Ultrasound image filtering using the mutiplicative model

NASA Astrophysics Data System (ADS)

Navarrete, Hugo; Frery, Alejandro C.; Sanchez, Fermin; Anto, Joan

2002-04-01

Ultrasound images, as a special case of coherent images, are normally corrupted with multiplicative noise i.e. speckle noise. Speckle noise reduction is a difficult task due to its multiplicative nature, but good statistical models of speckle formation are useful to design adaptive speckle reduction filters. In this article a new statistical model, emerging from the Multiplicative Model framework, is presented and compared to previous models (Rayleigh, Rice and K laws). It is shown that the proposed model gives the best performance when modeling the statistics of ultrasound images. Finally, the parameters of the model can be used to quantify the extent of speckle formation; this quantification is applied to adaptive speckle reduction filter design. The effectiveness of the filter is demonstrated on typical in-vivo log-compressed B-scan images obtained by a clinical ultrasound system.

Global Climate Change Adaptation Priorities for Biodiversity and Food Security

PubMed Central

Hannah, Lee; Ikegami, Makihiko; Hole, David G.; Seo, Changwan; Butchart, Stuart H. M.; Peterson, A. Townsend; Roehrdanz, Patrick R.

2013-01-01

International policy is placing increasing emphasis on adaptation to climate change, including the allocation of new funds to assist adaptation efforts. Climate change adaptation funding may be most effective where it meets integrated goals, but global geographic priorities based on multiple development and ecological criteria are not well characterized. Here we show that human and natural adaptation needs related to maintaining agricultural productivity and ecosystem integrity intersect in ten major areas globally, providing a coherent set of international priorities for adaptation funding. An additional seven regional areas are identified as worthy of additional study. The priority areas are locations where changes in crop suitability affecting impoverished farmers intersect with changes in ranges of restricted-range species. Agreement among multiple climate models and emissions scenarios suggests that these priorities are robust. Adaptation funding directed to these areas could simultaneously address multiple international policy goals, including poverty reduction, protecting agricultural production and safeguarding ecosystem services. PMID:23991125

Global climate change adaptation priorities for biodiversity and food security.

PubMed

Hannah, Lee; Ikegami, Makihiko; Hole, David G; Seo, Changwan; Butchart, Stuart H M; Peterson, A Townsend; Roehrdanz, Patrick R

2013-01-01

International policy is placing increasing emphasis on adaptation to climate change, including the allocation of new funds to assist adaptation efforts. Climate change adaptation funding may be most effective where it meets integrated goals, but global geographic priorities based on multiple development and ecological criteria are not well characterized. Here we show that human and natural adaptation needs related to maintaining agricultural productivity and ecosystem integrity intersect in ten major areas globally, providing a coherent set of international priorities for adaptation funding. An additional seven regional areas are identified as worthy of additional study. The priority areas are locations where changes in crop suitability affecting impoverished farmers intersect with changes in ranges of restricted-range species. Agreement among multiple climate models and emissions scenarios suggests that these priorities are robust. Adaptation funding directed to these areas could simultaneously address multiple international policy goals, including poverty reduction, protecting agricultural production and safeguarding ecosystem services.

The specificity of stimulus-specific adaptation in human auditory cortex increases with repeated exposure to the adapting stimulus.

PubMed

Briley, Paul M; Krumbholz, Katrin

2013-12-01

The neural response to a sensory stimulus tends to be more strongly reduced when the stimulus is preceded by the same, rather than a different, stimulus. This stimulus-specific adaptation (SSA) is ubiquitous across the senses. In hearing, SSA has been suggested to play a role in change detection as indexed by the mismatch negativity. This study sought to test whether SSA, measured in human auditory cortex, is caused by neural fatigue (reduction in neural responsiveness) or by sharpening of neural tuning to the adapting stimulus. For that, we measured event-related cortical potentials to pairs of pure tones with varying frequency separation and stimulus onset asynchrony (SOA). This enabled us to examine the relationship between the degree of specificity of adaptation as a function of frequency separation and the rate of decay of adaptation with increasing SOA. Using simulations of tonotopic neuron populations, we demonstrate that the fatigue model predicts independence of adaptation specificity and decay rate, whereas the sharpening model predicts interdependence. The data showed independence and thus supported the fatigue model. In a second experiment, we measured adaptation specificity after multiple presentations of the adapting stimulus. The multiple adapters produced more adaptation overall, but the effect was more specific to the adapting frequency. Within the context of the fatigue model, the observed increase in adaptation specificity could be explained by assuming a 2.5-fold increase in neural frequency selectivity. We discuss possible bottom-up and top-down mechanisms of this effect.

Static shape control for adaptive wings

NASA Astrophysics Data System (ADS)

Austin, Fred; Rossi, Michael J.; van Nostrand, William; Knowles, Gareth; Jameson, Antony

1994-09-01

A theoretical method was developed and experimentally validated, to control the static shape of flexible structures by employing internal translational actuators. A finite element model of the structure, without the actuators present, is employed to obtain the multiple-input, multiple-output control-system gain matrices for actuator-load control as well as actuator-displacement control. The method is applied to the quasistatic problem of maintaining an optimum-wing cross section during various transonic-cruise flight conditions to obtain significant reductions in the shock-induced drag. Only small, potentially achievable, adaptive modifications to the profile are required. The adaptive-wing concept employs actuators as truss elements of active ribs to reshape the wing cross section by deforming the structure. Finite element analyses of an adaptive-rib model verify the controlled-structure theory. Experiments on the model were conducted, and arbitrarily selected deformed shapes were accurately achieved.

Integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control for Lead-Wing close formation systems

NASA Astrophysics Data System (ADS)

Liu, Chun; Jiang, Bin; Zhang, Ke

2018-03-01

This paper investigates the attitude and position tracking control problem for Lead-Wing close formation systems in the presence of loss of effectiveness and lock-in-place or hardover failure. In close formation flight, Wing unmanned aerial vehicle movements are influenced by vortex effects of the neighbouring Lead unmanned aerial vehicle. This situation allows modelling of aerodynamic coupling vortex-effects and linearisation based on optimal close formation geometry. Linearised Lead-Wing close formation model is transformed into nominal robust H-infinity models with respect to Mach hold, Heading hold, and Altitude hold autopilots; static feedback H-infinity controller is designed to guarantee effective tracking of attitude and position while manoeuvring Lead unmanned aerial vehicle. Based on H-infinity control design, an integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control scheme is developed to guarantee asymptotic stability of close-loop systems, error signal boundedness, and attitude and position tracking properties. Simulation results for Lead-Wing close formation systems validate the efficiency of the proposed integrated multiple-model adaptive control algorithm.

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…

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.

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).

Adaptive vehicle motion estimation and prediction

NASA Astrophysics Data System (ADS)

Zhao, Liang; Thorpe, Chuck E.

1999-01-01

Accurate motion estimation and reliable maneuver prediction enable an automated car to react quickly and correctly to the rapid maneuvers of the other vehicles, and so allow safe and efficient navigation. In this paper, we present a car tracking system which provides motion estimation, maneuver prediction and detection of the tracked car. The three strategies employed - adaptive motion modeling, adaptive data sampling, and adaptive model switching probabilities - result in an adaptive interacting multiple model algorithm (AIMM). The experimental results on simulated and real data demonstrate that our tracking system is reliable, flexible, and robust. The adaptive tracking makes the system intelligent and useful in various autonomous driving tasks.

A Revised Version of the Norwegian Adaptation of the Test Anxiety Inventory in a Heterogeneous Population

ERIC Educational Resources Information Center

Oktedalen, Tuva; Hagtvet, Knut A.

2011-01-01

Confirmatory factor analysis and Multiple Indicators, Multiple Causes (MIMIC) modeling were employed to investigate psychometric properties of a revised adaptation of the Norwegian version of the Test Anxiety Inventory (RTAIN) in a sample of 456 students. The study supported the Norwegian version as a useful inventory for measuring the components…

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

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.…

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

Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing.

PubMed

Leong, Siow Hoo; Ong, Seng Huat

2017-01-01

This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index.

Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing

PubMed Central

Leong, Siow Hoo

2017-01-01

This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index. PMID:28686634

Investigation of the Multiple Method Adaptive Control (MMAC) method for flight control systems

NASA Technical Reports Server (NTRS)

Athans, M.; Baram, Y.; Castanon, D.; Dunn, K. P.; Green, C. S.; Lee, W. H.; Sandell, N. R., Jr.; Willsky, A. S.

1979-01-01

The stochastic adaptive control of the NASA F-8C digital-fly-by-wire aircraft using the multiple model adaptive control (MMAC) method is presented. The selection of the performance criteria for the lateral and the longitudinal dynamics, the design of the Kalman filters for different operating conditions, the identification algorithm associated with the MMAC method, the control system design, and simulation results obtained using the real time simulator of the F-8 aircraft at the NASA Langley Research Center are discussed.

Contextual cuing contributes to the independent modification of multiple internal models for vocal control

PubMed Central

Keough, Dwayne

2011-01-01

Research on the control of visually guided limb movements indicates that the brain learns and continuously updates an internal model that maps the relationship between motor commands and sensory feedback. A growing body of work suggests that an internal model that relates motor commands to sensory feedback also supports vocal control. There is evidence from arm-reaching studies that shows that when provided with a contextual cue, the motor system can acquire multiple internal models, which allows an animal to adapt to different perturbations in diverse contexts. In this study we show that trained singers can rapidly acquire multiple internal models regarding voice fundamental frequency (F0). These models accommodate different perturbations to ongoing auditory feedback. Participants heard three musical notes and reproduced each one in succession. The musical targets could serve as a contextual cue to indicate which direction (up or down) feedback would be altered on each trial; however, participants were not explicitly instructed to use this strategy. When participants were gradually exposed to altered feedback adaptation was observed immediately following vocal onset. Aftereffects were target specific and did not influence vocal productions on subsequent trials. When target notes were no longer a contextual cue, adaptation occurred during altered feedback trials and evidence for trial-by-trial adaptation was found. These findings indicate that the brain is exceptionally sensitive to the deviations between auditory feedback and the predicted consequence of a motor command during vocalization. Moreover, these results indicate that, with contextual cues, the vocal control system may maintain multiple internal models that are capable of independent modification during different tasks or environments. PMID:21346208

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…

A Multilateral Negotiation Model for Cloud Service Market

NASA Astrophysics Data System (ADS)

Yoo, Dongjin; Sim, Kwang Mong

Trading cloud services between consumers and providers is a complicated issue of cloud computing. Since a consumer can negotiate with multiple providers to acquire the same service and each provider can receive many requests from multiple consumers, to facilitate the trading of cloud services among multiple consumers and providers, a multilateral negotiation model for cloud market is necessary. The contribution of this work is the proposal of a business model supporting a multilateral price negotiation for trading cloud services. The design of proposed systems for cloud service market includes considering a many-to-many negotiation protocol, and price determining factor from service level feature. Two negotiation strategies are implemented: 1) MDA (Market Driven Agent); and 2) adaptive concession making responding to changes of bargaining position are proposed for cloud service market. Empirical results shows that MDA achieved better performance in some cases that the adaptive concession making strategy, it is noted that unlike the MDA, the adaptive concession making strategy does not assume that an agent has information of the number of competitors (e.g., a consumer agent adopting the adaptive concession making strategy need not know the number of consumer agents competing for the same service).

Determinants of Multiple Semantic Priming: A Meta-Analysis and Spike Frequency Adaptive Model of a Cortical Network

ERIC Educational Resources Information Center

Lavigne, Frederic; Dumercy, Laurent; Darmon, Nelly

2011-01-01

Recall and language comprehension while processing sequences of words involves multiple semantic priming between several related and/or unrelated words. Accounting for multiple and interacting priming effects in terms of underlying neuronal structure and dynamics is a challenge for current models of semantic priming. Further elaboration of current…

Robust design of configurations and parameters of adaptable products

NASA Astrophysics Data System (ADS)

Zhang, Jian; Chen, Yongliang; Xue, Deyi; Gu, Peihua

2014-03-01

An adaptable product can satisfy different customer requirements by changing its configuration and parameter values during the operation stage. Design of adaptable products aims at reducing the environment impact through replacement of multiple different products with single adaptable ones. Due to the complex architecture, multiple functional requirements, and changes of product configurations and parameter values in operation, impact of uncertainties to the functional performance measures needs to be considered in design of adaptable products. In this paper, a robust design approach is introduced to identify the optimal design configuration and parameters of an adaptable product whose functional performance measures are the least sensitive to uncertainties. An adaptable product in this paper is modeled by both configurations and parameters. At the configuration level, methods to model different product configuration candidates in design and different product configuration states in operation to satisfy design requirements are introduced. At the parameter level, four types of product/operating parameters and relations among these parameters are discussed. A two-level optimization approach is developed to identify the optimal design configuration and its parameter values of the adaptable product. A case study is implemented to illustrate the effectiveness of the newly developed robust adaptable design method.

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.

PubMed

Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing

2011-01-01

In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.

Genealogies of rapidly adapting populations

PubMed Central

Neher, Richard A.; Hallatschek, Oskar

2013-01-01

The genetic diversity of a species is shaped by its recent evolutionary history and can be used to infer demographic events or selective sweeps. Most inference methods are based on the null hypothesis that natural selection is a weak or infrequent evolutionary force. However, many species, particularly pathogens, are under continuous pressure to adapt in response to changing environments. A statistical framework for inference from diversity data of such populations is currently lacking. Towards this goal, we explore the properties of genealogies in a model of continual adaptation in asexual populations. We show that lineages trace back to a small pool of highly fit ancestors, in which almost simultaneous coalescence of more than two lineages frequently occurs. Whereas such multiple mergers are unlikely under the neutral coalescent, they create a unique genetic footprint in adapting populations. The site frequency spectrum of derived neutral alleles, for example, is nonmonotonic and has a peak at high frequencies, whereas Tajima’s D becomes more and more negative with increasing sample size. Because multiple merger coalescents emerge in many models of rapid adaptation, we argue that they should be considered as a null model for adapting populations. PMID:23269838

The Swedish version of the Acceptance of Chronic Health Conditions Scale for people with multiple sclerosis: Translation, cultural adaptation and psychometric properties.

PubMed

Forslin, Mia; Kottorp, Anders; Kierkegaard, Marie; Johansson, Sverker

2016-11-11

To translate and culturally adapt the Acceptance of Chronic Health Conditions (ACHC) Scale for people with multiple sclerosis into Swedish, and to analyse the psychometric properties of the Swedish version. Ten people with multiple sclerosis participated in translation and cultural adaptation of the ACHC Scale; 148 people with multiple sclerosis were included in evaluation of the psychometric properties of the scale. Translation and cultural adaptation were carried out through translation and back-translation, by expert committee evaluation and pre-test with cognitive interviews in people with multiple sclerosis. The psychometric properties of the Swedish version were evaluated using Rasch analysis. The Swedish version of the ACHC Scale was an acceptable equivalent to the original version. Seven of the original 10 items fitted the Rasch model and demonstrated ability to separate between groups. A 5-item version, including 2 items and 3 super-items, demonstrated better psychometric properties, but lower ability to separate between groups. The Swedish version of the ACHC Scale with the original 10 items did not fit the Rasch model. Two solutions, either with 7 items (ACHC-7) or with 2 items and 3 super-items (ACHC-5), demonstrated acceptable psychometric properties. Use of the ACHC-5 Scale with super-items is recommended, since this solution adjusts for local dependency among items.

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method.

PubMed

Tuta, Jure; Juric, Matjaz B

2018-03-24

This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

PubMed Central

Juric, Matjaz B.

2018-01-01

This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage. PMID:29587352

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.

Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks

NASA Astrophysics Data System (ADS)

Ma, Chuang; Bao, Zhong-Kui; Zhang, Hai-Feng

2017-10-01

So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an adaptive fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is adaptively determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.

Robustness of Ability Estimation to Multidimensionality in CAST with Implications to Test Assembly

ERIC Educational Resources Information Center

Zhang, Yanwei; Nandakumar, Ratna

2006-01-01

Computer Adaptive Sequential Testing (CAST) is a test delivery model that combines features of the traditional conventional paper-and-pencil testing and item-based computerized adaptive testing (CAT). The basic structure of CAST is a panel composed of multiple testlets adaptively administered to examinees at different stages. Current applications…

Multiple flood vulnerability assessment approach based on fuzzy comprehensive evaluation method and coordinated development degree model.

PubMed

Yang, Weichao; Xu, Kui; Lian, Jijian; Bin, Lingling; Ma, Chao

2018-05-01

Flood is a serious challenge that increasingly affects the residents as well as policymakers. Flood vulnerability assessment is becoming gradually relevant in the world. The purpose of this study is to develop an approach to reveal the relationship between exposure, sensitivity and adaptive capacity for better flood vulnerability assessment, based on the fuzzy comprehensive evaluation method (FCEM) and coordinated development degree model (CDDM). The approach is organized into three parts: establishment of index system, assessment of exposure, sensitivity and adaptive capacity, and multiple flood vulnerability assessment. Hydrodynamic model and statistical data are employed for the establishment of index system; FCEM is used to evaluate exposure, sensitivity and adaptive capacity; and CDDM is applied to express the relationship of the three components of vulnerability. Six multiple flood vulnerability types and four levels are proposed to assess flood vulnerability from multiple perspectives. Then the approach is applied to assess the spatiality of flood vulnerability in Hainan's eastern area, China. Based on the results of multiple flood vulnerability, a decision-making process for rational allocation of limited resources is proposed and applied to the study area. The study shows that multiple flood vulnerability assessment can evaluate vulnerability more completely, and help decision makers learn more information about making decisions in a more comprehensive way. In summary, this study provides a new way for flood vulnerability assessment and disaster prevention decision. Copyright © 2018 Elsevier Ltd. All rights reserved.

Studying the evolutionary significance of thermal adaptation in ectotherms: The diversification of amphibians' energetics.

PubMed

Nespolo, Roberto F; Figueroa, Julio; Solano-Iguaran, Jaiber J

2017-08-01

A fundamental problem in evolutionary biology is the understanding of the factors that promote or constrain adaptive evolution, and assessing the role of natural selection in this process. Here, comparative phylogenetics, that is, using phylogenetic information and traits to infer evolutionary processes has been a major paradigm . In this study, we discuss Ornstein-Uhlenbeck models (OU) in the context of thermal adaptation in ectotherms. We specifically applied this approach to study amphibians's evolution and energy metabolism. It has been hypothesized that amphibians exploit adaptive zones characterized by low energy expenditure, which generate specific predictions in terms of the patterns of diversification in standard metabolic rate (SMR). We complied whole-animal metabolic rates for 122 species of amphibians, and adjusted several models of diversification. According to the adaptive zone hypothesis, we expected: (1) to find "accelerated evolution" in SMR (i.e., diversification above Brownian Motion expectations, BM), (2) that a model assuming evolutionary optima (i.e., an OU model) fits better than a white-noise model and (3) that a model assuming multiple optima (according to the three amphibians's orders) fits better than a model assuming a single optimum. As predicted, we found that the diversification of SMR occurred most of the time, above BM expectations. Also, we found that a model assuming an optimum explained the data in a better way than a white-noise model. However, we did not find evidence that an OU model with multiple optima fits the data better, suggesting a single optimum in SMR for Anura, Caudata and Gymnophiona. These results show how comparative phylogenetics could be applied for testing adaptive hypotheses regarding history and physiological performance in ectotherms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Model-based scenario planning to develop climate change adaptation strategies for rare plant populations in grassland reserves

Treesearch

Laura Phillips-Mao; Susan M. Galatowitsch; Stephanie A. Snyder; Robert G. Haight

2016-01-01

Incorporating climate change into conservation decision-making at site and population scales is challenging due to uncertainties associated with localized climate change impacts and population responses to multiple interacting impacts and adaptation strategies. We explore the use of spatially explicit population models to facilitate scenario analysis, a conservation...

Diagnostic Classification Models and Multidimensional Adaptive Testing: A Commentary on Rupp and Templin

ERIC Educational Resources Information Center

Frey, Andreas; Carstensen, Claus H.

2009-01-01

On a general level, the objective of diagnostic classifications models (DCMs) lies in a classification of individuals regarding multiple latent skills. In this article, the authors show that this objective can be achieved by multidimensional adaptive testing (MAT) as well. The authors discuss whether or not the restricted applicability of DCMs can…

Multiple Auto-Adapting Color Balancing for Large Number of Images

NASA Astrophysics Data System (ADS)

Zhou, X.

2015-04-01

This paper presents a powerful technology of color balance between images. It does not only work for small number of images but also work for unlimited large number of images. Multiple adaptive methods are used. To obtain color seamless mosaic dataset, local color is adjusted adaptively towards the target color. Local statistics of the source images are computed based on the so-called adaptive dodging window. The adaptive target colors are statistically computed according to multiple target models. The gamma function is derived from the adaptive target and the adaptive source local stats. It is applied to the source images to obtain the color balanced output images. Five target color surface models are proposed. They are color point (or single color), color grid, 1st, 2nd and 3rd 2D polynomials. Least Square Fitting is used to obtain the polynomial target color surfaces. Target color surfaces are automatically computed based on all source images or based on an external target image. Some special objects such as water and snow are filtered by percentage cut or a given mask. Excellent results are achieved. The performance is extremely fast to support on-the-fly color balancing for large number of images (possible of hundreds of thousands images). Detailed algorithm and formulae are described. Rich examples including big mosaic datasets (e.g., contains 36,006 images) are given. Excellent results and performance are presented. The results show that this technology can be successfully used in various imagery to obtain color seamless mosaic. This algorithm has been successfully using in ESRI ArcGis.

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.

Discovery of a New Companion and Evidence of a Circumprimary Disk: Adaptive Optics Imaging of the Young Multiple System VW Chamaeleon

NASA Astrophysics Data System (ADS)

Brandeker, Alexis; Liseau, René; Artymowicz, Pawel; Jayawardhana, Ray

2001-11-01

Since a majority of young low-mass stars are members of multiple systems, the study of their stellar and disk configurations is crucial to our understanding of both star and planet formation processes. Here we present near-infrared adaptive optics observations of the young multiple star system VW Chamaeleon. The previously known 0.7" binary is clearly resolved already in our raw J- and K-band images. We report the discovery of a new faint companion to the secondary, at an apparent separation of only 0.1", or 16 AU. Our high-resolution photometric observations also make it possible to measure the J-K colors of each of the three components individually. We detect an infrared excess in the primary, consistent with theoretical models of a circumprimary disk. Analytical and numerical calculations of orbital stability show that VW Cha may be a stable triple system. Using models for the age and total mass of the secondary pair, we estimate the orbital period to be 74 yr. Thus, follow-up astrometric observations might yield direct dynamical masses within a few years and constrain evolutionary models of low-mass stars. Our results demonstrate that adaptive optics imaging in conjunction with deconvolution techniques is a powerful tool for probing close multiple systems. Based on observations collected at the European Southern Observatory, Chile.

Multiplatform Mission Planning and Operations Simulation Environment for Adaptive Remote Sensors

NASA Astrophysics Data System (ADS)

Smith, G.; Ball, C.; O'Brien, A.; Johnson, J. T.

2017-12-01

We report on the design and development of mission simulator libraries to support the emerging field of adaptive remote sensors. We will outline the current state of the art in adaptive sensing, provide analysis of how the current approach to performing observing system simulation experiments (OSSEs) must be changed to enable adaptive sensors for remote sensing, and present an architecture to enable their inclusion in future OSSEs.The growing potential of sensors capable of real-time adaptation of their operational parameters calls for a new class of mission planning and simulation tools. Existing simulation tools used in OSSEs assume a fixed set of sensor parameters in terms of observation geometry, frequencies used, resolution, or observation time, which allows simplifications to be made in the simulation and allows sensor observation errors to be characterized a priori. Adaptive sensors may vary these parameters depending on the details of the scene observed, so that sensor performance is not simple to model without conducting OSSE simulations that include sensor adaptation in response to varying observational environment. Adaptive sensors are of significance to resource-constrained, small satellite platforms because they enable the management of power and data volumes while providing methods for multiple sensors to collaborate.The new class of OSSEs required to utilize adaptive sensors located on multiple platforms must answer the question: If the physical act of sensing has a cost, how does the system determine if the science value of a measurement is worth the cost and how should that cost be shared among the collaborating sensors?Here we propose to answer this question using an architecture structured around three modules: ADAPT, MANAGE and COLLABORATE. The ADAPT module is a set of routines to facilitate modeling of adaptive sensors, the MANAGE module will implement a set of routines to facilitate simulations of sensor resource management when power and data volume are constrained, and the COLLABORATE module will support simulations of coordination among multiple platforms with adaptive sensors. When used together these modules will for a simulation OSSEs that can enable both the design of adaptive algorithms to support remote sensing and the prediction of the sensor performance.

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

Multiple Concurrent Visual-Motor Mappings: Implications for Models of Adaptation

NASA Technical Reports Server (NTRS)

Cunningham, H. A.; Welch, Robert B.

1994-01-01

Previous research on adaptation to visual-motor rearrangement suggests that the central nervous system represents accurately only 1 visual-motor mapping at a time. This idea was examined in 3 experiments where subjects tracked a moving target under repeated alternations between 2 initially interfering mappings (the 'normal' mapping characteristic of computer input devices and a 108' rotation of the normal mapping). Alternation between the 2 mappings led to significant reduction in error under the rotated mapping and significant reduction in the adaptation aftereffect ordinarily caused by switching between mappings. Color as a discriminative cue, interference versus decay in adaptation aftereffect, and intermanual transfer were also examined. The results reveal a capacity for multiple concurrent visual-motor mappings, possibly controlled by a parametric process near the motor output stage of processing.

Adaptive versus Learner Control in a Multiple Intelligence Learning Environment

ERIC Educational Resources Information Center

Kelly, Declan

2008-01-01

Within the field of technology enhanced learning, adaptive educational systems offer an advanced form of learning environment that attempts to meet the needs of different students. Such systems capture and represent, for each student, various characteristics such as knowledge and traits in an individual learner model. Subsequently, using the…

Adaptive and bounded investment returns promote cooperation in spatial public goods games.

PubMed

Chen, Xiaojie; Liu, Yongkui; Zhou, Yonghui; Wang, Long; Perc, Matjaž

2012-01-01

The public goods game is one of the most famous models for studying the evolution of cooperation in sizable groups. The multiplication factor in this game can characterize the investment return from the public good, which may be variable depending on the interactive environment in realistic situations. Instead of using the same universal value, here we consider that the multiplication factor in each group is updated based on the differences between the local and global interactive environments in the spatial public goods game, but meanwhile limited to within a certain range. We find that the adaptive and bounded investment returns can significantly promote cooperation. In particular, full cooperation can be achieved for high feedback strength when appropriate limitation is set for the investment return. Also, we show that the fraction of cooperators in the whole population can become larger if the lower and upper limits of the multiplication factor are increased. Furthermore, in comparison to the traditionally spatial public goods game where the multiplication factor in each group is identical and fixed, we find that cooperation can be better promoted if the multiplication factor is constrained to adjust between one and the group size in our model. Our results highlight the importance of the locally adaptive and bounded investment returns for the emergence and dominance of cooperative behavior in structured populations.

Adaptive and Bounded Investment Returns Promote Cooperation in Spatial Public Goods Games

PubMed Central

Chen, Xiaojie; Liu, Yongkui; Zhou, Yonghui; Wang, Long; Perc, Matjaž

2012-01-01

The public goods game is one of the most famous models for studying the evolution of cooperation in sizable groups. The multiplication factor in this game can characterize the investment return from the public good, which may be variable depending on the interactive environment in realistic situations. Instead of using the same universal value, here we consider that the multiplication factor in each group is updated based on the differences between the local and global interactive environments in the spatial public goods game, but meanwhile limited to within a certain range. We find that the adaptive and bounded investment returns can significantly promote cooperation. In particular, full cooperation can be achieved for high feedback strength when appropriate limitation is set for the investment return. Also, we show that the fraction of cooperators in the whole population can become larger if the lower and upper limits of the multiplication factor are increased. Furthermore, in comparison to the traditionally spatial public goods game where the multiplication factor in each group is identical and fixed, we find that cooperation can be better promoted if the multiplication factor is constrained to adjust between one and the group size in our model. Our results highlight the importance of the locally adaptive and bounded investment returns for the emergence and dominance of cooperative behavior in structured populations. PMID:22615836

Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

PubMed

Li, Zhijun; Su, Chun-Yi

2013-09-01

In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

Pattern Adaptation and Normalization Reweighting.

PubMed

Westrick, Zachary M; Heeger, David J; Landy, Michael S

2016-09-21

Adaptation to an oriented stimulus changes both the gain and preferred orientation of neural responses in V1. Neurons tuned near the adapted orientation are suppressed, and their preferred orientations shift away from the adapter. We propose a model in which weights of divisive normalization are dynamically adjusted to homeostatically maintain response products between pairs of neurons. We demonstrate that this adjustment can be performed by a very simple learning rule. Simulations of this model closely match existing data from visual adaptation experiments. We consider several alternative models, including variants based on homeostatic maintenance of response correlations or covariance, as well as feedforward gain-control models with multiple layers, and we demonstrate that homeostatic maintenance of response products provides the best account of the physiological data. Adaptation is a phenomenon throughout the nervous system in which neural tuning properties change in response to changes in environmental statistics. We developed a model of adaptation that combines normalization (in which a neuron's gain is reduced by the summed responses of its neighbors) and Hebbian learning (in which synaptic strength, in this case divisive normalization, is increased by correlated firing). The model is shown to account for several properties of adaptation in primary visual cortex in response to changes in the statistics of contour orientation. Copyright © 2016 the authors 0270-6474/16/369805-12$15.00/0.

Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yang, Boyuan

2017-09-01

It is a challenging problem to design excellent dictionaries to sparsely represent diverse fault information and simultaneously discriminate different fault sources. Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy. The proposed framework consists of four stages. Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition. Secondly, the noises are effectively eliminated through transform sparse coding techniques. Thirdly, the denoised signal is decoupled into discriminative feature subspaces by each tight frame filter. Finally, in guidance of elaborately designed fault related sensitive indexes, latent fault feature subspaces can be adaptively recognized and multiple faults are diagnosed simultaneously. Extensive numerical experiments are sequently implemented to investigate the sparsifying capability of the learned tight frame as well as its comprehensive denoising performance. Most importantly, the feasibility and superiority of the proposed framework is verified through performing multiple fault diagnosis of motor bearings. Compared with the state-of-the-art fault detection techniques, some important advantages have been observed: firstly, the proposed framework incorporates the physical prior with the data-driven strategy and naturally multiple fault feature with similar oscillation morphology can be adaptively decoupled. Secondly, the tight frame dictionary directly learned from the noisy observation can significantly promote the sparsity of fault features compared to analytical tight frames. Thirdly, a satisfactory complete signal space description property is guaranteed and thus weak feature leakage problem is avoided compared to typical learning methods.

Adaptive variation in Pinus ponderosa from Intermountain regions. II. Middle Columbia River system

Treesearch

Gerald Rehfeldt

1986-01-01

Seedling populations were grown and compared in common environments. Statistical analyses detected genetic differences between populations for numerous traits reflecting growth potential and periodicity of shoot elongation. Multiple regression models described an adaptive landscape in which populations from low elevations have a high growth potential while those from...

Forest Management Under Uncertainty for Multiple Bird Population Objectives

Treesearch

Clinton T. Moore; W. Todd Plummer; Michael J. Conroy

2005-01-01

We advocate adaptive programs of decision making and monitoring for the management of forest birds when responses by populations to management, and particularly management trade-offs among populations, are uncertain. Models are necessary components of adaptive management. Under this approach, uncertainty about the behavior of a managed system is explicitly captured in...

Adaptive Identification and Control of Flow-Induced Cavity Oscillations

NASA Technical Reports Server (NTRS)

Kegerise, M. A.; Cattafesta, L. N.; Ha, C.

2002-01-01

Progress towards an adaptive self-tuning regulator (STR) for the cavity tone problem is discussed in this paper. Adaptive system identification algorithms were applied to an experimental cavity-flow tested as a prerequisite to control. In addition, a simple digital controller and a piezoelectric bimorph actuator were used to demonstrate multiple tone suppression. The control tests at Mach numbers of 0.275, 0.40, and 0.60 indicated approx. = 7dB tone reductions at multiple frequencies. Several different adaptive system identification algorithms were applied at a single freestream Mach number of 0.275. Adaptive finite-impulse response (FIR) filters of orders up to N = 100 were found to be unsuitable for modeling the cavity flow dynamics. Adaptive infinite-impulse response (IIR) filters of comparable order better captured the system dynamics. Two recursive algorithms, the least-mean square (LMS) and the recursive-least square (RLS), were utilized to update the adaptive filter coefficients. Given the sample-time requirements imposed by the cavity flow dynamics, the computational simplicity of the least mean squares (LMS) algorithm is advantageous for real-time control.

A Decentralized Adaptive Approach to Fault Tolerant Flight Control

NASA Technical Reports Server (NTRS)

Wu, N. Eva; Nikulin, Vladimir; Heimes, Felix; Shormin, Victor

2000-01-01

This paper briefly reports some results of our study on the application of a decentralized adaptive control approach to a 6 DOF nonlinear aircraft model. The simulation results showed the potential of using this approach to achieve fault tolerant control. Based on this observation and some analysis, the paper proposes a multiple channel adaptive control scheme that makes use of the functionally redundant actuating and sensing capabilities in the model, and explains how to implement the scheme to tolerate actuator and sensor failures. The conditions, under which the scheme is applicable, are stated in the paper.

An adaptive two-stage dose-response design method for establishing proof of concept.

PubMed

Franchetti, Yoko; Anderson, Stewart J; Sampson, Allan R

2013-01-01

We propose an adaptive two-stage dose-response design where a prespecified adaptation rule is used to add and/or drop treatment arms between the stages. We extend the multiple comparison procedures-modeling (MCP-Mod) approach into a two-stage design. In each stage, we use the same set of candidate dose-response models and test for a dose-response relationship or proof of concept (PoC) via model-associated statistics. The stage-wise test results are then combined to establish "global" PoC using a conditional error function. Our simulation studies showed good and more robust power in our design method compared to conventional and fixed designs.

Manufacturing Methods and Technology for Digital Fault Isolation of Hybrid Microelectronic Assemblies.

DTIC Science & Technology

1982-03-01

Aircraft Company ARECAaSOENT CSR Ground Systems Group Task 007 Fullerton, California 92634 Project No. R1023 I$. =OTRS4.IWmOr SP NAnE lAD ABDASE it. REPORT...HMA feed mechanism, multiple type test sockets or adapters, and a localized UUT vessel for functional tests at temperature. The engineering model AP...test excluding (deactivated) microprocessor. * Models UUT and test adapter as a ROM. Independent latches or registers from interconnecting ports to

Experiments in Nonlinear Adaptive Control of Multi-Manipulator, Free-Flying Space Robots

NASA Technical Reports Server (NTRS)

Chen, Vincent Wei-Kang

1992-01-01

Sophisticated robots can greatly enhance the role of humans in space by relieving astronauts of low level, tedious assembly and maintenance chores and allowing them to concentrate on higher level tasks. Robots and astronauts can work together efficiently, as a team; but the robot must be capable of accomplishing complex operations and yet be easy to use. Multiple cooperating manipulators are essential to dexterity and can broaden greatly the types of activities the robot can achieve; adding adaptive control can ease greatly robot usage by allowing the robot to change its own controller actions, without human intervention, in response to changes in its environment. Previous work in the Aerospace Robotics Laboratory (ARL) have shown the usefulness of a space robot with cooperating manipulators. The research presented in this dissertation extends that work by adding adaptive control. To help achieve this high level of robot sophistication, this research made several advances to the field of nonlinear adaptive control of robotic systems. A nonlinear adaptive control algorithm developed originally for control of robots, but requiring joint positions as inputs, was extended here to handle the much more general case of manipulator endpoint-position commands. A new system modelling technique, called system concatenation was developed to simplify the generation of a system model for complicated systems, such as a free-flying multiple-manipulator robot system. Finally, the task-space concept was introduced wherein the operator's inputs specify only the robot's task. The robot's subsequent autonomous performance of each task still involves, of course, endpoint positions and joint configurations as subsets. The combination of these developments resulted in a new adaptive control framework that is capable of continuously providing full adaptation capability to the complex space-robot system in all modes of operation. The new adaptive control algorithm easily handles free-flying systems with multiple, interacting manipulators, and extends naturally to even larger systems. The new adaptive controller was experimentally demonstrated on an ideal testbed in the ARL-A first-ever experimental model of a multi-manipulator, free-flying space robot that is capable of capturing and manipulating free-floating objects without requiring human assistance. A graphical user interface enhanced the robot usability: it enabled an operator situated at a remote location to issue high-level task description commands to the robot, and to monitor robot activities as it then carried out each assignment autonomously.

Play It Again, Sam! Adapting Common Games into Multimedia Models Used for Student Reviews.

ERIC Educational Resources Information Center

Metcalf, Karen K.; Barlow, Amy; Hudson, Lisa; Jones, Elizabeth; Lyons, Dennis; Piersall, James; Munfus, Laureen

1998-01-01

Provides guidelines on how to adapt common games such as checkers, tic tac toe, obstacle courses, and memory joggers into interactive games in multimedia courseware. Emphasizes creating generic games that can be recycled and used for multiple topics to save development time and keep costs low. Discusses topic themes, game structure, and…

Adaptive multi-node multiple input and multiple output (MIMO) transmission for mobile wireless multimedia sensor networks.

PubMed

Cho, Sunghyun; Choi, Ji-Woong; You, Cheolwoo

2013-10-02

Mobile wireless multimedia sensor networks (WMSNs), which consist of mobile sink or sensor nodes and use rich sensing information, require much faster and more reliable wireless links than static wireless sensor networks (WSNs). This paper proposes an adaptive multi-node (MN) multiple input and multiple output (MIMO) transmission to improve the transmission reliability and capacity of mobile sink nodes when they experience spatial correlation. Unlike conventional single-node (SN) MIMO transmission, the proposed scheme considers the use of transmission antennas from more than two sensor nodes. To find an optimal antenna set and a MIMO transmission scheme, a MN MIMO channel model is introduced first, followed by derivation of closed-form ergodic capacity expressions with different MIMO transmission schemes, such as space-time transmit diversity coding and spatial multiplexing. The capacity varies according to the antenna correlation and the path gain from multiple sensor nodes. Based on these statistical results, we propose an adaptive MIMO mode and antenna set switching algorithm that maximizes the ergodic capacity of mobile sink nodes. The ergodic capacity of the proposed scheme is compared with conventional SN MIMO schemes, where the gain increases as the antenna correlation and path gain ratio increase.

Adaptive Multi-Node Multiple Input and Multiple Output (MIMO) Transmission for Mobile Wireless Multimedia Sensor Networks

PubMed Central

Cho, Sunghyun; Choi, Ji-Woong; You, Cheolwoo

2013-01-01

Mobile wireless multimedia sensor networks (WMSNs), which consist of mobile sink or sensor nodes and use rich sensing information, require much faster and more reliable wireless links than static wireless sensor networks (WSNs). This paper proposes an adaptive multi-node (MN) multiple input and multiple output (MIMO) transmission to improve the transmission reliability and capacity of mobile sink nodes when they experience spatial correlation. Unlike conventional single-node (SN) MIMO transmission, the proposed scheme considers the use of transmission antennas from more than two sensor nodes. To find an optimal antenna set and a MIMO transmission scheme, a MN MIMO channel model is introduced first, followed by derivation of closed-form ergodic capacity expressions with different MIMO transmission schemes, such as space-time transmit diversity coding and spatial multiplexing. The capacity varies according to the antenna correlation and the path gain from multiple sensor nodes. Based on these statistical results, we propose an adaptive MIMO mode and antenna set switching algorithm that maximizes the ergodic capacity of mobile sink nodes. The ergodic capacity of the proposed scheme is compared with conventional SN MIMO schemes, where the gain increases as the antenna correlation and path gain ratio increase. PMID:24152920

Is Interpersonal Psychotherapy Infinitely Adaptable? A Compendium of the Multiple Modifications of IPT

PubMed Central

FRANK, ELLEN; RITCHEY, FIONA C.; LEVENSON, JESSICA C.

2015-01-01

We employed standard literature search techniques and surveyed participants on the International Society for Interpersonal Psychotherapy listserve (isipt-list@googlegroups.com) to catalogue the multiple and highly creative ways in which Klerman’s and Weissman’s original concept of interpersonal psychotherapy (IPT) has been modified to meet the needs of a vast range of patient populations. Focusing first on adaptations of the individual treatment model for subgroups of adult patients, we next describe further adaptations of four major off-shoots of IPT: interpersonal counseling (IPC), IPT for adolescents (IPT-A), group IPT (IPT-G) and most recently, brief IPT (IPT-B). We then discuss IPT “in-laws,” those treatments that have married IPT with of other forms of psychotherapy for patients with bipolar disorder, panic symptomatology, and substance abuse. We conclude with that although there have been myriad successful adaptations of IPT, there remain some conditions for which IPT adaptations have not been found to be efficacious. PMID:26453344

Adaptive partially hidden Markov models with application to bilevel image coding.

PubMed

Forchhammer, S; Rasmussen, T S

1999-01-01

Partially hidden Markov models (PHMMs) have previously been introduced. The transition and emission/output probabilities from hidden states, as known from the HMMs, are conditioned on the past. This way, the HMM may be applied to images introducing the dependencies of the second dimension by conditioning. In this paper, the PHMM is extended to multiple sequences with a multiple token version and adaptive versions of PHMM coding are presented. The different versions of the PHMM are applied to lossless bilevel image coding. To reduce and optimize the model cost and size, the contexts are organized in trees and effective quantization of the parameters is introduced. The new coding methods achieve results that are better than the JBIG standard on selected test images, although at the cost of increased complexity. By the minimum description length principle, the methods presented for optimizing the code length may apply as guidance for training (P)HMMs for, e.g., segmentation or recognition purposes. Thereby, the PHMM models provide a new approach to image modeling.

The Use of Multiple Regression and Trend Analysis to Understand Enrollment Fluctuations. AIR Forum 1979 Paper.

ERIC Educational Resources Information Center

Campbell, S. Duke; Greenberg, Barry

The development of a predictive equation capable of explaining a significant percentage of enrollment variability at Florida International University is described. A model utilizing trend analysis and a multiple regression approach to enrollment forecasting was adapted to investigate enrollment dynamics at the university. Four independent…

A Word to the Wise: Advice for Scientists Engaged in Collaborative Adaptive Management

NASA Astrophysics Data System (ADS)

Hopkinson, Peter; Huber, Ann; Saah, David S.; Battles, John J.

2017-05-01

Collaborative adaptive management is a process for making decisions about the environment in the face of uncertainty and conflict. Scientists have a central role to play in these decisions. However, while scientists are well trained to reduce uncertainty by discovering new knowledge, most lack experience with the means to mitigate conflict in contested situations. To address this gap, we drew from our efforts coordinating a large collaborative adaptive management effort, the Sierra Nevada Adaptive Management Project, to offer advice to our fellow environmental scientists. Key challenges posed by collaborative adaptive management include the confusion caused by multiple institutional cultures, the need to provide information at management-relevant scales, frequent turnover in participants, fluctuations in enthusiasm among key constituencies, and diverse definitions of success among partners. Effective strategies included a dedication to consistency, a commitment to transparency, the willingness to communicate frequently via multiple forums, and the capacity for flexibility. Collaborative adaptive management represents a promising, new model for scientific engagement with the public. Learning the lessons of effective collaboration in environmental management is an essential task to achieve the shared goal of a sustainable future.

Optimal Appearance Model for Visual Tracking

PubMed Central

Wang, Yuru; Jiang, Longkui; Liu, Qiaoyuan; Yin, Minghao

2016-01-01

Many studies argue that integrating multiple cues in an adaptive way increases tracking performance. However, what is the definition of adaptiveness and how to realize it remains an open issue. On the premise that the model with optimal discriminative ability is also optimal for tracking the target, this work realizes adaptiveness and robustness through the optimization of multi-cue integration models. Specifically, based on prior knowledge and current observation, a set of discrete samples are generated to approximate the foreground and background distribution. With the goal of optimizing the classification margin, an objective function is defined, and the appearance model is optimized by introducing optimization algorithms. The proposed optimized appearance model framework is embedded into a particle filter for a field test, and it is demonstrated to be robust against various kinds of complex tracking conditions. This model is general and can be easily extended to other parameterized multi-cue models. PMID:26789639

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

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.

Adaptive walking of a quadrupedal robot based on layered biological reflexes

NASA Astrophysics Data System (ADS)

Zhang, Xiuli; Mingcheng, E.; Zeng, Xiangyu; Zheng, Haojun

2012-07-01

A multiple-legged robot is traditionally controlled by using its dynamic model. But the dynamic-model-based approach fails to acquire satisfactory performances when the robot faces rough terrains and unknown environments. Referring animals' neural control mechanisms, a control model is built for a quadruped robot walking adaptively. The basic rhythmic motion of the robot is controlled by a well-designed rhythmic motion controller(RMC) comprising a central pattern generator(CPG) for hip joints and a rhythmic coupler (RC) for knee joints. CPG and RC have relationships of motion-mapping and rhythmic couple. Multiple sensory-motor models, abstracted from the neural reflexes of a cat, are employed. These reflex models are organized and thus interact with the CPG in three layers, to meet different requirements of complexity and response time to the tasks. On the basis of the RMC and layered biological reflexes, a quadruped robot is constructed, which can clear obstacles and walk uphill and downhill autonomously, and make a turn voluntarily in uncertain environments, interacting with the environment in a way similar to that of an animal. The paper provides a biologically inspired architecture, with which a robot can walk adaptively in uncertain environments in a simple and effective way, and achieve better performances.

A multiple pointing-mount control strategy for space platforms

NASA Technical Reports Server (NTRS)

Johnson, C. D.

1992-01-01

A new disturbance-adaptive control strategy for multiple pointing-mount space platforms is proposed and illustrated by consideration of a simplified 3-link dynamic model of a multiple pointing-mount space platform. Simulation results demonstrate the effectiveness of the new platform control strategy. The simulation results also reveal a system 'destabilization phenomena' that can occur if the set of individual platform-mounted experiment controllers are 'too responsive.'

The Generalization of Visuomotor Learning to Untrained Movements and Movement Sequences Based on Movement Vector and Goal Location Remapping

PubMed Central

Wu, Howard G.

2013-01-01

The planning of goal-directed movements is highly adaptable; however, the basic mechanisms underlying this adaptability are not well understood. Even the features of movement that drive adaptation are hotly debated, with some studies suggesting remapping of goal locations and others suggesting remapping of the movement vectors leading to goal locations. However, several previous motor learning studies and the multiplicity of the neural coding underlying visually guided reaching movements stand in contrast to this either/or debate on the modes of motor planning and adaptation. Here we hypothesize that, during visuomotor learning, the target location and movement vector of trained movements are separately remapped, and we propose a novel computational model for how motor plans based on these remappings are combined during the control of visually guided reaching in humans. To test this hypothesis, we designed a set of experimental manipulations that effectively dissociated the effects of remapping goal location and movement vector by examining the transfer of visuomotor adaptation to untrained movements and movement sequences throughout the workspace. The results reveal that (1) motor adaptation differentially remaps goal locations and movement vectors, and (2) separate motor plans based on these features are effectively averaged during motor execution. We then show that, without any free parameters, the computational model we developed for combining movement-vector-based and goal-location-based planning predicts nearly 90% of the variance in novel movement sequences, even when multiple attributes are simultaneously adapted, demonstrating for the first time the ability to predict how motor adaptation affects movement sequence planning. PMID:23804099

Specificity, cross-talk and adaptation in Interferon signaling

NASA Astrophysics Data System (ADS)

Zilman, Anton

Innate immune system is the first line of defense of higher organisms against pathogens. It coordinates the behavior of millions of cells of multiple types, achieved through numerous signaling molecules. This talk focuses on the signaling specificity of a major class of signaling molecules - Type I Interferons - which are also used therapeutically in the treatment of a number of diseases, such as Hepatitis C, multiple sclerosis and some cancers. Puzzlingly, different Interferons act through the same cell surface receptor but have different effects on the target cells. They also exhibit a strange pattern of temporal cross-talk resulting in a serious clinical problem - loss of response to Interferon therapy. We combined mathematical modeling with quantitative experiments to develop a quantitative model of specificity and adaptation in the Interferon signaling pathway. The model resolves several outstanding experimental puzzles and directly affects the clinical use of Type I Interferons in treatment of viral hepatitis and other diseases.

Tracking of multiple targets using online learning for reference model adaptation.

PubMed

Pernkopf, Franz

2008-12-01

Recently, much work has been done in multiple object tracking on the one hand and on reference model adaptation for a single-object tracker on the other side. In this paper, we do both tracking of multiple objects (faces of people) in a meeting scenario and online learning to incrementally update the models of the tracked objects to account for appearance changes during tracking. Additionally, we automatically initialize and terminate tracking of individual objects based on low-level features, i.e., face color, face size, and object movement. Many methods unlike our approach assume that the target region has been initialized by hand in the first frame. For tracking, a particle filter is incorporated to propagate sample distributions over time. We discuss the close relationship between our implemented tracker based on particle filters and genetic algorithms. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach. Additionally, we provide an empirical verification of the reference model learning during tracking of indoor and outdoor scenes which supports a more robust tracking. Therefore, we report the average of the standard deviation of the trajectories over numerous tracking runs depending on the learning rate.

Adaptation of a Counseling Intervention to Address Multiple Cancer Risk Factors Among Overweight/Obese Latino Smokers

PubMed Central

Castro, Yessenia; Fernández, Maria E.; Strong, Larkin L.; Stewart, Diana W.; Krasny, Sarah; Robles, Eden Hernandez; Heredia, Natalia; Spears, Claire A.; Correa-Fernández, Virmarie; Eakin, Elizabeth; Resnicow, Ken; Basen-Engquist, Karen; Wetter, David W.

2015-01-01

More than 60% of cancer-related deaths in the United States are attributable to tobacco use, poor nutrition, and physical inactivity, and these risk factors tend to cluster together. Thus, strategies for cancer risk reduction would benefit from addressing multiple health risk behaviors. We adapted an evidence-based intervention grounded in social cognitive theory and principles of motivational interviewing originally developed for smoking cessation to also address physical activity and fruit/vegetable consumption among Latinos exhibiting multiple health risk behaviors. Literature reviews, focus groups, expert consultation, pretesting, and pilot testing were used to inform adaptation decisions. We identified common mechanisms underlying change in smoking, physical activity, and diet used as treatment targets; identified practical models of patient-centered cross-cultural service provision; and identified that family preferences and support as particularly strong concerns among the priority population. Adaptations made to the original intervention are described. The current study is a practical example of how an intervention can be adapted to maximize relevance and acceptability and also maintain the core elements of the original evidence-based intervention. The intervention has significant potential to influence cancer prevention efforts among Latinos in the United States and is being evaluated in a sample of 400 Latino overweight/obese smokers. PMID:25527143

Approaches to evaluating climate change impacts on species: A guide to initiating the adaptation planning process

Treesearch

Erika L. Rowland; Jennifer E. Davison; Lisa J. Graumlich

2011-01-01

Assessing the impact of climate change on species and associated management objectives is a critical initial step for engaging in the adaptation planning process. Multiple approaches are available. While all possess limitations to their application associated with the uncertainties inherent in the data and models that inform their results, conducting and incorporating...

Middle-Range Theory: Coping and Adaptation with Active Aging.

PubMed

Salazar-Barajas, Martha Elba; Salazar-González, Bertha Cecilia; Gallegos-Cabriales, Esther Carlota

2017-10-01

Various disciplines focus on a multiplicity of aspects of aging: lifestyles, personal biological factors, psychological conditions, health conditions, physical environment, and social and economic factors. The aforementioned are all related to the determinants of active aging. The aim is to describe the development of a middle-range theory based on coping and adaptation with active aging. Concepts and relationships derived from Roy's model of adaptation are included. The proposed concepts are hope, health habits, coping with aging, social relations, and active aging.

Risk assessment and adaptive runoff utilization in water resource system considering the complex relationship among water supply, electricity generation and environment

NASA Astrophysics Data System (ADS)

Zhou, J.; Zeng, X.; Mo, L.; Chen, L.; Jiang, Z.; Feng, Z.; Yuan, L.; He, Z.

2017-12-01

Generally, the adaptive utilization and regulation of runoff in the source region of China's southwest rivers is classified as a typical multi-objective collaborative optimization problem. There are grim competitions and incidence relation in the subsystems of water supply, electricity generation and environment, which leads to a series of complex problems represented by hydrological process variation, blocked electricity output and water environment risk. Mathematically, the difficulties of multi-objective collaborative optimization focus on the description of reciprocal relationships and the establishment of evolving model of adaptive systems. Thus, based on the theory of complex systems science, this project tries to carry out the research from the following aspects: the changing trend of coupled water resource, the covariant factor and driving mechanism, the dynamic evolution law of mutual feedback dynamic process in the supply-generation-environment coupled system, the environmental response and influence mechanism of coupled mutual feedback water resource system, the relationship between leading risk factor and multiple risk based on evolutionary stability and dynamic balance, the transfer mechanism of multiple risk response with the variation of the leading risk factor, the multidimensional coupled feedback system of multiple risk assessment index system and optimized decision theory. Based on the above-mentioned research results, the dynamic method balancing the efficiency of multiple objectives in the coupled feedback system and optimized regulation model of water resources is proposed, and the adaptive scheduling mode considering the internal characteristics and external response of coupled mutual feedback system of water resource is established. In this way, the project can make a contribution to the optimal scheduling theory and methodology of water resource management under uncertainty in the source region of Southwest River.

The impact of natural transformation on adaptation in spatially structured bacterial populations.

PubMed

Moradigaravand, Danesh; Engelstädter, Jan

2014-06-20

Recent studies have demonstrated that natural transformation and the formation of highly structured populations in bacteria are interconnected. In spite of growing evidence about this connection, little is known about the dynamics of natural transformation in spatially structured bacterial populations. In this work, we model the interdependency between the dynamics of the bacterial gene pool and those of environmental DNA in space to dissect the effect of transformation on adaptation. Our model reveals that even with only a single locus under consideration, transformation with a free DNA fragment pool results in complex adaptation dynamics that do not emerge in previous models focusing only on the gene shuffling effect of transformation at multiple loci. We demonstrate how spatial restriction on population growth and DNA diffusion in the environment affect the impact of transformation on adaptation. We found that in structured bacterial populations intermediate DNA diffusion rates predominantly cause transformation to impede adaptation by spreading deleterious alleles in the population. Overall, our model highlights distinctive evolutionary consequences of bacterial transformation in spatially restricted compared to planktonic bacterial populations.

Adaptation of flower and fruit colours to multiple, distinct mutualists.

PubMed

Renoult, Julien P; Valido, Alfredo; Jordano, Pedro; Schaefer, H Martin

2014-01-01

Communication in plant-animal mutualisms frequently involves multiple perceivers. A fundamental uncertainty is whether and how species adapt to communicate with groups of mutualists having distinct sensory abilities. We quantified the colour conspicuousness of flowers and fruits originating from one European and two South American plant communities, using visual models of pollinators (bee and fly) and seed dispersers (bird, primate and marten). We show that flowers are more conspicuous than fruits to pollinators, and the reverse to seed dispersers. In addition, flowers are more conspicuous to pollinators than to seed dispersers and the reverse for fruits. Thus, despite marked differences in the visual systems of mutualists, flower and fruit colours have evolved to attract multiple, distinct mutualists but not unintended perceivers. We show that this adaptation is facilitated by a limited correlation between flower and fruit colours, and by the fact that colour signals as coded at the photoreceptor level are more similar within than between functional groups (pollinators and seed dispersers). Overall, these results provide the first quantitative demonstration that flower and fruit colours are adaptations allowing plants to communicate simultaneously with distinct groups of mutualists. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

Model Selection for the Multiple Model Adaptive Algorithm for In-Flight Simulation.

DTIC Science & Technology

1987-12-01

of the two models, while the other model was given a probability of approximately zero. If the probabilties were exactly one and zero for the...Figures 6-103 through 6-107. From Figure 6-103, it can be seen that the probabilty of the model associated with the 10,000 ft, 0.35 Mach flight con

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

Uncertainty, learning, and the optimal management of wildlife

USGS Publications Warehouse

Williams, B.K.

2001-01-01

Wildlife management is limited by uncontrolled and often unrecognized environmental variation, by limited capabilities to observe and control animal populations, and by a lack of understanding about the biological processes driving population dynamics. In this paper I describe a comprehensive framework for management that includes multiple models and likelihood values to account for structural uncertainty, along with stochastic factors to account for environmental variation, random sampling, and partial controllability. Adaptive optimization is developed in terms of the optimal control of incompletely understood populations, with the expected value of perfect information measuring the potential for improving control through learning. The framework for optimal adaptive control is generalized by including partial observability and non-adaptive, sample-based updating of model likelihoods. Passive adaptive management is derived as a special case of constrained adaptive optimization, representing a potentially efficient suboptimal alternative that nonetheless accounts for structural uncertainty.

Testing a multiple mediation model of Asian American college students' willingness to see a counselor.

PubMed

Kim, Paul Youngbin; Park, Irene J K

2009-07-01

Adapting the theory of reasoned action, the present study examined help-seeking beliefs, attitudes, and intent among Asian American college students (N = 110). A multiple mediation model was tested to see if the relation between Asian values and willingness to see a counselor was mediated by attitudes toward seeking professional psychological help and subjective norm. A bootstrapping procedure was used to test the multiple mediation model. Results indicated that subjective norm was the sole significant mediator of the effect of Asian values on willingness to see a counselor. The findings highlight the importance of social influences on help-seeking intent among Asian American college students.

Restricted amide rotation with steric hindrance induced multiple conformations

NASA Astrophysics Data System (ADS)

Krishnan, V. V.; Vazquez, Salvador; Maitra, Kalyani; Maitra, Santanu

2017-12-01

The Csbnd N bond character is dependent directly upon the resonance-contributor structure population driven by the delocalized nitrogen lone-pair of electrons. In the case of N, N-dibenzyl-ortho-toluamide (o-DBET), the molecule adopts subpopulations of conformers with distinct NMR spectral features, particularly at low temperatures. This conformational adaptation is unique to o-DBET, while the corresponding meta- and para- forms do not show such behavior. Variable-temperature (VT) NMR, two-dimensional exchange spectroscopy (EXSY), and qualitative molecular modeling studies are used to demonstrate how multiple competing interactions such as restricted amide rotation and steric hindrance effects can lead to versatile molecular adaptations in the solution state.

Methods for the cultural adaptation of a diabetes lifestyle intervention for Latinas: an illustrative project.

PubMed

Osuna, Diego; Barrera, Manuel; Strycker, Lisa A; Toobert, Deborah J; Glasgow, Russell E; Geno, Cristy R; Almeida, Fabio; Perdomo, Malena; King, Diane; Doty, Alyssa Tinley

2011-05-01

Because Latinas experience a high prevalence of type 2 diabetes and its complications, there is an urgent need to reach them with interventions that promote healthful lifestyles. This article illustrates a sequential approach that took an effective multiple-risk-factor behavior-change program and adapted it for Latinas with type 2 diabetes. Adaptation stages include (a) information gathering from literature and focus groups, (b) preliminary adaptation design, and (c) preliminary adaptation test. In this third stage, a pilot study finds that participants were highly satisfied with the intervention and showed improvement across diverse outcomes. Key implications for applications include the importance of a model for guiding cultural adaptations, and the value of procedures for obtaining continuous feedback from staff and participants during the preliminary adaptation test.

Hidden Markov models of biological primary sequence information.

PubMed Central

Baldi, P; Chauvin, Y; Hunkapiller, T; McClure, M A

1994-01-01

Hidden Markov model (HMM) techniques are used to model families of biological sequences. A smooth and convergent algorithm is introduced to iteratively adapt the transition and emission parameters of the models from the examples in a given family. The HMM approach is applied to three protein families: globins, immunoglobulins, and kinases. In all cases, the models derived capture the important statistical characteristics of the family and can be used for a number of tasks, including multiple alignments, motif detection, and classification. For K sequences of average length N, this approach yields an effective multiple-alignment algorithm which requires O(KN2) operations, linear in the number of sequences. PMID:8302831

Culture adaptation of malaria parasites selects for convergent loss-of-function mutants.

PubMed

Claessens, Antoine; Affara, Muna; Assefa, Samuel A; Kwiatkowski, Dominic P; Conway, David J

2017-01-24

Cultured human pathogens may differ significantly from source populations. To investigate the genetic basis of laboratory adaptation in malaria parasites, clinical Plasmodium falciparum isolates were sampled from patients and cultured in vitro for up to three months. Genome sequence analysis was performed on multiple culture time point samples from six monoclonal isolates, and single nucleotide polymorphism (SNP) variants emerging over time were detected. Out of a total of five positively selected SNPs, four represented nonsense mutations resulting in stop codons, three of these in a single ApiAP2 transcription factor gene, and one in SRPK1. To survey further for nonsense mutants associated with culture, genome sequences of eleven long-term laboratory-adapted parasite strains were examined, revealing four independently acquired nonsense mutations in two other ApiAP2 genes, and five in Epac. No mutants of these genes exist in a large database of parasite sequences from uncultured clinical samples. This implicates putative master regulator genes in which multiple independent stop codon mutations have convergently led to culture adaptation, affecting most laboratory lines of P. falciparum. Understanding the adaptive processes should guide development of experimental models, which could include targeted gene disruption to adapt fastidious malaria parasite species to culture.

What is adaptive about adaptive decision making? A parallel constraint satisfaction account.

PubMed

Glöckner, Andreas; Hilbig, Benjamin E; Jekel, Marc

2014-12-01

There is broad consensus that human cognition is adaptive. However, the vital question of how exactly this adaptivity is achieved has remained largely open. Herein, we contrast two frameworks which account for adaptive decision making, namely broad and general single-mechanism accounts vs. multi-strategy accounts. We propose and fully specify a single-mechanism model for decision making based on parallel constraint satisfaction processes (PCS-DM) and contrast it theoretically and empirically against a multi-strategy account. To achieve sufficiently sensitive tests, we rely on a multiple-measure methodology including choice, reaction time, and confidence data as well as eye-tracking. Results show that manipulating the environmental structure produces clear adaptive shifts in choice patterns - as both frameworks would predict. However, results on the process level (reaction time, confidence), in information acquisition (eye-tracking), and from cross-predicting choice consistently corroborate single-mechanisms accounts in general, and the proposed parallel constraint satisfaction model for decision making in particular. Copyright © 2014 Elsevier B.V. All rights reserved.

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

Adaptation and Evaluation of a Multi-Criteria Decision Analysis Model for Lyme Disease Prevention

PubMed Central

Aenishaenslin, Cécile; Gern, Lise; Michel, Pascal; Ravel, André; Hongoh, Valérie; Waaub, Jean-Philippe; Milord, François; Bélanger, Denise

2015-01-01

Designing preventive programs relevant to vector-borne diseases such as Lyme disease (LD) can be complex given the need to include multiple issues and perspectives into prioritizing public health actions. A multi-criteria decision aid (MCDA) model was previously used to rank interventions for LD prevention in Quebec, Canada, where the disease is emerging. The aim of the current study was to adapt and evaluate the decision model constructed in Quebec under a different epidemiological context, in Switzerland, where LD has been endemic for the last thirty years. The model adaptation was undertaken with a group of Swiss stakeholders using a participatory approach. The PROMETHEE method was used for multi-criteria analysis. Key elements and results of the MCDA model are described and contrasted with the Quebec model. All criteria and most interventions of the MCDA model developed for LD prevention in Quebec were directly transferable to the Swiss context. Four new decision criteria were added, and the list of proposed interventions was modified. Based on the overall group ranking, interventions targeting human populations were prioritized in the Swiss model, with the top ranked action being the implementation of a large communication campaign. The addition of criteria did not significantly alter the intervention rankings, but increased the capacity of the model to discriminate between highest and lowest ranked interventions. The current study suggests that beyond the specificity of the MCDA models developed for Quebec and Switzerland, their general structure captures the fundamental and common issues that characterize the complexity of vector-borne disease prevention. These results should encourage public health organizations to adapt, use and share MCDA models as an effective and functional approach to enable the integration of multiple perspectives and considerations in the prevention and control of complex public health issues such as Lyme disease or other vector-borne and zoonotic diseases. PMID:26295344

Adaptation and Evaluation of a Multi-Criteria Decision Analysis Model for Lyme Disease Prevention.

PubMed

Aenishaenslin, Cécile; Gern, Lise; Michel, Pascal; Ravel, André; Hongoh, Valérie; Waaub, Jean-Philippe; Milord, François; Bélanger, Denise

2015-01-01

Designing preventive programs relevant to vector-borne diseases such as Lyme disease (LD) can be complex given the need to include multiple issues and perspectives into prioritizing public health actions. A multi-criteria decision aid (MCDA) model was previously used to rank interventions for LD prevention in Quebec, Canada, where the disease is emerging. The aim of the current study was to adapt and evaluate the decision model constructed in Quebec under a different epidemiological context, in Switzerland, where LD has been endemic for the last thirty years. The model adaptation was undertaken with a group of Swiss stakeholders using a participatory approach. The PROMETHEE method was used for multi-criteria analysis. Key elements and results of the MCDA model are described and contrasted with the Quebec model. All criteria and most interventions of the MCDA model developed for LD prevention in Quebec were directly transferable to the Swiss context. Four new decision criteria were added, and the list of proposed interventions was modified. Based on the overall group ranking, interventions targeting human populations were prioritized in the Swiss model, with the top ranked action being the implementation of a large communication campaign. The addition of criteria did not significantly alter the intervention rankings, but increased the capacity of the model to discriminate between highest and lowest ranked interventions. The current study suggests that beyond the specificity of the MCDA models developed for Quebec and Switzerland, their general structure captures the fundamental and common issues that characterize the complexity of vector-borne disease prevention. These results should encourage public health organizations to adapt, use and share MCDA models as an effective and functional approach to enable the integration of multiple perspectives and considerations in the prevention and control of complex public health issues such as Lyme disease or other vector-borne and zoonotic diseases.

The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science. Chapter 1

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia E.; Jones, James W.; Hatfield, Jerry L.; Antle, John M.; Ruane, Alexander C.; Mutter, Carolyn Z.

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) was founded in 2010. Its mission is to improve substantially the characterization of world food security as affected by climate variability and change, and to enhance adaptation capacity in both developing and developed countries. The objectives of AgMIP are to: Incorporate state-of-the-art climate, crop/livestock, and agricultural economic model improvements into coordinated multi-model regional and global assessments of future climate impacts and adaptation and other key aspects of the food system. Utilize multiple models, scenarios, locations, crops/livestock, and participants to explore uncertainty and the impact of data and methodological choices. Collaborate with regional experts in agronomy, animal sciences, economics, and climate to build a strong basis for model applications, addressing key climate related questions and sustainable intensification farming systems. Improve scientific and adaptive capacity in modeling for major agricultural regions in the developing and developed world, with a focus on vulnerable regions. Improve agricultural data and enhance data-sharing based on their intercomparison and evaluation using best scientific practices. Develop modeling frameworks to identify and evaluate promising adaptation technologies and policies and to prioritize strategies.

Spiking Neurons in a Hierarchical Self-Organizing Map Model Can Learn to Develop Spatial and Temporal Properties of Entorhinal Grid Cells and Hippocampal Place Cells

PubMed Central

Pilly, Praveen K.; Grossberg, Stephen

2013-01-01

Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation. PMID:23577130

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.

Poisson-Based Inference for Perturbation Models in Adaptive Spelling Training

ERIC Educational Resources Information Center

Baschera, Gian-Marco; Gross, Markus

2010-01-01

We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…

Online adaptive neural control of a robotic lower limb prosthesis

NASA Astrophysics Data System (ADS)

Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.

2018-02-01

Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.

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.

Multiple-try differential evolution adaptive Metropolis for efficient solution of highly parameterized models

NASA Astrophysics Data System (ADS)

Eric, L.; Vrugt, J. A.

2010-12-01

Spatially distributed hydrologic models potentially contain hundreds of parameters that need to be derived by calibration against a historical record of input-output data. The quality of this calibration strongly determines the predictive capability of the model and thus its usefulness for science-based decision making and forecasting. Unfortunately, high-dimensional optimization problems are typically difficult to solve. Here we present our recent developments to the Differential Evolution Adaptive Metropolis (DREAM) algorithm (Vrugt et al., 2009) to warrant efficient solution of high-dimensional parameter estimation problems. The algorithm samples from an archive of past states (Ter Braak and Vrugt, 2008), and uses multiple-try Metropolis sampling (Liu et al., 2000) to decrease the required burn-in time for each individual chain and increase efficiency of posterior sampling. This approach is hereafter referred to as MT-DREAM. We present results for 2 synthetic mathematical case studies, and 2 real-world examples involving from 10 to 240 parameters. Results for those cases show that our multiple-try sampler, MT-DREAM, can consistently find better solutions than other Bayesian MCMC methods. Moreover, MT-DREAM is admirably suited to be implemented and ran on a parallel machine and is therefore a powerful method for posterior inference.

Merging Multiple-Partial-Depth Data Time Series Using Objective Empirical Orthogonal Function Fitting

DTIC Science & Technology

2010-05-01

F. J. Lermusiaux, “Adaptive modeling, adaptive data assimilation and adaptive sampling,” Physica D, vol. 230, pp. 172–196, 2007 . [9] T. P. Sapsis and...DOI: 10.1016/j.physd.2009.09.017. [10] P. F. J. Lermusiaux, P. Malanotte-Rizzoli, D. Stammer , J. Carton, J. Cummings, and A. M. Moore, “Progress and...Oceanography, vol. 20, pp. 156–167, 2007 . [12] C. Wunsch, The Ocean Circulation Inverse Problem. Cambridge, U.K.: Cambridge Univ. Press, 1996, ch. 3. [13] H

Vibratory Adaptation of Cutaneous Mechanoreceptive Afferents

PubMed Central

Bensmaïa, S. J.; Leung, Y. Y.; Hsiao, S. S.; Johnson, K. O.

2007-01-01

The objective of this study was to investigate the effects of extended suprathreshold vibratory stimulation on the sensitivity of slowly adapting type 1 (SA1), rapidly adapting (RA), and Pacinian (PC) afferents. To that end, an algorithm was developed to track afferent absolute (I0) and entrainment (I1) thresholds as they change over time. We recorded afferent responses to periliminal vibratory test stimuli, which were interleaved with intense vibratory conditioning stimuli during the adaptation period of each experimental run. From these measurements, the algorithm allowed us to infer changes in the afferents’ sensitivity. We investigated the stimulus parameters that affect adaptation by assessing the degree to which adaptation depends on the amplitude and frequency of the adapting stimulus. For all three afferent types, I0 and I1 increased with increasing adaptation frequency and amplitude. The degree of adaptation seems to be independent of the firing rate evoked in the afferent by the conditioning stimulus. In the analysis, we distinguished between additive adaptation (in which I0 and I1 shift equally) and multiplicative effects (in which the ratio I1/I0 remains constant). RA threshold shifts are almost perfectly additive. SA1 threshold shifts are close to additive and far from multiplicative (I1 threshold shifts are twice the shifts). PC shifts are more difficult to classify. We used an I0 integrate-and-fire model to study the possible neural mechanisms. A change in transducer gain predicts a multiplicative change in I0 and I1 and is thus ruled out as a mechanism underlying SA1 and RA adaptation. A change in the resting action potential threshold predicts equal, additive change in I0 and I1 and thus accounts well for RA adaptation. A change in the degree of refractoriness during the relative refractory period predicts an additional change in I1 such as that observed for SA1 fibers. We infer that adaptation is caused by an increase in spiking thresholds produced by ion flow through transducer channels in the receptor membrane. In a companion paper, we describe the time-course of vibratory adaptation and recovery for SA1, RA, and PC fibers. PMID:16014802

Multiscale Sediment-Laden Flow Theory and Its Application in Flood Risk Management

NASA Astrophysics Data System (ADS)

Cao, Z. X.; Pender, G.; Hu, P.

2011-09-01

Sediment-laden flows over erodible bed normally feature multiple time scales. The time scales of sediment transport and bed deformation relative to the flow essentially measure how fast sediment transport adapts to capacity regime in line with local flow scenario and the bed deforms as compared to the flow, which literally dictate if a capacity based and/or decoupled model is justified. This paper synthesizes the recently developed multiscale theory for sediment-laden flows over erodible bed, with bed load and suspended load transport respectively. It is unravelled that bed load transport can adapt to capacity sufficiently rapidly even under highly unsteady flows and thus a capacity model is mostly applicable, whereas a non-capacity model is critical for suspended sediment because of the lower rate of adaptation to capacity. Physically coupled modeling is critical for cases characterized by rapid bed variation. Applications are outlined on flash floods and landslide dam break floods.

Multiple model self-tuning control for a class of nonlinear systems

NASA Astrophysics Data System (ADS)

Huang, Miao; Wang, Xin; Wang, Zhenlei

2015-10-01

This study develops a novel nonlinear multiple model self-tuning control method for a class of nonlinear discrete-time systems. An increment system model and a modified robust adaptive law are proposed to expand the application range, thus eliminating the assumption that either the nonlinear term of the nonlinear system or its differential term is global-bounded. The nonlinear self-tuning control method can address the situation wherein the nonlinear system is not subject to a globally uniformly asymptotically stable zero dynamics by incorporating the pole-placement scheme. A novel, nonlinear control structure based on this scheme is presented to improve control precision. Stability and convergence can be confirmed when the proposed multiple model self-tuning control method is applied. Furthermore, simulation results demonstrate the effectiveness of the proposed method.

Influence of experimental rat model of multiple organ dysfunction on cefepime and amikacin pharmacokinetics.

PubMed Central

Mimoz, O; Jacolot, A; Padoin, C; Quillard, J; Tod, M; Louchahi, K; Samii, K; Petitjean, O

1996-01-01

We adapted an experimental model of multiple organ dysfunction to study the alterations it induces in the pharmacology of cefepime and amikacin. The half-lives of both antibiotics were significantly prolonged because of nonsignificant enhancement of the volume of distribution and reduced renal elimination. In the presence of multiple organ dysfunction, the concentration of each antibiotic in the lungs, compared with that in the lungs of healthy controls, was significantly decreased, despite similar concentrations in plasma, indicating that the application of a standard antibiotic concentration in plasma could lead to underdosage in tissues during the initial days of therapy. PMID:8851623

Short-term electric power demand forecasting based on economic-electricity transmission model

NASA Astrophysics Data System (ADS)

Li, Wenfeng; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Wang, Yubin Mao; Wang, Jiangbo; He, Dandan

2018-04-01

Short-term electricity demand forecasting is the basic work to ensure safe operation of the power system. In this paper, a practical economic electricity transmission model (EETM) is built. With the intelligent adaptive modeling capabilities of Prognoz Platform 7.2, the econometric model consists of three industrial added value and income levels is firstly built, the electricity demand transmission model is also built. By multiple regression, moving averages and seasonal decomposition, the problem of multiple correlations between variables is effectively overcome in EETM. The validity of EETM is proved by comparison with the actual value of Henan Province. Finally, EETM model is used to forecast the electricity consumption of the 1-4 quarter of 2018.

Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology

PubMed Central

Grimm, Volker; Railsback, Steven F.

2012-01-01

Modern ecology recognizes that modelling systems across scales and at multiple levels—especially to link population and ecosystem dynamics to individual adaptive behaviour—is essential for making the science predictive. ‘Pattern-oriented modelling’ (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions. PMID:22144392

Adaptive selective relaying in cooperative free-space optical systems over atmospheric turbulence and misalignment fading channels.

PubMed

Boluda-Ruiz, Rubén; García-Zambrana, Antonio; Castillo-Vázquez, Carmen; Castillo-Vázquez, Beatriz

2014-06-30

In this paper, a novel adaptive cooperative protocol with multiple relays using detect-and-forward (DF) over atmospheric turbulence channels with pointing errors is proposed. The adaptive DF cooperative protocol here analyzed is based on the selection of the optical path, source-destination or different source-relay links, with a greater value of fading gain or irradiance, maintaining a high diversity order. Closed-form asymptotic bit error-rate (BER) expressions are obtained for a cooperative free-space optical (FSO) communication system with Nr relays, when the irradiance of the transmitted optical beam is susceptible to either a wide range of turbulence conditions, following a gamma-gamma distribution of parameters α and β, or pointing errors, following a misalignment fading model where the effect of beam width, detector size and jitter variance is considered. A greater robustness for different link distances and pointing errors is corroborated by the obtained results if compared with similar cooperative schemes or equivalent multiple-input multiple-output (MIMO) systems. Simulation results are further demonstrated to confirm the accuracy and usefulness of the derived results.

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

Beyond Adapting to Climate Change: Embedding Adaptation in Responses to Multiple Threats and Stresses

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

Wilbanks, Thomas J; Kates, Dr. Robert W.

2010-01-01

Climate change impacts are already being experienced in every region of the United States and every part of the world most severely in Arctic regions and adaptation is needed now. Although climate change adaptation research is still in its infancy, significant adaptation planning in the United States has already begun in a number of localities. This article seeks to broaden the adaptation effort by integrating it with broader frameworks of hazards research, sustainability science, and community and regional resilience. To extend the range of experience, we draw from ongoing case studies in the Southeastern United States and the environmental historymore » of New Orleans to consider the multiple threats and stresses that all communities and regions experience. Embedding climate adaptation in responses to multiple threats and stresses helps us to understand climate change impacts, themselves often products of multiple stresses, to achieve community acceptance of needed adaptations as co-benefits of addressing multiple threats, and to mainstream the process of climate adaptation through the larger envelope of social relationships, communication channels, and broad-based awareness of needs for risk management that accompany community resilience.« less

Application of the generalized vertical coordinate ocean model for better representing satellite data

NASA Technical Reports Server (NTRS)

Song, Y. T.

2002-01-01

It is found that two adaptive parametric functions can be introduced into the basic ocean equations for utilizing the optimal or hybrid features of commonly used z-level, terrain- following, isopycnal, and pressure coordinates in numerical ocean models. The two parametric functions are formulated by combining three techniques: the arbitrary vertical coordinate system of Kasahara (1 974), the Jacobian pressure gradient formulation of Song (1 998), and a newly developed metric factor that permits both compressible (non-Boussinesq) and incompressible (Boussinesq) approximations. Based on the new formulation, an adaptive modeling strategy is proposed and a staggered finite volume method is designed to ensure conservation of important physical properties and numerical accuracy. Implementation of the combined techniques to SCRUM (Song and Haidvogel1994) shows that the adaptive modeling strategy can be applied to any existing ocean model without incurring computational expense or altering the original numerical schemes. Such a generalized coordinate model is expected to benefit diverse ocean modelers for easily choosing optimal vertical structures and sharing modeling resources based on a common model platform. Several representing oceanographic problems with different scales and characteristics, such as coastal canyons, basin-scale circulation, and global ocean circulation, are used to demonstrate the model's capability for multiple applications. New results show that the model is capable of simultaneously resolving both Boussinesq and non-Boussinesq, and both small- and large-scale processes well. This talk will focus on its applications of multiple satellite sensing data in eddy-resolving simulations of Asian Marginal Sea and Kurosio. Attention will be given to how Topex/Poseidon SSH, TRMM SST; and GRACE ocean bottom pressure can be correctly represented in a non- Boussinesq model.

Mitochondrial phylogenomics of Hemiptera reveals adaptive innovations driving the diversification of true bugs

PubMed Central

Li, Hu; Leavengood, John M.; Chapman, Eric G.; Burkhardt, Daniel; Song, Fan; Jiang, Pei; Liu, Jinpeng; Cai, Wanzhi

2017-01-01

Hemiptera, the largest non-holometabolous order of insects, represents approximately 7% of metazoan diversity. With extraordinary life histories and highly specialized morphological adaptations, hemipterans have exploited diverse habitats and food sources through approximately 300 Myr of evolution. To elucidate the phylogeny and evolutionary history of Hemiptera, we carried out the most comprehensive mitogenomics analysis on the richest taxon sampling to date covering all the suborders and infraorders, including 34 newly sequenced and 94 published mitogenomes. With optimized branch length and sequence heterogeneity, Bayesian analyses using a site-heterogeneous mixture model resolved the higher-level hemipteran phylogeny as (Sternorrhyncha, (Auchenorrhyncha, (Coleorrhyncha, Heteroptera))). Ancestral character state reconstruction and divergence time estimation suggest that the success of true bugs (Heteroptera) is probably due to angiosperm coevolution, but key adaptive innovations (e.g. prognathous mouthpart, predatory behaviour, and haemelytron) facilitated multiple independent shifts among diverse feeding habits and multiple independent colonizations of aquatic habitats. PMID:28878063

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.

Multiple Imputation For Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys

PubMed Central

Rendall, Michael S.; Ghosh-Dastidar, Bonnie; Weden, Margaret M.; Baker, Elizabeth H.; Nazarov, Zafar

2013-01-01

Within-survey multiple imputation (MI) methods are adapted to pooled-survey regression estimation where one survey has more regressors, but typically fewer observations, than the other. This adaptation is achieved through: (1) larger numbers of imputations to compensate for the higher fraction of missing values; (2) model-fit statistics to check the assumption that the two surveys sample from a common universe; and (3) specificying the analysis model completely from variables present in the survey with the larger set of regressors, thereby excluding variables never jointly observed. In contrast to the typical within-survey MI context, cross-survey missingness is monotonic and easily satisfies the Missing At Random (MAR) assumption needed for unbiased MI. Large efficiency gains and substantial reduction in omitted variable bias are demonstrated in an application to sociodemographic differences in the risk of child obesity estimated from two nationally-representative cohort surveys. PMID:24223447

An adaptive actuator failure compensation scheme for two linked 2WD mobile robots

NASA Astrophysics Data System (ADS)

Ma, Yajie; Al-Dujaili, Ayad; Cocquempot, Vincent; El Badaoui El Najjar, Maan

2017-01-01

This paper develops a new adaptive compensation control scheme for two linked mobile robots with actuator failurs. A configuration with two linked two-wheel drive (2WD) mobile robots is proposed, and the modelling of its kinematics and dynamics are given. An adaptive failure compensation scheme is developed to compensate actuator failures, consisting of a kinematic controller and a multi-design integration based dynamic controller. The kinematic controller is a virtual one, and based on which, multiple adaptive dynamic control signals are designed which covers all possible failure cases. By combing these dynamic control signals, the dynamic controller is designed, which ensures system stability and asymptotic tracking properties. Simulation results verify the effectiveness of the proposed adaptive failure compensation scheme.

Learning and adaptation in the management of waterfowl harvests

USGS Publications Warehouse

Johnson, Fred A.

2011-01-01

A formal framework for the adaptive management of waterfowl harvests was adopted by the U.S. Fish and Wildlife Service in 1995. The process admits competing models of waterfowl population dynamics and harvest impacts, and relies on model averaging to compute optimal strategies for regulating harvest. Model weights, reflecting the relative ability of the alternative models to predict changes in population size, are used in the model averaging and are updated each year based on a comparison of model predictions and observations of population size. Since its inception the adaptive harvest program has focused principally on mallards (Anas platyrhynchos), which constitute a large portion of the U.S. waterfowl harvest. Four competing models, derived from a combination of two survival and two reproductive hypotheses, were originally assigned equal weights. In the last year of available information (2007), model weights favored the weakly density-dependent reproductive hypothesis over the strongly density-dependent one, and the additive mortality hypothesis over the compensatory one. The change in model weights led to a more conservative harvesting policy than what was in effect in the early years of the program. Adaptive harvest management has been successful in many ways, but nonetheless has exposed the difficulties in defining management objectives, in predicting and regulating harvests, and in coping with the tradeoffs inherent in managing multiple waterfowl stocks exposed to a common harvest. The key challenge now facing managers is whether adaptive harvest management as an institution can be sufficiently adaptive, and whether the knowledge and experience gained from the process can be reflected in higher-level policy decisions.

Can you sequence ecology? Metagenomics of adaptive diversification.

PubMed

Marx, Christopher J

2013-01-01

Few areas of science have benefited more from the expansion in sequencing capability than the study of microbial communities. Can sequence data, besides providing hypotheses of the functions the members possess, detect the evolutionary and ecological processes that are occurring? For example, can we determine if a species is adapting to one niche, or if it is diversifying into multiple specialists that inhabit distinct niches? Fortunately, adaptation of populations in the laboratory can serve as a model to test our ability to make such inferences about evolution and ecology from sequencing. Even adaptation to a single niche can give rise to complex temporal dynamics due to the transient presence of multiple competing lineages. If there are multiple niches, this complexity is augmented by segmentation of the population into multiple specialists that can each continue to evolve within their own niche. For a known example of parallel diversification that occurred in the laboratory, sequencing data gave surprisingly few obvious, unambiguous signs of the ecological complexity present. Whereas experimental systems are open to direct experimentation to test hypotheses of selection or ecological interaction, the difficulty in "seeing ecology" from sequencing for even such a simple system suggests translation to communities like the human microbiome will be quite challenging. This will require both improved empirical methods to enhance the depth and time resolution for the relevant polymorphisms and novel statistical approaches to rigorously examine time-series data for signs of various evolutionary and ecological phenomena within and between species.

Two Variations of Video Modeling Interventions for Teaching Play Skills to Children with Autism

ERIC Educational Resources Information Center

Sancho, Kimberly; Sidener, Tina M.; Reeve, Sharon A.; Sidener, David W.

2010-01-01

The current study employed an adapted alternating treatments design with reversal and multiple probe across participants components to compare the effects of traditional video priming and simultaneous video modeling on the acquisition of play skills in two children diagnosed with autism. Generalization was programmed across play sets, instructors,…

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.

Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons.

PubMed

Nicola, Wilten; Campbell, Sue Ann

2013-01-01

We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider includes the Izhikevich, adaptive exponential and quartic integrate and fire models. The heterogeneity in the parameters leads to different moment closure assumptions that can be made in the derivation of the mean-field model from the population density equation for the large network. Three different moment closure assumptions lead to three different mean-field systems. These systems can be used for distinct purposes such as bifurcation analysis of the large networks, prediction of steady state firing rate distributions, parameter estimation for actual neurons and faster exploration of the parameter space. We use the mean-field systems to analyze adaptation induced bursting under realistic sources of heterogeneity in multiple parameters. Our analysis demonstrates that the presence of heterogeneity causes the Hopf bifurcation associated with the emergence of bursting to change from sub-critical to super-critical. This is confirmed with numerical simulations of the full network for biologically reasonable parameter values. This change decreases the plausibility of adaptation being the cause of bursting in hippocampal area CA3, an area with a sizable population of heavily coupled, strongly adapting neurons.

Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons

PubMed Central

Nicola, Wilten; Campbell, Sue Ann

2013-01-01

We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider includes the Izhikevich, adaptive exponential and quartic integrate and fire models. The heterogeneity in the parameters leads to different moment closure assumptions that can be made in the derivation of the mean-field model from the population density equation for the large network. Three different moment closure assumptions lead to three different mean-field systems. These systems can be used for distinct purposes such as bifurcation analysis of the large networks, prediction of steady state firing rate distributions, parameter estimation for actual neurons and faster exploration of the parameter space. We use the mean-field systems to analyze adaptation induced bursting under realistic sources of heterogeneity in multiple parameters. Our analysis demonstrates that the presence of heterogeneity causes the Hopf bifurcation associated with the emergence of bursting to change from sub-critical to super-critical. This is confirmed with numerical simulations of the full network for biologically reasonable parameter values. This change decreases the plausibility of adaptation being the cause of bursting in hippocampal area CA3, an area with a sizable population of heavily coupled, strongly adapting neurons. PMID:24416013

Maximum-likelihood spectral estimation and adaptive filtering techniques with application to airborne Doppler weather radar. Thesis Technical Report No. 20

NASA Technical Reports Server (NTRS)

Lai, Jonathan Y.

1994-01-01

This dissertation focuses on the signal processing problems associated with the detection of hazardous windshears using airborne Doppler radar when weak weather returns are in the presence of strong clutter returns. In light of the frequent inadequacy of spectral-processing oriented clutter suppression methods, we model a clutter signal as multiple sinusoids plus Gaussian noise, and propose adaptive filtering approaches that better capture the temporal characteristics of the signal process. This idea leads to two research topics in signal processing: (1) signal modeling and parameter estimation, and (2) adaptive filtering in this particular signal environment. A high-resolution, low SNR threshold maximum likelihood (ML) frequency estimation and signal modeling algorithm is devised and proves capable of delineating both the spectral and temporal nature of the clutter return. Furthermore, the Least Mean Square (LMS) -based adaptive filter's performance for the proposed signal model is investigated, and promising simulation results have testified to its potential for clutter rejection leading to more accurate estimation of windspeed thus obtaining a better assessment of the windshear hazard.

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

PubMed Central

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-01-01

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.

PubMed

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-12-19

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

Joint U.S./Japan Conference on Adaptive Structures, 1st, Maui, HI, Nov. 13-15, 1990, Proceedings

NASA Technical Reports Server (NTRS)

Wada, Ben K. (Editor); Fanson, James L. (Editor); Miura, Koryo (Editor)

1991-01-01

The present volume of adaptive structures discusses the development of control laws for an orbiting tethered antenna/reflector system test scale model, the sizing of active piezoelectric struts for vibration suppression on a space-based interferometer, the control design of a space station mobile transporter with multiple constraints, and optimum configuration control of an intelligent truss structure. Attention is given to the formulation of full state feedback for infinite order structural systems, robustness issues in the design of smart structures, passive piezoelectric vibration damping, shape control experiments with a functional model for large optical reflectors, and a mathematical basis for the design optimization of adaptive trusses in precision control. Topics addressed include approaches to the optimal adaptive geometries of intelligent truss structures, the design of an automated manufacturing system for tubular smart structures, the Sandia structural control experiments, and the zero-gravity dynamics of space structures in parabolic aircraft flight.

Joint U.S./Japan Conference on Adaptive Structures, 1st, Maui, HI, Nov. 13-15, 1990, Proceedings

NASA Astrophysics Data System (ADS)

Wada, Ben K.; Fanson, James L.; Miura, Koryo

1991-11-01

The present volume of adaptive structures discusses the development of control laws for an orbiting tethered antenna/reflector system test scale model, the sizing of active piezoelectric struts for vibration suppression on a space-based interferometer, the control design of a space station mobile transporter with multiple constraints, and optimum configuration control of an intelligent truss structure. Attention is given to the formulation of full state feedback for infinite order structural systems, robustness issues in the design of smart structures, passive piezoelectric vibration damping, shape control experiments with a functional model for large optical reflectors, and a mathematical basis for the design optimization of adaptive trusses in precision control. Topics addressed include approaches to the optimal adaptive geometries of intelligent truss structures, the design of an automated manufacturing system for tubular smart structures, the Sandia structural control experiments, and the zero-gravity dynamics of space structures in parabolic aircraft flight.

Evaluating Training.

ERIC Educational Resources Information Center

Brethower, Karen S.; Rummler, Geary A.

1979-01-01

Presents general systems models (ballistic system, guided system, and adaptive system) and an evaluation matrix to help in examining training evaluation alternatives and in deciding what evaluation is appropriate. Includes some guidelines for conducting evaluation studies using four designs (control group, reversal, multiple baseline, and…

Real-Time Feedback Control of Flow-Induced Cavity Tones. Part 2; Adaptive Control

NASA Technical Reports Server (NTRS)

Kegerise, M. A.; Cabell, R. H.; Cattafesta, L. N., III

2006-01-01

An adaptive generalized predictive control (GPC) algorithm was formulated and applied to the cavity flow-tone problem. The algorithm employs gradient descent to update the GPC coefficients at each time step. Past input-output data and an estimate of the open-loop pulse response sequence are all that is needed to implement the algorithm for application at fixed Mach numbers. Transient measurements made during controller adaptation revealed that the controller coefficients converged to a steady state in the mean, and this implies that adaptation can be turned off at some point with no degradation in control performance. When converged, the control algorithm demonstrated multiple Rossiter mode suppression at fixed Mach numbers ranging from 0.275 to 0.38. However, as in the case of fixed-gain GPC, the adaptive GPC performance was limited by spillover in sidebands around the suppressed Rossiter modes. The algorithm was also able to maintain suppression of multiple cavity tones as the freestream Mach number was varied over a modest range (0.275 to 0.29). Beyond this range, stable operation of the control algorithm was not possible due to the fixed plant model in the algorithm.

A collaborative scheduling model for the supply-hub with multiple suppliers and multiple manufacturers.

PubMed

Li, Guo; Lv, Fei; Guan, Xu

2014-01-01

This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment.

A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers

PubMed Central

Lv, Fei; Guan, Xu

2014-01-01

This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment. PMID:24892104

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

Koniges, A.E.; Craddock, G.G.; Schnack, D.D.

The purpose of the workshop was to assemble workers, both within and outside of the fusion-related computations areas, for discussion regarding the issues of dynamically adaptive gridding. There were three invited talks related to adaptive gridding application experiences in various related fields of computational fluid dynamics (CFD), and nine short talks reporting on the progress of adaptive techniques in the specific areas of scrape-off-layer (SOL) modeling and magnetohydrodynamic (MHD) stability. Adaptive mesh methods have been successful in a number of diverse fields of CFD for over a decade. The method involves dynamic refinement of computed field profiles in a waymore » that disperses uniformly the numerical errors associated with discrete approximations. Because the process optimizes computational effort, adaptive mesh methods can be used to study otherwise the intractable physical problems that involve complex boundary shapes or multiple spatial/temporal scales. Recent results indicate that these adaptive techniques will be required for tokamak fluid-based simulations involving the diverted tokamak SOL modeling and MHD simulations problems related to the highest priority ITER relevant issues.Individual papers are indexed separately on the energy data bases.« less

Application of ANFIS to Phase Estimation for Multiple Phase Shift Keying

NASA Technical Reports Server (NTRS)

Drake, Jeffrey T.; Prasad, Nadipuram R.

2000-01-01

The paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for estimating phase in Multiple Phase Shift Keying (M-PSK) modulation. A brief overview of communications phase estimation is provided. The modeling of both general open-loop, and closed-loop phase estimation schemes for M-PSK symbols with unknown structure are discussed. Preliminary performance results from simulation of the above schemes are presented.

NASA Langley developments in response calculations needed for failure and life prediction

NASA Technical Reports Server (NTRS)

Housner, Jerrold M.

1993-01-01

NASA Langley developments in response calculations needed for failure and life predictions are discussed. Topics covered include: structural failure analysis in concurrent engineering; accuracy of independent regional modeling demonstrated on classical example; functional interface method accurately joins incompatible finite element models; interface method for insertion of local detail modeling extended to curve pressurized fuselage window panel; interface concept for joining structural regions; motivation for coupled 2D-3D analysis; compression panel with discontinuous stiffener coupled 2D-3D model and axial surface strains at the middle of the hat stiffener; use of adaptive refinement with multiple methods; adaptive mesh refinement; and studies on quantity effect of bow-type initial imperfections on reliability of stiffened panels.

Lessons learned: Optimization of a murine small bowel resection model

PubMed Central

Taylor, Janice A.; Martin, Colin A.; Nair, Rajalakshmi; Guo, Jun; Erwin, Christopher R.; Warner, Brad W.

2008-01-01

Background/Purpose Central to the use of murine models of disease is the ability to derive reproducible data. The purpose of this study was to determine factors contributing to variability in our murine model of small bowel resection (SBR). Methods Male C57Bl/6 mice were randomized to sham or 50% SBR. The effect of housing type (pathogen-free versus standard housing), nutrition (reconstituted powder versus tube feeding formulation), and correlates of intestinal morphology with gene expression changes were investigated Multiple linear regression modeling or one-way ANOVA was used for data analysis. Results Pathogen-free mice had significantly shorter ileal villi at baseline and demonstrated greater villus growth after SBR compared to mice housed in standard rooms. Food type did not affect adaptation. Gene expression changes were more consistent and significant in isolated crypt cells that demonstrated adaptive growth when compared with crypts that did not deepen after SBR. Conclusion Maintenance of mice in pathogen-free conditions and restricting gene expression analysis to individual animals exhibiting morphologic adaptation enhances sensitivity and specificity of data derived from this model. These refinements will minimize experimental variability and lead to improved understanding of the complex process of intestinal adaptation. PMID:18558176

Data matching for free-surface multiple attenuation by multidimensional deconvolution

NASA Astrophysics Data System (ADS)

van der Neut, Joost; Frijlink, Martijn; van Borselen, Roald

2012-09-01

A common strategy for surface-related multiple elimination of seismic data is to predict multiples by a convolutional model and subtract these adaptively from the input gathers. Problems can be posed by interfering multiples and primaries. Removing multiples by multidimensional deconvolution (MDD) (inversion) does not suffer from these problems. However, this approach requires data to be consistent, which is often not the case, especially not at interpolated near-offsets. A novel method is proposed to improve data consistency prior to inversion. This is done by backpropagating first-order multiples with a time-gated reference primary event and matching these with early primaries in the input gather. After data matching, multiple elimination by MDD can be applied with a deterministic inversion scheme.

Multiple Translocation of the AVR-Pita Effector Gene among Chromosomes of the Rice Blast Fungus Magnaporthe oryzae and Related Species

PubMed Central

Chuma, Izumi; Isobe, Chihiro; Hotta, Yuma; Ibaragi, Kana; Futamata, Natsuru; Kusaba, Motoaki; Yoshida, Kentaro; Terauchi, Ryohei; Fujita, Yoshikatsu; Nakayashiki, Hitoshi; Valent, Barbara; Tosa, Yukio

2011-01-01

Magnaporthe oryzae is the causal agent of rice blast disease, a devastating problem worldwide. This fungus has caused breakdown of resistance conferred by newly developed commercial cultivars. To address how the rice blast fungus adapts itself to new resistance genes so quickly, we examined chromosomal locations of AVR-Pita, a subtelomeric gene family corresponding to the Pita resistance gene, in various isolates of M. oryzae (including wheat and millet pathogens) and its related species. We found that AVR-Pita (AVR-Pita1 and AVR-Pita2) is highly variable in its genome location, occurring in chromosomes 1, 3, 4, 5, 6, 7, and supernumerary chromosomes, particularly in rice-infecting isolates. When expressed in M. oryzae, most of the AVR-Pita homologs could elicit Pita-mediated resistance, even those from non-rice isolates. AVR-Pita was flanked by a retrotransposon, which presumably contributed to its multiple translocation across the genome. On the other hand, family member AVR-Pita3, which lacks avirulence activity, was stably located on chromosome 7 in a vast majority of isolates. These results suggest that the diversification in genome location of AVR-Pita in the rice isolates is a consequence of recognition by Pita in rice. We propose a model that the multiple translocation of AVR-Pita may be associated with its frequent loss and recovery mediated by its transfer among individuals in asexual populations. This model implies that the high mobility of AVR-Pita is a key mechanism accounting for the rapid adaptation toward Pita. Dynamic adaptation of some fungal plant pathogens may be achieved by deletion and recovery of avirulence genes using a population as a unit of adaptation. PMID:21829350

Programming model for distributed intelligent systems

NASA Technical Reports Server (NTRS)

Sztipanovits, J.; Biegl, C.; Karsai, G.; Bogunovic, N.; Purves, B.; Williams, R.; Christiansen, T.

1988-01-01

A programming model and architecture which was developed for the design and implementation of complex, heterogeneous measurement and control systems is described. The Multigraph Architecture integrates artificial intelligence techniques with conventional software technologies, offers a unified framework for distributed and shared memory based parallel computational models and supports multiple programming paradigms. The system can be implemented on different hardware architectures and can be adapted to strongly different applications.

Segmentation of prostate boundaries from ultrasound images using statistical shape model.

PubMed

Shen, Dinggang; Zhan, Yiqiang; Davatzikos, Christos

2003-04-01

This paper presents a statistical shape model for the automatic prostate segmentation in transrectal ultrasound images. A Gabor filter bank is first used to characterize the prostate boundaries in ultrasound images in both multiple scales and multiple orientations. The Gabor features are further reconstructed to be invariant to the rotation of the ultrasound probe and incorporated in the prostate model as image attributes for guiding the deformable segmentation. A hierarchical deformation strategy is then employed, in which the model adaptively focuses on the similarity of different Gabor features at different deformation stages using a multiresolution technique, i.e., coarse features first and fine features later. A number of successful experiments validate the algorithm.

Model-on-Demand Predictive Control for Nonlinear Hybrid Systems With Application to Adaptive Behavioral Interventions

PubMed Central

Nandola, Naresh N.; Rivera, Daniel E.

2011-01-01

This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087

MURI: Adaptive Waveform Design for Full Spectral Dominance

DTIC Science & Technology

2011-03-11

a three- dimensional urban tracking model, based on the nonlinear measurement model (that uses the urban multipath geometry with different types of ... the time evolution of the scattering function with a high dimensional dynamic system; a multiple particle filter technique is used to sequentially...integration of space -time coding with a fixed set of beams. It complements the

Some Useful Cost-Benefit Criteria for Evaluating Computer-Based Test Delivery Models and Systems

ERIC Educational Resources Information Center

Luecht, Richard M.

2005-01-01

Computer-based testing (CBT) is typically implemented using one of three general test delivery models: (1) multiple fixed testing (MFT); (2) computer-adaptive testing (CAT); or (3) multistage testing (MSTs). This article reviews some of the real cost drivers associated with CBT implementation--focusing on item production costs, the costs…

Adaptive graph-based multiple testing procedures

PubMed Central

Klinglmueller, Florian; Posch, Martin; Koenig, Franz

2016-01-01

Multiple testing procedures defined by directed, weighted graphs have recently been proposed as an intuitive visual tool for constructing multiple testing strategies that reflect the often complex contextual relations between hypotheses in clinical trials. Many well-known sequentially rejective tests, such as (parallel) gatekeeping tests or hierarchical testing procedures are special cases of the graph based tests. We generalize these graph-based multiple testing procedures to adaptive trial designs with an interim analysis. These designs permit mid-trial design modifications based on unblinded interim data as well as external information, while providing strong family wise error rate control. To maintain the familywise error rate, it is not required to prespecify the adaption rule in detail. Because the adaptive test does not require knowledge of the multivariate distribution of test statistics, it is applicable in a wide range of scenarios including trials with multiple treatment comparisons, endpoints or subgroups, or combinations thereof. Examples of adaptations are dropping of treatment arms, selection of subpopulations, and sample size reassessment. If, in the interim analysis, it is decided to continue the trial as planned, the adaptive test reduces to the originally planned multiple testing procedure. Only if adaptations are actually implemented, an adjusted test needs to be applied. The procedure is illustrated with a case study and its operating characteristics are investigated by simulations. PMID:25319733

Unsupervised Domain Adaptation with Multiple Acoustic Models

DTIC Science & Technology

2010-12-01

Discriminative MAP Adaptation Standard ML-MAP has been extended to incorporate discrim- inative training criteria such as MMI and MPE [10]. Dis- criminative MAP...smoothing variable I . For example, the MMI - MAP mean is given by ( mmi -map) jm = fnumjm (O) den jm(O)g+Djm̂jm + I (ml-map) jm f numjm den... MMI training, and Djm is the Gaussian-dependent parameter for the extended Baum-Welch (EBW) algorithm. MMI -MAP has been successfully applied in

Adaptive Flight Control Research at NASA

NASA Technical Reports Server (NTRS)

Motter, Mark A.

2008-01-01

A broad overview of current adaptive flight control research efforts at NASA is presented, as well as some more detailed discussion of selected specific approaches. The stated objective of the Integrated Resilient Aircraft Control Project, one of NASA s Aviation Safety programs, is to advance the state-of-the-art of adaptive controls as a design option to provide enhanced stability and maneuverability margins for safe landing in the presence of adverse conditions such as actuator or sensor failures. Under this project, a number of adaptive control approaches are being pursued, including neural networks and multiple models. Validation of all the adaptive control approaches will use not only traditional methods such as simulation, wind tunnel testing and manned flight tests, but will be augmented with recently developed capabilities in unmanned flight testing.

Emergent Neutrality in Adaptive Asexual Evolution

PubMed Central

Schiffels, Stephan; Szöllősi, Gergely J.; Mustonen, Ville; Lässig, Michael

2011-01-01

In nonrecombining genomes, genetic linkage can be an important evolutionary force. Linkage generates interference interactions, by which simultaneously occurring mutations affect each other’s chance of fixation. Here, we develop a comprehensive model of adaptive evolution in linked genomes, which integrates interference interactions between multiple beneficial and deleterious mutations into a unified framework. By an approximate analytical solution, we predict the fixation rates of these mutations, as well as the probabilities of beneficial and deleterious alleles at fixed genomic sites. We find that interference interactions generate a regime of emergent neutrality: all genomic sites with selection coefficients smaller in magnitude than a characteristic threshold have nearly random fixed alleles, and both beneficial and deleterious mutations at these sites have nearly neutral fixation rates. We show that this dynamic limits not only the speed of adaptation, but also a population’s degree of adaptation in its current environment. We apply the model to different scenarios: stationary adaptation in a time-dependent environment and approach to equilibrium in a fixed environment. In both cases, the analytical predictions are in good agreement with numerical simulations. Our results suggest that interference can severely compromise biological functions in an adapting population, which sets viability limits on adaptive evolution under linkage. PMID:21926305

Estimating the Relevance of World Disturbances to Explain Savings, Interference and Long-Term Motor Adaptation Effects

PubMed Central

Berniker, Max; Kording, Konrad P.

2011-01-01

Recent studies suggest that motor adaptation is the result of multiple, perhaps linear processes each with distinct time scales. While these models are consistent with some motor phenomena, they can neither explain the relatively fast re-adaptation after a long washout period, nor savings on a subsequent day. Here we examined if these effects can be explained if we assume that the CNS stores and retrieves movement parameters based on their possible relevance. We formalize this idea with a model that infers not only the sources of potential motor errors, but also their relevance to the current motor circumstances. In our model adaptation is the process of re-estimating parameters that represent the body and the world. The likelihood of a world parameter being relevant is then based on the mismatch between an observed movement and that predicted when not compensating for the estimated world disturbance. As such, adapting to large motor errors in a laboratory setting should alert subjects that disturbances are being imposed on them, even after motor performance has returned to baseline. Estimates of this external disturbance should be relevant both now and in future laboratory settings. Estimated properties of our bodies on the other hand should always be relevant. Our model demonstrates savings, interference, spontaneous rebound and differences between adaptation to sudden and gradual disturbances. We suggest that many issues concerning savings and interference can be understood when adaptation is conditioned on the relevance of parameters. PMID:21998574

Spatial Selection and Local Adaptation Jointly Shape Life-History Evolution during Range Expansion.

PubMed

Van Petegem, Katrien H P; Boeye, Jeroen; Stoks, Robby; Bonte, Dries

2016-11-01

In the context of climate change and species invasions, range shifts increasingly gain attention because the rates at which they occur in the Anthropocene induce rapid changes in biological assemblages. During range shifts, species experience multiple selection pressures. For poleward expansions in particular, it is difficult to interpret observed evolutionary dynamics because of the joint action of evolutionary processes related to spatial selection and to adaptation toward local climatic conditions. To disentangle the effects of these two processes, we integrated stochastic modeling and data from a common garden experiment, using the spider mite Tetranychus urticae as a model species. By linking the empirical data with those derived form a highly parameterized individual-based model, we infer that both spatial selection and local adaptation contributed to the observed latitudinal life-history divergence. Spatial selection best described variation in dispersal behavior, while variation in development was best explained by adaptation to the local climate. Divergence in life-history traits in species shifting poleward could consequently be jointly determined by contemporary evolutionary dynamics resulting from adaptation to the environmental gradient and from spatial selection. The integration of modeling with common garden experiments provides a powerful tool to study the contribution of these evolutionary processes on life-history evolution during range expansion.

Fully implicit adaptive mesh refinement MHD algorithm

NASA Astrophysics Data System (ADS)

Philip, Bobby

2005-10-01

In the macroscopic simulation of plasmas, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. The former results in stiffness due to the presence of very fast waves. The latter requires one to resolve the localized features that the system develops. Traditional approaches based on explicit time integration techniques and fixed meshes are not suitable for this challenge, as such approaches prevent the modeler from using realistic plasma parameters to keep the computation feasible. We propose here a novel approach, based on implicit methods and structured adaptive mesh refinement (SAMR). Our emphasis is on both accuracy and scalability with the number of degrees of freedom. To our knowledge, a scalable, fully implicit AMR algorithm has not been accomplished before for MHD. As a proof-of-principle, we focus on the reduced resistive MHD model as a basic MHD model paradigm, which is truly multiscale. The approach taken here is to adapt mature physics-based technologyootnotetextL. Chac'on et al., J. Comput. Phys. 178 (1), 15- 36 (2002) to AMR grids, and employ AMR-aware multilevel techniques (such as fast adaptive composite --FAC-- algorithms) for scalability. We will demonstrate that the concept is indeed feasible, featuring optimal scalability under grid refinement. Results of fully-implicit, dynamically-adaptive AMR simulations will be presented on a variety of problems.

Fully implicit adaptive mesh refinement algorithm for reduced MHD

NASA Astrophysics Data System (ADS)

Philip, Bobby; Pernice, Michael; Chacon, Luis

2006-10-01

In the macroscopic simulation of plasmas, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. Traditional approaches based on explicit time integration techniques and fixed meshes are not suitable for this challenge, as such approaches prevent the modeler from using realistic plasma parameters to keep the computation feasible. We propose here a novel approach, based on implicit methods and structured adaptive mesh refinement (SAMR). Our emphasis is on both accuracy and scalability with the number of degrees of freedom. As a proof-of-principle, we focus on the reduced resistive MHD model as a basic MHD model paradigm, which is truly multiscale. The approach taken here is to adapt mature physics-based technology to AMR grids, and employ AMR-aware multilevel techniques (such as fast adaptive composite grid --FAC-- algorithms) for scalability. We demonstrate that the concept is indeed feasible, featuring near-optimal scalability under grid refinement. Results of fully-implicit, dynamically-adaptive AMR simulations in challenging dissipation regimes will be presented on a variety of problems that benefit from this capability, including tearing modes, the island coalescence instability, and the tilt mode instability. L. Chac'on et al., J. Comput. Phys. 178 (1), 15- 36 (2002) B. Philip, M. Pernice, and L. Chac'on, Lecture Notes in Computational Science and Engineering, accepted (2006)

An improved car-following model with multiple preceding cars' velocity fluctuation feedback

NASA Astrophysics Data System (ADS)

Guo, Lantian; Zhao, Xiangmo; Yu, Shaowei; Li, Xiuhai; Shi, Zhongke

2017-04-01

In order to explore and evaluate the effects of velocity variation trend of multiple preceding cars used in the Cooperative Adaptive Cruise Control (CACC) strategy on the dynamic characteristic, fuel economy and emission of the corresponding traffic flow, we conduct a study as follows: firstly, with the real-time car-following (CF) data, the close relationship between multiple preceding cars' velocity fluctuation feedback and the host car's behaviors is explored, the evaluation results clearly show that multiple preceding cars' velocity fluctuation with different time window-width are highly correlated to the host car's acceleration/deceleration. Then, a microscopic traffic flow model is proposed to evaluate the effects of multiple preceding cars' velocity fluctuation feedback in the CACC strategy on the traffic flow evolution process. Finally, numerical simulations on fuel economy and exhaust emission of the traffic flow are also implemented by utilizing VT-micro model. Simulation results prove that considering multiple preceding cars' velocity fluctuation feedback in the control strategy of the CACC system can improve roadway traffic mobility, fuel economy and exhaust emission performance.

Novel Multistatic Adaptive Microwave Imaging Methods for Early Breast Cancer Detection

NASA Astrophysics Data System (ADS)

Xie, Yao; Guo, Bin; Li, Jian; Stoica, Petre

2006-12-01

Multistatic adaptive microwave imaging (MAMI) methods are presented and compared for early breast cancer detection. Due to the significant contrast between the dielectric properties of normal and malignant breast tissues, developing microwave imaging techniques for early breast cancer detection has attracted much interest lately. MAMI is one of the microwave imaging modalities and employs multiple antennas that take turns to transmit ultra-wideband (UWB) pulses while all antennas are used to receive the reflected signals. MAMI can be considered as a special case of the multi-input multi-output (MIMO) radar with the multiple transmitted waveforms being either UWB pulses or zeros. Since the UWB pulses transmitted by different antennas are displaced in time, the multiple transmitted waveforms are orthogonal to each other. The challenge to microwave imaging is to improve resolution and suppress strong interferences caused by the breast skin, nipple, and so forth. The MAMI methods we investigate herein utilize the data-adaptive robust Capon beamformer (RCB) to achieve high resolution and interference suppression. We will demonstrate the effectiveness of our proposed methods for breast cancer detection via numerical examples with data simulated using the finite-difference time-domain method based on a 3D realistic breast model.

Toward A Scalable, Patient-Centered Community Health Worker Model: Adapting the IMPaCT Intervention for Use in the Outpatient Setting.

PubMed

Kangovi, Shreya; Carter, Tamala; Charles, Dorothy; Smith, Robyn A; Glanz, Karen; Long, Judith A; Grande, David

2016-12-01

Community health worker (CHW) programs are an increasingly popular strategy for patient-centered care. Many health care organizations are building CHW programs through trial and error, rather than implementing or adapting evidence-based interventions. This study used a qualitative design-mapping process to adapt an evidence-based CHW intervention, originally developed and tested in the hospital setting, for use among outpatients with multiple chronic conditions. The study involved qualitative in-depth, semi-structured interviews with chronically ill, uninsured, or Medicaid outpatients from low-income zip codes (n = 21) and their primary care practice staff (n = 30). Three key themes informed adaptation of the original intervention for outpatients with multiple conditions. First, outpatients were overwhelmed by their multiple conditions and wished they could focus on 1 at a time. Thus, the first major revision was to design a low-literacy decision aid that patients and providers could use to select a condition to focus on during the intervention. Second, motivation for health behavior change was a more prominent theme than in the original intervention. It was decided that in addition to providing tailored social support as in the original intervention, CHWs would help patients track progress toward their chronic disease management goals to motivate health behavior change. Third, patients were already connected to primary care; yet they still needed additional support to navigate their clinic once the intervention ended. The intervention was revised to include a weekly clinic-based support group. Structured adaptation using qualitative design mapping may allow for rapid adaptation and scale-up of evidence-based CHW interventions across new settings and populations.

Adapting Dialectical Behavior Therapy for College Counseling Centers

ERIC Educational Resources Information Center

Chugani, Carla D.

2017-01-01

College counseling centers report increased student presentation with severe psychological issues (Gallagher, 2012). Although dialectical behavior therapy (DBT) has demonstrated efficacy with multiple clinical populations, standard model DBT is not feasible for many traditional college counseling centers. This article describes the iterative…

Adaptive evolutionary walks require neutral intermediates in RNA fitness landscapes.

PubMed

Rendel, Mark D

2011-01-01

In RNA fitness landscapes with interconnected networks of neutral mutations, neutral precursor mutations can play an important role in facilitating the accessibility of epistatic adaptive mutant combinations. I use an exhaustively surveyed fitness landscape model based on short sequence RNA genotypes (and their secondary structure phenotypes) to calculate the minimum rate at which mutants initially appearing as neutral are incorporated into an adaptive evolutionary walk. I show first, that incorporating neutral mutations significantly increases the number of point mutations in a given evolutionary walk when compared to estimates from previous adaptive walk models. Second, that incorporating neutral mutants into such a walk significantly increases the final fitness encountered on that walk - indeed evolutionary walks including neutral steps often reach the global optimum in this model. Third, and perhaps most importantly, evolutionary paths of this kind are often extremely winding in their nature and have the potential to undergo multiple mutations at a given sequence position within a single walk; the potential of these winding paths to mislead phylogenetic reconstruction is briefly considered. Copyright © 2010 Elsevier Inc. All rights reserved.

Classification of multiple sclerosis lesions using adaptive dictionary learning.

PubMed

Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

2015-12-01

This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Recruitment dynamics in adaptive social networks

NASA Astrophysics Data System (ADS)

Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.

2013-06-01

We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime).

Recruitment dynamics in adaptive social networks.

PubMed

Shkarayev, Maxim S; Schwartz, Ira B; Shaw, Leah B

2013-01-01

We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime).

Theoretical Models of Protostellar Binary and Multiple Systems with AMR Simulations

NASA Astrophysics Data System (ADS)

Matsumoto, Tomoaki; Tokuda, Kazuki; Onishi, Toshikazu; Inutsuka, Shu-ichiro; Saigo, Kazuya; Takakuwa, Shigehisa

2017-05-01

We present theoretical models for protostellar binary and multiple systems based on the high-resolution numerical simulation with an adaptive mesh refinement (AMR) code, SFUMATO. The recent ALMA observations have revealed early phases of the binary and multiple star formation with high spatial resolutions. These observations should be compared with theoretical models with high spatial resolutions. We present two theoretical models for (1) a high density molecular cloud core, MC27/L1521F, and (2) a protobinary system, L1551 NE. For the model for MC27, we performed numerical simulations for gravitational collapse of a turbulent cloud core. The cloud core exhibits fragmentation during the collapse, and dynamical interaction between the fragments produces an arc-like structure, which is one of the prominent structures observed by ALMA. For the model for L1551 NE, we performed numerical simulations of gas accretion onto protobinary. The simulations exhibit asymmetry of a circumbinary disk. Such asymmetry has been also observed by ALMA in the circumbinary disk of L1551 NE.

Adaptive Conditioning of Multiple-Point Geostatistical Facies Simulation to Flow Data with Facies Probability Maps

NASA Astrophysics Data System (ADS)

Khodabakhshi, M.; Jafarpour, B.

2013-12-01

Characterization of complex geologic patterns that create preferential flow paths in certain reservoir systems requires higher-order geostatistical modeling techniques. Multipoint statistics (MPS) provides a flexible grid-based approach for simulating such complex geologic patterns from a conceptual prior model known as a training image (TI). In this approach, a stationary TI that encodes the higher-order spatial statistics of the expected geologic patterns is used to represent the shape and connectivity of the underlying lithofacies. While MPS is quite powerful for describing complex geologic facies connectivity, the nonlinear and complex relation between the flow data and facies distribution makes flow data conditioning quite challenging. We propose an adaptive technique for conditioning facies simulation from a prior TI to nonlinear flow data. Non-adaptive strategies for conditioning facies simulation to flow data can involves many forward flow model solutions that can be computationally very demanding. To improve the conditioning efficiency, we develop an adaptive sampling approach through a data feedback mechanism based on the sampling history. In this approach, after a short period of sampling burn-in time where unconditional samples are generated and passed through an acceptance/rejection test, an ensemble of accepted samples is identified and used to generate a facies probability map. This facies probability map contains the common features of the accepted samples and provides conditioning information about facies occurrence in each grid block, which is used to guide the conditional facies simulation process. As the sampling progresses, the initial probability map is updated according to the collective information about the facies distribution in the chain of accepted samples to increase the acceptance rate and efficiency of the conditioning. This conditioning process can be viewed as an optimization approach where each new sample is proposed based on the sampling history to improve the data mismatch objective function. We extend the application of this adaptive conditioning approach to the case where multiple training images are proposed to describe the geologic scenario in a given formation. We discuss the advantages and limitations of the proposed adaptive conditioning scheme and use numerical experiments from fluvial channel formations to demonstrate its applicability and performance compared to non-adaptive conditioning techniques.

Modified Hyperspheres Algorithm to Trace Homotopy Curves of Nonlinear Circuits Composed by Piecewise Linear Modelled Devices

PubMed Central

Vazquez-Leal, H.; Jimenez-Fernandez, V. M.; Benhammouda, B.; Filobello-Nino, U.; Sarmiento-Reyes, A.; Ramirez-Pinero, A.; Marin-Hernandez, A.; Huerta-Chua, J.

2014-01-01

We present a homotopy continuation method (HCM) for finding multiple operating points of nonlinear circuits composed of devices modelled by using piecewise linear (PWL) representations. We propose an adaptation of the modified spheres path tracking algorithm to trace the homotopy trajectories of PWL circuits. In order to assess the benefits of this proposal, four nonlinear circuits composed of piecewise linear modelled devices are analysed to determine their multiple operating points. The results show that HCM can find multiple solutions within a single homotopy trajectory. Furthermore, we take advantage of the fact that homotopy trajectories are PWL curves meant to replace the multidimensional interpolation and fine tuning stages of the path tracking algorithm with a simple and highly accurate procedure based on the parametric straight line equation. PMID:25184157

Single-process versus multiple-strategy models of decision making: evidence from an information intrusion paradigm.

PubMed

Söllner, Anke; Bröder, Arndt; Glöckner, Andreas; Betsch, Tilmann

2014-02-01

When decision makers are confronted with different problems and situations, do they use a uniform mechanism as assumed by single-process models (SPMs) or do they choose adaptively from a set of available decision strategies as multiple-strategy models (MSMs) imply? Both frameworks of decision making have gathered a lot of support, but only rarely have they been contrasted with each other. Employing an information intrusion paradigm for multi-attribute decisions from givens, SPM and MSM predictions on information search, decision outcomes, attention, and confidence judgments were derived and tested against each other in two experiments. The results consistently support the SPM view: Participants seemingly using a "take-the-best" (TTB) strategy do not ignore TTB-irrelevant information as MSMs would predict, but adapt the amount of information searched, choose alternative choice options, and show varying confidence judgments contingent on the quality of the "irrelevant" information. The uniformity of these findings underlines the adequacy of the novel information intrusion paradigm and comprehensively promotes the notion of a uniform decision making mechanism as assumed by single-process models. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Immunological changes in human skeletal muscle and blood after eccentric exercise and multiple biopsies

PubMed Central

Malm, Christer; Nyberg, Pernilla; Engström, Marianne; Sjödin, Bertil; Lenkei, Rodica; Ekblom, Björn; Lundberg, Ingrid

2000-01-01

A role of the immune system in muscular adaptation to physical exercise has been suggested but data from controlled human studies are scarce. The present study investigated immunological events in human blood and skeletal muscle by immunohistochemistry and flow cytometry after eccentric cycling exercise and multiple biopsies. Immunohistochemical detection of neutrophil- (CD11b, CD15), macrophage- (CD163), satellite cell- (CD56) and IL-1β-specific antigens increased similarly in human skeletal muscle after eccentric cycling exercise together with multiple muscle biopsies, or multiple biopsies only. Changes in immunological variables in blood and muscle were related, and monocytes and natural killer (NK) cells appeared to have governing functions over immunological events in human skeletal muscle. Delayed onset muscle soreness, serum creatine kinase activity and C-reactive protein concentration were not related to leukocyte infiltration in human skeletal muscle. Eccentric cycling and/or muscle biopsies did not result in T cell infiltration in human skeletal muscle. Modes of stress other than eccentric cycling should therefore be evaluated as a myositis model in human. Based on results from the present study, and in the light of previously published data, it appears plausible that muscular adaptation to physical exercise occurs without preceding muscle inflammation. Nevertheless, leukocytes seem important for repair, regeneration and adaptation of human skeletal muscle. PMID:11080266

Selective Bottlenecks Shape Evolutionary Pathways Taken during Mammalian Adaptation of a 1918-like Avian Influenza Virus.

PubMed

Moncla, Louise H; Zhong, Gongxun; Nelson, Chase W; Dinis, Jorge M; Mutschler, James; Hughes, Austin L; Watanabe, Tokiko; Kawaoka, Yoshihiro; Friedrich, Thomas C

2016-02-10

Avian influenza virus reassortants resembling the 1918 human pandemic virus can become transmissible among mammals by acquiring mutations in hemagglutinin (HA) and polymerase. Using the ferret model, we trace the evolutionary pathway by which an avian-like virus evolves the capacity for mammalian replication and airborne transmission. During initial infection, within-host HA diversity increased drastically. Then, airborne transmission fixed two polymerase mutations that do not confer a detectable replication advantage. In later transmissions, selection fixed advantageous HA1 variants. Transmission initially involved a "loose" bottleneck, which became strongly selective after additional HA mutations emerged. The stringency and evolutionary forces governing between-host bottlenecks may therefore change throughout host adaptation. Mutations occurred in multiple combinations in transmitted viruses, suggesting that mammalian transmissibility can evolve through multiple genetic pathways despite phenotypic constraints. Our data provide a glimpse into avian influenza virus adaptation in mammals, with broad implications for surveillance on potentially zoonotic viruses. Copyright © 2016 Elsevier Inc. All rights reserved.

Adaptive Horizontal Gene Transfers between Multiple Cheese-Associated Fungi.

PubMed

Ropars, Jeanne; Rodríguez de la Vega, Ricardo C; López-Villavicencio, Manuela; Gouzy, Jérôme; Sallet, Erika; Dumas, Émilie; Lacoste, Sandrine; Debuchy, Robert; Dupont, Joëlle; Branca, Antoine; Giraud, Tatiana

2015-10-05

Domestication is an excellent model for studies of adaptation because it involves recent and strong selection on a few, identified traits [1-5]. Few studies have focused on the domestication of fungi, with notable exceptions [6-11], despite their importance to bioindustry [12] and to a general understanding of adaptation in eukaryotes [5]. Penicillium fungi are ubiquitous molds among which two distantly related species have been independently selected for cheese making-P. roqueforti for blue cheeses like Roquefort and P. camemberti for soft cheeses like Camembert. The selected traits include morphology, aromatic profile, lipolytic and proteolytic activities, and ability to grow at low temperatures, in a matrix containing bacterial and fungal competitors [13-15]. By comparing the genomes of ten Penicillium species, we show that adaptation to cheese was associated with multiple recent horizontal transfers of large genomic regions carrying crucial metabolic genes. We identified seven horizontally transferred regions (HTRs) spanning more than 10 kb each, flanked by specific transposable elements, and displaying nearly 100% identity between distant Penicillium species. Two HTRs carried genes with functions involved in the utilization of cheese nutrients or competition and were found nearly identical in multiple strains and species of cheese-associated Penicillium fungi, indicating recent selective sweeps; they were experimentally associated with faster growth and greater competitiveness on cheese and contained genes highly expressed in the early stage of cheese maturation. These findings have industrial and food safety implications and improve our understanding of the processes of adaptation to rapid environmental changes. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Adaptive Horizontal Gene Transfers between Multiple Cheese-Associated Fungi

PubMed Central

Ropars, Jeanne; Rodríguez de la Vega, Ricardo C.; López-Villavicencio, Manuela; Gouzy, Jérôme; Sallet, Erika; Dumas, Émilie; Lacoste, Sandrine; Debuchy, Robert; Dupont, Joëlle; Branca, Antoine; Giraud, Tatiana

2015-01-01

Summary Domestication is an excellent model for studies of adaptation because it involves recent and strong selection on a few, identified traits [1–5]. Few studies have focused on the domestication of fungi, with notable exceptions [6–11], despite their importance to bioindustry [12] and to a general understanding of adaptation in eukaryotes [5]. Penicillium fungi are ubiquitous molds among which two distantly related species have been independently selected for cheese making—P. roqueforti for blue cheeses like Roquefort and P. camemberti for soft cheeses like Camembert. The selected traits include morphology, aromatic profile, lipolytic and proteolytic activities, and ability to grow at low temperatures, in a matrix containing bacterial and fungal competitors [13–15]. By comparing the genomes of ten Penicillium species, we show that adaptation to cheese was associated with multiple recent horizontal transfers of large genomic regions carrying crucial metabolic genes. We identified seven horizontally transferred regions (HTRs) spanning more than 10 kb each, flanked by specific transposable elements, and displaying nearly 100% identity between distant Penicillium species. Two HTRs carried genes with functions involved in the utilization of cheese nutrients or competition and were found nearly identical in multiple strains and species of cheese-associated Penicillium fungi, indicating recent selective sweeps; they were experimentally associated with faster growth and greater competitiveness on cheese and contained genes highly expressed in the early stage of cheese maturation. These findings have industrial and food safety implications and improve our understanding of the processes of adaptation to rapid environmental changes. PMID:26412136

A computational framework for modeling targets as complex adaptive systems

NASA Astrophysics Data System (ADS)

Santos, Eugene; Santos, Eunice E.; Korah, John; Murugappan, Vairavan; Subramanian, Suresh

2017-05-01

Modeling large military targets is a challenge as they can be complex systems encompassing myriad combinations of human, technological, and social elements that interact, leading to complex behaviors. Moreover, such targets have multiple components and structures, extending across multiple spatial and temporal scales, and are in a state of change, either in response to events in the environment or changes within the system. Complex adaptive system (CAS) theory can help in capturing the dynamism, interactions, and more importantly various emergent behaviors, displayed by the targets. However, a key stumbling block is incorporating information from various intelligence, surveillance and reconnaissance (ISR) sources, while dealing with the inherent uncertainty, incompleteness and time criticality of real world information. To overcome these challenges, we present a probabilistic reasoning network based framework called complex adaptive Bayesian Knowledge Base (caBKB). caBKB is a rigorous, overarching and axiomatic framework that models two key processes, namely information aggregation and information composition. While information aggregation deals with the union, merger and concatenation of information and takes into account issues such as source reliability and information inconsistencies, information composition focuses on combining information components where such components may have well defined operations. Since caBKBs can explicitly model the relationships between information pieces at various scales, it provides unique capabilities such as the ability to de-aggregate and de-compose information for detailed analysis. Using a scenario from the Network Centric Operations (NCO) domain, we will describe how our framework can be used for modeling targets with a focus on methodologies for quantifying NCO performance metrics.

Collaborative depression care: history, evolution and ways to enhance dissemination and sustainability.

PubMed

Katon, Wayne; Unützer, Jürgen; Wells, Kenneth; Jones, Loretta

2010-01-01

To describe the history and evolution of the collaborative depression care model and new research aimed at enhancing dissemination. Four keynote speakers from the 2009 NIMH Annual Mental Health Services Meeting collaborated in this article in order to describe the history and evolution of collaborative depression care, adaptation of collaborative care to new populations and medical settings, and optimal ways to enhance dissemination of this model. Extensive evidence across 37 randomized trials has shown the effectiveness of collaborative care vs. usual primary care in enhancing quality of depression care and in improving depressive outcomes for up to 2 to 5 years. Collaborative care is currently being disseminated in large health care organizations such as the Veterans Administration and Kaiser Permanente, as well as in fee-for-services systems and federally funded clinic systems of care in multiple states. New adaptations of collaborative care are being tested in pediatric and ob-gyn populations as well as in populations of patients with multiple comorbid medical illnesses. New NIMH-funded research is also testing community-based participatory research approaches to collaborative care to attempt to decrease disparities of care in underserved minority populations. Collaborative depression care has extensive research supporting the effectiveness of this model. New research and demonstration projects have focused on adapting this model to new populations and medical settings and on studying ways to optimally disseminate this approach to care, including developing financial models to incentivize dissemination and partnerships with community populations to enhance sustainability and to decrease disparities in quality of mental health care. Copyright © 2010 Elsevier Inc. All rights reserved.

Scale-adaptive compressive tracking with feature integration

NASA Astrophysics Data System (ADS)

Liu, Wei; Li, Jicheng; Chen, Xiao; Li, Shuxin

2016-05-01

Numerous tracking-by-detection methods have been proposed for robust visual tracking, among which compressive tracking (CT) has obtained some promising results. A scale-adaptive CT method based on multifeature integration is presented to improve the robustness and accuracy of CT. We introduce a keypoint-based model to achieve the accurate scale estimation, which can additionally give a prior location of the target. Furthermore, by the high efficiency of data-independent random projection matrix, multiple features are integrated into an effective appearance model to construct the naïve Bayes classifier. At last, an adaptive update scheme is proposed to update the classifier conservatively. Experiments on various challenging sequences demonstrate substantial improvements by our proposed tracker over CT and other state-of-the-art trackers in terms of dealing with scale variation, abrupt motion, deformation, and illumination changes.

Adaptive threshold control for auto-rate fallback algorithm in IEEE 802.11 multi-rate WLANs

NASA Astrophysics Data System (ADS)

Wu, Qilin; Lu, Yang; Zhu, Xiaolin; Ge, Fangzhen

2012-03-01

The IEEE 802.11 standard supports multiple rates for data transmission in the physical layer. Nowadays, to improve network performance, a rate adaptation scheme called auto-rate fallback (ARF) is widely adopted in practice. However, ARF scheme suffers performance degradation in multiple contending nodes environments. In this article, we propose a novel rate adaptation scheme called ARF with adaptive threshold control. In multiple contending nodes environment, the proposed scheme can effectively mitigate the frame collision effect on rate adaptation decision by adaptively adjusting rate-up and rate-down threshold according to the current collision level. Simulation results show that the proposed scheme can achieve significantly higher throughput than the other existing rate adaptation schemes. Furthermore, the simulation results also demonstrate that the proposed scheme can effectively respond to the varying channel condition.

Complex adaptive systems: A new approach for understanding health practices.

PubMed

Gomersall, Tim

2018-06-22

This article explores the potential of complex adaptive systems theory to inform behaviour change research. A complex adaptive system describes a collection of heterogeneous agents interacting within a particular context, adapting to each other's actions. In practical terms, this implies that behaviour change is 1) socially and culturally situated; 2) highly sensitive to small baseline differences in individuals, groups, and intervention components; and 3) determined by multiple components interacting "chaotically". Two approaches to studying complex adaptive systems are briefly reviewed. Agent-based modelling is a computer simulation technique that allows researchers to investigate "what if" questions in a virtual environment. Applied qualitative research techniques, on the other hand, offer a way to examine what happens when an intervention is pursued in real-time, and to identify the sorts of rules and assumptions governing social action. Although these represent very different approaches to complexity, there may be scope for mixing these methods - for example, by grounding models in insights derived from qualitative fieldwork. Finally, I will argue that the concept of complex adaptive systems offers one opportunity to gain a deepened understanding of health-related practices, and to examine the social psychological processes that produce health-promoting or damaging actions.

Taking Aim at the Cognitive Side of Learning in Sensorimotor Adaptation Tasks.

PubMed

McDougle, Samuel D; Ivry, Richard B; Taylor, Jordan A

2016-07-01

Sensorimotor adaptation tasks have been used to characterize processes responsible for calibrating the mapping between desired outcomes and motor commands. Research has focused on how this form of error-based learning takes place in an implicit and automatic manner. However, recent work has revealed the operation of multiple learning processes, even in this simple form of learning. This review focuses on the contribution of cognitive strategies and heuristics to sensorimotor learning, and how these processes enable humans to rapidly explore and evaluate novel solutions to enable flexible, goal-oriented behavior. This new work points to limitations in current computational models, and how these must be updated to describe the conjoint impact of multiple processes in sensorimotor learning. Copyright © 2016 Elsevier Ltd. All rights reserved.

Techniques and resources for storm-scale numerical weather prediction

NASA Technical Reports Server (NTRS)

Droegemeier, Kelvin; Grell, Georg; Doyle, James; Soong, Su-Tzai; Skamarock, William; Bacon, David; Staniforth, Andrew; Crook, Andrew; Wilhelmson, Robert

1993-01-01

The topics discussed include the following: multiscale application of the 5th-generation PSU/NCAR mesoscale model, the coupling of nonhydrostatic atmospheric and hydrostatic ocean models for air-sea interaction studies; a numerical simulation of cloud formation over complex topography; adaptive grid simulations of convection; an unstructured grid, nonhydrostatic meso/cloud scale model; efficient mesoscale modeling for multiple scales using variable resolution; initialization of cloud-scale models with Doppler radar data; and making effective use of future computing architectures, networks, and visualization software.

The DIAN-TU Next Generation Alzheimer’s prevention trial: adaptive design and disease progression model

PubMed Central

Bateman, Randall J.; Benzinger, Tammie L.; Berry, Scott; Clifford, David B.; Duggan, Cynthia; Fagan, Anne M.; Fanning, Kathleen; Farlow, Martin R.; Hassenstab, Jason; McDade, Eric M.; Mills, Susan; Paumier, Katrina; Quintana, Melanie; Salloway, Stephen P.; Santacruz, Anna; Schneider, Lon S.; Wang, Guoqiao; Xiong, Chengjie

2016-01-01

INTRODUCTION The Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU) trial is an adaptive platform trial testing multiple drugs to slow or prevent the progression of Alzheimer’s disease in autosomal dominant Alzheimer’s disease (ADAD) families. With completion of enrollment of the first two drug arms, the DIAN-TU now plans to add new drugs to the platform, designated as the Next Generation Prevention Trial (NexGen). METHODS In collaboration with ADAD families, philanthropic organizations, academic leaders, the DIAN-TU Pharma Consortium, the NIH, and regulatory colleagues, the DIAN-TU developed innovative clinical study designs for the DIAN-TU NexGen trial. RESULTS Our expanded trials toolbox consists of a Disease Progression Model for ADAD, primary endpoint DIAN-TU cognitive performance composite, biomarker development, self-administered cognitive assessments, adaptive dose adjustments, and blinded data collection through the last participant completion. CONCLUSION These steps represent elements to improve efficacy of the adaptive platform trial and a continued effort to optimize prevention and treatment trials in ADAD. PMID:27583651

Practical Implementation of Multiple Model Adaptive Estimation Using Neyman-Pearson Based Hypothesis Testing and Spectral Estimation Tools

DTIC Science & Technology

1996-09-01

Generalized Likelihood Ratio (GLR) and voting techniques. The third class consisted of multiple hypothesis filter detectors, specifically the MMAE. The...vector version, versus a tensor if we use the matrix version of the power spectral density estimate. Using this notation, we will derive an...as MATLAB , have an intrinsic sample covariance computation available, which makes this method quite easy to implement. In practice, the mean for the

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.

EM Adaptive LASSO—A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes

PubMed Central

Mallick, Himel; Tiwari, Hemant K.

2016-01-01

Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice. PMID:27066062

EM Adaptive LASSO-A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes.

PubMed

Mallick, Himel; Tiwari, Hemant K

2016-01-01

Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice.

Model-based adaptive 3D sonar reconstruction in reverberating environments.

PubMed

Saucan, Augustin-Alexandru; Sintes, Christophe; Chonavel, Thierry; Caillec, Jean-Marc Le

2015-10-01

In this paper, we propose a novel model-based approach for 3D underwater scene reconstruction, i.e., bathymetry, for side scan sonar arrays in complex and highly reverberating environments like shallow water areas. The presence of multipath echoes and volume reverberation generates false depth estimates. To improve the resulting bathymetry, this paper proposes and develops an adaptive filter, based on several original geometrical models. This multimodel approach makes it possible to track and separate the direction of arrival trajectories of multiple echoes impinging the array. Echo tracking is perceived as a model-based processing stage, incorporating prior information on the temporal evolution of echoes in order to reject cluttered observations generated by interfering echoes. The results of the proposed filter on simulated and real sonar data showcase the clutter-free and regularized bathymetric reconstruction. Model validation is carried out with goodness of fit tests, and demonstrates the importance of model-based processing for bathymetry reconstruction.

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

A STATISTICAL MODELING METHODOLOGY FOR THE DETECTION, QUANTIFICATION, AND PREDICTION OF ECOLOGICAL THRESHOLDS

EPA Science Inventory

This study will provide a general methodology for integrating threshold information from multiple species ecological metrics, allow for prediction of changes of alternative stable states, and provide a risk assessment tool that can be applied to adaptive management. The integr...

A nonlinear control method based on ANFIS and multiple models for a class of SISO nonlinear systems and its application.

PubMed

Zhang, Yajun; Chai, Tianyou; Wang, Hong

2011-11-01

This paper presents a novel nonlinear control strategy for a class of uncertain single-input and single-output discrete-time nonlinear systems with unstable zero-dynamics. The proposed method combines adaptive-network-based fuzzy inference system (ANFIS) with multiple models, where a linear robust controller, an ANFIS-based nonlinear controller and a switching mechanism are integrated using multiple models technique. It has been shown that the linear controller can ensure the boundedness of the input and output signals and the nonlinear controller can improve the dynamic performance of the closed loop system. Moreover, it has also been shown that the use of the switching mechanism can simultaneously guarantee the closed loop stability and improve its performance. As a result, the controller has the following three outstanding features compared with existing control strategies. First, this method relaxes the assumption of commonly-used uniform boundedness on the unmodeled dynamics and thus enhances its applicability. Second, since ANFIS is used to estimate and compensate the effect caused by the unmodeled dynamics, the convergence rate of neural network learning has been increased. Third, a "one-to-one mapping" technique is adapted to guarantee the universal approximation property of ANFIS. The proposed controller is applied to a numerical example and a pulverizing process of an alumina sintering system, respectively, where its effectiveness has been justified.

Modelling marine community responses to climate-driven species redistribution to guide monitoring and adaptive ecosystem-based management.

PubMed

Marzloff, Martin Pierre; Melbourne-Thomas, Jessica; Hamon, Katell G; Hoshino, Eriko; Jennings, Sarah; van Putten, Ingrid E; Pecl, Gretta T

2016-07-01

As a consequence of global climate-driven changes, marine ecosystems are experiencing polewards redistributions of species - or range shifts - across taxa and throughout latitudes worldwide. Research on these range shifts largely focuses on understanding and predicting changes in the distribution of individual species. The ecological effects of marine range shifts on ecosystem structure and functioning, as well as human coastal communities, can be large, yet remain difficult to anticipate and manage. Here, we use qualitative modelling of system feedback to understand the cumulative impacts of multiple species shifts in south-eastern Australia, a global hotspot for ocean warming. We identify range-shifting species that can induce trophic cascades and affect ecosystem dynamics and productivity, and evaluate the potential effectiveness of alternative management interventions to mitigate these impacts. Our results suggest that the negative ecological impacts of multiple simultaneous range shifts generally add up. Thus, implementing whole-of-ecosystem management strategies and regular monitoring of range-shifting species of ecological concern are necessary to effectively intervene against undesirable consequences of marine range shifts at the regional scale. Our study illustrates how modelling system feedback with only limited qualitative information about ecosystem structure and range-shifting species can predict ecological consequences of multiple co-occurring range shifts, guide ecosystem-based adaptation to climate change and help prioritise future research and monitoring. © 2016 John Wiley & Sons Ltd.

Locally adaptive, spatially explicit projection of US population for 2030 and 2050.

PubMed

McKee, Jacob J; Rose, Amy N; Bright, Edward A; Huynh, Timmy; Bhaduri, Budhendra L

2015-02-03

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Building on the spatial interpolation technique previously developed for high-resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically informed spatial distribution of projected population of the contiguous United States for 2030 and 2050, depicting one of many possible population futures. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection model departs from these by accounting for multiple components that affect population distribution. Modeled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the US Census's projection methodology, with the US Census's official projection as the benchmark. Applications of our model include incorporating multiple various scenario-driven events to produce a range of spatially explicit population futures for suitability modeling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.

An analysis of stakeholder views on children's mental health services.

PubMed

Rodríguez, Adriana; Southam-Gerow, Michael A; O'Connor, Mary Katherine; Allin, Robert B

2014-01-01

The purpose was to examine the perspectives of mental health stakeholders as a means to guide the adaptation of evidence-based treatments. The Mental Health System Ecological (MHSE) model was used to organize therapist, administrator, and parent perspectives gathered using qualitative methods. The MHSE model posits the influences of client-level, provider-level, intervention-specific, service delivery, organizational, and service system characteristics on implementation. Focus groups and interviews were conducted with community mental health stakeholders and included parents, therapists, and administrators (N = 21). Participants included 11 primarily Caucasian (90.48%) female participants, ranging in ages between 31 and 57 years. Data were analyzed according to the MHSE model. Frequency counts were tabulated for each theme and stakeholder group differences were determined using the Mann-Whitney test. Stakeholder groups mentioned needs at each level of the MHSE model. Stakeholder group differences most notably emerged with child and family themes, which included complexity of mental health issues, parenting differences, and family stressors. Stakeholders identified challenges for optimal mental health services for children across multiple levels of an ecological model. Implications of the findings are discussed, including the continued relevance of adapting mental health interventions by increasing their flexible application across multiple target problems and the promise of partnership approaches.

Internal model of gravity for hand interception: parametric adaptation to zero-gravity visual targets on Earth.

PubMed

Zago, Myrka; Lacquaniti, Francesco

2005-08-01

Internal model is a neural mechanism that mimics the dynamics of an object for sensory motor or cognitive functions. Recent research focuses on the issue of whether multiple internal models are learned and switched to cope with a variety of conditions, or single general models are adapted by tuning the parameters. Here we addressed this issue by investigating how the manual interception of a moving target changes with changes of the visual environment. In our paradigm, a virtual target moves vertically downward on a screen with different laws of motion. Subjects are asked to punch a hidden ball that arrives in synchrony with the visual target. By using several different protocols, we systematically found that subjects do not develop a new internal model appropriate for constant speed targets, but they use the default gravity model and reduce the central processing time. The results imply that adaptation to zero-gravity targets involves a compression of temporal processing through the cortical and subcortical regions interconnected with the vestibular cortex, which has previously been shown to be the site of storage of the internal model of gravity.

How protein materials balance strength, robustness, and adaptability

PubMed Central

Buehler, Markus J.; Yung, Yu Ching

2010-01-01

Proteins form the basis of a wide range of biological materials such as hair, skin, bone, spider silk, or cells, which play an important role in providing key functions to biological systems. The focus of this article is to discuss how protein materials are capable of balancing multiple, seemingly incompatible properties such as strength, robustness, and adaptability. To illustrate this, we review bottom-up materiomics studies focused on the mechanical behavior of protein materials at multiple scales, from nano to macro. We focus on alpha-helix based intermediate filament proteins as a model system to explain why the utilization of hierarchical structural features is vital to their ability to combine strength, robustness, and adaptability. Experimental studies demonstrating the activation of angiogenesis, the growth of new blood vessels, are presented as an example of how adaptability of structure in biological tissue is achieved through changes in gene expression that result in an altered material structure. We analyze the concepts in light of the universality and diversity of the structural makeup of protein materials and discuss the findings in the context of potential fundamental evolutionary principles that control their nanoscale structure. We conclude with a discussion of multiscale science in biology and de novo materials design. PMID:20676305

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

PubMed Central

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

2016-01-01

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

Climate change and health modeling: horses for courses.

PubMed

Ebi, Kristie L; Rocklöv, Joacim

2014-01-01

Mathematical and statistical models are needed to understand the extent to which weather, climate variability, and climate change are affecting current and may affect future health burdens in the context of other risk factors and a range of possible development pathways, and the temporal and spatial patterns of any changes. Such understanding is needed to guide the design and the implementation of adaptation and mitigation measures. Because each model projection captures only a narrow range of possible futures, and because models serve different purposes, multiple models are needed for each health outcome ('horses for courses'). Multiple modeling results can be used to bracket the ranges of when, where, and with what intensity negative health consequences could arise. This commentary explores some climate change and health modeling issues, particularly modeling exposure-response relationships, developing early warning systems, projecting health risks over coming decades, and modeling to inform decision-making. Research needs are also suggested.

Can multi-generational exposure to ocean warming and acidification lead to the adaptation of life history and physiology in a marine metazoan?

PubMed

Gibbin, Emma M; Chakravarti, Leela J; Jarrold, Michael D; Christen, Felix; Turpin, Vincent; Massamba N'Siala, Gloria; Blier, Pierre U; Calosi, Piero

2017-02-15

Ocean warming and acidification are concomitant global drivers that are currently threatening the survival of marine organisms. How species will respond to these changes depends on their capacity for plastic and adaptive responses. Little is known about the mechanisms that govern plasticity and adaptability or how global changes will influence these relationships across multiple generations. Here, we exposed the emerging model marine polychaete Ophryotrocha labronica to conditions simulating ocean warming and acidification, in isolation and in combination over five generations to identify: (i) how multiple versus single global change drivers alter both juvenile and adult life-history traits; (ii) the mechanistic link between adult physiological and fitness-related life-history traits; and (iii) whether the phenotypic changes observed over multiple generations are of plastic and/or adaptive origin. Two juvenile (developmental rate; survival to sexual maturity) and two adult (average reproductive body size; fecundity) life-history traits were measured in each generation, in addition to three physiological (cellular reactive oxygen species content, mitochondrial density, mitochondrial capacity) traits. We found that multi-generational exposure to warming alone caused an increase in juvenile developmental rate, reactive oxygen species production and mitochondrial density, decreases in average reproductive body size and fecundity, and fluctuations in mitochondrial capacity, relative to control conditions. Exposure to ocean acidification alone had only minor effects on juvenile developmental rate. Remarkably, when both drivers of global change were present, only mitochondrial capacity was significantly affected, suggesting that ocean warming and acidification act as opposing vectors of stress across multiple generations. © 2017. Published by The Company of Biologists Ltd.

Rapid Link of Innate Immune Signal to Adaptive Immunity by Brain–Fat Axis

PubMed Central

Kim, Min Soo; Yan, Jingqi; Wu, Wenhe; Zhang, Guo; Zhang, Yalin; Cai, Dongsheng

2015-01-01

Innate immunity signals induced by pathogen/damage-associated molecular patterns are essential for adaptive immune responses, but it is unclear if the brain plays a role in this process. Here we show that while tumor necrosis factor (TNF) quickly increased in the brain of mice following bacterial infection, intra-brain TNF delivery mimicked bacterial infection to rapidly increase peripheral lymphocytes, especially in the spleen and fat. Multiple mouse models revealed that hypothalamic responses to TNF were accountable for this increase of peripheral lymphocytes in response to bacterial infection. Finally, hypothalamic induction of lipolysis was found to mediate the brain's action in promoting this increase in peripheral adaptive immune response. Thus, the brain-fat axis is important for rapidly linking innate immunity to adaptive immunity. PMID:25848866

Evaluation of the effect of vibration nonlinearity on convergence behavior of adaptive higher harmonic controllers

NASA Technical Reports Server (NTRS)

Molusis, J. A.; Mookerjee, P.; Bar-Shalom, Y.

1983-01-01

Effect of nonlinearity on convergence of the local linear and global linear adaptive controllers is evaluated. A nonlinear helicopter vibration model is selected for the evaluation which has sufficient nonlinearity, including multiple minimum, to assess the vibration reduction capability of the adaptive controllers. The adaptive control algorithms are based upon a linear transfer matrix assumption and the presence of nonlinearity has a significant effect on algorithm behavior. Simulation results are presented which demonstrate the importance of the caution property in the global linear controller. Caution is represented by a time varying rate weighting term in the local linear controller and this improves the algorithm convergence. Nonlinearity in some cases causes Kalman filter divergence. Two forms of the Kalman filter covariance equation are investigated.

Importance of Branched-Chain Amino Acid Utilization in Francisella Intracellular Adaptation

PubMed Central

Gesbert, Gael; Ramond, Elodie; Tros, Fabiola; Dairou, Julien; Frapy, Eric; Barel, Monique

2014-01-01

Intracellular bacterial pathogens have adapted their metabolism to optimally utilize the nutrients available in infected host cells. We recently reported the identification of an asparagine transporter required specifically for cytosolic multiplication of Francisella. In the present work, we characterized a new member of the major super family (MSF) of transporters, involved in isoleucine uptake. We show that this transporter (here designated IleP) plays a critical role in intracellular metabolic adaptation of Francisella. Inactivation of IleP severely impaired intracellular F. tularensis subsp. novicida multiplication in all cell types tested and reduced bacterial virulence in the mouse model. To further establish the importance of the ileP gene in F. tularensis pathogenesis, we constructed a chromosomal deletion mutant of ileP (ΔFTL_1803) in the F. tularensis subsp. holarctica live vaccine strain (LVS). Inactivation of IleP in the F. tularensis LVS provoked comparable intracellular growth defects, confirming the critical role of this transporter in isoleucine uptake. The data presented establish, for the first time, the importance of isoleucine utilization for efficient phagosomal escape and cytosolic multiplication of Francisella and suggest that virulent F. tularensis subspecies have lost their branched-chain amino acid biosynthetic pathways and rely exclusively on dedicated uptake systems. This loss of function is likely to reflect an evolution toward a predominantly intracellular life style of the pathogen. Amino acid transporters should be thus considered major players in the adaptation of intracellular pathogens. PMID:25332124

Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

PubMed Central

Sun, Baoliang; Jiang, Chunlan; Li, Ming

2016-01-01

An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. PMID:27809271

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 to develop improved regional impact assessments for situations where multi-crop rotations better represent predominant agricultural systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Electrophysiological models of neural processing.

PubMed

Nelson, Mark E

2011-01-01

The brain is an amazing information processing system that allows organisms to adaptively monitor and control complex dynamic interactions with their environment across multiple spatial and temporal scales. Mathematical modeling and computer simulation techniques have become essential tools in understanding diverse aspects of neural processing ranging from sub-millisecond temporal coding in the sound localization circuity of barn owls to long-term memory storage and retrieval in humans that can span decades. The processing capabilities of individual neurons lie at the core of these models, with the emphasis shifting upward and downward across different levels of biological organization depending on the nature of the questions being addressed. This review provides an introduction to the techniques for constructing biophysically based models of individual neurons and local networks. Topics include Hodgkin-Huxley-type models of macroscopic membrane currents, Markov models of individual ion-channel currents, compartmental models of neuronal morphology, and network models involving synaptic interactions among multiple neurons.

Addressing Complex Challenges through Adaptive Leadership: A Promising Approach to Collaborative Problem Solving

ERIC Educational Resources Information Center

Nelson, Tenneisha; Squires, Vicki

2017-01-01

Organizations are faced with solving increasingly complex problems. Addressing these issues requires effective leadership that can facilitate a collaborative problem solving approach where multiple perspectives are leveraged. In this conceptual paper, we critique the effectiveness of earlier leadership models in tackling complex organizational…

Market Orientation in Managing Relationships with Multiple Constituencies of Croatian Higher Education

ERIC Educational Resources Information Center

Pavicic, Jurica; Alfirevic, Niksa; Mihanovic, Zoran

2009-01-01

In this paper, market orientation in Croatian higher education (HE) is discussed within the context of stakeholder-oriented management. Drawing on existing studies, the "classical" empirical model, describing the market orientation of generic nonprofit organisations, has been adapted to the contingencies of the Croatian HE sector.…

Case Study in Modeling Accessibility for Online Instruction

ERIC Educational Resources Information Center

Conway, Thomas Hayes

2017-01-01

The purpose of this qualitative multiple case study was to explore how accessibility standards are adapted to create online learning environments that are accessible to people who use assistive technology, or have navigational challenges due to physical or intellectual disabilities. Rogers diffusion of innovation was used as the contextual…

Combating speckle in SAR images - Vector filtering and sequential classification based on a multiplicative noise model

NASA Technical Reports Server (NTRS)

Lin, Qian; Allebach, Jan P.

1990-01-01

An adaptive vector linear minimum mean-squared error (LMMSE) filter for multichannel images with multiplicative noise is presented. It is shown theoretically that the mean-squared error in the filter output is reduced by making use of the correlation between image bands. The vector and conventional scalar LMMSE filters are applied to a three-band SIR-B SAR, and their performance is compared. Based on a mutliplicative noise model, the per-pel maximum likelihood classifier was derived. The authors extend this to the design of sequential and robust classifiers. These classifiers are also applied to the three-band SIR-B SAR image.

Dynamic optimization and adaptive controller design

NASA Astrophysics Data System (ADS)

Inamdar, S. R.

2010-10-01

In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.

Adaptive evolution of complex innovations through stepwise metabolic niche expansion.

PubMed

Szappanos, Balázs; Fritzemeier, Jonathan; Csörgő, Bálint; Lázár, Viktória; Lu, Xiaowen; Fekete, Gergely; Bálint, Balázs; Herczeg, Róbert; Nagy, István; Notebaart, Richard A; Lercher, Martin J; Pál, Csaba; Papp, Balázs

2016-05-20

A central challenge in evolutionary biology concerns the mechanisms by which complex metabolic innovations requiring multiple mutations arise. Here, we propose that metabolic innovations accessible through the addition of a single reaction serve as stepping stones towards the later establishment of complex metabolic features in another environment. We demonstrate the feasibility of this hypothesis through three complementary analyses. First, using genome-scale metabolic modelling, we show that complex metabolic innovations in Escherichia coli can arise via changing nutrient conditions. Second, using phylogenetic approaches, we demonstrate that the acquisition patterns of complex metabolic pathways during the evolutionary history of bacterial genomes support the hypothesis. Third, we show how adaptation of laboratory populations of E. coli to one carbon source facilitates the later adaptation to another carbon source. Our work demonstrates how complex innovations can evolve through series of adaptive steps without the need to invoke non-adaptive processes.

Adaptive evolution of complex innovations through stepwise metabolic niche expansion

PubMed Central

Szappanos, Balázs; Fritzemeier, Jonathan; Csörgő, Bálint; Lázár, Viktória; Lu, Xiaowen; Fekete, Gergely; Bálint, Balázs; Herczeg, Róbert; Nagy, István; Notebaart, Richard A.; Lercher, Martin J.; Pál, Csaba; Papp, Balázs

2016-01-01

A central challenge in evolutionary biology concerns the mechanisms by which complex metabolic innovations requiring multiple mutations arise. Here, we propose that metabolic innovations accessible through the addition of a single reaction serve as stepping stones towards the later establishment of complex metabolic features in another environment. We demonstrate the feasibility of this hypothesis through three complementary analyses. First, using genome-scale metabolic modelling, we show that complex metabolic innovations in Escherichia coli can arise via changing nutrient conditions. Second, using phylogenetic approaches, we demonstrate that the acquisition patterns of complex metabolic pathways during the evolutionary history of bacterial genomes support the hypothesis. Third, we show how adaptation of laboratory populations of E. coli to one carbon source facilitates the later adaptation to another carbon source. Our work demonstrates how complex innovations can evolve through series of adaptive steps without the need to invoke non-adaptive processes. PMID:27197754

Traffic off-balancing algorithm for energy efficient networks

NASA Astrophysics Data System (ADS)

Kim, Junhyuk; Lee, Chankyun; Rhee, June-Koo Kevin

2011-12-01

Physical layer of high-end network system uses multiple interface arrays. Under the load-balancing perspective, light load can be distributed to multiple interfaces. However, it can cause energy inefficiency in terms of the number of poor utilization interfaces. To tackle this energy inefficiency, traffic off-balancing algorithm for traffic adaptive interface sleep/awake is investigated. As a reference model, 40G/100G Ethernet is investigated. We report that suggested algorithm can achieve energy efficiency while satisfying traffic transmission requirement.

Force-induced bone growth and adaptation: A system theoretical approach to understanding bone mechanotransduction

NASA Astrophysics Data System (ADS)

Maldonado, Solvey; Findeisen, Rolf

2010-06-01

The modeling, analysis, and design of treatment therapies for bone disorders based on the paradigm of force-induced bone growth and adaptation is a challenging task. Mathematical models provide, in comparison to clinical, medical and biological approaches an structured alternative framework to understand the concurrent effects of the multiple factors involved in bone remodeling. By now, there are few mathematical models describing the appearing complex interactions. However, the resulting models are complex and difficult to analyze, due to the strong nonlinearities appearing in the equations, the wide range of variability of the states, and the uncertainties in parameters. In this work, we focus on analyzing the effects of changes in model structure and parameters/inputs variations on the overall steady state behavior using systems theoretical methods. Based on an briefly reviewed existing model that describes force-induced bone adaptation, the main objective of this work is to analyze the stationary behavior and to identify plausible treatment targets for remodeling related bone disorders. Identifying plausible targets can help in the development of optimal treatments combining both physical activity and drug-medication. Such treatments help to improve/maintain/restore bone strength, which deteriorates under bone disorder conditions, such as estrogen deficiency.

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

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.

Startle reduces recall of a recently learned internal model.

PubMed

Wright, Zachary; Patton, James L; Ravichandran, Venn

2011-01-01

Recent work has shown that preplanned motor programs are released early from subcortical areas by the using a startling acoustic stimulus (SAS). Our question is whether this response might also contain a recently learned internal model, which draws on experience to predict and compensate for expected perturbations in a feedforward manner. Studies of adaptation to robotic forces have shown some evidence of this, but were potentially confounded by cocontraction caused by startle. We performed a new adaptation experiment using a visually distorted field that could not be confounded by cocontraction. We found that in all subjects that exhibited startle, the startle stimulus (1) reduced performance of the recently learned task (2) reduced after-effect magnitudes. Because startle reduced but did not eliminate the recall of learned control, we suggest that multiple neural centers (cortical and subcortical) are involved in such learning and adaptation, which can impact training areas such as piloting, teleoperation, sports, and rehabilitation. © 2011 IEEE

Experimental verification of a real-time tuning method of a model-based controller by perturbations to its poles

NASA Astrophysics Data System (ADS)

Kajiwara, Itsuro; Furuya, Keiichiro; Ishizuka, Shinichi

2018-07-01

Model-based controllers with adaptive design variables are often used to control an object with time-dependent characteristics. However, the controller's performance is influenced by many factors such as modeling accuracy and fluctuations in the object's characteristics. One method to overcome these negative factors is to tune model-based controllers. Herein we propose an online tuning method to maintain control performance for an object that exhibits time-dependent variations. The proposed method employs the poles of the controller as design variables because the poles significantly impact performance. Specifically, we use the simultaneous perturbation stochastic approximation (SPSA) to optimize a model-based controller with multiple design variables. Moreover, a vibration control experiment of an object with time-dependent characteristics as the temperature is varied demonstrates that the proposed method allows adaptive control and stably maintains the closed-loop characteristics.

The adaptive safety analysis and monitoring system

NASA Astrophysics Data System (ADS)

Tu, Haiying; Allanach, Jeffrey; Singh, Satnam; Pattipati, Krishna R.; Willett, Peter

2004-09-01

The Adaptive Safety Analysis and Monitoring (ASAM) system is a hybrid model-based software tool for assisting intelligence analysts to identify terrorist threats, to predict possible evolution of the terrorist activities, and to suggest strategies for countering terrorism. The ASAM system provides a distributed processing structure for gathering, sharing, understanding, and using information to assess and predict terrorist network states. In combination with counter-terrorist network models, it can also suggest feasible actions to inhibit potential terrorist threats. In this paper, we will introduce the architecture of the ASAM system, and discuss the hybrid modeling approach embedded in it, viz., Hidden Markov Models (HMMs) to detect and provide soft evidence on the states of terrorist network nodes based on partial and imperfect observations, and Bayesian networks (BNs) to integrate soft evidence from multiple HMMs. The functionality of the ASAM system is illustrated by way of application to the Indian Airlines Hijacking, as modeled from open sources.

Systems Modeling at Multiple Levels of Regulation: Linking Systems and Genetic Networks to Spatially Explicit Plant Populations

PubMed Central

Kitchen, James L.; Allaby, Robin G.

2013-01-01

Selection and adaptation of individuals to their underlying environments are highly dynamical processes, encompassing interactions between the individual and its seasonally changing environment, synergistic or antagonistic interactions between individuals and interactions amongst the regulatory genes within the individual. Plants are useful organisms to study within systems modeling because their sedentary nature simplifies interactions between individuals and the environment, and many important plant processes such as germination or flowering are dependent on annual cycles which can be disrupted by climate behavior. Sedentism makes plants relevant candidates for spatially explicit modeling that is tied in with dynamical environments. We propose that in order to fully understand the complexities behind plant adaptation, a system that couples aspects from systems biology with population and landscape genetics is required. A suitable system could be represented by spatially explicit individual-based models where the virtual individuals are located within time-variable heterogeneous environments and contain mutable regulatory gene networks. These networks could directly interact with the environment, and should provide a useful approach to studying plant adaptation. PMID:27137364

Optimization study on multiple train formation scheme of urban rail transit

NASA Astrophysics Data System (ADS)

Xia, Xiaomei; Ding, Yong; Wen, Xin

2018-05-01

The new organization method, represented by the mixed operation of multi-marshalling trains, can adapt to the characteristics of the uneven distribution of passenger flow, but the research on this aspect is still not perfect enough. This paper introduced the passenger sharing rate and congestion penalty coefficient with different train formations. On this basis, this paper established an optimization model with the minimum passenger cost and operation cost as objective, and operation frequency and passenger demand as constraint. The ideal point method is used to solve this model. Compared with the fixed marshalling operation model, the overall cost of this scheme saves 9.24% and 4.43% respectively. This result not only validates the validity of the model, but also illustrate the advantages of the multiple train formations scheme.

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

PubMed

He, Dan; Kuhn, David; Parida, Laxmi

2016-06-15

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

Adaptive Helmholtz resonators and passive vibration absorbers for cylinder interior noise control

NASA Astrophysics Data System (ADS)

Estève, Simon J.; Johnson, Marty E.

2005-12-01

This paper presents an adaptive-passive solution to control the broadband sound transmission into rocket payload fairings. The treatment is composed of passive distributed vibration absorbers (DVAs) and adaptive Helmholtz resonators (HR). Both the frequency domain and time-domain model of a simply supported cylinder excited by an external plane wave are developed. To tune vibration absorbers to tonal excitation, a tuning strategy, based on the phase information between the velocity of the absorber mass and the velocity of the host structure is used here in a new fashion to tune resonators to peaks in the broadband acoustic spectrum of a cavity. This tuning law, called the dot-product method, only uses two microphone signals local to each HR, which allows the adaptive Helmholtz resonator (AHR) to be manufactured as an autonomous device with power supply, sensor, actuator and controller integrated. Numerical simulations corresponding to a 2.8 m long 2.5 m diameter composite cylinder prototype demonstrate that, as long as the structure modes, which strongly couple to the acoustic cavity, are damped with a DVA treatment, the dot-product method tune multiple HRs to a near-optimal solution over a broad frequency range (40-160 Hz). An adaptive HR prototype with variable opening is built and characterized. Experiments conducted on the cylinder prototype with eight AHRs demonstrate the ability of resonators adapted with the dot-product method to converge to near-optimal noise attenuation in a frequency band including multiple resonances.

Adaptation to seasonality and the winter freeze

PubMed Central

Preston, Jill C.; Sandve, Simen R.

2013-01-01

Flowering plants initially diversified during the Mesozoic era at least 140 million years ago in regions of the world where temperate seasonal environments were not encountered. Since then several cooling events resulted in the contraction of warm and wet environments and the establishment of novel temperate zones in both hemispheres. In response, less than half of modern angiosperm families have members that evolved specific adaptations to cold seasonal climates, including cold acclimation, freezing tolerance, endodormancy, and vernalization responsiveness. Despite compelling evidence for multiple independent origins, the level of genetic constraint on the evolution of adaptations to seasonal cold is not well understood. However, the recent increase in molecular genetic studies examining the response of model and crop species to seasonal cold offers new insight into the evolutionary lability of these traits. This insight has major implications for our understanding of complex trait evolution, and the potential role of local adaptation in response to past and future climate change. In this review, we discuss the biochemical, morphological, and developmental basis of adaptations to seasonal cold, and synthesize recent literature on the genetic basis of these traits in a phylogenomic context. We find evidence for multiple genetic links between distinct physiological responses to cold, possibly reinforcing the coordinated expression of these traits. Furthermore, repeated recruitment of the same or similar ancestral pathways suggests that land plants might be somewhat pre-adapted to dealing with temperature stress, perhaps making inducible cold traits relatively easy to evolve. PMID:23761798

Adaptive behaviour and multiple equilibrium states in a predator-prey model.

PubMed

Pimenov, Alexander; Kelly, Thomas C; Korobeinikov, Andrei; O'Callaghan, Michael J A; Rachinskii, Dmitrii

2015-05-01

There is evidence that multiple stable equilibrium states are possible in real-life ecological systems. Phenomenological mathematical models which exhibit such properties can be constructed rather straightforwardly. For instance, for a predator-prey system this result can be achieved through the use of non-monotonic functional response for the predator. However, while formal formulation of such a model is not a problem, the biological justification for such functional responses and models is usually inconclusive. In this note, we explore a conjecture that a multitude of equilibrium states can be caused by an adaptation of animal behaviour to changes of environmental conditions. In order to verify this hypothesis, we consider a simple predator-prey model, which is a straightforward extension of the classic Lotka-Volterra predator-prey model. In this model, we made an intuitively transparent assumption that the prey can change a mode of behaviour in response to the pressure of predation, choosing either "safe" of "risky" (or "business as usual") behaviour. In order to avoid a situation where one of the modes gives an absolute advantage, we introduce the concept of the "cost of a policy" into the model. A simple conceptual two-dimensional predator-prey model, which is minimal with this property, and is not relying on odd functional responses, higher dimensionality or behaviour change for the predator, exhibits two stable co-existing equilibrium states with basins of attraction separated by a separatrix of a saddle point. Copyright © 2015 Elsevier Inc. All rights reserved.

Adaptations to local environments in modern human populations.

PubMed

Jeong, Choongwon; Di Rienzo, Anna

2014-12-01

After leaving sub-Saharan Africa around 50000-100000 years ago, anatomically modern humans have quickly occupied extremely diverse environments. Human populations were exposed to further environmental changes resulting from cultural innovations, such as the spread of farming, which gave rise to new selective pressures related to pathogen exposures and dietary shifts. In addition to changing the frequency of individual adaptive alleles, natural selection may also shape the overall genetic architecture of adaptive traits. Here, we review recent advances in understanding the genetic architecture of adaptive human phenotypes based on insights from the studies of lactase persistence, skin pigmentation and high-altitude adaptation. These adaptations evolved in parallel in multiple human populations, providing a chance to investigate independent realizations of the evolutionary process. We suggest that the outcome of adaptive evolution is often highly variable even under similar selective pressures. Finally, we highlight a growing need for detecting adaptations that did not follow the classical sweep model and for incorporating new sources of genetic evidence such as information from ancient DNA. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling radiative transfer with the doubling and adding approach in a climate GCM setting

NASA Astrophysics Data System (ADS)

Lacis, A. A.

2017-12-01

The nonlinear dependence of multiply scattered radiation on particle size, optical depth, and solar zenith angle, makes accurate treatment of multiple scattering in the climate GCM setting problematic, due primarily to computational cost issues. In regard to the accurate methods of calculating multiple scattering that are available, their computational cost is far too prohibitive for climate GCM applications. Utilization of two-stream-type radiative transfer approximations may be computationally fast enough, but at the cost of reduced accuracy. We describe here a parameterization of the doubling/adding method that is being used in the GISS climate GCM, which is an adaptation of the doubling/adding formalism configured to operate with a look-up table utilizing a single gauss quadrature point with an extra-angle formulation. It is designed to closely reproduce the accuracy of full-angle doubling and adding for the multiple scattering effects of clouds and aerosols in a realistic atmosphere as a function of particle size, optical depth, and solar zenith angle. With an additional inverse look-up table, this single-gauss-point doubling/adding approach can be adapted to model fractional cloud cover for any GCM grid-box in the independent pixel approximation as a function of the fractional cloud particle sizes, optical depths, and solar zenith angle dependence.

Selection towards different adaptive optima drove the early diversification of locomotor phenotypes in the radiation of Neotropical geophagine cichlids.

PubMed

Astudillo-Clavijo, Viviana; Arbour, Jessica H; López-Fernández, Hernán

2015-05-01

Simpson envisaged a conceptual model of adaptive radiation in which lineages diversify into "adaptive zones" within a macroevolutionary adaptive landscape. However, only a handful of studies have empirically investigated this adaptive landscape and its consequences for our interpretation of the underlying mechanisms of phenotypic evolution. In fish radiations the evolution of locomotor phenotypes may represent an important dimension of ecomorphological diversification given the implications of locomotion for feeding and habitat use. Neotropical geophagine cichlids represent a newly identified adaptive radiation and provide a useful system for studying patterns of locomotor diversification and the implications of selective constraints on phenotypic divergence in general. We use multivariate ordination, models of phenotypic evolution and posterior predictive approaches to investigate the macroevolutionary adaptive landscape and test for evidence of early divergence of locomotor phenotypes in Geophagini. The evolution of locomotor phenotypes was characterized by selection towards at least two distinct adaptive peaks and the early divergence of modern morphological disparity. One adaptive peak included the benthic and epibenthic invertivores and was characterized by fishes with deep, laterally compressed bodies that optimize precise, slow-swimming manoeuvres. The second adaptive peak resulted from a shift in adaptive optima in the species-rich ram-feeding/rheophilic Crenicichla-Teleocichla clade and was characterized by species with streamlined bodies that optimize fast starts and rapid manoeuvres. Evolutionary models and posterior predictive approaches favoured an early shift to a new adaptive peak over decreasing rates of evolution as the underlying process driving the early divergence of locomotor phenotypes. The influence of multiple adaptive peaks on the divergence of locomotor phenotypes in Geophagini is compatible with the expectations of an ecologically driven adaptive radiation. This study confirms that the diversification of locomotor phenotypes represents an important dimension of phenotypic evolution in the geophagine adaptive radiation. It also suggests that the commonly observed early burst of phenotypic evolution during adaptive radiations may be better explained by the concentration of shifts to new adaptive peaks deep in the phylogeny rather than overall decreasing rates of evolution.

Optimizing the passenger air bag of an adaptive restraint system for multiple size occupants.

PubMed

Bai, Zhonghao; Jiang, Binhui; Zhu, Feng; Cao, Libo

2014-01-01

The development of the adaptive occupant restraint system (AORS) has led to an innovative way to optimize such systems for multiple size occupants. An AORS consists of multiple units such as adaptive air bags, seat belts, etc. During a collision, as a supplemental protective device, air bags can provide constraint force and play a role in dissipating the crash energy of the occupants' head and thorax. This article presents an investigation into an adaptive passenger air bag (PAB). The purpose of this study is to develop a base shape of a PAB for different size occupants using an optimization method. Four typical base shapes of a PAB were designed based on geometric data on the passenger side. Then 4 PAB finite element (FE) models and a validated sled with different size dummy models were developed in MADYMO (TNO, Rijswijk, The Netherlands) to conduct the optimization to obtain the best baseline PAB that would be used in the AORS. The objective functions-that is, the minimum total probability of injuries (∑Pcomb) of the 5th percentile female and 50th and 95th percentile male dummies-were adopted to evaluate the optimal configurations. The injury probability (Pcomb) for each dummy was adopted from the U.S. New Car Assessment Program (US-NCAP). The parameters of the AORS were first optimized for different types of PAB base shapes in a frontal impact. Then, contact time duration and force between the PAB and dummy head/chest were optimized by adjusting the parameters of the PAB, such as the number and position of tethers, lower the Pcomb of the 95th percentile male dummy. According to the optimization results, 4 typical PABs could provide effective protection to 5th and 50th percentile dummies. However, due to the heavy and large torsos of the 95th percentile occupants, the current occupant restraint system does not demonstrate satisfactory protective function, particularly for the thorax.

Combining Multiple Knowledge Sources for Speech Recognition

DTIC Science & Technology

1988-09-15

Thus, the first is thle to clarify the pronunciationt ( TASSEAJ for the acronym TASA !). best adaptation sentence, the second sentence, whens addled...10 rapid adapltati,,n sen- tenrces, and 15 spell-i,, de phrases. 6101 resource rirailageo lei SPEAKER-DEPENDENT DATABASE sentences were randortily...combining the smoothed phoneme models with the de - system tested on a standard database using two well de . tailed context models. BYBLOS makes maximal use

Research of maneuvering target prediction and tracking technology based on IMM algorithm

NASA Astrophysics Data System (ADS)

Cao, Zheng; Mao, Yao; Deng, Chao; Liu, Qiong; Chen, Jing

2016-09-01

Maneuvering target prediction and tracking technology is widely used in both military and civilian applications, the study of those technologies is all along the hotspot and difficulty. In the Electro-Optical acquisition-tracking-pointing system (ATP), the primary traditional maneuvering targets are ballistic target, large aircraft and other big targets. Those targets have the features of fast velocity and a strong regular trajectory and Kalman Filtering and polynomial fitting have good effects when they are used to track those targets. In recent years, the small unmanned aerial vehicles developed rapidly for they are small, nimble and simple operation. The small unmanned aerial vehicles have strong maneuverability in the observation system of ATP although they are close-in, slow and small targets. Moreover, those vehicles are under the manual operation, therefore, the acceleration of them changes greatly and they move erratically. So the prediction and tracking precision is low when traditional algorithms are used to track the maneuvering fly of those targets, such as speeding up, turning, climbing and so on. The interacting multiple model algorithm (IMM) use multiple models to match target real movement trajectory, there are interactions between each model. The IMM algorithm can switch model based on a Markov chain to adapt to the change of target movement trajectory, so it is suitable to solve the prediction and tracking problems of the small unmanned aerial vehicles because of the better adaptability of irregular movement. This paper has set up model set of constant velocity model (CV), constant acceleration model (CA), constant turning model (CT) and current statistical model. And the results of simulating and analyzing the real movement trajectory data of the small unmanned aerial vehicles show that the prediction and tracking technology based on the interacting multiple model algorithm can get relatively lower tracking error and improve tracking precision comparing with traditional algorithms.

Global Change adaptation in water resources management: the Water Change project.

PubMed

Pouget, Laurent; Escaler, Isabel; Guiu, Roger; Mc Ennis, Suzy; Versini, Pierre-Antoine

2012-12-01

In recent years, water resources management has been facing new challenges due to increasing changes and their associated uncertainties, such as changes in climate, water demand or land use, which can be grouped under the term Global Change. The Water Change project (LIFE+ funding) developed a methodology and a tool to assess the Global Change impacts on water resources, thus helping river basin agencies and water companies in their long term planning and in the definition of adaptation measures. The main result of the project was the creation of a step by step methodology to assess Global Change impacts and define strategies of adaptation. This methodology was tested in the Llobregat river basin (Spain) with the objective of being applicable to any water system. It includes several steps such as setting-up the problem with a DPSIR framework, developing Global Change scenarios, running river basin models and performing a cost-benefit analysis to define optimal strategies of adaptation. This methodology was supported by the creation of a flexible modelling system, which can link a wide range of models, such as hydrological, water quality, and water management models. The tool allows users to integrate their own models to the system, which can then exchange information among them automatically. This enables to simulate the interactions among multiple components of the water cycle, and run quickly a large number of Global Change scenarios. The outcomes of this project make possible to define and test different sets of adaptation measures for the basin that can be further evaluated through cost-benefit analysis. The integration of the results contributes to an efficient decision-making on how to adapt to Global Change impacts. Copyright © 2012 Elsevier B.V. All rights reserved.

The adaptation of Escherichia coli cells grown in simulated microgravity for an extended period is both phenotypic and genomic.

PubMed

Tirumalai, Madhan R; Karouia, Fathi; Tran, Quyen; Stepanov, Victor G; Bruce, Rebekah J; Ott, C Mark; Pierson, Duane L; Fox, George E

2017-01-01

Microorganisms impact spaceflight in a variety of ways. They play a positive role in biological systems, such as waste water treatment but can be problematic through buildups of biofilms that can affect advanced life support. Of special concern is the possibility that during extended missions, the microgravity environment will provide positive selection for undesirable genomic changes. Such changes could affect microbial antibiotic sensitivity and possibly pathogenicity. To evaluate this possibility, Escherichia coli (lac plus) cells were grown for over 1000 generations on Luria Broth medium under low-shear modeled microgravity conditions in a high aspect rotating vessel. This is the first study of its kind to grow bacteria for multiple generations over an extended period under low-shear modeled microgravity. Comparisons were made to a non-adaptive control strain using growth competitions. After 1000 generations, the final low-shear modeled microgravity-adapted strain readily outcompeted the unadapted lac minus strain. A portion of this advantage was maintained when the low-shear modeled microgravity strain was first grown in a shake flask environment for 10, 20, or 30 generations of growth. Genomic sequencing of the 1000 generation strain revealed 16 mutations. Of the five changes affecting codons, none were neutral. It is not clear how significant these mutations are as individual changes or as a group. It is concluded that part of the long-term adaptation to low-shear modeled microgravity is likely genomic. The strain was monitored for acquisition of antibiotic resistance by VITEK analysis throughout the adaptation period. Despite the evidence of genomic adaptation, resistance to a variety of antibiotics was never observed.

The Research of Multiple Attenuation Based on Feedback Iteration and Independent Component Analysis

NASA Astrophysics Data System (ADS)

Xu, X.; Tong, S.; Wang, L.

2017-12-01

How to solve the problem of multiple suppression is a difficult problem in seismic data processing. The traditional technology for multiple attenuation is based on the principle of the minimum output energy of the seismic signal, this criterion is based on the second order statistics, and it can't achieve the multiple attenuation when the primaries and multiples are non-orthogonal. In order to solve the above problems, we combine the feedback iteration method based on the wave equation and the improved independent component analysis (ICA) based on high order statistics to suppress the multiple waves. We first use iterative feedback method to predict the free surface multiples of each order. Then, in order to predict multiples from real multiple in amplitude and phase, we design an expanded pseudo multi-channel matching filtering method to get a more accurate matching multiple result. Finally, we present the improved fast ICA algorithm which is based on the maximum non-Gauss criterion of output signal to the matching multiples and get better separation results of the primaries and the multiples. The advantage of our method is that we don't need any priori information to the prediction of the multiples, and can have a better separation result. The method has been applied to several synthetic data generated by finite-difference model technique and the Sigsbee2B model multiple data, the primaries and multiples are non-orthogonal in these models. The experiments show that after three to four iterations, we can get the perfect multiple results. Using our matching method and Fast ICA adaptive multiple subtraction, we can not only effectively preserve the effective wave energy in seismic records, but also can effectively suppress the free surface multiples, especially the multiples related to the middle and deep areas.

Adaptive receiver structures for asynchronous CDMA systems

NASA Astrophysics Data System (ADS)

Rapajic, Predrag B.; Vucetic, Branka S.

1994-05-01

Adaptive linear and decision feedback receiver structures for coherent demodulation in asynchronous code division multiple access (CDMA) systems are considered. It is assumed that the adaptive receiver has no knowledge of the signature waveforms and timing of other users. The receiver is trained by a known training sequence prior to data transmission and continuously adjusted by an adaptive algorithm during data transmission. The proposed linear receiver is as simple as a standard single-user detector receiver consisting of a matched filter with constant coefficients, but achieves essential advantages with respect to timing recovery, multiple access interference elimination, near/far effect, narrowband and frequency-selective fading interference suppression, and user privacy. An adaptive centralized decision feedback receiver has the same advantages of the linear receiver but, in addition, achieves a further improvement in multiple access interference cancellation at the expense of higher complexity. The proposed receiver structures are tested by simulation over a channel with multipath propagation, multiple access interference, narrowband interference, and additive white Gaussian noise.

An Improved Adaptive Received Beamforming for Nested Frequency Offset and Nested Array FDA-MIMO Radar.

PubMed

Cheng, Sibei; Zhang, Qingjun; Bian, Mingming; Hao, Xinhong

2018-02-08

For the conventional FDA-MIMO (frequency diversity array multiple-input-multiple-output) Radar with uniform frequency offset and uniform linear array, the DOFs (degrees of freedom) of the adaptive beamformer are limited by the number of elements. A better performance-for example, a better suppression for strong interferences and a more desirable trade-off between the main lobe and side lobe-can be achieved with a greater number of DOFs. In order to obtain larger DOFs, this paper researches the signal model of the FDA-MIMO Radar with nested frequency offset and nested array, then proposes an improved adaptive beamforming method that uses the augmented matrix instead of the covariance matrix to calculate the optimum weight vectors and can be used to improve the output performances of FDA-MIMO Radar with the same element number or reduce the element number while maintain the approximate output performances such as the received beampattern, the main lobe width, side lobe depths and the output SINR (signal-to-interference-noise ratio). The effectiveness of the proposed scheme is verified by simulations.

An Improved Adaptive Received Beamforming for Nested Frequency Offset and Nested Array FDA-MIMO Radar

PubMed Central

Cheng, Sibei; Zhang, Qingjun; Bian, Mingming; Hao, Xinhong

2018-01-01

For the conventional FDA-MIMO (frequency diversity array multiple-input-multiple-output) Radar with uniform frequency offset and uniform linear array, the DOFs (degrees of freedom) of the adaptive beamformer are limited by the number of elements. A better performance—for example, a better suppression for strong interferences and a more desirable trade-off between the main lobe and side lobe—can be achieved with a greater number of DOFs. In order to obtain larger DOFs, this paper researches the signal model of the FDA-MIMO Radar with nested frequency offset and nested array, then proposes an improved adaptive beamforming method that uses the augmented matrix instead of the covariance matrix to calculate the optimum weight vectors and can be used to improve the output performances of FDA-MIMO Radar with the same element number or reduce the element number while maintain the approximate output performances such as the received beampattern, the main lobe width, side lobe depths and the output SINR (signal-to-interference-noise ratio). The effectiveness of the proposed scheme is verified by simulations. PMID:29419814

Signatures of positive selection: from selective sweeps at individual loci to subtle allele frequency changes in polygenic adaptation.

PubMed

Stephan, Wolfgang

2016-01-01

In the past 15 years, numerous methods have been developed to detect selective sweeps underlying adaptations. These methods are based on relatively simple population genetic models, including one or two loci at which positive directional selection occurs, and one or two marker loci at which the impact of selection on linked neutral variation is quantified. Information about the phenotype under selection is not included in these models (except for fitness). In contrast, in the quantitative genetic models of adaptation, selection acts on one or more phenotypic traits, such that a genotype-phenotype map is required to bridge the gap to population genetics theory. Here I describe the range of population genetic models from selective sweeps in a panmictic population of constant size to evolutionary traffic when simultaneous sweeps at multiple loci interfere, and I also consider the case of polygenic selection characterized by subtle allele frequency shifts at many loci. Furthermore, I present an overview of the statistical tests that have been proposed based on these population genetics models to detect evidence for positive selection in the genome. © 2015 John Wiley & Sons Ltd.

A biologically based model for the integration of sensory-motor contingencies in rules and plans: a prefrontal cortex based extension of the Distributed Adaptive Control architecture.

PubMed

Duff, Armin; Fibla, Marti Sanchez; Verschure, Paul F M J

2011-06-30

Intelligence depends on the ability of the brain to acquire and apply rules and representations. At the neuronal level these properties have been shown to critically depend on the prefrontal cortex. Here we present, in the context of the Distributed Adaptive Control architecture (DAC), a biologically based model for flexible control and planning based on key physiological properties of the prefrontal cortex, i.e. reward modulated sustained activity and plasticity of lateral connectivity. We test the model in a series of pertinent tasks, including multiple T-mazes and the Tower of London that are standard experimental tasks to assess flexible control and planning. We show that the model is both able to acquire and express rules that capture the properties of the task and to quickly adapt to changes. Further, we demonstrate that this biomimetic self-contained cognitive architecture generalizes to planning. In addition, we analyze the extended DAC architecture, called DAC 6, as a model that can be applied for the creation of intelligent and psychologically believable synthetic agents. Copyright © 2010 Elsevier Inc. All rights reserved.

The Aids' Requirements of Children with Severe Multiple Handicaps and the People Looking after Them.

ERIC Educational Resources Information Center

Anden, Gerd

The report presents findings from interviews with 10 families with children (4-19 years old) with severe mental retardation and multiple disabilities regarding the need for technical aids and adaptations in their homes. The following areas are addressed and examples of solutions proposed: hygienic aids (hot water adaptations, travel adaptations,…

The evolutionary basis of human social learning

PubMed Central

Morgan, T. J. H.; Rendell, L. E.; Ehn, M.; Hoppitt, W.; Laland, K. N.

2012-01-01

Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules. PMID:21795267

The evolutionary basis of human social learning.

PubMed

Morgan, T J H; Rendell, L E; Ehn, M; Hoppitt, W; Laland, K N

2012-02-22

Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules.

Taking Root in Foreign Soil: Adaptation Processes of Imported Universities

ERIC Educational Resources Information Center

Graham, Terrece F.

2016-01-01

The fall of the Berlin Wall in 1989 ushered in a period of change in higher-education systems across the former Eastern bloc. Reform-minded leaders in the region sought to introduce western models and policies promoted by foreign development aid agendas. Private higher-education institutions emerged. This qualitative multiple case study examines…

Using Faculty Characteristics to Predict Attitudes toward Developmental Education

ERIC Educational Resources Information Center

Sides, Meredith Louise Carr

2017-01-01

The study adapted Astin's I-E-O model and utilized multiple regression analyses to predict faculty attitudes toward developmental education. The study utilized a cross-sectional survey design to survey faculty members at 27 different higher education institutions in the state of Alabama. The survey instrument was a self-designed questionnaire that…

An Optimal Parameter Discretization Strategy for Multiple Model Adaptive Estimation and Control

DTIC Science & Technology

1989-12-01

Zicker . MMAE-Based Control with Space- Time Point Process Observations. IEEE Transactions on Aerospace and Elec- tronic Systems, AES-21 (3):292-300, 1985...Transactions of the Conference of Army Math- ematicians, Bethesda MD, 1982. (AD-POO1 033). 65. William L. Zicker . Pointing and Tracking of Particle

Integrating Science and Management to Assess Forest Ecosystem Vulnerability to Climate Change

Treesearch

Leslie A. Brandt; Patricia R. Butler; Stephen D. Handler; Maria K. Janowiak; P. Danielle Shannon; Christopher W. Swanston

2017-01-01

We developed the ecosystem vulnerability assessment approach (EVAA) to help inform potential adaptation actions in response to a changing climate. EVAA combines multiple quantitative models and expert elicitation from scientists and land managers. In each of eight assessment areas, a panel of local experts determined potential vulnerability of forest ecosystems to...

The Multiple Possibilities of Decency: Family and Society in American History.

ERIC Educational Resources Information Center

Schlossman, Steven L.

This paper focuses on three family-related issues: (1) the extraordinary complexity with which families perform educational and socializing functions and the corresponding danger of using simplistic cause and effect models to explain family behavior; (2) the ability of historical and contemporary American families to adapt to massive changes in…

Social Orientation: Problem Behavior and Motivations Toward Interpersonal Problem Solving Among High Risk Adolescents

PubMed Central

Kuperminc, Gabriel P.; Allen, Joseph P.

2006-01-01

A model of problematic adolescent behavior that expands current theories of social skill deficits in delinquent behavior to consider both social skills and orientation toward the use of adaptive skills was examined in an ethnically and socioeconomically diverse sample of 113 male and female adolescents. Adolescents were selected on the basis of moderate to serious risk for difficulties in social adaptation in order to focus on the population of youth most likely to be targeted by prevention efforts. Structural equation modeling was used to examine cross-sectional data using multiple informants (adolescents, peers, and parents) and multiple methods (performance test and self-report). Adolescent social orientation, as reflected in perceived problem solving effectiveness, identification with adult prosocial values, and self-efficacy expectations, exhibited a direct association to delinquent behavior and an indirect association to drug involvement mediated by demonstrated success in using problem solving skills. Results suggest that the utility of social skill theories of adolescent problem behaviors for informing preventive and remedial interventions can be enhanced by expanding them to consider adolescents’ orientation toward using the skills they may already possess. PMID:16929380

Active vibration control for piezoelectricity cantilever beam: an adaptive feedforward control method

NASA Astrophysics Data System (ADS)

Zhu, Qiao; Yue, Jun-Zhou; Liu, Wei-Qun; Wang, Xu-Dong; Chen, Jun; Hu, Guang-Di

2017-04-01

This work is focused on the active vibration control of piezoelectric cantilever beam, where an adaptive feedforward controller (AFC) is utilized to reject the vibration with unknown multiple frequencies. First, the experiment setup and its mathematical model are introduced. Due to that the channel between the disturbance and the vibration output is unknown in practice, a concept of equivalent input disturbance (EID) is employed to put an equivalent disturbance into the input channel. In this situation, the vibration control can be achieved by setting the control input be the identified EID. Then, for the EID with known multiple frequencies, the AFC is introduced to perfectly reject the vibration but is sensitive to the frequencies. In order to accurately identify the unknown frequencies of EID in presence of the random disturbances and un-modeled nonlinear dynamics, the time-frequency-analysis (TFA) method is employed to precisely identify the unknown frequencies. Consequently, a TFA-based AFC algorithm is proposed to the active vibration control with unknown frequencies. Finally, four cases are given to illustrate the efficiency of the proposed TFA-based AFC algorithm by experiment.

Evolution of morphology and locomotor performance in anurans: relationships with microhabitat diversification.

PubMed

Citadini, J M; Brandt, R; Williams, C R; Gomes, F R

2018-03-01

The relationships between morphology, performance, behavior and ecology provide evidence for multiple and complex phenotypic adaptations. The anuran body plan, for example, is evolutionarily conserved and shows clear specializations to jumping performance back at least to the early Jurassic. However, there are instances of more recent adaptation to habit diversity in the post-cranial skeleton, including relative limb length. The present study tested adaptive models of morphological evolution in anurans associated with the diversity of microhabitat use (semi-aquatic arboreal, fossorial, torrent, and terrestrial) in species of anuran amphibians from Brazil and Australia. We use phylogenetic comparative methods to determine which evolutionary models, including Brownian motion (BM) and Ornstein-Uhlenbeck (OU) are consistent with morphological variation observed across anuran species. Furthermore, this study investigated the relationship of maximum distance jumped as a function of components of morphological variables and microhabitat use. We found there are multiple optima of limb lengths associated to different microhabitats with a trend of increasing hindlimbs in torrent, arboreal, semi-aquatic whereas fossorial and terrestrial species evolve toward optima with shorter hindlimbs. Moreover, arboreal, semi-aquatic and torrent anurans have higher jumping performance and longer hindlimbs, when compared to terrestrial and fossorial species. We corroborate the hypothesis that evolutionary modifications of overall limb morphology have been important in the diversification of locomotor performance along the anuran phylogeny. Such evolutionary changes converged in different phylogenetic groups adapted to similar microhabitat use in two different zoogeographical regions. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

Global adaptation patterns of Australian and CIMMYT spring bread wheat.

PubMed

Mathews, Ky L; Chapman, Scott C; Trethowan, Richard; Pfeiffer, Wolfgang; van Ginkel, Maarten; Crossa, Jose; Payne, Thomas; Delacy, Ian; Fox, Paul N; Cooper, Mark

2007-10-01

The International Adaptation Trial (IAT) is a special purpose nursery designed to investigate the genotype-by-environment interactions and worldwide adaptation for grain yield of Australian and CIMMYT spring bread wheat (Triticum aestivum L.) and durum wheat (T. turgidum L. var. durum). The IAT contains lines representing Australian and CIMMYT wheat breeding programs and was distributed to 91 countries between 2000 and 2004. Yield data of 41 reference lines from 106 trials were analysed. A multiplicative mixed model accounted for trial variance heterogeneity and inter-trial correlations characteristic of multi-environment trials. A factor analytic model explained 48% of the genetic variance for the reference lines. Pedigree information was then incorporated to partition the genetic line effects into additive and non-additive components. This model explained 67 and 56% of the additive by environment and non-additive by environment genetic variances, respectively. Australian and CIMMYT germplasm showed good adaptation to their respective target production environments. In general, Australian lines performed well in south and west Australia, South America, southern Africa, Iran and high latitude European and Canadian locations. CIMMYT lines performed well at CIMMYT's key yield testing location in Mexico (CIANO), north-eastern Australia, the Indo-Gangetic plains, West Asia North Africa and locations in Europe and Canada. Maturity explained some of the global adaptation patterns. In general, southern Australian germplasm were later maturing than CIMMYT material. While CIANO continues to provide adapted lines to northern Australia, selecting for yield among later maturing CIMMYT material in CIANO may identify lines adapted to southern and western Australian environments.

Multiple-hypothesis multiple-model line tracking

NASA Astrophysics Data System (ADS)

Pace, Donald W.; Owen, Mark W.; Cox, Henry

2000-07-01

Passive sonar signal processing generally includes tracking of narrowband and/or broadband signature components observed on a Lofargram or on a Bearing-Time-Record (BTR) display. Fielded line tracking approaches to date have been recursive and single-hypthesis-oriented Kalman- or alpha-beta filters, with no mechanism for considering tracking alternatives beyond the most recent scan of measurements. While adaptivity is often built into the filter to handle changing track dynamics, these approaches are still extensions of single target tracking solutions to multiple target tracking environment. This paper describes an application of multiple-hypothesis, multiple target tracking technology to the sonar line tracking problem. A Multiple Hypothesis Line Tracker (MHLT) is developed which retains the recursive minimum-mean-square-error tracking behavior of a Kalman Filter in a maximum-a-posteriori delayed-decision multiple hypothesis context. Multiple line track filter states are developed and maintained using the interacting multiple model (IMM) state representation. Further, the data association and assignment problem is enhanced by considering line attribute information (line bandwidth and SNR) in addition to beam/bearing and frequency fit. MHLT results on real sonar data are presented to demonstrate the benefits of the multiple hypothesis approach. The utility of the system in cluttered environments and particularly in crossing line situations is shown.

Ensemble Data Assimilation Without Ensembles: Methodology and Application to Ocean Data Assimilation

NASA Technical Reports Server (NTRS)

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

2013-01-01

Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the ensemble Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an ensemble in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an ensemble sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time.

Methods for correcting tilt anisoplanatism in laser-guide-star-based multiconjugate adaptive optics.

PubMed

Ellerbroek, B L; Rigaut, F

2001-10-01

Multiconjugate adaptive optics (MCAO) is a technique for correcting turbulence-induced phase distortions in three dimensions instead of two, thereby greatly expanding the corrected field of view of an adaptive optics system. This is accomplished with use of multiple deformable mirrors conjugate to distinct ranges in the atmosphere, with actuator commands computed from wave-front sensor (WFS) measurements from multiple guide stars. Laser guide stars (LGSs) must be used (at least for the forseeable future) to achieve a useful degree of sky coverage in an astronomical MCAO system. Much as a single LGS cannot be used to measure overall wave-front tilt, a constellation of multiple LGSs at a common range cannot detect tilt anisoplanatism. This error alone will significantly degrade the performance of a MCAO system based on a single tilt-only natural guide star (NGS) and multiple tilt-removed LGSs at a common altitude. We present a heuristic, low-order model for the principal source of tilt anisoplanatism that suggests four possible approaches to eliminating this defect in LGS MCAO: (i) tip/tilt measurements from multiple NGS, (ii) a solution to the LGS tilt uncertainty problem, (iii) additional higher-order WFS measurements from a single NGS, or (iv) higher-order WFS measurements from both sodium and Rayleigh LGSs at different ranges. Sample numerical results for one particular MCAO system configuration indicate that approach (ii), if feasible, would provide the highest degree of tilt anisoplanatism compensation. Approaches (i) and (iv) also provide very useful levels of performance and do not require unrealistically low levels of WFS measurement noise. For a representative set of parameters for an 8-m telescope, the additional laser power required for approach (iv) is on the order of 2 W per Rayleigh LGS.

Modeling the interaction of biological cells with a solidifying interface

NASA Astrophysics Data System (ADS)

Chang, Anthony; Dantzig, Jonathan A.; Darr, Brian T.; Hubel, Allison

2007-10-01

In this article, we develop a modified level set method for modeling the interaction of particles with a solidifying interface. The dynamic computation of the van der Waals and drag forces between the particles and the solidification front leads to a problem of multiple length scales, which we resolve using adaptive grid techniques. We present a variety of example problems to demonstrate the accuracy and utility of the method. We also use the model to interpret experimental results obtained using directional solidification in a cryomicroscope.

A Stimulus-Locked Vector Autoregressive Model for Slow Event-Related fMRI Designs

PubMed Central

Siegle, Greg

2009-01-01

Summary Neuroscientists have become increasingly interested in exploring dynamic relationships among brain regions. Such a relationship, when directed from one region toward another, is denoted by “effective connectivity.” An fMRI experimental paradigm which is well-suited for examination of effective connectivity is the slow event-related design. This design presents stimuli at sufficient temporal spacing for determining within-trial trajectories of BOLD activation, allowing for the analysis of stimulus-locked temporal covariation of brain responses in multiple regions. This may be especially important for emotional stimuli processing, which can evolve over the course of several seconds, if not longer. However, while several methods have been devised for determining fMRI effective connectivity, few are adapted to event-related designs, which include non-stationary BOLD responses and multiple levels of nesting. We propose a model tailored for exploring effective connectivity of multiple brain regions in event-related fMRI designs - a semi-parametric adaptation of vector autoregressive (VAR) models, termed “stimulus-locked VAR” (SloVAR). Connectivity coefficients vary as a function of time relative to stimulus onset, are regularized via basis expansions, and vary randomly across subjects. SloVAR obtains flexible, data-driven estimates of effective connectivity and hence is useful for building connectivity models when prior information on dynamic regional relationships is sparse. Indices derived from the coefficient estimates can also be used to relate effective connectivity estimates to behavioral or clinical measures. We demonstrate the SloVAR model on a sample of clinically depressed and normal controls, showing that early but not late cortico-amygdala connectivity appears crucial to emotional control and early but not late cortico-cortico connectivity predicts depression severity in the depressed group, relationships that would have been missed in a more traditional VAR analysis. PMID:19236927

Adaptive oscillator networks with conserved overall coupling: Sequential firing and near-synchronized states

NASA Astrophysics Data System (ADS)

Picallo, Clara B.; Riecke, Hermann

2011-03-01

Motivated by recent observations in neuronal systems we investigate all-to-all networks of nonidentical oscillators with adaptive coupling. The adaptation models spike-timing-dependent plasticity in which the sum of the weights of all incoming links is conserved. We find multiple phase-locked states that fall into two classes: near-synchronized states and splay states. Among the near-synchronized states are states that oscillate with a frequency that depends only very weakly on the coupling strength and is essentially given by the frequency of one of the oscillators, which is, however, neither the fastest nor the slowest oscillator. In sufficiently large networks the adaptive coupling is found to develop effective network topologies dominated by one or two loops. This results in a multitude of stable splay states, which differ in their firing sequences. With increasing coupling strength their frequency increases linearly and the oscillators become less synchronized. The essential features of the two classes of states are captured analytically in perturbation analyses of the extended Kuramoto model used in the simulations.

A multilevel structural equation modeling analysis of vulnerabilities and resilience resources influencing affective adaptation to chronic pain.

PubMed

Sturgeon, John A; Zautra, Alex J; Arewasikporn, Anne

2014-02-01

The processes of individual adaptation to chronic pain are complex and occur across multiple domains. We examined the social, cognitive, and affective context of daily pain adaptation in individuals with fibromyalgia and osteoarthritis. By using a sample of 260 women with fibromyalgia or osteoarthritis, we examined the contributions of pain catastrophizing, negative interpersonal events, and positive interpersonal events to daily negative and positive affect across 30days of daily diary data. Individual differences and daily fluctuations in predictor variables were estimated simultaneously by utilizing multilevel structural equation modeling techniques. The relationships between pain and negative and positive affect were mediated by stable and day-to-day levels of pain catastrophizing as well as day-to-day positive interpersonal events, but not negative interpersonal events. There were significant and independent contributions of pain catastrophizing and positive interpersonal events to adaptation to pain and pain-related affective dysregulation. These effects occur both between persons and within a person's everyday life. Copyright © 2013 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

The DIAN-TU Next Generation Alzheimer's prevention trial: Adaptive design and disease progression model.

PubMed

Bateman, Randall J; Benzinger, Tammie L; Berry, Scott; Clifford, David B; Duggan, Cynthia; Fagan, Anne M; Fanning, Kathleen; Farlow, Martin R; Hassenstab, Jason; McDade, Eric M; Mills, Susan; Paumier, Katrina; Quintana, Melanie; Salloway, Stephen P; Santacruz, Anna; Schneider, Lon S; Wang, Guoqiao; Xiong, Chengjie

2017-01-01

The Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU) trial is an adaptive platform trial testing multiple drugs to slow or prevent the progression of Alzheimer's disease in autosomal dominant Alzheimer's disease (ADAD) families. With completion of enrollment of the first two drug arms, the DIAN-TU now plans to add new drugs to the platform, designated as the Next Generation (NexGen) prevention trial. In collaboration with ADAD families, philanthropic organizations, academic leaders, the DIAN-TU Pharma Consortium, the National Institutes of Health, and regulatory colleagues, the DIAN-TU developed innovative clinical study designs for the DIAN-TU NexGen prevention trial. Our expanded trial toolbox consists of a disease progression model for ADAD, primary end point DIAN-TU cognitive performance composite, biomarker development, self-administered cognitive assessments, adaptive dose adjustments, and blinded data collection through the last participant completion. These steps represent elements to improve efficacy of the adaptive platform trial and a continued effort to optimize prevention and treatment trials in ADAD. Copyright © 2016 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Sensor fusion V; Proceedings of the Meeting, Boston, MA, Nov. 15-17, 1992

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1992-01-01

Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)

Synergy of adaptive thresholds and multiple transmitters in free-space optical communication.

PubMed

Louthain, James A; Schmidt, Jason D

2010-04-26

Laser propagation through extended turbulence causes severe beam spread and scintillation. Airborne laser communication systems require special considerations in size, complexity, power, and weight. Rather than using bulky, costly, adaptive optics systems, we reduce the variability of the received signal by integrating a two-transmitter system with an adaptive threshold receiver to average out the deleterious effects of turbulence. In contrast to adaptive optics approaches, systems employing multiple transmitters and adaptive thresholds exhibit performance improvements that are unaffected by turbulence strength. Simulations of this system with on-off-keying (OOK) showed that reducing the scintillation variations with multiple transmitters improves the performance of low-frequency adaptive threshold estimators by 1-3 dB. The combination of multiple transmitters and adaptive thresholding provided at least a 10 dB gain over implementing only transmitter pointing and receiver tilt correction for all three high-Rytov number scenarios. The scenario with a spherical-wave Rytov number R=0.20 enjoyed a 13 dB reduction in the required SNR for BER's between 10(-5) to 10(-3), consistent with the code gain metric. All five scenarios between 0.06 and 0.20 Rytov number improved to within 3 dB of the SNR of the lowest Rytov number scenario.

Neurons in the inferior colliculus of the rat show stimulus-specific adaptation for frequency, but not for intensity

PubMed Central

Duque, Daniel; Wang, Xin; Nieto-Diego, Javier; Krumbholz, Katrin; Malmierca, Manuel S.

2016-01-01

Electrophysiological and psychophysical responses to a low-intensity probe sound tend to be suppressed by a preceding high-intensity adaptor sound. Nevertheless, rare low-intensity deviant sounds presented among frequent high-intensity standard sounds in an intensity oddball paradigm can elicit an electroencephalographic mismatch negativity (MMN) response. This has been taken to suggest that the MMN is a correlate of true change or “deviance” detection. A key question is where in the ascending auditory pathway true deviance sensitivity first emerges. Here, we addressed this question by measuring low-intensity deviant responses from single units in the inferior colliculus (IC) of anesthetized rats. If the IC exhibits true deviance sensitivity to intensity, IC neurons should show enhanced responses to low-intensity deviant sounds presented among high-intensity standards. Contrary to this prediction, deviant responses were only enhanced when the standards and deviants differed in frequency. The results could be explained with a model assuming that IC neurons integrate over multiple frequency-tuned channels and that adaptation occurs within each channel independently. We used an adaptation paradigm with multiple repeated adaptors to measure the tuning widths of these adaption channels in relation to the neurons’ overall tuning widths. PMID:27066835

Statistical Methods for Generalized Linear Models with Covariates Subject to Detection Limits.

PubMed

Bernhardt, Paul W; Wang, Huixia J; Zhang, Daowen

2015-05-01

Censored observations are a common occurrence in biomedical data sets. Although a large amount of research has been devoted to estimation and inference for data with censored responses, very little research has focused on proper statistical procedures when predictors are censored. In this paper, we consider statistical methods for dealing with multiple predictors subject to detection limits within the context of generalized linear models. We investigate and adapt several conventional methods and develop a new multiple imputation approach for analyzing data sets with predictors censored due to detection limits. We establish the consistency and asymptotic normality of the proposed multiple imputation estimator and suggest a computationally simple and consistent variance estimator. We also demonstrate that the conditional mean imputation method often leads to inconsistent estimates in generalized linear models, while several other methods are either computationally intensive or lead to parameter estimates that are biased or more variable compared to the proposed multiple imputation estimator. In an extensive simulation study, we assess the bias and variability of different approaches within the context of a logistic regression model and compare variance estimation methods for the proposed multiple imputation estimator. Lastly, we apply several methods to analyze the data set from a recently-conducted GenIMS study.

A Multidimensional Computerized Adaptive Short-Form Quality of Life Questionnaire Developed and Validated for Multiple Sclerosis: The MusiQoL-MCAT.

PubMed

Michel, Pierre; Baumstarck, Karine; Ghattas, Badih; Pelletier, Jean; Loundou, Anderson; Boucekine, Mohamed; Auquier, Pascal; Boyer, Laurent

2016-04-01

The aim was to develop a multidimensional computerized adaptive short-form questionnaire, the MusiQoL-MCAT, from a fixed-length QoL questionnaire for multiple sclerosis.A total of 1992 patients were enrolled in this international cross-sectional study. The development of the MusiQoL-MCAT was based on the assessment of between-items MIRT model fit followed by real-data simulations. The MCAT algorithm was based on Bayesian maximum a posteriori estimation of latent traits and Kullback-Leibler information item selection. We examined several simulations based on a fixed number of items. Accuracy was assessed using correlations (r) between initial IRT scores and MCAT scores. Precision was assessed using the standard error measurement (SEM) and the root mean square error (RMSE).The multidimensional graded response model was used to estimate item parameters and IRT scores. Among the MCAT simulations, the 16-item version of the MusiQoL-MCAT was selected because the accuracy and precision became stable with 16 items with satisfactory levels (r ≥ 0.9, SEM ≤ 0.55, and RMSE ≤ 0.3). External validity of the MusiQoL-MCAT was satisfactory.The MusiQoL-MCAT presents satisfactory properties and can individually tailor QoL assessment to each patient, making it less burdensome to patients and better adapted for use in clinical practice.

A Multidimensional Computerized Adaptive Short-Form Quality of Life Questionnaire Developed and Validated for Multiple Sclerosis

PubMed Central

Michel, Pierre; Baumstarck, Karine; Ghattas, Badih; Pelletier, Jean; Loundou, Anderson; Boucekine, Mohamed; Auquier, Pascal; Boyer, Laurent

2016-01-01

Abstract The aim was to develop a multidimensional computerized adaptive short-form questionnaire, the MusiQoL-MCAT, from a fixed-length QoL questionnaire for multiple sclerosis. A total of 1992 patients were enrolled in this international cross-sectional study. The development of the MusiQoL-MCAT was based on the assessment of between-items MIRT model fit followed by real-data simulations. The MCAT algorithm was based on Bayesian maximum a posteriori estimation of latent traits and Kullback–Leibler information item selection. We examined several simulations based on a fixed number of items. Accuracy was assessed using correlations (r) between initial IRT scores and MCAT scores. Precision was assessed using the standard error measurement (SEM) and the root mean square error (RMSE). The multidimensional graded response model was used to estimate item parameters and IRT scores. Among the MCAT simulations, the 16-item version of the MusiQoL-MCAT was selected because the accuracy and precision became stable with 16 items with satisfactory levels (r ≥ 0.9, SEM ≤ 0.55, and RMSE ≤ 0.3). External validity of the MusiQoL-MCAT was satisfactory. The MusiQoL-MCAT presents satisfactory properties and can individually tailor QoL assessment to each patient, making it less burdensome to patients and better adapted for use in clinical practice. PMID:27057832

More Water Resources but Less for Irrigation: Adaptation Strategy of the Yellow River in a Changing Environment

NASA Astrophysics Data System (ADS)

Tang, Q.; Yin, Y. Y.

2015-12-01

The Yellow River is the primary source of freshwater to the northern China. Increasing population and socio-economic development have put great pressure on water resources of the river basin. The anticipated climate and socio-economic changes may further increase water stress. Development of adaptation strategies would have significant implications for water and food security of this region. In this study, the outputs of multiple hydrological models forced with the bias-corrected climatic variables from multiple global climate models were used to assess the change in renewable water resources of the river basin in the 21st century. The outputs of multiple crop models were used to assess the change in agricultural water demand. The domestic and industrial water demands were estimated based on the future socio-economic conditions under the Shared Socio-economic Pathways (SSPs). Besides basic ecosystem needs for water which must be met, the water use in domestic and industrial sectors is considered to have a higher priority than the agricultural water use when water is insufficient. The results show that the renewable water resources of the basin would increase as global mean temperature increases while the water demand would grow much more rapidly, largely due to water demand increase in domestic and industrial sectors. In most of the sub-basins of the Yellow River basin, the available water resources can not sustain all the water use sectors starting from the next a few decades. As more water resources would be appropriated by domestic and industrial sectors, a part of irrigated area had to be converted to rainfed agriculture which led to a large reduction in food production. This study highlights the linked water and food security in a changing environment and suggests that the trade-off should be considered when developing regional adaptation strategies.

Simulation and analysis of main steam control system based on heat transfer calculation

NASA Astrophysics Data System (ADS)

Huang, Zhenqun; Li, Ruyan; Feng, Zhongbao; Wang, Songhan; Li, Wenbo; Cheng, Jiwei; Jin, Yingai

2018-05-01

In this paper, after thermal power plant 300MW boiler was studied, mat lab was used to write calculation program about heat transfer process between the main steam and boiler flue gas and amount of water was calculated to ensure the main steam temperature keeping in target temperature. Then heat transfer calculation program was introduced into Simulink simulation platform based on control system multiple models switching and heat transfer calculation. The results show that multiple models switching control system based on heat transfer calculation not only overcome the large inertia of main stream temperature, a large hysteresis characteristic of main stream temperature, but also adapted to the boiler load changing.

Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions.

PubMed

Brooks, Logan C; Farrow, David C; Hyun, Sangwon; Tibshirani, Ryan J; Rosenfeld, Roni

2018-06-15

Accurate and reliable forecasts of seasonal epidemics of infectious disease can assist in the design of countermeasures and increase public awareness and preparedness. This article describes two main contributions we made recently toward this goal: a novel approach to probabilistic modeling of surveillance time series based on "delta densities", and an optimization scheme for combining output from multiple forecasting methods into an adaptively weighted ensemble. Delta densities describe the probability distribution of the change between one observation and the next, conditioned on available data; chaining together nonparametric estimates of these distributions yields a model for an entire trajectory. Corresponding distributional forecasts cover more observed events than alternatives that treat the whole season as a unit, and improve upon multiple evaluation metrics when extracting key targets of interest to public health officials. Adaptively weighted ensembles integrate the results of multiple forecasting methods, such as delta density, using weights that can change from situation to situation. We treat selection of optimal weightings across forecasting methods as a separate estimation task, and describe an estimation procedure based on optimizing cross-validation performance. We consider some details of the data generation process, including data revisions and holiday effects, both in the construction of these forecasting methods and when performing retrospective evaluation. The delta density method and an adaptively weighted ensemble of other forecasting methods each improve significantly on the next best ensemble component when applied separately, and achieve even better cross-validated performance when used in conjunction. We submitted real-time forecasts based on these contributions as part of CDC's 2015/2016 FluSight Collaborative Comparison. Among the fourteen submissions that season, this system was ranked by CDC as the most accurate.

A Microswitch-Cluster Program to Foster Adaptive Responses and Head Control in Students with Multiple Disabilities: Replication and Validation Assessment

ERIC Educational Resources Information Center

Lancioni, Giulio E.; Singh, Nirbhay N.; O'Reilly, Mark F.; Sigafoos, Jeff; Oliva, Doretta; Gatti, Michela; Manfredi, Francesco; Megna, Gianfranco; La Martire, Maria L.; Tota, Alessia; Smaldone, Angela; Groeneweg, Jop

2008-01-01

A program relying on microswitch clusters (i.e., combinations of microswitches) and preferred stimuli was recently developed to foster adaptive responses and head control in persons with multiple disabilities. In the last version of this program, preferred stimuli (a) are scheduled for adaptive responses occurring in combination with head control…

Opportunistic Access in Frequency Hopping Cognitive Radio Networks

DTIC Science & Technology

2014-03-27

thresholding MA multiple access MFSK M-ary frequency shift keying MIMO multiple-input/multiple-output OFDM orthogonal frequency-division multiplexing x...adaptive BER performance as a function of ISR with orthogonal frequency-division multiplexing ( OFDM ) interference present. . . . . . . . . . 41 4.15 Non...adaptive BER performance as a function of EB/N0 with OFDM interfer- ence present

Metabolomic analysis reveals key metabolites related to the rapid adaptation of Saccharomyce cerevisiae to multiple inhibitors of furfural, acetic acid, and phenol.

PubMed

Wang, Xin; Li, Bing-Zhi; Ding, Ming-Zhu; Zhang, Wei-Wen; Yuan, Ying-Jin

2013-03-01

During hydrolysis of lignocellulosic biomass, a broad range of inhibitors are generated, which interfere with yeast growth and bioethanol production. In order to improve the strain tolerance to multiple inhibitors--acetic acid, furfural, and phenol (three representative lignocellulose-derived inhibitors) and uncover the underlying tolerant mechanism, an adaptation experiment was performed in which the industrial Saccharomyces cerevisiae was cultivated repeatedly in a medium containing multiple inhibitors. The adaptation occurred quickly, accompanied with distinct increase in growth rate, glucose utilization rate, furfural metabolism rate, and ethanol yield, only after the first transfer. A similar rapid adaptation was also observed for the lab strains of BY4742 and BY4743. The metabolomic analysis was employed to investigate the responses of the industrial S. cereviaise to three inhibitors during the adaptation. The results showed that higher levels of 2-furoic acid, 2, 3-butanediol, intermediates in glycolytic pathway, and amino acids derived from glycolysis, were discovered in the adapted strains, suggesting that enhanced metabolic activity in these pathways may relate to resistance against inhibitors. Additionally, through single-gene knockouts, several genes related to alanine metabolism, GABA shunt, and glycerol metabolism were verified to be crucial for the resistance to multiple inhibitors. This study provides new insights into the tolerance mechanism against multiple inhibitors, and guides for the improvement of tolerant ethanologenic yeast strains for lignocellulose-bioethanol fermentation.

A New Family of Models for the Multiple-Choice Item.

DTIC Science & Technology

1979-12-19

analysis of the verbal scholastic aptitude test using Birnhaum’s three-parameter logistic model. Educational and Psychological Measurement, 28, 989-1020...16. [8] McBride, J. R. Some properties of a Bayesian adaptive ability testing strategy. Applied Psychological Measurement, 1, 121-140, 1977. [9...University of Michigan Ann Arbor, MI 48106 ’~KL -137- Non Govt Mon Govt 1 Dr. Earl Hunt 1 Dr. Frederick N. Lord Dept. of Psychology Educational Testing

Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network.

PubMed

de Nijs, Patrick J; Berry, Nicholas J; Wells, Geoff J; Reay, Dave S

2014-10-20

The need for smallholder farmers to adapt their practices to a changing climate is well recognised, particularly in Africa. The cost of adapting to climate change in Africa is estimated to be $20 to $30 billion per year, but the total amount pledged to finance adaptation falls significantly short of this requirement. The difficulty of assessing and monitoring when adaptation is achieved is one of the key barriers to the disbursement of performance-based adaptation finance. To demonstrate the potential of Bayesian Belief Networks for describing the impacts of specific activities on climate change resilience, we developed a simple model that incorporates climate projections, local environmental data, information from peer-reviewed literature and expert opinion to account for the adaptation benefits derived from Climate-Smart Agriculture activities in Malawi. This novel approach allows assessment of vulnerability to climate change under different land use activities and can be used to identify appropriate adaptation strategies and to quantify biophysical adaptation benefits from activities that are implemented. We suggest that multiple-indicator Bayesian Belief Network approaches can provide insights into adaptation planning for a wide range of applications and, if further explored, could be part of a set of important catalysts for the expansion of adaptation finance.

Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network

NASA Astrophysics Data System (ADS)

de Nijs, Patrick J.; Berry, Nicholas J.; Wells, Geoff J.; Reay, Dave S.

2014-10-01

The need for smallholder farmers to adapt their practices to a changing climate is well recognised, particularly in Africa. The cost of adapting to climate change in Africa is estimated to be $20 to $30 billion per year, but the total amount pledged to finance adaptation falls significantly short of this requirement. The difficulty of assessing and monitoring when adaptation is achieved is one of the key barriers to the disbursement of performance-based adaptation finance. To demonstrate the potential of Bayesian Belief Networks for describing the impacts of specific activities on climate change resilience, we developed a simple model that incorporates climate projections, local environmental data, information from peer-reviewed literature and expert opinion to account for the adaptation benefits derived from Climate-Smart Agriculture activities in Malawi. This novel approach allows assessment of vulnerability to climate change under different land use activities and can be used to identify appropriate adaptation strategies and to quantify biophysical adaptation benefits from activities that are implemented. We suggest that multiple-indicator Bayesian Belief Network approaches can provide insights into adaptation planning for a wide range of applications and, if further explored, could be part of a set of important catalysts for the expansion of adaptation finance.

Robust lane detection and tracking using multiple visual cues under stochastic lane shape conditions

NASA Astrophysics Data System (ADS)

Huang, Zhi; Fan, Baozheng; Song, Xiaolin

2018-03-01

As one of the essential components of environment perception techniques for an intelligent vehicle, lane detection is confronted with challenges including robustness against the complicated disturbance and illumination, also adaptability to stochastic lane shapes. To overcome these issues, we proposed a robust lane detection method named classification-generation-growth-based (CGG) operator to the detected lines, whereby the linear lane markings are identified by synergizing multiple visual cues with the a priori knowledge and spatial-temporal information. According to the quality of linear lane fitting, the linear and linear-parabolic models are dynamically switched to describe the actual lane. The Kalman filter with adaptive noise covariance and the region of interests (ROI) tracking are applied to improve the robustness and efficiency. Experiments were conducted with images covering various challenging scenarios. The experimental results evaluate the effectiveness of the presented method for complicated disturbances, illumination, and stochastic lane shapes.

Multiple Levels of Family Factors and Oppositional Defiant Disorder Symptoms Among Chinese Children.

PubMed

Lin, Xiuyun; Li, Longfeng; Heath, Melissa A; Chi, Peilian; Xu, Shousen; Fang, Xiaoyi

2018-03-01

Family factors are closely associated with child developmental outcomes. This study examined the relationship of oppositional defiant disorder (ODD) symptoms and factors at whole family, dyadic, and individual levels in Chinese children. Participants, who were recruited from 14 primary schools in north, east, and south-west China, included 80 father-child dyads and 169 mother-child dyads. Children in the participating dyads were previously diagnosed with ODD. Results revealed that family cohesion/adaptability was indirectly associated with ODD symptoms via parent-child relationship and child emotion regulation. Parent-child relationship affected ODD symptoms directly and indirectly through child emotion regulation. In addition, the effects of family cohesion/adaptability on parent emotion regulation and child emotion regulation were mediated by the parent-child relationship. The tested model provides a comprehensive framework of how family factors at multiple levels are related to child ODD symptoms and highlights the importance of understanding child emotional and behavioral problems within the family context, more specifically within the multiple levels of family relationships. © 2016 Family Process Institute.

Towards More Comprehensive Projections of Urban Heat-Related Mortality: Estimates for New York City under Multiple Population, Adaptation, and Climate Scenarios

PubMed Central

Petkova, Elisaveta P.; Vink, Jan K.; Horton, Radley M.; Gasparrini, Antonio; Bader, Daniel A.; Francis, Joe D.; Kinney, Patrick L.

2016-01-01

Background: High temperatures have substantial impacts on mortality and, with growing concerns about climate change, numerous studies have developed projections of future heat-related deaths around the world. Projections of temperature-related mortality are often limited by insufficient information to formulate hypotheses about population sensitivity to high temperatures and future demographics. Objectives: The present study derived projections of temperature-related mortality in New York City by taking into account future patterns of adaptation or demographic change, both of which can have profound influences on future health burdens. Methods: We adopted a novel approach to modeling heat adaptation by incorporating an analysis of the observed population response to heat in New York City over the course of eight decades. This approach projected heat-related mortality until the end of the 21st century based on observed trends in adaptation over a substantial portion of the 20th century. In addition, we incorporated a range of new scenarios for population change until the end of the 21st century. We then estimated future heat-related deaths in New York City by combining the changing temperature–mortality relationship and population scenarios with downscaled temperature projections from the 33 global climate models (GCMs) and two Representative Concentration Pathways (RCPs). Results: The median number of projected annual heat-related deaths across the 33 GCMs varied greatly by RCP and adaptation and population change scenario, ranging from 167 to 3,331 in the 2080s compared with 638 heat-related deaths annually between 2000 and 2006. Conclusions: These findings provide a more complete picture of the range of potential future heat-related mortality risks across the 21st century in New York City, and they highlight the importance of both demographic change and adaptation responses in modifying future risks. Citation: Petkova EP, Vink JK, Horton RM, Gasparrini A, Bader DA, Francis JD, Kinney PL. 2017. Towards more comprehensive projections of urban heat-related mortality: estimates for New York City under multiple population, adaptation, and climate scenarios. Environ Health Perspect 125:47–55; http://dx.doi.org/10.1289/EHP166 PMID:27337737

Towards More Comprehensive Projections of Urban Heat-Related Mortality: Estimates for New York City under Multiple Population, Adaptation, and Climate Scenarios.

PubMed

Petkova, Elisaveta P; Vink, Jan K; Horton, Radley M; Gasparrini, Antonio; Bader, Daniel A; Francis, Joe D; Kinney, Patrick L

2017-01-01

High temperatures have substantial impacts on mortality and, with growing concerns about climate change, numerous studies have developed projections of future heat-related deaths around the world. Projections of temperature-related mortality are often limited by insufficient information to formulate hypotheses about population sensitivity to high temperatures and future demographics. The present study derived projections of temperature-related mortality in New York City by taking into account future patterns of adaptation or demographic change, both of which can have profound influences on future health burdens. We adopted a novel approach to modeling heat adaptation by incorporating an analysis of the observed population response to heat in New York City over the course of eight decades. This approach projected heat-related mortality until the end of the 21st century based on observed trends in adaptation over a substantial portion of the 20th century. In addition, we incorporated a range of new scenarios for population change until the end of the 21st century. We then estimated future heat-related deaths in New York City by combining the changing temperature-mortality relationship and population scenarios with downscaled temperature projections from the 33 global climate models (GCMs) and two Representative Concentration Pathways (RCPs). The median number of projected annual heat-related deaths across the 33 GCMs varied greatly by RCP and adaptation and population change scenario, ranging from 167 to 3,331 in the 2080s compared with 638 heat-related deaths annually between 2000 and 2006. These findings provide a more complete picture of the range of potential future heat-related mortality risks across the 21st century in New York City, and they highlight the importance of both demographic change and adaptation responses in modifying future risks. Citation: Petkova EP, Vink JK, Horton RM, Gasparrini A, Bader DA, Francis JD, Kinney PL. 2017. Towards more comprehensive projections of urban heat-related mortality: estimates for New York City under multiple population, adaptation, and climate scenarios. Environ Health Perspect 125:47-55; http://dx.doi.org/10.1289/EHP166.

Vector Adaptive/Predictive Encoding Of Speech

NASA Technical Reports Server (NTRS)

Chen, Juin-Hwey; Gersho, Allen

1989-01-01

Vector adaptive/predictive technique for digital encoding of speech signals yields decoded speech of very good quality after transmission at coding rate of 9.6 kb/s and of reasonably good quality at 4.8 kb/s. Requires 3 to 4 million multiplications and additions per second. Combines advantages of adaptive/predictive coding, and code-excited linear prediction, yielding speech of high quality but requires 600 million multiplications and additions per second at encoding rate of 4.8 kb/s. Vector adaptive/predictive coding technique bridges gaps in performance and complexity between adaptive/predictive coding and code-excited linear prediction.

Evolution of complex adaptations in molecular systems

PubMed Central

Pál, Csaba; Papp, Balázs

2017-01-01

A central challenge in evolutionary biology concerns the mechanisms by which complex adaptations arise. Such adaptations depend on the fixation of multiple, highly specific mutations, where intermediate stages of evolution seemingly provide little or no benefit. It is generally assumed that the establishment of complex adaptations is very slow in nature, as evolution of such traits demands special population genetic or environmental circumstances. However, blueprints of complex adaptations in molecular systems are pervasive, indicating that they can readily evolve. We discuss the prospects and limitations of non-adaptive scenarios, which assume multiple neutral or deleterious steps in the evolution of complex adaptations. Next, we examine how complex adaptations can evolve by natural selection in changing environment. Finally, we argue that molecular ’springboards’, such as phenotypic heterogeneity and promiscuous interactions facilitate this process by providing access to new adaptive paths. PMID:28782044

RRegrs: an R package for computer-aided model selection with multiple regression models.

PubMed

Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L

2015-01-01

Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling.

Comparing Anisotropic Output-Based Grid Adaptation Methods by Decomposition

NASA Technical Reports Server (NTRS)

Park, Michael A.; Loseille, Adrien; Krakos, Joshua A.; Michal, Todd

2015-01-01

Anisotropic grid adaptation is examined by decomposing the steps of flow solution, ad- joint solution, error estimation, metric construction, and simplex grid adaptation. Multiple implementations of each of these steps are evaluated by comparison to each other and expected analytic results when available. For example, grids are adapted to analytic metric fields and grid measures are computed to illustrate the properties of multiple independent implementations of grid adaptation mechanics. Different implementations of each step in the adaptation process can be evaluated in a system where the other components of the adaptive cycle are fixed. Detailed examination of these properties allows comparison of different methods to identify the current state of the art and where further development should be targeted.

Bayesian GGE biplot models applied to maize multi-environments trials.

PubMed

de Oliveira, L A; da Silva, C P; Nuvunga, J J; da Silva, A Q; Balestre, M

2016-06-17

The additive main effects and multiplicative interaction (AMMI) and the genotype main effects and genotype x environment interaction (GGE) models stand out among the linear-bilinear models used in genotype x environment interaction studies. Despite the advantages of their use to describe genotype x environment (AMMI) or genotype and genotype x environment (GGE) interactions, these methods have known limitations that are inherent to fixed effects models, including difficulty in treating variance heterogeneity and missing data. Traditional biplots include no measure of uncertainty regarding the principal components. The present study aimed to apply the Bayesian approach to GGE biplot models and assess the implications for selecting stable and adapted genotypes. Our results demonstrated that the Bayesian approach applied to GGE models with non-informative priors was consistent with the traditional GGE biplot analysis, although the credible region incorporated into the biplot enabled distinguishing, based on probability, the performance of genotypes, and their relationships with the environments in the biplot. Those regions also enabled the identification of groups of genotypes and environments with similar effects in terms of adaptability and stability. The relative position of genotypes and environments in biplots is highly affected by the experimental accuracy. Thus, incorporation of uncertainty in biplots is a key tool for breeders to make decisions regarding stability selection and adaptability and the definition of mega-environments.

Mechanisms in adaptive feedback control: photoisomerization in a liquid.

PubMed

Hoki, Kunihito; Brumer, Paul

2005-10-14

The underlying mechanism for Adaptive Feedback Control in the experimental photoisomerization of 3,3'-diethyl-2,2'-thiacyanine iodide (NK88) in methanol is exposed theoretically. With given laboratory limitations on laser output, the complicated electric fields are shown to achieve their targets in qualitatively simple ways. Further, control over the cis population without laser limitations reveals an incoherent pump-dump scenario as the optimal isomerization strategy. In neither case are there substantial contributions from quantum multiple-path interference or from nuclear wave packet coherence. Environmentally induced decoherence is shown to justify the use of a simplified theoretical model.

Interference and Shaping in Sensorimotor Adaptations with Rewards

PubMed Central

Darshan, Ran; Leblois, Arthur; Hansel, David

2014-01-01

When a perturbation is applied in a sensorimotor transformation task, subjects can adapt and maintain performance by either relying on sensory feedback, or, in the absence of such feedback, on information provided by rewards. For example, in a classical rotation task where movement endpoints must be rotated to reach a fixed target, human subjects can successfully adapt their reaching movements solely on the basis of binary rewards, although this proves much more difficult than with visual feedback. Here, we investigate such a reward-driven sensorimotor adaptation process in a minimal computational model of the task. The key assumption of the model is that synaptic plasticity is gated by the reward. We study how the learning dynamics depend on the target size, the movement variability, the rotation angle and the number of targets. We show that when the movement is perturbed for multiple targets, the adaptation process for the different targets can interfere destructively or constructively depending on the similarities between the sensory stimuli (the targets) and the overlap in their neuronal representations. Destructive interferences can result in a drastic slowdown of the adaptation. As a result of interference, the time to adapt varies non-linearly with the number of targets. Our analysis shows that these interferences are weaker if the reward varies smoothly with the subject's performance instead of being binary. We demonstrate how shaping the reward or shaping the task can accelerate the adaptation dramatically by reducing the destructive interferences. We argue that experimentally investigating the dynamics of reward-driven sensorimotor adaptation for more than one sensory stimulus can shed light on the underlying learning rules. PMID:24415925

3D fluid-structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS

NASA Astrophysics Data System (ADS)

Saeed, R. A.; Galybin, A. N.; Popov, V.

2013-01-01

This paper discusses condition monitoring and fault diagnosis in Francis turbine based on integration of numerical modelling with several different artificial intelligence (AI) techniques. In this study, a numerical approach for fluid-structure (turbine runner) analysis is presented. The results of numerical analysis provide frequency response functions (FRFs) data sets along x-, y- and z-directions under different operating load and different position and size of faults in the structure. To extract features and reduce the dimensionality of the obtained FRF data, the principal component analysis (PCA) has been applied. Subsequently, the extracted features are formulated and fed into multiple artificial neural networks (ANN) and multiple adaptive neuro-fuzzy inference systems (ANFIS) in order to identify the size and position of the damage in the runner and estimate the turbine operating conditions. The results demonstrated the effectiveness of this approach and provide satisfactory accuracy even when the input data are corrupted with certain level of noise.

[Patient-centred prescription model to improve adequate prescription and therapeutic adherence in patients with multiple disorders].

PubMed

Espaulella-Panicot, Joan; Molist-Brunet, Núria; Sevilla-Sánchez, Daniel; González-Bueno, Javier; Amblàs-Novellas, Jordi; Solà-Bonada, Núria; Codina-Jané, Carles

Patients with multiple disorders and on multiple medication are often associated with clinical complexity, defined as a situation of uncertainty conditioned by difficulties in establishing a situational diagnosis and decision-making. The patient-centred care approach in this population group seems to be one of the best therapeutic options. In this context, the preparation of an individualised therapeutic plan is the most relevant practical element, where the pharmacological plan maintains an important role. There has recently been a significant increase in knowledge in the area of adequacy of prescription and adherence. In this context, we must find a model must be found that incorporates this knowledge into clinical practice by the professionals. Person-centred prescription is a medication review model that includes different strategies in a single intervention. It is performed by a multidisciplinary team, and allows them to adapt the pharmacological plan of patients with clinical complexity. Copyright © 2017 SEGG. Publicado por Elsevier España, S.L.U. All rights reserved.

Fast Evolution from Precast Bricks: Genomics of Young Freshwater Populations of Threespine Stickleback Gasterosteus aculeatus

PubMed Central

Terekhanova, Nadezhda V.; Logacheva, Maria D.; Penin, Aleksey A.; Neretina, Tatiana V.; Barmintseva, Anna E.; Bazykin, Georgii A.; Kondrashov, Alexey S.; Mugue, Nikolai S.

2014-01-01

Adaptation is driven by natural selection; however, many adaptations are caused by weak selection acting over large timescales, complicating its study. Therefore, it is rarely possible to study selection comprehensively in natural environments. The threespine stickleback (Gasterosteus aculeatus) is a well-studied model organism with a short generation time, small genome size, and many genetic and genomic tools available. Within this originally marine species, populations have recurrently adapted to freshwater all over its range. This evolution involved extensive parallelism: pre-existing alleles that adapt sticklebacks to freshwater habitats, but are also present at low frequencies in marine populations, have been recruited repeatedly. While a number of genomic regions responsible for this adaptation have been identified, the details of selection remain poorly understood. Using whole-genome resequencing, we compare pooled genomic samples from marine and freshwater populations of the White Sea basin, and identify 19 short genomic regions that are highly divergent between them, including three known inversions. 17 of these regions overlap protein-coding genes, including a number of genes with predicted functions that are relevant for adaptation to the freshwater environment. We then analyze four additional independently derived young freshwater populations of known ages, two natural and two artificially established, and use the observed shifts of allelic frequencies to estimate the strength of positive selection. Adaptation turns out to be quite rapid, indicating strong selection acting simultaneously at multiple regions of the genome, with selection coefficients of up to 0.27. High divergence between marine and freshwater genotypes, lack of reduction in polymorphism in regions responsible for adaptation, and high frequencies of freshwater alleles observed even in young freshwater populations are all consistent with rapid assembly of G. aculeatus freshwater genotypes from pre-existing genomic regions of adaptive variation, with strong selection that favors this assembly acting simultaneously at multiple loci. PMID:25299485

Fast evolution from precast bricks: genomics of young freshwater populations of threespine stickleback Gasterosteus aculeatus.

PubMed

Terekhanova, Nadezhda V; Logacheva, Maria D; Penin, Aleksey A; Neretina, Tatiana V; Barmintseva, Anna E; Bazykin, Georgii A; Kondrashov, Alexey S; Mugue, Nikolai S

2014-10-01

Adaptation is driven by natural selection; however, many adaptations are caused by weak selection acting over large timescales, complicating its study. Therefore, it is rarely possible to study selection comprehensively in natural environments. The threespine stickleback (Gasterosteus aculeatus) is a well-studied model organism with a short generation time, small genome size, and many genetic and genomic tools available. Within this originally marine species, populations have recurrently adapted to freshwater all over its range. This evolution involved extensive parallelism: pre-existing alleles that adapt sticklebacks to freshwater habitats, but are also present at low frequencies in marine populations, have been recruited repeatedly. While a number of genomic regions responsible for this adaptation have been identified, the details of selection remain poorly understood. Using whole-genome resequencing, we compare pooled genomic samples from marine and freshwater populations of the White Sea basin, and identify 19 short genomic regions that are highly divergent between them, including three known inversions. 17 of these regions overlap protein-coding genes, including a number of genes with predicted functions that are relevant for adaptation to the freshwater environment. We then analyze four additional independently derived young freshwater populations of known ages, two natural and two artificially established, and use the observed shifts of allelic frequencies to estimate the strength of positive selection. Adaptation turns out to be quite rapid, indicating strong selection acting simultaneously at multiple regions of the genome, with selection coefficients of up to 0.27. High divergence between marine and freshwater genotypes, lack of reduction in polymorphism in regions responsible for adaptation, and high frequencies of freshwater alleles observed even in young freshwater populations are all consistent with rapid assembly of G. aculeatus freshwater genotypes from pre-existing genomic regions of adaptive variation, with strong selection that favors this assembly acting simultaneously at multiple loci.

Ecosystem evapotranspiration: challenges in measurements, estimates, and modeling

Treesearch

Devendra Amatya; S. Irmak; P. Gowda; Ge Sun; J.E. Nettles; K.R. Douglas-Mankin

2016-01-01

Evapotranspiration (ET) processes at the leaf to landscape scales in multiple land uses have important controls and feedbacks for local, regional, and global climate and water resource systems. Innovative methods, tools, and technologies for improved understanding and quantification of ET and crop water use are critical for adapting more effective management strategies...

Performance Factors Analysis -- A New Alternative to Knowledge Tracing

ERIC Educational Resources Information Center

Pavlik, Philip I., Jr.; Cen, Hao; Koedinger, Kenneth R.

2009-01-01

Knowledge tracing (KT)[1] has been used in various forms for adaptive computerized instruction for more than 40 years. However, despite its long history of application, it is difficult to use in domain model search procedures, has not been used to capture learning where multiple skills are needed to perform a single action, and has not been used…

Multiple Model Adaptive Estimation Applied to the Vista F-16 with Actuator and Sensor Failures. Volume 2

DTIC Science & Technology

1992-06-01

l,kj)-Tenpl/((tiaelagl/tsamp)-1.) DO Iii-irix(timelagl/tsaapbIFIX(tiaelag2/tsaap)-l Templ-Tenpl~probs( kid ,kj) END DO Meanprob( 2, kj ) Teapl...representing a fourth order * C * actuator C C * Porn : O(t) TPA * C --- a--------------------------------------------- C * 1(t) S^4 *TPBS^3 +TPCS𔃼

Adaptive Equilibrium Regulation: A Balancing Act in Two Timescales

PubMed Central

Boker, Steven M.

2015-01-01

An equilibrium involves a balancing of forces. Just as one maintains upright posture in standing or walking, many self-regulatory and interpersonal behaviors can be framed as a balancing act between an ever changing environment and within-person processes. The emerging balance between person and environment, the equilibria, are dynamic and adaptive in response to development and learning. A distinction is made between equilibrium achieved solely due to a short timescale balancing of forces and a longer timescale preferred equilibrium which we define as a state towards which the system slowly adapts. Together, these are developed into a framework that this article calls Adaptive Equilibrium Regulation (ÆR), which separates a regulatory process into two timescales: a faster regulation that automatically balances forces and a slower timescale adaptation process that reconfigures the fast regulation so as to move the system towards its preferred equilibrium when an environmental force persists over the longer timescale. This way of thinking leads to novel models for the interplay between multiple timescales of behavior, learning, and development. PMID:27066197

Adaptive Neural Tracking Control for Switched High-Order Stochastic Nonlinear Systems.

PubMed

Zhao, Xudong; Wang, Xinyong; Zong, Guangdeng; Zheng, Xiaolong

2017-10-01

This paper deals with adaptive neural tracking control design for a class of switched high-order stochastic nonlinear systems with unknown uncertainties and arbitrary deterministic switching. The considered issues are: 1) completely unknown uncertainties; 2) stochastic disturbances; and 3) high-order nonstrict-feedback system structure. The considered mathematical models can represent many practical systems in the actual engineering. By adopting the approximation ability of neural networks, common stochastic Lyapunov function method together with adding an improved power integrator technique, an adaptive state feedback controller with multiple adaptive laws is systematically designed for the systems. Subsequently, a controller with only two adaptive laws is proposed to solve the problem of over parameterization. Under the designed controllers, all the signals in the closed-loop system are bounded-input bounded-output stable in probability, and the system output can almost surely track the target trajectory within a specified bounded error. Finally, simulation results are presented to show the effectiveness of the proposed approaches.

Neural adaptive control for vibration suppression in composite fin-tip of aircraft.

PubMed

Suresh, S; Kannan, N; Sundararajan, N; Saratchandran, P

2008-06-01

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H(infinity) control scheme.

A Risk-based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

PubMed Central

Zafra-Cabeza, Ascensión; Rivera, Daniel E.; Collins, Linda M.; Ridao, Miguel A.; Camacho, Eduardo F.

2010-01-01

This paper examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based Model Predictive Control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this paper can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm. PMID:21643450

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

Combined adaptive multiple subtraction based on optimized event tracing and extended wiener filtering

NASA Astrophysics Data System (ADS)

Tan, Jun; Song, Peng; Li, Jinshan; Wang, Lei; Zhong, Mengxuan; Zhang, Xiaobo

2017-06-01

The surface-related multiple elimination (SRME) method is based on feedback formulation and has become one of the most preferred multiple suppression methods used. However, some differences are apparent between the predicted multiples and those in the source seismic records, which may result in conventional adaptive multiple subtraction methods being barely able to effectively suppress multiples in actual production. This paper introduces a combined adaptive multiple attenuation method based on the optimized event tracing technique and extended Wiener filtering. The method firstly uses multiple records predicted by SRME to generate a multiple velocity spectrum, then separates the original record to an approximate primary record and an approximate multiple record by applying the optimized event tracing method and short-time window FK filtering method. After applying the extended Wiener filtering method, residual multiples in the approximate primary record can then be eliminated and the damaged primary can be restored from the approximate multiple record. This method combines the advantages of multiple elimination based on the optimized event tracing method and the extended Wiener filtering technique. It is an ideal method for suppressing typical hyperbolic and other types of multiples, with the advantage of minimizing damage of the primary. Synthetic and field data tests show that this method produces better multiple elimination results than the traditional multi-channel Wiener filter method and is more suitable for multiple elimination in complicated geological areas.

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.

Ultimate explanations and suboptimal choice.

PubMed

Vasconcelos, Marco; Machado, Armando; Pandeirada, Josefa N S

2018-07-01

Researchers have unraveled multiple cases in which behavior deviates from rationality principles. We propose that such deviations are valuable tools to understand the adaptive significance of the underpinning mechanisms. To illustrate, we discuss in detail an experimental protocol in which animals systematically incur substantial foraging losses by preferring a lean but informative option over a rich but non-informative one. To understand how adaptive mechanisms may fail to maximize food intake, we review a model inspired by optimal foraging principles that reconciles sub-optimal choice with the view that current behavioral mechanisms were pruned by the optimizing action of natural selection. To move beyond retrospective speculation, we then review critical tests of the model, regarding both its assumptions and its (sometimes counterintuitive) predictions, all of which have been upheld. The overall contention is that (a) known mechanisms can be used to develop better ultimate accounts and that (b) to understand why mechanisms that generate suboptimal behavior evolved, we need to consider their adaptive value in the animal's characteristic ecology. Copyright © 2018 Elsevier B.V. All rights reserved.

Enhancing the effectiveness of human-robot teaming with a closed-loop system.

PubMed

Teo, Grace; Reinerman-Jones, Lauren; Matthews, Gerald; Szalma, James; Jentsch, Florian; Hancock, Peter

2018-02-01

With technological developments in robotics and their increasing deployment, human-robot teams are set to be a mainstay in the future. To develop robots that possess teaming capabilities, such as being able to communicate implicitly, the present study implemented a closed-loop system. This system enabled the robot to provide adaptive aid without the need for explicit commands from the human teammate, through the use of multiple physiological workload measures. Such measures of workload vary in sensitivity and there is large inter-individual variability in physiological responses to imposed taskload. Workload models enacted via closed-loop system should accommodate such individual variability. The present research investigated the effects of the adaptive robot aid vs. imposed aid on performance and workload. Results showed that adaptive robot aid driven by an individualized workload model for physiological response resulted in greater improvements in performance compared to aid that was simply imposed by the system. Copyright © 2017 Elsevier Ltd. All rights reserved.

Construction of multiple trade-offs to obtain arbitrary singularities of adaptive dynamics.

PubMed

Kisdi, Éva

2015-04-01

Evolutionary singularities are central to the adaptive dynamics of evolving traits. The evolutionary singularities are strongly affected by the shape of any trade-off functions a model assumes, yet the trade-off functions are often chosen in an ad hoc manner, which may unjustifiably constrain the evolutionary dynamics exhibited by the model. To avoid this problem, critical function analysis has been used to find a trade-off function that yields a certain evolutionary singularity such as an evolutionary branching point. Here I extend this method to multiple trade-offs parameterized with a scalar strategy. I show that the trade-off functions can be chosen such that an arbitrary point in the viability domain of the trait space is a singularity of an arbitrary type, provided (next to certain non-degeneracy conditions) that the model has at least two environmental feedback variables and at least as many trade-offs as feedback variables. The proof is constructive, i.e., it provides an algorithm to find trade-off functions that yield the desired singularity. I illustrate the construction of trade-offs with an example where the virulence of a pathogen evolves in a small ecosystem of a host, its pathogen, a predator that attacks the host and an alternative prey of the predator.

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

PubMed Central

Xie, Weihong; Yu, Yang

2017-01-01

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly. PMID:29124062

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach.

PubMed

Liang, Fan; Xie, Weihong; Yu, Yang

2017-01-01

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively "switch" from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.

The optimal hormonal replacement modality selection for multiple organ procurement from brain-dead organ donors

PubMed Central

Mi, Zhibao; Novitzky, Dimitri; Collins, Joseph F; Cooper, David KC

2015-01-01

The management of brain-dead organ donors is complex. The use of inotropic agents and replacement of depleted hormones (hormonal replacement therapy) is crucial for successful multiple organ procurement, yet the optimal hormonal replacement has not been identified, and the statistical adjustment to determine the best selection is not trivial. Traditional pair-wise comparisons between every pair of treatments, and multiple comparisons to all (MCA), are statistically conservative. Hsu’s multiple comparisons with the best (MCB) – adapted from the Dunnett’s multiple comparisons with control (MCC) – has been used for selecting the best treatment based on continuous variables. We selected the best hormonal replacement modality for successful multiple organ procurement using a two-step approach. First, we estimated the predicted margins by constructing generalized linear models (GLM) or generalized linear mixed models (GLMM), and then we applied the multiple comparison methods to identify the best hormonal replacement modality given that the testing of hormonal replacement modalities is independent. Based on 10-year data from the United Network for Organ Sharing (UNOS), among 16 hormonal replacement modalities, and using the 95% simultaneous confidence intervals, we found that the combination of thyroid hormone, a corticosteroid, antidiuretic hormone, and insulin was the best modality for multiple organ procurement for transplantation. PMID:25565890

Multiple ecosystem services in a working landscape

PubMed Central

Eastburn, Danny J.; O’Geen, Anthony T.; Tate, Kenneth W.; Roche, Leslie M.

2017-01-01

Policy makers and practitioners are in need of useful tools and models for assessing ecosystem service outcomes and the potential risks and opportunities of ecosystem management options. We utilize a state-and-transition model framework integrating dynamic soil and vegetation properties to examine multiple ecosystem services—specifically agricultural production, biodiversity and habitat, and soil health—across human created vegetation states in a managed oak woodland landscape in a Mediterranean climate. We found clear tradeoffs and synergies in management outcomes. Grassland states maximized agricultural productivity at a loss of soil health, biodiversity, and other ecosystem services. Synergies existed among multiple ecosystem services in savanna and woodland states with significantly larger nutrient pools, more diversity and native plant richness, and less invasive species. This integrative approach can be adapted to a diversity of working landscapes to provide useful information for science-based ecosystem service valuations, conservation decision making, and management effectiveness assessments. PMID:28301475

Multiple ecosystem services in a working landscape.

PubMed

Eastburn, Danny J; O'Geen, Anthony T; Tate, Kenneth W; Roche, Leslie M

2017-01-01

Policy makers and practitioners are in need of useful tools and models for assessing ecosystem service outcomes and the potential risks and opportunities of ecosystem management options. We utilize a state-and-transition model framework integrating dynamic soil and vegetation properties to examine multiple ecosystem services-specifically agricultural production, biodiversity and habitat, and soil health-across human created vegetation states in a managed oak woodland landscape in a Mediterranean climate. We found clear tradeoffs and synergies in management outcomes. Grassland states maximized agricultural productivity at a loss of soil health, biodiversity, and other ecosystem services. Synergies existed among multiple ecosystem services in savanna and woodland states with significantly larger nutrient pools, more diversity and native plant richness, and less invasive species. This integrative approach can be adapted to a diversity of working landscapes to provide useful information for science-based ecosystem service valuations, conservation decision making, and management effectiveness assessments.

Adaptive EAGLE dynamic solution adaptation and grid quality enhancement

NASA Technical Reports Server (NTRS)

Luong, Phu Vinh; Thompson, J. F.; Gatlin, B.; Mastin, C. W.; Kim, H. J.

1992-01-01

In the effort described here, the elliptic grid generation procedure in the EAGLE grid code was separated from the main code into a subroutine, and a new subroutine which evaluates several grid quality measures at each grid point was added. The elliptic grid routine can now be called, either by a computational fluid dynamics (CFD) code to generate a new adaptive grid based on flow variables and quality measures through multiple adaptation, or by the EAGLE main code to generate a grid based on quality measure variables through static adaptation. Arrays of flow variables can be read into the EAGLE grid code for use in static adaptation as well. These major changes in the EAGLE adaptive grid system make it easier to convert any CFD code that operates on a block-structured grid (or single-block grid) into a multiple adaptive code.

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.

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

Examining the Links between Challenging Behaviors in Youth with ASD and Parental Stress, Mental Health, and Involvement: Applying an Adaptation of the Family Stress Model to Families of Youth with ASD

ERIC Educational Resources Information Center

Schiltz, Hillary K.; McVey, Alana J.; Magnus, Brooke; Dolan, Bridget K.; Willar, Kirsten S.; Pleiss, Sheryl; Karst, Jeffrey; Carson, Audrey M.; Caiozzo, Christina; Vogt, Elisabeth; Van Hecke, Amy Vaughan

2018-01-01

Raising a child with autism spectrum disorder (ASD) poses unique challenges that may impact parents' mental health and parenting experiences. The current study analyzed self-report data from 77 parents of youth with ASD. A serial multiple mediation model revealed that parenting stress (SIPA) and parental mental health (BAI and BDI-II) appears to…

A Model of Network Porosity

DTIC Science & Technology

2016-02-04

indicative of what happens to the system in the steady state (Section 3.3, Equation 1). 3.4.3 Adaptation For this model, agents/entities do not exhibit...device or span multiple devices. MapDevicesToEnclaves For each device in the inventory of devices found in a hardware inventory, determine what enclave...service s in enclave ej filters(ei, ej , s) Determine what filter types are used on the information flow between enclave ei and service s in enclave ej

Language Adaptive LVCSR Through Polyphone Decision Tree Specialization

DTIC Science & Technology

2000-08-01

transfer models outperform monolingual ones [3], [14]. modeling. Since for the monolingual case the use of larger phonetic context windows has proven to...12.1 2. Multiple Languages German 11.8 61K 200 44.5 43 9.0 For our experiments we developed monolingual LVCSR sys- Japanese 10.0 22K 230 33.8 33 7.9... monolingual recognizer. Since the Japanese, Korean, Portuguese, Russian, Spanish, Swedish, engines are the same across the languages, differences in the

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.;

PSO-MISMO modeling strategy for multistep-ahead time series prediction.

PubMed

Bao, Yukun; Xiong, Tao; Hu, Zhongyi

2014-05-01

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

A Self-Adaptive Dynamic Recognition Model for Fatigue Driving Based on Multi-Source Information and Two Levels of Fusion

PubMed Central

Sun, Wei; Zhang, Xiaorui; Peeta, Srinivas; He, Xiaozheng; Li, Yongfu; Zhu, Senlai

2015-01-01

To improve the effectiveness and robustness of fatigue driving recognition, a self-adaptive dynamic recognition model is proposed that incorporates information from multiple sources and involves two sequential levels of fusion, constructed at the feature level and the decision level. Compared with existing models, the proposed model introduces a dynamic basic probability assignment (BPA) to the decision-level fusion such that the weight of each feature source can change dynamically with the real-time fatigue feature measurements. Further, the proposed model can combine the fatigue state at the previous time step in the decision-level fusion to improve the robustness of the fatigue driving recognition. An improved correction strategy of the BPA is also proposed to accommodate the decision conflict caused by external disturbances. Results from field experiments demonstrate that the effectiveness and robustness of the proposed model are better than those of models based on a single fatigue feature and/or single-source information fusion, especially when the most effective fatigue features are used in the proposed model. PMID:26393615

Fully implicit moving mesh adaptive algorithm

NASA Astrophysics Data System (ADS)

Chacon, Luis

2005-10-01

In many problems of interest, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. The former is best dealt with with fully implicit methods, which are able to step over fast frequencies to resolve the dynamical time scale of interest. The latter requires grid adaptivity for efficiency. Moving-mesh grid adaptive methods are attractive because they can be designed to minimize the numerical error for a given resolution. However, the required grid governing equations are typically very nonlinear and stiff, and of considerably difficult numerical treatment. Not surprisingly, fully coupled, implicit approaches where the grid and the physics equations are solved simultaneously are rare in the literature, and circumscribed to 1D geometries. In this study, we present a fully implicit algorithm for moving mesh methods that is feasible for multidimensional geometries. A crucial element is the development of an effective multilevel treatment of the grid equation.ootnotetextL. Chac'on, G. Lapenta, A fully implicit, nonlinear adaptive grid strategy, J. Comput. Phys., accepted (2005) We will show that such an approach is competitive vs. uniform grids both from the accuracy (due to adaptivity) and the efficiency standpoints. Results for a variety of models 1D and 2D geometries, including nonlinear diffusion, radiation-diffusion, Burgers equation, and gas dynamics will be presented.

Random Photon Absorption Model Elucidates How Early Gain Control in Fly Photoreceptors Arises from Quantal Sampling

PubMed Central

Song, Zhuoyi; Zhou, Yu; Juusola, Mikko

2016-01-01

Many diurnal photoreceptors encode vast real-world light changes effectively, but how this performance originates from photon sampling is unclear. A 4-module biophysically-realistic fly photoreceptor model, in which information capture is limited by the number of its sampling units (microvilli) and their photon-hit recovery time (refractoriness), can accurately simulate real recordings and their information content. However, sublinear summation in quantum bump production (quantum-gain-nonlinearity) may also cause adaptation by reducing the bump/photon gain when multiple photons hit the same microvillus simultaneously. Here, we use a Random Photon Absorption Model (RandPAM), which is the 1st module of the 4-module fly photoreceptor model, to quantify the contribution of quantum-gain-nonlinearity in light adaptation. We show how quantum-gain-nonlinearity already results from photon sampling alone. In the extreme case, when two or more simultaneous photon-hits reduce to a single sublinear value, quantum-gain-nonlinearity is preset before the phototransduction reactions adapt the quantum bump waveform. However, the contribution of quantum-gain-nonlinearity in light adaptation depends upon the likelihood of multi-photon-hits, which is strictly determined by the number of microvilli and light intensity. Specifically, its contribution to light-adaptation is marginal (≤ 1%) in fly photoreceptors with many thousands of microvilli, because the probability of simultaneous multi-photon-hits on any one microvillus is low even during daylight conditions. However, in cells with fewer sampling units, the impact of quantum-gain-nonlinearity increases with brightening light. PMID:27445779

Signatures of soft sweeps across the Dt1 locus underlying determinate growth habit in soya bean [Glycine max (L.) Merr.].

PubMed

Zhong, Limei; Yang, Qiaomei; Yan, Xin; Yu, Chao; Su, Liu; Zhang, Xifeng; Zhu, Youlin

2017-09-01

Determinate growth habit is an agronomically important trait associated with domestication in soya bean. Previous studies have demonstrated that the emergence of determinacy is correlated with artificial selection on four nonsynonymous mutations in the Dt1 gene. To better understand the signatures of the soft sweeps across the Dt1 locus and track the origins of the determinate alleles, we examined patterns of nucleotide variation in Dt1 and the surrounding genomic region of approximately 800 kb. Four local, asymmetrical hard sweeps on four determinate alleles, sized approximately 660, 120, 220 and 150 kb, were identified, which constitute the soft sweeps for the adaptation. These variable-sized sweeps substantially reflected the strength and timing of selection and indicated that the selection on the alleles had been completed rapidly within half a century. Statistics of EHH, iHS, H12 and H2/H1 based on haplotype data had the power to detect the soft sweeps, revealing distinct signatures of extensive long-range LD and haplotype homozygosity, and multiple frequent adaptive haplotypes. A haplotype network constructed for Dt1 and a phylogenetic tree based on its extended haplotype block implied independent sources of the adaptive alleles through de novo mutations or rare standing variation in quick succession during the selective phase, strongly supporting multiple origins of the determinacy. We propose that the adaptation of soya bean determinacy is guided by a model of soft sweeps and that this model might be indispensable during crop domestication or evolution. © 2017 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.

Assess and Adapt: Coordinated Ecoregional Forest Vulnerability Assessments Covering the Upper Midwest and Northeast in Support of Climate-informed Decision-making

NASA Astrophysics Data System (ADS)

Swanston, C.; Janowiak, M.; Handler, S.; Butler, P.; Brandt, L.; Iverson, L.; Thompson, F.; Ontl, T.; Shannon, D.

2016-12-01

Forest ecosystem vulnerability assessments are rapidly becoming an integral component of forest management planning, in which there is increasing public expectation that even near-term activities explicitly incorporate information about anticipated climate impacts and risks. There is a clear desire among forest managers for targeted assessments that address critical questions about species and ecosystem vulnerabilities while delivering this information in an accessible format. We developed the Ecosystem Vulnerability Assessment Approach (EVAA), which combines multiple quantitative models, expert elicitation from scientists and land managers, and a templated report structure oriented to natural resource managers. The report structure includes relevant information on the contemporary landscape, past climate, future climate projections, impact model results, and a transparent vulnerability assessment of species and ecosystems. We have used EVAA in seven ecoregional assessments covering 246 million acres of forestland across the upper Midwest and Northeast (www.forestadaptation.org; five published, two in review). We convened a panel of local forest ecology and management experts in each assessment area to examine projected climate effects on system drivers, stressors, and dominant species, as well as the current adaptive capacity of the major ecoregional forest ecosystems. The panels provided a qualitative assessment of the vulnerability of forest ecosystems to climate change over the next century. Over 130 authors from dozens of organizations collaborated on these peer-reviewed assessment publications, which are delivered to thousands of stakeholders through live and recorded webinars, online briefs, and in-person trainings and seminars. The assessments are designed to be used with the Adaptation Workbook (www.adaptationworkbook.org), a planning tool that works at multiple scales and has generated more than 200 real-world forest adaptation demonstration projects.

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts.

PubMed

Bokhorst, Stef; Pedersen, Stine Højlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W; Brown, Ross D; Ehrich, Dorothee; Essery, Richard L H; Heilig, Achim; Ingvander, Susanne; Johansson, Cecilia; Johansson, Margareta; Jónsdóttir, Ingibjörg Svala; Inga, Niila; Luojus, Kari; Macelloni, Giovanni; Mariash, Heather; McLennan, Donald; Rosqvist, Gunhild Ninis; Sato, Atsushi; Savela, Hannele; Schneebeli, Martin; Sokolov, Aleksandr; Sokratov, Sergey A; Terzago, Silvia; Vikhamar-Schuler, Dagrun; Williamson, Scott; Qiu, Yubao; Callaghan, Terry V

2016-09-01

Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.

Changing Arctic Snow Cover: A Review of Recent Developments and Assessment of Future Needs for Observations, Modelling, and Impacts

NASA Technical Reports Server (NTRS)

Bokhorst, Stef; Pedersen, Stine Hojlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W.; Brown, Ross D.; Ehrich, Dorothee; Essery, Richard L. H.; Heilig, Achim; Ingvander, Susanne;

Delinquent-Victim Youth-Adapting a Trauma-Informed Approach for the Juvenile Justice System.

PubMed

Rapp, Lisa

2016-01-01

The connection between victimization and later delinquency is well established and most youth involved with the juvenile justice system have at least one if not multiple victimizations in their history. Poly-victimized youth or those presenting with complex trauma require specialized assessment and services to prevent deleterious emotional, physical, and social life consequences. Empirical studies have provided information which can guide practitioners work with these youth and families, yet many of the policies and practices of the juvenile justice system are counter to this model. Many youth-serving organizations are beginning to review their operations to better match a trauma-informed approach and in this article the author will highlight how a trauma-informed care model could be utilized to adapt the juvenile justice system.

High Reliability Organizations--Medication Safety.

PubMed

Yip, Luke; Farmer, Brenna

2015-06-01

High reliability organizations (HROs), such as the aviation industry, successfully engage in high-risk endeavors and have low incidence of adverse events. HROs have a preoccupation with failure and errors. They analyze each event to effect system wide change in an attempt to mitigate the occurrence of similar errors. The healthcare industry can adapt HRO practices, specifically with regard to teamwork and communication. Crew resource management concepts can be adapted to healthcare with the use of certain tools such as checklists and the sterile cockpit to reduce medication errors. HROs also use The Swiss Cheese Model to evaluate risk and look for vulnerabilities in multiple protective barriers, instead of focusing on one failure. This model can be used in medication safety to evaluate medication management in addition to using the teamwork and communication tools of HROs.

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

Multiplicative noise removal through fractional order tv-based model and fast numerical schemes for its approximation

NASA Astrophysics Data System (ADS)

Ullah, Asmat; Chen, Wen; Khan, Mushtaq Ahmad

2017-07-01

This paper introduces a fractional order total variation (FOTV) based model with three different weights in the fractional order derivative definition for multiplicative noise removal purpose. The fractional-order Euler Lagrange equation which is a highly non-linear partial differential equation (PDE) is obtained by the minimization of the energy functional for image restoration. Two numerical schemes namely an iterative scheme based on the dual theory and majorization- minimization algorithm (MMA) are used. To improve the restoration results, we opt for an adaptive parameter selection procedure for the proposed model by applying the trial and error method. We report numerical simulations which show the validity and state of the art performance of the fractional-order model in visual improvement as well as an increase in the peak signal to noise ratio comparing to corresponding methods. Numerical experiments also demonstrate that MMAbased methodology is slightly better than that of an iterative scheme.

The UK Earth System Models Marine Biogeochemical Evaluation Toolkit, BGC-val

NASA Astrophysics Data System (ADS)

de Mora, Lee

2017-04-01

The Biogeochemical Validation toolkit, BGC-val, is a model and grid independent python-based marine model evaluation framework that automates much of the validation of the marine component of an Earth System Model. BGC-val was initially developed to be a flexible and extensible system to evaluate the spin up of the marine UK Earth System Model (UKESM). However, the grid-independence and flexibility means that it is straightforward to adapt the BGC-val framework to evaluate other marine models. In addition to the marine component of the UKESM, this toolkit has been adapted to compare multiple models, including models from the CMIP5 and iMarNet inter-comparison projects. The BGC-val toolkit produces multiple levels of analysis which are presented in a simple to use interactive html5 document. Level 1 contains time series analyses, showing the development over time of many important biogeochemical and physical ocean metrics, such as the Global primary production or the Drake passage current. The second level of BGC-val is an in-depth spatial analyses of a single point in time. This is a series of point to point comparison of model and data in various regions, such as a comparison of Surface Nitrate in the model vs data from the world ocean atlas. The third level analyses are specialised ad-hoc packages to go in-depth on a specific question, such as the development of Oxygen minimum zones in the Equatorial Pacific. In additional to the three levels, the html5 document opens with a Level 0 table showing a summary of the status of the model run. The beta version of this toolkit is available via the Plymouth Marine Laboratory Gitlab server and uses the BSD 3 clause license.

The cerebellum in action: a simulation and robotics study.

PubMed

Hofstötter, Constanze; Mintz, Matti; Verschure, Paul F M J

2002-10-01

The control or prediction of the precise timing of events are central aspects of the many tasks assigned to the cerebellum. Despite much detailed knowledge of its physiology and anatomy, it remains unclear how the cerebellar circuitry can achieve such an adaptive timing function. We present a computational model pursuing this question for one extensively studied type of cerebellar-mediated learning: the classical conditioning of discrete motor responses. This model combines multiple current assumptions on the function of the cerebellar circuitry and was used to investigate whether plasticity in the cerebellar cortex alone can mediate adaptive conditioned response timing. In particular, we studied the effect of changes in the strength of the synapses formed between parallel fibres and Purkinje cells under the control of a negative feedback loop formed between inferior olive, cerebellar cortex and cerebellar deep nuclei. The learning performance of the model was evaluated at the circuit level in simulated conditioning experiments as well as at the behavioural level using a mobile robot. We demonstrate that the model supports adaptively timed responses under real-world conditions. Thus, in contrast to many other models that have focused on cerebellar-mediated conditioning, we investigated whether and how the suggested underlying mechanisms could give rise to behavioural phenomena.

An adaptive semi-Lagrangian advection model for transport of volcanic emissions in the atmosphere

NASA Astrophysics Data System (ADS)

Gerwing, Elena; Hort, Matthias; Behrens, Jörn; Langmann, Bärbel

2018-06-01

The dispersion of volcanic emissions in the Earth atmosphere is of interest for climate research, air traffic control and human wellbeing. Current volcanic emission dispersion models rely on fixed-grid structures that often are not able to resolve the fine filamented structure of volcanic emissions being transported in the atmosphere. Here we extend an existing adaptive semi-Lagrangian advection model for volcanic emissions including the sedimentation of volcanic ash. The advection of volcanic emissions is driven by a precalculated wind field. For evaluation of the model, the explosive eruption of Mount Pinatubo in June 1991 is chosen, which was one of the largest eruptions in the 20th century. We compare our simulations of the climactic eruption on 15 June 1991 to satellite data of the Pinatubo ash cloud and evaluate different sets of input parameters. We could reproduce the general advection of the Pinatubo ash cloud and, owing to the adaptive mesh, simulations could be performed at a high local resolution while minimizing computational cost. Differences to the observed ash cloud are attributed to uncertainties in the input parameters and the course of Typhoon Yunya, which is probably not completely resolved in the wind data used to drive the model. The best results were achieved for simulations with multiple ash particle sizes.

Phylogenetic analysis reveals positive correlations between adaptations to diverse hosts in a group of pathogen-like herbivores.

PubMed

Peterson, Daniel A; Hardy, Nate B; Morse, Geoffrey E; Stocks, Ian C; Okusu, Akiko; Normark, Benjamin B

2015-10-01

A jack of all trades can be master of none-this intuitive idea underlies most theoretical models of host-use evolution in plant-feeding insects, yet empirical support for trade-offs in performance on distinct host plants is weak. Trade-offs may influence the long-term evolution of host use while being difficult to detect in extant populations, but host-use evolution may also be driven by adaptations for generalism. Here we used host-use data from insect collection records to parameterize a phylogenetic model of host-use evolution in armored scale insects, a large family of plant-feeding insects with a simple, pathogen-like life history. We found that a model incorporating positive correlations between evolutionary changes in host performance best fit the observed patterns of diaspidid presence and absence on nearly all focal host taxa, suggesting that adaptations to particular hosts also enhance performance on other hosts. In contrast to the widely invoked trade-off model, we advocate a "toolbox" model of host-use evolution in which armored scale insects accumulate a set of independent genetic tools, each of which is under selection for a single function but may be useful on multiple hosts. © 2015 The Author(s).

Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy

NASA Astrophysics Data System (ADS)

Sardinha-Lourenço, A.; Andrade-Campos, A.; Antunes, A.; Oliveira, M. S.

2018-03-01

Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24-48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models.

Tridimensional Acculturation and Adaptation among Jamaican Adolescent-Mother Dyads in the United States

PubMed Central

Ferguson, Gail M.; Bornstein, Marc H.; Pottinger, Audrey M.

2011-01-01

A bidimensional acculturation framework cannot account for multiple destination cultures within contemporary settlement societies. We propose and test a tridimensional model among Jamaican adolescent-mother dyads in the United States compared with Jamaican Islander, European American, African American, and other Black and non-Black U.S. immigrant dyads (473 dyads, M adolescent age = 14 years). Jamaican immigrants evidence tridimensional acculturation, orienting toward Jamaican, African American, and European American cultures. Integration is favored (70%), particularly tricultural integration; moreover, Jamaican and other Black U.S. immigrants are more oriented toward African American than European American culture. Jamaican immigrant youth adapt at least as well as non-immigrant Jamaican and U.S. peers, although assimilated adolescents, particularly first generation, have worse sociocultural adaptation than integrated and separated adolescents. PMID:22966917

Comparison of Models for Bubonic Plague Reveals Unique Pathogen Adaptations to the Dermis.

PubMed

Gonzalez, Rodrigo J; Weening, Eric H; Lane, M Chelsea; Miller, Virginia L

2015-07-01

Vector-borne pathogens are inoculated in the skin of mammals, most likely in the dermis. Despite this, subcutaneous (s.c.) models of infection are broadly used in many fields, including Yersinia pestis pathogenesis. We expand on a previous report where we implemented intradermal (i.d.) inoculations to study bacterial dissemination during bubonic plague and compare this model with an s.c. We found that i.d. inoculations result in faster kinetics of infection and that bacterial dose influenced mouse survival after i.d. but not s.c. inoculation. Moreover, a deletion mutant of rovA, previously shown to be moderately attenuated in the s.c. model, was severely attenuated in the i.d. Lastly, based on previous observations where a population bottleneck from the skin to lymph nodes was observed after i.d., but not after s.c., inoculations, we used the latter model as a strategy to identify an additional bottleneck in bacterial dissemination from lymph nodes to the bloodstream. Our data indicate that the more biologically relevant i.d. model of bubonic plague differs significantly from the s.c. model in multiple aspects of infection. These findings reveal adaptations of Y. pestis to the dermis and how these adaptations can define the progression of disease. They also emphasize the importance of using a relevant route of infection when addressing host-pathogen interactions. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Science Opportunity Analyzer (SOA): Science Planning Made Simple

NASA Technical Reports Server (NTRS)

Streiffert, Barbara A.; Polanskey, Carol A.

2004-01-01

.For the first time at JPL, the Cassini mission to Saturn is using distributed science operations for developing their experiments. Remote scientists needed the ability to: a) Identify observation opportunities; b) Create accurate, detailed designs for their observations; c) Verify that their designs meet their objectives; d) Check their observations against project flight rules and constraints; e) Communicate their observations to other scientists. Many existing tools provide one or more of these functions, but Science Opportunity Analyzer (SOA) has been built to unify these tasks into a single application. Accurate: Utilizes JPL Navigation and Ancillary Information Facility (NAIF) SPICE* software tool kit - Provides high fidelity modeling. - Facilitates rapid adaptation to other flight projects. Portable: Available in Unix, Windows and Linux. Adaptable: Designed to be a multi-mission tool so it can be readily adapted to other flight projects. Implemented in Java, Java 3D and other innovative technologies. Conclusion: SOA is easy to use. It only requires 6 simple steps. SOA's ability to show the same accurate information in multiple ways (multiple visualization formats, data plots, listings and file output) is essential to meet the needs of a diverse, distributed science operations environment.

Adapting Word Embeddings from Multiple Domains to Symptom Recognition from Psychiatric Notes

PubMed Central

Zhang, Yaoyun; Li, Hee-Jin; Wang, Jingqi; Cohen, Trevor; Roberts, Kirk; Xu, Hua

2018-01-01

Mental health is increasingly recognized an important topic in healthcare. Information concerning psychiatric symptoms is critical for the timely diagnosis of mental disorders, as well as for the personalization of interventions. However, the diversity and sparsity of psychiatric symptoms make it challenging for conventional natural language processing techniques to automatically extract such information from clinical text. To address this problem, this study takes the initiative to use and adapt word embeddings from four source domains – intensive care, biomedical literature, Wikipedia and Psychiatric Forum – to recognize symptoms in the target domain of psychiatry. We investigated four different approaches including 1) only using word embeddings of the source domain, 2) directly combining data of the source and target to generate word embeddings, 3) assigning different weights to word embeddings, and 4) retraining the word embedding model of the source domain using a corpus of the target domain. To the best of our knowledge, this is the first work of adapting multiple word embeddings of external domains to improve psychiatric symptom recognition in clinical text. Experimental results showed that the last two approaches outperformed the baseline methods, indicating the effectiveness of our new strategies to leverage embeddings from other domains. PMID:29888086

Multiple-block grid adaption for an airplane geometry

NASA Technical Reports Server (NTRS)

Abolhassani, Jamshid Samareh; Smith, Robert E.

1988-01-01

Grid-adaption methods are developed with the capability of moving grid points in accordance with several variables for a three-dimensional multiple-block grid system. These methods are algebraic, and they are implemented for the computation of high-speed flow over an airplane configuration.

Fractional-order leaky integrate-and-fire model with long-term memory and power law dynamics.

PubMed

Teka, Wondimu W; Upadhyay, Ranjit Kumar; Mondal, Argha

2017-09-01

Pyramidal neurons produce different spiking patterns to process information, communicate with each other and transform information. These spiking patterns have complex and multiple time scale dynamics that have been described with the fractional-order leaky integrate-and-Fire (FLIF) model. Models with fractional (non-integer) order differentiation that generalize power law dynamics can be used to describe complex temporal voltage dynamics. The main characteristic of FLIF model is that it depends on all past values of the voltage that causes long-term memory. The model produces spikes with high interspike interval variability and displays several spiking properties such as upward spike-frequency adaptation and long spike latency in response to a constant stimulus. We show that the subthreshold voltage and the firing rate of the fractional-order model make transitions from exponential to power law dynamics when the fractional order α decreases from 1 to smaller values. The firing rate displays different types of spike timing adaptation caused by changes on initial values. We also show that the voltage-memory trace and fractional coefficient are the causes of these different types of spiking properties. The voltage-memory trace that represents the long-term memory has a feedback regulatory mechanism and affects spiking activity. The results suggest that fractional-order models might be appropriate for understanding multiple time scale neuronal dynamics. Overall, a neuron with fractional dynamics displays history dependent activities that might be very useful and powerful for effective information processing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

NASA Astrophysics Data System (ADS)

Lane, Peter C. R.; Gobet, Fernand

2013-03-01

Creating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the `speciated non-dominated sorting genetic algorithm' for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data.

Quantification of the Effects of Salt Stress and Physiological State on Thermotolerance of Bacillus cereus ATCC 10987 and ATCC 14579

PubMed Central

den Besten, Heidy M. W.; Mataragas, Marios; Moezelaar, Roy; Abee, Tjakko; Zwietering, Marcel H.

2006-01-01

The food-borne pathogen Bacillus cereus can acquire enhanced thermal resistance through multiple mechanisms. Two Bacillus cereus strains, ATCC 10987 and ATCC 14579, were used to quantify the effects of salt stress and physiological state on thermotolerance. Cultures were exposed to increasing concentrations of sodium chloride for 30 min, after which their thermotolerance was assessed at 50°C. Linear and nonlinear microbial survival models, which cover a wide range of known inactivation curvatures for vegetative cells, were fitted to the inactivation data and evaluated. Based on statistical indices and model characteristics, biphasic models with a shoulder were selected and used for quantification. Each model parameter reflected a survival characteristic, and both models were flexible, allowing a reduction of parameters when certain phenomena were not present. Both strains showed enhanced thermotolerance after preexposure to (non)lethal salt stress conditions in the exponential phase. The maximum adaptive stress response due to salt preexposure demonstrated for exponential-phase cells was comparable to the effect of physiological state on thermotolerance in both strains. However, the adaptive salt stress response was less pronounced for transition- and stationary-phase cells. The distinct tailing of strain ATCC 10987 was attributed to the presence of a subpopulation of spores. The existence of a stable heat-resistant subpopulation of vegetative cells could not be demonstrated for either of the strains. Quantification of the adaptive stress response might be instrumental in understanding adaptation mechanisms and will allow the food industry to develop more accurate and reliable stress-integrated predictive modeling to optimize minimal processing conditions. PMID:16957208

Neuron specific metabolic adaptations following multi-day exposures to oxygen glucose deprivation.

PubMed

Zeiger, Stephanie L H; McKenzie, Jennifer R; Stankowski, Jeannette N; Martin, Jacob A; Cliffel, David E; McLaughlin, BethAnn

2010-11-01

Prior exposure to sub toxic insults can induce a powerful endogenous neuroprotective program known as ischemic preconditioning. Current models typically rely on a single stress episode to induce neuroprotection whereas the clinical reality is that patients may experience multiple transient ischemic attacks (TIAs) prior to suffering a stroke. We sought to develop a neuron-enriched preconditioning model using multiple oxygen glucose deprivation (OGD) episodes to assess the endogenous protective mechanisms neurons implement at the metabolic and cellular level. We found that neurons exposed to a five minute period of glucose deprivation recovered oxygen utilization and lactate production using novel microphysiometry techniques. Using the non-toxic and energetically favorable five minute exposure, we developed a preconditioning paradigm where neurons are exposed to this brief OGD for three consecutive days. These cells experienced a 45% greater survival following an otherwise lethal event and exhibited a longer lasting window of protection in comparison to our previous in vitro preconditioning model using a single stress. As in other models, preconditioned cells exhibited mild caspase activation, an increase in oxidized proteins and a requirement for reactive oxygen species for neuroprotection. Heat shock protein 70 was upregulated during preconditioning, yet the majority of this protein was released extracellularly. We believe coupling this neuron-enriched multi-day model with microphysiometry will allow us to assess neuronal specific real-time metabolic adaptations necessary for preconditioning. Copyright © 2010 Elsevier B.V. All rights reserved.

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 experimental data, we conclude that selection for low faecal worm egg count should be stable over a short time frame (e.g. 20 years). We are further exploring model outcomes with multiple loci and comparing outcomes to other control strategies. PMID:23714384

Multidimensional adaptive evolution of a feed-forward network and the illusion of compensation

PubMed Central

Bullaughey, Kevin

2016-01-01

When multiple substitutions affect a trait in opposing ways, they are often assumed to be compensatory, not only with respect to the trait, but also with respect to fitness. This type of compensatory evolution has been suggested to underlie the evolution of protein structures and interactions, RNA secondary structures, and gene regulatory modules and networks. The possibility for compensatory evolution results from epistasis. Yet if epistasis is widespread, then it is also possible that the opposing substitutions are individually adaptive. I term this possibility an adaptive reversal. Although possible for arbitrary phenotype-fitness mappings, it has not yet been investigated whether such epistasis is prevalent in a biologically-realistic setting. I investigate a particular regulatory circuit, the type I coherent feed-forward loop, which is ubiquitous in natural systems and is accurately described by a simple mathematical model. I show that such reversals are common during adaptive evolution, can result solely from the topology of the fitness landscape, and can occur even when adaptation follows a modest environmental change and the network was well adapted to the original environment. The possibility of adaptive reversals warrants a systems perspective when interpreting substitution patterns in gene regulatory networks. PMID:23289561

The role of goal management for successful adaptation to arthritis.

PubMed

Arends, Roos Y; Bode, Christina; Taal, Erik; Van de Laar, Mart A F J

2013-10-01

Persons with polyarthritis often experience difficulties in attaining personal goals due to disease symptoms such as pain, fatigue and reduced mobility. This study examines the relationship of goal management strategies - goal maintenance, goal adjustment, goal disengagement, goal reengagement - with indicators of adaptation to polyarthritis, namely, depression, anxiety, purpose in life, positive affect, participation, and work participation. 305 patients diagnosed with polyarthritis participated in a questionnaire study (62% female, 29% employed, mean age: 62 years). Hierarchical multiple-regression-analyses were conducted to examine the relative importance of the goal management strategies for adaptation. Self-efficacy in relation to goal management was also studied. For all adaptation indicators, the goal management strategies added substantial explained variance to the models (R(2): .07-.27). Goal maintenance and goal adjustment were significant predictors of adaptation to polyarthritis. Self-efficacy partly mediated the influence of goal management strategies. Goal management strategies were found to be important predictors of successful adaptation to polyarthritis. Overall, adjusting goals to personal ability and circumstances and striving for goals proved to be the most beneficial strategies. Designing interventions that focus on the effective management of goals may help people to adapt to polyarthritis. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Design and Implementation of Multi-Input Adaptive Signal Extractions.

DTIC Science & Technology

1982-09-01

deflected gradient) algorithm requiring only N+ l multiplications per adaptation step. Additional quantization is introduced to eliminate all multiplications...noise cancellation for intermittent-signal applications," IEEE Trans. Information Theory, Vol. IT-26. Nov. 1980, pp. 746-750. 1-2 J. Kazakoff and W. A...cancellation," Proc. IEEE, July 1981, Vol. 69, pp. 846-847. *I-10 P. L . Kelly and W. A. Gardner, "Pilot-Directed Adaptive Signal Extraction," Dept. of

Coping as Part of Motivational Resilience in School: A Multidimensional Measure of Families, Allocations, and Profiles of Academic Coping

ERIC Educational Resources Information Center

Skinner, Ellen; Pitzer, Jennifer; Steele, Joel

2013-01-01

A study was designed to examine a multidimensional measure of children's coping in the academic domain as part of a larger model of motivational resilience. Using items tapping multiple ways of dealing with academic problems, including five adaptive ways (strategizing, help-seeking, comfort-seeking, self-encouragement, and commitment) and six…

New Multiple-Choice Measures of Historical Thinking: An Investigation of Cognitive Validity

ERIC Educational Resources Information Center

Smith, Mark D.

2018-01-01

History education scholars have recognized the need for test validity research in recent years and have called for empirical studies that explore how to best measure historical thinking processes. The present study was designed to help answer this call and to provide a model that others can adapt to carry this line of research forward. It employed…

Development of a Microcomputer-Based Adaptive Testing System. Phase I. Specification of Requirements and Preliminary Design.

DTIC Science & Technology

1982-06-30

treatments, and cure (or kill ) a patient. Administratively, the items were in a multiple-choice format and the simulation proceeded by branching...Discs: dual 5 1/4 inch floppies (IM) Bus: N/A Operating System: CP/M, MmmOST Price: $3,495 -14 ~-174- - ’i~ Model 820 Xerox 1341 West Mockingbird Lane

Important ingredients for health adaptive information systems.

PubMed

Senathirajah, Yalini; Bakken, Suzanne

2011-01-01

Healthcare information systems frequently do not truly meet clinician needs, due to the complexity, variability, and rapid change in medical contexts. Recently the internet world has been transformed by approaches commonly termed 'Web 2.0'. This paper proposes a Web 2.0 model for a healthcare adaptive architecture. The vision includes creating modular, user-composable systems which aim to make all necessary information from multiple internal and external sources available via a platform, for the user to use, arrange, recombine, author, and share at will, using rich interfaces where advisable. Clinicians can create a set of 'widgets' and 'views' which can transform data, reflect their domain knowledge and cater to their needs, using simple drag and drop interfaces without the intervention of programmers. We have built an example system, MedWISE, embodying the user-facing parts of the model. This approach to HIS is expected to have several advantages, including greater suitability to user needs (reflecting clinician rather than programmer concepts and priorities), incorporation of multiple information sources, agile reconfiguration to meet emerging situations and new treatment deployment, capture of user domain expertise and tacit knowledge, efficiencies due to workflow and human-computer interaction improvements, and greater user acceptance.

Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Anbazhagan, Ashwin; Chen, Min; Reutens, David C.

2017-12-01

Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

Adjustment modes in the trajectory of progressive multiple sclerosis: a qualitative study and conceptual model.

PubMed

Bogosian, Angeliki; Morgan, Myfanwy; Bishop, Felicity L; Day, Fern; Moss-Morris, Rona

2017-03-01

We examined cognitive and behavioural challenges and adaptations for people with progressive multiple sclerosis (MS) and developed a preliminary conceptual model of changes in adjustment over time. Using theoretical sampling, 34 semi-structured interviews were conducted with people with MS. Participants were between 41 and 77 years of age. Thirteen were diagnosed with primary progressive MS and 21 with secondary progressive MS. Data were analysed using a grounded theory approach. Participants described initially bracketing the illness off and carrying on their usual activities but this became problematic as the condition progressed and they employed different adjustment modes to cope with increased disabilities. Some scaled back their activities to live a more comfortable life, others identified new activities or adapted old ones, whereas at times, people disengaged from the adjustment process altogether and resigned to their condition. Relationships with partners, emotional reactions, environment and perception of the environment influenced adjustment, while people were often flexible and shifted among modes. Adjusting to a progressive condition is a fluid process. Future interventions can be tailored to address modifiable factors at different stages of the condition and may involve addressing emotional reactions concealing/revealing the condition and perceptions of the environment.

Adaptive Digital Signature Design and Short-Data-Record Adaptive Filtering

DTIC Science & Technology

2008-04-01

rate BPSK binary phase shift keying CA − CFAR cell averaging− constant false alarm rate CDMA code − division multiple − access CFAR constant false...Cotae, “Spreading sequence design for multiple cell synchronous DS-CDMA systems under total weighted squared correlation criterion,” EURASIP Journal...415-428, Mar. 2002. [6] P. Cotae, “Spreading sequence design for multiple cell synchronous DS-CDMA systems under total weighted squared correlation

3D CSEM inversion based on goal-oriented adaptive finite element method

NASA Astrophysics Data System (ADS)

Zhang, Y.; Key, K.

2016-12-01

We present a parallel 3D frequency domain controlled-source electromagnetic inversion code name MARE3DEM. Non-linear inversion of observed data is performed with the Occam variant of regularized Gauss-Newton optimization. The forward operator is based on the goal-oriented finite element method that efficiently calculates the responses and sensitivity kernels in parallel using a data decomposition scheme where independent modeling tasks contain different frequencies and subsets of the transmitters and receivers. To accommodate complex 3D conductivity variation with high flexibility and precision, we adopt the dual-grid approach where the forward mesh conforms to the inversion parameter grid and is adaptively refined until the forward solution converges to the desired accuracy. This dual-grid approach is memory efficient, since the inverse parameter grid remains independent from fine meshing generated around the transmitter and receivers by the adaptive finite element method. Besides, the unstructured inverse mesh efficiently handles multiple scale structures and allows for fine-scale model parameters within the region of interest. Our mesh generation engine keeps track of the refinement hierarchy so that the map of conductivity and sensitivity kernel between the forward and inverse mesh is retained. We employ the adjoint-reciprocity method to calculate the sensitivity kernels which establish a linear relationship between changes in the conductivity model and changes in the modeled responses. Our code uses a direcy solver for the linear systems, so the adjoint problem is efficiently computed by re-using the factorization from the primary problem. Further computational efficiency and scalability is obtained in the regularized Gauss-Newton portion of the inversion using parallel dense matrix-matrix multiplication and matrix factorization routines implemented with the ScaLAPACK library. We show the scalability, reliability and the potential of the algorithm to deal with complex geological scenarios by applying it to the inversion of synthetic marine controlled source EM data generated for a complex 3D offshore model with significant seafloor topography.

De-noising of 3D multiple-coil MR images using modified LMMSE estimator.

PubMed

Yaghoobi, Nima; Hasanzadeh, Reza P R

2018-06-20

De-noising is a crucial topic in Magnetic Resonance Imaging (MRI) which focuses on less loss of Magnetic Resonance (MR) image information and details preservation during the noise suppression. Nowadays multiple-coil MRI system is preferred to single one due to its acceleration in the imaging process. Due to the fact that the model of noise in single-coil and multiple-coil MRI systems are different, the de-noising methods that mostly are adapted to single-coil MRI systems, do not work appropriately with multiple-coil one. The model of noise in single-coil MRI systems is Rician while in multiple-coil one (if no subsampling occurs in k-space or GRAPPA reconstruction process is being done in the coils), it obeys noncentral Chi (nc-χ). In this paper, a new filtering method based on the Linear Minimum Mean Square Error (LMMSE) estimator is proposed for multiple-coil MR Images ruined by nc-χ noise. In the presented method, to have an optimum similarity selection of voxels, the Bayesian Mean Square Error (BMSE) criterion is used and proved for nc-χ noise model and also a nonlocal voxel selection methodology is proposed for nc-χ distribution. The results illustrate robust and accurate performance compared to the related state-of-the-art methods, either on ideal nc-χ images or GRAPPA reconstructed ones. Copyright © 2018. Published by Elsevier Inc.

Modified Multiple Model Adaptive Estimation (M3AE) for Simultaneous Parameter and State Estimation

DTIC Science & Technology

1998-03-01

Contents Page Dedication : iv Acknowledgments v Table Of Contents vi List of Figures . . ; x List of Tables xv Abstract xvii Chapter 1 ...INTRODUCTION 1 1.1 Overview 1 1.2 Background 7 1.2.1 The Chi-Square Test 9 1.2.2 Generalized Likelihood Ratio (GLR) Testing 10 1.2.3 Multiple...M3AE Covariance Analysis 115 4.1.3 Simulations and Performance Analysis 121 4.1.3.1 Test Case 1 : aT = 32.0 124 4.1.3.2 Test Case 2: aT = 37.89, and

Estimating the optimal dynamic antipsychotic treatment regime: Evidence from the sequential multiple assignment randomized CATIE Schizophrenia Study

PubMed Central

Shortreed, Susan M.; Moodie, Erica E. M.

2012-01-01

Summary Treatment of schizophrenia is notoriously difficult and typically requires personalized adaption of treatment due to lack of efficacy of treatment, poor adherence, or intolerable side effects. The Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE) Schizophrenia Study is a sequential multiple assignment randomized trial comparing the typical antipsychotic medication, perphenazine, to several newer atypical antipsychotics. This paper describes the marginal structural modeling method for estimating optimal dynamic treatment regimes and applies the approach to the CATIE Schizophrenia Study. Missing data and valid estimation of confidence intervals are also addressed. PMID:23087488

Adaptive Topographies and Equilibrium Selection in an Evolutionary Game

PubMed Central

Osinga, Hinke M.; Marshall, James A. R.

2015-01-01

It has long been known in the field of population genetics that adaptive topographies, in which population equilibria maximise mean population fitness for a trait regardless of its genetic bases, do not exist. Whether one chooses to model selection acting on a single locus or multiple loci does matter. In evolutionary game theory, analysis of a simple and general game involving distinct roles for the two players has shown that whether strategies are modelled using a single ‘locus’ or one ‘locus’ for each role, the stable population equilibria are unchanged and correspond to the fitness-maximising evolutionary stable strategies of the game. This is curious given the aforementioned population genetical results on the importance of the genetic bases of traits. Here we present a dynamical systems analysis of the game with roles detailing how, while the stable equilibria in this game are unchanged by the number of ‘loci’ modelled, equilibrium selection may differ under the two modelling approaches. PMID:25706762

Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models

NASA Astrophysics Data System (ADS)

Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.

2015-03-01

We present Π4U, an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.

Improved compensation of atmospheric turbulence effects by multiple adaptive mirror systems.

PubMed

Shamir, J; Crowe, D G; Beletic, J W

1993-08-20

Optical wave-front propagation in a layered model for the atmosphere is analyzed by the use of diffraction theory, leading to a novel approach for utilizing artificial guide stars. Considering recent observations of layering in the atmospheric turbulence, the results of this paper indicate that, even for very large telescopes, a substantial enlargement of the compensated angular field of view is possible when two adaptive mirrors and four or five artificial guide stars are employed. The required number of guide stars increases as the thickness of the turbulent layers increases, converging to the conventional results at the limit of continuously turbulent atmosphere.

EBT Fidelity Trajectories Across Training Cohorts Using the Interagency Collaborative Team Strategy

PubMed Central

Hecht, Debra; Aarons, Greg; Fettes, Danielle; Hurlburt, Michael; Ledesma, Karla

2015-01-01

The Interdisciplinary Collaborative Team (ICT) strategy uses front-line providers as adaptation, training and quality control agents for multi-agency EBT implementation. This study tests whether an ICT transmits fidelity to subsequent provider cohorts. SafeCare was implemented by home visitors from multiple community-based agencies contracting with child welfare. Client-reported fidelity trajectories for 5,769 visits, 957 clients and 45 providers were compared using three-level growth models. Provider cohorts trained and live-coached by the ICT attained benchmark fidelity after 12 weeks, and this was sustained. Hispanic clients reported high cultural competency, supporting a cultural adaptation crafted by the ICT. PMID:25586878

EBT Fidelity Trajectories Across Training Cohorts Using the Interagency Collaborative Team Strategy.

PubMed

Chaffin, Mark; Hecht, Debra; Aarons, Greg; Fettes, Danielle; Hurlburt, Michael; Ledesma, Karla

2016-03-01

The Interdisciplinary Collaborative Team (ICT) strategy uses front-line providers as adaptation, training and quality control agents for multi-agency EBT implementation. This study tests whether an ICT transmits fidelity to subsequent provider cohorts. SafeCare was implemented by home visitors from multiple community-based agencies contracting with child welfare. Client-reported fidelity trajectories for 5,769 visits, 957 clients and 45 providers were compared using three-level growth models. Provider cohorts trained and live-coached by the ICT attained benchmark fidelity after 12 weeks, and this was sustained. Hispanic clients reported high cultural competency, supporting a cultural adaptation crafted by the ICT.

Weighted Iterative Bayesian Compressive Sensing (WIBCS) for High Dimensional Polynomial Surrogate Construction

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Debusschere, B.; Najm, H. N.; Thornton, P. E.

2016-12-01

Surrogate construction has become a routine procedure when facing computationally intensive studies requiring multiple evaluations of complex models. In particular, surrogate models, otherwise called emulators or response surfaces, replace complex models in uncertainty quantification (UQ) studies, including uncertainty propagation (forward UQ) and parameter estimation (inverse UQ). Further, surrogates based on Polynomial Chaos (PC) expansions are especially convenient for forward UQ and global sensitivity analysis, also known as variance-based decomposition. However, the PC surrogate construction strongly suffers from the curse of dimensionality. With a large number of input parameters, the number of model simulations required for accurate surrogate construction is prohibitively large. Relatedly, non-adaptive PC expansions typically include infeasibly large number of basis terms far exceeding the number of available model evaluations. We develop Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth and PC surrogate construction leading to a sparse, high-dimensional PC surrogate with a very few model evaluations. The surrogate is then readily employed for global sensitivity analysis leading to further dimensionality reduction. Besides numerical tests, we demonstrate the construction on the example of Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Keck Adaptive Optics Imaging of Nearby Young Stars: Detection of Close Multiple Systems

NASA Astrophysics Data System (ADS)

Brandeker, Alexis; Jayawardhana, Ray; Najita, Joan

2003-10-01

Using adaptive optics on the Keck II 10 m telescope on Mauna Kea, we have surveyed 24 of the nearest young stars known in search of close companions. Our sample includes members of the MBM 12 and TW Hydrae young associations and the classical T Tauri binary UY Aurigae in the Taurus star-forming region. We present relative photometry and accurate astrometry for 10 close multiple systems. The multiplicity frequency in the TW Hydrae and MBM 12 groups are high in comparison to other young regions, although the significance of this result is low because of the small number statistics. We resolve S18 into a triple system, including a tight 63 mas (projected separation of 17 AU at a distance of 275 pc) binary, for the first time, with a hierarchical configuration reminiscent of VW Chamaeleontis and T Tauri. Another tight binary in our sample-TWA 5Aab (54 mas or 3 AU at 55 pc)-offers the prospect of dynamical mass measurement using astrometric observations within a few years and thus could be important for testing pre-main-sequence evolutionary models. Our observations confirm with 9 σ confidence that the brown dwarf TWA 5B is bound to TWA 5A. We find that the flux ratio of UY Aur has changed dramatically, by more than a magnitude in the H band, possibly as a result of variable extinction. With the smaller flux difference, the system may once again become detectable as an optical binary, as it was at the time of its discovery in 1944. Taken together, our results demonstrate that adaptive optics on large telescopes is a powerful tool for detecting tight companions and thus exploring the frequency and configurations of close multiple systems.

Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays.

PubMed

Niu, Ben; Li, Lu

2018-06-01

This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.

Li-ion battery thermal runaway suppression system using microchannel coolers and refrigerant injections

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

Bandhauer, Todd M.; Farmer, Joseph C.

A battery management system with thermally integrated fire suppression includes a multiplicity of individual battery cells in a housing; a multiplicity of cooling passages in the housing within or between the multiplicity of individual battery cells; a multiplicity of sensors operably connected to the individual battery cells, the sensors adapted to detect a thermal runaway event related to one or more of the multiplicity of individual battery cells; and a management system adapted to inject coolant into at least one of the multiplicity of cooling passages upon the detection of the thermal runaway event by the any one of themore » multiplicity of sensors, so that the thermal runaway event is rapidly quenched.« less

Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures.

PubMed

Tao, Yebin; Sánchez, Brisa N; Mukherjee, Bhramar

2015-03-30

Many existing cohort studies designed to investigate health effects of environmental exposures also collect data on genetic markers. The Early Life Exposures in Mexico to Environmental Toxicants project, for instance, has been genotyping single nucleotide polymorphisms on candidate genes involved in mental and nutrient metabolism and also in potentially shared metabolic pathways with the environmental exposures. Given the longitudinal nature of these cohort studies, rich exposure and outcome data are available to address novel questions regarding gene-environment interaction (G × E). Latent variable (LV) models have been effectively used for dimension reduction, helping with multiple testing and multicollinearity issues in the presence of correlated multivariate exposures and outcomes. In this paper, we first propose a modeling strategy, based on LV models, to examine the association between repeated outcome measures (e.g., child weight) and a set of correlated exposure biomarkers (e.g., prenatal lead exposure). We then construct novel tests for G × E effects within the LV framework to examine effect modification of outcome-exposure association by genetic factors (e.g., the hemochromatosis gene). We consider two scenarios: one allowing dependence of the LV models on genes and the other assuming independence between the LV models and genes. We combine the two sets of estimates by shrinkage estimation to trade off bias and efficiency in a data-adaptive way. Using simulations, we evaluate the properties of the shrinkage estimates, and in particular, we demonstrate the need for this data-adaptive shrinkage given repeated outcome measures, exposure measures possibly repeated and time-varying gene-environment association. Copyright © 2014 John Wiley & Sons, Ltd.

The evolution of high summit metabolism and cold tolerance in birds and its impact on present-day distributions.

PubMed

Swanson, David L; Garland, Theodore

2009-01-01

Summit metabolic rate (M(sum), maximum cold-induced metabolic rate) is positively correlated with cold tolerance in birds, suggesting that high M(sum) is important for residency in cold climates. However, the phylogenetic distribution of high M(sum) among birds and the impact of its evolution on current distributions are not well understood. Two potential adaptive hypotheses might explain the phylogenetic distribution of high M(sum) among birds. The cold adaptation hypothesis contends that species wintering in cold climates should have higher M(sum) than species wintering in warmer climates. The flight adaptation hypothesis suggests that volant birds might be capable of generating high M(sum) as a byproduct of their muscular capacity for flight; thus, variation in M(sum) should be associated with capacity for sustained flight, one indicator of which is migration. We collected M(sum) data from the literature for 44 bird species and conducted both conventional and phylogenetically informed statistical analyses to examine the predictors of M(sum) variation. Significant phylogenetic signal was present for log body mass, log mass-adjusted M(sum), and average temperature in the winter range. In multiple regression models, log body mass, winter temperature, and clade were significant predictors of log M(sum). These results are consistent with a role for climate in determining M(sum) in birds, but also indicate that phylogenetic signal remains even after accounting for associations indicative of adaptation to winter temperature. Migratory strategy was never a significant predictor of log M(sum) in multiple regressions, a result that is not consistent with the flight adaptation hypothesis.

New techniques for the analysis of manual control systems. [mathematical models of human operator behavior

NASA Technical Reports Server (NTRS)

Bekey, G. A.

1971-01-01

Studies are summarized on the application of advanced analytical and computational methods to the development of mathematical models of human controllers in multiaxis manual control systems. Specific accomplishments include the following: (1) The development of analytical and computer methods for the measurement of random parameters in linear models of human operators. (2) Discrete models of human operator behavior in a multiple display situation were developed. (3) Sensitivity techniques were developed which make possible the identification of unknown sampling intervals in linear systems. (4) The adaptive behavior of human operators following particular classes of vehicle failures was studied and a model structure proposed.

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

Modeling month-season of birth as a risk factor in mouse models of chronic disease: from multiple sclerosis to autoimmune encephalomyelitis.

PubMed

Reynolds, Jacob D; Case, Laure K; Krementsov, Dimitry N; Raza, Abbas; Bartiss, Rose; Teuscher, Cory

2017-06-01

Month-season of birth (M-SOB) is a risk factor in multiple chronic diseases, including multiple sclerosis (MS), where the lowest and greatest risk of developing MS coincide with the lowest and highest birth rates, respectively. To determine whether M-SOB effects in such chronic diseases as MS can be experimentally modeled, we examined the effect of M-SOB on susceptibility of C57BL/6J mice to experimental autoimmune encephalomyelitis (EAE). As in MS, mice that were born during the M-SOB with the lowest birth rate were less susceptible to EAE than mice born during the M-SOB with the highest birth rate. We also show that the M-SOB effect on EAE susceptibility is associated with differential production of multiple cytokines/chemokines by neuroantigen-specific T cells that are known to play a role in EAE pathogenesis. Taken together, these results support the existence of an M-SOB effect that may reflect seasonally dependent developmental differences in adaptive immune responses to self-antigens independent of external stimuli, including exposure to sunlight and vitamin D. Moreover, our documentation of an M-SOB effect on EAE susceptibility in mice allows for modeling and detailed analysis of mechanisms that underlie the M-SOB effect in not only MS but in numerous other diseases in which M-SOB impacts susceptibility.-Reynolds, J. D., Case, L. K., Krementsov, D. N., Raza, A., Bartiss, R., Teuscher, C. Modeling month-season of birth as a risk factor in mouse models of chronic disease: from multiple sclerosis to autoimmune encephalomyelitis. © FASEB.

Patterns of gene flow and selection across multiple species of Acrocephalus warblers: footprints of parallel selection on the Z chromosome.

PubMed

Reifová, Radka; Majerová, Veronika; Reif, Jiří; Ahola, Markus; Lindholm, Antero; Procházka, Petr

2016-06-16

Understanding the mechanisms and selective forces leading to adaptive radiations and origin of biodiversity is a major goal of evolutionary biology. Acrocephalus warblers are small passerines that underwent an adaptive radiation in the last approximately 10 million years that gave rise to 37 extant species, many of which still hybridize in nature. Acrocephalus warblers have served as model organisms for a wide variety of ecological and behavioral studies, yet our knowledge of mechanisms and selective forces driving their radiation is limited. Here we studied patterns of interspecific gene flow and selection across three European Acrocephalus warblers to get a first insight into mechanisms of radiation of this avian group. We analyzed nucleotide variation at eight nuclear loci in three hybridizing Acrocephalus species with overlapping breeding ranges in Europe. Using an isolation-with-migration model for multiple populations, we found evidence for unidirectional gene flow from A. scirpaceus to A. palustris and from A. palustris to A. dumetorum. Gene flow was higher between genetically more closely related A. scirpaceus and A. palustris than between ecologically more similar A. palustris and A. dumetorum, suggesting that gradual accumulation of intrinsic barriers rather than divergent ecological selection are more efficient in restricting interspecific gene flow in Acrocephalus warblers. Although levels of genetic differentiation between different species pairs were in general not correlated, we found signatures of apparently independent instances of positive selection at the same two Z-linked loci in multiple species. Our study brings the first evidence that gene flow occurred during Acrocephalus radiation and not only between sister species. Interspecific gene flow could thus be an important source of genetic variation in individual Acrocephalus species and could have accelerated adaptive evolution and speciation rate in this avian group by creating novel genetic combinations and new phenotypes. Independent instances of positive selection at the same loci in multiple species indicate an interesting possibility that the same loci might have contributed to reproductive isolation in several speciation events.

Texture-preserved penalized weighted least-squares reconstruction of low-dose CT image via image segmentation and high-order MRF modeling

NASA Astrophysics Data System (ADS)

Han, Hao; Zhang, Hao; Wei, Xinzhou; Moore, William; Liang, Zhengrong

2016-03-01

In this paper, we proposed a low-dose computed tomography (LdCT) image reconstruction method with the help of prior knowledge learning from previous high-quality or normal-dose CT (NdCT) scans. The well-established statistical penalized weighted least squares (PWLS) algorithm was adopted for image reconstruction, where the penalty term was formulated by a texture-based Gaussian Markov random field (gMRF) model. The NdCT scan was firstly segmented into different tissue types by a feature vector quantization (FVQ) approach. Then for each tissue type, a set of tissue-specific coefficients for the gMRF penalty was statistically learnt from the NdCT image via multiple-linear regression analysis. We also proposed a scheme to adaptively select the order of gMRF model for coefficients prediction. The tissue-specific gMRF patterns learnt from the NdCT image were finally used to form an adaptive MRF penalty for the PWLS reconstruction of LdCT image. The proposed texture-adaptive PWLS image reconstruction algorithm was shown to be more effective to preserve image textures than the conventional PWLS image reconstruction algorithm, and we further demonstrated the gain of high-order MRF modeling for texture-preserved LdCT PWLS image reconstruction.

In-Flight Suppression of an Unstable F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen S.; Gilligan, Eric T.; Wall, John H.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

2015-01-01

NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off-nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using post-flight frequency-domain reconstruction, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

On the Evolution of Behaviors through Embodied Imitation.

PubMed

Erbas, Mehmet D; Bull, Larry; Winfield, Alan F T

2015-01-01

This article describes research in which embodied imitation and behavioral adaptation are investigated in collective robotics. We model social learning in artificial agents with real robots. The robots are able to observe and learn each others' movement patterns using their on-board sensors only, so that imitation is embodied. We show that the variations that arise from embodiment allow certain behaviors that are better adapted to the process of imitation to emerge and evolve during multiple cycles of imitation. As these behaviors are more robust to uncertainties in the real robots' sensors and actuators, they can be learned by other members of the collective with higher fidelity. Three different types of learned-behavior memory have been experimentally tested to investigate the effect of memory capacity on the evolution of movement patterns, and results show that as the movement patterns evolve through multiple cycles of imitation, selection, and variation, the robots are able to, in a sense, agree on the structure of the behaviors that are imitated.

Testing for a slope-based decoupling algorithm in a woofer-tweeter adaptive optics system.

PubMed

Cheng, Tao; Liu, WenJin; Yang, KangJian; He, Xin; Yang, Ping; Xu, Bing

2018-05-01

It is well known that using two or more deformable mirrors (DMs) can improve the compensation ability of an adaptive optics (AO) system. However, to keep the stability of an AO system, the correlation between the multiple DMs must be suppressed during the correction. In this paper, we proposed a slope-based decoupling algorithm to simultaneous control the multiple DMs. In order to examine the validity and practicality of this algorithm, a typical woofer-tweeter (W-T) AO system was set up. For the W-T system, a theory model was simulated and the results indicated in theory that the algorithm we presented can selectively make woofer and tweeter correct different spatial frequency aberration and suppress the cross coupling between the dual DMs. At the same time, the experimental results for the W-T AO system were consistent with the results of the simulation, which demonstrated in practice that this algorithm is practical for the AO system with dual DMs.

An Indirect Adaptive Control Scheme in the Presence of Actuator and Sensor Failures

NASA Technical Reports Server (NTRS)

Sun, Joy Z.; Josh, Suresh M.

2009-01-01

The problem of controlling a system in the presence of unknown actuator and sensor faults is addressed. The system is assumed to have groups of actuators, and groups of sensors, with each group consisting of multiple redundant similar actuators or sensors. The types of actuator faults considered consist of unknown actuators stuck in unknown positions, as well as reduced actuator effectiveness. The sensor faults considered include unknown biases and outages. The approach employed for fault detection and estimation consists of a bank of Kalman filters based on multiple models, and subsequent control reconfiguration to mitigate the effect of biases caused by failed components as well as to obtain stability and satisfactory performance using the remaining actuators and sensors. Conditions for fault identifiability are presented, and the adaptive scheme is applied to an aircraft flight control example in the presence of actuator failures. Simulation results demonstrate that the method can rapidly and accurately detect faults and estimate the fault values, thus enabling safe operation and acceptable performance in spite of failures.

Factor analysis of auto-associative neural networks with application in speaker verification.

PubMed

Garimella, Sri; Hermansky, Hynek

2013-04-01

Auto-associative neural network (AANN) is a fully connected feed-forward neural network, trained to reconstruct its input at its output through a hidden compression layer, which has fewer numbers of nodes than the dimensionality of input. AANNs are used to model speakers in speaker verification, where a speaker-specific AANN model is obtained by adapting (or retraining) the universal background model (UBM) AANN, an AANN trained on multiple held out speakers, using corresponding speaker data. When the amount of speaker data is limited, this adaptation procedure may lead to overfitting as all the parameters of UBM-AANN are adapted. In this paper, we introduce and develop the factor analysis theory of AANNs to alleviate this problem. We hypothesize that only the weight matrix connecting the last nonlinear hidden layer and the output layer is speaker-specific, and further restrict it to a common low-dimensional subspace during adaptation. The subspace is learned using large amounts of development data, and is held fixed during adaptation. Thus, only the coordinates in a subspace, also known as i-vector, need to be estimated using speaker-specific data. The update equations are derived for learning both the common low-dimensional subspace and the i-vectors corresponding to speakers in the subspace. The resultant i-vector representation is used as a feature for the probabilistic linear discriminant analysis model. The proposed system shows promising results on the NIST-08 speaker recognition evaluation (SRE), and yields a 23% relative improvement in equal error rate over the previously proposed weighted least squares-based subspace AANNs system. The experiments on NIST-10 SRE confirm that these improvements are consistent and generalize across datasets.

Downscaling Land Surface Temperature in Complex Regions by Using Multiple Scale Factors with Adaptive Thresholds

PubMed Central

Yang, Yingbao; Li, Xiaolong; Pan, Xin; Zhang, Yong; Cao, Chen

2017-01-01

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mixed areas with three or four surface types. The correlation coefficients (CCs) between LST and the scale factors were used to assess the importance of the scale factors within a moving window. CC thresholds determined which factors participated in the fitting of the regression equation. The proposed downscaling approach, which involves an adaptive selection of the scale factors, was evaluated using the LST derived from four Landsat 8 thermal imageries of Nanjing City in different seasons. Results of the visual and quantitative analyses show that the proposed approach achieves relatively satisfactory downscaling results on 11 August, with coefficient of determination and root-mean-square error of 0.87 and 1.13 °C, respectively. Relative to other approaches, our approach shows the similar accuracy and the availability in all seasons. The best (worst) availability occurred in the region of vegetation (water). Thus, the approach is an efficient and reliable LST downscaling method. Future tasks include reliable LST downscaling in challenging regions and the application of our model in middle and low spatial resolutions. PMID:28368301

Attitude determination using an adaptive multiple model filtering Scheme

NASA Technical Reports Server (NTRS)

Lam, Quang; Ray, Surendra N.

1995-01-01

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

Attitude determination using an adaptive multiple model filtering Scheme

NASA Astrophysics Data System (ADS)

Lam, Quang; Ray, Surendra N.

1995-05-01

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

Adaptive testing for multiple traits in a proportional odds model with applications to detect SNP-brain network associations.

PubMed

Kim, Junghi; Pan, Wei

2017-04-01

There has been increasing interest in developing more powerful and flexible statistical tests to detect genetic associations with multiple traits, as arising from neuroimaging genetic studies. Most of existing methods treat a single trait or multiple traits as response while treating an SNP as a predictor coded under an additive inheritance mode. In this paper, we follow an earlier approach in treating an SNP as an ordinal response while treating traits as predictors in a proportional odds model (POM). In this way, it is not only easier to handle mixed types of traits, e.g., some quantitative and some binary, but it is also potentially more robust to the commonly adopted additive inheritance mode. More importantly, we develop an adaptive test in a POM so that it can maintain high power across many possible situations. Compared to the existing methods treating multiple traits as responses, e.g., in a generalized estimating equation (GEE) approach, the proposed method can be applied to a high dimensional setting where the number of phenotypes (p) can be larger than the sample size (n), in addition to a usual small P setting. The promising performance of the proposed method was demonstrated with applications to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, in which either structural MRI driven phenotypes or resting-state functional MRI (rs-fMRI) derived brain functional connectivity measures were used as phenotypes. The applications led to the identification of several top SNPs of biological interest. Furthermore, simulation studies showed competitive performance of the new method, especially for p>n. © 2017 WILEY PERIODICALS, INC.

The Relationship of Item-Level Response Times with Test-Taker and Item Variables in an Operational CAT Environment. LSAC Research Report Series.

ERIC Educational Resources Information Center

Swygert, Kimberly A.

In this study, data from an operational computerized adaptive test (CAT) were examined in order to gather information concerning item response times in a CAT environment. The CAT under study included multiple-choice items measuring verbal, quantitative, and analytical reasoning. The analyses included the fitting of regression models describing the…

Adaptive Estimation and Heuristic Optimization of Nonlinear Spacecraft Attitude Dynamics

DTIC Science & Technology

2016-09-15

Algorithm GPS Global Positioning System HOUF Higher Order Unscented Filter IC initial conditions IMM Interacting Multiple Model IMU Inertial Measurement Unit ...sources ranging from inertial measurement units to star sensors are used to construct observations for attitude estimation algorithms. The sensor...parameters. A single vector measurement will provide two independent parameters, as a unit vector constraint removes a DOF making the problem underdetermined

A Novel Electrocardiogram Segmentation Algorithm Using a Multiple Model Adaptive Estimator

DTIC Science & Technology

2002-03-01

2-5 Figure 2-3. Typical Pulse Oximeter Placement [20].....................................................2-5 Figure 2-4...the heart contracts and then decreases when the heart relaxes. The pulse oximeter is typically place on a toe, finger, or earlobe as shown in Figure...2-3. Figure 2-2. Absorption as Light Passes Through the Body [24]. Figure 2-3. Typical Pulse Oximeter Placement [19]. The pulse

Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks.

PubMed

Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Ferrigno, Giancarlo; D'Angelo, Egidio; Pedrocchi, Alessandra

2015-01-01

The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. Modeling it and embedding it into sensorimotor tasks allows us to create functional links between plasticity mechanisms, neural circuits and behavioral learning. Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning. A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used. Two cerebellum-mediated paradigms have been designed: an associative Pavlovian task and a vestibulo-ocular reflex, with multiple sessions of acquisition and extinction and with different stimuli and perturbation patterns. The cerebellar controller succeeded to generate conditioned responses and finely tuned eye movement compensation, thus reproducing human-like behaviors. Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli.

Multiple Memory Systems Are Unnecessary to Account for Infant Memory Development: An Ecological Model

PubMed Central

Rovee-Collier, Carolyn; Cuevas, Kimberly

2009-01-01

How the memory of adults evolves from the memory abilities of infants is a central problem in cognitive development. The popular solution holds that the multiple memory systems of adults mature at different rates during infancy. The early-maturing system (implicit or nondeclarative memory) functions automatically from birth, whereas the late-maturing system (explicit or declarative memory) functions intentionally, with awareness, from late in the first year. Data are presented from research on deferred imitation, sensory preconditioning, potentiation, and context for which this solution cannot account and present an alternative model that eschews the need for multiple memory systems. The ecological model of infant memory development (N. E. Spear, 1984) holds that members of all species are perfectly adapted to their niche at each point in ontogeny and exhibit effective, evolutionarily selected solutions to whatever challenges each new niche poses. Because adults and infants occupy different niches, what they perceive, learn, and remember about the same event differs, but their raw capacity to learn and remember does not. PMID:19209999

Using landscape limnology to classify freshwater ecosystems for multi-ecosystem management and conservation

USGS Publications Warehouse

Soranno, Patricia A.; Cheruvelil, Kendra Spence; Webster, Katherine E.; Bremigan, Mary T.; Wagner, Tyler; Stow, Craig A.

2010-01-01

Governmental entities are responsible for managing and conserving large numbers of lake, river, and wetland ecosystems that can be addressed only rarely on a case-by-case basis. We present a system for predictive classification modeling, grounded in the theoretical foundation of landscape limnology, that creates a tractable number of ecosystem classes to which management actions may be tailored. We demonstrate our system by applying two types of predictive classification modeling approaches to develop nutrient criteria for eutrophication management in 1998 north temperate lakes. Our predictive classification system promotes the effective management of multiple ecosystems across broad geographic scales by explicitly connecting management and conservation goals to the classification modeling approach, considering multiple spatial scales as drivers of ecosystem dynamics, and acknowledging the hierarchical structure of freshwater ecosystems. Such a system is critical for adaptive management of complex mosaics of freshwater ecosystems and for balancing competing needs for ecosystem services in a changing world.

Cerebellar subjects show impaired adaptation of anticipatory EMG during catching.

PubMed

Lang, C E; Bastian, A J

1999-11-01

We evaluated the role of the cerebellum in adapting anticipatory muscle activity during a multijointed catching task. Individuals with and without cerebellar damage caught a series of balls of different weights dropped from above. In Experiment 1 (light-heavy-light), each subject was required to catch light balls (baseline phase), heavy balls (adaptation phase), and then light balls again (postadaptation phase). Subjects were not told when the balls would be switched, and they were required to keep their hand within a vertical spatial "window" during the catch. During the series of trials, we measured three-dimensional (3-D) position and electromyogram (EMG) from the catching arm. We modeled the adaptation process using an exponential decay function; this model allowed us to dissociate adaptation from performance variability. Results from the position data show that cerebellar subjects did not adapt or adapted very slowly to the changed ball weight when compared with the control subjects. The cerebellar group required an average of 30.9 +/- 8.7 trials (mean +/- SE) to progress approximately two-thirds of the way through the adaptation compared with 1.7 +/- 0.2 trials for the control group. Only control subjects showed a negative aftereffect indicating storage of the adaptation. No difference in performance variability existed between the two groups. EMG data show that control subjects increased their anticipatory muscle activity in the flexor muscles of the arm to control the momentum of the ball at impact. Cerebellar subjects were unable to differentially increase the anticipatory muscle activity across three joints to perform the task successfully. In Experiment 2 (heavy-light-heavy), we tested to see whether the rate of adaptation changed when adapting to a light ball versus a heavy ball. Subjects caught the heavy balls (baseline phase), the light balls (adaptation phase), and then heavy balls again (postadaptation phase). Comparison of rates of adaptation between Experiment 1 and Experiment 2 showed that the rate of adaptation was unchanged whether adapting to a light ball or a heavy ball. Given these findings, we conclude that the cerebellum is important in generating the appropriate anticipatory muscle activity across multiple muscles and modifying it in response to changing demands though trial-and-error practice.

Effects of Estimation Bias on Multiple-Category Classification with an IRT-Based Adaptive Classification Procedure

ERIC Educational Resources Information Center

Yang, Xiangdong; Poggio, John C.; Glasnapp, Douglas R.

2006-01-01

The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following…

Underwater wireless optical MIMO system with spatial modulation and adaptive power allocation

NASA Astrophysics Data System (ADS)

Huang, Aiping; Tao, Linwei; Niu, Yilong

2018-04-01

In this paper, we investigate the performance of underwater wireless optical multiple-input multiple-output communication system combining spatial modulation (SM-UOMIMO) with flag dual amplitude pulse position modulation (FDAPPM). Channel impulse response for coastal and harbor ocean water links are obtained by Monte Carlo (MC) simulation. Moreover, we obtain the closed-form and upper bound average bit error rate (BER) expressions for receiver diversity including optical combining, equal gain combining and selected combining. And a novel adaptive power allocation algorithm (PAA) is proposed to minimize the average BER of SM-UOMIMO system. Our numeric results indicate an excellent match between the analytical results and numerical simulations, which confirms the accuracy of our derived expressions. Furthermore, the results show that adaptive PAA outperforms conventional fixed factor PAA and equal PAA obviously. Multiple-input single-output system with adaptive PAA obtains even better BER performance than MIMO one, at the same time reducing receiver complexity effectively.

Quality of experience enhancement of high efficiency video coding video streaming in wireless packet networks using multiple description coding

NASA Astrophysics Data System (ADS)

Boumehrez, Farouk; Brai, Radhia; Doghmane, Noureddine; Mansouri, Khaled

2018-01-01

Recently, video streaming has attracted much attention and interest due to its capability to process and transmit large data. We propose a quality of experience (QoE) model relying on high efficiency video coding (HEVC) encoder adaptation scheme, in turn based on the multiple description coding (MDC) for video streaming. The main contributions of the paper are (1) a performance evaluation of the new and emerging video coding standard HEVC/H.265, which is based on the variation of quantization parameter (QP) values depending on different video contents to deduce their influence on the sequence to be transmitted, (2) QoE support multimedia applications in wireless networks are investigated, so we inspect the packet loss impact on the QoE of transmitted video sequences, (3) HEVC encoder parameter adaptation scheme based on MDC is modeled with the encoder parameter and objective QoE model. A comparative study revealed that the proposed MDC approach is effective for improving the transmission with a peak signal-to-noise ratio (PSNR) gain of about 2 to 3 dB. Results show that a good choice of QP value can compensate for transmission channel effects and improve received video quality, although HEVC/H.265 is also sensitive to packet loss. The obtained results show the efficiency of our proposed method in terms of PSNR and mean-opinion-score.

Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface.

PubMed

Matsubara, Takamitsu; Morimoto, Jun

2013-08-01

In this study, we propose a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different electromyography (EMG) signals are measured. When different users perform the same motion (e.g., grasping), different EMG signals are also measured. Therefore, designing a myoelectric interface that can be used by multiple users to perform multiple motions is difficult. To cope with this problem, we propose for EMG signals a bilinear model that is composed of two linear factors: 1) user dependent and 2) motion dependent. By decomposing the EMG signals into these two factors, the extracted motion-dependent factors can be used as user-independent features. We can construct a motion classifier on the extracted feature space to develop the multiuser interface. For novel users, the proposed adaptation method estimates the user-dependent factor through only a few interactions. The bilinear EMG model with the estimated user-dependent factor can extract the user-independent features from the novel user data. We applied our proposed method to a recognition task of five hand gestures for robotic hand control using four-channel EMG signals measured from subject forearms. Our method resulted in 73% accuracy, which was statistically significantly different from the accuracy of standard nonmultiuser interfaces, as the result of a two-sample t -test at a significance level of 1%.

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 initial outputs on migration and in-situ adaptation. In doing so we illustrate some key causal relationships between changes in the environment, livelihoods and migration decision.

A Multi-Moment Bulkwater Ice Microphysics Scheme with Consideration of the Adaptive Growth Habit and Apparent Density for Pristine Ice in the WRF Model

NASA Astrophysics Data System (ADS)

Tsai, T. C.; Chen, J. P.; Dearden, C.

2014-12-01

The wide variety of ice crystal shapes and growth habits makes it a complicated issue in cloud models. This study developed the bulk ice adaptive habit parameterization based on the theoretical approach of Chen and Lamb (1994) and introduced a 6-class hydrometeors double-moment (mass and number) bulk microphysics scheme with gamma-type size distribution function. Both the proposed schemes have been implemented into the Weather Research and Forecasting model (WRF) model forming a new multi-moment bulk microphysics scheme. Two new moments of ice crystal shape and volume are included for tracking pristine ice's adaptive habit and apparent density. A closure technique is developed to solve the time evolution of the bulk moments. For the verification of the bulk ice habit parameterization, some parcel-type (zero-dimension) calculations were conducted and compared with binned numerical calculations. The results showed that: a flexible size spectrum is important in numerical accuracy, the ice shape can significantly enhance the diffusional growth, and it is important to consider the memory of growth habit (adaptive growth) under varying environmental conditions. Also, the derived results with the 3-moment method were much closer to the binned calculations. A field campaign of DIAMET was selected to simulate in the WRF model for real-case studies. The simulations were performed with the traditional spherical ice and the new adaptive shape schemes to evaluate the effect of crystal habits. Some main features of narrow rain band, as well as the embedded precipitation cells, in the cold front case were well captured by the model. Furthermore, the simulations produced a good agreement in the microphysics against the aircraft observations in ice particle number concentration, ice crystal aspect ratio, and deposition heating rate especially within the temperature region of ice secondary multiplication production.

Implications of Modeling Uncertainty for Water Quality Decision Making

NASA Astrophysics Data System (ADS)

Shabman, L.

2002-05-01

The report, National Academy of Sciences report, "Assessing the TMDL Approach to Water Quality Management" endorsed the "watershed" and "ambient water quality focused" approach" to water quality management called for in the TMDL program. The committee felt that available data and models were adequate to move such a program forward, if the EPA and all stakeholders better understood the nature of the scientific enterprise and its application to the TMDL program. Specifically, the report called for a greater acknowledgement of model prediction uncertinaity in making and implementing TMDL plans. To assure that such uncertinaity was addressed in water quality decision making the committee called for a commitment to "adaptive implementation" of water quality management plans. The committee found that the number and complexity of the interactions of multiple stressors, combined with model prediction uncertinaity means that we need to avoid the temptation to make assurances that specific actions will result in attainment of particular water quality standards. Until the work on solving a water quality problem begins, analysts and decision makers cannot be sure what the correct solutions are, or even what water quality goals a community should be seeking. In complex systems we need to act in order to learn; adaptive implementation is a concurrent process of action and learning. Learning requires (1) continued monitoring of the waterbody to determine how it responds to the actions taken and (2) carefully designed experiments in the watershed. If we do not design learning into what we attempt we are not doing adaptive implementation. Therefore, there needs to be an increased commitment to monitoring and experiments in watersheds that will lead to learning. This presentation will 1) explain the logic for adaptive implementation; 2) discuss the ways that water quality modelers could characterize and explain model uncertinaity to decision makers; 3) speculate on the implications of the adaptive implementation for setting of water quality standards, for design of watershed monitoring programs and for the regulatory rules governing the TMDL program implementation.

Adaptive iterative design (AID): a novel approach for evaluating the interactive effects of multiple stressors on aquatic organisms.

PubMed

Glaholt, Stephen P; Chen, Celia Y; Demidenko, Eugene; Bugge, Deenie M; Folt, Carol L; Shaw, Joseph R

2012-08-15

The study of stressor interactions by eco-toxicologists using nonlinear response variables is limited by required amounts of a priori knowledge, complexity of experimental designs, the use of linear models, and the lack of use of optimal designs of nonlinear models to characterize complex interactions. Therefore, we developed AID, an adaptive-iterative design for eco-toxicologist to more accurately and efficiently examine complex multiple stressor interactions. AID incorporates the power of the general linear model and A-optimal criteria with an iterative process that: 1) minimizes the required amount of a priori knowledge, 2) simplifies the experimental design, and 3) quantifies both individual and interactive effects. Once a stable model is determined, the best fit model is identified and the direction and magnitude of stressors, individually and all combinations (including complex interactions) are quantified. To validate AID, we selected five commonly co-occurring components of polluted aquatic systems, three metal stressors (Cd, Zn, As) and two water chemistry parameters (pH, hardness) to be tested using standard acute toxicity tests in which Daphnia mortality is the (nonlinear) response variable. We found after the initial data input of experimental data, although literature values (e.g. EC-values) may also be used, and after only two iterations of AID, our dose response model was stable. The model ln(Cd)*ln(Zn) was determined the best predictor of Daphnia mortality response to the combined effects of Cd, Zn, As, pH, and hardness. This model was then used to accurately identify and quantify the strength of both greater- (e.g. As*Cd) and less-than additive interactions (e.g. Cd*Zn). Interestingly, our study found only binary interactions significant, not higher order interactions. We conclude that AID is more efficient and effective at assessing multiple stressor interactions than current methods. Other applications, including life-history endpoints commonly used by regulators, could benefit from AID's efficiency in assessing water quality criteria. Copyright © 2012 Elsevier B.V. All rights reserved.

A new parameterization for integrated population models to document amphibian reintroductions

USGS Publications Warehouse

Duarte, Adam; Pearl, Christopher; Adams, Michael J.; Peterson, James T.

2017-01-01

Managers are increasingly implementing reintroduction programs as part of a global effort to alleviate amphibian declines. Given uncertainty in factors affecting populations and a need to make recurring decisions to achieve objectives, adaptive management is a useful component of these efforts. A major impediment to the estimation of demographic rates often used to parameterize and refine decision-support models is that life-stage-specific monitoring data are frequently sparse for amphibians. We developed a new parameterization for integrated population models to match the ecology of amphibians and capitalize on relatively inexpensive monitoring data to document amphibian reintroductions. We evaluate the capability of this model by fitting it to Oregon spotted frog (Rana pretiosa) monitoring data collected from 2007 to 2014 following their reintroduction within the Klamath Basin, Oregon, USA. The number of egg masses encountered and the estimated adult and metamorph abundances generally increased following reintroduction. We found that survival probability from egg to metamorph ranged from 0.01 in 2008 to 0.09 in 2009 and was not related to minimum spring temperatures, metamorph survival probability ranged from 0.13 in 2010–2011 to 0.86 in 2012–2013 and was positively related to mean monthly temperatures (logit-scale slope = 2.37), adult survival probability was lower for founders (0.40) than individuals recruited after reintroduction (0.56), and the mean number of egg masses per adult female was 0.74. Our study is the first to test hypotheses concerning Oregon spotted frog egg-to-metamorph and metamorph-to-adult transition probabilities in the wild and document their response at multiple life stages following reintroduction. Furthermore, we provide an example to illustrate how the structure of our integrated population model serves as a useful foundation for amphibian decision-support models within adaptive management programs. The integration of multiple, but related, data sets has an advantage of being able to estimate complex ecological relationships across multiple life stages, offering a modeling framework that accommodates uncertainty, enforces parsimony, and ensures all model parameters can be confronted with monitoring data.

A new parameterization for integrated population models to document amphibian reintroductions.

PubMed

Duarte, Adam; Pearl, Christopher A; Adams, Michael J; Peterson, James T

2017-09-01

Managers are increasingly implementing reintroduction programs as part of a global effort to alleviate amphibian declines. Given uncertainty in factors affecting populations and a need to make recurring decisions to achieve objectives, adaptive management is a useful component of these efforts. A major impediment to the estimation of demographic rates often used to parameterize and refine decision-support models is that life-stage-specific monitoring data are frequently sparse for amphibians. We developed a new parameterization for integrated population models to match the ecology of amphibians and capitalize on relatively inexpensive monitoring data to document amphibian reintroductions. We evaluate the capability of this model by fitting it to Oregon spotted frog (Rana pretiosa) monitoring data collected from 2007 to 2014 following their reintroduction within the Klamath Basin, Oregon, USA. The number of egg masses encountered and the estimated adult and metamorph abundances generally increased following reintroduction. We found that survival probability from egg to metamorph ranged from 0.01 in 2008 to 0.09 in 2009 and was not related to minimum spring temperatures, metamorph survival probability ranged from 0.13 in 2010-2011 to 0.86 in 2012-2013 and was positively related to mean monthly temperatures (logit-scale slope = 2.37), adult survival probability was lower for founders (0.40) than individuals recruited after reintroduction (0.56), and the mean number of egg masses per adult female was 0.74. Our study is the first to test hypotheses concerning Oregon spotted frog egg-to-metamorph and metamorph-to-adult transition probabilities in the wild and document their response at multiple life stages following reintroduction. Furthermore, we provide an example to illustrate how the structure of our integrated population model serves as a useful foundation for amphibian decision-support models within adaptive management programs. The integration of multiple, but related, data sets has an advantage of being able to estimate complex ecological relationships across multiple life stages, offering a modeling framework that accommodates uncertainty, enforces parsimony, and ensures all model parameters can be confronted with monitoring data. © 2017 by the Ecological Society of America.

Distributed adaptive asymptotically consensus tracking control of uncertain Euler-Lagrange systems under directed graph condition.

PubMed

Wang, Wei; Wen, Changyun; Huang, Jiangshuai; Fan, Huijin

2017-11-01

In this paper, a backstepping based distributed adaptive control scheme is proposed for multiple uncertain Euler-Lagrange systems under directed graph condition. The common desired trajectory is allowed totally unknown by part of the subsystems and the linearly parameterized trajectory model assumed in currently available results is no longer needed. To compensate the effects due to unknown trajectory information, a smooth function of consensus errors and certain positive integrable functions are introduced in designing virtual control inputs. Besides, to overcome the difficulty of completely counteracting the coupling terms of distributed consensus errors and parameter estimation errors in the presence of asymmetric Laplacian matrix, extra information transmission of local parameter estimates are introduced among linked subsystem and adaptive gain technique is adopted to generate distributed torque inputs. It is shown that with the proposed distributed adaptive control scheme, global uniform boundedness of all the closed-loop signals and asymptotically output consensus tracking can be achieved. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Making acuity-adaptable units work: lessons from the field.

PubMed

Zimring, Craig; Seo, Hyun-Bo

2012-01-01

Because there have been no clear directions on how to implement acuity-adaptable units (AAUs), this paper describes actual tactics and strategies that have worked in multiple institutions. AAUs have been used in hospitals for the past decade, but reports in the literature have indicated both successes and difficulties in meeting operational goals and objectives. Despite various views regarding acuity adaptability, there is little in the literature that suggests why it works in some hospitals and not in others. As part of a larger construction project, this project team interviewed the leaders of six hospitals to determine what was associated with the successful implementation of AAUs. This paper reports on themes that emerged from these interviews, namely: choose the right specialty for medical centers; adopt the AAU model for the entire facility in community hospitals; bring in and train the right people; change culture through communication; and use acuity-adaptable unit clusters. Main themes, predictable patient progress, and culture change are further discussed and key recommendations are described.

Adaptive Fuzzy Bounded Control for Consensus of Multiple Strict-Feedback Nonlinear Systems.

PubMed

Wang, Wei; Tong, Shaocheng

2018-02-01

This paper studies the adaptive fuzzy bounded control problem for leader-follower multiagent systems, where each follower is modeled by the uncertain nonlinear strict-feedback system. Combining the fuzzy approximation with the dynamic surface control, an adaptive fuzzy control scheme is developed to guarantee the output consensus of all agents under directed communication topologies. Different from the existing results, the bounds of the control inputs are known as a priori, and they can be determined by the feedback control gains. To realize smooth and fast learning, a predictor is introduced to estimate each error surface, and the corresponding predictor error is employed to learn the optimal fuzzy parameter vector. It is proved that the developed adaptive fuzzy control scheme guarantees the uniformly ultimate boundedness of the closed-loop systems, and the tracking error converges to a small neighborhood of the origin. The simulation results and comparisons are provided to show the validity of the control strategy presented in this paper.

Multi-gene phylogenetic analysis reveals the multiple origin and evolution of mangrove physiological traits through exaptation

NASA Astrophysics Data System (ADS)

Sahu, Sunil Kumar; Singh, Reena; Kathiresan, Kandasamy

2016-12-01

Mangroves are taxonomically diverse group of salt-tolerant, mainly arboreal, flowering plants that grow in tropical and sub-tropical regions and have adapted themselves to thrive in such obdurate surroundings. While evolution is often understood exclusively in terms of adaptation, innovation often begins when a feature adapted for one function is co-opted for a different purpose and the co-opted features are called exaptations. Thus, one of the fundamental issues is what features of mangroves have evolved through exaptation. We attempt to address these questions through molecular phylogenetic approach using chloroplast and nuclear markers. First, we determined if these mangroves specific traits have evolved multiple times in the phylogeny. Once the multiple origins were established, we then looked at related non-mangrove species for characters that could have been co-opted by mangrove species. We also assessed the efficacy of these molecular sequences in distinguishing mangroves at the species level. This study revealed the multiple origin of mangroves and shed light on the ancestral characters that might have led certain lineages of plants to adapt to estuarine conditions and also traces the evolutionary history of mangroves and hitherto unexplained theory that mangroves traits (aerial roots and viviparous propagules) evolved as a result of exaptation rather than adaptation to saline habitats.

Suppressing multiples using an adaptive multichannel filter based on L1-norm

NASA Astrophysics Data System (ADS)

Shi, Ying; Jing, Hongliang; Zhang, Wenwu; Ning, Dezhi

2017-08-01

Adaptive subtraction is an important link for removing surface-related multiples in the wave equation-based method. In this paper, we propose an adaptive multichannel subtraction method based on the L1-norm. We achieve enhanced compensation for the mismatch between the input seismogram and the predicted multiples in terms of the amplitude, phase, frequency band, and travel time. Unlike the conventional L2-norm, the proposed method does not rely on the assumption that the primary and the multiples are orthogonal, and also takes advantage of the fact that the L1-norm is more robust when dealing with outliers. In addition, we propose a frequency band extension via modulation to reconstruct the high frequencies to compensate for the frequency misalignment. We present a parallel computing scheme to accelerate the subtraction algorithm on graphic processing units (GPUs), which significantly reduces the computational cost. The synthetic and field seismic data tests show that the proposed method effectively suppresses the multiples.

The Cultural Adaptability of Intermediate Measures of Functional Outcome in Schizophrenia*

PubMed Central

Rubin, Maureen; Fredrick, Megan M.; Mintz, Jim; Nuechterlein, Keith H.; Schooler, Nina R.; Jaeger, Judith; Peters, Nancy M.; Buller, Raimund; Marder, Stephen R.; Dube, Sanjay

2012-01-01

The Measurement and Treatment Research to Improve Cognition in Schizophrenia initiative was designed to encourage the development of cognitive enhancing agents for schizophrenia. For a medication to receive this indication, regulatory agencies require evidence of improvement in both cognition and functional outcome. Because medication trials are conducted across multiple countries, we examined ratings of the cross-cultural adaptability of 4 intermediate measures of functional outcome (Independent Living Scales, UCSD Performance-based Skills Assessment, Test of Adaptive Behavior in Schizophrenia, Cognitive Assessment Interview [CAI]) made by experienced clinical researchers at 31 sites in 8 countries. English-speaking research staff familiar with conducting medication trials rated the extent to which each subscale of each intermediate measure could be applied to their culture and to subgroups within their culture based on gender, geographic region, ethnicity, and socioeconomic status on the Cultural Adaptation Rating Scale. Ratings suggested that the CAI would be easiest to adapt across cultures. However, in a recent study, the CAI was found to have weaker psychometric properties than some of the other measures. Problems were identified for specific subscales on all the performance-based assessments across multiple countries. India, China, and Mexico presented the greatest challenges in adaptation. For international clinical trials, it would be important to use the measures that are most adaptable, to adapt subscales that are problematic for specific countries or regions, or to develop a battery composed of the subscales from different instruments that may be most acceptable across multiple cultures with minimal adaptation. PMID:21134973

Managing adaptively for multifunctionality in agricultural systems

USGS Publications Warehouse

Hodbod, Jennifer; Barreteau, Olivier; Allen, Craig R.; Magda, Danièle

2016-01-01

The critical importance of agricultural systems for food security and as a dominant global landcover requires management that considers the full dimensions of system functions at appropriate scales, i.e. multifunctionality. We propose that adaptive management is the most suitable management approach for such goals, given its ability to reduce uncertainty over time and support multiple objectives within a system, for multiple actors. As such, adaptive management may be the most appropriate method for sustainably intensifying production whilst increasing the quantity and quality of ecosystem services. However, the current assessment of performance of agricultural systems doesn’t reward ecosystem service provision. Therefore, we present an overview of the ecosystem functions agricultural systems should and could provide, coupled with a revised definition for assessing the performance of agricultural systems from a multifunctional perspective that, when all satisfied, would create adaptive agricultural systems that can increase production whilst ensuring food security and the quantity and quality of ecosystem services. The outcome of this high level of performance is the capacity to respond to multiple shocks without collapse, equity and triple bottom line sustainability. Through the assessment of case studies, we find that alternatives to industrialized agricultural systems incorporate more functional goals, but that there are mixed findings as to whether these goals translate into positive measurable outcomes. We suggest that an adaptive management perspective would support the implementation of a systematic analysis of the social, ecological and economic trade-offs occurring within such systems, particularly between ecosystem services and functions, in order to provide suitable and comparable assessments. We also identify indicators to monitor performance at multiple scales in agricultural systems which can be used within an adaptive management framework to increase resilience at multiple scales.

Managing adaptively for multifunctionality in agricultural systems.

PubMed

Hodbod, Jennifer; Barreteau, Olivier; Allen, Craig; Magda, Danièle

2016-12-01

The critical importance of agricultural systems for food security and as a dominant global landcover requires management that considers the full dimensions of system functions at appropriate scales, i.e. multifunctionality. We propose that adaptive management is the most suitable management approach for such goals, given its ability to reduce uncertainty over time and support multiple objectives within a system, for multiple actors. As such, adaptive management may be the most appropriate method for sustainably intensifying production whilst increasing the quantity and quality of ecosystem services. However, the current assessment of performance of agricultural systems doesn't reward ecosystem service provision. Therefore, we present an overview of the ecosystem functions agricultural systems should and could provide, coupled with a revised definition for assessing the performance of agricultural systems from a multifunctional perspective that, when all satisfied, would create adaptive agricultural systems that can increase production whilst ensuring food security and the quantity and quality of ecosystem services. The outcome of this high level of performance is the capacity to respond to multiple shocks without collapse, equity and triple bottom line sustainability. Through the assessment of case studies, we find that alternatives to industrialized agricultural systems incorporate more functional goals, but that there are mixed findings as to whether these goals translate into positive measurable outcomes. We suggest that an adaptive management perspective would support the implementation of a systematic analysis of the social, ecological and economic trade-offs occurring within such systems, particularly between ecosystem services and functions, in order to provide suitable and comparable assessments. We also identify indicators to monitor performance at multiple scales in agricultural systems which can be used within an adaptive management framework to increase resilience at multiple scales. Copyright © 2016 Elsevier Ltd. All rights reserved.

The National Astronomy Consortium - An Adaptable Model for OAD?

NASA Astrophysics Data System (ADS)

Sheth, Kartik

2015-08-01

The National Astronomy Consortium (NAC) is a program led by the National Radio Astronomy Observatory (NRAO) and Associated Universities Inc., (AUI) in partnership with the National Society of Black Physicists (NSBP), and a number of minority and majority universities to increase the numbers of students from underrepresented groups and those otherwise overlooked by the traditional academic pipeline into STEM or STEM-related careers. The seed for the NAC was a partnership between NRAO and Howard University which began with an exchange of a few summer students five years ago. Since then the NAC has grown tremendously. Today the NAC aims to host between 4 to 5 cohorts nationally in an innovative model in which the students are mentored throughout the year with multiple mentors and peer mentoring, continued engagement in research and professional development / career training throughout the academic year and throughout their careers.The NAC model has already shown success and is a very promising and innovative model for increasing participation of young people in STEM and STEM-related careers. I will discuss how this model could be adapted in various countries at all levels of education.

Cosine problem in EPRL/FK spinfoam model

NASA Astrophysics Data System (ADS)

Vojinović, Marko

2014-01-01

We calculate the classical limit effective action of the EPRL/FK spinfoam model of quantum gravity coupled to matter fields. By employing the standard QFT background field method adapted to the spinfoam setting, we find that the model has many different classical effective actions. Most notably, these include the ordinary Einstein-Hilbert action coupled to matter, but also an action which describes antigravity. All those multiple classical limits appear as a consequence of the fact that the EPRL/FK vertex amplitude has cosine-like large spin asymptotics. We discuss some possible ways to eliminate the unwanted classical limits.

A predictive model to inform adaptive management of double-crested cormorants and fisheries in Michigan

USGS Publications Warehouse

Tsehaye, Iyob; Jones, Michael L.; Irwin, Brian J.; Fielder, David G.; Breck, James E.; Luukkonen, David R.

2015-01-01

The proliferation of double-crested cormorants (DCCOs; Phalacrocorax auritus) in North America has raised concerns over their potential negative impacts on game, cultured and forage fishes, island and terrestrial resources, and other colonial water birds, leading to increased public demands to reduce their abundance. By combining fish surplus production and bird functional feeding response models, we developed a deterministic predictive model representing bird–fish interactions to inform an adaptive management process for the control of DCCOs in multiple colonies in Michigan. Comparisons of model predictions with observations of changes in DCCO numbers under management measures implemented from 2004 to 2012 suggested that our relatively simple model was able to accurately reconstruct past DCCO population dynamics. These comparisons helped discriminate among alternative parameterizations of demographic processes that were poorly known, especially site fidelity. Using sensitivity analysis, we also identified remaining critical uncertainties (mainly in the spatial distributions of fish vs. DCCO feeding areas) that can be used to prioritize future research and monitoring needs. Model forecasts suggested that continuation of existing control efforts would be sufficient to achieve long-term DCCO control targets in Michigan and that DCCO control may be necessary to achieve management goals for some DCCO-impacted fisheries in the state. Finally, our model can be extended by accounting for parametric or ecological uncertainty and including more complex assumptions on DCCO–fish interactions as part of the adaptive management process.

Distributed and self-adaptive vehicle speed estimation in the composite braking case for four-wheel drive hybrid electric car

NASA Astrophysics Data System (ADS)

Zhao, Z.-G.; Zhou, L.-J.; Zhang, J.-T.; Zhu, Q.; Hedrick, J.-K.

2017-05-01

Considering the controllability and observability of the braking torques of the hub motor, Integrated Starter Generator (ISG), and hydraulic brake for four-wheel drive (4WD) hybrid electric cars, a distributed and self-adaptive vehicle speed estimation algorithm for different braking situations has been proposed by fully utilising the Electronic Stability Program (ESP) sensor signals and multiple powersource signals. Firstly, the simulation platform of a 4WD hybrid electric car was established, which integrates an electronic-hydraulic composited braking system model and its control strategy, a nonlinear seven degrees-of-freedom vehicle dynamics model, and the Burckhardt tyre model. Secondly, combining the braking torque signals with the ESP signals, self-adaptive unscented Kalman sub-filter and main-filter adaptable to the observation noise were, respectively, designed. Thirdly, the fusion rules for the sub-filters and master filter were proposed herein, and the estimation results were compared with the simulated value of a real vehicle speed. Finally, based on the hardware in-the-loop platform and by picking up the regenerative motor torque signals and wheel cylinder pressure signals, the proposed speed estimation algorithm was tested under the case of moderate braking on the highly adhesive road, and the case of Antilock Braking System (ABS) action on the slippery road, as well as the case of ABS action on the icy road. Test results show that the presented vehicle speed estimation algorithm has not only a high precision but also a strong adaptability in the composite braking case.

LDA merging and splitting with applications to multiagent cooperative learning and system alteration.

PubMed

Pang, Shaoning; Ban, Tao; Kadobayashi, Youki; Kasabov, Nikola K

2012-04-01

To adapt linear discriminant analysis (LDA) to real-world applications, there is a pressing need to equip it with an incremental learning ability to integrate knowledge presented by one-pass data streams, a functionality to join multiple LDA models to make the knowledge sharing between independent learning agents more efficient, and a forgetting functionality to avoid reconstruction of the overall discriminant eigenspace caused by some irregular changes. To this end, we introduce two adaptive LDA learning methods: LDA merging and LDA splitting. These provide the benefits of ability of online learning with one-pass data streams, retained class separability identical to the batch learning method, high efficiency for knowledge sharing due to condensed knowledge representation by the eigenspace model, and more preferable time and storage costs than traditional approaches under common application conditions. These properties are validated by experiments on a benchmark face image data set. By a case study on the application of the proposed method to multiagent cooperative learning and system alternation of a face recognition system, we further clarified the adaptability of the proposed methods to complex dynamic learning tasks.

Adaptation and learning: characteristic time scales of performance dynamics.

PubMed

Newell, Karl M; Mayer-Kress, Gottfried; Hong, S Lee; Liu, Yeou-Teh

2009-12-01

A multiple time scales landscape model is presented that reveals structures of performance dynamics that were not resolved in the traditional power law analysis of motor learning. It shows the co-existence of separate processes during and between practice sessions that evolve in two independent dimensions characterized by time scales that differ by about an order of magnitude. Performance along the slow persistent dimension of learning improves often as much and sometimes more during rest (memory consolidation and/or insight generation processes) than during a practice session itself. In contrast, the process characterized by the fast, transient dimension of adaptation reverses direction between practice sessions, thereby significantly degrading performance at the beginning of the next practice session (warm-up decrement). The theoretical model fits qualitatively and quantitatively the data from Snoddy's [Snoddy, G. S. (1926). Learning and stability. Journal of Applied Psychology, 10, 1-36] classic learning study of mirror tracing and other averaged and individual data sets, and provides a new account of the processes of change in adaptation and learning. 2009 Elsevier B.V. All rights reserved.

Do ecohydrology and community dynamics feed back to banded-ecosystem structure and productivity?

NASA Astrophysics Data System (ADS)

Callegaro, Chiara; Ursino, Nadia

2016-04-01

Mixed communities including grass, shrubs and trees are often reported to populate self-organized vegetation patterns. Patterns of survey data suggest that species diversity and complementarity strengthen the dynamics of banded environments. Resource scarcity and local facilitation trigger self organization, whereas coexistence of multiple species in vegetated self-organizing patches, implying competition for water and nutrients and favorable reproduction sites, is made possible by differing adaptation strategies. Mixed community spatial self-organization has so far received relatively little attention, compared with local net facilitation of isolated species. We assumed that soil moisture availability is a proxy for the environmental niche of plant species according to Ursino and Callegaro (2016). Our modelling effort was focused on niche differentiation of coexisting species within a tiger bush type ecosystem. By minimal numerical modelling and stability analysis we try to answer a few open scientific questions: Is there an adaptation strategy that increases biodiversity and ecosystem functioning? Does specific adaptation to environmental niches influence the structure of self-organizing vegetation pattern? What specific niche distribution along the environmental gradient gives the highest global productivity?

High-resolution multi-code implementation of unsteady Navier-Stokes flow solver based on paralleled overset adaptive mesh refinement and high-order low-dissipation hybrid schemes

NASA Astrophysics Data System (ADS)

Li, Gaohua; Fu, Xiang; Wang, Fuxin

2017-10-01

The low-dissipation high-order accurate hybrid up-winding/central scheme based on fifth-order weighted essentially non-oscillatory (WENO) and sixth-order central schemes, along with the Spalart-Allmaras (SA)-based delayed detached eddy simulation (DDES) turbulence model, and the flow feature-based adaptive mesh refinement (AMR), are implemented into a dual-mesh overset grid infrastructure with parallel computing capabilities, for the purpose of simulating vortex-dominated unsteady detached wake flows with high spatial resolutions. The overset grid assembly (OGA) process based on collection detection theory and implicit hole-cutting algorithm achieves an automatic coupling for the near-body and off-body solvers, and the error-and-try method is used for obtaining a globally balanced load distribution among the composed multiple codes. The results of flows over high Reynolds cylinder and two-bladed helicopter rotor show that the combination of high-order hybrid scheme, advanced turbulence model, and overset adaptive mesh refinement can effectively enhance the spatial resolution for the simulation of turbulent wake eddies.

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 specified criteria (SPI/SPEI value, time scale, RCP, etc.) can provide professionals in a variety of disciplines with necessary climatic insight to develop adaptation strategies.

Legal and Institutional Foundations of Adaptive Environmental Governance

EPA Science Inventory

Legal and institutional structures fundamentally shape opportunities for adaptive governance of environmental resources at multiple ecological and societal scales. Properties of adaptive governance are widely studied. However, these studies have not resulted in consolidated frame...

Multivariate adaptive regression splines analysis to predict biomarkers of spontaneous preterm birth.

PubMed

Menon, Ramkumar; Bhat, Geeta; Saade, George R; Spratt, Heidi

2014-04-01

To develop classification models of demographic/clinical factors and biomarker data from spontaneous preterm birth in African Americans and Caucasians. Secondary analysis of biomarker data using multivariate adaptive regression splines (MARS), a supervised machine learning algorithm method. Analysis of data on 36 biomarkers from 191 women was reduced by MARS to develop predictive models for preterm birth in African Americans and Caucasians. Maternal plasma, cord plasma collected at admission for preterm or term labor and amniotic fluid at delivery. Data were partitioned into training and testing sets. Variable importance, a relative indicator (0-100%) and area under the receiver operating characteristic curve (AUC) characterized results. Multivariate adaptive regression splines generated models for combined and racially stratified biomarker data. Clinical and demographic data did not contribute to the model. Racial stratification of data produced distinct models in all three compartments. In African Americans maternal plasma samples IL-1RA, TNF-α, angiopoietin 2, TNFRI, IL-5, MIP1α, IL-1β and TGF-α modeled preterm birth (AUC train: 0.98, AUC test: 0.86). In Caucasians TNFR1, ICAM-1 and IL-1RA contributed to the model (AUC train: 0.84, AUC test: 0.68). African Americans cord plasma samples produced IL-12P70, IL-8 (AUC train: 0.82, AUC test: 0.66). Cord plasma in Caucasians modeled IGFII, PDGFBB, TGF-β1 , IL-12P70, and TIMP1 (AUC train: 0.99, AUC test: 0.82). Amniotic fluid in African Americans modeled FasL, TNFRII, RANTES, KGF, IGFI (AUC train: 0.95, AUC test: 0.89) and in Caucasians, TNF-α, MCP3, TGF-β3 , TNFR1 and angiopoietin 2 (AUC train: 0.94 AUC test: 0.79). Multivariate adaptive regression splines models multiple biomarkers associated with preterm birth and demonstrated racial disparity. © 2014 Nordic Federation of Societies of Obstetrics and Gynecology.

Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model

PubMed Central

Ryu, Stephen I.

2017-01-01

Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain–machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses. SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis. PMID:28087767

Deficiency for endoglin in tumor vasculature weakens the endothelial barrier to metastatic dissemination

PubMed Central

Anderberg, Charlotte; Cunha, Sara I.; Zhai, Zhenhua; Cortez, Eliane; Pardali, Evangelia; Johnson, Jill R.; Franco, Marcela; Páez-Ribes, Marta; Cordiner, Ross; Fuxe, Jonas; Johansson, Bengt R.; Goumans, Marie-José; Casanovas, Oriol; ten Dijke, Peter; Arthur, Helen M.

2013-01-01

Therapy-induced resistance remains a significant hurdle to achieve long-lasting responses and cures in cancer patients. We investigated the long-term consequences of genetically impaired angiogenesis by engineering multiple tumor models deprived of endoglin, a co-receptor for TGF-β in endothelial cells actively engaged in angiogenesis. Tumors from endoglin-deficient mice adapted to the weakened angiogenic response, and refractoriness to diminished endoglin signaling was accompanied by increased metastatic capability. Mechanistic studies in multiple mouse models of cancer revealed that deficiency for endoglin resulted in a tumor vasculature that displayed hallmarks of endothelial-to-mesenchymal transition, a process of previously unknown significance in cancer biology, but shown by us to be associated with a reduced capacity of the vasculature to avert tumor cell intra- and extravasation. Nevertheless, tumors deprived of endoglin exhibited a delayed onset of resistance to anti-VEGF (vascular endothelial growth factor) agents, illustrating the therapeutic utility of combinatorial targeting of multiple angiogenic pathways for the treatment of cancer. PMID:23401487

An augmented parametric response map with consideration of image registration error: towards guidance of locally adaptive radiotherapy

NASA Astrophysics Data System (ADS)

Lausch, Anthony; Chen, Jeff; Ward, Aaron D.; Gaede, Stewart; Lee, Ting-Yim; Wong, Eugene

2014-11-01

Parametric response map (PRM) analysis is a voxel-wise technique for predicting overall treatment outcome, which shows promise as a tool for guiding personalized locally adaptive radiotherapy (RT). However, image registration error (IRE) introduces uncertainty into this analysis which may limit its use for guiding RT. Here we extend the PRM method to include an IRE-related PRM analysis confidence interval and also incorporate multiple graded classification thresholds to facilitate visualization. A Gaussian IRE model was used to compute an expected value and confidence interval for PRM analysis. The augmented PRM (A-PRM) was evaluated using CT-perfusion functional image data from patients treated with RT for glioma and hepatocellular carcinoma. Known rigid IREs were simulated by applying one thousand different rigid transformations to each image set. PRM and A-PRM analyses of the transformed images were then compared to analyses of the original images (ground truth) in order to investigate the two methods in the presence of controlled IRE. The A-PRM was shown to help visualize and quantify IRE-related analysis uncertainty. The use of multiple graded classification thresholds also provided additional contextual information which could be useful for visually identifying adaptive RT targets (e.g. sub-volume boosts). The A-PRM should facilitate reliable PRM guided adaptive RT by allowing the user to identify if a patient’s unique IRE-related PRM analysis uncertainty has the potential to influence target delineation.

Situating adaptation: How governance challenges and perceptions of uncertainty influence adaptation in the Rocky Mountains

Treesearch

Carina Wyborn; Laurie Yung; Daniel Murphy; Daniel R. Williams

2015-01-01

Adaptation is situated within multiple, interacting social, political, and economic forces. Adaptation pathways envision adaptation as a continual pathway of change and response embedded within this broader sociopolitical context. Pathways emphasize that current decisions are both informed by past actions and shape the landscape of future options. This research...

Smooth adaptive sliding mode vibration control of a flexible parallel manipulator with multiple smart linkages in modal space

NASA Astrophysics Data System (ADS)

Zhang, Quan; Li, Chaodong; Zhang, Jiantao; Zhang, Jianhui

2017-12-01

This paper addresses the dynamic model and active vibration control of a rigid-flexible parallel manipulator with three smart links actuated by three linear ultrasonic motors. To suppress the vibration of three flexible intermediate links under high speed and acceleration, multiple Lead Zirconium Titanate (PZT) sensors and actuators are collocated mounted on each link, forming a smart structure which can achieve self-sensing and self-actuating. The dynamic characteristics and equations of the flexible link incorporated with the PZT sensors and actuator are analyzed and formulated. The smooth adaptive sliding mode based active vibration control is proposed to suppress the vibration of the smart links, and the first and second modes of the three links are targeted to be suppressed in modal space to avoid the spillover phenomenon. Simulations and experiments are implemented to validate the effectiveness of the smart structures and the proposed control laws. Experimental results show that the vibration of the first mode around 92 Hz and the second mode around 240 Hz of the three smart links are reduced respectively by 64.98%, 59.47%, 62.28%, and 45.80%, 36.79%, 33.33%, which further verify the multi-mode vibration control ability of the smooth adaptive sliding mode control law.

Development of a GCR Event-based Risk Model

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Ponomarev, Artem L.; Plante, Ianik; Carra, Claudio; Kim, Myung-Hee

2009-01-01

A goal at NASA is to develop event-based systems biology models of space radiation risks that will replace the current dose-based empirical models. Complex and varied biochemical signaling processes transmit the initial DNA and oxidative damage from space radiation into cellular and tissue responses. Mis-repaired damage or aberrant signals can lead to genomic instability, persistent oxidative stress or inflammation, which are causative of cancer and CNS risks. Protective signaling through adaptive responses or cell repopulation is also possible. We are developing a computational simulation approach to galactic cosmic ray (GCR) effects that is based on biological events rather than average quantities such as dose, fluence, or dose equivalent. The goal of the GCR Event-based Risk Model (GERMcode) is to provide a simulation tool to describe and integrate physical and biological events into stochastic models of space radiation risks. We used the quantum multiple scattering model of heavy ion fragmentation (QMSFRG) and well known energy loss processes to develop a stochastic Monte-Carlo based model of GCR transport in spacecraft shielding and tissue. We validated the accuracy of the model by comparing to physical data from the NASA Space Radiation Laboratory (NSRL). Our simulation approach allows us to time-tag each GCR proton or heavy ion interaction in tissue including correlated secondary ions often of high multiplicity. Conventional space radiation risk assessment employs average quantities, and assumes linearity and additivity of responses over the complete range of GCR charge and energies. To investigate possible deviations from these assumptions, we studied several biological response pathway models of varying induction and relaxation times including the ATM, TGF -Smad, and WNT signaling pathways. We then considered small volumes of interacting cells and the time-dependent biophysical events that the GCR would produce within these tissue volumes to estimate how GCR event rates mapped to biological signaling induction and relaxation times. We considered several hypotheses related to signaling and cancer risk, and then performed simulations for conditions where aberrant or adaptive signaling would occur on long-duration space mission. Our results do not support the conventional assumptions of dose, linearity and additivity. A discussion on how event-based systems biology models, which focus on biological signaling as the mechanism to propagate damage or adaptation, can be further developed for cancer and CNS space radiation risk projections is given.

Resolving z ~2 galaxy using adaptive coadded source plane reconstruction

NASA Astrophysics Data System (ADS)

Sharma, Soniya; Richard, Johan; Kewley, Lisa; Yuan, Tiantian

2018-06-01

Natural magnification provided by gravitational lensing coupled with Integral field spectrographic observations (IFS) and adaptive optics (AO) imaging techniques have become the frontier of spatially resolved studies of high redshift galaxies (z>1). Mass models of gravitational lenses hold the key for understanding the spatially resolved source–plane (unlensed) physical properties of the background lensed galaxies. Lensing mass models very sensitively control the accuracy and precision of source-plane reconstructions of the observed lensed arcs. Effective source-plane resolution defined by image-plane (observed) point spread function (PSF) makes it challenging to recover the unlensed (source-plane) surface brightness distribution.We conduct a detailed study to recover the source-plane physical properties of z=2 lensed galaxy using spatially resolved observations from two different multiple images of the lensed target. To deal with PSF’s from two data sets on different multiple images of the galaxy, we employ a forward (Source to Image) approach to merge these independent observations. Using our novel technique, we are able to present a detailed analysis of the source-plane dynamics at scales much better than previously attainable through traditional image inversion methods. Moreover, our technique is adapted to magnification, thus allowing us to achieve higher resolution in highly magnified regions of the source. We find that this lensed system is highly evident of a minor merger. In my talk, I present this case study of z=2 lensed galaxy and also discuss the applications of our algorithm to study plethora of lensed systems, which will be available through future telescopes like JWST and GMT.

A mechanistic modeling system for estimating large scale emissions and transport of pollen and co-allergens

PubMed Central

Efstathiou, Christos; Isukapalli, Sastry

2011-01-01

Allergic airway diseases represent a complex health problem which can be exacerbated by the synergistic action of pollen particles and air pollutants such as ozone. Understanding human exposures to aeroallergens requires accurate estimates of the spatial distribution of airborne pollen levels as well as of various air pollutants at different times. However, currently there are no established methods for estimating allergenic pollen emissions and concentrations over large geographic areas such as the United States. A mechanistic modeling system for describing pollen emissions and transport over extensive domains has been developed by adapting components of existing regional scale air quality models and vegetation databases. First, components of the Biogenic Emissions Inventory System (BEIS) were adapted to predict pollen emission patterns. Subsequently, the transport module of the Community Multiscale Air Quality (CMAQ) modeling system was modified to incorporate description of pollen transport. The combined model, CMAQ-pollen, allows for simultaneous prediction of multiple air pollutants and pollen levels in a single model simulation, and uses consistent assumptions related to the transport of multiple chemicals and pollen species. Application case studies for evaluating the combined modeling system included the simulation of birch and ragweed pollen levels for the year 2002, during their corresponding peak pollination periods (April for birch and September for ragweed). The model simulations were driven by previously evaluated meteorological model outputs and emissions inventories for the eastern United States for the simulation period. A semi-quantitative evaluation of CMAQ-pollen was performed using tree and ragweed pollen counts in Newark, NJ for the same time periods. The peak birch pollen concentrations were predicted to occur within two days of the peak measurements, while the temporal patterns closely followed the measured profiles of overall tree pollen. For the case of ragweed pollen, the model was able to capture the patterns observed during September 2002, but did not predict an early peak; this can be associated with a wider species pollination window and inadequate spatial information in current land cover databases. An additional sensitivity simulation was performed to comparatively evaluate the dispersion patterns predicted by CMAQ-pollen with those predicted by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is used extensively in aerobiological studies. The CMAQ estimated concentration plumes matched the equivalent pollen scenario modeled with HYSPLIT. The novel pollen modeling approach presented here allows simultaneous estimation of multiple airborne allergens and other air pollutants, and is being developed as a central component of an integrated population exposure modeling system, the Modeling Environment for Total Risk studies (MENTOR) for multiple, co-occurring contaminants that include aeroallergens and irritants. PMID:21516207

A mechanistic modeling system for estimating large-scale emissions and transport of pollen and co-allergens

NASA Astrophysics Data System (ADS)

Efstathiou, Christos; Isukapalli, Sastry; Georgopoulos, Panos

2011-04-01

Allergic airway diseases represent a complex health problem which can be exacerbated by the synergistic action of pollen particles and air pollutants such as ozone. Understanding human exposures to aeroallergens requires accurate estimates of the spatial distribution of airborne pollen levels as well as of various air pollutants at different times. However, currently there are no established methods for estimating allergenic pollen emissions and concentrations over large geographic areas such as the United States. A mechanistic modeling system for describing pollen emissions and transport over extensive domains has been developed by adapting components of existing regional scale air quality models and vegetation databases. First, components of the Biogenic Emissions Inventory System (BEIS) were adapted to predict pollen emission patterns. Subsequently, the transport module of the Community Multiscale Air Quality (CMAQ) modeling system was modified to incorporate description of pollen transport. The combined model, CMAQ-pollen, allows for simultaneous prediction of multiple air pollutants and pollen levels in a single model simulation, and uses consistent assumptions related to the transport of multiple chemicals and pollen species. Application case studies for evaluating the combined modeling system included the simulation of birch and ragweed pollen levels for the year 2002, during their corresponding peak pollination periods (April for birch and September for ragweed). The model simulations were driven by previously evaluated meteorological model outputs and emissions inventories for the eastern United States for the simulation period. A semi-quantitative evaluation of CMAQ-pollen was performed using tree and ragweed pollen counts in Newark, NJ for the same time periods. The peak birch pollen concentrations were predicted to occur within two days of the peak measurements, while the temporal patterns closely followed the measured profiles of overall tree pollen. For the case of ragweed pollen, the model was able to capture the patterns observed during September 2002, but did not predict an early peak; this can be associated with a wider species pollination window and inadequate spatial information in current land cover databases. An additional sensitivity simulation was performed to comparatively evaluate the dispersion patterns predicted by CMAQ-pollen with those predicted by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is used extensively in aerobiological studies. The CMAQ estimated concentration plumes matched the equivalent pollen scenario modeled with HYSPLIT. The novel pollen modeling approach presented here allows simultaneous estimation of multiple airborne allergens and other air pollutants, and is being developed as a central component of an integrated population exposure modeling system, the Modeling Environment for Total Risk studies (MENTOR) for multiple, co-occurring contaminants that include aeroallergens and irritants.

Persons with Multiple Disabilities Use Orientation Technology to Find Room Entrances during Indoor Traveling

ERIC Educational Resources Information Center

Lancioni, Giulio E.; Singh, Nirbhay N.; O'Reilly, Mark F.; Sigafoos, Jeff; Alberti, Gloria; Scigliuzzo, Francesca; Signorino, Mario; Oliva, Doretta; Smaldone, Angela; La Martire, Maria L.

2010-01-01

These two studies assessed adapted orientation technology for promoting correct direction and room identification during indoor traveling by persons with multiple (e.g., sensory, motor and intellectual/adaptive) disabilities. In Study I, two adults were included who had severe visual impairment or total blindness and deafness and used a wheelchair…

Two Children with Multiple Disabilities Increase Adaptive Object Manipulation and Reduce Inappropriate Behavior via a Technology-Assisted Program

ERIC Educational Resources Information Center

Lancioni, Giulio E.; O'Reilly, Mark F.; Singh, Nirbhay N.; Sigafoos, Jeff; Didden, Robert; Oliva, Doretta; Campodonico, Francesca

2010-01-01

Persons with severe to profound multiple disabilities, such as intellectual, visual, and motor disabilities, may be characterized by low levels of adaptive engagement with the environment. They may also display forms of inappropriate, stereotypical behavior (like hand mouthing, that is, putting their fingers into or over their mouths) or…

A Multiple Objective Test Assembly Approach for Exposure Control Problems in Computerized Adaptive Testing

ERIC Educational Resources Information Center

Veldkamp, Bernard P.; Verschoor, Angela J.; Eggen, Theo J. H. M.

2010-01-01

Overexposure and underexposure of items in the bank are serious problems in operational computerized adaptive testing (CAT) systems. These exposure problems might result in item compromise, or point at a waste of investments. The exposure control problem can be viewed as a test assembly problem with multiple objectives. Information in the test has…

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.

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

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

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

2016-04-01

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

Development of a stress response therapy targeting aggressive prostate cancer.

PubMed

Nguyen, Hao G; Conn, Crystal S; Kye, Yae; Xue, Lingru; Forester, Craig M; Cowan, Janet E; Hsieh, Andrew C; Cunningham, John T; Truillet, Charles; Tameire, Feven; Evans, Michael J; Evans, Christopher P; Yang, Joy C; Hann, Byron; Koumenis, Constantinos; Walter, Peter; Carroll, Peter R; Ruggero, Davide

2018-05-02

Oncogenic lesions up-regulate bioenergetically demanding cellular processes, such as protein synthesis, to drive cancer cell growth and continued proliferation. However, the hijacking of these key processes by oncogenic pathways imposes onerous cell stress that must be mitigated by adaptive responses for cell survival. The mechanism by which these adaptive responses are established, their functional consequences for tumor development, and their implications for therapeutic interventions remain largely unknown. Using murine and humanized models of prostate cancer (PCa), we show that one of the three branches of the unfolded protein response is selectively activated in advanced PCa. This adaptive response activates the phosphorylation of the eukaryotic initiation factor 2-α (P-eIF2α) to reset global protein synthesis to a level that fosters aggressive tumor development and is a marker of poor patient survival upon the acquisition of multiple oncogenic lesions. Using patient-derived xenograft models and an inhibitor of P-eIF2α activity, ISRIB, our data show that targeting this adaptive brake for protein synthesis selectively triggers cytotoxicity against aggressive metastatic PCa, a disease for which presently there is no cure. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

A Phase Correction Technique Based on Spatial Movements of Antennas in Real-Time (S.M.A.R.T.) for Designing Self-Adapting Conformal Array Antennas

NASA Astrophysics Data System (ADS)

Roy, Sayan

This research presents a real-time adaptive phase correction technique for flexible phased array antennas on conformal surfaces of variable shapes. Previously reported pattern correctional methods for flexible phased array antennas require prior knowledge on the possible non-planar shapes in which the array may adapt for conformal applications. For the first time, this initial requirement of shape curvature knowledge is no longer needed and the instantaneous information on the relative location of array elements is used here for developing a geometrical model based on a set of Bezier curves. Specifically, by using an array of inclinometer sensors and an adaptive phase-correctional algorithm, it has been shown that the proposed geometrical model can successfully predict different conformal orientations of a 1-by-4 antenna array in real-time without the requirement of knowing the shape-changing characteristics of the surface the array is attached upon. Moreover, the phase correction technique is validated by determining the field patterns and broadside gain of the 1-by-4 antenna array on four different conformal surfaces with multiple points of curvatures. Throughout this work, measurements are shown to agree with the analytical solutions and full-wave simulations.

The Oregon Model of Behavior Family Therapy: From Intervention Design to Promoting Large-Scale System Change

PubMed Central

Dishion, Thomas; Forgatch, Marion; Chamberlain, Patricia; Pelham, William E.

2017-01-01

This paper reviews the evolution of the Oregon model of family behavior therapy over the past four decades. Inspired by basic research on family interaction and innovation in behavior change theory, a set of intervention strategies were developed that were effective for reducing multiple forms of problem behavior in children (e.g., Patterson, Chamberlain, & Reid, 1982). Over the ensuing decades, the behavior family therapy principles were applied and adapted to promote children’s adjustment to address family formation and adaptation (Family Check-Up model), family disruption and maladaptation (Parent Management Training–Oregon model), and family attenuation and dissolution (Treatment Foster Care–Oregon model). We provide a brief overview of each intervention model and summarize randomized trials of intervention effectiveness. We review evidence on the viability of effective implementation, as well as barriers and solutions to adopting these evidence-based practices. We conclude by proposing an integrated family support system for the three models applied to the goal of reducing the prevalence of severe problem behavior, addiction, and mental problems for children and families, as well as reducing the need for costly and largely ineffective residential placements. PMID:27993335

Resilience thinking: integrating resilience, adaptability and transformability

Treesearch

Carl Folke; Stephen R. Carpenter; Brian Walker; Marten Scheffer; Terry Chapin; Johan Rockstrom

2010-01-01

Resilience thinking addresses the dynamics and development of complex social-ecological systems (SES). Three aspects are central: resilience, adaptability and transformability. These aspects interrelate across multiple scales. Resilience in this context is the capacity of a SES to continually change and adapt yet remain within critical thresholds. Adaptability is part...

Somatic stem cell heterogeneity: diversity in the blood, skin and intestinal stem cell compartments

PubMed Central

Goodell, Margaret A.; Nguyen, Hoang; Shroyer, Noah

2017-01-01

Somatic stem cells replenish many tissues throughout life to repair damage and to maintain tissue homeostasis. Stem cell function is frequently described as following a hierarchical model in which a single master cell undergoes self-renewal and differentiation into multiple cell types and is responsible for most regenerative activity. However, recent data from studies on blood, skin and intestinal epithelium all point to the concomitant action of multiple types of stem cells with distinct everyday roles. Under stress conditions such as acute injury, the surprising developmental flexibility of these stem cells enables them to adapt to diverse roles and to acquire different regeneration capabilities. This paradigm shift raises many new questions about the developmental origins, inter-relationships and molecular regulation of these multiple stem cell types. PMID:25907613

An adaptive strategy for reducing Feral Cat predation on endangered hawaiian birds

USGS Publications Warehouse

Hess, S.C.; Banko, P.C.; Hansen, H.

2009-01-01

Despite the long history of Feral Cats Felis catus in Hawai'i, there has been little research to provide strategies to improve control programmes and reduce depredation on endangered species. Our objective Was to develop a predictive model to determine how landscape features on Mauna Kea, such as habitat, elevation, and proximity to roads, may affect the number of Feral Cats captured at each trap. We used log-link generalized linear models and QAIC c model ranking criteria to determine the effect of these factors. We found that The number of cats captured per trap Was related to effort, habitat type, and Whether traps Were located on The West or North Slope of Mauna Kea. We recommend an adaptive management strategy to minimize trapping interference by non-target Small Indian Mongoose Herpestes auropunctatus with toxicants, to focus trapping efforts in M??mane Sophora chrysophylla habitat on the West slope of Mauna Kea, and to cluster traps near others that have previously captured multiple cats.

The Mitochondrial Lon Protease Is Required for Age-Specific and Sex-Specific Adaptation to Oxidative Stress.

PubMed

Pomatto, Laura C D; Carney, Caroline; Shen, Brenda; Wong, Sarah; Halaszynski, Kelly; Salomon, Matthew P; Davies, Kelvin J A; Tower, John

2017-01-09

Multiple human diseases involving chronic oxidative stress show a significant sex bias, including neurodegenerative diseases, cancer, immune dysfunction, diabetes, and cardiovascular disease. However, a possible molecular mechanism for the sex bias in physiological adaptation to oxidative stress remains unclear. Here, we report that Drosophila melanogaster females but not males adapt to hydrogen peroxide stress, whereas males but not females adapt to paraquat (superoxide) stress. Stress adaptation in each sex requires the conserved mitochondrial Lon protease and is associated with sex-specific expression of Lon protein isoforms and proteolytic activity. Adaptation to oxidative stress is lost with age in both sexes. Transgenic expression of transformer gene during development transforms chromosomal males into pseudo-females and confers the female-specific pattern of Lon isoform expression, Lon proteolytic activity induction, and H 2 O 2 stress adaptation; these effects were also observed using adult-specific transformation. Conversely, knockdown of transformer in chromosomal females eliminates the female-specific Lon isoform expression, Lon proteolytic activity induction, and H 2 O 2 stress adaptation and produces the male-specific paraquat (superoxide) stress adaptation. Sex-specific expression of alternative Lon isoforms was also observed in mouse tissues. The results develop Drosophila melanogaster as a model for sex-specific stress adaptation regulated by the Lon protease, with potential implications for understanding sexual dimorphism in human disease. Copyright © 2017 Elsevier Ltd. All rights reserved.

Behavioral training promotes multiple adaptive processes following acute hearing loss.

PubMed

Keating, Peter; Rosenior-Patten, Onayomi; Dahmen, Johannes C; Bell, Olivia; King, Andrew J

2016-03-23

The brain possesses a remarkable capacity to compensate for changes in inputs resulting from a range of sensory impairments. Developmental studies of sound localization have shown that adaptation to asymmetric hearing loss can be achieved either by reinterpreting altered spatial cues or by relying more on those cues that remain intact. Adaptation to monaural deprivation in adulthood is also possible, but appears to lack such flexibility. Here we show, however, that appropriate behavioral training enables monaurally-deprived adult humans to exploit both of these adaptive processes. Moreover, cortical recordings in ferrets reared with asymmetric hearing loss suggest that these forms of plasticity have distinct neural substrates. An ability to adapt to asymmetric hearing loss using multiple adaptive processes is therefore shared by different species and may persist throughout the lifespan. This highlights the fundamental flexibility of neural systems, and may also point toward novel therapeutic strategies for treating sensory disorders.

Adaptively Parameterized Tomography of the Western Hellenic Subduction Zone

NASA Astrophysics Data System (ADS)

Hansen, S. E.; Papadopoulos, G. A.

2017-12-01

The Hellenic subduction zone (HSZ) is the most seismically active region in Europe and plays a major role in the active tectonics of the eastern Mediterranean. This complicated environment has the potential to generate both large magnitude (M > 8) earthquakes and tsunamis. Situated above the western end of the HSZ, Greece faces a high risk from these geologic hazards, and characterizing this risk requires detailed understanding of the geodynamic processes occurring in this area. However, despite previous investigations, the kinematics of the HSZ are still controversial. Regional tomographic studies have yielded important information about the shallow seismic structure of the HSZ, but these models only image down to 150 km depth within small geographic areas. Deeper structure is constrained by global tomographic models but with coarser resolution ( 200-300 km). Additionally, current tomographic models focused on the HSZ were generated with regularly-spaced gridding, and this type of parameterization often over-emphasizes poorly sampled regions of the model or under-represents small-scale structure. Therefore, we are developing a new, high-resolution image of the mantle structure beneath the western HSZ using an adaptively parameterized seismic tomography approach. By combining multiple, regional travel-time datasets in the context of a global model, with adaptable gridding based on the sampling density of high-frequency data, this method generates a composite model of mantle structure that is being used to better characterize geodynamic processes within the HSZ, thereby allowing for improved hazard assessment. Preliminary results will be shown.

Parallel three-dimensional magnetotelluric inversion using adaptive finite-element method. Part I: theory and synthetic study

NASA Astrophysics Data System (ADS)

Grayver, Alexander V.

2015-07-01

This paper presents a distributed magnetotelluric inversion scheme based on adaptive finite-element method (FEM). The key novel aspect of the introduced algorithm is the use of automatic mesh refinement techniques for both forward and inverse modelling. These techniques alleviate tedious and subjective procedure of choosing a suitable model parametrization. To avoid overparametrization, meshes for forward and inverse problems were decoupled. For calculation of accurate electromagnetic (EM) responses, automatic mesh refinement algorithm based on a goal-oriented error estimator has been adopted. For further efficiency gain, EM fields for each frequency were calculated using independent meshes in order to account for substantially different spatial behaviour of the fields over a wide range of frequencies. An automatic approach for efficient initial mesh design in inverse problems based on linearized model resolution matrix was developed. To make this algorithm suitable for large-scale problems, it was proposed to use a low-rank approximation of the linearized model resolution matrix. In order to fill a gap between initial and true model complexities and resolve emerging 3-D structures better, an algorithm for adaptive inverse mesh refinement was derived. Within this algorithm, spatial variations of the imaged parameter are calculated and mesh is refined in the neighborhoods of points with the largest variations. A series of numerical tests were performed to demonstrate the utility of the presented algorithms. Adaptive mesh refinement based on the model resolution estimates provides an efficient tool to derive initial meshes which account for arbitrary survey layouts, data types, frequency content and measurement uncertainties. Furthermore, the algorithm is capable to deliver meshes suitable to resolve features on multiple scales while keeping number of unknowns low. However, such meshes exhibit dependency on an initial model guess. Additionally, it is demonstrated that the adaptive mesh refinement can be particularly efficient in resolving complex shapes. The implemented inversion scheme was able to resolve a hemisphere object with sufficient resolution starting from a coarse discretization and refining mesh adaptively in a fully automatic process. The code is able to harness the computational power of modern distributed platforms and is shown to work with models consisting of millions of degrees of freedom. Significant computational savings were achieved by using locally refined decoupled meshes.

Effect of Split-File Digital Workflow on Crown Margin Adaptation

DTIC Science & Technology

2017-03-30

you in your future publication/presentation efforts. LINDA STEEL -GOODWIN, Col, USAF, BSC Director, Clinical Investigations & Research Support...METHODS Multiple pilot studies were completed to define a working model with appropriate restoration settings ( cement gap 20 µm, extra cement gap 40...depressions for standardization. Right: Zirconia and e.max restorations had a cement gap (CG) = 20 µm ; extra cement gap (ECG) = 40 µm, and distance to

Local adaptation in migrated interior Douglas-fir seedlings is mediated by ectomycorrhizas and other soil factors.

PubMed

Pickles, Brian J; Twieg, Brendan D; O'Neill, Gregory A; Mohn, William W; Simard, Suzanne W

2015-08-01

Separating edaphic impacts on tree distributions from those of climate and geography is notoriously difficult. Aboveground and belowground factors play important roles, and determining their relative contribution to tree success will greatly assist in refining predictive models and forestry strategies in a changing climate. In a common glasshouse, seedlings of interior Douglas-fir (Pseudotsuga menziesii var. glauca) from multiple populations were grown in multiple forest soils. Fungicide was applied to half of the seedlings to separate soil fungal and nonfungal impacts on seedling performance. Soils of varying geographic and climatic distance from seed origin were compared, using a transfer function approach. Seedling height and biomass were optimized following seed transfer into drier soils, whereas survival was optimized when elevation transfer was minimised. Fungicide application reduced ectomycorrhizal root colonization by c. 50%, with treated seedlings exhibiting greater survival but reduced biomass. Local adaptation of Douglas-fir populations to soils was mediated by soil fungi to some extent in 56% of soil origin by response variable combinations. Mediation by edaphic factors in general occurred in 81% of combinations. Soil biota, hitherto unaccounted for in climate models, interacts with biogeography to influence plant ranges in a changing climate. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

PubMed Central

Lindén, Henrik; Lansner, Anders

2016-01-01

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison. PMID:27213810

Community resilience under multi-hazards: time series measurement and it's strategies for improvement

NASA Astrophysics Data System (ADS)

Tian, Cong-shan; Fang, Yi-ping

2017-04-01

Multi - hazards stress is a big obsession that hampers the social and economic development in disaster - prone areas. There is a need to understand and manage drivers of vulnerability and adaptive capacity to the system of multiple geological hazards. Here we pilot three methods namely the multi - hazards resilience assessment model (new framework), the entropy weight method, and the assess social resilience to flood hazards model to measure the resilience to natural hazards of landslide and debris flow on community scale. Using one typical multi - hazards affected county in southwest China, 32 resilience indicators belonging to antecedent conditions, coping responses, adaptation (including learning), and hazard exposure are selected, and a composite index was calculated under the three methods mentioned above. Results show that the new framework reflected a more detailed fluctuation among the 16 years, despite of the overall similar trend between 2000 and 2015 under the three methods. Medical insurance coverage, unemployment insurance coverage, education degree, and hazard exposure are the main drivers of resilience. The most effective strategies for improving community resilience to multiple hazards are likely to be accelerating the development of education, improving the level of medical security, increasing unemployment insurance, and establishing multi - hazards prevention and mitigation systems.

In-Flight Suppression of a Destabilized F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

NASA Technical Reports Server (NTRS)

Wall, John H.; VanZwieten, Tannen S.; Gilligan, Eric T.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

2015-01-01

NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using frequency-domain reconstruction of flight data, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

Psychological adaptations for assessing gossip veracity.

PubMed

Hess, Nicole H; Hagen, Edward H

2006-09-01

Evolutionary models of human cooperation are increasingly emphasizing the role of reputation and the requisite truthful "gossiping" about reputation-relevant behavior. If resources were allocated among individuals according to their reputations, competition for resources via competition for "good" reputations would have created incentives for exaggerated or deceptive gossip about oneself and one's competitors in ancestral societies. Correspondingly, humans should have psychological adaptations to assess gossip veracity. Using social psychological methods, we explored cues of gossip veracity in four experiments. We found that simple reiteration increased gossip veracity, but only for those who found the gossip relatively uninteresting. Multiple sources of gossip increased its veracity, as did the independence of those sources. Information that suggested alternative, benign interpretations of gossip decreased its veracity. Competition between a gossiper and her target decreased gossip veracity. These results provide preliminary evidence for psychological adaptations for assessing gossip veracity, mechanisms that might be used to assess veracity in other domains involving social exchange of information.

Adaptive Markov Random Fields for Example-Based Super-resolution of Faces

NASA Astrophysics Data System (ADS)

Stephenson, Todd A.; Chen, Tsuhan

2006-12-01

Image enhancement of low-resolution images can be done through methods such as interpolation, super-resolution using multiple video frames, and example-based super-resolution. Example-based super-resolution, in particular, is suited to images that have a strong prior (for those frameworks that work on only a single image, it is more like image restoration than traditional, multiframe super-resolution). For example, hallucination and Markov random field (MRF) methods use examples drawn from the same domain as the image being enhanced to determine what the missing high-frequency information is likely to be. We propose to use even stronger prior information by extending MRF-based super-resolution to use adaptive observation and transition functions, that is, to make these functions region-dependent. We show with face images how we can adapt the modeling for each image patch so as to improve the resolution.

Adaptive magnetorheological seat suspension for shock mitigation

NASA Astrophysics Data System (ADS)

Singh, Harinder J.; Wereley, Norman M.

2013-04-01

An adaptive magnetorheological seat suspension (AMSS) was analyzed for optimal protection of occupants from shock loads caused by the impact of a helicopter with the ground. The AMSS system consists of an adaptive linear stroke magnetorheological shock absorber (MRSA) integrated into the seat structure of a helicopter. The MRSA provides a large controllability yield force to accommodate a wide spectrum for shock mitigation. A multiple degrees-of-freedom nonlinear biodynamic model for a 50th percentile male occupant was integrated with the dynamics of MRSA and the governing equations of motion were investigated theoretically. The load-stroke profile of MRSA was optimized with the goal of minimizing the potential for injuries. The MRSA yield force and the shock absorber stroke limitations were the most crucial parameters for improved biodynamic response mitigation. An assessment of injuries based on established injury criteria for different body parts was carried out.

Cultivating Resilience in Families Who Foster: Understanding How Families Cope and Adapt Over Time.

PubMed

Lietz, Cynthia A; Julien-Chinn, Francie J; Geiger, Jennifer M; Hayes Piel, Megan

2016-12-01

Families who foster offer essential care for children and youth when their own parents are unable to provide for their safety and well-being. Foster caregivers face many challenges including increased workload, emotional distress, and the difficulties associated with health and mental health problems that are more common in children in foster care. Despite these stressors, many families are able to sustain fostering while maintaining or enhancing functioning of their unit. This qualitative study applied an adaptational process model of family resilience that emerged in previous studies to examine narratives of persistent, long-term, and multiple fostering experiences. Data corroborated previous research in two ways. Family resilience was again described as a transactional process of coping and adaptation that evolves over time. This process was cultivated through the activation of 10 family strengths that are important in different ways, during varied phases. © 2016 Family Process Institute.

Emerging hierarchies in dynamically adapting webs

NASA Astrophysics Data System (ADS)

Katifori, Eleni; Graewer, Johannes; Magnasco, Marcelo; Modes, Carl

Transport networks play a key role across four realms of eukaryotic life: slime molds, fungi, plants, and animals. In addition to the developmental algorithms that build them, many also employ adaptive strategies to respond to stimuli, damage, and other environmental changes. We model these adapting network architectures using a generic dynamical system on weighted graphs and find in simulation that these networks ultimately develop a hierarchical organization of the final weighted architecture accompanied by the formation of a system-spanning backbone. We quantify the hierarchical organization of the networks by developing an algorithm that decomposes the architecture to multiple scales and analyzes how the organization in each scale relates to that of the scale above and below it. The methodologies developed in this work are applicable to a wide range of systems including the slime mold physarum polycephalum, human microvasculature, and force chains in granular media.

Transatlantic invasion routes and adaptive potential in North American populations of the invasive glossy buckthorn, Frangula alnus

PubMed Central

De Kort, Hanne; Mergeay, Joachim; Jacquemyn, Hans; Honnay, Olivier

2016-01-01

Background and Aims Many invasive species severely threaten native biodiversity and ecosystem functioning. One of the most prominent questions in invasion genetics is how invasive populations can overcome genetic founder effects to establish stable populations after colonization of new habitats. High native genetic diversity and multiple introductions are expected to increase genetic diversity and adaptive potential in the invasive range. Our aim was to identify the European source populations of Frangula alnus (glossy buckthorn), an ornamental and highly invasive woody species that was deliberately introduced into North America at the end of the 18th century. A second aim of this study was to assess the adaptive potential as an explanation for the invasion success of this species. Methods Using a set of annotated single-nucleotide polymorphisms (SNPs) that were assigned a putative function based on sequence comparison with model species, a total of 38 native European and 21 invasive North American populations were subjected to distance-based structure and assignment analyses combined with population genomic tools. Genetic diversity at SNPs with ecologically relevant functions was considered as a proxy for adaptive potential. Key Results Patterns of invasion coincided with early modern transatlantic trading routes. Multiple introductions through transatlantic trade from a limited number of European port regions to American urban areas led to the establishment of bridgehead populations with high allelic richness and expected heterozygosity, allowing continuous secondary migration to natural areas. Conclusions Targeted eradication of the urban populations, where the highest genetic diversity and adaptive potential were observed, offers a promising strategy to arrest further invasion of native American prairies and forests. PMID:27539599

The physical, behavioral, and psychosocial consequences of Internet use in college students.

PubMed

Clark, Deborah J; Frith, Karen H; Demi, Alice S

2004-01-01

The purposes of this study were to identify the physical, behavioral, and psychosocial consequences of Internet use in undergraduate college students; and to evaluate whether time, social norms, and adopter category predict the consequences of Internet use. Rogers' model for studying consequences of innovation was adapted for this study. A descriptive, correlational design was used. Convenience sampling yielded 293 undergraduate students who answered the online survey. Consequences of Internet use were assessed with the researcher-developed instrument, the Internet Consequences Scale (ICONS). Mean scores on the behavioral and psychosocial subscales of the ICONS indicated positive consequences of Internet use, while the physical consequences subscale revealed negative consequences. Multiple regression analyses revealed a small, but significant, amount of variance in consequences of Internet use that could be explained by time, social norms, and adopter category, thus supporting the adapted model for the study of consequences of Internet use in college students.

Sensor modeling and demonstration of a multi-object spectrometer for performance-driven sensing

NASA Astrophysics Data System (ADS)

Kerekes, John P.; Presnar, Michael D.; Fourspring, Kenneth D.; Ninkov, Zoran; Pogorzala, David R.; Raisanen, Alan D.; Rice, Andrew C.; Vasquez, Juan R.; Patel, Jeffrey P.; MacIntyre, Robert T.; Brown, Scott D.

2009-05-01

A novel multi-object spectrometer (MOS) is being explored for use as an adaptive performance-driven sensor that tracks moving targets. Developed originally for astronomical applications, the instrument utilizes an array of micromirrors to reflect light to a panchromatic imaging array. When an object of interest is detected the individual micromirrors imaging the object are tilted to reflect the light to a spectrometer to collect a full spectrum. This paper will present example sensor performance from empirical data collected in laboratory experiments, as well as our approach in designing optical and radiometric models of the MOS channels and the micromirror array. Simulation of moving vehicles in a highfidelity, hyperspectral scene is used to generate a dynamic video input for the adaptive sensor. Performance-driven algorithms for feature-aided target tracking and modality selection exploit multiple electromagnetic observables to track moving vehicle targets.

RNA-Interference Pathways Display High Rates of Adaptive Protein Evolution in Multiple Invertebrates

PubMed Central

Palmer, William H.; Hadfield, Jarrod D.; Obbard, Darren J.

2018-01-01

Conflict between organisms can lead to a reciprocal adaptation that manifests as an increased evolutionary rate in genes mediating the conflict. This adaptive signature has been observed in RNA-interference (RNAi) pathway genes involved in the suppression of viruses and transposable elements in Drosophila melanogaster, suggesting that a subset of Drosophila RNAi genes may be locked in an arms race with these parasites. However, it is not known whether rapid evolution of RNAi genes is a general phenomenon across invertebrates, or which RNAi genes generally evolve adaptively. Here we use population genomic data from eight invertebrate species to infer rates of adaptive sequence evolution, and to test for past and ongoing selective sweeps in RNAi genes. We assess rates of adaptive protein evolution across species using a formal meta-analytic framework to combine data across species and by implementing a multispecies generalized linear mixed model of mutation counts. Across species, we find that RNAi genes display a greater rate of adaptive protein substitution than other genes, and that this is primarily mediated by positive selection acting on the genes most likely to defend against viruses and transposable elements. In contrast, evidence for recent selective sweeps is broadly spread across functional classes of RNAi genes and differs substantially among species. Finally, we identify genes that exhibit elevated adaptive evolution across the analyzed insect species, perhaps due to concurrent parasite-mediated arms races. PMID:29437826

A dual-learning paradigm can simultaneously train multiple characteristics of walking

PubMed Central

Toliver, Alexis; Bastian, Amy J.

2016-01-01

Impairments in human motor patterns are complex: what is often observed as a single global deficit (e.g., limping when walking) is actually the sum of several distinct abnormalities. Motor adaptation can be useful to teach patients more normal motor patterns, yet conventional training paradigms focus on individual features of a movement, leaving others unaddressed. It is known that under certain conditions, distinct movement components can be simultaneously adapted without interference. These previous “dual-learning” studies focused solely on short, planar reaching movements, yet it is unknown whether these findings can generalize to a more complex behavior like walking. Here we asked whether a dual-learning paradigm, incorporating two distinct motor adaptation tasks, can be used to simultaneously train multiple components of the walking pattern. We developed a joint-angle learning task that provided biased visual feedback of sagittal joint angles to increase peak knee or hip flexion during the swing phase of walking. Healthy, young participants performed this task independently or concurrently with another locomotor adaptation task, split-belt treadmill adaptation, where subjects adapted their step length symmetry. We found that participants were able to successfully adapt both components of the walking pattern simultaneously, without interference, and at the same rate as adapting either component independently. This leads us to the interesting possibility that combining rehabilitation modalities within a single training session could be used to help alleviate multiple deficits at once in patients with complex gait impairments. PMID:26961100

System health monitoring using multiple-model adaptive estimation techniques

NASA Astrophysics Data System (ADS)

Sifford, Stanley Ryan

Monitoring system health for fault detection and diagnosis by tracking system parameters concurrently with state estimates is approached using a new multiple-model adaptive estimation (MMAE) method. This novel method is called GRid-based Adaptive Parameter Estimation (GRAPE). GRAPE expands existing MMAE methods by using new techniques to sample the parameter space. GRAPE expands on MMAE with the hypothesis that sample models can be applied and resampled without relying on a predefined set of models. GRAPE is initially implemented in a linear framework using Kalman filter models. A more generalized GRAPE formulation is presented using extended Kalman filter (EKF) models to represent nonlinear systems. GRAPE can handle both time invariant and time varying systems as it is designed to track parameter changes. Two techniques are presented to generate parameter samples for the parallel filter models. The first approach is called selected grid-based stratification (SGBS). SGBS divides the parameter space into equally spaced strata. The second approach uses Latin Hypercube Sampling (LHS) to determine the parameter locations and minimize the total number of required models. LHS is particularly useful when the parameter dimensions grow. Adding more parameters does not require the model count to increase for LHS. Each resample is independent of the prior sample set other than the location of the parameter estimate. SGBS and LHS can be used for both the initial sample and subsequent resamples. Furthermore, resamples are not required to use the same technique. Both techniques are demonstrated for both linear and nonlinear frameworks. The GRAPE framework further formalizes the parameter tracking process through a general approach for nonlinear systems. These additional methods allow GRAPE to either narrow the focus to converged values within a parameter range or expand the range in the appropriate direction to track the parameters outside the current parameter range boundary. Customizable rules define the specific resample behavior when the GRAPE parameter estimates converge. Convergence itself is determined from the derivatives of the parameter estimates using a simple moving average window to filter out noise. The system can be tuned to match the desired performance goals by making adjustments to parameters such as the sample size, convergence criteria, resample criteria, initial sampling method, resampling method, confidence in prior sample covariances, sample delay, and others.

Multiple testing with discrete data: Proportion of true null hypotheses and two adaptive FDR procedures.

PubMed

Chen, Xiongzhi; Doerge, Rebecca W; Heyse, Joseph F

2018-05-11

We consider multiple testing with false discovery rate (FDR) control when p values have discrete and heterogeneous null distributions. We propose a new estimator of the proportion of true null hypotheses and demonstrate that it is less upwardly biased than Storey's estimator and two other estimators. The new estimator induces two adaptive procedures, that is, an adaptive Benjamini-Hochberg (BH) procedure and an adaptive Benjamini-Hochberg-Heyse (BHH) procedure. We prove that the adaptive BH (aBH) procedure is conservative nonasymptotically. Through simulation studies, we show that these procedures are usually more powerful than their nonadaptive counterparts and that the adaptive BHH procedure is usually more powerful than the aBH procedure and a procedure based on randomized p-value. The adaptive procedures are applied to a study of HIV vaccine efficacy, where they identify more differentially polymorphic positions than the BH procedure at the same FDR level. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Widespread Chromosomal Inversion Polymorphism Contributes to a Major Life-History Transition, Local Adaptation, and Reproductive Isolation

PubMed Central

Lowry, David B.; Willis, John H.

2010-01-01

The role of chromosomal inversions in adaptation and speciation is controversial. Historically, inversions were thought to contribute to these processes either by directly causing hybrid sterility or by facilitating the maintenance of co-adapted gene complexes. Because inversions suppress recombination when heterozygous, a recently proposed local adaptation mechanism predicts that they will spread if they capture alleles at multiple loci involved in divergent adaptation to contrasting environments. Many empirical studies have found inversion polymorphisms linked to putatively adaptive phenotypes or distributed along environmental clines. However, direct involvement of an inversion in local adaptation and consequent ecological reproductive isolation has not to our knowledge been demonstrated in nature. In this study, we discovered that a chromosomal inversion polymorphism is geographically widespread, and we test the extent to which it contributes to adaptation and reproductive isolation under natural field conditions. Replicated crosses between the prezygotically reproductively isolated annual and perennial ecotypes of the yellow monkeyflower, Mimulus guttatus, revealed that alternative chromosomal inversion arrangements are associated with life-history divergence over thousands of kilometers across North America. The inversion polymorphism affected adaptive flowering time divergence and other morphological traits in all replicated crosses between four pairs of annual and perennial populations. To determine if the inversion contributes to adaptation and reproductive isolation in natural populations, we conducted a novel reciprocal transplant experiment involving outbred lines, where alternative arrangements of the inversion were reciprocally introgressed into the genetic backgrounds of each ecotype. Our results demonstrate for the first time in nature the contribution of an inversion to adaptation, an annual/perennial life-history shift, and multiple reproductive isolating barriers. These results are consistent with the local adaptation mechanism being responsible for the distribution of the two inversion arrangements across the geographic range of M. guttatus and that locally adaptive inversion effects contribute directly to reproductive isolation. Such a mechanism may be partially responsible for the observation that closely related species often differ by multiple chromosomal rearrangements. PMID:20927411

Acute adaptation of the vestibuloocular reflex: signal processing by floccular and ventral parafloccular Purkinje cells.

PubMed

Hirata, Y; Highstein, S M

2001-05-01

The gain of the vertical vestibuloocular reflex (VVOR), defined as eye velocity/head velocity was adapted in squirrel monkeys by employing visual-vestibular mismatch stimuli. VVOR gain, measured in the dark, could be trained to values between 0.4 and 1.5. Single-unit activity of vertical zone Purkinje cells was recorded from the flocculus and ventral paraflocculus in alert squirrel monkeys before and during the gain change training. Our goal was to evaluate the site(s) of learning of the gain change. To aid in the evaluation, a model of the vertical optokinetic reflex (VOKR) and VVOR was constructed consisting of floccular and nonfloccular systems divided into subsystems based on the known anatomy and input and output parameters. Three kinds of input to floccular Purkinje cells via mossy fibers were explicitly described, namely vestibular, visual (retinal slip), and efference copy of eye movement. The characteristics of each subsystem (gain and phase) were identified at different VOR gains by reconstructing single-unit activity of Purkinje cells during VOKR and VVOR with multiple linear regression models consisting of sensory input and motor output signals. Model adequacy was checked by evaluating the residual following the regressions and by predicting Purkinje cells' activity during visual-vestibular mismatch paradigms. As a result, parallel changes in identified characteristics with VVOR adaptation were found in the prefloccular/floccular subsystem that conveys vestibular signals and in the nonfloccular subsystem that conveys vestibular signals, while no change was found in other subsystems, namely prefloccular/floccular subsystems conveying efference copy or visual signals, nonfloccular subsystem conveying visual signals, and postfloccular subsystem transforming Purkinje cell activity to eye movements. The result suggests multiple sites for VVOR motor learning including both flocculus and nonflocculus pathways. The gain change in the nonfloccular vestibular subsystem was in the correct direction to cause VOR gain adaptation while the change in the prefloccular/floccular vestibular subsystem was incorrect (anti-compensatory). This apparent incorrect directional change might serve to prevent instability of the VOR caused by positive feedback via the efference copy pathway.

Mutual coupling, channel model, and BER for curvilinear antenna arrays

NASA Astrophysics Data System (ADS)

Huang, Zhiyong

This dissertation introduces a wireless communications system with an adaptive beam-former and investigates its performance with different antenna arrays. Mutual coupling, real antenna elements and channel models are included to examine the system performance. In a beamforming system, mutual coupling (MC) among the elements can significantly degrade the system performance. However, MC effects can be compensated if an accurate model of mutual coupling is available. A mutual coupling matrix model is utilized to compensate mutual coupling in the beamforming of a uniform circular array (UCA). Its performance is compared with other models in uplink and downlink beamforming scenarios. In addition, the predictions are compared with measurements and verified with results from full-wave simulations. In order to accurately investigate the minimum mean-square-error (MSE) of an adaptive array in MC, two different noise models, the environmental and the receiver noise, are modeled. The minimum MSEs with and without data domain MC compensation are analytically compared. The influence of mutual coupling on the convergence is also examined. In addition, the weight compensation method is proposed to attain the desired array pattern. Adaptive arrays with different geometries are implemented with the minimum MSE algorithm in the wireless communications system to combat interference at the same frequency. The bit-error-rate (BER) of systems with UCA, uniform rectangular array (URA) and UCA with center element are investigated in additive white Gaussian noise plus well-separated signals or random direction signals scenarios. The output SINR of an adaptive array with multiple interferers is analytically examined. The influence of the adaptive algorithm convergence on the BER is investigated. The UCA is then investigated in a narrowband Rician fading channel. The channel model is built and the space correlations are examined. The influence of the number of signal paths, number of the interferers, Doppler spread and convergence are investigated. The tracking mode is introduced to the adaptive array system, and it further improves the BER. The benefit of using faster data rate (wider bandwidth) is discussed. In order to have better performance in a 3D space, the geometries of uniform spherical array (USAs) are presented and different configurations of USAs are discussed. The LMS algorithm based on temporal a priori information is applied to UCAs and USAs to beamform the patterns. Their performances are compared based on simulation results. Based on the analytical and simulation results, it can be concluded that mutual coupling slightly influences the performance of the adaptive array in communication systems. In addition, arrays with curvilinear geometries perform well in AWGN and fading channels.

Impacts of Future Climate Change on California Perennial Crop Yields: Model Projections with Climate and Crop Uncertainties

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

Lobell, D; Field, C; Cahill, K

2006-01-10

Most research on the agricultural impacts of climate change has focused on the major annual crops, yet perennial cropping systems are less adaptable and thus potentially more susceptible to damage. Improved assessments of yield responses to future climate are needed to prioritize adaptation strategies in the many regions where perennial crops are economically and culturally important. These impact assessments, in turn, must rely on climate and crop models that contain often poorly defined uncertainties. We evaluated the impact of climate change on six major perennial crops in California: wine grapes, almonds, table grapes, oranges, walnuts, and avocados. Outputs from multiplemore » climate models were used to evaluate climate uncertainty, while multiple statistical crop models, derived by resampling historical databases, were used to address crop response uncertainties. We find that, despite these uncertainties, climate change in California is very likely to put downward pressure on yields of almonds, walnuts, avocados, and table grapes by 2050. Without CO{sub 2} fertilization or adaptation measures, projected losses range from 0 to >40% depending on the crop and the trajectory of climate change. Climate change uncertainty generally had a larger impact on projections than crop model uncertainty, although the latter was substantial for several crops. Opportunities for expansion into cooler regions are identified, but this adaptation would require substantial investments and may be limited by non-climatic constraints. Given the long time scales for growth and production of orchards and vineyards ({approx}30 years), climate change should be an important factor in selecting perennial varieties and deciding whether and where perennials should be planted.« less

Neuronal Spike Timing Adaptation Described with a Fractional Leaky Integrate-and-Fire Model

PubMed Central

Teka, Wondimu; Marinov, Toma M.; Santamaria, Fidel

2014-01-01

The voltage trace of neuronal activities can follow multiple timescale dynamics that arise from correlated membrane conductances. Such processes can result in power-law behavior in which the membrane voltage cannot be characterized with a single time constant. The emergent effect of these membrane correlations is a non-Markovian process that can be modeled with a fractional derivative. A fractional derivative is a non-local process in which the value of the variable is determined by integrating a temporal weighted voltage trace, also called the memory trace. Here we developed and analyzed a fractional leaky integrate-and-fire model in which the exponent of the fractional derivative can vary from 0 to 1, with 1 representing the normal derivative. As the exponent of the fractional derivative decreases, the weights of the voltage trace increase. Thus, the value of the voltage is increasingly correlated with the trajectory of the voltage in the past. By varying only the fractional exponent, our model can reproduce upward and downward spike adaptations found experimentally in neocortical pyramidal cells and tectal neurons in vitro. The model also produces spikes with longer first-spike latency and high inter-spike variability with power-law distribution. We further analyze spike adaptation and the responses to noisy and oscillatory input. The fractional model generates reliable spike patterns in response to noisy input. Overall, the spiking activity of the fractional leaky integrate-and-fire model deviates from the spiking activity of the Markovian model and reflects the temporal accumulated intrinsic membrane dynamics that affect the response of the neuron to external stimulation. PMID:24675903

Between-Trial Forgetting Due to Interference and Time in Motor Adaptation.

PubMed

Kim, Sungshin; Oh, Youngmin; Schweighofer, Nicolas

2015-01-01

Learning a motor task with temporally spaced presentations or with other tasks intermixed between presentations reduces performance during training, but can enhance retention post training. These two effects are known as the spacing and contextual interference effect, respectively. Here, we aimed at testing a unifying hypothesis of the spacing and contextual interference effects in visuomotor adaptation, according to which forgetting between trials due to either spaced presentations or interference by another task will promote between-trial forgetting, which will depress performance during acquisition, but will promote retention. We first performed an experiment with three visuomotor adaptation conditions: a short inter-trial-interval (ITI) condition (SHORT-ITI); a long ITI condition (LONG-ITI); and an alternating condition with two alternated opposite tasks (ALT), with the same single-task ITI as in LONG-ITI. In the SHORT-ITI condition, there was fastest increase in performance during training and largest immediate forgetting in the retention tests. In contrast, in the ALT condition, there was slowest increase in performance during training and little immediate forgetting in the retention tests. Compared to these two conditions, in the LONG-ITI, we found intermediate increase in performance during training and intermediate immediate forgetting. To account for these results, we fitted to the data six possible adaptation models with one or two time scales, and with interference in the fast, or in the slow, or in both time scales. Model comparison confirmed that two time scales and some degree of interferences in either time scale are needed to account for our experimental results. In summary, our results suggest that retention following adaptation is modulated by the degree of between-trial forgetting, which is due to time-based decay in single adaptation task and interferences in multiple adaptation tasks.

No adaptive response is induced by chronic low-dose radiation from Ra-226 in the CHSE/F fish embryonic cell line and the HaCaT human epithelial cell line

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

Shi, Xiaopei, E-mail: shix22@mcmaster.ca; Mothersi

Purpose: To determine whether chronic low-dose α-particle radiation from Ra-226 over multiple cell generations can lead to an adaptive response in CHSE/F fish embryonic cells or HaCaT human epithelial cells receiving subsequent acute high-dose γ-ray radiation. Methods: CHSE/F and HaCaT cells were exposed to very low doses of Ra-226 in medium for multiple generations prior to being challenged by a higher dose γ-ray radiation. The clonogenic assay was used to test the clonogenic survival of cells with or without being pretreated by radiation from Ra-226. Results: In general, pretreatment with chronic radiation has no significant influence on the reaction ofmore » cells to the subsequent challenge radiation. Compared to unprimed cells, the change in clonogenic survival of primed cells after receiving challenge radiation is mainly due to the influence of the chronic exposure, and there's little adaptive response induced. However at several dose points, pretreatment of CHSE/F fish cells with chronic radiation resulted in a radiosensitive response to a challenge dose of γ-ray radiation, and pretreatment of HaCaT cells resulted in no effect except for a slightly radioresistant response to the challenge radiation which was not significant. Conclusion: The results suggest that chronic low-dose radiation is not effective enough to induce adaptive response. There was a difference between human and fish cells and it may be important to consider results from multiple species before making conclusions about effects of chronic or low doses of radiation in the environment. The term “radiosensitive” or “adaptive” make no judgment about whether such responses are ultimately beneficial or harmful. - Highlights: • No obvious adaptive response is induced by chronic low-dose radiation from Ra-226. • Priming radiation from Ra-226 sensitized CHSE/F cells to the challenge radiation. • Linear model is inconsistent with current work using chronic low-dose radiation.« less

Two Boys with Multiple Disabilities Increasing Adaptive Responding and Curbing Dystonic/Spastic Behavior via a Microswitch-Based Program

ERIC Educational Resources Information Center

Lancioni, Giulio E.; Singh, Nirbhay N.; O'Reilly, Mark F.; Sigafoos, Jeff; Didden, Robert; Oliva, Doretta

2009-01-01

A recent study has shown that microswitch clusters (i.e., combinations of microswitches) and contingent stimulation could be used to increase adaptive responding and reduce dystonic/spastic behavior in two children with multiple disabilities [Lancioni, G. E., Singh, N. N., Oliva, D., Scalini, L., & Groeneweg, J. (2003). Microswitch clusters to…

Adaptation of a Counseling Intervention to Address Multiple Cancer Risk Factors among Overweight/Obese Latino Smokers

ERIC Educational Resources Information Center

Castro, Yessenia; Fernández, Maria E.; Strong, Larkin L.; Stewart, Diana W.; Krasny, Sarah; Hernandez Robles, Eden; Heredia, Natalia; Spears, Claire A.; Correa-Fernández, Virmarie; Eakin, Elizabeth; Resnicow, Ken; Basen-Engquist, Karen; Wetter, David W.

2015-01-01

More than 60% of cancer-related deaths in the United States are attributable to tobacco use, poor nutrition, and physical inactivity, and these risk factors tend to cluster together. Thus, strategies for cancer risk reduction would benefit from addressing multiple health risk behaviors. We adapted an evidence-based intervention grounded in social…

Persons with Multiple Disabilities Exercise Adaptive Response Schemes with the Help of Technology-Based Programs: Three Single-Case Studies

ERIC Educational Resources Information Center

Lancioni, Giulio E.; Singh, Nirbhay N.; O'Reilly, Mark F.; Sigafoos, Jeff; Oliva, Doretta; Campodonico, Francesca; Lang, Russell

2012-01-01

The present three single-case studies assessed the effectiveness of technology-based programs to help three persons with multiple disabilities exercise adaptive response schemes independently. The response schemes included (a) left and right head movements for a man who kept his head increasingly static on his wheelchair's headrest (Study I), (b)…

A comparison of the adaptations of strains of Lymantria dispar multiple nucleopolyhedrovirus to hosts from spatially isolated populations

Treesearch

V.V. Martemyanov; J.D. Podgwaite; I.A. Belousova; S.V. Pavlushin; J.M. Slavicek; O.A. Baturina; M.R. Kabilov; A.V. Ilyinykh

2017-01-01

The adaptation of pathogens to either their hosts or to environmental conditions is the focus of many current ecological studies. In this work we compared the ability of six spatially-distant Lymantria dispar (gypsy moth) multiple nucleopolyhedrovirus (LdMNPV) strains (three from eastern North America and three from central Asia) to induce acute...

Genomic signatures of adaptation to wine biological ageing conditions in biofilm-forming flor yeasts.

PubMed

Coi, A L; Bigey, F; Mallet, S; Marsit, S; Zara, G; Gladieux, P; Galeote, V; Budroni, M; Dequin, S; Legras, J L

2017-04-01

The molecular and evolutionary processes underlying fungal domestication remain largely unknown despite the importance of fungi to bioindustry and for comparative adaptation genomics in eukaryotes. Wine fermentation and biological ageing are performed by strains of S. cerevisiae with, respectively, pelagic fermentative growth on glucose and biofilm aerobic growth utilizing ethanol. Here, we use environmental samples of wine and flor yeasts to investigate the genomic basis of yeast adaptation to contrasted anthropogenic environments. Phylogenetic inference and population structure analysis based on single nucleotide polymorphisms revealed a group of flor yeasts separated from wine yeasts. A combination of methods revealed several highly differentiated regions between wine and flor yeasts, and analyses using codon-substitution models for detecting molecular adaptation identified sites under positive selection in the high-affinity transporter gene ZRT1. The cross-population composite likelihood ratio revealed selective sweeps at three regions, including in the hexose transporter gene HXT7, the yapsin gene YPS6 and the membrane protein coding gene MTS27. Our analyses also revealed that the biological ageing environment has led to the accumulation of numerous mutations in proteins from several networks, including Flo11 regulation and divalent metal transport. Together, our findings suggest that the tuning of FLO11 expression and zinc transport networks are a distinctive feature of the genetic changes underlying the domestication of flor yeasts. Our study highlights the multiplicity of genomic changes underlying yeast adaptation to man-made habitats and reveals that flor/wine yeast lineage can serve as a useful model for studying the genomics of adaptive divergence. © 2017 John Wiley & Sons Ltd.

A chimera grid scheme. [multiple overset body-conforming mesh system for finite difference adaptation to complex aircraft configurations

NASA Technical Reports Server (NTRS)

Steger, J. L.; Dougherty, F. C.; Benek, J. A.

1983-01-01

A mesh system composed of multiple overset body-conforming grids is described for adapting finite-difference procedures to complex aircraft configurations. In this so-called 'chimera mesh,' a major grid is generated about a main component of the configuration and overset minor grids are used to resolve all other features. Methods for connecting overset multiple grids and modifications of flow-simulation algorithms are discussed. Computational tests in two dimensions indicate that the use of multiple overset grids can simplify the task of grid generation without an adverse effect on flow-field algorithms and computer code complexity.

Adaptive optics in spinning disk microscopy: improved contrast and brightness by a simple and fast method.

PubMed

Fraisier, V; Clouvel, G; Jasaitis, A; Dimitrov, A; Piolot, T; Salamero, J

2015-09-01

Multiconfocal microscopy gives a good compromise between fast imaging and reasonable resolution. However, the low intensity of live fluorescent emitters is a major limitation to this technique. Aberrations induced by the optical setup, especially the mismatch of the refractive index and the biological sample itself, distort the point spread function and further reduce the amount of detected photons. Altogether, this leads to impaired image quality, preventing accurate analysis of molecular processes in biological samples and imaging deep in the sample. The amount of detected fluorescence can be improved with adaptive optics. Here, we used a compact adaptive optics module (adaptive optics box for sectioning optical microscopy), which was specifically designed for spinning disk confocal microscopy. The module overcomes undesired anomalies by correcting for most of the aberrations in confocal imaging. Existing aberration detection methods require prior illumination, which bleaches the sample. To avoid multiple exposures of the sample, we established an experimental model describing the depth dependence of major aberrations. This model allows us to correct for those aberrations when performing a z-stack, gradually increasing the amplitude of the correction with depth. It does not require illumination of the sample for aberration detection, thus minimizing photobleaching and phototoxicity. With this model, we improved both signal-to-background ratio and image contrast. Here, we present comparative studies on a variety of biological samples. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

When should we expect early bursts of trait evolution in comparative data? Predictions from an evolutionary food web model.

PubMed

Ingram, T; Harmon, L J; Shurin, J B

2012-09-01

Conceptual models of adaptive radiation predict that competitive interactions among species will result in an early burst of speciation and trait evolution followed by a slowdown in diversification rates. Empirical studies often show early accumulation of lineages in phylogenetic trees, but usually fail to detect early bursts of phenotypic evolution. We use an evolutionary simulation model to assemble food webs through adaptive radiation, and examine patterns in the resulting phylogenetic trees and species' traits (body size and trophic position). We find that when foraging trade-offs result in food webs where all species occupy integer trophic levels, lineage diversity and trait disparity are concentrated early in the tree, consistent with the early burst model. In contrast, in food webs in which many omnivorous species feed at multiple trophic levels, high levels of turnover of species' identities and traits tend to eliminate the early burst signal. These results suggest testable predictions about how the niche structure of ecological communities may be reflected by macroevolutionary patterns. © 2012 The Authors. Journal of Evolutionary Biology © 2012 European Society For Evolutionary Biology.

Multiscale modeling of metabolism, flows, and exchanges in heterogeneous organs

PubMed Central

Bassingthwaighte, James B.; Raymond, Gary M.; Butterworth, Erik; Alessio, Adam; Caldwell, James H.

2010-01-01

Large-scale models accounting for the processes supporting metabolism and function in an organ or tissue with a marked heterogeneity of flows and metabolic rates are computationally complex and tedious to compute. Their use in the analysis of data from positron emission tomography (PET) and magnetic resonance imaging (MRI) requires model reduction since the data are composed of concentration–time curves from hundreds of regions of interest (ROI) within the organ. Within each ROI, one must account for blood flow, intracapillary gradients in concentrations, transmembrane transport, and intracellular reactions. Using modular design, we configured a whole organ model, GENTEX, to allow adaptive usage for multiple reacting molecular species while omitting computation of unused components. The temporal and spatial resolution and the number of species are adaptable and the numerical accuracy and computational speed is adjustable during optimization runs, which increases accuracy and spatial resolution as convergence approaches. An application to the interpretation of PET image sequences after intravenous injection of 13NH3 provides functional image maps of regional myocardial blood flows. PMID:20201893

Quick and Easy Adaptations and Accommodations for Early Childhood Students

ERIC Educational Resources Information Center

Breitfelder, Leisa M.

2008-01-01

Research-based information is used to support the idea of the use of adaptations and accommodations for early childhood students who have varying disabilities. Multiple adaptations and accommodations are outlined. A step-by-step plan is provided on how to make specific adaptations and accommodations to fit the specific needs of early childhood…

Legal and institutional foundations of adaptive environmental governance

PubMed Central

DeCaro, Daniel A.; Chaffin, Brian C.; Schlager, Edella; Garmestani, Ahjond S.; Ruhl, J.B.

2018-01-01

Legal and institutional structures fundamentally shape opportunities for adaptive governance of environmental resources at multiple ecological and societal scales. Properties of adaptive governance are widely studied. However, these studies have not resulted in consolidated frameworks for legal and institutional design, limiting our ability to promote adaptation and social-ecological resilience. We develop an overarching framework that describes the current and potential role of law in enabling adaptation. We apply this framework to different social-ecological settings, centers of activity, and scales, illustrating the multidimensional and polycentric nature of water governance. Adaptation typically emerges organically among multiple centers of agency and authority in society as a relatively self-organized or autonomous process marked by innovation, social learning, and political deliberation. This self-directed and emergent process is difficult to create in an exogenous, top-down fashion. However, traditional centers of authority may establish enabling conditions for adaptation using a suite of legal, economic, and democratic tools to legitimize and facilitate self-organization, coordination, and collaboration across scales. The principles outlined here provide preliminary legal and institutional foundations for adaptive environmental governance, which may inform institutional design and guide future scholarship. PMID:29780428

Legal and institutional foundations of adaptive environmental governance.

PubMed

DeCaro, Daniel A; Chaffin, Brian C; Schlager, Edella; Garmestani, Ahjond S; Ruhl, J B

2017-03-17

Legal and institutional structures fundamentally shape opportunities for adaptive governance of environmental resources at multiple ecological and societal scales. Properties of adaptive governance are widely studied. However, these studies have not resulted in consolidated frameworks for legal and institutional design, limiting our ability to promote adaptation and social-ecological resilience. We develop an overarching framework that describes the current and potential role of law in enabling adaptation. We apply this framework to different social-ecological settings, centers of activity, and scales, illustrating the multidimensional and polycentric nature of water governance. Adaptation typically emerges organically among multiple centers of agency and authority in society as a relatively self-organized or autonomous process marked by innovation, social learning, and political deliberation. This self-directed and emergent process is difficult to create in an exogenous, top-down fashion. However, traditional centers of authority may establish enabling conditions for adaptation using a suite of legal, economic, and democratic tools to legitimize and facilitate self-organization, coordination, and collaboration across scales. The principles outlined here provide preliminary legal and institutional foundations for adaptive environmental governance, which may inform institutional design and guide future scholarship.

Two Case Studies to Quantify Resilience across Food-Energy-Water Systems: the Columbia River Treaty and Adaptation in Yakima River Basin Irrigation Systems

NASA Astrophysics Data System (ADS)

Malek, K.; Adam, J. C.; Richey, A.; Rushi, B. R.; Stockle, C.; Yoder, J.; Barik, M.; Lee, S. Y.; Rajagopalan, K.; Brady, M.; Barber, M. E.; Boll, J.; Padowski, J.

2017-12-01

The U.S. Pacific Northwest (PNW) plays a significant role in meeting agricultural and hydroelectric demands nationwide. Climatic and anthropogenic stressors, however, potentially threaten the productivity, resilience, and environmental health of the region. Our objective is to understand how resilience of each Food-Energy-Water (FEW) sector, and the combined Nexus, respond to exogenous perturbations and the extent to which technological and institutional advances can buffer these perturbations. In the process of taking information from complex integrated models and assessing resilience across FEW sectors, we start with two case studies: 1) Columbia River Treaty (CRT) with Canada that determines how multiple reservoirs in the Columbia River basin (CRB) are operated, and 2) climate change adaptation actions in the Yakima River basin (YRB). We discuss these case studies in terms of the similarities and contrasts related to FEW sectors and management complexities. Both the CRB and YBP systems are highly sensitive to climate change (they are both snowmelt-dominant) and already experience water conflict. The CRT is currently undergoing renegotiation; a new CRT will need to consider a much more comprehensive approach, e.g., treating environmental flows explicitly. The YRB also already experiences significant water conflict and thus the comprehensive Yakima Basin Integrated Plan (YBIP) is being pursued. We apply a new modeling framework that mechanistically captures the interactions between the FEW sectors to quantify the impacts of CRT and YBIP planning (as well as adaptation decisions taken by individuals, e.g., irrigators) on resilience in each sector. Proposed modification to the CRT may relieve impacts to multiple sectors. However, in the YRB, irrigators' actions to adapt to climate change (through investing in more efficient irrigation technology) could reduce downstream water availability for other users. Developing a process to quantify resilience to perturbations, such as climate change, will enable innovative solutions that co-balance benefits, and ultimately increase resilience, across all FEW sectors.

A theory of cerebellar cortex and adaptive motor control based on two types of universal function approximation capability.

PubMed

Fujita, Masahiko

2016-03-01

Lesions of the cerebellum result in large errors in movements. The cerebellum adaptively controls the strength and timing of motor command signals depending on the internal and external environments of movements. The present theory describes how the cerebellar cortex can control signals for accurate and timed movements. A model network of the cerebellar Golgi and granule cells is shown to be equivalent to a multiple-input (from mossy fibers) hierarchical neural network with a single hidden layer of threshold units (granule cells) that receive a common recurrent inhibition (from a Golgi cell). The weighted sum of the hidden unit signals (Purkinje cell output) is theoretically analyzed regarding the capability of the network to perform two types of universal function approximation. The hidden units begin firing as the excitatory inputs exceed the recurrent inhibition. This simple threshold feature leads to the first approximation theory, and the network final output can be any continuous function of the multiple inputs. When the input is constant, this output becomes stationary. However, when the recurrent unit activity is triggered to decrease or the recurrent inhibition is triggered to increase through a certain mechanism (metabotropic modulation or extrasynaptic spillover), the network can generate any continuous signals for a prolonged period of change in the activity of recurrent signals, as the second approximation theory shows. By incorporating the cerebellar capability of two such types of approximations to a motor system, in which learning proceeds through repeated movement trials with accompanying corrections, accurate and timed responses for reaching the target can be adaptively acquired. Simple models of motor control can solve the motor error vs. sensory error problem, as well as the structural aspects of credit (or error) assignment problem. Two physiological experiments are proposed for examining the delay and trace conditioning of eyelid responses, as well as saccade adaptation, to investigate this novel idea of cerebellar processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Agriculture Model Intercomparison and Improvement Project (AgMIP) (Invited)

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2010-12-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a distributed climate-scenario simulation exercise for historical model intercomparison and future climate change conditions with participation of multiple crop and world agricultural trade modeling groups around the world. The goals of AgMIP are to improve substantially the characterization of risk of hunger and world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Historical period results will spur model improvement and interaction among major modeling groups, while future period results will lead directly to tests of adaptation and mitigation strategies across a range of scales. AgMIP will consist of a multi-scale impact assessment utilizing the latest methods for climate and agricultural scenario generation. Scenarios and modeling protocols will be distributed on the web, and multi-model results will be collated and analyzed to ensure the widest possible coverage of agricultural crops and regions. AgMIP will place regional changes in agricultural production in a global context that reflects new trading opportunities, imbalances, and shortages in world markets resulting from climate change and other driving forces for food supply. Such projections are essential inputs from the Vulnerability, Impacts, and Adaptation (VIA) research community to the Intergovernmental Panel on Climate Change Fifth Assessment (AR5), now underway, and the UN Framework Convention on Climate Change. They will set the context for local-scale vulnerability and adaptation studies, supply test scenarios for national-scale development of trade policy instruments, provide critical information on changing supply and demand for water resources, and elucidate interactive effects of climate change and land use change. AgMIP will not only provide crucially-needed new global estimates of how climate change will affect food supply and hunger in the agricultural regions of the world, but it will also build the capabilities of developing countries to estimate how climate change will affect their supply and demand for food.

Patterns of Nucleotide Diversity at Photoperiod Related Genes in Norway Spruce [Picea abies (L.) Karst.

PubMed Central

Källman, Thomas; De Mita, Stéphane; Larsson, Hanna; Gyllenstrand, Niclas; Heuertz, Myriam; Parducci, Laura; Suyama, Yoshihisa; Lagercrantz, Ulf; Lascoux, Martin

2014-01-01

The ability of plants to track seasonal changes is largely dependent on genes assigned to the photoperiod pathway, and variation in those genes is thereby important for adaptation to local day length conditions. Extensive physiological data in several temperate conifer species suggest that populations are adapted to local light conditions, but data on the genes underlying this adaptation are more limited. Here we present nucleotide diversity data from 19 genes putatively involved in photoperiodic response in Norway spruce (Picea abies). Based on similarity to model plants the genes were grouped into three categories according to their presumed position in the photoperiod pathway: photoreceptors, circadian clock genes, and downstream targets. An HKA (Hudson, Kreitman and Aquade) test showed a significant excess of diversity at photoreceptor genes, but no departure from neutrality at circadian genes and downstream targets. Departures from neutrality were also tested with Tajima's D and Fay and Wu's H statistics under three demographic scenarios: the standard neutral model, a population expansion model, and a more complex population split model. Only one gene, the circadian clock gene PaPRR3 with a highly positive Tajima's D value, deviates significantly from all tested demographic scenarios. As the PaPRR3 gene harbours multiple non-synonymous variants it appears as an excellent candidate gene for control of photoperiod response in Norway spruce. PMID:24810273

Patterns of nucleotide diversity at photoperiod related genes in Norway spruce [Picea abies (L.) Karst].

PubMed

Källman, Thomas; De Mita, Stéphane; Larsson, Hanna; Gyllenstrand, Niclas; Heuertz, Myriam; Parducci, Laura; Suyama, Yoshihisa; Lagercrantz, Ulf; Lascoux, Martin

2014-01-01

The ability of plants to track seasonal changes is largely dependent on genes assigned to the photoperiod pathway, and variation in those genes is thereby important for adaptation to local day length conditions. Extensive physiological data in several temperate conifer species suggest that populations are adapted to local light conditions, but data on the genes underlying this adaptation are more limited. Here we present nucleotide diversity data from 19 genes putatively involved in photoperiodic response in Norway spruce (Picea abies). Based on similarity to model plants the genes were grouped into three categories according to their presumed position in the photoperiod pathway: photoreceptors, circadian clock genes, and downstream targets. An HKA (Hudson, Kreitman and Aquade) test showed a significant excess of diversity at photoreceptor genes, but no departure from neutrality at circadian genes and downstream targets. Departures from neutrality were also tested with Tajima's D and Fay and Wu's H statistics under three demographic scenarios: the standard neutral model, a population expansion model, and a more complex population split model. Only one gene, the circadian clock gene PaPRR3 with a highly positive Tajima's D value, deviates significantly from all tested demographic scenarios. As the PaPRR3 gene harbours multiple non-synonymous variants it appears as an excellent candidate gene for control of photoperiod response in Norway spruce.

Systematic, Multimethod Assessment of Adaptations Across Four Diverse Health Systems Interventions.

PubMed

Rabin, Borsika A; McCreight, Marina; Battaglia, Catherine; Ayele, Roman; Burke, Robert E; Hess, Paul L; Frank, Joseph W; Glasgow, Russell E

2018-01-01

Many health outcomes and implementation science studies have demonstrated the importance of tailoring evidence-based care interventions to local context to improve fit. By adapting to local culture, history, resources, characteristics, and priorities, interventions are more likely to lead to improved outcomes. However, it is unclear how best to adapt evidence-based programs and promising innovations. There are few guides or examples of how to best categorize or assess health-care adaptations, and even fewer that are brief and practical for use by non-researchers. This study describes the importance and potential of assessing adaptations before, during, and after the implementation of health systems interventions. We present a promising multilevel and multimethod approach developed and being applied across four different health systems interventions. Finally, we discuss implications and opportunities for future research. The four case studies are diverse in the conditions addressed, interventions, and implementation strategies. They include two nurse coordinator-based transition of care interventions, a data and training-driven multimodal pain management project, and a cardiovascular patient-reported outcomes project, all of which are using audit and feedback. We used the same modified adaptation framework to document changes made to the interventions and implementation strategies. To create the modified framework, we started with the adaptation and modification model developed by Stirman and colleagues and expanded it by adding concepts from the RE-AIM framework. Our assessments address the intuitive domains of Who, How, When, What, and Why to classify and organize adaptations. For each case study, we discuss how the modified framework was operationalized, the multiple methods used to collect data, results to date and approaches utilized for data analysis. These methods include a real-time tracking system and structured interviews at key times during the intervention. We provide descriptive data on the types and categories of adaptations made and discuss lessons learned. The multimethod approaches demonstrate utility across diverse health systems interventions. The modified adaptations model adequately captures adaptations across the various projects and content areas. We recommend systematic documentation of adaptations in future clinical and public health research and have made our assessment materials publicly available.

Virtual Systems Pharmacology (ViSP) software for simulation from mechanistic systems-level models.

PubMed

Ermakov, Sergey; Forster, Peter; Pagidala, Jyotsna; Miladinov, Marko; Wang, Albert; Baillie, Rebecca; Bartlett, Derek; Reed, Mike; Leil, Tarek A

2014-01-01

Multiple software programs are available for designing and running large scale system-level pharmacology models used in the drug development process. Depending on the problem, scientists may be forced to use several modeling tools that could increase model development time, IT costs and so on. Therefore, it is desirable to have a single platform that allows setting up and running large-scale simulations for the models that have been developed with different modeling tools. We developed a workflow and a software platform in which a model file is compiled into a self-contained executable that is no longer dependent on the software that was used to create the model. At the same time the full model specifics is preserved by presenting all model parameters as input parameters for the executable. This platform was implemented as a model agnostic, therapeutic area agnostic and web-based application with a database back-end that can be used to configure, manage and execute large-scale simulations for multiple models by multiple users. The user interface is designed to be easily configurable to reflect the specifics of the model and the user's particular needs and the back-end database has been implemented to store and manage all aspects of the systems, such as Models, Virtual Patients, User Interface Settings, and Results. The platform can be adapted and deployed on an existing cluster or cloud computing environment. Its use was demonstrated with a metabolic disease systems pharmacology model that simulates the effects of two antidiabetic drugs, metformin and fasiglifam, in type 2 diabetes mellitus patients.

Virtual Systems Pharmacology (ViSP) software for simulation from mechanistic systems-level models

PubMed Central

Ermakov, Sergey; Forster, Peter; Pagidala, Jyotsna; Miladinov, Marko; Wang, Albert; Baillie, Rebecca; Bartlett, Derek; Reed, Mike; Leil, Tarek A.

2014-01-01

Multiple software programs are available for designing and running large scale system-level pharmacology models used in the drug development process. Depending on the problem, scientists may be forced to use several modeling tools that could increase model development time, IT costs and so on. Therefore, it is desirable to have a single platform that allows setting up and running large-scale simulations for the models that have been developed with different modeling tools. We developed a workflow and a software platform in which a model file is compiled into a self-contained executable that is no longer dependent on the software that was used to create the model. At the same time the full model specifics is preserved by presenting all model parameters as input parameters for the executable. This platform was implemented as a model agnostic, therapeutic area agnostic and web-based application with a database back-end that can be used to configure, manage and execute large-scale simulations for multiple models by multiple users. The user interface is designed to be easily configurable to reflect the specifics of the model and the user's particular needs and the back-end database has been implemented to store and manage all aspects of the systems, such as Models, Virtual Patients, User Interface Settings, and Results. The platform can be adapted and deployed on an existing cluster or cloud computing environment. Its use was demonstrated with a metabolic disease systems pharmacology model that simulates the effects of two antidiabetic drugs, metformin and fasiglifam, in type 2 diabetes mellitus patients. PMID:25374542

ED(MF)n: Humidity-Convection Feedbacks in a Mass Flux Scheme Based on Resolved Size Densities

NASA Astrophysics Data System (ADS)

Neggers, R.

2014-12-01

Cumulus cloud populations remain at least partially unresolved in present-day numerical simulations of global weather and climate, and accordingly their impact on the larger-scale flow has to be represented through parameterization. Various methods have been developed over the years, ranging in complexity from the early bulk models relying on a single plume to more recent approaches that attempt to reconstruct the underlying probability density functions, such as statistical schemes and multiple plume approaches. Most of these "classic" methods capture key aspects of cumulus cloud populations, and have been successfully implemented in operational weather and climate models. However, the ever finer discretizations of operational circulation models, driven by advances in the computational efficiency of supercomputers, is creating new problems for existing sub-grid schemes. Ideally, a sub-grid scheme should automatically adapt its impact on the resolved scales to the dimension of the grid-box within which it is supposed to act. It can be argued that this is only possible when i) the scheme is aware of the range of scales of the processes it represents, and ii) it can distinguish between contributions as a function of size. How to conceptually represent this knowledge of scale in existing parameterization schemes remains an open question that is actively researched. This study considers a relatively new class of models for sub-grid transport in which ideas from the field of population dynamics are merged with the concept of multi plume modelling. More precisely, a multiple mass flux framework for moist convective transport is formulated in which the ensemble of plumes is created in "size-space". It is argued that thus resolving the underlying size-densities creates opportunities for introducing scale-awareness and scale-adaptivity in the scheme. The behavior of an implementation of this framework in the Eddy Diffusivity Mass Flux (EDMF) model, named ED(MF)n, is examined for a standard case of subtropical marine shallow cumulus. We ask if a system of multiple independently resolved plumes is able to automatically create the vertical profile of bulk (mass) flux at which the sub-grid scale transport balances the imposed larger-scale forcings in the cloud layer.

Adaptive method with intercessory feedback control for an intelligent agent

DOEpatents

Goldsmith, Steven Y.

2004-06-22

An adaptive architecture method with feedback control for an intelligent agent provides for adaptively integrating reflexive and deliberative responses to a stimulus according to a goal. An adaptive architecture method with feedback control for multiple intelligent agents provides for coordinating and adaptively integrating reflexive and deliberative responses to a stimulus according to a goal. Re-programming of the adaptive architecture is through a nexus which coordinates reflexive and deliberator components.

Tackling the workforce crisis in district nursing: can the Dutch Buurtzorg model offer a solution and a better patient experience? A mixed methods case study.

PubMed

Drennan, Vari M; Calestani, Melania; Ross, Fiona; Saunders, Mary; West, Peter

2018-06-06

Despite policy intentions for more healthcare out of hospital, district nursing services face multiple funding and staffing challenges, which compromise the care delivered and policy objectives. What is the impact of the adapted Buurtzorg model on feasibility, acceptability and effective outcomes in an English district nursing service? Mixed methods case study. Primary care. Neighbourhood nursing team (Buurtzorg model), patients and carers, general practitioners (GPs), other health professionals, managers and conventional district nurses. The adapted Buurtzorg model of community nursing demonstrated feasibility and acceptability to patients, carers, GPs and other health professionals. For many patients, it was preferable to previous experiences of district nursing in terms of continuity in care, improved support of multiple long-term conditions (encompassing physical, mental and social factors) and proactive care. For the neighbourhood nurses, the ability to make operational and clinical decisions at team level meant adopting practices that made the service more responsive, accessible and efficient and offered a more attractive working environment. Challenges were reported by nurses and managers in relation to the recognition and support of the concept of self-managing teams within a large bureaucratic healthcare organisation. While there were some reports of clinical effectiveness and efficiency, this was not possible to quantify, cost or compare with the standard district nursing service. The adapted Buurtzorg model of neighbourhood nursing holds potential for addressing issues of concern to patients, carers and staff in the community. The two interacting innovations, that is, a renewed focus on patient and carer-centred care and the self-managing team, were implemented in ways that patients, carers, other health professionals and nurses could identify difference for both the nursing care and also the nurses' working lives. It now requires longer term investigation to understand both the mechanism for change and also the sustainability. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Advanced Atmospheric Ensemble Modeling Techniques

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

Buckley, R.; Chiswell, S.; Kurzeja, R.

Ensemble modeling (EM), the creation of multiple atmospheric simulations for a given time period, has become an essential tool for characterizing uncertainties in model predictions. We explore two novel ensemble modeling techniques: (1) perturbation of model parameters (Adaptive Programming, AP), and (2) data assimilation (Ensemble Kalman Filter, EnKF). The current research is an extension to work from last year and examines transport on a small spatial scale (<100 km) in complex terrain, for more rigorous testing of the ensemble technique. Two different release cases were studied, a coastal release (SF6) and an inland release (Freon) which consisted of two releasemore » times. Observations of tracer concentration and meteorology are used to judge the ensemble results. In addition, adaptive grid techniques have been developed to reduce required computing resources for transport calculations. Using a 20- member ensemble, the standard approach generated downwind transport that was quantitatively good for both releases; however, the EnKF method produced additional improvement for the coastal release where the spatial and temporal differences due to interior valley heating lead to the inland movement of the plume. The AP technique showed improvements for both release cases, with more improvement shown in the inland release. This research demonstrated that transport accuracy can be improved when models are adapted to a particular location/time or when important local data is assimilated into the simulation and enhances SRNL’s capability in atmospheric transport modeling in support of its current customer base and local site missions, as well as our ability to attract new customers within the intelligence community.« less

Legal and Institutional Foundations of Adaptive Environmental ...

EPA Pesticide Factsheets

Legal and institutional structures fundamentally shape opportunities for adaptive governance of environmental resources at multiple ecological and societal scales. Properties of adaptive governance are widely studied. However, these studies have not resulted in consolidated frameworks for legal and institutional design, limiting our ability to promote adaptation and social-ecological resilience. We develop an overarching framework that describes the current and potential role of law in enabling adaptation. We apply this framework to different social-ecological settings, centers of activity, and scales, illustrating the multidimensional and polycentric nature of water governance. Adaptation typically emerges organically among multiple centers of agency and authority in society as a relatively self-organized or autonomous process marked by innovation, social learning, and political deliberation. This self-directed and emergent process is difficult to create in an exogenous, top-down fashion. However, traditional centers of authority may establish enabling conditions for adaptation using a suite of legal, economic, and democratic tools to legitimize and facilitate self-organization, coordination, and collaboration across scales. The principles outlined here provide preliminary legal and institutional foundations for adaptive environmental governance, which may inform institutional design and guide future scholarship. Adaptation typically emerges organically among m

Broadband low-frequency sound isolation by lightweight adaptive metamaterials

NASA Astrophysics Data System (ADS)

Liao, Yunhong; Chen, Yangyang; Huang, Guoliang; Zhou, Xiaoming

2018-03-01

Blocking broadband low-frequency airborne noises is highly desirable in lots of engineering applications, while it is extremely difficult to be realized with lightweight materials and/or structures. Recently, a new class of lightweight adaptive metamaterials with hybrid shunting circuits has been proposed, demonstrating super broadband structure-borne bandgaps. In this study, we aim at examining their potentials in broadband sound isolation by establishing an analytical model that rigorously combines the piezoelectric dynamic couplings between adaptive metamaterials and acoustics. Sound transmission loss of the adaptive metamaterial is investigated with respect to both the frequency and angular spectrum to demonstrate their sound-insulation effects. We find that efficient sound isolation can indeed be pursued in the broadband bi-spectrum for not only the case of the small resonator's periodicity where only one mode relevant to the mass-spring resonance exists, but also for the large-periodicity scenario, so that the total weight can be even lighter, in which the multiple plate-resonator coupling modes appear. In the latter case, the negative spring stiffness provided by the piezoelectric stack has been utilized to suppress the resonance-induced high acoustic transmission. Such kinds of adaptive metamaterials could open a new approach for broadband noise isolation with extremely lightweight structures.

A latent variable approach to study gene-environment interactions in the presence of multiple correlated exposures.

PubMed

Sánchez, Brisa N; Kang, Shan; Mukherjee, Bhramar

2012-06-01

Many existing cohort studies initially designed to investigate disease risk as a function of environmental exposures have collected genomic data in recent years with the objective of testing for gene-environment interaction (G × E) effects. In environmental epidemiology, interest in G × E arises primarily after a significant effect of the environmental exposure has been documented. Cohort studies often collect rich exposure data; as a result, assessing G × E effects in the presence of multiple exposure markers further increases the burden of multiple testing, an issue already present in both genetic and environment health studies. Latent variable (LV) models have been used in environmental epidemiology to reduce dimensionality of the exposure data, gain power by reducing multiplicity issues via condensing exposure data, and avoid collinearity problems due to presence of multiple correlated exposures. We extend the LV framework to characterize gene-environment interaction in presence of multiple correlated exposures and genotype categories. Further, similar to what has been done in case-control G × E studies, we use the assumption of gene-environment (G-E) independence to boost the power of tests for interaction. The consequences of making this assumption, or the issue of how to explicitly model G-E association has not been previously investigated in LV models. We postulate a hierarchy of assumptions about the LV model regarding the different forms of G-E dependence and show that making such assumptions may influence inferential results on the G, E, and G × E parameters. We implement a class of shrinkage estimators to data adaptively trade-off between the most restrictive to most flexible form of G-E dependence assumption and note that such class of compromise estimators can serve as a benchmark of model adequacy in LV models. We demonstrate the methods with an example from the Early Life Exposures in Mexico City to Neuro-Toxicants Study of lead exposure, iron metabolism genes, and birth weight. © 2011, The International Biometric Society.

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters.

PubMed

Wang, Dandan; Zong, Qun; Tian, Bailing; Shao, Shikai; Zhang, Xiuyun; Zhao, Xinyi

2018-02-01

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation.

PubMed

Peternel, Luka; Noda, Tomoyuki; Petrič, Tadej; Ude, Aleš; Morimoto, Jun; Babič, Jan

2016-01-01

In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.

Adaptive Optimal Control Using Frequency Selective Information of the System Uncertainty With Application to Unmanned Aircraft.

PubMed

Maity, Arnab; Hocht, Leonhard; Heise, Christian; Holzapfel, Florian

2018-01-01

A new efficient adaptive optimal control approach is presented in this paper based on the indirect model reference adaptive control (MRAC) architecture for improvement of adaptation and tracking performance of the uncertain system. The system accounts here for both matched and unmatched unknown uncertainties that can act as plant as well as input effectiveness failures or damages. For adaptation of the unknown parameters of these uncertainties, the frequency selective learning approach is used. Its idea is to compute a filtered expression of the system uncertainty using multiple filters based on online instantaneous information, which is used for augmentation of the update law. It is capable of adjusting a sudden change in system dynamics without depending on high adaptation gains and can satisfy exponential parameter error convergence under certain conditions in the presence of structured matched and unmatched uncertainties as well. Additionally, the controller of the MRAC system is designed using a new optimal control method. This method is a new linear quadratic regulator-based optimal control formulation for both output regulation and command tracking problems. It provides a closed-form control solution. The proposed overall approach is applied in a control of lateral dynamics of an unmanned aircraft problem to show its effectiveness.

Cultural Adaptation of a Neurobiologically Informed Intervention in Local and International Contexts.

PubMed

Pakulak, Eric; Hampton Wray, Amanda; Longoria, Zayra; Garcia Isaza, Alejandra; Stevens, Courtney; Bell, Theodore; Burlingame, Sarah; Klein, Scott; Berlinski, Samuel; Attanasio, Orazio; Neville, Helen

2017-12-01

The relationship between early adversity and numerous negative outcomes across the lifespan is evident in a wide range of societies and cultures (e.g., Pakulak, Stevens, & Neville, 2018). Among the most affected neural systems are those supporting attention, self-regulation, and stress regulation. As such, these systems represent targets for neurobiologically informed interventions addressing early adversity. In prior work with monolingual native English-speaking families, we showed that a two-generation intervention targeting these systems in families improves outcomes across multiple domains including child brain function for selective attention (for detail, see Neville et al., 2013). Here, we discuss the translation and cultural adaptation (CA) of this intervention in local and international contexts, which required systematic consideration of cultural differences that could affect program acceptability. First, we conducted a translation and CA of our program to serve Latino families in the United States using the Cultural Adaptation Process (CAP), a model that works closely with stakeholders in a systematic, iterative process. Second, to implement the adapted program in Medellín, Colombia, we conducted a subsequent adaptation for Colombian culture using the same CAP. Our experience underscores the importance of consideration of cultural differences and a systematic approach to adaptation before assessing the efficacy of neurobiologically informed interventions in different cultural contexts. © 2017 Wiley Periodicals, Inc.

Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation

PubMed Central

Peternel, Luka; Noda, Tomoyuki; Petrič, Tadej; Ude, Aleš; Morimoto, Jun; Babič, Jan

2016-01-01

In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion. PMID:26881743