in non-urban (rural) areas include the Small Baseline Subset Algorithm (SBAS). However, it involves spatial phase unwrapping, and phase unwrapping errors are typically encountered in rural areas and are difficult to detect. In addition, the SBAS technique involves a rectangular multilooking of the differential interferograms to reduce phase noise, resulting in a loss of resolution and superposition of different objects on ground. In this paper, we introduce a new approach for deformation monitoring with a focus on DSs, wherein, there is no need to unwrap the differential interferograms and the deformation is mapped at object resolution. It is based on a robust object adaptive parameter estimation using single look differential interferograms, where, the local tilts of deformation velocity and local slopes of residual DEM in range and azimuth directions are estimated. We present here the technical details and a processing example of this newly developed algorithm.

Augmenting epidemiological models with point-of-care diagnostics data

DOE PAGES

Pullum, Laura L.; Ramanathan, Arvind; Nutaro, James J.; ...

2016-04-20

Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnosticsmore » data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. As a result, calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions.« less

Remote estimation of colored dissolved organic matter and chlorophyll-a in Lake Huron using Sentinel-2 measurements

NASA Astrophysics Data System (ADS)

Chen, Jiang; Zhu, Weining; Tian, Yong Q.; Yu, Qian; Zheng, Yuhan; Huang, Litong

2017-07-01

Colored dissolved organic matter (CDOM) and chlorophyll-a (Chla) are important water quality parameters and play crucial roles in aquatic environment. Remote sensing of CDOM and Chla concentrations for inland lakes is often limited by low spatial resolution. The newly launched Sentinel-2 satellite is equipped with high spatial resolution (10, 20, and 60 m). Empirical band ratio models were developed to derive CDOM and Chla concentrations in Lake Huron. The leave-one-out cross-validation method was used for model calibration and validation. The best CDOM retrieval algorithm is a B3/B5 model with accuracy coefficient of determination (R2)=0.884, root-mean-squared error (RMSE)=0.731 m-1, relative root-mean-squared error (RRMSE)=28.02%, and bias=-0.1 m-1. The best Chla retrieval algorithm is a B5/B4 model with accuracy R2=0.49, RMSE=9.972 mg/m3, RRMSE=48.47%, and bias=-0.116 mg/m3. Neural network models were further implemented to improve inversion accuracy. The applications of the two best band ratio models to Sentinel-2 imagery with 10 m×10 m pixel size presented the high potential of the sensor for monitoring water quality of inland lakes.

Autonomous Rover Traverse and Precise Arm Placement on Remotely Designated Targets

NASA Technical Reports Server (NTRS)

Felder, Michael; Nesnas, Issa A.; Pivtoraiko, Mihail; Kelly, Alonzo; Volpe, Richard

2011-01-01

Exploring planetary surfaces typically involves traversing challenging and unknown terrain and acquiring in-situ measurements at designated locations using arm-mounted instruments. We present field results for a new implementation of an autonomous capability that enables a rover to traverse and precisely place an arm-mounted instrument on remote targets. Using point-and-click mouse commands, a scientist designates targets in the initial imagery acquired from the rover's mast cameras. The rover then autonomously traverse the rocky terrain for a distance of 10 - 15 m, tracks the target(s) of interest during the traverse, positions itself for approaching the target, and then precisely places an arm-mounted instrument within 2-3 cm from the originally designated target. The rover proceeds to acquire science measurements with the instrument. This work advances what has been previously developed and integrated on the Mars Exploration Rovers by using algorithms that are capable of traversing more rock-dense terrains, enabling tight thread-the-needle maneuvers. We integrated these algorithms on the newly refurbished Athena Mars research rover and fielded them in the JPL Mars Yard. We conducted 43 runs with targets at distances ranging from 5 m to 15 m and achieved a success rate of 93% for placement of the instrument within 2-3 cm.

CentiServer: A Comprehensive Resource, Web-Based Application and R Package for Centrality Analysis.

PubMed

Jalili, Mahdi; Salehzadeh-Yazdi, Ali; Asgari, Yazdan; Arab, Seyed Shahriar; Yaghmaie, Marjan; Ghavamzadeh, Ardeshir; Alimoghaddam, Kamran

2015-01-01

Various disciplines are trying to solve one of the most noteworthy queries and broadly used concepts in biology, essentiality. Centrality is a primary index and a promising method for identifying essential nodes, particularly in biological networks. The newly created CentiServer is a comprehensive online resource that provides over 110 definitions of different centrality indices, their computational methods, and algorithms in the form of an encyclopedia. In addition, CentiServer allows users to calculate 55 centralities with the help of an interactive web-based application tool and provides a numerical result as a comma separated value (csv) file format or a mapped graphical format as a graph modeling language (GML) file. The standalone version of this application has been developed in the form of an R package. The web-based application (CentiServer) and R package (centiserve) are freely available at http://www.centiserver.org/.

A Novel Collection of snRNA-Like Promoters with Tissue-Specific Transcription Properties

PubMed Central

Garritano, Sonia; Gigoni, Arianna; Costa, Delfina; Malatesta, Paolo; Florio, Tullio; Cancedda, Ranieri; Pagano, Aldo

2012-01-01

We recently identified a novel dataset of snRNA-like trascriptional units in the human genome. The investigation of a subset of these elements showed that they play relevant roles in physiology and/or pathology. In this work we expand our collection of small RNAs taking advantage of a newly developed algorithm able to identify genome sequence stretches with RNA polymerase (pol) III type 3 promoter features thus constituting putative pol III binding sites. The bioinformatic analysis of a subset of these elements that map in introns of protein-coding genes in antisense configuration suggest their association with alternative splicing, similarly to other recently characterized small RNAs. Interestingly, the analysis of the transcriptional activity of these novel promoters shows that they are active in a cell-type specific manner, in accordance with the emerging body of evidence of a tissue/cell-specific activity of pol III. PMID:23109855

A novel collection of snRNA-like promoters with tissue-specific transcription properties.

PubMed

Garritano, Sonia; Gigoni, Arianna; Costa, Delfina; Malatesta, Paolo; Florio, Tullio; Cancedda, Ranieri; Pagano, Aldo

2012-01-01

We recently identified a novel dataset of snRNA-like trascriptional units in the human genome. The investigation of a subset of these elements showed that they play relevant roles in physiology and/or pathology. In this work we expand our collection of small RNAs taking advantage of a newly developed algorithm able to identify genome sequence stretches with RNA polymerase (pol) III type 3 promoter features thus constituting putative pol III binding sites. The bioinformatic analysis of a subset of these elements that map in introns of protein-coding genes in antisense configuration suggest their association with alternative splicing, similarly to other recently characterized small RNAs. Interestingly, the analysis of the transcriptional activity of these novel promoters shows that they are active in a cell-type specific manner, in accordance with the emerging body of evidence of a tissue/cell-specific activity of pol III.

CentiServer: A Comprehensive Resource, Web-Based Application and R Package for Centrality Analysis

PubMed Central

Jalili, Mahdi; Salehzadeh-Yazdi, Ali; Asgari, Yazdan; Arab, Seyed Shahriar; Yaghmaie, Marjan; Ghavamzadeh, Ardeshir; Alimoghaddam, Kamran

2015-01-01

Various disciplines are trying to solve one of the most noteworthy queries and broadly used concepts in biology, essentiality. Centrality is a primary index and a promising method for identifying essential nodes, particularly in biological networks. The newly created CentiServer is a comprehensive online resource that provides over 110 definitions of different centrality indices, their computational methods, and algorithms in the form of an encyclopedia. In addition, CentiServer allows users to calculate 55 centralities with the help of an interactive web-based application tool and provides a numerical result as a comma separated value (csv) file format or a mapped graphical format as a graph modeling language (GML) file. The standalone version of this application has been developed in the form of an R package. The web-based application (CentiServer) and R package (centiserve) are freely available at http://www.centiserver.org/ PMID:26571275

Phase Diversity Applied to Sunspot Observations

NASA Astrophysics Data System (ADS)

Tritschler, A.; Schmidt, W.; Knolker, M.

We present preliminary results of a multi-colour phase diversity experiment carried out with the Multichannel Filter System of the Vacuum Tower Telescope at the Observatorio del Teide on Tenerife. We apply phase-diversity imaging to a time sequence of sunspot filtergrams taken in three continuum bands and correct the seeing influence for each image. A newly developed phase diversity device allowing for the projection of both the focused and the defocused image onto a single CCD chip was used in one of the wavelength channels. With the information about the wavefront obtained by the image reconstruction algorithm the restoration of the other two bands can be performed as well. The processed and restored data set will then be used to derive the temperature and proper motion of the umbral dots. Data analysis is still under way, and final results will be given in a forthcoming article.

Laplacian scale-space behavior of planar curve corners.

PubMed

Zhang, Xiaohong; Qu, Ying; Yang, Dan; Wang, Hongxing; Kymer, Jeff

2015-11-01

Scale-space behavior of corners is important for developing an efficient corner detection algorithm. In this paper, we analyze the scale-space behavior with the Laplacian of Gaussian (LoG) operator on a planar curve which constructs Laplacian Scale Space (LSS). The analytical expression of a Laplacian Scale-Space map (LSS map) is obtained, demonstrating the Laplacian Scale-Space behavior of the planar curve corners, based on a newly defined unified corner model. With this formula, some Laplacian Scale-Space behavior is summarized. Although LSS demonstrates some similarities to Curvature Scale Space (CSS), there are still some differences. First, no new extreme points are generated in the LSS. Second, the behavior of different cases of a corner model is consistent and simple. This makes it easy to trace the corner in a scale space. At last, the behavior of LSS is verified in an experiment on a digital curve.

Moiré-reduction method for slanted-lenticular-based quasi-three-dimensional displays

NASA Astrophysics Data System (ADS)

Zhuang, Zhenfeng; Surman, Phil; Zhang, Lei; Rawat, Rahul; Wang, Shizheng; Zheng, Yuanjin; Sun, Xiao Wei

2016-12-01

In this paper we present a method for determining the preferred slanted angle for a lenticular film that minimizes moiré patterns in quasi-three-dimensional (Q3D) displays. We evaluate the preferred slanted angles of the lenticular film for the stripe-type sub-pixel structure liquid crystal display (LCD) panel. Additionally, the sub-pixels mapping algorithm of the specific angle is proposed to assign the images to either the right or left eye channel. A Q3D display prototype is built. Compared with the conventional SLF, this newly implemented Q3D display can not only eliminate moiré patterns but also provide 3D images in both portrait and landscape orientations. It is demonstrated that the developed slanted lenticular film (SLF) provides satisfactory 3D images by employing a compact structure, minimum moiré patterns and stabilized 3D contrast.

3D hybrid carbon composed of multigraphene bridged by carbon chains

NASA Astrophysics Data System (ADS)

Liu, Lingyu; Hu, Meng; Liu, Chao; Shao, Cancan; Pan, Yilong; Ma, Mengdong; Wu, Yingju; Zhao, Zhisheng; Gao, Guoying; He, Julong

2018-01-01

The element carbon possesses various stable and metastable allotropes; some of them have been applied in diverse fields. The experimental evidences of both carbon chain and graphdiyne have been reported. Here, we reveal the mystery of an enchanting carbon allotrope with sp-, sp2-, and sp3-hybridized carbon atoms using a newly developed ab initio particle-swarm optimization algorithm for crystal structure prediction. This crystalline allotrope, namely m-C12, can be viewed as braided mesh architecture interwoven with multigraphene and carbon chains. The m-C12 meets the criteria for dynamic and mechanical stabilities and is energetically more stable than carbyne and graphdiyne. Analysis of the B/G and Poisson's ratio indicates that this allotrope is ductile. Notably, m-C12 is a superconducting carbon with Tc of 1.13 K, which is rare in the family of carbon allotropes.

LAMMPS integrated materials engine (LIME) for efficient automation of particle-based simulations: application to equation of state generation

NASA Astrophysics Data System (ADS)

Barnes, Brian C.; Leiter, Kenneth W.; Becker, Richard; Knap, Jaroslaw; Brennan, John K.

2017-07-01

We describe the development, accuracy, and efficiency of an automation package for molecular simulation, the large-scale atomic/molecular massively parallel simulator (LAMMPS) integrated materials engine (LIME). Heuristics and algorithms employed for equation of state (EOS) calculation using a particle-based model of a molecular crystal, hexahydro-1,3,5-trinitro-s-triazine (RDX), are described in detail. The simulation method for the particle-based model is energy-conserving dissipative particle dynamics, but the techniques used in LIME are generally applicable to molecular dynamics simulations with a variety of particle-based models. The newly created tool set is tested through use of its EOS data in plate impact and Taylor anvil impact continuum simulations of solid RDX. The coarse-grain model results from LIME provide an approach to bridge the scales from atomistic simulations to continuum simulations.

Enhancing resolution in coherent x-ray diffraction imaging.

PubMed

Noh, Do Young; Kim, Chan; Kim, Yoonhee; Song, Changyong

2016-12-14

Achieving a resolution near 1 nm is a critical issue in coherent x-ray diffraction imaging (CDI) for applications in materials and biology. Albeit with various advantages of CDI based on synchrotrons and newly developed x-ray free electron lasers, its applications would be limited without improving resolution well below 10 nm. Here, we review the issues and efforts in improving CDI resolution including various methods for resolution determination. Enhancing diffraction signal at large diffraction angles, with the aid of interference between neighboring strong scatterers or templates, is reviewed and discussed in terms of increasing signal-to-noise ratio. In addition, we discuss errors in image reconstruction algorithms-caused by the discreteness of the Fourier transformations involved-which degrade the spatial resolution, and suggest ways to correct them. We expect this review to be useful for applications of CDI in imaging weakly scattering soft matters using coherent x-ray sources including x-ray free electron lasers.

Testing the limits: cautions and concerns regarding the new Wechsler IQ and Memory scales.

PubMed

Loring, David W; Bauer, Russell M

2010-02-23

The Wechsler Adult Intelligence Scale (WAIS) and the Wechsler Memory Scale (WMS) are 2 of the most common psychological tests used in clinical care and research in neurology. Newly revised versions of both instruments (WAIS-IV and WMS-IV) have recently been published and are increasingly being adopted by the neuropsychology community. There have been significant changes in the structure and content of both scales, leading to the potential for inaccurate patient classification if algorithms developed using their predecessors are employed. There are presently insufficient clinical data in neurologic populations to insure their appropriate application to neuropsychological evaluations. We provide a perspective on these important new neuropsychological instruments, comment on the pressures to adopt these tests in the absence of an appropriate evidence base supporting their incremental validity, and describe the potential negative impact on both patient care and continuing research applications.

Measurements of a Newly Designed BPM for the Tevatron Electron Lens 2

NASA Astrophysics Data System (ADS)

Scarpine, V. E.; Kamerdzhiev, V.; Fellenz, B.; Olson, M.; Kuznetsov, G.; Kamerdzhiev, V.; Shiltsev, V. D.; Zhang, X. L.

2006-11-01

Fermilab has developed a second electron lens (TEL-2) for beam-beam compensation in the Tevatron as part of its Run II upgrade program. Operation of the beam position monitors (BPMs) in the first electron lens (TEL-1) showed a systematic transverse position difference between short proton bunches (2 ns sigma) and long electron pulses (˜1 us) of up to ˜1.5 mm. This difference was attributed to frequency dependence in the BPM system. The TEL-2 BPMs utilize a new, compact four-plate design with grounding strips between plates to minimize crosstalk. In-situ measurements of these new BPMs are made using a stretched wire pulsed with both proton and electron beam formats. In addition, longitudinal impedance measurements of the TEL-2 are presented. Signal processing algorithm studies indicate that the frequency-dependent transverse position offset may be reduced to ˜0.1 mm for the beam structures of interest.

Missile signal processing common computer architecture for rapid technology upgrade

NASA Astrophysics Data System (ADS)

Rabinkin, Daniel V.; Rutledge, Edward; Monticciolo, Paul

2004-10-01

Interceptor missiles process IR images to locate an intended target and guide the interceptor towards it. Signal processing requirements have increased as the sensor bandwidth increases and interceptors operate against more sophisticated targets. A typical interceptor signal processing chain is comprised of two parts. Front-end video processing operates on all pixels of the image and performs such operations as non-uniformity correction (NUC), image stabilization, frame integration and detection. Back-end target processing, which tracks and classifies targets detected in the image, performs such algorithms as Kalman tracking, spectral feature extraction and target discrimination. In the past, video processing was implemented using ASIC components or FPGAs because computation requirements exceeded the throughput of general-purpose processors. Target processing was performed using hybrid architectures that included ASICs, DSPs and general-purpose processors. The resulting systems tended to be function-specific, and required custom software development. They were developed using non-integrated toolsets and test equipment was developed along with the processor platform. The lifespan of a system utilizing the signal processing platform often spans decades, while the specialized nature of processor hardware and software makes it difficult and costly to upgrade. As a result, the signal processing systems often run on outdated technology, algorithms are difficult to update, and system effectiveness is impaired by the inability to rapidly respond to new threats. A new design approach is made possible three developments; Moore's Law - driven improvement in computational throughput; a newly introduced vector computing capability in general purpose processors; and a modern set of open interface software standards. Today's multiprocessor commercial-off-the-shelf (COTS) platforms have sufficient throughput to support interceptor signal processing requirements. This application may be programmed under existing real-time operating systems using parallel processing software libraries, resulting in highly portable code that can be rapidly migrated to new platforms as processor technology evolves. Use of standardized development tools and 3rd party software upgrades are enabled as well as rapid upgrade of processing components as improved algorithms are developed. The resulting weapon system will have a superior processing capability over a custom approach at the time of deployment as a result of a shorter development cycles and use of newer technology. The signal processing computer may be upgraded over the lifecycle of the weapon system, and can migrate between weapon system variants enabled by modification simplicity. This paper presents a reference design using the new approach that utilizes an Altivec PowerPC parallel COTS platform. It uses a VxWorks-based real-time operating system (RTOS), and application code developed using an efficient parallel vector library (PVL). A quantification of computing requirements and demonstration of interceptor algorithm operating on this real-time platform are provided.

Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data.

PubMed

Chen, Shuonan; Mar, Jessica C

2018-06-19

A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data. Standard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or simulated single cell data, which demonstrates their lack of performance for this task. Using default settings, network methods were applied to the same datasets. Comparisons of the learned networks highlighted the uniqueness of some predicted edges for each method. The fact that different methods infer networks that vary substantially reflects the underlying mathematical rationale and assumptions that distinguish network methods from each other. This study provides a comprehensive evaluation of network modeling algorithms applied to experimental single cell gene expression data and in silico simulated datasets where the network structure is known. Comparisons demonstrate that most of these assessed network methods are not able to predict network structures from single cell expression data accurately, even if they are specifically developed for single cell methods. Also, single cell methods, which usually depend on more elaborative algorithms, in general have less similarity to each other in the sets of edges detected. The results from this study emphasize the importance for developing more accurate optimized network modeling methods that are compatible for single cell data. Newly-developed single cell methods may uniquely capture particular features of potential gene-gene relationships, and caution should be taken when we interpret these results.

Field Performance of a Newly Developed Upflow Filtration Device

EPA Science Inventory

The objective of this research is to examine the removal capacities of a newly developed Upflow filtration device for treatment of stormwater. The device was developed by engineers at the University of Alabama through a Small Business Innovative Research (SBIR) grant from the U....

Fast leaf-fitting with generalized underdose/overdose constraints for real-time MLC tracking

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

Moore, Douglas, E-mail: douglas.moore@utsouthwestern.edu; Sawant, Amit; Ruan, Dan

2016-01-15

Purpose: Real-time multileaf collimator (MLC) tracking is a promising approach to the management of intrafractional tumor motion during thoracic and abdominal radiotherapy. MLC tracking is typically performed in two steps: transforming a planned MLC aperture in response to patient motion and refitting the leaves to the newly generated aperture. One of the challenges of this approach is the inability to faithfully reproduce the desired motion-adapted aperture. This work presents an optimization-based framework with which to solve this leaf-fitting problem in real-time. Methods: This optimization framework is designed to facilitate the determination of leaf positions in real-time while accounting for themore » trade-off between coverage of the PTV and avoidance of organs at risk (OARs). Derived within this framework, an algorithm is presented that can account for general linear transformations of the planned MLC aperture, particularly 3D translations and in-plane rotations. This algorithm, together with algorithms presented in Sawant et al. [“Management of three-dimensional intrafraction motion through real-time DMLC tracking,” Med. Phys. 35, 2050–2061 (2008)] and Ruan and Keall [Presented at the 2011 IEEE Power Engineering and Automation Conference (PEAM) (2011) (unpublished)], was applied to apertures derived from eight lung intensity modulated radiotherapy plans subjected to six-degree-of-freedom motion traces acquired from lung cancer patients using the kilovoltage intrafraction monitoring system developed at the University of Sydney. A quality-of-fit metric was defined, and each algorithm was evaluated in terms of quality-of-fit and computation time. Results: This algorithm is shown to perform leaf-fittings of apertures, each with 80 leaf pairs, in 0.226 ms on average as compared to 0.082 and 64.2 ms for the algorithms of Sawant et al., Ruan, and Keall, respectively. The algorithm shows approximately 12% improvement in quality-of-fit over the Sawant et al. approach, while performing comparably to Ruan and Keall. Conclusions: This work improves upon the quality of the Sawant et al. approach, but does so without sacrificing run-time performance. In addition, using this framework allows for complex leaf-fitting strategies that can be used to account for PTV/OAR trade-off during real-time MLC tracking.« less

Designing an Algorithm to Preserve Privacy for Medical Record Linkage With Error-Prone Data

PubMed Central

Pal, Doyel; Chen, Tingting; Khethavath, Praveen

2014-01-01

Background Linking medical records across different medical service providers is important to the enhancement of health care quality and public health surveillance. In records linkage, protecting the patients’ privacy is a primary requirement. In real-world health care databases, records may well contain errors due to various reasons such as typos. Linking the error-prone data and preserving data privacy at the same time are very difficult. Existing privacy preserving solutions for this problem are only restricted to textual data. Objective To enable different medical service providers to link their error-prone data in a private way, our aim was to provide a holistic solution by designing and developing a medical record linkage system for medical service providers. Methods To initiate a record linkage, one provider selects one of its collaborators in the Connection Management Module, chooses some attributes of the database to be matched, and establishes the connection with the collaborator after the negotiation. In the Data Matching Module, for error-free data, our solution offered two different choices for cryptographic schemes. For error-prone numerical data, we proposed a newly designed privacy preserving linking algorithm named the Error-Tolerant Linking Algorithm, that allows the error-prone data to be correctly matched if the distance between the two records is below a threshold. Results We designed and developed a comprehensive and user-friendly software system that provides privacy preserving record linkage functions for medical service providers, which meets the regulation of Health Insurance Portability and Accountability Act. It does not require a third party and it is secure in that neither entity can learn the records in the other’s database. Moreover, our novel Error-Tolerant Linking Algorithm implemented in this software can work well with error-prone numerical data. We theoretically proved the correctness and security of our Error-Tolerant Linking Algorithm. We have also fully implemented the software. The experimental results showed that it is reliable and efficient. The design of our software is open so that the existing textual matching methods can be easily integrated into the system. Conclusions Designing algorithms to enable medical records linkage for error-prone numerical data and protect data privacy at the same time is difficult. Our proposed solution does not need a trusted third party and is secure in that in the linking process, neither entity can learn the records in the other’s database. PMID:25600786

Tracing Forest Change through 40 Years on Two Continents with the BULC Algorithm and Google Earth Engine

NASA Astrophysics Data System (ADS)

Cardille, J. A.; Crowley, M.; Fortin, J. A.; Lee, J.; Perez, E.; Sleeter, B. M.; Thau, D.

2016-12-01

With the opening of the Landsat archive, researchers have a vast new data source teeming with imagery and potential. Beyond Landsat, data from other sensors is newly available as well: these include ALOS/PALSAR, Sentinel-1 and -2, MERIS, and many more. Google Earth Engine, developed to organize and provide analysis tools for these immense data sets, is an ideal platform for researchers trying to sift through huge image stacks. It offers nearly unlimited processing power and storage with a straightforward programming interface. Yet labeling land-cover change through time remains challenging given the current state of the art for interpreting remote sensing image sequences. Moreover, combining data from very different image platforms remains quite difficult. To address these challenges, we developed the BULC algorithm (Bayesian Updating of Land Cover), designed for the continuous updating of land-cover classifications through time in large data sets. The algorithm ingests data from any of the wide variety of earth-resources sensors; it maintains a running estimate of land-cover probabilities and the most probable class at all time points along a sequence of events. Here we compare BULC results from two study sites that witnessed considerable forest change in the last 40 years: the Pacific Northwest of the United States and the Mato Grosso region of Brazil. In Brazil, we incorporated rough classifications from more than 100 images of varying quality, mixing imagery from more than 10 different sensors. In the Pacific Northwest, we used BULC to identify forest changes due to logging and urbanization from 1973 to the present. Both regions had classification sequences that were better than many of the component days, effectively ignoring clouds and other unwanted noise while fusing the information contained on several platforms. As we leave remote sensing's data-poor era and enter a period with multiple looks at Earth's surface from multiple sensors over a short period of time, the BULC algorithm can help to sift through images of varying quality in Google Earth Engine to extract the most useful information for mapping the state and history of Earth's land cover.

Tracing Forest Change through 40 Years on Two Continents with the BULC Algorithm and Google Earth Engine

NASA Astrophysics Data System (ADS)

Cardille, J. A.

2015-12-01

With the opening of the Landsat archive, researchers have a vast new data source teeming with imagery and potential. Beyond Landsat, data from other sensors is newly available as well: these include ALOS/PALSAR, Sentinel-1 and -2, MERIS, and many more. Google Earth Engine, developed to organize and provide analysis tools for these immense data sets, is an ideal platform for researchers trying to sift through huge image stacks. It offers nearly unlimited processing power and storage with a straightforward programming interface. Yet labeling forest change through time remains challenging given the current state of the art for interpreting remote sensing image sequences. Moreover, combining data from very different image platforms remains quite difficult. To address these challenges, we developed the BULC algorithm (Bayesian Updating of Land Cover), designed for the continuous updating of land-cover classifications through time in large data sets. The algorithm ingests data from any of the wide variety of earth-resources sensors; it maintains a running estimate of land-cover probabilities and the most probable class at all time points along a sequence of events. Here we compare BULC results from two study sites that witnessed considerable forest change in the last 40 years: the Pacific Northwest of the United States and the Mato Grosso region of Brazil. In Brazil, we incorporated rough classifications from more than 100 images of varying quality, mixing imagery from more than 10 different sensors. In the Pacific Northwest, we used BULC to identify forest changes due to logging and urbanization from 1973 to the present. Both regions had classification sequences that were better than many of the component days, effectively ignoring clouds and other unwanted signal while fusing the information contained on several platforms. As we leave remote sensing's data-poor era and enter a period with multiple looks at Earth's surface from multiple sensors over a short period of time, this algorithm may help to sift through images of varying quality in Google Earth Engine to extract the most useful information for mapping.

Field-Scale Modeling of Local Capillary Trapping During CO2 Injection into a Saline Aquifer

NASA Astrophysics Data System (ADS)

Ren, B.; Lake, L. W.; Bryant, S. L.

2015-12-01

Local capillary trapping is the small-scale (10-2 to 10+1 m) CO2 trapping that is caused by the capillary pressure heterogeneity. The benefit of LCT, applied specially to CO2 sequestration, is that saturation of stored CO2 is larger than the residual gas, yet these CO2 are not susceptible to leakage through failed seals. Thus quantifying the extent of local capillary trapping is valuable in design and risk assessment of geologic storage projects. Modeling local capillary trapping is computationally expensive and may even be intractable using a conventional reservoir simulator. In this paper, we propose a novel method to model local capillary trapping by combining geologic criteria and connectivity analysis. The connectivity analysis originally developed for characterizing well-to-reservoir connectivity is adapted to this problem by means of a newly defined edge weight property between neighboring grid blocks, which accounts for the multiphase flow properties, injection rate, and gravity effect. Then the connectivity is estimated from shortest path algorithm to predict the CO2 migration behavior and plume shape during injection. A geologic criteria algorithm is developed to estimate the potential local capillary traps based only on the entry capillary pressure field. The latter is correlated to a geostatistical realization of permeability field. The extended connectivity analysis shows a good match of CO2 plume computed by the full-physics simulation. We then incorporate it into the geologic algorithm to quantify the amount of LCT structures identified within the entry capillary pressure field that can be filled during CO2 injection. Several simulations are conducted in the reservoirs with different level of heterogeneity (measured by the Dykstra-Parsons coefficient) under various injection scenarios. We find that there exists a threshold Dykstra-Parsons coefficient, below which low injection rate gives rise to more LCT; whereas higher injection rate increases LCT in heterogeneous reservoirs. Both the geologic algorithm and connectivity analysis are very fast; therefore, the integrated methodology can be used as a quick tool to estimate local capillary trapping. It can also be used as a potential complement to the full-physics simulation to evaluate safe storage capacity.

Accurate force field for molybdenum by machine learning large materials data

NASA Astrophysics Data System (ADS)

Chen, Chi; Deng, Zhi; Tran, Richard; Tang, Hanmei; Chu, Iek-Heng; Ong, Shyue Ping

2017-09-01

In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning techniques on large materials data sets. Despite Mo's importance as a structural metal, existing force fields for Mo based on the embedded atom and modified embedded atom methods do not provide satisfactory accuracy on many properties. We will show that by fitting to the energies, forces, and stress tensors of a large density functional theory (DFT)-computed dataset on a diverse set of Mo structures, a Mo SNAP model can be developed that achieves close to DFT accuracy in the prediction of a broad range of properties, including elastic constants, melting point, phonon spectra, surface energies, grain boundary energies, etc. We will outline a systematic model development process, which includes a rigorous approach to structural selection based on principal component analysis, as well as a differential evolution algorithm for optimizing the hyperparameters in the model fitting so that both the model error and the property prediction error can be simultaneously lowered. We expect that this newly developed Mo SNAP model will find broad applications in large and long-time scale simulations.

I-TASSER: fully automated protein structure prediction in CASP8.

PubMed

Zhang, Yang

2009-01-01

The I-TASSER algorithm for 3D protein structure prediction was tested in CASP8, with the procedure fully automated in both the Server and Human sections. The quality of the server models is close to that of human ones but the human predictions incorporate more diverse templates from other servers which improve the human predictions in some of the distant homology targets. For the first time, the sequence-based contact predictions from machine learning techniques are found helpful for both template-based modeling (TBM) and template-free modeling (FM). In TBM, although the accuracy of the sequence based contact predictions is on average lower than that from template-based ones, the novel contacts in the sequence-based predictions, which are complementary to the threading templates in the weakly or unaligned regions, are important to improve the global and local packing in these regions. Moreover, the newly developed atomic structural refinement algorithm was tested in CASP8 and found to improve the hydrogen-bonding networks and the overall TM-score, which is mainly due to its ability of removing steric clashes so that the models can be generated from cluster centroids. Nevertheless, one of the major issues of the I-TASSER pipeline is the model selection where the best models could not be appropriately recognized when the correct templates are detected only by the minority of the threading algorithms. There are also problems related with domain-splitting and mirror image recognition which mainly influences the performance of I-TASSER modeling in the FM-based structure predictions. Copyright 2009 Wiley-Liss, Inc.

A collaborative filtering approach for protein-protein docking scoring functions.

PubMed

Bourquard, Thomas; Bernauer, Julie; Azé, Jérôme; Poupon, Anne

2011-04-22

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions. In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately. This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures.

A Collaborative Filtering Approach for Protein-Protein Docking Scoring Functions

PubMed Central

Bourquard, Thomas; Bernauer, Julie; Azé, Jérôme; Poupon, Anne

2011-01-01

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions. In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately. This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures. PMID:21526112

Deep Blue Retrievals of Asian Aerosol Properties During ACE-Asia

NASA Technical Reports Server (NTRS)

Hsu, N. Christina; Tsay, Si-Cee; King, Michael D.; Herman, Jay R.

2006-01-01

During the ACE-Asia field campaign, unprecedented amounts of aerosol property data in East Asia during springtime were collected from an array of aircraft, shipboard, and surface instruments. However, most of the observations were obtained in areas downwind of the source regions. In this paper, the newly developed satellite aerosol algorithm called "Deep Blue" was employed to characterize the properties of aerosols over source regions using radiance measurements from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS). Based upon the ngstr m exponent derived from the Deep Blue algorithm, it was demonstrated that this new algorithm is able to distinguish dust plumes from fine-mode pollution particles even in complex aerosol environments such as the one over Beijing. Furthermore, these results were validated by comparing them with observations from AERONET sites in China and Mongolia during spring 2001. These comparisons show that the values of satellite-retrieved aerosol optical thickness from Deep Blue are generally within 20%-30% of those measured by sunphotometers. The analyses also indicate that the roles of mineral dust and anthropogenic particles are comparable in contributing to the overall aerosol distributions during spring in northern China, while fine-mode particles are dominant over southern China. The spring season in East Asia consists of one of the most complex environments in terms of frequent cloudiness and wide ranges of aerosol loadings and types. This paper will discuss how the factors contributing to this complexity influence the resulting aerosol monthly averages from various satellite sensors and, thus, the synergy among satellite aerosol products.

Robust tracking and quantification of C. elegans body shape and locomotion through coiling, entanglement, and omega bends

PubMed Central

Roussel, Nicolas; Sprenger, Jeff; Tappan, Susan J; Glaser, Jack R

2014-01-01

The behavior of the well-characterized nematode, Caenorhabditis elegans (C. elegans), is often used to study the neurologic control of sensory and motor systems in models of health and neurodegenerative disease. To advance the quantification of behaviors to match the progress made in the breakthroughs of genetics, RNA, proteins, and neuronal circuitry, analysis must be able to extract subtle changes in worm locomotion across a population. The analysis of worm crawling motion is complex due to self-overlap, coiling, and entanglement. Using current techniques, the scope of the analysis is typically restricted to worms to their non-occluded, uncoiled state which is incomplete and fundamentally biased. Using a model describing the worm shape and crawling motion, we designed a deformable shape estimation algorithm that is robust to coiling and entanglement. This model-based shape estimation algorithm has been incorporated into a framework where multiple worms can be automatically detected and tracked simultaneously throughout the entire video sequence, thereby increasing throughput as well as data validity. The newly developed algorithms were validated against 10 manually labeled datasets obtained from video sequences comprised of various image resolutions and video frame rates. The data presented demonstrate that tracking methods incorporated in WormLab enable stable and accurate detection of these worms through coiling and entanglement. Such challenging tracking scenarios are common occurrences during normal worm locomotion. The ability for the described approach to provide stable and accurate detection of C. elegans is critical to achieve unbiased locomotory analysis of worm motion. PMID:26435884

Detecting Horizontal Gene Transfer between Closely Related Taxa

PubMed Central

Adato, Orit; Ninyo, Noga; Gophna, Uri; Snir, Sagi

2015-01-01

Horizontal gene transfer (HGT), the transfer of genetic material between organisms, is crucial for genetic innovation and the evolution of genome architecture. Existing HGT detection algorithms rely on a strong phylogenetic signal distinguishing the transferred sequence from ancestral (vertically derived) genes in its recipient genome. Detecting HGT between closely related species or strains is challenging, as the phylogenetic signal is usually weak and the nucleotide composition is normally nearly identical. Nevertheless, there is a great importance in detecting HGT between congeneric species or strains, especially in clinical microbiology, where understanding the emergence of new virulent and drug-resistant strains is crucial, and often time-sensitive. We developed a novel, self-contained technique named Near HGT, based on the synteny index, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the constant relative mutability (CRM). Using CRM, the algorithm assigns a confidence score based on “unusual” sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three E. coli strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set. When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain. PMID:26439115

Photogrammetric DSM denoising

NASA Astrophysics Data System (ADS)

Nex, F.; Gerke, M.

2014-08-01

Image matching techniques can nowadays provide very dense point clouds and they are often considered a valid alternative to LiDAR point cloud. However, photogrammetric point clouds are often characterized by a higher level of random noise compared to LiDAR data and by the presence of large outliers. These problems constitute a limitation in the practical use of photogrammetric data for many applications but an effective way to enhance the generated point cloud has still to be found. In this paper we concentrate on the restoration of Digital Surface Models (DSM), computed from dense image matching point clouds. A photogrammetric DSM, i.e. a 2.5D representation of the surface is still one of the major products derived from point clouds. Four different algorithms devoted to DSM denoising are presented: a standard median filter approach, a bilateral filter, a variational approach (TGV: Total Generalized Variation), as well as a newly developed algorithm, which is embedded into a Markov Random Field (MRF) framework and optimized through graph-cuts. The ability of each algorithm to recover the original DSM has been quantitatively evaluated. To do that, a synthetic DSM has been generated and different typologies of noise have been added to mimic the typical errors of photogrammetric DSMs. The evaluation reveals that standard filters like median and edge preserving smoothing through a bilateral filter approach cannot sufficiently remove typical errors occurring in a photogrammetric DSM. The TGV-based approach much better removes random noise, but large areas with outliers still remain. Our own method which explicitly models the degradation properties of those DSM outperforms the others in all aspects.

Human Computation in Visualization: Using Purpose Driven Games for Robust Evaluation of Visualization Algorithms.

PubMed

Ahmed, N; Zheng, Ziyi; Mueller, K

2012-12-01

Due to the inherent characteristics of the visualization process, most of the problems in this field have strong ties with human cognition and perception. This makes the human brain and sensory system the only truly appropriate evaluation platform for evaluating and fine-tuning a new visualization method or paradigm. However, getting humans to volunteer for these purposes has always been a significant obstacle, and thus this phase of the development process has traditionally formed a bottleneck, slowing down progress in visualization research. We propose to take advantage of the newly emerging field of Human Computation (HC) to overcome these challenges. HC promotes the idea that rather than considering humans as users of the computational system, they can be made part of a hybrid computational loop consisting of traditional computation resources and the human brain and sensory system. This approach is particularly successful in cases where part of the computational problem is considered intractable using known computer algorithms but is trivial to common sense human knowledge. In this paper, we focus on HC from the perspective of solving visualization problems and also outline a framework by which humans can be easily seduced to volunteer their HC resources. We introduce a purpose-driven game titled "Disguise" which serves as a prototypical example for how the evaluation of visualization algorithms can be mapped into a fun and addicting activity, allowing this task to be accomplished in an extensive yet cost effective way. Finally, we sketch out a framework that transcends from the pure evaluation of existing visualization methods to the design of a new one.

Assessing College Students' Perceptions of a Case Teacher's Pedagogical Content Knowledge Using a Newly Developed Instrument

ERIC Educational Resources Information Center

Jang, Syh-Jong

2011-01-01

Ongoing professional development for college teachers has been much emphasized. However, previous research on learning environments has seldom addressed college students' perceptions of teachers' PCK. This study aimed to evaluate college students' perceptions of a physics teacher's PCK development using a newly developed instrument and workshop…

Preparing School Leaders: The Professional Development Needs of Newly Appointed Principals

ERIC Educational Resources Information Center

Ng, Shun-wing; Szeto, Sing-ying Elson

2016-01-01

In Hong Kong, there is an acute need to provide newly appointed principals with opportunities for continuous professional development so that they could face the impact of reforms and globalization on school development. The Education Bureau has commissioned the tertiary institutions to provide structured professional development courses to cater…

PROGRESS REPORT ON THE DSSTOX DATABASE NETWORK: NEWLY LAUNCHED WEBSITE, APPLICATIONS, FUTURE PLANS

EPA Science Inventory

Progress Report on the DSSTox Database Network: Newly Launched Website, Applications, Future Plans

Progress will be reported on development of the Distributed Structure-Searchable Toxicity (DSSTox) Database Network and the newly launched public website that coordinates and...

GIS embedded hydrological modeling: the SID&GRID project

NASA Astrophysics Data System (ADS)

Borsi, I.; Rossetto, R.; Schifani, C.

2012-04-01

The SID&GRID research project, started April 2010 and funded by Regione Toscana (Italy) under the POR FSE 2007-2013, aims to develop a Decision Support System (DSS) for water resource management and planning based on open source and public domain solutions. In order to quantitatively assess water availability in space and time and to support the planning decision processes, the SID&GRID solution consists of hydrological models (coupling 3D existing and newly developed surface- and ground-water and unsaturated zone modeling codes) embedded in a GIS interface, applications and library, where all the input and output data are managed by means of DataBase Management System (DBMS). A graphical user interface (GUI) to manage, analyze and run the SID&GRID hydrological models based on open source gvSIG GIS framework (Asociación gvSIG, 2011) and a Spatial Data Infrastructure to share and interoperate with distributed geographical data is being developed. Such a GUI is thought as a "master control panel" able to guide the user from pre-processing spatial and temporal data, running the hydrological models, and analyzing the outputs. To achieve the above-mentioned goals, the following codes have been selected and are being integrated: 1. Postgresql/PostGIS (PostGIS, 2011) for the Geo Data base Management System; 2. gvSIG with Sextante (Olaya, 2011) geo-algorithm library capabilities and Grass tools (GRASS Development Team, 2011) for the desktop GIS; 3. Geoserver and Geonetwork to share and discover spatial data on the web according to Open Geospatial Consortium; 4. new tools based on the Sextante GeoAlgorithm framework; 5. MODFLOW-2005 (Harbaugh, 2005) groundwater modeling code; 6. MODFLOW-LGR (Mehl and Hill 2005) for local grid refinement; 7. VSF (Thoms et al., 2006) for the variable saturated flow component; 8. new developed routines for overland flow; 9. new algorithms in Jython integrated in gvSIG to compute the net rainfall rate reaching the soil surface, as input for the unsaturated/saturated flow model. At this stage of the research (which will end April 2013), two primary components of the master control panel are being developed: i. a SID&GRID toolbar integrated into gvSIG map context; ii. a new Sextante set of geo-algorithm to pre- and post-process the spatial data to run the hydrological models. The groundwater part of the code has been fully integrated and tested and 3D visualization tools are being developed. The LGR capability has been extended to the 3D solution of the Richards' equation in order to solve in detail the unsaturated zone where required. To be updated about the project, please follow us at the website: http://ut11.isti.cnr.it/SIDGRID/

Rate distortion optimal bit allocation methods for volumetric data using JPEG 2000.

PubMed

Kosheleva, Olga M; Usevitch, Bryan E; Cabrera, Sergio D; Vidal, Edward

2006-08-01

Computer modeling programs that generate three-dimensional (3-D) data on fine grids are capable of generating very large amounts of information. These data sets, as well as 3-D sensor/measured data sets, are prime candidates for the application of data compression algorithms. A very flexible and powerful compression algorithm for imagery data is the newly released JPEG 2000 standard. JPEG 2000 also has the capability to compress volumetric data, as described in Part 2 of the standard, by treating the 3-D data as separate slices. As a decoder standard, JPEG 2000 does not describe any specific method to allocate bits among the separate slices. This paper proposes two new bit allocation algorithms for accomplishing this task. The first procedure is rate distortion optimal (for mean squared error), and is conceptually similar to postcompression rate distortion optimization used for coding codeblocks within JPEG 2000. The disadvantage of this approach is its high computational complexity. The second bit allocation algorithm, here called the mixed model (MM) approach, mathematically models each slice's rate distortion curve using two distinct regions to get more accurate modeling at low bit rates. These two bit allocation algorithms are applied to a 3-D Meteorological data set. Test results show that the MM approach gives distortion results that are nearly identical to the optimal approach, while significantly reducing computational complexity.

Mapping subsurface in proximity to newly-developed sinkhole along roadway.

DOT National Transportation Integrated Search

2013-02-01

MS&T acquired electrical resistivity tomography profiles in immediate proximity to a newly-developed sinkhole in Nixa Missouri : The sinkhole has closed a well-traveled municipal roadway and threatens proximal infrastructure. The intent of this inves...

Application of an uncertainty analysis approach to strategic environmental assessment for urban planning.

PubMed

Liu, Yi; Chen, Jining; He, Weiqi; Tong, Qingyuan; Li, Wangfeng

2010-04-15

Urban planning has been widely applied as a regulatory measure to guide a city's construction and management. It represents official expectations on future population and economic growth and land use over the urban area. No doubt, significant variations often occur between planning schemes and actual development; in particular in China, the world's largest developing country experiencing rapid urbanization and industrialization. This in turn leads to difficulty in estimating the environmental consequences of the urban plan. Aiming to quantitatively analyze the uncertain environmental impacts of the urban plan's implementation, this article developed an integrated methodology combining a scenario analysis approach and a stochastic simulation technique for strategic environmental assessment (SEA). Based on industrial development scenarios, Monte Carlo sampling is applied to generate all possibilities of the spatial distribution of newly emerged industries. All related environmental consequences can be further estimated given the industrial distributions as input to environmental quality models. By applying a HSY algorithm, environmentally unacceptable urban growth, regarding both economic development and land use spatial layout, can be systematically identified, providing valuable information to urban planners and decision makers. A case study in Dalian Municipality, Northeast China, is used to illustrate applicability of this methodology. The impacts of Urban Development Plan for Dalian Municipality (2003-2020) (UDP) on atmospheric environment are also discussed in this article.

SU-E-CAMPUS-T-05: Preliminary Results On a 2D Dosimetry System Based On the Optically Stimulated Luminescence of Al2O3

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

Ahmed, M; Eller, S; Yukihara, E

2014-06-15

Purpose: To develop a precise 2D dose mapping technique based on the optically stimulated luminescence (OSL) from Al{sub 2}O{sub 3} films for medical applications. Methods: A 2D laser scanning reader was developed using fast F{sup +}-center (lifetime of <7 ns) and slow F-center (lifetime of 35 ms) OSL emission from newly developed Al{sub 2}O{sub 3} films (Landauer Inc.). An algorithm was developed to correct images for both material and system properties. Since greater contribution of the F??-center emission in the recorded signal increases the readout efficiency and robustness of image corrections, Al{sub 2}O{sub 3}:C,Mg film samples are being investigated inmore » addition to Al{sub 2}O{sub 3}:C samples. Preliminary investigations include exposure of the films to a 6 MV photon beam at 10 cm depth in solid water phantom with an SSD of 100 cm, using a 10 cm × 10 cm flat field or a 4 cm × 4 cm field with a 60° wedge filter. Kodak EDR2 radiographic film and EBT2 Gafchromic film were also exposed for comparison. Results: The results indicate that the algorithm is able to correct images and calculate 2D dose. For the wedge field irradiation, the calculated dose at the center of the field was 0.9 Gy for Al{sub 2}O{sub 3}:C and 0.87 Gy for Al{sub 2}O{sub 3}:C,Mg, whereas, the delivered dose was 0.95 Gy. A good qualitative agreement of the dose profiles was obtained between the OSL films and EDR2 and EBT2 films. Laboratory tests using a beta source suggest that a large dynamic range (10{sup −2}−10{sup 2} Gy) can be achieved using this technique. Conclusion: A 2D dosimetry system and an in-house image correction algorithm were developed for 2D film dosimetry in medical applications. The system is in the preliminary stage of development, but the data demonstrates the feasibility of this approach. This work was supported by Landauer, Inc.« less

Adapting MODIS Dust Mask Algorithm to Suomi NPP VIIRS for Air Quality Applications

NASA Astrophysics Data System (ADS)

Ciren, P.; Liu, H.; Kondragunta, S.; Laszlo, I.

2012-12-01

Despite pollution reduction control strategies enforced by the Environmental Protection Agency (EPA), large regions of the United States are often under exceptional events such as biomass burning and dust outbreaks that lead to non-attainment of particulate matter standards. This has warranted the National Weather Service (NWS) to provide smoke and dust forecast guidance to the general public. The monitoring and forecasting of dust outbreaks relies on satellite data. Currently, Aqua/MODIS (MODerate resolution Imaging Spectrometer) and Terra/MODIS provide measurements needed to derive dust mask and Aerosol Optical Thickness (AOT) products. The newly launched Suomi NPP VIIRS (Visible/Infrared Imaging Radiometer Suite) instrument has a Suspended Matter (SM) product that indicates the presence of dust, smoke, volcanic ash, sea salt, and unknown aerosol types in a given pixel. The algorithm to identify dust is different over land and ocean but for both, the information comes from AOT retrieval algorithm. Over land, the selection of dust aerosol model in the AOT retrieval algorithm indicates the presence of dust and over ocean a fine mode fraction smaller than 20% indicates dust. Preliminary comparisons of VIIRS SM to CALIPSO Vertical Feature Mask (VFM) aerosol type product indicate that the Probability of Detection (POD) is at ~10% and the product is not mature for operational use. As an alternate approach, NESDIS dust mask algorithm developed for NWS dust forecast verification that uses MODIS deep blue, visible, and mid-IR channels using spectral differencing techniques and spatial variability tests was applied to VIIRS radiances. This algorithm relies on the spectral contrast of dust absorption at 412 and 440 nm and an increase in reflectivity at 2.13 μm when dust is present in the atmosphere compared to a clear sky. To avoid detecting bright desert surface as airborne dust, the algorithm uses the reflectances at 1.24 μm and 2.25 μm to flag bright pixels. The algorithm flags pixels that fall into the glint region so sun glint is not picked up as dust. The algorithm also has a spatial variability test that uses reflectances at 0.86 μm to screen for clouds over water. Analysis of one granule for a known dust event on May 2, 2012 shows that the agreement between VIIRS and MODIS is 82% and VIIRS and CALIPSO is 71%. The probability of detection for VIIRS when compared to MODIS and CALIPSO is 53% and 45% respectively whereas the false alarm ratio for VIIRS when compared to MODIS and CALIPSO is 20% and 37% respectively. The algorithm details, results from the test cases, and the use of the dust flag product in NWS applications will be presented.

The Passive Microwave Neural Network Precipitation Retrieval (PNPR) for AMSU/MHS and ATMS cross-track scanning radiometers

NASA Astrophysics Data System (ADS)

Sano', Paolo; Casella, Daniele; Panegrossi, Giulia; Cinzia Marra, Anna; Dietrich, Stefano

2016-04-01

Spaceborne microwave cross-track scanning radiometers, originally developed for temperature and humidity sounding, have shown great capabilities to provide a significant contribution in precipitation monitoring both in terms of measurement quality and spatial/temporal coverage. The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for cross-track scanning radiometers, originally developed for the Advanced Microwave Sounding Unit/Microwave Humidity Sounder (AMSU-A/MHS) radiometers (on board the European MetOp and U.S. NOAA satellites), was recently newly designed to exploit the Advanced Technology Microwave Sounder (ATMS) on board the Suomi-NPP satellite and the future JPSS satellites. The PNPR algorithm is based on the Artificial Neural Network (ANN) approach. The main PNPR-ATMS algorithm changes with respect to PNPR-AMSU/MHS are the design and implementation of a new ANN able to manage the information derived from the additional ATMS channels (respect to the AMSU-A/MHS radiometer) and a new screening procedure for not-precipitating pixels. In order to achieve maximum consistency of the retrieved surface precipitation, both PNPR algorithms are based on the same physical foundation. The PNPR is optimized for the European and the African area. The neural network was trained using a cloud-radiation database built upon 94 cloud-resolving simulations over Europe and the Mediterranean and over the African area and radiative transfer model simulations of TB vectors consistent with the AMSU-A/MHS and ATMS channel frequencies, viewing angles, and view-angle dependent IFOV sizes along the scan projections. As opposed to other ANN precipitation retrieval algorithms, PNPR uses a unique ANN that retrieves the surface precipitation rate for all types of surface backgrounds represented in the training database, i.e., land (vegetated or arid), ocean, snow/ice or coast. This approach prevents different precipitation estimates from being inconsistent with one another when an observed precipitation system extends over two or more types of surfaces. As input data, the PNPR algorithm incorporates the TBs from selected channels, and various additional TBs-derived variables. Ancillary geographical/geophysical inputs (i.e., latitude, terrain height, surface type, season) are also considered during the training phase. The PNPR algorithm outputs consist of both the surface precipitation rate (along with the information on precipitation phase: liquid, mixed, solid) and a pixel-based quality index. We will illustrate the main features of the PNPR algorithm and will show results of a verification study over Europe and Africa. The study is based on the available ground-based radar and/or rain gauge network observations over the European area. In addition, results of the comparison with rainfall products available from the NASA/JAXA Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) (over the African area) and Global Precipitation Measurement (GPM) Dual frequency Precipitation Radar (DPR) will be shown. The analysis is built upon a two-years coincidence dataset of AMSU/MHS and ATMS observations with PR (2013-2014) and DPR (2014-2015). The PNPR is developed within the EUMETSAT H/SAF program (Satellite Application Facility for Operational Hydrology and Water Management), where it is used operationally towards the full exploitation of all microwave radiometers available in the GPM era. The algorithm will be tailored to the future European Microwave Sounder (MWS) onboard the MetOp-Second Generation (MetOp-SG) satellites.

Chandra ACIS Sub-pixel Resolution

NASA Astrophysics Data System (ADS)

Kim, Dong-Woo; Anderson, C. S.; Mossman, A. E.; Allen, G. E.; Fabbiano, G.; Glotfelty, K. J.; Karovska, M.; Kashyap, V. L.; McDowell, J. C.

2011-05-01

We investigate how to achieve the best possible ACIS spatial resolution by binning in ACIS sub-pixel and applying an event repositioning algorithm after removing pixel-randomization from the pipeline data. We quantitatively assess the improvement in spatial resolution by (1) measuring point source sizes and (2) detecting faint point sources. The size of a bright (but no pile-up), on-axis point source can be reduced by about 20-30%. With the improve resolution, we detect 20% more faint sources when embedded on the extended, diffuse emission in a crowded field. We further discuss the false source rate of about 10% among the newly detected sources, using a few ultra-deep observations. We also find that the new algorithm does not introduce a grid structure by an aliasing effect for dithered observations and does not worsen the positional accuracy

Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images

PubMed Central

Jang, Yeonggul; Jung, Ho Yub; Hong, Youngtaek; Cho, Iksung; Shim, Hackjoon; Chang, Hyuk-Jae

2016-01-01

This paper presents a method for the automatic 3D segmentation of the ascending aorta from coronary computed tomography angiography (CCTA). The segmentation is performed in three steps. First, the initial seed points are selected by minimizing a newly proposed energy function across the Hough circles. Second, the ascending aorta is segmented by geodesic distance transformation. Third, the seed points are effectively transferred through the next axial slice by a novel transfer function. Experiments are performed using a database composed of 10 patients' CCTA images. For the experiment, the ground truths are annotated manually on the axial image slices by a medical expert. A comparative evaluation with state-of-the-art commercial aorta segmentation algorithms shows that our approach is computationally more efficient and accurate under the DSC (Dice Similarity Coefficient) measurements. PMID:26904151

Temporally consistent segmentation of point clouds

NASA Astrophysics Data System (ADS)

Owens, Jason L.; Osteen, Philip R.; Daniilidis, Kostas

2014-06-01

We consider the problem of generating temporally consistent point cloud segmentations from streaming RGB-D data, where every incoming frame extends existing labels to new points or contributes new labels while maintaining the labels for pre-existing segments. Our approach generates an over-segmentation based on voxel cloud connectivity, where a modified k-means algorithm selects supervoxel seeds and associates similar neighboring voxels to form segments. Given the data stream from a potentially mobile sensor, we solve for the camera transformation between consecutive frames using a joint optimization over point correspondences and image appearance. The aligned point cloud may then be integrated into a consistent model coordinate frame. Previously labeled points are used to mask incoming points from the new frame, while new and previous boundary points extend the existing segmentation. We evaluate the algorithm on newly-generated RGB-D datasets.

Evaluation of new binders using newly developed fracture energy test.

DOT National Transportation Integrated Search

2013-07-01

This study evaluated a total of seven asphalt binders with various additives : using the newly developed binder fracture energy test. The researchers prepared and : tested PAV-aged and RTFO-plus-PAV-aged specimens. This study confirmed previous : res...

13 CFR 120.812 - Probationary period for newly certified CDCs.

Code of Federal Regulations, 2013 CFR

2013-01-01

... certified CDCs. 120.812 Section 120.812 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION BUSINESS LOANS Development Company Loan Program (504) Certification Procedures to Become A Cdc § 120.812 Probationary period for newly certified CDCs. (a) Newly certified CDCs will be on probation for a period of two...

13 CFR 120.812 - Probationary period for newly certified CDCs.

Code of Federal Regulations, 2014 CFR

2014-01-01

... certified CDCs. 120.812 Section 120.812 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION BUSINESS LOANS Development Company Loan Program (504) Certification Procedures to Become A Cdc § 120.812 Probationary period for newly certified CDCs. (a) Newly certified CDCs will be on probation for a period of two...

13 CFR 120.812 - Probationary period for newly certified CDCs.

Code of Federal Regulations, 2012 CFR

2012-01-01

... certified CDCs. 120.812 Section 120.812 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION BUSINESS LOANS Development Company Loan Program (504) Certification Procedures to Become A Cdc § 120.812 Probationary period for newly certified CDCs. (a) Newly certified CDCs will be on probation for a period of two...

13 CFR 120.812 - Probationary period for newly certified CDCs.

Code of Federal Regulations, 2011 CFR

2011-01-01

... certified CDCs. 120.812 Section 120.812 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION BUSINESS LOANS Development Company Loan Program (504) Certification Procedures to Become A Cdc § 120.812 Probationary period for newly certified CDCs. (a) Newly certified CDCs will be on probation for a period of two...

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods

PubMed Central

Hogervorst, Maarten A.; Pinkus, Alan R.

2016-01-01

The fusion and enhancement of multiband nighttime imagery for surveillance and navigation has been the subject of extensive research for over two decades. Despite the ongoing efforts in this area there is still only a small number of static multiband test images available for the development and evaluation of new image fusion and enhancement methods. Moreover, dynamic multiband imagery is also currently lacking. To fill this gap we present the TRICLOBS dynamic multi-band image data set containing sixteen registered visual (0.4–0.7μm), near-infrared (NIR, 0.7–1.0μm) and long-wave infrared (LWIR, 8–14μm) motion sequences. They represent different military and civilian surveillance scenarios registered in three different scenes. Scenes include (military and civilian) people that are stationary, walking or running, or carrying various objects. Vehicles, foliage, and buildings or other man-made structures are also included in the scenes. This data set is primarily intended for the development and evaluation of image fusion, enhancement and color mapping algorithms for short-range surveillance applications. The imagery was collected during several field trials with our newly developed TRICLOBS (TRI-band Color Low-light OBServation) all-day all-weather surveillance system. This system registers a scene in the Visual, NIR and LWIR part of the electromagnetic spectrum using three optically aligned sensors (two digital image intensifiers and an uncooled long-wave infrared microbolometer). The three sensor signals are mapped to three individual RGB color channels, digitized, and stored as uncompressed RGB (false) color frames. The TRICLOBS data set enables the development and evaluation of (both static and dynamic) image fusion, enhancement and color mapping algorithms. To allow the development of realistic color remapping procedures, the data set also contains color photographs of each of the three scenes. The color statistics derived from these photographs can be used to define color mappings that give the multi-band imagery a realistic color appearance. PMID:28036328

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods.

PubMed

Toet, Alexander; Hogervorst, Maarten A; Pinkus, Alan R

2016-01-01

The fusion and enhancement of multiband nighttime imagery for surveillance and navigation has been the subject of extensive research for over two decades. Despite the ongoing efforts in this area there is still only a small number of static multiband test images available for the development and evaluation of new image fusion and enhancement methods. Moreover, dynamic multiband imagery is also currently lacking. To fill this gap we present the TRICLOBS dynamic multi-band image data set containing sixteen registered visual (0.4-0.7μm), near-infrared (NIR, 0.7-1.0μm) and long-wave infrared (LWIR, 8-14μm) motion sequences. They represent different military and civilian surveillance scenarios registered in three different scenes. Scenes include (military and civilian) people that are stationary, walking or running, or carrying various objects. Vehicles, foliage, and buildings or other man-made structures are also included in the scenes. This data set is primarily intended for the development and evaluation of image fusion, enhancement and color mapping algorithms for short-range surveillance applications. The imagery was collected during several field trials with our newly developed TRICLOBS (TRI-band Color Low-light OBServation) all-day all-weather surveillance system. This system registers a scene in the Visual, NIR and LWIR part of the electromagnetic spectrum using three optically aligned sensors (two digital image intensifiers and an uncooled long-wave infrared microbolometer). The three sensor signals are mapped to three individual RGB color channels, digitized, and stored as uncompressed RGB (false) color frames. The TRICLOBS data set enables the development and evaluation of (both static and dynamic) image fusion, enhancement and color mapping algorithms. To allow the development of realistic color remapping procedures, the data set also contains color photographs of each of the three scenes. The color statistics derived from these photographs can be used to define color mappings that give the multi-band imagery a realistic color appearance.

Extended AIC model based on high order moments and its application in the financial market

NASA Astrophysics Data System (ADS)

Mao, Xuegeng; Shang, Pengjian

2018-07-01

In this paper, an extended method of traditional Akaike Information Criteria(AIC) is proposed to detect the volatility of time series by combining it with higher order moments, such as skewness and kurtosis. Since measures considering higher order moments are powerful in many aspects, the properties of asymmetry and flatness can be observed. Furthermore, in order to reduce the effect of noise and other incoherent features, we combine the extended AIC algorithm with multiscale wavelet analysis, in which the newly extended AIC algorithm is applied to wavelet coefficients at several scales and the time series are reconstructed by wavelet transform. After that, we create AIC planes to derive the relationship among AIC values using variance, skewness and kurtosis respectively. When we test this technique on the financial market, the aim is to analyze the trend and volatility of the closing price of stock indices and classify them. And we also adapt multiscale analysis to measure complexity of time series over a range of scales. Empirical results show that the singularity of time series in stock market can be detected via extended AIC algorithm.

SEVEN NEW BINARIES DISCOVERED IN THE KEPLER LIGHT CURVES THROUGH THE BEER METHOD CONFIRMED BY RADIAL-VELOCITY OBSERVATIONS

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

Faigler, S.; Mazeh, T.; Tal-Or, L.

We present seven newly discovered non-eclipsing short-period binary systems with low-mass companions, identified by the recently introduced BEER algorithm, applied to the publicly available 138-day photometric light curves obtained by the Kepler mission. The detection is based on the beaming effect (sometimes called Doppler boosting), which increases (decreases) the brightness of any light source approaching (receding from) the observer, enabling a prediction of the stellar Doppler radial-velocity (RV) modulation from its precise photometry. The BEER algorithm identifies the BEaming periodic modulation, with a combination of the well-known Ellipsoidal and Reflection/heating periodic effects, induced by short-period companions. The seven detections weremore » confirmed by spectroscopic RV follow-up observations, indicating minimum secondary masses in the range 0.07-0.4 M{sub Sun }. The binaries discovered establish for the first time the feasibility of the BEER algorithm as a new detection method for short-period non-eclipsing binaries, with the potential to detect in the near future non-transiting brown-dwarf secondaries, or even massive planets.« less

Fuzzy Performance between Surface Fitting and Energy Distribution in Turbulence Runner

PubMed Central

Liang, Zhongwei; Liu, Xiaochu; Ye, Bangyan; Brauwer, Richard Kars

2012-01-01

Because the application of surface fitting algorithms exerts a considerable fuzzy influence on the mathematical features of kinetic energy distribution, their relation mechanism in different external conditional parameters must be quantitatively analyzed. Through determining the kinetic energy value of each selected representative position coordinate point by calculating kinetic energy parameters, several typical algorithms of complicated surface fitting are applied for constructing microkinetic energy distribution surface models in the objective turbulence runner with those obtained kinetic energy values. On the base of calculating the newly proposed mathematical features, we construct fuzzy evaluation data sequence and present a new three-dimensional fuzzy quantitative evaluation method; then the value change tendencies of kinetic energy distribution surface features can be clearly quantified, and the fuzzy performance mechanism discipline between the performance results of surface fitting algorithms, the spatial features of turbulence kinetic energy distribution surface, and their respective environmental parameter conditions can be quantitatively analyzed in detail, which results in the acquirement of final conclusions concerning the inherent turbulence kinetic energy distribution performance mechanism and its mathematical relation. A further turbulence energy quantitative study can be ensured. PMID:23213287

An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution.

PubMed

Biswas, Subhodip; Kundu, Souvik; Das, Swagatam

2014-10-01

In real life, we often need to find multiple optimally sustainable solutions of an optimization problem. Evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations in their actual framework. Differential evolution (DE) is a powerful evolutionary algorithm (EA) well-known for its ability and efficiency as a single peak global optimizer for continuous spaces. This article suggests a niching scheme integrated with DE for achieving a stable and efficient niching behavior by combining the newly proposed parent-centric mutation operator with synchronous crowding replacement rule. The proposed approach is designed by considering the difficulties associated with the problem dependent niching parameters (like niche radius) and does not make use of such control parameter. The mutation operator helps to maintain the population diversity at an optimum level by using well-defined local neighborhoods. Based on a comparative study involving 13 well-known state-of-the-art niching EAs tested on an extensive collection of benchmarks, we observe a consistent statistical superiority enjoyed by our proposed niching algorithm.

Molecular image-directed biopsies: improving clinical biopsy selection in patients with multiple tumors

NASA Astrophysics Data System (ADS)

Harmon, Stephanie A.; Tuite, Michael J.; Jeraj, Robert

2016-10-01

Site selection for image-guided biopsies in patients with multiple lesions is typically based on clinical feasibility and physician preference. This study outlines the development of a selection algorithm that, in addition to clinical requirements, incorporates quantitative imaging data for automatic identification of candidate lesions for biopsy. The algorithm is designed to rank potential targets by maximizing a lesion-specific score, incorporating various criteria separated into two categories: (1) physician-feasibility category including physician-preferred lesion location and absolute volume scores, and (2) imaging-based category including various modality and application-specific metrics. This platform was benchmarked in two clinical scenarios, a pre-treatment setting and response-based setting using imaging from metastatic prostate cancer patients with high disease burden (multiple lesions) undergoing conventional treatment and receiving whole-body [18F]NaF PET/CT scans pre- and mid-treatment. Targeting of metastatic lesions was robust to different weighting ratios and candidacy for biopsy was physician confirmed. Lesion ranked as top targets for biopsy remained so for all patients in pre-treatment and post-treatment biopsy selection after sensitivity testing was completed for physician-biased or imaging-biased scenarios. After identifying candidates, biopsy feasibility was evaluated by a physician and confirmed for 90% (32/36) of high-ranking lesions, of which all top choices were confirmed. The remaining cases represented lesions with high anatomical difficulty for targeting, such as proximity to sciatic nerve. This newly developed selection method was successfully used to quantitatively identify candidate lesions for biopsies in patients with multiple lesions. In a prospective study, we were able to successfully plan, develop, and implement this technique for the selection of a pre-treatment biopsy location.

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

PubMed Central

Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-01-01

Background Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Objective Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Methods Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Results Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. Conclusions To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. PMID:29506966

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation.

PubMed

Sadat, Md Nazmus; Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-03-05

Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. ©Md Nazmus Sadat, Xiaoqian Jiang, Md Momin Al Aziz, Shuang Wang, Noman Mohammed. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.03.2018.

Using Smartphone Sensors for Improving Energy Expenditure Estimation

PubMed Central

Zhu, Jindan; Das, Aveek K.; Zeng, Yunze; Mohapatra, Prasant; Han, Jay J.

2015-01-01

Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of EE estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings. PMID:27170901

Using Smartphone Sensors for Improving Energy Expenditure Estimation.

PubMed

Pande, Amit; Zhu, Jindan; Das, Aveek K; Zeng, Yunze; Mohapatra, Prasant; Han, Jay J

2015-01-01

Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of EE estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings.

Modelling of wildlife migrations and its economic impacts

NASA Astrophysics Data System (ADS)

Myšková, Kateřina; Žák, Jaroslav

2013-10-01

Natural wildlife migrations are often disrupted by the construction of line structures, especially freeways. Various overpasses and underpasses (migration objects) are being designed to enable species to pass through line structures. The newly developed original procedure for the quantification of the utility of migration objects (migration potentials) and sections of line structures retrieves the deficiencies of the previous ones. The procedure has been developed under bulk information obtained by monitoring migrations using camera system and spy games. The log-normal distribution functions are used. The procedure for the evaluation of the probability of the permeability of the line structures sectors is also presented. The above mentioned procedures and algorithms can be used while planning, preparing, constructing or verifying the measures to assure the permeability of line structures for selected feral species. Using the procedures can significantly reduce financial costs and improve the permeability. When the migration potentials are correctly determined and the whole sector is taken into account for the migrations, some originally designed objects may be found to be redundant and not building them will bring financial savings.

Spectroscopic photon localization microscopy: breaking the resolution limit of single molecule localization microscopy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Dong, Biqin; Almassalha, Luay Matthew; Urban, Ben E.; Nguyen, The-Quyen; Khuon, Satya; Chew, Teng-Leong; Backman, Vadim; Sun, Cheng; Zhang, Hao F.

2017-02-01

Distinguishing minute differences in spectroscopic signatures is crucial for revealing the fluorescence heterogeneity among fluorophores to achieve a high molecular specificity. Here we report spectroscopic photon localization microscopy (SPLM), a newly developed far-field spectroscopic imaging technique, to achieve nanoscopic resolution based on the principle of single-molecule localization microscopy while simultaneously uncovering the inherent molecular spectroscopic information associated with each stochastic event (Dong et al., Nature Communications 2016, in press). In SPLM, by using a slit-less monochromator, both the zero-order and the first-order diffractions from a grating were recorded simultaneously by an electron multiplying charge-coupled device to reveal the spatial distribution and the associated emission spectra of individual stochastic radiation events, respectively. As a result, the origins of photon emissions from different molecules can be identified according to their spectral differences with sub-nm spectral resolution, even when the molecules are within close proximity. With the newly developed algorithms including background subtraction and spectral overlap unmixing, we established and tested a method which can significantly extend the fundamental spatial resolution limit of single molecule localization microscopy by molecular discrimination through spectral regression. Taking advantage of this unique capability, we demonstrated improvement in spatial resolution of PALM/STORM up to ten fold with selected fluorophores. This technique can be readily adopted by other research groups to greatly enhance the optical resolution of single molecule localization microscopy without the need to modify their existing staining methods and protocols. This new resolving capability can potentially provide new insights into biological phenomena and enable significant research progress to be made in the life sciences.

ROBUST ONLINE MONITORING FOR CALIBRATION ASSESSMENT OF TRANSMITTERS AND INSTRUMENTATION

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

Ramuhalli, Pradeep; Tipireddy, Ramakrishna; Lerchen, Megan E.

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. Specifically, the next generation of OLM technology is expected to include newly developed advanced algorithms that improve monitoring of sensor/system performance and enable the use of plant data to derive information that currently cannot be measured. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this paper, we discuss an overview of research beingmore » performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or more sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation – fault detection and selection of acceptance criteria • Virtual sensing – signal value prediction and acceptance criteria • Response-time assessment – fault detection and acceptance criteria selection A GP-based uncertainty quantification (UQ) method previously developed for UQ in OLM, was adapted for use in sensor-fault detection and virtual sensing. For signal validation, the various components to the OLM residual (which is computed using an AAKR model) were explicitly defined and modeled using a GP. Evaluation was conducted using flow loop data from multiple sources. Results using experimental data from laboratory-scale flow loops indicate that the approach, while capable of detecting sensor drift, may be incapable of discriminating between sensor drift and model inadequacy. This may be due to a simplification applied in the initial modeling, where the sensor degradation is assumed to be stationary. In the case of virtual sensors, the GP model was used in a predictive mode to estimate the correct sensor reading for sensors that may have failed. Results have indicated the viability of using this approach for virtual sensing. However, the GP model has proven to be computationally expensive, and so alternative algorithms for virtual sensing are being evaluated. Finally, automated approaches to performing noise analysis for extracting sensor response time were developed. Evaluation of this technique using laboratory-scale data indicates that it compares well with manual techniques previously used for noise analysis. Moreover, the automated and manual approaches for noise analysis also compare well with the current “gold standard”, hydraulic ramp testing, for response time monitoring. Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.« less

Mapping the Recent US Hurricanes Triggered Flood Events in Near Real Time

NASA Astrophysics Data System (ADS)

Shen, X.; Lazin, R.; Anagnostou, E. N.; Wanik, D. W.; Brakenridge, G. R.

2017-12-01

Synthetic Aperture Radar (SAR) observations is the only reliable remote sensing data source to map flood inundation during severe weather events. Unfortunately, since state-of-art data processing algorithms cannot meet the automation and quality standard of a near-real-time (NRT) system, quality controlled inundation mapping by SAR currently depends heavily on manual processing, which limits our capability to quickly issue flood inundation maps at global scale. Specifically, most SAR-based inundation mapping algorithms are not fully automated, while those that are automated exhibit severe over- and/or under-detection errors that limit their potential. These detection errors are primarily caused by the strong overlap among the SAR backscattering probability density functions (PDF) of different land cover types. In this study, we tested a newly developed NRT SAR-based inundation mapping system, named Radar Produced Inundation Diary (RAPID), using Sentinel-1 dual polarized SAR data over recent flood events caused by Hurricanes Harvey, Irma, and Maria (2017). The system consists of 1) self-optimized multi-threshold classification, 2) over-detection removal using land-cover information and change detection, 3) under-detection compensation, and 4) machine-learning based correction. Algorithm details are introduced in another poster, H53J-1603. Good agreements were obtained by comparing the result from RAPID with visual interpretation of SAR images and manual processing from Dartmouth Flood Observatory (DFO) (See Figure 1). Specifically, the over- and under-detections that is typically noted in automated methods is significantly reduced to negligible levels. This performance indicates that RAPID can address the automation and accuracy issues of current state-of-art algorithms and has the potential to apply operationally on a number of satellite SAR missions, such as SWOT, ALOS, Sentinel etc. RAPID data can support many applications such as rapid assessment of damage losses and disaster alleviation/rescue at global scale.

Fast emission estimates in China and South Africa constrained by satellite observations

NASA Astrophysics Data System (ADS)

Mijling, Bas; van der A, Ronald

2013-04-01

Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Unfortunately, bottom-up emission inventories, compiled from large quantities of statistical data, are easily outdated for emerging economies such as China and South Africa, where rapid economic growth change emissions accordingly. Alternatively, top-down emission estimates from satellite observations of air constituents have important advantages of being spatial consistent, having high temporal resolution, and enabling emission updates shortly after the satellite data become available. However, constraining emissions from observations of concentrations is computationally challenging. Within the GlobEmission project (part of the Data User Element programme of ESA) a new algorithm has been developed, specifically designed for fast daily emission estimates of short-lived atmospheric species on a mesoscopic scale (0.25 × 0.25 degree) from satellite observations of column concentrations. The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates in East China and South Africa, using the CHIMERE chemical transport model together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. The observations are used to construct a monthly emission time series, which reveal important emission trends such as the emission reduction measures during the Beijing Olympic Games, and the impact and recovery from the global economic crisis. The algorithm is also able to detect emerging sources (e.g. new power plants) and improve emission information for areas where proxy data are not or badly known (e.g. shipping emissions). The new emission inventories result in a better agreement between observations and simulations of air pollutant concentrations, facilitating improved air quality forecasts.

Leaf area and photosynthesis of newly emerged trifoliolate leaves are regulated by mature leaves in soybean.

PubMed

Wu, Yushan; Gong, Wanzhuo; Wang, Yangmei; Yong, Taiwen; Yang, Feng; Liu, Weigui; Wu, Xiaoling; Du, Junbo; Shu, Kai; Liu, Jiang; Liu, Chunyan; Yang, Wenyu

2018-03-29

Leaf anatomy and the stomatal development of developing leaves of plants have been shown to be regulated by the same light environment as that of mature leaves, but no report has yet been written on whether such a long-distance signal from mature leaves regulates the total leaf area of newly emerged leaves. To explore this question, we created an investigation in which we collected data on the leaf area, leaf mass per area (LMA), leaf anatomy, cell size, cell number, gas exchange and soluble sugar content of leaves from three soybean varieties grown under full sunlight (NS), shaded mature leaves (MS) or whole plants grown in shade (WS). Our results show that MS or WS cause a marked decline both in leaf area and LMA in newly developing leaves. Leaf anatomy also showed characteristics of shade leaves with decreased leaf thickness, palisade tissue thickness, sponge tissue thickness, cell size and cell numbers. In addition, in the MS and WS treatments, newly developed leaves exhibited lower net photosynthetic rate (Pn), stomatal conductance (Gs) and transpiration rate (E), but higher carbon dioxide (CO 2 ) concentration in the intercellular space (Ci) than plants grown in full sunlight. Moreover, soluble sugar content was significantly decreased in newly developed leaves in MS and WS treatments. These results clearly indicate that (1) leaf area, leaf anatomical structure, and photosynthetic function of newly developing leaves are regulated by a systemic irradiance signal from mature leaves; (2) decreased cell size and cell number are the major cause of smaller and thinner leaves in shade; and (3) sugars could possibly act as candidate signal substances to regulate leaf area systemically.

Japan Society of Gynecologic Oncology guidelines 2013 for the treatment of uterine body neoplasms.

PubMed

Ebina, Yasuhiko; Katabuchi, Hidetaka; Mikami, Mikio; Nagase, Satoru; Yaegashi, Nobuo; Udagawa, Yasuhiro; Kato, Hidenori; Kubushiro, Kaneyuki; Takamatsu, Kiyoshi; Ino, Kazuhiko; Yoshikawa, Hiroyuki

2016-06-01

The third version of the Japan Society of Gynecologic Oncology guidelines for the treatment of uterine body neoplasms was published in 2013. The guidelines comprise nine chapters and nine algorithms. Each chapter includes a clinical question, recommendations, background, objectives, explanations, and references. This revision was intended to collect up-to-date international evidence. The highlights of this revision are to (1) newly specify costs and conflicts of interest; (2) describe the clinical significance of pelvic lymph node dissection and para-aortic lymphadenectomy, including variant histologic types; (3) describe more clearly the indications for laparoscopic surgery as the standard treatment; (4) provide guidelines for post-treatment hormone replacement therapy; (5) clearly differentiate treatment of advanced or recurrent cancer between the initial treatment and the treatment carried out after the primary operation; (6) collectively describe fertility-sparing therapy for both atypical endometrial hyperplasia and endometrioid adenocarcinoma (corresponding to G1) and newly describe relapse therapy after fertility-preserving treatment; and (7) newly describe the treatment of trophoblastic disease. Overall, the objective of these guidelines is to clearly delineate the standard of care for uterine body neoplasms in Japan with the goal of ensuring a high standard of care for all Japanese women diagnosed with uterine body neoplasms.

Real-Time PPP Based on the Coupling Estimation of Clock Bias and Orbit Error with Broadcast Ephemeris.

PubMed

Pan, Shuguo; Chen, Weirong; Jin, Xiaodong; Shi, Xiaofei; He, Fan

2015-07-22

Satellite orbit error and clock bias are the keys to precise point positioning (PPP). The traditional PPP algorithm requires precise satellite products based on worldwide permanent reference stations. Such an algorithm requires considerable work and hardly achieves real-time performance. However, real-time positioning service will be the dominant mode in the future. IGS is providing such an operational service (RTS) and there are also commercial systems like Trimble RTX in operation. On the basis of the regional Continuous Operational Reference System (CORS), a real-time PPP algorithm is proposed to apply the coupling estimation of clock bias and orbit error. The projection of orbit error onto the satellite-receiver range has the same effects on positioning accuracy with clock bias. Therefore, in satellite clock estimation, part of the orbit error can be absorbed by the clock bias and the effects of residual orbit error on positioning accuracy can be weakened by the evenly distributed satellite geometry. In consideration of the simple structure of pseudorange equations and the high precision of carrier-phase equations, the clock bias estimation method coupled with orbit error is also improved. Rovers obtain PPP results by receiving broadcast ephemeris and real-time satellite clock bias coupled with orbit error. By applying the proposed algorithm, the precise orbit products provided by GNSS analysis centers are rendered no longer necessary. On the basis of previous theoretical analysis, a real-time PPP system was developed. Some experiments were then designed to verify this algorithm. Experimental results show that the newly proposed approach performs better than the traditional PPP based on International GNSS Service (IGS) real-time products. The positioning accuracies of the rovers inside and outside the network are improved by 38.8% and 36.1%, respectively. The PPP convergence speeds are improved by up to 61.4% and 65.9%. The new approach can change the traditional PPP mode because of its advantages of independence, high positioning precision, and real-time performance. It could be an alternative solution for regional positioning service before global PPP service comes into operation.

Real-Time PPP Based on the Coupling Estimation of Clock Bias and Orbit Error with Broadcast Ephemeris

PubMed Central

Pan, Shuguo; Chen, Weirong; Jin, Xiaodong; Shi, Xiaofei; He, Fan

2015-01-01

Satellite orbit error and clock bias are the keys to precise point positioning (PPP). The traditional PPP algorithm requires precise satellite products based on worldwide permanent reference stations. Such an algorithm requires considerable work and hardly achieves real-time performance. However, real-time positioning service will be the dominant mode in the future. IGS is providing such an operational service (RTS) and there are also commercial systems like Trimble RTX in operation. On the basis of the regional Continuous Operational Reference System (CORS), a real-time PPP algorithm is proposed to apply the coupling estimation of clock bias and orbit error. The projection of orbit error onto the satellite-receiver range has the same effects on positioning accuracy with clock bias. Therefore, in satellite clock estimation, part of the orbit error can be absorbed by the clock bias and the effects of residual orbit error on positioning accuracy can be weakened by the evenly distributed satellite geometry. In consideration of the simple structure of pseudorange equations and the high precision of carrier-phase equations, the clock bias estimation method coupled with orbit error is also improved. Rovers obtain PPP results by receiving broadcast ephemeris and real-time satellite clock bias coupled with orbit error. By applying the proposed algorithm, the precise orbit products provided by GNSS analysis centers are rendered no longer necessary. On the basis of previous theoretical analysis, a real-time PPP system was developed. Some experiments were then designed to verify this algorithm. Experimental results show that the newly proposed approach performs better than the traditional PPP based on International GNSS Service (IGS) real-time products. The positioning accuracies of the rovers inside and outside the network are improved by 38.8% and 36.1%, respectively. The PPP convergence speeds are improved by up to 61.4% and 65.9%. The new approach can change the traditional PPP mode because of its advantages of independence, high positioning precision, and real-time performance. It could be an alternative solution for regional positioning service before global PPP service comes into operation. PMID:26205276

FBP and BPF reconstruction methods for circular X-ray tomography with off-center detector.

PubMed

Schäfer, Dirk; Grass, Michael; van de Haar, Peter

2011-07-01

Circular scanning with an off-center planar detector is an acquisition scheme that allows to save detector area while keeping a large field of view (FOV). Several filtered back-projection (FBP) algorithms have been proposed earlier. The purpose of this work is to present two newly developed back-projection filtration (BPF) variants and evaluate the image quality of these methods compared to the existing state-of-the-art FBP methods. The first new BPF algorithm applies redundancy weighting of overlapping opposite projections before differentiation in a single projection. The second one uses the Katsevich-type differentiation involving two neighboring projections followed by redundancy weighting and back-projection. An averaging scheme is presented to mitigate streak artifacts inherent to circular BPF algorithms along the Hilbert filter lines in the off-center transaxial slices of the reconstructions. The image quality is assessed visually on reconstructed slices of simulated and clinical data. Quantitative evaluation studies are performed with the Forbild head phantom by calculating root-mean-squared-deviations (RMSDs) to the voxelized phantom for different detector overlap settings and by investigating the noise resolution trade-off with a wire phantom in the full detector and off-center scenario. The noise-resolution behavior of all off-center reconstruction methods corresponds to their full detector performance with the best resolution for the FDK based methods with the given imaging geometry. With respect to RMSD and visual inspection, the proposed BPF with Katsevich-type differentiation outperforms all other methods for the smallest chosen detector overlap of about 15 mm. The best FBP method is the algorithm that is also based on the Katsevich-type differentiation and subsequent redundancy weighting. For wider overlap of about 40-50 mm, these two algorithms produce similar results outperforming the other three methods. The clinical case with a detector overlap of about 17 mm confirms these results. The BPF-type reconstructions with Katsevich differentiation are widely independent of the size of the detector overlap and give the best results with respect to RMSD and visual inspection for minimal detector overlap. The increased homogeneity will improve correct assessment of lesions in the entire field of view.

An Efficient and Robust Moving Shadow Removal Algorithm and Its Applications in ITS

NASA Astrophysics Data System (ADS)

Lin, Chin-Teng; Yang, Chien-Ting; Shou, Yu-Wen; Shen, Tzu-Kuei

2010-12-01

We propose an efficient algorithm for removing shadows of moving vehicles caused by non-uniform distributions of light reflections in the daytime. This paper presents a brand-new and complete structure in feature combination as well as analysis for orientating and labeling moving shadows so as to extract the defined objects in foregrounds more easily in each snapshot of the original files of videos which are acquired in the real traffic situations. Moreover, we make use of Gaussian Mixture Model (GMM) for background removal and detection of moving shadows in our tested images, and define two indices for characterizing non-shadowed regions where one indicates the characteristics of lines and the other index can be characterized by the information in gray scales of images which helps us to build a newly defined set of darkening ratios (modified darkening factors) based on Gaussian models. To prove the effectiveness of our moving shadow algorithm, we carry it out with a practical application of traffic flow detection in ITS (Intelligent Transportation System)—vehicle counting. Our algorithm shows the faster processing speed, 13.84 ms/frame, and can improve the accuracy rate in 4% ~ 10% for our three tested videos in the experimental results of vehicle counting.

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm.

PubMed

Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

2016-10-01

Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm

PubMed Central

Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

2016-01-01

Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses. PMID:27706086

Solving the problem of negative populations in approximate accelerated stochastic simulations using the representative reaction approach.

PubMed

Kadam, Shantanu; Vanka, Kumar

2013-02-15

Methods based on the stochastic formulation of chemical kinetics have the potential to accurately reproduce the dynamical behavior of various biochemical systems of interest. However, the computational expense makes them impractical for the study of real systems. Attempts to render these methods practical have led to the development of accelerated methods, where the reaction numbers are modeled by Poisson random numbers. However, for certain systems, such methods give rise to physically unrealistic negative numbers for species populations. The methods which make use of binomial variables, in place of Poisson random numbers, have since become popular, and have been partially successful in addressing this problem. In this manuscript, the development of two new computational methods, based on the representative reaction approach (RRA), has been discussed. The new methods endeavor to solve the problem of negative numbers, by making use of tools like the stochastic simulation algorithm and the binomial method, in conjunction with the RRA. It is found that these newly developed methods perform better than other binomial methods used for stochastic simulations, in resolving the problem of negative populations. Copyright © 2012 Wiley Periodicals, Inc.

Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study.

PubMed

Lowres, Nicole; Neubeck, Lis; Salkeld, Glenn; Krass, Ines; McLachlan, Andrew J; Redfern, Julie; Bennett, Alexandra A; Briffa, Tom; Bauman, Adrian; Martinez, Carlos; Wallenhorst, Christopher; Lau, Jerrett K; Brieger, David B; Sy, Raymond W; Freedman, S Ben

2014-06-01

Atrial fibrillation (AF) causes a third of all strokes, but often goes undetected before stroke. Identification of unknown AF in the community and subsequent anti-thrombotic treatment could reduce stroke burden. We investigated community screening for unknown AF using an iPhone electrocardiogram (iECG) in pharmacies, and determined the cost-effectiveness of this strategy.Pharmacists performedpulse palpation and iECG recordings, with cardiologist iECG over-reading. General practitioner review/12-lead ECG was facilitated for suspected new AF. An automated AF algorithm was retrospectively applied to collected iECGs. Cost-effectiveness analysis incorporated costs of iECG screening, and treatment/outcome data from a United Kingdom cohort of 5,555 patients with incidentally detected asymptomatic AF. A total of 1,000 pharmacy customers aged ≥65 years (mean 76 ± 7 years; 44% male) were screened. Newly identified AF was found in 1.5% (95% CI, 0.8-2.5%); mean age 79 ± 6 years; all had CHA2DS2-VASc score ≥2. AF prevalence was 6.7% (67/1,000). The automated iECG algorithm showed 98.5% (CI, 92-100%) sensitivity for AF detection and 91.4% (CI, 89-93%) specificity. The incremental cost-effectiveness ratio of extending iECG screening into the community, based on 55% warfarin prescription adherence, would be $AUD5,988 (€3,142; $USD4,066) per Quality Adjusted Life Year gained and $AUD30,481 (€15,993; $USD20,695) for preventing one stroke. Sensitivity analysis indicated cost-effectiveness improved with increased treatment adherence.Screening with iECG in pharmacies with an automated algorithm is both feasible and cost-effective. The high and largely preventable stroke/thromboembolism risk of those with newly identified AF highlights the likely benefits of community AF screening. Guideline recommendation of community iECG AF screening should be considered.

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

Grzetic, S; Weldon, M; Noa, K

Purpose: This study compares the newly released MaxFOV Revision 1 EFOV reconstruction algorithm for GE RT590 to the older WideView EFOV algorithm. Two radiotherapy overlays from Q-fix and Diacor, are included in our analysis. Hounsfield Units (HU) generated with the WideView algorithm varied in the extended field (beyond 50cm) and the scanned object’s border varied from slice to slice. A validation of HU consistency between the two reconstruction algorithms is performed. Methods: A CatPhan 504 and CIRS062 Electron Density Phantom were scanned on a GE RT590 CT-Simulator. The phantoms were positioned in multiple locations within the scan field of viewmore » so some of the density plugs were outside the 50cm reconstruction circle. Images were reconstructed using both the WideView and MaxFOV algorithms. The HU for each scan were characterized both in average over a volume and in profile. Results: HU values are consistent between the two algorithms. Low-density material will have a slight increase in HU value and high-density material will have a slight decrease in HU value as the distance from the sweet spot increases. Border inconsistencies and shading artifacts are still present with the MaxFOV reconstruction on the Q-fix overlay but not the Diacor overlay (It should be noted that the Q-fix overlay is not currently GE-certified). HU values for water outside the 50cm FOV are within 40HU of reconstructions at the sweet spot of the scanner. CatPhan HU profiles show improvement with the MaxFOV algorithm as it approaches the scanner edge. Conclusion: The new MaxFOV algorithm improves the contour border for objects outside of the standard FOV when using a GE-approved tabletop. Air cavities outside of the standard FOV create inconsistent object borders. HU consistency is within GE specifications and the accuracy of the phantom edge improves. Further adjustments to the algorithm are being investigated by GE.« less

Detecting Faults in Southern California using Computer-Vision Techniques and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Interferometry

NASA Astrophysics Data System (ADS)

Barba, M.; Rains, C.; von Dassow, W.; Parker, J. W.; Glasscoe, M. T.

2013-12-01

Knowing the location and behavior of active faults is essential for earthquake hazard assessment and disaster response. In Interferometric Synthetic Aperture Radar (InSAR) images, faults are revealed as linear discontinuities. Currently, interferograms are manually inspected to locate faults. During the summer of 2013, the NASA-JPL DEVELOP California Disasters team contributed to the development of a method to expedite fault detection in California using remote-sensing technology. The team utilized InSAR images created from polarimetric L-band data from NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) project. A computer-vision technique known as 'edge-detection' was used to automate the fault-identification process. We tested and refined an edge-detection algorithm under development through NASA's Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) project. To optimize the algorithm we used both UAVSAR interferograms and synthetic interferograms generated through Disloc, a web-based modeling program available through NASA's QuakeSim project. The edge-detection algorithm detected seismic, aseismic, and co-seismic slip along faults that were identified and compared with databases of known fault systems. Our optimization process was the first step toward integration of the edge-detection code into E-DECIDER to provide decision support for earthquake preparation and disaster management. E-DECIDER partners that will use the edge-detection code include the California Earthquake Clearinghouse and the US Department of Homeland Security through delivery of products using the Unified Incident Command and Decision Support (UICDS) service. Through these partnerships, researchers, earthquake disaster response teams, and policy-makers will be able to use this new methodology to examine the details of ground and fault motions for moderate to large earthquakes. Following an earthquake, the newly discovered faults can be paired with infrastructure overlays, allowing emergency response teams to identify sites that may have been exposed to damage. The faults will also be incorporated into a database for future integration into fault models and earthquake simulations, improving future earthquake hazard assessment. As new faults are mapped, they will further understanding of the complex fault systems and earthquake hazards within the seismically dynamic state of California.

Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space.

PubMed

Loewenstein, Yaniv; Portugaly, Elon; Fromer, Menachem; Linial, Michal

2008-07-01

UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets. We present a novel class of memory-constrained UPGMA (MC-UPGMA) algorithms. Given any practical memory size constraint, this framework guarantees the correct clustering solution without explicitly requiring all dissimilarities in memory. The algorithms are general and are applicable to any dataset. We present a data-dependent characterization of hardness and clustering efficiency. The presented concepts are applicable to any agglomerative clustering formulation. We apply our algorithm to the entire collection of protein sequences, to automatically build a comprehensive evolutionary-driven hierarchy of proteins from sequence alone. The newly created tree captures protein families better than state-of-the-art large-scale methods such as CluSTr, ProtoNet4 or single-linkage clustering. We demonstrate that leveraging the entire mass embodied in all sequence similarities allows to significantly improve on current protein family clusterings which are unable to directly tackle the sheer mass of this data. Furthermore, we argue that non-metric constraints are an inherent complexity of the sequence space and should not be overlooked. The robustness of UPGMA allows significant improvement, especially for multidomain proteins, and for large or divergent families. A comprehensive tree built from all UniProt sequence similarities, together with navigation and classification tools will be made available as part of the ProtoNet service. A C++ implementation of the algorithm is available on request.

mTOR inhibitor-induced interstitial lung disease in cancer patients: Comprehensive review and a practical management algorithm.

PubMed

Willemsen, Annelieke E C A B; Grutters, Jan C; Gerritsen, Winald R; van Erp, Nielka P; van Herpen, Carla M L; Tol, Jolien

2016-05-15

Mammalian target of rapamycin inhibitors (mTORi) have clinically significant activity against various malignancies, such as renal cell carcinoma and breast cancer, but their use can be complicated by several toxicities. Interstitial lung disease (ILD) is an adverse event of particular importance. Mostly, mTORi-induced ILD remains asymptomatic or mildly symptomatic, but it can also lead to severe morbidity and even mortality. Therefore, careful diagnosis and management of ILD is warranted. The reported incidence of mTORi-induced ILD varies widely because of a lack of uniform diagnostic criteria and active surveillance. Because of the nonspecific clinical features, a broad differential diagnosis that includes (opportunistic) infections should be considered in case of suspicion of mTORi-induced ILD. The exact mechanism or interplay of mechanisms leading to the development of ILD remains to be defined. Suggested mechanisms are either a direct toxic effect or immune-mediated mechanisms, considering mTOR inhibitors have several effects on the immune system. The clinical course of ILD varies widely and is difficult to predict. Consequently, the discrimination between when mTOR inhibitors can be continued safely and when discontinuation is indicated is challenging. In this review, we give a comprehensive review of the incidence, clinical presentation and pathophysiology of mTORi-induced ILD in cancer patients. We present newly developed diagnostic criteria for ILD, which include clinical symptoms as well as basic pulmonary function tests and radiological abnormalities. In conjunction with these diagnostic criteria, we provide a detailed and easily applicable clinical management algorithm. © 2015 UICC.

Simultaneous elastic parameter inversion in 2-D/3-D TTI medium combined later arrival times

NASA Astrophysics Data System (ADS)

Bai, Chao-ying; Wang, Tao; Yang, Shang-bei; Li, Xing-wang; Huang, Guo-jiao

2016-04-01

Traditional traveltime inversion for anisotropic medium is, in general, based on a "weak" assumption in the anisotropic property, which simplifies both the forward part (ray tracing is performed once only) and the inversion part (a linear inversion solver is possible). But for some real applications, a general (both "weak" and "strong") anisotropic medium should be considered. In such cases, one has to develop a ray tracing algorithm to handle with the general (including "strong") anisotropic medium and also to design a non-linear inversion solver for later tomography. Meanwhile, it is constructive to investigate how much the tomographic resolution can be improved by introducing the later arrivals. For this motivation, we incorporated our newly developed ray tracing algorithm (multistage irregular shortest-path method) for general anisotropic media with a non-linear inversion solver (a damped minimum norm, constrained least squares problem with a conjugate gradient approach) to formulate a non-linear inversion solver for anisotropic medium. This anisotropic traveltime inversion procedure is able to combine the later (reflected) arrival times. Both 2-D/3-D synthetic inversion experiments and comparison tests show that (1) the proposed anisotropic traveltime inversion scheme is able to recover the high contrast anomalies and (2) it is possible to improve the tomographic resolution by introducing the later (reflected) arrivals, but not as expected in the isotropic medium, because the different velocity (qP, qSV and qSH) sensitivities (or derivatives) respective to the different elastic parameters are not the same but are also dependent on the inclination angle.

Molecular Pathology of Patient Tumors, Patient-Derived Xenografts, and Cancer Cell Lines.

PubMed

Guo, Sheng; Qian, Wubin; Cai, Jie; Zhang, Likun; Wery, Jean-Pierre; Li, Qi-Xiang

2016-08-15

The Cancer Genome Atlas (TCGA) project has generated abundant genomic data for human cancers of various histopathology types and enabled exploring cancer molecular pathology per big data approach. We developed a new algorithm based on most differentially expressed genes (DEG) per pairwise comparisons to calculate correlation coefficients to be used to quantify similarity within and between cancer types. We systematically compared TCGA cancers, demonstrating high correlation within types and low correlation between types, thus establishing molecular specificity of cancer types and an alternative diagnostic method largely equivalent to histopathology. Different coefficients for different cancers in study may reveal that the degree of the within-type homogeneity varies by cancer types. We also performed the same calculation using the TCGA-derived DEGs on patient-derived xenografts (PDX) of different histopathology types corresponding to the TCGA types, as well as on cancer cell lines. We, for the first time, demonstrated highly similar patterns for within- and between-type correlation between PDXs and patient samples in a systematic study, confirming the high relevance of PDXs as surrogate experimental models for human diseases. In contrast, cancer cell lines have drastically reduced expression similarity to both PDXs and patient samples. The studies also revealed high similarity between some types, for example, LUSC and HNSCC, but low similarity between certain subtypes, for example, LUAD and LUSC. Our newly developed algorithm seems to be a practical diagnostic method to classify and reclassify a disease, either human or xenograft, with better accuracy than traditional histopathology. Cancer Res; 76(16); 4619-26. ©2016 AACR. ©2016 American Association for Cancer Research.

The ArF laser for the next-generation multiple-patterning immersion lithography supporting green operations

NASA Astrophysics Data System (ADS)

Ishida, Keisuke; Ohta, Takeshi; Miyamoto, Hirotaka; Kumazaki, Takahito; Tsushima, Hiroaki; Kurosu, Akihiko; Matsunaga, Takashi; Mizoguchi, Hakaru

2016-03-01

Multiple patterning ArF immersion lithography has been expected as the promising technology to satisfy tighter leading edge device requirements. One of the most important features of the next generation lasers will be the ability to support green operations while further improving cost of ownership and performance. Especially, the dependence on rare gases, such as Neon and Helium, is becoming a critical issue for high volume manufacturing process. The new ArF excimer laser, GT64A has been developed to cope with the reduction of operational costs, the prevention against rare resource shortage and the improvement of device yield in multiple-patterning lithography. GT64A has advantages in efficiency and stability based on the field-proven injection-lock twin-chamber platform (GigaTwin platform). By the combination of GigaTwin platform and the advanced gas control algorithm, the consumption of rare gases such as Neon is reduced to a half. And newly designed Line Narrowing Module can realize completely Helium free operation. For the device yield improvement, spectral bandwidth stability is important to increase image contrast and contribute to the further reduction of CD variation. The new spectral bandwidth control algorithm and high response actuator has been developed to compensate the offset due to thermal change during the interval such as the period of wafer exchange operation. And REDeeM Cloud™, new monitoring system for managing light source performance and operations, is on-board and provides detailed light source information such as wavelength, energy, E95, etc.

GRAPE-5: A Special-Purpose Computer for N-Body Simulations

NASA Astrophysics Data System (ADS)

Kawai, Atsushi; Fukushige, Toshiyuki; Makino, Junichiro; Taiji, Makoto

2000-08-01

We have developed a special-purpose computer for gravitational many-body simulations, GRAPE-5. GRAPE-5 accelerates the force calculation which dominates the calculation cost of the simulation. All other calculations, such as the time integration of orbits, are performed on a general-purpose computer (host computer) connected to GRAPE-5. A GRAPE-5 board consists of eight custom pipeline chips (G5 chip) and its peak performance is 38.4 Gflops. GRAPE-5 is the successor of GRAPE-3. The differences between GRAPE-5 and GRAPE-3 are: (1) The newly developed G5 chip contains two pipelines operating at 80 MHz, while the GRAPE chip, which was used for GRAPE-3, had one at 20 MHz. The calculation speed of GRAPE-5 is 8-times faster than that of GRAPE-3. (2) The GRAPE-5 board adopted a PCI bus as the interface to the host computer instead of VME of GRAPE-3, resulting in a communication speed one order of magnitude faster. (3) In addition to the pure 1/r potential, the G5 chip can calculate forces with arbitrary cutoff functions, so that it can be applied to the Ewald or P3M methods. (4) The pairwise force calculated on GRAPE-5 is about 10-times more accurate than that on GRAPE-3. On one GRAPE-5 board, one timestep with a direct summation algorithm takes 14 (N/128 k)2 seconds. With the Barnes-Hut tree algorithm (theta = 0.75), one timestep can be done in 15 (N/106) seconds.

The preprocessed connectomes project repository of manually corrected skull-stripped T1-weighted anatomical MRI data.

PubMed

Puccio, Benjamin; Pooley, James P; Pellman, John S; Taverna, Elise C; Craddock, R Cameron

2016-10-25

Skull-stripping is the procedure of removing non-brain tissue from anatomical MRI data. This procedure can be useful for calculating brain volume and for improving the quality of other image processing steps. Developing new skull-stripping algorithms and evaluating their performance requires gold standard data from a variety of different scanners and acquisition methods. We complement existing repositories with manually corrected brain masks for 125 T1-weighted anatomical scans from the Nathan Kline Institute Enhanced Rockland Sample Neurofeedback Study. Skull-stripped images were obtained using a semi-automated procedure that involved skull-stripping the data using the brain extraction based on nonlocal segmentation technique (BEaST) software, and manually correcting the worst results. Corrected brain masks were added into the BEaST library and the procedure was repeated until acceptable brain masks were available for all images. In total, 85 of the skull-stripped images were hand-edited and 40 were deemed to not need editing. The results are brain masks for the 125 images along with a BEaST library for automatically skull-stripping other data. Skull-stripped anatomical images from the Neurofeedback sample are available for download from the Preprocessed Connectomes Project. The resulting brain masks can be used by researchers to improve preprocessing of the Neurofeedback data, as training and testing data for developing new skull-stripping algorithms, and for evaluating the impact on other aspects of MRI preprocessing. We have illustrated the utility of these data as a reference for comparing various automatic methods and evaluated the performance of the newly created library on independent data.

An Ambulatory Tremor Score for Parkinson's Disease.

PubMed

Braybrook, Michelle; O'Connor, Sam; Churchward, Philip; Perera, Thushara; Farzanehfar, Parisa; Horne, Malcolm

2016-10-19

While tremor in Parkinson's Disease (PD) can be characterised in the consulting room, its relationship to treatment and fluctuations can be clinically helpful. To develop an ambulatory assessment of tremor of PD. Accelerometry data was collected using the Parkinson's KinetiGraph System (PKG, Global Kinetics). An algorithm was developed, which could successfully distinguish been subjects with a resting or postural tremor that involved the wrist whose frequency was greater than 3 Hz. Percent of time that tremor was present (PTT) between 09 : 00 and 18 : 00 was calculated. This algorithm was applied to 85 people with PD who had been assessed clinically for the presence and nature of tremor. The Sensitivity and Selectivity of a PTT ≥0.8% was 92.5% and 92.9% in identifying tremor, providing that the tremor was not a fine kinetic and postural tremor or was not in the upper limb. A PTT >1% provide high likely hood of the presence of clinical meaningful tremor. These cut-offs were retested on a second cohort (n = 87) with a similar outcome. The Sensitivity and Selectivity of the combined group was 88.7% and 89.5% respectively. Using the PTT, 50% of 22 newly diagnosed patients had a PTT >1.0%.The PKG's simultaneous bradykinesia scores was used to find a threshold for the emergence of tremor. Tremor produced artefactual increase in the PKG's dyskinesia score in 1% of this sample. We propose this as a means of assessing the presence of tremor and its relationship to bradykinesia.

Global and Regional Trends of Aerosol Optical Depth over Land and Ocean Using SeaWiFS Measurements from 1997 to 2010

NASA Technical Reports Server (NTRS)

Hsu, N. C.; Gautam, R.; Sayer, A. M.; Bettenhausen, C.; Li, C.; Jeong, M. J.; Tsay, S. C.; Holben, B. N.

2012-01-01

Both sensor calibration and satellite retrieval algorithm play an important role in the ability to determine accurately long-term trends from satellite data. Owing to the unprecedented accuracy and long-term stability of its radiometric calibration, the SeaWiFS measurements exhibit minimal uncertainty with respect to sensor calibration. In this study, we take advantage of this well-calibrated set of measurements by applying a newly-developed aerosol optical depth (AOD) retrieval algorithm over land and ocean to investigate the distribution of AOD, and to identify emerging patterns and trends in global and regional aerosol loading during its 13-year mission. Our results indicate that the averaged AOD trend over global ocean is weakly positive from 1998 to 2010 and comparable to that observed by MODIS but opposite in sign to that observed by AVHRR during overlapping years. On a smaller scale, different trends are found for different regions. For example, large upward trends are found over the Arabian Peninsula that indicate a strengthening of the seasonal cycle of dust emission and transport processes over the whole region as well as over downwind oceanic regions. In contrast, a negative-neutral tendency is observed over the desert/arid Saharan region as well as in the associated dust outflow over the north Atlantic. Additionally, we found decreasing trends over the eastern US and Europe, and increasing trends over countries such as China and India that are experiencing rapid economic development. In general, these results are consistent with those derived from ground-based AERONET measurements.

Joint Optimization of Vertical Component Gravity and Seismic P-wave First Arrivals by Simulated Annealing

NASA Astrophysics Data System (ADS)

Louie, J. N.; Basler-Reeder, K.; Kent, G. M.; Pullammanappallil, S. K.

2015-12-01

Simultaneous joint seismic-gravity optimization improves P-wave velocity models in areas with sharp lateral velocity contrasts. Optimization is achieved using simulated annealing, a metaheuristic global optimization algorithm that does not require an accurate initial model. Balancing the seismic-gravity objective function is accomplished by a novel approach based on analysis of Pareto charts. Gravity modeling uses a newly developed convolution algorithm, while seismic modeling utilizes the highly efficient Vidale eikonal equation traveltime generation technique. Synthetic tests show that joint optimization improves velocity model accuracy and provides velocity control below the deepest headwave raypath. Detailed first arrival picking followed by trial velocity modeling remediates inconsistent data. We use a set of highly refined first arrival picks to compare results of a convergent joint seismic-gravity optimization to the Plotrefa™ and SeisOpt® Pro™ velocity modeling packages. Plotrefa™ uses a nonlinear least squares approach that is initial model dependent and produces shallow velocity artifacts. SeisOpt® Pro™ utilizes the simulated annealing algorithm and is limited to depths above the deepest raypath. Joint optimization increases the depth of constrained velocities, improving reflector coherency at depth. Kirchoff prestack depth migrations reveal that joint optimization ameliorates shallow velocity artifacts caused by limitations in refraction ray coverage. Seismic and gravity data from the San Emidio Geothermal field of the northwest Basin and Range province demonstrate that joint optimization changes interpretation outcomes. The prior shallow-valley interpretation gives way to a deep valley model, while shallow antiformal reflectors that could have been interpreted as antiformal folds are flattened. Furthermore, joint optimization provides a clearer image of the rangefront fault. This technique can readily be applied to existing datasets and could replace the existing strategy of forward modeling to match gravity data.

Retrievals of ice cloud microphysical properties of deep convective systems using radar measurements

NASA Astrophysics Data System (ADS)

Tian, Jingjing; Dong, Xiquan; Xi, Baike; Wang, Jingyu; Homeyer, Cameron R.; McFarquhar, Greg M.; Fan, Jiwen

2016-09-01

This study presents newly developed algorithms for retrieving ice cloud microphysical properties (ice water content (IWC) and median mass diameter (Dm)) for the stratiform rain and thick anvil regions of deep convective systems (DCSs) using Next Generation Radar (NEXRAD) reflectivity and empirical relationships from aircraft in situ measurements. A typical DCS case (20 May 2011) during the Midlatitude Continental Convective Clouds Experiment (MC3E) is selected as an example to demonstrate the 4-D retrievals. The vertical distributions of retrieved IWC are compared with previous studies and cloud-resolving model simulations. The statistics from six selected cases during MC3E show that the aircraft in situ derived IWC and Dm are 0.47 ± 0.29 g m-3 and 2.02 ± 1.3 mm, while the mean values of retrievals have a positive bias of 0.19 g m-3 (40%) and negative bias of 0.41 mm (20%), respectively. To evaluate the new retrieval algorithms, IWC and Dm are retrieved for other DCSs observed during the Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX) using NEXRAD reflectivity and compared with aircraft in situ measurements. During BAMEX, a total of 63, 1 min collocated aircraft and radar samples are available for comparisons, and the averages of radar retrieved and aircraft in situ measured IWC values are 1.52 g m-3 and 1.25 g m-3 with a correlation of 0.55, and their averaged Dm values are 2.08 and 1.77 mm. In general, the new retrieval algorithms are suitable for continental DCSs during BAMEX, especially within stratiform rain and thick anvil regions.

Using ultrahigh sensitive optical microangiography to achieve comprehensive depth resolved microvasculature mapping for human retina

NASA Astrophysics Data System (ADS)

An, Lin; Shen, Tueng T.; Wang, Ruikang K.

2011-10-01

This paper presents comprehensive and depth-resolved retinal microvasculature images within human retina achieved by a newly developed ultrahigh sensitive optical microangiography (UHS-OMAG) system. Due to its high flow sensitivity, UHS-OMAG is much more sensitive to tissue motion due to the involuntary movement of the human eye and head compared to the traditional OMAG system. To mitigate these motion artifacts on final imaging results, we propose a new phase compensation algorithm in which the traditional phase-compensation algorithm is repeatedly used to efficiently minimize the motion artifacts. Comparatively, this new algorithm demonstrates at least 8 to 25 times higher motion tolerability, critical for the UHS-OMAG system to achieve retinal microvasculature images with high quality. Furthermore, the new UHS-OMAG system employs a high speed line scan CMOS camera (240 kHz A-line scan rate) to capture 500 A-lines for one B-frame at a 400 Hz frame rate. With this system, we performed a series of in vivo experiments to visualize the retinal microvasculature in humans. Two featured imaging protocols are utilized. The first is of the low lateral resolution (16 μm) and a wide field of view (4 × 3 mm2 with single scan and 7 × 8 mm2 for multiple scans), while the second is of the high lateral resolution (5 μm) and a narrow field of view (1.5 × 1.2 mm2 with single scan). The great imaging performance delivered by our system suggests that UHS-OMAG can be a promising noninvasive alternative to the current clinical retinal microvasculature imaging techniques for the diagnosis of eye diseases with significant vascular involvement, such as diabetic retinopathy and age-related macular degeneration.

Management of disease-modifying treatments in neurological autoimmune diseases of the central nervous system.

PubMed

Salmen, A; Gold, R; Chan, A

2014-05-01

The therapeutic armamentarium for autoimmune diseases of the central nervous system, specifically multiple sclerosis and neuromyelitis optica, is steadily increasing, with a large spectrum of immunomodulatory and immunosuppressive agents targeting different mechanisms of the immune system. However, increasingly efficacious treatment options also entail higher potential for severe adverse drug reactions. Especially in cases failing first-line treatment, thorough evaluation of the risk-benefit profile of treatment alternatives is necessary. This argues for the need of algorithms to identify patients more likely to benefit from a specific treatment. Moreover, paradigms to stratify the risk for severe adverse drug reactions need to be established. In addition to clinical/paraclinical measures, biomarkers may aid in individualized risk-benefit assessment. A recent example is the routine testing for anti-John Cunningham virus antibodies in natalizumab-treated multiple sclerosis patients to assess the risk for the development of progressive multi-focal leucoencephalopathy. Refined algorithms for individualized risk assessment may also facilitate early initiation of induction treatment schemes in patient groups with high disease activity rather than classical escalation concepts. In this review, we will discuss approaches for individiualized risk-benefit assessment both for newly introduced agents as well as medications with established side-effect profiles. In addition to clinical parameters, we will also focus on biomarkers that may assist in patient selection. © 2013 British Society for Immunology.

2014 KLCSG-NCC Korea Practice Guidelines for the management of hepatocellular carcinoma: HCC diagnostic algorithm.

PubMed

Lee, Jeong Min; Park, Joong-Won; Choi, Byung Ihn

2014-01-01

Hepatocellular carcinoma (HCC) is the fifth most commonly occurring cancer in Korea and typically has a poor prognosis with a 5-year survival rate of only 28.6%. Therefore, it is of paramount importance to achieve the earliest possible diagnosis of HCC and to recommend the most up-to-date optimal treatment strategy in order to increase the survival rate of patients who develop this disease. After the establishment of the Korean Liver Cancer Study Group (KLCSG) and the National Cancer Center (NCC), Korea jointly produced for the first time the Clinical Practice Guidelines for HCC in 2003, revised them in 2009, and published the newest revision of the guidelines in 2014, including changes in the diagnostic criteria of HCC and incorporating the most recent medical advances over the past 5 years. In this review, we will address the noninvasive diagnostic criteria and diagnostic algorithm of HCC included in the newly established KLCSG-NCC guidelines in 2014, and review the differences in the criteria for a diagnosis of HCC between the KLCSG-NCC guidelines and the most recent imaging guidelines endorsed by the European Organisation for Research and Treatment of Cancer (EORTC), the Liver Imaging Reporting and Data System (LI-RADS), the Organ Procurement and Transplantation Network (OPTN) system, the Asian Pacific Association for the Study of the Liver (APASL) and the Japan Society of Hepatology (JSH).

Enhanced Software for Scheduling Space-Shuttle Processing

NASA Technical Reports Server (NTRS)

Barretta, Joseph A.; Johnson, Earl P.; Bierman, Rocky R.; Blanco, Juan; Boaz, Kathleen; Stotz, Lisa A.; Clark, Michael; Lebovitz, George; Lotti, Kenneth J.; Moody, James M.;

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

PubMed

Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

2016-05-01

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

NASA Astrophysics Data System (ADS)

Jiang, Guo-Qian; Xie, Ping; Wang, Xiao; Chen, Meng; He, Qun

2017-11-01

The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

Dynamic Staffing and Rescheduling in Software Project Management: A Hybrid Approach.

PubMed

Ge, Yujia; Xu, Bin

2016-01-01

Resource allocation could be influenced by various dynamic elements, such as the skills of engineers and the growth of skills, which requires managers to find an effective and efficient tool to support their staffing decision-making processes. Rescheduling happens commonly and frequently during the project execution. Control options have to be made when new resources are added or tasks are changed. In this paper we propose a software project staffing model considering dynamic elements of staff productivity with a Genetic Algorithm (GA) and Hill Climbing (HC) based optimizer. Since a newly generated reschedule dramatically different from the initial schedule could cause an obvious shifting cost increase, our rescheduling strategies consider both efficiency and stability. The results of real world case studies and extensive simulation experiments show that our proposed method is effective and could achieve comparable performance to other heuristic algorithms in most cases.

The Cross-Correlation and Reshuffling Tests in Discerning Induced Seismicity

NASA Astrophysics Data System (ADS)

Schultz, Ryan; Telesca, Luciano

2018-05-01

In recent years, cases of newly emergent induced clusters have increased seismic hazard and risk in locations with social, environmental, and economic consequence. Thus, the need for a quantitative and robust means to discern induced seismicity has become a critical concern. This paper reviews a Matlab-based algorithm designed to quantify the statistical confidence between two time-series datasets. Similar to prior approaches, our method utilizes the cross-correlation to delineate the strength and lag of correlated signals. In addition, use of surrogate reshuffling tests allows for the dynamic testing against statistical confidence intervals of anticipated spurious correlations. We demonstrate the robust nature of our algorithm in a suite of synthetic tests to determine the limits of accurate signal detection in the presence of noise and sub-sampling. Overall, this routine has considerable merit in terms of delineating the strength of correlated signals, one of which includes the discernment of induced seismicity from natural.

Spinning BTZ black hole versus Kerr black hole: A closer look

NASA Astrophysics Data System (ADS)

Kim, Hongsu

1999-03-01

By applying Newman's algorithm, the AdS3 rotating black hole solution is ``derived'' from the nonrotating black hole solution of Bañados, Teitelboim, and Zanelli (BTZ). The rotating BTZ solution derived in this fashion is given in ``Boyer-Lindquist-type'' coordinates whereas the form of the solution originally given by BTZ is given in kind of ``unfamiliar'' coordinates which are related to each other by a transformation of time coordinate alone. The relative physical meaning between these two time coordinates is carefully studied. Since the Kerr-type and Boyer-Lindquist-type coordinates for rotating BTZ solution are newly found via Newman's algorithm, the transformation to Kerr-Schild-type coordinates is looked for. Indeed, such a transformation is found to exist. In these Kerr-Schild-type coordinates, a truly maximal extension of its global structure by analytically continuing to an ``antigravity universe'' region is carried out.

Improvements on non-equilibrium and transport Green function techniques: The next-generation TRANSIESTA

NASA Astrophysics Data System (ADS)

Papior, Nick; Lorente, Nicolás; Frederiksen, Thomas; García, Alberto; Brandbyge, Mads

2017-03-01

We present novel methods implemented within the non-equilibrium Green function code (NEGF) TRANSIESTA based on density functional theory (DFT). Our flexible, next-generation DFT-NEGF code handles devices with one or multiple electrodes (Ne ≥ 1) with individual chemical potentials and electronic temperatures. We describe its novel methods for electrostatic gating, contour optimizations, and assertion of charge conservation, as well as the newly implemented algorithms for optimized and scalable matrix inversion, performance-critical pivoting, and hybrid parallelization. Additionally, a generic NEGF "post-processing" code (TBTRANS/PHTRANS) for electron and phonon transport is presented with several novelties such as Hamiltonian interpolations, Ne ≥ 1 electrode capability, bond-currents, generalized interface for user-defined tight-binding transport, transmission projection using eigenstates of a projected Hamiltonian, and fast inversion algorithms for large-scale simulations easily exceeding 106 atoms on workstation computers. The new features of both codes are demonstrated and bench-marked for relevant test systems.

Gravitational Microlensing Observations of Two New Exoplanets Using the Deep Impact High Resolution Instrument

NASA Astrophysics Data System (ADS)

Barry, Richard K.; Bennett, D. P.; Klaasen, K.; Becker, A. C.; Christiansen, J.; Albrow, M.

2014-01-01

We have worked to characterize two exoplanets newly detected from the ground: OGLE-2012-BLG-0406 and OGLE-2012-BLG-0838, using microlensing observations of the Galactic Bulge recently obtained by NASA’s Deep Impact (DI) spacecraft, in combination with ground data. These observations of the crowded Bulge fields from Earth and from an observatory at a distance of ~1 AU have permitted the extraction of a microlensing parallax signature - critical for breaking exoplanet model degeneracies. For this effort, we used DI’s High Resolution Instrument, launched with a permanent defocus aberration due to an error in cryogenic testing. We show how the effects of a very large, chromatic PSF can be reduced in differencing photometry. We also compare two approaches to differencing photometry - one of which employs the Bramich algorithm and another using the Fruchter & Hook drizzle algorithm.

On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection.

PubMed

Tsinganos, Panagiotis; Skodras, Athanassios

2018-02-14

In the context of the ageing global population, researchers and scientists have tried to find solutions to many challenges faced by older people. Falls, the leading cause of injury among elderly, are usually severe enough to require immediate medical attention; thus, their detection is of primary importance. To this effect, many fall detection systems that utilize wearable and ambient sensors have been proposed. In this study, we compare three newly proposed data fusion schemes that have been applied in human activity recognition and fall detection. Furthermore, these algorithms are compared to our recent work regarding fall detection in which only one type of sensor is used. The results show that fusion algorithms differ in their performance, whereas a machine learning strategy should be preferred. In conclusion, the methods presented and the comparison of their performance provide useful insights into the problem of fall detection.

A new modulated Hebbian learning rule--biologically plausible method for local computation of a principal subspace.

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2003-08-01

This paper presents one possible implementation of a transformation that performs linear mapping to a lower-dimensional subspace. Principal component subspace will be the one that will be analyzed. Idea implemented in this paper represents generalization of the recently proposed infinity OH neural method for principal component extraction. The calculations in the newly proposed method are performed locally--a feature which is usually considered as desirable from the biological point of view. Comparing to some other wellknown methods, proposed synaptic efficacy learning rule requires less information about the value of the other efficacies to make single efficacy modification. Synaptic efficacies are modified by implementation of Modulated Hebb-type (MH) learning rule. Slightly modified MH algorithm named Modulated Hebb Oja (MHO) algorithm, will be also introduced. Structural similarity of the proposed network with part of the retinal circuit will be presented, too.

In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm

PubMed Central

Chung, Yongchul G.; Gómez-Gualdrón, Diego A.; Li, Peng; Leperi, Karson T.; Deria, Pravas; Zhang, Hongda; Vermeulen, Nicolaas A.; Stoddart, J. Fraser; You, Fengqi; Hupp, Joseph T.; Farha, Omar K.; Snurr, Randall Q.

2016-01-01

Discovery of new adsorbent materials with a high CO2 working capacity could help reduce CO2 emissions from newly commissioned power plants using precombustion carbon capture. High-throughput computational screening efforts can accelerate the discovery of new adsorbents but sometimes require significant computational resources to explore the large space of possible materials. We report the in silico discovery of high-performing adsorbents for precombustion CO2 capture by applying a genetic algorithm to efficiently search a large database of metal-organic frameworks (MOFs) for top candidates. High-performing MOFs identified from the in silico search were synthesized and activated and show a high CO2 working capacity and a high CO2/H2 selectivity. One of the synthesized MOFs shows a higher CO2 working capacity than any MOF reported in the literature under the operating conditions investigated here. PMID:27757420

Dynamic Staffing and Rescheduling in Software Project Management: A Hybrid Approach

PubMed Central

Ge, Yujia; Xu, Bin

2016-01-01

Resource allocation could be influenced by various dynamic elements, such as the skills of engineers and the growth of skills, which requires managers to find an effective and efficient tool to support their staffing decision-making processes. Rescheduling happens commonly and frequently during the project execution. Control options have to be made when new resources are added or tasks are changed. In this paper we propose a software project staffing model considering dynamic elements of staff productivity with a Genetic Algorithm (GA) and Hill Climbing (HC) based optimizer. Since a newly generated reschedule dramatically different from the initial schedule could cause an obvious shifting cost increase, our rescheduling strategies consider both efficiency and stability. The results of real world case studies and extensive simulation experiments show that our proposed method is effective and could achieve comparable performance to other heuristic algorithms in most cases. PMID:27285420

Constructing Practical Knowledge of Teaching: Eleven Newly Qualified Language Teachers' Discursive Agency

ERIC Educational Resources Information Center

Ruohotie-Lyhty, Maria

2011-01-01

This paper explores the professional development of 11 newly qualified foreign language teachers. It draws on a qualitative longitudinal study conducted at the University of Jyvaskyla, Finland between 2002 and 2009. The paper concentrates on the personal side of teacher development by analysing participants' discourses concerning language…

Gray matter segmentation of the spinal cord with active contours in MR images.

PubMed

Datta, Esha; Papinutto, Nico; Schlaeger, Regina; Zhu, Alyssa; Carballido-Gamio, Julio; Henry, Roland G

2017-02-15

Fully or partially automated spinal cord gray matter segmentation techniques for spinal cord gray matter segmentation will allow for pivotal spinal cord gray matter measurements in the study of various neurological disorders. The objective of this work was multi-fold: (1) to develop a gray matter segmentation technique that uses registration methods with an existing delineation of the cord edge along with Morphological Geodesic Active Contour (MGAC) models; (2) to assess the accuracy and reproducibility of the newly developed technique on 2D PSIR T1 weighted images; (3) to test how the algorithm performs on different resolutions and other contrasts; (4) to demonstrate how the algorithm can be extended to 3D scans; and (5) to show the clinical potential for multiple sclerosis patients. The MGAC algorithm was developed using a publicly available implementation of a morphological geodesic active contour model and the spinal cord segmentation tool of the software Jim (Xinapse Systems) for initial estimate of the cord boundary. The MGAC algorithm was demonstrated on 2D PSIR images of the C2/C3 level with two different resolutions, 2D T2* weighted images of the C2/C3 level, and a 3D PSIR image. These images were acquired from 45 healthy controls and 58 multiple sclerosis patients selected for the absence of evident lesions at the C2/C3 level. Accuracy was assessed though visual assessment, Hausdorff distances, and Dice similarity coefficients. Reproducibility was assessed through interclass correlation coefficients. Validity was assessed through comparison of segmented gray matter areas in images with different resolution for both manual and MGAC segmentations. Between MGAC and manual segmentations in healthy controls, the mean Dice similarity coefficient was 0.88 (0.82-0.93) and the mean Hausdorff distance was 0.61 (0.46-0.76) mm. The interclass correlation coefficient from test and retest scans of healthy controls was 0.88. The percent change between the manual segmentations from high and low-resolution images was 25%, while the percent change between the MGAC segmentations from high and low resolution images was 13%. Between MGAC and manual segmentations in MS patients, the average Dice similarity coefficient was 0.86 (0.8-0.92) and the average Hausdorff distance was 0.83 (0.29-1.37) mm. We demonstrate that an automatic segmentation technique, based on a morphometric geodesic active contours algorithm, can provide accurate and precise spinal cord gray matter segmentations on 2D PSIR images. We have also shown how this automated technique can potentially be extended to other imaging protocols. Copyright © 2016 Elsevier Inc. All rights reserved.

A Steady-State Visual Evoked Potential Brain-Computer Interface System Evaluation as an In-Vehicle Warning Device

NASA Astrophysics Data System (ADS)

Riyahi, Pouria

This thesis is part of current research at Center for Intelligence Systems Research (CISR) at The George Washington University for developing new in-vehicle warning systems via Brain-Computer Interfaces (BCIs). The purpose of conducting this research is to contribute to the current gap between BCI and in-vehicle safety studies. It is based on the premise that accurate and timely monitoring of human (driver) brain's signal to external stimuli could significantly aide in detection of driver's intentions and development of effective warning systems. The thesis starts with introducing the concept of BCI and its development history while it provides a literature review on the nature of brain signals. The current advancement and increasing demand for commercial and non-medical BCI products are described. In addition, the recent research attempts in transportation safety to study drivers' behavior or responses through brain signals are reviewed. The safety studies, which are focused on employing a reliable and practical BCI system as an in-vehicle assistive device, are also introduced. A major focus of this thesis research has been on the evaluation and development of the signal processing algorithms which can effectively filter and process brain signals when the human subject is subjected to Visual LED (Light Emitting Diodes) stimuli at different frequencies. The stimulated brain generates a voltage potential, referred to as Steady-State Visual Evoked Potential (SSVEP). Therefore, a newly modified analysis algorithm for detecting the brain visual signals is proposed. These algorithms are designed to reach a satisfactory accuracy rate without preliminary trainings, hence focusing on eliminating the need for lengthy training of human subjects. Another important concern is the ability of the algorithms to find correlation of brain signals with external visual stimuli in real-time. The developed analysis models are based on algorithms which are capable of generating results for real-time processing of BCI devices. All of these methods are evaluated through two sets of recorded brain signals which were recorded by g.TEC CO. as an external source and recorded brain signals during our car driving simulator experiments. The final discussion is about how the presence of an SSVEP based warning system could affect drivers' performances which is defined by their reaction distance and Time to Collision (TTC). Three different scenarios with and without warning LEDs were planned to measure the subjects' normal driving behavior and their performance while they use a warning system during their driving task. Finally, warning scenarios are divided into short and long warning periods without and with informing the subjects, respectively. The long warning period scenario attempts to determine the level of drivers' distraction or vigilance during driving. The good outcome of warning scenarios can bridge between vehicle safety studies and online BCI system design research. The preliminary results show some promise of the developed methods for in-vehicle safety systems. However, for any decisive conclusion that considers using a BCI system as a helpful in-vehicle assistive device requires far deeper scrutinizing.

STAR Algorithm Integration Team - Facilitating operational algorithm development

NASA Astrophysics Data System (ADS)

Mikles, V. J.

2015-12-01

The NOAA/NESDIS Center for Satellite Research and Applications (STAR) provides technical support of the Joint Polar Satellite System (JPSS) algorithm development and integration tasks. Utilizing data from the S-NPP satellite, JPSS generates over thirty Environmental Data Records (EDRs) and Intermediate Products (IPs) spanning atmospheric, ocean, cryosphere, and land weather disciplines. The Algorithm Integration Team (AIT) brings technical expertise and support to product algorithms, specifically in testing and validating science algorithms in a pre-operational environment. The AIT verifies that new and updated algorithms function in the development environment, enforces established software development standards, and ensures that delivered packages are functional and complete. AIT facilitates the development of new JPSS-1 algorithms by implementing a review approach based on the Enterprise Product Lifecycle (EPL) process. Building on relationships established during the S-NPP algorithm development process and coordinating directly with science algorithm developers, the AIT has implemented structured reviews with self-contained document suites. The process has supported algorithm improvements for products such as ozone, active fire, vegetation index, and temperature and moisture profiles.

76 FR 33419 - Nationally Recognized Statistical Rating Organizations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-08

... documentation of the internal control structure) or should the factors focus on the design (i.e., establishment... related to implementing them. a. Controls reasonably designed to ensure that a newly developed methodology... U.S.C. 78o-7(r)(1)(A). b. Controls reasonably designed to ensure that a newly developed methodology...

The Spelling Project. Technical Report 1992-2.

ERIC Educational Resources Information Center

Green, Kathy E.; Schroeder, David H.

Results of an analysis of a newly developed spelling test and several related measures are reported. Information about the reliability of a newly developed spelling test; its distribution of scores; its relationship with the standard battery of aptitude tests of the Johnson O'Connor Research Foundation; and its relationships with sex, age,…

How Schools Can Promote Healthy Development for Newly Arrived Immigrant and Refugee Adolescents: Research Priorities

ERIC Educational Resources Information Center

McNeely, Clea A.; Morland, Lyn; Doty, S. Benjamin; Meschke, Laurie L.; Awad, Summer; Husain, Altaf; Nashwan, Ayat

2017-01-01

Background: The US education system must find creative and effective ways to foster the healthy development of the approximately 2 million newly arrived immigrant and refugee adolescents, many of whom contend with language barriers, limited prior education, trauma, and discrimination. We identify research priorities for promoting the school…

Rainfall Estimation over the Nile Basin using an Adapted Version of the SCaMPR Algorithm

NASA Astrophysics Data System (ADS)

Habib, E. H.; Kuligowski, R. J.; Elshamy, M. E.; Ali, M. A.; Haile, A.; Amin, D.; Eldin, A.

2011-12-01

Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite-derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). This study reports on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self-Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application over the Nile Basin. The algorithm uses a set of rainfall predictors from multi-spectral Infrared cloud top observations and self-calibrates them to a set of predictands from Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as SSM/I, SSMIS, AMSU, AMSR-E, and TMI. The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real-time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static coefficients that are derived from IR-MW data from past observations. We also compare the SCaMPR algorithm to other global-scale satellite rainfall algorithms (e.g., 'Tropical Rainfall Measuring Mission (TRMM) and other sources' (TRMM-3B42) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) CMORPH product. The algorithm has several potential future applications such as: improving the performance accuracy of hydrologic forecasting models over the Nile Basin, and utilizing the enhanced rainfall datasets and better-calibrated hydrologic models to assess the impacts of climate change on the region's water availability.

Effect of a culture-based screening algorithm on tuberculosis incidence in immigrants and refugees bound for the United States: a population-based cross-sectional study.

PubMed

Liu, Yecai; Posey, Drew L; Cetron, Martin S; Painter, John A

2015-03-17

Before 2007, immigrants and refugees bound for the United States were screened for tuberculosis (TB) by a smear-based algorithm that could not diagnose smear-negative/culture-positive TB. In 2007, the Centers for Disease Control and Prevention implemented a culture-based algorithm. To evaluate the effect of the culture-based algorithm on preventing the importation of TB to the United States by immigrants and refugees from foreign countries. Population-based, cross-sectional study. Panel physician sites for overseas medical examination. Immigrants and refugees with TB. Comparison of the increase of smear-negative/culture-positive TB cases diagnosed overseas among immigrants and refugees by the culture-based algorithm with the decline of reported cases among foreign-born persons within 1 year after arrival in the United States from 2007 to 2012. Of the 3 212 421 arrivals of immigrants and refugees from 2007 to 2012, a total of 1 650 961 (51.4%) were screened by the smear-based algorithm and 1 561 460 (48.6%) were screened by the culture-based algorithm. Among the 4032 TB cases diagnosed by the culture-based algorithm, 2195 (54.4%) were smear-negative/culture-positive. Before implementation (2002 to 2006), the annual number of reported cases among foreign-born persons within 1 year after arrival was relatively constant (range, 1424 to 1626 cases; mean, 1504 cases) but decreased from 1511 to 940 cases during implementation (2007 to 2012). During the same period, the annual number of smear-negative/culture-positive TB cases diagnosed overseas among immigrants and refugees bound for the United States by the culture-based algorithm increased from 4 to 629. This analysis did not control for the decline in new arrivals of nonimmigrant visitors to the United States and the decrease of incidence of TB in their countries of origin. Implementation of the culture-based algorithm may have substantially reduced the incidence of TB among newly arrived, foreign-born persons in the United States. None.

Practices influenced by policy? An exploration of newly hired science teachers at sites in South Africa and the United States

NASA Astrophysics Data System (ADS)

Navy, S. L.; Luft, J. A.; Toerien, R.; Hewson, P. W.

2018-05-01

In many parts of the world, newly hired science teachers' practices are developing in a complex policy environment. However, little is known about how newly hired science teachers' practices are enacted throughout a cycle of instruction and how these practices can be influenced by macro-, meso-, and micro-policies. Knowing how policies impact practice can result in better policies or better support for certain policies in order to enhance the instruction of newly hired teachers. This comparative study investigated how 12 newly hired science teachers at sites in South Africa (SA) and the United States (US) progressed through an instructional cycle of planning, teaching, and reflection. The qualitative data were analysed through beginning teacher competency frameworks, the cycle of instruction, and institutional theory. Data analysis revealed prevailing areas of practice and connections to levels of policy within the instructional cycle phases. There were some differences between the SA and US teachers and among first-, second-, and third-year teachers. More importantly, this study indicates that newly hired teachers are susceptible to micro-policies and are progressively developing their practice. It also shows the importance of meso-level connectors. It suggests that teacher educators and policy makers must consider how to prepare and support newly hired science teachers to achieve the shared global visions of science teaching.

Sea ice type dynamics in the Arctic based on Sentinel-1 Data

NASA Astrophysics Data System (ADS)

Babiker, Mohamed; Korosov, Anton; Park, Jeong-Won

2017-04-01

Sea ice observation from satellites has been carried out for more than four decades and is one of the most important applications of EO data in operational monitoring as well as in climate change studies. Several sensors and retrieval methods have been developed and successfully utilized to measure sea ice area, concentration, drift, type, thickness, etc [e.g. Breivik et al., 2009]. Today operational sea ice monitoring and analysis is fully dependent on use of satellite data. However, new and improved satellite systems, such as multi-polarisation Synthetic Apperture Radar (SAR), require further studies to develop more advanced and automated sea ice monitoring methods. In addition, the unprecedented volume of data available from recently launched Sentinel missions provides both challenges and opportunities for studying sea ice dynamics. In this study we investigate sea ice type dynamics in the Fram strait based on Sentinel-1 A, B SAR data. Series of images for the winter season are classified into 4 ice types (young ice, first year ice, multiyear ice and leads) using the new algorithm developed by us for sea ice classification, which is based on segmentation, GLCM calculation, Haralick texture feature extraction, unsupervised and supervised classifications and Support Vector Machine (SVM) [Zakhvatkina et al., 2016; Korosov et al., 2016]. This algorithm is further improved by applying thermal and scalloping noise removal [Park et al. 2016]. Sea ice drift is retrieved from the same series of Sentinel-1 images using the newly developed algorithm based on combination of feature tracking and pattern matching [Mukenhuber et al., 2016]. Time series of these two products (sea ice type and sea ice drift) are combined in order to study sea ice deformation processes at small scales. Zones of sea ice convergence and divergence identified from sea ice drift are compared with ridges and leads identified from texture features. That allows more specific interpretation of SAR imagery and more accurate automatic classification. In addition, the map of four ice types calculated using the texture features from one SAR image is propagated forward using the sea ice drift vectors. The propagated ice type is compared with ice type derived from the next image. The comparison identifies changes in ice type which occurred during drift and allows to reduce uncertainties in sea ice type calculation.

Statistical properties of correlated solar flares and coronal mass ejections in cycles 23 and 24

NASA Astrophysics Data System (ADS)

Aarnio, Alicia

2018-01-01

Outstanding problems in understanding early stellar systems include mass loss, angular momentum evolution, and the effects of energetic events on the surrounding environs. The latter of these drives much research into our own system's space weather and the development of predictive algorithms for geomagnetic storms. So dually motivated, we have leveraged a big-data approach to combine two decades of GOES and LASCO data to identify a large sample of spatially and temporally correlated solar flares and CMEs. In this presentation, we revisit the analysis of Aarnio et al. (2011), adding 10 years of data and further exploring the relationships between correlated flare and CME properties. We compare the updated data set results to those previously obtained, and discuss the effects of selecting smaller time windows within solar cycles 23 and 24 on the empirically defined relationships between correlated flare and CME properties. Finally, we discuss a newly identified large sample of potentially interesting correlated flares and CMEs perhaps erroneously excluded from previous searches.

Application of LANDSAT system for improving methodology for inventory and classification of wetlands

NASA Technical Reports Server (NTRS)

Gilmer, D. S. (Principal Investigator)

1976-01-01

The author has identified the following significant results. A newly developed software system for generating statistics on surface water features was tested using LANDSAT data acquired previous to 1975. This software test provided a satisfactory evaluation of the system and also allowed expansion of data base on prairie water features. The software system recognizes water on the basis of a classification algorithm. This classification is accomplished by level thresholding a single near infrared data channel. After each pixel is classified as water or nonwater, the software system then recognizes ponds or lakes as sets of contiguous pixels or as single isolated pixels in the case of very small ponds. Pixels are considered to be contiguous if they are adjacent between successive scan lines. After delineating each water feature, the software system then assigns the feature a position based upon a geographic grid system and calculates the feature's planimetric area, its perimeter, and a parameter known as the shape factor.

Efficient Parallelization of a Dynamic Unstructured Application on the Tera MTA

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Biswas, Rupak

1999-01-01

The success of parallel computing in solving real-life computationally-intensive problems relies on their efficient mapping and execution on large-scale multiprocessor architectures. Many important applications are both unstructured and dynamic in nature, making their efficient parallel implementation a daunting task. This paper presents the parallelization of a dynamic unstructured mesh adaptation algorithm using three popular programming paradigms on three leading supercomputers. We examine an MPI message-passing implementation on the Cray T3E and the SGI Origin2OOO, a shared-memory implementation using cache coherent nonuniform memory access (CC-NUMA) of the Origin2OOO, and a multi-threaded version on the newly-released Tera Multi-threaded Architecture (MTA). We compare several critical factors of this parallel code development, including runtime, scalability, programmability, and memory overhead. Our overall results demonstrate that multi-threaded systems offer tremendous potential for quickly and efficiently solving some of the most challenging real-life problems on parallel computers.

Line-scan spatially offset Raman spectroscopy for inspecting subsurface food safety and quality

NASA Astrophysics Data System (ADS)

Qin, Jianwei; Chao, Kuanglin; Kim, Moon S.

2016-05-01

This paper presented a method for subsurface food inspection using a newly developed line-scan spatially offset Raman spectroscopy (SORS) technique. A 785 nm laser was used as a Raman excitation source. The line-shape SORS data was collected in a wavenumber range of 0-2815 cm-1 using a detection module consisting of an imaging spectrograph and a CCD camera. A layered sample, which was created by placing a plastic sheet cut from the original container on top of cane sugar, was used to test the capability for subsurface food inspection. A whole set of SORS data was acquired in an offset range of 0-36 mm (two sides of the laser) with a spatial interval of 0.07 mm. Raman spectrum from the cane sugar under the plastic sheet was resolved using self-modeling mixture analysis algorithms, demonstrating the potential of the technique for authenticating foods and ingredients through packaging. The line-scan SORS measurement technique provides a new method for subsurface inspection of food safety and quality.