Sample records for adaptive background model

  1. Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding.

    PubMed

    Zhang, Xianguo; Huang, Tiejun; Tian, Yonghong; Gao, Wen

    2014-02-01

    The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.

  2. Equivalent background speed in recovery from motion adaptation.

    PubMed

    Simpson, W A; Newman, A; Aasland, W

    1997-01-01

    We measured, in the same observers, (1) the detectability, d, of a small rotational jump following adaptation to rotational motion and (2) the detectability of the same jump when superimposed on one of several background rotation speeds. Following 90 s of motion adaptation the detectability of the jump was impaired, and sensitivity slowly recovered over the course of 60 s. The detectability of the jump was also impaired by the background speed in a way consistent with a quadratic form of Weber's law. We propose that motion adaptation impairs the detectability of the small jump because it is as if an equivalent background speed has been superimposed on the display. We measured the equivalent background by finding the real background speed that produced the same d' at each instant in the recovery from motion adaptation. The equivalent background started at approximately one to two thirds the speed of the adapting motion, declined rapidly, rose to a small peak at 30 s, then disappeared by 60 s. Since the equivalent background speed corresponds to the speed of the motion aftereffect, we have measured the time course of the motion aftereffect with objective psychophysics.

  3. Incremental principal component pursuit for video background modeling

    DOEpatents

    Rodriquez-Valderrama, Paul A.; Wohlberg, Brendt

    2017-03-14

    An incremental Principal Component Pursuit (PCP) algorithm for video background modeling that is able to process one frame at a time while adapting to changes in background, with a computational complexity that allows for real-time processing, having a low memory footprint and is robust to translational and rotational jitter.

  4. Influence of background size, luminance and eccentricity on different adaptation mechanisms

    PubMed Central

    Gloriani, Alejandro H.; Matesanz, Beatriz M.; Barrionuevo, Pablo A.; Arranz, Isabel; Issolio, Luis; Mar, Santiago; Aparicio, Juan A.

    2016-01-01

    Mechanisms of light adaptation have been traditionally explained with reference to psychophysical experimentation. However, the neural substrata involved in those mechanisms remain to be elucidated. Our study analyzed links between psychophysical measurements and retinal physiological evidence with consideration for the phenomena of rod-cone interactions, photon noise, and spatial summation. Threshold test luminances were obtained with steady background fields at mesopic and photopic light levels (i.e., 0.06–110 cd/m2) for retinal eccentricities from 0° to 15° using three combinations of background/test field sizes (i.e., 10°/2°, 10°/0.45°, and 1°/0.45°). A two-channel Maxwellian view optical system was employed to eliminate pupil effects on the measured thresholds. A model based on visual mechanisms that were described in the literature was optimized to fit the measured luminance thresholds in all experimental conditions. Our results can be described by a combination of visual mechanisms. We determined how spatial summation changed with eccentricity and how subtractive adaptation changed with eccentricity and background field size. According to our model, photon noise plays a significant role to explain contrast detection thresholds measured with the 1/0.45° background/test size combination at mesopic luminances and at off-axis eccentricities. In these conditions, our data reflect the presence of rod-cone interaction for eccentricities between 6° and 9° and luminances between 0.6 and 5 cd/m2. In spite of the increasing noise effects with eccentricity, results also show that the visual system tends to maintain a constant signal-to-noise ratio in the off-axis detection task over the whole mesopic range. PMID:27210038

  5. Influence of background size, luminance and eccentricity on different adaptation mechanisms.

    PubMed

    Gloriani, Alejandro H; Matesanz, Beatriz M; Barrionuevo, Pablo A; Arranz, Isabel; Issolio, Luis; Mar, Santiago; Aparicio, Juan A

    2016-08-01

    Mechanisms of light adaptation have been traditionally explained with reference to psychophysical experimentation. However, the neural substrata involved in those mechanisms remain to be elucidated. Our study analyzed links between psychophysical measurements and retinal physiological evidence with consideration for the phenomena of rod-cone interactions, photon noise, and spatial summation. Threshold test luminances were obtained with steady background fields at mesopic and photopic light levels (i.e., 0.06-110cd/m(2)) for retinal eccentricities from 0° to 15° using three combinations of background/test field sizes (i.e., 10°/2°, 10°/0.45°, and 1°/0.45°). A two-channel Maxwellian view optical system was employed to eliminate pupil effects on the measured thresholds. A model based on visual mechanisms that were described in the literature was optimized to fit the measured luminance thresholds in all experimental conditions. Our results can be described by a combination of visual mechanisms. We determined how spatial summation changed with eccentricity and how subtractive adaptation changed with eccentricity and background field size. According to our model, photon noise plays a significant role to explain contrast detection thresholds measured with the 1/0.45° background/test size combination at mesopic luminances and at off-axis eccentricities. In these conditions, our data reflect the presence of rod-cone interaction for eccentricities between 6° and 9° and luminances between 0.6 and 5cd/m(2). In spite of the increasing noise effects with eccentricity, results also show that the visual system tends to maintain a constant signal-to-noise ratio in the off-axis detection task over the whole mesopic range. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Background Noise Reduction Using Adaptive Noise Cancellation Determined by the Cross-Correlation

    NASA Technical Reports Server (NTRS)

    Spalt, Taylor B.; Brooks, Thomas F.; Fuller, Christopher R.

    2012-01-01

    Background noise due to flow in wind tunnels contaminates desired data by decreasing the Signal-to-Noise Ratio. The use of Adaptive Noise Cancellation to remove background noise at measurement microphones is compromised when the reference sensor measures both background and desired noise. The technique proposed modifies the classical processing configuration based on the cross-correlation between the reference and primary microphone. Background noise attenuation is achieved using a cross-correlation sample width that encompasses only the background noise and a matched delay for the adaptive processing. A present limitation of the method is that a minimum time delay between the background noise and desired signal must exist in order for the correlated parts of the desired signal to be separated from the background noise in the crosscorrelation. A simulation yields primary signal recovery which can be predicted from the coherence of the background noise between the channels. Results are compared with two existing methods.

  7. Target detection using the background model from the topological anomaly detection algorithm

    NASA Astrophysics Data System (ADS)

    Dorado Munoz, Leidy P.; Messinger, David W.; Ziemann, Amanda K.

    2013-05-01

    The Topological Anomaly Detection (TAD) algorithm has been used as an anomaly detector in hyperspectral and multispectral images. TAD is an algorithm based on graph theory that constructs a topological model of the background in a scene, and computes an anomalousness ranking for all of the pixels in the image with respect to the background in order to identify pixels with uncommon or strange spectral signatures. The pixels that are modeled as background are clustered into groups or connected components, which could be representative of spectral signatures of materials present in the background. Therefore, the idea of using the background components given by TAD in target detection is explored in this paper. In this way, these connected components are characterized in three different approaches, where the mean signature and endmembers for each component are calculated and used as background basis vectors in Orthogonal Subspace Projection (OSP) and Adaptive Subspace Detector (ASD). Likewise, the covariance matrix of those connected components is estimated and used in detectors: Constrained Energy Minimization (CEM) and Adaptive Coherence Estimator (ACE). The performance of these approaches and the different detectors is compared with a global approach, where the background characterization is derived directly from the image. Experiments and results using self-test data set provided as part of the RIT blind test target detection project are shown.

  8. Adaptive regularization network based neural modeling paradigm for nonlinear adaptive estimation of cerebral evoked potentials.

    PubMed

    Zhang, Jian-Hua; Böhme, Johann F

    2007-11-01

    In this paper we report an adaptive regularization network (ARN) approach to realizing fast blind separation of cerebral evoked potentials (EPs) from background electroencephalogram (EEG) activity with no need to make any explicit assumption on the statistical (or deterministic) signal model. The ARNs are proposed to construct nonlinear EEG and EP signal models. A novel adaptive regularization training (ART) algorithm is proposed to improve the generalization performance of the ARN. Two adaptive neural modeling methods based on the ARN are developed and their implementation and performance analysis are also presented. The computer experiments using simulated and measured visual evoked potential (VEP) data have shown that the proposed ARN modeling paradigm yields computationally efficient and more accurate VEP signal estimation owing to its intrinsic model-free and nonlinear processing characteristics.

  9. Adaptive removal of background and white space from document images using seam categorization

    NASA Astrophysics Data System (ADS)

    Fillion, Claude; Fan, Zhigang; Monga, Vishal

    2011-03-01

    Document images are obtained regularly by rasterization of document content and as scans of printed documents. Resizing via background and white space removal is often desired for better consumption of these images, whether on displays or in print. While white space and background are easy to identify in images, existing methods such as naïve removal and content aware resizing (seam carving) each have limitations that can lead to undesirable artifacts, such as uneven spacing between lines of text or poor arrangement of content. An adaptive method based on image content is hence needed. In this paper we propose an adaptive method to intelligently remove white space and background content from document images. Document images are different from pictorial images in structure. They typically contain objects (text letters, pictures and graphics) separated by uniform background, which include both white paper space and other uniform color background. Pixels in uniform background regions are excellent candidates for deletion if resizing is required, as they introduce less change in document content and style, compared with deletion of object pixels. We propose a background deletion method that exploits both local and global context. The method aims to retain the document structural information and image quality.

  10. Olfactory cortical adaptation facilitates detection of odors against background.

    PubMed

    Kadohisa, Mikiko; Wilson, Donald A

    2006-03-01

    Detection and discrimination of odors generally, if not always, occurs against an odorous background. On any given inhalation, olfactory receptor neurons will be activated by features of both the target odorant and features of background stimuli. To identify a target odorant against a background therefore, the olfactory system must be capable of grouping a subset of features into an odor object distinct from the background. Our previous work has suggested that rapid homosynaptic depression of afferents to the anterior piriform cortex (aPCX) contributes to both cortical odor adaptation to prolonged stimulation and habituation of simple odor-evoked behaviors. We hypothesize here that this process may also contribute to figure-ground separation of a target odorant from background stimulation. Single-unit recordings were made from both mitral/tufted cells and aPCX neurons in urethan-anesthetized rats and mice. Single-unit responses to odorant stimuli and their binary mixtures were determined. One of the odorants was randomly selected as the background and presented for 50 s. Forty seconds after the onset of the background stimulus, the second target odorant was presented, producing a binary mixture. The results suggest that mitral/tufted cells continue to respond to the background odorant and, when the target odorant is presented, had response magnitudes similar to that evoked by the binary mixture. In contrast, aPCX neurons filter out the background stimulus while maintaining responses to the target stimulus. Thus the aPCX acts as a filter driven most strongly by changing stimuli, providing a potential mechanism for olfactory figure-ground separation and selective reading of olfactory bulb output.

  11. An analog retina model for detecting dim moving objects against a bright moving background

    NASA Technical Reports Server (NTRS)

    Searfus, R. M.; Colvin, M. E.; Eeckman, F. H.; Teeters, J. L.; Axelrod, T. S.

    1991-01-01

    We are interested in applications that require the ability to track a dim target against a bright, moving background. Since the target signal will be less than or comparable to the variations in the background signal intensity, sophisticated techniques must be employed to detect the target. We present an analog retina model that adapts to the motion of the background in order to enhance targets that have a velocity difference with respect to the background. Computer simulation results and our preliminary concept of an analog 'Z' focal plane implementation are also presented.

  12. A 3D image sensor with adaptable charge subtraction scheme for background light suppression

    NASA Astrophysics Data System (ADS)

    Shin, Jungsoon; Kang, Byongmin; Lee, Keechang; Kim, James D. K.

    2013-02-01

    We present a 3D ToF (Time-of-Flight) image sensor with adaptive charge subtraction scheme for background light suppression. The proposed sensor can alternately capture high resolution color image and high quality depth map in each frame. In depth-mode, the sensor requires enough integration time for accurate depth acquisition, but saturation will occur in high background light illumination. We propose to divide the integration time into N sub-integration times adaptively. In each sub-integration time, our sensor captures an image without saturation and subtracts the charge to prevent the pixel from the saturation. In addition, the subtraction results are cumulated N times obtaining a final result image without background illumination at full integration time. Experimental results with our own ToF sensor show high background suppression performance. We also propose in-pixel storage and column-level subtraction circuit for chiplevel implementation of the proposed method. We believe the proposed scheme will enable 3D sensors to be used in out-door environment.

  13. Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update.

    PubMed

    Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

    2016-04-15

    Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the "good" models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm.

  14. Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

    PubMed Central

    Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

    2016-01-01

    Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm. PMID:27092505

  15. Hybrid Adaptive Flight Control with Model Inversion Adaptation

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan

    2011-01-01

    This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.

  16. TIMSS 2011 User Guide for the International Database. Supplement 2: National Adaptations of International Background Questionnaires

    ERIC Educational Resources Information Center

    Foy, Pierre, Ed.; Arora, Alka, Ed.; Stanco, Gabrielle M., Ed.

    2013-01-01

    This supplement describes national adaptations made to the international version of the TIMSS 2011 background questionnaires. This information provides users with a guide to evaluate the availability of internationally comparable data for use in secondary analyses involving the TIMSS 2011 background variables. Background questionnaire adaptations…

  17. Adaptive Modeling of the International Space Station Electrical Power System

    NASA Technical Reports Server (NTRS)

    Thomas, Justin Ray

    2007-01-01

    Software simulations provide NASA engineers the ability to experiment with spacecraft systems in a computer-imitated environment. Engineers currently develop software models that encapsulate spacecraft system behavior. These models can be inaccurate due to invalid assumptions, erroneous operation, or system evolution. Increasing accuracy requires manual calibration and domain-specific knowledge. This thesis presents a method for automatically learning system models without any assumptions regarding system behavior. Data stream mining techniques are applied to learn models for critical portions of the International Space Station (ISS) Electrical Power System (EPS). We also explore a knowledge fusion approach that uses traditional engineered EPS models to supplement the learned models. We observed that these engineered EPS models provide useful background knowledge to reduce predictive error spikes when confronted with making predictions in situations that are quite different from the training scenarios used when learning the model. Evaluations using ISS sensor data and existing EPS models demonstrate the success of the adaptive approach. Our experimental results show that adaptive modeling provides reductions in model error anywhere from 80% to 96% over these existing models. Final discussions include impending use of adaptive modeling technology for ISS mission operations and the need for adaptive modeling in future NASA lunar and Martian exploration.

  18. Background field removal using a region adaptive kernel for quantitative susceptibility mapping of human brain

    NASA Astrophysics Data System (ADS)

    Fang, Jinsheng; Bao, Lijun; Li, Xu; van Zijl, Peter C. M.; Chen, Zhong

    2017-08-01

    Background field removal is an important MR phase preprocessing step for quantitative susceptibility mapping (QSM). It separates the local field induced by tissue magnetic susceptibility sources from the background field generated by sources outside a region of interest, e.g. brain, such as air-tissue interface. In the vicinity of air-tissue boundary, e.g. skull and paranasal sinuses, where large susceptibility variations exist, present background field removal methods are usually insufficient and these regions often need to be excluded by brain mask erosion at the expense of losing information of local field and thus susceptibility measures in these regions. In this paper, we propose an extension to the variable-kernel sophisticated harmonic artifact reduction for phase data (V-SHARP) background field removal method using a region adaptive kernel (R-SHARP), in which a scalable spherical Gaussian kernel (SGK) is employed with its kernel radius and weights adjustable according to an energy "functional" reflecting the magnitude of field variation. Such an energy functional is defined in terms of a contour and two fitting functions incorporating regularization terms, from which a curve evolution model in level set formation is derived for energy minimization. We utilize it to detect regions of with a large field gradient caused by strong susceptibility variation. In such regions, the SGK will have a small radius and high weight at the sphere center in a manner adaptive to the voxel energy of the field perturbation. Using the proposed method, the background field generated from external sources can be effectively removed to get a more accurate estimation of the local field and thus of the QSM dipole inversion to map local tissue susceptibility sources. Numerical simulation, phantom and in vivo human brain data demonstrate improved performance of R-SHARP compared to V-SHARP and RESHARP (regularization enabled SHARP) methods, even when the whole paranasal sinus regions

  19. A Flexible Cosmic Ultraviolet Background Model

    NASA Astrophysics Data System (ADS)

    McQuinn, Matthew

    2016-10-01

    HST studies of the IGM, of the CGM, and of reionization-era galaxies are all aided by ionizing background models, which are a critical input in modeling the ionization state of diffuse, 10^4 K gas. The ionization state in turn enables the determination of densities and sizes of absorbing clouds and, when applied to the Ly-a forest, the global ionizing emissivity of sources. Unfortunately, studies that use these background models have no way of gauging the amount of uncertainty in the adopted model other than to recompute their results using previous background models with outdated observational inputs. As of yet there has been no systematic study of uncertainties in the background model and there unfortunately is no publicly available ultraviolet background code. A public code would enable users to update the calculation with the latest observational constraints, and it would allow users to experiment with varying the background model's assumptions regarding emissions and absorptions. We propose to develop a publicly available ionizing background code and, as an initial application, quantify the level of uncertainty in the ionizing background spectrum across cosmic time. As the background model improves, so does our understanding of (1) the sources that dominate ionizing emissions across cosmic time and (2) the properties of diffuse gas in the circumgalactic medium, the WHIM, and the Ly-a forest. HST is the primary telescope for studying both the highest redshift galaxies and low-redshift diffuse gas. The proposed program would benefit HST studies of the Universe at z 0 all the way up to z = 10, including of high-z galaxies observed in the HST Frontier Fields.

  20. An Adapted Dialogic Reading Program for Turkish Kindergarteners from Low Socio-Economic Backgrounds

    ERIC Educational Resources Information Center

    Ergül, Cevriye; Akoglu, Gözde; Sarica, Ayse D.; Karaman, Gökçe; Tufan, Mümin; Bahap-Kudret, Zeynep; Zülfikar, Deniz

    2016-01-01

    The study aimed to examine the effectiveness of the Adapted Dialogic Reading Program (ADR) on the language and early literacy skills of Turkish kindergarteners from low socio-economic (SES) backgrounds. The effectiveness of ADR was investigated across six different treatment conditions including classroom and home based implementations in various…

  1. An efficient background modeling approach based on vehicle detection

    NASA Astrophysics Data System (ADS)

    Wang, Jia-yan; Song, Li-mei; Xi, Jiang-tao; Guo, Qing-hua

    2015-10-01

    The existing Gaussian Mixture Model(GMM) which is widely used in vehicle detection suffers inefficiency in detecting foreground image during the model phase, because it needs quite a long time to blend the shadows in the background. In order to overcome this problem, an improved method is proposed in this paper. First of all, each frame is divided into several areas(A, B, C and D), Where area A, B, C and D are decided by the frequency and the scale of the vehicle access. For each area, different new learning rate including weight, mean and variance is applied to accelerate the elimination of shadows. At the same time, the measure of adaptive change for Gaussian distribution is taken to decrease the total number of distributions and save memory space effectively. With this method, different threshold value and different number of Gaussian distribution are adopted for different areas. The results show that the speed of learning and the accuracy of the model using our proposed algorithm surpass the traditional GMM. Probably to the 50th frame, interference with the vehicle has been eliminated basically, and the model number only 35% to 43% of the standard, the processing speed for every frame approximately has a 20% increase than the standard. The proposed algorithm has good performance in terms of elimination of shadow and processing speed for vehicle detection, it can promote the development of intelligent transportation, which is very meaningful to the other Background modeling methods.

  2. Background field removal using a region adaptive kernel for quantitative susceptibility mapping of human brain.

    PubMed

    Fang, Jinsheng; Bao, Lijun; Li, Xu; van Zijl, Peter C M; Chen, Zhong

    2017-08-01

    Background field removal is an important MR phase preprocessing step for quantitative susceptibility mapping (QSM). It separates the local field induced by tissue magnetic susceptibility sources from the background field generated by sources outside a region of interest, e.g. brain, such as air-tissue interface. In the vicinity of air-tissue boundary, e.g. skull and paranasal sinuses, where large susceptibility variations exist, present background field removal methods are usually insufficient and these regions often need to be excluded by brain mask erosion at the expense of losing information of local field and thus susceptibility measures in these regions. In this paper, we propose an extension to the variable-kernel sophisticated harmonic artifact reduction for phase data (V-SHARP) background field removal method using a region adaptive kernel (R-SHARP), in which a scalable spherical Gaussian kernel (SGK) is employed with its kernel radius and weights adjustable according to an energy "functional" reflecting the magnitude of field variation. Such an energy functional is defined in terms of a contour and two fitting functions incorporating regularization terms, from which a curve evolution model in level set formation is derived for energy minimization. We utilize it to detect regions of with a large field gradient caused by strong susceptibility variation. In such regions, the SGK will have a small radius and high weight at the sphere center in a manner adaptive to the voxel energy of the field perturbation. Using the proposed method, the background field generated from external sources can be effectively removed to get a more accurate estimation of the local field and thus of the QSM dipole inversion to map local tissue susceptibility sources. Numerical simulation, phantom and in vivo human brain data demonstrate improved performance of R-SHARP compared to V-SHARP and RESHARP (regularization enabled SHARP) methods, even when the whole paranasal sinus regions

  3. Asynchronous adaptive time step in quantitative cellular automata modeling

    PubMed Central

    Zhu, Hao; Pang, Peter YH; Sun, Yan; Dhar, Pawan

    2004-01-01

    Background The behaviors of cells in metazoans are context dependent, thus large-scale multi-cellular modeling is often necessary, for which cellular automata are natural candidates. Two related issues are involved in cellular automata based multi-cellular modeling: how to introduce differential equation based quantitative computing to precisely describe cellular activity, and upon it, how to solve the heavy time consumption issue in simulation. Results Based on a modified, language based cellular automata system we extended that allows ordinary differential equations in models, we introduce a method implementing asynchronous adaptive time step in simulation that can considerably improve efficiency yet without a significant sacrifice of accuracy. An average speedup rate of 4–5 is achieved in the given example. Conclusions Strategies for reducing time consumption in simulation are indispensable for large-scale, quantitative multi-cellular models, because even a small 100 × 100 × 100 tissue slab contains one million cells. Distributed and adaptive time step is a practical solution in cellular automata environment. PMID:15222901

  4. Mutational Effects and Population Dynamics During Viral Adaptation Challenge Current Models

    PubMed Central

    Miller, Craig R.; Joyce, Paul; Wichman, Holly A.

    2011-01-01

    Adaptation in haploid organisms has been extensively modeled but little tested. Using a microvirid bacteriophage (ID11), we conducted serial passage adaptations at two bottleneck sizes (104 and 106), followed by fitness assays and whole-genome sequencing of 631 individual isolates. Extensive genetic variation was observed including 22 beneficial, several nearly neutral, and several deleterious mutations. In the three large bottleneck lines, up to eight different haplotypes were observed in samples of 23 genomes from the final time point. The small bottleneck lines were less diverse. The small bottleneck lines appeared to operate near the transition between isolated selective sweeps and conditions of complex dynamics (e.g., clonal interference). The large bottleneck lines exhibited extensive interference and less stochasticity, with multiple beneficial mutations establishing on a variety of backgrounds. Several leapfrog events occurred. The distribution of first-step adaptive mutations differed significantly from the distribution of second-steps, and a surprisingly large number of second-step beneficial mutations were observed on a highly fit first-step background. Furthermore, few first-step mutations appeared as second-steps and second-steps had substantially smaller selection coefficients. Collectively, the results indicate that the fitness landscape falls between the extremes of smooth and fully uncorrelated, violating the assumptions of many current mutational landscape models. PMID:21041559

  5. Do common mechanisms of adaptation mediate color discrimination and appearance? Contrast adaptation

    NASA Astrophysics Data System (ADS)

    Hillis, James M.; Brainard, David H.

    2007-08-01

    Are effects of background contrast on color appearance and sensitivity controlled by the same mechanism of adaptation? We examined the effects of background color contrast on color appearance and on color-difference sensitivity under well-matched conditions. We linked the data using Fechner's hypothesis that the rate of apparent stimulus change is proportional to sensitivity and examined a family of parametric models of adaptation. Our results show that both appearance and discrimination are consistent with the same mechanism of adaptation.

  6. Background Error Correlation Modeling with Diffusion Operators

    DTIC Science & Technology

    2013-01-01

    RESPONSIBLE PERSON 19b. TELEPHONE NUMBER (Include area code) 07-10-2013 Book Chapter Background Error Correlation Modeling with Diffusion Operators...normalization Unclassified Unclassified Unclassified UU 27 Max Yaremchuk (228) 688-5259 Reset Chapter 8 Background error correlation modeling with diffusion ...field, then a structure like this simulates enhanced diffusive transport of model errors in the regions of strong cur- rents on the background of

  7. Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality

    PubMed Central

    Hondula, David M.; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

    2017-01-01

    Background: Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to “adaptation uncertainty” (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. Objectives: This study had three aims: a) Compare the range in projected impacts that arises from using different adaptation modeling methods; b) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c) recommend modeling method(s) to use in future impact assessments. Methods: We estimated impacts for 2070–2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. Results: The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Conclusions: Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634 PMID:28885979

  8. A diversified portfolio model of adaptability.

    PubMed

    Chandra, Siddharth; Leong, Frederick T L

    2016-12-01

    A new model of adaptability, the diversified portfolio model (DPM) of adaptability, is introduced. In the 1950s, Markowitz developed the financial portfolio model by demonstrating that investors could optimize the ratio of risk and return on their portfolios through risk diversification. The DPM integrates attractive features of a variety of models of adaptability, including Linville's self-complexity model, the risk and resilience model, and Bandura's social cognitive theory. The DPM draws on the concept of portfolio diversification, positing that diversified investment in multiple life experiences, life roles, and relationships promotes positive adaptation to life's challenges. The DPM provides a new integrative model of adaptability across the biopsychosocial levels of functioning. More importantly, the DPM addresses a gap in the literature by illuminating the antecedents of adaptive processes studied in a broad array of psychological models. The DPM is described in relation to the biopsychosocial model and propositions are offered regarding its utility in increasing adaptiveness. Recommendations for future research are also offered. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  9. Sensorimotor adaptation is influenced by background music.

    PubMed

    Bock, Otmar

    2010-06-01

    It is well established that listening to music can modify subjects' cognitive performance. The present study evaluates whether this so-called Mozart Effect extends beyond cognitive tasks and includes sensorimotor adaptation. Three subject groups listened to musical pieces that in the author's judgment were serene, neutral, or sad, respectively. This judgment was confirmed by the subjects' introspective reports. While listening to music, subjects engaged in a pointing task that required them to adapt to rotated visual feedback. All three groups adapted successfully, but the speed and magnitude of adaptive improvement was more pronounced with serene music than with the other two music types. In contrast, aftereffects upon restoration of normal feedback were independent of music type. These findings support the existence of a "Mozart effect" for strategic movement control, but not for adaptive recalibration. Possibly, listening to music modifies neural activity in an intertwined cognitive-emotional network.

  10. Background modeling for the GERDA experiment

    NASA Astrophysics Data System (ADS)

    Becerici-Schmidt, N.; Gerda Collaboration

    2013-08-01

    The neutrinoless double beta (0νββ) decay experiment GERDA at the LNGS of INFN has started physics data taking in November 2011. This paper presents an analysis aimed at understanding and modeling the observed background energy spectrum, which plays an essential role in searches for a rare signal like 0νββ decay. A very promising preliminary model has been obtained, with the systematic uncertainties still under study. Important information can be deduced from the model such as the expected background and its decomposition in the signal region. According to the model the main background contributions around Qββ come from 214Bi, 228Th, 42K, 60Co and α emitting isotopes in the 226Ra decay chain, with a fraction depending on the assumed source positions.

  11. Background odour induces adaptation and sensitization of olfactory receptors in the antennae of houseflies.

    PubMed

    Kelling, F J; Ialenti, F; Den Otter, C J

    2002-06-01

    The presence of background odour was found to have a small but significant effect on the sensitivity of the antennal olfactory system of houseflies, Musca domestica Linnaeus (Diptera: Muscidae), to new pulses of odour. We show that cross-adaptation and cross-sensitization between a background odour of (+/-)-1-octen-3-ol and pulses of (+/-)-1-octen-3-ol, 2-pentanone and R-(+)-limonene can occur, confirming that olfactory receptor cells are sensitive to different odours. Background odour can increase the responses to low concentration odour pulses and decrease the responses to higher concentration odour pulses. It is suggested that background odour has a larger effect on olfactory receptor cells that respond with a tonic increase of spike frequency, giving information about the level of odour concentration, i.e. the 'static' environment. Cells that respond in a phasic way only provide information on the dynamics of the olfactory environment.

  12. [Detection of Weak Speech Signals from Strong Noise Background Based on Adaptive Stochastic Resonance].

    PubMed

    Lu, Huanhuan; Wang, Fuzhong; Zhang, Huichun

    2016-04-01

    Traditional speech detection methods regard the noise as a jamming signal to filter,but under the strong noise background,these methods lost part of the original speech signal while eliminating noise.Stochastic resonance can use noise energy to amplify the weak signal and suppress the noise.According to stochastic resonance theory,a new method based on adaptive stochastic resonance to extract weak speech signals is proposed.This method,combined with twice sampling,realizes the detection of weak speech signals from strong noise.The parameters of the systema,b are adjusted adaptively by evaluating the signal-to-noise ratio of the output signal,and then the weak speech signal is optimally detected.Experimental simulation analysis showed that under the background of strong noise,the output signal-to-noise ratio increased from the initial value-7dB to about 0.86 dB,with the gain of signalto-noise ratio is 7.86 dB.This method obviously raises the signal-to-noise ratio of the output speech signals,which gives a new idea to detect the weak speech signals in strong noise environment.

  13. Modeled summer background concentration nutrients and ...

    EPA Pesticide Factsheets

    We used regression models to predict background concentration of four water quality indictors: total nitrogen (N), total phosphorus (P), chloride, and total suspended solids (TSS), in the mid-continent (USA) great rivers, the Upper Mississippi, the Lower Missouri, and the Ohio. From best-model linear regressions of water quality indicators with land use and other stressor variables, we determined the concentration of the indicators when the land use and stressor variables were all set to zero the y-intercept. Except for total P on the Upper Mississippi River and chloride on the Ohio River, we were able to predict background concentration from significant regression models. In every model with more than one predictor variable, the model included at least one variable representing agricultural land use and one variable representing development. Predicted background concentration of total N was the same on the Upper Mississippi and Lower Missouri rivers (350 ug l-1), which was much lower than a published eutrophication threshold and percentile-based thresholds (25th percentile of concentration at all sites in the population) but was similar to a threshold derived from the response of sestonic chlorophyll a to great river total N concentration. Background concentration of total P on the Lower Missouri (53 ug l-1) was also lower than published and percentile-based thresholds. Background TSS concentration was higher on the Lower Missouri (30 mg l-1) than the other ri

  14. Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

    NASA Technical Reports Server (NTRS)

    Keppenne, Christian L.; Rienecker, Michele; Kovach, Robin M.; Vernieres, Guillaume

    2014-01-01

    An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

  15. [Acculturation orientations and psychosocial adaptation among adolescents with immigrant background].

    PubMed

    Goutaudier, N; Chauchard, E; Melioli, T; Valls, M; van Leeuwen, N; Chabrol, H

    2015-09-01

    The aim of the study was to explore the typology of adolescents with immigrant background based on the orientations of acculturation and to estimate the psychosocial adaptation of the various subtypes. A sample of 228 French high school students with an immigrant background completed a questionnaire assessing acculturation orientations (Immigrant Acculturation Scale; Barrette et al., 2004), antisocial behaviors, depressive symptoms and self-esteem. Cluster analysis based on acculturation orientations was performed using the k-means method. Cluster analysis produced four distinct acculturation profiles: bicultural (31%), separated (28%), marginalized (21%), and assimilated-individualistic (20%). Adolescents in the separated and marginalized clusters, both characterized by rejection of the host culture, reported higher levels of antisocial behavior. Depressive symptoms and self-esteem did not differ between clusters. Several hypotheses may explain the association between separation and delinquency. First, separation and rejection of the host culture may lead to rebellious behavior such as delinquency. Conversely, delinquent behavior may provoke rejection or discrimination by peers or school, or legal sanctions that induce a reciprocal process of rejection of the host culture and separation. The relationship between separation and antisocial behavior may be bidirectional, each one reinforcing the other, resulting in a negative spiral. This study confirms the interest of the study of the orientations of acculturation in the understanding of the antisocial behavior of adolescents with immigrant background. Copyright © 2014 L’Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

  16. Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

    NASA Technical Reports Server (NTRS)

    Keppenne, Christian L.; Rienecker, Michele M.; Kovach, Robin M.; Vernieres, Guillaume; Koster, Randal D. (Editor)

    2014-01-01

    An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

  17. Stacked Multilayer Self-Organizing Map for Background Modeling.

    PubMed

    Zhao, Zhenjie; Zhang, Xuebo; Fang, Yongchun

    2015-09-01

    In this paper, a new background modeling method called stacked multilayer self-organizing map background model (SMSOM-BM) is proposed, which presents several merits such as strong representative ability for complex scenarios, easy to use, and so on. In order to enhance the representative ability of the background model and make the parameters learned automatically, the recently developed idea of representative learning (or deep learning) is elegantly employed to extend the existing single-layer self-organizing map background model to a multilayer one (namely, the proposed SMSOM-BM). As a consequence, the SMSOM-BM gains several merits including strong representative ability to learn background model of challenging scenarios, and automatic determination for most network parameters. More specifically, every pixel is modeled by a SMSOM, and spatial consistency is considered at each layer. By introducing a novel over-layer filtering process, we can train the background model layer by layer in an efficient manner. Furthermore, for real-time performance consideration, we have implemented the proposed method using NVIDIA CUDA platform. Comparative experimental results show superior performance of the proposed approach.

  18. Detecting consistent patterns of directional adaptation using differential selection codon models.

    PubMed

    Parto, Sahar; Lartillot, Nicolas

    2017-06-23

    Phylogenetic codon models are often used to characterize the selective regimes acting on protein-coding sequences. Recent methodological developments have led to models explicitly accounting for the interplay between mutation and selection, by modeling the amino acid fitness landscape along the sequence. However, thus far, most of these models have assumed that the fitness landscape is constant over time. Fluctuations of the fitness landscape may often be random or depend on complex and unknown factors. However, some organisms may be subject to systematic changes in selective pressure, resulting in reproducible molecular adaptations across independent lineages subject to similar conditions. Here, we introduce a codon-based differential selection model, which aims to detect and quantify the fine-grained consistent patterns of adaptation at the protein-coding level, as a function of external conditions experienced by the organism under investigation. The model parameterizes the global mutational pressure, as well as the site- and condition-specific amino acid selective preferences. This phylogenetic model is implemented in a Bayesian MCMC framework. After validation with simulations, we applied our method to a dataset of HIV sequences from patients with known HLA genetic background. Our differential selection model detects and characterizes differentially selected coding positions specifically associated with two different HLA alleles. Our differential selection model is able to identify consistent molecular adaptations as a function of repeated changes in the environment of the organism. These models can be applied to many other problems, ranging from viral adaptation to evolution of life-history strategies in plants or animals.

  19. Adapting to life: ocean biogeochemical modelling and adaptive remeshing

    NASA Astrophysics Data System (ADS)

    Hill, J.; Popova, E. E.; Ham, D. A.; Piggott, M. D.; Srokosz, M.

    2014-05-01

    An outstanding problem in biogeochemical modelling of the ocean is that many of the key processes occur intermittently at small scales, such as the sub-mesoscale, that are not well represented in global ocean models. This is partly due to their failure to resolve sub-mesoscale phenomena, which play a significant role in vertical nutrient supply. Simply increasing the resolution of the models may be an inefficient computational solution to this problem. An approach based on recent advances in adaptive mesh computational techniques may offer an alternative. Here the first steps in such an approach are described, using the example of a simple vertical column (quasi-1-D) ocean biogeochemical model. We present a novel method of simulating ocean biogeochemical behaviour on a vertically adaptive computational mesh, where the mesh changes in response to the biogeochemical and physical state of the system throughout the simulation. We show that the model reproduces the general physical and biological behaviour at three ocean stations (India, Papa and Bermuda) as compared to a high-resolution fixed mesh simulation and to observations. The use of an adaptive mesh does not increase the computational error, but reduces the number of mesh elements by a factor of 2-3. Unlike previous work the adaptivity metric used is flexible and we show that capturing the physical behaviour of the model is paramount to achieving a reasonable solution. Adding biological quantities to the adaptivity metric further refines the solution. We then show the potential of this method in two case studies where we change the adaptivity metric used to determine the varying mesh sizes in order to capture the dynamics of chlorophyll at Bermuda and sinking detritus at Papa. We therefore demonstrate that adaptive meshes may provide a suitable numerical technique for simulating seasonal or transient biogeochemical behaviour at high vertical resolution whilst minimising the number of elements in the mesh. More

  20. Relationship between Family Adaptability, Cohesion and Adolescent Problem Behaviors: Curvilinearity of Circumplex Model

    PubMed Central

    Joh, Ju Youn; Kim, Sun; Park, Jun Li

    2013-01-01

    Background The Family Adaptability and Cohesion Evaluation Scale (FACES) III using the circumplex model has been widely used in investigating family function. However, the criticism of the curvilinear hypothesis of the circumplex model has always been from an empirical point of view. This study examined the relationship between adolescent adaptability, cohesion, and adolescent problem behaviors, and especially testing the consistency of the curvilinear hypotheses with FACES III. Methods We used the data from 398 adolescent participants who were in middle school. A self-reported questionnaire was used to evaluate the FACES III and Youth Self Report. Results According to the level of family adaptability, significant differences were evident in internalizing problems (P = 0.014). But, in externalizing problems, the results were not significant (P = 0.305). Also, according to the level of family cohesion, significant differences were in internalizing problems (P = 0.002) and externalizing problems (P = 0.004). Conclusion The relationship between the dimensions of adaptability, cohesion and adolescent problem behaviors was not curvilinear. In other words, adolescents with high adaptability and high cohesion showed low problem behaviors. PMID:23730484

  1. An Optimal Control Modification to Model-Reference Adaptive Control for Fast Adaptation

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan T.; Krishnakumar, Kalmanje; Boskovic, Jovan

    2008-01-01

    This paper presents a method that can achieve fast adaptation for a class of model-reference adaptive control. It is well-known that standard model-reference adaptive control exhibits high-gain control behaviors when a large adaptive gain is used to achieve fast adaptation in order to reduce tracking error rapidly. High gain control creates high-frequency oscillations that can excite unmodeled dynamics and can lead to instability. The fast adaptation approach is based on the minimization of the squares of the tracking error, which is formulated as an optimal control problem. The necessary condition of optimality is used to derive an adaptive law using the gradient method. This adaptive law is shown to result in uniform boundedness of the tracking error by means of the Lyapunov s direct method. Furthermore, this adaptive law allows a large adaptive gain to be used without causing undesired high-gain control effects. The method is shown to be more robust than standard model-reference adaptive control. Simulations demonstrate the effectiveness of the proposed method.

  2. A Background Noise Reduction Technique Using Adaptive Noise Cancellation for Microphone Arrays

    NASA Technical Reports Server (NTRS)

    Spalt, Taylor B.; Fuller, Christopher R.; Brooks, Thomas F.; Humphreys, William M., Jr.; Brooks, Thomas F.

    2011-01-01

    Background noise in wind tunnel environments poses a challenge to acoustic measurements due to possible low or negative Signal to Noise Ratios (SNRs) present in the testing environment. This paper overviews the application of time domain Adaptive Noise Cancellation (ANC) to microphone array signals with an intended application of background noise reduction in wind tunnels. An experiment was conducted to simulate background noise from a wind tunnel circuit measured by an out-of-flow microphone array in the tunnel test section. A reference microphone was used to acquire a background noise signal which interfered with the desired primary noise source signal at the array. The technique s efficacy was investigated using frequency spectra from the array microphones, array beamforming of the point source region, and subsequent deconvolution using the Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) algorithm. Comparisons were made with the conventional techniques for improving SNR of spectral and Cross-Spectral Matrix subtraction. The method was seen to recover the primary signal level in SNRs as low as -29 dB and outperform the conventional methods. A second processing approach using the center array microphone as the noise reference was investigated for more general applicability of the ANC technique. It outperformed the conventional methods at the -29 dB SNR but yielded less accurate results when coherence over the array dropped. This approach could possibly improve conventional testing methodology but must be investigated further under more realistic testing conditions.

  3. Spatiotemporal models for the simulation of infrared backgrounds

    NASA Astrophysics Data System (ADS)

    Wilkes, Don M.; Cadzow, James A.; Peters, R. Alan, II; Li, Xingkang

    1992-09-01

    It is highly desirable for designers of automatic target recognizers (ATRs) to be able to test their algorithms on targets superimposed on a wide variety of background imagery. Background imagery in the infrared spectrum is expensive to gather from real sources, consequently, there is a need for accurate models for producing synthetic IR background imagery. We have developed a model for such imagery that will do the following: Given a real, infrared background image, generate another image, distinctly different from the one given, that has the same general visual characteristics as well as the first and second-order statistics of the original image. The proposed model consists of a finite impulse response (FIR) kernel convolved with an excitation function, and histogram modification applied to the final solution. A procedure for deriving the FIR kernel using a signal enhancement algorithm has been developed, and the histogram modification step is a simple memoryless nonlinear mapping that imposes the first order statistics of the original image onto the synthetic one, thus the overall model is a linear system cascaded with a memoryless nonlinearity. It has been found that the excitation function relates to the placement of features in the image, the FIR kernel controls the sharpness of the edges and the global spectrum of the image, and the histogram controls the basic coloration of the image. A drawback to this method of simulating IR backgrounds is that a database of actual background images must be collected in order to produce accurate FIR and histogram models. If this database must include images of all types of backgrounds obtained at all times of the day and all times of the year, the size of the database would be prohibitive. In this paper we propose improvements to the model described above that enable time-dependent modeling of the IR background. This approach can greatly reduce the number of actual IR backgrounds that are required to produce a

  4. Patterns of coral bleaching: Modeling the adaptive bleaching hypothesis

    USGS Publications Warehouse

    Ware, J.R.; Fautin, D.G.; Buddemeier, R.W.

    1996-01-01

    Bleaching - the loss of symbiotic dinoflagellates (zooxanthellae) from animals normally possessing them - can be induced by a variety of stresses, of which temperature has received the most attention. Bleaching is generally considered detrimental, but Buddemeier and Fautin have proposed that bleaching is also adaptive, providing an opportunity for recombining hosts with alternative algal types to form symbioses that might be better adapted to altered circumstances. Our mathematical model of this "adaptive bleaching hypothesis" provides insight into how animal-algae symbioses might react under various circumstances. It emulates many aspects of the coral bleaching phenomenon including: corals bleaching in response to a temperature only slightly greater than their average local maximum temperature; background bleaching; bleaching events being followed by bleaching of lesser magnitude in the subsequent one to several years; higher thermal tolerance of corals subject to environmental variability compared with those living under more constant conditions; patchiness in bleaching; and bleaching at temperatures that had not previously resulted in bleaching. ?? 1996 Elsevier Science B.V. All rights reserved.

  5. Modeling background radiation in Southern Nevada

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

    Haber, Daniel A.; Burnley, Pamela C.; Adcock, Christopher T.

    Aerial gamma ray surveys are an important tool for national security, scientific, and industrial interests in determining locations of both anthropogenic and natural sources of radioactivity. There is a relationship between radioactivity and geology and in the past this relationship has been used to predict geology from an aerial survey. The purpose of this project is to develop a method to predict the radiologic exposure rate of the geologic materials by creating a high resolution background model. The intention is for this method to be used in an emergency response scenario where the background radiation envi-ronment is unknown. Two studymore » areas in Southern Nevada have been modeled using geologic data, images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), geochemical data, and pre-existing low resolution aerial surveys from the National Uranium Resource Evaluation (NURE) Survey. Using these data, geospatial areas that are homogenous in terms of K, U, and Th, referred to as background radiation units, are defined and the gamma ray exposure rate is predicted. The prediction is compared to data collected via detailed aerial survey by the Department of Energy's Remote Sensing Lab - Nellis, allowing for the refinement of the technique. By using geologic units to define radiation background units of exposed bedrock and ASTER visualizations to subdivide and define radiation background units within alluvium, successful models have been produced for Government Wash, north of Lake Mead, and for the western shore of Lake Mohave, east of Searchlight, NV.« less

  6. Modeling background radiation in Southern Nevada

    DOE PAGES

    Haber, Daniel A.; Burnley, Pamela C.; Adcock, Christopher T.; ...

    2017-02-06

    Aerial gamma ray surveys are an important tool for national security, scientific, and industrial interests in determining locations of both anthropogenic and natural sources of radioactivity. There is a relationship between radioactivity and geology and in the past this relationship has been used to predict geology from an aerial survey. The purpose of this project is to develop a method to predict the radiologic exposure rate of the geologic materials by creating a high resolution background model. The intention is for this method to be used in an emergency response scenario where the background radiation envi-ronment is unknown. Two studymore » areas in Southern Nevada have been modeled using geologic data, images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), geochemical data, and pre-existing low resolution aerial surveys from the National Uranium Resource Evaluation (NURE) Survey. Using these data, geospatial areas that are homogenous in terms of K, U, and Th, referred to as background radiation units, are defined and the gamma ray exposure rate is predicted. The prediction is compared to data collected via detailed aerial survey by the Department of Energy's Remote Sensing Lab - Nellis, allowing for the refinement of the technique. By using geologic units to define radiation background units of exposed bedrock and ASTER visualizations to subdivide and define radiation background units within alluvium, successful models have been produced for Government Wash, north of Lake Mead, and for the western shore of Lake Mohave, east of Searchlight, NV.« less

  7. Predictor-Based Model Reference Adaptive Control

    NASA Technical Reports Server (NTRS)

    Lavretsky, Eugene; Gadient, Ross; Gregory, Irene M.

    2010-01-01

    This paper is devoted to the design and analysis of a predictor-based model reference adaptive control. Stable adaptive laws are derived using Lyapunov framework. The proposed architecture is compared with the now classical model reference adaptive control. A simulation example is presented in which numerical evidence indicates that the proposed controller yields improved transient characteristics.

  8. An Adaptive Critic Approach to Reference Model Adaptation

    NASA Technical Reports Server (NTRS)

    Krishnakumar, K.; Limes, G.; Gundy-Burlet, K.; Bryant, D.

    2003-01-01

    Neural networks have been successfully used for implementing control architectures for different applications. In this work, we examine a neural network augmented adaptive critic as a Level 2 intelligent controller for a C- 17 aircraft. This intelligent control architecture utilizes an adaptive critic to tune the parameters of a reference model, which is then used to define the angular rate command for a Level 1 intelligent controller. The present architecture is implemented on a high-fidelity non-linear model of a C-17 aircraft. The goal of this research is to improve the performance of the C-17 under degraded conditions such as control failures and battle damage. Pilot ratings using a motion based simulation facility are included in this paper. The benefits of using an adaptive critic are documented using time response comparisons for severe damage situations.

  9. Standard Model Background of the Cosmological Collider.

    PubMed

    Chen, Xingang; Wang, Yi; Xianyu, Zhong-Zhi

    2017-06-30

    The inflationary universe can be viewed as a "cosmological collider" with an energy of the Hubble scale, producing very massive particles and recording their characteristic signals in primordial non-Gaussianities. To utilize this collider to explore any new physics at very high scales, it is a prerequisite to understand the background signals from the particle physics standard model. In this Letter we describe the standard model background of the cosmological collider.

  10. Modelling the effect of religion on human empathy based on an adaptive temporal-causal network model.

    PubMed

    van Ments, Laila; Roelofsma, Peter; Treur, Jan

    2018-01-01

    Religion is a central aspect of many individuals' lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. By first developing a conceptual representation of a network model based on the literature, and then formalizing this model into a numerical representation, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus was mainly on the influence of religion on human empathy and dis-empathy, a topic very relevant today. The developed model could be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals. It is illustrated for a number of different scenarios based on different characteristics of the persons and of the religion.

  11. Adaptive Wavelet Modeling of Geophysical Data

    NASA Astrophysics Data System (ADS)

    Plattner, A.; Maurer, H.; Dahmen, W.; Vorloeper, J.

    2009-12-01

    Despite the ever-increasing power of modern computers, realistic modeling of complex three-dimensional Earth models is still a challenging task and requires substantial computing resources. The overwhelming majority of current geophysical modeling approaches includes either finite difference or non-adaptive finite element algorithms, and variants thereof. These numerical methods usually require the subsurface to be discretized with a fine mesh to accurately capture the behavior of the physical fields. However, this may result in excessive memory consumption and computing times. A common feature of most of these algorithms is that the modeled data discretizations are independent of the model complexity, which may be wasteful when there are only minor to moderate spatial variations in the subsurface parameters. Recent developments in the theory of adaptive numerical solvers have the potential to overcome this problem. Here, we consider an adaptive wavelet based approach that is applicable to a large scope of problems, also including nonlinear problems. To the best of our knowledge such algorithms have not yet been applied in geophysics. Adaptive wavelet algorithms offer several attractive features: (i) for a given subsurface model, they allow the forward modeling domain to be discretized with a quasi minimal number of degrees of freedom, (ii) sparsity of the associated system matrices is guaranteed, which makes the algorithm memory efficient, and (iii) the modeling accuracy scales linearly with computing time. We have implemented the adaptive wavelet algorithm for solving three-dimensional geoelectric problems. To test its performance, numerical experiments were conducted with a series of conductivity models exhibiting varying degrees of structural complexity. Results were compared with a non-adaptive finite element algorithm, which incorporates an unstructured mesh to best fit subsurface boundaries. Such algorithms represent the current state-of-the-art in

  12. Predictor-Based Model Reference Adaptive Control

    NASA Technical Reports Server (NTRS)

    Lavretsky, Eugene; Gadient, Ross; Gregory, Irene M.

    2009-01-01

    This paper is devoted to robust, Predictor-based Model Reference Adaptive Control (PMRAC) design. The proposed adaptive system is compared with the now-classical Model Reference Adaptive Control (MRAC) architecture. Simulation examples are presented. Numerical evidence indicates that the proposed PMRAC tracking architecture has better than MRAC transient characteristics. In this paper, we presented a state-predictor based direct adaptive tracking design methodology for multi-input dynamical systems, with partially known dynamics. Efficiency of the design was demonstrated using short period dynamics of an aircraft. Formal proof of the reported PMRAC benefits constitute future research and will be reported elsewhere.

  13. Model of aircraft noise adaptation

    NASA Technical Reports Server (NTRS)

    Dempsey, T. K.; Coates, G. D.; Cawthorn, J. M.

    1977-01-01

    Development of an aircraft noise adaptation model, which would account for much of the variability in the responses of subjects participating in human response to noise experiments, was studied. A description of the model development is presented. The principal concept of the model, was the determination of an aircraft adaptation level which represents an annoyance calibration for each individual. Results showed a direct correlation between noise level of the stimuli and annoyance reactions. Attitude-personality variables were found to account for varying annoyance judgements.

  14. Adaptive nonlinear control for autonomous ground vehicles

    NASA Astrophysics Data System (ADS)

    Black, William S.

    We present the background and motivation for ground vehicle autonomy, and focus on uses for space-exploration. Using a simple design example of an autonomous ground vehicle we derive the equations of motion. After providing the mathematical background for nonlinear systems and control we present two common methods for exactly linearizing nonlinear systems, feedback linearization and backstepping. We use these in combination with three adaptive control methods: model reference adaptive control, adaptive sliding mode control, and extremum-seeking model reference adaptive control. We show the performances of each combination through several simulation results. We then consider disturbances in the system, and design nonlinear disturbance observers for both single-input-single-output and multi-input-multi-output systems. Finally, we show the performance of these observers with simulation results.

  15. Adaptive-network models of collective dynamics

    NASA Astrophysics Data System (ADS)

    Zschaler, G.

    2012-09-01

    Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge

  16. Identifying traits for genotypic adaptation using crop models.

    PubMed

    Ramirez-Villegas, Julian; Watson, James; Challinor, Andrew J

    2015-06-01

    Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  17. Adaptive Modeling Procedure Selection by Data Perturbation.

    PubMed

    Zhang, Yongli; Shen, Xiaotong

    2015-10-01

    Many procedures have been developed to deal with the high-dimensional problem that is emerging in various business and economics areas. To evaluate and compare these procedures, modeling uncertainty caused by model selection and parameter estimation has to be assessed and integrated into a modeling process. To do this, a data perturbation method estimates the modeling uncertainty inherited in a selection process by perturbing the data. Critical to data perturbation is the size of perturbation, as the perturbed data should resemble the original dataset. To account for the modeling uncertainty, we derive the optimal size of perturbation, which adapts to the data, the model space, and other relevant factors in the context of linear regression. On this basis, we develop an adaptive data-perturbation method that, unlike its nonadaptive counterpart, performs well in different situations. This leads to a data-adaptive model selection method. Both theoretical and numerical analysis suggest that the data-adaptive model selection method adapts to distinct situations in that it yields consistent model selection and optimal prediction, without knowing which situation exists a priori. The proposed method is applied to real data from the commodity market and outperforms its competitors in terms of price forecasting accuracy.

  18. Adaptive Weibull Multiplicative Model and Multilayer Perceptron Neural Networks for Dark-Spot Detection from SAR Imagery

    PubMed Central

    Taravat, Alireza; Oppelt, Natascha

    2014-01-01

    Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR), as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM) and MultiLayer Perceptron (MLP) neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN) model generates poor accuracies. PMID:25474376

  19. Chandra ACIS-I particle background: an analytical model

    NASA Astrophysics Data System (ADS)

    Bartalucci, I.; Mazzotta, P.; Bourdin, H.; Vikhlinin, A.

    2014-06-01

    Aims: Imaging and spectroscopy of X-ray extended sources require a proper characterisation of a spatially unresolved background signal. This background includes sky and instrumental components, each of which are characterised by its proper spatial and spectral behaviour. While the X-ray sky background has been extensively studied in previous work, here we analyse and model the instrumental background of the ACIS-I detector on board the Chandra X-ray observatory in very faint mode. Methods: Caused by interaction of highly energetic particles with the detector, the ACIS-I instrumental background is spectrally characterised by the superimposition of several fluorescence emission lines onto a continuum. To isolate its flux from any sky component, we fitted an analytical model of the continuum to observations performed in very faint mode with the detector in the stowed position shielded from the sky, and gathered over the eight-year period starting in 2001. The remaining emission lines were fitted to blank-sky observations of the same period. We found 11 emission lines. Analysing the spatial variation of the amplitude, energy and width of these lines has further allowed us to infer that three lines of these are presumably due to an energy correction artefact produced in the frame store. Results: We provide an analytical model that predicts the instrumental background with a precision of 2% in the continuum and 5% in the lines. We use this model to measure the flux of the unresolved cosmic X-ray background in the Chandra deep field south. We obtain a flux of 10.2+0.5-0.4 × 10-13 erg cm-2 deg-2 s-1 for the [1-2] keV band and (3.8 ± 0.2) × 10-12 erg cm-2 deg-2 s-1 for the [2-8] keV band.

  20. Adaptation in Tunably Rugged Fitness Landscapes: The Rough Mount Fuji Model

    PubMed Central

    Neidhart, Johannes; Szendro, Ivan G.; Krug, Joachim

    2014-01-01

    Much of the current theory of adaptation is based on Gillespie’s mutational landscape model (MLM), which assumes that the fitness values of genotypes linked by single mutational steps are independent random variables. On the other hand, a growing body of empirical evidence shows that real fitness landscapes, while possessing a considerable amount of ruggedness, are smoother than predicted by the MLM. In the present article we propose and analyze a simple fitness landscape model with tunable ruggedness based on the rough Mount Fuji (RMF) model originally introduced by Aita et al. in the context of protein evolution. We provide a comprehensive collection of results pertaining to the topographical structure of RMF landscapes, including explicit formulas for the expected number of local fitness maxima, the location of the global peak, and the fitness correlation function. The statistics of single and multiple adaptive steps on the RMF landscape are explored mainly through simulations, and the results are compared to the known behavior in the MLM model. Finally, we show that the RMF model can explain the large number of second-step mutations observed on a highly fit first-step background in a recent evolution experiment with a microvirid bacteriophage. PMID:25123507

  1. Image Discrimination Models Predict Object Detection in Natural Backgrounds

    NASA Technical Reports Server (NTRS)

    Ahumada, Albert J., Jr.; Rohaly, A. M.; Watson, Andrew B.; Null, Cynthia H. (Technical Monitor)

    1994-01-01

    Object detection involves looking for one of a large set of object sub-images in a large set of background images. Image discrimination models only predict the probability that an observer will detect a difference between two images. In a recent study based on only six different images, we found that discrimination models can predict the relative detectability of objects in those images, suggesting that these simpler models may be useful in some object detection applications. Here we replicate this result using a new, larger set of images. Fifteen images of a vehicle in an other-wise natural setting were altered to remove the vehicle and mixed with the original image in a proportion chosen to make the target neither perfectly recognizable nor unrecognizable. The target was also rotated about a vertical axis through its center and mixed with the background. Sixteen observers rated these 30 target images and the 15 background-only images for the presence of a vehicle. The likelihoods of the observer responses were computed from a Thurstone scaling model with the assumption that the detectabilities are proportional to the predictions of an image discrimination model. Three image discrimination models were used: a cortex transform model, a single channel model with a contrast sensitivity function filter, and the Root-Mean-Square (RMS) difference of the digital target and background-only images. As in the previous study, the cortex transform model performed best; the RMS difference predictor was second best; and last, but still a reasonable predictor, was the single channel model. Image discrimination models can predict the relative detectabilities of objects in natural backgrounds.

  2. Cosmic microwave background probes models of inflation

    NASA Technical Reports Server (NTRS)

    Davis, Richard L.; Hodges, Hardy M.; Smoot, George F.; Steinhardt, Paul J.; Turner, Michael S.

    1992-01-01

    Inflation creates both scalar (density) and tensor (gravity wave) metric perturbations. We find that the tensor-mode contribution to the cosmic microwave background anisotropy on large-angular scales can only exceed that of the scalar mode in models where the spectrum of perturbations deviates significantly from scale invariance. If the tensor mode dominates at large-angular scales, then the value of DeltaT/T predicted on 1 deg is less than if the scalar mode dominates, and, for cold-dark-matter models, bias factors greater than 1 can be made consistent with Cosmic Background Explorer (COBE) DMR results.

  3. Adaptation and visual salience

    PubMed Central

    McDermott, Kyle C.; Malkoc, Gokhan; Mulligan, Jeffrey B.; Webster, Michael A.

    2011-01-01

    We examined how the salience of color is affected by adaptation to different color distributions. Observers searched for a color target on a dense background of distractors varying along different directions in color space. Prior adaptation to the backgrounds enhanced search on the same background while adaptation to orthogonal background directions slowed detection. Advantages of adaptation were seen for both contrast adaptation (to different color axes) and chromatic adaptation (to different mean chromaticities). Control experiments, including analyses of eye movements during the search, suggest that these aftereffects are unlikely to reflect simple learning or changes in search strategies on familiar backgrounds, and instead result from how adaptation alters the relative salience of the target and background colors. Comparable effects were observed along different axes in the chromatic plane or for axes defined by different combinations of luminance and chromatic contrast, consistent with visual search and adaptation mediated by multiple color mechanisms. Similar effects also occurred for color distributions characteristic of natural environments with strongly selective color gamuts. Our results are consistent with the hypothesis that adaptation may play an important functional role in highlighting the salience of novel stimuli by discounting ambient properties of the visual environment. PMID:21106682

  4. Adapting to life: ocean biogeochemical modelling and adaptive remeshing

    NASA Astrophysics Data System (ADS)

    Hill, J.; Popova, E. E.; Ham, D. A.; Piggott, M. D.; Srokosz, M.

    2013-11-01

    An outstanding problem in biogeochemical modelling of the ocean is that many of the key processes occur intermittently at small scales, such as the sub-mesoscale, that are not well represented in global ocean models. As an example, state-of-the-art models give values of primary production approximately two orders of magnitude lower than those observed in the ocean's oligotrophic gyres, which cover a third of the Earth's surface. This is partly due to their failure to resolve sub-mesoscale phenomena, which play a significant role in nutrient supply. Simply increasing the resolution of the models may be an inefficient computational solution to this problem. An approach based on recent advances in adaptive mesh computational techniques may offer an alternative. Here the first steps in such an approach are described, using the example of a~simple vertical column (quasi 1-D) ocean biogeochemical model. We present a novel method of simulating ocean biogeochemical behaviour on a vertically adaptive computational mesh, where the mesh changes in response to the biogeochemical and physical state of the system throughout the simulation. We show that the model reproduces the general physical and biological behaviour at three ocean stations (India, Papa and Bermuda) as compared to a high-resolution fixed mesh simulation and to observations. The simulations capture both the seasonal and inter-annual variations. The use of an adaptive mesh does not increase the computational error, but reduces the number of mesh elements by a factor of 2-3, so reducing computational overhead. We then show the potential of this method in two case studies where we change the metric used to determine the varying mesh sizes in order to capture the dynamics of chlorophyll at Bermuda and sinking detritus at Papa. We therefore demonstrate adaptive meshes may provide a~suitable numerical technique for simulating seasonal or transient biogeochemical behaviour at high spatial resolution whilst minimising

  5. A Nonlinear Model for Transient Responses from Light-Adapted Wolf Spider Eyes

    PubMed Central

    DeVoe, Robert D.

    1967-01-01

    A quantitative model is proposed to test the hypothesis that the dynamics of nonlinearities in retinal action potentials from light-adapted wolf spider eyes may be due to delayed asymmetries in responses of the visual cells. For purposes of calculation, these delayed asymmetries are generated in an analogue by a time-variant resistance. It is first shown that for small incremental stimuli, the linear behavior of such a resistance describes peaking and low frequency phase lead in frequency responses of the eye to sinusoidal modulations of background illumination. It also describes the overshoots in linear step responses. It is next shown that the analogue accounts for nonlinear transient and short term DC responses to large positive and negative step stimuli and for the variations in these responses with changes in degree of light adaptation. Finally, a physiological model is proposed in which the delayed asymmetries in response are attributed to delayed rectification by the visual cell membrane. In this model, cascaded chemical reactions may serve to transduce visual stimuli into membrane resistance changes. PMID:6056011

  6. Adaptive System Modeling for Spacecraft Simulation

    NASA Technical Reports Server (NTRS)

    Thomas, Justin

    2011-01-01

    This invention introduces a methodology and associated software tools for automatically learning spacecraft system models without any assumptions regarding system behavior. Data stream mining techniques were used to learn models for critical portions of the International Space Station (ISS) Electrical Power System (EPS). Evaluation on historical ISS telemetry data shows that adaptive system modeling reduces simulation error anywhere from 50 to 90 percent over existing approaches. The purpose of the methodology is to outline how someone can create accurate system models from sensor (telemetry) data. The purpose of the software is to support the methodology. The software provides analysis tools to design the adaptive models. The software also provides the algorithms to initially build system models and continuously update them from the latest streaming sensor data. The main strengths are as follows: Creates accurate spacecraft system models without in-depth system knowledge or any assumptions about system behavior. Automatically updates/calibrates system models using the latest streaming sensor data. Creates device specific models that capture the exact behavior of devices of the same type. Adapts to evolving systems. Can reduce computational complexity (faster simulations).

  7. A Readout Integrated Circuit (ROIC) employing self-adaptive background current compensation technique for Infrared Focal Plane Array (IRFPA)

    NASA Astrophysics Data System (ADS)

    Zhou, Tong; Zhao, Jian; He, Yong; Jiang, Bo; Su, Yan

    2018-05-01

    A novel self-adaptive background current compensation circuit applied to infrared focal plane array is proposed in this paper, which can compensate the background current generated in different conditions. Designed double-threshold detection strategy is to estimate and eliminate the background currents, which could significantly reduce the hardware overhead and improve the uniformity among different pixels. In addition, the circuit is well compatible to various categories of infrared thermo-sensitive materials. The testing results of a 4 × 4 experimental chip showed that the proposed circuit achieves high precision, wide application and high intelligence. Tape-out of the 320 × 240 readout circuit, as well as the bonding, encapsulation and imaging verification of uncooled infrared focal plane array, have also been completed.

  8. Biological adaptive control model: a mechanical analogue of multi-factorial bone density adaptation.

    PubMed

    Davidson, Peter L; Milburn, Peter D; Wilson, Barry D

    2004-03-21

    The mechanism of how bone adapts to every day demands needs to be better understood to gain insight into situations in which the musculoskeletal system is perturbed. This paper offers a novel multi-factorial mathematical model of bone density adaptation which combines previous single-factor models in a single adaptation system as a means of gaining this insight. Unique aspects of the model include provision for interaction between factors and an estimation of the relative contribution of each factor. This interacting system is considered analogous to a Newtonian mechanical system and the governing response equation is derived as a linear version of the adaptation process. The transient solution to sudden environmental change is found to be exponential or oscillatory depending on the balance between cellular activation and deactivation frequencies.

  9. Infrared images target detection based on background modeling in the discrete cosine domain

    NASA Astrophysics Data System (ADS)

    Ye, Han; Pei, Jihong

    2018-02-01

    Background modeling is the critical technology to detect the moving target for video surveillance. Most background modeling techniques are aimed at land monitoring and operated in the spatial domain. A background establishment becomes difficult when the scene is a complex fluctuating sea surface. In this paper, the background stability and separability between target are analyzed deeply in the discrete cosine transform (DCT) domain, on this basis, we propose a background modeling method. The proposed method models each frequency point as a single Gaussian model to represent background, and the target is extracted by suppressing the background coefficients. Experimental results show that our approach can establish an accurate background model for seawater, and the detection results outperform other background modeling methods in the spatial domain.

  10. Wind profiling for a coherent wind Doppler lidar by an auto-adaptive background subtraction approach.

    PubMed

    Wu, Yanwei; Guo, Pan; Chen, Siying; Chen, He; Zhang, Yinchao

    2017-04-01

    Auto-adaptive background subtraction (AABS) is proposed as a denoising method for data processing of the coherent Doppler lidar (CDL). The method is proposed specifically for a low-signal-to-noise-ratio regime, in which the drifting power spectral density of CDL data occurs. Unlike the periodogram maximum (PM) and adaptive iteratively reweighted penalized least squares (airPLS), the proposed method presents reliable peaks and is thus advantageous in identifying peak locations. According to the analysis results of simulated and actually measured data, the proposed method outperforms the airPLS method and the PM algorithm in the furthest detectable range. The proposed method improves the detection range approximately up to 16.7% and 40% when compared to the airPLS method and the PM method, respectively. It also has smaller mean wind velocity and standard error values than the airPLS and PM methods. The AABS approach improves the quality of Doppler shift estimates and can be applied to obtain the whole wind profiling by the CDL.

  11. Adaptation of gastrointestinal nematode parasites to host genotype: single locus simulation models

    PubMed Central

    2013-01-01

    Background Breeding livestock for improved resistance to disease is an increasingly important selection goal. However, the risk of pathogens adapting to livestock bred for improved disease resistance is difficult to quantify. Here, we explore the possibility of gastrointestinal worms adapting to sheep bred for low faecal worm egg count using computer simulation. Our model assumes sheep and worm genotypes interact at a single locus, such that the effect of an A allele in sheep is dependent on worm genotype, and the B allele in worms is favourable for parasitizing the A allele sheep but may increase mortality on pasture. We describe the requirements for adaptation and test if worm adaptation (1) is slowed by non-genetic features of worm infections and (2) can occur with little observable change in faecal worm egg count. Results Adaptation in worms was found to be primarily influenced by overall worm fitness, viz. the balance between the advantage of the B allele during the parasitic stage in sheep and its disadvantage on pasture. Genetic variation at the interacting locus in worms could be from de novo or segregating mutations, but de novo mutations are rare and segregating mutations are likely constrained to have (near) neutral effects on worm fitness. Most other aspects of the worm infection we modelled did not affect the outcomes. However, the host-controlled mechanism to reduce faecal worm egg count by lowering worm fecundity reduced the selection pressure on worms to adapt compared to other mechanisms, such as increasing worm mortality. Temporal changes in worm egg count were unreliable for detecting adaptation, despite the steady environment assumed in the simulations. Conclusions Adaptation of worms to sheep selected for low faecal worm egg count requires an allele segregating in worms that is favourable in animals with improved resistance but less favourable in other animals. Obtaining alleles with this specific property seems unlikely. With support from

  12. Adaptive finite element modelling of three-dimensional magnetotelluric fields in general anisotropic media

    NASA Astrophysics Data System (ADS)

    Liu, Ying; Xu, Zhenhuan; Li, Yuguo

    2018-04-01

    We present a goal-oriented adaptive finite element (FE) modelling algorithm for 3-D magnetotelluric fields in generally anisotropic conductivity media. The model consists of a background layered structure, containing anisotropic blocks. Each block and layer might be anisotropic by assigning to them 3 × 3 conductivity tensors. The second-order partial differential equations are solved using the adaptive finite element method (FEM). The computational domain is subdivided into unstructured tetrahedral elements, which allow for complex geometries including bathymetry and dipping interfaces. The grid refinement process is guided by a global posteriori error estimator and is performed iteratively. The system of linear FE equations for electric field E is solved with a direct solver MUMPS. Then the magnetic field H can be found, in which the required derivatives are computed numerically using cubic spline interpolation. The 3-D FE algorithm has been validated by comparisons with both the 3-D finite-difference solution and 2-D FE results. Two model types are used to demonstrate the effects of anisotropy upon 3-D magnetotelluric responses: horizontal and dipping anisotropy. Finally, a 3D sea hill model is modelled to study the effect of oblique interfaces and the dipping anisotropy.

  13. Effect of vergence adaptation on convergence-accommodation: model simulations.

    PubMed

    Sreenivasan, Vidhyapriya; Bobier, William R; Irving, Elizabeth L; Lakshminarayanan, Vasudevan

    2009-10-01

    Several theoretical control models depict the adaptation effects observed in the accommodation and vergence mechanisms of the human visual system. Two current quantitative models differ in their approach of defining adaptation and in identifying the effect of controller adaptation on their respective cross-links between the vergence and accommodative systems. Here, we compare the simulation results of these adaptation models with empirical data obtained from emmetropic adults when they performed sustained near task through + 2D lens addition. The results of our experimental study showed an initial increase in exophoria (a divergent open-loop vergence position) and convergence-accommodation (CA) when viewing through +2D lenses. Prolonged fixation through the near addition lenses initiated vergence adaptation, which reduced the lens-induced exophoria and resulted in a concurrent reduction of CA. Both models showed good agreement with empirical measures of vergence adaptation. However, only one model predicted the experimental time course of reduction in CA. The pattern of our empirical results seem to be best described by the adaptation model that indicates the total vergence response to be a sum of two controllers, phasic and tonic, with the output of phasic controller providing input to the cross-link interactions.

  14. A radon daughter deposition model for low background experiments

    NASA Astrophysics Data System (ADS)

    Rielage, K.; Guiseppe, V. E.; Mastbaum, A.; Elliott, S. R.; Hime, A.

    2009-05-01

    The next generation low-background detectors operating underground, such as dark matter searches and neutrinoless double-beta decay, aim for unprecedented low levels of radioactive backgrounds. Although the radioactive decays of airborne radon (particularly ^222Rn) and its subsequent daughters present in an experiment are potential backgrounds, more troublesome is the deposition of radon daughters on detector materials. Exposure to radon at any stage of assembly of an experiment can result in surface contamination by daughters supported by the long half life (22 y) of ^210Pb on sensitive locations of a detector. An understanding of the potential surface contamination will enable requirements of radon-reduced air and clean room environments for the assembly of low background experiments. It is known that there are a number of environmental factors that govern the deposition of daughters onto surfaces. However, existing models have not explored the impact of some environmental factors important for low background experiments. A test stand has been constructed to deposit radon daughters on various surfaces under a controlled environment in order to develop a deposition model. Results from this test stand and the resulting deposition model will be presented.

  15. Adaptive Finite Element Methods for Continuum Damage Modeling

    NASA Technical Reports Server (NTRS)

    Min, J. B.; Tworzydlo, W. W.; Xiques, K. E.

    1995-01-01

    The paper presents an application of adaptive finite element methods to the modeling of low-cycle continuum damage and life prediction of high-temperature components. The major objective is to provide automated and accurate modeling of damaged zones through adaptive mesh refinement and adaptive time-stepping methods. The damage modeling methodology is implemented in an usual way by embedding damage evolution in the transient nonlinear solution of elasto-viscoplastic deformation problems. This nonlinear boundary-value problem is discretized by adaptive finite element methods. The automated h-adaptive mesh refinements are driven by error indicators, based on selected principal variables in the problem (stresses, non-elastic strains, damage, etc.). In the time domain, adaptive time-stepping is used, combined with a predictor-corrector time marching algorithm. The time selection is controlled by required time accuracy. In order to take into account strong temperature dependency of material parameters, the nonlinear structural solution a coupled with thermal analyses (one-way coupling). Several test examples illustrate the importance and benefits of adaptive mesh refinements in accurate prediction of damage levels and failure time.

  16. Background noise model development for seismic stations of KMA

    NASA Astrophysics Data System (ADS)

    Jeon, Youngsoo

    2010-05-01

    The background noise recorded at seismometer is exist at any seismic signal due to the natural phenomena of the medium which the signal passed through. Reducing the seismic noise is very important to improve the data quality in seismic studies. But, the most important aspect of reducing seismic noise is to find the appropriate place before installing the seismometer. For this reason, NIMR(National Institution of Meteorological Researches) starts to develop a model of standard background noise for the broadband seismic stations of the KMA(Korea Meteorological Administration) using a continuous data set obtained from 13 broadband stations during the period of 2007 and 2008. We also developed the model using short period seismic data from 10 stations at the year of 2009. The method of Mcmara and Buland(2004) is applied to analyse background noise of Korean Peninsula. The fact that borehole seismometer records show low noise level at frequency range greater than 1 Hz compared with that of records at the surface indicate that the cultural noise of inland Korean Peninsula should be considered to process the seismic data set. Reducing Double Frequency peak also should be regarded because the Korean Peninsula surrounded by the seas from eastern, western and southern part. The development of KMA background model shows that the Peterson model(1993) is not applicable to fit the background noise signal generated from Korean Peninsula.

  17. Unstructured mesh adaptivity for urban flooding modelling

    NASA Astrophysics Data System (ADS)

    Hu, R.; Fang, F.; Salinas, P.; Pain, C. C.

    2018-05-01

    Over the past few decades, urban floods have been gaining more attention due to their increase in frequency. To provide reliable flooding predictions in urban areas, various numerical models have been developed to perform high-resolution flood simulations. However, the use of high-resolution meshes across the whole computational domain causes a high computational burden. In this paper, a 2D control-volume and finite-element flood model using adaptive unstructured mesh technology has been developed. This adaptive unstructured mesh technique enables meshes to be adapted optimally in time and space in response to the evolving flow features, thus providing sufficient mesh resolution where and when it is required. It has the advantage of capturing the details of local flows and wetting and drying front while reducing the computational cost. Complex topographic features are represented accurately during the flooding process. For example, the high-resolution meshes around the buildings and steep regions are placed when the flooding water reaches these regions. In this work a flooding event that happened in 2002 in Glasgow, Scotland, United Kingdom has been simulated to demonstrate the capability of the adaptive unstructured mesh flooding model. The simulations have been performed using both fixed and adaptive unstructured meshes, and then results have been compared with those published 2D and 3D results. The presented method shows that the 2D adaptive mesh model provides accurate results while having a low computational cost.

  18. Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.

    PubMed

    Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui

    2017-01-01

    To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.

  19. Data-Adaptable Modeling and Optimization for Runtime Adaptable Systems

    DTIC Science & Technology

    2016-06-08

    execution scenarios e . Enables model -guided optimization algorithms that outperform state-of-the-art f. Understands the overhead of system...the Data-Adaptable System Model (DASM), that facilitates design by enabling the designer to: 1) specify both an application’s task flow as well as...systems. The MILAN [3] framework specializes in the design, simulation , and synthesis of System On Chip (SoC) applications using model -based techniques

  20. Roy's Adaptation Model-Guided Education and Promoting the Adaptation of Veterans With Lower Extremities Amputation.

    PubMed

    Azarmi, Somayeh; Farsi, Zahra

    2015-10-01

    Any defect in extremities of the body can affect different life aspects. The purpose of this study was to investigate the effect of Roy's adaptation model-guided education on promoting the adaptation of veterans with lower extremities amputation. In a randomized clinical trial, 60 veterans with lower extremities amputation referring to Kowsar Orthotics and Prosthetics Center of veterans clinic in Tehran, Iran, were recruited with convenience method and were randomly assigned to intervention and control groups during 2013 - 2014. For data collection, Roy's adaptation model questionnaire was used. After completing the questionnaires in both groups, maladaptive behaviors were determined in the intervention group and an education program based on Roy's adaptation model was implemented. After two months, both groups completed the questionnaires again. Data was analyzed with SPSS software. Independent t-test showed statistically significant differences between the two groups in the post-test stage in terms of the total score of adaptation (P = 0.001) as well as physiologic (P = 0.0001) and role function modes (P = 0.004). The total score of adaptation (139.43 ± 5.45 to 127.54 ± 14.55, P = 0.006) as well as the scores of physiologic (60.26 ± 5.45 to 53.73 ± 7.79, P = 0.001) and role function (20.30 ± 2.42 to 18.13 ± 3.18, P = 0.01) modes in the intervention group significantly increased, whereas the scores of self-concept (42.10 ± 4.71 to 39.40 ± 5.67, P = 0.21) and interdependence (16.76 ± 2.22 to 16.30 ± 2.57, P = 0.44) modes in the two stages did not have a significant difference. Findings of this research indicated that the Roy's adaptation model-guided education promoted the adaptation level of physiologic and role function modes in veterans with lower extremities amputation. However, this intervention could not promote adaptation in self-concept and interdependence modes. More intervention is advised based on Roy's adaptation model for improving the

  1. Adaptive h -refinement for reduced-order models: ADAPTIVE h -refinement for reduced-order models

    DOE PAGES

    Carlberg, Kevin T.

    2014-11-05

    Our work presents a method to adaptively refine reduced-order models a posteriori without requiring additional full-order-model solves. The technique is analogous to mesh-adaptive h-refinement: it enriches the reduced-basis space online by ‘splitting’ a given basis vector into several vectors with disjoint support. The splitting scheme is defined by a tree structure constructed offline via recursive k-means clustering of the state variables using snapshot data. This method identifies the vectors to split online using a dual-weighted-residual approach that aims to reduce error in an output quantity of interest. The resulting method generates a hierarchy of subspaces online without requiring large-scale operationsmore » or full-order-model solves. Furthermore, it enables the reduced-order model to satisfy any prescribed error tolerance regardless of its original fidelity, as a completely refined reduced-order model is mathematically equivalent to the original full-order model. Experiments on a parameterized inviscid Burgers equation highlight the ability of the method to capture phenomena (e.g., moving shocks) not contained in the span of the original reduced basis.« less

  2. Latent variable method for automatic adaptation to background states in motor imagery BCI

    NASA Astrophysics Data System (ADS)

    Dagaev, Nikolay; Volkova, Ksenia; Ossadtchi, Alexei

    2018-02-01

    Objective. Brain-computer interface (BCI) systems are known to be vulnerable to variabilities in background states of a user. Usually, no detailed information on these states is available even during the training stage. Thus there is a need in a method which is capable of taking background states into account in an unsupervised way. Approach. We propose a latent variable method that is based on a probabilistic model with a discrete latent variable. In order to estimate the model’s parameters, we suggest to use the expectation maximization algorithm. The proposed method is aimed at assessing characteristics of background states without any corresponding data labeling. In the context of asynchronous motor imagery paradigm, we applied this method to the real data from twelve able-bodied subjects with open/closed eyes serving as background states. Main results. We found that the latent variable method improved classification of target states compared to the baseline method (in seven of twelve subjects). In addition, we found that our method was also capable of background states recognition (in six of twelve subjects). Significance. Without any supervised information on background states, the latent variable method provides a way to improve classification in BCI by taking background states into account at the training stage and then by making decisions on target states weighted by posterior probabilities of background states at the prediction stage.

  3. Gas leak detection in infrared video with background modeling

    NASA Astrophysics Data System (ADS)

    Zeng, Xiaoxia; Huang, Likun

    2018-03-01

    Background modeling plays an important role in the task of gas detection based on infrared video. VIBE algorithm is a widely used background modeling algorithm in recent years. However, the processing speed of the VIBE algorithm sometimes cannot meet the requirements of some real time detection applications. Therefore, based on the traditional VIBE algorithm, we propose a fast prospect model and optimize the results by combining the connected domain algorithm and the nine-spaces algorithm in the following processing steps. Experiments show the effectiveness of the proposed method.

  4. Cardiovascular Adaptations Induced by Resistance Training in Animal Models.

    PubMed

    Melo, S F S; da Silva Júnior, N D; Barauna, V G; Oliveira, E M

    2018-01-01

    In the last 10 years the number of studies showing the benefits of resistance training (RT) to the cardiovascular system, have grown. In comparison to aerobic training, RT-induced favorable adaptations to the cardiovascular system have been ignored for many years, thus the mechanisms of the RT-induced cardiovascular adaptations are still uncovered. The lack of animal models with comparable protocols to the RT performed by humans hampers the knowledge. We have used squat-exercise model, which is widely used by many others laboratories. However, to a lesser extent, other models are also employed to investigate the cardiovascular adaptations. In the subsequent sections we will review the information regarding cardiac morphological adaptations, signaling pathway of the cardiac cell, cardiac function and the vascular adaptation induced by RT using this animal model developed by Tamaki et al. in 1992. Furthermore, we also describe cardiovascular findings observed using other animal models of RT.

  5. Completing and Adapting Models of Biological Processes

    NASA Technical Reports Server (NTRS)

    Margaria, Tiziana; Hinchey, Michael G.; Raffelt, Harald; Rash, James L.; Rouff, Christopher A.; Steffen, Bernhard

    2006-01-01

    We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.

  6. An Effective Method for Modeling Two-dimensional Sky Background of LAMOST

    NASA Astrophysics Data System (ADS)

    Haerken, Hasitieer; Duan, Fuqing; Zhang, Jiannan; Guo, Ping

    2017-06-01

    Each CCD of LAMOST accommodates 250 spectra, while about 40 are used to observe sky background during real observations. How to estimate the unknown sky background information hidden in the observed 210 celestial spectra by using the known 40 sky spectra is the problem we solve. In order to model the sky background, usually a pre-observation is performed with all fibers observing sky background. We use the observed 250 skylight spectra as training data, where those observed by the 40 fibers are considered as a base vector set. The Locality-constrained Linear Coding (LLC) technique is utilized to represent the skylight spectra observed by the 210 fibers with the base vector set. We also segment each spectrum into small parts, and establish the local sky background model for each part. Experimental results validate the proposed method, and show the local model is better than the global model.

  7. A Roy model study of adapting to being HIV positive.

    PubMed

    Perrett, Stephanie E; Biley, Francis C

    2013-10-01

    Roy's adaptation model outlines a generic process of adaptation useful to nurses in any situation where a patient is facing change. To advance nursing practice, nursing theories and frameworks must be constantly tested and developed through research. This article describes how the results of a qualitative grounded theory study have been used to test components of the Roy adaptation model. A framework for "negotiating uncertainty" was the result of a grounded theory study exploring adaptation to HIV. This framework has been compared to the Roy adaptation model, strengthening concepts such as focal and contextual stimuli, Roy's definition of adaptation and her description of adaptive modes, while suggesting areas for further development including the role of perception. The comparison described in this article demonstrates the usefulness of qualitative research in developing nursing models, specifically highlighting opportunities to continue refining Roy's work.

  8. Modeling Two Types of Adaptation to Climate Change

    EPA Science Inventory

    Mitigation and adaptation are the two key responses available to policymakers to reduce the risks of climate change. We model these two policies together in a new DICE-based integrated assessment model that characterizes adaptation as either short-lived flow spending or long-live...

  9. A Conceptual Model of Childhood Adaptation to Type 1 Diabetes

    PubMed Central

    Whittemore, Robin; Jaser, Sarah; Guo, Jia; Grey, Margaret

    2010-01-01

    The Childhood Adaptation Model to Chronic Illness: Diabetes Mellitus was developed to identify factors that influence childhood adaptation to type 1 diabetes (T1D). Since this model was proposed, considerable research has been completed. The purpose of this paper is to update the model on childhood adaptation to T1D using research conducted since the original model was proposed. The framework suggests that individual and family characteristics, such as age, socioeconomic status, and in children with T1D, treatment modality (pump vs. injections), psychosocial responses (depressive symptoms and anxiety), and individual and family responses (self-management, coping, self-efficacy, family functioning, social competence) influence the level of adaptation. Adaptation has both physiologic (metabolic control) and psychosocial (QOL) components. This revised model provides greater specificity to the factors that influence adaptation to chronic illness in children. Research and clinical implications are discussed. PMID:20934079

  10. Speech understanding in background noise with the two-microphone adaptive beamformer BEAM in the Nucleus Freedom Cochlear Implant System.

    PubMed

    Spriet, Ann; Van Deun, Lieselot; Eftaxiadis, Kyriaky; Laneau, Johan; Moonen, Marc; van Dijk, Bas; van Wieringen, Astrid; Wouters, Jan

    2007-02-01

    This paper evaluates the benefit of the two-microphone adaptive beamformer BEAM in the Nucleus Freedom cochlear implant (CI) system for speech understanding in background noise by CI users. A double-blind evaluation of the two-microphone adaptive beamformer BEAM and a hardware directional microphone was carried out with five adult Nucleus CI users. The test procedure consisted of a pre- and post-test in the lab and a 2-wk trial period at home. In the pre- and post-test, the speech reception threshold (SRT) with sentences and the percentage correct phoneme scores for CVC words were measured in quiet and background noise at different signal-to-noise ratios. Performance was assessed for two different noise configurations (with a single noise source and with three noise sources) and two different noise materials (stationary speech-weighted noise and multitalker babble). During the 2-wk trial period at home, the CI users evaluated the noise reduction performance in different listening conditions by means of the SSQ questionnaire. In addition to the perceptual evaluation, the noise reduction performance of the beamformer was measured physically as a function of the direction of the noise source. Significant improvements of both the SRT in noise (average improvement of 5-16 dB) and the percentage correct phoneme scores (average improvement of 10-41%) were observed with BEAM compared to the standard hardware directional microphone. In addition, the SSQ questionnaire and subjective evaluation in controlled and real-life scenarios suggested a possible preference for the beamformer in noisy environments. The evaluation demonstrates that the adaptive noise reduction algorithm BEAM in the Nucleus Freedom CI-system may significantly increase the speech perception by cochlear implantees in noisy listening conditions. This is the first monolateral (adaptive) noise reduction strategy actually implemented in a mainstream commercial CI.

  11. A conceptual model of childhood adaptation to type 1 diabetes.

    PubMed

    Whittemore, Robin; Jaser, Sarah; Guo, Jia; Grey, Margaret

    2010-01-01

    The Childhood Adaptation Model to Chronic Illness: Diabetes Mellitus was developed to identify factors that influence childhood adaptation to type 1 diabetes (T1D). Since this model was proposed, considerable research has been completed. The purpose of this article is to update the model on childhood adaptation to T1D using research conducted since the original model was proposed. The framework suggests that, in individuals and families, characteristics such as age and socioeconomic status as well as the individuals' and families' responses (self-management, coping, self-efficacy, family functioning, social competence) influence the level of adaptation; in children with T1D, characteristics such as treatment modality (pump vs injections) and psychosocial responses (depressive symptoms and anxiety) also influence the level of adaptation. Adaptation has both physiologic (metabolic control) and psychosocial (Quality of Life [QOL]) components. This revised model provides greater specificity to the factors that influence adaptation to chronic illness in children. Research and clinical implications are discussed. Copyright © 2010 Mosby, Inc. All rights reserved.

  12. Switching Adaptability in Human-Inspired Sidesteps: A Minimal Model.

    PubMed

    Fujii, Keisuke; Yoshihara, Yuki; Tanabe, Hiroko; Yamamoto, Yuji

    2017-01-01

    Humans can adapt to abruptly changing situations by coordinating redundant components, even in bipedality. Conventional adaptability has been reproduced by various computational approaches, such as optimal control, neural oscillator, and reinforcement learning; however, the adaptability in bipedal locomotion necessary for biological and social activities, such as unpredicted direction change in chase-and-escape, is unknown due to the dynamically unstable multi-link closed-loop system. Here we propose a switching adaptation model for performing bipedal locomotion by improving autonomous distributed control, where autonomous actuators interact without central control and switch the roles for propulsion, balancing, and leg swing. Our switching mobility model achieved direction change at any time using only three actuators, although it showed higher motor costs than comparable models without direction change. Our method of evaluating such adaptation at any time should be utilized as a prerequisite for understanding universal motor control. The proposed algorithm may simply explain and predict the adaptation mechanism in human bipedality to coordinate the actuator functions within and between limbs.

  13. Adaptive and non-adaptive models of depression: A comparison using register data on antidepressant medication during divorce.

    PubMed

    Rosenström, Tom; Fawcett, Tim W; Higginson, Andrew D; Metsä-Simola, Niina; Hagen, Edward H; Houston, Alasdair I; Martikainen, Pekka

    2017-01-01

    Divorce is associated with an increased probability of a depressive episode, but the causation of events remains unclear. Adaptive models of depression propose that depression is a social strategy in part, whereas non-adaptive models tend to propose a diathesis-stress mechanism. We compare an adaptive evolutionary model of depression to three alternative non-adaptive models with respect to their ability to explain the temporal pattern of depression around the time of divorce. Register-based data (304,112 individuals drawn from a random sample of 11% of Finnish people) on antidepressant purchases is used as a proxy for depression. This proxy affords an unprecedented temporal resolution (a 3-monthly prevalence estimates over 10 years) without any bias from non-compliance, and it can be linked with underlying episodes via a statistical model. The evolutionary-adaptation model (all time periods with risk of divorce are depressogenic) was the best quantitative description of the data. The non-adaptive stress-relief model (period before divorce is depressogenic and period afterwards is not) provided the second best quantitative description of the data. The peak-stress model (periods before and after divorce can be depressogenic) fit the data less well, and the stress-induction model (period following divorce is depressogenic and the preceding period is not) did not fit the data at all. The evolutionary model was the most detailed mechanistic description of the divorce-depression link among the models, and the best fit in terms of predicted curvature; thus, it offers most rigorous hypotheses for further study. The stress-relief model also fit very well and was the best model in a sensitivity analysis, encouraging development of more mechanistic models for that hypothesis.

  14. Adaptive MPC based on MIMO ARX-Laguerre model.

    PubMed

    Ben Abdelwahed, Imen; Mbarek, Abdelkader; Bouzrara, Kais

    2017-03-01

    This paper proposes a method for synthesizing an adaptive predictive controller using a reduced complexity model. This latter is given by the projection of the ARX model on Laguerre bases. The resulting model is entitled MIMO ARX-Laguerre and it is characterized by an easy recursive representation. The adaptive predictive control law is computed based on multi-step-ahead finite-element predictors, identified directly from experimental input/output data. The model is tuned in each iteration by an online identification algorithms of both model parameters and Laguerre poles. The proposed approach avoids time consuming numerical optimization algorithms associated with most common linear predictive control strategies, which makes it suitable for real-time implementation. The method is used to synthesize and test in numerical simulations adaptive predictive controllers for the CSTR process benchmark. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Reference analysis of the signal + background model in counting experiments

    NASA Astrophysics Data System (ADS)

    Casadei, D.

    2012-01-01

    The model representing two independent Poisson processes, labelled as ``signal'' and ``background'' and both contributing additively to the total number of counted events, is considered from a Bayesian point of view. This is a widely used model for the searches of rare or exotic events in presence of a background source, as for example in the searches performed by high-energy physics experiments. In the assumption of prior knowledge about the background yield, a reference prior is obtained for the signal alone and its properties are studied. Finally, the properties of the full solution, the marginal reference posterior, are illustrated with few examples.

  16. Adapted Lethality: What We Can Learn from Guinea Pig-Adapted Ebola Virus Infection Model

    PubMed Central

    Cheresiz, S. V.; Semenova, E. A.; Chepurnov, A. A.

    2016-01-01

    Establishment of small animal models of Ebola virus (EBOV) infection is important both for the study of genetic determinants involved in the complex pathology of EBOV disease and for the preliminary screening of antivirals, production of therapeutic heterologic immunoglobulins, and experimental vaccine development. Since the wild-type EBOV is avirulent in rodents, the adaptation series of passages in these animals are required for the virulence/lethality to emerge in these models. Here, we provide an overview of our several adaptation series in guinea pigs, which resulted in the establishment of guinea pig-adapted EBOV (GPA-EBOV) variants different in their characteristics, while uniformly lethal for the infected animals, and compare the virologic, genetic, pathomorphologic, and immunologic findings with those obtained in the adaptation experiments of the other research groups. PMID:26989413

  17. Adapted Lethality: What We Can Learn from Guinea Pig-Adapted Ebola Virus Infection Model.

    PubMed

    Cheresiz, S V; Semenova, E A; Chepurnov, A A

    2016-01-01

    Establishment of small animal models of Ebola virus (EBOV) infection is important both for the study of genetic determinants involved in the complex pathology of EBOV disease and for the preliminary screening of antivirals, production of therapeutic heterologic immunoglobulins, and experimental vaccine development. Since the wild-type EBOV is avirulent in rodents, the adaptation series of passages in these animals are required for the virulence/lethality to emerge in these models. Here, we provide an overview of our several adaptation series in guinea pigs, which resulted in the establishment of guinea pig-adapted EBOV (GPA-EBOV) variants different in their characteristics, while uniformly lethal for the infected animals, and compare the virologic, genetic, pathomorphologic, and immunologic findings with those obtained in the adaptation experiments of the other research groups.

  18. Modeling surface backgrounds from radon progeny plate-out

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

    Perumpilly, G.; Guiseppe, V. E.; Snyder, N.

    2013-08-08

    The next generation low-background detectors operating deep underground aim for unprecedented low levels of radioactive backgrounds. The surface deposition and subsequent implantation of radon progeny in detector materials will be a source of energetic background events. We investigate Monte Carlo and model-based simulations to understand the surface implantation profile of radon progeny. Depending on the material and region of interest of a rare event search, these partial energy depositions can be problematic. Motivated by the use of Ge crystals for the detection of neutrinoless double-beta decay, we wish to understand the detector response of surface backgrounds from radon progeny. Wemore » look at the simulation of surface decays using a validated implantation distribution based on nuclear recoils and a realistic surface texture. Results of the simulations and measured α spectra are presented.« less

  19. Adaptive subdomain modeling: A multi-analysis technique for ocean circulation models

    NASA Astrophysics Data System (ADS)

    Altuntas, Alper; Baugh, John

    2017-07-01

    Many coastal and ocean processes of interest operate over large temporal and geographical scales and require a substantial amount of computational resources, particularly when engineering design and failure scenarios are also considered. This study presents an adaptive multi-analysis technique that improves the efficiency of these computations when multiple alternatives are being simulated. The technique, called adaptive subdomain modeling, concurrently analyzes any number of child domains, with each instance corresponding to a unique design or failure scenario, in addition to a full-scale parent domain providing the boundary conditions for its children. To contain the altered hydrodynamics originating from the modifications, the spatial extent of each child domain is adaptively adjusted during runtime depending on the response of the model. The technique is incorporated in ADCIRC++, a re-implementation of the popular ADCIRC ocean circulation model with an updated software architecture designed to facilitate this adaptive behavior and to utilize concurrent executions of multiple domains. The results of our case studies confirm that the method substantially reduces computational effort while maintaining accuracy.

  20. Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications

    PubMed Central

    Kim, Byeong Hak; Kim, Min Young; Chae, You Seong

    2017-01-01

    Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC. PMID:29280970

  1. Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications.

    PubMed

    Kim, Byeong Hak; Kim, Min Young; Chae, You Seong

    2017-12-27

    Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.

  2. Adaptive and non-adaptive models of depression: A comparison using register data on antidepressant medication during divorce

    PubMed Central

    Fawcett, Tim W.; Higginson, Andrew D.; Metsä-Simola, Niina; Hagen, Edward H.; Houston, Alasdair I.; Martikainen, Pekka

    2017-01-01

    Divorce is associated with an increased probability of a depressive episode, but the causation of events remains unclear. Adaptive models of depression propose that depression is a social strategy in part, whereas non-adaptive models tend to propose a diathesis-stress mechanism. We compare an adaptive evolutionary model of depression to three alternative non-adaptive models with respect to their ability to explain the temporal pattern of depression around the time of divorce. Register-based data (304,112 individuals drawn from a random sample of 11% of Finnish people) on antidepressant purchases is used as a proxy for depression. This proxy affords an unprecedented temporal resolution (a 3-monthly prevalence estimates over 10 years) without any bias from non-compliance, and it can be linked with underlying episodes via a statistical model. The evolutionary-adaptation model (all time periods with risk of divorce are depressogenic) was the best quantitative description of the data. The non-adaptive stress-relief model (period before divorce is depressogenic and period afterwards is not) provided the second best quantitative description of the data. The peak-stress model (periods before and after divorce can be depressogenic) fit the data less well, and the stress-induction model (period following divorce is depressogenic and the preceding period is not) did not fit the data at all. The evolutionary model was the most detailed mechanistic description of the divorce-depression link among the models, and the best fit in terms of predicted curvature; thus, it offers most rigorous hypotheses for further study. The stress-relief model also fit very well and was the best model in a sensitivity analysis, encouraging development of more mechanistic models for that hypothesis. PMID:28614385

  3. Adaptive cyber-attack modeling system

    NASA Astrophysics Data System (ADS)

    Gonsalves, Paul G.; Dougherty, Edward T.

    2006-05-01

    The pervasiveness of software and networked information systems is evident across a broad spectrum of business and government sectors. Such reliance provides an ample opportunity not only for the nefarious exploits of lone wolf computer hackers, but for more systematic software attacks from organized entities. Much effort and focus has been placed on preventing and ameliorating network and OS attacks, a concomitant emphasis is required to address protection of mission critical software. Typical software protection technique and methodology evaluation and verification and validation (V&V) involves the use of a team of subject matter experts (SMEs) to mimic potential attackers or hackers. This manpower intensive, time-consuming, and potentially cost-prohibitive approach is not amenable to performing the necessary multiple non-subjective analyses required to support quantifying software protection levels. To facilitate the evaluation and V&V of software protection solutions, we have designed and developed a prototype adaptive cyber attack modeling system. Our approach integrates an off-line mechanism for rapid construction of Bayesian belief network (BN) attack models with an on-line model instantiation, adaptation and knowledge acquisition scheme. Off-line model construction is supported via a knowledge elicitation approach for identifying key domain requirements and a process for translating these requirements into a library of BN-based cyber-attack models. On-line attack modeling and knowledge acquisition is supported via BN evidence propagation and model parameter learning.

  4. Adapting the ALP Model for Student and Institutional Needs

    ERIC Educational Resources Information Center

    Sides, Meredith

    2016-01-01

    With the increasing adoption of accelerated models of learning comes the necessary step of adapting these models to fit the unique needs of the student population at each individual institution. One such college adapted the ALP (Accelerated Learning Program) model and made specific changes to the target population, structure and scheduling, and…

  5. The Family Adaptation Model: A Life Course Perspective. Technical Report 880.

    ERIC Educational Resources Information Center

    Bowen, Gary L.

    This conceptual model for explaining the factors and processes that underlie family adaptation in the Army relies heavily upon two traditions: the "Double ABCX" model of family stress and adaptation and the "Person-Environment Fit" model. The new model has three major parts: the environmental system, the personal system, and family adaptation.…

  6. Adaptive Numerical Algorithms in Space Weather Modeling

    NASA Technical Reports Server (NTRS)

    Toth, Gabor; vanderHolst, Bart; Sokolov, Igor V.; DeZeeuw, Darren; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Nakib, Dalal; Powell, Kenneth G.; hide

    2010-01-01

    Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different physics in different domains. A multi-physics system can be modeled by a software framework comprising of several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solar wind Roe Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamics (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical

  7. Reducing computation in an i-vector speaker recognition system using a tree-structured universal background model

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

    McClanahan, Richard; De Leon, Phillip L.

    The majority of state-of-the-art speaker recognition systems (SR) utilize speaker models that are derived from an adapted universal background model (UBM) in the form of a Gaussian mixture model (GMM). This is true for GMM supervector systems, joint factor analysis systems, and most recently i-vector systems. In all of the identified systems, the posterior probabilities and sufficient statistics calculations represent a computational bottleneck in both enrollment and testing. We propose a multi-layered hash system, employing a tree-structured GMM–UBM which uses Runnalls’ Gaussian mixture reduction technique, in order to reduce the number of these calculations. Moreover, with this tree-structured hash, wemore » can trade-off reduction in computation with a corresponding degradation of equal error rate (EER). As an example, we also reduce this computation by a factor of 15× while incurring less than 10% relative degradation of EER (or 0.3% absolute EER) when evaluated with NIST 2010 speaker recognition evaluation (SRE) telephone data.« less

  8. Reducing computation in an i-vector speaker recognition system using a tree-structured universal background model

    DOE PAGES

    McClanahan, Richard; De Leon, Phillip L.

    2014-08-20

    The majority of state-of-the-art speaker recognition systems (SR) utilize speaker models that are derived from an adapted universal background model (UBM) in the form of a Gaussian mixture model (GMM). This is true for GMM supervector systems, joint factor analysis systems, and most recently i-vector systems. In all of the identified systems, the posterior probabilities and sufficient statistics calculations represent a computational bottleneck in both enrollment and testing. We propose a multi-layered hash system, employing a tree-structured GMM–UBM which uses Runnalls’ Gaussian mixture reduction technique, in order to reduce the number of these calculations. Moreover, with this tree-structured hash, wemore » can trade-off reduction in computation with a corresponding degradation of equal error rate (EER). As an example, we also reduce this computation by a factor of 15× while incurring less than 10% relative degradation of EER (or 0.3% absolute EER) when evaluated with NIST 2010 speaker recognition evaluation (SRE) telephone data.« less

  9. A Model for Climate Change Adaptation

    NASA Astrophysics Data System (ADS)

    Pasqualini, D.; Keating, G. N.

    2009-12-01

    Climate models predict serious impacts on the western U.S. in the next few decades, including increased temperatures and reduced precipitation. In combination, these changes are linked to profound impacts on fundamental systems, such as water and energy supplies, agriculture, population stability, and the economy. Global and national imperatives for climate change mitigation and adaptation are made actionable at the state level, for instance through greenhouse gas (GHG) emission regulations and incentives for renewable energy sources. However, adaptation occurs at the local level, where energy and water usage can be understood relative to local patterns of agriculture, industry, and culture. In response to the greenhouse gas emission reductions required by California’s Assembly Bill 32 (2006), Sonoma County has committed to sharp emissions reductions across several sectors, including water, energy, and transportation. To assist Sonoma County develop a renewable energy (RE) portfolio to achieve this goal we have developed an integrated assessment model, CLEAR (CLimate-Energy Assessment for Resiliency) model. Building on Sonoma County’s existing baseline studies of energy use, carbon emissions and potential RE sources, the CLEAR model simulates the complex interactions among technology deployment, economics and social behavior. This model enables assessment of these and other components with specific analysis of their coupling and feedbacks because, due to the complex nature of the problem, the interrelated sectors cannot be studied independently. The goal is an approach to climate change mitigation and adaptation that is replicable for use by other interested communities. The model user interfaces helps stakeholders and policymakers understand options for technology implementation.

  10. Cultural Adaptation of the Strengthening Families Program 10-14 to Italian Families

    ERIC Educational Resources Information Center

    Ortega, Enrique; Giannotta, Fabrizia; Latina, Delia; Ciairano, Silvia

    2012-01-01

    Background: The family context has proven to be a useful target in which to apply prevention efforts aimed at child and adolescent health risk behaviors. There are currently a variety of cultural adaptation models that serve to guide the international adaptation of intervention programs. Objective: The cultural adaptation process and program…

  11. Effect of a care plan based on Roy adaptation model biological dimension on stroke patients' physiologic adaptation level.

    PubMed

    Alimohammadi, Nasrollah; Maleki, Bibi; Shahriari, Mohsen; Chitsaz, Ahmad

    2015-01-01

    Stroke is a stressful event with several functional, physical, psychological, social, and economic problems that affect individuals' different living balances. With coping strategies, patients try to control these problems and return to their natural life. The aim of this study is to investigate the effect of a care plan based on Roy adaptation model biological dimension on stroke patients' physiologic adaptation level. This study is a clinical trial in which 50 patients, affected by brain stroke and being admitted in the neurology ward of Kashani and Alzahra hospitals, were randomly assigned to control and study groups in Isfahan in 2013. Roy adaptation model care plan was administered in biological dimension in the form of four sessions and phone call follow-ups for 1 month. The forms related to Roy adaptation model were completed before and after intervention in the two groups. Chi-square test and t-test were used to analyze the data through SPSS 18. There was a significant difference in mean score of adaptation in physiological dimension in the study group after intervention (P < 0.001) compared to before intervention. Comparison of the mean scores of changes of adaptation in the patients affected by brain stroke in the study and control groups showed a significant increase in physiological dimension in the study group by 47.30 after intervention (P < 0.001). The results of study showed that Roy adaptation model biological dimension care plan can result in an increase in adaptation in patients with stroke in physiological dimension. Nurses can use this model for increasing patients' adaptation.

  12. Dynamics modeling and adaptive control of flexible manipulators

    NASA Technical Reports Server (NTRS)

    Sasiadek, J. Z.

    1991-01-01

    An application of Model Reference Adaptive Control (MRAC) to the position and force control of flexible manipulators and robots is presented. A single-link flexible manipulator is analyzed. The problem was to develop a mathematical model of a flexible robot that is accurate. The objective is to show that the adaptive control works better than 'conventional' systems and is suitable for flexible structure control.

  13. Hybrid active contour model for inhomogeneous image segmentation with background estimation

    NASA Astrophysics Data System (ADS)

    Sun, Kaiqiong; Li, Yaqin; Zeng, Shan; Wang, Jun

    2018-03-01

    This paper proposes a hybrid active contour model for inhomogeneous image segmentation. The data term of the energy function in the active contour consists of a global region fitting term in a difference image and a local region fitting term in the original image. The difference image is obtained by subtracting the background from the original image. The background image is dynamically estimated from a linear filtered result of the original image on the basis of the varying curve locations during the active contour evolution process. As in existing local models, fitting the image to local region information makes the proposed model robust against an inhomogeneous background and maintains the accuracy of the segmentation result. Furthermore, fitting the difference image to the global region information makes the proposed model robust against the initial contour location, unlike existing local models. Experimental results show that the proposed model can obtain improved segmentation results compared with related methods in terms of both segmentation accuracy and initial contour sensitivity.

  14. AMModels: An R package for storing models, data, and metadata to facilitate adaptive management

    PubMed Central

    Katz, Jonathan E.

    2018-01-01

    Agencies are increasingly called upon to implement their natural resource management programs within an adaptive management (AM) framework. This article provides the background and motivation for the R package, AMModels. AMModels was developed under R version 3.2.2. The overall goal of AMModels is simple: To codify knowledge in the form of models and to store it, along with models generated from numerous analyses and datasets that may come our way, so that it can be used or recalled in the future. AMModels facilitates this process by storing all models and datasets in a single object that can be saved to an .RData file and routinely augmented to track changes in knowledge through time. Through this process, AMModels allows the capture, development, sharing, and use of knowledge that may help organizations achieve their mission. While AMModels was designed to facilitate adaptive management, its utility is far more general. Many R packages exist for creating and summarizing models, but to our knowledge, AMModels is the only package dedicated not to the mechanics of analysis but to organizing analysis inputs, analysis outputs, and preserving descriptive metadata. We anticipate that this package will assist users hoping to preserve the key elements of an analysis so they may be more confidently revisited at a later date. PMID:29489825

  15. AMModels: An R package for storing models, data, and metadata to facilitate adaptive management.

    PubMed

    Donovan, Therese M; Katz, Jonathan E

    2018-01-01

    Agencies are increasingly called upon to implement their natural resource management programs within an adaptive management (AM) framework. This article provides the background and motivation for the R package, AMModels. AMModels was developed under R version 3.2.2. The overall goal of AMModels is simple: To codify knowledge in the form of models and to store it, along with models generated from numerous analyses and datasets that may come our way, so that it can be used or recalled in the future. AMModels facilitates this process by storing all models and datasets in a single object that can be saved to an .RData file and routinely augmented to track changes in knowledge through time. Through this process, AMModels allows the capture, development, sharing, and use of knowledge that may help organizations achieve their mission. While AMModels was designed to facilitate adaptive management, its utility is far more general. Many R packages exist for creating and summarizing models, but to our knowledge, AMModels is the only package dedicated not to the mechanics of analysis but to organizing analysis inputs, analysis outputs, and preserving descriptive metadata. We anticipate that this package will assist users hoping to preserve the key elements of an analysis so they may be more confidently revisited at a later date.

  16. Adaptive Modeling Language and Its Derivatives

    NASA Technical Reports Server (NTRS)

    Chemaly, Adel

    2006-01-01

    Adaptive Modeling Language (AML) is the underlying language of an object-oriented, multidisciplinary, knowledge-based engineering framework. AML offers an advanced modeling paradigm with an open architecture, enabling the automation of the entire product development cycle, integrating product configuration, design, analysis, visualization, production planning, inspection, and cost estimation.

  17. Modeling adaptive kernels from probabilistic phylogenetic trees.

    PubMed

    Nicotra, Luca; Micheli, Alessio

    2009-01-01

    Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

  18. Consensus time and conformity in the adaptive voter model

    NASA Astrophysics Data System (ADS)

    Rogers, Tim; Gross, Thilo

    2013-09-01

    The adaptive voter model is a paradigmatic model in the study of opinion formation. Here we propose an extension for this model, in which conflicts are resolved by obtaining another opinion, and analytically study the time required for consensus to emerge. Our results shed light on the rich phenomenology of both the original and extended adaptive voter models, including a dynamical phase transition in the scaling behavior of the mean time to consensus.

  19. Adaptive invasive species distribution models: A framework for modeling incipient invasions

    USGS Publications Warehouse

    Uden, Daniel R.; Allen, Craig R.; Angeler, David G.; Corral, Lucia; Fricke, Kent A.

    2015-01-01

    The utilization of species distribution model(s) (SDM) for approximating, explaining, and predicting changes in species’ geographic locations is increasingly promoted for proactive ecological management. Although frameworks for modeling non-invasive species distributions are relatively well developed, their counterparts for invasive species—which may not be at equilibrium within recipient environments and often exhibit rapid transformations—are lacking. Additionally, adaptive ecological management strategies address the causes and effects of biological invasions and other complex issues in social-ecological systems. We conducted a review of biological invasions, species distribution models, and adaptive practices in ecological management, and developed a framework for adaptive, niche-based, invasive species distribution model (iSDM) development and utilization. This iterative, 10-step framework promotes consistency and transparency in iSDM development, allows for changes in invasive drivers and filters, integrates mechanistic and correlative modeling techniques, balances the avoidance of type 1 and type 2 errors in predictions, encourages the linking of monitoring and management actions, and facilitates incremental improvements in models and management across space, time, and institutional boundaries. These improvements are useful for advancing coordinated invasive species modeling, management and monitoring from local scales to the regional, continental and global scales at which biological invasions occur and harm native ecosystems and economies, as well as for anticipating and responding to biological invasions under continuing global change.

  20. Multiview road sign detection via self-adaptive color model and shape context matching

    NASA Astrophysics Data System (ADS)

    Liu, Chunsheng; Chang, Faliang; Liu, Chengyun

    2016-09-01

    The multiview appearance of road signs in uncontrolled environments has made the detection of road signs a challenging problem in computer vision. We propose a road sign detection method to detect multiview road signs. This method is based on several algorithms, including the classical cascaded detector, the self-adaptive weighted Gaussian color model (SW-Gaussian model), and a shape context matching method. The classical cascaded detector is used to detect the frontal road signs in video sequences and obtain the parameters for the SW-Gaussian model. The proposed SW-Gaussian model combines the two-dimensional Gaussian model and the normalized red channel together, which can largely enhance the contrast between the red signs and background. The proposed shape context matching method can match shapes with big noise, which is utilized to detect road signs in different directions. The experimental results show that compared with previous detection methods, the proposed multiview detection method can reach higher detection rate in detecting signs with different directions.

  1. Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

    NASA Technical Reports Server (NTRS)

    Lee, Chuen-Chien

    1991-01-01

    Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

  2. Modeling radon daughter deposition rates for low background detectors

    NASA Astrophysics Data System (ADS)

    Westerdale, S.; Guiseppe, V. E.; Rielage, K.; Elliot, S. R.; Hime, A.

    2009-10-01

    Detectors such as those looking for dark matter and those working to detect neutrinoless double-beta decay require record low levels of background radiation. One major source of background radiation is from radon daughters that decay from airborne radon. In particular, ^222Rn decay products may be deposited on any detector materials that are exposed to environmental radon. Long-lasting daughters, especially ^210Pb, can pose a long-term background radiation source that can interfere with the detectors' measurements by emitting alpha particles into sensitive parts of the detectors. A better understanding of this radon daughter deposition will allow for preventative actions to be taken to minimize the amount of noise from this source. A test stand has therefore been set up to study the impact of various environmental factors on the rate of radon daughter deposition so that a model can be constructed. Results from the test stand and a model of radon daughter deposition will be presented.

  3. Industry Cluster's Adaptive Co-competition Behavior Modeling Inspired by Swarm Intelligence

    NASA Astrophysics Data System (ADS)

    Xiang, Wei; Ye, Feifan

    Adaptation helps the individual enterprise to adjust its behavior to uncertainties in environment and hence determines a healthy growth of both the individuals and the whole industry cluster as well. This paper is focused on the study on co-competition adaptation behavior of industry cluster, which is inspired by swarm intelligence mechanisms. By referencing to ant cooperative transportation and ant foraging behavior and their related swarm intelligence approaches, the cooperative adaptation and competitive adaptation behavior are studied and relevant models are proposed. Those adaptive co-competition behaviors model can be integrated to the multi-agent system of industry cluster to make the industry cluster model more realistic.

  4. Linear model for fast background subtraction in oligonucleotide microarrays.

    PubMed

    Kroll, K Myriam; Barkema, Gerard T; Carlon, Enrico

    2009-11-16

    One important preprocessing step in the analysis of microarray data is background subtraction. In high-density oligonucleotide arrays this is recognized as a crucial step for the global performance of the data analysis from raw intensities to expression values. We propose here an algorithm for background estimation based on a model in which the cost function is quadratic in a set of fitting parameters such that minimization can be performed through linear algebra. The model incorporates two effects: 1) Correlated intensities between neighboring features in the chip and 2) sequence-dependent affinities for non-specific hybridization fitted by an extended nearest-neighbor model. The algorithm has been tested on 360 GeneChips from publicly available data of recent expression experiments. The algorithm is fast and accurate. Strong correlations between the fitted values for different experiments as well as between the free-energy parameters and their counterparts in aqueous solution indicate that the model captures a significant part of the underlying physical chemistry.

  5. Adaptation problems with nursing home application for elderly persons: an application of the Roy adaptation nursing model.

    PubMed

    Farkas, L

    1981-09-01

    This study sought to identify adaptation problems on the part of elderly persons and significant others that were associated with nursing home applications for elderly persons. Within the framework of the Roy Adaptation Nursing Model, an ex post facto research design was utilized to identify these adaptation problems. The four adaptive modes in this model are: physiological, self-concept, role function, and interdependence. The study group (n = 22) and a control group of elderly persons living in Calgary, as well as their significant others, were administered a structured questionnaire. Five hypotheses relating to overall adaptation problems, powerlessness, role reversal, guilt, and knowledge and utilization of services were formulated and tested. Only the hypothesis indicating role reversal on the part of the significant others was accepted. Adaptation problems encountered by the elderly which were associated with nursing home applications occurred in the self-concept and interdependence adaptive modes. The adaptation problems perceived by the significant others, which were associated with nursing home applications for their elderly, occurred in the self-concept, role function, and interdependence modes. Adaptation problems from the significant others' perception rather than from the elderly persons' perception appear to be more significantly associated with nursing home applications.

  6. ViBe: a universal background subtraction algorithm for video sequences.

    PubMed

    Barnich, Olivier; Van Droogenbroeck, Marc

    2011-06-01

    This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.

  7. The Role of Scale and Model Bias in ADAPT's Photospheric Eatimation

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

    Godinez Vazquez, Humberto C.; Hickmann, Kyle Scott; Arge, Charles Nicholas

    2015-05-20

    The Air Force Assimilative Photospheric flux Transport model (ADAPT), is a magnetic flux propagation based on Worden-Harvey (WH) model. ADAPT would be used to provide a global photospheric map of the Earth. A data assimilation method based on the Ensemble Kalman Filter (EnKF), a method of Monte Carlo approximation tied with Kalman filtering, is used in calculating the ADAPT models.

  8. In silico biology of bone modelling and remodelling: adaptation.

    PubMed

    Gerhard, Friederike A; Webster, Duncan J; van Lenthe, G Harry; Müller, Ralph

    2009-05-28

    Modelling and remodelling are the processes by which bone adapts its shape and internal structure to external influences. However, the cellular mechanisms triggering osteoclastic resorption and osteoblastic formation are still unknown. In order to investigate current biological theories, in silico models can be applied. In the past, most of these models were based on the continuum assumption, but some questions related to bone adaptation can be addressed better by models incorporating the trabecular microstructure. In this paper, existing simulation models are reviewed and one of the microstructural models is extended to test the hypothesis that bone adaptation can be simulated without particular knowledge of the local strain distribution in the bone. Validation using an experimental murine loading model showed that this is possible. Furthermore, the experimental model revealed that bone formation cannot be attributed only to an increase in trabecular thickness but also to structural reorganization including the growth of new trabeculae. How these new trabeculae arise is still an unresolved issue and might be better addressed by incorporating other levels of hierarchy, especially the cellular level. The cellular level sheds light on the activity and interplay between the different cell types, leading to the effective change in the whole bone. For this reason, hierarchical multi-scale simulations might help in the future to better understand the biomathematical laws behind bone adaptation.

  9. Object detection in natural backgrounds predicted by discrimination performance and models

    NASA Technical Reports Server (NTRS)

    Rohaly, A. M.; Ahumada, A. J. Jr; Watson, A. B.

    1997-01-01

    Many models of visual performance predict image discriminability, the visibility of the difference between a pair of images. We compared the ability of three image discrimination models to predict the detectability of objects embedded in natural backgrounds. The three models were: a multiple channel Cortex transform model with within-channel masking; a single channel contrast sensitivity filter model; and a digital image difference metric. Each model used a Minkowski distance metric (generalized vector magnitude) to summate absolute differences between the background and object plus background images. For each model, this summation was implemented with three different exponents: 2, 4 and infinity. In addition, each combination of model and summation exponent was implemented with and without a simple contrast gain factor. The model outputs were compared to measures of object detectability obtained from 19 observers. Among the models without the contrast gain factor, the multiple channel model with a summation exponent of 4 performed best, predicting the pattern of observer d's with an RMS error of 2.3 dB. The contrast gain factor improved the predictions of all three models for all three exponents. With the factor, the best exponent was 4 for all three models, and their prediction errors were near 1 dB. These results demonstrate that image discrimination models can predict the relative detectability of objects in natural scenes.

  10. A Hybrid Acoustic and Pronunciation Model Adaptation Approach for Non-native Speech Recognition

    NASA Astrophysics Data System (ADS)

    Oh, Yoo Rhee; Kim, Hong Kook

    In this paper, we propose a hybrid model adaptation approach in which pronunciation and acoustic models are adapted by incorporating the pronunciation and acoustic variabilities of non-native speech in order to improve the performance of non-native automatic speech recognition (ASR). Specifically, the proposed hybrid model adaptation can be performed at either the state-tying or triphone-modeling level, depending at which acoustic model adaptation is performed. In both methods, we first analyze the pronunciation variant rules of non-native speakers and then classify each rule as either a pronunciation variant or an acoustic variant. The state-tying level hybrid method then adapts pronunciation models and acoustic models by accommodating the pronunciation variants in the pronunciation dictionary and by clustering the states of triphone acoustic models using the acoustic variants, respectively. On the other hand, the triphone-modeling level hybrid method initially adapts pronunciation models in the same way as in the state-tying level hybrid method; however, for the acoustic model adaptation, the triphone acoustic models are then re-estimated based on the adapted pronunciation models and the states of the re-estimated triphone acoustic models are clustered using the acoustic variants. From the Korean-spoken English speech recognition experiments, it is shown that ASR systems employing the state-tying and triphone-modeling level adaptation methods can relatively reduce the average word error rates (WERs) by 17.1% and 22.1% for non-native speech, respectively, when compared to a baseline ASR system.

  11. Adaptive tracking for complex systems using reduced-order models

    NASA Technical Reports Server (NTRS)

    Carnigan, Craig R.

    1990-01-01

    Reduced-order models are considered in the context of parameter adaptive controllers for tracking workspace trajectories. A dual-arm manipulation task is used to illustrate the methodology and provide simulation results. A parameter adaptive controller is designed to track a payload trajectory using a four-parameter model instead of the full-order, nine-parameter model. Several simulations with different payload-to-arm mass ratios are used to illustrate the capabilities of the reduced-order model in tracking the desired trajectory.

  12. Adaptive LINE-P: An Adaptive Linear Energy Prediction Model for Wireless Sensor Network Nodes.

    PubMed

    Ahmed, Faisal; Tamberg, Gert; Le Moullec, Yannick; Annus, Paul

    2018-04-05

    In the context of wireless sensor networks, energy prediction models are increasingly useful tools that can facilitate the power management of the wireless sensor network (WSN) nodes. However, most of the existing models suffer from the so-called fixed weighting parameter, which limits their applicability when it comes to, e.g., solar energy harvesters with varying characteristics. Thus, in this article we propose the Adaptive LINE-P (all cases) model that calculates adaptive weighting parameters based on the stored energy profiles. Furthermore, we also present a profile compression method to reduce the memory requirements. To determine the performance of our proposed model, we have used real data for the solar and wind energy profiles. The simulation results show that our model achieves 90-94% accuracy and that the compressed method reduces memory overheads by 50% as compared to state-of-the-art models.

  13. Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.

    PubMed

    Lee, Wen-Chung; Wu, Yun-Chun

    2016-01-01

    The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.

  14. Extracting harmonic signal from a chaotic background with local linear model

    NASA Astrophysics Data System (ADS)

    Li, Chenlong; Su, Liyun

    2017-02-01

    In this paper, the problems of blind detection and estimation of harmonic signal in strong chaotic background are analyzed, and new methods by using local linear (LL) model are put forward. The LL model has been exhaustively researched and successfully applied for fitting and forecasting chaotic signal in many chaotic fields. We enlarge the modeling capacity substantially. Firstly, we can predict the short-term chaotic signal and obtain the fitting error based on the LL model. Then we detect the frequencies from the fitting error by periodogram, a property on the fitting error is proposed which has not been addressed before, and this property ensures that the detected frequencies are similar to that of harmonic signal. Secondly, we establish a two-layer LL model to estimate the determinate harmonic signal in strong chaotic background. To estimate this simply and effectively, we develop an efficient backfitting algorithm to select and optimize the parameters that are hard to be exhaustively searched for. In the method, based on sensitivity to initial value of chaos motion, the minimum fitting error criterion is used as the objective function to get the estimation of the parameters of the two-layer LL model. Simulation shows that the two-layer LL model and its estimation technique have appreciable flexibility to model the determinate harmonic signal in different chaotic backgrounds (Lorenz, Henon and Mackey-Glass (M-G) equations). Specifically, the harmonic signal can be extracted well with low SNR and the developed background algorithm satisfies the condition of convergence in repeated 3-5 times.

  15. A conceptual model of children's cognitive adaptation to physical disability.

    PubMed

    Bernardo, M L

    1982-11-01

    Increasing numbers of children are being required to adapt to lifelong illness and disability. While numerous studies exist on theories of adaptation, reaction to illness, and children's concepts of self and of illness, an integrated view of children's ability to conceptualize themselves, their disabilities and possible adaptations has not been formulated. In this article an attempt has been made to integrate models of adaptation to disability and knowledge about children's cognitive development using Piagetian theory of cognitive development and Crate's stages of adaptation to chronic illness. This conceptually integrated model can be used as a departure point for studies to validate the applicability of Piaget's theory to the development of the physically disabled child and to clinically assess the adaptational stages available to the child at various developmental stages.

  16. Complex interplay between neutral and adaptive evolution shaped differential genomic background and disease susceptibility along the Italian peninsula.

    PubMed

    Sazzini, Marco; Gnecchi Ruscone, Guido Alberto; Giuliani, Cristina; Sarno, Stefania; Quagliariello, Andrea; De Fanti, Sara; Boattini, Alessio; Gentilini, Davide; Fiorito, Giovanni; Catanoso, Mariagrazia; Boiardi, Luigi; Croci, Stefania; Macchioni, Pierluigi; Mantovani, Vilma; Di Blasio, Anna Maria; Matullo, Giuseppe; Salvarani, Carlo; Franceschi, Claudio; Pettener, Davide; Garagnani, Paolo; Luiselli, Donata

    2016-09-01

    The Italian peninsula has long represented a natural hub for human migrations across the Mediterranean area, being involved in several prehistoric and historical population movements. Coupled with a patchy environmental landscape entailing different ecological/cultural selective pressures, this might have produced peculiar patterns of population structure and local adaptations responsible for heterogeneous genomic background of present-day Italians. To disentangle this complex scenario, genome-wide data from 780 Italian individuals were generated and set into the context of European/Mediterranean genomic diversity by comparison with genotypes from 50 populations. To maximize possibility of pinpointing functional genomic regions that have played adaptive roles during Italian natural history, our survey included also ~250,000 exomic markers and ~20,000 coding/regulatory variants with well-established clinical relevance. This enabled fine-grained dissection of Italian population structure through the identification of clusters of genetically homogeneous provinces and of genomic regions underlying their local adaptations. Description of such patterns disclosed crucial implications for understanding differential susceptibility to some inflammatory/autoimmune disorders, coronary artery disease and type 2 diabetes of diverse Italian subpopulations, suggesting the evolutionary causes that made some of them particularly exposed to the metabolic and immune challenges imposed by dietary and lifestyle shifts that involved western societies in the last centuries.

  17. Adaptive User Model for Web-Based Learning Environment.

    ERIC Educational Resources Information Center

    Garofalakis, John; Sirmakessis, Spiros; Sakkopoulos, Evangelos; Tsakalidis, Athanasios

    This paper describes the design of an adaptive user model and its implementation in an advanced Web-based Virtual University environment that encompasses combined and synchronized adaptation between educational material and well-known communication facilities. The Virtual University environment has been implemented to support a postgraduate…

  18. Adaptive Control with Reference Model Modification

    NASA Technical Reports Server (NTRS)

    Stepanyan, Vahram; Krishnakumar, Kalmanje

    2012-01-01

    This paper presents a modification of the conventional model reference adaptive control (MRAC) architecture in order to improve transient performance of the input and output signals of uncertain systems. A simple modification of the reference model is proposed by feeding back the tracking error signal. It is shown that the proposed approach guarantees tracking of the given reference command and the reference control signal (one that would be designed if the system were known) not only asymptotically but also in transient. Moreover, it prevents generation of high frequency oscillations, which are unavoidable in conventional MRAC systems for large adaptation rates. The provided design guideline makes it possible to track a reference commands of any magnitude from any initial position without re-tuning. The benefits of the method are demonstrated with a simulation example

  19. Adaptive tracking for complex systems using reduced-order models

    NASA Technical Reports Server (NTRS)

    Carignan, Craig R.

    1990-01-01

    Reduced-order models are considered in the context of parameter adaptive controllers for tracking workspace trajectories. A dual-arm manipulation task is used to illustrate the methodology and provide simulation results. A parameter adaptive controller is designed to track the desired position trajectory of a payload using a four-parameter model instead of a full-order, nine-parameter model. Several simulations with different payload-to-arm mass ratios are used to illustrate the capabilities of the reduced-order model in tracking the desired trajectory.

  20. RAD-ADAPT: Software for modelling clonogenic assay data in radiation biology.

    PubMed

    Zhang, Yaping; Hu, Kaiqiang; Beumer, Jan H; Bakkenist, Christopher J; D'Argenio, David Z

    2017-04-01

    We present a comprehensive software program, RAD-ADAPT, for the quantitative analysis of clonogenic assays in radiation biology. Two commonly used models for clonogenic assay analysis, the linear-quadratic model and single-hit multi-target model, are included in the software. RAD-ADAPT uses maximum likelihood estimation method to obtain parameter estimates with the assumption that cell colony count data follow a Poisson distribution. The program has an intuitive interface, generates model prediction plots, tabulates model parameter estimates, and allows automatic statistical comparison of parameters between different groups. The RAD-ADAPT interface is written using the statistical software R and the underlying computations are accomplished by the ADAPT software system for pharmacokinetic/pharmacodynamic systems analysis. The use of RAD-ADAPT is demonstrated using an example that examines the impact of pharmacologic ATM and ATR kinase inhibition on human lung cancer cell line A549 after ionizing radiation. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Adaptive LINE-P: An Adaptive Linear Energy Prediction Model for Wireless Sensor Network Nodes

    PubMed Central

    Ahmed, Faisal

    2018-01-01

    In the context of wireless sensor networks, energy prediction models are increasingly useful tools that can facilitate the power management of the wireless sensor network (WSN) nodes. However, most of the existing models suffer from the so-called fixed weighting parameter, which limits their applicability when it comes to, e.g., solar energy harvesters with varying characteristics. Thus, in this article we propose the Adaptive LINE-P (all cases) model that calculates adaptive weighting parameters based on the stored energy profiles. Furthermore, we also present a profile compression method to reduce the memory requirements. To determine the performance of our proposed model, we have used real data for the solar and wind energy profiles. The simulation results show that our model achieves 90–94% accuracy and that the compressed method reduces memory overheads by 50% as compared to state-of-the-art models. PMID:29621169

  2. Computerized Instructional Adaptive Testing Model: Formulation and Validation.

    DTIC Science & Technology

    1980-02-01

    AD-AO1 855 CONTROL DATA EDUCATION CO MINNEAPOLIS MN F/6 5/9MPUTERIZED INSTRUCTIONAL ADAPTIVE TESTING MODELS FORMULATION --EC(U) FEB 80 S J KALISCH...final report wus submitted by Control Data Education Company, 8100 34th Avenue, South, Minneapolis, Minnesota 55440, under contract F33615-17-C.0071... DATA EDUCATION CO MINNEAPOLIS MN p/e 5/9 I COMPULTERIZED :LSTUCTIONAL ADAPTIVE TESTING MODELS FORMULATION --EIC(U) FEB 80 S J KALISCH F33615-77-C-0O71

  3. AMModels: An R package for storing models, data, and metadata to facilitate adaptive management

    USGS Publications Warehouse

    Donovan, Therese M.; Katz, Jonathan

    2018-01-01

    Agencies are increasingly called upon to implement their natural resource management programs within an adaptive management (AM) framework. This article provides the background and motivation for the R package, AMModels. AMModels was developed under R version 3.2.2. The overall goal of AMModels is simple: To codify knowledge in the form of models and to store it, along with models generated from numerous analyses and datasets that may come our way, so that it can be used or recalled in the future. AMModels facilitates this process by storing all models and datasets in a single object that can be saved to an .RData file and routinely augmented to track changes in knowledge through time. Through this process, AMModels allows the capture, development, sharing, and use of knowledge that may help organizations achieve their mission. While AMModels was designed to facilitate adaptive management, its utility is far more general. Many R packages exist for creating and summarizing models, but to our knowledge, AMModels is the only package dedicated not to the mechanics of analysis but to organizing analysis inputs, analysis outputs, and preserving descriptive metadata. We anticipate that this package will assist users hoping to preserve the key elements of an analysis so they may be more confidently revisited at a later date.

  4. Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

    NASA Technical Reports Server (NTRS)

    Ahumada, A. J., Jr.; Watson, A. B.; Rohaly, A. M.; Null, Cynthia H. (Technical Monitor)

    1995-01-01

    In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

  5. Infrared image background modeling based on improved Susan filtering

    NASA Astrophysics Data System (ADS)

    Yuehua, Xia

    2018-02-01

    When SUSAN filter is used to model the infrared image, the Gaussian filter lacks the ability of direction filtering. After filtering, the edge information of the image cannot be preserved well, so that there are a lot of edge singular points in the difference graph, increase the difficulties of target detection. To solve the above problems, the anisotropy algorithm is introduced in this paper, and the anisotropic Gauss filter is used instead of the Gauss filter in the SUSAN filter operator. Firstly, using anisotropic gradient operator to calculate a point of image's horizontal and vertical gradient, to determine the long axis direction of the filter; Secondly, use the local area of the point and the neighborhood smoothness to calculate the filter length and short axis variance; And then calculate the first-order norm of the difference between the local area of the point's gray-scale and mean, to determine the threshold of the SUSAN filter; Finally, the built SUSAN filter is used to convolution the image to obtain the background image, at the same time, the difference between the background image and the original image is obtained. The experimental results show that the background modeling effect of infrared image is evaluated by Mean Squared Error (MSE), Structural Similarity (SSIM) and local Signal-to-noise Ratio Gain (GSNR). Compared with the traditional filtering algorithm, the improved SUSAN filter has achieved better background modeling effect, which can effectively preserve the edge information in the image, and the dim small target is effectively enhanced in the difference graph, which greatly reduces the false alarm rate of the image.

  6. Gravitoinertial force background level affects adaptation to coriolis force perturbations of reaching movements

    NASA Technical Reports Server (NTRS)

    Lackner, J. R.; Dizio, P.

    1998-01-01

    We evaluated the combined effects on reaching movements of the transient, movement-dependent Coriolis forces and the static centrifugal forces generated in a rotating environment. Specifically, we assessed the effects of comparable Coriolis force perturbations in different static force backgrounds. Two groups of subjects made reaching movements toward a just-extinguished visual target before rotation began, during 10 rpm counterclockwise rotation, and after rotation ceased. One group was seated on the axis of rotation, the other 2.23 m away. The resultant of gravity and centrifugal force on the hand was 1.0 g for the on-center group during 10 rpm rotation, and 1.031 g for the off-center group because of the 0.25 g centrifugal force present. For both groups, rightward Coriolis forces, approximately 0.2 g peak, were generated during voluntary arm movements. The endpoints and paths of the initial per-rotation movements were deviated rightward for both groups by comparable amounts. Within 10 subsequent reaches, the on-center group regained baseline accuracy and straight-line paths; however, even after 40 movements the off-center group had not resumed baseline endpoint accuracy. Mirror-image aftereffects occurred when rotation stopped. These findings demonstrate that manual control is disrupted by transient Coriolis force perturbations and that adaptation can occur even in the absence of visual feedback. An increase, even a small one, in background force level above normal gravity does not affect the size of the reaching errors induced by Coriolis forces nor does it affect the rate of reacquiring straight reaching paths; however, it does hinder restoration of reaching accuracy.

  7. Automated adaptive inference of phenomenological dynamical models.

    PubMed

    Daniels, Bryan C; Nemenman, Ilya

    2015-08-21

    Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved.

  8. Automated adaptive inference of phenomenological dynamical models

    PubMed Central

    Daniels, Bryan C.; Nemenman, Ilya

    2015-01-01

    Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved. PMID:26293508

  9. [Analysis of the stability and adaptability of near infrared spectra qualitative analysis model].

    PubMed

    Cao, Wu; Li, Wei-jun; Wang, Ping; Zhang, Li-ping

    2014-06-01

    The stability and adaptability of model of near infrared spectra qualitative analysis were studied. Method of separate modeling can significantly improve the stability and adaptability of model; but its ability of improving adaptability of model is limited. Method of joint modeling can not only improve the adaptability of the model, but also the stability of model, at the same time, compared to separate modeling, the method can shorten the modeling time, reduce the modeling workload; extend the term of validity of model, and improve the modeling efficiency. The experiment of model adaptability shows that, the correct recognition rate of separate modeling method is relatively low, which can not meet the requirements of application, and joint modeling method can reach the correct recognition rate of 90%, and significantly enhances the recognition effect. The experiment of model stability shows that, the identification results of model by joint modeling are better than the model by separate modeling, and has good application value.

  10. Modeling Adaptation as a Flow and Stock Decsion with Mitigation

    EPA Science Inventory

    Mitigation and adaptation are the two key responses available to policymakers to reduce the risks of climate change. We model these two policies together in a new DICE-based integrated assessment model that characterizes adaptation as either short-lived flow spending or long-live...

  11. Modeling Adaptation as a Flow and Stock Decision with Mitigation

    EPA Science Inventory

    Mitigation and adaptation are the two key responses available to policymakers to reduce the risks of climate change. We model these two policies together in a new DICE-based integrated assessment model that characterizes adaptation as either short-lived flow spending or long-liv...

  12. Saccade adaptation goes for the goal

    PubMed Central

    Madelain, Laurent; Herman, James P.; Harwood, Mark R.

    2013-01-01

    The oculomotor system maintains saccade accuracy by adjusting saccades that are consistently inaccurate. Four experiments were performed to determine the relative contribution of background and target postsaccadic displacement. Unlike typical saccade adaptation experiments, we used natural image scenes and masked target and background displacements during the saccade to exclude motion signals from allowing detection of the displacements. We found that the background had no effect on saccade gain while the target drove gain changes. Only when the target was blanked after the saccade did we observe some adaptation in the direction of the background displacement. We conclude that target selection is critical to saccade adaptation, and operates effectively against natural image backgrounds. PMID:23492925

  13. A model-based exploration of the role of pattern generating circuits during locomotor adaptation.

    PubMed

    Marjaninejad, Ali; Finley, James M

    2016-08-01

    In this study, we used a model-based approach to explore the potential contributions of central pattern generating circuits (CPGs) during adaptation to external perturbations during locomotion. We constructed a neuromechanical modeled of locomotion using a reduced-phase CPG controller and an inverted pendulum mechanical model. Two different forms of locomotor adaptation were examined in this study: split-belt treadmill adaptation and adaptation to a unilateral, elastic force field. For each simulation, we first examined the effects of phase resetting and varying the model's initial conditions on the resulting adaptation. After evaluating the effect of phase resetting on the adaptation of step length symmetry, we examined the extent to which the results from these simple models could explain previous experimental observations. We found that adaptation of step length symmetry during split-belt treadmill walking could be reproduced using our model, but this model failed to replicate patterns of adaptation observed in response to force field perturbations. Given that spinal animal models can adapt to both of these types of perturbations, our findings suggest that there may be distinct features of pattern generating circuits that mediate each form of adaptation.

  14. Alertness function of thalamus in conflict adaptation.

    PubMed

    Wang, Xiangpeng; Zhao, Xiaoyue; Xue, Gui; Chen, Antao

    2016-05-15

    Conflict adaptation reflects the ability to improve current conflict resolution based on previously experienced conflict, which is crucial for our goal-directed behaviors. In recent years, the roles of alertness are attracting increasing attention when discussing the generation of conflict adaptation. However, due to the difficulty of manipulating alertness, very limited progress has been made in this line. Inspired by that color may affect alertness, we manipulated background color of experimental task and found that conflict adaptation significantly presented in gray and red backgrounds but did not in blue background. Furthermore, behavioral and functional magnetic resonance imaging results revealed that the modulation of color on conflict adaptation was implemented through changing alertness level. In particular, blue background eliminated conflict adaptation by damping the alertness regulating function of thalamus and the functional connectivity between thalamus and inferior frontal gyrus (IFG). In contrast, in gray and red backgrounds where alertness levels are typically high, the thalamus and the right IFG functioned normally and conflict adaptations were significant. Therefore, the alertness function of thalamus is determinant to conflict adaptation, and thalamus and right IFG are crucial nodes of the neural circuit subserving this ability. Present findings provide new insights into the neural mechanisms of conflict adaptation. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Statistical Modeling of Natural Backgrounds in Hyperspectral LWIR Data

    DTIC Science & Technology

    2016-09-06

    extremely important for studying performance trades. First, we study the validity of this model using real hyperspectral data, and compare the relative...difficult to validate any statistical model created for a target of interest. However, since background measurements are plentiful, it is reasonable to...Golden, S., Less, D., Jin, X., and Rynes, P., “ Modeling and analysis of LWIR signature variability associated with 3d and BRDF effects,” 98400P (May 2016

  16. Decentralized model reference adaptive control of large flexible structures

    NASA Technical Reports Server (NTRS)

    Lee, Fu-Ming; Fong, I-Kong; Lin, Yu-Hwan

    1988-01-01

    A decentralized model reference adaptive control (DMRAC) method is developed for large flexible structures (LFS). The development follows that of a centralized model reference adaptive control for LFS that have been shown to be feasible. The proposed method is illustrated using a simply supported beam with collocated actuators and sensors. Results show that the DMRAC can achieve either output regulation or output tracking with adequate convergence, provided the reference model inputs and their time derivatives are integrable, bounded, and approach zero as t approaches infinity.

  17. A model for homeopathic remedy effects: low dose nanoparticles, allostatic cross-adaptation, and time-dependent sensitization in a complex adaptive system

    PubMed Central

    2012-01-01

    Background This paper proposes a novel model for homeopathic remedy action on living systems. Research indicates that homeopathic remedies (a) contain measurable source and silica nanoparticles heterogeneously dispersed in colloidal solution; (b) act by modulating biological function of the allostatic stress response network (c) evoke biphasic actions on living systems via organism-dependent adaptive and endogenously amplified effects; (d) improve systemic resilience. Discussion The proposed active components of homeopathic remedies are nanoparticles of source substance in water-based colloidal solution, not bulk-form drugs. Nanoparticles have unique biological and physico-chemical properties, including increased catalytic reactivity, protein and DNA adsorption, bioavailability, dose-sparing, electromagnetic, and quantum effects different from bulk-form materials. Trituration and/or liquid succussions during classical remedy preparation create “top-down” nanostructures. Plants can biosynthesize remedy-templated silica nanostructures. Nanoparticles stimulate hormesis, a beneficial low-dose adaptive response. Homeopathic remedies prescribed in low doses spaced intermittently over time act as biological signals that stimulate the organism’s allostatic biological stress response network, evoking nonlinear modulatory, self-organizing change. Potential mechanisms include time-dependent sensitization (TDS), a type of adaptive plasticity/metaplasticity involving progressive amplification of host responses, which reverse direction and oscillate at physiological limits. To mobilize hormesis and TDS, the remedy must be appraised as a salient, but low level, novel threat, stressor, or homeostatic disruption for the whole organism. Silica nanoparticles adsorb remedy source and amplify effects. Properly-timed remedy dosing elicits disease-primed compensatory reversal in direction of maladaptive dynamics of the allostatic network, thus promoting resilience and recovery from

  18. Why background colour matters to bees and flowers.

    PubMed

    Bukovac, Zoë; Shrestha, Mani; Garcia, Jair E; Burd, Martin; Dorin, Alan; Dyer, Adrian G

    2017-05-01

    Flowers are often viewed by bee pollinators against a variety of different backgrounds. On the Australian continent, backgrounds are very diverse and include surface examples of all major geological stages of the Earth's history, which have been present during the entire evolutionary period of Angiosperms. Flower signals in Australia are also representative of typical worldwide evolutionary spectral adaptations that enable successful pollination. We measured the spectral properties of 581 natural surfaces, including rocks, sand, green leaves, and dry plant materials, sampled from tropical Cairns through to the southern tip of mainland Australia. We modelled in a hexagon colour space, how interactions between background spectra and flower-like colour stimuli affect reliable discrimination and detection in bee pollinators. We calculated the extent to which a given locus would be conflated with the loci of a different flower-colour stimulus using empirically determined colour discrimination regions for bee vision. Our results reveal that whilst colour signals are robust in homogeneous background viewing conditions, there could be significant pressure on plant flowers to evolve saliently-different colours to overcome background spectral noise. We thus show that perceptual noise has a large influence on how colour information can be used in natural conditions.

  19. Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach

    PubMed Central

    Cavagnaro, Daniel R.; Gonzalez, Richard; Myung, Jay I.; Pitt, Mark A.

    2014-01-01

    Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called Adaptive Design Optimization (ADO), adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate Expected Utility, Weighted Expected Utility, Original Prospect Theory, and Cumulative Prospect Theory models. PMID:24532856

  20. Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach.

    PubMed

    Cavagnaro, Daniel R; Gonzalez, Richard; Myung, Jay I; Pitt, Mark A

    2013-02-01

    Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called Adaptive Design Optimization (ADO), adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate Expected Utility, Weighted Expected Utility, Original Prospect Theory, and Cumulative Prospect Theory models.

  1. Nonlinear adaptive inverse control via the unified model neural network

    NASA Astrophysics Data System (ADS)

    Jeng, Jin-Tsong; Lee, Tsu-Tian

    1999-03-01

    In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

  2. Integrating Climate Change Adaptation into Public Health Practice: Using Adaptive Management to Increase Adaptive Capacity and Build Resilience

    PubMed Central

    McDowell, Julia Z.; Luber, George

    2011-01-01

    Background: Climate change is expected to have a range of health impacts, some of which are already apparent. Public health adaptation is imperative, but there has been little discussion of how to increase adaptive capacity and resilience in public health systems. Objectives: We explored possible explanations for the lack of work on adaptive capacity, outline climate–health challenges that may lie outside public health’s coping range, and consider changes in practice that could increase public health’s adaptive capacity. Methods: We conducted a substantive, interdisciplinary literature review focused on climate change adaptation in public health, social learning, and management of socioeconomic systems exhibiting dynamic complexity. Discussion: There are two competing views of how public health should engage climate change adaptation. Perspectives differ on whether climate change will primarily amplify existing hazards, requiring enhancement of existing public health functions, or present categorically distinct threats requiring innovative management strategies. In some contexts, distinctly climate-sensitive health threats may overwhelm public health’s adaptive capacity. Addressing these threats will require increased emphasis on institutional learning, innovative management strategies, and new and improved tools. Adaptive management, an iterative framework that embraces uncertainty, uses modeling, and integrates learning, may be a useful approach. We illustrate its application to extreme heat in an urban setting. Conclusions: Increasing public health capacity will be necessary for certain climate–health threats. Focusing efforts to increase adaptive capacity in specific areas, promoting institutional learning, embracing adaptive management, and developing tools to facilitate these processes are important priorities and can improve the resilience of local public health systems to climate change. PMID:21997387

  3. Improved Discovery of Molecular Interactions in Genome-Scale Data with Adaptive Model-Based Normalization

    PubMed Central

    Brown, Patrick O.

    2013-01-01

    Background High throughput molecular-interaction studies using immunoprecipitations (IP) or affinity purifications are powerful and widely used in biology research. One of many important applications of this method is to identify the set of RNAs that interact with a particular RNA-binding protein (RBP). Here, the unique statistical challenge presented is to delineate a specific set of RNAs that are enriched in one sample relative to another, typically a specific IP compared to a non-specific control to model background. The choice of normalization procedure critically impacts the number of RNAs that will be identified as interacting with an RBP at a given significance threshold – yet existing normalization methods make assumptions that are often fundamentally inaccurate when applied to IP enrichment data. Methods In this paper, we present a new normalization methodology that is specifically designed for identifying enriched RNA or DNA sequences in an IP. The normalization (called adaptive or AD normalization) uses a basic model of the IP experiment and is not a variant of mean, quantile, or other methodology previously proposed. The approach is evaluated statistically and tested with simulated and empirical data. Results and Conclusions The adaptive (AD) normalization method results in a greatly increased range in the number of enriched RNAs identified, fewer false positives, and overall better concordance with independent biological evidence, for the RBPs we analyzed, compared to median normalization. The approach is also applicable to the study of pairwise RNA, DNA and protein interactions such as the analysis of transcription factors via chromatin immunoprecipitation (ChIP) or any other experiments where samples from two conditions, one of which contains an enriched subset of the other, are studied. PMID:23349766

  4. Importance of the cutoff value in the quadratic adaptive integrate-and-fire model.

    PubMed

    Touboul, Jonathan

    2009-08-01

    The quadratic adaptive integrate-and-fire model (Izhikevich, 2003 , 2007 ) is able to reproduce various firing patterns of cortical neurons and is widely used in large-scale simulations of neural networks. This model describes the dynamics of the membrane potential by a differential equation that is quadratic in the voltage, coupled to a second equation for adaptation. Integration is stopped during the rise phase of a spike at a voltage cutoff value V(c) or when it blows up. Subsequently the membrane potential is reset, and the adaptation variable is increased by a fixed amount. We show in this note that in the absence of a cutoff value, not only the voltage but also the adaptation variable diverges in finite time during spike generation in the quadratic model. The divergence of the adaptation variable makes the system very sensitive to the cutoff: changing V(c) can dramatically alter the spike patterns. Furthermore, from a computational viewpoint, the divergence of the adaptation variable implies that the time steps for numerical simulation need to be small and adaptive. However, divergence of the adaptation variable does not occur for the quartic model (Touboul, 2008 ) and the adaptive exponential integrate-and-fire model (Brette & Gerstner, 2005 ). Hence, these models are robust to changes in the cutoff value.

  5. User Modeling in Adaptive Hypermedia Educational Systems

    ERIC Educational Resources Information Center

    Martins, Antonio Constantino; Faria, Luiz; Vaz de Carvalho, Carlos; Carrapatoso, Eurico

    2008-01-01

    This document is a survey in the research area of User Modeling (UM) for the specific field of Adaptive Learning. The aims of this document are: To define what it is a User Model; To present existing and well known User Models; To analyze the existent standards related with UM; To compare existing systems. In the scientific area of User Modeling…

  6. Firing patterns in the adaptive exponential integrate-and-fire model.

    PubMed

    Naud, Richard; Marcille, Nicolas; Clopath, Claudia; Gerstner, Wulfram

    2008-11-01

    For simulations of large spiking neuron networks, an accurate, simple and versatile single-neuron modeling framework is required. Here we explore the versatility of a simple two-equation model: the adaptive exponential integrate-and-fire neuron. We show that this model generates multiple firing patterns depending on the choice of parameter values, and present a phase diagram describing the transition from one firing type to another. We give an analytical criterion to distinguish between continuous adaption, initial bursting, regular bursting and two types of tonic spiking. Also, we report that the deterministic model is capable of producing irregular spiking when stimulated with constant current, indicating low-dimensional chaos. Lastly, the simple model is fitted to real experiments of cortical neurons under step current stimulation. The results provide support for the suitability of simple models such as the adaptive exponential integrate-and-fire neuron for large network simulations.

  7. Adaptive Nonparametric Kinematic Modeling of Concentric Tube Robots.

    PubMed

    Fagogenis, Georgios; Bergeles, Christos; Dupont, Pierre E

    2016-10-01

    Concentric tube robots comprise telescopic precurved elastic tubes. The robot's tip and shape are controlled via relative tube motions, i.e. tube rotations and translations. Non-linear interactions between the tubes, e.g. friction and torsion, as well as uncertainty in the physical properties of the tubes themselves, e.g. the Young's modulus, curvature, or stiffness, hinder accurate kinematic modelling. In this paper, we present a machine-learning-based methodology for kinematic modelling of concentric tube robots and in situ model adaptation. Our approach is based on Locally Weighted Projection Regression (LWPR). The model comprises an ensemble of linear models, each of which locally approximates the original complex kinematic relation. LWPR can accommodate for model deviations by adjusting the respective local models at run-time, resulting in an adaptive kinematics framework. We evaluated our approach on data gathered from a three-tube robot, and report high accuracy across the robot's configuration space.

  8. The ADaptation and Anticipation Model (ADAM) of sensorimotor synchronization

    PubMed Central

    van der Steen, M. C. (Marieke); Keller, Peter E.

    2013-01-01

    A constantly changing environment requires precise yet flexible timing of movements. Sensorimotor synchronization (SMS)—the temporal coordination of an action with events in a predictable external rhythm—is a fundamental human skill that contributes to optimal sensory-motor control in daily life. A large body of research related to SMS has focused on adaptive error correction mechanisms that support the synchronization of periodic movements (e.g., finger taps) with events in regular pacing sequences. The results of recent studies additionally highlight the importance of anticipatory mechanisms that support temporal prediction in the context of SMS with sequences that contain tempo changes. To investigate the role of adaptation and anticipatory mechanisms in SMS we introduce ADAM: an ADaptation and Anticipation Model. ADAM combines reactive error correction processes (adaptation) with predictive temporal extrapolation processes (anticipation) inspired by the computational neuroscience concept of internal models. The combination of simulations and experimental manipulations based on ADAM creates a novel and promising approach for exploring adaptation and anticipation in SMS. The current paper describes the conceptual basis and architecture of ADAM. PMID:23772211

  9. Incorporating Midbrain Adaptation to Mean Sound Level Improves Models of Auditory Cortical Processing

    PubMed Central

    Schoppe, Oliver; King, Andrew J.; Schnupp, Jan W.H.; Harper, Nicol S.

    2016-01-01

    Adaptation to stimulus statistics, such as the mean level and contrast of recently heard sounds, has been demonstrated at various levels of the auditory pathway. It allows the nervous system to operate over the wide range of intensities and contrasts found in the natural world. Yet current standard models of the response properties of auditory neurons do not incorporate such adaptation. Here we present a model of neural responses in the ferret auditory cortex (the IC Adaptation model), which takes into account adaptation to mean sound level at a lower level of processing: the inferior colliculus (IC). The model performs high-pass filtering with frequency-dependent time constants on the sound spectrogram, followed by half-wave rectification, and passes the output to a standard linear–nonlinear (LN) model. We find that the IC Adaptation model consistently predicts cortical responses better than the standard LN model for a range of synthetic and natural stimuli. The IC Adaptation model introduces no extra free parameters, so it improves predictions without sacrificing parsimony. Furthermore, the time constants of adaptation in the IC appear to be matched to the statistics of natural sounds, suggesting that neurons in the auditory midbrain predict the mean level of future sounds and adapt their responses appropriately. SIGNIFICANCE STATEMENT An ability to accurately predict how sensory neurons respond to novel stimuli is critical if we are to fully characterize their response properties. Attempts to model these responses have had a distinguished history, but it has proven difficult to improve their predictive power significantly beyond that of simple, mostly linear receptive field models. Here we show that auditory cortex receptive field models benefit from a nonlinear preprocessing stage that replicates known adaptation properties of the auditory midbrain. This improves their predictive power across a wide range of stimuli but keeps model complexity low as it

  10. Adaptive Shape Functions and Internal Mesh Adaptation for Modelling Progressive Failure in Adhesively Bonded Joints

    NASA Technical Reports Server (NTRS)

    Stapleton, Scott; Gries, Thomas; Waas, Anthony M.; Pineda, Evan J.

    2014-01-01

    Enhanced finite elements are elements with an embedded analytical solution that can capture detailed local fields, enabling more efficient, mesh independent finite element analysis. The shape functions are determined based on the analytical model rather than prescribed. This method was applied to adhesively bonded joints to model joint behavior with one element through the thickness. This study demonstrates two methods of maintaining the fidelity of such elements during adhesive non-linearity and cracking without increasing the mesh needed for an accurate solution. The first method uses adaptive shape functions, where the shape functions are recalculated at each load step based on the softening of the adhesive. The second method is internal mesh adaption, where cracking of the adhesive within an element is captured by further discretizing the element internally to represent the partially cracked geometry. By keeping mesh adaptations within an element, a finer mesh can be used during the analysis without affecting the global finite element model mesh. Examples are shown which highlight when each method is most effective in reducing the number of elements needed to capture adhesive nonlinearity and cracking. These methods are validated against analogous finite element models utilizing cohesive zone elements.

  11. Anisotropic mesh adaptation for marine ice-sheet modelling

    NASA Astrophysics Data System (ADS)

    Gillet-Chaulet, Fabien; Tavard, Laure; Merino, Nacho; Peyaud, Vincent; Brondex, Julien; Durand, Gael; Gagliardini, Olivier

    2017-04-01

    Improving forecasts of ice-sheets contribution to sea-level rise requires, amongst others, to correctly model the dynamics of the grounding line (GL), i.e. the line where the ice detaches from its underlying bed and goes afloat on the ocean. Many numerical studies, including the intercomparison exercises MISMIP and MISMIP3D, have shown that grid refinement in the GL vicinity is a key component to obtain reliable results. Improving model accuracy while maintaining the computational cost affordable has then been an important target for the development of marine icesheet models. Adaptive mesh refinement (AMR) is a method where the accuracy of the solution is controlled by spatially adapting the mesh size. It has become popular in models using the finite element method as they naturally deal with unstructured meshes, but block-structured AMR has also been successfully applied to model GL dynamics. The main difficulty with AMR is to find efficient and reliable estimators of the numerical error to control the mesh size. Here, we use the estimator proposed by Frey and Alauzet (2015). Based on the interpolation error, it has been found effective in practice to control the numerical error, and has some flexibility, such as its ability to combine metrics for different variables, that makes it attractive. Routines to compute the anisotropic metric defining the mesh size have been implemented in the finite element ice flow model Elmer/Ice (Gagliardini et al., 2013). The mesh adaptation is performed using the freely available library MMG (Dapogny et al., 2014) called from Elmer/Ice. Using a setup based on the inter-comparison exercise MISMIP+ (Asay-Davis et al., 2016), we study the accuracy of the solution when the mesh is adapted using various variables (ice thickness, velocity, basal drag, …). We show that combining these variables allows to reduce the number of mesh nodes by more than one order of magnitude, for the same numerical accuracy, when compared to uniform mesh

  12. A heuristic model on the role of plasticity in adaptive evolution: plasticity increases adaptation, population viability and genetic variation.

    PubMed

    Gomez-Mestre, Ivan; Jovani, Roger

    2013-11-22

    An ongoing new synthesis in evolutionary theory is expanding our view of the sources of heritable variation beyond point mutations of fixed phenotypic effects to include environmentally sensitive changes in gene regulation. This expansion of the paradigm is necessary given ample evidence for a heritable ability to alter gene expression in response to environmental cues. In consequence, single genotypes are often capable of adaptively expressing different phenotypes in different environments, i.e. are adaptively plastic. We present an individual-based heuristic model to compare the adaptive dynamics of populations composed of plastic or non-plastic genotypes under a wide range of scenarios where we modify environmental variation, mutation rate and costs of plasticity. The model shows that adaptive plasticity contributes to the maintenance of genetic variation within populations, reduces bottlenecks when facing rapid environmental changes and confers an overall faster rate of adaptation. In fluctuating environments, plasticity is favoured by selection and maintained in the population. However, if the environment stabilizes and costs of plasticity are high, plasticity is reduced by selection, leading to genetic assimilation, which could result in species diversification. More broadly, our model shows that adaptive plasticity is a common consequence of selection under environmental heterogeneity, and hence a potentially common phenomenon in nature. Thus, taking adaptive plasticity into account substantially extends our view of adaptive evolution.

  13. Fantastic animals as an experimental model to teach animal adaptation

    PubMed Central

    Guidetti, Roberto; Baraldi, Laura; Calzolai, Caterina; Pini, Lorenza; Veronesi, Paola; Pederzoli, Aurora

    2007-01-01

    Background Science curricula and teachers should emphasize evolution in a manner commensurate with its importance as a unifying concept in science. The concept of adaptation represents a first step to understand the results of natural selection. We settled an experimental project of alternative didactic to improve knowledge of organism adaptation. Students were involved and stimulated in learning processes by creative activities. To set adaptation in a historic frame, fossil records as evidence of past life and evolution were considered. Results The experimental project is schematized in nine phases: review of previous knowledge; lesson on fossils; lesson on fantastic animals; planning an imaginary world; creation of an imaginary animal; revision of the imaginary animals; adaptations of real animals; adaptations of fossil animals; and public exposition. A rubric to evaluate the student's performances is reported. The project involved professors and students of the University of Modena and Reggio Emilia and of the "G. Marconi" Secondary School of First Degree (Modena, Italy). Conclusion The educational objectives of the project are in line with the National Indications of the Italian Ministry of Public Instruction: knowledge of the characteristics of living beings, the meanings of the term "adaptation", the meaning of fossils, the definition of ecosystem, and the particularity of the different biomes. At the end of the project, students will be able to grasp particular adaptations of real organisms and to deduce information about the environment in which the organism evolved. This project allows students to review previous knowledge and to form their personalities. PMID:17767729

  14. Stochastic Models of Emerging Infectious Disease Transmission on Adaptive Random Networks

    PubMed Central

    Pipatsart, Navavat; Triampo, Wannapong

    2017-01-01

    We presented adaptive random network models to describe human behavioral change during epidemics and performed stochastic simulations of SIR (susceptible-infectious-recovered) epidemic models on adaptive random networks. The interplay between infectious disease dynamics and network adaptation dynamics was investigated in regard to the disease transmission and the cumulative number of infection cases. We found that the cumulative case was reduced and associated with an increasing network adaptation probability but was increased with an increasing disease transmission probability. It was found that the topological changes of the adaptive random networks were able to reduce the cumulative number of infections and also to delay the epidemic peak. Our results also suggest the existence of a critical value for the ratio of disease transmission and adaptation probabilities below which the epidemic cannot occur. PMID:29075314

  15. A model of excitation and adaptation in bacterial chemotaxis.

    PubMed Central

    Hauri, D C; Ross, J

    1995-01-01

    We present a model of the chemotactic mechanism of Escherichia coli that exhibits both initial excitation and eventual complete adaptation to any and all levels of stimulus ("exact" adaptation). In setting up the reaction network, we use only known interactions and experimentally determined cytosolic concentrations. Whenever possible, rate coefficients are first assigned experimentally measured values; second, we permit some variation in these rate coefficients by using a multiple-well optimization technique and incremental adjustment to obtain values that are sufficient to engender initial response to stimuli (excitation) and an eventual return of behavior to baseline (adaptation). The predictions of the model are similar to the observed behavior of wild-type bacteria in regard to the time scale of excitation in the presence of both attractant and repellent. The model predicts a weaker response to attractant than that observed experimentally, and the time scale of adaptation does not depend as strongly upon stimulant concentration as does that for wild-type bacteria. The mechanism responsible for long-term adaptation is local rather than global: on addition of a repellent or attractant, the receptor types not sensitive to that attractant or repellent do not change their average methylation level in the long term, although transient changes do occur. By carrying out a phenomenological simulation of bacterial chemotaxis, we find that the model is insufficiently sensitive to effect taxis in a gradient of attractant. However, by arbitrarily increasing the sensitivity of the motor to the tumble effector (phosphorylated CheY), we can obtain chemotactic behavior. Images FIGURE 6 FIGURE 7 PMID:7696522

  16. Mixture Modeling for Background and Sources Separation in x-ray Astronomical Images

    NASA Astrophysics Data System (ADS)

    Guglielmetti, Fabrizia; Fischer, Rainer; Dose, Volker

    2004-11-01

    A probabilistic technique for the joint estimation of background and sources in high-energy astrophysics is described. Bayesian probability theory is applied to gain insight into the coexistence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. The present analysis is applied to ROSAT PSPC data (0.1-2.4 keV) in Survey Mode. A background map is modelled using a Thin-Plate spline. Source probability maps are obtained for each pixel (45 arcsec) independently and for larger correlation lengths, revealing faint and extended sources. We will demonstrate that the described probabilistic method allows for detection improvement of faint extended celestial sources compared to the Standard Analysis Software System (SASS) used for the production of the ROSAT All-Sky Survey (RASS) catalogues.

  17. Adaptation Method for Overall and Local Performances of Gas Turbine Engine Model

    NASA Astrophysics Data System (ADS)

    Kim, Sangjo; Kim, Kuisoon; Son, Changmin

    2018-04-01

    An adaptation method was proposed to improve the modeling accuracy of overall and local performances of gas turbine engine. The adaptation method was divided into two steps. First, the overall performance parameters such as engine thrust, thermal efficiency, and pressure ratio were adapted by calibrating compressor maps, and second, the local performance parameters such as temperature of component intersection and shaft speed were adjusted by additional adaptation factors. An optimization technique was used to find the correlation equation of adaptation factors for compressor performance maps. The multi-island genetic algorithm (MIGA) was employed in the present optimization. The correlations of local adaptation factors were generated based on the difference between the first adapted engine model and performance test data. The proposed adaptation method applied to a low-bypass ratio turbofan engine of 12,000 lb thrust. The gas turbine engine model was generated and validated based on the performance test data in the sea-level static condition. In flight condition at 20,000 ft and 0.9 Mach number, the result of adapted engine model showed improved prediction in engine thrust (overall performance parameter) by reducing the difference from 14.5 to 3.3%. Moreover, there was further improvement in the comparison of low-pressure turbine exit temperature (local performance parameter) as the difference is reduced from 3.2 to 0.4%.

  18. Response normalization and blur adaptation: Data and multi-scale model

    PubMed Central

    Elliott, Sarah L.; Georgeson, Mark A.; Webster, Michael A.

    2011-01-01

    Adapting to blurred or sharpened images alters perceived blur of a focused image (M. A. Webster, M. A. Georgeson, & S. M. Webster, 2002). We asked whether blur adaptation results in (a) renormalization of perceived focus or (b) a repulsion aftereffect. Images were checkerboards or 2-D Gaussian noise, whose amplitude spectra had (log–log) slopes from −2 (strongly blurred) to 0 (strongly sharpened). Observers adjusted the spectral slope of a comparison image to match different test slopes after adaptation to blurred or sharpened images. Results did not show repulsion effects but were consistent with some renormalization. Test blur levels at and near a blurred or sharpened adaptation level were matched by more focused slopes (closer to 1/f) but with little or no change in appearance after adaptation to focused (1/f) images. A model of contrast adaptation and blur coding by multiple-scale spatial filters predicts these blur aftereffects and those of Webster et al. (2002). A key proposal is that observers are pre-adapted to natural spectra, and blurred or sharpened spectra induce changes in the state of adaptation. The model illustrates how norms might be encoded and recalibrated in the visual system even when they are represented only implicitly by the distribution of responses across multiple channels. PMID:21307174

  19. Water System Adaptation To Hydrological Changes: Module 12, Models and Tools for Stormwater and Wastewater System Adaptation

    EPA Science Inventory

    This course will introduce students to the fundamental principles of water system adaptation to hydrological changes, with emphasis on data analysis and interpretation, technical planning, and computational modeling. Starting with real-world scenarios and adaptation needs, the co...

  20. A Direct Adaptive Control Approach in the Presence of Model Mismatch

    NASA Technical Reports Server (NTRS)

    Joshi, Suresh M.; Tao, Gang; Khong, Thuan

    2009-01-01

    This paper considers the problem of direct model reference adaptive control when the plant-model matching conditions are violated due to abnormal changes in the plant or incorrect knowledge of the plant's mathematical structure. The approach consists of direct adaptation of state feedback gains for state tracking, and simultaneous estimation of the plant-model mismatch. Because of the mismatch, the plant can no longer track the state of the original reference model, but may be able to track a new reference model that still provides satisfactory performance. The reference model is updated if the estimated plant-model mismatch exceeds a bound that is determined via robust stability and/or performance criteria. The resulting controller is a hybrid direct-indirect adaptive controller that offers asymptotic state tracking in the presence of plant-model mismatch as well as parameter deviations.

  1. Sensorimotor synchronization with tempo-changing auditory sequences: Modeling temporal adaptation and anticipation.

    PubMed

    van der Steen, M C Marieke; Jacoby, Nori; Fairhurst, Merle T; Keller, Peter E

    2015-11-11

    The current study investigated the human ability to synchronize movements with event sequences containing continuous tempo changes. This capacity is evident, for example, in ensemble musicians who maintain precise interpersonal coordination while modulating the performance tempo for expressive purposes. Here we tested an ADaptation and Anticipation Model (ADAM) that was developed to account for such behavior by combining error correction processes (adaptation) with a predictive temporal extrapolation process (anticipation). While previous computational models of synchronization incorporate error correction, they do not account for prediction during tempo-changing behavior. The fit between behavioral data and computer simulations based on four versions of ADAM was assessed. These versions included a model with adaptation only, one in which adaptation and anticipation act in combination (error correction is applied on the basis of predicted tempo changes), and two models in which adaptation and anticipation were linked in a joint module that corrects for predicted discrepancies between the outcomes of adaptive and anticipatory processes. The behavioral experiment required participants to tap their finger in time with three auditory pacing sequences containing tempo changes that differed in the rate of change and the number of turning points. Behavioral results indicated that sensorimotor synchronization accuracy and precision, while generally high, decreased with increases in the rate of tempo change and number of turning points. Simulations and model-based parameter estimates showed that adaptation mechanisms alone could not fully explain the observed precision of sensorimotor synchronization. Including anticipation in the model increased the precision of simulated sensorimotor synchronization and improved the fit of model to behavioral data, especially when adaptation and anticipation mechanisms were linked via a joint module based on the notion of joint internal

  2. Background-Error Correlation Model Based on the Implicit Solution of a Diffusion Equation

    DTIC Science & Technology

    2010-01-01

    1 Background- Error Correlation Model Based on the Implicit Solution of a Diffusion Equation Matthew J. Carrier* and Hans Ngodock...4. TITLE AND SUBTITLE Background- Error Correlation Model Based on the Implicit Solution of a Diffusion Equation 5a. CONTRACT NUMBER 5b. GRANT...2001), which sought to model error correlations based on the explicit solution of a generalized diffusion equation. The implicit solution is

  3. On the role of model-based monitoring for adaptive planning under uncertainty

    NASA Astrophysics Data System (ADS)

    Raso, Luciano; Kwakkel, Jan; Timmermans, Jos; Haasnoot, Mariolijn

    2016-04-01

    , triggered by the challenge of uncertainty in operational control, may offer solutions from which monitoring for adaptive planning can benefit. Specifically: (i) in control, observations are incorporated into the model through data assimilation, updating the present state, boundary conditions, and parameters based on new observations, diminishing the shadow of the past; (ii) adaptive control is a way to modify the characteristics of the internal model, incorporating new knowledge on the system, countervailing the inhibition of learning; and (iii) in closed-loop control, a continuous system update equips the controller with "inherent robustness", i.e. to capacity to adapts to new conditions even when these were not initially considered. We aim to explore how inherent robustness addresses the challenge of surprise. Innovations in model-based control might help to improve and adapt the models used to support adaptive delta management to new information (reducing uncertainty). Moreover, this would offer a starting point for using these models not only in the design of adaptive plans, but also as part of the monitoring. The proposed research requires multidisciplinary cooperation between control theory, the policy sciences, and integrated assessment modeling.

  4. Adaptation, saturation, and physiological masking in single auditory-nerve fibers.

    PubMed

    Smith, R L

    1979-01-01

    Results are reviewed concerning some effects, at a units's characteristic frequency, of a short-term conditioning stimulus on the responses to perstimulatory and poststimulatory test tones. A phenomenological equation is developed from the poststimulatory results and shown to be consistent with the perstimulatory results. According to the results and equation, the response to a test tone equals the unconditioned or unadapted response minus the decrement produced by adaptation to the conditioning tone. Furthermore, the decrement is proportional to the driven response to the conditioning tone and does not depend on sound intensity per se. The equation has a simple interpretation in terms of two processes in cascade--a static saturating nonlinearity followed by additive adaptation. Results are presented to show that this functional model is sufficient to account for the "physiological masking" produced by wide-band backgrounds. According to this interpretation, a sufficiently intense background produces saturation. Consequently, a superimposed test tone cause no change in response. In addition, when the onset of the background precedes the onset of the test tone, the total firing rate is reduced by adaptation. Evidence is reviewed concerning the possible correspondence between the variables in the model and intracellular events in the auditory periphery.

  5. Generalized background error covariance matrix model (GEN_BE v2.0)

    NASA Astrophysics Data System (ADS)

    Descombes, G.; Auligné, T.; Vandenberghe, F.; Barker, D. M.; Barré, J.

    2015-03-01

    The specification of state background error statistics is a key component of data assimilation since it affects the impact observations will have on the analysis. In the variational data assimilation approach, applied in geophysical sciences, the dimensions of the background error covariance matrix (B) are usually too large to be explicitly determined and B needs to be modeled. Recent efforts to include new variables in the analysis such as cloud parameters and chemical species have required the development of the code to GENerate the Background Errors (GEN_BE) version 2.0 for the Weather Research and Forecasting (WRF) community model. GEN_BE allows for a simpler, flexible, robust, and community-oriented framework that gathers methods used by some meteorological operational centers and researchers. We present the advantages of this new design for the data assimilation community by performing benchmarks of different modeling of B and showing some of the new features in data assimilation test cases. As data assimilation for clouds remains a challenge, we present a multivariate approach that includes hydrometeors in the control variables and new correlated errors. In addition, the GEN_BE v2.0 code is employed to diagnose error parameter statistics for chemical species, which shows that it is a tool flexible enough to implement new control variables. While the generation of the background errors statistics code was first developed for atmospheric research, the new version (GEN_BE v2.0) can be easily applied to other domains of science and chosen to diagnose and model B. Initially developed for variational data assimilation, the model of the B matrix may be useful for variational ensemble hybrid methods as well.

  6. Background stratospheric aerosol and polar stratospheric cloud reference models

    NASA Technical Reports Server (NTRS)

    Mccormick, M. P.; Wang, P.-H.; Pitts, M. C.

    1993-01-01

    A global aerosol climatology is evolving from the NASA satellite experiments SAM II, SAGE I, and SAGE II. In addition, polar stratospheric cloud (PSC) data have been obtained from these experiments over the last decade. An undated reference model of the optical characteristics of the background aerosol is described and a new aerosol reference model derived from the latest available data is proposed. The aerosol models are referenced to the height above the tropopause. The impact of a number of volcanic eruptions is described. In addition, a model describing the seasonal, longitudinal, and interannual variations in PSCs is presented.

  7. Performance Optimizing Adaptive Control with Time-Varying Reference Model Modification

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan T.; Hashemi, Kelley E.

    2017-01-01

    This paper presents a new adaptive control approach that involves a performance optimization objective. The control synthesis involves the design of a performance optimizing adaptive controller from a subset of control inputs. The resulting effect of the performance optimizing adaptive controller is to modify the initial reference model into a time-varying reference model which satisfies the performance optimization requirement obtained from an optimal control problem. The time-varying reference model modification is accomplished by the real-time solutions of the time-varying Riccati and Sylvester equations coupled with the least-squares parameter estimation of the sensitivities of the performance metric. The effectiveness of the proposed method is demonstrated by an application of maneuver load alleviation control for a flexible aircraft.

  8. Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.

    PubMed

    Aprasoff, Jonathan; Donchin, Opher

    2012-04-01

    Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.

  9. REVIEW: Internal models in sensorimotor integration: perspectives from adaptive control theory

    NASA Astrophysics Data System (ADS)

    Tin, Chung; Poon, Chi-Sang

    2005-09-01

    Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models' architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods, such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning, are reviewed and their possible relevance to motor control is discussed. Possible applicability of a Luenberger observer and an extended Kalman filter to state estimation problems—such as sensorimotor prediction or the resolution of vestibular sensory ambiguity—is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal models in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future.

  10. Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality.

    PubMed

    Gosling, Simon N; Hondula, David M; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

    2017-08-16

    Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to "adaptation uncertainty" (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. This study had three aims: a ) Compare the range in projected impacts that arises from using different adaptation modeling methods; b ) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c ) recommend modeling method(s) to use in future impact assessments. We estimated impacts for 2070-2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634.

  11. Adaptive filter design using recurrent cerebellar model articulation controller.

    PubMed

    Lin, Chih-Min; Chen, Li-Yang; Yeung, Daniel S

    2010-07-01

    A novel adaptive filter is proposed using a recurrent cerebellar-model-articulation-controller (CMAC). The proposed locally recurrent globally feedforward recurrent CMAC (RCMAC) has favorable properties of small size, good generalization, rapid learning, and dynamic response, thus it is more suitable for high-speed signal processing. To provide fast training, an efficient parameter learning algorithm based on the normalized gradient descent method is presented, in which the learning rates are on-line adapted. Then the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so the stability of the filtering error can be guaranteed. To demonstrate the performance of the proposed adaptive RCMAC filter, it is applied to a nonlinear channel equalization system and an adaptive noise cancelation system. The advantages of the proposed filter over other adaptive filters are verified through simulations.

  12. The Application of Adaptive Behaviour Models: A Systematic Review

    PubMed Central

    Price, Jessica A.; Morris, Zoe A.; Costello, Shane

    2018-01-01

    Adaptive behaviour has been viewed broadly as an individual’s ability to meet the standards of social responsibilities and independence; however, this definition has been a source of debate amongst researchers and clinicians. Based on the rich history and the importance of the construct of adaptive behaviour, the current study aimed to provide a comprehensive overview of the application of adaptive behaviour models to assessment tools, through a systematic review. A plethora of assessment measures for adaptive behaviour have been developed in order to adequately assess the construct; however, it appears that the only definition on which authors seem to agree is that adaptive behaviour is what adaptive behaviour scales measure. The importance of the construct for diagnosis, intervention and planning has been highlighted throughout the literature. It is recommended that researchers and clinicians critically review what measures of adaptive behaviour they are utilising and it is suggested that the definition and theory is revisited. PMID:29342927

  13. Moving target detection method based on improved Gaussian mixture model

    NASA Astrophysics Data System (ADS)

    Ma, J. Y.; Jie, F. R.; Hu, Y. J.

    2017-07-01

    Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.

  14. Adaptive Failure Compensation for Aircraft Tracking Control Using Engine Differential Based Model

    NASA Technical Reports Server (NTRS)

    Liu, Yu; Tang, Xidong; Tao, Gang; Joshi, Suresh M.

    2006-01-01

    An aircraft model that incorporates independently adjustable engine throttles and ailerons is employed to develop an adaptive control scheme in the presence of actuator failures. This model captures the key features of aircraft flight dynamics when in the engine differential mode. Based on this model an adaptive feedback control scheme for asymptotic state tracking is developed and applied to a transport aircraft model in the presence of two types of failures during operation, rudder failure and aileron failure. Simulation results are presented to demonstrate the adaptive failure compensation scheme.

  15. DNDO Report: Predicting Solar Modulation Potentials for Modeling Cosmic Background Radiation

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

    Behne, Patrick Alan

    The modeling of the detectability of special nuclear material (SNM) at ports and border crossings requires accurate knowledge of the background radiation at those locations. Background radiation originates from two main sources, cosmic and terrestrial. Cosmic background is produced by high-energy galactic cosmic rays (GCR) entering the atmosphere and inducing a cascade of particles that eventually impact the earth’s surface. The solar modulation potential represents one of the primary inputs to modeling cosmic background radiation. Usosokin et al. formally define solar modulation potential as “the mean energy loss [per unit charge] of a cosmic ray particle inside the heliosphere…” Modulationmore » potential, a function of elevation, location, and time, shares an inverse relationship with cosmic background radiation. As a result, radiation detector thresholds require adjustment to account for differing background levels, caused partly by differing solar modulations. Failure to do so can result in higher rates of false positives and failed detection of SNM for low and high levels of solar modulation potential, respectively. This study focuses on solar modulation’s time dependence, and seeks the best method to predict modulation for future dates using Python. To address the task of predicting future solar modulation, we utilize both non-linear least squares sinusoidal curve fitting and cubic spline interpolation. This material will be published in transactions of the ANS winter meeting of November, 2016.« less

  16. Adaptive Modeling of Details for Physically-Based Sound Synthesis and Propagation

    DTIC Science & Technology

    2015-03-21

    the interface that ensures the consistency and validity of the solution given by the two methods. Transfer functions are used to model two-way...release; distribution is unlimited. Adaptive modeling of details for physically-based sound synthesis and propagation The views, opinions and/or...Research Triangle Park, NC 27709-2211 Applied sciences, Adaptive modeling , Physcially-based, Sound synthesis, Propagation, Virtual world REPORT

  17. On valuing information in adaptive-management models.

    PubMed

    Moore, Alana L; McCarthy, Michael A

    2010-08-01

    Active adaptive management looks at the benefit of using strategies that may be suboptimal in the near term but may provide additional information that will facilitate better management in the future. In many adaptive-management problems that have been studied, the optimal active and passive policies (accounting for learning when designing policies and designing policy on the basis of current best information, respectively) are very similar. This seems paradoxical; when faced with uncertainty about the best course of action, managers should spend very little effort on actively designing programs to learn about the system they are managing. We considered two possible reasons why active and passive adaptive solutions are often similar. First, the benefits of learning are often confined to the particular case study in the modeled scenario, whereas in reality information gained from local studies is often applied more broadly. Second, management objectives that incorporate the variance of an estimate may place greater emphasis on learning than more commonly used objectives that aim to maximize an expected value. We explored these issues in a case study of Merri Creek, Melbourne, Australia, in which the aim was to choose between two options for revegetation. We explicitly incorporated monitoring costs in the model. The value of the terminal rewards and the choice of objective both influenced the difference between active and passive adaptive solutions. Explicitly considering the cost of monitoring provided a different perspective on how the terminal reward and management objective affected learning. The states for which it was optimal to monitor did not always coincide with the states in which active and passive adaptive management differed. Our results emphasize that spending resources on monitoring is only optimal when the expected benefits of the options being considered are similar and when the pay-off for learning about their benefits is large.

  18. Mean-variance model for portfolio optimization with background risk based on uncertainty theory

    NASA Astrophysics Data System (ADS)

    Zhai, Jia; Bai, Manying

    2018-04-01

    The aim of this paper is to develop a mean-variance model for portfolio optimization considering the background risk, liquidity and transaction cost based on uncertainty theory. In portfolio selection problem, returns of securities and assets liquidity are assumed as uncertain variables because of incidents or lacking of historical data, which are common in economic and social environment. We provide crisp forms of the model and a hybrid intelligent algorithm to solve it. Under a mean-variance framework, we analyze the portfolio frontier characteristic considering independently additive background risk. In addition, we discuss some effects of background risk and liquidity constraint on the portfolio selection. Finally, we demonstrate the proposed models by numerical simulations.

  19. Parent Management Training-Oregon Model (PMTO™) in Mexico City: Integrating Cultural Adaptation Activities in an Implementation Model

    PubMed Central

    Baumann, Ana A.; Domenech Rodríguez, Melanie M.; Amador, Nancy G.; Forgatch, Marion S.; Parra-Cardona, J. Rubén

    2015-01-01

    This article describes the process of cultural adaptation at the start of the implementation of the Parent Management Training intervention-Oregon model (PMTO) in Mexico City. The implementation process was guided by the model, and the cultural adaptation of PMTO was theoretically guided by the cultural adaptation process (CAP) model. During the process of the adaptation, we uncovered the potential for the CAP to be embedded in the implementation process, taking into account broader training and economic challenges and opportunities. We discuss how cultural adaptation and implementation processes are inextricably linked and iterative and how maintaining a collaborative relationship with the treatment developer has guided our work and has helped expand our research efforts, and how building human capital to implement PMTO in Mexico supported the implementation efforts of PMTO in other places in the United States. PMID:26052184

  20. Parent Management Training-Oregon Model (PMTO™) in Mexico City: Integrating Cultural Adaptation Activities in an Implementation Model.

    PubMed

    Baumann, Ana A; Domenech Rodríguez, Melanie M; Amador, Nancy G; Forgatch, Marion S; Parra-Cardona, J Rubén

    2014-03-01

    This article describes the process of cultural adaptation at the start of the implementation of the Parent Management Training intervention-Oregon model (PMTO) in Mexico City. The implementation process was guided by the model, and the cultural adaptation of PMTO was theoretically guided by the cultural adaptation process (CAP) model. During the process of the adaptation, we uncovered the potential for the CAP to be embedded in the implementation process, taking into account broader training and economic challenges and opportunities. We discuss how cultural adaptation and implementation processes are inextricably linked and iterative and how maintaining a collaborative relationship with the treatment developer has guided our work and has helped expand our research efforts, and how building human capital to implement PMTO in Mexico supported the implementation efforts of PMTO in other places in the United States.

  1. 9: ADAPTATION OF PREGNANCY RISK ASSESSMENT MONITORING SYSTEM (PRAMS) AND PROVIDE A MODEL ON IT

    PubMed Central

    Kharaghani, Roghieh; Shariati, Mohammad; Keramat, Afsaneh; Yunesian, Masud; Moghisi, Alireza

    2017-01-01

    Background and aims A surveillance system helps to detect epidemics and the pattern of the problems incidence in the community and it is essential part of evidence based decision making process. This study aimed to adapt of PRAMS and provide a model on it. Methods This study was performed in 7 steps as follows: Surveillance systems in pregnancy were reviewed and appropriate system was selected for Iran by nominal group technique. Two comparative studies were conducted to determine the similarities and differences between Iran and the selected community. PRAMS method and system were adapted based on the results of the comparative studies and experts opinions. The study tool was adapted. A field trial was conducted to assess adapted PRAMS feasibility based on TELOS (technical, economic, legal, operational, and schedule) model in the city of Shahriar, located in the west of Tehran, and to compare data collection methods. Then, based on the results and consultation with related executive managers, the final model of PRAMS was suggested for Iranian health system. Results Review of the surveillance systems in pregnancy, identified six models. The results of the nominal group technique showed that, the appropriate model for Iran is PRAMS. Based on the comparative studies and expert opinions, the appropriate system and method for program was as follows: the sampling frame was composed of data in thyroid screening forms and hospital records, the sampling method was systematic, data collection methods were home and phone based surveys, and participants were women within 2 to 6 months postpartum who had a live or still birth. The study tool was adapted. Thirty-seven health volunteers collected the data in this study (technical feasibility). Any home based completed questionnaire cost 2.45 and a phone cost 1.89 USD. Many indices were achieved from the study, which were worth much more than the expenses (economic feasibility). The project was consistent with legal requirements

  2. A Context-Adaptive Teacher Training Model in a Ubiquitous Learning Environment

    ERIC Educational Resources Information Center

    Chen, Min; Chiang, Feng Kuang; Jiang, Ya Na; Yu, Sheng Quan

    2017-01-01

    In view of the discrepancies in teacher training and teaching practice, this paper put forward a context-adaptive teacher training model in a ubiquitous learning (u-learning) environment. The innovative model provides teachers of different subjects with adaptive and personalized learning content in a u-learning environment, implements intra- and…

  3. Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach

    PubMed Central

    2016-01-01

    Background Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach. PMID:26812235

  4. A Comparison between High-Energy Radiation Background Models and SPENVIS Trapped-Particle Radiation Models

    NASA Technical Reports Server (NTRS)

    Krizmanic, John F.

    2013-01-01

    We have been assessing the effects of background radiation in low-Earth orbit for the next generation of X-ray and Cosmic-ray experiments, in particular for International Space Station orbit. Outside the areas of high fluxes of trapped radiation, we have been using parameterizations developed by the Fermi team to quantify the high-energy induced background. For the low-energy background, we have been using the AE8 and AP8 SPENVIS models to determine the orbit fractions where the fluxes of trapped particles are too high to allow for useful operation of the experiment. One area we are investigating is how the fluxes of SPENVIS predictions at higher energies match the fluxes at the low-energy end of our parameterizations. I will summarize our methodology for background determination from the various sources of cosmogenic and terrestrial radiation and how these compare to SPENVIS predictions in overlapping energy ranges.

  5. Generalized Background Error covariance matrix model (GEN_BE v2.0)

    NASA Astrophysics Data System (ADS)

    Descombes, G.; Auligné, T.; Vandenberghe, F.; Barker, D. M.

    2014-07-01

    The specification of state background error statistics is a key component of data assimilation since it affects the impact observations will have on the analysis. In the variational data assimilation approach, applied in geophysical sciences, the dimensions of the background error covariance matrix (B) are usually too large to be explicitly determined and B needs to be modeled. Recent efforts to include new variables in the analysis such as cloud parameters and chemical species have required the development of the code to GENerate the Background Errors (GEN_BE) version 2.0 for the Weather Research and Forecasting (WRF) community model to allow for a simpler, flexible, robust, and community-oriented framework that gathers methods used by meteorological operational centers and researchers. We present the advantages of this new design for the data assimilation community by performing benchmarks and showing some of the new features on data assimilation test cases. As data assimilation for clouds remains a challenge, we present a multivariate approach that includes hydrometeors in the control variables and new correlated errors. In addition, the GEN_BE v2.0 code is employed to diagnose error parameter statistics for chemical species, which shows that it is a tool flexible enough to involve new control variables. While the generation of the background errors statistics code has been first developed for atmospheric research, the new version (GEN_BE v2.0) can be easily extended to other domains of science and be chosen as a testbed for diagnostic and new modeling of B. Initially developed for variational data assimilation, the model of the B matrix may be useful for variational ensemble hybrid methods as well.

  6. A Universal Model of Giftedness--An Adaptation of the Munich Model

    ERIC Educational Resources Information Center

    Jessurun, J. H.; Shearer, C. B.; Weggeman, M. C. D. P.

    2016-01-01

    The Munich Model of Giftedness (MMG) by Heller and his colleagues, developed for the identification of gifted children, is adapted and expanded, with the aim of making it more universally usable as a model for the pathway from talents to performance. On the side of the talent-factors, the concept of multiple intelligences is introduced, and the…

  7. Adaptive hyperspectral imager: design, modeling, and control

    NASA Astrophysics Data System (ADS)

    McGregor, Scot; Lacroix, Simon; Monmayrant, Antoine

    2015-08-01

    An adaptive, hyperspectral imager is presented. We propose a system with easily adaptable spectral resolution, adjustable acquisition time, and high spatial resolution which is independent of spectral resolution. The system yields the possibility to define a variety of acquisition schemes, and in particular near snapshot acquisitions that may be used to measure the spectral content of given or automatically detected regions of interest. The proposed system is modelled and simulated, and tests on a first prototype validate the approach to achieve near snapshot spectral acquisitions without resorting to any computationally heavy post-processing, nor cumbersome calibration

  8. Brugga basin's TACD Model Adaptation to current GIS PCRaster 4.1

    NASA Astrophysics Data System (ADS)

    Lopez Rozo, Nicolas Antonio; Corzo Perez, Gerald Augusto; Santos Granados, Germán Ricardo

    2017-04-01

    The process-oriented catchment model TACD (Tracer-Aided Catchment model - Distributed) was developed in the Brugga Basin (Dark Forest, Germany) with a modular structure in the Geographic Information System PCRaster Version 2, in order to dynamically model the natural processes of a complex Basin, such as rainfall, air temperature, solar radiation, evapotranspiration and flow routing among others. Further research and application on this model has been done, such as adapting other meso-scaled basins and adding erosion processes in the hydrological model. However, TACD model is computationally intensive. This has made it not efficient on large and well discretized river basins. Aswell, the current version is not compatible with latest PCRaster Version 4.1, which offers new capabilities on 64-bit hardware architecture, hydraulic calculation improvements, in maps creation, some error and bug fixes. The current work studied and adapted TACD model into the latest GIS PCRaster Version 4.1. This was done by editing the original scripts, replacing deprecated functionalities without losing correctness of the TACD model. The correctness of the adapted TACD model was verified by using the original study case of the Brugga Basin and comparing the adapted model results with the original model results by Stefan Roser in 2001. Small differences were found due to the fact that some hydraulic and hydrological routines were optimized since version 2 of GIS PCRaster. Therefore, the hydraulic and hydrological processes are well represented. With this new working model, further research and development on current topics like uncertainty analysis, GCM downscaling techniques and spatio-temporal modelling are encouraged.

  9. Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks

    NASA Astrophysics Data System (ADS)

    Kanevski, Mikhail

    2015-04-01

    The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press

  10. Modeling spatial tuning of adaptation of the angular vestibulo-ocular reflex

    PubMed Central

    Yakushin, Sergei B.

    2012-01-01

    Gain adaptation of the yaw angular vestibular ocular reflex (aVOR) induced in side-down positions has gravity-independent (global) and -dependent (localized) components. When the head oscillation angles are small during adaptation, localized gain changes are maximal in the approximate position of adaptation. Concurrently, polarization vectors of canal–otolith vestibular neurons adapt their orientations during these small-angle adaptation paradigms. Whether there is orientation adaptation with large amplitude head oscillations, when the head is not localized to a specific position, is unknown. Yaw aVOR gains were decreased by oscillating monkeys about a yaw axis in a side-down position in a subject–stationary visual surround for 2 h. Amplitudes of head oscillation ranged from 15° to 180°. The yaw aVOR gain was tested in darkness at 0.5 Hz, with small angles of oscillation (±15°) while upright and in tilted positions. The peak value of the gain change was highly tuned for small angular oscillations during adaptation and significantly broadened with larger oscillation angles during adaptation. When the orientation of the polarization vectors associated with the gravity-dependent component of the neural network model was adapted toward the direction of gravity, it predicted the localized learning for small angles and the broadening when the orientation adaptation was diminished. The model-based analysis suggests that the otolith orientation adaptation plays an important role in the localized behavior of aVOR as a function of gravity and in regulating the relationship between global and localized adaptation. PMID:22660376

  11. Bayesian Analysis for Exponential Random Graph Models Using the Adaptive Exchange Sampler.

    PubMed

    Jin, Ick Hoon; Yuan, Ying; Liang, Faming

    2013-10-01

    Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the intractable normalizing constant and model degeneracy. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the intractable normalizing constant and model degeneracy issues encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.

  12. Experimental aeroelastic control using adaptive wing model concepts

    NASA Astrophysics Data System (ADS)

    Costa, Antonio P.; Moniz, Paulo A.; Suleman, Afzal

    2001-06-01

    The focus of this study is to evaluate the aeroelastic performance and control of adaptive wings. Ailerons and flaps have been designed and implemented into 3D wings for comparison with adaptive structures and active aerodynamic surface control methods. The adaptive structures concept, the experimental setup and the control design are presented. The wind-tunnel tests of the wing models are presented for the open- and closed-loop systems. The wind tunnel testing has allowed for quantifying the effectiveness of the piezoelectric vibration control of the wings, and also provided performance data for comparison with conventional aerodynamic control surfaces. The results indicate that a wing utilizing skins as active structural elements with embedded piezoelectric actuators can be effectively used to improve the aeroelastic response of aeronautical components. It was also observed that the control authority of adaptive wings is much greater than wings using conventional aerodynamic control surfaces.

  13. Modeling hospitals' adaptive capacity during a loss of infrastructure services.

    PubMed

    Vugrin, Eric D; Verzi, Stephen J; Finley, Patrick D; Turnquist, Mark A; Griffin, Anne R; Ricci, Karen A; Wyte-Lake, Tamar

    2015-01-01

    Resilience in hospitals - their ability to withstand, adapt to, and rapidly recover from disruptive events - is vital to their role as part of national critical infrastructure. This paper presents a model to provide planning guidance to decision makers about how to make hospitals more resilient against possible disruption scenarios. This model represents a hospital's adaptive capacities that are leveraged to care for patients during loss of infrastructure services (power, water, etc.). The model is an optimization that reallocates and substitutes resources to keep patients in a high care state or allocates resources to allow evacuation if necessary. An illustrative example demonstrates how the model might be used in practice.

  14. Testing the Nanoparticle-Allostatic Cross Adaptation-Sensitization Model for Homeopathic Remedy Effects

    PubMed Central

    Bell, Iris R.; Koithan, Mary; Brooks, Audrey J.

    2012-01-01

    Key concepts of the Nanoparticle-Allostatic Cross-Adaptation-Sensitization (NPCAS) Model for the action of homeopathic remedies in living systems include source nanoparticles as low level environmental stressors, heterotypic hormesis, cross-adaptation, allostasis (stress response network), time-dependent sensitization with endogenous amplification and bidirectional change, and self-organizing complex adaptive systems. The model accommodates the requirement for measurable physical agents in the remedy (source nanoparticles and/or source adsorbed to silica nanoparticles). Hormetic adaptive responses in the organism, triggered by nanoparticles; bipolar, metaplastic change, dependent on the history of the organism. Clinical matching of the patient’s symptom picture, including modalities, to the symptom pattern that the source material can cause (cross-adaptation and cross-sensitization). Evidence for nanoparticle-related quantum macro-entanglement in homeopathic pathogenetic trials. This paper examines research implications of the model, discussing the following hypotheses: Variability in nanoparticle size, morphology, and aggregation affects remedy properties and reproducibility of findings. Homeopathic remedies modulate adaptive allostatic responses, with multiple dynamic short- and long-term effects. Simillimum remedy nanoparticles, as novel mild stressors corresponding to the organism’s dysfunction initiate time-dependent cross-sensitization, reversing the direction of dysfunctional reactivity to environmental stressors. The NPCAS model suggests a way forward for systematic research on homeopathy. The central proposition is that homeopathic treatment is a form of nanomedicine acting by modulation of endogenous adaptation and metaplastic amplification processes in the organism to enhance long-term systemic resilience and health. PMID:23290882

  15. Variational dynamic background model for keyword spotting in handwritten documents

    NASA Astrophysics Data System (ADS)

    Kumar, Gaurav; Wshah, Safwan; Govindaraju, Venu

    2013-12-01

    We propose a bayesian framework for keyword spotting in handwritten documents. This work is an extension to our previous work where we proposed dynamic background model, DBM for keyword spotting that takes into account the local character level scores and global word level scores to learn a logistic regression classifier to separate keywords from non-keywords. In this work, we add a bayesian layer on top of the DBM called the variational dynamic background model, VDBM. The logistic regression classifier uses the sigmoid function to separate keywords from non-keywords. The sigmoid function being neither convex nor concave, exact inference of VDBM becomes intractable. An expectation maximization step is proposed to do approximate inference. The advantage of VDBM over the DBM is multi-fold. Firstly, being bayesian, it prevents over-fitting of data. Secondly, it provides better modeling of data and an improved prediction of unseen data. VDBM is evaluated on the IAM dataset and the results prove that it outperforms our prior work and other state of the art line based word spotting system.

  16. The company they keep: Background similarity influences transfer of aftereffects from second- to first-order stimuli

    PubMed Central

    Qian, Ning; Dayan, Peter

    2013-01-01

    A wealth of studies has found that adapting to second-order visual stimuli has little effect on the perception of first-order stimuli. This is physiologically and psychologically troubling, since many cells show similar tuning to both classes of stimuli, and since adapting to first-order stimuli leads to aftereffects that do generalize to second-order stimuli. Focusing on high-level visual stimuli, we recently proposed the novel explanation that the lack of transfer arises partially from the characteristically different backgrounds of the two stimulus classes. Here, we consider the effect of stimulus backgrounds in the far more prevalent, lower-level, case of the orientation tilt aftereffect. Using a variety of first- and second-order oriented stimuli, we show that we could increase or decrease both within- and cross-class adaptation aftereffects by increasing or decreasing the similarity of the otherwise apparently uninteresting or irrelevant backgrounds of adapting and test patterns. Our results suggest that similarity between background statistics of the adapting and test stimuli contributes to low-level visual adaptation, and that these backgrounds are thus not discarded by visual processing but provide contextual modulation of adaptation. Null cross-adaptation aftereffects must also be interpreted cautiously. These findings reduce the apparent inconsistency between psychophysical and neurophysiological data about first- and second-order stimuli. PMID:23732217

  17. Design of a Model Reference Adaptive Controller for an Unmanned Air Vehicle

    NASA Technical Reports Server (NTRS)

    Crespo, Luis G.; Matsutani, Megumi; Annaswamy, Anuradha M.

    2010-01-01

    This paper presents the "Adaptive Control Technology for Safe Flight (ACTS)" architecture, which consists of a non-adaptive controller that provides satisfactory performance under nominal flying conditions, and an adaptive controller that provides robustness under off nominal ones. The design and implementation procedures of both controllers are presented. The aim of these procedures, which encompass both theoretical and practical considerations, is to develop a controller suitable for flight. The ACTS architecture is applied to the Generic Transport Model developed by NASA-Langley Research Center. The GTM is a dynamically scaled test model of a transport aircraft for which a flight-test article and a high-fidelity simulation are available. The nominal controller at the core of the ACTS architecture has a multivariable LQR-PI structure while the adaptive one has a direct, model reference structure. The main control surfaces as well as the throttles are used as control inputs. The inclusion of the latter alleviates the pilot s workload by eliminating the need for cancelling the pitch coupling generated by changes in thrust. Furthermore, the independent usage of the throttles by the adaptive controller enables their use for attitude control. Advantages and potential drawbacks of adaptation are demonstrated by performing high fidelity simulations of a flight-validated controller and of its adaptive augmentation.

  18. Adaptability in linkage of soil carbon nutrient cycles - the SEAM model

    NASA Astrophysics Data System (ADS)

    Wutzler, Thomas; Zaehle, Sönke; Schrumpf, Marion; Ahrens, Bernhard; Reichstein, Markus

    2017-04-01

    In order to understand the coupling of carbon (C) and nitrogen (N) cycles, it is necessary to understand C and N-use efficiencies of microbial soil organic matter (SOM) decomposition. While important controls of those efficiencies by microbial community adaptations have been shown at the scale of a soil pore, an abstract simplified representation of community adaptations is needed at ecosystem scale. Therefore we developed the soil enzyme allocation model (SEAM), which takes a holistic, partly optimality based approach to describe C and N dynamics at the spatial scale of an ecosystem and time-scales of years and longer. We explicitly modelled community adaptation strategies of resource allocation to extracellular enzymes and enzyme limitations on SOM decomposition. Using SEAM, we explored whether alternative strategy-hypotheses can have strong effects on SOM and inorganic N cycling. Results from prototypical simulations and a calibration to observations of an intensive pasture site showed that the so-called revenue enzyme allocation strategy was most viable. This strategy accounts for microbial adaptations to both, stoichiometry and amount of different SOM resources, and supported the largest microbial biomass under a wide range of conditions. Predictions of the SEAM model were qualitatively similar to models explicitly representing competing microbial groups. With adaptive enzyme allocation under conditions of high C/N ratio of litter inputs, N in formerly locked in slowly degrading SOM pools was made accessible, whereas with high N inputs, N was sequestered in SOM and protected from leaching. The finding that adaptation in enzyme allocation changes C and N-use efficiencies of SOM decomposition implies that concepts of C-nutrient cycle interactions should take account for the effects of such adaptations. This can be done using a holistic optimality approach.

  19. An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources.

    PubMed

    Doulamis, A D; Doulamis, N D; Kollias, S D

    2003-01-01

    Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)-based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neural-network architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates the network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neural-network architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neural-network architecture presents better performance than other examined techniques.

  20. Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

    NASA Astrophysics Data System (ADS)

    Yu, Chao; Tan, Guozhen; Lv, Hongtao; Wang, Zhen; Meng, Jun; Hao, Jianye; Ren, Fenghui

    2016-06-01

    Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people’s adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics.

  1. Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies.

    PubMed

    Yu, Chao; Tan, Guozhen; Lv, Hongtao; Wang, Zhen; Meng, Jun; Hao, Jianye; Ren, Fenghui

    2016-06-10

    Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people's adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics.

  2. Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

    PubMed Central

    Yu, Chao; Tan, Guozhen; Lv, Hongtao; Wang, Zhen; Meng, Jun; Hao, Jianye; Ren, Fenghui

    2016-01-01

    Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people’s adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics. PMID:27282089

  3. An adaptive multi-feature segmentation model for infrared image

    NASA Astrophysics Data System (ADS)

    Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa

    2016-04-01

    Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

  4. Modeling Students' Memory for Application in Adaptive Educational Systems

    ERIC Educational Resources Information Center

    Pelánek, Radek

    2015-01-01

    Human memory has been thoroughly studied and modeled in psychology, but mainly in laboratory setting under simplified conditions. For application in practical adaptive educational systems we need simple and robust models which can cope with aspects like varied prior knowledge or multiple-choice questions. We discuss and evaluate several models of…

  5. Adaptation of a multi-resolution adversarial model for asymmetric warfare

    NASA Astrophysics Data System (ADS)

    Rosenberg, Brad; Gonsalves, Paul G.

    2006-05-01

    Recent military operations have demonstrated the use by adversaries of non-traditional or asymmetric military tactics to offset US military might. Rogue nations with links to trans-national terrorists have created a highly unpredictable and potential dangerous environment for US military operations. Several characteristics of these threats include extremism in beliefs, global in nature, non-state oriented, and highly networked and adaptive, thus making these adversaries less vulnerable to conventional military approaches. Additionally, US forces must also contend with more traditional state-based threats that are further evolving their military fighting strategies and capabilities. What are needed are solutions to assist our forces in the prosecution of operations against these diverse threat types and their atypical strategies and tactics. To address this issue, we present a system that allows for the adaptation of a multi-resolution adversarial model. The developed model can then be used to support both training and simulation based acquisition requirements to effectively respond to such an adversary. The described system produces a combined adversarial model by merging behavior modeling at the individual level with aspects at the group and organizational level via network analysis. Adaptation of this adversarial model is performed by means of an evolutionary algorithm to build a suitable model for the chosen adversary.

  6. Enhanced identification and biological validation of differential gene expression via Illumina whole-genome expression arrays through the use of the model-based background correction methodology

    PubMed Central

    Ding, Liang-Hao; Xie, Yang; Park, Seongmi; Xiao, Guanghua; Story, Michael D.

    2008-01-01

    Despite the tremendous growth of microarray usage in scientific studies, there is a lack of standards for background correction methodologies, especially in single-color microarray platforms. Traditional background subtraction methods often generate negative signals and thus cause large amounts of data loss. Hence, some researchers prefer to avoid background corrections, which typically result in the underestimation of differential expression. Here, by utilizing nonspecific negative control features integrated into Illumina whole genome expression arrays, we have developed a method of model-based background correction for BeadArrays (MBCB). We compared the MBCB with a method adapted from the Affymetrix robust multi-array analysis algorithm and with no background subtraction, using a mouse acute myeloid leukemia (AML) dataset. We demonstrated that differential expression ratios obtained by using the MBCB had the best correlation with quantitative RT–PCR. MBCB also achieved better sensitivity in detecting differentially expressed genes with biological significance. For example, we demonstrated that the differential regulation of Tnfr2, Ikk and NF-kappaB, the death receptor pathway, in the AML samples, could only be detected by using data after MBCB implementation. We conclude that MBCB is a robust background correction method that will lead to more precise determination of gene expression and better biological interpretation of Illumina BeadArray data. PMID:18450815

  7. Soft sensor modelling by time difference, recursive partial least squares and adaptive model updating

    NASA Astrophysics Data System (ADS)

    Fu, Y.; Yang, W.; Xu, O.; Zhou, L.; Wang, J.

    2017-04-01

    To investigate time-variant and nonlinear characteristics in industrial processes, a soft sensor modelling method based on time difference, moving-window recursive partial least square (PLS) and adaptive model updating is proposed. In this method, time difference values of input and output variables are used as training samples to construct the model, which can reduce the effects of the nonlinear characteristic on modelling accuracy and retain the advantages of recursive PLS algorithm. To solve the high updating frequency of the model, a confidence value is introduced, which can be updated adaptively according to the results of the model performance assessment. Once the confidence value is updated, the model can be updated. The proposed method has been used to predict the 4-carboxy-benz-aldehyde (CBA) content in the purified terephthalic acid (PTA) oxidation reaction process. The results show that the proposed soft sensor modelling method can reduce computation effectively, improve prediction accuracy by making use of process information and reflect the process characteristics accurately.

  8. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

    PubMed Central

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines. PMID:23408775

  9. Domain Modeling for Adaptive Training and Education in Support of the US Army Learning Model-Research Outline

    DTIC Science & Technology

    2015-06-01

    Definitions are provided for this section to distinguish between adaptive training and education elements and also to highlight their relationships ...illustrate this point Franke (2011) asserts that through the use of case study examples, instruction can provide the pedagogical foundation for decision...a prime example of an adaptive training and education system: a learner or trainee model, an instructional or pedagogical model, a domain model

  10. Data Assimilation in the ADAPT Photospheric Flux Transport Model

    DOE PAGES

    Hickmann, Kyle S.; Godinez, Humberto C.; Henney, Carl J.; ...

    2015-03-17

    Global maps of the solar photospheric magnetic flux are fundamental drivers for simulations of the corona and solar wind and therefore are important predictors of geoeffective events. However, observations of the solar photosphere are only made intermittently over approximately half of the solar surface. The Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model uses localized ensemble Kalman filtering techniques to adjust a set of photospheric simulations to agree with the available observations. At the same time, this information is propagated to areas of the simulation that have not been observed. ADAPT implements a local ensemble transform Kalman filter (LETKF)more » to accomplish data assimilation, allowing the covariance structure of the flux-transport model to influence assimilation of photosphere observations while eliminating spurious correlations between ensemble members arising from a limited ensemble size. We give a detailed account of the implementation of the LETKF into ADAPT. Advantages of the LETKF scheme over previously implemented assimilation methods are highlighted.« less

  11. Urn models for response-adaptive randomized designs: a simulation study based on a non-adaptive randomized trial.

    PubMed

    Ghiglietti, Andrea; Scarale, Maria Giovanna; Miceli, Rosalba; Ieva, Francesca; Mariani, Luigi; Gavazzi, Cecilia; Paganoni, Anna Maria; Edefonti, Valeria

    2018-03-22

    Recently, response-adaptive designs have been proposed in randomized clinical trials to achieve ethical and/or cost advantages by using sequential accrual information collected during the trial to dynamically update the probabilities of treatment assignments. In this context, urn models-where the probability to assign patients to treatments is interpreted as the proportion of balls of different colors available in a virtual urn-have been used as response-adaptive randomization rules. We propose the use of Randomly Reinforced Urn (RRU) models in a simulation study based on a published randomized clinical trial on the efficacy of home enteral nutrition in cancer patients after major gastrointestinal surgery. We compare results with the RRU design with those previously published with the non-adaptive approach. We also provide a code written with the R software to implement the RRU design in practice. In detail, we simulate 10,000 trials based on the RRU model in three set-ups of different total sample sizes. We report information on the number of patients allocated to the inferior treatment and on the empirical power of the t-test for the treatment coefficient in the ANOVA model. We carry out a sensitivity analysis to assess the effect of different urn compositions. For each sample size, in approximately 75% of the simulation runs, the number of patients allocated to the inferior treatment by the RRU design is lower, as compared to the non-adaptive design. The empirical power of the t-test for the treatment effect is similar in the two designs.

  12. Electromagnetic PIC modeling with a background gas

    NASA Astrophysics Data System (ADS)

    Verboncoeur, J. P.; Cooperberg, D.

    1997-02-01

    Modeling the interaction of relativistic electromagnetic plasmas with a background gas is described. The timescales range over many orders of magnitude, from the electromagnetic Courant condition (˜10-12 sec) to electron-neutral collision times (˜10-7 sec) to ion transit times (˜10-5 sec). For this work, the traditional Monte Carlo algorithm [1] is described for relativistic electrons. Subcycling is employed to improve efficiency, and smoothing is employed to reduce particle noise. Applications include plasma-focused electron guns, gas-filled microwave tubes, surface wave discharges driven at microwave frequencies, and electron-cyclotron resonance discharges. The method is implemented in the OOPIC code [2].

  13. Three-dimensional geoelectric modelling with optimal work/accuracy rate using an adaptive wavelet algorithm

    NASA Astrophysics Data System (ADS)

    Plattner, A.; Maurer, H. R.; Vorloeper, J.; Dahmen, W.

    2010-08-01

    Despite the ever-increasing power of modern computers, realistic modelling of complex 3-D earth models is still a challenging task and requires substantial computing resources. The overwhelming majority of current geophysical modelling approaches includes either finite difference or non-adaptive finite element algorithms and variants thereof. These numerical methods usually require the subsurface to be discretized with a fine mesh to accurately capture the behaviour of the physical fields. However, this may result in excessive memory consumption and computing times. A common feature of most of these algorithms is that the modelled data discretizations are independent of the model complexity, which may be wasteful when there are only minor to moderate spatial variations in the subsurface parameters. Recent developments in the theory of adaptive numerical solvers have the potential to overcome this problem. Here, we consider an adaptive wavelet-based approach that is applicable to a large range of problems, also including nonlinear problems. In comparison with earlier applications of adaptive solvers to geophysical problems we employ here a new adaptive scheme whose core ingredients arose from a rigorous analysis of the overall asymptotically optimal computational complexity, including in particular, an optimal work/accuracy rate. Our adaptive wavelet algorithm offers several attractive features: (i) for a given subsurface model, it allows the forward modelling domain to be discretized with a quasi minimal number of degrees of freedom, (ii) sparsity of the associated system matrices is guaranteed, which makes the algorithm memory efficient and (iii) the modelling accuracy scales linearly with computing time. We have implemented the adaptive wavelet algorithm for solving 3-D geoelectric problems. To test its performance, numerical experiments were conducted with a series of conductivity models exhibiting varying degrees of structural complexity. Results were compared

  14. Psychosocial Adaptation to Chronic Illness and Disability: A Virtue Based Model.

    PubMed

    Kim, Jeong Han; McMahon, Brian T; Hawley, Carolyn; Brickham, Dana; Gonzalez, Rene; Lee, Dong-Hun

    2016-03-01

    Psychosocial adaptation to chronic illness and disability (CID) is an area of study where a positive psychology perspective, especially the study of virtues and character strengths, can be implemented within the rehabilitation framework. A carefully developed theory to guide future interdisciplinary research is now timely. A traditional literature review between philosophy and rehabilitation psychology was conducted in order to develop a virtue-based psychosocial adaptation theory, merging important perspectives from the fields of rehabilitation and positive psychology. The virtue-based psychosocial adaptation model (V-PAM) to CID is proposed in the present study. The model involves five qualities or constructs: courage, practical wisdom, commitment to action, integrity and emotional transcendence. Each of these components of virtue contributes to an understanding of psychosocial adaptation. The present study addresses the implications and applications of V-PAM that will advance this understanding.

  15. Adaptive typography for dynamic mapping environments

    NASA Astrophysics Data System (ADS)

    Bardon, Didier

    1991-08-01

    When typography moves across a map, it passes over areas of different colors, densities, and textures. In such a dynamic environment, the aspect of typography must be constantly adapted to provide disernibility for every new background. Adaptive typography undergoes two adaptive operations: background control and contrast control. The background control prevents the features of the map (edges, lines, abrupt changes of densities) from destroying the integrity of the letterform. This is achieved by smoothing the features of the map in the area where a text label is displayed. The modified area is limited to the space covered by the characters of the label. Dispositions are taken to insure that the smoothing operation does not introduce any new visual noise. The contrast control assures that there are sufficient lightness differences between the typography and its ever-changing background. For every new situation, background color and foreground color are compared and the foreground color lightness is adjusted according to a chosen contrast value. Criteria and methods of choosing the appropriate contrast value are presented as well as the experiments that led to them.

  16. Model Adaptation for Prognostics in a Particle Filtering Framework

    NASA Technical Reports Server (NTRS)

    Saha, Bhaskar; Goebel, Kai Frank

    2011-01-01

    One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

  17. Adaptive time-variant models for fuzzy-time-series forecasting.

    PubMed

    Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

    2010-12-01

    A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

  18. Hawkes process model with a time-dependent background rate and its application to high-frequency financial data.

    PubMed

    Omi, Takahiro; Hirata, Yoshito; Aihara, Kazuyuki

    2017-07-01

    A Hawkes process model with a time-varying background rate is developed for analyzing the high-frequency financial data. In our model, the logarithm of the background rate is modeled by a linear model with a relatively large number of variable-width basis functions, and the parameters are estimated by a Bayesian method. Our model can capture not only the slow time variation, such as in the intraday seasonality, but also the rapid one, which follows a macroeconomic news announcement. By analyzing the tick data of the Nikkei 225 mini, we find that (i) our model is better fitted to the data than the Hawkes models with a constant background rate or a slowly varying background rate, which have been commonly used in the field of quantitative finance; (ii) the improvement in the goodness-of-fit to the data by our model is significant especially for sessions where considerable fluctuation of the background rate is present; and (iii) our model is statistically consistent with the data. The branching ratio, which quantifies the level of the endogeneity of markets, estimated by our model is 0.41, suggesting the relative importance of exogenous factors in the market dynamics. We also demonstrate that it is critically important to appropriately model the time-dependent background rate for the branching ratio estimation.

  19. Hawkes process model with a time-dependent background rate and its application to high-frequency financial data

    NASA Astrophysics Data System (ADS)

    Omi, Takahiro; Hirata, Yoshito; Aihara, Kazuyuki

    2017-07-01

    A Hawkes process model with a time-varying background rate is developed for analyzing the high-frequency financial data. In our model, the logarithm of the background rate is modeled by a linear model with a relatively large number of variable-width basis functions, and the parameters are estimated by a Bayesian method. Our model can capture not only the slow time variation, such as in the intraday seasonality, but also the rapid one, which follows a macroeconomic news announcement. By analyzing the tick data of the Nikkei 225 mini, we find that (i) our model is better fitted to the data than the Hawkes models with a constant background rate or a slowly varying background rate, which have been commonly used in the field of quantitative finance; (ii) the improvement in the goodness-of-fit to the data by our model is significant especially for sessions where considerable fluctuation of the background rate is present; and (iii) our model is statistically consistent with the data. The branching ratio, which quantifies the level of the endogeneity of markets, estimated by our model is 0.41, suggesting the relative importance of exogenous factors in the market dynamics. We also demonstrate that it is critically important to appropriately model the time-dependent background rate for the branching ratio estimation.

  20. Adaptive supervision: a theoretical model for social workers.

    PubMed

    Latting, J E

    1986-01-01

    Two models of leadership styles are prominent in the management field: Blake and Mouton's managerial Grid and Hersey and Blanchard's Situational Leadership Model. Much of the research on supervisory styles in social work has been based on the former. A recent public debate between the two sets of theorists suggests that both have strengths and limitations. Accordingly, an adaptive model of social work supervision that combines elements of both theories is proposed.

  1. Coupling internal cerebellar models enhances online adaptation and supports offline consolidation in sensorimotor tasks

    PubMed Central

    Passot, Jean-Baptiste; Luque, Niceto R.; Arleo, Angelo

    2013-01-01

    The cerebellum is thought to mediate sensorimotor adaptation through the acquisition of internal models of the body-environment interaction. These representations can be of two types, identified as forward and inverse models. The first predicts the sensory consequences of actions, while the second provides the correct commands to achieve desired state transitions. In this paper, we propose a composite architecture consisting of multiple cerebellar internal models to account for the adaptation performance of humans during sensorimotor learning. The proposed model takes inspiration from the cerebellar microcomplex circuit, and employs spiking neurons to process information. We investigate the intrinsic properties of the cerebellar circuitry subserving efficient adaptation properties, and we assess the complementary contributions of internal representations by simulating our model in a procedural adaptation task. Our simulation results suggest that the coupling of internal models enhances learning performance significantly (compared with independent forward and inverse models), and it allows for the reproduction of human adaptation capabilities. Furthermore, we provide a computational explanation for the performance improvement observed after one night of sleep in a wide range of sensorimotor tasks. We predict that internal model coupling is a necessary condition for the offline consolidation of procedural memories. PMID:23874289

  2. Adapting crop rotations to climate change in regional impact modelling assessments.

    PubMed

    Teixeira, Edmar I; de Ruiter, John; Ausseil, Anne-Gaelle; Daigneault, Adam; Johnstone, Paul; Holmes, Allister; Tait, Andrew; Ewert, Frank

    2018-03-01

    The environmental and economic sustainability of future cropping systems depends on adaptation to climate change. Adaptation studies commonly rely on agricultural systems models to integrate multiple components of production systems such as crops, weather, soil and farmers' management decisions. Previous adaptation studies have mostly focused on isolated monocultures. However, in many agricultural regions worldwide, multi-crop rotations better represent local production systems. It is unclear how adaptation interventions influence crops grown in sequences. We develop a catchment-scale assessment to investigate the effects of tactical adaptations (choice of genotype and sowing date) on yield and underlying crop-soil factors of rotations. Based on locally surveyed data, a silage-maize followed by catch-crop-wheat rotation was simulated with the APSIM model for the RCP 8.5 emission scenario, two time periods (1985-2004 and 2080-2100) and six climate models across the Kaituna catchment in New Zealand. Results showed that direction and magnitude of climate change impacts, and the response to adaptation, varied spatially and were affected by rotation carryover effects due to agronomical (e.g. timing of sowing and harvesting) and soil (e.g. residual nitrogen, N) aspects. For example, by adapting maize to early-sowing dates under a warmer climate, there was an advance in catch crop establishment which enhanced residual soil N uptake. This dynamics, however, differed with local environment and choice of short- or long-cycle maize genotypes. Adaptation was insufficient to neutralize rotation yield losses in lowlands but consistently enhanced yield gains in highlands, where other constraints limited arable cropping. The positive responses to adaptation were mainly due to increases in solar radiation interception across the entire growth season. These results provide deeper insights on the dynamics of climate change impacts for crop rotation systems. Such knowledge can be used

  3. Model reference adaptive control of robots

    NASA Technical Reports Server (NTRS)

    Steinvorth, Rodrigo

    1991-01-01

    This project presents the results of controlling two types of robots using new Command Generator Tracker (CGT) based Direct Model Reference Adaptive Control (MRAC) algorithms. Two mathematical models were used to represent a single-link, flexible joint arm and a Unimation PUMA 560 arm; and these were then controlled in simulation using different MRAC algorithms. Special attention was given to the performance of the algorithms in the presence of sudden changes in the robot load. Previously used CGT based MRAC algorithms had several problems. The original algorithm that was developed guaranteed asymptotic stability only for almost strictly positive real (ASPR) plants. This condition is very restrictive, since most systems do not satisfy this assumption. Further developments to the algorithm led to an expansion of the number of plants that could be controlled, however, a steady state error was introduced in the response. These problems led to the introduction of some modifications to the algorithms so that they would be able to control a wider class of plants and at the same time would asymptotically track the reference model. This project presents the development of two algorithms that achieve the desired results and simulates the control of the two robots mentioned before. The results of the simulations are satisfactory and show that the problems stated above have been corrected in the new algorithms. In addition, the responses obtained show that the adaptively controlled processes are resistant to sudden changes in the load.

  4. Extracting Optical Fiber Background from Surface-Enhanced Raman Spectroscopy Spectra Based on Bi-Objective Optimization Modeling.

    PubMed

    Huang, Jie; Shi, Tielin; Tang, Zirong; Zhu, Wei; Liao, Guanglan; Li, Xiaoping; Gong, Bo; Zhou, Tengyuan

    2017-08-01

    We propose a bi-objective optimization model for extracting optical fiber background from the measured surface-enhanced Raman spectroscopy (SERS) spectrum of the target sample in the application of fiber optic SERS. The model is built using curve fitting to resolve the SERS spectrum into several individual bands, and simultaneously matching some resolved bands with the measured background spectrum. The Pearson correlation coefficient is selected as the similarity index and its maximum value is pursued during the spectral matching process. An algorithm is proposed, programmed, and demonstrated successfully in extracting optical fiber background or fluorescence background from the measured SERS spectra of rhodamine 6G (R6G) and crystal violet (CV). The proposed model not only can be applied to remove optical fiber background or fluorescence background for SERS spectra, but also can be transferred to conventional Raman spectra recorded using fiber optic instrumentation.

  5. Coastal Adaptation Planning for Sea Level Rise and Extremes: A Global Model for Adaptation Decision-making at the Local Level Given Uncertain Climate Projections

    NASA Astrophysics Data System (ADS)

    Turner, D.

    2014-12-01

    Understanding the potential economic and physical impacts of climate change on coastal resources involves evaluating a number of distinct adaptive responses. This paper presents a tool for such analysis, a spatially-disaggregated optimization model for adaptation to sea level rise (SLR) and storm surge, the Coastal Impact and Adaptation Model (CIAM). This decision-making framework fills a gap between very detailed studies of specific locations and overly aggregate global analyses. While CIAM is global in scope, the optimal adaptation strategy is determined at the local level, evaluating over 12,000 coastal segments as described in the DIVA database (Vafeidis et al. 2006). The decision to pursue a given adaptation measure depends on local socioeconomic factors like income, population, and land values and how they develop over time, relative to the magnitude of potential coastal impacts, based on geophysical attributes like inundation zones and storm surge. For example, the model's decision to protect or retreat considers the costs of constructing and maintaining coastal defenses versus those of relocating people and capital to minimize damages from land inundation and coastal storms. Uncertain storm surge events are modeled with a generalized extreme value distribution calibrated to data on local surge extremes. Adaptation is optimized for the near-term outlook, in an "act then learn then act" framework that is repeated over the model time horizon. This framework allows the adaptation strategy to be flexibly updated, reflecting the process of iterative risk management. CIAM provides new estimates of the economic costs of SLR; moreover, these detailed results can be compactly represented in a set of adaptation and damage functions for use in integrated assessment models. Alongside the optimal result, CIAM evaluates suboptimal cases and finds that global costs could increase by an order of magnitude, illustrating the importance of adaptive capacity and coastal policy.

  6. Model and experiments to optimize co-adaptation in a simplified myoelectric control system.

    PubMed

    Couraud, M; Cattaert, D; Paclet, F; Oudeyer, P Y; de Rugy, A

    2018-04-01

    To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly different from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to be used in myoelectric controls. We combined a simplified myoelectric control with a perturbation for which human adaptation is well characterized and modeled, in order to explore co-adaptation settings in a principled manner. First, we reproduced results obtained in a classical visuomotor rotation paradigm in our simplified myoelectric context, where we rotate the muscle pulling vectors used to reconstruct wrist force from EMG. Then, a model of human adaptation in response to directional error was used to simulate various co-adaptation settings, where perturbations and machine co-adaptation are both applied on muscle pulling vectors. These simulations established that a relatively low gain of machine co-adaptation that minimizes final errors generates slow and incomplete adaptation, while higher gains increase adaptation rate but also errors by amplifying noise. After experimental verification on real subjects, we tested a variable gain that cumulates the advantages of both, and implemented it with directionally tuned neurons similar to those used to model human adaptation. This enables machine co-adaptation to locally improve myoelectric control, and to absorb more challenging perturbations. The simplified context used here enabled to explore co-adaptation settings in both simulations and experiments, and to raise important considerations such as the need for a variable gain encoded locally. The benefits and limits of extending this approach to more complex and functional

  7. Model and experiments to optimize co-adaptation in a simplified myoelectric control system

    NASA Astrophysics Data System (ADS)

    Couraud, M.; Cattaert, D.; Paclet, F.; Oudeyer, P. Y.; de Rugy, A.

    2018-04-01

    Objective. To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly different from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to be used in myoelectric controls. Approach. We combined a simplified myoelectric control with a perturbation for which human adaptation is well characterized and modeled, in order to explore co-adaptation settings in a principled manner. Results. First, we reproduced results obtained in a classical visuomotor rotation paradigm in our simplified myoelectric context, where we rotate the muscle pulling vectors used to reconstruct wrist force from EMG. Then, a model of human adaptation in response to directional error was used to simulate various co-adaptation settings, where perturbations and machine co-adaptation are both applied on muscle pulling vectors. These simulations established that a relatively low gain of machine co-adaptation that minimizes final errors generates slow and incomplete adaptation, while higher gains increase adaptation rate but also errors by amplifying noise. After experimental verification on real subjects, we tested a variable gain that cumulates the advantages of both, and implemented it with directionally tuned neurons similar to those used to model human adaptation. This enables machine co-adaptation to locally improve myoelectric control, and to absorb more challenging perturbations. Significance. The simplified context used here enabled to explore co-adaptation settings in both simulations and experiments, and to raise important considerations such as the need for a variable gain encoded locally. The benefits and limits of extending this

  8. A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

    PubMed

    Liu, Zitao; Hauskrecht, Milos

    2017-11-01

    Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

  9. Adaptive model training system and method

    DOEpatents

    Bickford, Randall L; Palnitkar, Rahul M; Lee, Vo

    2014-04-15

    An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.

  10. Adaptive model training system and method

    DOEpatents

    Bickford, Randall L; Palnitkar, Rahul M

    2014-11-18

    An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.

  11. Optimal region of latching activity in an adaptive Potts model for networks of neurons

    NASA Astrophysics Data System (ADS)

    Abdollah-nia, Mohammad-Farshad; Saeedghalati, Mohammadkarim; Abbassian, Abdolhossein

    2012-02-01

    In statistical mechanics, the Potts model is a model for interacting spins with more than two discrete states. Neural networks which exhibit features of learning and associative memory can also be modeled by a system of Potts spins. A spontaneous behavior of hopping from one discrete attractor state to another (referred to as latching) has been proposed to be associated with higher cognitive functions. Here we propose a model in which both the stochastic dynamics of Potts models and an adaptive potential function are present. A latching dynamics is observed in a limited region of the noise(temperature)-adaptation parameter space. We hence suggest noise as a fundamental factor in such alternations alongside adaptation. From a dynamical systems point of view, the noise-adaptation alternations may be the underlying mechanism for multi-stability in attractor-based models. An optimality criterion for realistic models is finally inferred.

  12. Multiple Model Adaptive Attitude Control of LEO Satellite with Angular Velocity Constraints

    NASA Astrophysics Data System (ADS)

    Shahrooei, Abolfazl; Kazemi, Mohammad Hosein

    2018-04-01

    In this paper, the multiple model adaptive control is utilized to improve the transient response of attitude control system for a rigid spacecraft. An adaptive output feedback control law is proposed for attitude control under angular velocity constraints and its almost global asymptotic stability is proved. The multiple model adaptive control approach is employed to counteract large uncertainty in parameter space of the inertia matrix. The nonlinear dynamics of a low earth orbit satellite is simulated and the proposed control algorithm is implemented. The reported results show the effectiveness of the suggested scheme.

  13. Model reference, sliding mode adaptive control for flexible structures

    NASA Technical Reports Server (NTRS)

    Yurkovich, S.; Ozguner, U.; Al-Abbass, F.

    1988-01-01

    A decentralized model reference adaptive approach using a variable-structure sliding model control has been developed for the vibration suppression of large flexible structures. Local models are derived based upon the desired damping and response time in a model-following scheme, and variable structure controllers are then designed which employ colocated angular rate and position feedback. Numerical simulations have been performed using NASA's flexible grid experimental apparatus.

  14. Plant adaptive behaviour in hydrological models (Invited)

    NASA Astrophysics Data System (ADS)

    van der Ploeg, M. J.; Teuling, R.

    2013-12-01

    Models that will be able to cope with future precipitation and evaporation regimes need a solid base that describes the essence of the processes involved [1]. Micro-behaviour in the soil-vegetation-atmosphere system may have a large impact on patterns emerging at larger scales. A complicating factor in the micro-behaviour is the constant interaction between vegetation and geology in which water plays a key role. The resilience of the coupled vegetation-soil system critically depends on its sensitivity to environmental changes. As a result of environmental changes vegetation may wither and die, but such environmental changes may also trigger gene adaptation. Constant exposure to environmental stresses, biotic or abiotic, influences plant physiology, gene adaptations, and flexibility in gene adaptation [2-6]. Gene expression as a result of different environmental conditions may profoundly impact drought responses across the same plant species. Differences in response to an environmental stress, has consequences for the way species are currently being treated in models (single plant to global scale). In particular, model parameters that control root water uptake and plant transpiration are generally assumed to be a property of the plant functional type. Assigning plant functional types does not allow for local plant adaptation to be reflected in the model parameters, nor does it allow for correlations that might exist between root parameters and soil type. Models potentially provide a means to link root water uptake and transport to large scale processes (e.g. Rosnay and Polcher 1998, Feddes et al. 2001, Jung 2010), especially when powered with an integrated hydrological, ecological and physiological base. We explore the experimental evidence from natural vegetation to formulate possible alternative modeling concepts. [1] Seibert, J. 2000. Multi-criteria calibration of a conceptual runoff model using a genetic algorithm. Hydrology and Earth System Sciences 4(2): 215

  15. Space station dynamic modeling, disturbance accommodation, and adaptive control

    NASA Technical Reports Server (NTRS)

    Wang, S. J.; Ih, C. H.; Lin, Y. H.; Metter, E.

    1985-01-01

    Dynamic models for two space station configurations were derived. Space shuttle docking disturbances and their effects on the station and solar panels are quantified. It is shown that hard shuttle docking can cause solar panel buckling. Soft docking and berthing can substantially reduce structural loads at the expense of large shuttle and station attitude excursions. It is found predocking shuttle momentum reduction is necessary to achieve safe and routine operations. A direct model reference adaptive control is synthesized and evaluated for the station model parameter errors and plant dynamics truncations. The rigid body and the flexible modes are treated. It is shown that convergence of the adaptive algorithm can be achieved in 100 seconds with reasonable performance even during shuttle hard docking operations in which station mass and inertia are instantaneously changed by more than 100%.

  16. Language Model Combination and Adaptation Using Weighted Finite State Transducers

    NASA Technical Reports Server (NTRS)

    Liu, X.; Gales, M. J. F.; Hieronymus, J. L.; Woodland, P. C.

    2010-01-01

    In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaption may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences

  17. A Nonlinear Dynamic Inversion Predictor-Based Model Reference Adaptive Controller for a Generic Transport Model

    NASA Technical Reports Server (NTRS)

    Campbell, Stefan F.; Kaneshige, John T.

    2010-01-01

    Presented here is a Predictor-Based Model Reference Adaptive Control (PMRAC) architecture for a generic transport aircraft. At its core, this architecture features a three-axis, non-linear, dynamic-inversion controller. Command inputs for this baseline controller are provided by pilot roll-rate, pitch-rate, and sideslip commands. This paper will first thoroughly present the baseline controller followed by a description of the PMRAC adaptive augmentation to this control system. Results are presented via a full-scale, nonlinear simulation of NASA s Generic Transport Model (GTM).

  18. Adaptive surrogate model based multiobjective optimization for coastal aquifer management

    NASA Astrophysics Data System (ADS)

    Song, Jian; Yang, Yun; Wu, Jianfeng; Wu, Jichun; Sun, Xiaomin; Lin, Jin

    2018-06-01

    In this study, a novel surrogate model assisted multiobjective memetic algorithm (SMOMA) is developed for optimal pumping strategies of large-scale coastal groundwater problems. The proposed SMOMA integrates an efficient data-driven surrogate model with an improved non-dominated sorted genetic algorithm-II (NSGAII) that employs a local search operator to accelerate its convergence in optimization. The surrogate model based on Kernel Extreme Learning Machine (KELM) is developed and evaluated as an approximate simulator to generate the patterns of regional groundwater flow and salinity levels in coastal aquifers for reducing huge computational burden. The KELM model is adaptively trained during evolutionary search to satisfy desired fidelity level of surrogate so that it inhibits error accumulation of forecasting and results in correctly converging to true Pareto-optimal front. The proposed methodology is then applied to a large-scale coastal aquifer management in Baldwin County, Alabama. Objectives of minimizing the saltwater mass increase and maximizing the total pumping rate in the coastal aquifers are considered. The optimal solutions achieved by the proposed adaptive surrogate model are compared against those solutions obtained from one-shot surrogate model and original simulation model. The adaptive surrogate model does not only improve the prediction accuracy of Pareto-optimal solutions compared with those by the one-shot surrogate model, but also maintains the equivalent quality of Pareto-optimal solutions compared with those by NSGAII coupled with original simulation model, while retaining the advantage of surrogate models in reducing computational burden up to 94% of time-saving. This study shows that the proposed methodology is a computationally efficient and promising tool for multiobjective optimizations of coastal aquifer managements.

  19. Modelling difficulties in abstract thinking in psychosis: the importance of socio-developmental background.

    PubMed

    Berg, A O; Melle, I; Zuber, V; Simonsen, C; Nerhus, M; Ueland, T; Andreassen, O A; Sundet, K; Vaskinn, A

    2017-01-01

    Abstract thinking is important in modern understanding of neurocognitive abilities, and a symptom of thought disorder in psychosis. In patients with psychosis, we assessed if socio-developmental background influences abstract thinking, and the association with executive functioning and clinical psychosis symptoms. Participants (n = 174) had a diagnosis of psychotic or bipolar disorder, were 17-65 years, intelligence quotient (IQ) > 70, fluent in a Scandinavian language, and their full primary education in Norway. Immigrants (N = 58) were matched (1:2) with participants without a history of migration (N = 116). All participants completed a neurocognitive and clinical assessment. Socio-developmental background was operationalised as human developmental index (HDI) of country of birth, at year of birth. Structural equation modelling was used to assess the model with best fit. The model with best fit, χ 2  = 96.591, df = 33, p < .001, confirmed a significant indirect effect of HDI scores on abstract thinking through executive functioning, but not through clinical psychosis symptoms. This study found that socio-developmental background influences abstract thinking in psychosis by indirect effect through executive functioning. We should take into account socio-developmental background in the interpretation of neurocognitive performance in patients with psychosis, and prioritise cognitive remediation in treatment of immigrant patients.

  20. Animal Models of Inflammasomopathies Reveal Roles for Innate but not Adaptive Immunity

    PubMed Central

    Brydges, Susannah D; Mueller, James L; McGeough, Matthew D; Pena, Carla A; Misaghi, Amirhossein; Gandhi, Chhavi; Putnam, Chris D; Boyle, David L; Firestein, Gary S; Horner, Anthony A; Soroosh, Pejman; Watford, Wendy T; O’Shea, John J; Kastner, Daniel L; Hoffman, Hal M

    2009-01-01

    SUMMARY Cryopyrin (NALP3) mediates formation of the inflammasome, a protein complex responsible for cleavage of pro-IL-1β to its active form. Mutations in the cryopyrin gene, NLRP3, cause the autoinflammatory disease spectrum: cryopyrin-associated periodic syndromes (CAPS). The central role of IL-1β in CAPS is supported by the remarkable response to IL-1 targeted therapy. We developed two novel Nlrp3 mutant knock-in mouse strains to model CAPS to examine the role of other inflammatory mediators and adaptive immune responses in an innate immune driven disease. These mice had systemic inflammation and poor growth, similar to some human CAPS patients, and demonstrated early mortality, primarily mediated by myeloid cells. Mating these mutant mice to various knock-out backgrounds confirmed the mouse disease phenotype required an intact inflammasome, was only partially dependent on IL-1β, and was independent of T cells. This data suggests CAPS are true inflammasomopathies and provide insight for more common inflammatory disorders. PMID:19501000

  1. Decentralized Adaptive Control of Systems with Uncertain Interconnections, Plant-Model Mismatch and Actuator Failures

    NASA Technical Reports Server (NTRS)

    Patre, Parag; Joshi, Suresh M.

    2011-01-01

    Decentralized adaptive control is considered for systems consisting of multiple interconnected subsystems. It is assumed that each subsystem s parameters are uncertain and the interconnection parameters are not known. In addition, mismatch can exist between each subsystem and its reference model. A strictly decentralized adaptive control scheme is developed, wherein each subsystem has access only to its own state but has the knowledge of all reference model states. The mismatch is estimated online for each subsystem and the mismatch estimates are used to adaptively modify the corresponding reference models. The adaptive control scheme is extended to the case with actuator failures in addition to mismatch.

  2. Distinguishing Models of Professional Development: The Case of an Adaptive Model's Impact on Teachers' Knowledge, Instruction, and Student Achievement

    ERIC Educational Resources Information Center

    Koellner, Karen; Jacobs, Jennifer

    2015-01-01

    We posit that professional development (PD) models fall on a continuum from highly adaptive to highly specified, and that these constructs provide a productive way to characterize and distinguish among models. The study reported here examines the impact of an adaptive mathematics PD model on teachers' knowledge and instructional practices as well…

  3. Computational model for behavior shaping as an adaptive health intervention strategy.

    PubMed

    Berardi, Vincent; Carretero-González, Ricardo; Klepeis, Neil E; Ghanipoor Machiani, Sahar; Jahangiri, Arash; Bellettiere, John; Hovell, Melbourne

    2018-03-01

    Adaptive behavioral interventions that automatically adjust in real-time to participants' changing behavior, environmental contexts, and individual history are becoming more feasible as the use of real-time sensing technology expands. This development is expected to improve shortcomings associated with traditional behavioral interventions, such as the reliance on imprecise intervention procedures and limited/short-lived effects. JITAI adaptation strategies often lack a theoretical foundation. Increasing the theoretical fidelity of a trial has been shown to increase effectiveness. This research explores the use of shaping, a well-known process from behavioral theory for engendering or maintaining a target behavior, as a JITAI adaptation strategy. A computational model of behavior dynamics and operant conditioning was modified to incorporate the construct of behavior shaping by adding the ability to vary, over time, the range of behaviors that were reinforced when emitted. Digital experiments were performed with this updated model for a range of parameters in order to identify the behavior shaping features that optimally generated target behavior. Narrowing the range of reinforced behaviors continuously in time led to better outcomes compared with a discrete narrowing of the reinforcement window. Rapid narrowing followed by more moderate decreases in window size was more effective in generating target behavior than the inverse scenario. The computational shaping model represents an effective tool for investigating JITAI adaptation strategies. Model parameters must now be translated from the digital domain to real-world experiments so that model findings can be validated.

  4. An adaptable neuromorphic model of orientation selectivity based on floating gate dynamics

    PubMed Central

    Gupta, Priti; Markan, C. M.

    2014-01-01

    The biggest challenge that the neuromorphic community faces today is to build systems that can be considered truly cognitive. Adaptation and self-organization are the two basic principles that underlie any cognitive function that the brain performs. If we can replicate this behavior in hardware, we move a step closer to our goal of having cognitive neuromorphic systems. Adaptive feature selectivity is a mechanism by which nature optimizes resources so as to have greater acuity for more abundant features. Developing neuromorphic feature maps can help design generic machines that can emulate this adaptive behavior. Most neuromorphic models that have attempted to build self-organizing systems, follow the approach of modeling abstract theoretical frameworks in hardware. While this is good from a modeling and analysis perspective, it may not lead to the most efficient hardware. On the other hand, exploiting hardware dynamics to build adaptive systems rather than forcing the hardware to behave like mathematical equations, seems to be a more robust methodology when it comes to developing actual hardware for real world applications. In this paper we use a novel time-staggered Winner Take All circuit, that exploits the adaptation dynamics of floating gate transistors, to model an adaptive cortical cell that demonstrates Orientation Selectivity, a well-known biological phenomenon observed in the visual cortex. The cell performs competitive learning, refining its weights in response to input patterns resembling different oriented bars, becoming selective to a particular oriented pattern. Different analysis performed on the cell such as orientation tuning, application of abnormal inputs, response to spatial frequency and periodic patterns reveal close similarity between our cell and its biological counterpart. Embedded in a RC grid, these cells interact diffusively exhibiting cluster formation, making way for adaptively building orientation selective maps in silicon. PMID

  5. Adaptive Multiscale Modeling of Geochemical Impacts on Fracture Evolution

    NASA Astrophysics Data System (ADS)

    Molins, S.; Trebotich, D.; Steefel, C. I.; Deng, H.

    2016-12-01

    Understanding fracture evolution is essential for many subsurface energy applications, including subsurface storage, shale gas production, fracking, CO2 sequestration, and geothermal energy extraction. Geochemical processes in particular play a significant role in the evolution of fractures through dissolution-driven widening, fines migration, and/or fracture sealing due to precipitation. One obstacle to understanding and exploiting geochemical fracture evolution is that it is a multiscale process. However, current geochemical modeling of fractures cannot capture this multi-scale nature of geochemical and mechanical impacts on fracture evolution, and is limited to either a continuum or pore-scale representation. Conventional continuum-scale models treat fractures as preferential flow paths, with their permeability evolving as a function (often, a cubic law) of the fracture aperture. This approach has the limitation that it oversimplifies flow within the fracture in its omission of pore scale effects while also assuming well-mixed conditions. More recently, pore-scale models along with advanced characterization techniques have allowed for accurate simulations of flow and reactive transport within the pore space (Molins et al., 2014, 2015). However, these models, even with high performance computing, are currently limited in their ability to treat tractable domain sizes (Steefel et al., 2013). Thus, there is a critical need to develop an adaptive modeling capability that can account for separate properties and processes, emergent and otherwise, in the fracture and the rock matrix at different spatial scales. Here we present an adaptive modeling capability that treats geochemical impacts on fracture evolution within a single multiscale framework. Model development makes use of the high performance simulation capability, Chombo-Crunch, leveraged by high resolution characterization and experiments. The modeling framework is based on the adaptive capability in Chombo

  6. An adaptive time-stepping strategy for solving the phase field crystal model

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

    Zhang, Zhengru, E-mail: zrzhang@bnu.edu.cn; Ma, Yuan, E-mail: yuner1022@gmail.com; Qiao, Zhonghua, E-mail: zqiao@polyu.edu.hk

    2013-09-15

    In this work, we will propose an adaptive time step method for simulating the dynamics of the phase field crystal (PFC) model. The numerical simulation of the PFC model needs long time to reach steady state, and then large time-stepping method is necessary. Unconditionally energy stable schemes are used to solve the PFC model. The time steps are adaptively determined based on the time derivative of the corresponding energy. It is found that the use of the proposed time step adaptivity cannot only resolve the steady state solution, but also the dynamical development of the solution efficiently and accurately. Themore » numerical experiments demonstrate that the CPU time is significantly saved for long time simulations.« less

  7. A User-Centered Approach to Adaptive Hypertext Based on an Information Relevance Model

    NASA Technical Reports Server (NTRS)

    Mathe, Nathalie; Chen, James

    1994-01-01

    Rapid and effective to information in large electronic documentation systems can be facilitated if information relevant in an individual user's content can be automatically supplied to this user. However most of this knowledge on contextual relevance is not found within the contents of documents, it is rather established incrementally by users during information access. We propose a new model for interactively learning contextual relevance during information retrieval, and incrementally adapting retrieved information to individual user profiles. The model, called a relevance network, records the relevance of references based on user feedback for specific queries and user profiles. It also generalizes such knowledge to later derive relevant references for similar queries and profiles. The relevance network lets users filter information by context of relevance. Compared to other approaches, it does not require any prior knowledge nor training. More importantly, our approach to adaptivity is user-centered. It facilitates acceptance and understanding by users by giving them shared control over the adaptation without disturbing their primary task. Users easily control when to adapt and when to use the adapted system. Lastly, the model is independent of the particular application used to access information, and supports sharing of adaptations among users.

  8. Physician behavioral adaptability: A model to outstrip a "one size fits all" approach.

    PubMed

    Carrard, Valérie; Schmid Mast, Marianne

    2015-10-01

    Based on a literature review, we propose a model of physician behavioral adaptability (PBA) with the goal of inspiring new research. PBA means that the physician adapts his or her behavior according to patients' different preferences. The PBA model shows how physicians infer patients' preferences and adapt their interaction behavior from one patient to the other. We claim that patients will benefit from better outcomes if their physicians show behavioral adaptability rather than a "one size fits all" approach. This literature review is based on a literature search of the PsycINFO(®) and MEDLINE(®) databases. The literature review and first results stemming from the authors' research support the validity and viability of parts of the PBA model. There is evidence suggesting that physicians are able to show behavioral flexibility when interacting with their different patients, that a match between patients' preferences and physician behavior is related to better consultation outcomes, and that physician behavioral adaptability is related to better consultation outcomes. Training of physicians' behavioral flexibility and their ability to infer patients' preferences can facilitate physician behavioral adaptability and positive patient outcomes. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  9. Modeling the time--varying subjective quality of HTTP video streams with rate adaptations.

    PubMed

    Chen, Chao; Choi, Lark Kwon; de Veciana, Gustavo; Caramanis, Constantine; Heath, Robert W; Bovik, Alan C

    2014-05-01

    Newly developed hypertext transfer protocol (HTTP)-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' quality of experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for online TVSQ prediction in HTTP-based streaming.

  10. Zealotry effects on opinion dynamics in the adaptive voter model

    NASA Astrophysics Data System (ADS)

    Klamser, Pascal P.; Wiedermann, Marc; Donges, Jonathan F.; Donner, Reik V.

    2017-11-01

    The adaptive voter model has been widely studied as a conceptual model for opinion formation processes on time-evolving social networks. Past studies on the effect of zealots, i.e., nodes aiming to spread their fixed opinion throughout the system, only considered the voter model on a static network. Here we extend the study of zealotry to the case of an adaptive network topology co-evolving with the state of the nodes and investigate opinion spreading induced by zealots depending on their initial density and connectedness. Numerical simulations reveal that below the fragmentation threshold a low density of zealots is sufficient to spread their opinion to the whole network. Beyond the transition point, zealots must exhibit an increased degree as compared to ordinary nodes for an efficient spreading of their opinion. We verify the numerical findings using a mean-field approximation of the model yielding a low-dimensional set of coupled ordinary differential equations. Our results imply that the spreading of the zealots' opinion in the adaptive voter model is strongly dependent on the link rewiring probability and the average degree of normal nodes in comparison with that of the zealots. In order to avoid a complete dominance of the zealots' opinion, there are two possible strategies for the remaining nodes: adjusting the probability of rewiring and/or the number of connections with other nodes, respectively.

  11. Improvement of individual camouflage through background choice in ground-nesting birds.

    PubMed

    Stevens, Martin; Troscianko, Jolyon; Wilson-Aggarwal, Jared K; Spottiswoode, Claire N

    2017-09-01

    Animal camouflage is a longstanding example of adaptation. Much research has tested how camouflage prevents detection and recognition, largely focusing on changes to an animal's own appearance over evolution. However, animals could also substantially alter their camouflage by behaviourally choosing appropriate substrates. Recent studies suggest that individuals from several animal taxa could select backgrounds or positions to improve concealment. Here, we test whether individual wild animals choose backgrounds in complex environments, and whether this improves camouflage against predator vision. We studied nest site selection by nine species of ground-nesting birds (nightjars, plovers and coursers) in Zambia, and used image analysis and vision modeling to quantify egg and plumage camouflage to predator vision. Individual birds chose backgrounds that enhanced their camouflage, being better matched to their chosen backgrounds than to other potential backgrounds with respect to multiple aspects of camouflage. This occurred at all three spatial scales tested (a few cm and five meters from the nest, and compared to other sites chosen by conspecifics), and was the case for the eggs of all bird groups studied, and for adult nightjar plumage. Thus, individual wild animals improve their camouflage through active background choice, with choices highly refined across multiple spatial scales.

  12. Improvement of individual camouflage through background choice in ground-nesting birds

    PubMed Central

    Stevens, Martin; Troscianko, Jolyon; Wilson-Aggarwal, Jared K.; Spottiswoode, Claire N.

    2017-01-01

    Animal camouflage is a longstanding example of adaptation. Much research has tested how camouflage prevents detection and recognition, largely focusing on changes to an animal's own appearance over evolution. However, animals could also substantially alter their camouflage by behaviourally choosing appropriate substrates. Recent studies suggest that individuals from several animal taxa could select backgrounds or positions to improve concealment. Here, we test whether individual wild animals choose backgrounds in complex environments, and whether this improves camouflage against predator vision. We studied nest site selection by nine species of ground-nesting birds (nightjars, plovers and coursers) in Zambia, and used image analysis and vision modeling to quantify egg and plumage camouflage to predator vision. Individual birds chose backgrounds that enhanced their camouflage, being better matched to their chosen backgrounds than to other potential backgrounds with respect to multiple aspects of camouflage. This occurred at all three spatial scales tested (a few cm and five meters from the nest, and compared to other sites chosen by conspecifics), and was the case for the eggs of all bird groups studied, and for adult nightjar plumage. Thus, individual wild animals improve their camouflage through active background choice, with choices highly refined across multiple spatial scales. PMID:28890937

  13. Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops.

    PubMed

    Hammer, Graeme L; van Oosterom, Erik; McLean, Greg; Chapman, Scott C; Broad, Ian; Harland, Peter; Muchow, Russell C

    2010-05-01

    Progress in molecular plant breeding is limited by the ability to predict plant phenotype based on its genotype, especially for complex adaptive traits. Suitably constructed crop growth and development models have the potential to bridge this predictability gap. A generic cereal crop growth and development model is outlined here. It is designed to exhibit reliable predictive skill at the crop level while also introducing sufficient physiological rigour for complex phenotypic responses to become emergent properties of the model dynamics. The approach quantifies capture and use of radiation, water, and nitrogen within a framework that predicts the realized growth of major organs based on their potential and whether the supply of carbohydrate and nitrogen can satisfy that potential. The model builds on existing approaches within the APSIM software platform. Experiments on diverse genotypes of sorghum that underpin the development and testing of the adapted crop model are detailed. Genotypes differing in height were found to differ in biomass partitioning among organs and a tall hybrid had significantly increased radiation use efficiency: a novel finding in sorghum. Introducing these genetic effects associated with plant height into the model generated emergent simulated phenotypic differences in green leaf area retention during grain filling via effects associated with nitrogen dynamics. The relevance to plant breeding of this capability in complex trait dissection and simulation is discussed.

  14. Support of surgical process modeling by using adaptable software user interfaces

    NASA Astrophysics Data System (ADS)

    Neumuth, T.; Kaschek, B.; Czygan, M.; Goldstein, D.; Strauß, G.; Meixensberger, J.; Burgert, O.

    2010-03-01

    Surgical Process Modeling (SPM) is a powerful method for acquiring data about the evolution of surgical procedures. Surgical Process Models are used in a variety of use cases including evaluation studies, requirements analysis and procedure optimization, surgical education, and workflow management scheme design. This work proposes the use of adaptive, situation-aware user interfaces for observation support software for SPM. We developed a method to support the modeling of the observer by using an ontological knowledge base. This is used to drive the graphical user interface for the observer to restrict the search space of terminology depending on the current situation. In the evaluation study it is shown, that the workload of the observer was decreased significantly by using adaptive user interfaces. 54 SPM observation protocols were analyzed by using the NASA Task Load Index and it was shown that the use of the adaptive user interface disburdens the observer significantly in workload criteria effort, mental demand and temporal demand, helping him to concentrate on his essential task of modeling the Surgical Process.

  15. Adaptive modeling, identification, and control of dynamic structural systems. I. Theory

    USGS Publications Warehouse

    Safak, Erdal

    1989-01-01

    A concise review of the theory of adaptive modeling, identification, and control of dynamic structural systems based on discrete-time recordings is presented. Adaptive methods have four major advantages over the classical methods: (1) Removal of the noise from the signal is done over the whole frequency band; (2) time-varying characteristics of systems can be tracked; (3) systems with unknown characteristics can be controlled; and (4) a small segment of the data is needed during the computations. Included in the paper are the discrete-time representation of single-input single-output (SISO) systems, models for SISO systems with noise, the concept of stochastic approximation, recursive prediction error method (RPEM) for system identification, and the adaptive control. Guidelines for model selection and model validation and the computational aspects of the method are also discussed in the paper. The present paper is the first of two companion papers. The theory given in the paper is limited to that which is necessary to follow the examples for applications in structural dynamics presented in the second paper.

  16. Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations

    PubMed Central

    Good, Benjamin H.; Rouzine, Igor M.; Balick, Daniel J.; Hallatschek, Oskar; Desai, Michael M.

    2012-01-01

    When large asexual populations adapt, competition between simultaneously segregating mutations slows the rate of adaptation and restricts the set of mutations that eventually fix. This phenomenon of interference arises from competition between mutations of different strengths as well as competition between mutations that arise on different fitness backgrounds. Previous work has explored each of these effects in isolation, but the way they combine to influence the dynamics of adaptation remains largely unknown. Here, we describe a theoretical model to treat both aspects of interference in large populations. We calculate the rate of adaptation and the distribution of fixed mutational effects accumulated by the population. We focus particular attention on the case when the effects of beneficial mutations are exponentially distributed, as well as on a more general class of exponential-like distributions. In both cases, we show that the rate of adaptation and the influence of genetic background on the fixation of new mutants is equivalent to an effective model with a single selection coefficient and rescaled mutation rate, and we explicitly calculate these effective parameters. We find that the effective selection coefficient exactly coincides with the most common fixed mutational effect. This equivalence leads to an intuitive picture of the relative importance of different types of interference effects, which can shift dramatically as a function of the population size, mutation rate, and the underlying distribution of fitness effects. PMID:22371564

  17. Adaptive control of servo system based on LuGre model

    NASA Astrophysics Data System (ADS)

    Jin, Wang; Niancong, Liu; Jianlong, Chen; Weitao, Geng

    2018-03-01

    This paper established a mechanical model of feed system based on LuGre model. In order to solve the influence of nonlinear factors on the system running stability, a nonlinear single observer is designed to estimate the parameter z in the LuGre model and an adaptive friction compensation controller is designed. Simulink simulation results show that the control method can effectively suppress the adverse effects of friction and external disturbances. The simulation show that the adaptive parameter kz is between 0.11-0.13, and the value of gamma1 is between 1.9-2.1. Position tracking error reaches level 10-3 and is stabilized near 0 values within 0.3 seconds, the compensation method has better tracking accuracy and robustness.

  18. Computerized Adaptive Test (CAT) Applications and Item Response Theory Models for Polytomous Items

    ERIC Educational Resources Information Center

    Aybek, Eren Can; Demirtasli, R. Nukhet

    2017-01-01

    This article aims to provide a theoretical framework for computerized adaptive tests (CAT) and item response theory models for polytomous items. Besides that, it aims to introduce the simulation and live CAT software to the related researchers. Computerized adaptive test algorithm, assumptions of item response theory models, nominal response…

  19. "Your Model Is Predictive-- but Is It Useful?" Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation

    ERIC Educational Resources Information Center

    González-Brenes, José P.; Huang, Yun

    2015-01-01

    Classification evaluation metrics are often used to evaluate adaptive tutoring systems-- programs that teach and adapt to humans. Unfortunately, it is not clear how intuitive these metrics are for practitioners with little machine learning background. Moreover, our experiments suggest that existing convention for evaluating tutoring systems may…

  20. Agenda Setting for Health Promotion: Exploring an Adapted Model for the Social Media Era

    PubMed Central

    2015-01-01

    Background The foundation of best practice in health promotion is a robust theoretical base that informs design, implementation, and evaluation of interventions that promote the public’s health. This study provides a novel contribution to health promotion through the adaptation of the agenda-setting approach in response to the contribution of social media. This exploration and proposed adaptation is derived from a study that examined the effectiveness of Twitter in influencing agenda setting among users in relation to road traffic accidents in Saudi Arabia. Objective The proposed adaptations to the agenda-setting model to be explored reflect two levels of engagement: agenda setting within the social media sphere and the position of social media within classic agenda setting. This exploratory research aims to assess the veracity of the proposed adaptations on the basis of the hypotheses developed to test these two levels of engagement. Methods To validate the hypotheses, we collected and analyzed data from two primary sources: Twitter activities and Saudi national newspapers. Keyword mentions served as indicators of agenda promotion; for Twitter, interactions were used to measure the process of agenda setting within the platform. The Twitter final dataset comprised 59,046 tweets and 38,066 users who contributed by tweeting, replying, or retweeting. Variables were collected for each tweet and user. In addition, 518 keyword mentions were recorded from six popular Saudi national newspapers. Results The results showed significant ratification of the study hypotheses at both levels of engagement that framed the proposed adaptions. The results indicate that social media facilitates the contribution of individuals in influencing agendas (individual users accounted for 76.29%, 67.79%, and 96.16% of retweet impressions, total impressions, and amplification multipliers, respectively), a component missing from traditional constructions of agenda-setting models. The influence

  1. How Career Variety Promotes the Adaptability of Managers: A Theoretical Model

    ERIC Educational Resources Information Center

    Karaevli, Ayse; Tim Hall, Douglas T.

    2006-01-01

    This paper presents a theoretical model showing how managerial adaptability develops from career variety over the span of the person's career. By building on the literature of career theory, adult learning and development, and career adjustment, we offer a new conceptualization of managerial adaptability by identifying its behavioral, cognitive,…

  2. Analytical approach to an integrate-and-fire model with spike-triggered adaptation

    NASA Astrophysics Data System (ADS)

    Schwalger, Tilo; Lindner, Benjamin

    2015-12-01

    The calculation of the steady-state probability density for multidimensional stochastic systems that do not obey detailed balance is a difficult problem. Here we present the analytical derivation of the stationary joint and various marginal probability densities for a stochastic neuron model with adaptation current. Our approach assumes weak noise but is valid for arbitrary adaptation strength and time scale. The theory predicts several effects of adaptation on the statistics of the membrane potential of a tonically firing neuron: (i) a membrane potential distribution with a convex shape, (ii) a strongly increased probability of hyperpolarized membrane potentials induced by strong and fast adaptation, and (iii) a maximized variability associated with the adaptation current at a finite adaptation time scale.

  3. Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation.

    PubMed

    Nikzad-Langerodi, Ramin; Lughofer, Edwin; Cernuda, Carlos; Reischer, Thomas; Kantner, Wolfgang; Pawliczek, Marcin; Brandstetter, Markus

    2018-07-12

    The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of vibrational spectroscopy has recently emerged as a promising technique to monitor the DP of MF resins. However, spectroscopic determination of the DP relies on chemometric models, which are usually sensitive to drifts caused by instrumental and/or sample-associated changes occurring over time. In order to detect the time point when drifts start causing prediction bias, we here explore a universal drift detector based on a faded version of the Page-Hinkley (PH) statistic, which we test in three data streams from an industrial MF resin production process. We employ committee disagreement (CD), computed as the variance of model predictions from an ensemble of partial least squares (PLS) models, as a measure for sample-wise prediction uncertainty and use the PH statistic to detect changes in this quantity. We further explore supervised and unsupervised strategies for (semi-)automatic model adaptation upon detection of a drift. For the former, manual reference measurements are requested whenever statistical thresholds on Hotelling's T 2 and/or Q-Residuals are violated. Models are subsequently re-calibrated using weighted partial least squares in order to increase the influence of newer samples, which increases the flexibility when adapting to new (drifted) states. Unsupervised model adaptation is carried out exploiting the dual antecedent-consequent structure of a recently developed fuzzy systems variant of PLS termed FLEXFIS-PLS. In particular, antecedent parts are updated while maintaining the internal structure of the local linear predictors (i.e. the consequents). We found improved drift detection capability of the CD compared to Hotelling's T 2 and Q-Residuals when used in combination with the proposed PH test. Furthermore, we found that active

  4. Ship detection using STFT sea background statistical modeling for large-scale oceansat remote sensing image

    NASA Astrophysics Data System (ADS)

    Wang, Lixia; Pei, Jihong; Xie, Weixin; Liu, Jinyuan

    2018-03-01

    Large-scale oceansat remote sensing images cover a big area sea surface, which fluctuation can be considered as a non-stationary process. Short-Time Fourier Transform (STFT) is a suitable analysis tool for the time varying nonstationary signal. In this paper, a novel ship detection method using 2-D STFT sea background statistical modeling for large-scale oceansat remote sensing images is proposed. First, the paper divides the large-scale oceansat remote sensing image into small sub-blocks, and 2-D STFT is applied to each sub-block individually. Second, the 2-D STFT spectrum of sub-blocks is studied and the obvious different characteristic between sea background and non-sea background is found. Finally, the statistical model for all valid frequency points in the STFT spectrum of sea background is given, and the ship detection method based on the 2-D STFT spectrum modeling is proposed. The experimental result shows that the proposed algorithm can detect ship targets with high recall rate and low missing rate.

  5. Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection

    USGS Publications Warehouse

    Jarnevich, Catherine S.; Talbert, Marian; Morisette, Jeffrey T.; Aldridge, Cameron L.; Brown, Cynthia; Kumar, Sunil; Manier, Daniel; Talbert, Colin; Holcombe, Tracy R.

    2017-01-01

    Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.

  6. Online Denoising Based on the Second-Order Adaptive Statistics Model.

    PubMed

    Yi, Sheng-Lun; Jin, Xue-Bo; Su, Ting-Li; Tang, Zhen-Yun; Wang, Fa-Fa; Xiang, Na; Kong, Jian-Lei

    2017-07-20

    Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter. The proposed method consists of two parts within a closed loop: the first one is to estimate the system state based on the second-order adaptive statistics model and the other is to update the adaptive parameter in the model using the Yule-Walker algorithm. Specifically, the state estimation process was implemented via the Kalman filter in a recursive way, and the online purpose was therefore attained. Experimental data in a reinforced concrete structure test was used to verify the effectiveness of the proposed method. Results show the proposed method not only dealt with the signals with colored noise, but also achieved a tradeoff between efficiency and accuracy.

  7. Real-Time Robust Adaptive Modeling and Scheduling for an Electronic Commerce Server

    NASA Astrophysics Data System (ADS)

    Du, Bing; Ruan, Chun

    With the increasing importance and pervasiveness of Internet services, it is becoming a challenge for the proliferation of electronic commerce services to provide performance guarantees under extreme overload. This paper describes a real-time optimization modeling and scheduling approach for performance guarantee of electronic commerce servers. We show that an electronic commerce server may be simulated as a multi-tank system. A robust adaptive server model is subject to unknown additive load disturbances and uncertain model matching. Overload control techniques are based on adaptive admission control to achieve timing guarantees. We evaluate the performance of the model using a complex simulation that is subjected to varying model parameters and massive overload.

  8. Mosaic evolution and adaptive brain component alteration under domestication seen on the background of evolutionary theory.

    PubMed

    Rehkämper, Gerd; Frahm, Heiko D; Cnotka, Julia

    2008-01-01

    Brain sizes and brain component sizes of five domesticated pigeon breeds including homing (racing) pigeons are compared with rock doves (Columba livia) based on an allometric approach to test the influence of domestication on brain and brain component size. Net brain volume, the volumes of cerebellum and telencephalon as a whole are significantly smaller in almost all domestic pigeons. Inside the telencephalon, mesopallium, nidopallium (+ entopallium + arcopallium) and septum are smaller as well. The hippocampus is significantly larger, particularly in homing pigeons. This finding is in contrast to the predictions of the 'regression hypothesis' of brain alteration under domestication. Among the domestic pigeons homing pigeons have significantly larger olfactory bulbs. These data are interpreted as representing a functional adaptation to homing that is based on spatial cognition and sensory integration. We argue that domestication as seen in domestic pigeons is not principally different from evolution in the wild, but represents a heuristic model to understand the evolutionary process in terms of adaptation and optimization. Copyright 2007 S. Karger AG, Basel.

  9. A model of adaptation for families of elderly patients with dementia: focusing on family resilience.

    PubMed

    Kim, Geun Myun; Lim, Ji Young; Kim, Eun Joo; Kim, Sang Suk

    2017-07-19

    We constructed a model explaining families' positive adaptation in chronic crisis situations such as the problematic behavior of elderly patients with dementia and attendant caregiving stress, based on the family resilience model. Our aim was to devise an adaptation model for families of elderly patients with dementia. A survey of problematic behavior in elderly patients with dementia, family stress, family resilience, and family adaptation was conducted with 292 consenting individuals. The collected data were analyzed using structural equation modeling. The communication process, family stress, and problematic behavior of elderly patients with dementia had direct and indirect effects on family adaptation, while belief system, organization pattern, and social support had indirect effects. Specifically, family stress and more severe problematic behavior by elderly patients with dementia negatively influenced family adaptation, while greater family resilience improved such adaptation. Interventions aiming to enhance family resilience, based on the results of this study, are required to help families with positive adaptation. Such family programs might involve practical support such as education on the characteristics of elderly persons with dementia and coping methods for their problematic behavior; forming self-help groups for families; revitalizing communication within families; and activating communication channels with experts.

  10. Background Model for the Majorana Demonstrator

    NASA Astrophysics Data System (ADS)

    Cuesta, C.; Abgrall, N.; Aguayo, E.; Avignone, F. T.; Barabash, A. S.; Bertrand, F. E.; Boswell, M.; Brudanin, V.; Busch, M.; Byram, D.; Caldwell, A. S.; Chan, Y.-D.; Christofferson, C. D.; Combs, D. C.; Detwiler, J. A.; Doe, P. J.; Efremenko, Yu.; Egorov, V.; Ejiri, H.; Elliott, S. R.; Fast, J. E.; Finnerty, P.; Fraenkle, F. M.; Galindo-Uribarri, A.; Giovanetti, G. K.; Goett, J.; Green, M. P.; Gruszko, J.; Guiseppe, V. E.; Gusev, K.; Hallin, A. L.; Hazama, R.; Hegai, A.; Henning, R.; Hoppe, E. W.; Howard, S.; Howe, M. A.; Keeter, K. J.; Kidd, M. F.; Kochetov, O.; Konovalov, S. I.; Kouzes, R. T.; LaFerriere, B. D.; Leon, J.; Leviner, L. E.; Loach, J. C.; MacMullin, J.; MacMullin, S.; Martin, R. D.; Meijer, S.; Mertens, S.; Nomachi, M.; Orrell, J. L.; O'Shaughnessy, C.; Overman, N. R.; Phillips, D. G.; Poon, A. W. P.; Pushkin, K.; Radford, D. C.; Rager, J.; Rielage, K.; Robertson, R. G. H.; Romero-Romero, E.; Ronquest, M. C.; Schubert, A. G.; Shanks, B.; Shima, T.; Shirchenko, M.; Snavely, K. J.; Snyder, N.; Suriano, A. M.; Thompson, J.; Timkin, V.; Tornow, W.; Trimble, J. E.; Varner, R. L.; Vasilyev, S.; Vetter, K.; Vorren, K.; White, B. R.; Wilkerson, J. F.; Wiseman, C.; Xu, W.; Yakushev, E.; Young, A. R.; Yu, C.-H.; Yumatov, V.

    The Majorana Collaboration is constructing a system containing 40 kg of HPGe detectors to demonstrate the feasibility and potential of a future tonne-scale experiment capable of probing the neutrino mass scale in the inverted-hierarchy region. To realize this, a major goal of the Majorana Demonstrator is to demonstrate a path forward to achieving a background rate at or below 1 cnt/(ROI-t-y) in the 4 keV region of interest around the Q-value at 2039 keV. This goal is pursued through a combination of a significant reduction of radioactive impurities in construction materials with analytical methods for background rejection, for example using powerful pulse shape analysis techniques profiting from the p-type point contact HPGe detectors technology. The effectiveness of these methods is assessed using simulations of the different background components whose purity levels are constrained from radioassay measurements.

  11. Background model for the Majorana Demonstrator

    DOE PAGES

    Cuesta, C.; Abgrall, N.; Aguayo, E.; ...

    2015-01-01

    The Majorana Collaboration is constructing a system containing 40 kg of HPGe detectors to demonstrate the feasibility and potential of a future tonne-scale experiment capable of probing the neutrino mass scale in the inverted-hierarchy region. To realize this, a major goal of the Majorana Demonstrator is to demonstrate a path forward to achieving a background rate at or below 1 cnt/(ROI-t-y) in the 4 keV region of interest around the Q-value at 2039 keV. This goal is pursued through a combination of a significant reduction of radioactive impurities in construction materials with analytical methods for background rejection, for example usingmore » powerful pulse shape analysis techniques profiting from the p-type point contact HPGe detectors technology. The effectiveness of these methods is assessed using simulations of the different background components whose purity levels are constrained from radioassay measurements.« less

  12. The Adaptive Calibration Model of stress responsivity

    PubMed Central

    Ellis, Bruce J.; Shirtcliff, Elizabeth A.

    2010-01-01

    This paper presents the Adaptive Calibration Model (ACM), an evolutionary-developmental theory of individual differences in the functioning of the stress response system. The stress response system has three main biological functions: (1) to coordinate the organism’s allostatic response to physical and psychosocial challenges; (2) to encode and filter information about the organism’s social and physical environment, mediating the organism’s openness to environmental inputs; and (3) to regulate the organism’s physiology and behavior in a broad range of fitness-relevant areas including defensive behaviors, competitive risk-taking, learning, attachment, affiliation and reproductive functioning. The information encoded by the system during development feeds back on the long-term calibration of the system itself, resulting in adaptive patterns of responsivity and individual differences in behavior. Drawing on evolutionary life history theory, we build a model of the development of stress responsivity across life stages, describe four prototypical responsivity patterns, and discuss the emergence and meaning of sex differences. The ACM extends the theory of biological sensitivity to context (BSC) and provides an integrative framework for future research in the field. PMID:21145350

  13. Adaptive surrogate model based multi-objective transfer trajectory optimization between different libration points

    NASA Astrophysics Data System (ADS)

    Peng, Haijun; Wang, Wei

    2016-10-01

    An adaptive surrogate model-based multi-objective optimization strategy that combines the benefits of invariant manifolds and low-thrust control toward developing a low-computational-cost transfer trajectory between libration orbits around the L1 and L2 libration points in the Sun-Earth system has been proposed in this paper. A new structure for a multi-objective transfer trajectory optimization model that divides the transfer trajectory into several segments and gives the dominations for invariant manifolds and low-thrust control in different segments has been established. To reduce the computational cost of multi-objective transfer trajectory optimization, a mixed sampling strategy-based adaptive surrogate model has been proposed. Numerical simulations show that the results obtained from the adaptive surrogate-based multi-objective optimization are in agreement with the results obtained using direct multi-objective optimization methods, and the computational workload of the adaptive surrogate-based multi-objective optimization is only approximately 10% of that of direct multi-objective optimization. Furthermore, the generating efficiency of the Pareto points of the adaptive surrogate-based multi-objective optimization is approximately 8 times that of the direct multi-objective optimization. Therefore, the proposed adaptive surrogate-based multi-objective optimization provides obvious advantages over direct multi-objective optimization methods.

  14. Evaluating mallard adaptive management models with time series

    USGS Publications Warehouse

    Conn, P.B.; Kendall, W.L.

    2004-01-01

    Wildlife practitioners concerned with midcontinent mallard (Anas platyrhynchos) management in the United States have instituted a system of adaptive harvest management (AHM) as an objective format for setting harvest regulations. Under the AHM paradigm, predictions from a set of models that reflect key uncertainties about processes underlying population dynamics are used in coordination with optimization software to determine an optimal set of harvest decisions. Managers use comparisons of the predictive abilities of these models to gauge the relative truth of different hypotheses about density-dependent recruitment and survival, with better-predicting models giving more weight to the determination of harvest regulations. We tested the effectiveness of this strategy by examining convergence rates of 'predictor' models when the true model for population dynamics was known a priori. We generated time series for cases when the a priori model was 1 of the predictor models as well as for several cases when the a priori model was not in the model set. We further examined the addition of different levels of uncertainty into the variance structure of predictor models, reflecting different levels of confidence about estimated parameters. We showed that in certain situations, the model-selection process favors a predictor model that incorporates the hypotheses of additive harvest mortality and weakly density-dependent recruitment, even when the model is not used to generate data. Higher levels of predictor model variance led to decreased rates of convergence to the model that generated the data, but model weight trajectories were in general more stable. We suggest that predictive models should incorporate all sources of uncertainty about estimated parameters, that the variance structure should be similar for all predictor models, and that models with different functional forms for population dynamics should be considered for inclusion in predictor model! sets. All of these

  15. ForCent model development and testing using the Enriched Background Isotope Study experiment

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

    Parton, W.J.; Hanson, P. J.; Swanston, C.

    The ForCent forest ecosystem model was developed by making major revisions to the DayCent model including: (1) adding a humus organic pool, (2) incorporating a detailed root growth model, and (3) including plant phenological growth patterns. Observed plant production and soil respiration data from 1993 to 2000 were used to demonstrate that the ForCent model could accurately simulate ecosystem carbon dynamics for the Oak Ridge National Laboratory deciduous forest. A comparison of ForCent versus observed soil pool {sup 14}C signature ({Delta} {sup 14}C) data from the Enriched Background Isotope Study {sup 14}C experiment (1999-2006) shows that the model correctly simulatesmore » the temporal dynamics of the {sup 14}C label as it moved from the surface litter and roots into the mineral soil organic matter pools. ForCent model validation was performed by comparing the observed Enriched Background Isotope Study experimental data with simulated live and dead root biomass {Delta} {sup 14}C data, and with soil respiration {Delta} {sup 14}C (mineral soil, humus layer, leaf litter layer, and total soil respiration) data. Results show that the model correctly simulates the impact of the Enriched Background Isotope Study {sup 14}C experimental treatments on soil respiration {Delta} {sup 14}C values for the different soil organic matter pools. Model results suggest that a two-pool root growth model correctly represents root carbon dynamics and inputs to the soil. The model fitting process and sensitivity analysis exposed uncertainty in our estimates of the fraction of mineral soil in the slow and passive pools, dissolved organic carbon flux out of the litter layer into the mineral soil, and mixing of the humus layer into the mineral soil layer.« less

  16. Progress in modelling agricultural impacts of and adaptations to climate change.

    PubMed

    Rötter, R P; Hoffmann, M P; Koch, M; Müller, C

    2018-06-01

    Modelling is a key tool to explore agricultural impacts of and adaptations to climate change. Here we report recent progress made especially referring to the large project initiatives MACSUR and AgMIP; in particular, in modelling potential crop impacts from field to global using multi-model ensembles. We identify two main fields where further progress is necessary: a more mechanistic understanding of climate impacts and management options for adaptation and mitigation; and focusing on cropping systems and integrative multi-scale assessments instead of single season and crops, especially in complex tropical and neglected but important cropping systems. Stronger linking of experimentation with statistical and eco-physiological crop modelling could facilitate the necessary methodological advances. Copyright © 2018 Elsevier Ltd. All rights reserved.

  17. Adaptive Counseling and Therapy: An Integrative, Eclectic Model.

    ERIC Educational Resources Information Center

    Howard, George S.; And Others

    1986-01-01

    Presents an integrative model, Adaptive Counseling and Therapy (ACT), for selecting a progression of therapist styles as clients move through developmental stages during the course of counseling and psychotherapy. ACT is intended to be useful to practitioners in case conceptualization and in the application of effective treatment planning.…

  18. Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Nguyen, Nhan T.; Griffin, Brian J.

    2010-01-01

    In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.

  19. Individual-based models for adaptive diversification in high-dimensional phenotype spaces.

    PubMed

    Ispolatov, Iaroslav; Madhok, Vaibhav; Doebeli, Michael

    2016-02-07

    Most theories of evolutionary diversification are based on equilibrium assumptions: they are either based on optimality arguments involving static fitness landscapes, or they assume that populations first evolve to an equilibrium state before diversification occurs, as exemplified by the concept of evolutionary branching points in adaptive dynamics theory. Recent results indicate that adaptive dynamics may often not converge to equilibrium points and instead generate complicated trajectories if evolution takes place in high-dimensional phenotype spaces. Even though some analytical results on diversification in complex phenotype spaces are available, to study this problem in general we need to reconstruct individual-based models from the adaptive dynamics generating the non-equilibrium dynamics. Here we first provide a method to construct individual-based models such that they faithfully reproduce the given adaptive dynamics attractor without diversification. We then show that a propensity to diversify can be introduced by adding Gaussian competition terms that generate frequency dependence while still preserving the same adaptive dynamics. For sufficiently strong competition, the disruptive selection generated by frequency-dependence overcomes the directional evolution along the selection gradient and leads to diversification in phenotypic directions that are orthogonal to the selection gradient. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Modeling posttraumatic growth among cancer patients: The roles of social support, appraisals, and adaptive coping.

    PubMed

    Cao, Weidan; Qi, Xiaona; Cai, Deborah A; Han, Xuanye

    2018-01-01

    The purpose of the study was to build a model to explain the relationships between social support, uncontrollability appraisal, adaptive coping, and posttraumatic growth (PTG) among cancer patients in China. The participants who were cancer patients in a cancer hospital in China filled out a survey. The final sample size was 201. Structural equation modeling was used to build a model explaining PTG. Structural equation modeling results indicated that higher levels of social support predicted higher levels of adaptive coping, higher levels of uncontrollability appraisal predicted lower levels of adaptive coping, and higher levels of adaptive coping predicted higher levels of PTG. Moreover, adaptive coping was a mediator between social support and growth, as well as a mediator between uncontrollability and growth. The direct effects of social support and uncontrollability on PTG were insignificant. The model demonstrated the relationships between social support, uncontrollability appraisal, adaptive coping, and PTG. It could be concluded that uncontrollability appraisal was a required but not sufficient condition for PTG. Neither social support nor uncontrollability appraisal had direct influence on PTG. However, social support and uncontrollability might indirectly influence PTG, through adaptive coping. It implies that both internal factors (eg, cognitive appraisal and coping) and external factors (eg, social support) are required in order for growth to happen. Copyright © 2017 John Wiley & Sons, Ltd.

  1. MRSA model of learning and adaptation: a qualitative study among the general public

    PubMed Central

    2012-01-01

    Background More people in the US now die from Methicillin Resistant Staphylococcus aureus (MRSA) infections than from HIV/AIDS. Often acquired in healthcare facilities or during healthcare procedures, the extremely high incidence of MRSA infections and the dangerously low levels of literacy regarding antibiotic resistance in the general public are on a collision course. Traditional medical approaches to infection control and the conventional attitude healthcare practitioners adopt toward public education are no longer adequate to avoid this collision. This study helps us understand how people acquire and process new information and then adapt behaviours based on learning. Methods Using constructivist theory, semi-structured face-to-face and phone interviews were conducted to gather pertinent data. This allowed participants to tell their stories so their experiences could deepen our understanding of this crucial health issue. Interview transcripts were analysed using grounded theory and sensitizing concepts. Results Our findings were classified into two main categories, each of which in turn included three subthemes. First, in the category of Learning, we identified how individuals used their Experiences with MRSA, to answer the questions: What was learned? and, How did learning occur? The second category, Adaptation gave us insights into Self-reliance, Reliance on others, and Reflections on the MRSA journey. Conclusions This study underscores the critical importance of educational programs for patients, and improved continuing education for healthcare providers. Five specific results of this study can reduce the vacuum that currently exists between the knowledge and information available to healthcare professionals, and how that information is conveyed to the public. These points include: 1) a common model of MRSA learning and adaptation; 2) the self-directed nature of adult learning; 3) the focus on general MRSA information, care and prevention, and antibiotic

  2. Effect of Treatment Education Based on the Roy Adaptation Model on Adjustment of Hemodialysis Patients.

    PubMed

    Kacaroglu Vicdan, Ayse; Gulseven Karabacak, Bilgi

    2016-01-01

    The Roy Adaptation Model examines the individual in 4 fields: physiological mode, self-concept mode, role function mode, and interdependence mode. Hemodialysis treatment is associated with the Roy Adaptation Model as it involves fields that might be needed by the individual with chronic renal disease. This research was conducted as randomized controlled experiment with the aim of determining the effect of the education given in accordance with the Roy Adaptation Model on physiological, psychological, and social adaptation of individuals undergoing hemodialysis treatment. This was a random controlled experimental study. The study was conducted at a dialysis center in Konya-Aksehir in Turkey between July 1 and December 31, 2012. The sample was composed of 82 individuals-41 experimental and 41 control. In the second interview, there was a decrease in the systolic blood pressures and body weights of the experimental group, an increase in the scores of functional performance and self-respect, and a decrease in the scores of psychosocial adaptation. In the control group, on the other hand, there was a decrease in the scores of self-respect and an increase in the scores of psychosocial adaptation. The 2 groups were compared in terms of adaptation variables and a difference was determined on behalf of the experimental group. The training that was provided and evaluated for individuals receiving hemodialysis according to 4 modes of the Roy Adaptation Model increased physical, psychological, and social adaptation.

  3. Location- and lesion-dependent estimation of background tissue complexity for anthropomorphic model observer

    NASA Astrophysics Data System (ADS)

    Avanaki, Ali R. N.; Espig, Kathryn; Knippel, Eddie; Kimpe, Tom R. L.; Xthona, Albert; Maidment, Andrew D. A.

    2016-03-01

    In this paper, we specify a notion of background tissue complexity (BTC) as perceived by a human observer that is suited for use with model observers. This notion of BTC is a function of image location and lesion shape and size. We propose four unsupervised BTC estimators based on: (i) perceived pre- and post-lesion similarity of images, (ii) lesion border analysis (LBA; conspicuous lesion should be brighter than its surround), (iii) tissue anomaly detection, and (iv) mammogram density measurement. The latter two are existing methods we adapt for location- and lesion-dependent BTC estimation. To validate the BTC estimators, we ask human observers to measure BTC as the visibility threshold amplitude of an inserted lesion at specified locations in a mammogram. Both human-measured and computationally estimated BTC varied with lesion shape (from circular to oval), size (from small circular to larger circular), and location (different points across a mammogram). BTCs measured by different human observers are correlated (ρ=0.67). BTC estimators are highly correlated to each other (0.84model observer, with applications such as optimization of contrast-enhanced medical imaging systems, and creation of a diversified image dataset with characteristics of a desired population.

  4. Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona

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

    Marsac, Kara E.; Burnley, Pamela C.; Adcock, Christopher T.

    Here, this study compares high-resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (Nationalmore » Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likely due to a number of factors including the need to take into account the evolution of soil geochemistry during chemical weathering and the influence of aeolian addition. Refinements to the forward models were made using ASTER visualizations to create subunits of similar exposure rate within the Chinle Formation, which contains multiple lithologies and by grouping alluvial units by drainage basin rather than age.« less

  5. Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona

    DOE PAGES

    Marsac, Kara E.; Burnley, Pamela C.; Adcock, Christopher T.; ...

    2016-09-16

    Here, this study compares high-resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (Nationalmore » Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likely due to a number of factors including the need to take into account the evolution of soil geochemistry during chemical weathering and the influence of aeolian addition. Refinements to the forward models were made using ASTER visualizations to create subunits of similar exposure rate within the Chinle Formation, which contains multiple lithologies and by grouping alluvial units by drainage basin rather than age.« less

  6. Use of Time Information in Models behind Adaptive System for Building Fluency in Mathematics

    ERIC Educational Resources Information Center

    Rihák, Jirí

    2015-01-01

    In this work we introduce the system for adaptive practice of foundations of mathematics. Adaptivity of the system is primarily provided by selection of suitable tasks, which uses information from a domain model and a student model. The domain model does not use prerequisites but works with splitting skills to more concrete sub-skills. The student…

  7. Multithreaded Model for Dynamic Load Balancing Parallel Adaptive PDE Computations

    NASA Technical Reports Server (NTRS)

    Chrisochoides, Nikos

    1995-01-01

    We present a multithreaded model for the dynamic load-balancing of numerical, adaptive computations required for the solution of Partial Differential Equations (PDE's) on multiprocessors. Multithreading is used as a means of exploring concurrency in the processor level in order to tolerate synchronization costs inherent to traditional (non-threaded) parallel adaptive PDE solvers. Our preliminary analysis for parallel, adaptive PDE solvers indicates that multithreading can be used an a mechanism to mask overheads required for the dynamic balancing of processor workloads with computations required for the actual numerical solution of the PDE's. Also, multithreading can simplify the implementation of dynamic load-balancing algorithms, a task that is very difficult for traditional data parallel adaptive PDE computations. Unfortunately, multithreading does not always simplify program complexity, often makes code re-usability not an easy task, and increases software complexity.

  8. Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller.

    PubMed

    Lin, Chih-Min; Yang, Ming-Shu; Chao, Fei; Hu, Xiao-Min; Zhang, Jun

    2016-10-01

    This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties, type-2 fuzzy sets can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so that the convergence of the filtering error can be guaranteed. In order to demonstrate the performance of the proposed adaptive T2FCMAC filter, it is tested in signal processing applications, including a nonlinear channel equalization system, a time-varying channel equalization system, and an adaptive noise cancellation system. The advantages of the proposed filter over the other adaptive filters are verified through simulations.

  9. In Vivo Rodent Models of Skeletal Muscle Adaptation to Decreased Use.

    PubMed

    Cho, Su Han; Kim, Jang Hoe; Song, Wook

    2016-03-01

    Skeletal muscle possesses plasticity and adaptability to external and internal physiological changes. Due to these characteristics, skeletal muscle shows dramatic changes depending on its response to stimuli such as physical activity, nutritional changes, disease status, and environmental changes. Modulation of the rate of protein synthesis/degradation plays an important role in atrophic responses. The purpose of this review is to describe different features of skeletal muscle adaptation with various models of deceased use. In this review, four models were addressed: immobilization, spinal cord transection, hindlimb unloading, and aging. Immobilization is a form of decreased use in which skeletal muscle shows electrical activity, tension development, and motion. These results differ by muscle group. Spinal cord transection was selected to simulate spinal cord injury. Similar to the immobilization model, dramatic atrophy occurs in addition to fiber type conversion in this model. Despite the fact that electromyography shows unremarkable changes in muscle after hindlimb unloading, decreased muscle mass and contractile force are observed. Lastly, aging significantly decreases the numbers of muscle fibers and motor units. Skeletal muscle responses to decreased use include decreased strength, decreased fiber numbers, and fiber type transformation. These four models demonstrated different changes in the skeletal muscle. This review elucidates the different skeletal muscle adaptations in these four decreased use animal models and encourages further studies.

  10. Particle Swarm Social Adaptive Model for Multi-Agent Based Insurgency Warfare Simulation

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

    Cui, Xiaohui; Potok, Thomas E

    2009-12-01

    To better understand insurgent activities and asymmetric warfare, a social adaptive model for modeling multiple insurgent groups attacking multiple military and civilian targets is proposed and investigated. This report presents a pilot study using the particle swarm modeling, a widely used non-linear optimal tool to model the emergence of insurgency campaign. The objective of this research is to apply the particle swarm metaphor as a model of insurgent social adaptation for the dynamically changing environment and to provide insight and understanding of insurgency warfare. Our results show that unified leadership, strategic planning, and effective communication between insurgent groups are notmore » the necessary requirements for insurgents to efficiently attain their objective.« less

  11. Nursing Approach Based on Roy Adaptation Model in a Patient Undergoing Breast Conserving Surgery for Breast Cancer.

    PubMed

    Ursavaş, Figen Erol; Karayurt, Özgül; İşeri, Özge

    2014-07-01

    The use of models in nursing provides nurses to focus on the role of nursing and its applications rather than medical practice. In addition, it helps patient care to be systematic, purposeful, controlled and effective. One of the commonly used models in nursing is Roy Adaptation Model. According to Roy adaptation model, the aim of nursing is to increase compliance and life expectancy. Roy Adaptation Model evaluates the patient in physiologic mode, self-concept mode, role function mode and interdependence mode aiming to provide holistic care. This article describes the use of Roy Adaptation Model in the care of a patient who has been diagnosed with breast cancer and had breast-conserving surgery. Patient data was evaluated in the four modes of Roy adaptation model (physiologic, self-concept, role function, and interdependence modes) and the nursing process was applied.

  12. Reach Adaptation: What Determines Whether We Learn an Internal Model of the Tool or Adapt the Model of Our Arm?

    PubMed Central

    Kluzik, JoAnn; Diedrichsen, Jörn; Shadmehr, Reza; Bastian, Amy J.

    2008-01-01

    We make errors when learning to use a new tool. However, the cause of error may be ambiguous: is it because we misestimated properties of the tool or of our own arm? We considered a well-studied adaptation task in which people made goal-directed reaching movements while holding the handle of a robotic arm. The robot produced viscous forces that perturbed reach trajectories. As reaching improved with practice, did people recalibrate an internal model of their arm, or did they build an internal model of the novel tool (robot), or both? What factors influenced how the brain solved this credit assignment problem? To investigate these questions, we compared transfer of adaptation between three conditions: catch trials in which robot forces were turned off unannounced, robot-null trials in which subjects were told that forces were turned off, and free-space trials in which subjects still held the handle but watched as it was detached from the robot. Transfer to free space was 40% of that observed in unannounced catch trials. We next hypothesized that transfer to free space might increase if the training field changed gradually, rather than abruptly. Indeed, this method increased transfer to free space from 40 to 60%. Therefore although practice with a novel tool resulted in formation of an internal model of the tool, it also appeared to produce a transient change in the internal model of the subject's arm. Gradual changes in the tool's dynamics increased the extent to which the nervous system recalibrated the model of the subject's own arm. PMID:18596187

  13. Classrooms as Complex Adaptive Systems: A Relational Model

    ERIC Educational Resources Information Center

    Burns, Anne; Knox, John S.

    2011-01-01

    In this article, we describe and model the language classroom as a complex adaptive system (see Logan & Schumann, 2005). We argue that linear, categorical descriptions of classroom processes and interactions do not sufficiently explain the complex nature of classrooms, and cannot account for how classroom change occurs (or does not occur), over…

  14. Dynamical information encoding in neural adaptation.

    PubMed

    Luozheng Li; Wenhao Zhang; Yuanyuan Mi; Dahui Wang; Xiaohan Lin; Si Wu

    2016-08-01

    Adaptation refers to the general phenomenon that a neural system dynamically adjusts its response property according to the statistics of external inputs. In response to a prolonged constant stimulation, neuronal firing rates always first increase dramatically at the onset of the stimulation; and afterwards, they decrease rapidly to a low level close to background activity. This attenuation of neural activity seems to be contradictory to our experience that we can still sense the stimulus after the neural system is adapted. Thus, it prompts a question: where is the stimulus information encoded during the adaptation? Here, we investigate a computational model in which the neural system employs a dynamical encoding strategy during the neural adaptation: at the early stage of the adaptation, the stimulus information is mainly encoded in the strong independent firings; and as time goes on, the information is shifted into the weak but concerted responses of neurons. We find that short-term plasticity, a general feature of synapses, provides a natural mechanism to achieve this goal. Furthermore, we demonstrate that with balanced excitatory and inhibitory inputs, this correlation-based information can be read out efficiently. The implications of this study on our understanding of neural information encoding are discussed.

  15. Adaptive control of a jet turboshaft engine driving a variable pitch propeller using multiple models

    NASA Astrophysics Data System (ADS)

    Ahmadian, Narjes; Khosravi, Alireza; Sarhadi, Pouria

    2017-08-01

    In this paper, a multiple model adaptive control (MMAC) method is proposed for a gas turbine engine. The model of a twin spool turbo-shaft engine driving a variable pitch propeller includes various operating points. Variations in fuel flow and propeller pitch inputs produce different operating conditions which force the controller to be adopted rapidly. Important operating points are three idle, cruise and full thrust cases for the entire flight envelope. A multi-input multi-output (MIMO) version of second level adaptation using multiple models is developed. Also, stability analysis using Lyapunov method is presented. The proposed method is compared with two conventional first level adaptation and model reference adaptive control techniques. Simulation results for JetCat SPT5 turbo-shaft engine demonstrate the performance and fidelity of the proposed method.

  16. Evolution of adaptation mechanisms: Adaptation energy, stress, and oscillating death.

    PubMed

    Gorban, Alexander N; Tyukina, Tatiana A; Smirnova, Elena V; Pokidysheva, Lyudmila I

    2016-09-21

    In 1938, Selye proposed the notion of adaptation energy and published 'Experimental evidence supporting the conception of adaptation energy.' Adaptation of an animal to different factors appears as the spending of one resource. Adaptation energy is a hypothetical extensive quantity spent for adaptation. This term causes much debate when one takes it literally, as a physical quantity, i.e. a sort of energy. The controversial points of view impede the systematic use of the notion of adaptation energy despite experimental evidence. Nevertheless, the response to many harmful factors often has general non-specific form and we suggest that the mechanisms of physiological adaptation admit a very general and nonspecific description. We aim to demonstrate that Selye׳s adaptation energy is the cornerstone of the top-down approach to modelling of non-specific adaptation processes. We analyze Selye׳s axioms of adaptation energy together with Goldstone׳s modifications and propose a series of models for interpretation of these axioms. Adaptation energy is considered as an internal coordinate on the 'dominant path' in the model of adaptation. The phenomena of 'oscillating death' and 'oscillating remission' are predicted on the base of the dynamical models of adaptation. Natural selection plays a key role in the evolution of mechanisms of physiological adaptation. We use the fitness optimization approach to study of the distribution of resources for neutralization of harmful factors, during adaptation to a multifactor environment, and analyze the optimal strategies for different systems of factors. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Background Model for the Majorana Demonstrator

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

    Cuesta, C.; Abgrall, N.; Aguayo, Estanislao

    2015-06-01

    The Majorana Collaboration is constructing a prototype system containing 40 kg of HPGe detectors to demonstrate the feasibility and potential of a future tonne-scale experiment to search for neutrinoless double-beta (0v BB) decay in 76Ge. In view of the requirement that the next generation of tonne-scale Ge-based 0vBB-decay experiment be capable of probing the neutrino mass scale in the inverted-hierarchy region, a major goal of theMajorana Demonstrator is to demonstrate a path forward to achieving a background rate at or below 1 cnt/(ROI-t-y) in the 4 keV region of interest around the Q-value at 2039 keV. This goal is pursuedmore » through a combination of a significant reduction of radioactive impurities in construction materials with analytical methods for background rejection, for example using powerful pulse shape analysis techniques profiting from the p-type point contact HPGe detectors technology. The effectiveness of these methods is assessed using Geant4 simulations of the different background components whose purity levels are constrained from radioassay measurements.« less

  18. Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona.

    PubMed

    Marsac, Kara E; Burnley, Pamela C; Adcock, Christopher T; Haber, Daniel A; Malchow, Russell L; Hausrath, Elisabeth M

    2016-12-01

    This study compares high resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (National Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likely due to a number of factors including the need to take into account the evolution of soil geochemistry during chemical weathering and the influence of aeolian addition. Refinements to the forward models were made using ASTER visualizations to create subunits of similar exposure rate within the Chinle Formation, which contains multiple lithologies and by grouping alluvial units by drainage basin rather than age. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  19. Comparing model-based adaptive LMS filters and a model-free hysteresis loop analysis method for structural health monitoring

    NASA Astrophysics Data System (ADS)

    Zhou, Cong; Chase, J. Geoffrey; Rodgers, Geoffrey W.; Xu, Chao

    2017-02-01

    The model-free hysteresis loop analysis (HLA) method for structural health monitoring (SHM) has significant advantages over the traditional model-based SHM methods that require a suitable baseline model to represent the actual system response. This paper provides a unique validation against both an experimental reinforced concrete (RC) building and a calibrated numerical model to delineate the capability of the model-free HLA method and the adaptive least mean squares (LMS) model-based method in detecting, localizing and quantifying damage that may not be visible, observable in overall structural response. Results clearly show the model-free HLA method is capable of adapting to changes in how structures transfer load or demand across structural elements over time and multiple events of different size. However, the adaptive LMS model-based method presented an image of greater spread of lesser damage over time and story when the baseline model is not well defined. Finally, the two algorithms are tested over a simpler hysteretic behaviour typical steel structure to quantify the impact of model mismatch between the baseline model used for identification and the actual response. The overall results highlight the need for model-based methods to have an appropriate model that can capture the observed response, in order to yield accurate results, even in small events where the structure remains linear.

  20. Model Adaptation in Parametric Space for POD-Galerkin Models

    NASA Astrophysics Data System (ADS)

    Gao, Haotian; Wei, Mingjun

    2017-11-01

    The development of low-order POD-Galerkin models is largely motivated by the expectation to use the model developed with a set of parameters at their native values to predict the dynamic behaviors of the same system under different parametric values, in other words, a successful model adaptation in parametric space. However, most of time, even small deviation of parameters from their original value may lead to large deviation or unstable results. It has been shown that adding more information (e.g. a steady state, mean value of a different unsteady state, or an entire different set of POD modes) may improve the prediction of flow with other parametric states. For a simple case of the flow passing a fixed cylinder, an orthogonal mean mode at a different Reynolds number may stabilize the POD-Galerkin model when Reynolds number is changed. For a more complicated case of the flow passing an oscillatory cylinder, a global POD-Galerkin model is first applied to handle the moving boundaries, then more information (e.g. more POD modes) is required to predicate the flow under different oscillatory frequencies. Supported by ARL.

  1. Optimizing the learning rate for adaptive estimation of neural encoding models

    PubMed Central

    2018-01-01

    Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel

  2. Optimizing the learning rate for adaptive estimation of neural encoding models.

    PubMed

    Hsieh, Han-Lin; Shanechi, Maryam M

    2018-05-01

    Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel

  3. Adaptive Optics Communications Performance Analysis

    NASA Technical Reports Server (NTRS)

    Srinivasan, M.; Vilnrotter, V.; Troy, M.; Wilson, K.

    2004-01-01

    The performance improvement obtained through the use of adaptive optics for deep-space communications in the presence of atmospheric turbulence is analyzed. Using simulated focal-plane signal-intensity distributions, uncoded pulse-position modulation (PPM) bit-error probabilities are calculated assuming the use of an adaptive focal-plane detector array as well as an adaptively sized single detector. It is demonstrated that current practical adaptive optics systems can yield performance gains over an uncompensated system ranging from approximately 1 dB to 6 dB depending upon the PPM order and background radiation level.

  4. Energetics, adaptation, and adaptability.

    PubMed

    Ulijaszek, Stanley J

    1996-01-01

    Energy capture and conversion are fundamental to human existence, and over the past three decades biological anthropologists have used a number of approaches which incorporate energetics measures in studies of human population biology. Human groups can vary enormously in their energy expenditure. This review considers evidence for genetic adaptation and presents models for physiological adaptability to reduced physiological energy availability and/or negative energy balance. In industrialized populations, different aspects of energy expenditure have been shown to have a genetic component, including basal metabolic rate, habitual physical activity level, mechanical efficiency of work performance, and thermic effect of food. Metabolic adaptation to low energy intakes has been demonstrated in populations in both developing and industrialized nations. Thyroid hormone-related effects on energy metabolic responses to low physiological energy availability are unified in a model, linking energetic adaptability in physical activity and maintenance metabolism. Negative energy balance has been shown to be associated with reduced reproductive function in women experiencing seasonal environments in some developing countries. Existing models relating negative energy balance to menstrual or ovulatory function are largely descriptive, and do not propose any physiological mechanisms for this phenomenon. A model is proposed whereby reduced physiological energy availability could influence ovulatory function via low serum levels of the amino acid aspartate and reduced sympathetic nervous system activity. © 1996 Wiley-Liss, Inc. Copyright © 1996 Wiley-Liss, Inc.

  5. An Ad-Hoc Adaptive Pilot Model for Pitch Axis Gross Acquisition Tasks

    NASA Technical Reports Server (NTRS)

    Hanson, Curtis E.

    2012-01-01

    An ad-hoc algorithm is presented for real-time adaptation of the well-known crossover pilot model and applied to pitch axis gross acquisition tasks in a generic fighter aircraft. Off-line tuning of the crossover model to human pilot data gathered in a fixed-based high fidelity simulation is first accomplished for a series of changes in aircraft dynamics to provide expected values for model parameters. It is shown that in most cases, for this application, the traditional crossover model can be reduced to a gain and a time delay. The ad-hoc adaptive pilot gain algorithm is shown to have desirable convergence properties for most types of changes in aircraft dynamics.

  6. A plastic corticostriatal circuit model of adaptation in perceptual decision making

    PubMed Central

    Hsiao, Pao-Yueh; Lo, Chung-Chuan

    2013-01-01

    The ability to optimize decisions and adapt them to changing environments is a crucial brain function that increase survivability. Although much has been learned about the neuronal activity in various brain regions that are associated with decision making, and about how the nervous systems may learn to achieve optimization, the underlying neuronal mechanisms of how the nervous systems optimize decision strategies with preference given to speed or accuracy, and how the systems adapt to changes in the environment, remain unclear. Based on extensive empirical observations, we addressed the question by extending a previously described cortico-basal ganglia circuit model of perceptual decisions with the inclusion of a dynamic dopamine (DA) system that modulates spike-timing dependent plasticity (STDP). We found that, once an optimal model setting that maximized the reward rate was selected, the same setting automatically optimized decisions across different task environments through dynamic balancing between the facilitating and depressing components of the DA dynamics. Interestingly, other model parameters were also optimal if we considered the reward rate that was weighted by the subject's preferences for speed or accuracy. Specifically, the circuit model favored speed if we increased the phasic DA response to the reward prediction error, whereas the model favored accuracy if we reduced the tonic DA activity or the phasic DA responses to the estimated reward probability. The proposed model provides insight into the roles of different components of DA responses in decision adaptation and optimization in a changing environment. PMID:24339814

  7. ForCent Model Development and Testing using the Enriched Background Isotope Study (EBIS) Experiment

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

    Parton, William; Hanson, Paul J; Swanston, Chris

    The ForCent forest ecosystem model was developed by making major revisions to the DayCent model including: (1) adding a humus organic pool, (2) incorporating a detailed root growth model, and (3) including plant phenological growth patterns. Observed plant production and soil respiration data from 1993 to 2000 were used to demonstrate that the ForCent model could accurately simulate ecosystem carbon dynamics for the Oak Ridge National Laboratory deciduous forest. A comparison of ForCent versus observed soil pool 14C signature (? 14C) data from the Enriched Background Isotope Study 14C experiment (1999-2006) shows that the model correctly simulates the temporal dynamicsmore » of the 14C label as it moved from the surface litter and roots into the mineral soil organic matter pools. ForCent model validation was performed by comparing the observed Enriched Background Isotope Study experimental data with simulated live and dead root biomass ? 14C data, and with soil respiration ? 14C (mineral soil, humus layer, leaf litter layer, and total soil respiration) data. Results show that the model correctly simulates the impact of the Enriched Background Isotope Study 14C experimental treatments on soil respiration ? 14C values for the different soil organic matter pools. Model results suggest that a two-pool root growth model correctly represents root carbon dynamics and inputs to the soil. The model fitting process and sensitivity analysis exposed uncertainty in our estimates of the fraction of mineral soil in the slow and passive pools, dissolved organic carbon flux out of the litter layer into the mineral soil, and mixing of the humus layer into the mineral soil layer.« less

  8. Model reference tracking control of an aircraft: a robust adaptive approach

    NASA Astrophysics Data System (ADS)

    Tanyer, Ilker; Tatlicioglu, Enver; Zergeroglu, Erkan

    2017-05-01

    This work presents the design and the corresponding analysis of a nonlinear robust adaptive controller for model reference tracking of an aircraft that has parametric uncertainties in its system matrices and additive state- and/or time-dependent nonlinear disturbance-like terms in its dynamics. Specifically, robust integral of the sign of the error feedback term and an adaptive term is fused with a proportional integral controller. Lyapunov-based stability analysis techniques are utilised to prove global asymptotic convergence of the output tracking error. Extensive numerical simulations are presented to illustrate the performance of the proposed robust adaptive controller.

  9. Nyx: Adaptive mesh, massively-parallel, cosmological simulation code

    NASA Astrophysics Data System (ADS)

    Almgren, Ann; Beckner, Vince; Friesen, Brian; Lukic, Zarija; Zhang, Weiqun

    2017-12-01

    Nyx code solves equations of compressible hydrodynamics on an adaptive grid hierarchy coupled with an N-body treatment of dark matter. The gas dynamics in Nyx use a finite volume methodology on an adaptive set of 3-D Eulerian grids; dark matter is represented as discrete particles moving under the influence of gravity. Particles are evolved via a particle-mesh method, using Cloud-in-Cell deposition/interpolation scheme. Both baryonic and dark matter contribute to the gravitational field. In addition, Nyx includes physics for accurately modeling the intergalactic medium; in optically thin limits and assuming ionization equilibrium, the code calculates heating and cooling processes of the primordial-composition gas in an ionizing ultraviolet background radiation field.

  10. Climatic influence of background and volcanic stratosphere aerosol models

    NASA Technical Reports Server (NTRS)

    Deschamps, P. Y.; Herman, M.; Lenoble, J.; Tanre, D.

    1982-01-01

    A simple modelization of the earth atmosphere system including tropospheric and stratospheric aerosols has been derived and tested. Analytical expressions are obtained for the albedo variation due to a thin stratospheric aerosol layer. Also outlined are the physical procedures and the respective influence of the main parameters: aerosol optical thickness, single scattering albedo and asymmetry factor, and sublayer albedo. The method is applied to compute the variation of the zonal and planetary albedos due to a stratospheric layer of background H2SO4 particles and of volcanic ash.

  11. Towards Increased Relevance: Context-Adapted Models of the Learning Organization

    ERIC Educational Resources Information Center

    Örtenblad, Anders

    2015-01-01

    Purpose: The purposes of this paper are to take a closer look at the relevance of the idea of the learning organization for organizations in different generalized organizational contexts; to open up for the existence of multiple, context-adapted models of the learning organization; and to suggest a number of such models.…

  12. Heuristic-Leadership Model: Adapting to Current Training and Changing Times.

    ERIC Educational Resources Information Center

    Danielson, Mary Ann

    A model was developed for training individuals to adapt better to the changing work environment by focusing on the subordinate to supervisor relationship and providing a heuristic approach to leadership. The model emphasizes a heuristic approach to decision-making through the active participation of both members of the dyad. The demand among…

  13. A Mixture Rasch Model-Based Computerized Adaptive Test for Latent Class Identification

    ERIC Educational Resources Information Center

    Jiao, Hong; Macready, George; Liu, Junhui; Cho, Youngmi

    2012-01-01

    This study explored a computerized adaptive test delivery algorithm for latent class identification based on the mixture Rasch model. Four item selection methods based on the Kullback-Leibler (KL) information were proposed and compared with the reversed and the adaptive KL information under simulated testing conditions. When item separation was…

  14. Colour and pattern change against visually heterogeneous backgrounds in the tree frog Hyla japonica

    PubMed Central

    Kang, Changku; Kim, Ye Eun; Jang, Yikweon

    2016-01-01

    Colour change in animals can be adaptive phenotypic plasticity in heterogeneous environments. Camouflage through background colour matching has been considered a primary force that drives the evolution of colour changing ability. However, the mechanism to which animals change their colour and patterns under visually heterogeneous backgrounds (i.e. consisting of more than one colour) has only been identified in limited taxa. Here, we investigated the colour change process of the Japanese tree frog (Hyla japonica) against patterned backgrounds and elucidated how the expression of dorsal patterns changes against various achromatic/chromatic backgrounds with/without patterns. Our main findings are i) frogs primarily responded to the achromatic differences in background, ii) their contrasting dorsal patterns were conditionally expressed dependent on the brightness of backgrounds, iii) against mixed coloured background, frogs adopted intermediate forms between two colours. Using predator (avian and snake) vision models, we determined that colour differences against different backgrounds yielded perceptible changes in dorsal colours. We also found substantial individual variation in colour changing ability and the levels of dorsal pattern expression between individuals. We discuss the possibility of correlational selection on colour changing ability and resting behaviour that maintains the high variation in colour changing ability within population. PMID:26932675

  15. Colour and pattern change against visually heterogeneous backgrounds in the tree frog Hyla japonica.

    PubMed

    Kang, Changku; Kim, Ye Eun; Jang, Yikweon

    2016-03-02

    Colour change in animals can be adaptive phenotypic plasticity in heterogeneous environments. Camouflage through background colour matching has been considered a primary force that drives the evolution of colour changing ability. However, the mechanism to which animals change their colour and patterns under visually heterogeneous backgrounds (i.e. consisting of more than one colour) has only been identified in limited taxa. Here, we investigated the colour change process of the Japanese tree frog (Hyla japonica) against patterned backgrounds and elucidated how the expression of dorsal patterns changes against various achromatic/chromatic backgrounds with/without patterns. Our main findings are i) frogs primarily responded to the achromatic differences in background, ii) their contrasting dorsal patterns were conditionally expressed dependent on the brightness of backgrounds, iii) against mixed coloured background, frogs adopted intermediate forms between two colours. Using predator (avian and snake) vision models, we determined that colour differences against different backgrounds yielded perceptible changes in dorsal colours. We also found substantial individual variation in colour changing ability and the levels of dorsal pattern expression between individuals. We discuss the possibility of correlational selection on colour changing ability and resting behaviour that maintains the high variation in colour changing ability within population.

  16. A randomized controlled trial of culturally adapted motivational interviewing for Hispanic heavy drinkers: Theory of Adaptation and Study Protocol

    PubMed Central

    Lee, Christina S.; Colby, Suzanne M.; Magill, Molly; Almeida, Joanna; Tavares, Tonya; Rohsenow, Damaris J.

    2016-01-01

    Background The NIH Strategic Plan prioritizes health disparities research for socially disadvantaged Hispanics, to reduce the disproportionate burden of alcohol-related negative consequences compared to other racial/ethnic groups. Cultural adaptation of evidence-based treatments, such as motivational interviewing (MI), can improve access and response to alcohol treatment. However, the lack of rigorous clinical trials designed to test the efficacy and theoretical underpinnings of cultural adaptation has made proof of concept difficult. Objective The CAMI2 (Culturally Adapted Motivational Interviewing) study design and its theoretical model, is described to illustrate how MI adapted to social and cultural factors (CAMI) can be discriminated against non-adapted MI. Methods and Design CAMI2, a large, 12 month randomized prospective trial, examines the efficacy of CAMI and MI among heavy drinking Hispanics recruited from the community (n=257). Outcomes are reductions in heavy drinking days (Time Line Follow-Back) and negative consequences of drinking among Hispanics (Drinkers Inventory of Consequences). A second aim examines perceived acculturation stress as a moderator of treatment outcomes in the CAMI condition. Summary The CAMI2 study design protocol is presented and the theory of adaptation is presented. Findings from the trial described may yield important recommendations on the science of cultural adaptation and improve MI dissemination to Hispanics with alcohol risk. PMID:27565832

  17. Water System Adaptation To Hydrological Changes: Module 11, Methods and Tools: Computational Models

    EPA Science Inventory

    This course will introduce students to the fundamental principles of water system adaptation to hydrological changes, with emphasis on data analysis and interpretation, technical planning, and computational modeling. Starting with real-world scenarios and adaptation needs, the co...

  18. Robust Optimal Adaptive Control Method with Large Adaptive Gain

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan T.

    2009-01-01

    In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.

  19. Modeling Speed-Accuracy Tradeoff in Adaptive System for Practicing Estimation

    ERIC Educational Resources Information Center

    Nižnan, Juraj

    2015-01-01

    Estimation is useful in situations where an exact answer is not as important as a quick answer that is good enough. A web-based adaptive system for practicing estimates is currently being developed. We propose a simple model for estimating student's latent skill of estimation. This model combines a continuous measure of correctness and response…

  20. Multiconjugate adaptive optics applied to an anatomically accurate human eye model.

    PubMed

    Bedggood, P A; Ashman, R; Smith, G; Metha, A B

    2006-09-04

    Aberrations of both astronomical telescopes and the human eye can be successfully corrected with conventional adaptive optics. This produces diffraction-limited imagery over a limited field of view called the isoplanatic patch. A new technique, known as multiconjugate adaptive optics, has been developed recently in astronomy to increase the size of this patch. The key is to model atmospheric turbulence as several flat, discrete layers. A human eye, however, has several curved, aspheric surfaces and a gradient index lens, complicating the task of correcting aberrations over a wide field of view. Here we utilize a computer model to determine the degree to which this technology may be applied to generate high resolution, wide-field retinal images, and discuss the considerations necessary for optimal use with the eye. The Liou and Brennan schematic eye simulates the aspheric surfaces and gradient index lens of real human eyes. We show that the size of the isoplanatic patch of the human eye is significantly increased through multiconjugate adaptive optics.

  1. Long-range Acoustic Interactions in Insect Swarms - An Adaptive Gravity Model

    NASA Astrophysics Data System (ADS)

    Gorbonos, Dan; Ianconescu, Reuven; Puckett, James G.; Ni, Rui; Ouellette, Nicholas T.; Gov, Nir S.

    The collective motion of groups of animals emerges from the net effect of the interactions between individual members of the group. In many cases, such as birds, fish, or ungulates, these interactions are mediated by sensory stimuli that predominantly arise from nearby neighbors. But not all stimuli in animal groups are short range. We consider mating swarms of midges, which are thought to interact primarily via long-range acoustic stimuli. We exploit the similarity in form between the decay of acoustic and gravitational sources to build a model for swarm behavior. By accounting for the adaptive nature of the midges' acoustic sensing, we show that our ``adaptive gravity'' model makes mean-field predictions that agree well with experimental observations of laboratory swarms. Our results highlight the role of sensory mechanisms and interaction range in collective animal behavior. Additionally, the adaptive interactions open a new class of equations of motion, which may appear in other biological contexts.

  2. Long-range Acoustic Interactions in Insect Swarms: An Adaptive Gravity Model

    NASA Astrophysics Data System (ADS)

    Gorbonos, Dan; Ianconescu, Reuven; Puckett, James G.; Ni, Rui; Ouellette, Nicholas T.; Gov, Nir S.

    The collective motion of groups of animals emerges from the net effect of the interactions between individual members of the group. In many cases, such as birds, fish, or ungulates, these interactions are mediated by sensory stimuli that predominantly arise from nearby neighbors. But not all stimuli in animal groups are short range. Here, we consider mating swarms of midges, which interact primarily via long-range acoustic stimuli. We exploit the similarity in form between the decay of acoustic and gravitational sources to build a model for swarm behavior. By accounting for the adaptive nature of the midges' acoustic sensing, we show that our ``adaptive gravity'' model makes mean-field predictions that agree well with experimental observations of laboratory swarms. Our results highlight the role of sensory mechanisms and interaction range in collective animal behavior. The adaptive interactions that we present here open a new class of equations of motion, which may appear in other biological contexts.

  3. Long-range acoustic interactions in insect swarms: an adaptive gravity model

    NASA Astrophysics Data System (ADS)

    Gorbonos, Dan; Ianconescu, Reuven; Puckett, James G.; Ni, Rui; Ouellette, Nicholas T.; Gov, Nir S.

    2016-07-01

    The collective motion of groups of animals emerges from the net effect of the interactions between individual members of the group. In many cases, such as birds, fish, or ungulates, these interactions are mediated by sensory stimuli that predominantly arise from nearby neighbors. But not all stimuli in animal groups are short range. Here, we consider mating swarms of midges, which are thought to interact primarily via long-range acoustic stimuli. We exploit the similarity in form between the decay of acoustic and gravitational sources to build a model for swarm behavior. By accounting for the adaptive nature of the midges’ acoustic sensing, we show that our ‘adaptive gravity’ model makes mean-field predictions that agree well with experimental observations of laboratory swarms. Our results highlight the role of sensory mechanisms and interaction range in collective animal behavior. Additionally, the adaptive interactions that we present here open a new class of equations of motion, which may appear in other biological contexts.

  4. Microcomputer pollution model for civilian airports and Air Force Bases. Model application and background

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

    Segal, H.M.

    1988-08-01

    This is one of three reports describing the Emissions and Dispersion Modeling System (EDMS). All reports use the same main title--A MICROCOMPUTER MODEL FOR CIVILIAN AIRPORTS AND AIR FORCE BASES--but different subtitles. The subtitles are: (1) USER'S GUIDE - ISSUE 2 (FAA-EE-88-3/ESL-TR-88-54); (2) MODEL DESCRIPTION (FAA-EE-88-4/ESL-TR-88-53); (S) MODEL APPLICATION AND BACKGROUND (FAA-EE-88-5/ESL-TR-88-55). The first and second reports above describe the EDMS model and provide instructions for its use. This is the third report. IT consists of an accumulation of five key documents describing the development and use of the EDMS model. This report is prepared in accordance with discussions withmore » the EPA and requirements outlined in the March 27, 1980 Federal Register for submitting air-quality models to the EPA. Contents: Model Development and Use - Its Chronology and Reports; Monitoring Concorde EMissions; The Influence of Aircraft Operations on Air Quality at Airports; Simplex A - A simplified Atmospheric Dispersion Model for Airport Use -(User's Guide); Microcomputer Graphics in Atmospheric Dispersion Modeling; Pollution from Motor Vehicles and Aircraft at Stapleton International Airport (Abbreviated Report).« less

  5. Retrospective cost adaptive Reynolds-averaged Navier-Stokes k-ω model for data-driven unsteady turbulent simulations

    NASA Astrophysics Data System (ADS)

    Li, Zhiyong; Hoagg, Jesse B.; Martin, Alexandre; Bailey, Sean C. C.

    2018-03-01

    This paper presents a data-driven computational model for simulating unsteady turbulent flows, where sparse measurement data is available. The model uses the retrospective cost adaptation (RCA) algorithm to automatically adjust the closure coefficients of the Reynolds-averaged Navier-Stokes (RANS) k- ω turbulence equations to improve agreement between the simulated flow and the measurements. The RCA-RANS k- ω model is verified for steady flow using a pipe-flow test case and for unsteady flow using a surface-mounted-cube test case. Measurements used for adaptation of the verification cases are obtained from baseline simulations with known closure coefficients. These verification test cases demonstrate that the RCA-RANS k- ω model can successfully adapt the closure coefficients to improve agreement between the simulated flow field and a set of sparse flow-field measurements. Furthermore, the RCA-RANS k- ω model improves agreement between the simulated flow and the baseline flow at locations at which measurements do not exist. The RCA-RANS k- ω model is also validated with experimental data from 2 test cases: steady pipe flow, and unsteady flow past a square cylinder. In both test cases, the adaptation improves agreement with experimental data in comparison to the results from a non-adaptive RANS k- ω model that uses the standard values of the k- ω closure coefficients. For the steady pipe flow, adaptation is driven by mean stream-wise velocity measurements at 24 locations along the pipe radius. The RCA-RANS k- ω model reduces the average velocity error at these locations by over 35%. For the unsteady flow over a square cylinder, adaptation is driven by time-varying surface pressure measurements at 2 locations on the square cylinder. The RCA-RANS k- ω model reduces the average surface-pressure error at these locations by 88.8%.

  6. An implicit adaptation algorithm for a linear model reference control system

    NASA Technical Reports Server (NTRS)

    Mabius, L.; Kaufman, H.

    1975-01-01

    This paper presents a stable implicit adaptation algorithm for model reference control. The constraints for stability are found using Lyapunov's second method and do not depend on perfect model following between the system and the reference model. Methods are proposed for satisfying these constraints without estimating the parameters on which the constraints depend.

  7. Sum rules for the uniform-background model of an atomic-sharp metal corner

    NASA Astrophysics Data System (ADS)

    Streitenberger, P.

    1994-04-01

    Analytical results are derived for the electrostatic potential of an atomic-sharp 90° metal corner in the uniform-background model. The electrostatic potential at a free jellium edge and the jellium corner, respectively, is determined exactly in terms of the energy per electron of the uniform electron gas integrated over the background density. The surface energy, the edge formation energy and the derivative of the corner formation energy with respect to the background density are given as integrals over the electrostatic potential. The present approach represents a novel approach to such sum rules, inclusive of the Budd-Vannimenus sum rules for a free jellium surface, based on general properties of linear response functions.

  8. Building Adaptive Capacity with the Delphi Method and Mediated Modeling for Water Quality and Climate Change Adaptation in Lake Champlain Basin

    NASA Astrophysics Data System (ADS)

    Coleman, S.; Hurley, S.; Koliba, C.; Zia, A.; Exler, S.

    2014-12-01

    Eutrophication and nutrient pollution of surface waters occur within complex governance, social, hydrologic and biophysical basin contexts. The pervasive and perennial nutrient pollution in Lake Champlain Basin, despite decades of efforts, exemplifies problems found across the world's surface waters. Stakeholders with diverse values, interests, and forms of explicit and tacit knowledge determine water quality impacts through land use, agricultural and water resource decisions. Uncertainty, ambiguity and dynamic feedback further complicate the ability to promote the continual provision of water quality and ecosystem services. Adaptive management of water resources and land use requires mechanisms to allow for learning and integration of new information over time. The transdisciplinary Research on Adaptation to Climate Change (RACC) team is working to build regional adaptive capacity in Lake Champlain Basin while studying and integrating governance, land use, hydrological, and biophysical systems to evaluate implications for adaptive management. The RACC team has engaged stakeholders through mediated modeling workshops, online forums, surveys, focus groups and interviews. In March 2014, CSS2CC.org, an interactive online forum to source and identify adaptive interventions from a group of stakeholders across sectors was launched. The forum, based on the Delphi Method, brings forward the collective wisdom of stakeholders and experts to identify potential interventions and governance designs in response to scientific uncertainty and ambiguity surrounding the effectiveness of any strategy, climate change impacts, and the social and natural systems governing water quality and eutrophication. A Mediated Modeling Workshop followed the forum in May 2014, where participants refined and identified plausible interventions under different governance, policy and resource scenarios. Results from the online forum and workshop can identify emerging consensus across scales and sectors

  9. Relationship between Family Adaptability, Cohesion and Adolescent Problem Behaviors: Curvilinearity of Circumplex Model.

    PubMed

    Joh, Ju Youn; Kim, Sun; Park, Jun Li; Kim, Yeon Pyo

    2013-05-01

    The Family Adaptability and Cohesion Evaluation Scale (FACES) III using the circumplex model has been widely used in investigating family function. However, the criticism of the curvilinear hypothesis of the circumplex model has always been from an empirical point of view. This study examined the relationship between adolescent adaptability, cohesion, and adolescent problem behaviors, and especially testing the consistency of the curvilinear hypotheses with FACES III. We used the data from 398 adolescent participants who were in middle school. A self-reported questionnaire was used to evaluate the FACES III and Youth Self Report. According to the level of family adaptability, significant differences were evident in internalizing problems (P = 0.014). But, in externalizing problems, the results were not significant (P = 0.305). Also, according to the level of family cohesion, significant differences were in internalizing problems (P = 0.002) and externalizing problems (P = 0.004). The relationship between the dimensions of adaptability, cohesion and adolescent problem behaviors was not curvilinear. In other words, adolescents with high adaptability and high cohesion showed low problem behaviors.

  10. From epidemics to information propagation: Striking differences in structurally similar adaptive network models

    NASA Astrophysics Data System (ADS)

    Trajanovski, Stojan; Guo, Dongchao; Van Mieghem, Piet

    2015-09-01

    The continuous-time adaptive susceptible-infected-susceptible (ASIS) epidemic model and the adaptive information diffusion (AID) model are two adaptive spreading processes on networks, in which a link in the network changes depending on the infectious state of its end nodes, but in opposite ways: (i) In the ASIS model a link is removed between two nodes if exactly one of the nodes is infected to suppress the epidemic, while a link is created in the AID model to speed up the information diffusion; (ii) a link is created between two susceptible nodes in the ASIS model to strengthen the healthy part of the network, while a link is broken in the AID model due to the lack of interest in informationless nodes. The ASIS and AID models may be considered as first-order models for cascades in real-world networks. While the ASIS model has been exploited in the literature, we show that the AID model is realistic by obtaining a good fit with Facebook data. Contrary to the common belief and intuition for such similar models, we show that the ASIS and AID models exhibit different but not opposite properties. Most remarkably, a unique metastable state always exists in the ASIS model, while there an hourglass-shaped region of instability in the AID model. Moreover, the epidemic threshold is a linear function in the effective link-breaking rate in the AID model, while it is almost constant but noisy in the AID model.

  11. Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

    PubMed Central

    Carmena, Jose M.

    2016-01-01

    Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to

  12. A Model of Internal Communication in Adaptive Communication Systems.

    ERIC Educational Resources Information Center

    Williams, M. Lee

    A study identified and categorized different types of internal communication systems and developed an applied model of internal communication in adaptive organizational systems. Twenty-one large organizations were selected for their varied missions and diverse approaches to managing internal communication. Individual face-to-face or telephone…

  13. Towards a large-scale scalable adaptive heart model using shallow tree meshes

    NASA Astrophysics Data System (ADS)

    Krause, Dorian; Dickopf, Thomas; Potse, Mark; Krause, Rolf

    2015-10-01

    Electrophysiological heart models are sophisticated computational tools that place high demands on the computing hardware due to the high spatial resolution required to capture the steep depolarization front. To address this challenge, we present a novel adaptive scheme for resolving the deporalization front accurately using adaptivity in space. Our adaptive scheme is based on locally structured meshes. These tensor meshes in space are organized in a parallel forest of trees, which allows us to resolve complicated geometries and to realize high variations in the local mesh sizes with a minimal memory footprint in the adaptive scheme. We discuss both a non-conforming mortar element approximation and a conforming finite element space and present an efficient technique for the assembly of the respective stiffness matrices using matrix representations of the inclusion operators into the product space on the so-called shallow tree meshes. We analyzed the parallel performance and scalability for a two-dimensional ventricle slice as well as for a full large-scale heart model. Our results demonstrate that the method has good performance and high accuracy.

  14. Adaptive scenarios: a training model for today's public health workforce.

    PubMed

    Uden-Holman, Tanya; Bedet, Jennifer; Walkner, Laurie; Abd-Hamid, Nor Hashidah

    2014-01-01

    With the current economic climate, money for training is scarce. In addition, time is a major barrier to participation in trainings. To meet the public health workforce's rising demand for training, while struggling with less time and fewer resources, the Upper Midwest Preparedness and Emergency Response Learning Center has developed a model of online training that provides the public health workforce with individually customized, needs-based training experiences. Adaptive scenarios are rooted in case-based reasoning, a learning approach that focuses on the specific knowledge needed to solve a problem. Proponents of case-based reasoning argue that learners benefit from being able to remember previous similar situations and reusing information and knowledge from that situation. Adaptive scenarios based on true-to-life job performance provide an opportunity to assess skills by presenting the user with choices to make in a problem-solving context. A team approach was used to develop the adaptive scenarios. Storylines were developed that incorporated situations aligning with the knowledge, skills, and attitudes outlined in the Public Health Preparedness and Response Core Competency Model. This article examines 2 adaptive scenarios: "Ready or Not? A Family Preparedness Scenario" and "Responding to a Crisis: Managing Emotions and Stress Scenario." The scenarios are available on Upper Midwest Preparedness and Emergency Response Learning Center's Learning Management System, the Training Source (http://training-source.org). Evaluation data indicate that users' experiences have been positive. Integrating the assessment and training elements of the scenarios so that the training experience is uniquely adaptive to each user is one of the most efficient ways to provide training. The opportunity to provide individualized, needs-based training without having to administer separate assessments has the potential to save time and resources. These adaptive scenarios continue to be

  15. Application of model reference adaptive control to a flexible remote manipulator arm

    NASA Technical Reports Server (NTRS)

    Meldrum, D. R.; Balas, M. J.

    1986-01-01

    An exact modal state-space representation is derived in detail for a single-link, flexible remote manipulator with a noncollocated sensor and actuator. A direct model following adaptive controller is designed to control the torque at the pinned end of the arm so as to command the free end to track a prescribed sinusoidal motion. Conditions that must be satisfied in order for the controller to work are stated. Simulation results to date are discussed along with the potential of the model following adaptive control scheme in robotics and space environments.

  16. Oxidative DNA damage background estimated by a system model of base excision repair

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

    Sokhansanj, B A; Wilson, III, D M

    Human DNA can be damaged by natural metabolism through free radical production. It has been suggested that the equilibrium between innate damage and cellular DNA repair results in an oxidative DNA damage background that potentially contributes to disease and aging. Efforts to quantitatively characterize the human oxidative DNA damage background level based on measuring 8-oxoguanine lesions as a biomarker have led to estimates varying over 3-4 orders of magnitude, depending on the method of measurement. We applied a previously developed and validated quantitative pathway model of human DNA base excision repair, integrating experimentally determined endogenous damage rates and model parametersmore » from multiple sources. Our estimates of at most 100 8-oxoguanine lesions per cell are consistent with the low end of data from biochemical and cell biology experiments, a result robust to model limitations and parameter variation. Our results show the power of quantitative system modeling to interpret composite experimental data and make biologically and physiologically relevant predictions for complex human DNA repair pathway mechanisms and capacity.« less

  17. Adaptive Behaviour Assessment System: Indigenous Australian Adaptation Model (ABAS: IAAM)

    ERIC Educational Resources Information Center

    du Plessis, Santie

    2015-01-01

    The study objectives were to develop, trial and evaluate a cross-cultural adaptation of the Adaptive Behavior Assessment System-Second Edition Teacher Form (ABAS-II TF) ages 5-21 for use with Indigenous Australian students ages 5-14. This study introduced a multiphase mixed-method design with semi-structured and informal interviews, school…

  18. Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.

    PubMed

    Wilson, Emma D; Assaf, Tareq; Pearson, Martin J; Rossiter, Jonathan M; Dean, Paul; Anderson, Sean R; Porrill, John

    2015-01-01

    The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.

  19. The background in the $$0\

    DOE PAGES

    Agostini, M.; Allardt, M.; Andreotti, E.; ...

    2014-04-04

    The GERmanium Detector Array (Gerda) experiment at the Gran Sasso underground laboratory (LNGS) of INFN is searching for neutrinoless double beta (0νββ) decay of 76 Ge. The signature of the signal is a monoenergetic peak at 2039 keV, the Q ββ value of the decay. To avoid bias in the signal search, the present analysis does not consider all those events, that fall in a 40 keV wide region centered around Q ββ. The main parameters needed for the 0νββ analysis are described. A background model was developed to describe the observed energy spectrum. The model contains severalmore » contributions, that are expected on the basis of material screening or that are established by the observation of characteristic structures in the energy spectrum. The model predicts a flat energy spectrum for the blinding window around Qββ with a background index ranging from 17.6 to 23.8 × 10 -3 cts/(keV kg yr). A part of the data not considered before has been used to test if the predictions of the background model are consistent. The observed number of events in this energy region is consistent with the background model. The background at Q ββ is dominated by close sources, mainly due to 42 K, 214 Bi, 228 60 Co and α emitting isotopes from the 226 Ra decay chain. The individual fractions depend on the assumed locations of the contaminants. It is shown, that after removal of the known γ peaks, the energy spectrum can be fitted in an energy range of 200 keV around Q ββ with a constant background. This gives a background index consistent with the full model and uncertainties of the same size.« less

  20. First results of GERDA Phase II and consistency with background models

    NASA Astrophysics Data System (ADS)

    Agostini, M.; Allardt, M.; Bakalyarov, A. M.; Balata, M.; Barabanov, I.; Baudis, L.; Bauer, C.; Bellotti, E.; Belogurov, S.; Belyaev, S. T.; Benato, G.; Bettini, A.; Bezrukov, L.; Bode1, T.; Borowicz, D.; Brudanin, V.; Brugnera, R.; Caldwell, A.; Cattadori, C.; Chernogorov, A.; D'Andrea, V.; Demidova, E. V.; Di Marco, N.; Domula, A.; Doroshkevich, E.; Egorov, V.; Falkenstein, R.; Frodyma, N.; Gangapshev, A.; Garfagnini, A.; Gooch, C.; Grabmayr, P.; Gurentsov, V.; Gusev, K.; Hakenmüller, J.; Hegai, A.; Heisel, M.; Hemmer, S.; Hofmann, W.; Hult, M.; Inzhechik, L. V.; Janicskó Csáthy, J.; Jochum, J.; Junker, M.; Kazalov, V.; Kihm, T.; Kirpichnikov, I. V.; Kirsch, A.; Kish, A.; Klimenko, A.; Kneißl, R.; Knöpfle, K. T.; Kochetov, O.; Kornoukhov, V. N.; Kuzminov, V. V.; Laubenstein, M.; Lazzaro, A.; Lebedev, V. I.; Lehnert, B.; Liao, H. Y.; Lindner, M.; Lippi, I.; Lubashevskiy, A.; Lubsandorzhiev, B.; Lutter, G.; Macolino, C.; Majorovits, B.; Maneschg, W.; Medinaceli, E.; Miloradovic, M.; Mingazheva, R.; Misiaszek, M.; Moseev, P.; Nemchenok, I.; Palioselitis, D.; Panas, K.; Pandola, L.; Pelczar, K.; Pullia, A.; Riboldi, S.; Rumyantseva, N.; Sada, C.; Salamida, F.; Salathe, M.; Schmitt, C.; Schneider, B.; Schönert, S.; Schreiner, J.; Schulz, O.; Schütz, A.-K.; Schwingenheuer, B.; Selivanenko, O.; Shevzik, E.; Shirchenko, M.; Simgen, H.; Smolnikov, A.; Stanco, L.; Vanhoefer, L.; Vasenko, A. A.; Veresnikova, A.; von Sturm, K.; Wagner, V.; Wegmann, A.; Wester, T.; Wiesinger, C.; Wojcik, M.; Yanovich, E.; Zhitnikov, I.; Zhukov, S. V.; Zinatulina, D.; Zuber, K.; Zuzel, G.

    2017-01-01

    The GERDA (GERmanium Detector Array) is an experiment for the search of neutrinoless double beta decay (0νββ) in 76Ge, located at Laboratori Nazionali del Gran Sasso of INFN (Italy). GERDA operates bare high purity germanium detectors submersed in liquid Argon (LAr). Phase II of data-taking started in Dec 2015 and is currently ongoing. In Phase II 35 kg of germanium detectors enriched in 76Ge including thirty newly produced Broad Energy Germanium (BEGe) detectors is operating to reach an exposure of 100 kg·yr within about 3 years data taking. The design goal of Phase II is to reduce the background by one order of magnitude to get the sensitivity for T1/20ν = O≤ft( {{{10}26}} \\right){{ yr}}. To achieve the necessary background reduction, the setup was complemented with LAr veto. Analysis of the background spectrum of Phase II demonstrates consistency with the background models. Furthermore 226Ra and 232Th contamination levels consistent with screening results. In the first Phase II data release we found no hint for a 0νββ decay signal and place a limit of this process T1/20ν > 5.3 \\cdot {1025} yr (90% C.L., sensitivity 4.0·1025 yr). First results of GERDA Phase II will be presented.

  1. Radiofrequency ablation: importance of background tissue electrical conductivity--an agar phantom and computer modeling study.

    PubMed

    Solazzo, Stephanie A; Liu, Zhengjun; Lobo, S Melvyn; Ahmed, Muneeb; Hines-Peralta, Andrew U; Lenkinski, Robert E; Goldberg, S Nahum

    2005-08-01

    To determine whether radiofrequency (RF)-induced heating can be correlated with background electrical conductivity in a controlled experimental phantom environment mimicking different background tissue electrical conductivities and to determine the potential electrical and physical basis for such a correlation by using computer modeling. The effect of background tissue electrical conductivity on RF-induced heating was studied in a controlled system of 80 two-compartment agar phantoms (with inner wells of 0.3%, 1.0%, or 36.0% NaCl) with background conductivity that varied from 0.6% to 5.0% NaCl. Mathematical modeling of the relationship between electrical conductivity and temperatures 2 cm from the electrode (T2cm) was performed. Next, computer simulation of RF heating by using two-dimensional finite-element analysis (ETherm) was performed with parameters selected to approximate the agar phantoms. Resultant heating, in terms of both the T2cm and the distance of defined thermal isotherms from the electrode surface, was calculated and compared with the phantom data. Additionally, electrical and thermal profiles were determined by using the computer modeling data and correlated by using linear regression analysis. For each inner compartment NaCl concentration, a negative exponential relationship was established between increased background NaCl concentration and the T2cm (R2= 0.64-0.78). Similar negative exponential relationships (r2 > 0.97%) were observed for the computer modeling. Correlation values (R2) between the computer and experimental data were 0.9, 0.9, and 0.55 for the 0.3%, 1.0%, and 36.0% inner NaCl concentrations, respectively. Plotting of the electrical field generated around the RF electrode identified the potential for a dramatic local change in electrical field distribution (ie, a second electrical peak ["E-peak"]) occurring at the interface between the two compartments of varied electrical background conductivity. Linear correlations between the E

  2. Performance Optimizing Multi-Objective Adaptive Control with Time-Varying Model Reference Modification

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan T.; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

    2017-01-01

    This paper presents a new adaptive control approach that involves a performance optimization objective. The problem is cast as a multi-objective optimal control. The control synthesis involves the design of a performance optimizing controller from a subset of control inputs. The effect of the performance optimizing controller is to introduce an uncertainty into the system that can degrade tracking of the reference model. An adaptive controller from the remaining control inputs is designed to reduce the effect of the uncertainty while maintaining a notion of performance optimization in the adaptive control system.

  3. An Adaptive ANOVA-based PCKF for High-Dimensional Nonlinear Inverse Modeling

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

    LI, Weixuan; Lin, Guang; Zhang, Dongxiao

    2014-02-01

    The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos bases in the expansion helps to capture uncertainty more accurately but increases computational cost. Bases selection is particularly importantmore » for high-dimensional stochastic problems because the number of polynomial chaos bases required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE bases are pre-set based on users’ experience. Also, for sequential data assimilation problems, the bases kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE bases for different problems and automatically adjusts the number of bases in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm is tested with different examples and demonstrated great effectiveness in comparison with non-adaptive PCKF and En

  4. An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling

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

    Li, Weixuan, E-mail: weixuan.li@usc.edu; Lin, Guang, E-mail: guang.lin@pnnl.gov; Zhang, Dongxiao, E-mail: dxz@pku.edu.cn

    2014-02-01

    The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos basis functions in the expansion helps to capture uncertainty more accurately but increases computational cost. Selection of basis functionsmore » is particularly important for high-dimensional stochastic problems because the number of polynomial chaos basis functions required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE basis functions are pre-set based on users' experience. Also, for sequential data assimilation problems, the basis functions kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE basis functions for different problems and automatically adjusts the number of basis functions in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm was tested with different examples and

  5. Highly Adaptable Triple-Negative Breast Cancer Cells as a Functional Model for Testing Anticancer Agents

    PubMed Central

    Singh, Balraj; Shamsnia, Anna; Raythatha, Milan R.; Milligan, Ryan D.; Cady, Amanda M.; Madan, Simran; Lucci, Anthony

    2014-01-01

    A major obstacle in developing effective therapies against solid tumors stems from an inability to adequately model the rare subpopulation of panresistant cancer cells that may often drive the disease. We describe a strategy for optimally modeling highly abnormal and highly adaptable human triple-negative breast cancer cells, and evaluating therapies for their ability to eradicate such cells. To overcome the shortcomings often associated with cell culture models, we incorporated several features in our model including a selection of highly adaptable cancer cells based on their ability to survive a metabolic challenge. We have previously shown that metabolically adaptable cancer cells efficiently metastasize to multiple organs in nude mice. Here we show that the cancer cells modeled in our system feature an embryo-like gene expression and amplification of the fat mass and obesity associated gene FTO. We also provide evidence of upregulation of ZEB1 and downregulation of GRHL2 indicating increased epithelial to mesenchymal transition in metabolically adaptable cancer cells. Our results obtained with a variety of anticancer agents support the validity of the model of realistic panresistance and suggest that it could be used for developing anticancer agents that would overcome panresistance. PMID:25279830

  6. Highly adaptable triple-negative breast cancer cells as a functional model for testing anticancer agents.

    PubMed

    Singh, Balraj; Shamsnia, Anna; Raythatha, Milan R; Milligan, Ryan D; Cady, Amanda M; Madan, Simran; Lucci, Anthony

    2014-01-01

    A major obstacle in developing effective therapies against solid tumors stems from an inability to adequately model the rare subpopulation of panresistant cancer cells that may often drive the disease. We describe a strategy for optimally modeling highly abnormal and highly adaptable human triple-negative breast cancer cells, and evaluating therapies for their ability to eradicate such cells. To overcome the shortcomings often associated with cell culture models, we incorporated several features in our model including a selection of highly adaptable cancer cells based on their ability to survive a metabolic challenge. We have previously shown that metabolically adaptable cancer cells efficiently metastasize to multiple organs in nude mice. Here we show that the cancer cells modeled in our system feature an embryo-like gene expression and amplification of the fat mass and obesity associated gene FTO. We also provide evidence of upregulation of ZEB1 and downregulation of GRHL2 indicating increased epithelial to mesenchymal transition in metabolically adaptable cancer cells. Our results obtained with a variety of anticancer agents support the validity of the model of realistic panresistance and suggest that it could be used for developing anticancer agents that would overcome panresistance.

  7. Observations and modeling of seismic background noise

    USGS Publications Warehouse

    Peterson, Jon R.

    1993-01-01

    The preparation of this report had two purposes. One was to present a catalog of seismic background noise spectra obtained from a worldwide network of seismograph stations. The other purpose was to refine and document models of seismic background noise that have been in use for several years. The second objective was, in fact, the principal reason that this study was initiated and influenced the procedures used in collecting and processing the data.With a single exception, all of the data used in this study were extracted from the digital data archive at the U.S. Geological Survey's Albuquerque Seismological Laboratory (ASL). This archive dates from 1972 when ASL first began deploying digital seismograph systems and collecting and distributing digital data under the sponsorship of the Defense Advanced Research Projects Agency (DARPA). There have been many changes and additions to the global seismograph networks during the past twenty years, but perhaps none as significant as the current deployment of very broadband seismographs by the U.S. Geological Survey (USGS) and the University of California San Diego (UCSD) under the scientific direction of the IRIS consortium. The new data acquisition systems have extended the bandwidth and resolution of seismic recording, and they utilize high-density recording media that permit the continuous recording of broadband data. The data improvements and continuous recording greatly benefit and simplify surveys of seismic background noise.Although there are many other sources of digital data, the ASL archive data were used almost exclusively because of accessibility and because the data systems and their calibration are well documented for the most part. Fortunately, the ASL archive contains high-quality data from other stations in addition to those deployed by the USGS. Included are data from UCSD IRIS/IDA stations, the Regional Seismic Test Network (RSTN) deployed by Sandia National Laboratories (SNL), and the TERRAscope network

  8. Right adrenal vein: comparison between adaptive statistical iterative reconstruction and model-based iterative reconstruction.

    PubMed

    Noda, Y; Goshima, S; Nagata, S; Miyoshi, T; Kawada, H; Kawai, N; Tanahashi, Y; Matsuo, M

    2018-06-01

    To compare right adrenal vein (RAV) visualisation and contrast enhancement degree on adrenal venous phase images reconstructed using adaptive statistical iterative reconstruction (ASiR) and model-based iterative reconstruction (MBIR) techniques. This prospective study was approved by the institutional review board, and written informed consent was waived. Fifty-seven consecutive patients who underwent adrenal venous phase imaging were enrolled. The same raw data were reconstructed using ASiR 40% and MBIR. The expert and beginner independently reviewed computed tomography (CT) images. RAV visualisation rates, background noise, and CT attenuation of the RAV, right adrenal gland, inferior vena cava (IVC), hepatic vein, and bilateral renal veins were compared between the two reconstruction techniques. RAV visualisation rates were higher with MBIR than with ASiR (95% versus 88%, p=0.13 in expert and 93% versus 75%, p=0.002 in beginner, respectively). RAV visualisation confidence ratings with MBIR were significantly greater than with ASiR (p<0.0001, both in the beginner and the expert). The mean background noise was significantly lower with MBIR than with ASiR (p<0.0001). Mean CT attenuation values of the RAV, right adrenal gland, IVC, and hepatic vein were comparable between the two techniques (p=0.12-0.91). Mean CT attenuation values of the bilateral renal veins were significantly higher with MBIR than with ASiR (p=0.0013 and 0.02). Reconstruction of adrenal venous phase images using MBIR significantly reduces background noise, leading to an improvement in the RAV visualisation compared with ASiR. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  9. Regional and global modeling estimates of policy relevant background ozone over the United States

    NASA Astrophysics Data System (ADS)

    Emery, Christopher; Jung, Jaegun; Downey, Nicole; Johnson, Jeremiah; Jimenez, Michele; Yarwood, Greg; Morris, Ralph

    2012-02-01

    Policy Relevant Background (PRB) ozone, as defined by the US Environmental Protection Agency (EPA), refers to ozone concentrations that would occur in the absence of all North American anthropogenic emissions. PRB enters into the calculation of health risk benefits, and as the US ozone standard approaches background levels, PRB is increasingly important in determining the feasibility and cost of compliance. As PRB is a hypothetical construct, modeling is a necessary tool. Since 2006 EPA has relied on global modeling to establish PRB for their regulatory analyses. Recent assessments with higher resolution global models exhibit improved agreement with remote observations and modest upward shifts in PRB estimates. This paper shifts the paradigm to a regional model (CAMx) run at 12 km resolution, for which North American boundary conditions were provided by a low-resolution version of the GEOS-Chem global model. We conducted a comprehensive model inter-comparison, from which we elucidate differences in predictive performance against ozone observations and differences in temporal and spatial background variability over the US. In general, CAMx performed better in replicating observations at remote monitoring sites, and performance remained better at higher concentrations. While spring and summer mean PRB predicted by GEOS-Chem ranged 20-45 ppb, CAMx predicted PRB ranged 25-50 ppb and reached well over 60 ppb in the west due to event-oriented phenomena such as stratospheric intrusion and wildfires. CAMx showed a higher correlation between modeled PRB and total observed ozone, which is significant for health risk assessments. A case study during April 2006 suggests that stratospheric exchange of ozone is underestimated in both models on an event basis. We conclude that wildfires, lightning NO x and stratospheric intrusions contribute a significant level of uncertainty in estimating PRB, and that PRB will require careful consideration in the ozone standard setting process.

  10. FPGA implementation for real-time background subtraction based on Horprasert model.

    PubMed

    Rodriguez-Gomez, Rafael; Fernandez-Sanchez, Enrique J; Diaz, Javier; Ros, Eduardo

    2012-01-01

    Background subtraction is considered the first processing stage in video surveillance systems, and consists of determining objects in movement in a scene captured by a static camera. It is an intensive task with a high computational cost. This work proposes an embedded novel architecture on FPGA which is able to extract the background on resource-limited environments and offers low degradation (produced because of the hardware-friendly model modification). In addition, the original model is extended in order to detect shadows and improve the quality of the segmentation of the moving objects. We have analyzed the resource consumption and performance in Spartan3 Xilinx FPGAs and compared to others works available on the literature, showing that the current architecture is a good trade-off in terms of accuracy, performance and resources utilization. With less than a 65% of the resources utilization of a XC3SD3400 Spartan-3A low-cost family FPGA, the system achieves a frequency of 66.5 MHz reaching 32.8 fps with resolution 1,024 × 1,024 pixels, and an estimated power consumption of 5.76 W.

  11. FPGA Implementation for Real-Time Background Subtraction Based on Horprasert Model

    PubMed Central

    Rodriguez-Gomez, Rafael; Fernandez-Sanchez, Enrique J.; Diaz, Javier; Ros, Eduardo

    2012-01-01

    Background subtraction is considered the first processing stage in video surveillance systems, and consists of determining objects in movement in a scene captured by a static camera. It is an intensive task with a high computational cost. This work proposes an embedded novel architecture on FPGA which is able to extract the background on resource-limited environments and offers low degradation (produced because of the hardware-friendly model modification). In addition, the original model is extended in order to detect shadows and improve the quality of the segmentation of the moving objects. We have analyzed the resource consumption and performance in Spartan3 Xilinx FPGAs and compared to others works available on the literature, showing that the current architecture is a good trade-off in terms of accuracy, performance and resources utilization. With less than a 65% of the resources utilization of a XC3SD3400 Spartan-3A low-cost family FPGA, the system achieves a frequency of 66.5 MHz reaching 32.8 fps with resolution 1,024 × 1,024 pixels, and an estimated power consumption of 5.76 W. PMID:22368487

  12. Cultural ecologies of adaptive vs. maladaptive traits: A simple nonlinear model

    NASA Astrophysics Data System (ADS)

    Antoci, Angelo; Russu, Paolo; Sacco, Pier Luigi

    2018-05-01

    In this paper, we generalize a model by Enquist and Ghirlanda [12] to analyze the "macro" dynamics of cumulative culture in a context where there is a coexistence of adaptive and maladaptive cultural traits. In particular, we introduce a different, nonlinear specification of the main processes at work in the cumulative culture dynamics: imperfect transmission of traits, generation of new traits, and switches from adaptive to maladaptive and vice-versa. We find that the system exhibits a variety of dynamic behaviors where the crucial force is the switching between the adaptive and maladaptive nature of a certain trait, with the other processes playing a modulating role. We identify in particular a number of dynamic regimes with distinctive characteristics.

  13. Addressing potential local adaptation in species distribution models: implications for conservation under climate change

    USGS Publications Warehouse

    Hällfors, Maria Helena; Liao, Jishan; Dzurisin, Jason D. K.; Grundel, Ralph; Hyvärinen, Marko; Towle, Kevin; Wu, Grace C.; Hellmann, Jessica J.

    2016-01-01

    Species distribution models (SDMs) have been criticized for involving assumptions that ignore or categorize many ecologically relevant factors such as dispersal ability and biotic interactions. Another potential source of model error is the assumption that species are ecologically uniform in their climatic tolerances across their range. Typically, SDMs to treat a species as a single entity, although populations of many species differ due to local adaptation or other genetic differentiation. Not taking local adaptation into account, may lead to incorrect range prediction and therefore misplaced conservation efforts. A constraint is that we often do not know the degree to which populations are locally adapted, however. Lacking experimental evidence, we still can evaluate niche differentiation within a species' range to promote better conservation decisions. We explore possible conservation implications of making type I or type II errors in this context. For each of two species, we construct three separate MaxEnt models, one considering the species as a single population and two of disjunct populations. PCA analyses and response curves indicate different climate characteristics in the current environments of the populations. Model projections into future climates indicate minimal overlap between areas predicted to be climatically suitable by the whole species versus population-based models. We present a workflow for addressing uncertainty surrounding local adaptation in SDM application and illustrate the value of conducting population-based models to compare with whole-species models. These comparisons might result in more cautious management actions when alternative range outcomes are considered.

  14. Semantic Description of Educational Adaptive Hypermedia Based on a Conceptual Model

    ERIC Educational Resources Information Center

    Papasalouros, Andreas; Retalis, Symeon; Papaspyrou, Nikolaos

    2004-01-01

    The role of conceptual modeling in Educational Adaptive Hypermedia Applications (EAHA) is especially important. A conceptual model of an educational application depicts the instructional solution that is implemented, containing information about concepts that must be ac-quired by learners, tasks in which learners must be involved and resources…

  15. Second-order sliding mode controller with model reference adaptation for automatic train operation

    NASA Astrophysics Data System (ADS)

    Ganesan, M.; Ezhilarasi, D.; Benni, Jijo

    2017-11-01

    In this paper, a new approach to model reference based adaptive second-order sliding mode control together with adaptive state feedback is presented to control the longitudinal dynamic motion of a high speed train for automatic train operation with the objective of minimal jerk travel by the passengers. The nonlinear dynamic model for the longitudinal motion of the train comprises of a locomotive and coach subsystems is constructed using multiple point-mass model by considering the forces acting on the vehicle. An adaptation scheme using Lyapunov criterion is derived to tune the controller gains by considering a linear, stable reference model that ensures the stability of the system in closed loop. The effectiveness of the controller tracking performance is tested under uncertain passenger load, coupler-draft gear parameters, propulsion resistance coefficients variations and environmental disturbances due to side wind and wet rail conditions. The results demonstrate improved tracking performance of the proposed control scheme with a least jerk under maximum parameter uncertainties when compared to constant gain second-order sliding mode control.

  16. Accurate Modeling of the Terrestrial Gamma-Ray Background for Homeland Security Applications

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

    Sandness, Gerald A.; Schweppe, John E.; Hensley, Walter K.

    2009-10-24

    Abstract–The Pacific Northwest National Laboratory has developed computer models to simulate the use of radiation portal monitors to screen vehicles and cargo for the presence of illicit radioactive material. The gamma radiation emitted by the vehicles or cargo containers must often be measured in the presence of a relatively large gamma-ray background mainly due to the presence of potassium, uranium, and thorium (and progeny isotopes) in the soil and surrounding building materials. This large background is often a significant limit to the detection sensitivity for items of interest and must be modeled accurately for analyzing homeland security situations. Calculations ofmore » the expected gamma-ray emission from a disk of soil and asphalt were made using the Monte Carlo transport code MCNP and were compared to measurements made at a seaport with a high-purity germanium detector. Analysis revealed that the energy spectrum of the measured background could not be reproduced unless the model included gamma rays coming from the ground out to distances of at least 300 m. The contribution from beyond about 50 m was primarily due to gamma rays that scattered in the air before entering the detectors rather than passing directly from the ground to the detectors. These skyshine gamma rays contribute tens of percent to the total gamma-ray spectrum, primarily at energies below a few hundred keV. The techniques that were developed to efficiently calculate the contributions from a large soil disk and a large air volume in a Monte Carlo simulation are described and the implications of skyshine in portal monitoring applications are discussed.« less

  17. An Adaptive Complex Network Model for Brain Functional Networks

    PubMed Central

    Gomez Portillo, Ignacio J.; Gleiser, Pablo M.

    2009-01-01

    Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution. PMID:19738902

  18. Space Object Classification and Characterization Via Multiple Model Adaptive Estimation

    DTIC Science & Technology

    2014-07-14

    BRDF ) which models light distribution scattered from the surface due to the incident light. The BRDF at any point on the surface is a function of two...uu B vu B nu obs I u sun I u I hu (b) Reflection Geometry Fig. 2: Reflection Geometry and Space Object Shape Model of the BRDF is ρdiff(i...Space Object Classification and Characterization Via Multiple Model Adaptive Estimation Richard Linares Director’s Postdoctoral Fellow Space Science

  19. Data-adaptive harmonic spectra and multilayer Stuart-Landau models

    NASA Astrophysics Data System (ADS)

    Chekroun, Mickaël D.; Kondrashov, Dmitri

    2017-09-01

    Harmonic decompositions of multivariate time series are considered for which we adopt an integral operator approach with periodic semigroup kernels. Spectral decomposition theorems are derived that cover the important cases of two-time statistics drawn from a mixing invariant measure. The corresponding eigenvalues can be grouped per Fourier frequency and are actually given, at each frequency, as the singular values of a cross-spectral matrix depending on the data. These eigenvalues obey, furthermore, a variational principle that allows us to define naturally a multidimensional power spectrum. The eigenmodes, as far as they are concerned, exhibit a data-adaptive character manifested in their phase which allows us in turn to define a multidimensional phase spectrum. The resulting data-adaptive harmonic (DAH) modes allow for reducing the data-driven modeling effort to elemental models stacked per frequency, only coupled at different frequencies by the same noise realization. In particular, the DAH decomposition extracts time-dependent coefficients stacked by Fourier frequency which can be efficiently modeled—provided the decay of temporal correlations is sufficiently well-resolved—within a class of multilayer stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators. Applications to the Lorenz 96 model and to a stochastic heat equation driven by a space-time white noise are considered. In both cases, the DAH decomposition allows for an extraction of spatio-temporal modes revealing key features of the dynamics in the embedded phase space. The multilayer Stuart-Landau models (MSLMs) are shown to successfully model the typical patterns of the corresponding time-evolving fields, as well as their statistics of occurrence.

  20. The Genetic Basis of Phenotypic Adaptation II: The Distribution of Adaptive Substitutions in the Moving Optimum Model

    PubMed Central

    Kopp, Michael; Hermisson, Joachim

    2009-01-01

    {equation*}{\\mathrm{{\\gamma}}}=\\tilde {{\\upsilon}}/({\\mathrm{\\tilde {{\\sigma}}}}{\\Theta}{\\mathrm{{\\omega}}}^{3})\\end{equation*}\\end{document}, which combines the ecological and genetic parameters; (ii) depending on γ, we can distinguish two distinct adaptive regimes: for large γ the adaptive process is mutation limited and dominated by genetic constraints, whereas for small γ it is environmentally limited and dominated by the external ecological dynamics; (iii) deviations from the adaptive-walk approximation occur for large mutation rates, when different mutant alleles interact via linkage or epistasis; and (iv) in contrast to predictions from previous models assuming constant selection, the distribution of adaptive substitutions is generally not exponential. PMID:19805820

  1. Command generator tracker based direct model reference adaptive control of a PUMA 560 manipulator. Thesis

    NASA Technical Reports Server (NTRS)

    Swift, David C.

    1992-01-01

    This project dealt with the application of a Direct Model Reference Adaptive Control algorithm to the control of a PUMA 560 Robotic Manipulator. This chapter will present some motivation for using Direct Model Reference Adaptive Control, followed by a brief historical review, the project goals, and a summary of the subsequent chapters.

  2. Understanding Migration as an Adaptation in Deltas Using a Bayesian Network Model

    NASA Astrophysics Data System (ADS)

    Lázár, A. N.; Adams, H.; de Campos, R. S.; Mortreux, C. C.; Clarke, D.; Nicholls, R. J.; Amisigo, B. A.

    2016-12-01

    Deltas are hotspots of high population density, fertile lands and dramatic environmental and anthropogenic pressures and changes. Amongst other environmental factors, sea level rise, soil salinization, water shortages and erosion threaten people's livelihoods and wellbeing. As a result, there is a growing concern that significant environmental change induced migration might occur from these areas. Migration, however, is already happening for economic, education and other reasons (e.g. livelihood change, marriage, planned relocation, etc.). Migration hence has multiple, interlinked drivers and depending on the perspective, can be considered as a positive or negative phenomenon. The DECCMA project (Deltas, Vulnerability & Climate Change: Migration & Adaptation) studies migration as part of a suite of adaptation options available to the coastal populations in the Ganges delta in Bangladesh, the Mahanadi delta in India and the Volta delta in Ghana. It aims to develop a holistic framework of analysis that assesses the impact of climate and environmental change on the migration patterns of these areas. This assessment framework will couple environmental, socio-economics and governance dimensions in an attempt to synthesise drivers and barriers and allow testing of plausible future scenarios. One of the integrative methods of DECCMA is a Bayesian Belief Network (BBN) model describing the decision-making of a coastal household. BBN models are built on qualitative and quantitative observations/expert knowledge and describe the probability of different events/responses etc. BBN models are especially useful to capture uncertainties of large systems and engaging with stakeholders. The DECCMA BBN model is based on household survey results from delta migrant sending areas. This presentation will describe model elements (livelihood sensitivity to climate change, local and national adaptation options, household characteristics/attitude, social networks, household decision) and

  3. Modeling Adaptable Business Service for Enterprise Collaboration

    NASA Astrophysics Data System (ADS)

    Boukadi, Khouloud; Vincent, Lucien; Burlat, Patrick

    Nowadays, a Service Oriented Architecture (SOA) seems to be one of the most promising paradigms for leveraging enterprise information systems. SOA creates opportunities for enterprises to provide value added service tailored for on demand enterprise collaboration. With the emergence and rapid development of Web services technologies, SOA is being paid increasing attention and has become widespread. In spite of the popularity of SOA, a standardized framework for modeling and implementing business services are still in progress. For the purpose of supporting these service-oriented solutions, we adopt a model driven development approach. This paper outlines the Contextual Service Oriented Modeling and Analysis (CSOMA) methodology and presents UML profiles for the PIM level service-oriented architectural modeling, as well as its corresponding meta-models. The proposed PIM (Platform Independent Model) describes the business SOA at a high level of abstraction regardless of techniques involved in the application employment. In addition, all essential service-specific concerns required for delivering quality and context-aware service are covered. Some of the advantages of this approach are that it is generic and thus not closely allied with Web service technology as well as specifically treating the service adaptability during the design stage.

  4. Neuro- and sensoriphysiological Adaptations to Microgravity using Fish as Model System

    NASA Astrophysics Data System (ADS)

    Anken, R.

    The phylogenetic development of all organisms took place under constant gravity conditions, against which they achieved specific countermeasures for compensation and adaptation. On this background, it is still an open question to which extent altered gravity such as hyper- or microgravity (centrifuge/spaceflight) affects the normal individual development, either on the systemic level of the whole organism or on the level of individual organs or even single cells. The present review provides information on this topic, focusing on the effects of altered gravity on developing fish as model systems even for higher vertebrates including humans, with special emphasis on the effect of altered gravity on behaviour and particularly on the developing brain and vestibular system. Overall, the results speak in favour of the following concept: Short-term altered gravity (˜ 1 day) can induce transient sensorimotor disorders (kinetoses) due to malfunctions of the inner ear, originating from asymmetric otoliths. The regain of normal postural control is likely due to a reweighing of sensory inputs. During long-term altered gravity (several days and more), complex adptations on the level of the central and peripheral vestibular system occur. This work was financially supported by the German Aerospace Center (DLR) e.V. (FKZ: 50 WB 9997).

  5. The background in the experiment Gerda

    NASA Astrophysics Data System (ADS)

    Agostini, M.; Allardt, M.; Andreotti, E.; Bakalyarov, A. M.; Balata, M.; Barabanov, I.; Barnabé Heider, M.; Barros, N.; Baudis, L.; Bauer, C.; Becerici-Schmidt, N.; Bellotti, E.; Belogurov, S.; Belyaev, S. T.; Benato, G.; Bettini, A.; Bezrukov, L.; Bode, T.; Brudanin, V.; Brugnera, R.; Budjáš, D.; Caldwell, A.; Cattadori, C.; Chernogorov, A.; Cossavella, F.; Demidova, E. V.; Domula, A.; Egorov, V.; Falkenstein, R.; Ferella, A.; Freund, K.; Frodyma, N.; Gangapshev, A.; Garfagnini, A.; Gotti, C.; Grabmayr, P.; Gurentsov, V.; Gusev, K.; Guthikonda, K. K.; Hampel, W.; Hegai, A.; Heisel, M.; Hemmer, S.; Heusser, G.; Hofmann, W.; Hult, M.; Inzhechik, L. V.; Ioannucci, L.; Csáthy, J. Janicskó; Jochum, J.; Junker, M.; Kihm, T.; Kirpichnikov, I. V.; Kirsch, A.; Klimenko, A.; Knöpfle, K. T.; Kochetov, O.; Kornoukhov, V. N.; Kuzminov, V. V.; Laubenstein, M.; Lazzaro, A.; Lebedev, V. I.; Lehnert, B.; Liao, H. Y.; Lindner, M.; Lippi, I.; Liu, X.; Lubashevskiy, A.; Lubsandorzhiev, B.; Lutter, G.; Macolino, C.; Machado, A. A.; Majorovits, B.; Maneschg, W.; Nemchenok, I.; Nisi, S.; O'Shaughnessy, C.; Palioselitis, D.; Pandola, L.; Pelczar, K.; Pessina, G.; Pullia, A.; Riboldi, S.; Sada, C.; Salathe, M.; Schmitt, C.; Schreiner, J.; Schulz, O.; Schwingenheuer, B.; Schönert, S.; Shevchik, E.; Shirchenko, M.; Simgen, H.; Smolnikov, A.; Stanco, L.; Strecker, H.; Tarka, M.; Ur, C. A.; Vasenko, A. A.; Volynets, O.; von Sturm, K.; Wagner, V.; Walter, M.; Wegmann, A.; Wester, T.; Wojcik, M.; Yanovich, E.; Zavarise, P.; Zhitnikov, I.; Zhukov, S. V.; Zinatulina, D.; Zuber, K.; Zuzel, G.

    2014-04-01

    The GERmanium Detector Array ( Gerda) experiment at the Gran Sasso underground laboratory (LNGS) of INFN is searching for neutrinoless double beta () decay of Ge. The signature of the signal is a monoenergetic peak at 2039 keV, the value of the decay. To avoid bias in the signal search, the present analysis does not consider all those events, that fall in a 40 keV wide region centered around . The main parameters needed for the analysis are described. A background model was developed to describe the observed energy spectrum. The model contains several contributions, that are expected on the basis of material screening or that are established by the observation of characteristic structures in the energy spectrum. The model predicts a flat energy spectrum for the blinding window around with a background index ranging from 17.6 to 23.8 cts/(keV kg yr). A part of the data not considered before has been used to test if the predictions of the background model are consistent. The observed number of events in this energy region is consistent with the background model. The background at is dominated by close sources, mainly due to K, Bi, Th, Co and emitting isotopes from the Ra decay chain. The individual fractions depend on the assumed locations of the contaminants. It is shown, that after removal of the known peaks, the energy spectrum can be fitted in an energy range of 200 keV around with a constant background. This gives a background index consistent with the full model and uncertainties of the same size.

  6. A new multiscale air quality transport model (Fluidity, 4.1.9) using fully unstructured anisotropic adaptive mesh technology

    NASA Astrophysics Data System (ADS)

    Zheng, J.; Zhu, J.; Wang, Z.; Fang, F.; Pain, C. C.; Xiang, J.

    2015-06-01

    A new anisotropic hr-adaptive mesh technique has been applied to modelling of multiscale transport phenomena, which is based on a discontinuous Galerkin/control volume discretization on unstructured meshes. Over existing air quality models typically based on static-structured grids using a locally nesting technique, the advantage of the anisotropic hr-adaptive model has the ability to adapt the mesh according to the evolving pollutant distribution and flow features. That is, the mesh resolution can be adjusted dynamically to simulate the pollutant transport process accurately and effectively. To illustrate the capability of the anisotropic adaptive unstructured mesh model, three benchmark numerical experiments have been setup for two-dimensional (2-D) transport phenomena. Comparisons have been made between the results obtained using uniform resolution meshes and anisotropic adaptive resolution meshes.

  7. Development of adaptive observation strategy using retrospective optimal interpolation

    NASA Astrophysics Data System (ADS)

    Noh, N.; Kim, S.; Song, H.; Lim, G.

    2011-12-01

    Retrospective optimal interpolation (ROI) is a method that is used to minimize cost functions with multiple minima without using adjoint models. Song and Lim (2011) perform the experiments to reduce the computational costs for implementing ROI by transforming the control variables into eigenvectors of background error covariance. We adapt the ROI algorithm to compute sensitivity estimates of severe weather events over the Korean peninsula. The eigenvectors of the ROI algorithm is modified every time the observations are assimilated. This implies that the modified eigenvectors shows the error distribution of control variables which are updated by assimilating observations. So, We can estimate the effects of the specific observations. In order to verify the adaptive observation strategy, High-impact weather over the Korean peninsula is simulated and interpreted using WRF modeling system and sensitive regions for each high-impact weather is calculated. The effects of assimilation for each observation type is discussed.

  8. A Review: Development of a Microdose Model for Analysis of Adaptive Response and Bystander Dose Response Behavior

    PubMed Central

    Leonard, Bobby E.

    2008-01-01

    Prior work has provided incremental phases to a microdosimetry modeling program to describe the dose response behavior of the radio-protective adaptive response effect. We have here consolidated these prior works (Leonard 2000, 2005, 2007a, 2007b, 2007c) to provide a composite, comprehensive Microdose Model that is also herein modified to include the bystander effect. The nomenclature for the model is also standardized for the benefit of the experimental cellular radio-biologist. It extends the prior work to explicitly encompass separately the analysis of experimental data that is 1.) only dose dependent and reflecting only adaptive response radio-protection, 2.) both dose and dose-rate dependent data and reflecting only adaptive response radio-protection for spontaneous and challenge dose damage, 3.) only dose dependent data and reflecting both bystander deleterious damage and adaptive response radio-protection (AR-BE model). The Appendix cites the various applications of the model. Here we have used the Microdose Model to analyze the, much more human risk significant, Elmore et al (2006) data for the dose and dose rate influence on the adaptive response radio-protective behavior of HeLa x Skin cells for naturally occurring, spontaneous chromosome damage from a Brachytherapy type 125I photon radiation source. We have also applied the AR-BE Microdose Model to the Chromosome inversion data of Hooker et al (2004) reflecting both low LET bystander and adaptive response effects. The micro-beam facility data of Miller et al (1999), Nagasawa and Little (1999) and Zhou et al (2003) is also examined. For the Zhou et al (2003) data, we use the AR-BE model to estimate the threshold for adaptive response reduction of the bystander effect. The mammogram and diagnostic X-ray induction of AR and protective BE are observed. We show that bystander damage is reduced in the similar manner as spontaneous and challenge dose damage as shown by the Azzam et al (1996) data. We cite

  9. A review: Development of a microdose model for analysis of adaptive response and bystander dose response behavior.

    PubMed

    Leonard, Bobby E

    2008-02-27

    Prior work has provided incremental phases to a microdosimetry modeling program to describe the dose response behavior of the radio-protective adaptive response effect. We have here consolidated these prior works (Leonard 2000, 2005, 2007a, 2007b, 2007c) to provide a composite, comprehensive Microdose Model that is also herein modified to include the bystander effect. The nomenclature for the model is also standardized for the benefit of the experimental cellular radio-biologist. It extends the prior work to explicitly encompass separately the analysis of experimental data that is 1.) only dose dependent and reflecting only adaptive response radio-protection, 2.) both dose and dose-rate dependent data and reflecting only adaptive response radio-protection for spontaneous and challenge dose damage, 3.) only dose dependent data and reflecting both bystander deleterious damage and adaptive response radio-protection (AR-BE model). The Appendix cites the various applications of the model. Here we have used the Microdose Model to analyze the, much more human risk significant, Elmore et al (2006) data for the dose and dose rate influence on the adaptive response radio-protective behavior of HeLa x Skin cells for naturally occurring, spontaneous chromosome damage from a Brachytherapy type (125)I photon radiation source. We have also applied the AR-BE Microdose Model to the Chromosome inversion data of Hooker et al (2004) reflecting both low LET bystander and adaptive response effects. The micro-beam facility data of Miller et al (1999), Nagasawa and Little (1999) and Zhou et al (2003) is also examined. For the Zhou et al (2003) data, we use the AR-BE model to estimate the threshold for adaptive response reduction of the bystander effect. The mammogram and diagnostic X-ray induction of AR and protective BE are observed. We show that bystander damage is reduced in the similar manner as spontaneous and challenge dose damage as shown by the Azzam et al (1996) data. We cite

  10. Model-Based Adaptive Event-Triggered Control of Strict-Feedback Nonlinear Systems.

    PubMed

    Li, Yuan-Xin; Yang, Guang-Hong

    2018-04-01

    This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated. A positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. The closed-loop stability of the resulting nonlinear impulsive dynamical system is rigorously proved via Lyapunov analysis under an adaptive event sampling condition. In comparing with the traditional adaptive backstepping design with a fixed sample period, the event-triggered method samples the state and updates the NN weights only when it is necessary. Therefore, the number of transmissions can be significantly reduced. Finally, two simulation examples are presented to show the effectiveness of the proposed control method.

  11. Capabilities of wind tunnels with two-adaptive walls to minimize boundary interference in 3-D model testing

    NASA Technical Reports Server (NTRS)

    Rebstock, Rainer; Lee, Edwin E., Jr.

    1989-01-01

    An initial wind tunnel test was made to validate a new wall adaptation method for 3-D models in test sections with two adaptive walls. First part of the adaptation strategy is an on-line assessment of wall interference at the model position. The wall induced blockage was very small at all test conditions. Lift interference occurred at higher angles of attack with the walls set aerodynamically straight. The adaptation of the top and bottom tunnel walls is aimed at achieving a correctable flow condition. The blockage was virtually zero throughout the wing planform after the wall adjustment. The lift curve measured with the walls adapted agreed very well with interference free data for Mach 0.7, regardless of the vertical position of the wing in the test section. The 2-D wall adaptation can significantly improve the correctability of 3-D model data. Nevertheless, residual spanwise variations of wall interference are inevitable.

  12. Adaptive Urban Stormwater Management Using a Two-stage Stochastic Optimization Model

    NASA Astrophysics Data System (ADS)

    Hung, F.; Hobbs, B. F.; McGarity, A. E.

    2014-12-01

    In many older cities, stormwater results in combined sewer overflows (CSOs) and consequent water quality impairments. Because of the expense of traditional approaches for controlling CSOs, cities are considering the use of green infrastructure (GI) to reduce runoff and pollutants. Examples of GI include tree trenches, rain gardens, green roofs, and rain barrels. However, the cost and effectiveness of GI are uncertain, especially at the watershed scale. We present a two-stage stochastic extension of the Stormwater Investment Strategy Evaluation (StormWISE) model (A. McGarity, JWRPM, 2012, 111-24) to explicitly model and optimize these uncertainties in an adaptive management framework. A two-stage model represents the immediate commitment of resources ("here & now") followed by later investment and adaptation decisions ("wait & see"). A case study is presented for Philadelphia, which intends to extensively deploy GI over the next two decades (PWD, "Green City, Clean Water - Implementation and Adaptive Management Plan," 2011). After first-stage decisions are made, the model updates the stochastic objective and constraints (learning). We model two types of "learning" about GI cost and performance. One assumes that learning occurs over time, is automatic, and does not depend on what has been done in stage one (basic model). The other considers learning resulting from active experimentation and learning-by-doing (advanced model). Both require expert probability elicitations, and learning from research and monitoring is modelled by Bayesian updating (as in S. Jacobi et al., JWRPM, 2013, 534-43). The model allocates limited financial resources to GI investments over time to achieve multiple objectives with a given reliability. Objectives include minimizing construction and O&M costs; achieving nutrient, sediment, and runoff volume targets; and community concerns, such as aesthetics, CO2 emissions, heat islands, and recreational values. CVaR (Conditional Value at Risk) and

  13. A new technique for measuring aerosols with moonlight observations and a sky background model

    NASA Astrophysics Data System (ADS)

    Jones, Amy; Noll, Stefan; Kausch, Wolfgang; Kimeswenger, Stefan; Szyszka, Ceszary; Unterguggenberger, Stefanie

    2014-05-01

    There have been an ample number of studies on aerosols in urban, daylight conditions, but few for remote, nocturnal aerosols. We have developed a new technique for investigating such aerosols using our sky background model and astronomical observations. With a dedicated observing proposal we have successfully tested this technique for nocturnal, remote aerosol studies. This technique relies on three requirements: (a) sky background model, (b) observations taken with scattered moonlight, and (c) spectrophotometric standard star observations for flux calibrations. The sky background model was developed for the European Southern Observatory and is optimized for the Very Large Telescope at Cerro Paranal in the Atacama desert in Chile. This is a remote location with almost no urban aerosols. It is well suited for studying remote background aerosols that are normally difficult to detect. Our sky background model has an uncertainty of around 20 percent and the scattered moonlight portion is even more accurate. The last two requirements are having astronomical observations with moonlight and of standard stars at different airmasses, all during the same night. We had a dedicated observing proposal at Cerro Paranal with the instrument X-Shooter to use as a case study for this method. X-Shooter is a medium resolution, echelle spectrograph which covers the wavelengths from 0.3 to 2.5 micrometers. We observed plain sky at six different distances (7, 13, 20, 45, 90, and 110 degrees) to the Moon for three different Moon phases (between full and half). Also direct observations of spectrophotometric standard stars were taken at two different airmasses for each night to measure the extinction curve via the Langley method. This is an ideal data set for testing this technique. The underlying assumption is that all components, other than the atmospheric conditions (specifically aerosols and airglow), can be calculated with the model for the given observing parameters. The scattered

  14. A generic efficient adaptive grid scheme for rocket propulsion modeling

    NASA Technical Reports Server (NTRS)

    Mo, J. D.; Chow, Alan S.

    1993-01-01

    The objective of this research is to develop an efficient, time-accurate numerical algorithm to discretize the Navier-Stokes equations for the predictions of internal one-, two-dimensional and axisymmetric flows. A generic, efficient, elliptic adaptive grid generator is implicitly coupled with the Lower-Upper factorization scheme in the development of ALUNS computer code. The calculations of one-dimensional shock tube wave propagation and two-dimensional shock wave capture, wave-wave interactions, shock wave-boundary interactions show that the developed scheme is stable, accurate and extremely robust. The adaptive grid generator produced a very favorable grid network by a grid speed technique. This generic adaptive grid generator is also applied in the PARC and FDNS codes and the computational results for solid rocket nozzle flowfield and crystal growth modeling by those codes will be presented in the conference, too. This research work is being supported by NASA/MSFC.

  15. A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

    PubMed Central

    Othman, Zulaiha Ali

    2014-01-01

    Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748

  16. A modified active appearance model based on an adaptive artificial bee colony.

    PubMed

    Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali

    2014-01-01

    Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

  17. Modelling airway smooth muscle passive length adaptation via thick filament length distributions

    PubMed Central

    Donovan, Graham M.

    2013-01-01

    We present a new model of airway smooth muscle (ASM), which surrounds and constricts every airway in the lung and thus plays a central role in the airway constriction associated with asthma. This new model of ASM is based on an extension of sliding filament/crossbridge theory, which explicitly incorporates the length distribution of thick sliding filaments to account for a phenomenon known as dynamic passive length adaptation; the model exhibits good agreement with experimental data for ASM force–length behaviour across multiple scales. Principally these are (nonlinear) force–length loops at short timescales (seconds), parabolic force–length curves at medium timescales (minutes) and length adaptation at longer timescales. This represents a significant improvement on the widely-used cross-bridge models which work so well in or near the isometric regime, and may have significant implications for studies which rely on crossbridge or other dynamic airway smooth muscle models, and thus both airway and lung dynamics. PMID:23721681

  18. Adaptive inferential sensors based on evolving fuzzy models.

    PubMed

    Angelov, Plamen; Kordon, Arthur

    2010-04-01

    A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can address the

  19. Adaptive skin detection based on online training

    NASA Astrophysics Data System (ADS)

    Zhang, Ming; Tang, Liang; Zhou, Jie; Rong, Gang

    2007-11-01

    Skin is a widely used cue for porn image classification. Most conventional methods are off-line training schemes. They usually use a fixed boundary to segment skin regions in the images and are effective only in restricted conditions: e.g. good lightness and unique human race. This paper presents an adaptive online training scheme for skin detection which can handle these tough cases. In our approach, skin detection is considered as a classification problem on Gaussian mixture model. For each image, human face is detected and the face color is used to establish a primary estimation of skin color distribution. Then an adaptive online training algorithm is used to find the real boundary between skin color and background color in current image. Experimental results on 450 images showed that the proposed method is more robust in general situations than the conventional ones.

  20. Maximizing effectiveness of adaptation action in Pacific Island communities using coastal wave attenuation models

    NASA Astrophysics Data System (ADS)

    Jung, H.; Carruthers, T.; Allison, M. A.; Weathers, D.; Moss, L.; Timmermans, H.

    2017-12-01

    Pacific Island communities are highly vulnerable to the effects of climate change, specifically accelerating rates of sea level rise, changes to storm intensity and associated rainfall patterns resulting in flooding and shoreline erosion. Nature-based adaptation is being planned not only to reduce the risk from shoreline erosion, but also to support benefits of a healthy ecosystem (e.g., supporting fisheries or coral reefs). In order to assess potential effectiveness of the nature-based actions to dissipate wave energy, two-dimensional X-Beach models were developed to predict the wave attenuation effect of coastal adaptation actions at the pilot sites—the villages of Naselesele and Somosomo on Taveuni island, Fiji. Both sites are experiencing serious shoreline erosion due to sea level rise and storm wave. The water depth (single-beam bathymetry), land elevation (truck-based LiDAR), and vegetation data including stem density and height were collected in both locations in a June 2017 field experiment. Wave height and water velocity were also measured for the model setup and calibration using a series of bottom-mounted instruments deployed in the 0-15 m water depth portions of the study grid. The calibrated model will be used to evaluate a range of possible adaptation actions identified by the community members of Naselesele and Somosomo. Particularly, multiple storm scenario runs with management-relevant shoreline restoration/adaptation options will be implemented to evaluate efficiencies of each adaptation action (e.g., no action, with additional planted trees, with sand mining, with seawalls constructed with natural materials, etc.). These model results will help to better understand how proposed adaption actions may influence future shoreline change and maximize benefits to communities in island nations across the SW Pacific.

  1. Geometry Modeling and Adaptive Control of Air-Breathing Hypersonic Vehicles

    NASA Astrophysics Data System (ADS)

    Vick, Tyler Joseph

    Air-breathing hypersonic vehicles have the potential to provide global reach and affordable access to space. Recent technological advancements have made scramjet-powered flight achievable, as evidenced by the successes of the X-43A and X-51A flight test programs over the last decade. Air-breathing hypersonic vehicles present unique modeling and control challenges in large part due to the fact that scramjet propulsion systems are highly integrated into the airframe, resulting in strongly coupled and often unstable dynamics. Additionally, the extreme flight conditions and inability to test fully integrated vehicle systems larger than X-51 before flight leads to inherent uncertainty in hypersonic flight. This thesis presents a means to design vehicle geometries, simulate vehicle dynamics, and develop and analyze control systems for hypersonic vehicles. First, a software tool for generating three-dimensional watertight vehicle surface meshes from simple design parameters is developed. These surface meshes are compatible with existing vehicle analysis tools, with which databases of aerodynamic and propulsive forces and moments can be constructed. A six-degree-of-freedom nonlinear dynamics simulation model which incorporates this data is presented. Inner-loop longitudinal and lateral control systems are designed and analyzed utilizing the simulation model. The first is an output feedback proportional-integral linear controller designed using linear quadratic regulator techniques. The second is a model reference adaptive controller (MRAC) which augments this baseline linear controller with an adaptive element. The performance and robustness of each controller are analyzed through simulated time responses to angle-of-attack and bank angle commands, while various uncertainties are introduced. The MRAC architecture enables the controller to adapt in a nonlinear fashion to deviations from the desired response, allowing for improved tracking performance, stability, and

  2. Holistic approach for automated background EEG assessment in asphyxiated full-term infants

    NASA Astrophysics Data System (ADS)

    Matic, Vladimir; Cherian, Perumpillichira J.; Koolen, Ninah; Naulaers, Gunnar; Swarte, Renate M.; Govaert, Paul; Van Huffel, Sabine; De Vos, Maarten

    2014-12-01

    Objective. To develop an automated algorithm to quantify background EEG abnormalities in full-term neonates with hypoxic ischemic encephalopathy. Approach. The algorithm classifies 1 h of continuous neonatal EEG (cEEG) into a mild, moderate or severe background abnormality grade. These classes are well established in the literature and a clinical neurophysiologist labeled 272 1 h cEEG epochs selected from 34 neonates. The algorithm is based on adaptive EEG segmentation and mapping of the segments into the so-called segments’ feature space. Three features are suggested and further processing is obtained using a discretized three-dimensional distribution of the segments’ features represented as a 3-way data tensor. Further classification has been achieved using recently developed tensor decomposition/classification methods that reduce the size of the model and extract a significant and discriminative set of features. Main results. Effective parameterization of cEEG data has been achieved resulting in high classification accuracy (89%) to grade background EEG abnormalities. Significance. For the first time, the algorithm for the background EEG assessment has been validated on an extensive dataset which contained major artifacts and epileptic seizures. The demonstrated high robustness, while processing real-case EEGs, suggests that the algorithm can be used as an assistive tool to monitor the severity of hypoxic insults in newborns.

  3. INITIAL APPL;ICATION OF THE ADAPTIVE GRID AIR POLLUTION MODEL

    EPA Science Inventory

    The paper discusses an adaptive-grid algorithm used in air pollution models. The algorithm reduces errors related to insufficient grid resolution by automatically refining the grid scales in regions of high interest. Meanwhile the grid scales are coarsened in other parts of the d...

  4. Dynamics of the exponential integrate-and-fire model with slow currents and adaptation.

    PubMed

    Barranca, Victor J; Johnson, Daniel C; Moyher, Jennifer L; Sauppe, Joshua P; Shkarayev, Maxim S; Kovačič, Gregor; Cai, David

    2014-08-01

    In order to properly capture spike-frequency adaptation with a simplified point-neuron model, we study approximations of Hodgkin-Huxley (HH) models including slow currents by exponential integrate-and-fire (EIF) models that incorporate the same types of currents. We optimize the parameters of the EIF models under the external drive consisting of AMPA-type conductance pulses using the current-voltage curves and the van Rossum metric to best capture the subthreshold membrane potential, firing rate, and jump size of the slow current at the neuron's spike times. Our numerical simulations demonstrate that, in addition to these quantities, the approximate EIF-type models faithfully reproduce bifurcation properties of the HH neurons with slow currents, which include spike-frequency adaptation, phase-response curves, critical exponents at the transition between a finite and infinite number of spikes with increasing constant external drive, and bifurcation diagrams of interspike intervals in time-periodically forced models. Dynamics of networks of HH neurons with slow currents can also be approximated by corresponding EIF-type networks, with the approximation being at least statistically accurate over a broad range of Poisson rates of the external drive. For the form of external drive resembling realistic, AMPA-like synaptic conductance response to incoming action potentials, the EIF model affords great savings of computation time as compared with the corresponding HH-type model. Our work shows that the EIF model with additional slow currents is well suited for use in large-scale, point-neuron models in which spike-frequency adaptation is important.

  5. Adapting Evidence-Based Mental Health Treatments in Community Settings: Preliminary Results from a Partnership Approach

    ERIC Educational Resources Information Center

    Southam-Gerow, Michael A.; Hourigan, Shannon E.; Allin, Robert B., Jr.

    2009-01-01

    This article describes the application of a university-community partnership model to the problem of adapting evidence-based treatment approaches in a community mental health setting. Background on partnership research is presented, with consideration of methodological and practical issues related to this kind of research. Then, a rationale for…

  6. Resolution-Adapted All-Atomic and Coarse-Grained Model for Biomolecular Simulations.

    PubMed

    Shen, Lin; Hu, Hao

    2014-06-10

    We develop here an adaptive multiresolution method for the simulation of complex heterogeneous systems such as the protein molecules. The target molecular system is described with the atomistic structure while maintaining concurrently a mapping to the coarse-grained models. The theoretical model, or force field, used to describe the interactions between two sites is automatically adjusted in the simulation processes according to the interaction distance/strength. Therefore, all-atomic, coarse-grained, or mixed all-atomic and coarse-grained models would be used together to describe the interactions between a group of atoms and its surroundings. Because the choice of theory is made on the force field level while the sampling is always carried out in the atomic space, the new adaptive method preserves naturally the atomic structure and thermodynamic properties of the entire system throughout the simulation processes. The new method will be very useful in many biomolecular simulations where atomistic details are critically needed.

  7. Adaptive model-based control systems and methods for controlling a gas turbine

    NASA Technical Reports Server (NTRS)

    Brunell, Brent Jerome (Inventor); Mathews, Jr., Harry Kirk (Inventor); Kumar, Aditya (Inventor)

    2004-01-01

    Adaptive model-based control systems and methods are described so that performance and/or operability of a gas turbine in an aircraft engine, power plant, marine propulsion, or industrial application can be optimized under normal, deteriorated, faulted, failed and/or damaged operation. First, a model of each relevant system or component is created, and the model is adapted to the engine. Then, if/when deterioration, a fault, a failure or some kind of damage to an engine component or system is detected, that information is input to the model-based control as changes to the model, constraints, objective function, or other control parameters. With all the information about the engine condition, and state and directives on the control goals in terms of an objective function and constraints, the control then solves an optimization so the optimal control action can be determined and taken. This model and control may be updated in real-time to account for engine-to-engine variation, deterioration, damage, faults and/or failures using optimal corrective control action command(s).

  8. Non-negative infrared patch-image model: Robust target-background separation via partial sum minimization of singular values

    NASA Astrophysics Data System (ADS)

    Dai, Yimian; Wu, Yiquan; Song, Yu; Guo, Jun

    2017-03-01

    To further enhance the small targets and suppress the heavy clutters simultaneously, a robust non-negative infrared patch-image model via partial sum minimization of singular values is proposed. First, the intrinsic reason behind the undesirable performance of the state-of-the-art infrared patch-image (IPI) model when facing extremely complex backgrounds is analyzed. We point out that it lies in the mismatching of IPI model's implicit assumption of a large number of observations with the reality of deficient observations of strong edges. To fix this problem, instead of the nuclear norm, we adopt the partial sum of singular values to constrain the low-rank background patch-image, which could provide a more accurate background estimation and almost eliminate all the salient residuals in the decomposed target image. In addition, considering the fact that the infrared small target is always brighter than its adjacent background, we propose an additional non-negative constraint to the sparse target patch-image, which could not only wipe off more undesirable components ulteriorly but also accelerate the convergence rate. Finally, an algorithm based on inexact augmented Lagrange multiplier method is developed to solve the proposed model. A large number of experiments are conducted demonstrating that the proposed model has a significant improvement over the other nine competitive methods in terms of both clutter suppressing performance and convergence rate.

  9. Speed adaptation in a powered transtibial prosthesis controlled with a neuromuscular model.

    PubMed

    Markowitz, Jared; Krishnaswamy, Pavitra; Eilenberg, Michael F; Endo, Ken; Barnhart, Chris; Herr, Hugh

    2011-05-27

    Control schemes for powered ankle-foot prostheses would benefit greatly from a means to make them inherently adaptive to different walking speeds. Towards this goal, one may attempt to emulate the intact human ankle, as it is capable of seamless adaptation. Human locomotion is governed by the interplay among legged dynamics, morphology and neural control including spinal reflexes. It has been suggested that reflexes contribute to the changes in ankle joint dynamics that correspond to walking at different speeds. Here, we use a data-driven muscle-tendon model that produces estimates of the activation, force, length and velocity of the major muscles spanning the ankle to derive local feedback loops that may be critical in the control of those muscles during walking. This purely reflexive approach ignores sources of non-reflexive neural drive and does not necessarily reflect the biological control scheme, yet can still closely reproduce the muscle dynamics estimated from biological data. The resulting neuromuscular model was applied to control a powered ankle-foot prosthesis and tested by an amputee walking at three speeds. The controller produced speed-adaptive behaviour; net ankle work increased with walking speed, highlighting the benefits of applying neuromuscular principles in the control of adaptive prosthetic limbs.

  10. Modeling Adaptive Educational Methods with IMS Learning Design

    ERIC Educational Resources Information Center

    Specht, Marcus; Burgos, Daniel

    2007-01-01

    The paper describes a classification system for adaptive methods developed in the area of adaptive educational hypermedia based on four dimensions: What components of the educational system are adapted? To what features of the user and the current context does the system adapt? Why does the system adapt? How does the system get the necessary…

  11. A biological hierarchical model based underwater moving object detection.

    PubMed

    Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

    2014-01-01

    Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.

  12. A Biological Hierarchical Model Based Underwater Moving Object Detection

    PubMed Central

    Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

    2014-01-01

    Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results. PMID:25140194

  13. Direct model reference adaptive control with application to flexible robots

    NASA Technical Reports Server (NTRS)

    Steinvorth, Rodrigo; Kaufman, Howard; Neat, Gregory W.

    1992-01-01

    A modification to a direct command generator tracker-based model reference adaptive control (MRAC) system is suggested in this paper. This modification incorporates a feedforward into the reference model's output as well as the plant's output. Its purpose is to eliminate the bounded model following error present in steady state when previous MRAC systems were used. The algorithm was evaluated using the dynamics for a single-link flexible-joint arm. The results of these simulations show a response with zero steady state model following error. These results encourage further use of MRAC for various types of nonlinear plants.

  14. Variable-Length Computerized Adaptive Testing Based on Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Hsu, Chia-Ling; Wang, Wen-Chung; Chen, Shu-Ying

    2013-01-01

    Interest in developing computerized adaptive testing (CAT) under cognitive diagnosis models (CDMs) has increased recently. CAT algorithms that use a fixed-length termination rule frequently lead to different degrees of measurement precision for different examinees. Fixed precision, in which the examinees receive the same degree of measurement…

  15. Frequency-specific adaptation and its underlying circuit model in the auditory midbrain.

    PubMed

    Shen, Li; Zhao, Lingyun; Hong, Bo

    2015-01-01

    Receptive fields of sensory neurons are considered to be dynamic and depend on the stimulus history. In the auditory system, evidence of dynamic frequency-receptive fields has been found following stimulus-specific adaptation (SSA). However, the underlying mechanism and circuitry of SSA have not been fully elucidated. Here, we studied how frequency-receptive fields of neurons in rat inferior colliculus (IC) changed when exposed to a biased tone sequence. Pure tone with one specific frequency (adaptor) was presented markedly more often than others. The adapted tuning was compared with the original tuning measured with an unbiased sequence. We found inhomogeneous changes in frequency tuning in IC, exhibiting a center-surround pattern with respect to the neuron's best frequency. Central adaptors elicited strong suppressive and repulsive changes while flank adaptors induced facilitative and attractive changes. Moreover, we proposed a two-layer model of the underlying network, which not only reproduced the adaptive changes in the receptive fields but also predicted novelty responses to oddball sequences. These results suggest that frequency-specific adaptation in auditory midbrain can be accounted for by an adapted frequency channel and its lateral spreading of adaptation, which shed light on the organization of the underlying circuitry.

  16. Asian medaka fishes offer new models for studying mechanisms of seawater adaptation.

    PubMed

    Inoue, Koji; Takei, Yoshio

    2003-12-01

    Japanese medaka (Oryzias latipes) is a freshwater (FW) teleost that is popular throughout the world for laboratory use. In this paper, we discuss the utility of Japanese medaka and related species for studying mechanisms of seawater (SW) adaptation. In addition to general advantages as an experimental animal such as their daily spawning activity, transparency of embryos, short generation time and established transgenic techniques, Japanese medaka have some adaptability to SW unlike the strictly stenohaline zebrafish (Danio rerio). Since other species in the genus Oryzias exhibit different degrees of adaptability to SW, comparative studies between Japanese medaka, where molecular-biological and genetic information is abundant, and other Oryzias species are expected to present varying approaches to solving the problems of SW adaptation. We introduce some examples of interspecies comparison for SW adaptabilities both in adult fish and in embryos. Oryzias species are good models for evolutionary, ecological and zoogeographical studies and a relationship between SW adaptability and geographic distribution has been suggested. Medaka fishes may thus deliver new insights into our understanding of how fish have expanded their distribution to a wide variety of osmotic environments.

  17. Constraints on universe models with cosmological constant from cosmic microwave background anisotropy

    NASA Astrophysics Data System (ADS)

    Sugiyama, Naoshi; Gouda, Naoteru; Sasaki, Misao

    1990-12-01

    Thorough numerical calculations of the fluctuations in the cosmic microwave background radiation using the gage-invariant formalism are carried out for various cosmological models with the cosmological constant. It is shown that a spatially flat cold dark matter-dominated universe of Omega(0) = 0.1 to about 0.4 and H(0) = 50 to about 100 km/s per Mpc with adiabatic perturbations has the possibility of giving the final answer to cosmological puzzles. It is also found that the introduction of the cosmological constant may revive pure baryonic universe models.

  18. Neural network-based model reference adaptive control system.

    PubMed

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  19. An adaptive signal-processing approach to online adaptive tutoring.

    PubMed

    Bergeron, Bryan; Cline, Andrew

    2011-01-01

    Conventional intelligent or adaptive tutoring online systems rely on domain-specific models of learner behavior based on rules, deep domain knowledge, and other resource-intensive methods. We have developed and studied a domain-independent methodology of adaptive tutoring based on domain-independent signal-processing approaches that obviate the need for the construction of explicit expert and student models. A key advantage of our method over conventional approaches is a lower barrier to entry for educators who want to develop adaptive online learning materials.

  20. Does Visuomotor Adaptation Proceed in Stages? An Examination of the Learning Model by Chein and Schneider (2012).

    PubMed

    Simon, Anja; Bock, Otmar

    2015-01-01

    A new 3-stage model based on neuroimaging evidence is proposed by Chein and Schneider (2012). Each stage is associated with different brain regions, and draws on cognitive abilities: the first stage on creativity, the second on selective attention, and the third on automatic processing. The purpose of the present study was to scrutinize the validity of this model for 1 popular learning paradigm, visuomotor adaptation. Participants completed tests for creativity, selective attention and automated processing before attending in a pointing task with adaptation to a 60° rotation of visual feedback. To examine the relationship between cognitive abilities and motor learning at different times of practice, associations between cognitive and adaptation scores were calculated repeatedly throughout adaptation. The authors found no benefit of high creativity for adaptive performance. High levels of selective attention were positively associated with early adaptation, but hardly with late adaptation and de-adaptation. High levels of automated execution were beneficial for late adaptation, but hardly for early and de-adaptation. From this we conclude that Chein and Schneider's first learning stage is difficult to confirm by research on visuomotor adaptation, and that the other 2 learning stages rather relate to workaround strategies than to actual adaptive recalibration.

  1. Introducing Enabling Computational Tools to the Climate Sciences: Multi-Resolution Climate Modeling with Adaptive Cubed-Sphere Grids

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

    Jablonowski, Christiane

    The research investigates and advances strategies how to bridge the scale discrepancies between local, regional and global phenomena in climate models without the prohibitive computational costs of global cloud-resolving simulations. In particular, the research explores new frontiers in computational geoscience by introducing high-order Adaptive Mesh Refinement (AMR) techniques into climate research. AMR and statically-adapted variable-resolution approaches represent an emerging trend for atmospheric models and are likely to become the new norm in future-generation weather and climate models. The research advances the understanding of multi-scale interactions in the climate system and showcases a pathway how to model these interactions effectively withmore » advanced computational tools, like the Chombo AMR library developed at the Lawrence Berkeley National Laboratory. The research is interdisciplinary and combines applied mathematics, scientific computing and the atmospheric sciences. In this research project, a hierarchy of high-order atmospheric models on cubed-sphere computational grids have been developed that serve as an algorithmic prototype for the finite-volume solution-adaptive Chombo-AMR approach. The foci of the investigations have lied on the characteristics of both static mesh adaptations and dynamically-adaptive grids that can capture flow fields of interest like tropical cyclones. Six research themes have been chosen. These are (1) the introduction of adaptive mesh refinement techniques into the climate sciences, (2) advanced algorithms for nonhydrostatic atmospheric dynamical cores, (3) an assessment of the interplay between resolved-scale dynamical motions and subgrid-scale physical parameterizations, (4) evaluation techniques for atmospheric model hierarchies, (5) the comparison of AMR refinement strategies and (6) tropical cyclone studies with a focus on multi-scale interactions and variable-resolution modeling. The results of this research

  2. Grey-Markov prediction model based on background value optimization and central-point triangular whitenization weight function

    NASA Astrophysics Data System (ADS)

    Ye, Jing; Dang, Yaoguo; Li, Bingjun

    2018-01-01

    Grey-Markov forecasting model is a combination of grey prediction model and Markov chain which show obvious optimization effects for data sequences with characteristics of non-stationary and volatility. However, the state division process in traditional Grey-Markov forecasting model is mostly based on subjective real numbers that immediately affects the accuracy of forecasting values. To seek the solution, this paper introduces the central-point triangular whitenization weight function in state division to calculate possibilities of research values in each state which reflect preference degrees in different states in an objective way. On the other hand, background value optimization is applied in the traditional grey model to generate better fitting data. By this means, the improved Grey-Markov forecasting model is built. Finally, taking the grain production in Henan Province as an example, it verifies this model's validity by comparing with GM(1,1) based on background value optimization and the traditional Grey-Markov forecasting model.

  3. A model of adaptation of overseas nurses: exploring the experiences of Japanese nurses working in Australia.

    PubMed

    Kishi, Yuka; Inoue, Kumiyo; Crookes, Patrick; Shorten, Allison

    2014-04-01

    The purpose of the study was to investigate the experiences of Japanese nurses and their adaptation to their work environment in Australia. Using a qualitative research method and semistructured interviews, the study aimed to discover, describe, and analyze the experiences of 14 Japanese nurses participating in the study. A qualitative study. Fourteen Japanese registered nurses working in Australian hospitals participated in the study. Individual semistructured interviews were conducted from April to June in 2008. Thematic analysis was used to identify themes within the data. Analysis of qualitative open-ended questions revealed the participants' adaptation process. It consists of three themes or phases: seeking (S), acclimatizing (A), and settling (S), subsequently named the S.A.S. model. The conceptual model of the adaptation processes of 14 Japanese nurses working in Australia includes the seeking, acclimatizing, and settling phases. Although these phases are not mutually exclusive and the process is not necessarily uniformly linear, all participants in this study passed through this S.A.S. model in order to adapt to their new environment. The S.A.S. model of adaptation helps to describe the experiences of Japanese overseas qualified nurses working in Australian hospitals. Future research is needed to examine whether this model can be applied to nurses from other countries and in other settings outside Australia.

  4. Direct model reference adaptive control of a flexible robotic manipulator

    NASA Technical Reports Server (NTRS)

    Meldrum, D. R.

    1985-01-01

    Quick, precise control of a flexible manipulator in a space environment is essential for future Space Station repair and satellite servicing. Numerous control algorithms have proven successful in controlling rigid manipulators wih colocated sensors and actuators; however, few have been tested on a flexible manipulator with noncolocated sensors and actuators. In this thesis, a model reference adaptive control (MRAC) scheme based on command generator tracker theory is designed for a flexible manipulator. Quicker, more precise tracking results are expected over nonadaptive control laws for this MRAC approach. Equations of motion in modal coordinates are derived for a single-link, flexible manipulator with an actuator at the pinned-end and a sensor at the free end. An MRAC is designed with the objective of controlling the torquing actuator so that the tip position follows a trajectory that is prescribed by the reference model. An appealing feature of this direct MRAC law is that it allows the reference model to have fewer states than the plant itself. Direct adaptive control also adjusts the controller parameters directly with knowledge of only the plant output and input signals.

  5. A tissue adaptation model based on strain-dependent collagen degradation and contact-guided cell traction.

    PubMed

    Heck, T A M; Wilson, W; Foolen, J; Cilingir, A C; Ito, K; van Donkelaar, C C

    2015-03-18

    Soft biological tissues adapt their collagen network to the mechanical environment. Collagen remodeling and cell traction are both involved in this process. The present study presents a collagen adaptation model which includes strain-dependent collagen degradation and contact-guided cell traction. Cell traction is determined by the prevailing collagen structure and is assumed to strive for tensional homeostasis. In addition, collagen is assumed to mechanically fail if it is over-strained. Care is taken to use principally measurable and physiologically meaningful relationships. This model is implemented in a fibril-reinforced biphasic finite element model for soft hydrated tissues. The versatility and limitations of the model are demonstrated by corroborating the predicted transient and equilibrium collagen adaptation under distinct mechanical constraints against experimental observations from the literature. These experiments include overloading of pericardium explants until failure, static uniaxial and biaxial loading of cell-seeded gels in vitro and shortening of periosteum explants. In addition, remodeling under hypothetical conditions is explored to demonstrate how collagen might adapt to small differences in constraints. Typical aspects of all essentially different experimental conditions are captured quantitatively or qualitatively. Differences between predictions and experiments as well as new insights that emerge from the present simulations are discussed. This model is anticipated to evolve into a mechanistic description of collagen adaptation, which may assist in developing load-regimes for functional tissue engineered constructs, or may be employed to improve our understanding of the mechanisms behind physiological and pathological collagen remodeling. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. Effect of wine-based marinades on the behavior of Salmonella Typhimurium and background flora in beef fillets.

    PubMed

    Nisiotou, A; Chorianopoulos, N G; Gounadaki, A; Panagou, E Z; Nychas, G-J E

    2013-06-17

    The aim of this study was to evaluate the wine-based marinades to control the survival of acid-adapted and non-adapted Salmonella Typhimurium and background flora of fresh beef stored aerobically or under modified atmosphere. Beef slices were inoculated with a 3-strain cocktail of acid-adapted or non-adapted Salmonella Typhimurium strains DT 193, 4/74 and DSM 554 and marinated by immersion in wine (W) or wine supplemented with 0.3% thyme essential oil (WEO), for 12h at 4°C. Marinated slices were then stored under air or modified atmosphere conditions at 5°C. S. Typhimurium and background flora were followed for a 19-day period of storage. S. Typhimurium individual strains were monitored by pulsed field gel electrophoresis. Marination with wine significantly (P<0.05) reduced the background flora compared to the control (non-marinated). Furthermore, immersion of fillets in W or WEO marinades for 12h significantly (P<0.05) reduced the levels of S. Typhimurium compared to the non-marinated (control) samples by 1.1 and 1.4logCFU/g or 2.0 and 1.9logCFU/g for acid-adapted and non-adapted cells, respectively. Acid-adapted cells were more susceptible (P<0.05) to the addition of thyme essential oil in the wine marinade. The epidemic multi-drug resistant DT 193, the 4/74 and DSM 554 strains survived marination (for both W and WEO) and were detected at about similar proportions as revealed by PFGE results. Present results indicate that wine-based marinades are efficient, from a safety and shelf life stand point, in reducing pathogen's levels as well as the background beef flora. Copyright © 2013 Elsevier B.V. All rights reserved.

  7. Restructuring Introductory Physics by Adapting an Active Learning Studio Model

    ERIC Educational Resources Information Center

    Gatch, Delena

    2010-01-01

    Despite efforts to engage students in the traditional lecture environment, faculty in Georgia Southern University's Physics Department became dissatisfied with lecture as the primary means of instruction. During the fall semester of 2006, our department began adapting the studio model to suit the needs of introductory calculus-based physics…

  8. A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents

    PubMed Central

    Griol, David

    2016-01-01

    Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user's intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user's needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users. PMID:26819592

  9. Wind adaptive modeling of transmission lines using minimum description length

    NASA Astrophysics Data System (ADS)

    Jaw, Yoonseok; Sohn, Gunho

    2017-03-01

    The transmission lines are moving objects, which positions are dynamically affected by wind-induced conductor motion while they are acquired by airborne laser scanners. This wind effect results in a noisy distribution of laser points, which often hinders accurate representation of transmission lines and thus, leads to various types of modeling errors. This paper presents a new method for complete 3D transmission line model reconstruction in the framework of inner and across span analysis. The highlighted fact is that the proposed method is capable of indirectly estimating noise scales, which corrupts the quality of laser observations affected by different wind speeds through a linear regression analysis. In the inner span analysis, individual transmission line models of each span are evaluated based on the Minimum Description Length theory and erroneous transmission line segments are subsequently replaced by precise transmission line models with wind-adaptive noise scale estimated. In the subsequent step of across span analysis, detecting the precise start and end positions of the transmission line models, known as the Point of Attachment, is the key issue for correcting partial modeling errors, as well as refining transmission line models. Finally, the geometric and topological completion of transmission line models are achieved over the entire network. A performance evaluation was conducted over 138.5 km long corridor data. In a modest wind condition, the results demonstrates that the proposed method can improve the accuracy of non-wind-adaptive initial models on an average of 48% success rate to produce complete transmission line models in the range between 85% and 99.5% with the positional accuracy of 9.55 cm transmission line models and 28 cm Point of Attachment in the root-mean-square error.

  10. Adaptation of hidden Markov models for recognizing speech of reduced frame rate.

    PubMed

    Lee, Lee-Min; Jean, Fu-Rong

    2013-12-01

    The frame rate of the observation sequence in distributed speech recognition applications may be reduced to suit a resource-limited front-end device. In order to use models trained using full-frame-rate data in the recognition of reduced frame-rate (RFR) data, we propose a method for adapting the transition probabilities of hidden Markov models (HMMs) to match the frame rate of the observation. Experiments on the recognition of clean and noisy connected digits are conducted to evaluate the proposed method. Experimental results show that the proposed method can effectively compensate for the frame-rate mismatch between the training and the test data. Using our adapted model to recognize the RFR speech data, one can significantly reduce the computation time and achieve the same level of accuracy as that of a method, which restores the frame rate using data interpolation.

  11. Adaptive control using neural networks and approximate models.

    PubMed

    Narendra, K S; Mukhopadhyay, S

    1997-01-01

    The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input-output models warrant a detailed study in their own right.

  12. CHAMP: a locally adaptive unmixing-based hyperspectral anomaly detection algorithm

    NASA Astrophysics Data System (ADS)

    Crist, Eric P.; Thelen, Brian J.; Carrara, David A.

    1998-10-01

    Anomaly detection offers a means by which to identify potentially important objects in a scene without prior knowledge of their spectral signatures. As such, this approach is less sensitive to variations in target class composition, atmospheric and illumination conditions, and sensor gain settings than would be a spectral matched filter or similar algorithm. The best existing anomaly detectors generally fall into one of two categories: those based on local Gaussian statistics, and those based on linear mixing moles. Unmixing-based approaches better represent the real distribution of data in a scene, but are typically derived and applied on a global or scene-wide basis. Locally adaptive approaches allow detection of more subtle anomalies by accommodating the spatial non-homogeneity of background classes in a typical scene, but provide a poorer representation of the true underlying background distribution. The CHAMP algorithm combines the best attributes of both approaches, applying a linear-mixing model approach in a spatially adaptive manner. The algorithm itself, and teste results on simulated and actual hyperspectral image data, are presented in this paper.

  13. Modelling interactions between mitigation, adaptation and sustainable development

    NASA Astrophysics Data System (ADS)

    Reusser, D. E.; Siabatto, F. A. P.; Garcia Cantu Ros, A.; Pape, C.; Lissner, T.; Kropp, J. P.

    2012-04-01

    Managing the interdependence of climate mitigation, adaptation and sustainable development requires a good understanding of the dominant socioecological processes that have determined the pathways in the past. Key variables include water and food availability which depend on climate and overall ecosystem services, as well as energy supply and social, political and economic conditions. We present our initial steps to build a system dynamic model of nations that represents a minimal set of relevant variables of the socio- ecological development. The ultimate goal of the modelling exercise is to derive possible future scenarios and test those for their compatibility with sustainability boundaries. Where dynamics go beyond sustainability boundaries intervention points in the dynamics can be searched.

  14. Parametric recursive system identification and self-adaptive modeling of the human energy metabolism for adaptive control of fat weight.

    PubMed

    Őri, Zsolt P

    2017-05-01

    A mathematical model has been developed to facilitate indirect measurements of difficult to measure variables of the human energy metabolism on a daily basis. The model performs recursive system identification of the parameters of the metabolic model of the human energy metabolism using the law of conservation of energy and principle of indirect calorimetry. Self-adaptive models of the utilized energy intake prediction, macronutrient oxidation rates, and daily body composition changes were created utilizing Kalman filter and the nominal trajectory methods. The accuracy of the models was tested in a simulation study utilizing data from the Minnesota starvation and overfeeding study. With biweekly macronutrient intake measurements, the average prediction error of the utilized carbohydrate intake was -23.2 ± 53.8 kcal/day, fat intake was 11.0 ± 72.3 kcal/day, and protein was 3.7 ± 16.3 kcal/day. The fat and fat-free mass changes were estimated with an error of 0.44 ± 1.16 g/day for fat and -2.6 ± 64.98 g/day for fat-free mass. The daily metabolized macronutrient energy intake and/or daily macronutrient oxidation rate and the daily body composition change from directly measured serial data are optimally predicted with a self-adaptive model with Kalman filter that uses recursive system identification.

  15. A conceptual model for the development process of confirmatory adaptive clinical trials within an emergency research network.

    PubMed

    Mawocha, Samkeliso C; Fetters, Michael D; Legocki, Laurie J; Guetterman, Timothy C; Frederiksen, Shirley; Barsan, William G; Lewis, Roger J; Berry, Donald A; Meurer, William J

    2017-06-01

    Adaptive clinical trials use accumulating data from enrolled subjects to alter trial conduct in pre-specified ways based on quantitative decision rules. In this research, we sought to characterize the perspectives of key stakeholders during the development process of confirmatory-phase adaptive clinical trials within an emergency clinical trials network and to build a model to guide future development of adaptive clinical trials. We used an ethnographic, qualitative approach to evaluate key stakeholders' views about the adaptive clinical trial development process. Stakeholders participated in a series of multidisciplinary meetings during the development of five adaptive clinical trials and completed a Strengths-Weaknesses-Opportunities-Threats questionnaire. In the analysis, we elucidated overarching themes across the stakeholders' responses to develop a conceptual model. Four major overarching themes emerged during the analysis of stakeholders' responses to questioning: the perceived statistical complexity of adaptive clinical trials and the roles of collaboration, communication, and time during the development process. Frequent and open communication and collaboration were viewed by stakeholders as critical during the development process, as were the careful management of time and logistical issues related to the complexity of planning adaptive clinical trials. The Adaptive Design Development Model illustrates how statistical complexity, time, communication, and collaboration are moderating factors in the adaptive design development process. The intensity and iterative nature of this process underscores the need for funding mechanisms for the development of novel trial proposals in academic settings.

  16. Generative electronic background music system

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

    Mazurowski, Lukasz

    In this short paper-extended abstract the new approach to generation of electronic background music has been presented. The Generative Electronic Background Music System (GEBMS) has been located between other related approaches within the musical algorithm positioning framework proposed by Woller et al. The music composition process is performed by a number of mini-models parameterized by further described properties. The mini-models generate fragments of musical patterns used in output composition. Musical pattern and output generation are controlled by container for the mini-models - a host-model. General mechanism has been presented including the example of the synthesized output compositions.

  17. Adaptive microfluidic gradient generator for quantitative chemotaxis experiments.

    PubMed

    Anielski, Alexander; Pfannes, Eva K B; Beta, Carsten

    2017-03-01

    Chemotactic motion in a chemical gradient is an essential cellular function that controls many processes in the living world. For a better understanding and more detailed modelling of the underlying mechanisms of chemotaxis, quantitative investigations in controlled environments are needed. We developed a setup that allows us to separately address the dependencies of the chemotactic motion on the average background concentration and on the gradient steepness of the chemoattractant. In particular, both the background concentration and the gradient steepness can be kept constant at the position of the cell while it moves along in the gradient direction. This is achieved by generating a well-defined chemoattractant gradient using flow photolysis. In this approach, the chemoattractant is released by a light-induced reaction from a caged precursor in a microfluidic flow chamber upstream of the cell. The flow photolysis approach is combined with an automated real-time cell tracker that determines changes in the cell position and triggers movement of the microscope stage such that the cell motion is compensated and the cell remains at the same position in the gradient profile. The gradient profile can be either determined experimentally using a caged fluorescent dye or may be alternatively determined by numerical solutions of the corresponding physical model. To demonstrate the function of this adaptive microfluidic gradient generator, we compare the chemotactic motion of Dictyostelium discoideum cells in a static gradient and in a gradient that adapts to the position of the moving cell.

  18. Modelling of celestial backgrounds

    NASA Astrophysics Data System (ADS)

    Hickman, Duncan L.; Smith, Moira I.; Lim, Jae-Wan; Jeon, Yun-Ho

    2018-05-01

    For applications where a sensor's image includes the celestial background, stars and Solar System Bodies compromise the ability of the sensor system to correctly classify a target. Such false targets are particularly significant for the detection of weak target signatures which only have a small relative angular motion. The detection of celestial features is well established in the visible spectral band. However, given the increasing sensitivity and low noise afforded by emergent infrared focal plane array technology together with larger and more efficient optics, the signatures of celestial features can also impact performance at infrared wavelengths. A methodology has been developed which allows the rapid generation of celestial signatures in any required spectral band using star data from star catalogues and other open-source information. Within this paper, the radiometric calculations are presented to determine the irradiance values of stars and planets in any spectral band.

  19. The Roy Adaptation Model: A Theoretical Framework for Nurses Providing Care to Individuals With Anorexia Nervosa.

    PubMed

    Jennings, Karen M

    Using a nursing theoretical framework to understand, elucidate, and propose nursing research is fundamental to knowledge development. This article presents the Roy Adaptation Model as a theoretical framework to better understand individuals with anorexia nervosa during acute treatment, and the role of nursing assessments and interventions in the promotion of weight restoration. Nursing assessments and interventions situated within the Roy Adaptation Model take into consideration how weight restoration does not occur in isolation but rather reflects an adaptive process within external and internal environments, and has the potential for more holistic care.

  20. Communicating to Farmers about Skin Cancer: The Behavior Adaptation Model.

    ERIC Educational Resources Information Center

    Parrott, Roxanne; Monahan, Jennifer; Ainsworth, Stuart; Steiner, Carol

    1998-01-01

    States health campaign messages designed to encourage behavior adaptation have greater likelihood of success than campaigns promoting avoidance of at-risk behaviors that cannot be avoided. Tests a model of health risk behavior using four different behaviors in a communication campaign aimed at reducing farmers' risk for skin cancer--questions…

  1. Adapting the Transtheoretical Model of Change to the Bereavement Process

    ERIC Educational Resources Information Center

    Calderwood, Kimberly A.

    2011-01-01

    Theorists currently believe that bereaved people undergo some transformation of self rather than returning to their original state. To advance our understanding of this process, this article presents an adaptation of Prochaska and DiClemente's transtheoretical model of change as it could be applied to the journey that bereaved individuals…

  2. Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions.

    PubMed

    Dong, Yuwen; Deshpande, Sunil; Rivera, Daniel E; Downs, Danielle S; Savage, Jennifer S

    2014-06-01

    Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or "just-in-time" behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.

  3. Adult zebrafish intestine resection: a novel model of short bowel syndrome, adaptation, and intestinal stem cell regeneration.

    PubMed

    Schall, K A; Holoyda, K A; Grant, C N; Levin, D E; Torres, E R; Maxwell, A; Pollack, H A; Moats, R A; Frey, M R; Darehzereshki, A; Al Alam, D; Lien, C; Grikscheit, T C

    2015-08-01

    Loss of significant intestinal length from congenital anomaly or disease may lead to short bowel syndrome (SBS); intestinal failure may be partially offset by a gain in epithelial surface area, termed adaptation. Current in vivo models of SBS are costly and technically challenging. Operative times and survival rates have slowed extension to transgenic models. We created a new reproducible in vivo model of SBS in zebrafish, a tractable vertebrate model, to facilitate investigation of the mechanisms of intestinal adaptation. Proximal intestinal diversion at segment 1 (S1, equivalent to jejunum) was performed in adult male zebrafish. SBS fish emptied distal intestinal contents via stoma as in the human disease. After 2 wk, S1 was dilated compared with controls and villus ridges had increased complexity, contributing to greater villus epithelial perimeter. The number of intervillus pockets, the intestinal stem cell zone of the zebrafish increased and contained a higher number of bromodeoxyuridine (BrdU)-labeled cells after 2 wk of SBS. Egf receptor and a subset of its ligands, also drivers of adaptation, were upregulated in SBS fish. Igf has been reported as a driver of intestinal adaptation in other animal models, and SBS fish exposed to a pharmacological inhibitor of the Igf receptor failed to demonstrate signs of intestinal adaptation, such as increased inner epithelial perimeter and BrdU incorporation. We describe a technically feasible model of human SBS in the zebrafish, a faster and less expensive tool to investigate intestinal stem cell plasticity as well as the mechanisms that drive intestinal adaptation. Copyright © 2015 the American Physiological Society.

  4. Adult zebrafish intestine resection: a novel model of short bowel syndrome, adaptation, and intestinal stem cell regeneration

    PubMed Central

    Schall, K. A.; Holoyda, K. A.; Grant, C. N.; Levin, D. E.; Torres, E. R.; Maxwell, A.; Pollack, H. A.; Moats, R. A.; Frey, M. R.; Darehzereshki, A.; Al Alam, D.; Lien, C.

    2015-01-01

    Loss of significant intestinal length from congenital anomaly or disease may lead to short bowel syndrome (SBS); intestinal failure may be partially offset by a gain in epithelial surface area, termed adaptation. Current in vivo models of SBS are costly and technically challenging. Operative times and survival rates have slowed extension to transgenic models. We created a new reproducible in vivo model of SBS in zebrafish, a tractable vertebrate model, to facilitate investigation of the mechanisms of intestinal adaptation. Proximal intestinal diversion at segment 1 (S1, equivalent to jejunum) was performed in adult male zebrafish. SBS fish emptied distal intestinal contents via stoma as in the human disease. After 2 wk, S1 was dilated compared with controls and villus ridges had increased complexity, contributing to greater villus epithelial perimeter. The number of intervillus pockets, the intestinal stem cell zone of the zebrafish increased and contained a higher number of bromodeoxyuridine (BrdU)-labeled cells after 2 wk of SBS. Egf receptor and a subset of its ligands, also drivers of adaptation, were upregulated in SBS fish. Igf has been reported as a driver of intestinal adaptation in other animal models, and SBS fish exposed to a pharmacological inhibitor of the Igf receptor failed to demonstrate signs of intestinal adaptation, such as increased inner epithelial perimeter and BrdU incorporation. We describe a technically feasible model of human SBS in the zebrafish, a faster and less expensive tool to investigate intestinal stem cell plasticity as well as the mechanisms that drive intestinal adaptation. PMID:26089336

  5. Tsunami modelling with adaptively refined finite volume methods

    USGS Publications Warehouse

    LeVeque, R.J.; George, D.L.; Berger, M.J.

    2011-01-01

    Numerical modelling of transoceanic tsunami propagation, together with the detailed modelling of inundation of small-scale coastal regions, poses a number of algorithmic challenges. The depth-averaged shallow water equations can be used to reduce this to a time-dependent problem in two space dimensions, but even so it is crucial to use adaptive mesh refinement in order to efficiently handle the vast differences in spatial scales. This must be done in a 'wellbalanced' manner that accurately captures very small perturbations to the steady state of the ocean at rest. Inundation can be modelled by allowing cells to dynamically change from dry to wet, but this must also be done carefully near refinement boundaries. We discuss these issues in the context of Riemann-solver-based finite volume methods for tsunami modelling. Several examples are presented using the GeoClaw software, and sample codes are available to accompany the paper. The techniques discussed also apply to a variety of other geophysical flows. ?? 2011 Cambridge University Press.

  6. Adaptive Value Normalization in the Prefrontal Cortex Is Reduced by Memory Load.

    PubMed

    Holper, L; Van Brussel, L D; Schmidt, L; Schulthess, S; Burke, C J; Louie, K; Seifritz, E; Tobler, P N

    2017-01-01

    Adaptation facilitates neural representation of a wide range of diverse inputs, including reward values. Adaptive value coding typically relies on contextual information either obtained from the environment or retrieved from and maintained in memory. However, it is unknown whether having to retrieve and maintain context information modulates the brain's capacity for value adaptation. To address this issue, we measured hemodynamic responses of the prefrontal cortex (PFC) in two studies on risky decision-making. In each trial, healthy human subjects chose between a risky and a safe alternative; half of the participants had to remember the risky alternatives, whereas for the other half they were presented visually. The value of safe alternatives varied across trials. PFC responses adapted to contextual risk information, with steeper coding of safe alternative value in lower-risk contexts. Importantly, this adaptation depended on working memory load, such that response functions relating PFC activity to safe values were steeper with presented versus remembered risk. An independent second study replicated the findings of the first study and showed that similar slope reductions also arose when memory maintenance demands were increased with a secondary working memory task. Formal model comparison showed that a divisive normalization model fitted effects of both risk context and working memory demands on PFC activity better than alternative models of value adaptation, and revealed that reduced suppression of background activity was the critical parameter impairing normalization with increased memory maintenance demand. Our findings suggest that mnemonic processes can constrain normalization of neural value representations.

  7. Adaptive Value Normalization in the Prefrontal Cortex Is Reduced by Memory Load

    PubMed Central

    Burke, C. J.; Seifritz, E.; Tobler, P. N.

    2017-01-01

    Abstract Adaptation facilitates neural representation of a wide range of diverse inputs, including reward values. Adaptive value coding typically relies on contextual information either obtained from the environment or retrieved from and maintained in memory. However, it is unknown whether having to retrieve and maintain context information modulates the brain’s capacity for value adaptation. To address this issue, we measured hemodynamic responses of the prefrontal cortex (PFC) in two studies on risky decision-making. In each trial, healthy human subjects chose between a risky and a safe alternative; half of the participants had to remember the risky alternatives, whereas for the other half they were presented visually. The value of safe alternatives varied across trials. PFC responses adapted to contextual risk information, with steeper coding of safe alternative value in lower-risk contexts. Importantly, this adaptation depended on working memory load, such that response functions relating PFC activity to safe values were steeper with presented versus remembered risk. An independent second study replicated the findings of the first study and showed that similar slope reductions also arose when memory maintenance demands were increased with a secondary working memory task. Formal model comparison showed that a divisive normalization model fitted effects of both risk context and working memory demands on PFC activity better than alternative models of value adaptation, and revealed that reduced suppression of background activity was the critical parameter impairing normalization with increased memory maintenance demand. Our findings suggest that mnemonic processes can constrain normalization of neural value representations. PMID:28462394

  8. Comparison of wavefront sensor models for simulation of adaptive optics.

    PubMed

    Wu, Zhiwen; Enmark, Anita; Owner-Petersen, Mette; Andersen, Torben

    2009-10-26

    The new generation of extremely large telescopes will have adaptive optics. Due to the complexity and cost of such systems, it is important to simulate their performance before construction. Most systems planned will have Shack-Hartmann wavefront sensors. Different mathematical models are available for simulation of such wavefront sensors. The choice of wavefront sensor model strongly influences computation time and simulation accuracy. We have studied the influence of three wavefront sensor models on performance calculations for a generic, adaptive optics (AO) system designed for K-band operation of a 42 m telescope. The performance of this AO system has been investigated both for reduced wavelengths and for reduced r(0) in the K band. The telescope AO system was designed for K-band operation, that is both the subaperture size and the actuator pitch were matched to a fixed value of r(0) in the K-band. We find that under certain conditions, such as investigating limiting guide star magnitude for large Strehl-ratios, a full model based on Fraunhofer propagation to the subimages is significantly more accurate. It does however require long computation times. The shortcomings of simpler models based on either direct use of average wavefront tilt over the subapertures for actuator control, or use of the average tilt to move a precalculated point spread function in the subimages are most pronounced for studies of system limitations to operating parameter variations. In the long run, efficient parallelization techniques may be developed to overcome the problem.

  9. Genetic Algorithm-Guided, Adaptive Model Order Reduction of Flexible Aircrafts

    NASA Technical Reports Server (NTRS)

    Zhu, Jin; Wang, Yi; Pant, Kapil; Suh, Peter; Brenner, Martin J.

    2017-01-01

    This paper presents a methodology for automated model order reduction (MOR) of flexible aircrafts to construct linear parameter-varying (LPV) reduced order models (ROM) for aeroservoelasticity (ASE) analysis and control synthesis in broad flight parameter space. The novelty includes utilization of genetic algorithms (GAs) to automatically determine the states for reduction while minimizing the trial-and-error process and heuristics requirement to perform MOR; balanced truncation for unstable systems to achieve locally optimal realization of the full model; congruence transformation for "weak" fulfillment of state consistency across the entire flight parameter space; and ROM interpolation based on adaptive grid refinement to generate a globally functional LPV ASE ROM. The methodology is applied to the X-56A MUTT model currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that X-56A ROM with less than one-seventh the number of states relative to the original model is able to accurately predict system response among all input-output channels for pitch, roll, and ASE control at various flight conditions. The GA-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The adaptive refinement allows selective addition of the grid points in the parameter space where flight dynamics varies dramatically to enhance interpolation accuracy without over-burdening controller synthesis and onboard memory efforts downstream. The present MOR framework can be used by control engineers for robust ASE controller synthesis and novel vehicle design.

  10. A novel model for simultaneous study of neointestinal regeneration and intestinal adaptation.

    PubMed

    Jwo, Shyh-Chuan; Tang, Shye-Jye; Chen, Jim-Ray; Chiang, Kun-Chun; Huang, Ting-Shou; Chen, Huang-Yang

    2013-01-01

    The use of autologous grafts, fabricated from tissue-engineered neointestine, to enhance insufficient compensation of intestinal adaptation for severe short bowel syndrome is a compelling idea. Unfortunately, current approaches and knowledge for neointestinal regeneration, unlike intestinal adaptation, are still unsatisfactory. Thus, we have designed a novel model of intestinal adaptation with simultaneous neointestinal regeneration and evaluated its feasibility for future basic research and clinical application. Fifty male Sprague-Dawley rats weighing 250-350 g underwent this procedure and sacrificed at 4, 8, and 12 weeks postoperatively. Spatiotemporal analyses were carried out by gross, histology, and DNA/protein quantification. Three rats died of operative complications. In early experiments, the use of hard silicone stent as tissue scaffold in 11 rats was unsatisfactory for neointestinal regeneration. In later experiments, when a soft silastic tube was used, the success rate increased up to 90.9%. Further analyses revealed that no neointestine developed without donor intestine; regenerated lengths of mucosa and muscle were positively related to time postsurgery but independent of donor length with 0.5 or 1 cm. Other parameters of neointestinal regeneration or intestinal adaptation showed no relationship to both time postsurgery and donor length. In conclusion, this is a potentially important model for investigators searching for solutions to short bowel syndrome. © 2013 by the Wound Healing Society.

  11. Frequency-specific adaptation and its underlying circuit model in the auditory midbrain

    PubMed Central

    Shen, Li; Zhao, Lingyun; Hong, Bo

    2015-01-01

    Receptive fields of sensory neurons are considered to be dynamic and depend on the stimulus history. In the auditory system, evidence of dynamic frequency-receptive fields has been found following stimulus-specific adaptation (SSA). However, the underlying mechanism and circuitry of SSA have not been fully elucidated. Here, we studied how frequency-receptive fields of neurons in rat inferior colliculus (IC) changed when exposed to a biased tone sequence. Pure tone with one specific frequency (adaptor) was presented markedly more often than others. The adapted tuning was compared with the original tuning measured with an unbiased sequence. We found inhomogeneous changes in frequency tuning in IC, exhibiting a center-surround pattern with respect to the neuron's best frequency. Central adaptors elicited strong suppressive and repulsive changes while flank adaptors induced facilitative and attractive changes. Moreover, we proposed a two-layer model of the underlying network, which not only reproduced the adaptive changes in the receptive fields but also predicted novelty responses to oddball sequences. These results suggest that frequency-specific adaptation in auditory midbrain can be accounted for by an adapted frequency channel and its lateral spreading of adaptation, which shed light on the organization of the underlying circuitry. PMID:26483641

  12. Modeling of Adaptive Optics-Based Free-Space Communications Systems

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

    Wilks, S C; Morris, J R; Brase, J M

    2002-08-06

    We introduce a wave-optics based simulation code written for air-optic laser communications links, that includes a detailed model of an adaptive optics compensation system. We present the results obtained by this model, where the phase of a communications laser beam is corrected, after it propagates through a turbulent atmosphere. The phase of the received laser beam is measured using a Shack-Hartmann wavefront sensor, and the correction method utilizes a MEMS mirror. Strehl improvement and amount of power coupled to the receiving fiber for both 1 km horizontal and 28 km slant paths are presented.

  13. AN OPTIMAL ADAPTIVE LOCAL GRID REFINEMENT APPROACH TO MODELING CONTAMINANT TRANSPORT

    EPA Science Inventory

    A Lagrangian-Eulerian method with an optimal adaptive local grid refinement is used to model contaminant transport equations. pplication of this approach to two bench-mark problems indicates that it completely resolves difficulties of peak clipping, numerical diffusion, and spuri...

  14. Deep Impact Sequence Planning Using Multi-Mission Adaptable Planning Tools With Integrated Spacecraft Models

    NASA Technical Reports Server (NTRS)

    Wissler, Steven S.; Maldague, Pierre; Rocca, Jennifer; Seybold, Calina

    2006-01-01

    The Deep Impact mission was ambitious and challenging. JPL's well proven, easily adaptable multi-mission sequence planning tools combined with integrated spacecraft subsystem models enabled a small operations team to develop, validate, and execute extremely complex sequence-based activities within very short development times. This paper focuses on the core planning tool used in the mission, APGEN. It shows how the multi-mission design and adaptability of APGEN made it possible to model spacecraft subsystems as well as ground assets throughout the lifecycle of the Deep Impact project, starting with models of initial, high-level mission objectives, and culminating in detailed predictions of spacecraft behavior during mission-critical activities.

  15. Application of Multi-Model CMIP5 Analysis in Future Drought Adaptation Strategies

    NASA Astrophysics Data System (ADS)

    Casey, M.; Luo, L.; Lang, Y.

    2014-12-01

    Drought influences the efficacy of numerous natural and artificial systems including species diversity, agriculture, and infrastructure. Global climate change raises concerns that extend well beyond atmospheric and hydrological disciplines - as climate changes with time, the need for system adaptation becomes apparent. Drought, as a natural phenomenon, is typically defined relative to the climate in which it occurs. Typically a 30-year reference time frame (RTF) is used to determine the severity of a drought event. This study investigates the projected future droughts over North America with different RTFs. Confidence in future hydroclimate projection is characterized by the agreement of long term (2005-2100) multi-model precipitation (P) and temperature (T) projections within the Coupled model Intercomparison Project Phase 5 (CMIP5). Drought severity and the propensity of extreme conditions are measured by the multi-scalar, probabilistic, RTF-based Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI). SPI considers only P while SPEI incorporates Evapotranspiration (E) via T; comparing the two reveals the role of temperature change in future hydroclimate change. Future hydroclimate conditions, hydroclimate extremity, and CMIP5 model agreement are assessed for each Representative Concentration Pathway (RCP 2.6, 4.5, 6.0, 8.5) in regions throughout North America for the entire year and for the boreal seasons. In addition, multiple time scales of SPI and SPEI are calculated to characterize drought at time scales ranging from short to long term. The study explores a simple, standardized method for considering adaptation in future drought assessment, which provides a novel perspective to incorporate adaptation with climate change. The result of the analysis is a multi-dimension, probabilistic summary of the hydrological (P, E) environment a natural or artificial system must adapt to over time. Studies similar to this with

  16. Moving vehicles segmentation based on Gaussian motion model

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Fang, Xiang Z.; Lin, Wei Y.

    2005-07-01

    Moving objects segmentation is a challenge in computer vision. This paper focuses on the segmentation of moving vehicles in dynamic scene. We analyses the psychology of human vision and present a framework for segmenting moving vehicles in the highway. The proposed framework consists of two parts. Firstly, we propose an adaptive background update method in which the background is updated according to the change of illumination conditions and thus can adapt to the change of illumination sensitively. Secondly, we construct a Gaussian motion model to segment moving vehicles, in which the motion vectors of the moving pixels are modeled as a Gaussian model and an on-line EM algorithm is used to update the model. The Gaussian distribution of the adaptive model is elevated to determine which moving vectors result from moving vehicles and which from other moving objects such as waving trees. Finally, the pixels with motion vector result from the moving vehicles are segmented. Experimental results of several typical scenes show that the proposed model can detect the moving vehicles correctly and is immune from influence of the moving objects caused by the waving trees and the vibration of camera.

  17. Compact Ocean Models Enable Onboard AUV Autonomy and Decentralized Adaptive Sampling

    DTIC Science & Technology

    2014-09-30

    1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Compact Ocean Models Enable Onboard AUV Autonomy and...transmitted onboard an AUV . 3. Develop algorithms for adaptive planning of AUV surveys. 4. Demonstrate use of compact ocean models onboard a long...range AUV during a field deployment. Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of

  18. Adaptive relative pose control of spacecraft with model couplings and uncertainties

    NASA Astrophysics Data System (ADS)

    Sun, Liang; Zheng, Zewei

    2018-02-01

    The spacecraft pose tracking control problem for an uncertain pursuer approaching to a space target is researched in this paper. After modeling the nonlinearly coupled dynamics for relative translational and rotational motions between two spacecraft, position tracking and attitude synchronization controllers are developed independently by using a robust adaptive control approach. The unknown kinematic couplings, parametric uncertainties, and bounded external disturbances are handled with adaptive updating laws. It is proved via Lyapunov method that the pose tracking errors converge to zero asymptotically. Spacecraft close-range rendezvous and proximity operations are introduced as an example to validate the effectiveness of the proposed control approach.

  19. The Differentiation of Adaptive Behaviours: Evidence from High and Low Performers

    ERIC Educational Resources Information Center

    Kane, Harrison; Oakland, Thomas David

    2015-01-01

    Background: Professionals who use measures of adaptive behaviour when working with special populations may assume that adaptive behaviour is a consistent and linear construct at various ability levels and thus believe the construct of adaptive behaviour is the same for high and low performers. That is, highly adaptive people simply are assumed to…

  20. Ibuprofen Differentially Affects Supraspinatus Muscle and Tendon Adaptations to Exercise in a Rat Model

    PubMed Central

    Rooney, Sarah Ilkhanipour; Baskin, Rachel; Torino, Daniel J.; Vafa, Rameen P.; Khandekar, Pooja S.; Kuntz, Andrew F.; Soslowsky, Louis J.

    2017-01-01

    Background Previous studies have shown that ibuprofen is detrimental to tissue healing following acute injury; however, the effects of ibuprofen when combined with non-injurious exercise are debated. Hypothesis We hypothesized that administration of ibuprofen to rats undergoing a non-injurious treadmill exercise protocol would abolish the beneficial adaptations found with exercise but have no effect on sedentary muscle and tendon properties. Study Design Controlled laboratory study Methods Rats were divided into exercise or cage activity (sedentary) groups and acute (a single bout of exercise followed by 24 hours of rest) and chronic (2 or 8 weeks of repeated exercise) time points. Half of the rats received ibuprofen to investigate the effects of this drug over time when combined with different activity levels (exercise and sedentary). Supraspinatus tendons were used for mechanical testing and histology (organization, cell shape, cellularity), and supraspinatus muscles were used for morphological (fiber CSA, centrally nucleated fibers) and fiber type analysis. Results Chronic intake of ibuprofen did not impair supraspinatus tendon organization or mechanical adaptations (stiffness, modulus, max load, max stress, dynamic modulus, or viscoelastic properties) to exercise. Tendon mechanical properties were not diminished and in some instances increased with ibuprofen. In contrast, total supraspinatus muscle fiber cross-sectional area decreased with ibuprofen at chronic time points, and some fiber type-specific changes were detected. Conclusions Chronic administration of ibuprofen does not impair supraspinatus tendon mechanical properties in a rat model of exercise but does decrease supraspinatus muscle fiber cross-sectional area. Clinically, these findings suggest that ibuprofen does not detrimentally affect regulation of supraspinatus tendon adaptions to exercise but does decrease muscle growth. Individuals should be advised on the risk of decreased muscle hypertrophy

  1. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

    NASA Astrophysics Data System (ADS)

    Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

    2012-06-01

    SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

  2. Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions

    NASA Astrophysics Data System (ADS)

    Galimzianova, Alfiia; Lesjak, Žiga; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

    2015-03-01

    Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov random field (MRF) models are known to give reliable and smooth NABT segmentation. However, the presence of pathology, MR intensity bias and natural tissue-dependent intensity variability altogether represent difficult challenges for a reliable estimation of NABT intensity model based on MR images. In this paper, we propose a novel method for segmentation of normal and pathological structures in brain MR images of multiple sclerosis (MS) patients that is based on locally-adaptive NABT model, a robust method for the estimation of model parameters and a MRF-based segmentation framework. Experiments on multi-sequence brain MR images of 27 MS patients show that, compared to whole-brain model and compared to the widely used Expectation-Maximization Segmentation (EMS) method, the locally-adaptive NABT model increases the accuracy of MS lesion segmentation.

  3. Adapting forest management to climate change using bioclimate models with topographic drivers

    Treesearch

    Gerald E. Rehfeldt; James J. Worrall; Suzanne B. Marchetti; Nicholas L. Crookston

    2015-01-01

    Bioclimate models incorporating topographic predictors as surrogates for microclimate effects are developed for Populus tremuloides and Picea engelmannii to provide the fine-grained specificity to local terrain required for adapting management of three Colorado (USA) national forests (1.28 million ha) and their periphery to climate change. Models were built with the...

  4. Dynamic modeling, property investigation, and adaptive controller design of serial robotic manipulators modeled with structural compliance

    NASA Technical Reports Server (NTRS)

    Tesar, Delbert; Tosunoglu, Sabri; Lin, Shyng-Her

    1990-01-01

    Research results on general serial robotic manipulators modeled with structural compliances are presented. Two compliant manipulator modeling approaches, distributed and lumped parameter models, are used in this study. System dynamic equations for both compliant models are derived by using the first and second order influence coefficients. Also, the properties of compliant manipulator system dynamics are investigated. One of the properties, which is defined as inaccessibility of vibratory modes, is shown to display a distinct character associated with compliant manipulators. This property indicates the impact of robot geometry on the control of structural oscillations. Example studies are provided to illustrate the physical interpretation of inaccessibility of vibratory modes. Two types of controllers are designed for compliant manipulators modeled by either lumped or distributed parameter techniques. In order to maintain the generality of the results, neither linearization is introduced. Example simulations are given to demonstrate the controller performance. The second type controller is also built for general serial robot arms and is adaptive in nature which can estimate uncertain payload parameters on-line and simultaneously maintain trajectory tracking properties. The relation between manipulator motion tracking capability and convergence of parameter estimation properties is discussed through example case studies. The effect of control input update delays on adaptive controller performance is also studied.

  5. Formation of model-free motor memories during motor adaptation depends on perturbation schedule

    PubMed Central

    Lefèvre, Philippe

    2015-01-01

    Motor adaptation to an external perturbation relies on several mechanisms such as model-based, model-free, strategic, or repetition-dependent learning. Depending on the experimental conditions, each of these mechanisms has more or less weight in the final adaptation state. Here we focused on the conditions that lead to the formation of a model-free motor memory (Huang VS, Haith AM, Mazzoni P, Krakauer JW. Neuron 70: 787–801, 2011), i.e., a memory that does not depend on an internal model or on the size or direction of the errors experienced during the learning. The formation of such model-free motor memory was hypothesized to depend on the schedule of the perturbation (Orban de Xivry JJ, Ahmadi-Pajouh MA, Harran MD, Salimpour Y, Shadmehr R. J Neurophysiol 109: 124–136, 2013). Here we built on this observation by directly testing the nature of the motor memory after abrupt or gradual introduction of a visuomotor rotation, in an experimental paradigm where the presence of model-free motor memory can be identified (Huang VS, Haith AM, Mazzoni P, Krakauer JW. Neuron 70: 787–801, 2011). We found that relearning was faster after abrupt than gradual perturbation, which suggests that model-free learning is reduced during gradual adaptation to a visuomotor rotation. In addition, the presence of savings after abrupt introduction of the perturbation but gradual extinction of the motor memory suggests that unexpected errors are necessary to induce a model-free motor memory. Overall, these data support the hypothesis that different perturbation schedules do not lead to a more or less stabilized motor memory but to distinct motor memories with different attributes and neural representations. PMID:25673736

  6. Model reference adaptive control (MRAC)-based parameter identification applied to surface-mounted permanent magnet synchronous motor

    NASA Astrophysics Data System (ADS)

    Zhong, Chongquan; Lin, Yaoyao

    2017-11-01

    In this work, a model reference adaptive control-based estimated algorithm is proposed for online multi-parameter identification of surface-mounted permanent magnet synchronous machines. By taking the dq-axis equations of a practical motor as the reference model and the dq-axis estimation equations as the adjustable model, a standard model-reference-adaptive-system-based estimator was established. Additionally, the Popov hyperstability principle was used in the design of the adaptive law to guarantee accurate convergence. In order to reduce the oscillation of identification result, this work introduces a first-order low-pass digital filter to improve precision regarding the parameter estimation. The proposed scheme was then applied to an SPM synchronous motor control system without any additional circuits and implemented using a DSP TMS320LF2812. For analysis, the experimental results reveal the effectiveness of the proposed method.

  7. Geographic divergence and colour change in response to visual backgrounds and illumination intensity in bearded dragons.

    PubMed

    Cadena, Viviana; Smith, Kathleen R; Endler, John A; Stuart-Fox, Devi

    2017-03-15

    Animals may improve camouflage by both dynamic colour change and local evolutionary adaptation of colour but we have little understanding of their relative importance in colour-changing species. We tested for differences in colour change in response to background colour and light intensity in two populations of central bearded dragon lizards ( Pogona vitticeps ) representing the extremes in body coloration and geographical range. We found that bearded dragons change colour in response to various backgrounds and that colour change is affected by illumination intensity. Within-individual colour change was similar in magnitude in the two populations but varied between backgrounds. However, at the endpoints of colour change, each population showed greater similarity to backgrounds that were representative of the local habitat compared with the other population, indicating local adaptation to visual backgrounds. Our results suggest that even in species that change colour, both phenotypic plasticity and geographic divergence of coloration may contribute to improved camouflage. © 2017. Published by The Company of Biologists Ltd.

  8. Social Background and School Continuation Decisions.

    ERIC Educational Resources Information Center

    Mare, Robert D.

    1980-01-01

    Presents a model of the relationship between social background and school continuation decisions among White males born between 1900 and 1950. The model predicts a decline in the effects of social background by the last school transition. Reprint available from Institute for Research on Poverty, University of Wisconsin, Madison, WI 53706. (AM)

  9. Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

    PubMed Central

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

    2015-01-01

    Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models

  10. A hybrid skull-stripping algorithm based on adaptive balloon snake models

    NASA Astrophysics Data System (ADS)

    Liu, Hung-Ting; Sheu, Tony W. H.; Chang, Herng-Hua

    2013-02-01

    Skull-stripping is one of the most important preprocessing steps in neuroimage analysis. We proposed a hybrid algorithm based on an adaptive balloon snake model to handle this challenging task. The proposed framework consists of two stages: first, the fuzzy possibilistic c-means (FPCM) is used for voxel clustering, which provides a labeled image for the snake contour initialization. In the second stage, the contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of the balloon snake model, which drives the contour with an adaptive inward normal force to capture the boundary of the brain. The similarity indices indicate that our method outperformed the BSE and BET methods in skull-stripping the MR image volumes in the IBSR data set. Experimental results show the effectiveness of this new scheme and potential applications in a wide variety of skull-stripping applications.

  11. Adaptive Quadrature for Item Response Models. Research Report. ETS RR-06-29

    ERIC Educational Resources Information Center

    Haberman, Shelby J.

    2006-01-01

    Adaptive quadrature is applied to marginal maximum likelihood estimation for item response models with normal ability distributions. Even in one dimension, significant gains in speed and accuracy of computation may be achieved.

  12. Adapting to a Changing Environment: Modeling the Interaction of Directional Selection and Plasticity.

    PubMed

    Nunney, Leonard

    2016-01-01

    Human-induced habitat loss and fragmentation constrains the range of many species, making them unable to respond to climate change by moving. For such species to avoid extinction, they must respond with some combination of phenotypic plasticity and genetic adaptation. Haldane's "cost of natural selection" limits the rate of adaptation, but, although modeling has shown that in very large populations long-term adaptation can be maintained at rates substantially faster than Haldane's suggested limit, maintaining large populations is often an impossibility, so phenotypic plasticity may be crucial in enhancing the long-term survival of small populations. The potential importance of plasticity is in "buying time" for populations subject to directional environmental change: if genotypes can encompass a greater environmental range, then populations can maintain high fitness for a longer period of time. Alternatively, plasticity could be detrimental by lessening the effectiveness of natural selection in promoting genetic adaptation. Here, I modeled a directionally changing environment in which a genotype's adaptive phenotypic plasticity is centered around the environment where its fitness is highest. Plasticity broadens environmental tolerance and, provided it is not too costly, is favored by natural selection. However, a paradoxical result of the individually advantageous spread of plasticity is that, unless the adaptive trait is determined by very few loci, the long-term extinction risk of a population increases. This effect reflects a conflict between the short-term individual benefit of plasticity and a long-term detriment to population persistence, adding to the multiple threats facing small populations under conditions of climate change. © The American Genetic Association. 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  13. Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

    PubMed Central

    Qin, Lei; Snoussi, Hichem; Abdallah, Fahed

    2014-01-01

    We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences. PMID:24865883

  14. The design and implementation of radar clutter modelling and adaptive target detection techniques

    NASA Astrophysics Data System (ADS)

    Ali, Mohammed Hussain

    The analysis and reduction of radar clutter is investigated. Clutter is the term applied to unwanted radar reflections from land, sea, precipitation, and/or man-made objects. A great deal of useful information regarding the characteristics of clutter can be obtained by the application of frequency domain analytical methods. Thus, some considerable time was spent assessing the various techniques available and their possible application to radar clutter. In order to better understand clutter, use of a clutter model was considered desirable. There are many techniques which will enable a target to be detected in the presence of clutter. One of the most flexible of these is that of adaptive filtering. This technique was thoroughly investigated and a method for improving its efficacy was devised. The modified adaptive filter employed differential adaption times to enhance detectability. Adaptation time as a factor relating to target detectability is a new concept and was investigated in some detail. It was considered desirable to implement the theoretical work in dedicated hardware to confirm that the modified clutter model and the adaptive filter technique actually performed as predicted. The equipment produced is capable of operation in real time and provides an insight into real time DSP applications. This equipment is sufficiently rapid to produce a real time display on the actual PPI system. Finally a software package was also produced which would simulate the operation of a PPI display and thus ease the interpretation of the filter outputs.

  15. A new adaptive multiple modelling approach for non-linear and non-stationary systems

    NASA Astrophysics Data System (ADS)

    Chen, Hao; Gong, Yu; Hong, Xia

    2016-07-01

    This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.

  16. The Effects of Climate Model Similarity on Local, Risk-Based Adaptation Planning

    NASA Astrophysics Data System (ADS)

    Steinschneider, S.; Brown, C. M.

    2014-12-01

    The climate science community has recently proposed techniques to develop probabilistic projections of climate change from ensemble climate model output. These methods provide a means to incorporate the formal concept of risk, i.e., the product of impact and probability, into long-term planning assessments for local systems under climate change. However, approaches for pdf development often assume that different climate models provide independent information for the estimation of probabilities, despite model similarities that stem from a common genealogy. Here we utilize an ensemble of projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to develop probabilistic climate information, with and without an accounting of inter-model correlations, and use it to estimate climate-related risks to a local water utility in Colorado, U.S. We show that the tail risk of extreme climate changes in both mean precipitation and temperature is underestimated if model correlations are ignored. When coupled with impact models of the hydrology and infrastructure of the water utility, the underestimation of extreme climate changes substantially alters the quantification of risk for water supply shortages by mid-century. We argue that progress in climate change adaptation for local systems requires the recognition that there is less information in multi-model climate ensembles than previously thought. Importantly, adaptation decisions cannot be limited to the spread in one generation of climate models.

  17. Reference analysis of the signal + background model in counting experiments II. Approximate reference prior

    NASA Astrophysics Data System (ADS)

    Casadei, D.

    2014-10-01

    The objective Bayesian treatment of a model representing two independent Poisson processes, labelled as ``signal'' and ``background'' and both contributing additively to the total number of counted events, is considered. It is shown that the reference prior for the parameter of interest (the signal intensity) can be well approximated by the widely (ab)used flat prior only when the expected background is very high. On the other hand, a very simple approximation (the limiting form of the reference prior for perfect prior background knowledge) can be safely used over a large portion of the background parameters space. The resulting approximate reference posterior is a Gamma density whose parameters are related to the observed counts. This limiting form is simpler than the result obtained with a flat prior, with the additional advantage of representing a much closer approximation to the reference posterior in all cases. Hence such limiting prior should be considered a better default or conventional prior than the uniform prior. On the computing side, it is shown that a 2-parameter fitting function is able to reproduce extremely well the reference prior for any background prior. Thus, it can be useful in applications requiring the evaluation of the reference prior for a very large number of times.

  18. Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise

    NASA Technical Reports Server (NTRS)

    Eckstein, M. P.; Ahumada, A. J. Jr; Watson, A. B.

    1997-01-01

    Studies of visual detection of a signal superimposed on one of two identical backgrounds show performance degradation when the background has high contrast and is similar in spatial frequency and/or orientation to the signal. To account for this finding, models include a contrast gain control mechanism that pools activity across spatial frequency, orientation and space to inhibit (divisively) the response of the receptor sensitive to the signal. In tasks in which the observer has to detect a known signal added to one of M different backgrounds grounds due to added visual noise, the main sources of degradation are the stochastic noise in the image and the suboptimal visual processing. We investigate how these two sources of degradation (contrast gain control and variations in the background) interact in a task in which the signal is embedded in one of M locations in a complex spatially varying background (structured background). We use backgrounds extracted from patient digital medical images. To isolate effects of the fixed deterministic background (the contrast gain control) from the effects of the background variations, we conduct detection experiments with three different background conditions: (1) uniform background, (2) a repeated sample of structured background, and (3) different samples of structured background. Results show that human visual detection degrades from the uniform background condition to the repeated background condition and degrades even further in the different backgrounds condition. These results suggest that both the contrast gain control mechanism and the background random variations degrade human performance in detection of a signal in a complex, spatially varying background. A filter model and added white noise are used to generate estimates of sampling efficiencies, an equivalent internal noise, an equivalent contrast-gain-control-induced noise, and an equivalent noise due to the variations in the structured background.

  19. Adaptive-Grid Methods for Phase Field Models of Microstructure Development

    NASA Technical Reports Server (NTRS)

    Provatas, Nikolas; Goldenfeld, Nigel; Dantzig, Jonathan A.

    1999-01-01

    In this work the authors show how the phase field model can be solved in a computationally efficient manner that opens a new large-scale simulational window on solidification physics. Our method uses a finite element, adaptive-grid formulation, and exploits the fact that the phase and temperature fields vary significantly only near the interface. We illustrate how our method allows efficient simulation of phase-field models in very large systems, and verify the predictions of solvability theory at intermediate undercooling. We then present new results at low undercoolings that suggest that solvability theory may not give the correct tip speed in that regime. We model solidification using the phase-field model used by Karma and Rappel.

  20. Particle systems for adaptive, isotropic meshing of CAD models

    PubMed Central

    Levine, Joshua A.; Whitaker, Ross T.

    2012-01-01

    We present a particle-based approach for generating adaptive triangular surface and tetrahedral volume meshes from computer-aided design models. Input shapes are treated as a collection of smooth, parametric surface patches that can meet non-smoothly on boundaries. Our approach uses a hierarchical sampling scheme that places particles on features in order of increasing dimensionality. These particles reach a good distribution by minimizing an energy computed in 3D world space, with movements occurring in the parametric space of each surface patch. Rather than using a pre-computed measure of feature size, our system automatically adapts to both curvature as well as a notion of topological separation. It also enforces a measure of smoothness on these constraints to construct a sizing field that acts as a proxy to piecewise-smooth feature size. We evaluate our technique with comparisons against other popular triangular meshing techniques for this domain. PMID:23162181

  1. BEATBOX v1.0: Background Error Analysis Testbed with Box Models

    NASA Astrophysics Data System (ADS)

    Knote, Christoph; Barré, Jérôme; Eckl, Max

    2018-02-01

    The Background Error Analysis Testbed (BEATBOX) is a new data assimilation framework for box models. Based on the BOX Model eXtension (BOXMOX) to the Kinetic Pre-Processor (KPP), this framework allows users to conduct performance evaluations of data assimilation experiments, sensitivity analyses, and detailed chemical scheme diagnostics from an observation simulation system experiment (OSSE) point of view. The BEATBOX framework incorporates an observation simulator and a data assimilation system with the possibility of choosing ensemble, adjoint, or combined sensitivities. A user-friendly, Python-based interface allows for the tuning of many parameters for atmospheric chemistry and data assimilation research as well as for educational purposes, for example observation error, model covariances, ensemble size, perturbation distribution in the initial conditions, and so on. In this work, the testbed is described and two case studies are presented to illustrate the design of a typical OSSE experiment, data assimilation experiments, a sensitivity analysis, and a method for diagnosing model errors. BEATBOX is released as an open source tool for the atmospheric chemistry and data assimilation communities.

  2. Role of Forcing Uncertainty and Background Model Error Characterization in Snow Data Assimilation

    NASA Technical Reports Server (NTRS)

    Kumar, Sujay V.; Dong, Jiarul; Peters-Lidard, Christa D.; Mocko, David; Gomez, Breogan

    2017-01-01

    Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This article examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, the article demonstrates that the use of hybrid forcing input strategies (either through the use of an ensemble of forcing products or through the added use of the forcing climatology) provide a better characterization of the background model error, which leads to improved data assimilation results, especially during the snow accumulation and melt-time periods. The use of hybrid forcing ensembles is then employed for assimilating snow depth retrievals from the AMSR2 (Advanced Microwave Scanning Radiometer 2) instrument over two domains in the continental USA with different snow evolution characteristics. Over a region near the Great Lakes, where the snow evolution tends to be ephemeral, the use of hybrid forcing ensembles provides significant improvements relative to the use of a single forcing dataset. Over the Colorado headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results of the article demonstrate that improving the background model error through the use of a forcing ensemble enables the assimilation system to better incorporate the observational information.

  3. A generalized spatiotemporal covariance model for stationary background in analysis of MEG data.

    PubMed

    Plis, S M; Schmidt, D M; Jun, S C; Ranken, D M

    2006-01-01

    Using a noise covariance model based on a single Kronecker product of spatial and temporal covariance in the spatiotemporal analysis of MEG data was demonstrated to provide improvement in the results over that of the commonly used diagonal noise covariance model. In this paper we present a model that is a generalization of all of the above models. It describes models based on a single Kronecker product of spatial and temporal covariance as well as more complicated multi-pair models together with any intermediate form expressed as a sum of Kronecker products of spatial component matrices of reduced rank and their corresponding temporal covariance matrices. The model provides a framework for controlling the tradeoff between the described complexity of the background and computational demand for the analysis using this model. Ways to estimate the value of the parameter controlling this tradeoff are also discussed.

  4. Adaptive Core Simulation Employing Discrete Inverse Theory - Part II: Numerical Experiments

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

    Abdel-Khalik, Hany S.; Turinsky, Paul J.

    2005-07-15

    Use of adaptive simulation is intended to improve the fidelity and robustness of important core attribute predictions such as core power distribution, thermal margins, and core reactivity. Adaptive simulation utilizes a selected set of past and current reactor measurements of reactor observables, i.e., in-core instrumentation readings, to adapt the simulation in a meaningful way. The companion paper, ''Adaptive Core Simulation Employing Discrete Inverse Theory - Part I: Theory,'' describes in detail the theoretical background of the proposed adaptive techniques. This paper, Part II, demonstrates several computational experiments conducted to assess the fidelity and robustness of the proposed techniques. The intentmore » is to check the ability of the adapted core simulator model to predict future core observables that are not included in the adaption or core observables that are recorded at core conditions that differ from those at which adaption is completed. Also, this paper demonstrates successful utilization of an efficient sensitivity analysis approach to calculate the sensitivity information required to perform the adaption for millions of input core parameters. Finally, this paper illustrates a useful application for adaptive simulation - reducing the inconsistencies between two different core simulator code systems, where the multitudes of input data to one code are adjusted to enhance the agreement between both codes for important core attributes, i.e., core reactivity and power distribution. Also demonstrated is the robustness of such an application.« less

  5. Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation.

    PubMed

    Zhang, Xiangjun; Wu, Xiaolin

    2008-06-01

    The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.

  6. A model-adaptivity method for the solution of Lennard-Jones based adhesive contact problems

    NASA Astrophysics Data System (ADS)

    Ben Dhia, Hachmi; Du, Shuimiao

    2018-05-01

    The surface micro-interaction model of Lennard-Jones (LJ) is used for adhesive contact problems (ACP). To address theoretical and numerical pitfalls of this model, a sequence of partitions of contact models is adaptively constructed to both extend and approximate the LJ model. It is formed by a combination of the LJ model with a sequence of shifted-Signorini (or, alternatively, -Linearized-LJ) models, indexed by a shift parameter field. For each model of this sequence, a weak formulation of the associated local ACP is developed. To track critical localized adhesive areas, a two-step strategy is developed: firstly, a macroscopic frictionless (as first approach) linear-elastic contact problem is solved once to detect contact separation zones. Secondly, at each shift-adaptive iteration, a micro-macro ACP is re-formulated and solved within the multiscale Arlequin framework, with significant reduction of computational costs. Comparison of our results with available analytical and numerical solutions shows the effectiveness of our global strategy.

  7. ADAPT

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

    Reynolds, John; Jankovsky, Zachary; Metzroth, Kyle G

    2018-04-04

    The purpose of the ADAPT code is to generate Dynamic Event Trees (DET) using a user specified set of simulators. ADAPT can utilize any simulation tool which meets a minimal set of requirements. ADAPT is based on the concept of DET which uses explicit modeling of the deterministic dynamic processes that take place during a nuclear reactor plant system (or other complex system) evolution along with stochastic modeling. When DET are used to model various aspects of Probabilistic Risk Assessment (PRA), all accident progression scenarios starting from an initiating event are considered simultaneously. The DET branching occurs at user specifiedmore » times and/or when an action is required by the system and/or the operator. These outcomes then decide how the dynamic system variables will evolve in time for each DET branch. Since two different outcomes at a DET branching may lead to completely different paths for system evolution, the next branching for these paths may occur not only at separate times, but can be based on different branching criteria. The computational infrastructure allows for flexibility in ADAPT to link with different system simulation codes, parallel processing of the scenarios under consideration, on-line scenario management (initiation as well as termination), analysis of results, and user friendly graphical capabilities. The ADAPT system is designed for a distributed computing environment; the scheduler can track multiple concurrent branches simultaneously. The scheduler is modularized so that the DET branching strategy can be modified (e.g. biasing towards the worst-case scenario/event). Independent database systems store data from the simulation tasks and the DET structure so that the event tree can be constructed and analyzed later. ADAPT is provided with a user-friendly client which can easily sort through and display the results of an experiment, precluding the need for the user to manually inspect individual simulator runs.« less

  8. Limitations to Thermoregulation and Acclimatization Challenge Human Adaptation to Global Warming

    PubMed Central

    Hanna, Elizabeth G.; Tait, Peter W.

    2015-01-01

    Human thermoregulation and acclimatization are core components of the human coping mechanism for withstanding variations in environmental heat exposure. Amidst growing recognition that curtailing global warming to less than two degrees is becoming increasing improbable, human survival will require increasing reliance on these mechanisms. The projected several fold increase in extreme heat events suggests we need to recalibrate health protection policies and ratchet up adaptation efforts. Climate researchers, epidemiologists, and policy makers engaged in climate change adaptation and health protection are not commonly drawn from heat physiology backgrounds. Injecting a scholarly consideration of physiological limitations to human heat tolerance into the adaptation and policy literature allows for a broader understanding of heat health risks to support effective human adaptation and adaptation planning. This paper details the physiological and external environmental factors that determine human thermoregulation and acclimatization. We present a model to illustrate the interrelationship between elements that modulate the physiological process of thermoregulation. Limitations inherent in these processes, and the constraints imposed by differing exposure levels, and thermal comfort seeking on achieving acclimatization, are then described. Combined, these limitations will restrict the likely contribution that acclimatization can play in future human adaptation to global warming. We postulate that behavioral and technological adaptations will need to become the dominant means for human individual and societal adaptations as global warming progresses. PMID:26184272

  9. Limitations to Thermoregulation and Acclimatization Challenge Human Adaptation to Global Warming.

    PubMed

    Hanna, Elizabeth G; Tait, Peter W

    2015-07-15

    Human thermoregulation and acclimatization are core components of the human coping mechanism for withstanding variations in environmental heat exposure. Amidst growing recognition that curtailing global warming to less than two degrees is becoming increasing improbable, human survival will require increasing reliance on these mechanisms. The projected several fold increase in extreme heat events suggests we need to recalibrate health protection policies and ratchet up adaptation efforts. Climate researchers, epidemiologists, and policy makers engaged in climate change adaptation and health protection are not commonly drawn from heat physiology backgrounds. Injecting a scholarly consideration of physiological limitations to human heat tolerance into the adaptation and policy literature allows for a broader understanding of heat health risks to support effective human adaptation and adaptation planning. This paper details the physiological and external environmental factors that determine human thermoregulation and acclimatization. We present a model to illustrate the interrelationship between elements that modulate the physiological process of thermoregulation. Limitations inherent in these processes, and the constraints imposed by differing exposure levels, and thermal comfort seeking on achieving acclimatization, are then described. Combined, these limitations will restrict the likely contribution that acclimatization can play in future human adaptation to global warming. We postulate that behavioral and technological adaptations will need to become the dominant means for human individual and societal adaptations as global warming progresses.

  10. Adaptation of health care for migrants: whose responsibility?

    PubMed Central

    2014-01-01

    Background In a context of increasing ethnic diversity, culturally competent strategies have been recommended to improve care quality and access to health care for ethnic minorities and migrants; their implementation by health professionals, however, has remained patchy. Most programs of cultural competence assume that health professionals accept that they have a responsibility to adapt to migrants, but this assumption has often remained at the level of theory. In this paper, we surveyed health professionals’ views on their responsibility to adapt. Methods Five hundred-and-sixty-nine health professionals from twenty-four inpatient and outpatient health services were selected according to their geographic location. All health care professionals were requested to complete a questionnaire about who should adapt to ethnic diversity: health professionals or patients. After a factorial analysis to identify the underlying responsibility dimensions, we performed a multilevel regression model in order to investigate individual and service covariates of responsibility attribution. Results Three dimensions emerged from the factor analysis: responsibility for the adaptation of communication, responsibility for the adaptation to the negotiation of values, and responsibility for the adaptation to health beliefs. Our results showed that the sense of responsibility for the adaptation of health care depended on the nature of the adaptation required: when the adaptation directly concerned communication with the patient, health professionals declared that they should be the ones to adapt; in relation to cultural preferences, however, the responsibility felt on the patient’s shoulders. Most respondents were unclear in relation to adaptation to health beliefs. Regression indicated that being Belgian, not being a physician, and working in a primary-care service were associated with placing the burden of responsibility on the patient. Conclusions Health care professionals do not

  11. Application of positive-real functions in hyperstable discrete model-reference adaptive system design.

    NASA Technical Reports Server (NTRS)

    Karmarkar, J. S.

    1972-01-01

    Proposal of an algorithmic procedure, based on mathematical programming methods, to design compensators for hyperstable discrete model-reference adaptive systems (MRAS). The objective of the compensator is to render the MRAS insensitive to initial parameter estimates within a maximized hypercube in the model parameter space.

  12. General mental ability and two types of adaptation to unforeseen change: applying discontinuous growth models to the task-change paradigm.

    PubMed

    Lang, Jonas W B; Bliese, Paul D

    2009-03-01

    The present research provides new insights into the relationship between general mental ability (GMA) and adaptive performance by applying a discontinuous growth modeling framework to a study of unforeseen change on a complex decision-making task. The proposed framework provides a way to distinguish 2 types of adaptation (transition adaptation and reacquisition adaptation) from 2 common performance components (skill acquisition and basal task performance). Transition adaptation refers to an immediate loss of performance following a change, whereas reacquisition adaptation refers to the ability to relearn a changed task over time. Analyses revealed that GMA was negatively related to transition adaptation and found no evidence for a relationship between GMA and reacquisition adaptation. The results are integrated within the context of adaptability research, and implications of using the described discontinuous growth modeling framework to study adaptability are discussed. (c) 2009 APA, all rights reserved.

  13. Mathematical modelling of bone adaptation of the metacarpal subchondral bone in racehorses.

    PubMed

    Hitchens, Peta L; Pivonka, Peter; Malekipour, Fatemeh; Whitton, R Chris

    2018-06-01

    In Thoroughbred racehorses, fractures of the distal limb are commonly catastrophic. Most of these fractures occur due to the accumulation of fatigue damage from repetitive loading, as evidenced by microdamage at the predilection sites for fracture. Adaptation of the bone in response to training loads is important for fatigue resistance. In order to better understand the mechanism of subchondral bone adaptation to its loading environment, we utilised a square root function defining the relationship between bone volume fraction [Formula: see text] and specific surface [Formula: see text] of the subchondral bone of the lateral condyles of the third metacarpal bone (MCIII) of the racehorse, and using this equation, developed a mathematical model of subchondral bone that adapts to loading conditions observed in vivo. The model is expressed as an ordinary differential equation incorporating a formation rate that is dependent on strain energy density. The loading conditions applied to a selected subchondral region, i.e. volume of interest, were estimated based on joint contact forces sustained by racehorses in training. For each of the initial conditions of [Formula: see text] we found no difference between subsequent homoeostatic [Formula: see text] at any given loading condition, but the time to reach equilibrium differed by initial [Formula: see text] and loading condition. We found that the observed values for [Formula: see text] from the mathematical model output were a good approximation to the existing data for racehorses in training or at rest. This model provides the basis for understanding the effect of changes to training strategies that may reduce the risk of racehorse injury.

  14. Multi-talker background and semantic priming effect

    PubMed Central

    Dekerle, Marie; Boulenger, Véronique; Hoen, Michel; Meunier, Fanny

    2014-01-01

    The reported studies have aimed to investigate whether informational masking in a multi-talker background relies on semantic interference between the background and target using an adapted semantic priming paradigm. In 3 experiments, participants were required to perform a lexical decision task on a target item embedded in backgrounds composed of 1–4 voices. These voices were Semantically Consistent (SC) voices (i.e., pronouncing words sharing semantic features with the target) or Semantically Inconsistent (SI) voices (i.e., pronouncing words semantically unrelated to each other and to the target). In the first experiment, backgrounds consisted of 1 or 2 SC voices. One and 2 SI voices were added in Experiments 2 and 3, respectively. The results showed a semantic priming effect only in the conditions where the number of SC voices was greater than the number of SI voices, suggesting that semantic priming depended on prime intelligibility and strategic processes. However, even if backgrounds were composed of 3 or 4 voices, reducing intelligibility, participants were able to recognize words from these backgrounds, although no semantic priming effect on the targets was observed. Overall this finding suggests that informational masking can occur at a semantic level if intelligibility is sufficient. Based on the Effortfulness Hypothesis, we also suggest that when there is an increased difficulty in extracting target signals (caused by a relatively high number of voices in the background), more cognitive resources were allocated to formal processes (i.e., acoustic and phonological), leading to a decrease in available resources for deeper semantic processing of background words, therefore preventing semantic priming from occurring. PMID:25400572

  15. Dissipation function and adaptive gradient reconstruction based smoke detection in video

    NASA Astrophysics Data System (ADS)

    Li, Bin; Zhang, Qiang; Shi, Chunlei

    2017-11-01

    A method for smoke detection in video is proposed. The camera monitoring the scene is assumed to be stationary. With the atmospheric scattering model, dissipation function is reflected transmissivity between the background objects in the scene and the camera. Dark channel prior and fast bilateral filter are used for estimating dissipation function which is only the function of the depth of field. Based on dissipation function, visual background extractor (ViBe) can be used for detecting smoke as a result of smoke's motion characteristics as well as detecting other moving targets. Since smoke has semi-transparent parts, the things which are covered by these parts can be recovered by poisson equation adaptively. The similarity between the recovered parts and the original background parts in the same position is calculated by Normalized Cross Correlation (NCC) and the original background's value is selected from the frame which is nearest to the current frame. The parts with high similarity are considered as smoke parts.

  16. Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia.

    PubMed

    Fang, Xin; Wallqvist, Anders; Reifman, Jaques

    2012-01-01

    The ability to adapt to different conditions is key for Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of M. tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an in silico approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA) program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of dosR, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of M. tuberculosis activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to differential gene

  17. Parallel goal-oriented adaptive finite element modeling for 3D electromagnetic exploration

    NASA Astrophysics Data System (ADS)

    Zhang, Y.; Key, K.; Ovall, J.; Holst, M.

    2014-12-01

    We present a parallel goal-oriented adaptive finite element method for accurate and efficient electromagnetic (EM) modeling of complex 3D structures. An unstructured tetrahedral mesh allows this approach to accommodate arbitrarily complex 3D conductivity variations and a priori known boundaries. The total electric field is approximated by the lowest order linear curl-conforming shape functions and the discretized finite element equations are solved by a sparse LU factorization. Accuracy of the finite element solution is achieved through adaptive mesh refinement that is performed iteratively until the solution converges to the desired accuracy tolerance. Refinement is guided by a goal-oriented error estimator that uses a dual-weighted residual method to optimize the mesh for accurate EM responses at the locations of the EM receivers. As a result, the mesh refinement is highly efficient since it only targets the elements where the inaccuracy of the solution corrupts the response at the possibly distant locations of the EM receivers. We compare the accuracy and efficiency of two approaches for estimating the primary residual error required at the core of this method: one uses local element and inter-element residuals and the other relies on solving a global residual system using a hierarchical basis. For computational efficiency our method follows the Bank-Holst algorithm for parallelization, where solutions are computed in subdomains of the original model. To resolve the load-balancing problem, this approach applies a spectral bisection method to divide the entire model into subdomains that have approximately equal error and the same number of receivers. The finite element solutions are then computed in parallel with each subdomain carrying out goal-oriented adaptive mesh refinement independently. We validate the newly developed algorithm by comparison with controlled-source EM solutions for 1D layered models and with 2D results from our earlier 2D goal oriented

  18. Automated adaptive inference of phenomenological dynamical models

    NASA Astrophysics Data System (ADS)

    Daniels, Bryan

    Understanding the dynamics of biochemical systems can seem impossibly complicated at the microscopic level: detailed properties of every molecular species, including those that have not yet been discovered, could be important for producing macroscopic behavior. The profusion of data in this area has raised the hope that microscopic dynamics might be recovered in an automated search over possible models, yet the combinatorial growth of this space has limited these techniques to systems that contain only a few interacting species. We take a different approach inspired by coarse-grained, phenomenological models in physics. Akin to a Taylor series producing Hooke's Law, forgoing microscopic accuracy allows us to constrain the search over dynamical models to a single dimension. This makes it feasible to infer dynamics with very limited data, including cases in which important dynamical variables are unobserved. We name our method Sir Isaac after its ability to infer the dynamical structure of the law of gravitation given simulated planetary motion data. Applying the method to output from a microscopically complicated but macroscopically simple biological signaling model, it is able to adapt the level of detail to the amount of available data. Finally, using nematode behavioral time series data, the method discovers an effective switch between behavioral attractors after the application of a painful stimulus.

  19. Histone Deacetylase Adaptation in Single Ventricle Heart Disease and a Young Animal Model of Right Ventricular Hypertrophy

    PubMed Central

    Blakeslee, Weston W.; Demos-Davies, Kimberly M.; Lemon, Douglas D.; Lutter, Katharina M.; Cavasin, Maria A.; Payne, Sam; Nunley, Karin; Long, Carlin S.; McKinsey, Timothy A.; Miyamoto, Shelley D.

    2017-01-01

    Background Histone deacetylase (HDAC) inhibitors are promising therapeutics for various forms of cardiac disease. The purpose of this study was to assess cardiac HDAC catalytic activity and expression in children with single ventricle heart disease of right ventricular morphology (SV), as well as in a rodent model of right ventricular hypertrophy (RVH). Methods Homogenates of RV explants from non-failing controls and SV children were assayed for HDAC catalytic activity and HDAC isoform expression. Postnatal 1-day old rat pups were placed in hypoxic conditions and echocardiographic analysis, gene expression, HDAC catalytic activity and isoform expression studies of the RV were performed. Results Class I, IIa, and IIb HDAC catalytic activity and protein expression were elevated in hearts of SV children. Hypoxic neonatal rats demonstrated RVH, abnormal gene expression and elevated class I and class IIb HDAC catalytic activity and protein expression in the RV compared to control. Conclusions These data suggest that myocardial HDAC adaptations occur in the SV heart and could represent a novel therapeutic target. While further characterization of the hypoxic neonatal rat is needed, this animal model may be suitable for pre-clinical investigations of pediatric RV disease and could serve as a useful model for future mechanistic studies. PMID:28549058

  20. Online Instructors as Thinking Advisors: A Model for Online Learner Adaptation

    ERIC Educational Resources Information Center

    Benedetti, Christopher

    2015-01-01

    This article examines the characteristics and challenges of online instruction and presents a model for improving learner adaptation in an online classroom. Instruction in an online classroom presents many challenges, including learner individualization. Individual differences in learning styles and preferences are often not considered in the…