Statistical Analysis of Notational AFL Data Using Continuous Time Markov Chains.

PubMed

Meyer, Denny; Forbes, Don; Clarke, Stephen R

2006-01-01

Animal biologists commonly use continuous time Markov chain models to describe patterns of animal behaviour. In this paper we consider the use of these models for describing AFL football. In particular we test the assumptions for continuous time Markov chain models (CTMCs), with time, distance and speed values associated with each transition. Using a simple event categorisation it is found that a semi-Markov chain model is appropriate for this data. This validates the use of Markov Chains for future studies in which the outcomes of AFL matches are simulated. Key PointsA comparison of four AFL matches suggests similarity in terms of transition probabilities for events and the mean times, distances and speeds associated with each transition.The Markov assumption appears to be valid.However, the speed, time and distance distributions associated with each transition are not exponential suggesting that semi-Markov model can be used to model and simulate play.Team identified events and directions associated with transitions are required to develop the model into a tool for the prediction of match outcomes.

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

PubMed Central

Liu, Yu-Ying; Li, Shuang; Li, Fuxin; Song, Le; Rehg, James M.

2016-01-01

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer’s disease dataset. PMID:27019571

Financial Data Analysis by means of Coupled Continuous-Time Random Walk in Rachev-Rűschendorf Model

NASA Astrophysics Data System (ADS)

Jurlewicz, A.; Wyłomańska, A.; Żebrowski, P.

2008-09-01

We adapt the continuous-time random walk formalism to describe asset price evolution. We expand the idea proposed by Rachev and Rűschendorf who analyzed the binomial pricing model in the discrete time with randomization of the number of price changes. As a result, in the framework of the proposed model we obtain a mixture of the Gaussian and a generalized arcsine laws as the limiting distribution of log-returns. Moreover, we derive an European-call-option price that is an extension of the Black-Scholes formula. We apply the obtained theoretical results to model actual financial data and try to show that the continuous-time random walk offers alternative tools to deal with several complex issues of financial markets.

CTPPL: A Continuous Time Probabilistic Programming Language

DTIC Science & Technology

2009-07-01

recent years there has been a flurry of interest in continuous time models, mostly focused on continuous time Bayesian networks ( CTBNs ) [Nodelman, 2007... CTBNs are built on homogenous Markov processes. A homogenous Markov pro- cess is a finite state, continuous time process, consisting of an initial...q1 : xn()] ... Some state transitions can produce emissions. In a CTBN , each variable has a conditional inten- sity matrix Qu for every combination of

A latent class multiple constraint multiple discrete-continuous extreme value model of time use and goods consumption.

DOT National Transportation Integrated Search

2016-06-01

This paper develops a microeconomic theory-based multiple discrete continuous choice model that considers: (a) that both goods consumption and time allocations (to work and non-work activities) enter separately as decision variables in the utility fu...

A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders

USGS Publications Warehouse

Dorazio, Robert; Karanth, K. Ullas

2017-01-01

MotivationSeveral spatial capture-recapture (SCR) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data.Model and data analysisWe developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data.BenefitsOur approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. We illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. Our continuous-time SCR model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. We plan to extend this model to estimate the population dynamics of animals detected during multiple years of SCR surveys.

Modeling of water treatment plant using timed continuous Petri nets

NASA Astrophysics Data System (ADS)

Nurul Fuady Adhalia, H.; Subiono, Adzkiya, Dieky

2017-08-01

Petri nets represent graphically certain conditions and rules. In this paper, we construct a model of the Water Treatment Plant (WTP) using timed continuous Petri nets. Specifically, we consider that (1) the water pump always active and (2) the water source is always available. After obtaining the model, the flow through the transitions and token conservation laws are calculated.

HIGH TIME-RESOLVED COMPARISONS FOR IN-DEPTH PROBING OF CMAQ FINE-PARTICLES AND GAS PREDICTIONS

EPA Science Inventory

Model evaluation is important to develop confidence in models and develop an understanding of their predictions. Most comparisons in the U.S. involve time-integrated measurements of 24-hours or longer. Comparisons against continuous or semi-continuous particle and gaseous measur...

Variable selection in discrete survival models including heterogeneity.

PubMed

Groll, Andreas; Tutz, Gerhard

2017-04-01

Several variable selection procedures are available for continuous time-to-event data. However, if time is measured in a discrete way and therefore many ties occur models for continuous time are inadequate. We propose penalized likelihood methods that perform efficient variable selection in discrete survival modeling with explicit modeling of the heterogeneity in the population. The method is based on a combination of ridge and lasso type penalties that are tailored to the case of discrete survival. The performance is studied in simulation studies and an application to the birth of the first child.

Models for Serially Correlated Over or Underdispersed Unequally Spaced Longitudinal Count Data with Applications to Asthma Inhaler Use

DTIC Science & Technology

2007-08-01

the gamma prior and Poisson counts are conditioned on an unobserved AR( 1 ) process that accounts for the time since the last observation . This model did...to the observation equation. For unequally spaced observations the AR( 1 ) errors are replaced by a continuous time AR( 1 ) process , and the distance...unequal spaced observations are handled in the XJG model by assuming an underlying continuous time AR( 1 ) (CAR(l)) process . It is implemented by

Modelling breast cancer tumour growth for a stable disease population.

PubMed

Isheden, Gabriel; Humphreys, Keith

2017-01-01

Statistical models of breast cancer tumour progression have been used to further our knowledge of the natural history of breast cancer, to evaluate mammography screening in terms of mortality, to estimate overdiagnosis, and to estimate the impact of lead-time bias when comparing survival times between screen detected cancers and cancers found outside of screening programs. Multi-state Markov models have been widely used, but several research groups have proposed other modelling frameworks based on specifying an underlying biological continuous tumour growth process. These continuous models offer some advantages over multi-state models and have been used, for example, to quantify screening sensitivity in terms of mammographic density, and to quantify the effect of body size covariates on tumour growth and time to symptomatic detection. As of yet, however, the continuous tumour growth models are not sufficiently developed and require extensive computing to obtain parameter estimates. In this article, we provide a detailed description of the underlying assumptions of the continuous tumour growth model, derive new theoretical results for the model, and show how these results may help the development of this modelling framework. In illustrating the approach, we develop a model for mammography screening sensitivity, using a sample of 1901 post-menopausal women diagnosed with invasive breast cancer.

Nonergodic property of the space-time coupled CTRW: Dependence on the long-tailed property and correlation

NASA Astrophysics Data System (ADS)

Liu, Jian; Li, Baohe; Chen, Xiaosong

2018-02-01

The space-time coupled continuous time random walk model is a stochastic framework of anomalous diffusion with many applications in physics, geology and biology. In this manuscript the time averaged mean squared displacement and nonergodic property of a space-time coupled continuous time random walk model is studied, which is a prototype of the coupled continuous time random walk presented and researched intensively with various methods. The results in the present manuscript show that the time averaged mean squared displacements increase linearly with lag time which means ergodicity breaking occurs, besides, we find that the diffusion coefficient is intrinsically random which shows both aging and enhancement, the analysis indicates that the either aging or enhancement phenomena are determined by the competition between the correlation exponent γ and the waiting time's long-tailed index α.

Fault-tolerant continuous flow systems modelling

NASA Astrophysics Data System (ADS)

Tolbi, B.; Tebbikh, H.; Alla, H.

2017-01-01

This paper presents a structural modelling of faults with hybrid Petri nets (HPNs) for the analysis of a particular class of hybrid dynamic systems, continuous flow systems. HPNs are first used for the behavioural description of continuous flow systems without faults. Then, faults' modelling is considered using a structural method without having to rebuild the model to new. A translation method is given in hierarchical way, it gives a hybrid automata (HA) from an elementary HPN. This translation preserves the behavioural semantics (timed bisimilarity), and reflects the temporal behaviour by giving semantics for each model in terms of timed transition systems. Thus, advantages of the power modelling of HPNs and the analysis ability of HA are taken. A simple example is used to illustrate the ideas.

Atomic clocks and the continuous-time random-walk

NASA Astrophysics Data System (ADS)

Formichella, Valerio; Camparo, James; Tavella, Patrizia

2017-11-01

Atomic clocks play a fundamental role in many fields, most notably they generate Universal Coordinated Time and are at the heart of all global navigation satellite systems. Notwithstanding their excellent timekeeping performance, their output frequency does vary: it can display deterministic frequency drift; diverse continuous noise processes result in nonstationary clock noise (e.g., random-walk frequency noise, modelled as a Wiener process), and the clock frequency may display sudden changes (i.e., "jumps"). Typically, the clock's frequency instability is evaluated by the Allan or Hadamard variances, whose functional forms can identify the different operative noise processes. Here, we show that the Allan and Hadamard variances of a particular continuous-time random-walk, the compound Poisson process, have the same functional form as for a Wiener process with drift. The compound Poisson process, introduced as a model for observed frequency jumps, is an alternative to the Wiener process for modelling random walk frequency noise. This alternate model fits well the behavior of the rubidium clocks flying on GPS Block-IIR satellites. Further, starting from jump statistics, the model can be improved by considering a more general form of continuous-time random-walk, and this could bring new insights into the physics of atomic clocks.

Model documentation for relations between continuous real-time and discrete water-quality constituents in the North Fork Ninnescah River upstream from Cheney Reservoir, south-central Kansas, 1999--2009

USGS Publications Warehouse

Stone, Mandy L.; Graham, Jennifer L.; Gatotho, Jackline W.

2013-01-01

Cheney Reservoir in south-central Kansas is one of the primary sources of water for the city of Wichita. The North Fork Ninnescah River is the largest contributing tributary to Cheney Reservoir. The U.S. Geological Survey has operated a continuous real-time water-quality monitoring station since 1998 on the North Fork Ninnescah River. Continuously measured water-quality physical properties include streamflow, specific conductance, pH, water temperature, dissolved oxygen, and turbidity. Discrete water-quality samples were collected during 1999 through 2009 and analyzed for sediment, nutrients, bacteria, and other water-quality constituents. Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physical properties to estimate concentrations of those constituents of interest that are not easily measured in real time because of limitations in sensor technology and fiscal constraints. Regression models were published in 2006 that were based on a different dataset collected during 1997 through 2003. This report updates those models using discrete and continuous data collected during January 1999 through December 2009. Models also were developed for five new constituents, including additional nutrient species and indicator bacteria. The water-quality information in this report is important to the city of Wichita because it allows the concentrations of many potential pollutants of interest, including nutrients and sediment, to be estimated in real time and characterized over conditions and time scales that would not be possible otherwise.

Continuous-time discrete-space models for animal movement

USGS Publications Warehouse

Hanks, Ephraim M.; Hooten, Mevin B.; Alldredge, Mat W.

2015-01-01

The processes influencing animal movement and resource selection are complex and varied. Past efforts to model behavioral changes over time used Bayesian statistical models with variable parameter space, such as reversible-jump Markov chain Monte Carlo approaches, which are computationally demanding and inaccessible to many practitioners. We present a continuous-time discrete-space (CTDS) model of animal movement that can be fit using standard generalized linear modeling (GLM) methods. This CTDS approach allows for the joint modeling of location-based as well as directional drivers of movement. Changing behavior over time is modeled using a varying-coefficient framework which maintains the computational simplicity of a GLM approach, and variable selection is accomplished using a group lasso penalty. We apply our approach to a study of two mountain lions (Puma concolor) in Colorado, USA.

An Indirect System Identification Technique for Stable Estimation of Continuous-Time Parameters of the Vestibulo-Ocular Reflex (VOR)

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.; Wallin, Ragnar; Boyle, Richard D.

2013-01-01

The vestibulo-ocular reflex (VOR) is a well-known dual mode bifurcating system that consists of slow and fast modes associated with nystagmus and saccade, respectively. Estimation of continuous-time parameters of nystagmus and saccade models are known to be sensitive to estimation methodology, noise and sampling rate. The stable and accurate estimation of these parameters are critical for accurate disease modelling, clinical diagnosis, robotic control strategies, mission planning for space exploration and pilot safety, etc. This paper presents a novel indirect system identification method for the estimation of continuous-time parameters of VOR employing standardised least-squares with dual sampling rates in a sparse structure. This approach permits the stable and simultaneous estimation of both nystagmus and saccade data. The efficacy of this approach is demonstrated via simulation of a continuous-time model of VOR with typical parameters found in clinical studies and in the presence of output additive noise.

Multiplicative Forests for Continuous-Time Processes

PubMed Central

Weiss, Jeremy C.; Natarajan, Sriraam; Page, David

2013-01-01

Learning temporal dependencies between variables over continuous time is an important and challenging task. Continuous-time Bayesian networks effectively model such processes but are limited by the number of conditional intensity matrices, which grows exponentially in the number of parents per variable. We develop a partition-based representation using regression trees and forests whose parameter spaces grow linearly in the number of node splits. Using a multiplicative assumption we show how to update the forest likelihood in closed form, producing efficient model updates. Our results show multiplicative forests can be learned from few temporal trajectories with large gains in performance and scalability. PMID:25284967

Multiplicative Forests for Continuous-Time Processes.

PubMed

Weiss, Jeremy C; Natarajan, Sriraam; Page, David

2012-01-01

Learning temporal dependencies between variables over continuous time is an important and challenging task. Continuous-time Bayesian networks effectively model such processes but are limited by the number of conditional intensity matrices, which grows exponentially in the number of parents per variable. We develop a partition-based representation using regression trees and forests whose parameter spaces grow linearly in the number of node splits. Using a multiplicative assumption we show how to update the forest likelihood in closed form, producing efficient model updates. Our results show multiplicative forests can be learned from few temporal trajectories with large gains in performance and scalability.

State-space prediction model for chaotic time series

NASA Astrophysics Data System (ADS)

Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

1998-08-01

A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

Unraveling the sub-processes of selective attention: insights from dynamic modeling and continuous behavior.

PubMed

Frisch, Simon; Dshemuchadse, Maja; Görner, Max; Goschke, Thomas; Scherbaum, Stefan

2015-11-01

Selective attention biases information processing toward stimuli that are relevant for achieving our goals. However, the nature of this bias is under debate: Does it solely rely on the amplification of goal-relevant information or is there a need for additional inhibitory processes that selectively suppress currently distracting information? Here, we explored the processes underlying selective attention with a dynamic, modeling-based approach that focuses on the continuous evolution of behavior over time. We present two dynamic neural field models incorporating the diverging theoretical assumptions. Simulations with both models showed that they make similar predictions with regard to response times but differ markedly with regard to their continuous behavior. Human data observed via mouse tracking as a continuous measure of performance revealed evidence for the model solely based on amplification but no indication of persisting selective distracter inhibition.

Flood mapping in ungauged basins using fully continuous hydrologic-hydraulic modeling

NASA Astrophysics Data System (ADS)

Grimaldi, Salvatore; Petroselli, Andrea; Arcangeletti, Ettore; Nardi, Fernando

2013-04-01

SummaryIn this work, a fully-continuous hydrologic-hydraulic modeling framework for flood mapping is introduced and tested. It is characterized by a simulation of a long rainfall time series at sub-daily resolution that feeds a continuous rainfall-runoff model producing a discharge time series that is directly given as an input to a bi-dimensional hydraulic model. The main advantage of the proposed approach is to avoid the use of the design hyetograph and the design hydrograph that constitute the main source of subjective analysis and uncertainty for standard methods. The proposed procedure is optimized for small and ungauged watersheds where empirical models are commonly applied. Results of a simple real case study confirm that this experimental fully-continuous framework may pave the way for the implementation of a less subjective and potentially automated procedure for flood hazard mapping.

Application of stochastic automata networks for creation of continuous time Markov chain models of voltage gating of gap junction channels.

PubMed

Snipas, Mindaugas; Pranevicius, Henrikas; Pranevicius, Mindaugas; Pranevicius, Osvaldas; Paulauskas, Nerijus; Bukauskas, Feliksas F

2015-01-01

The primary goal of this work was to study advantages of numerical methods used for the creation of continuous time Markov chain models (CTMC) of voltage gating of gap junction (GJ) channels composed of connexin protein. This task was accomplished by describing gating of GJs using the formalism of the stochastic automata networks (SANs), which allowed for very efficient building and storing of infinitesimal generator of the CTMC that allowed to produce matrices of the models containing a distinct block structure. All of that allowed us to develop efficient numerical methods for a steady-state solution of CTMC models. This allowed us to accelerate CPU time, which is necessary to solve CTMC models, ~20 times.

The disruption management model.

PubMed

McAlister, James

2011-10-01

Within all organisations, business continuity disruptions present a set of dilemmas that managers may not have dealt with before in their normal daily duties. The disruption management model provides a simple but effective management tool to enable crisis management teams to stay focused on recovery in the midst of a business continuity incident. The model has four chronological primary headlines, which steer the team through a quick-time crisis decision-making process. The procedure facilitates timely, systematic, rationalised and justified decisions, which can withstand post-event scrutiny. The disruption management model has been thoroughly tested within an emergency services environment and is proven to significantly support clear and concise decision making in a business continuity context.

Continuous time random walk model with asymptotical probability density of waiting times via inverse Mittag-Leffler function

NASA Astrophysics Data System (ADS)

Liang, Yingjie; Chen, Wen

2018-04-01

The mean squared displacement (MSD) of the traditional ultraslow diffusion is a logarithmic function of time. Recently, the continuous time random walk model is employed to characterize this ultraslow diffusion dynamics by connecting the heavy-tailed logarithmic function and its variation as the asymptotical waiting time density. In this study we investigate the limiting waiting time density of a general ultraslow diffusion model via the inverse Mittag-Leffler function, whose special case includes the traditional logarithmic ultraslow diffusion model. The MSD of the general ultraslow diffusion model is analytically derived as an inverse Mittag-Leffler function, and is observed to increase even more slowly than that of the logarithmic function model. The occurrence of very long waiting time in the case of the inverse Mittag-Leffler function has the largest probability compared with the power law model and the logarithmic function model. The Monte Carlo simulations of one dimensional sample path of a single particle are also performed. The results show that the inverse Mittag-Leffler waiting time density is effective in depicting the general ultraslow random motion.

Stability of continuous-time quantum filters with measurement imperfections

NASA Astrophysics Data System (ADS)

Amini, H.; Pellegrini, C.; Rouchon, P.

2014-07-01

The fidelity between the state of a continuously observed quantum system and the state of its associated quantum filter, is shown to be always a submartingale. The observed system is assumed to be governed by a continuous-time Stochastic Master Equation (SME), driven simultaneously by Wiener and Poisson processes and that takes into account incompleteness and errors in measurements. This stability result is the continuous-time counterpart of a similar stability result already established for discrete-time quantum systems and where the measurement imperfections are modelled by a left stochastic matrix.

FracFit: A Robust Parameter Estimation Tool for Anomalous Transport Problems

NASA Astrophysics Data System (ADS)

Kelly, J. F.; Bolster, D.; Meerschaert, M. M.; Drummond, J. D.; Packman, A. I.

2016-12-01

Anomalous transport cannot be adequately described with classical Fickian advection-dispersion equations (ADE). Rather, fractional calculus models may be used, which capture non-Fickian behavior (e.g. skewness and power-law tails). FracFit is a robust parameter estimation tool based on space- and time-fractional models used to model anomalous transport. Currently, four fractional models are supported: 1) space fractional advection-dispersion equation (sFADE), 2) time-fractional dispersion equation with drift (TFDE), 3) fractional mobile-immobile equation (FMIE), and 4) tempered fractional mobile-immobile equation (TFMIE); additional models may be added in the future. Model solutions using pulse initial conditions and continuous injections are evaluated using stable distribution PDFs and CDFs or subordination integrals. Parameter estimates are extracted from measured breakthrough curves (BTCs) using a weighted nonlinear least squares (WNLS) algorithm. Optimal weights for BTCs for pulse initial conditions and continuous injections are presented, facilitating the estimation of power-law tails. Two sample applications are analyzed: 1) continuous injection laboratory experiments using natural organic matter and 2) pulse injection BTCs in the Selke river. Model parameters are compared across models and goodness-of-fit metrics are presented, assisting model evaluation. The sFADE and time-fractional models are compared using space-time duality (Baeumer et. al., 2009), which links the two paradigms.

Global exponential periodicity and stability of discrete-time complex-valued recurrent neural networks with time-delays.

PubMed

Hu, Jin; Wang, Jun

2015-06-01

In recent years, complex-valued recurrent neural networks have been developed and analysed in-depth in view of that they have good modelling performance for some applications involving complex-valued elements. In implementing continuous-time dynamical systems for simulation or computational purposes, it is quite necessary to utilize a discrete-time model which is an analogue of the continuous-time system. In this paper, we analyse a discrete-time complex-valued recurrent neural network model and obtain the sufficient conditions on its global exponential periodicity and exponential stability. Simulation results of several numerical examples are delineated to illustrate the theoretical results and an application on associative memory is also given. Copyright © 2015 Elsevier Ltd. All rights reserved.

Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables.

PubMed

Heck, Daniel W; Erdfelder, Edgar; Kieslich, Pascal J

2018-05-24

Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.

Application of Stochastic Automata Networks for Creation of Continuous Time Markov Chain Models of Voltage Gating of Gap Junction Channels

PubMed Central

Pranevicius, Henrikas; Pranevicius, Mindaugas; Pranevicius, Osvaldas; Bukauskas, Feliksas F.

2015-01-01

The primary goal of this work was to study advantages of numerical methods used for the creation of continuous time Markov chain models (CTMC) of voltage gating of gap junction (GJ) channels composed of connexin protein. This task was accomplished by describing gating of GJs using the formalism of the stochastic automata networks (SANs), which allowed for very efficient building and storing of infinitesimal generator of the CTMC that allowed to produce matrices of the models containing a distinct block structure. All of that allowed us to develop efficient numerical methods for a steady-state solution of CTMC models. This allowed us to accelerate CPU time, which is necessary to solve CTMC models, ∼20 times. PMID:25705700

Glucose Prediction Algorithms from Continuous Monitoring Data: Assessment of Accuracy via Continuous Glucose Error-Grid Analysis.

PubMed

Zanderigo, Francesca; Sparacino, Giovanni; Kovatchev, Boris; Cobelli, Claudio

2007-09-01

The aim of this article was to use continuous glucose error-grid analysis (CG-EGA) to assess the accuracy of two time-series modeling methodologies recently developed to predict glucose levels ahead of time using continuous glucose monitoring (CGM) data. We considered subcutaneous time series of glucose concentration monitored every 3 minutes for 48 hours by the minimally invasive CGM sensor Glucoday® (Menarini Diagnostics, Florence, Italy) in 28 type 1 diabetic volunteers. Two prediction algorithms, based on first-order polynomial and autoregressive (AR) models, respectively, were considered with prediction horizons of 30 and 45 minutes and forgetting factors (ff) of 0.2, 0.5, and 0.8. CG-EGA was used on the predicted profiles to assess their point and dynamic accuracies using original CGM profiles as reference. Continuous glucose error-grid analysis showed that the accuracy of both prediction algorithms is overall very good and that their performance is similar from a clinical point of view. However, the AR model seems preferable for hypoglycemia prevention. CG-EGA also suggests that, irrespective of the time-series model, the use of ff = 0.8 yields the highest accurate readings in all glucose ranges. For the first time, CG-EGA is proposed as a tool to assess clinically relevant performance of a prediction method separately at hypoglycemia, euglycemia, and hyperglycemia. In particular, we have shown that CG-EGA can be helpful in comparing different prediction algorithms, as well as in optimizing their parameters.

40 CFR 60.2570 - What are the principal components of the model rule?

Code of Federal Regulations, 2012 CFR

2012-07-01

... the model rule? 60.2570 Section 60.2570 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Use of Model Rule...

40 CFR 60.2570 - What are the principal components of the model rule?

Code of Federal Regulations, 2013 CFR

2013-07-01

... the model rule? 60.2570 Section 60.2570 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Use of Model Rule...

40 CFR 60.2570 - What are the principal components of the model rule?

Code of Federal Regulations, 2014 CFR

2014-07-01

... the model rule? 60.2570 Section 60.2570 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Use of Model Rule...

Retrospective estimation of breeding phenology of American Goldfinch (Carduelis tristis) using pattern oriented modeling

EPA Science Inventory

Avian seasonal productivity is often modeled as a time-limited stochastic process. Many mathematical formulations have been proposed, including individual based models, continuous-time differential equations, and discrete Markov models. All such models typically include paramete...

Developmental trajectories of marital happiness in continuously married individuals: a group-based modeling approach.

PubMed

Anderson, Jared R; Van Ryzin, Mark J; Doherty, William J

2010-10-01

Most contemporary studies of change in marital quality over time have used growth curve modeling to describe continuously declining mean curves. However, there is some evidence that different trajectories of marital quality exist for different subpopulations. Group-based trajectory modeling provides the opportunity to conduct an empirical investigation of the variance in marital quality trajectories. We applied this method to analyze data from continuously married individuals from the Marital Instability over the Life Course Study (N = 706). Instead of a single continuously declining trajectory of marital happiness, we found 5 distinct trajectories. Nearly two thirds of participants reported high and stable levels of happiness over time, and the other one third showed either a pattern of continuous low happiness, low happiness that subsequently declined, or a curvilinear pattern of high happiness, decline, and recovery. Marital problems, time spent in shared activities, and (to a lesser degree) economic hardship were able to distinguish trajectory group membership. Our results suggest that marital happiness may have multiple distinct trajectories across reasonably diverse populations. Implications for theory, research, and practice are discussed.

On the continuous differentiability of inter-spike intervals of synaptically connected cortical spiking neurons in a neuronal network.

PubMed

Kumar, Gautam; Kothare, Mayuresh V

2013-12-01

We derive conditions for continuous differentiability of inter-spike intervals (ISIs) of spiking neurons with respect to parameters (decision variables) of an external stimulating input current that drives a recurrent network of synaptically connected neurons. The dynamical behavior of individual neurons is represented by a class of discontinuous single-neuron models. We report here that ISIs of neurons in the network are continuously differentiable with respect to decision variables if (1) a continuously differentiable trajectory of the membrane potential exists between consecutive action potentials with respect to time and decision variables and (2) the partial derivative of the membrane potential of spiking neurons with respect to time is not equal to the partial derivative of their firing threshold with respect to time at the time of action potentials. Our theoretical results are supported by showing fulfillment of these conditions for a class of known bidimensional spiking neuron models.

Detectability of Granger causality for subsampled continuous-time neurophysiological processes.

PubMed

Barnett, Lionel; Seth, Anil K

2017-01-01

Granger causality is well established within the neurosciences for inference of directed functional connectivity from neurophysiological data. These data usually consist of time series which subsample a continuous-time biophysiological process. While it is well known that subsampling can lead to imputation of spurious causal connections where none exist, less is known about the effects of subsampling on the ability to reliably detect causal connections which do exist. We present a theoretical analysis of the effects of subsampling on Granger-causal inference. Neurophysiological processes typically feature signal propagation delays on multiple time scales; accordingly, we base our analysis on a distributed-lag, continuous-time stochastic model, and consider Granger causality in continuous time at finite prediction horizons. Via exact analytical solutions, we identify relationships among sampling frequency, underlying causal time scales and detectability of causalities. We reveal complex interactions between the time scale(s) of neural signal propagation and sampling frequency. We demonstrate that detectability decays exponentially as the sample time interval increases beyond causal delay times, identify detectability "black spots" and "sweet spots", and show that downsampling may potentially improve detectability. We also demonstrate that the invariance of Granger causality under causal, invertible filtering fails at finite prediction horizons, with particular implications for inference of Granger causality from fMRI data. Our analysis emphasises that sampling rates for causal analysis of neurophysiological time series should be informed by domain-specific time scales, and that state-space modelling should be preferred to purely autoregressive modelling. On the basis of a very general model that captures the structure of neurophysiological processes, we are able to help identify confounds, and offer practical insights, for successful detection of causal connectivity from neurophysiological recordings. Copyright © 2016 Elsevier B.V. All rights reserved.

A novel condition for stable nonlinear sampled-data models using higher-order discretized approximations with zero dynamics.

PubMed

Zeng, Cheng; Liang, Shan; Xiang, Shuwen

2017-05-01

Continuous-time systems are usually modelled by the form of ordinary differential equations arising from physical laws. However, the use of these models in practice and utilizing, analyzing or transmitting these data from such systems must first invariably be discretized. More importantly, for digital control of a continuous-time nonlinear system, a good sampled-data model is required. This paper investigates the new consistency condition which is weaker than the previous similar results presented. Moreover, given the stability of the high-order approximate model with stable zero dynamics, the novel condition presented stabilizes the exact sampled-data model of the nonlinear system for sufficiently small sampling periods. An insightful interpretation of the obtained results can be made in terms of the stable sampling zero dynamics, and the new consistency condition is surprisingly associated with the relative degree of the nonlinear continuous-time system. Our controller design, based on the higher-order approximate discretized model, extends the existing methods which mainly deal with the Euler approximation. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

40 CFR 60.5080 - What are the principal components of the model rule?

Code of Federal Regulations, 2011 CFR

2011-07-01

... the model rule? 60.5080 Section 60.5080 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge Incineration Units Use of Model Rule § 60.5080 What...

MIMICKING COUNTERFACTUAL OUTCOMES TO ESTIMATE CAUSAL EFFECTS.

PubMed

Lok, Judith J

2017-04-01

In observational studies, treatment may be adapted to covariates at several times without a fixed protocol, in continuous time. Treatment influences covariates, which influence treatment, which influences covariates, and so on. Then even time-dependent Cox-models cannot be used to estimate the net treatment effect. Structural nested models have been applied in this setting. Structural nested models are based on counterfactuals: the outcome a person would have had had treatment been withheld after a certain time. Previous work on continuous-time structural nested models assumes that counterfactuals depend deterministically on observed data, while conjecturing that this assumption can be relaxed. This article proves that one can mimic counterfactuals by constructing random variables, solutions to a differential equation, that have the same distribution as the counterfactuals, even given past observed data. These "mimicking" variables can be used to estimate the parameters of structural nested models without assuming the treatment effect to be deterministic.

The stochastic system approach for estimating dynamic treatments effect.

PubMed

Commenges, Daniel; Gégout-Petit, Anne

2015-10-01

The problem of assessing the effect of a treatment on a marker in observational studies raises the difficulty that attribution of the treatment may depend on the observed marker values. As an example, we focus on the analysis of the effect of a HAART on CD4 counts, where attribution of the treatment may depend on the observed marker values. This problem has been treated using marginal structural models relying on the counterfactual/potential response formalism. Another approach to causality is based on dynamical models, and causal influence has been formalized in the framework of the Doob-Meyer decomposition of stochastic processes. Causal inference however needs assumptions that we detail in this paper and we call this approach to causality the "stochastic system" approach. First we treat this problem in discrete time, then in continuous time. This approach allows incorporating biological knowledge naturally. When working in continuous time, the mechanistic approach involves distinguishing the model for the system and the model for the observations. Indeed, biological systems live in continuous time, and mechanisms can be expressed in the form of a system of differential equations, while observations are taken at discrete times. Inference in mechanistic models is challenging, particularly from a numerical point of view, but these models can yield much richer and reliable results.

Trial-dependent psychometric functions accounting for perceptual learning in 2-AFC discrimination tasks.

PubMed

Kattner, Florian; Cochrane, Aaron; Green, C Shawn

2017-09-01

The majority of theoretical models of learning consider learning to be a continuous function of experience. However, most perceptual learning studies use thresholds estimated by fitting psychometric functions to independent blocks, sometimes then fitting a parametric function to these block-wise estimated thresholds. Critically, such approaches tend to violate the basic principle that learning is continuous through time (e.g., by aggregating trials into large "blocks" for analysis that each assume stationarity, then fitting learning functions to these aggregated blocks). To address this discrepancy between base theory and analysis practice, here we instead propose fitting a parametric function to thresholds from each individual trial. In particular, we implemented a dynamic psychometric function whose parameters were allowed to change continuously with each trial, thus parameterizing nonstationarity. We fit the resulting continuous time parametric model to data from two different perceptual learning tasks. In nearly every case, the quality of the fits derived from the continuous time parametric model outperformed the fits derived from a nonparametric approach wherein separate psychometric functions were fit to blocks of trials. Because such a continuous trial-dependent model of perceptual learning also offers a number of additional advantages (e.g., the ability to extrapolate beyond the observed data; the ability to estimate performance on individual critical trials), we suggest that this technique would be a useful addition to each psychophysicist's analysis toolkit.

Increased dead space in face mask continuous positive airway pressure in neonates.

PubMed

Hishikawa, Kenji; Fujinaga, Hideshi; Ito, Yushi

2017-01-01

Continuous positive airway pressure (CPAP) by face mask is commonly performed in newborn resuscitation. We evaluated the effect of face mask CPAP on system dead space. Face mask CPAP increases dead space. A CPAP model study. We estimated the volume of the inner space of the mask. We devised a face mask CPAP model, in which the outlet of the mask was covered with plastic; and three modified face mask CPAP models, in which holes were drilled near to the cushion of the covered face mask to alter the air exit. We passed a continuous flow of 21% oxygen through each model and we controlled the inner pressure to 5 cmH 2 O by adjusting the flow-relief valve. To evaluate the ventilation in the inner space of each model, we measured the oxygen concentration rise time, that is, the time needed for the oxygen concentration of each model to reach 35% after the oxygen concentration of the continuous flow was raised from 21% to 40%. The volume of inner space of the face mask was 38.3 ml. Oxygen concentration rise time in the face mask CPAP model was significantly longer at various continuous flow rates and points of the inner space of the face mask compared with that of the modified face mask CPAP model. Our study indicates that face mask CPAP leads to an increase in dead space and a decrease in ventilation efficiency under certain circumstances. Pediatr Pulmonol. 2017;52:107-111. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Estimating latent time of maturation and survival costs of reproduction in continuous time from capture-recapture data

USGS Publications Warehouse

Ergon, T.; Yoccoz, N.G.; Nichols, J.D.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

In many species, age or time of maturation and survival costs of reproduction may vary substantially within and among populations. We present a capture-mark-recapture model to estimate the latent individual trait distribution of time of maturation (or other irreversible transitions) as well as survival differences associated with the two states (representing costs of reproduction). Maturation can take place at any point in continuous time, and mortality hazard rates for each reproductive state may vary according to continuous functions over time. Although we explicitly model individual heterogeneity in age/time of maturation, we make the simplifying assumption that death hazard rates do not vary among individuals within groups of animals. However, the estimates of the maturation distribution are fairly robust against individual heterogeneity in survival as long as there is no individual level correlation between mortality hazards and latent time of maturation. We apply the model to biweekly capture?recapture data of overwintering field voles (Microtus agrestis) in cyclically fluctuating populations to estimate time of maturation and survival costs of reproduction. Results show that onset of seasonal reproduction is particularly late and survival costs of reproduction are particularly large in declining populations.

Dependability and performability analysis

NASA Technical Reports Server (NTRS)

Trivedi, Kishor S.; Ciardo, Gianfranco; Malhotra, Manish; Sahner, Robin A.

1993-01-01

Several practical issues regarding specifications and solution of dependability and performability models are discussed. Model types with and without rewards are compared. Continuous-time Markov chains (CTMC's) are compared with (continuous-time) Markov reward models (MRM's) and generalized stochastic Petri nets (GSPN's) are compared with stochastic reward nets (SRN's). It is shown that reward-based models could lead to more concise model specifications and solution of a variety of new measures. With respect to the solution of dependability and performability models, three practical issues were identified: largeness, stiffness, and non-exponentiality, and a variety of approaches are discussed to deal with them, including some of the latest research efforts.

Non-Linear System Identification for Aeroelastic Systems with Application to Experimental Data

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2008-01-01

Representation and identification of a non-linear aeroelastic pitch-plunge system as a model of the NARMAX class is considered. A non-linear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (i) the outputs of the NARMAX model match closely those generated using continuous-time methods and (ii) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.

2D discontinuous piecewise linear map: Emergence of fashion cycles.

PubMed

Gardini, L; Sushko, I; Matsuyama, K

2018-05-01

We consider a discrete-time version of the continuous-time fashion cycle model introduced in Matsuyama, 1992. Its dynamics are defined by a 2D discontinuous piecewise linear map depending on three parameters. In the parameter space of the map periodicity, regions associated with attracting cycles of different periods are organized in the period adding and period incrementing bifurcation structures. The boundaries of all the periodicity regions related to border collision bifurcations are obtained analytically in explicit form. We show the existence of several partially overlapping period incrementing structures, that is, a novelty for the considered class of maps. Moreover, we show that if the time-delay in the discrete time formulation of the model shrinks to zero, the number of period incrementing structures tends to infinity and the dynamics of the discrete time fashion cycle model converges to those of continuous-time fashion cycle model.

Intermittent control: a computational theory of human control.

PubMed

Gawthrop, Peter; Loram, Ian; Lakie, Martin; Gollee, Henrik

2011-02-01

The paradigm of continuous control using internal models has advanced understanding of human motor control. However, this paradigm ignores some aspects of human control, including intermittent feedback, serial ballistic control, triggered responses and refractory periods. It is shown that event-driven intermittent control provides a framework to explain the behaviour of the human operator under a wider range of conditions than continuous control. Continuous control is included as a special case, but sampling, system matched hold, an intermittent predictor and an event trigger allow serial open-loop trajectories using intermittent feedback. The implementation here may be described as "continuous observation, intermittent action". Beyond explaining unimodal regulation distributions in common with continuous control, these features naturally explain refractoriness and bimodal stabilisation distributions observed in double stimulus tracking experiments and quiet standing, respectively. Moreover, given that human control systems contain significant time delays, a biological-cybernetic rationale favours intermittent over continuous control: intermittent predictive control is computationally less demanding than continuous predictive control. A standard continuous-time predictive control model of the human operator is used as the underlying design method for an event-driven intermittent controller. It is shown that when event thresholds are small and sampling is regular, the intermittent controller can masquerade as the underlying continuous-time controller and thus, under these conditions, the continuous-time and intermittent controller cannot be distinguished. This explains why the intermittent control hypothesis is consistent with the continuous control hypothesis for certain experimental conditions.

A continuous time delay-difference type model (CTDDM) applied to stock assessment of the southern Atlantic albacore Thunnus alalunga

NASA Astrophysics Data System (ADS)

Liao, Baochao; Liu, Qun; Zhang, Kui; Baset, Abdul; Memon, Aamir Mahmood; Memon, Khadim Hussain; Han, Yanan

2016-09-01

A continuous time delay-diff erence model (CTDDM) has been established that considers continuous time delays of biological processes. The southern Atlantic albacore ( Thunnus alalunga) stock is the one of the commercially important tuna population in the marine world. The age structured production model (ASPM) and the surplus production model (SPM) have already been used to assess the albacore stock. However, the ASPM requires detailed biological information and the SPM lacks the biological realism. In this study, we focus on applying a CTDDM to the southern Atlantic albacore ( T. alalunga) species, which provides an alternative method to assess this fishery. It is the first time that CTDDM has been provided for assessing the Atlantic albacore ( T. alalunga) fishery. CTDDM obtained the 80% confidence interval of MSY (maximum sustainable yield) of (21 510 t, 23 118t). The catch in 2011 (24 100 t) is higher than the MSY values and the relative fishing mortality ratio ( F 2011/ F MSY) is higher than 1.0. The results of CTDDM were analyzed to verify the proposed methodology and provide reference information for the sustainable management of the southern Atlantic albacore stock. The CTDDM treats the recruitment, the growth, and the mortality rates as all varying continuously over time and fills gaps between ASPM and SPM in this stock assessment.

Improving Global Mass Flux Solutions from Gravity Recovery and Climate Experiment (GRACE) Through Forward Modeling and Continuous Time Correlation

NASA Technical Reports Server (NTRS)

Sabaka, T. J.; Rowlands, D. D.; Luthcke, S. B.; Boy, J.-P.

2010-01-01

We describe Earth's mass flux from April 2003 through November 2008 by deriving a time series of mas cons on a global 2deg x 2deg equal-area grid at 10 day intervals. We estimate the mass flux directly from K band range rate (KBRR) data provided by the Gravity Recovery and Climate Experiment (GRACE) mission. Using regularized least squares, we take into account the underlying process dynamics through continuous space and time-correlated constraints. In addition, we place the mascon approach in the context of other filtering techniques, showing its equivalence to anisotropic, nonsymmetric filtering, least squares collocation, and Kalman smoothing. We produce mascon time series from KBRR data that have and have not been corrected (forward modeled) for hydrological processes and fmd that the former produce superior results in oceanic areas by minimizing signal leakage from strong sources on land. By exploiting the structure of the spatiotemporal constraints, we are able to use a much more efficient (in storage and computation) inversion algorithm based upon the conjugate gradient method. This allows us to apply continuous rather than piecewise continuous time-correlated constraints, which we show via global maps and comparisons with ocean-bottom pressure gauges, to produce time series with reduced random variance and full systematic signal. Finally, we present a preferred global model, a hybrid whose oceanic portions are derived using forward modeling of hydrology but whose land portions are not, and thus represent a pure GRACE-derived signal.

Inference for Continuous-Time Probabilistic Programming

DTIC Science & Technology

2017-12-01

Parzen window density estimator to jointly model the inter-camera travel time intervals, locations of exit/entrances, and velocities of ob- jects...asked to travel across the scene multiple times . Even in such a scenario they formed groups and made social interactions, which Fig. 7: Topology of...INFERENCE FOR CONTINUOUS- TIME PROBABILISTIC PROGRAMMING UNIVERSITY OF CALIFORNIA AT RIVERSIDE DECEMBER 2017 FINAL TECHNICAL REPORT APPROVED FOR

A continuous-time neural model for sequential action.

PubMed

Kachergis, George; Wyatte, Dean; O'Reilly, Randall C; de Kleijn, Roy; Hommel, Bernhard

2014-11-05

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Complex discrete dynamics from simple continuous population models.

PubMed

Gamarra, Javier G P; Solé, Ricard V

2002-05-01

Nonoverlapping generations have been classically modelled as difference equations in order to account for the discrete nature of reproductive events. However, other events such as resource consumption or mortality are continuous and take place in the within-generation time. We have realistically assumed a hybrid ODE bidimensional model of resources and consumers with discrete events for reproduction. Numerical and analytical approaches showed that the resulting dynamics resembles a Ricker map, including the doubling route to chaos. Stochastic simulations with a handling-time parameter for indirect competition of juveniles may affect the qualitative behaviour of the model.

Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0)

NASA Astrophysics Data System (ADS)

Santos, Léonard; Thirel, Guillaume; Perrin, Charles

2018-04-01

In many conceptual rainfall-runoff models, the water balance differential equations are not explicitly formulated. These differential equations are solved sequentially by splitting the equations into terms that can be solved analytically with a technique called operator splitting. As a result, only the solutions of the split equations are used to present the different models. This article provides a methodology to make the governing water balance equations of a bucket-type rainfall-runoff model explicit and to solve them continuously. This is done by setting up a comprehensive state-space representation of the model. By representing it in this way, the operator splitting, which makes the structural analysis of the model more complex, could be removed. In this state-space representation, the lag functions (unit hydrographs), which are frequent in rainfall-runoff models and make the resolution of the representation difficult, are first replaced by a so-called Nash cascade and then solved with a robust numerical integration technique. To illustrate this methodology, the GR4J model is taken as an example. The substitution of the unit hydrographs with a Nash cascade, even if it modifies the model behaviour when solved using operator splitting, does not modify it when the state-space representation is solved using an implicit integration technique. Indeed, the flow time series simulated by the new representation of the model are very similar to those simulated by the classic model. The use of a robust numerical technique that approximates a continuous-time model also improves the lag parameter consistency across time steps and provides a more time-consistent model with time-independent parameters.

Discretization and control of an SEIR epidemic model under equilibrium Wiener noise disturbances

NASA Astrophysics Data System (ADS)

Alonso, Santiago; De la Sen, Manuel; Nistal, Raul; Ibeas, Asier

2017-11-01

A discretized SEIR epidemic model, subject to Wiener noise disturbances of the equilibrium points, is studied. The discrete-time model is got from a general discretization technique applied to its continuous-time counterpart so that its behaviour be close to its continuous-time counterpart irrespective of the size of the discretization period. The positivity and stability of a normalized version of such a discrete-time model are emphasized. The paper also proposes the design of a periodic impulsive vaccination which is periodically injected to the susceptible subpopulation in order to eradicate the propagation of the disease or, at least, to reduce its unsuitable infective effects within the potentially susceptible subpopulation. The existence and asymptotic stability of a disease-free periodic solution are proved. In particular, both the exposed and infectious subpopulations converge asymptotically to zero as time tends to infinity while the normalized subpopulations of susceptible and recovered by immunization oscillate.

Continuous-Time Random Walk with multi-step memory: an application to market dynamics

NASA Astrophysics Data System (ADS)

Gubiec, Tomasz; Kutner, Ryszard

2017-11-01

An extended version of the Continuous-Time Random Walk (CTRW) model with memory is herein developed. This memory involves the dependence between arbitrary number of successive jumps of the process while waiting times between jumps are considered as i.i.d. random variables. This dependence was established analyzing empirical histograms for the stochastic process of a single share price on a market within the high frequency time scale. Then, it was justified theoretically by considering bid-ask bounce mechanism containing some delay characteristic for any double-auction market. Our model appeared exactly analytically solvable. Therefore, it enables a direct comparison of its predictions with their empirical counterparts, for instance, with empirical velocity autocorrelation function. Thus, the present research significantly extends capabilities of the CTRW formalism. Contribution to the Topical Issue "Continuous Time Random Walk Still Trendy: Fifty-year History, Current State and Outlook", edited by Ryszard Kutner and Jaume Masoliver.

Nonlinear System Identification for Aeroelastic Systems with Application to Experimental Data

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2008-01-01

Representation and identification of a nonlinear aeroelastic pitch-plunge system as a model of the Nonlinear AutoRegressive, Moving Average eXogenous (NARMAX) class is considered. A nonlinear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (1) the outputs of the NARMAX model closely match those generated using continuous-time methods, and (2) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.

DEVELOPMENT OF A MODEL FOR REAL TIME CO CONCENTRATIONS NEAR ROADWAYS

EPA Science Inventory

Although emission standards for mobile sources continue to be tightened, tailpipe emissions in urban areas continue to be a major source of human exposure to air toxics. Current human exposure models using simplified assumptions based on fixed air monitoring stations and region...

Experimental Measurements of Sonic Boom Signatures Using a Continuous Data Acquisition Technique

NASA Technical Reports Server (NTRS)

Wilcox, Floyd J.; Elmiligui, Alaa A.

2013-01-01

A wind tunnel investigation was conducted in the Langley Unitary Plan Wind Tunnel to determine the effectiveness of a technique to measure aircraft sonic boom signatures using a single conical survey probe while continuously moving the model past the probe. Sonic boom signatures were obtained using both move-pause and continuous data acquisition methods for comparison. The test was conducted using a generic business jet model at a constant angle of attack and a single model-to-survey-probe separation distance. The sonic boom signatures were obtained at a Mach number of 2.0 and a unit Reynolds number of 2 million per foot. The test results showed that it is possible to obtain sonic boom signatures while continuously moving the model and that the time required to acquire the signature is at least 10 times faster than the move-pause method. Data plots are presented with a discussion of the results. No tabulated data or flow visualization photographs are included.

40 CFR 60.5075 - How does the model rule relate to the required elements of my state plan?

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 6 2011-07-01 2011-07-01 false How does the model rule relate to the... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge Incineration Units Use of Model Rule...

Backward jump continuous-time random walk: An application to market trading

NASA Astrophysics Data System (ADS)

Gubiec, Tomasz; Kutner, Ryszard

2010-10-01

The backward jump modification of the continuous-time random walk model or the version of the model driven by the negative feedback was herein derived for spatiotemporal continuum in the context of a share price evolution on a stock exchange. In the frame of the model, we described stochastic evolution of a typical share price on a stock exchange with a moderate liquidity within a high-frequency time scale. The model was validated by satisfactory agreement of the theoretical velocity autocorrelation function with its empirical counterpart obtained for the continuous quotation. This agreement is mainly a result of a sharp backward correlation found and considered in this article. This correlation is a reminiscence of such a bid-ask bounce phenomenon where backward price jump has the same or almost the same length as preceding jump. We suggested that this correlation dominated the dynamics of the stock market with moderate liquidity. Although assumptions of the model were inspired by the market high-frequency empirical data, its potential applications extend beyond the financial market, for instance, to the field covered by the Le Chatelier-Braun principle of contrariness.

Backward jump continuous-time random walk: an application to market trading.

PubMed

Gubiec, Tomasz; Kutner, Ryszard

2010-10-01

The backward jump modification of the continuous-time random walk model or the version of the model driven by the negative feedback was herein derived for spatiotemporal continuum in the context of a share price evolution on a stock exchange. In the frame of the model, we described stochastic evolution of a typical share price on a stock exchange with a moderate liquidity within a high-frequency time scale. The model was validated by satisfactory agreement of the theoretical velocity autocorrelation function with its empirical counterpart obtained for the continuous quotation. This agreement is mainly a result of a sharp backward correlation found and considered in this article. This correlation is a reminiscence of such a bid-ask bounce phenomenon where backward price jump has the same or almost the same length as preceding jump. We suggested that this correlation dominated the dynamics of the stock market with moderate liquidity. Although assumptions of the model were inspired by the market high-frequency empirical data, its potential applications extend beyond the financial market, for instance, to the field covered by the Le Chatelier-Braun principle of contrariness.

Continuous Video Modeling to Assist with Completion of Multi-Step Home Living Tasks by Young Adults with Moderate Intellectual Disability

ERIC Educational Resources Information Center

Mechling, Linda C.; Ayres, Kevin M.; Bryant, Kathryn J.; Foster, Ashley L.

2014-01-01

The current study evaluated a relatively new video-based procedure, continuous video modeling (CVM), to teach multi-step cleaning tasks to high school students with moderate intellectual disability. CVM in contrast to video modeling and video prompting allows repetition of the video model (looping) as many times as needed while the user completes…

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

PubMed Central

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-01-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity. PMID:23592970

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

PubMed

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-04-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

DEVELOPMENT AND PEER REVIEW OF TIME-TO-EFFECT MODELS FOR THE ANALYSIS OF NEUROTOXICITY AND OTHER TIME DEPENDENT DATA

EPA Science Inventory

Neurobehavioral studies pose unique challenges for dose-response modeling, including small sample size and relatively large intra-subject variation, repeated measurements over time, multiple endpoints with both continuous and ordinal scales, and time dependence of risk characteri...

Life extending control for rocket engines

NASA Technical Reports Server (NTRS)

Lorenzo, C. F.; Saus, J. R.; Ray, A.; Carpino, M.; Wu, M.-K.

1992-01-01

The concept of life extending control is defined. A brief discussion of current fatigue life prediction methods is given and the need for an alternative life prediction model based on a continuous functional relationship is established. Two approaches to life extending control are considered: (1) the implicit approach which uses cyclic fatigue life prediction as a basis for control design; and (2) the continuous life prediction approach which requires a continuous damage law. Progress on an initial formulation of a continuous (in time) fatigue model is presented. Finally, nonlinear programming is used to develop initial results for life extension for a simplified rocket engine (model).

The predictive ability of six pharmacokinetic models of rocuronium developed using a single bolus: evaluation with bolus and continuous infusion regimen.

PubMed

Sasakawa, Tomoki; Masui, Kenichi; Kazama, Tomiei; Iwasaki, Hiroshi

2016-08-01

Rocuronium concentration prediction using pharmacokinetic (PK) models would be useful for controlling rocuronium effects because neuromuscular monitoring throughout anesthesia can be difficult. This study assessed whether six different compartmental PK models developed from data obtained after bolus administration only could predict the measured plasma concentration (Cp) values of rocuronium delivered by bolus followed by continuous infusion. Rocuronium Cp values from 19 healthy subjects who received a bolus dose followed by continuous infusion in a phase III multicenter trial in Japan were used retrospectively as evaluation datasets. Six different compartmental PK models of rocuronium were used to simulate rocuronium Cp time course values, which were compared with measured Cp values. Prediction error (PE) derivatives of median absolute PE (MDAPE), median PE (MDPE), wobble, divergence absolute PE, and divergence PE were used to assess inaccuracy, bias, intra-individual variability, and time-related trends in APE and PE values. MDAPE and MDPE values were acceptable only for the Magorian and Kleijn models. The divergence PE value for the Kleijn model was lower than -10 %/h, indicating unstable prediction over time. The Szenohradszky model had the lowest divergence PE (-2.7 %/h) and wobble (5.4 %) values with negative bias (MDPE = -25.9 %). These three models were developed using the mixed-effects modeling approach. The Magorian model showed the best PE derivatives among the models assessed. A PK model developed from data obtained after single-bolus dosing can predict Cp values during bolus and continuous infusion. Thus, a mixed-effects modeling approach may be preferable in extrapolating such data.

40 CFR 60.2725 - May I conduct a repeat performance test to establish new operating limits?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model... during any performance test used to demonstrate compliance. Model Rule—Monitoring ...

40 CFR 60.2665 - What if all the qualified operators are temporarily not accessible?

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Operator... accessible and that you are resuming operation. Model Rule—Emission Limitations and Operating Limits ...

40 CFR 60.2725 - May I conduct a repeat performance test to establish new operating limits?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model... during any performance test used to demonstrate compliance. Model Rule—Monitoring ...

40 CFR 60.2665 - What if all the qualified operators are temporarily not accessible?

Code of Federal Regulations, 2013 CFR

2013-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Operator... accessible and that you are resuming operation. Model Rule—Emission Limitations and Operating Limits ...

Relations between continuous real-time physical properties and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998-2014

USGS Publications Warehouse

Rasmussen, Patrick P.; Eslick, Patrick J.; Ziegler, Andrew C.

2016-08-11

Water from the Little Arkansas River is used as source water for artificial recharge of the Equus Beds aquifer, one of the primary water-supply sources for the city of Wichita, Kansas. The U.S. Geological Survey has operated two continuous real-time water-quality monitoring stations since 1995 on the Little Arkansas River in Kansas. Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physical properties to compute concentrations of those constituents of interest. Site-specific regression models were originally published in 2000 for the near Halstead and near Sedgwick U.S. Geological Survey streamgaging stations and the site-specific regression models were then updated in 2003. This report updates those regression models using discrete and continuous data collected during May 1998 through August 2014. In addition to the constituents listed in the 2003 update, new regression models were developed for total organic carbon. The real-time computations of water-quality concentrations and loads are available at http://nrtwq.usgs.gov. The water-quality information in this report is important to the city of Wichita because water-quality information allows for real-time quantification and characterization of chemicals of concern (including chloride), in addition to nutrients, sediment, bacteria, and atrazine transported in the Little Arkansas River. The water-quality information in this report aids in the decision making for water treatment before artificial recharge.

Modeling commodity salam contract between two parties for discrete and continuous time series

NASA Astrophysics Data System (ADS)

Hisham, Azie Farhani Badrol; Jaffar, Maheran Mohd

2017-08-01

In order for Islamic finance to remain competitive as the conventional, there needs a new development of Islamic compliance product such as Islamic derivative that can be used to manage the risk. However, under syariah principles and regulations, all financial instruments must not be conflicting with five syariah elements which are riba (interest paid), rishwah (corruption), gharar (uncertainty or unnecessary risk), maysir (speculation or gambling) and jahl (taking advantage of the counterparty's ignorance). This study has proposed a traditional Islamic contract namely salam that can be built as an Islamic derivative product. Although a lot of studies has been done on discussing and proposing the implementation of salam contract as the Islamic product however they are more into qualitative and law issues. Since there is lack of quantitative study of salam contract being developed, this study introduces mathematical models that can value the appropriate salam price for a commodity salam contract between two parties. In modeling the commodity salam contract, this study has modified the existing conventional derivative model and come out with some adjustments to comply with syariah rules and regulations. The cost of carry model has been chosen as the foundation to develop the commodity salam model between two parties for discrete and continuous time series. However, the conventional time value of money results from the concept of interest that is prohibited in Islam. Therefore, this study has adopted the idea of Islamic time value of money which is known as the positive time preference, in modeling the commodity salam contract between two parties for discrete and continuous time series.

Logistic and linear regression model documentation for statistical relations between continuous real-time and discrete water-quality constituents in the Kansas River, Kansas, July 2012 through June 2015

USGS Publications Warehouse

Foster, Guy M.; Graham, Jennifer L.

2016-04-06

The Kansas River is a primary source of drinking water for about 800,000 people in northeastern Kansas. Source-water supplies are treated by a combination of chemical and physical processes to remove contaminants before distribution. Advanced notification of changing water-quality conditions and cyanobacteria and associated toxin and taste-and-odor compounds provides drinking-water treatment facilities time to develop and implement adequate treatment strategies. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas State Water Plan Fund), and the City of Lawrence, the City of Topeka, the City of Olathe, and Johnson County Water One, began a study in July 2012 to develop statistical models at two Kansas River sites located upstream from drinking-water intakes. Continuous water-quality monitors have been operated and discrete-water quality samples have been collected on the Kansas River at Wamego (USGS site number 06887500) and De Soto (USGS site number 06892350) since July 2012. Continuous and discrete water-quality data collected during July 2012 through June 2015 were used to develop statistical models for constituents of interest at the Wamego and De Soto sites. Logistic models to continuously estimate the probability of occurrence above selected thresholds were developed for cyanobacteria, microcystin, and geosmin. Linear regression models to continuously estimate constituent concentrations were developed for major ions, dissolved solids, alkalinity, nutrients (nitrogen and phosphorus species), suspended sediment, indicator bacteria (Escherichia coli, fecal coliform, and enterococci), and actinomycetes bacteria. These models will be used to provide real-time estimates of the probability that cyanobacteria and associated compounds exceed thresholds and of the concentrations of other water-quality constituents in the Kansas River. The models documented in this report are useful for characterizing changes in water-quality conditions through time, characterizing potentially harmful cyanobacterial events, and indicating changes in water-quality conditions that may affect drinking-water treatment processes.

Future supply chains enabled by continuous processing--opportunities and challenges. May 20-21, 2014 Continuous Manufacturing Symposium.

PubMed

Srai, Jagjit Singh; Badman, Clive; Krumme, Markus; Futran, Mauricio; Johnston, Craig

2015-03-01

This paper examines the opportunities and challenges facing the pharmaceutical industry in moving to a primarily "continuous processing"-based supply chain. The current predominantly "large batch" and centralized manufacturing system designed for the "blockbuster" drug has driven a slow-paced, inventory heavy operating model that is increasingly regarded as inflexible and unsustainable. Indeed, new markets and the rapidly evolving technology landscape will drive more product variety, shorter product life-cycles, and smaller drug volumes, which will exacerbate an already unsustainable economic model. Future supply chains will be required to enhance affordability and availability for patients and healthcare providers alike despite the increased product complexity. In this more challenging supply scenario, we examine the potential for a more pull driven, near real-time demand-based supply chain, utilizing continuous processing where appropriate as a key element of a more "flow-through" operating model. In this discussion paper on future supply chain models underpinned by developments in the continuous manufacture of pharmaceuticals, we have set out; The significant opportunities to moving to a supply chain flow-through operating model, with substantial opportunities in inventory reduction, lead-time to patient, and radically different product assurance/stability regimes. Scenarios for decentralized production models producing a greater variety of products with enhanced volume flexibility. Production, supply, and value chain footprints that are radically different from today's monolithic and centralized batch manufacturing operations. Clinical trial and drug product development cost savings that support more rapid scale-up and market entry models with early involvement of SC designers within New Product Development. The major supply chain and industrial transformational challenges that need to be addressed. The paper recognizes that although current batch operational performance in pharma is far from optimal and not necessarily an appropriate end-state benchmark for batch technology, the adoption of continuous supply chain operating models underpinned by continuous production processing, as full or hybrid solutions in selected product supply chains, can support industry transformations to deliver right-first-time quality at substantially lower inventory profiles. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

General personality and psychopathology in referred and nonreferred children and adolescents: an investigation of continuity, pathoplasty, and complication models.

PubMed

De Bolle, Marleen; Beyers, Wim; De Clercq, Barbara; De Fruyt, Filip

2012-11-01

This study investigated the continuity, pathoplasty, and complication models as plausible explanations for personality-psychopathology relations in a combined sample of community (n = 571) and referred (n = 146) children and adolescents. Multivariate structural equation modeling was used to examine the structural relations between latent personality and psychopathology change across a 2-year period. Item response theory models were fitted as an additional test of the continuity hypothesis. Even after correcting for item overlap, the results provided strong support for the continuity model, demonstrating that personality and psychopathology displayed dynamic change patterns across time. Item response theory models further supported the continuity conceptualization for understanding the association between internalizing problems and emotional stability and extraversion as well as between externalizing problems and benevolence and conscientiousness. In addition to the continuity model, particular personality and psychopathology combinations provided evidence for the pathoplasty and complication models. The theoretical and practical implications of these results are discussed, and suggestions for future research are provided. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm.

PubMed

Stoll, Gautier; Viara, Eric; Barillot, Emmanuel; Calzone, Laurence

2012-08-29

Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time. There exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature. Here, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) on the Boolean state space. This software, parallelized and optimized, computes the temporal evolution of probability distributions and estimates stationary distributions. Applications of the Boolean Kinetic Monte-Carlo are demonstrated for three qualitative models: a toy model, a published model of p53/Mdm2 interaction and a published model of the mammalian cell cycle. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations.

Cavity master equation for the continuous time dynamics of discrete-spin models.

PubMed

Aurell, E; Del Ferraro, G; Domínguez, E; Mulet, R

2017-05-01

We present an alternate method to close the master equation representing the continuous time dynamics of interacting Ising spins. The method makes use of the theory of random point processes to derive a master equation for local conditional probabilities. We analytically test our solution studying two known cases, the dynamics of the mean-field ferromagnet and the dynamics of the one-dimensional Ising system. We present numerical results comparing our predictions with Monte Carlo simulations in three different models on random graphs with finite connectivity: the Ising ferromagnet, the random field Ising model, and the Viana-Bray spin-glass model.

Cavity master equation for the continuous time dynamics of discrete-spin models

NASA Astrophysics Data System (ADS)

Aurell, E.; Del Ferraro, G.; Domínguez, E.; Mulet, R.

2017-05-01

We present an alternate method to close the master equation representing the continuous time dynamics of interacting Ising spins. The method makes use of the theory of random point processes to derive a master equation for local conditional probabilities. We analytically test our solution studying two known cases, the dynamics of the mean-field ferromagnet and the dynamics of the one-dimensional Ising system. We present numerical results comparing our predictions with Monte Carlo simulations in three different models on random graphs with finite connectivity: the Ising ferromagnet, the random field Ising model, and the Viana-Bray spin-glass model.

Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes

PubMed Central

Nakamura, Tomoaki; Nagai, Takayuki; Mochihashi, Daichi; Kobayashi, Ichiro; Asoh, Hideki; Kaneko, Masahide

2017-01-01

Humans divide perceived continuous information into segments to facilitate recognition. For example, humans can segment speech waves into recognizable morphemes. Analogously, continuous motions are segmented into recognizable unit actions. People can divide continuous information into segments without using explicit segment points. This capacity for unsupervised segmentation is also useful for robots, because it enables them to flexibly learn languages, gestures, and actions. In this paper, we propose a Gaussian process-hidden semi-Markov model (GP-HSMM) that can divide continuous time series data into segments in an unsupervised manner. Our proposed method consists of a generative model based on the hidden semi-Markov model (HSMM), the emission distributions of which are Gaussian processes (GPs). Continuous time series data is generated by connecting segments generated by the GP. Segmentation can be achieved by using forward filtering-backward sampling to estimate the model's parameters, including the lengths and classes of the segments. In an experiment using the CMU motion capture dataset, we tested GP-HSMM with motion capture data containing simple exercise motions; the results of this experiment showed that the proposed GP-HSMM was comparable with other methods. We also conducted an experiment using karate motion capture data, which is more complex than exercise motion capture data; in this experiment, the segmentation accuracy of GP-HSMM was 0.92, which outperformed other methods. PMID:29311889

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data.

PubMed

de Haan-Rietdijk, Silvia; Voelkle, Manuel C; Keijsers, Loes; Hamaker, Ellen L

2017-01-01

The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector) autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT) models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1) and VAR(1) models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (V)AR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available.

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data

PubMed Central

de Haan-Rietdijk, Silvia; Voelkle, Manuel C.; Keijsers, Loes; Hamaker, Ellen L.

2017-01-01

The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector) autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT) models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1) and VAR(1) models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (V)AR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available. PMID:29104554

Parent-Child Attachment Working Models and Self-Esteem in Adolescence.

ERIC Educational Resources Information Center

McCormick, Cynthia B.; Kennedy, Janice H.

1994-01-01

Continuity over time in parent-child attachments and the relationship between these attachments and current self-esteem were studied for 218 nonparent college students. Results indicate continuity over time of attachment. Self-esteem is related to childhood and adolescent styles of attachment and dimensions of independence encouragement and…

Mathematical modeling of continuous ethanol fermentation in a membrane bioreactor by pervaporation compared to conventional system: Genetic algorithm.

PubMed

Esfahanian, Mehri; Shokuhi Rad, Ali; Khoshhal, Saeed; Najafpour, Ghasem; Asghari, Behnam

2016-07-01

In this paper, genetic algorithm was used to investigate mathematical modeling of ethanol fermentation in a continuous conventional bioreactor (CCBR) and a continuous membrane bioreactor (CMBR) by ethanol permselective polydimethylsiloxane (PDMS) membrane. A lab scale CMBR with medium glucose concentration of 100gL(-1) and Saccharomyces cerevisiae microorganism was designed and fabricated. At dilution rate of 0.14h(-1), maximum specific cell growth rate and productivity of 0.27h(-1) and 6.49gL(-1)h(-1) were respectively found in CMBR. However, at very high dilution rate, the performance of CMBR was quite similar to conventional fermentation on account of insufficient incubation time. In both systems, genetic algorithm modeling of cell growth, ethanol production and glucose concentration were conducted based on Monod and Moser kinetic models during each retention time at unsteady condition. The results showed that Moser kinetic model was more satisfactory and desirable than Monod model. Copyright © 2016 Elsevier Ltd. All rights reserved.

A discrete epidemic model for bovine Babesiosis disease and tick populations

NASA Astrophysics Data System (ADS)

Aranda, Diego F.; Trejos, Deccy Y.; Valverde, Jose C.

2017-06-01

In this paper, we provide and study a discrete model for the transmission of Babesiosis disease in bovine and tick populations. This model supposes a discretization of the continuous-time model developed by us previously. The results, here obtained by discrete methods as opposed to continuous ones, show that similar conclusions can be obtained for the discrete model subject to the assumption of some parametric constraints which were not necessary in the continuous case. We prove that these parametric constraints are not artificial and, in fact, they can be deduced from the biological significance of the model. Finally, some numerical simulations are given to validate the model and verify our theoretical study.

40 CFR 60.2615 - What must I do if I plan to permanently close my CISWI unit and not restart it?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model... plan is due. Model Rule—Waste Management Plan ...

40 CFR 60.2615 - What must I do if I plan to permanently close my CISWI unit and not restart it?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model... plan is due. Model Rule—Waste Management Plan ...

Discrete event simulation tool for analysis of qualitative models of continuous processing systems

NASA Technical Reports Server (NTRS)

Malin, Jane T. (Inventor); Basham, Bryan D. (Inventor); Harris, Richard A. (Inventor)

1990-01-01

An artificial intelligence design and qualitative modeling tool is disclosed for creating computer models and simulating continuous activities, functions, and/or behavior using developed discrete event techniques. Conveniently, the tool is organized in four modules: library design module, model construction module, simulation module, and experimentation and analysis. The library design module supports the building of library knowledge including component classes and elements pertinent to a particular domain of continuous activities, functions, and behavior being modeled. The continuous behavior is defined discretely with respect to invocation statements, effect statements, and time delays. The functionality of the components is defined in terms of variable cluster instances, independent processes, and modes, further defined in terms of mode transition processes and mode dependent processes. Model construction utilizes the hierarchy of libraries and connects them with appropriate relations. The simulation executes a specialized initialization routine and executes events in a manner that includes selective inherency of characteristics through a time and event schema until the event queue in the simulator is emptied. The experimentation and analysis module supports analysis through the generation of appropriate log files and graphics developments and includes the ability of log file comparisons.

A Measurement Model for Likert Responses that Incorporates Response Time

ERIC Educational Resources Information Center

Ferrando, Pere J.; Lorenzo-Seva, Urbano

2007-01-01

This article describes a model for response times that is proposed as a supplement to the usual factor-analytic model for responses to graded or more continuous typical-response items. The use of the proposed model together with the factor model provides additional information about the respondent and can potentially increase the accuracy of the…

Correlated continuous time random walk and option pricing

NASA Astrophysics Data System (ADS)

Lv, Longjin; Xiao, Jianbin; Fan, Liangzhong; Ren, Fuyao

2016-04-01

In this paper, we study a correlated continuous time random walk (CCTRW) with averaged waiting time, whose probability density function (PDF) is proved to follow stretched Gaussian distribution. Then, we apply this process into option pricing problem. Supposing the price of the underlying is driven by this CCTRW, we find this model captures the subdiffusive characteristic of financial markets. By using the mean self-financing hedging strategy, we obtain the closed-form pricing formulas for a European option with and without transaction costs, respectively. At last, comparing the obtained model with the classical Black-Scholes model, we find the price obtained in this paper is higher than that obtained from the Black-Scholes model. A empirical analysis is also introduced to confirm the obtained results can fit the real data well.

SEM Based CARMA Time Series Modeling for Arbitrary N.

PubMed

Oud, Johan H L; Voelkle, Manuel C; Driver, Charles C

2018-01-01

This article explains in detail the state space specification and estimation of first and higher-order autoregressive moving-average models in continuous time (CARMA) in an extended structural equation modeling (SEM) context for N = 1 as well as N > 1. To illustrate the approach, simulations will be presented in which a single panel model (T = 41 time points) is estimated for a sample of N = 1,000 individuals as well as for samples of N = 100 and N = 50 individuals, followed by estimating 100 separate models for each of the one-hundred N = 1 cases in the N = 100 sample. Furthermore, we will demonstrate how to test the difference between the full panel model and each N = 1 model by means of a subject-group-reproducibility test. Finally, the proposed analyses will be applied in an empirical example, in which the relationships between mood at work and mood at home are studied in a sample of N = 55 women. All analyses are carried out by ctsem, an R-package for continuous time modeling, interfacing to OpenMx.

A global time-dependent model of thunderstorm electricity. I - Mathematical properties of the physical and numerical models

NASA Technical Reports Server (NTRS)

Browning, G. L.; Tzur, I.; Roble, R. G.

1987-01-01

A time-dependent model is introduced that can be used to simulate the interaction of a thunderstorm with its global electrical environment. The model solves the continuity equation of the Maxwell current, which is assumed to be composed of the conduction, displacement, and source currents. Boundary conditions which can be used in conjunction with the continuity equation to form a well-posed initial-boundary value problem are determined. Properties of various components of solutions of the initial-boundary value problem are analytically determined. The results indicate that the problem has two time scales, one determined by the background electrical conductivity and the other by the time variation of the source function. A numerical method for obtaining quantitative results is introduced, and its properties are studied. Some simulation results on the evolution of the displacement and conduction currents during the electrification of a storm are presented.

Continuous distribution of emission states from single CdSe/ZnS quantum dots.

PubMed

Zhang, Kai; Chang, Hauyee; Fu, Aihua; Alivisatos, A Paul; Yang, Haw

2006-04-01

The photoluminescence dynamics of colloidal CdSe/ZnS/streptavidin quantum dots were studied using time-resolved single-molecule spectroscopy. Statistical tests of the photon-counting data suggested that the simple "on/off" discrete state model is inconsistent with experimental results. Instead, a continuous emission state distribution model was found to be more appropriate. Autocorrelation analysis of lifetime and intensity fluctuations showed a nonlinear correlation between them. These results were consistent with the model that charged quantum dots were also emissive, and that time-dependent charge migration gave rise to the observed photoluminescence dynamics.

40 CFR 60.2720 - May I conduct performance testing less often?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Continuous...

40 CFR 60.2720 - May I conduct performance testing less often?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Continuous...

Impulsive control of a continuous-culture and flocculation harvest chemostat model

NASA Astrophysics Data System (ADS)

Zhang, Tongqian; Ma, Wanbiao; Meng, Xinzhu

2017-12-01

In this paper, a new mathematical model describing the process of continuous culture and harvest of microalgaes is proposed. By inputting medium and flocculant at two different fixed moments periodically, continuous culture and harvest of microalgaes is implemented. The mathematical analysis is conducted and the whole dynamics of model is investigated by using theory of impulsive differential equations. We find that the model has a microalgaes-extinction periodic solution and it is globally asymptotically stable when some certain threshold value is less than the unit. And the model is permanent when some certain threshold value is larger than the unit. Then, according to the threshold, the control strategies of continuous culture and harvest of microalgaes are discussed. The results show that continuous culture and harvest of microalgaes can be archived by adjusting suitable input time, input amount of medium or flocculant. Finally, some numerical simulations are carried out to verify the control strategy.

Continuous-variable gate decomposition for the Bose-Hubbard model

NASA Astrophysics Data System (ADS)

Kalajdzievski, Timjan; Weedbrook, Christian; Rebentrost, Patrick

2018-06-01

In this work, we decompose the time evolution of the Bose-Hubbard model into a sequence of logic gates that can be implemented on a continuous-variable photonic quantum computer. We examine the structure of the circuit that represents this time evolution for one-dimensional and two-dimensional lattices. The elementary gates needed for the implementation are counted as a function of lattice size. We also include the contribution of the leading dipole interaction term which may be added to the Hamiltonian and its corresponding circuit.

Time series models on analysing mortality rates and acute childhood lymphoid leukaemia.

PubMed

Kis, Maria

2005-01-01

In this paper we demonstrate applying time series models on medical research. The Hungarian mortality rates were analysed by autoregressive integrated moving average models and seasonal time series models examined the data of acute childhood lymphoid leukaemia.The mortality data may be analysed by time series methods such as autoregressive integrated moving average (ARIMA) modelling. This method is demonstrated by two examples: analysis of the mortality rates of ischemic heart diseases and analysis of the mortality rates of cancer of digestive system. Mathematical expressions are given for the results of analysis. The relationships between time series of mortality rates were studied with ARIMA models. Calculations of confidence intervals for autoregressive parameters by tree methods: standard normal distribution as estimation and estimation of the White's theory and the continuous time case estimation. Analysing the confidence intervals of the first order autoregressive parameters we may conclude that the confidence intervals were much smaller than other estimations by applying the continuous time estimation model.We present a new approach to analysing the occurrence of acute childhood lymphoid leukaemia. We decompose time series into components. The periodicity of acute childhood lymphoid leukaemia in Hungary was examined using seasonal decomposition time series method. The cyclic trend of the dates of diagnosis revealed that a higher percent of the peaks fell within the winter months than in the other seasons. This proves the seasonal occurrence of the childhood leukaemia in Hungary.

Markov Chain Models for Stochastic Behavior in Resonance Overlap Regions

NASA Astrophysics Data System (ADS)

McCarthy, Morgan; Quillen, Alice

2018-01-01

We aim to predict lifetimes of particles in chaotic zoneswhere resonances overlap. A continuous-time Markov chain model isconstructed using mean motion resonance libration timescales toestimate transition times between resonances. The model is applied todiffusion in the co-rotation region of a planet. For particles begunat low eccentricity, the model is effective for early diffusion, butnot at later time when particles experience close encounters to the planet.

Hidden Semi-Markov Models and Their Application

NASA Astrophysics Data System (ADS)

Beyreuther, M.; Wassermann, J.

2008-12-01

In the framework of detection and classification of seismic signals there are several different approaches. Our choice for a more robust detection and classification algorithm is to adopt Hidden Markov Models (HMM), a technique showing major success in speech recognition. HMM provide a powerful tool to describe highly variable time series based on a double stochastic model and therefore allow for a broader class description than e.g. template based pattern matching techniques. Being a fully probabilistic model, HMM directly provide a confidence measure of an estimated classification. Furthermore and in contrast to classic artificial neuronal networks or support vector machines, HMM are incorporating the time dependence explicitly in the models thus providing a adequate representation of the seismic signal. As the majority of detection algorithms, HMM are not based on the time and amplitude dependent seismogram itself but on features estimated from the seismogram which characterize the different classes. Features, or in other words characteristic functions, are e.g. the sonogram bands, instantaneous frequency, instantaneous bandwidth or centroid time. In this study we apply continuous Hidden Semi-Markov Models (HSMM), an extension of continuous HMM. The duration probability of a HMM is an exponentially decaying function of the time, which is not a realistic representation of the duration of an earthquake. In contrast HSMM use Gaussians as duration probabilities, which results in an more adequate model. The HSMM detection and classification system is running online as an EARTHWORM module at the Bavarian Earthquake Service. Here the signals that are to be classified simply differ in epicentral distance. This makes it possible to easily decide whether a classification is correct or wrong and thus allows to better evaluate the advantages and disadvantages of the proposed algorithm. The evaluation is based on several month long continuous data and the results are additionally compared to the previously published discrete HMM, continuous HMM and a classic STA/LTA. The intermediate evaluation results are very promising.

Real-time, continuous water-quality monitoring in Indiana and Kentucky

USGS Publications Warehouse

Shoda, Megan E.; Lathrop, Timothy R.; Risch, Martin R.

2015-01-01

Water-quality “super” gages (also known as “sentry” gages) provide real-time, continuous measurements of the physical and chemical characteristics of stream water at or near selected U.S. Geological Survey (USGS) streamgages in Indiana and Kentucky. A super gage includes streamflow and water-quality instrumentation and representative stream sample collection for laboratory analysis. USGS scientists can use statistical surrogate models to relate instrument values to analyzed chemical concentrations at a super gage. Real-time, continuous and laboratory-analyzed concentration and load data are publicly accessible on USGS Web pages.

Random walk to a nonergodic equilibrium concept

NASA Astrophysics Data System (ADS)

Bel, G.; Barkai, E.

2006-01-01

Random walk models, such as the trap model, continuous time random walks, and comb models, exhibit weak ergodicity breaking, when the average waiting time is infinite. The open question is, what statistical mechanical theory replaces the canonical Boltzmann-Gibbs theory for such systems? In this paper a nonergodic equilibrium concept is investigated, for a continuous time random walk model in a potential field. In particular we show that in the nonergodic phase the distribution of the occupation time of the particle in a finite region of space approaches U- or W-shaped distributions related to the arcsine law. We show that when conditions of detailed balance are applied, these distributions depend on the partition function of the problem, thus establishing a relation between the nonergodic dynamics and canonical statistical mechanics. In the ergodic phase the distribution function of the occupation times approaches a δ function centered on the value predicted based on standard Boltzmann-Gibbs statistics. The relation of our work to single-molecule experiments is briefly discussed.

Anomalous diffusion with linear reaction dynamics: from continuous time random walks to fractional reaction-diffusion equations.

PubMed

Henry, B I; Langlands, T A M; Wearne, S L

2006-09-01

We have revisited the problem of anomalously diffusing species, modeled at the mesoscopic level using continuous time random walks, to include linear reaction dynamics. If a constant proportion of walkers are added or removed instantaneously at the start of each step then the long time asymptotic limit yields a fractional reaction-diffusion equation with a fractional order temporal derivative operating on both the standard diffusion term and a linear reaction kinetics term. If the walkers are added or removed at a constant per capita rate during the waiting time between steps then the long time asymptotic limit has a standard linear reaction kinetics term but a fractional order temporal derivative operating on a nonstandard diffusion term. Results from the above two models are compared with a phenomenological model with standard linear reaction kinetics and a fractional order temporal derivative operating on a standard diffusion term. We have also developed further extensions of the CTRW model to include more general reaction dynamics.

POLYNOMIAL-BASED DISAGGREGATION OF HOURLY RAINFALL FOR CONTINUOUS HYDROLOGIC SIMULATION

EPA Science Inventory

Hydrologic modeling of urban watersheds for designs and analyses of stormwater conveyance facilities can be performed in either an event-based or continuous fashion. Continuous simulation requires, among other things, the use of a time series of rainfall amounts. However, for urb...

Beyond ROC Curvature: Strength Effects and Response Time Data Support Continuous-Evidence Models of Recognition Memory

ERIC Educational Resources Information Center

Dube, Chad; Starns, Jeffrey J.; Rotello, Caren M.; Ratcliff, Roger

2012-01-01

A classic question in the recognition memory literature is whether retrieval is best described as a continuous-evidence process consistent with signal detection theory (SDT), or a threshold process consistent with many multinomial processing tree (MPT) models. Because receiver operating characteristics (ROCs) based on confidence ratings are…

Mixed and Mixture Regression Models for Continuous Bounded Responses Using the Beta Distribution

ERIC Educational Resources Information Center

Verkuilen, Jay; Smithson, Michael

2012-01-01

Doubly bounded continuous data are common in the social and behavioral sciences. Examples include judged probabilities, confidence ratings, derived proportions such as percent time on task, and bounded scale scores. Dependent variables of this kind are often difficult to analyze using normal theory models because their distributions may be quite…

Incorporating temporal and clinical reasoning in a new measure of continuity of care.

PubMed Central

Spooner, S. A.

1994-01-01

Previously described quantitative methods for measuring continuity of care have assumed that perfect continuity exists when a patient sees only one provider, regardless of the temporal pattern and clinical context of the visits. This paper describes an implementation of a new operational model of continuity--the Temporal Continuity Index--that takes into account time intervals between well visits in a pediatric residency continuity clinic. Ideal continuity in this model is achieved when intervals between visits are appropriate based on the age of the patient and clinical context of the encounters. The fundamental concept in this model is the expectation interval, which contains the length of the maximum ideal follow-up interval for a visit and the maximum follow-up interval. This paper describes an initial implementation of the TCI model and compares TCI calculations to previous quantitative methods and proposes its use as part of the assessment of resident education in outpatient settings. PMID:7950019

Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling

PubMed Central

Wittmann, Dominik M; Krumsiek, Jan; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Klamt, Steffen; Theis, Fabian J

2009-01-01

Background The understanding of regulatory and signaling networks has long been a core objective in Systems Biology. Knowledge about these networks is mainly of qualitative nature, which allows the construction of Boolean models, where the state of a component is either 'off' or 'on'. While often able to capture the essential behavior of a network, these models can never reproduce detailed time courses of concentration levels. Nowadays however, experiments yield more and more quantitative data. An obvious question therefore is how qualitative models can be used to explain and predict the outcome of these experiments. Results In this contribution we present a canonical way of transforming Boolean into continuous models, where the use of multivariate polynomial interpolation allows transformation of logic operations into a system of ordinary differential equations (ODE). The method is standardized and can readily be applied to large networks. Other, more limited approaches to this task are briefly reviewed and compared. Moreover, we discuss and generalize existing theoretical results on the relation between Boolean and continuous models. As a test case a logical model is transformed into an extensive continuous ODE model describing the activation of T-cells. We discuss how parameters for this model can be determined such that quantitative experimental results are explained and predicted, including time-courses for multiple ligand concentrations and binding affinities of different ligands. This shows that from the continuous model we may obtain biological insights not evident from the discrete one. Conclusion The presented approach will facilitate the interaction between modeling and experiments. Moreover, it provides a straightforward way to apply quantitative analysis methods to qualitatively described systems. PMID:19785753

A theoretical model of continuity in anxiety and links to academic achievement in disaster-exposed school children.

PubMed

Weems, Carl F; Scott, Brandon G; Taylor, Leslie K; Cannon, Melinda F; Romano, Dawn M; Perry, Andre M

2013-08-01

This study tested a theoretical model of continuity in anxious emotion and its links to academic achievement in disaster-exposed youth. An urban school based sample of youths (n = 191; Grades 4-8) exposed to Hurricane Katrina were assessed at 24 months (Time 1) and then again at 30 months (Time 2) postdisaster. Academic achievement was assessed through end of the school year standardized test scores (~31 months after Katrina). The results suggest that the association of traumatic stress to academic achievement was indirect via linkages from earlier (Time 1) posttraumatic stress disorder symptoms that predicted later (Time 2) test anxiety. Time 2 test anxiety was then negatively associated with academic achievement. Age and gender invariance testing suggested strong consistency across gender and minor developmental variation in the age range examined. The model presented advances the developmental understanding of the expression of anxious emotion and its links to student achievement among disaster-exposed urban school children. The findings highlight the importance of identifying heterotypic continuity in anxiety and suggest potential applied and policy directions for disaster-exposed youth. Avenues for future theoretical refinement are also discussed.

Functional linear models to test for differences in prairie wetland hydraulic gradients

USGS Publications Warehouse

Greenwood, Mark C.; Sojda, Richard S.; Preston, Todd M.; Swayne, David A.; Yang, Wanhong; Voinov, A.A.; Rizzoli, A.; Filatova, T.

2010-01-01

Functional data analysis provides a framework for analyzing multiple time series measured frequently in time, treating each series as a continuous function of time. Functional linear models are used to test for effects on hydraulic gradient functional responses collected from three types of land use in Northeastern Montana at fourteen locations. Penalized regression-splines are used to estimate the underlying continuous functions based on the discretely recorded (over time) gradient measurements. Permutation methods are used to assess the statistical significance of effects. A method for accommodating missing observations in each time series is described. Hydraulic gradients may be an initial and fundamental ecosystem process that responds to climate change. We suggest other potential uses of these methods for detecting evidence of climate change.

Integrating continuous stocks and flows into state-and-transition simulation models of landscape change

USGS Publications Warehouse

Daniel, Colin J.; Sleeter, Benjamin M.; Frid, Leonardo; Fortin, Marie-Josée

2018-01-01

State-and-transition simulation models (STSMs) provide a general framework for forecasting landscape dynamics, including projections of both vegetation and land-use/land-cover (LULC) change. The STSM method divides a landscape into spatially-referenced cells and then simulates the state of each cell forward in time, as a discrete-time stochastic process using a Monte Carlo approach, in response to any number of possible transitions. A current limitation of the STSM method, however, is that all of the state variables must be discrete.Here we present a new approach for extending a STSM, in order to account for continuous state variables, called a state-and-transition simulation model with stocks and flows (STSM-SF). The STSM-SF method allows for any number of continuous stocks to be defined for every spatial cell in the STSM, along with a suite of continuous flows specifying the rates at which stock levels change over time. The change in the level of each stock is then simulated forward in time, for each spatial cell, as a discrete-time stochastic process. The method differs from the traditional systems dynamics approach to stock-flow modelling in that the stocks and flows can be spatially-explicit, and the flows can be expressed as a function of the STSM states and transitions.We demonstrate the STSM-SF method by integrating a spatially-explicit carbon (C) budget model with a STSM of LULC change for the state of Hawai'i, USA. In this example, continuous stocks are pools of terrestrial C, while the flows are the possible fluxes of C between these pools. Importantly, several of these C fluxes are triggered by corresponding LULC transitions in the STSM. Model outputs include changes in the spatial and temporal distribution of C pools and fluxes across the landscape in response to projected future changes in LULC over the next 50 years.The new STSM-SF method allows both discrete and continuous state variables to be integrated into a STSM, including interactions between them. With the addition of stocks and flows, STSMs provide a conceptually simple yet powerful approach for characterizing uncertainties in projections of a wide range of questions regarding landscape change.

Predicting declines in perceived relationship continuity using practice deprivation scores: a longitudinal study in primary care.

PubMed

Levene, Louis S; Baker, Richard; Walker, Nicola; Williams, Christopher; Wilson, Andrew; Bankart, John

2018-06-01

Increased relationship continuity in primary care is associated with better health outcomes, greater patient satisfaction, and fewer hospital admissions. Greater socioeconomic deprivation is associated with lower levels of continuity, as well as poorer health outcomes. To investigate whether deprivation scores predicted variations in the decline over time of patient-perceived relationship continuity of care, after adjustment for practice organisational and population factors. An observational study in 6243 primary care practices with more than one GP, in England, using a longitudinal multilevel linear model, 2012-2017 inclusive. Patient-perceived relationship continuity was calculated using two questions from the GP Patient Survey. The effect of deprivation on the linear slope of continuity over time was modelled, adjusting for nine confounding variables (practice population and organisational factors). Clustering of measurements within general practices was adjusted for by using a random intercepts and random slopes model. Descriptive statistics and univariable analyses were also undertaken. Relationship continuity declined by 27.5% between 2012 and 2017, and at all deprivation levels. Deprivation scores from 2012 did not predict variations in the decline of relationship continuity at practice level, after accounting for the effects of organisational and population confounding variables, which themselves did not predict, or weakly predicted with very small effect sizes, the decline of continuity. Cross-sectionally, continuity and deprivation were negatively correlated within each year. The decline in relationship continuity of care has been marked and widespread. Measures to maximise continuity will need to be feasible for individual practices with diverse population and organisational characteristics. © British Journal of General Practice 2018.

Time-domain approach for the transient responses in stratified viscoelastic Earth models

NASA Technical Reports Server (NTRS)

Hanyk, L.; Moser, J.; Yuen, D. A.; Matyska, C.

1995-01-01

We have developed the numerical algorithm for the computation of transient viscoelastic responses in the time domain for a radially stratified Earth model. Stratifications in both the elastic parameters and the viscosity profile have been considered. The particular viscosity profile employed has a viscosity maximum with a constrast of O(100) in the mid lower mantle. The distribution of relaxation times reveals the presence of a continuous spectrum situated between O(100) and O(exp 4) years. The principal mode is embedded within this continuous spectrum. From this initial-value approach we have found that for the low degree harmonics the non-modal contributions are comparable to the modal contributions. For this viscosity model the differences between the time-domain and normal-mode results are found to decrease strongly with increasing angular order. These calculations also show that a time-dependent effective relaxation time can be defined, which can be bounded by the relaxation times of the principal modes.

Future Supply Chains Enabled by Continuous Processing-Opportunities Challenges May 20-21 2014 Continuous Manufacturing Symposium.

PubMed

Srai, Jagjit Singh; Badman, Clive; Krumme, Markus; Futran, Mauricio; Johnston, Craig

2015-03-01

This paper examines the opportunities and challenges facing the pharmaceutical industry in moving to a primarily "continuous processing"-based supply chain. The current predominantly "large batch" and centralized manufacturing system designed for the "blockbuster" drug has driven a slow-paced, inventory heavy operating model that is increasingly regarded as inflexible and unsustainable. Indeed, new markets and the rapidly evolving technology landscape will drive more product variety, shorter product life-cycles, and smaller drug volumes, which will exacerbate an already unsustainable economic model. Future supply chains will be required to enhance affordability and availability for patients and healthcare providers alike despite the increased product complexity. In this more challenging supply scenario, we examine the potential for a more pull driven, near real-time demand-based supply chain, utilizing continuous processing where appropriate as a key element of a more "flow-through" operating model. In this discussion paper on future supply chain models underpinned by developments in the continuous manufacture of pharmaceuticals, we have set out; The paper recognizes that although current batch operational performance in pharma is far from optimal and not necessarily an appropriate end-state benchmark for batch technology, the adoption of continuous supply chain operating models underpinned by continuous production processing, as full or hybrid solutions in selected product supply chains, can support industry transformations to deliver right-first-time quality at substantially lower inventory profiles. © 2015 The Authors. Journal of Pharmaceutical Sciences published by Wiley Periodicals, Inc. and the American Pharmacists Association. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Modeling the Atmospheric Phase Effects of a Digital Antenna Array Communications System

NASA Technical Reports Server (NTRS)

Tkacenko, A.

2006-01-01

In an antenna array system such as that used in the Deep Space Network (DSN) for satellite communication, it is often necessary to account for the effects due to the atmosphere. Typically, the atmosphere induces amplitude and phase fluctuations on the transmitted downlink signal that invalidate the assumed stationarity of the signal model. The degree to which these perturbations affect the stationarity of the model depends both on parameters of the atmosphere, including wind speed and turbulence strength, and on parameters of the communication system, such as the sampling rate used. In this article, we focus on modeling the atmospheric phase fluctuations in a digital antenna array communications system. Based on a continuous-time statistical model for the atmospheric phase effects, we show how to obtain a related discrete-time model based on sampling the continuous-time process. The effects of the nonstationarity of the resulting signal model are investigated using the sample matrix inversion (SMI) algorithm for minimum mean-squared error (MMSE) equalization of the received signal

Path integration of head direction: updating a packet of neural activity at the correct speed using neuronal time constants.

PubMed

Walters, D M; Stringer, S M

2010-07-01

A key question in understanding the neural basis of path integration is how individual, spatially responsive, neurons may self-organize into networks that can, through learning, integrate velocity signals to update a continuous representation of location within an environment. It is of vital importance that this internal representation of position is updated at the correct speed, and in real time, to accurately reflect the motion of the animal. In this article, we present a biologically plausible model of velocity path integration of head direction that can solve this problem using neuronal time constants to effect natural time delays, over which associations can be learned through associative Hebbian learning rules. The model comprises a linked continuous attractor network and competitive network. In simulation, we show that the same model is able to learn two different speeds of rotation when implemented with two different values for the time constant, and without the need to alter any other model parameters. The proposed model could be extended to path integration of place in the environment, and path integration of spatial view.

Simulation-Based Prediction of Equivalent Continuous Noises during Construction Processes

PubMed Central

Zhang, Hong; Pei, Yun

2016-01-01

Quantitative prediction of construction noise is crucial to evaluate construction plans to help make decisions to address noise levels. Considering limitations of existing methods for measuring or predicting the construction noise and particularly the equivalent continuous noise level over a period of time, this paper presents a discrete-event simulation method for predicting the construction noise in terms of equivalent continuous level. The noise-calculating models regarding synchronization, propagation and equivalent continuous level are presented. The simulation framework for modeling the noise-affected factors and calculating the equivalent continuous noise by incorporating the noise-calculating models into simulation strategy is proposed. An application study is presented to demonstrate and justify the proposed simulation method in predicting the equivalent continuous noise during construction. The study contributes to provision of a simulation methodology to quantitatively predict the equivalent continuous noise of construction by considering the relevant uncertainties, dynamics and interactions. PMID:27529266

Simulation-Based Prediction of Equivalent Continuous Noises during Construction Processes.

PubMed

Zhang, Hong; Pei, Yun

2016-08-12

Quantitative prediction of construction noise is crucial to evaluate construction plans to help make decisions to address noise levels. Considering limitations of existing methods for measuring or predicting the construction noise and particularly the equivalent continuous noise level over a period of time, this paper presents a discrete-event simulation method for predicting the construction noise in terms of equivalent continuous level. The noise-calculating models regarding synchronization, propagation and equivalent continuous level are presented. The simulation framework for modeling the noise-affected factors and calculating the equivalent continuous noise by incorporating the noise-calculating models into simulation strategy is proposed. An application study is presented to demonstrate and justify the proposed simulation method in predicting the equivalent continuous noise during construction. The study contributes to provision of a simulation methodology to quantitatively predict the equivalent continuous noise of construction by considering the relevant uncertainties, dynamics and interactions.

Continuous measurement of an atomic current

NASA Astrophysics Data System (ADS)

Laflamme, C.; Yang, D.; Zoller, P.

2017-04-01

We are interested in dynamics of quantum many-body systems under continuous observation, and its physical realizations involving cold atoms in lattices. In the present work we focus on continuous measurement of atomic currents in lattice models, including the Hubbard model. We describe a Cavity QED setup, where measurement of a homodyne current provides a faithful representation of the atomic current as a function of time. We employ the quantum optical description in terms of a diffusive stochastic Schrödinger equation to follow the time evolution of the atomic system conditional to observing a given homodyne current trajectory, thus accounting for the competition between the Hamiltonian evolution and measurement back action. As an illustration, we discuss minimal models of atomic dynamics and continuous current measurement on rings with synthetic gauge fields, involving both real space and synthetic dimension lattices (represented by internal atomic states). Finally, by "not reading" the current measurements the time evolution of the atomic system is governed by a master equation, where—depending on the microscopic details of our CQED setups—we effectively engineer a current coupling of our system to a quantum reservoir. This provides interesting scenarios of dissipative dynamics generating "dark" pure quantum many-body states.

40 CFR 60.2640 - When must the operator training course be completed?

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Operator...

40 CFR 60.2640 - When must the operator training course be completed?

Code of Federal Regulations, 2013 CFR

2013-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Operator...

Path statistics, memory, and coarse-graining of continuous-time random walks on networks

PubMed Central

Kion-Crosby, Willow; Morozov, Alexandre V.

2015-01-01

Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with coarse-grained states (for example, those employed in studies of molecular kinetics) or spatial disorder can give rise to memory and non-exponential distributions of waiting times and first-passage statistics. However, existing methods for analyzing CTRWs on complex energy landscapes do not address these effects. Here we use statistical mechanics of the nonequilibrium path ensemble to characterize first-passage CTRWs on networks with arbitrary connectivity, energy landscape, and waiting time distributions. Our approach can be applied to calculating higher moments (beyond the mean) of path length, time, and action, as well as statistics of any conservative or non-conservative force along a path. For homogeneous networks, we derive exact relations between length and time moments, quantifying the validity of approximating a continuous-time process with its discrete-time projection. For more general models, we obtain recursion relations, reminiscent of transfer matrix and exact enumeration techniques, to efficiently calculate path statistics numerically. We have implemented our algorithm in PathMAN (Path Matrix Algorithm for Networks), a Python script that users can apply to their model of choice. We demonstrate the algorithm on a few representative examples which underscore the importance of non-exponential distributions, memory, and coarse-graining in CTRWs. PMID:26646868

Model documentation for relations between continuous real-time and discrete water-quality constituents in Cheney Reservoir near Cheney, Kansas, 2001--2009

USGS Publications Warehouse

Stone, Mandy L.; Graham, Jennifer L.; Gatotho, Jackline W.

2013-01-01

Cheney Reservoir, located in south-central Kansas, is one of the primary water supplies for the city of Wichita, Kansas. The U.S. Geological Survey has operated a continuous real-time water-quality monitoring station in Cheney Reservoir since 2001; continuously measured physicochemical properties include specific conductance, pH, water temperature, dissolved oxygen, turbidity, fluorescence (wavelength range 650 to 700 nanometers; estimate of total chlorophyll), and reservoir elevation. Discrete water-quality samples were collected during 2001 through 2009 and analyzed for sediment, nutrients, taste-and-odor compounds, cyanotoxins, phytoplankton community composition, actinomycetes bacteria, and other water-quality measures. Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physicochemical properties to compute concentrations of constituents that are not easily measured in real time. The water-quality information in this report is important to the city of Wichita because it allows quantification and characterization of potential constituents of concern in Cheney Reservoir. This report updates linear regression models published in 2006 that were based on data collected during 2001 through 2003. The update uses discrete and continuous data collected during May 2001 through December 2009. Updated models to compute dissolved solids, sodium, chloride, and suspended solids were similar to previously published models. However, several other updated models changed substantially from previously published models. In addition to updating relations that were previously developed, models also were developed for four new constituents, including magnesium, dissolved phosphorus, actinomycetes bacteria, and the cyanotoxin microcystin. In addition, a conversion factor of 0.74 was established to convert the Yellow Springs Instruments (YSI) model 6026 turbidity sensor measurements to the newer YSI model 6136 sensor at the Cheney Reservoir site. Because a high percentage of geosmin and microcystin data were below analytical detection thresholds (censored data), multiple logistic regression was used to develop models that best explained the probability of geosmin and microcystin concentrations exceeding relevant thresholds. The geosmin and microcystin models are particularly important because geosmin is a taste-and-odor compound and microcystin is a cyanotoxin.

40 CFR 60.2760 - What information must I submit following my initial performance test?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.2860 - What are the emission limitations for air curtain incinerators?

Code of Federal Regulations, 2014 CFR

2014-07-01

... AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Air...

40 CFR 60.2670 - What emission limitations must I meet and by when?

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Emission...

40 CFR 60.2760 - What information must I submit following my initial performance test?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.2670 - What emission limitations must I meet and by when?

Code of Federal Regulations, 2013 CFR

2013-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Emission...

40 CFR 60.2715 - By what date must I conduct the annual performance test?

Code of Federal Regulations, 2013 CFR

2013-07-01

...) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Continuous...

40 CFR 60.2715 - By what date must I conduct the annual performance test?

Code of Federal Regulations, 2014 CFR

2014-07-01

...) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Continuous...

40 CFR 60.2865 - How must I monitor opacity for air curtain incinerators?

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Air...

Continuous Time Random Walks with memory and financial distributions

NASA Astrophysics Data System (ADS)

Montero, Miquel; Masoliver, Jaume

2017-11-01

We study financial distributions from the perspective of Continuous Time Random Walks with memory. We review some of our previous developments and apply them to financial problems. We also present some new models with memory that can be useful in characterizing tendency effects which are inherent in most markets. We also briefly study the effect on return distributions of fractional behaviors in the distribution of pausing times between successive transactions.

"It's about Improving My Practice": The Learner Experience of Real-Time Coaching

ERIC Educational Resources Information Center

Sharplin, Erica J.; Stahl, Garth; Kehrwald, Ben

2016-01-01

This article reports on pre-service teachers' experience of the Real-Time Coaching model, an innovative technology-based approach to teacher training. The Real-Time Coaching model uses multiple feedback cycles via wireless technology to develop within pre-service teachers the specific skills and mindset toward continual improvement. Results of…

A simple mathematical model of gradual Darwinian evolution: emergence of a Gaussian trait distribution in adaptation along a fitness gradient.

PubMed

Biktashev, Vadim N

2014-04-01

We consider a simple mathematical model of gradual Darwinian evolution in continuous time and continuous trait space, due to intraspecific competition for common resource in an asexually reproducing population in constant environment, while far from evolutionary stable equilibrium. The model admits exact analytical solution. In particular, Gaussian distribution of the trait emerges from generic initial conditions.

A Dynamical System Approach Explaining the Process of Development by Introducing Different Time-scales.

PubMed

Hashemi Kamangar, Somayeh Sadat; Moradimanesh, Zahra; Mokhtari, Setareh; Bakouie, Fatemeh

2018-06-11

A developmental process can be described as changes through time within a complex dynamic system. The self-organized changes and emergent behaviour during development can be described and modeled as a dynamical system. We propose a dynamical system approach to answer the main question in human cognitive development i.e. the changes during development happens continuously or in discontinuous stages. Within this approach there is a concept; the size of time scales, which can be used to address the aforementioned question. We introduce a framework, by considering the concept of time-scale, in which "fast" and "slow" is defined by the size of time-scales. According to our suggested model, the overall pattern of development can be seen as one continuous function, with different time-scales in different time intervals.

Heterogeneous continuous-time random walks

NASA Astrophysics Data System (ADS)

Grebenkov, Denis S.; Tupikina, Liubov

2018-01-01

We introduce a heterogeneous continuous-time random walk (HCTRW) model as a versatile analytical formalism for studying and modeling diffusion processes in heterogeneous structures, such as porous or disordered media, multiscale or crowded environments, weighted graphs or networks. We derive the exact form of the propagator and investigate the effects of spatiotemporal heterogeneities onto the diffusive dynamics via the spectral properties of the generalized transition matrix. In particular, we show how the distribution of first-passage times changes due to local and global heterogeneities of the medium. The HCTRW formalism offers a unified mathematical language to address various diffusion-reaction problems, with numerous applications in material sciences, physics, chemistry, biology, and social sciences.

Classical integrable defects as quasi Bäcklund transformations

NASA Astrophysics Data System (ADS)

Doikou, Anastasia

2016-10-01

We consider the algebraic setting of classical defects in discrete and continuous integrable theories. We derive the ;equations of motion; on the defect point via the space-like and time-like description. We then exploit the structural similarity of these equations with the discrete and continuous Bäcklund transformations. And although these equations are similar they are not exactly the same to the Bäcklund transformations. We also consider specific examples of integrable models to demonstrate our construction, i.e. the Toda chain and the sine-Gordon model. The equations of the time (space) evolution of the defect (discontinuity) degrees of freedom for these models are explicitly derived.

Occupation times and ergodicity breaking in biased continuous time random walks

NASA Astrophysics Data System (ADS)

Bel, Golan; Barkai, Eli

2005-12-01

Continuous time random walk (CTRW) models are widely used to model diffusion in condensed matter. There are two classes of such models, distinguished by the convergence or divergence of the mean waiting time. Systems with finite average sojourn time are ergodic and thus Boltzmann-Gibbs statistics can be applied. We investigate the statistical properties of CTRW models with infinite average sojourn time; in particular, the occupation time probability density function is obtained. It is shown that in the non-ergodic phase the distribution of the occupation time of the particle on a given lattice point exhibits bimodal U or trimodal W shape, related to the arcsine law. The key points are as follows. (a) In a CTRW with finite or infinite mean waiting time, the distribution of the number of visits on a lattice point is determined by the probability that a member of an ensemble of particles in equilibrium occupies the lattice point. (b) The asymmetry parameter of the probability distribution function of occupation times is related to the Boltzmann probability and to the partition function. (c) The ensemble average is given by Boltzmann-Gibbs statistics for either finite or infinite mean sojourn time, when detailed balance conditions hold. (d) A non-ergodic generalization of the Boltzmann-Gibbs statistical mechanics for systems with infinite mean sojourn time is found.

40 CFR Table 5 to Subpart Dddd of... - Model Rule-Summary of Reporting Requirements a

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 6 2010-07-01 2010-07-01 false Model Rule-Summary of Reporting Requirements a 5 Table 5 to Subpart DDDD of Part 60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial...

40 CFR Table 5 to Subpart Dddd of... - Model Rule-Summary of Reporting Requirements a

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 6 2011-07-01 2011-07-01 false Model Rule-Summary of Reporting Requirements a 5 Table 5 to Subpart DDDD of Part 60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial...

40 CFR Table 5 to Subpart Mmmm of... - Model Rule-Toxic Equivalency Factors

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 7 2013-07-01 2013-07-01 false Model Rule-Toxic Equivalency Factors 5 Table 5 to Subpart MMMM of Part 60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge...

40 CFR Table 5 to Subpart Mmmm of... - Model Rule-Toxic Equivalency Factors

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 7 2014-07-01 2014-07-01 false Model Rule-Toxic Equivalency Factors 5 Table 5 to Subpart MMMM of Part 60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge...

40 CFR Table 5 to Subpart Dddd of... - Model Rule-Summary of Reporting Requirements a

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 7 2013-07-01 2013-07-01 false Model Rule-Summary of Reporting Requirements a 5 Table 5 to Subpart DDDD of Part 60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial...

40 CFR Table 5 to Subpart Dddd of... - Model Rule-Summary of Reporting Requirements a

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 7 2014-07-01 2014-07-01 false Model Rule-Summary of Reporting Requirements a 5 Table 5 to Subpart DDDD of Part 60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial...

40 CFR Table 5 to Subpart Dddd of... - Model Rule-Summary of Reporting Requirements a

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 7 2012-07-01 2012-07-01 false Model Rule-Summary of Reporting Requirements a 5 Table 5 to Subpart DDDD of Part 60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial...

Educational Aspirations: Markov and Poisson Models. Rural Industrial Development Project Working Paper Number 14, August 1971.

ERIC Educational Resources Information Center

Kayser, Brian D.

The fit of educational aspirations of Illinois rural high school youths to 3 related one-parameter mathematical models was investigated. The models used were the continuous-time Markov chain model, the discrete-time Markov chain, and the Poisson distribution. The sample of 635 students responded to questionnaires from 1966 to 1969 as part of an…

Non-invasive continuous blood pressure measurement based on mean impact value method, BP neural network, and genetic algorithm.

PubMed

Tan, Xia; Ji, Zhong; Zhang, Yadan

2018-04-25

Non-invasive continuous blood pressure monitoring can provide an important reference and guidance for doctors wishing to analyze the physiological and pathological status of patients and to prevent and diagnose cardiovascular diseases in the clinical setting. Therefore, it is very important to explore a more accurate method of non-invasive continuous blood pressure measurement. To address the shortcomings of existing blood pressure measurement models based on pulse wave transit time or pulse wave parameters, a new method of non-invasive continuous blood pressure measurement - the GA-MIV-BP neural network model - is presented. The mean impact value (MIV) method is used to select the factors that greatly influence blood pressure from the extracted pulse wave transit time and pulse wave parameters. These factors are used as inputs, and the actual blood pressure values as outputs, to train the BP neural network model. The individual parameters are then optimized using a genetic algorithm (GA) to establish the GA-MIV-BP neural network model. Bland-Altman consistency analysis indicated that the measured and predicted blood pressure values were consistent and interchangeable. Therefore, this algorithm is of great significance to promote the clinical application of a non-invasive continuous blood pressure monitoring method.

Future Supply Chains Enabled by Continuous Processing—Opportunities and Challenges. May 20–21, 2014 Continuous Manufacturing Symposium

PubMed Central

Srai, Jagjit Singh; Badman, Clive; Krumme, Markus; Futran, Mauricio; Johnston, Craig

2015-01-01

This paper examines the opportunities and challenges facing the pharmaceutical industry in moving to a primarily “continuous processing”-based supply chain. The current predominantly “large batch” and centralized manufacturing system designed for the “blockbuster” drug has driven a slow-paced, inventory heavy operating model that is increasingly regarded as inflexible and unsustainable. Indeed, new markets and the rapidly evolving technology landscape will drive more product variety, shorter product life-cycles, and smaller drug volumes, which will exacerbate an already unsustainable economic model. Future supply chains will be required to enhance affordability and availability for patients and healthcare providers alike despite the increased product complexity. In this more challenging supply scenario, we examine the potential for a more pull driven, near real-time demand-based supply chain, utilizing continuous processing where appropriate as a key element of a more “flow-through” operating model. In this discussion paper on future supply chain models underpinned by developments in the continuous manufacture of pharmaceuticals, we have set out; The significant opportunities to moving to a supply chain flow-through operating model, with substantial opportunities in inventory reduction, lead-time to patient, and radically different product assurance/stability regimes. Scenarios for decentralized production models producing a greater variety of products with enhanced volume flexibility. Production, supply, and value chain footprints that are radically different from today's monolithic and centralized batch manufacturing operations. Clinical trial and drug product development cost savings that support more rapid scale-up and market entry models with early involvement of SC designers within New Product Development. The major supply chain and industrial transformational challenges that need to be addressed. The paper recognizes that although current batch operational performance in pharma is far from optimal and not necessarily an appropriate end-state benchmark for batch technology, the adoption of continuous supply chain operating models underpinned by continuous production processing, as full or hybrid solutions in selected product supply chains, can support industry transformations to deliver right-first-time quality at substantially lower inventory profiles. © 2015 The Authors. Journal of Pharmaceutical Sciences published by Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:840–849, 2015 PMID:25631279

Retrieval-travel-time model for free-fall-flow-rack automated storage and retrieval system

NASA Astrophysics Data System (ADS)

Metahri, Dhiyaeddine; Hachemi, Khalid

2018-03-01

Automated storage and retrieval systems (AS/RSs) are material handling systems that are frequently used in manufacturing and distribution centers. The modelling of the retrieval-travel time of an AS/RS (expected product delivery time) is practically important, because it allows us to evaluate and improve the system throughput. The free-fall-flow-rack AS/RS has emerged as a new technology for drug distribution. This system is a new variation of flow-rack AS/RS that uses an operator or a single machine for storage operations, and uses a combination between the free-fall movement and a transport conveyor for retrieval operations. The main contribution of this paper is to develop an analytical model of the expected retrieval-travel time for the free-fall flow-rack under a dedicated storage assignment policy. The proposed model, which is based on a continuous approach, is compared for accuracy, via simulation, with discrete model. The obtained results show that the maximum deviation between the continuous model and the simulation is less than 5%, which shows the accuracy of our model to estimate the retrieval time. The analytical model is useful to optimise the dimensions of the rack, assess the system throughput, and evaluate different storage policies.

Return probability after a quench from a domain wall initial state in the spin-1/2 XXZ chain

NASA Astrophysics Data System (ADS)

Stéphan, Jean-Marie

2017-10-01

We study the return probability and its imaginary (τ) time continuation after a quench from a domain wall initial state in the XXZ spin chain, focusing mainly on the region with anisotropy \\vert Δ\\vert < 1 . We establish exact Fredholm determinant formulas for those, by exploiting a connection to the six-vertex model with domain wall boundary conditions. In imaginary time, we find the expected scaling for a partition function of a statistical mechanical model of area proportional to τ2 , which reflects the fact that the model exhibits the limit shape phenomenon. In real time, we observe that in the region \\vert Δ\\vert <1 the decay for long time t is nowhere continuous as a function of anisotropy: it is Gaussian at roots of unity and exponential otherwise. We also determine that the front moves as x_f(t)=t\\sqrt{1-Δ^2} , by the analytic continuation of known arctic curves in the six-vertex model. Exactly at \\vert Δ\\vert =1 , we find the return probability decays as e-\\zeta(3/2) \\sqrt{t/π}t1/2O(1) . It is argued that this result provides an upper bound on spin transport. In particular, it suggests that transport should be diffusive at the isotropic point for this quench.

Continuous Time Random Walk and Migration-Proliferation Dichotomy of Brain Cancer

NASA Astrophysics Data System (ADS)

Iomin, A.

A theory of fractional kinetics of glial cancer cells is presented. A role of the migration-proliferation dichotomy in the fractional cancer cell dynamics in the outer-invasive zone is discussed and explained in the framework of a continuous time random walk. The main suggested model is based on a construction of a 3D comb model, where the migration-proliferation dichotomy becomes naturally apparent and the outer-invasive zone of glioma cancer is considered as a fractal composite with a fractal dimension Dfr < 3.

40 CFR 60.5115 - How do I comply with the increment of progress for achieving final compliance?

Code of Federal Regulations, 2011 CFR

2011-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge Incineration Units Model Rule...

40 CFR 60.2790 - Are there any other notifications or reports that I must submit?

Code of Federal Regulations, 2013 CFR

2013-07-01

... AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule...

40 CFR 60.2590 - When must I submit the notifications of achievement of increments of progress?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.2590 - When must I submit the notifications of achievement of increments of progress?

Code of Federal Regulations, 2012 CFR

2012-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.2790 - Are there any other notifications or reports that I must submit?

Code of Federal Regulations, 2014 CFR

2014-07-01

... AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule...

40 CFR 60.2830 - When must I submit the notifications of achievement of increments of progress?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.2590 - When must I submit the notifications of achievement of increments of progress?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.5100 - When must I submit the notifications of achievement of increments of progress?

Code of Federal Regulations, 2011 CFR

2011-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge Incineration Units Model Rule...

Continuing Development of a Hybrid Model (VSH) of the Neutral Thermosphere

NASA Technical Reports Server (NTRS)

Burns, Alan

1996-01-01

We propose to continue the development of a new operational model of neutral thermospheric density, composition, temperatures and winds to improve current engineering environment definitions of the neutral thermosphere. This model will be based on simulations made with the National Center for Atmospheric Research (NCAR) Thermosphere-Ionosphere- Electrodynamic General Circulation Model (TIEGCM) and on empirical data. It will be capable of using real-time geophysical indices or data from ground-based and satellite inputs and provides neutral variables at specified locations and times. This "hybrid" model will be based on a Vector Spherical Harmonic (VSH) analysis technique developed (over the last 8 years) at the University of Michigan that permits the incorporation of the TIGCM outputs and data into the model. The VSH model will be a more accurate version of existing models of the neutral thermospheric, and will thus improve density specification for satellites flying in low Earth orbit (LEO).

A probabilistic method for constructing wave time-series at inshore locations using model scenarios

USGS Publications Warehouse

Long, Joseph W.; Plant, Nathaniel G.; Dalyander, P. Soupy; Thompson, David M.

2014-01-01

Continuous time-series of wave characteristics (height, period, and direction) are constructed using a base set of model scenarios and simple probabilistic methods. This approach utilizes an archive of computationally intensive, highly spatially resolved numerical wave model output to develop time-series of historical or future wave conditions without performing additional, continuous numerical simulations. The archive of model output contains wave simulations from a set of model scenarios derived from an offshore wave climatology. Time-series of wave height, period, direction, and associated uncertainties are constructed at locations included in the numerical model domain. The confidence limits are derived using statistical variability of oceanographic parameters contained in the wave model scenarios. The method was applied to a region in the northern Gulf of Mexico and assessed using wave observations at 12 m and 30 m water depths. Prediction skill for significant wave height is 0.58 and 0.67 at the 12 m and 30 m locations, respectively, with similar performance for wave period and direction. The skill of this simplified, probabilistic time-series construction method is comparable to existing large-scale, high-fidelity operational wave models but provides higher spatial resolution output at low computational expense. The constructed time-series can be developed to support a variety of applications including climate studies and other situations where a comprehensive survey of wave impacts on the coastal area is of interest.

Coupled continuous time-random walks in quenched random environment

NASA Astrophysics Data System (ADS)

Magdziarz, M.; Szczotka, W.

2018-02-01

We introduce a coupled continuous-time random walk with coupling which is characteristic for Lévy walks. Additionally we assume that the walker moves in a quenched random environment, i.e. the site disorder at each lattice point is fixed in time. We analyze the scaling limit of such a random walk. We show that for large times the behaviour of the analyzed process is exactly the same as in the case of uncoupled quenched trap model for Lévy flights.

Generalized classes of continuous symmetries in two-mode Dicke models

NASA Astrophysics Data System (ADS)

Moodie, Ryan I.; Ballantine, Kyle E.; Keeling, Jonathan

2018-03-01

As recently realized experimentally [Nature (London) 543, 87 (2017), 10.1038/nature21067], one can engineer models with continuous symmetries by coupling two cavity modes to trapped atoms via a Raman pumping geometry. Considering specifically cases where internal states of the atoms couple to the cavity, we show an extended range of parameters for which continuous symmetry breaking can occur, and we classify the distinct steady states and time-dependent states that arise for different points in this extended parameter regime.

Susceptible-infected-susceptible epidemics on networks with general infection and cure times.

PubMed

Cator, E; van de Bovenkamp, R; Van Mieghem, P

2013-06-01

The classical, continuous-time susceptible-infected-susceptible (SIS) Markov epidemic model on an arbitrary network is extended to incorporate infection and curing or recovery times each characterized by a general distribution (rather than an exponential distribution as in Markov processes). This extension, called the generalized SIS (GSIS) model, is believed to have a much larger applicability to real-world epidemics (such as information spread in online social networks, real diseases, malware spread in computer networks, etc.) that likely do not feature exponential times. While the exact governing equations for the GSIS model are difficult to deduce due to their non-Markovian nature, accurate mean-field equations are derived that resemble our previous N-intertwined mean-field approximation (NIMFA) and so allow us to transfer the whole analytic machinery of the NIMFA to the GSIS model. In particular, we establish the criterion to compute the epidemic threshold in the GSIS model. Moreover, we show that the average number of infection attempts during a recovery time is the more natural key parameter, instead of the effective infection rate in the classical, continuous-time SIS Markov model. The relative simplicity of our mean-field results enables us to treat more general types of SIS epidemics, while offering an easier key parameter to measure the average activity of those general viral agents.

Susceptible-infected-susceptible epidemics on networks with general infection and cure times

NASA Astrophysics Data System (ADS)

Cator, E.; van de Bovenkamp, R.; Van Mieghem, P.

2013-06-01

The classical, continuous-time susceptible-infected-susceptible (SIS) Markov epidemic model on an arbitrary network is extended to incorporate infection and curing or recovery times each characterized by a general distribution (rather than an exponential distribution as in Markov processes). This extension, called the generalized SIS (GSIS) model, is believed to have a much larger applicability to real-world epidemics (such as information spread in online social networks, real diseases, malware spread in computer networks, etc.) that likely do not feature exponential times. While the exact governing equations for the GSIS model are difficult to deduce due to their non-Markovian nature, accurate mean-field equations are derived that resemble our previous N-intertwined mean-field approximation (NIMFA) and so allow us to transfer the whole analytic machinery of the NIMFA to the GSIS model. In particular, we establish the criterion to compute the epidemic threshold in the GSIS model. Moreover, we show that the average number of infection attempts during a recovery time is the more natural key parameter, instead of the effective infection rate in the classical, continuous-time SIS Markov model. The relative simplicity of our mean-field results enables us to treat more general types of SIS epidemics, while offering an easier key parameter to measure the average activity of those general viral agents.

Temperature-Dependent Kinetic Prediction for Reactions Described by Isothermal Mathematics

DOE PAGES

Dinh, L. N.; Sun, T. C.; McLean, W.

2016-09-12

Most kinetic models are expressed in isothermal mathematics. In addition, this may lead unaware scientists either to the misconception that classical isothermal kinetic models cannot be used for any chemical process in an environment with a time-dependent temperature profile or, even worse, to a misuse of them. In reality, classical isothermal models can be employed to make kinetic predictions for reactions in environments with time-dependent temperature profiles, provided that there is a continuity/conservation in the reaction extent at every temperature–time step. In this article, fundamental analyses, illustrations, guiding tables, and examples are given to help the interested readers using eithermore » conventional isothermal reacted fraction curves or rate equations to make proper kinetic predictions for chemical reactions in environments with temperature profiles that vary, even arbitrarily, with time simply by the requirement of continuity/conservation of reaction extent whenever there is an external temperature change.« less

Ergodic Transition in a Simple Model of the Continuous Double Auction

PubMed Central

Radivojević, Tijana; Anselmi, Jonatha; Scalas, Enrico

2014-01-01

We study a phenomenological model for the continuous double auction, whose aggregate order process is equivalent to two independent queues. The continuous double auction defines a continuous-time random walk for trade prices. The conditions for ergodicity of the auction are derived and, as a consequence, three possible regimes in the behavior of prices and logarithmic returns are observed. In the ergodic regime, prices are unstable and one can observe a heteroskedastic behavior in the logarithmic returns. On the contrary, non-ergodicity triggers stability of prices, even if two different regimes can be seen. PMID:24558377

Ergodic transition in a simple model of the continuous double auction.

PubMed

Radivojević, Tijana; Anselmi, Jonatha; Scalas, Enrico

2014-01-01

We study a phenomenological model for the continuous double auction, whose aggregate order process is equivalent to two independent M/M/1 queues. The continuous double auction defines a continuous-time random walk for trade prices. The conditions for ergodicity of the auction are derived and, as a consequence, three possible regimes in the behavior of prices and logarithmic returns are observed. In the ergodic regime, prices are unstable and one can observe a heteroskedastic behavior in the logarithmic returns. On the contrary, non-ergodicity triggers stability of prices, even if two different regimes can be seen.

Introduction to focus issue: Synchronization in large networks and continuous media—data, models, and supermodels

NASA Astrophysics Data System (ADS)

Duane, Gregory S.; Grabow, Carsten; Selten, Frank; Ghil, Michael

2017-12-01

The synchronization of loosely coupled chaotic systems has increasingly found applications to large networks of differential equations and to models of continuous media. These applications are at the core of the present Focus Issue. Synchronization between a system and its model, based on limited observations, gives a new perspective on data assimilation. Synchronization among different models of the same system defines a supermodel that can achieve partial consensus among models that otherwise disagree in several respects. Finally, novel methods of time series analysis permit a better description of synchronization in a system that is only observed partially and for a relatively short time. This Focus Issue discusses synchronization in extended systems or in components thereof, with particular attention to data assimilation, supermodeling, and their applications to various areas, from climate modeling to macroeconomics.

Introduction to focus issue: Synchronization in large networks and continuous media-data, models, and supermodels.

PubMed

Duane, Gregory S; Grabow, Carsten; Selten, Frank; Ghil, Michael

2017-12-01

The synchronization of loosely coupled chaotic systems has increasingly found applications to large networks of differential equations and to models of continuous media. These applications are at the core of the present Focus Issue. Synchronization between a system and its model, based on limited observations, gives a new perspective on data assimilation. Synchronization among different models of the same system defines a supermodel that can achieve partial consensus among models that otherwise disagree in several respects. Finally, novel methods of time series analysis permit a better description of synchronization in a system that is only observed partially and for a relatively short time. This Focus Issue discusses synchronization in extended systems or in components thereof, with particular attention to data assimilation, supermodeling, and their applications to various areas, from climate modeling to macroeconomics.

Finite State Models of Manned Systems: Validation, Simplification, and Extension.

DTIC Science & Technology

1979-11-01

model a time set is needed. A time set is some set T together with a binary relation defined on T which linearly orders the set. If "model time" is...discrete, so is T ; continuous time is represented by a set corresponding to a subset of the non-negative real numbers. In the following discussion time...defined as sequences, over time, of input and outIut values. The notion of sequences or trajectories is formalized as: AT = xx: T -- Al BT = tyIy: T -4BJ AT

Why is past depression the best predictor of future depression? Stress generation as a mechanism of depression continuity in girls.

PubMed

Rudolph, Karen D; Flynn, Megan; Abaied, Jamie L; Groot, Alison; Thompson, Renee

2009-07-01

This study examined whether a transactional interpersonal life stress model helps to explain the continuity in depression over time in girls. Youth (86 girls, 81 boys; M age = 12.41, SD = 1.19) and their caregivers participated in a three-wave longitudinal study. Depression and episodic life stress were assessed with semistructured interviews. Path analysis provided support for a transactional interpersonal life stress model in girls but not in boys, wherein depression predicted the generation of interpersonal stress, which predicted subsequent depression. Moreover, self-generated interpersonal stress partially accounted for the continuity of depression over time. Although depression predicted noninterpersonal stress generation in girls (but not in boys), noninterpersonal stress did not predict subsequent depression.

A Second-Order Conditionally Linear Mixed Effects Model with Observed and Latent Variable Covariates

ERIC Educational Resources Information Center

Harring, Jeffrey R.; Kohli, Nidhi; Silverman, Rebecca D.; Speece, Deborah L.

2012-01-01

A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a…

Dealing with Time in Health Economic Evaluation: Methodological Issues and Recommendations for Practice.

PubMed

O'Mahony, James F; Newall, Anthony T; van Rosmalen, Joost

2015-12-01

Time is an important aspect of health economic evaluation, as the timing and duration of clinical events, healthcare interventions and their consequences all affect estimated costs and effects. These issues should be reflected in the design of health economic models. This article considers three important aspects of time in modelling: (1) which cohorts to simulate and how far into the future to extend the analysis; (2) the simulation of time, including the difference between discrete-time and continuous-time models, cycle lengths, and converting rates and probabilities; and (3) discounting future costs and effects to their present values. We provide a methodological overview of these issues and make recommendations to help inform both the conduct of cost-effectiveness analyses and the interpretation of their results. For choosing which cohorts to simulate and how many, we suggest analysts carefully assess potential reasons for variation in cost effectiveness between cohorts and the feasibility of subgroup-specific recommendations. For the simulation of time, we recommend using short cycles or continuous-time models to avoid biases and the need for half-cycle corrections, and provide advice on the correct conversion of transition probabilities in state transition models. Finally, for discounting, analysts should not only follow current guidance and report how discounting was conducted, especially in the case of differential discounting, but also seek to develop an understanding of its rationale. Our overall recommendations are that analysts explicitly state and justify their modelling choices regarding time and consider how alternative choices may impact on results.

Consensus for linear multi-agent system with intermittent information transmissions using the time-scale theory

NASA Astrophysics Data System (ADS)

Taousser, Fatima; Defoort, Michael; Djemai, Mohamed

2016-01-01

This paper investigates the consensus problem for linear multi-agent system with fixed communication topology in the presence of intermittent communication using the time-scale theory. Since each agent can only obtain relative local information intermittently, the proposed consensus algorithm is based on a discontinuous local interaction rule. The interaction among agents happens at a disjoint set of continuous-time intervals. The closed-loop multi-agent system can be represented using mixed linear continuous-time and linear discrete-time models due to intermittent information transmissions. The time-scale theory provides a powerful tool to combine continuous-time and discrete-time cases and study the consensus protocol under a unified framework. Using this theory, some conditions are derived to achieve exponential consensus under intermittent information transmissions. Simulations are performed to validate the theoretical results.

Bayesian functional integral method for inferring continuous data from discrete measurements.

PubMed

Heuett, William J; Miller, Bernard V; Racette, Susan B; Holloszy, John O; Chow, Carson C; Periwal, Vipul

2012-02-08

Inference of the insulin secretion rate (ISR) from C-peptide measurements as a quantification of pancreatic β-cell function is clinically important in diseases related to reduced insulin sensitivity and insulin action. ISR derived from C-peptide concentration is an example of nonparametric Bayesian model selection where a proposed ISR time-course is considered to be a "model". An inferred value of inaccessible continuous variables from discrete observable data is often problematic in biology and medicine, because it is a priori unclear how robust the inference is to the deletion of data points, and a closely related question, how much smoothness or continuity the data actually support. Predictions weighted by the posterior distribution can be cast as functional integrals as used in statistical field theory. Functional integrals are generally difficult to evaluate, especially for nonanalytic constraints such as positivity of the estimated parameters. We propose a computationally tractable method that uses the exact solution of an associated likelihood function as a prior probability distribution for a Markov-chain Monte Carlo evaluation of the posterior for the full model. As a concrete application of our method, we calculate the ISR from actual clinical C-peptide measurements in human subjects with varying degrees of insulin sensitivity. Our method demonstrates the feasibility of functional integral Bayesian model selection as a practical method for such data-driven inference, allowing the data to determine the smoothing timescale and the width of the prior probability distribution on the space of models. In particular, our model comparison method determines the discrete time-step for interpolation of the unobservable continuous variable that is supported by the data. Attempts to go to finer discrete time-steps lead to less likely models. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

40 CFR 60.2610 - What must I do if I close my CISWI unit and then restart it?

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule...

40 CFR 60.2845 - How do I comply with the increment of progress for achieving final compliance?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.5085 - What are my requirements for meeting increments of progress and achieving final compliance?

Code of Federal Regulations, 2011 CFR

2011-07-01

... ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge Incineration Units Model Rule...

40 CFR 60.2610 - What must I do if I close my CISWI unit and then restart it?

Code of Federal Regulations, 2013 CFR

2013-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule...

40 CFR 60.5095 - What must I include in the notifications of achievement of increments of progress?

Code of Federal Regulations, 2011 CFR

2011-07-01

... ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge Incineration Units Model Rule...

40 CFR 60.2605 - How do I comply with the increment of progress for achieving final compliance?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.2605 - How do I comply with the increment of progress for achieving final compliance?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

How parasitism affects critical patch-size in a host-parasitoid model: application to the forest tent caterpillar.

PubMed

Cobbold, C A; Lewis, M A; Lutscher, F; Roland, J

2005-03-01

Habitat structure has broad impacts on many biological systems. In particular, habitat fragmentation can increase the probability of species extinction and on the other hand it can lead to population outbreaks in response to a decline in natural enemies. An extreme consequence of fragmentation is the isolation of small regions of suitable habitat surrounded by a large region of hostile matrix. This scenario can be interpreted as a critical patch-size problem, well studied in a continuous time framework, but relatively new to discrete time models. In this paper we present an integrodifference host-parasitoid model, discrete in time and continuous in space, to study how the critical habitat-size necessary for parasitoid survival changes in response to parasitoid life history traits, such as emergence time. We show that early emerging parasitoids may be able to persist in smaller habitats than late emerging species. The model predicts that these early emerging parasitoids lead to more severe host outbreaks. We hypothesise that promoting efficient late emerging parasitoids may be key in reducing outbreak severity, an approach requiring large continuous regions of suitable habitat. We parameterise the model for the host species of the forest tent caterpillar Malacosoma disstria Hbn., a pest insect for which fragmented landscape increases the severity of outbreaks. This host is known to have several parasitoids, due to paucity of data and as a first step in the modelling we consider a single generic parasitoid. The model findings are related to observations of the forest tent caterpillar offering insight into this host-parasitoid response to habitat structure.

An Expert System for the Evaluation of Cost Models

DTIC Science & Technology

1990-09-01

contrast to the condition of equal error variance, called homoscedasticity. (Reference: Applied Linear Regression Models by John Neter - page 423...normal. (Reference: Applied Linear Regression Models by John Neter - page 125) Click Here to continue -> Autocorrelation Click Here for the index - Index...over time. Error terms correlated over time are said to be autocorrelated or serially correlated. (REFERENCE: Applied Linear Regression Models by John

Reducing cognitive skill decay and diagnostic error: theory-based practices for continuing education in health care.

PubMed

Weaver, Sallie J; Newman-Toker, David E; Rosen, Michael A

2012-01-01

Missed, delayed, or wrong diagnoses can have a severe impact on patients, providers, and the entire health care system. One mechanism implicated in such diagnostic errors is the deterioration of cognitive diagnostic skills that are used rarely or not at all over a prolonged period of time. Existing evidence regarding maintenance of effective cognitive reasoning skills in the clinical education, organizational training, and human factors literatures suggest that continuing education plays a critical role in mitigating and managing diagnostic skill decay. Recent models also underscore the role of system level factors (eg, cognitive decision support tools, just-in-time training opportunities) in supporting clinical reasoning process. The purpose of this manuscript is to offer a multidisciplinary review of cognitive models of clinical decision making skills in order to provide a list of best practices for supporting continuous improvement and maintenance of cognitive diagnostic processes through continuing education. Copyright © 2012 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on CME, Association for Hospital Medical Education.

Subordinated continuous-time AR processes and their application to modeling behavior of mechanical system

NASA Astrophysics Data System (ADS)

Gajda, Janusz; Wyłomańska, Agnieszka; Zimroz, Radosław

2016-12-01

Many real data exhibit behavior adequate to subdiffusion processes. Very often it is manifested by so-called ;trapping events;. The visible evidence of subdiffusion we observe not only in financial time series but also in technical data. In this paper we propose a model which can be used for description of such kind of data. The model is based on the continuous time autoregressive time series with stable noise delayed by the infinitely divisible inverse subordinator. The proposed system can be applied to real datasets with short-time dependence, visible jumps and mentioned periods of stagnation. In this paper we extend the theoretical considerations in analysis of subordinated processes and propose a new model that exhibits mentioned properties. We concentrate on the main characteristics of the examined subordinated process expressed mainly in the language of the measures of dependence which are main tools used in statistical investigation of real data. We present also the simulation procedure of the considered system and indicate how to estimate its parameters. The theoretical results we illustrate by the analysis of real technical data.

Transfer Function Identification Using Orthogonal Fourier Transform Modeling Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2013-01-01

A method for transfer function identification, including both model structure determination and parameter estimation, was developed and demonstrated. The approach uses orthogonal modeling functions generated from frequency domain data obtained by Fourier transformation of time series data. The method was applied to simulation data to identify continuous-time transfer function models and unsteady aerodynamic models. Model fit error, estimated model parameters, and the associated uncertainties were used to show the effectiveness of the method for identifying accurate transfer function models from noisy data.

Determination of recharge fraction of injection water in combined abstraction-injection wells using continuous radon monitoring.

PubMed

Lee, Kil Yong; Kim, Yong-Chul; Cho, Soo Young; Kim, Seong Yun; Yoon, Yoon Yeol; Koh, Dong Chan; Ha, Kyucheol; Ko, Kyung-Seok

2016-12-01

The recharge fractions of injection water in combined abstraction-injection wells (AIW) were determined using continuous radon monitoring and radon mass balance model. The recharge system consists of three combined abstraction-injection wells, an observation well, a collection tank, an injection tank, and tubing for heating and transferring used groundwater. Groundwater was abstracted from an AIW and sprayed on the water-curtain heating facility and then the used groundwater was injected into the same AIW well by the recharge system. Radon concentrations of fresh groundwater in the AIWs and of used groundwater in the injection tank were measured continuously using a continuous radon monitoring system. Radon concentrations of fresh groundwater in the AIWs and used groundwater in the injection tank were in the ranges of 10,830-13,530 Bq/m 3 and 1500-5600 Bq/m 3 , respectively. A simple radon mass balance model was developed to estimate the recharge fraction of used groundwater in the AIWs. The recharge fraction in the 3 AIWs was in the range of 0.595-0.798. The time series recharge fraction could be obtained using the continuous radon monitoring system with a simple radon mass balance model. The results revealed that the radon mass balance model using continuous radon monitoring was effective for determining the time series recharge fractions in AIWs as well as for characterizing the recharge system. Copyright © 2016 Elsevier Ltd. All rights reserved.

Noise in Neuronal and Electronic Circuits: A General Modeling Framework and Non-Monte Carlo Simulation Techniques.

PubMed

Kilinc, Deniz; Demir, Alper

2017-08-01

The brain is extremely energy efficient and remarkably robust in what it does despite the considerable variability and noise caused by the stochastic mechanisms in neurons and synapses. Computational modeling is a powerful tool that can help us gain insight into this important aspect of brain mechanism. A deep understanding and computational design tools can help develop robust neuromorphic electronic circuits and hybrid neuroelectronic systems. In this paper, we present a general modeling framework for biological neuronal circuits that systematically captures the nonstationary stochastic behavior of ion channels and synaptic processes. In this framework, fine-grained, discrete-state, continuous-time Markov chain models of both ion channels and synaptic processes are treated in a unified manner. Our modeling framework features a mechanism for the automatic generation of the corresponding coarse-grained, continuous-state, continuous-time stochastic differential equation models for neuronal variability and noise. Furthermore, we repurpose non-Monte Carlo noise analysis techniques, which were previously developed for analog electronic circuits, for the stochastic characterization of neuronal circuits both in time and frequency domain. We verify that the fast non-Monte Carlo analysis methods produce results with the same accuracy as computationally expensive Monte Carlo simulations. We have implemented the proposed techniques in a prototype simulator, where both biological neuronal and analog electronic circuits can be simulated together in a coupled manner.

Continuous-time random-walk model for financial distributions

NASA Astrophysics Data System (ADS)

Masoliver, Jaume; Montero, Miquel; Weiss, George H.

2003-02-01

We apply the formalism of the continuous-time random walk to the study of financial data. The entire distribution of prices can be obtained once two auxiliary densities are known. These are the probability densities for the pausing time between successive jumps and the corresponding probability density for the magnitude of a jump. We have applied the formalism to data on the U.S. dollar deutsche mark future exchange, finding good agreement between theory and the observed data.

40 CFR 60.2850 - What must I do if I close my air curtain incinerator and then restart it?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.2600 - How do I comply with the increment of progress for submittal of a control plan?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.2600 - How do I comply with the increment of progress for submittal of a control plan?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.2840 - How do I comply with the increment of progress for submittal of a control plan?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

40 CFR 60.2600 - How do I comply with the increment of progress for submittal of a control plan?

Code of Federal Regulations, 2012 CFR

2012-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model...

Diffusion theory of decision making in continuous report.

PubMed

Smith, Philip L

2016-07-01

I present a diffusion model for decision making in continuous report tasks, in which a continuous, circularly distributed, stimulus attribute in working memory is matched to a representation of the attribute in the stimulus display. Memory retrieval is modeled as a 2-dimensional diffusion process with vector-valued drift on a disk, whose bounding circle represents the decision criterion. The direction and magnitude of the drift vector describe the identity of the stimulus and the quality of its representation in memory, respectively. The point at which the diffusion exits the disk determines the reported value of the attribute and the time to exit the disk determines the decision time. Expressions for the joint distribution of decision times and report outcomes are obtained by means of the Girsanov change-of-measure theorem, which allows the properties of the nonzero-drift diffusion process to be characterized as a function of a Euclidian-distance Bessel process. Predicted report precision is equal to the product of the decision criterion and the drift magnitude and follows a von Mises distribution, in agreement with the treatment of precision in the working memory literature. Trial-to-trial variability in criterion and drift rate leads, respectively, to direct and inverse relationships between report accuracy and decision times, in agreement with, and generalizing, the standard diffusion model of 2-choice decisions. The 2-dimensional model provides a process account of working memory precision and its relationship with the diffusion model, and a new way to investigate the properties of working memory, via the distributions of decision times. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Spatiotemporal stochastic models for earth science and engineering applications

NASA Astrophysics Data System (ADS)

Luo, Xiaochun

1998-12-01

Spatiotemporal processes occur in many areas of earth sciences and engineering. However, most of the available theoretical tools and techniques of space-time daft processing have been designed to operate exclusively in time or in space, and the importance of spatiotemporal variability was not fully appreciated until recently. To address this problem, a systematic framework of spatiotemporal random field (S/TRF) models for geoscience/engineering applications is presented and developed in this thesis. The space-tune continuity characterization is one of the most important aspects in S/TRF modelling, where the space-time continuity is displayed with experimental spatiotemporal variograms, summarized in terms of space-time continuity hypotheses, and modelled using spatiotemporal variogram functions. Permissible spatiotemporal covariance/variogram models are addressed through permissibility criteria appropriate to spatiotemporal processes. The estimation of spatiotemporal processes is developed in terms of spatiotemporal kriging techniques. Particular emphasis is given to the singularity analysis of spatiotemporal kriging systems. The impacts of covariance, functions, trend forms, and data configurations on the singularity of spatiotemporal kriging systems are discussed. In addition, the tensorial invariance of universal spatiotemporal kriging systems is investigated in terms of the space-time trend. The conditional simulation of spatiotemporal processes is proposed with the development of the sequential group Gaussian simulation techniques (SGGS), which is actually a series of sequential simulation algorithms associated with different group sizes. The simulation error is analyzed with different covariance models and simulation grids. The simulated annealing technique honoring experimental variograms, is also proposed, providing a way of conditional simulation without the covariance model fitting which is prerequisite for most simulation algorithms. The proposed techniques were first applied for modelling of the pressure system in a carbonate reservoir, and then applied for modelling of springwater contents in the Dyle watershed. The results of these case studies as well as the theory suggest that these techniques are realistic and feasible.

Fluid limit of nonintegrable continuous-time random walks in terms of fractional differential equations.

PubMed

Sánchez, R; Carreras, B A; van Milligen, B Ph

2005-01-01

The fluid limit of a recently introduced family of nonintegrable (nonlinear) continuous-time random walks is derived in terms of fractional differential equations. In this limit, it is shown that the formalism allows for the modeling of the interaction between multiple transport mechanisms with not only disparate spatial scales but also different temporal scales. For this reason, the resulting fluid equations may find application in the study of a large number of nonlinear multiscale transport problems, ranging from the study of self-organized criticality to the modeling of turbulent transport in fluids and plasmas.

Improved continuity of care in a resident clinic.

PubMed

Butler, Melissa; Kim, Hyungkoo; Sansone, Randy

2017-02-01

For residents in the out-patient clinic, continuity in patient care is an integral and vital aspect of internal medicine training, but is frequently compromised by resident in-patient schedules, the structure of the out-patient clinic and the need to comply with the increasing regulation of duty hours. In this study, we examined whether the creation and implementation of a new team approach, the Firms Model, would improve the continuity of patient care in the internal medicine resident out-patient clinic. Before the implementation of the Firms Model, an examination of a consecutive clinic sample indicated that patients were seen by their assigned resident providers 41.9 per cent of the time (n = 1319 clinic visits). After implementation of the Firms Model, an examination of a consecutive clinic sample indicated that patients were seen by their assigned Firm resident providers 88.9 per cent of the time (n = 1341 clinic visits). Implementation of the Firms Model resulted in a statistically significant increase in the percentage of patients seen by assigned resident providers in an internal medicine out-patient clinic, culminating in a substantial improvement in continuity of care within our resident out-patient clinic. We discuss the implications of these findings. Continuity in patient care is an integral and vital aspect of internal medicine training, but is frequently compromised. © 2016 John Wiley & Sons Ltd.

Simulation Modeling of Software Development Processes

NASA Technical Reports Server (NTRS)

Calavaro, G. F.; Basili, V. R.; Iazeolla, G.

1996-01-01

A simulation modeling approach is proposed for the prediction of software process productivity indices, such as cost and time-to-market, and the sensitivity analysis of such indices to changes in the organization parameters and user requirements. The approach uses a timed Petri Net and Object Oriented top-down model specification. Results demonstrate the model representativeness, and its usefulness in verifying process conformance to expectations, and in performing continuous process improvement and optimization.

Application of DIVWAG at Rodman Laboratory

DTIC Science & Technology

1976-03-01

Continue on reveree eide It neceaemry mnd identify by block number) DIVWAG War Game Simulation Mathematical Model 20. ABSTRACT (Continue on...parameters. Rodman Laboratory is using DIVWAG in a simulation mode. In this model of operation, once a game has been completed, a representative...a period of play by a blue (red) artillery battery as a function of range and game time). t Ü UNCLASSIFIED SECURITY CLASSIFICATION OF THIS

The continuous similarity model of bulk soil-water evaporation

NASA Technical Reports Server (NTRS)

Clapp, R. B.

1983-01-01

The continuous similarity model of evaporation is described. In it, evaporation is conceptualized as a two stage process. For an initially moist soil, evaporation is first climate limited, but later it becomes soil limited. During the latter stage, the evaporation rate is termed evaporability, and mathematically it is inversely proportional to the evaporation deficit. A functional approximation of the moisture distribution within the soil column is also included in the model. The model was tested using data from four experiments conducted near Phoenix, Arizona; and there was excellent agreement between the simulated and observed evaporation. The model also predicted the time of transition to the soil limited stage reasonably well. For one of the experiments, a third stage of evaporation, when vapor diffusion predominates, was observed. The occurrence of this stage was related to the decrease in moisture at the surface of the soil. The continuous similarity model does not account for vapor flow. The results show that climate, through the potential evaporation rate, has a strong influence on the time of transition to the soil limited stage. After this transition, however, bulk evaporation is independent of climate until the effects of vapor flow within the soil predominate.

Why is Past Depression the Best Predictor of Future Depression? Stress Generation as a Mechanism of Depression Continuity in Girls

PubMed Central

Rudolph, Karen D.; Flynn, Megan; Abaied, Jamie; Groot, Alison; Thompson, Renee

2009-01-01

This study examined whether a transactional interpersonal life stress model helps to explain the continuity in depression over time in girls. Youth (86 girls, 81 boys; M age = 12.41, SD = 1.19) and their caregivers participated in a three-wave longitudinal study. Depression and episodic life stress were assessed with semi-structured interviews. Path analysis provided support for a transactional interpersonal life stress model in girls but not in boys, wherein depression predicted the generation of interpersonal stress, which predicted subsequent depression. Moreover, self-generated interpersonal stress partially accounted for the continuity of depression over time. Although depression predicted noninterpersonal stress generation in girls (but not in boys), noninterpersonal stress did not predict subsequent depression. PMID:20183635

Hybrid Discrete-Continuous Markov Decision Processes

NASA Technical Reports Server (NTRS)

Feng, Zhengzhu; Dearden, Richard; Meuleau, Nicholas; Washington, Rich

2003-01-01

This paper proposes a Markov decision process (MDP) model that features both discrete and continuous state variables. We extend previous work by Boyan and Littman on the mono-dimensional time-dependent MDP to multiple dimensions. We present the principle of lazy discretization, and piecewise constant and linear approximations of the model. Having to deal with several continuous dimensions raises several new problems that require new solutions. In the (piecewise) linear case, we use techniques from partially- observable MDPs (POMDPS) to represent value functions as sets of linear functions attached to different partitions of the state space.

Continuous versus short-term infusion of cefuroxime: assessment of concept based on plasma, subcutaneous tissue, and bone pharmacokinetics in an animal model.

PubMed

Tøttrup, Mikkel; Bibby, Bo M; Hardlei, Tore F; Bue, Mats; Kerrn-Jespersen, Sigrid; Fuursted, Kurt; Søballe, Kjeld; Birke-Sørensen, Hanne

2015-01-01

The relatively short half-lives of most β-lactams suggest that continuous infusion of these time-dependent antimicrobials may be favorable compared to short-term infusion. Nevertheless, only limited solid-tissue pharmacokinetic data are available to support this theory. In this study, we randomly assigned 12 pigs to receive cefuroxime as either a short-term or continuous infusion. Measurements of cefuroxime were obtained every 30 min in plasma, subcutaneous tissue, and bone. For the measurements in solid tissues, microdialysis was applied. A two-compartment population model was fitted separately to the drug concentration data for the different tissues using a nonlinear mixed-effects regression model. Estimates of the pharmacokinetic parameters and time with concentrations above the MIC were derived using Monte Carlo simulations. Except for subcutaneous tissue in the short-term infusion group, the tissue penetration was incomplete for all tissues. For short-term infusion, the tissue penetration ratios were 0.97 (95% confidence interval [CI], 0.67 to 1.39), 0.61 (95% CI, 0.51 to 0.73), and 0.45 (95% CI, 0.36 to 0.56) for subcutaneous tissue, cancellous bone, and cortical bone, respectively. For continuous infusion, they were 0.53 (95% CI, 0.33 to 0.84), 0.38 (95% CI, 0.23 to 0.57), and 0.27 (95% CI, 0.13 to 0.48) for the same tissues, respectively. The absolute areas under the concentration-time curve were also lower in the continuous infusion group. Nevertheless, a significantly longer time with concentrations above the MIC was found for continuous infusion up until MICs of 4, 2, 2, and 0.5 μg/ml for plasma and the same three tissues mentioned above, respectively. For drugs with a short half-life, like cefuroxime, continuous infusion seems to be favorable compared to short-term infusion; however, incomplete tissue penetration and high MIC strains may jeopardize the continuous infusion approach. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Serial recall of colors: Two models of memory for serial order applied to continuous visual stimuli.

PubMed

Peteranderl, Sonja; Oberauer, Klaus

2018-01-01

This study investigated the effects of serial position and temporal distinctiveness on serial recall of simple visual stimuli. Participants observed lists of five colors presented at varying, unpredictably ordered interitem intervals, and their task was to reproduce the colors in their order of presentation by selecting colors on a continuous-response scale. To control for the possibility of verbal labeling, articulatory suppression was required in one of two experimental sessions. The predictions were derived through simulation from two computational models of serial recall: SIMPLE represents the class of temporal-distinctiveness models, whereas SOB-CS represents event-based models. According to temporal-distinctiveness models, items that are temporally isolated within a list are recalled more accurately than items that are temporally crowded. In contrast, event-based models assume that the time intervals between items do not affect recall performance per se, although free time following an item can improve memory for that item because of extended time for the encoding. The experimental and the simulated data were fit to an interference measurement model to measure the tendency to confuse items with other items nearby on the list-the locality constraint-in people as well as in the models. The continuous-reproduction performance showed a pronounced primacy effect with no recency, as well as some evidence for transpositions obeying the locality constraint. Though not entirely conclusive, this evidence favors event-based models over a role for temporal distinctiveness. There was also a strong detrimental effect of articulatory suppression, suggesting that verbal codes can be used to support serial-order memory of simple visual stimuli.

When Dread Risks Are More Dreadful than Continuous Risks: Comparing Cumulative Population Losses over Time.

PubMed

Bodemer, Nicolai; Ruggeri, Azzurra; Galesic, Mirta

2013-01-01

People show higher sensitivity to dread risks, rare events that kill many people at once, compared with continuous risks, relatively frequent events that kill many people over a longer period of time. The different reaction to dread risks is often considered a bias: If the continuous risk causes the same number of fatalities, it should not be perceived as less dreadful. We test the hypothesis that a dread risk may have a stronger negative impact on the cumulative population size over time in comparison with a continuous risk causing the same number of fatalities. This difference should be particularly strong when the risky event affects children and young adults who would have produced future offspring if they had survived longer. We conducted a series of simulations, with varying assumptions about population size, population growth, age group affected by risky event, and the underlying demographic model. Results show that dread risks affect the population more severely over time than continuous risks that cause the same number of fatalities, suggesting that fearing a dread risk more than a continuous risk is an ecologically rational strategy.

Modeling of switching regulator power stages with and without zero-inductor-current dwell time

NASA Technical Reports Server (NTRS)

Lee, F. C. Y.; Yu, Y.

1979-01-01

State-space techniques are employed to derive accurate models for the three basic switching converter power stages: buck, boost, and buck/boost operating with and without zero-inductor-current dwell time. A generalized procedure is developed which treats the continuous-inductor-current mode without dwell time as a special case of the discontinuous-current mode when the dwell time vanishes. Abrupt changes of system behavior, including a reduction of the system order when the dwell time appears, are shown both analytically and experimentally. Merits resulting from the present modeling technique in comparison with existing modeling techniques are illustrated.

A brief review of models of DC-DC power electronic converters for analysis of their stability

NASA Astrophysics Data System (ADS)

Siewniak, Piotr; Grzesik, Bogusław

2014-10-01

A brief review of models of DC-DC power electronic converters (PECs) is presented in this paper. It contains the most popular, continuous-time and discrete-time models used for PEC simulation, design, stability analysis and other applications. Both large-signal and small-signal models are considered. Special attention is paid to models that are used in practice for the analysis of the global and local stability of PECs.

42 CFR § 512.200 - Time periods for EPM episodes.

Code of Federal Regulations, 2010 CFR

2017-10-01

... 42 Public Health 5 2017-10-01 2017-10-01 false Time periods for EPM episodes. § 512.200 Section § 512.200 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) HEALTH CARE INFRASTRUCTURE AND MODEL PROGRAMS EPISODE PAYMENT MODEL Scope of Episodes...

MODELING AND MEASUREMENT OF REAL-TIME CO CONCENTRATIONS IN ROADWAY MICROENVIRONMENTS

EPA Science Inventory

Although emission standards for motor vehicles continue to be tightened, tailpipe emissions continue to be a major source of human exposure to air toxics. The United States Environmental protection Agency's national Exposure Research laboratory has initiated a project to impro...

Continuous-time quantum Monte Carlo calculation of multiorbital vertex asymptotics

NASA Astrophysics Data System (ADS)

Kaufmann, Josef; Gunacker, Patrik; Held, Karsten

2017-07-01

We derive the equations for calculating the high-frequency asymptotics of the local two-particle vertex function for a multiorbital impurity model. These relate the asymptotics for a general local interaction to equal-time two-particle Green's functions, which we sample using continuous-time quantum Monte Carlo simulations with a worm algorithm. As specific examples we study the single-orbital Hubbard model and the three t2 g orbitals of SrVO3 within dynamical mean-field theory (DMFT). We demonstrate how the knowledge of the high-frequency asymptotics reduces the statistical uncertainties of the vertex and further eliminates finite-box-size effects. The proposed method benefits the calculation of nonlocal susceptibilities in DMFT and diagrammatic extensions of DMFT.

An approach to the drone fleet survivability assessment based on a stochastic continues-time model

NASA Astrophysics Data System (ADS)

Kharchenko, Vyacheslav; Fesenko, Herman; Doukas, Nikos

2017-09-01

An approach and the algorithm to the drone fleet survivability assessment based on a stochastic continues-time model are proposed. The input data are the number of the drones, the drone fleet redundancy coefficient, the drone stability and restoration rate, the limit deviation from the norms of the drone fleet recovery, the drone fleet operational availability coefficient, the probability of the drone failure-free operation, time needed for performing the required tasks by the drone fleet. The ways for improving the recoverable drone fleet survivability taking into account amazing factors of system accident are suggested. Dependencies of the drone fleet survivability rate both on the drone stability and the number of the drones are analysed.

The distribution of genome shared identical by descent for a pair of full sibs by means of the continuous time Markov chain

NASA Astrophysics Data System (ADS)

Julie, Hongki; Pasaribu, Udjianna S.; Pancoro, Adi

2015-12-01

This paper will allow Markov Chain's application in genome shared identical by descent by two individual at full sibs model. The full sibs model was a continuous time Markov Chain with three state. In the full sibs model, we look for the cumulative distribution function of the number of sub segment which have 2 IBD haplotypes from a segment of the chromosome which the length is t Morgan and the cumulative distribution function of the number of sub segment which have at least 1 IBD haplotypes from a segment of the chromosome which the length is t Morgan. This cumulative distribution function will be developed by the moment generating function.

Time Exceedances for High Intensity Solar Proton Fluxes

NASA Technical Reports Server (NTRS)

Xapsos, Michael A.; Stauffer, Craig A.; Jordan, Thomas M.; Adam, James H., Jr.; Dietrich, William F.

2011-01-01

A model is presented for times during a space mission that specified solar proton flux levels are exceeded. This includes both total time and continuous time periods during missions. Results for the solar maximum and solar minimum phases of the solar cycle are presented and compared for a broad range of proton energies and shielding levels. This type of approach is more amenable to reliability analysis for spacecraft systems and instrumentation than standard statistical models.

Analysis of Phase-Type Stochastic Petri Nets With Discrete and Continuous Timing

NASA Technical Reports Server (NTRS)

Jones, Robert L.; Goode, Plesent W. (Technical Monitor)

2000-01-01

The Petri net formalism is useful in studying many discrete-state, discrete-event systems exhibiting concurrency, synchronization, and other complex behavior. As a bipartite graph, the net can conveniently capture salient aspects of the system. As a mathematical tool, the net can specify an analyzable state space. Indeed, one can reason about certain qualitative properties (from state occupancies) and how they arise (the sequence of events leading there). By introducing deterministic or random delays, the model is forced to sojourn in states some amount of time, giving rise to an underlying stochastic process, one that can be specified in a compact way and capable of providing quantitative, probabilistic measures. We formalize a new non-Markovian extension to the Petri net that captures both discrete and continuous timing in the same model. The approach affords efficient, stationary analysis in most cases and efficient transient analysis under certain restrictions. Moreover, this new formalism has the added benefit in modeling fidelity stemming from the simultaneous capture of discrete- and continuous-time events (as opposed to capturing only one and approximating the other). We show how the underlying stochastic process, which is non-Markovian, can be resolved into simpler Markovian problems that enjoy efficient solutions. Solution algorithms are provided that can be easily programmed.

Time-dependent correlation of cerebral blood flow with oxygen metabolism in activated human visual cortex as measured by fMRI.

PubMed

Lin, Ai-Ling; Fox, Peter T; Yang, Yihong; Lu, Hanzhang; Tan, Li-Hai; Gao, Jia-Hong

2009-01-01

The aim of this study was to investigate the relationship between relative cerebral blood flow (delta CBF) and relative cerebral metabolic rate of oxygen (delta CMRO(2)) during continuous visual stimulation (21 min at 8 Hz) with fMRI biophysical models by simultaneously measuring of BOLD, CBF and CBV fMRI signals. The delta CMRO(2) was determined by both a newly calibrated single-compartment model (SCM) and a multi-compartment model (MCM) and was in agreement between these two models (P>0.5). The duration-varying delta CBF and delta CMRO(2) showed a negative correlation with time (r=-0.97, P<0.001); i.e., delta CBF declines while delta CMRO(2) increases during continuous stimulation. This study also illustrated that without properly calibrating the critical parameters employed in the SCM, an incorrect and even an opposite appearance of the flow-metabolism relationship during prolonged visual stimulation (positively linear coupling) can result. The time-dependent negative correlation between flow and metabolism demonstrated in this fMRI study is consistent with a previous PET observation and further supports the view that the increase in CBF is driven by factors other than oxygen demand and the energy demands will eventually require increased aerobic metabolism as stimulation continues.

Impact of Lead Time and Safety Factor in Mixed Inventory Models with Backorder Discounts

NASA Astrophysics Data System (ADS)

Lo, Ming-Cheng; Chao-Hsien Pan, Jason; Lin, Kai-Cing; Hsu, Jia-Wei

This study investigates the impact of safety factor on the continuous review inventory model involving controllable lead time with mixture of backorder discount and partial lost sales. The objective is to minimize the expected total annual cost with respect to order quantity, backorder price discount, safety factor and lead time. A model with normal demand is also discussed. Numerical examples are presented to illustrate the procedures of the algorithms and the effects of parameters on the result of the proposed models are analyzed.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

Matrix models for the black hole information paradox

NASA Astrophysics Data System (ADS)

Iizuka, Norihiro; Okuda, Takuya; Polchinski, Joseph

2010-02-01

We study various matrix models with a charge-charge interaction as toy models of the gauge dual of the AdS black hole. These models show a continuous spectrum and power-law decay of correlators at late time and infinite N, implying information loss in this limit. At finite N, the spectrum is discrete and correlators have recurrences, so there is no information loss. We study these models by a variety of techniques, such as Feynman graph expansion, loop equations, and sum over Young tableaux, and we obtain explicitly the leading 1/ N 2 corrections for the spectrum and correlators. These techniques are suggestive of possible dual bulk descriptions. At fixed order in 1/ N 2 the spectrum remains continuous and no recurrence occurs, so information loss persists. However, the interchange of the long-time and large- N limits is subtle and requires further study.

A CTRW-based model of time-resolved fluorescence lifetime imaging in a turbid medium

NASA Astrophysics Data System (ADS)

Chernomordik, Victor; Gandjbakhche, Amir H.; Hassan, Moinuddin; Pajevic, Sinisa; Weiss, George H.

2010-12-01

We develop an analytic model of time-resolved fluorescent imaging of photons migrating through a semi-infinite turbid medium bounded by an infinite plane in the presence of a single stationary point fluorophore embedded in the medium. In contrast to earlier models of fluorescent imaging in which photon motion is assumed to be some form of continuous diffusion process, the present analysis is based on a continuous-time random walk (CTRW) on a simple cubic lattice, the objective being to estimate the position and lifetime of the fluorophore. This can provide information related to local variations in pH and temperature with potential medical significance. Aspects of the theory were tested using time-resolved measurements of the fluorescence from small inclusions inside tissue-like phantoms. The experimental results were found to be in good agreement with theoretical predictions provided that the fluorophore was not located too close to the planar boundary, a common problem in many diffusive systems.

Continuous and Pulsatile Pediatric Ventricular Assist Device Hemodynamics with a Viscoelastic Blood Model

PubMed Central

Good, Bryan C.; Deutsch, Steven; Manning, Keefe B.

2015-01-01

Purpose To investigate the effects of pulsatile and continuous pediatric ventricular assist (PVAD) flow and pediatric blood viscoelasticity on hemodynamics in a pediatric aortic graft model. Methods Hemodynamic parameters of pulsatility, along with velocity and wall shear stress (WSS), are analyzed and compared between Newtonian and viscoelastic blood models at a range of physiological pediatric hematocrits using computational fluid dynamics. Results Both pulsatile and continuous PVAD flow lead to a decrease in pulsatility (surplus hemodynamic energy (SHE), ergs/cm3) compared to healthy aortic flow but with continuous PVAD pulsatility up to 2.4 times lower than pulsatile PVAD pulsatility at each aortic outlet. Significant differences are also seen between the two flow modes in velocity and WSS. The higher velocity jet during systole with pulsatile flow leads to higher WSSs at the anastomotic toe and at the aortic branch bifurcations. The lower velocity but continuous flow jet leads to a much different flow field and higher WSSs into diastole. Under a range of physiological pediatric hematocrit (20-60%), both velocity and WSS can vary significantly with the higher hematocrit blood model generally leading to higher peak WSSs but also lower WSSs in regions of flow separation. Conclusions The large decrease in pulsatility seen from continuous PVAD flow could lead to complications in pediatric vascular development while the high WSSs during peak systole from pulsatile PVAD flow could lead to blood damage. Both flow modes lead to similar regions prone to intimal hyperplasia (IH) resulting from low time-averaged WSS (TAWSS) and high oscillatory shear index (OSI). PMID:26643646

A fast quadrature-based numerical method for the continuous spectrum biphasic poroviscoelastic model of articular cartilage.

PubMed

Stuebner, Michael; Haider, Mansoor A

2010-06-18

A new and efficient method for numerical solution of the continuous spectrum biphasic poroviscoelastic (BPVE) model of articular cartilage is presented. Development of the method is based on a composite Gauss-Legendre quadrature approximation of the continuous spectrum relaxation function that leads to an exponential series representation. The separability property of the exponential terms in the series is exploited to develop a numerical scheme that can be reduced to an update rule requiring retention of the strain history at only the previous time step. The cost of the resulting temporal discretization scheme is O(N) for N time steps. Application and calibration of the method is illustrated in the context of a finite difference solution of the one-dimensional confined compression BPVE stress-relaxation problem. Accuracy of the numerical method is demonstrated by comparison to a theoretical Laplace transform solution for a range of viscoelastic relaxation times that are representative of articular cartilage. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Continuous assimilation of simulated Geosat altimetric sea level into an eddy-resolving numerical ocean model. I - Sea level differences. II - Referenced sea level differences

NASA Technical Reports Server (NTRS)

White, Warren B.; Tai, Chang-Kou; Holland, William R.

1990-01-01

The optimal interpolation method of Lorenc (1981) was used to conduct continuous assimilation of altimetric sea level differences from the simulated Geosat exact repeat mission (ERM) into a three-layer quasi-geostrophic eddy-resolving numerical ocean box model that simulates the statistics of mesoscale eddy activity in the western North Pacific. Assimilation was conducted continuously as the Geosat tracks appeared in simulated real time/space, with each track repeating every 17 days, but occurring at different times and locations within the 17-day period, as would have occurred in a realistic nowcast situation. This interpolation method was also used to conduct the assimilation of referenced altimetric sea level differences into the same model, performing the referencing of altimetric sea sevel differences by using the simulated sea level. The results of this dynamical interpolation procedure are compared with those of a statistical (i.e., optimum) interpolation procedure.

Qubit models of weak continuous measurements: markovian conditional and open-system dynamics

NASA Astrophysics Data System (ADS)

Gross, Jonathan A.; Caves, Carlton M.; Milburn, Gerard J.; Combes, Joshua

2018-04-01

In this paper we approach the theory of continuous measurements and the associated unconditional and conditional (stochastic) master equations from the perspective of quantum information and quantum computing. We do so by showing how the continuous-time evolution of these master equations arises from discretizing in time the interaction between a system and a probe field and by formulating quantum-circuit diagrams for the discretized evolution. We then reformulate this interaction by replacing the probe field with a bath of qubits, one for each discretized time segment, reproducing all of the standard quantum-optical master equations. This provides an economical formulation of the theory, highlighting its fundamental underlying assumptions.

An advanced environment for hybrid modeling of biological systems based on modelica.

PubMed

Pross, Sabrina; Bachmann, Bernhard

2011-01-20

Biological systems are often very complex so that an appropriate formalism is needed for modeling their behavior. Hybrid Petri Nets, consisting of time-discrete Petri Net elements as well as continuous ones, have proven to be ideal for this task. Therefore, a new Petri Net library was implemented based on the object-oriented modeling language Modelica which allows the modeling of discrete, stochastic and continuous Petri Net elements by differential, algebraic and discrete equations. An appropriate Modelica-tool performs the hybrid simulation with discrete events and the solution of continuous differential equations. A special sub-library contains so-called wrappers for specific reactions to simplify the modeling process. The Modelica-models can be connected to Simulink-models for parameter optimization, sensitivity analysis and stochastic simulation in Matlab. The present paper illustrates the implementation of the Petri Net component models, their usage within the modeling process and the coupling between the Modelica-tool Dymola and Matlab/Simulink. The application is demonstrated by modeling the metabolism of Chinese Hamster Ovary Cells.

LMI-based stability and performance conditions for continuous-time nonlinear systems in Takagi-Sugeno's form.

PubMed

Lam, H K; Leung, Frank H F

2007-10-01

This correspondence presents the stability analysis and performance design of the continuous-time fuzzy-model-based control systems. The idea of the nonparallel-distributed-compensation (non-PDC) control laws is extended to the continuous-time fuzzy-model-based control systems. A nonlinear controller with non-PDC control laws is proposed to stabilize the continuous-time nonlinear systems in Takagi-Sugeno's form. To produce the stability-analysis result, a parameter-dependent Lyapunov function (PDLF) is employed. However, two difficulties are usually encountered: 1) the time-derivative terms produced by the PDLF will complicate the stability analysis and 2) the stability conditions are not in the form of linear-matrix inequalities (LMIs) that aid the design of feedback gains. To tackle the first difficulty, the time-derivative terms are represented by some weighted-sum terms in some existing approaches, which will increase the number of stability conditions significantly. In view of the second difficulty, some positive-definitive terms are added in order to cast the stability conditions into LMIs. In this correspondence, the favorable properties of the membership functions and nonlinear control laws, which allow the introduction of some free matrices, are employed to alleviate the two difficulties while retaining the favorable properties of PDLF-based approach. LMI-based stability conditions are derived to ensure the system stability. Furthermore, based on a common scalar performance index, LMI-based performance conditions are derived to guarantee the system performance. Simulation examples are given to illustrate the effectiveness of the proposed approach.

Modeling of switching regulator power stages with and without zero-inductor-current dwell time

NASA Technical Reports Server (NTRS)

Lee, F. C.; Yu, Y.; Triner, J. E.

1976-01-01

State space techniques are employed to derive accurate models for buck, boost, and buck/boost converter power stages operating with and without zero-inductor-current dwell time. A generalized procedure is developed which treats the continuous-inductor-current mode without the dwell time as a special case of the discontinuous-current mode, when the dwell time vanishes. An abrupt change of system behavior including a reduction of the system order when the dwell time appears is shown both analytically and experimentally.

40 CFR 60.2770 - What information must I include in my annual report?

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule... calibration drift specification in the applicable performance specification or in the relevant standard. (2...

40 CFR 60.2770 - What information must I include in my annual report?

Code of Federal Regulations, 2013 CFR

2013-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule... calibration drift specification in the applicable performance specification or in the relevant standard. (2...

Gradient-based controllers for timed continuous Petri nets

NASA Astrophysics Data System (ADS)

Lefebvre, Dimitri; Leclercq, Edouard; Druaux, Fabrice; Thomas, Philippe

2015-07-01

This paper is about control design for timed continuous Petri nets that are described as piecewise affine systems. In this context, the marking vector is considered as the state space vector, weighted marking of place subsets are defined as the model outputs and the model inputs correspond to multiplicative control actions that slow down the firing rate of some controllable transitions. Structural and functional sensitivity of the outputs with respect to the inputs are discussed in terms of Petri nets. Then, gradient-based controllers (GBC) are developed in order to adapt the control actions of the controllable transitions according to desired trajectories of the outputs.

Relating Factor Models for Longitudinal Data to Quasi-Simplex and NARMA Models

ERIC Educational Resources Information Center

Rovine, Michael J.; Molenaar, Peter C. M.

2005-01-01

In this article we show the one-factor model can be rewritten as a quasi-simplex model. Using this result along with addition theorems from time series analysis, we describe a common general model, the nonstationary autoregressive moving average (NARMA) model, that includes as a special case, any latent variable model with continuous indicators…

Automated time activity classification based on global positioning system (GPS) tracking data

PubMed Central

2011-01-01

Background Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. Methods We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Results Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Conclusions Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns. PMID:22082316

Automated time activity classification based on global positioning system (GPS) tracking data.

PubMed

Wu, Jun; Jiang, Chengsheng; Houston, Douglas; Baker, Dean; Delfino, Ralph

2011-11-14

Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.

Analysis of continuous GPS measurements from southern Victoria Land, Antarctica

USGS Publications Warehouse

Willis, Michael J.

2007-01-01

Several years of continuous data have been collected at remote bedrock Global Positioning System (GPS) sites in southern Victoria Land, Antarctica. Annual to sub-annual variations are observed in the position time-series. An atmospheric pressure loading (APL) effect is calculated from pressure field anomalies supplied by the European Centre for Medium-Range Weather Forecasts (ECMWF) model loading an elastic Earth model. The predicted APL signal has a moderate correlation with the vertical position time-series at McMurdo, Ross Island (International Global Navigation Satellite System Service (IGS) station MCM4), produced using a global solution. In contrast, a local solution in which MCM4 is the fiducial site generates a vertical time series for a remote site in Victoria Land (Cape Roberts, ROB4) which exhibits a low, inverse correlation with the predicted atmospheric pressure loading signal. If, in the future, known and well modeled geophysical loads can be separated from the time-series, then local hydrological loading, of interest for glaciological and climate applications, can potentially be extracted from the GPS time-series.

Bayesian dynamic mediation analysis.

PubMed

Huang, Jing; Yuan, Ying

2017-12-01

Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Will the use of a carbon tax for revenue generation produce an incentive to continue carbon emissions?

NASA Astrophysics Data System (ADS)

Wang, Rong; Moreno-Cruz, Juan; Caldeira, Ken

2017-05-01

Integrated assessment models are commonly used to generate optimal carbon prices based on an objective function that maximizes social welfare. Such models typically project an initially low carbon price that increases with time. This framework does not reflect the incentives of decision makers who are responsible for generating tax revenue. If a rising carbon price is to result in near-zero emissions, it must ultimately result in near-zero carbon tax revenue. That means that at some point, policy makers will be asked to increase the tax rate on carbon emissions to such an extent that carbon tax revenue will fall. Therefore, there is a risk that the use of a carbon tax to generate revenue could eventually create a perverse incentive to continue carbon emissions in order to provide a continued stream of carbon tax revenue. Using the Dynamic Integrated Climate Economy (DICE) model, we provide evidence that this risk is not a concern for the immediate future but that a revenue-generating carbon tax could create this perverse incentive as time goes on. This incentive becomes perverse at about year 2085 under the default configuration of DICE, but the timing depends on a range of factors including the cost of climate damages and the cost of decarbonizing the global energy system. While our study is based on a schematic model, it highlights the importance of considering a broader spectrum of incentives in studies using more comprehensive integrated assessment models. Our study demonstrates that the use of a carbon tax for revenue generation could potentially motivate implementation of such a tax today, but this source of revenue generation risks motivating continued carbon emissions far into the future.

Analysis of discrete and continuous distributions of ventilatory time constants from dynamic computed tomography

NASA Astrophysics Data System (ADS)

Doebrich, Marcus; Markstaller, Klaus; Karmrodt, Jens; Kauczor, Hans-Ulrich; Eberle, Balthasar; Weiler, Norbert; Thelen, Manfred; Schreiber, Wolfgang G.

2005-04-01

In this study, an algorithm was developed to measure the distribution of pulmonary time constants (TCs) from dynamic computed tomography (CT) data sets during a sudden airway pressure step up. Simulations with synthetic data were performed to test the methodology as well as the influence of experimental noise. Furthermore the algorithm was applied to in vivo data. In five pigs sudden changes in airway pressure were imposed during dynamic CT acquisition in healthy lungs and in a saline lavage ARDS model. The fractional gas content in the imaged slice (FGC) was calculated by density measurements for each CT image. Temporal variations of the FGC were analysed assuming a model with a continuous distribution of exponentially decaying time constants. The simulations proved the feasibility of the method. The influence of experimental noise could be well evaluated. Analysis of the in vivo data showed that in healthy lungs ventilation processes can be more likely characterized by discrete TCs whereas in ARDS lungs continuous distributions of TCs are observed. The temporal behaviour of lung inflation and deflation can be characterized objectively using the described new methodology. This study indicates that continuous distributions of TCs reflect lung ventilation mechanics more accurately compared to discrete TCs.

Experiment and simulation for CSI: What are the missing links?

NASA Technical Reports Server (NTRS)

Belvin, W. Keith; Park, K. C.

1989-01-01

Viewgraphs on experiment and simulation for control structure interaction (CSI) are presented. Topics covered include: control structure interaction; typical control/structure interaction system; CSI problem classification; actuator/sensor models; modeling uncertainty; noise models; real-time computations; and discrete versus continuous.

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

NASA Astrophysics Data System (ADS)

Zhu, Z.; Woodcock, C. E.

2012-12-01

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

ECS: efficient communication scheduling for underwater sensor networks.

PubMed

Hong, Lu; Hong, Feng; Guo, Zhongwen; Li, Zhengbao

2011-01-01

TDMA protocols have attracted a lot of attention for underwater acoustic sensor networks (UWSNs), because of the unique characteristics of acoustic signal propagation such as great energy consumption in transmission, long propagation delay and long communication range. Previous TDMA protocols all allocated transmission time to nodes based on discrete time slots. This paper proposes an efficient continuous time scheduling TDMA protocol (ECS) for UWSNs, including the continuous time based and sender oriented conflict analysis model, the transmission moment allocation algorithm and the distributed topology maintenance algorithm. Simulation results confirm that ECS improves network throughput by 20% on average, compared to existing MAC protocols.

A stochastical event-based continuous time step rainfall generator based on Poisson rectangular pulse and microcanonical random cascade models

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Zha, Tingting; Schümberg, Sabine; Müller, Hannes; Maurer, Thomas; Hinz, Christoph

2017-04-01

Rainfall variability within a storm is of major importance for fast hydrological processes, e.g. surface runoff, erosion and solute dissipation from surface soils. To investigate and simulate the impacts of within-storm variabilities on these processes, long time series of rainfall with high resolution are required. Yet, observed precipitation records of hourly or higher resolution are in most cases available only for a small number of stations and only for a few years. To obtain long time series of alternating rainfall events and interstorm periods while conserving the statistics of observed rainfall events, the Poisson model can be used. Multiplicative microcanonical random cascades have been widely applied to disaggregate rainfall time series from coarse to fine temporal resolution. We present a new coupling approach of the Poisson rectangular pulse model and the multiplicative microcanonical random cascade model that preserves the characteristics of rainfall events as well as inter-storm periods. In the first step, a Poisson rectangular pulse model is applied to generate discrete rainfall events (duration and mean intensity) and inter-storm periods (duration). The rainfall events are subsequently disaggregated to high-resolution time series (user-specified, e.g. 10 min resolution) by a multiplicative microcanonical random cascade model. One of the challenges of coupling these models is to parameterize the cascade model for the event durations generated by the Poisson model. In fact, the cascade model is best suited to downscale rainfall data with constant time step such as daily precipitation data. Without starting from a fixed time step duration (e.g. daily), the disaggregation of events requires some modifications of the multiplicative microcanonical random cascade model proposed by Olsson (1998): Firstly, the parameterization of the cascade model for events of different durations requires continuous functions for the probabilities of the multiplicative weights, which we implemented through sigmoid functions. Secondly, the branching of the first and last box is constrained to preserve the rainfall event durations generated by the Poisson rectangular pulse model. The event-based continuous time step rainfall generator has been developed and tested using 10 min and hourly rainfall data of four stations in North-Eastern Germany. The model performs well in comparison to observed rainfall in terms of event durations and mean event intensities as well as wet spell and dry spell durations. It is currently being tested using data from other stations across Germany and in different climate zones. Furthermore, the rainfall event generator is being applied in modelling approaches aimed at understanding the impact of rainfall variability on hydrological processes. Reference Olsson, J.: Evaluation of a scaling cascade model for temporal rainfall disaggregation, Hydrology and Earth System Sciences, 2, 19.30

The Embedding Problem for Markov Models of Nucleotide Substitution

PubMed Central

Verbyla, Klara L.; Yap, Von Bing; Pahwa, Anuj; Shao, Yunli; Huttley, Gavin A.

2013-01-01

Continuous-time Markov processes are often used to model the complex natural phenomenon of sequence evolution. To make the process of sequence evolution tractable, simplifying assumptions are often made about the sequence properties and the underlying process. The validity of one such assumption, time-homogeneity, has never been explored. Violations of this assumption can be found by identifying non-embeddability. A process is non-embeddable if it can not be embedded in a continuous time-homogeneous Markov process. In this study, non-embeddability was demonstrated to exist when modelling sequence evolution with Markov models. Evidence of non-embeddability was found primarily at the third codon position, possibly resulting from changes in mutation rate over time. Outgroup edges and those with a deeper time depth were found to have an increased probability of the underlying process being non-embeddable. Overall, low levels of non-embeddability were detected when examining individual edges of triads across a diverse set of alignments. Subsequent phylogenetic reconstruction analyses demonstrated that non-embeddability could impact on the correct prediction of phylogenies, but at extremely low levels. Despite the existence of non-embeddability, there is minimal evidence of violations of the local time homogeneity assumption and consequently the impact is likely to be minor. PMID:23935949

Bacteria transport simulation using APEX model in the Toenepi watershed, New Zealand

USDA-ARS?s Scientific Manuscript database

The Agricultural Policy/Environmental eXtender (APEX) model is a distributed, continuous, daily-time step small watershed-scale hydrologic and water quality model. In this study, the newly developed fecal-derived bacteria fate and transport subroutine was applied and evalated using APEX model. The e...

A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers

ERIC Educational Resources Information Center

Klein Entink, R. H.; Fox, J. P.; van der Linden, W. J.

2009-01-01

Response times on test items are easily collected in modern computerized testing. When collecting both (binary) responses and (continuous) response times on test items, it is possible to measure the accuracy and speed of test takers. To study the relationships between these two constructs, the model is extended with a multivariate multilevel…

Integrated Thermal Response Modeling System For Hypersonic Entry Vehicles

NASA Technical Reports Server (NTRS)

Chen, Y.-K.; Milos, F. S.; Partridge, Harry (Technical Monitor)

2000-01-01

We describe all extension of the Markov decision process model in which a continuous time dimension is included ill the state space. This allows for the representation and exact solution of a wide range of problems in which transitions or rewards vary over time. We examine problems based on route planning with public transportation and telescope observation scheduling.

An Assessment of Meyer and Allen's (1991) Three-Component Model of Organizational Commitment and Turnover Intentions.

ERIC Educational Resources Information Center

Jaros, Stephen J.

1997-01-01

Data from 158 part-time graduate students employed full time and 160 aerospace engineers were used to test a model of organizational commitment. Contrary to expectations, the three components of commitment (affective, normative, continuance) differed in their effects on intention to quit. Affective commitment had a significantly stronger…

Azimuthally Anisotropic 3D Velocity Continuation

DOE PAGES

Burnett, William; Fomel, Sergey

2011-01-01

We extend time-domain velocity continuation to the zero-offset 3D azimuthally anisotropic case. Velocity continuation describes how a seismic image changes given a change in migration velocity. This description turns out to be of a wave propagation process, in which images change along a velocity axis. In the anisotropic case, the velocity model is multiparameter. Therefore, anisotropic image propagation is multidimensional. We use a three-parameter slowness model, which is related to azimuthal variations in velocity, as well as their principal directions. This information is useful for fracture and reservoir characterization from seismic data. We provide synthetic diffraction imaging examples to illustratemore » the concept and potential applications of azimuthal velocity continuation and to analyze the impulse response of the 3D velocity continuation operator.« less

Continuous estimation of evapotranspiration and gross primary productivity from an Unmanned Aerial System

NASA Astrophysics Data System (ADS)

Wang, S.; Bandini, F.; Jakobsen, J.; J Zarco-Tejada, P.; Liu, X.; Haugård Olesen, D.; Ibrom, A.; Bauer-Gottwein, P.; Garcia, M.

2017-12-01

Model prediction of evapotranspiration (ET) and gross primary productivity (GPP) using optical and thermal satellite imagery is biased towards clear-sky conditions. Unmanned Aerial Systems (UAS) can collect optical and thermal signals at unprecedented very high spatial resolution (< 1 meter) under sunny and cloudy weather conditions. However, methods to obtain model outputs between image acquisitions are still needed. This study uses UAS based optical and thermal observations to continuously estimate daily ET and GPP in a Danish willow forest for an entire growing season of 2016. A hexacopter equipped with multispectral and thermal infrared cameras and a real-time kinematic Global Navigation Satellite System was used. The Normalized Differential Vegetation Index (NDVI) and the Temperature Vegetation Dryness Index (TVDI) were used as proxies for leaf area index and soil moisture conditions, respectively. To obtain continuously daily records between UAS acquisitions, UAS surface temperature was assimilated by the ensemble Kalman filter into a prognostic land surface model (Noilhan and Planton, 1989), which relies on the force-restore method, to simulate the continuous land surface temperature. NDVI was interpolated into daily time steps by the cubic spline method. Using these continuous datasets, a joint ET and GPP model, which combines the Priestley-Taylor Jet Propulsion Laboratory ET model (Fisher et al., 2008; Garcia et al., 2013) and the Light Use Efficiency GPP model (Potter et al., 1993), was applied. The simulated ET and GPP were compared with the footprint of eddy covariance observations. The simulated daily ET has a RMSE of 14.41 W•m-2 and a correlation coefficient of 0.83. The simulated daily GPP has a root mean square error (RMSE) of 1.56 g•C•m-2•d-1 and a correlation coefficient of 0.87. This study demonstrates the potential of UAS based multispectral and thermal mapping to continuously estimate ET and GPP for both sunny and cloudy weather conditions.

Combining the ASA Physical Classification System and Continuous Intraoperative Surgical Apgar Score Measurement in Predicting Postoperative Risk.

PubMed

Jering, Monika Zdenka; Marolen, Khensani N; Shotwell, Matthew S; Denton, Jason N; Sandberg, Warren S; Ehrenfeld, Jesse Menachem

2015-11-01

The surgical Apgar score predicts major 30-day postoperative complications using data assessed at the end of surgery. We hypothesized that evaluating the surgical Apgar score continuously during surgery may identify patients at high risk for postoperative complications. We retrospectively identified general, vascular, and general oncology patients at Vanderbilt University Medical Center. Logistic regression methods were used to construct a series of predictive models in order to continuously estimate the risk of major postoperative complications, and to alert care providers during surgery should the risk exceed a given threshold. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the discriminative ability of a model utilizing a continuously measured surgical Apgar score relative to models that use only preoperative clinical factors or continuously monitored individual constituents of the surgical Apgar score (i.e. heart rate, blood pressure, and blood loss). AUROC estimates were validated internally using a bootstrap method. 4,728 patients were included. Combining the ASA PS classification with continuously measured surgical Apgar score demonstrated improved discriminative ability (AUROC 0.80) in the pooled cohort compared to ASA (0.73) and the surgical Apgar score alone (0.74). To optimize the tradeoff between inadequate and excessive alerting with future real-time notifications, we recommend a threshold probability of 0.24. Continuous assessment of the surgical Apgar score is predictive for major postoperative complications. In the future, real-time notifications might allow for detection and mitigation of changes in a patient's accumulating risk of complications during a surgical procedure.

Aperiodic Robust Model Predictive Control for Constrained Continuous-Time Nonlinear Systems: An Event-Triggered Approach.

PubMed

Liu, Changxin; Gao, Jian; Li, Huiping; Xu, Demin

2018-05-01

The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achieve robust constraint satisfaction, and an event-triggered scheduling strategy is designed in the framework of dual-mode MPC. Second, the sufficient conditions for ensuring feasibility and closed-loop robust stability are developed, respectively. We show that robust stability can be ensured and communication load can be reduced with the proposed MPC algorithm. Finally, numerical simulations and comparison studies are performed to verify the theoretical results.

Low-traffic limit and first-passage times for a simple model of the continuous double auction

NASA Astrophysics Data System (ADS)

Scalas, Enrico; Rapallo, Fabio; Radivojević, Tijana

2017-11-01

We consider a simplified model of the continuous double auction where prices are integers varying from 1 to N with limit orders and market orders, but quantity per order limited to a single share. For this model, the order process is equivalent to two M / M / 1 queues. We study the behavior of the auction in the low-traffic limit where limit orders are immediately matched by market orders. In this limit, the distribution of prices can be computed exactly and gives a reasonable approximation of the price distribution when the ratio between the rate of order arrivals and the rate of order executions is below 1 / 2. This is further confirmed by the analysis of the first-passage time in 1 or N.

Reply to Steele & Ferrer: Modeling Oscillation, Approximately or Exactly?

ERIC Educational Resources Information Center

Oud, Johan H. L.; Folmer, Henk

2011-01-01

This article addresses modeling oscillation in continuous time. It criticizes Steele and Ferrer's article "Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes" (2011), particularly the approximate estimation procedure applied. This procedure is the latent version of the local linear approximation procedure…

Real-time hypoglycemia detection from continuous glucose monitoring data of subjects with type 1 diabetes.

PubMed

Jensen, Morten Hasselstrøm; Christensen, Toke Folke; Tarnow, Lise; Seto, Edmund; Dencker Johansen, Mette; Hejlesen, Ole Kristian

2013-07-01

Hypoglycemia is a potentially fatal condition. Continuous glucose monitoring (CGM) has the potential to detect hypoglycemia in real time and thereby reduce time in hypoglycemia and avoid any further decline in blood glucose level. However, CGM is inaccurate and shows a substantial number of cases in which the hypoglycemic event is not detected by the CGM. The aim of this study was to develop a pattern classification model to optimize real-time hypoglycemia detection. Features such as time since last insulin injection and linear regression, kurtosis, and skewness of the CGM signal in different time intervals were extracted from data of 10 male subjects experiencing 17 insulin-induced hypoglycemic events in an experimental setting. Nondiscriminative features were eliminated with SEPCOR and forward selection. The feature combinations were used in a Support Vector Machine model and the performance assessed by sample-based sensitivity and specificity and event-based sensitivity and number of false-positives. The best model was composed by using seven features and was able to detect 17 of 17 hypoglycemic events with one false-positive compared with 12 of 17 hypoglycemic events with zero false-positives for the CGM alone. Lead-time was 14 min and 0 min for the model and the CGM alone, respectively. This optimized real-time hypoglycemia detection provides a unique approach for the diabetes patient to reduce time in hypoglycemia and learn about patterns in glucose excursions. Although these results are promising, the model needs to be validated on CGM data from patients with spontaneous hypoglycemic events.

Predicting coexistence of plants subject to a tolerance-competition trade-off.

PubMed

Haegeman, Bart; Sari, Tewfik; Etienne, Rampal S

2014-06-01

Ecological trade-offs between species are often invoked to explain species coexistence in ecological communities. However, few mathematical models have been proposed for which coexistence conditions can be characterized explicitly in terms of a trade-off. Here we present a model of a plant community which allows such a characterization. In the model plant species compete for sites where each site has a fixed stress condition. Species differ both in stress tolerance and competitive ability. Stress tolerance is quantified as the fraction of sites with stress conditions low enough to allow establishment. Competitive ability is quantified as the propensity to win the competition for empty sites. We derive the deterministic, discrete-time dynamical system for the species abundances. We prove the conditions under which plant species can coexist in a stable equilibrium. We show that the coexistence conditions can be characterized graphically, clearly illustrating the trade-off between stress tolerance and competitive ability. We compare our model with a recently proposed, continuous-time dynamical system for a tolerance-fecundity trade-off in plant communities, and we show that this model is a special case of the continuous-time version of our model.

Nonparametric model validations for hidden Markov models with applications in financial econometrics.

PubMed

Zhao, Zhibiao

2011-06-01

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.

Generalized Jaynes-Cummings model as a quantum search algorithm

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

Romanelli, A.

2009-07-15

We propose a continuous time quantum search algorithm using a generalization of the Jaynes-Cummings model. In this model the states of the atom are the elements among which the algorithm realizes the search, exciting resonances between the initial and the searched states. This algorithm behaves like Grover's algorithm; the optimal search time is proportional to the square root of the size of the search set and the probability to find the searched state oscillates periodically in time. In this frame, it is possible to reinterpret the usual Jaynes-Cummings model as a trivial case of the quantum search algorithm.

40 CFR 60.5225 - What are the monitoring and calibration requirements for compliance with my operating limits?

Code of Federal Regulations, 2011 CFR

2011-07-01

... requirements for compliance with my operating limits? 60.5225 Section 60.5225 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge Incineration Units Model Rule...

40 CFR 60.5120 - What must I do if I close my SSI unit and then restart it?

Code of Federal Regulations, 2011 CFR

2011-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge Incineration Units Model Rule-Increments of Progress... standards, and operating limits on the date your unit restarts operation. ...

Simulation to coating weight control for galvanizing

NASA Astrophysics Data System (ADS)

Wang, Junsheng; Yan, Zhang; Wu, Kunkui; Song, Lei

2013-05-01

Zinc coating weight control is one of the most critical issues for continuous galvanizing line. The process has the characteristic of variable-time large time delay, nonlinear, multivariable. It can result in seriously coating weight error and non-uniform coating. We develop a control system, which can automatically control the air knives pressure and its position to give a constant and uniform zinc coating, in accordance with customer-order specification through an auto-adaptive empirical model-based feed forward adaptive controller, and two model-free adaptive feedback controllers . The proposed models with controller were applied to continuous galvanizing line (CGL) at Angang Steel Works. By the production results, the precise and stability of the control model reduces over-coating weight and improves coating uniform. The product for this hot dip galvanizing line does not only satisfy the customers' quality requirement but also save the zinc consumption.

Fermion bag approach to Hamiltonian lattice field theories in continuous time

NASA Astrophysics Data System (ADS)

Huffman, Emilie; Chandrasekharan, Shailesh

2017-12-01

We extend the idea of fermion bags to Hamiltonian lattice field theories in the continuous time formulation. Using a class of models we argue that the temperature is a parameter that splits the fermion dynamics into small spatial regions that can be used to identify fermion bags. Using this idea we construct a continuous time quantum Monte Carlo algorithm and compute critical exponents in the 3 d Ising Gross-Neveu universality class using a single flavor of massless Hamiltonian staggered fermions. We find η =0.54 (6 ) and ν =0.88 (2 ) using lattices up to N =2304 sites. We argue that even sizes up to N =10 ,000 sites should be accessible with supercomputers available today.

Impulsive Control for Continuous-Time Markov Decision Processes: A Linear Programming Approach

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

Dufour, F., E-mail: dufour@math.u-bordeaux1.fr; Piunovskiy, A. B., E-mail: piunov@liv.ac.uk

2016-08-15

In this paper, we investigate an optimization problem for continuous-time Markov decision processes with both impulsive and continuous controls. We consider the so-called constrained problem where the objective of the controller is to minimize a total expected discounted optimality criterion associated with a cost rate function while keeping other performance criteria of the same form, but associated with different cost rate functions, below some given bounds. Our model allows multiple impulses at the same time moment. The main objective of this work is to study the associated linear program defined on a space of measures including the occupation measures ofmore » the controlled process and to provide sufficient conditions to ensure the existence of an optimal control.« less

Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements.

PubMed

Burggraeve, A; Van den Kerkhof, T; Hellings, M; Remon, J P; Vervaet, C; De Beer, T

2011-04-18

Fluid bed granulation is a batch process, which is characterized by the processing of raw materials for a predefined period of time, consisting of a fixed spraying phase and a subsequent drying period. The present study shows the multivariate statistical modeling and control of a fluid bed granulation process based on in-line particle size distribution (PSD) measurements (using spatial filter velocimetry) combined with continuous product temperature registration using a partial least squares (PLS) approach. Via the continuous in-line monitoring of the PSD and product temperature during granulation of various reference batches, a statistical batch model was developed allowing the real-time evaluation and acceptance or rejection of future batches. Continuously monitored PSD and product temperature process data of 10 reference batches (X-data) were used to develop a reference batch PLS model, regressing the X-data versus the batch process time (Y-data). Two PLS components captured 98.8% of the variation in the X-data block. Score control charts in which the average batch trajectory and upper and lower control limits are displayed were developed. Next, these control charts were used to monitor 4 new test batches in real-time and to immediately detect any deviations from the expected batch trajectory. By real-time evaluation of new batches using the developed control charts and by computation of contribution plots of deviating process behavior at a certain time point, batch losses or reprocessing can be prevented. Immediately after batch completion, all PSD and product temperature information (i.e., a batch progress fingerprint) was used to estimate some granule properties (density and flowability) at an early stage, which can improve batch release time. Individual PLS models relating the computed scores (X) of the reference PLS model (based on the 10 reference batches) and the density, respectively, flowabililty as Y-matrix, were developed. The scores of the 4 test batches were used to examine the predictive ability of the model. Copyright © 2011 Elsevier B.V. All rights reserved.

An enhanced lumped element electrical model of a double barrier memristive device

NASA Astrophysics Data System (ADS)

Solan, Enver; Dirkmann, Sven; Hansen, Mirko; Schroeder, Dietmar; Kohlstedt, Hermann; Ziegler, Martin; Mussenbrock, Thomas; Ochs, Karlheinz

2017-05-01

The massive parallel approach of neuromorphic circuits leads to effective methods for solving complex problems. It has turned out that resistive switching devices with a continuous resistance range are potential candidates for such applications. These devices are memristive systems—nonlinear resistors with memory. They are fabricated in nanotechnology and hence parameter spread during fabrication may aggravate reproducible analyses. This issue makes simulation models of memristive devices worthwhile. Kinetic Monte-Carlo simulations based on a distributed model of the device can be used to understand the underlying physical and chemical phenomena. However, such simulations are very time-consuming and neither convenient for investigations of whole circuits nor for real-time applications, e.g. emulation purposes. Instead, a concentrated model of the device can be used for both fast simulations and real-time applications, respectively. We introduce an enhanced electrical model of a valence change mechanism (VCM) based double barrier memristive device (DBMD) with a continuous resistance range. This device consists of an ultra-thin memristive layer sandwiched between a tunnel barrier and a Schottky-contact. The introduced model leads to very fast simulations by using usual circuit simulation tools while maintaining physically meaningful parameters. Kinetic Monte-Carlo simulations based on a distributed model and experimental data have been utilized as references to verify the concentrated model.

Distinguishing transient signals and instrumental disturbances in semi-coherent searches for continuous gravitational waves with line-robust statistics

NASA Astrophysics Data System (ADS)

Keitel, David

2016-05-01

Non-axisymmetries in rotating neutron stars emit quasi-monochromatic gravitational waves. These long-duration ‘continuous wave’ signals are among the main search targets of ground-based interferometric detectors. However, standard detection methods are susceptible to false alarms from instrumental artefacts that resemble a continuous-wave signal. Past work [Keitel, Prix, Papa, Leaci and Siddiqi 2014, Phys. Rev. D 89 064023] showed that a Bayesian approach, based on an explicit model of persistent single-detector disturbances, improves robustness against such artefacts. Since many strong outliers in semi-coherent searches of LIGO data are caused by transient disturbances that last only a few hours or days, I describe in a recent paper [Keitel D 2015, LIGO-P1500159] how to extend this approach to cover transient disturbances, and demonstrate increased sensitivity in realistic simulated data. Additionally, neutron stars could emit transient signals which, for a limited time, also follow the continuous-wave signal model. As a pragmatic alternative to specialized transient searches, I demonstrate how to make standard semi-coherent continuous-wave searches more sensitive to transient signals. Focusing on the time-scale of a single segment in the semi-coherent search, Bayesian model selection yields a simple detection statistic without a significant increase in computational cost. This proceedings contribution gives a brief overview of both works.

An Approach To Using All Location Tagged Numerical Data Sets As Continuous Fields With User-Assigned Continuity As A Basis For User-Driven Data Assimilation

NASA Astrophysics Data System (ADS)

Vernon, F.; Arrott, M.; Orcutt, J. A.; Mueller, C.; Case, J.; De Wardener, G.; Kerfoot, J.; Schofield, O.

2013-12-01

Any approach sophisticated enough to handle a variety of data sources and scale, yet easy enough to promote wide use and mainstream adoption is required to address the following mappings: - From the authored domain of observation to the requested domain of interest; - From the authored spatiotemporal resolution to the requested resolution; and - From the representation of data placed on wide variety of discrete mesh types to the use of that data as a continuos field with a selectable continuity. The Open Geospatial Consortium's (OGC) Reference Model[1] with its direct association with the ISO 19000 series standards provides a comprehensive foundation to represent all data on any type of mesh structure, aka "Discrete Coverages". The Reference Model also provides the specification for the core operations required to utilize any Discrete Coverage. The FEniCS Project[2] provides a comprehensive model for how to represent the Basis Functions on mesh structures as "Degrees of Freedom" to present discrete data as continuous fields with variable continuity. In this talk, we will present the research and development the OOI Cyberinfrastructure Project is pursuing to integrate these approaches into a comprehensive Application Programming Interface (API) to author, acquire and operate on the broad range of data formulation from time series, trajectories and tables through to time variant finite difference grids and finite element meshes.

Space and time-resolved probing of heterogeneous catalysis reactions using lab-on-a-chip

NASA Astrophysics Data System (ADS)

Navin, Chelliah V.; Krishna, Katla Sai; Theegala, Chandra S.; Kumar, Challa S. S. R.

2016-03-01

Probing catalytic reactions on a catalyst surface in real time is a major challenge. Herein, we demonstrate the utility of a continuous flow millifluidic chip reactor coated with a nanostructured gold catalyst as an effective platform for in situ investigation of the kinetics of catalytic reactions by taking 5-(hydroxymethyl)furfural (HMF) to 2,5-furandicarboxylic acid (FDCA) conversion as a model reaction. The idea conceptualized in this paper can not only dramatically change the ability to probe the time-resolved kinetics of heterogeneous catalysis reactions but also used for investigating other chemical and biological catalytic processes, thereby making this a broad platform for probing reactions as they occur within continuous flow reactors.Probing catalytic reactions on a catalyst surface in real time is a major challenge. Herein, we demonstrate the utility of a continuous flow millifluidic chip reactor coated with a nanostructured gold catalyst as an effective platform for in situ investigation of the kinetics of catalytic reactions by taking 5-(hydroxymethyl)furfural (HMF) to 2,5-furandicarboxylic acid (FDCA) conversion as a model reaction. The idea conceptualized in this paper can not only dramatically change the ability to probe the time-resolved kinetics of heterogeneous catalysis reactions but also used for investigating other chemical and biological catalytic processes, thereby making this a broad platform for probing reactions as they occur within continuous flow reactors. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr06752a

Markov reward processes

NASA Technical Reports Server (NTRS)

Smith, R. M.

1991-01-01

Numerous applications in the area of computer system analysis can be effectively studied with Markov reward models. These models describe the behavior of the system with a continuous-time Markov chain, where a reward rate is associated with each state. In a reliability/availability model, upstates may have reward rate 1 and down states may have reward rate zero associated with them. In a queueing model, the number of jobs of certain type in a given state may be the reward rate attached to that state. In a combined model of performance and reliability, the reward rate of a state may be the computational capacity, or a related performance measure. Expected steady-state reward rate and expected instantaneous reward rate are clearly useful measures of the Markov reward model. More generally, the distribution of accumulated reward or time-averaged reward over a finite time interval may be determined from the solution of the Markov reward model. This information is of great practical significance in situations where the workload can be well characterized (deterministically, or by continuous functions e.g., distributions). The design process in the development of a computer system is an expensive and long term endeavor. For aerospace applications the reliability of the computer system is essential, as is the ability to complete critical workloads in a well defined real time interval. Consequently, effective modeling of such systems must take into account both performance and reliability. This fact motivates our use of Markov reward models to aid in the development and evaluation of fault tolerant computer systems.

Distributed Optimization Design of Continuous-Time Multiagent Systems With Unknown-Frequency Disturbances.

PubMed

Wang, Xinghu; Hong, Yiguang; Yi, Peng; Ji, Haibo; Kang, Yu

2017-05-24

In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.

40 CFR 60.2560 - What is the “model rule” in this subpart?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Use of Model Rule § 60.2560 What is the “model rule” in this subpart? (a) The model rule is the portion of these emission...

40 CFR 60.2560 - What is the “model rule” in this subpart?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Use of Model Rule § 60.2560 What is the “model rule” in this subpart? (a) The model rule is the portion of these emission...

40 CFR 60.2560 - What is the “model rule” in this subpart?

Code of Federal Regulations, 2012 CFR

2012-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Use of Model Rule § 60.2560 What is the “model rule” in this subpart? (a) The model rule is the portion of these emission...

Evaluation of the AnnAGNPS model for predicting runoff and sediment yield in a small Mediterranean agricultural watershed in Navarre (Spain)

USDA-ARS?s Scientific Manuscript database

AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model) is a system of computer models developed to predict non-point source pollutant loadings within agricultural watersheds. It contains a daily time step distributed parameter continuous simulation surface runoff model designed to assis...

A Systematic Approach to Determining the Identifiability of Multistage Carcinogenesis Models.

PubMed

Brouwer, Andrew F; Meza, Rafael; Eisenberg, Marisa C

2017-07-01

Multistage clonal expansion (MSCE) models of carcinogenesis are continuous-time Markov process models often used to relate cancer incidence to biological mechanism. Identifiability analysis determines what model parameter combinations can, theoretically, be estimated from given data. We use a systematic approach, based on differential algebra methods traditionally used for deterministic ordinary differential equation (ODE) models, to determine identifiable combinations for a generalized subclass of MSCE models with any number of preinitation stages and one clonal expansion. Additionally, we determine the identifiable combinations of the generalized MSCE model with up to four clonal expansion stages, and conjecture the results for any number of clonal expansion stages. The results improve upon previous work in a number of ways and provide a framework to find the identifiable combinations for further variations on the MSCE models. Finally, our approach, which takes advantage of the Kolmogorov backward equations for the probability generating functions of the Markov process, demonstrates that identifiability methods used in engineering and mathematics for systems of ODEs can be applied to continuous-time Markov processes. © 2016 Society for Risk Analysis.

Continuous-time interval model identification of blood glucose dynamics for type 1 diabetes

NASA Astrophysics Data System (ADS)

Kirchsteiger, Harald; Johansson, Rolf; Renard, Eric; del Re, Luigi

2014-07-01

While good physiological models of the glucose metabolism in type 1 diabetic patients are well known, their parameterisation is difficult. The high intra-patient variability observed is a further major obstacle. This holds for data-based models too, so that no good patient-specific models are available. Against this background, this paper proposes the use of interval models to cover the different metabolic conditions. The control-oriented models contain a carbohydrate and insulin sensitivity factor to be used for insulin bolus calculators directly. Available clinical measurements were sampled on an irregular schedule which prompts the use of continuous-time identification, also for the direct estimation of the clinically interpretable factors mentioned above. An identification method is derived and applied to real data from 28 diabetic patients. Model estimation was done on a clinical data-set, whereas validation results shown were done on an out-of-clinic, everyday life data-set. The results show that the interval model approach allows a much more regular estimation of the parameters and avoids physiologically incompatible parameter estimates.

PDF turbulence modeling and DNS

NASA Technical Reports Server (NTRS)

Hsu, A. T.

1992-01-01

The problem of time discontinuity (or jump condition) in the coalescence/dispersion (C/D) mixing model is addressed in probability density function (pdf). A C/D mixing model continuous in time is introduced. With the continuous mixing model, the process of chemical reaction can be fully coupled with mixing. In the case of homogeneous turbulence decay, the new model predicts a pdf very close to a Gaussian distribution, with finite higher moments also close to that of a Gaussian distribution. Results from the continuous mixing model are compared with both experimental data and numerical results from conventional C/D models. The effect of Coriolis forces on compressible homogeneous turbulence is studied using direct numerical simulation (DNS). The numerical method used in this study is an eight order compact difference scheme. Contrary to the conclusions reached by previous DNS studies on incompressible isotropic turbulence, the present results show that the Coriolis force increases the dissipation rate of turbulent kinetic energy, and that anisotropy develops as the Coriolis force increases. The Taylor-Proudman theory does apply since the derivatives in the direction of the rotation axis vanishes rapidly. A closer analysis reveals that the dissipation rate of the incompressible component of the turbulent kinetic energy indeed decreases with a higher rotation rate, consistent with incompressible flow simulations (Bardina), while the dissipation rate of the compressible part increases; the net gain is positive. Inertial waves are observed in the simulation results.

Direct use of linear time-domain aerodynamics in aeroservoelastic analysis: Aerodynamic model

NASA Technical Reports Server (NTRS)

Woods, J. A.; Gilbert, Michael G.

1990-01-01

The work presented here is the first part of a continuing effort to expand existing capabilities in aeroelasticity by developing the methodology which is necessary to utilize unsteady time-domain aerodynamics directly in aeroservoelastic design and analysis. The ultimate objective is to define a fully integrated state-space model of an aeroelastic vehicle's aerodynamics, structure and controls which may be used to efficiently determine the vehicle's aeroservoelastic stability. Here, the current status of developing a state-space model for linear or near-linear time-domain indicial aerodynamic forces is presented.

Effective pore-scale dispersion upscaling with a correlated continuous time random walk approach

NASA Astrophysics Data System (ADS)

Le Borgne, T.; Bolster, D.; Dentz, M.; de Anna, P.; Tartakovsky, A.

2011-12-01

We investigate the upscaling of dispersion from a pore-scale analysis of Lagrangian velocities. A key challenge in the upscaling procedure is to relate the temporal evolution of spreading to the pore-scale velocity field properties. We test the hypothesis that one can represent Lagrangian velocities at the pore scale as a Markov process in space. The resulting effective transport model is a continuous time random walk (CTRW) characterized by a correlated random time increment, here denoted as correlated CTRW. We consider a simplified sinusoidal wavy channel model as well as a more complex heterogeneous pore space. For both systems, the predictions of the correlated CTRW model, with parameters defined from the velocity field properties (both distribution and correlation), are found to be in good agreement with results from direct pore-scale simulations over preasymptotic and asymptotic times. In this framework, the nontrivial dependence of dispersion on the pore boundary fluctuations is shown to be related to the competition between distribution and correlation effects. In particular, explicit inclusion of spatial velocity correlation in the effective CTRW model is found to be important to represent incomplete mixing in the pore throats.

Curve Number Application in Continuous Runoff Models: An Exercise in Futility?

NASA Astrophysics Data System (ADS)

Lamont, S. J.; Eli, R. N.

2006-12-01

The suitability of applying the NRCS (Natural Resource Conservation Service) Curve Number (CN) to continuous runoff prediction is examined by studying the dependence of CN on several hydrologic variables in the context of a complex nonlinear hydrologic model. The continuous watershed model Hydrologic Simulation Program-FORTRAN (HSPF) was employed using a simple theoretical watershed in two numerical procedures designed to investigate the influence of soil type, soil depth, storm depth, storm distribution, and initial abstraction ratio value on the calculated CN value. This study stems from a concurrent project involving the design of a hydrologic modeling system to support the Cumulative Hydrologic Impact Assessments (CHIA) of over 230 coal-mined watersheds throughout West Virginia. Because of the large number of watersheds and limited availability of data necessary for HSPF calibration, it was initially proposed that predetermined CN values be used as a surrogate for those HSPF parameters controlling direct runoff. A soil physics model was developed to relate CN values to those HSPF parameters governing soil moisture content and infiltration behavior, with the remaining HSPF parameters being adopted from previous calibrations on real watersheds. A numerical procedure was then adopted to back-calculate CN values from the theoretical watershed using antecedent moisture conditions equivalent to the NRCS Antecedent Runoff Condition (ARC) II. This procedure used the direct runoff produced from a cyclic synthetic storm event time series input to HSPF. A second numerical method of CN determination, using real time series rainfall data, was used to provide a comparison to those CN values determined using the synthetic storm event time series. It was determined that the calculated CN values resulting from both numerical methods demonstrated a nonlinear dependence on all of the computational variables listed above. It was concluded that the use of the Curve Number as a surrogate for the selected subset of HPSF parameters could not be justified. These results suggest that use of the Curve Number in other complex continuous time series hydrologic models may not be appropriate, given the limitations inherent in the definition of the NRCS CN method.

New deconvolution method for microscopic images based on the continuous Gaussian radial basis function interpolation model.

PubMed

Chen, Zhaoxue; Chen, Hao

2014-01-01

A deconvolution method based on the Gaussian radial basis function (GRBF) interpolation is proposed. Both the original image and Gaussian point spread function are expressed as the same continuous GRBF model, thus image degradation is simplified as convolution of two continuous Gaussian functions, and image deconvolution is converted to calculate the weighted coefficients of two-dimensional control points. Compared with Wiener filter and Lucy-Richardson algorithm, the GRBF method has an obvious advantage in the quality of restored images. In order to overcome such a defect of long-time computing, the method of graphic processing unit multithreading or increasing space interval of control points is adopted, respectively, to speed up the implementation of GRBF method. The experiments show that based on the continuous GRBF model, the image deconvolution can be efficiently implemented by the method, which also has a considerable reference value for the study of three-dimensional microscopic image deconvolution.

Understanding Activity Engagement Across Weekdays and Weekend Days: A Multivariate Multiple Discrete-Continuous Modeling Approach

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

Garikapati, Venu; Astroza, Sebastian; Bhat, Prerna C.

This paper is motivated by the increasing recognition that modeling activity-travel demand for a single day of the week, as is done in virtually all travel forecasting models, may be inadequate in capturing underlying processes that govern activity-travel scheduling behavior. The considerable variability in daily travel suggests that there are important complementary relationships and competing tradeoffs involved in scheduling and allocating time to various activities across days of the week. Both limited survey data availability and methodological challenges in modeling week-long activity-travel schedules have precluded the development of multi-day activity-travel demand models. With passive and technology-based data collection methods increasinglymore » in vogue, the collection of multi-day travel data may become increasingly commonplace in the years ahead. This paper addresses the methodological challenge associated with modeling multi-day activity-travel demand by formulating a multivariate multiple discrete-continuous probit (MDCP) model system. The comprehensive framework ties together two MDCP model components, one corresponding to weekday time allocation and the other to weekend activity-time allocation. By tying the two MDCP components together, the model system also captures relationships in activity-time allocation between weekdays on the one hand and weekend days on the other. Model estimation on a week-long travel diary data set from the United Kingdom shows that there are significant inter-relationships between weekdays and weekend days in activity-travel scheduling behavior. The model system presented in this paper may serve as a higher-level multi-day activity scheduler in conjunction with existing daily activity-based travel models.« less

Continuous time limits of the utterance selection model

NASA Astrophysics Data System (ADS)

Michaud, Jérôme

2017-02-01

In this paper we derive alternative continuous time limits of the utterance selection model (USM) for language change [G. J. Baxter et al., Phys. Rev. E 73, 046118 (2006), 10.1103/PhysRevE.73.046118]. This is motivated by the fact that the Fokker-Planck continuous time limit derived in the original version of the USM is only valid for a small range of parameters. We investigate the consequences of relaxing these constraints on parameters. Using the normal approximation of the multinomial approximation, we derive a continuous time limit of the USM in the form of a weak-noise stochastic differential equation. We argue that this weak noise, not captured by the Kramers-Moyal expansion, cannot be neglected. We then propose a coarse-graining procedure, which takes the form of a stochastic version of the heterogeneous mean field approximation. This approximation groups the behavior of nodes of the same degree, reducing the complexity of the problem. With the help of this approximation, we study in detail two simple families of networks: the regular networks and the star-shaped networks. The analysis reveals and quantifies a finite-size effect of the dynamics. If we increase the size of the network by keeping all the other parameters constant, we transition from a state where conventions emerge to a state where no convention emerges. Furthermore, we show that the degree of a node acts as a time scale. For heterogeneous networks such as star-shaped networks, the time scale difference can become very large, leading to a noisier behavior of highly connected nodes.

A continuous analog of run length distributions reflecting accumulated fractionation events.

PubMed

Yu, Zhe; Sankoff, David

2016-11-11

We propose a new, continuous model of the fractionation process (duplicate gene deletion after polyploidization) on the real line. The aim is to infer how much DNA is deleted at a time, based on segment lengths for alternating deleted (invisible) and undeleted (visible) regions. After deriving a number of analytical results for "one-sided" fractionation, we undertake a series of simulations that help us identify the distribution of segment lengths as a gamma with shape and rate parameters evolving over time. This leads to an inference procedure based on observed length distributions for visible and invisible segments. We suggest extensions of this mathematical and simulation work to biologically realistic discrete models, including two-sided fractionation.

Continuing care and long-term substance use outcomes in managed care: early evidence for a primary care-based model.

PubMed

Chi, Felicia W; Parthasarathy, Sujaya; Mertens, Jennifer R; Weisner, Constance M

2011-10-01

How best to provide ongoing services to patients with substance use disorders to sustain long-term recovery is a significant clinical and policy question that has not been adequately addressed. Analyzing nine years of prospective data for 991 adults who entered substance abuse treatment in a private, nonprofit managed care health plan, this study aimed to examine the components of a continuing care model (primary care, specialty substance abuse treatment, and psychiatric services) and their combined effect on outcomes over nine years after treatment entry. In a longitudinal observational study, follow-up measures included self-reported alcohol and drug use, Addiction Severity Index scores, and service utilization data extracted from the health plan databases. Remission, defined as abstinence or nonproblematic use, was the outcome measure. A mixed-effects logistic random intercept model controlling for time and other covariates found that yearly primary care, and specialty care based on need as measured at the prior time point, were positively associated with remission over time. Persons receiving continuing care (defined as having yearly primary care and specialty substance abuse treatment and psychiatric services when needed) had twice the odds of achieving remission at follow-ups (p<.001) as those without. Continuing care that included both primary care and specialty care management to support ongoing monitoring, self-care, and treatment as needed was important for long-term recovery of patients with substance use disorders.

Modeling multiple time series annotations as noisy distortions of the ground truth: An Expectation-Maximization approach.

PubMed

Gupta, Rahul; Audhkhasi, Kartik; Jacokes, Zach; Rozga, Agata; Narayanan, Shrikanth

2018-01-01

Studies of time-continuous human behavioral phenomena often rely on ratings from multiple annotators. Since the ground truth of the target construct is often latent, the standard practice is to use ad-hoc metrics (such as averaging annotator ratings). Despite being easy to compute, such metrics may not provide accurate representations of the underlying construct. In this paper, we present a novel method for modeling multiple time series annotations over a continuous variable that computes the ground truth by modeling annotator specific distortions. We condition the ground truth on a set of features extracted from the data and further assume that the annotators provide their ratings as modification of the ground truth, with each annotator having specific distortion tendencies. We train the model using an Expectation-Maximization based algorithm and evaluate it on a study involving natural interaction between a child and a psychologist, to predict confidence ratings of the children's smiles. We compare and analyze the model against two baselines where: (i) the ground truth in considered to be framewise mean of ratings from various annotators and, (ii) each annotator is assumed to bear a distinct time delay in annotation and their annotations are aligned before computing the framewise mean.

Discrete-Time Mapping for an Impulsive Goodwin Oscillator with Three Delays

NASA Astrophysics Data System (ADS)

Churilov, Alexander N.; Medvedev, Alexander; Zhusubaliyev, Zhanybai T.

A popular biomathematics model of the Goodwin oscillator has been previously generalized to a more biologically plausible construct by introducing three time delays to portray the transport phenomena arising due to the spatial distribution of the model states. The present paper addresses a similar conversion of an impulsive version of the Goodwin oscillator that has found application in mathematical modeling, e.g. in endocrine systems with pulsatile hormone secretion. While the cascade structure of the linear continuous part pertinent to the Goodwin oscillator is preserved in the impulsive Goodwin oscillator, the static nonlinear feedback of the former is substituted with a pulse modulation mechanism thus resulting in hybrid dynamics of the closed-loop system. To facilitate the analysis of the mathematical model under investigation, a discrete mapping propagating the continuous state variables through the firing times of the impulsive feedback is derived. Due to the presence of multiple time delays in the considered model, previously developed mapping derivation approaches are not applicable here and a novel technique is proposed and applied. The mapping captures the dynamics of the original hybrid system and is instrumental in studying complex nonlinear phenomena arising in the impulsive Goodwin oscillator. A simulation example is presented to demonstrate the utility of the proposed approach in bifurcation analysis.

Modelling of slaughterhouse solid waste anaerobic digestion: determination of parameters and continuous reactor simulation.

PubMed

López, Iván; Borzacconi, Liliana

2010-10-01

A model based on the work of Angelidaki et al. (1993) was applied to simulate the anaerobic biodegradation of ruminal contents. In this study, two fractions of solids with different biodegradation rates were considered. A first-order kinetic was used for the easily biodegradable fraction and a kinetic expression that is function of the extracellular enzyme concentration was used for the slowly biodegradable fraction. Batch experiments were performed to obtain an accumulated methane curve that was then used to obtain the model parameters. For this determination, a methodology derived from the "multiple-shooting" method was successfully used. Monte Carlo simulations allowed a confidence range to be obtained for each parameter. Simulations of a continuous reactor were performed using the optimal set of model parameters. The final steady-states were determined as functions of the operational conditions (solids load and residence time). The simulations showed that methane flow peaked at a flow rate of 0.5-0.8 Nm(3)/d/m(reactor)(3) at a residence time of 10-20 days. Simulations allow the adequate selection of operating conditions of a continuous reactor. (c) 2010 Elsevier Ltd. All rights reserved.

Describing and Predicting Developmental Profiles of Externalizing Problems from Childhood to Adulthood

PubMed Central

Petersen, Isaac T.; Bates, John E.; Dodge, Kenneth A.; Lansford, Jennifer E.; Pettit, Gregory S.

2014-01-01

This longitudinal study considers externalizing behavior problems from ages 5 to 27 (N = 585). Externalizing problem ratings by mothers, fathers, teachers, peers, and self-report were modeled with growth curves. Risk and protective factors across many different domains and time frames were included as predictors of the trajectories. A major contribution of the study is in demonstrating how heterotypic continuity and changing measures can be handled in modeling changes in externalizing behavior over long developmental periods. On average, externalizing problems decreased from early childhood to preadolescence, increased during adolescence, and decreased from late adolescence to adulthood. There was strong nonlinear continuity in externalizing problems over time. Family process, peer process, stress, and individual characteristics predicted externalizing problems beyond the strong continuity of externalizing problems. The model accounted for 70% of the variability in the development of externalizing problems. The model’s predicted values showed moderate sensitivity and specificity in prediction of arrests, illegal drug use, and drunk driving. Overall, the study showed that by using changing, developmentally-relevant measures and simultaneously taking into account numerous characteristics of children and their living situations, research can model lengthy spans of development and improve predictions of the development of later, severe externalizing problems. PMID:25166430

Modeling and Measurement of Correlation between Blood and Interstitial Glucose Changes

PubMed Central

Shi, Ting; Li, Dachao; Li, Guoqing; Zhang, Yiming; Xu, Kexin; Lu, Luo

2016-01-01

One of the most effective methods for continuous blood glucose monitoring is to continuously measure glucose in the interstitial fluid (ISF). However, multiple physiological factors can modulate glucose concentrations and affect the lag phase between blood and ISF glucose changes. This study aims to develop a compensatory tool for measuring the delay in ISF glucose variations in reference to blood glucose changes. A theoretical model was developed based on biophysics and physiology of glucose transport in the microcirculation system. Blood and interstitial fluid glucose changes were measured in mice and rats by fluorescent and isotope methods, respectively. Computer simulation mimicked curves were fitted with data resulting from fluorescent measurements of mice and isotope measurements of rats, indicating that there were lag times for ISF glucose changes. It also showed that there was a required diffusion distance for glucose to travel from center of capillaries to interstitial space in both mouse and rat models. We conclude that it is feasible with the developed model to continuously monitor dynamic changes of blood glucose concentration through measuring glucose changes in ISF with high accuracy, which requires correct parameters for determining and compensating for the delay time of glucose changes in ISF. PMID:27239479

40 CFR 60.2630 - What should I include in my waste management plan?

Code of Federal Regulations, 2014 CFR

2014-07-01

...) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Waste Management.... Model Rule—Operator Training and Qualification ...

40 CFR 60.2630 - What should I include in my waste management plan?

Code of Federal Regulations, 2013 CFR

2013-07-01

...) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Waste Management.... Model Rule—Operator Training and Qualification ...

On a Nonlinear Model for Tumor Growth: Global in Time Weak Solutions

NASA Astrophysics Data System (ADS)

Donatelli, Donatella; Trivisa, Konstantina

2014-07-01

We investigate the dynamics of a class of tumor growth models known as mixed models. The key characteristic of these type of tumor growth models is that the different populations of cells are continuously present everywhere in the tumor at all times. In this work we focus on the evolution of tumor growth in the presence of proliferating, quiescent and dead cells as well as a nutrient. The system is given by a multi-phase flow model and the tumor is described as a growing continuum Ω with boundary ∂Ω both of which evolve in time. Global-in-time weak solutions are obtained using an approach based on penalization of the boundary behavior, diffusion and viscosity in the weak formulation.

Continuous-Time Semi-Markov Models in Health Economic Decision Making: An Illustrative Example in Heart Failure Disease Management.

PubMed

Cao, Qi; Buskens, Erik; Feenstra, Talitha; Jaarsma, Tiny; Hillege, Hans; Postmus, Douwe

2016-01-01

Continuous-time state transition models may end up having large unwieldy structures when trying to represent all relevant stages of clinical disease processes by means of a standard Markov model. In such situations, a more parsimonious, and therefore easier-to-grasp, model of a patient's disease progression can often be obtained by assuming that the future state transitions do not depend only on the present state (Markov assumption) but also on the past through time since entry in the present state. Despite that these so-called semi-Markov models are still relatively straightforward to specify and implement, they are not yet routinely applied in health economic evaluation to assess the cost-effectiveness of alternative interventions. To facilitate a better understanding of this type of model among applied health economic analysts, the first part of this article provides a detailed discussion of what the semi-Markov model entails and how such models can be specified in an intuitive way by adopting an approach called vertical modeling. In the second part of the article, we use this approach to construct a semi-Markov model for assessing the long-term cost-effectiveness of 3 disease management programs for heart failure. Compared with a standard Markov model with the same disease states, our proposed semi-Markov model fitted the observed data much better. When subsequently extrapolating beyond the clinical trial period, these relatively large differences in goodness-of-fit translated into almost a doubling in mean total cost and a 60-d decrease in mean survival time when using the Markov model instead of the semi-Markov model. For the disease process considered in our case study, the semi-Markov model thus provided a sensible balance between model parsimoniousness and computational complexity. © The Author(s) 2015.

Hybrid deterministic/stochastic simulation of complex biochemical systems.

PubMed

Lecca, Paola; Bagagiolo, Fabio; Scarpa, Marina

2017-11-21

In a biological cell, cellular functions and the genetic regulatory apparatus are implemented and controlled by complex networks of chemical reactions involving genes, proteins, and enzymes. Accurate computational models are indispensable means for understanding the mechanisms behind the evolution of a complex system, not always explored with wet lab experiments. To serve their purpose, computational models, however, should be able to describe and simulate the complexity of a biological system in many of its aspects. Moreover, it should be implemented by efficient algorithms requiring the shortest possible execution time, to avoid enlarging excessively the time elapsing between data analysis and any subsequent experiment. Besides the features of their topological structure, the complexity of biological networks also refers to their dynamics, that is often non-linear and stiff. The stiffness is due to the presence of molecular species whose abundance fluctuates by many orders of magnitude. A fully stochastic simulation of a stiff system is computationally time-expensive. On the other hand, continuous models are less costly, but they fail to capture the stochastic behaviour of small populations of molecular species. We introduce a new efficient hybrid stochastic-deterministic computational model and the software tool MoBioS (MOlecular Biology Simulator) implementing it. The mathematical model of MoBioS uses continuous differential equations to describe the deterministic reactions and a Gillespie-like algorithm to describe the stochastic ones. Unlike the majority of current hybrid methods, the MoBioS algorithm divides the reactions' set into fast reactions, moderate reactions, and slow reactions and implements a hysteresis switching between the stochastic model and the deterministic model. Fast reactions are approximated as continuous-deterministic processes and modelled by deterministic rate equations. Moderate reactions are those whose reaction waiting time is greater than the fast reaction waiting time but smaller than the slow reaction waiting time. A moderate reaction is approximated as a stochastic (deterministic) process if it was classified as a stochastic (deterministic) process at the time at which it crosses the threshold of low (high) waiting time. A Gillespie First Reaction Method is implemented to select and execute the slow reactions. The performances of MoBios were tested on a typical example of hybrid dynamics: that is the DNA transcription regulation. The simulated dynamic profile of the reagents' abundance and the estimate of the error introduced by the fully deterministic approach were used to evaluate the consistency of the computational model and that of the software tool.

An exploration of the midwifery continuity of care program at one Australian University as a symbiotic clinical education model.

PubMed

Sweet, Linda P; Glover, Pauline

2013-03-01

This discussion paper analyses a midwifery Continuity of Care program at an Australian University with the symbiotic clinical education model, to identify strengths and weakness, and identify ways in which this new pedagogical approach can be improved. In 2002 a major change in Australian midwifery curricula was the introduction of a pedagogical innovation known as the Continuity of Care experience. This innovation contributes a significant portion of clinical experience for midwifery students. It is intended as a way to give midwifery students the opportunity to provide continuity of care in partnership with women, through their pregnancy and childbirth, thus imitating a model of continuity of care and continuity of carer. A qualitative study was conducted in 2008/9 as part of an Australian Learning and Teaching Council Associate Fellowship. Evidence and findings from this project (reported elsewhere) are used in this paper to illustrate the evaluation of midwifery Continuity of Care experience program at an Australian university with the symbiotic clinical education model. Strengths of the current Continuity of Care experience are the strong focus on relationships between midwifery students and women, and early clinical exposure to professional practice. Improved facilitation through the development of stronger relationships with clinicians will improve learning, and result in improved access to authentic supported learning and increased provision of formative feedback. This paper presents a timely review of the Continuity of Care experience for midwifery student learning and highlights the potential of applying the symbiotic clinical education model to enhance learning. Applying the symbiotic clinical education framework to evidence gathered about the Continuity of Care experience in Australian midwifery education highlights strengths and weaknesses which may be used to guide curricula and pedagogical improvements. Copyright © 2011 Elsevier Ltd. All rights reserved.

Modelling a demand driven biogas system for production of electricity at peak demand and for production of biomethane at other times.

PubMed

O'Shea, R; Wall, D; Murphy, J D

2016-09-01

Four feedstocks were assessed for use in a demand driven biogas system. Biomethane potential (BMP) assays were conducted for grass silage, food waste, Laminaria digitata and dairy cow slurry. Semi-continuous trials were undertaken for all feedstocks, assessing biogas and biomethane production. Three kinetic models of the semi-continuous trials were compared. A first order model most accurately correlated with gas production in the pulse fed semi-continuous system. This model was developed for production of electricity on demand, and biomethane upgrading. The model examined a theoretical grass silage digester that would produce 435kWe in a continuous fed system. Adaptation to demand driven biogas required 187min to produce sufficient methane to run a 2MWe combined heat and power (CHP) unit for 60min. The upgrading system was dispatched 71min following CHP shutdown. Of the biogas produced 21% was used in the CHP and 79% was used in the upgrading system. Copyright © 2016 Elsevier Ltd. All rights reserved.

ECS: Efficient Communication Scheduling for Underwater Sensor Networks

PubMed Central

Hong, Lu; Hong, Feng; Guo, Zhongwen; Li, Zhengbao

2011-01-01

TDMA protocols have attracted a lot of attention for underwater acoustic sensor networks (UWSNs), because of the unique characteristics of acoustic signal propagation such as great energy consumption in transmission, long propagation delay and long communication range. Previous TDMA protocols all allocated transmission time to nodes based on discrete time slots. This paper proposes an efficient continuous time scheduling TDMA protocol (ECS) for UWSNs, including the continuous time based and sender oriented conflict analysis model, the transmission moment allocation algorithm and the distributed topology maintenance algorithm. Simulation results confirm that ECS improves network throughput by 20% on average, compared to existing MAC protocols. PMID:22163775

A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.

PubMed

Deng, Zhimin; Tian, Tianhai

2014-07-29

The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging. To address the issue of inadequate experimental data, we propose a continuous optimization approach for making reliable inference of model parameters. This approach first uses a spline interpolation to generate continuous functions of system dynamics as well as the first and second order derivatives of continuous functions. The expanded dataset is the basis to infer unknown model parameters using various continuous optimization criteria, including the error of simulation only, error of both simulation and the first derivative, or error of simulation as well as the first and second derivatives. We use three case studies to demonstrate the accuracy and reliability of the proposed new approach. Compared with the corresponding discrete criteria using experimental data at the measurement time points only, numerical results of the ERK kinase activation module show that the continuous absolute-error criteria using both function and high order derivatives generate estimates with better accuracy. This result is also supported by the second and third case studies for the G1/S transition network and the MAP kinase pathway, respectively. This suggests that the continuous absolute-error criteria lead to more accurate estimates than the corresponding discrete criteria. We also study the robustness property of these three models to examine the reliability of estimates. Simulation results show that the models with estimated parameters using continuous fitness functions have better robustness properties than those using the corresponding discrete fitness functions. The inference studies and robustness analysis suggest that the proposed continuous optimization criteria are effective and robust for estimating unknown parameters in mathematical models.

Reduced order modelling in searches for continuous gravitational waves - I. Barycentring time delays

NASA Astrophysics Data System (ADS)

Pitkin, M.; Doolan, S.; McMenamin, L.; Wette, K.

2018-06-01

The frequencies and phases of emission from extra-solar sources measured by Earth-bound observers are modulated by the motions of the observer with respect to the source, and through relativistic effects. These modulations depend critically on the source's sky-location. Precise knowledge of the modulations are required to coherently track the source's phase over long observations, for example, in pulsar timing, or searches for continuous gravitational waves. The modulations can be modelled as sky-location and time-dependent time delays that convert arrival times at the observer to the inertial frame of the source, which can often be the Solar system barycentre. We study the use of reduced order modelling for speeding up the calculation of this time delay for any sky-location. We find that the time delay model can be decomposed into just four basis vectors, and with these the delay for any sky-location can be reconstructed to sub-nanosecond accuracy. When compared to standard routines for time delay calculation in gravitational wave searches, using the reduced basis can lead to speed-ups of 30 times. We have also studied components of time delays for sources in binary systems. Assuming eccentricities <0.25, we can reconstruct the delays to within 100 s of nanoseconds, with best case speed-ups of a factor of 10, or factors of two when interpolating the basis for different orbital periods or time stamps. In long-duration phase-coherent searches for sources with sky-position uncertainties, or binary parameter uncertainties, these speed-ups could allow enhancements in their scopes without large additional computational burdens.

A Bayesian Approach for Analyzing Longitudinal Structural Equation Models

ERIC Educational Resources Information Center

Song, Xin-Yuan; Lu, Zhao-Hua; Hser, Yih-Ing; Lee, Sik-Yum

2011-01-01

This article considers a Bayesian approach for analyzing a longitudinal 2-level nonlinear structural equation model with covariates, and mixed continuous and ordered categorical variables. The first-level model is formulated for measures taken at each time point nested within individuals for investigating their characteristics that are dynamically…

Time to refine key climate policy models

NASA Astrophysics Data System (ADS)

Barron, Alexander R.

2018-05-01

Ambition regarding climate change at the national level is critical but is often calibrated with the projected costs — as estimated by a small suite of energy-economic models. Weaknesses in several key areas in these models will continue to distort policy design unless collectively addressed by a diversity of researchers.

On modeling of integrated communication and control systems

NASA Technical Reports Server (NTRS)

Liou, Luen-Woei; Ray, Asok

1990-01-01

The mathematical modeling scheme proposed by Ray and Halevi (1988) for integrated communication and control systems is considered analytically, with an emphasis on the effect of introducing varying and distributed time delays to account for asynchronous time-division multiplexing in the communication part of the system. Ray and Halevi applied a state-transition concept to transform the original continuous-time model into a discrete-time model; the same approach was used by Kalman and Bertram (1959) to model various types of sampled data systems which are not subject to induced delays. The relationship between the two modeling schemes is explored, and it is shown that, although the Kalman-Bertram method has the advantage of a unified approach, it becomes inconvenient when varying delays appear in the control loop.

The continuous adjoint approach to the k-ε turbulence model for shape optimization and optimal active control of turbulent flows

NASA Astrophysics Data System (ADS)

Papoutsis-Kiachagias, E. M.; Zymaris, A. S.; Kavvadias, I. S.; Papadimitriou, D. I.; Giannakoglou, K. C.

2015-03-01

The continuous adjoint to the incompressible Reynolds-averaged Navier-Stokes equations coupled with the low Reynolds number Launder-Sharma k-ε turbulence model is presented. Both shape and active flow control optimization problems in fluid mechanics are considered, aiming at minimum viscous losses. In contrast to the frequently used assumption of frozen turbulence, the adjoint to the turbulence model equations together with appropriate boundary conditions are derived, discretized and solved. This is the first time that the adjoint equations to the Launder-Sharma k-ε model have been derived. Compared to the formulation that neglects turbulence variations, the impact of additional terms and equations is evaluated. Sensitivities computed using direct differentiation and/or finite differences are used for comparative purposes. To demonstrate the need for formulating and solving the adjoint to the turbulence model equations, instead of merely relying upon the 'frozen turbulence assumption', the gain in the optimization turnaround time offered by the proposed method is quantified.

Parsimonious Continuous Time Random Walk Models and Kurtosis for Diffusion in Magnetic Resonance of Biological Tissue

NASA Astrophysics Data System (ADS)

Ingo, Carson; Sui, Yi; Chen, Yufen; Parrish, Todd; Webb, Andrew; Ronen, Itamar

2015-03-01

In this paper, we provide a context for the modeling approaches that have been developed to describe non-Gaussian diffusion behavior, which is ubiquitous in diffusion weighted magnetic resonance imaging of water in biological tissue. Subsequently, we focus on the formalism of the continuous time random walk theory to extract properties of subdiffusion and superdiffusion through novel simplifications of the Mittag-Leffler function. For the case of time-fractional subdiffusion, we compute the kurtosis for the Mittag-Leffler function, which provides both a connection and physical context to the much-used approach of diffusional kurtosis imaging. We provide Monte Carlo simulations to illustrate the concepts of anomalous diffusion as stochastic processes of the random walk. Finally, we demonstrate the clinical utility of the Mittag-Leffler function as a model to describe tissue microstructure through estimations of subdiffusion and kurtosis with diffusion MRI measurements in the brain of a chronic ischemic stroke patient.

Dynamic density functional theory with hydrodynamic interactions: theoretical development and application in the study of phase separation in gas-liquid systems.

PubMed

Kikkinides, E S; Monson, P A

2015-03-07

Building on recent developments in dynamic density functional theory, we have developed a version of the theory that includes hydrodynamic interactions. This is achieved by combining the continuity and momentum equations eliminating velocity fields, so the resulting model equation contains only terms related to the fluid density and its time and spatial derivatives. The new model satisfies simultaneously continuity and momentum equations under the assumptions of constant dynamic or kinematic viscosity and small velocities and/or density gradients. We present applications of the theory to spinodal decomposition of subcritical temperatures for one-dimensional and three-dimensional density perturbations for both a van der Waals fluid and for a lattice gas model in mean field theory. In the latter case, the theory provides a hydrodynamic extension to the recently studied dynamic mean field theory. We find that the theory correctly describes the transition from diffusive phase separation at short times to hydrodynamic behaviour at long times.

Dynamic density functional theory with hydrodynamic interactions: Theoretical development and application in the study of phase separation in gas-liquid systems

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

Kikkinides, E. S.; Monson, P. A.

Building on recent developments in dynamic density functional theory, we have developed a version of the theory that includes hydrodynamic interactions. This is achieved by combining the continuity and momentum equations eliminating velocity fields, so the resulting model equation contains only terms related to the fluid density and its time and spatial derivatives. The new model satisfies simultaneously continuity and momentum equations under the assumptions of constant dynamic or kinematic viscosity and small velocities and/or density gradients. We present applications of the theory to spinodal decomposition of subcritical temperatures for one-dimensional and three-dimensional density perturbations for both a van dermore » Waals fluid and for a lattice gas model in mean field theory. In the latter case, the theory provides a hydrodynamic extension to the recently studied dynamic mean field theory. We find that the theory correctly describes the transition from diffusive phase separation at short times to hydrodynamic behaviour at long times.« less

Estimates of grassland biomass and turnover time on the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Xia, Jiangzhou; Ma, Minna; Liang, Tiangang; Wu, Chaoyang; Yang, Yuanhe; Zhang, Li; Zhang, Yangjian; Yuan, Wenping

2018-01-01

The grassland of the Tibetan Plateau forms a globally significant biome, which represents 6% of the world’s grasslands and 44% of China’s grasslands. However, large uncertainties remain concerning the vegetation carbon storage and turnover time in this biome. In this study, we quantified the pool size of both the aboveground and belowground biomass and turnover time of belowground biomass across the Tibetan Plateau by combining systematic measurements taken from a substantial number of surveys (i.e. 1689 sites for aboveground biomass, 174 sites for belowground biomass) with a machine learning technique (i.e. random forest, RF). Our study demonstrated that the RF model is effective tool for upscaling local biomass observations to the regional scale, and for producing continuous biomass estimates of the Tibetan Plateau. On average, the models estimated 46.57 Tg (1 Tg = 1012g) C of aboveground biomass and 363.71 Tg C of belowground biomass in the Tibetan grasslands covering an area of 1.32 × 106 km2. The turnover time of belowground biomass demonstrated large spatial heterogeneity, with a median turnover time of 4.25 years. Our results also demonstrated large differences in the biomass simulations among the major ecosystem models used for the Tibetan Plateau, largely because of inadequate model parameterization and validation. This study provides a spatially continuous measure of vegetation carbon storage and turnover time, and provides useful information for advancing ecosystem models and improving their performance.

Stochastic satisficing account of confidence in uncertain value-based decisions

PubMed Central

Bahrami, Bahador; Keramati, Mehdi

2018-01-01

Every day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. uncertainty about waiting time in a queue, affects decisions and confidence when outcome is stochastic and continuous. How does one evaluate and choose between an option with unreliable but high expected reward, and an option with more certain but lower expected reward? Here we used an experimental design where two choices’ payoffs took continuous values, to examine the effect of outcome variance on decision and confidence. We found that our participants’ probability of choosing the good (high expected reward) option decreased when the good or the bad options’ payoffs were more variable. Their confidence ratings were affected by outcome variability, but only when choosing the good option. Unlike perceptual detection tasks, confidence ratings correlated only weakly with decisions’ time, but correlated with the consistency of trial-by-trial choices. Inspired by the satisficing heuristic, we propose a “stochastic satisficing” (SSAT) model for evaluating options with continuous uncertain outcomes. In this model, options are evaluated by their probability of exceeding an acceptability threshold, and confidence reports scale with the chosen option’s thus-defined satisficing probability. Participants’ decisions were best explained by an expected reward model, while the SSAT model provided the best prediction of decision confidence. We further tested and verified the predictions of this model in a second experiment. Our model and experimental results generalize the models of metacognition from perceptual detection tasks to continuous-value based decisions. Finally, we discuss how the stochastic satisficing account of decision confidence serves psychological and social purposes associated with the evaluation, communication and justification of decision-making. PMID:29621325

Real-time SWMF-Geospace at CCMC: assessing the quality of output from continuous operational simulations

NASA Astrophysics Data System (ADS)

Liemohn, M. W.; Welling, D. T.; De Zeeuw, D.; Kuznetsova, M. M.; Rastaetter, L.; Ganushkina, N. Y.; Ilie, R.; Toth, G.; Gombosi, T. I.; van der Holst, B.

2016-12-01

The ground-based magnetometer index Dst is a decent measure of the near-Earth current systems, in particular those in the storm-time inner magnetosphere. The ability of a large-scale, physics-based model to reproduce, or even predict, this index is therefore a tangible measure of the overall validity of the code for space weather research and space weather operational usage. Experimental real-time simulations of the Space Weather Modeling Framework (SWMF) are conducted at the Community Coordinated Modeling Center (CCMC), with results available there (http://ccmc.gsfc.nasa.gov/realtime.php), through the CCMC Integrated Space Weather Analysis (iSWA) site (http://iswa.ccmc.gsfc.nasa.gov/IswaSystemWebApp/), and the Michigan SWMF site (http://csem.engin.umich.edu/realtime). Presently, two configurations of the SWMF are running in real time at CCMC, both focusing on the geospace modules, using the BATS-R-US magnetohydrodynamic model, the Ridley Ionosphere Model, and with and without the Rice Convection Model for inner magnetospheric drift physics. While both have been running for several years, nearly continuous results are available since July 2015. Dst from the model output is compared against the Kyoto real-time Dst. Various quantitative measures are presented to assess the goodness of fit between the models and observations. In particular, correlation coefficients, RMSE and prediction efficiency are calculated and discussed. In addition, contingency tables are presented, demonstrating the ability of the model to predict "disturbed times" as defined by Dst values below some critical threshold. It is shown that the SWMF run with the inner magnetosphere model is significantly better at reproducing storm-time values, with prediction efficiencies above 0.25 and Heidke skill scores above 0.5. This work was funded by NASA and NSF grants, and the European Union's Horizon 2020 research and innovation programme under grant agreement 637302 PROGRESS.

Transient nucleation induction time from the birth-death equations

NASA Technical Reports Server (NTRS)

Shneidman, Vitaly A.; Weinberg, Michael C.

1992-01-01

For the set of finite-difference equations of Becker-Doering an exact formula for the induction time, which is expressed in terms of rapidly convergent sums, is presented. The form of the result is particularly amenable for analytical study, and the latter is carried out to obtain approximations of the exact expression in a rigorous manner and to assess its sensitivity to the choice of the nucleation model. The induction time is found to be governed by two main nucleation parameters, the normalized barrier height, and the number of molecules in the critical cluster. The ratio of these two parameters provides an assessment of the importance of discreteness effects. The exact expression is studied in both the continuous and the asymptotic limits. The accuracy of the Zeldovich equation, which is produced in the continuous limit, is discussed for several nucleation models.

Principles of continuous quality improvement applied to intravenous therapy.

PubMed

Dunavin, M K; Lane, C; Parker, P E

1994-01-01

Documentation of the application of the principles of continuous quality improvement (CQI) to the health care setting is crucial for understanding the transition from traditional management models to CQI models. A CQI project was designed and implemented by the IV Therapy Department at Lawrence Memorial Hospital to test the application of these principles to intravenous therapy and as a learning tool for the entire organization. Through a prototype inventory project, significant savings in cost and time were demonstrated using check sheets, flow diagrams, control charts, and other statistical tools, as well as using the Plan-Do-Check-Act cycle. As a result, a primary goal, increased time for direct patient care, was achieved. Eight hours per week in nursing time was saved, relationships between two work areas were improved, and $6,000 in personnel costs, storage space, and inventory were saved.

Finite-dimensional modeling of network-induced delays for real-time control systems

NASA Technical Reports Server (NTRS)

Ray, Asok; Halevi, Yoram

1988-01-01

In integrated control systems (ICS), a feedback loop is closed by the common communication channel, which multiplexes digital data from the sensor to the controller and from the controller to the actuator along with the data traffic from other control loops and management functions. Due to asynchronous time-division multiplexing in the network access protocols, time-varying delays are introduced in the control loop, which degrade the system dynamic performance and are a potential source of instability. The delayed control system is represented by a finite-dimensional, time-varying, discrete-time model which is less complex than the existing continuous-time models for time-varying delays; this approach allows for simpler schemes for analysis and simulation of the ICS.

Taylor O(h³) Discretization of ZNN Models for Dynamic Equality-Constrained Quadratic Programming With Application to Manipulators.

PubMed

Liao, Bolin; Zhang, Yunong; Jin, Long

2016-02-01

In this paper, a new Taylor-type numerical differentiation formula is first presented to discretize the continuous-time Zhang neural network (ZNN), and obtain higher computational accuracy. Based on the Taylor-type formula, two Taylor-type discrete-time ZNN models (termed Taylor-type discrete-time ZNNK and Taylor-type discrete-time ZNNU models) are then proposed and discussed to perform online dynamic equality-constrained quadratic programming. For comparison, Euler-type discrete-time ZNN models (called Euler-type discrete-time ZNNK and Euler-type discrete-time ZNNU models) and Newton iteration, with interesting links being found, are also presented. It is proved herein that the steady-state residual errors of the proposed Taylor-type discrete-time ZNN models, Euler-type discrete-time ZNN models, and Newton iteration have the patterns of O(h(3)), O(h(2)), and O(h), respectively, with h denoting the sampling gap. Numerical experiments, including the application examples, are carried out, of which the results further substantiate the theoretical findings and the efficacy of Taylor-type discrete-time ZNN models. Finally, the comparisons with Taylor-type discrete-time derivative model and other Lagrange-type discrete-time ZNN models for dynamic equality-constrained quadratic programming substantiate the superiority of the proposed Taylor-type discrete-time ZNN models once again.

Noncontact Infrared-Mediated Heat Transfer During Continuous Freeze-Drying of Unit Doses.

PubMed

Van Bockstal, Pieter-Jan; De Meyer, Laurens; Corver, Jos; Vervaet, Chris; De Beer, Thomas

2017-01-01

Recently, an innovative continuous freeze-drying concept for unit doses was proposed, based on spinning the vials during freezing. An efficient heat transfer during drying is essential to continuously process these spin frozen vials. Therefore, the applicability of noncontact infrared (IR) radiation was examined. The impact of several process and formulation variables on the mass of sublimed ice after 15 min of primary drying (i.e., sublimation rate) and the total drying time was examined. Two experimental designs were performed in which electrical power to the IR heaters, distance between the IR heaters and the spin frozen vial, chamber pressure, product layer thickness, and 5 model formulations were included as factors. A near-infrared spectroscopy method was developed to determine the end point of primary and secondary drying. The sublimation rate was mainly influenced by the electrical power to the IR heaters and the distance between the IR heaters and the vial. The layer thickness had the largest effect on total drying time. The chamber pressure and the 5 model formulations had no significant impact on sublimation rate and total drying time, respectively. This study shows that IR radiation is suitable to provide the energy during the continuous processing of spin frozen vials. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

40 CFR 60.5110 - How do I comply with the increment of progress for submittal of a control plan?

Code of Federal Regulations, 2011 CFR

2011-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge Incineration Units Model Rule... pollution control and process changes that you will use to comply with the emission limits and standards and...

Satellite Geodetic Constraints On Earthquake Processes: Implications of the 1999 Turkish Earthquakes for Fault Mechanics and Seismic Hazards on the San Andreas Fault

NASA Technical Reports Server (NTRS)

Reilinger, Robert

2005-01-01

Our principal activities during the initial phase of this project include: 1) Continued monitoring of postseismic deformation for the 1999 Izmit and Duzce, Turkey earthquakes from repeated GPS survey measurements and expansion of the Marmara Continuous GPS Network (MAGNET), 2) Establishing three North Anatolian fault crossing profiles (10 sitedprofile) at locations that experienced major surface-fault earthquakes at different times in the past to examine strain accumulation as a function of time in the earthquake cycle (2004), 3) Repeat observations of selected sites in the fault-crossing profiles (2005), 4) Repeat surveys of the Marmara GPS network to continue to monitor postseismic deformation, 5) Refining block models for the Marmara Sea seismic gap area to better understand earthquake hazards in the Greater Istanbul area, 6) Continuing development of models for afterslip and distributed viscoelastic deformation for the earthquake cycle. We are keeping close contact with MIT colleagues (Brad Hager, and Eric Hetland) who are developing models for S. California and for the earthquake cycle in general (Hetland, 2006). In addition, our Turkish partners at the Marmara Research Center have undertaken repeat, micro-gravity measurements at the MAGNET sites and have provided us estimates of gravity change during the period 2003 - 2005.

A Hybrid Secure Scheme for Wireless Sensor Networks against Timing Attacks Using Continuous-Time Markov Chain and Queueing Model.

PubMed

Meng, Tianhui; Li, Xiaofan; Zhang, Sha; Zhao, Yubin

2016-09-28

Wireless sensor networks (WSNs) have recently gained popularity for a wide spectrum of applications. Monitoring tasks can be performed in various environments. This may be beneficial in many scenarios, but it certainly exhibits new challenges in terms of security due to increased data transmission over the wireless channel with potentially unknown threats. Among possible security issues are timing attacks, which are not prevented by traditional cryptographic security. Moreover, the limited energy and memory resources prohibit the use of complex security mechanisms in such systems. Therefore, balancing between security and the associated energy consumption becomes a crucial challenge. This paper proposes a secure scheme for WSNs while maintaining the requirement of the security-performance tradeoff. In order to proceed to a quantitative treatment of this problem, a hybrid continuous-time Markov chain (CTMC) and queueing model are put forward, and the tradeoff analysis of the security and performance attributes is carried out. By extending and transforming this model, the mean time to security attributes failure is evaluated. Through tradeoff analysis, we show that our scheme can enhance the security of WSNs, and the optimal rekeying rate of the performance and security tradeoff can be obtained.

A Hybrid Secure Scheme for Wireless Sensor Networks against Timing Attacks Using Continuous-Time Markov Chain and Queueing Model

PubMed Central

Meng, Tianhui; Li, Xiaofan; Zhang, Sha; Zhao, Yubin

2016-01-01

Wireless sensor networks (WSNs) have recently gained popularity for a wide spectrum of applications. Monitoring tasks can be performed in various environments. This may be beneficial in many scenarios, but it certainly exhibits new challenges in terms of security due to increased data transmission over the wireless channel with potentially unknown threats. Among possible security issues are timing attacks, which are not prevented by traditional cryptographic security. Moreover, the limited energy and memory resources prohibit the use of complex security mechanisms in such systems. Therefore, balancing between security and the associated energy consumption becomes a crucial challenge. This paper proposes a secure scheme for WSNs while maintaining the requirement of the security-performance tradeoff. In order to proceed to a quantitative treatment of this problem, a hybrid continuous-time Markov chain (CTMC) and queueing model are put forward, and the tradeoff analysis of the security and performance attributes is carried out. By extending and transforming this model, the mean time to security attributes failure is evaluated. Through tradeoff analysis, we show that our scheme can enhance the security of WSNs, and the optimal rekeying rate of the performance and security tradeoff can be obtained. PMID:27690042

Model documentation for relations between continuous real-time and discrete water-quality constituents in Indian Creek, Johnson County, Kansas, June 2004 through May 2013

USGS Publications Warehouse

Stone, Mandy L.; Graham, Jennifer L.

2014-01-01

Johnson County is the fastest growing county in Kansas, with a population of about 560,000 people in 2012. Urban growth and development can have substantial effects on water quality, and streams in Johnson County are affected by nonpoint-source pollutants from stormwater runoff and point-source discharges such as municipal wastewater effluent. Understanding of current (2014) water-quality conditions and the effects of urbanization is critical for the protection and remediation of aquatic resources in Johnson County, Kansas and downstream reaches located elsewhere. The Indian Creek Basin is 194 square kilometers and includes parts of Johnson County, Kansas and Jackson County, Missouri. Approximately 86 percent of the Indian Creek Basin is located in Johnson County, Kansas. The U.S. Geological Survey, in cooperation with Johnson County Wastewater, operated a series of six continuous real-time water-quality monitoring stations in the Indian Creek Basin during June 2011 through May 2013; one of these sites has been operating since February 2004. Five monitoring sites were located on Indian Creek and one site was located on Tomahawk Creek. The purpose of this report is to document regression models that establish relations between continuously measured water-quality properties and discretely collected water-quality constituents. Continuously measured water-quality properties include streamflow, specific conductance, pH, water temperature, dissolved oxygen, turbidity, and nitrate. Discrete water-quality samples were collected during June 2011 through May 2013 at five new sites and June 2004 through May 2013 at a long-term site and analyzed for sediment, nutrients, bacteria, and other water-quality constituents. Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physical properties to estimate concentrations of those constituents of interest that are not easily measured in real time because of limitations in sensor technology and fiscal constraints. Regression models for 28 water-quality constituents were developed and documented. The water-quality information in this report is important to Johnson County Wastewater because it allows the concentrations of many potential pollutants of interest, including nutrients and sediment, to be estimated in real time and characterized during conditions and time scales that would not be possible otherwise.

Galerkin v. discrete-optimal projection in nonlinear model reduction

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

Carlberg, Kevin Thomas; Barone, Matthew Franklin; Antil, Harbir

Discrete-optimal model-reduction techniques such as the Gauss{Newton with Approximated Tensors (GNAT) method have shown promise, as they have generated stable, accurate solutions for large-scale turbulent, compressible ow problems where standard Galerkin techniques have failed. However, there has been limited comparative analysis of the two approaches. This is due in part to difficulties arising from the fact that Galerkin techniques perform projection at the time-continuous level, while discrete-optimal techniques do so at the time-discrete level. This work provides a detailed theoretical and experimental comparison of the two techniques for two common classes of time integrators: linear multistep schemes and Runge{Kutta schemes.more » We present a number of new ndings, including conditions under which the discrete-optimal ROM has a time-continuous representation, conditions under which the two techniques are equivalent, and time-discrete error bounds for the two approaches. Perhaps most surprisingly, we demonstrate both theoretically and experimentally that decreasing the time step does not necessarily decrease the error for the discrete-optimal ROM; instead, the time step should be `matched' to the spectral content of the reduced basis. In numerical experiments carried out on a turbulent compressible- ow problem with over one million unknowns, we show that increasing the time step to an intermediate value decreases both the error and the simulation time of the discrete-optimal reduced-order model by an order of magnitude.« less

Asynchronous discrete control of continuous processes

NASA Astrophysics Data System (ADS)

Kaliski, M. E.; Johnson, T. L.

1984-07-01

The research during this second contract year continued to deal with the development of sound theoretical models for asynchronous systems. Two criteria served to shape the research pursued: the first, that the developed models extend and generalize previously developed research for synchronous discrete control; the second, that the models explicitly address the question of how to incorporate system transition times into themselves. The following sections of this report concisely delineate this year's work. Our original proposal for this research identified four general tasks of investigation: (1.1) Analysis of Qualitative Properties of Asynchronous Hybrid Systems; (1.2) Acceptance and Control for Asynchronous Hybrid Systems.

40 CFR 60.2695 - How are the performance test data used?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Performance Testing.... [76 FR 15773, Mar. 21, 2011] Model Rule—Initial Compliance Requirements ...

40 CFR 60.2695 - How are the performance test data used?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Performance Testing.... [76 FR 15773, Mar. 21, 2011] Model Rule—Initial Compliance Requirements ...

Statistics of particle time-temperature histories.

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

Hewson, John C.; Lignell, David O.; Sun, Guangyuan

2014-10-01

Particles in non - isothermal turbulent flow are subject to a stochastic environment tha t produces a distribution of particle time - temperature histories. This distribution is a function of the dispersion of the non - isothermal (continuous) gas phase and the distribution of particles relative to that gas phase. In this work we extend the one - dimensional turbulence (ODT) model to predict the joint dispersion of a dispersed particle phase and a continuous phase. The ODT model predicts the turbulent evolution of continuous scalar fields with a model for the cascade of fluctuations to smaller sc ales (themore » 'triplet map') at a rate that is a function of the fully resolved one - dimens ional velocity field . Stochastic triplet maps also drive Lagrangian particle dispersion with finite Stokes number s including inertial and eddy trajectory - crossing effect s included. Two distinct approaches to this coupling between triplet maps and particle dispersion are developed and implemented along with a hybrid approach. An 'instantaneous' particle displacement model matches the tracer particle limit and provide s an accurate description of particle dispersion. A 'continuous' particle displacement m odel translates triplet maps into a continuous velocity field to which particles respond. Particles can alter the turbulence, and modifications to the stochastic rate expr ession are developed for two - way coupling between particles and the continuous phase. Each aspect of model development is evaluated in canonical flows (homogeneous turbulence, free - shear flows and wall - bounded flows) for which quality measurements are ava ilable. ODT simulations of non - isothermal flows provide statistics for particle heating. These simulations show the significance of accurately predicting the joint statistics of particle and fluid dispersion . Inhomogeneous turbulence coupled with the in fluence of the mean flow fields on particles of varying properties alter s particle dispersion. The joint particle - temperature dispersion leads to a distribution of temperature histories predicted by the ODT . Predictions are shown for the lower moments an d the full distributions of the particle positions, particle - observed gas temperatures and particle temperatures. An analysis of the time scales affecting particle - temperature interactions covers Lagrangian integral time scales based on temperature autoco rrelations, rates of temperature change associated with particle motion relative to the temperature field and rates of diffusional change of temperatures. These latter two time scales have not been investigated previously; they are shown to be strongly in termittent having peaked distributions with long tails. The logarithm of the absolute value of these time scales exhibits a distribution closer to normal. A cknowledgements This work is supported by the Defense Threat Reduction Agency (DTRA) under their Counter - Weapons of Mass Destruction Basic Research Program in the area of Chemical and Biological Agent Defeat under award number HDTRA1 - 11 - 4503I to Sandia National Laboratories. The authors would like to express their appreciation for the guidance provi ded by Dr. Suhithi Peiris to this project and to the Science to Defeat Weapons of Mass Destruction program.« less

40 CFR 60.1570 - What is the “model rule” in this subpart?

Code of Federal Regulations, 2010 CFR

2010-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small Municipal Waste Combustion Units Constructed on or Before August 30, 1999 Use of Model Rule § 60.1570 What is the “model rule” in this subpart? (a) The model rule is the portion of the...

40 CFR 60.5070 - What is the “model rule” in this subpart?

Code of Federal Regulations, 2011 CFR

2011-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Existing Sewage Sludge Incineration Units Use of Model Rule § 60.5070 What is the “model rule” in this subpart? (a) The model rule is the portion of these emission guidelines (§§ 60.5085 through...

Program Helps Generate Boundary-Element Mathematical Models

NASA Technical Reports Server (NTRS)

Goldberg, R. K.

1995-01-01

Composite Model Generation-Boundary Element Method (COM-GEN-BEM) computer program significantly reduces time and effort needed to construct boundary-element mathematical models of continuous-fiber composite materials at micro-mechanical (constituent) scale. Generates boundary-element models compatible with BEST-CMS boundary-element code for anlaysis of micromechanics of composite material. Written in PATRAN Command Language (PCL).

Three-part joint modeling methods for complex functional data mixed with zero-and-one-inflated proportions and zero-inflated continuous outcomes with skewness.

PubMed

Li, Haocheng; Staudenmayer, John; Wang, Tianying; Keadle, Sarah Kozey; Carroll, Raymond J

2018-02-20

We take a functional data approach to longitudinal studies with complex bivariate outcomes. This work is motivated by data from a physical activity study that measured 2 responses over time in 5-minute intervals. One response is the proportion of time active in each interval, a continuous proportions with excess zeros and ones. The other response, energy expenditure rate in the interval, is a continuous variable with excess zeros and skewness. This outcome is complex because there are 3 possible activity patterns in each interval (inactive, partially active, and completely active), and those patterns, which are observed, induce both nonrandom and random associations between the responses. More specifically, the inactive pattern requires a zero value in both the proportion for active behavior and the energy expenditure rate; a partially active pattern means that the proportion of activity is strictly between zero and one and that the energy expenditure rate is greater than zero and likely to be moderate, and the completely active pattern means that the proportion of activity is exactly one, and the energy expenditure rate is greater than zero and likely to be higher. To address these challenges, we propose a 3-part functional data joint modeling approach. The first part is a continuation-ratio model to reorder the ordinal valued 3 activity patterns. The second part models the proportions when they are in interval (0,1). The last component specifies the skewed continuous energy expenditure rate with Box-Cox transformations when they are greater than zero. In this 3-part model, the regression structures are specified as smooth curves measured at various time points with random effects that have a correlation structure. The smoothed random curves for each variable are summarized using a few important principal components, and the association of the 3 longitudinal components is modeled through the association of the principal component scores. The difficulties in handling the ordinal and proportional variables are addressed using a quasi-likelihood type approximation. We develop an efficient algorithm to fit the model that also involves the selection of the number of principal components. The method is applied to physical activity data and is evaluated empirically by a simulation study. Copyright © 2017 John Wiley & Sons, Ltd.

Nonparametric model validations for hidden Markov models with applications in financial econometrics

PubMed Central

Zhao, Zhibiao

2011-01-01

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise. PMID:21750601

Single-particle dispersion in stably stratified turbulence

NASA Astrophysics Data System (ADS)

Sujovolsky, N. E.; Mininni, P. D.; Rast, M. P.

2018-03-01

We present models for single-particle dispersion in vertical and horizontal directions of stably stratified flows. The model in the vertical direction is based on the observed Lagrangian spectrum of the vertical velocity, while the model in the horizontal direction is a combination of a continuous-time eddy-constrained random walk process with a contribution to transport from horizontal winds. Transport at times larger than the Lagrangian turnover time is not universal and dependent on these winds. The models yield results in good agreement with direct numerical simulations of stratified turbulence, for which single-particle dispersion differs from the well-studied case of homogeneous and isotropic turbulence.

Using meta-differential evolution to enhance a calculation of a continuous blood glucose level.

PubMed

Koutny, Tomas

2016-09-01

We developed a new model of glucose dynamics. The model calculates blood glucose level as a function of transcapillary glucose transport. In previous studies, we validated the model with animal experiments. We used analytical method to determine model parameters. In this study, we validate the model with subjects with type 1 diabetes. In addition, we combine the analytic method with meta-differential evolution. To validate the model with human patients, we obtained a data set of type 1 diabetes study that was coordinated by Jaeb Center for Health Research. We calculated a continuous blood glucose level from continuously measured interstitial fluid glucose level. We used 6 different scenarios to ensure robust validation of the calculation. Over 96% of calculated blood glucose levels fit A+B zones of the Clarke Error Grid. No data set required any correction of model parameters during the time course of measuring. We successfully verified the possibility of calculating a continuous blood glucose level of subjects with type 1 diabetes. This study signals a successful transition of our research from an animal experiment to a human patient. Researchers can test our model with their data on-line at https://diabetes.zcu.cz. Copyright © 2016 The Author. Published by Elsevier Ireland Ltd.. All rights reserved.

Evolution of Particle Size Distributions in Fragmentation Over Time

NASA Astrophysics Data System (ADS)

Charalambous, C. A.; Pike, W. T.

2013-12-01

We present a new model of fragmentation based on a probabilistic calculation of the repeated fracture of a particle population. The resulting continuous solution, which is in closed form, gives the evolution of fragmentation products from an initial block, through a scale-invariant power-law relationship to a final comminuted powder. Models for the fragmentation of particles have been developed separately in mainly two different disciplines: the continuous integro-differential equations of batch mineral grinding (Reid, 1965) and the fractal analysis of geophysics (Turcotte, 1986) based on a discrete model with a single probability of fracture. The first gives a time-dependent development of the particle-size distribution, but has resisted a closed-form solution, while the latter leads to the scale-invariant power laws, but with no time dependence. Bird (2009) recently introduced a bridge between these two approaches with a step-wise iterative calculation of the fragmentation products. The development of the particle-size distribution occurs with discrete steps: during each fragmentation event, the particles will repeatedly fracture probabilistically, cascading down the length scales to a final size distribution reached after all particles have failed to further fragment. We have identified this process as the equivalent to a sequence of trials for each particle with a fixed probability of fragmentation. Although the resulting distribution is discrete, it can be reformulated as a continuous distribution in maturity over time and particle size. In our model, Turcotte's power-law distribution emerges at a unique maturation index that defines a regime boundary. Up to this index, the fragmentation is in an erosional regime with the initial particle size setting the scaling. Fragmentation beyond this index is in a regime of comminution with rebreakage of the particles down to the size limit of fracture. The maturation index can increment continuously, for example under grinding conditions, or as discrete steps, such as with impact events. In both cases our model gives the energy associated with the fragmentation in terms of the developing surface area of the population. We show the agreement of our model to the evolution of particle size distributions associated with episodic and continuous fragmentation and how the evolution of some popular fractals may be represented using this approach. C. A. Charalambous and W. T. Pike (2013). Multi-Scale Particle Size Distributions of Mars, Moon and Itokawa based on a time-maturation dependent fragmentation model. Abstract Submitted to the AGU 46th Fall Meeting. Bird, N. R. A., Watts, C. W., Tarquis, A. M., & Whitmore, A. P. (2009). Modeling dynamic fragmentation of soil. Vadose Zone Journal, 8(1), 197-201. Reid, K. J. (1965). A solution to the batch grinding equation. Chemical Engineering Science, 20(11), 953-963. Turcotte, D. L. (1986). Fractals and fragmentation. Journal of Geophysical Research: Solid Earth 91(B2), 1921-1926.

Robustness of neuroprosthetic decoding algorithms.

PubMed

Serruya, Mijail; Hatsopoulos, Nicholas; Fellows, Matthew; Paninski, Liam; Donoghue, John

2003-03-01

We assessed the ability of two algorithms to predict hand kinematics from neural activity as a function of the amount of data used to determine the algorithm parameters. Using chronically implanted intracortical arrays, single- and multineuron discharge was recorded during trained step tracking and slow continuous tracking tasks in macaque monkeys. The effect of increasing the amount of data used to build a neural decoding model on the ability of that model to predict hand kinematics accurately was examined. We evaluated how well a maximum-likelihood model classified discrete reaching directions and how well a linear filter model reconstructed continuous hand positions over time within and across days. For each of these two models we asked two questions: (1) How does classification performance change as the amount of data the model is built upon increases? (2) How does varying the time interval between the data used to build the model and the data used to test the model affect reconstruction? Less than 1 min of data for the discrete task (8 to 13 neurons) and less than 3 min (8 to 18 neurons) for the continuous task were required to build optimal models. Optimal performance was defined by a cost function we derived that reflects both the ability of the model to predict kinematics accurately and the cost of taking more time to build such models. For both the maximum-likelihood classifier and the linear filter model, increasing the duration between the time of building and testing the model within a day did not cause any significant trend of degradation or improvement in performance. Linear filters built on one day and tested on neural data on a subsequent day generated error-measure distributions that were not significantly different from those generated when the linear filters were tested on neural data from the initial day (p<0.05, Kolmogorov-Smirnov test). These data show that only a small amount of data from a limited number of cortical neurons appears to be necessary to construct robust models to predict kinematic parameters for the subsequent hours. Motor-control signals derived from neurons in motor cortex can be reliably acquired for use in neural prosthetic devices. Adequate decoding models can be built rapidly from small numbers of cells and maintained with daily calibration sessions.

A queueing theory based model for business continuity in hospitals.

PubMed

Miniati, R; Cecconi, G; Dori, F; Frosini, F; Iadanza, E; Biffi Gentili, G; Niccolini, F; Gusinu, R

2013-01-01

Clinical activities can be seen as results of precise and defined events' succession where every single phase is characterized by a waiting time which includes working duration and possible delay. Technology makes part of this process. For a proper business continuity management, planning the minimum number of devices according to the working load only is not enough. A risk analysis on the whole process should be carried out in order to define which interventions and extra purchase have to be made. Markov models and reliability engineering approaches can be used for evaluating the possible interventions and to protect the whole system from technology failures. The following paper reports a case study on the application of the proposed integrated model, including risk analysis approach and queuing theory model, for defining the proper number of device which are essential to guarantee medical activity and comply the business continuity management requirements in hospitals.

Spatial generalised linear mixed models based on distances.

PubMed

Melo, Oscar O; Mateu, Jorge; Melo, Carlos E

2016-10-01

Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.

DSCOVR Transcendance

NASA Astrophysics Data System (ADS)

Herman, J. R.; Boccara, M.; Albers, S. C.

2017-12-01

The Earth Polychromatic Imaging Camera (EPIC) onboard the DSCOVR satellite continuously views the sun-illuminated portion of the Earth with spectral coverage in the visible band, among others. Ideally, such a system would be able to provide a video with continuous coverage up to real time. However due to limits in onboard storage, bandwidth, and antenna coverage on the ground, we can receive at most 20 images a day, separated by at least one hour. Also, the processing time to generate the visible image out of the separate RGB channels delays public images delivery by a day or two. Finally, occasional remote tuning of instruments can cause several day periods where the imagery is completely missing. We are proposing a model-based method to fill these gaps and restore images lost in real-time processing. We are combining two sets of algorithms. The first, called Blueturn, interpolates successive images while projecting them on a 3-D model of the Earth, all this being done in real-time using the GPU. The second, called Simulated Weather Imagery (SWIM), makes EPIC-like images utilizing a ray-tracing model of scattering and absorption of sunlight by clouds, atmospheric gases, aerosols, and land surface. Clouds are obtained from 3-D gridded analyses and forecasts using weather modeling systems such as the Local Analysis and Prediction System (LAPS), and the Flow-following finite-volume Finite Icosahedral Model (FIM). SWIM uses EPIC images to validate its models. Typical model grid spacing is about 20km and is roughly commensurate with the EPIC imagery. Calculating one image per hour is enough for Blueturn to generate a smooth video. The synthetic images are designed to be visually realistic and aspire to be indistinguishable from the real ones. Resulting interframe transitions become seamless, and real-time delay is reduced to 1 hour. With Blueturn already available as a free online app, streaming EPIC images directly from NASA's public website, and with another SWIM server to ensure constant interval between key images, this work brings transcendance to EPIC's tribute. Enriched by two years of actual service in space, the most real holistic view of the Earth will be continued at a high degree of fidelity, regardless of EPIC limitations or interruptions.

ARMA models for earthquake ground motions. Seismic safety margins research program

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

Chang, M. K.; Kwiatkowski, J. W.; Nau, R. F.

1981-02-01

Four major California earthquake records were analyzed by use of a class of discrete linear time-domain processes commonly referred to as ARMA (Autoregressive/Moving-Average) models. It was possible to analyze these different earthquakes, identify the order of the appropriate ARMA model(s), estimate parameters, and test the residuals generated by these models. It was also possible to show the connections, similarities, and differences between the traditional continuous models (with parameter estimates based on spectral analyses) and the discrete models with parameters estimated by various maximum-likelihood techniques applied to digitized acceleration data in the time domain. The methodology proposed is suitable for simulatingmore » earthquake ground motions in the time domain, and appears to be easily adapted to serve as inputs for nonlinear discrete time models of structural motions. 60 references, 19 figures, 9 tables.« less

Spatiotemporal interpolation of discharge across a river network by using synthetic SWOT satellite data

NASA Astrophysics Data System (ADS)

Paiva, Rodrigo C. D.; Durand, Michael T.; Hossain, Faisal

2015-01-01

Recent efforts have sought to estimate river discharge and other surface water-related quantities using spaceborne sensors, with better spatial coverage but worse temporal sampling as compared with in situ measurements. The Surface Water and Ocean Topography (SWOT) mission will provide river discharge estimates globally from space. However, questions on how to optimally use the spatially distributed but asynchronous satellite observations to generate continuous fields still exist. This paper presents a statistical model (River Kriging-RK), for estimating discharge time series in a river network in the context of the SWOT mission. RK uses discharge estimates at different locations and times to produce a continuous field using spatiotemporal kriging. A key component of RK is the space-time river discharge covariance, which was derived analytically from the diffusive wave approximation of Saint Venant's equations. The RK covariance also accounts for the loss of correlation at confluences. The model performed well in a case study on Ganges-Brahmaputra-Meghna (GBM) River system in Bangladesh using synthetic SWOT observations. The correlation model reproduced empirically derived values. RK (R2=0.83) outperformed other kriging-based methods (R2=0.80), as well as a simple time series linear interpolation (R2=0.72). RK was used to combine discharge from SWOT and in situ observations, improving estimates when the latter is included (R2=0.91). The proposed statistical concepts may eventually provide a feasible framework to estimate continuous discharge time series across a river network based on SWOT data, other altimetry missions, and/or in situ data.

Dynamic frailty models based on compound birth-death processes.

PubMed

Putter, Hein; van Houwelingen, Hans C

2015-07-01

Frailty models are used in survival analysis to model unobserved heterogeneity. They accommodate such heterogeneity by the inclusion of a random term, the frailty, which is assumed to multiply the hazard of a subject (individual frailty) or the hazards of all subjects in a cluster (shared frailty). Typically, the frailty term is assumed to be constant over time. This is a restrictive assumption and extensions to allow for time-varying or dynamic frailties are of interest. In this paper, we extend the auto-correlated frailty models of Henderson and Shimakura and of Fiocco, Putter and van Houwelingen, developed for longitudinal count data and discrete survival data, to continuous survival data. We present a rigorous construction of the frailty processes in continuous time based on compound birth-death processes. When the frailty processes are used as mixtures in models for survival data, we derive the marginal hazards and survival functions and the marginal bivariate survival functions and cross-ratio function. We derive distributional properties of the processes, conditional on observed data, and show how to obtain the maximum likelihood estimators of the parameters of the model using a (stochastic) expectation-maximization algorithm. The methods are applied to a publicly available data set. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A novel grey-fuzzy-Markov and pattern recognition model for industrial accident forecasting

NASA Astrophysics Data System (ADS)

Edem, Inyeneobong Ekoi; Oke, Sunday Ayoola; Adebiyi, Kazeem Adekunle

2017-10-01

Industrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions. The scope of this research domain continues to expand due to the continuous knowledge ignition motivated by scholars in the area. So, more intelligent and intellectual contributions on current research issues in the accident domain will potentially spark more lively academic, value-added discussions that will be of practical significance to members of the safety community. In this communication, a new grey-fuzzy-Markov time series model, developed from nondifferential grey interval analytical framework has been presented for the first time. This instrument forecasts future accident occurrences under time-invariance assumption. The actual contribution made in the article is to recognise accident occurrence patterns and decompose them into grey state principal pattern components. The architectural framework of the developed grey-fuzzy-Markov pattern recognition (GFMAPR) model has four stages: fuzzification, smoothening, defuzzification and whitenisation. The results of application of the developed novel model signify that forecasting could be effectively carried out under uncertain conditions and hence, positions the model as a distinctly superior tool for accident forecasting investigations. The novelty of the work lies in the capability of the model in making highly accurate predictions and forecasts based on the availability of small or incomplete accident data.

Time-Related Decay or Interference-Based Forgetting in Working Memory?

ERIC Educational Resources Information Center

Portrat, Sophie; Barrouillet, Pierre; Camos, Valerie

2008-01-01

The time-based resource-sharing model of working memory assumes that memory traces suffer from a time-related decay when attention is occupied by concurrent activities. Using complex continuous span tasks in which temporal parameters are carefully controlled, P. Barrouillet, S. Bernardin, S. Portrat, E. Vergauwe, & V. Camos (2007) recently…

Is It Time to Retire the Hybrid Atomic Orbital?

ERIC Educational Resources Information Center

Grushow, Alexander

2011-01-01

A rationale for the removal of the hybrid atomic orbital from the chemistry curriculum is examined. Although the hybrid atomic orbital model does not accurately predict spectroscopic energies, many chemical educators continue to use and teach the model despite the confusion it can cause for students. Three arguments for retaining the model in the…

Evaluation of impact of length of calibration time period on the APEX model streamflow simulation

USDA-ARS?s Scientific Manuscript database

Due to resource constraints, continuous long-term measured data for model calibration and validation (C/V) are rare. As a result, most hydrologic and water quality models are calibrated and, if possible, validated using limited available measured data. However, little research has been carried out t...

Impact of length of calibration period on the APEX model water quantity and quality simulation performance

USDA-ARS?s Scientific Manuscript database

Availability of continuous long-term measured data for model calibration and validation is limited due to time and resources constraints. As a result, hydrologic and water quality models are calibrated and, if possible, validated when measured data is available. Past work reported on the impact of t...

Integrated modeling of long-term vegetation and hydrologic dynamics in Rocky Mountain watersheds

Treesearch

Robert Steven Ahl

2007-01-01

Changes in forest structure resulting from natural disturbances, or managed treatments, can have negative and long lasting impacts on water resources. To facilitate integrated management of forest and water resources, a System for Long-Term Integrated Management Modeling (SLIMM) was developed. By combining two spatially explicit, continuous time models, vegetation...

The role of change in the relationship between commitment and turnover: a latent growth modeling approach.

PubMed

Bentein, Kathleen; Vandenberghe, Christian; Vandenberg, Robert; Stinglhamber, Florence

2005-05-01

Through the use of affective, normative, and continuance commitment in a multivariate 2nd-order factor latent growth modeling approach, the authors observed linear negative trajectories that characterized the changes in individuals across time in both affective and normative commitment. In turn, an individual's intention to quit the organization was characterized by a positive trajectory. A significant association was also found between the change trajectories such that the steeper the decline in an individual's affective and normative commitments across time, the greater the rate of increase in that individual's intention to quit, and, further, the greater the likelihood that the person actually left the organization over the next 9 months. Findings regarding continuance commitment and its components were mixed.

Dynamic modeling and parameter estimation of a radial and loop type distribution system network

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

Jun Qui; Heng Chen; Girgis, A.A.

1993-05-01

This paper presents a new identification approach to three-phase power system modeling and model reduction taking power system network as multi-input, multi-output (MIMO) processes. The model estimate can be obtained in discrete-time input-output form, discrete- or continuous-time state-space variable form, or frequency-domain impedance transfer function matrix form. An algorithm for determining the model structure of this MIMO process is described. The effect of measurement noise on the approach is also discussed. This approach has been applied on a sample system and simulation results are also presented in this paper.

A mathematical approach to HIV infection dynamics

NASA Astrophysics Data System (ADS)

Ida, A.; Oharu, S.; Oharu, Y.

2007-07-01

In order to obtain a comprehensive form of mathematical models describing nonlinear phenomena such as HIV infection process and AIDS disease progression, it is efficient to introduce a general class of time-dependent evolution equations in such a way that the associated nonlinear operator is decomposed into the sum of a differential operator and a perturbation which is nonlinear in general and also satisfies no global continuity condition. An attempt is then made to combine the implicit approach (usually adapted for convective diffusion operators) and explicit approach (more suited to treat continuous-type operators representing various physiological interactions), resulting in a semi-implicit product formula. Decomposing the operators in this way and considering their individual properties, it is seen that approximation-solvability of the original model is verified under suitable conditions. Once appropriate terms are formulated to describe treatment by antiretroviral therapy, the time-dependence of the reaction terms appears, and such product formula is useful for generating approximate numerical solutions to the governing equations. With this knowledge, a continuous model for HIV disease progression is formulated and physiological interpretations are provided. The abstract theory is then applied to show existence of unique solutions to the continuous model describing the behavior of the HIV virus in the human body and its reaction to treatment by antiretroviral therapy. The product formula suggests appropriate discrete models describing the dynamics of host pathogen interactions with HIV1 and is applied to perform numerical simulations based on the model of the HIV infection process and disease progression. Finally, the results of our numerical simulations are visualized and it is observed that our results agree with medical and physiological aspects.

Decomposition of heterogeneous organic matterand its long-term stabilization in soils

USGS Publications Warehouse

Sierra, Carlos A.; Harmon, Mark E.; Perakis, Steven S.

2011-01-01

Soil organic matter is a complex mixture of material with heterogeneous biological, physical, and chemical properties. Decomposition models represent this heterogeneity either as a set of discrete pools with different residence times or as a continuum of qualities. It is unclear though, whether these two different approaches yield comparable predictions of organic matter dynamics. Here, we compare predictions from these two different approaches and propose an intermediate approach to study organic matter decomposition based on concepts from continuous models implemented numerically. We found that the disagreement between discrete and continuous approaches can be considerable depending on the degree of nonlinearity of the model and simulation time. The two approaches can diverge substantially for predicting long-term processes in soils. Based on our alternative approach, which is a modification of the continuous quality theory, we explored the temporal patterns that emerge by treating substrate heterogeneity explicitly. The analysis suggests that the pattern of carbon mineralization over time is highly dependent on the degree and form of nonlinearity in the model, mostly expressed as differences in microbial growth and efficiency for different substrates. Moreover, short-term stabilization and destabilization mechanisms operating simultaneously result in long-term accumulation of carbon characterized by low decomposition rates, independent of the characteristics of the incoming litter. We show that representation of heterogeneity in the decomposition process can lead to substantial improvements in our understanding of carbon mineralization and its long-term stability in soils.

Salmonella Fecal Shedding and Immune Responses are Dose- and Serotype- Dependent in Pigs

PubMed Central

Ivanek, Renata; Österberg, Julia; Gautam, Raju; Sternberg Lewerin, Susanna

2012-01-01

Despite the public health importance of Salmonella infection in pigs, little is known about the associated dynamics of fecal shedding and immunity. In this study, we investigated the transitions of pigs through the states of Salmonella fecal shedding and immune response post-Salmonella inoculation as affected by the challenge dose and serotype. Continuous-time multistate Markov models were developed using published experimental data. The model for shedding had four transient states, of which two were shedding (continuous and intermittent shedding) and two non-shedding (latency and intermittent non-shedding), and one absorbing state representing permanent cessation of shedding. The immune response model had two transient states representing responses below and above the seroconversion level. The effects of two doses [low (0.65×106 CFU/pig) and high (0.65×109 CFU/pig)] and four serotypes (Salmonella Yoruba, Salmonella Cubana, Salmonella Typhimurium, and Salmonella Derby) on the models' transition intensities were evaluated using a proportional intensities model. Results indicated statistically significant effects of the challenge dose and serotype on the dynamics of shedding and immune response. The time spent in the specific states was also estimated. Continuous shedding was on average 10–26 days longer, while intermittent non-shedding was 2–4 days shorter, in pigs challenged with the high compared to low dose. Interestingly, among pigs challenged with the high dose, the continuous and intermittent shedding states were on average up to 10–17 and 3–4 days longer, respectively, in pigs infected with S. Cubana compared to the other three serotypes. Pigs challenged with the high dose of S. Typhimurium or S. Derby seroconverted on average up to 8–11 days faster compared to the low dose. These findings highlight that Salmonella fecal shedding and immune response following Salmonella challenge are dose- and serotype-dependent and that the detection of specific Salmonella strains and immune responses in pigs are time-sensitive. PMID:22523553

Supercritical nonlinear parametric dynamics of Timoshenko microbeams

NASA Astrophysics Data System (ADS)

Farokhi, Hamed; Ghayesh, Mergen H.

2018-06-01

The nonlinear supercritical parametric dynamics of a Timoshenko microbeam subject to an axial harmonic excitation force is examined theoretically, by means of different numerical techniques, and employing a high-dimensional analysis. The time-variant axial load is assumed to consist of a mean value along with harmonic fluctuations. In terms of modelling, a continuous expression for the elastic potential energy of the system is developed based on the modified couple stress theory, taking into account small-size effects; the kinetic energy of the system is also modelled as a continuous function of the displacement field. Hamilton's principle is employed to balance the energies and to obtain the continuous model of the system. Employing the Galerkin scheme along with an assumed-mode technique, the energy terms are reduced, yielding a second-order reduced-order model with finite number of degrees of freedom. A transformation is carried out to convert the second-order reduced-order model into a double-dimensional first order one. A bifurcation analysis is performed for the system in the absence of the axial load fluctuations. Moreover, a mean value for the axial load is selected in the supercritical range, and the principal parametric resonant response, due to the time-variant component of the axial load, is obtained - as opposed to transversely excited systems, for parametrically excited system (such as our problem here), the nonlinear resonance occurs in the vicinity of twice any natural frequency of the linear system; this is accomplished via use of the pseudo-arclength continuation technique, a direct time integration, an eigenvalue analysis, and the Floquet theory for stability. The natural frequencies of the system prior to and beyond buckling are also determined. Moreover, the effect of different system parameters on the nonlinear supercritical parametric dynamics of the system is analysed, with special consideration to the effect of the length-scale parameter.

40 CFR 60.2805 - Am I required to apply for and obtain a Title V operating permit for my unit?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model... permit for my unit? Yes. Each CISWI unit and air curtain incinerator subject to standards under this...

40 CFR 60.2805 - Am I required to apply for and obtain a Title V operating permit for my unit?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model... permit for my unit? Yes. Each CISWI unit and air curtain incinerator subject to standards under this...

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

NASA Astrophysics Data System (ADS)

Galiana-Merino, J. J.; Pla, C.; Fernandez-Cortes, A.; Cuezva, S.; Ortiz, J.; Benavente, D.

2014-10-01

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of several environmental time series, particularly focused on the analyses of cave monitoring data. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to provide a fast and precise time-period examination of the time series at different period bands. Moreover, statistic methods to examine the relation between two signals have been included. Finally, the entropy of curves and splines based methods have also been developed for segmenting and modeling the analyzed time series. All these methods together provide a user-friendly and fast program for the environmental signal analysis, with useful, practical and understandable results.

Modeling of wastewater treatment system of car parks from petroleum products

NASA Astrophysics Data System (ADS)

Savdur, S. N.; Stepanova, Yu V.; Kodolova, I. A.; Fesina, E. L.

2018-05-01

The paper discusses the technological complex of wastewater treatment of car parks from petroleum products. Based on the review of the main modeling methods of discrete-continuous chemical and engineering processes, it substantiates expediency of using the theory of Petri nets (PN) for modeling the process of wastewater treatment of car parks from petroleum products. It is proposed to use a modification of Petri nets which is focused on modeling and analysis of discrete-continuous chemical and engineering processes by prioritizing transitions, timing marks in positions and transitions. A model in the form of modified Petri nets (MPN) is designed. A software package to control the process for wastewater treatment is designed by means of SCADA TRACE MODE.

ON CONTINUOUS-REVIEW (S-1,S) INVENTORY POLICIES WITH STATE-DEPENDENT LEADTIMES,

DTIC Science & Technology

INVENTORY CONTROL, *REPLACEMENT THEORY), MATHEMATICAL MODELS, LEAD TIME , MANAGEMENT ENGINEERING, DISTRIBUTION FUNCTIONS, PROBABILITY, QUEUEING THEORY, COSTS, OPTIMIZATION, STATISTICAL PROCESSES, DIFFERENCE EQUATIONS

Robotic reactions: delay-induced patterns in autonomous vehicle systems.

PubMed

Orosz, Gábor; Moehlis, Jeff; Bullo, Francesco

2010-02-01

Fundamental design principles are presented for vehicle systems governed by autonomous cruise control devices. By analyzing the corresponding delay differential equations, it is shown that for any car-following model short-wavelength oscillations can appear due to robotic reaction times, and that there are tradeoffs between the time delay and the control gains. The analytical findings are demonstrated on an optimal velocity model using numerical continuation and numerical simulation.

Robotic reactions: Delay-induced patterns in autonomous vehicle systems

NASA Astrophysics Data System (ADS)

Orosz, Gábor; Moehlis, Jeff; Bullo, Francesco

2010-02-01

Fundamental design principles are presented for vehicle systems governed by autonomous cruise control devices. By analyzing the corresponding delay differential equations, it is shown that for any car-following model short-wavelength oscillations can appear due to robotic reaction times, and that there are tradeoffs between the time delay and the control gains. The analytical findings are demonstrated on an optimal velocity model using numerical continuation and numerical simulation.

CyberArc: a non-coplanar-arc optimization algorithm for CyberKnife

NASA Astrophysics Data System (ADS)

Kearney, Vasant; Cheung, Joey P.; McGuinness, Christopher; Solberg, Timothy D.

2017-07-01

The goal of this study is to demonstrate the feasibility of a novel non-coplanar-arc optimization algorithm (CyberArc). This method aims to reduce the delivery time of conventional CyberKnife treatments by allowing for continuous beam delivery. CyberArc uses a 4 step optimization strategy, in which nodes, beams, and collimator sizes are determined, source trajectories are calculated, intermediate radiation models are generated, and final monitor units are calculated, for the continuous radiation source model. The dosimetric results as well as the time reduction factors for CyberArc are presented for 7 prostate and 2 brain cases. The dosimetric quality of the CyberArc plans are evaluated using conformity index, heterogeneity index, local confined normalized-mutual-information, and various clinically relevant dosimetric parameters. The results indicate that the CyberArc algorithm dramatically reduces the treatment time of CyberKnife plans while simultaneously preserving the dosimetric quality of the original plans.

CyberArc: a non-coplanar-arc optimization algorithm for CyberKnife.

PubMed

Kearney, Vasant; Cheung, Joey P; McGuinness, Christopher; Solberg, Timothy D

2017-06-26

The goal of this study is to demonstrate the feasibility of a novel non-coplanar-arc optimization algorithm (CyberArc). This method aims to reduce the delivery time of conventional CyberKnife treatments by allowing for continuous beam delivery. CyberArc uses a 4 step optimization strategy, in which nodes, beams, and collimator sizes are determined, source trajectories are calculated, intermediate radiation models are generated, and final monitor units are calculated, for the continuous radiation source model. The dosimetric results as well as the time reduction factors for CyberArc are presented for 7 prostate and 2 brain cases. The dosimetric quality of the CyberArc plans are evaluated using conformity index, heterogeneity index, local confined normalized-mutual-information, and various clinically relevant dosimetric parameters. The results indicate that the CyberArc algorithm dramatically reduces the treatment time of CyberKnife plans while simultaneously preserving the dosimetric quality of the original plans.

Convergence of discrete Aubry–Mather model in the continuous limit

NASA Astrophysics Data System (ADS)

Su, Xifeng; Thieullen, Philippe

2018-05-01

We develop two approximation schemes for solving the cell equation and the discounted cell equation using Aubry–Mather–Fathi theory. The Hamiltonian is supposed to be Tonelli, time-independent and periodic in space. By Legendre transform it is equivalent to find a fixed point of some nonlinear operator, called Lax-Oleinik operator, which may be discounted or not. By discretizing in time, we are led to solve an additive eigenvalue problem involving a discrete Lax–Oleinik operator. We show how to approximate the effective Hamiltonian and some weak KAM solutions by letting the time step in the discrete model tend to zero. We also obtain a selected discrete weak KAM solution as in Davini et al (2016 Invent. Math. 206 29–55), and show that it converges to a particular solution of the cell equation. In order to unify the two settings, continuous and discrete, we develop a more general formalism of the short-range interactions.

Green's functions in equilibrium and nonequilibrium from real-time bold-line Monte Carlo

NASA Astrophysics Data System (ADS)

Cohen, Guy; Gull, Emanuel; Reichman, David R.; Millis, Andrew J.

2014-03-01

Green's functions for the Anderson impurity model are obtained within a numerically exact formalism. We investigate the limits of analytical continuation for equilibrium systems, and show that with real time methods even sharp high-energy features can be reliably resolved. Continuing to an Anderson impurity in a junction, we evaluate two-time correlation functions, spectral properties, and transport properties, showing how the correspondence between the spectral function and the differential conductance breaks down when nonequilibrium effects are taken into account. Finally, a long-standing dispute regarding this model has involved the voltage splitting of the Kondo peak, an effect which was predicted over a decade ago by approximate analytical methods but never successfully confirmed by numerics. We settle the issue by demonstrating in an unbiased manner that this splitting indeed occurs. Yad Hanadiv-Rothschild Foundation, TG-DMR120085, TG-DMR130036, NSF CHE-1213247, NSF DMR 1006282, DOE ER 46932.

Multistability and hidden attractors in an impulsive Goodwin oscillator with time delay

NASA Astrophysics Data System (ADS)

Zhusubaliyev, Z. T.; Mosekilde, E.; Churilov, A. N.; Medvedev, A.

2015-07-01

The release of luteinizing hormone (LH) is driven by intermittent bursts of activity in the hypothalamic nerve centers of the brain. Luteinizing hormone again stimulates release of the male sex hormone testosterone (Te) and, via the circulating concentration of Te, the hypothalamic nerve centers are subject to a negative feedback regulation that is capable of modifying the intermittent bursts into more regular pulse trains. Bifurcation analysis of a hybrid model that attempts to integrate the intermittent bursting activity with a continuous hormone secretion has recently demonstrated a number of interesting nonlinear dynamic phenomena, including bistability and deterministic chaos. The present paper focuses on the additional complexity that arises when the time delay in the continuous part of the model exceeds the typical bursting interval of the feedback. Under these conditions, the hybrid model is capable of displaying quasiperiodicity and border collisions as well as multistability and hidden attractors.

Scale models: A proven cost-effective tool for outage planning

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

Lee, R.; Segroves, R.

1995-03-01

As generation costs for operating nuclear stations have risen, more nuclear utilities have initiated efforts to improve cost effectiveness. Nuclear plant owners are also being challenged with lower radiation exposure limits and new revised radiation protection related regulations (10 CFR 20), which places further stress on their budgets. As source term reduction activities continue to lower radiation fields, reducing the amount of time spent in radiation fields becomes one of the most cost-effective ways of reducing radiation exposure. An effective approach for minimizing time spent in radiation areas is to use a physical scale model for worker orientation planning andmore » monitoring maintenance, modifications, and outage activities. To meet the challenge of continued reduction in the annual cumulative radiation exposures, new cost-effective tools are required. One field-tested and proven tool is the physical scale model.« less

A Health Production Model with Endogenous Retirement

PubMed Central

Galama, Titus; Kapteyn, Arie; Fonseca, Raquel; Michaud, Pierre-Carl

2012-01-01

We formulate a stylized structural model of health, wealth accumulation and retirement decisions building on the human capital framework of health and derive analytic solutions for the time paths of consumption, health, health investment, savings and retirement. We argue that the literature has been unnecessarily restrictive in assuming that health is always at the “optimal” health level. Exploring the properties of corner solutions we find that advances in population health decrease the retirement age, while at the same time individuals retire when their health has deteriorated. This potentially explains why retirees point to deteriorating health as an important reason for early retirement, while retirement ages have continued to fall in the developed world, despite continued improvements in population health and mortality. In our model, workers with higher human capital invest more in health and because they stay healthier retire later than those with lower human capital whose health deteriorates faster. PMID:22888062

Continuous, Real-Time Monitoring of Cocaine in Undiluted Blood Serum via a Microfluidic, Electrochemical Aptamer-Based Sensor

PubMed Central

Swensen, James S.; Xiao, Yi; Ferguson, Brian S.; Lubin, Arica A.; Lai, Rebecca Y.; Heeger, Alan J.; Plaxco, Kevin W.; Soh, H. Tom.

2009-01-01

The development of a biosensor system capable of continuous, real-time measurement of small-molecule analytes directly in complex, unprocessed aqueous samples has been a significant challenge, and successful implementation has been achieved for only a limited number of targets. Towards a general solution to this problem, we report here the Microfluidic Electrochemical Aptamer-based Sensor (MECAS) chip wherein we integrate target-specific DNA aptamers that fold, and thus generate an electrochemical signal, in response to the analyte with a microfluidic detection system. As a model, we demonstrate the continuous, real-time (~1 minute time resolution) detection of the small molecule drug cocaine at near physiological, low micromolar concentrations directly in undiluted, otherwise unmodified blood serum. We believe our approach of integrating folding-based electrochemical sensors with miniaturized detection systems may lay the ground work for the real-time, point-of-care detection of a wide variety of molecular targets. PMID:19271708

An Illustration of Generalised Arma (garma) Time Series Modeling of Forest Area in Malaysia

NASA Astrophysics Data System (ADS)

Pillai, Thulasyammal Ramiah; Shitan, Mahendran

Forestry is the art and science of managing forests, tree plantations, and related natural resources. The main goal of forestry is to create and implement systems that allow forests to continue a sustainable provision of environmental supplies and services. Forest area is land under natural or planted stands of trees, whether productive or not. Forest area of Malaysia has been observed over the years and it can be modeled using time series models. A new class of GARMA models have been introduced in the time series literature to reveal some hidden features in time series data. For these models to be used widely in practice, we illustrate the fitting of GARMA (1, 1; 1, δ) model to the Annual Forest Area data of Malaysia which has been observed from 1987 to 2008. The estimation of the model was done using Hannan-Rissanen Algorithm, Whittle's Estimation and Maximum Likelihood Estimation.

Social networks: Evolving graphs with memory dependent edges

NASA Astrophysics Data System (ADS)

Grindrod, Peter; Parsons, Mark

2011-10-01

The plethora of digital communication technologies, and their mass take up, has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the existence or otherwise of certain infinite products and series involving age dependent model parameters. We show how to differentiate between the alternatives based on a finite set of observations. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

Finding the Key Periods for Assimilating HJ-1A/B CCD Data and the WOFOST Model to Evaluate Heavy Metal Stress in Rice.

PubMed

Zhao, Shuang; Qian, Xu; Liu, Xiangnan; Xu, Zhao

2018-04-17

Accurately monitoring heavy metal stress in crops is vital for food security and agricultural production. The assimilation of remote sensing images into the World Food Studies (WOFOST) model provides an efficient way to solve this problem. In this study, we aimed at investigating the key periods of the assimilation framework for continuous monitoring of heavy metal stress in rice. The Harris algorithm was used for the leaf area index (LAI) curves to select the key period for an optimized assimilation. To obtain accurate LAI values, the measured dry weight of rice roots (WRT), which have been proven to be the most stress-sensitive indicator of heavy metal stress, were incorporated into the improved WOFOST model. Finally, the key periods, which contain four dominant time points, were used to select remote sensing images for the RS-WOFOST model for continuous monitoring of heavy metal stress. Compared with the key period which contains all the available remote sensing images, the results showed that the optimal key period can significantly improve the time efficiency of the assimilation framework by shortening the model operation time by more than 50%, while maintaining its accuracy. This result is highly significant when monitoring heavy metals in rice on a large-scale. Furthermore, it can also offer a reference for the timing of field measurements in monitoring heavy metal stress in rice.

A study of tumour growth based on stoichiometric principles: a continuous model and its discrete analogue.

PubMed

Saleem, M; Agrawal, Tanuja; Anees, Afzal

2014-01-01

In this paper, we consider a continuous mathematically tractable model and its discrete analogue for the tumour growth. The model formulation is based on stoichiometric principles considering tumour-immune cell interactions in potassium (K (+))-limited environment. Our both continuous and discrete models illustrate 'cancer immunoediting' as a dynamic process having all three phases namely elimination, equilibrium and escape. The stoichiometric principles introduced into the model allow us to study its dynamics with the variation in the total potassium in the surrounding of the tumour region. It is found that an increase in the total potassium may help the patient fight the disease for a longer period of time. This result seems to be in line with the protective role of the potassium against the risk of pancreatic cancer as has been reported by Bravi et al. [Dietary intake of selected micronutrients and risk of pancreatic cancer: An Italian case-control study, Ann. Oncol. 22 (2011), pp. 202-206].

A study of tumour growth based on stoichiometric principles: a continuous model and its discrete analogue

PubMed Central

Saleem, M.; Agrawal, Tanuja; Anees, Afzal

2014-01-01

In this paper, we consider a continuous mathematically tractable model and its discrete analogue for the tumour growth. The model formulation is based on stoichiometric principles considering tumour-immune cell interactions in potassium (K +)-limited environment. Our both continuous and discrete models illustrate ‘cancer immunoediting’ as a dynamic process having all three phases namely elimination, equilibrium and escape. The stoichiometric principles introduced into the model allow us to study its dynamics with the variation in the total potassium in the surrounding of the tumour region. It is found that an increase in the total potassium may help the patient fight the disease for a longer period of time. This result seems to be in line with the protective role of the potassium against the risk of pancreatic cancer as has been reported by Bravi et al. [Dietary intake of selected micronutrients and risk of pancreatic cancer: An Italian case-control study, Ann. Oncol. 22 (2011), pp. 202–206]. PMID:24963981

Rapid Processing of Turner Designs Model 10-Au-005 Internally Logged Fluorescence Data

EPA Science Inventory

Continuous recording of dye fluorescence using field fluorometers at selected sampling sites facilitates acquisition of real-time dye tracing data. The Turner Designs Model 10-AU-005 field fluorometer allows for frequent fluorescence readings, data logging, and easy downloading t...

High performance hybrid functional Petri net simulations of biological pathway models on CUDA.

PubMed

Chalkidis, Georgios; Nagasaki, Masao; Miyano, Satoru

2011-01-01

Hybrid functional Petri nets are a wide-spread tool for representing and simulating biological models. Due to their potential of providing virtual drug testing environments, biological simulations have a growing impact on pharmaceutical research. Continuous research advancements in biology and medicine lead to exponentially increasing simulation times, thus raising the demand for performance accelerations by efficient and inexpensive parallel computation solutions. Recent developments in the field of general-purpose computation on graphics processing units (GPGPU) enabled the scientific community to port a variety of compute intensive algorithms onto the graphics processing unit (GPU). This work presents the first scheme for mapping biological hybrid functional Petri net models, which can handle both discrete and continuous entities, onto compute unified device architecture (CUDA) enabled GPUs. GPU accelerated simulations are observed to run up to 18 times faster than sequential implementations. Simulating the cell boundary formation by Delta-Notch signaling on a CUDA enabled GPU results in a speedup of approximately 7x for a model containing 1,600 cells.

A Multiple Items EPQ/EOQ Model for a Vendor and Multiple Buyers System with Considering Continuous and Discrete Demand Simultaneously

NASA Astrophysics Data System (ADS)

Jonrinaldi; Rahman, T.; Henmaidi; Wirdianto, E.; Zhang, D. Z.

2018-03-01

This paper proposed a mathematical model for multiple items Economic Production and Order Quantity (EPQ/EOQ) with considering continuous and discrete demand simultaneously in a system consisting of a vendor and multiple buyers. This model is used to investigate the optimal production lot size of the vendor and the number of shipments policy of orders to multiple buyers. The model considers the multiple buyers’ holding cost as well as transportation cost, which minimize the total production and inventory costs of the system. The continuous demand from any other customers can be fulfilled anytime by the vendor while the discrete demand from multiple buyers can be fulfilled by the vendor using the multiple delivery policy with a number of shipments of items in the production cycle time. A mathematical model is developed to illustrate the system based on EPQ and EOQ model. Solution procedures are proposed to solve the model using a Mixed Integer Non Linear Programming (MINLP) and algorithm methods. Then, the numerical example is provided to illustrate the system and results are discussed.

Fundamentals of continuum mechanics – classical approaches and new trends

NASA Astrophysics Data System (ADS)

Altenbach, H.

2018-04-01

Continuum mechanics is a branch of mechanics that deals with the analysis of the mechanical behavior of materials modeled as a continuous manifold. Continuum mechanics models begin mostly by introducing of three-dimensional Euclidean space. The points within this region are defined as material points with prescribed properties. Each material point is characterized by a position vector which is continuous in time. Thus, the body changes in a way which is realistic, globally invertible at all times and orientation-preserving, so that the body cannot intersect itself and as transformations which produce mirror reflections are not possible in nature. For the mathematical formulation of the model it is also assumed to be twice continuously differentiable, so that differential equations describing the motion may be formulated. Finally, the kinematical relations, the balance equations, the constitutive and evolution equations and the boundary and/or initial conditions should be defined. If the physical fields are non-smooth jump conditions must be taken into account. The basic equations of continuum mechanics are presented following a short introduction. Additionally, some examples of solid deformable continua will be discussed within the presentation. Finally, advanced models of continuum mechanics will be introduced. The paper is dedicated to Alexander Manzhirov’s 60th birthday.

Continuous protein concentration via free-flow moving reaction boundary electrophoresis.

PubMed

Kong, Fanzhi; Zhang, Min; Chen, Jingjing; Fan, Liuyin; Xiao, Hua; Liu, Shaorong; Cao, Chengxi

2017-07-28

In this work, we developed the model and theory of free-flow moving reaction boundary electrophoresis (FFMRB) for continuous protein concentration for the first time. The theoretical results indicated that (i) the moving reaction boundary (MRB) can be quantitatively designed in free-flow electrophoresis (FFE) system; (ii) charge-to-mass ratio (Z/M) analysis could provide guidance for protein concentration optimization; and (iii) the maximum processing capacity could be predicted. To demonstrate the model and theory, three model proteins of hemoglobin (Hb), cytochrome C (Cyt C) and C-phycocyanin (C-PC) were chosen for the experiments. The experimental results verified that (i) stable MRBs with different velocities could be established in FFE apparatus with weak acid/weak base neutralization reaction system; (ii) proteins of Hb, Cyt C and C-PC were well concentrated with FFMRB; and (iii) a maximum processing capacity and recovery ratio of Cyt C enrichment were 126mL/h and 95.5% respectively, and a maximum enrichment factor was achieved 12.6 times for Hb. All of the experiments demonstrated the protein concentration model and theory. In contrast to other methods, the continuous processing ability enables FFMRB to efficiently enrich diluted protein or peptide in large volume solution. Copyright © 2017 Elsevier B.V. All rights reserved.

Continued Bullying Victimization in Adolescents: Maladaptive Schemas as a Mediational Mechanism.

PubMed

Calvete, Esther; Fernández-González, Liria; González-Cabrera, Joaquín M; Gámez-Guadix, Manuel

2018-03-01

Bullying victimization in adolescence is a significant social problem that can become persistent over time for some victims. However, there is an overall paucity of research examining the factors that contribute to continued bullying victimization. Schema therapy proposes a model that can help us understand why bullying victimization can be persistent for some victims. This study examines the role of maladaptive schemas, the key concept in schema therapy, as a mechanism of continued bullying victimization. The hypothesis was that maladaptive schemas of rejection mediate the predictive association between victimization in both the family and at school and future bullying victimization. Social anxiety was also considered, as previous research suggests that it can increase the risk of victimization. The participants were 1328 adolescents (45% female) with a mean age of 15.05 years (SD = 1.37), who completed questionnaires at three time points with a 6-month interval between them. Time 2 maladaptive schemas of rejection significantly mediated the predictive association from Time 1 bullying victimization, family abuse and social anxiety to Time 3 bullying victimization. The findings pertaining to potentially malleable factors, such as maladaptive schemas that maintain continued interpersonal victimization, have important implications for prevention and treatment strategies with adolescents.

Landscape genetics of a pollinator longhorn beetle [Typocerus v. velutinus (Olivier)] on a continuous habitat surface.

PubMed

Abdel Moniem, H E M; Schemerhorn, B J; DeWoody, J A; Holland, J D

2016-10-01

Landscape connectivity, the degree to which the landscape structure facilitates or impedes organismal movement and gene flow, is increasingly important to conservationists and land managers. Metrics for describing the undulating shape of continuous habitat surfaces can expand the usefulness of continuous gradient surfaces that describe habitat and predict the flow of organisms and genes. We adopted a landscape gradient model of habitat and used surface metrics of connectivity to model the genetic continuity between populations of the banded longhorn beetle [Typocerus v. velutinus (Olivier)] collected at 17 sites across a fragmentation gradient in Indiana, USA. We tested the hypothesis that greater habitat connectivity facilitates gene flow between beetle populations against a null model of isolation by distance (IBD). We used next-generation sequencing to develop 10 polymorphic microsatellite loci and genotype the individual beetles to assess the population genetic structure. Isolation by distance did not explain the population genetic structure. The surface metrics model of habitat connectivity explained the variance in genetic dissimilarities 30 times better than the IBD model. We conclude that surface metrology of habitat maps is a powerful extension of landscape genetics in heterogeneous landscapes. © 2016 John Wiley & Sons Ltd.

East Pacific Rise axial structure from a joint tomographic inversion of traveltimes picked on downward continued and standard shot gathers collected by 3D MCS surveying

NASA Astrophysics Data System (ADS)

Newman, Kori; Nedimović, Mladen; Delescluse, Matthias; Menke, William; Canales, J. Pablo; Carbotte, Suzanne; Carton, Helene; Mutter, John

2010-05-01

We present traveltime tomographic models along closely spaced (~250 m), strike-parallel profiles that flank the axis of the East Pacific Rise at 9°41' - 9°57' N. The data were collected during a 3D (multi-streamer) multichannel seismic (MCS) survey of the ridge. Four 6-km long hydrophone streamers were towed by the ship along three along-axis sail lines, yielding twelve possible profiles over which to compute tomographic models. Based on the relative location between source-receiver midpoints and targeted subsurface structures, we have chosen to compute models for four of those lines. MCS data provide for a high density of seismic ray paths with which to constrain the model. Potentially, travel times for ~250,000 source-receiver pairs can be picked over the 30 km length of each model. However, such data density does not enhance the model resolution, so, for computational efficiency, the data are decimated so that ~15,000 picks per profile are used. Downward continuation of the shot gathers simulates an experimental geometry in which the sources and receivers are positioned just above the sea floor. This allows the shallowest sampling refracted arrivals to be picked and incorporated into the inversion whereas they would otherwise not be usable with traditional first-arrival travel-time tomographic techniques. Some of the far-offset deep-penetrating 2B refractions cannot be picked on the downward continued gathers due to signal processing artifacts. For this reason, we run a joint inversion by also including 2B traveltime picks from standard shot gathers. Uppermost velocity structure (seismic layer 2A thickness and velocity) is primarily constrained from 1D inversion of the nearest offset (<500 m) source-receiver travel-time picks for each downward continued shot gather. Deeper velocities are then computed in a joint 2D inversion that uses all picks from standard and downward continued shot gathers and incorporates the 1D results into the starting model. The resulting velocity models extend ~1 km into the crust. Preliminary results show thicker layer 2A and faster layer 2A velocities at fourth order ridge segment boundaries. Additionally, layer 2A thickens north of 9° 52' N, which is consistent with earlier investigations of this ridge segment. Slower layer 2B velocities are resolved in the vicinity of documented hydrothermal vent fields. We anticipate that additional analyses of the results will yield further insight into fine scale variations in near-axis mid-ocean ridge structure.

Microseismic response characteristics modeling and locating of underground water supply pipe leak

NASA Astrophysics Data System (ADS)

Wang, J.; Liu, J.

2015-12-01

In traditional methods of pipeline leak location, geophones must be located on the pipe wall. If the exact location of the pipeline is unknown, the leaks cannot be identified accurately. To solve this problem, taking into account the characteristics of the pipeline leak, we propose a continuous random seismic source model and construct geological models to investigate the proposed method for locating underground pipeline leaks. Based on two dimensional (2D) viscoacoustic equations and the staggered grid finite-difference (FD) algorithm, the microseismic wave field generated by a leaking pipe is modeled. Cross-correlation analysis and the simulated annealing (SA) algorithm were utilized to obtain the time difference and the leak location. We also analyze and discuss the effect of the number of recorded traces, the survey layout, and the offset and interval of the traces on the accuracy of the estimated location. The preliminary results of the simulation and data field experiment indicate that (1) a continuous random source can realistically represent the leak microseismic wave field in a simulation using 2D visco-acoustic equations and a staggered grid FD algorithm. (2) The cross-correlation method is effective for calculating the time difference of the direct wave relative to the reference trace. However, outside the refraction blind zone, the accuracy of the time difference is reduced by the effects of the refracted wave. (3) The acquisition method of time difference based on the microseismic theory and SA algorithm has a great potential for locating leaks from underground pipelines from an array located on the ground surface. Keywords: Viscoacoustic finite-difference simulation; continuous random source; simulated annealing algorithm; pipeline leak location

Acquisition with partial and continuous reinforcement in pigeon autoshaping.

PubMed

Gottlieb, Daniel A

2004-08-01

Contemporary time accumulation models make the unique prediction that acquisition of a conditioned response will be equally rapid with partial and continuous reinforcement, if the time between conditioned stimuli is held constant. To investigate this, acquisition of conditioned responding was examined in pigeon autoshaping under conditions of 100% and 25% reinforcement, holding intertrial interval constant. Contrary to what was predicted, evidence for slowed acquisition in partially reinforced animals was observed with several response measures. However, asymptotic performance was superior with 25% reinforcement. A switching of reinforcement contingencies after initial acquisition did not immediately affect responding. After further sessions, partial reinforcement augmented responding, whereas continuous reinforcement did not, irrespective of an animal's reinforcement history. Subsequent training with a novel stimulus maintained the response patterns. These acquisition results generally support associative, rather than time accumulation, accounts of conditioning.

Consequences of Base Time for Redundant Signals Experiments

PubMed Central

Townsend, James T.; Honey, Christopher

2007-01-01

We report analytical and computational investigations into the effects of base time on the diagnosticity of two popular theoretical tools in the redundant signals literature: (1) the race model inequality and (2) the capacity coefficient. We show analytically and without distributional assumptions that the presence of base time decreases the sensitivity of both of these measures to model violations. We further use simulations to investigate the statistical power model selection tools based on the race model inequality, both with and without base time. Base time decreases statistical power, and biases the race model test toward conservatism. The magnitude of this biasing effect increases as we increase the proportion of total reaction time variance contributed by base time. We marshal empirical evidence to suggest that the proportion of reaction time variance contributed by base time is relatively small, and that the effects of base time on the diagnosticity of our model-selection tools are therefore likely to be minor. However, uncertainty remains concerning the magnitude and even the definition of base time. Experimentalists should continue to be alert to situations in which base time may contribute a large proportion of the total reaction time variance. PMID:18670591

Modeling the time-varying and level-dependent effects of the medial olivocochlear reflex in auditory nerve responses.

PubMed

Smalt, Christopher J; Heinz, Michael G; Strickland, Elizabeth A

2014-04-01

The medial olivocochlear reflex (MOCR) has been hypothesized to provide benefit for listening in noisy environments. This advantage can be attributed to a feedback mechanism that suppresses auditory nerve (AN) firing in continuous background noise, resulting in increased sensitivity to a tone or speech. MOC neurons synapse on outer hair cells (OHCs), and their activity effectively reduces cochlear gain. The computational model developed in this study implements the time-varying, characteristic frequency (CF) and level-dependent effects of the MOCR within the framework of a well-established model for normal and hearing-impaired AN responses. A second-order linear system was used to model the time-course of the MOCR using physiological data in humans. The stimulus-level-dependent parameters of the efferent pathway were estimated by fitting AN sensitivity derived from responses in decerebrate cats using a tone-in-noise paradigm. The resulting model uses a binaural, time-varying, CF-dependent, level-dependent OHC gain reduction for both ipsilateral and contralateral stimuli that improves detection of a tone in noise, similarly to recorded AN responses. The MOCR may be important for speech recognition in continuous background noise as well as for protection from acoustic trauma. Further study of this model and its efferent feedback loop may improve our understanding of the effects of sensorineural hearing loss in noisy situations, a condition in which hearing aids currently struggle to restore normal speech perception.

Dynamical initial-state model for relativistic heavy-ion collisions

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

Shen, Chun; Schenke, Bjorn

We present a fully three-dimensional model providing initial conditions for energy and net-baryon density distributions in heavy ion collisions at arbitrary collision energy. The model includes the dynamical deceleration of participating nucleons or valence quarks, depending on the implementation. The duration of the deceleration continues until the string spanned between colliding participants is assumed to thermalize, which is either after a fixed proper time, or a uctuating time depending on sampled final rapidities. Energy is deposited in space-time along the string, which in general will span a range of space-time rapidities and proper times. We study various observables obtained directlymore » from the initial state model, including net-baryon rapidity distributions, 2-particle rapidity correlations, as well as the rapidity decorrelation of the transverse geometry. Their dependence on the model implementation and parameter values is investigated. Here, we also present the implementation of the model with 3+1 dimensional hydrodynamics, which involves the addition of source terms that deposit energy and net-baryon densities produced by the initial state model at proper times greater than the initial time for the hydrodynamic simulation.« less

Dynamical initial-state model for relativistic heavy-ion collisions

NASA Astrophysics Data System (ADS)

Shen, Chun; Schenke, Björn

2018-02-01

We present a fully three-dimensional model providing initial conditions for energy and net-baryon density distributions in heavy-ion collisions at arbitrary collision energy. The model includes the dynamical deceleration of participating nucleons or valence quarks, depending on the implementation. The duration of the deceleration continues until the string spanned between colliding participants is assumed to thermalize, which is either after a fixed proper time, or a fluctuating time depending on sampled final rapidities. Energy is deposited in space time along the string, which in general will span a range of space-time rapidities and proper times. We study various observables obtained directly from the initial-state model, including net-baryon rapidity distributions, two-particle rapidity correlations, as well as the rapidity decorrelation of the transverse geometry. Their dependence on the model implementation and parameter values is investigated. We also present the implementation of the model with 3+1-dimensional hydrodynamics, which involves the addition of source terms that deposit energy and net-baryon densities produced by the initial-state model at proper times greater than the initial time for the hydrodynamic simulation.

Dynamical initial-state model for relativistic heavy-ion collisions

DOE PAGES

Shen, Chun; Schenke, Bjorn

2018-02-15

We present a fully three-dimensional model providing initial conditions for energy and net-baryon density distributions in heavy ion collisions at arbitrary collision energy. The model includes the dynamical deceleration of participating nucleons or valence quarks, depending on the implementation. The duration of the deceleration continues until the string spanned between colliding participants is assumed to thermalize, which is either after a fixed proper time, or a uctuating time depending on sampled final rapidities. Energy is deposited in space-time along the string, which in general will span a range of space-time rapidities and proper times. We study various observables obtained directlymore » from the initial state model, including net-baryon rapidity distributions, 2-particle rapidity correlations, as well as the rapidity decorrelation of the transverse geometry. Their dependence on the model implementation and parameter values is investigated. Here, we also present the implementation of the model with 3+1 dimensional hydrodynamics, which involves the addition of source terms that deposit energy and net-baryon densities produced by the initial state model at proper times greater than the initial time for the hydrodynamic simulation.« less

Real-time imaging of subarachnoid hemorrhage in piglets with electrical impedance tomography.

PubMed

Dai, Meng; Wang, Liang; Xu, Canhua; Li, Lianfeng; Gao, Guodong; Dong, Xiuzhen

2010-09-01

Subarachnoid hemorrhage (SAH) is one of the most severe medical emergencies in neurosurgery. Early detection or diagnosis would significantly reduce the rate of disability and mortality, and improve the prognosis of the patients. Although the present medical imaging techniques generally have high sensitivity to identify bleeding, the use of an additional, non-invasive imaging technique capable of continuously monitoring SAH is required to prevent contingent bleeding or re-bleeding. In this study, electrical impedance tomography (EIT) was applied to detect the onset of SAH modeled on eight piglets in real time, with the subsequent process being monitored continuously. The experimental SAH model was introduced by one-time injection of 5 ml fresh autologous arterial blood into the cisterna magna. Results showed that resistivity variations within the brain caused by the added blood could be detected using the EIT method and may be associated not only with the resistivity difference among brain tissues, but also with variations of cerebrospinal fluid dynamics. In conclusion, EIT has unique potential for use in clinical practice to provide invaluable real-time neuroimaging data for SAH after the improvement of electrode design, anisotropic realistic modeling and instrumentation.

Identification of individualised empirical models of carbohydrate and insulin effects on T1DM blood glucose dynamics

NASA Astrophysics Data System (ADS)

Cescon, Marzia; Johansson, Rolf; Renard, Eric; Maran, Alberto

2014-07-01

One of the main limiting factors in improving glucose control for type 1 diabetes mellitus (T1DM) subjects is the lack of a precise description of meal and insulin intake effects on blood glucose. Knowing the magnitude and duration of such effects would be useful not only for patients and physicians, but also for the development of a controller targeting glycaemia regulation. Therefore, in this paper we focus on estimating low-complexity yet physiologically sound and individualised multi-input single-output (MISO) models of the glucose metabolism in T1DM able to reflect the basic dynamical features of the glucose-insulin metabolic system in response to a meal intake or an insulin injection. The models are continuous-time second-order transfer functions relating the amount of carbohydrate of a meal and the insulin units of the accordingly administered dose (inputs) to plasma glucose evolution (output) and consist of few parameters clinically relevant to be estimated. The estimation strategy is continuous-time data-driven system identification and exploits a database in which meals and insulin boluses are separated in time, allowing the unique identification of the model parameters.

Continuous-time quantum Monte Carlo impurity solvers

NASA Astrophysics Data System (ADS)

Gull, Emanuel; Werner, Philipp; Fuchs, Sebastian; Surer, Brigitte; Pruschke, Thomas; Troyer, Matthias

2011-04-01

Continuous-time quantum Monte Carlo impurity solvers are algorithms that sample the partition function of an impurity model using diagrammatic Monte Carlo techniques. The present paper describes codes that implement the interaction expansion algorithm originally developed by Rubtsov, Savkin, and Lichtenstein, as well as the hybridization expansion method developed by Werner, Millis, Troyer, et al. These impurity solvers are part of the ALPS-DMFT application package and are accompanied by an implementation of dynamical mean-field self-consistency equations for (single orbital single site) dynamical mean-field problems with arbitrary densities of states. Program summaryProgram title: dmft Catalogue identifier: AEIL_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEIL_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: ALPS LIBRARY LICENSE version 1.1 No. of lines in distributed program, including test data, etc.: 899 806 No. of bytes in distributed program, including test data, etc.: 32 153 916 Distribution format: tar.gz Programming language: C++ Operating system: The ALPS libraries have been tested on the following platforms and compilers: Linux with GNU Compiler Collection (g++ version 3.1 and higher), and Intel C++ Compiler (icc version 7.0 and higher) MacOS X with GNU Compiler (g++ Apple-version 3.1, 3.3 and 4.0) IBM AIX with Visual Age C++ (xlC version 6.0) and GNU (g++ version 3.1 and higher) compilers Compaq Tru64 UNIX with Compq C++ Compiler (cxx) SGI IRIX with MIPSpro C++ Compiler (CC) HP-UX with HP C++ Compiler (aCC) Windows with Cygwin or coLinux platforms and GNU Compiler Collection (g++ version 3.1 and higher) RAM: 10 MB-1 GB Classification: 7.3 External routines: ALPS [1], BLAS/LAPACK, HDF5 Nature of problem: (See [2].) Quantum impurity models describe an atom or molecule embedded in a host material with which it can exchange electrons. They are basic to nanoscience as representations of quantum dots and molecular conductors and play an increasingly important role in the theory of "correlated electron" materials as auxiliary problems whose solution gives the "dynamical mean field" approximation to the self-energy and local correlation functions. Solution method: Quantum impurity models require a method of solution which provides access to both high and low energy scales and is effective for wide classes of physically realistic models. The continuous-time quantum Monte Carlo algorithms for which we present implementations here meet this challenge. Continuous-time quantum impurity methods are based on partition function expansions of quantum impurity models that are stochastically sampled to all orders using diagrammatic quantum Monte Carlo techniques. For a review of quantum impurity models and their applications and of continuous-time quantum Monte Carlo methods for impurity models we refer the reader to [2]. Additional comments: Use of dmft requires citation of this paper. Use of any ALPS program requires citation of the ALPS [1] paper. Running time: 60 s-8 h per iteration.

Continuous Shape Estimation of Continuum Robots Using X-ray Images

PubMed Central

Lobaton, Edgar J.; Fu, Jinghua; Torres, Luis G.; Alterovitz, Ron

2015-01-01

We present a new method for continuously and accurately estimating the shape of a continuum robot during a medical procedure using a small number of X-ray projection images (e.g., radiographs or fluoroscopy images). Continuum robots have curvilinear structure, enabling them to maneuver through constrained spaces by bending around obstacles. Accurately estimating the robot’s shape continuously over time is crucial for the success of procedures that require avoidance of anatomical obstacles and sensitive tissues. Online shape estimation of a continuum robot is complicated by uncertainty in its kinematic model, movement of the robot during the procedure, noise in X-ray images, and the clinical need to minimize the number of X-ray images acquired. Our new method integrates kinematics models of the robot with data extracted from an optimally selected set of X-ray projection images. Our method represents the shape of the continuum robot over time as a deformable surface which can be described as a linear combination of time and space basis functions. We take advantage of probabilistic priors and numeric optimization to select optimal camera configurations, thus minimizing the expected shape estimation error. We evaluate our method using simulated concentric tube robot procedures and demonstrate that obtaining between 3 and 10 images from viewpoints selected by our method enables online shape estimation with errors significantly lower than using the kinematic model alone or using randomly spaced viewpoints. PMID:26279960

Continuous Shape Estimation of Continuum Robots Using X-ray Images.

PubMed

Lobaton, Edgar J; Fu, Jinghua; Torres, Luis G; Alterovitz, Ron

2013-05-06

We present a new method for continuously and accurately estimating the shape of a continuum robot during a medical procedure using a small number of X-ray projection images (e.g., radiographs or fluoroscopy images). Continuum robots have curvilinear structure, enabling them to maneuver through constrained spaces by bending around obstacles. Accurately estimating the robot's shape continuously over time is crucial for the success of procedures that require avoidance of anatomical obstacles and sensitive tissues. Online shape estimation of a continuum robot is complicated by uncertainty in its kinematic model, movement of the robot during the procedure, noise in X-ray images, and the clinical need to minimize the number of X-ray images acquired. Our new method integrates kinematics models of the robot with data extracted from an optimally selected set of X-ray projection images. Our method represents the shape of the continuum robot over time as a deformable surface which can be described as a linear combination of time and space basis functions. We take advantage of probabilistic priors and numeric optimization to select optimal camera configurations, thus minimizing the expected shape estimation error. We evaluate our method using simulated concentric tube robot procedures and demonstrate that obtaining between 3 and 10 images from viewpoints selected by our method enables online shape estimation with errors significantly lower than using the kinematic model alone or using randomly spaced viewpoints.

Exact Solutions to Time-dependent Mdps

NASA Technical Reports Server (NTRS)

Boyan, Justin A.; Littman, Michael L.

2000-01-01

We describe an extension of the Markov decision process model in which a continuous time dimension is included in the state space. This allows for the representation and exact solution of a wide range of problems in which transitions or rewards vary over time. We examine problems based on route planning with public transportation and telescope observation scheduling.

Ready to Assemble: A Model State Higher Education Accountability System

ERIC Educational Resources Information Center

Carey, Kevin; Aldeman, Chad

2008-01-01

At a time when college degrees are increasingly a prerequisite for middle-class wages, less than 40 percent of college students are able to demonstrate proficiency on literacy tests, barely half of college students graduate on time, and many do not graduate at all. At the same time. the price of college continues to increase quickly and…

Fast-slow asymptotics for a Markov chain model of fast sodium current

NASA Astrophysics Data System (ADS)

Starý, Tomáš; Biktashev, Vadim N.

2017-09-01

We explore the feasibility of using fast-slow asymptotics to eliminate the computational stiffness of discrete-state, continuous-time deterministic Markov chain models of ionic channels underlying cardiac excitability. We focus on a Markov chain model of fast sodium current, and investigate its asymptotic behaviour with respect to small parameters identified in different ways.

User Inspired Management of Scientific Jobs in Grids and Clouds

ERIC Educational Resources Information Center

Withana, Eran Chinthaka

2011-01-01

From time-critical, real time computational experimentation to applications which process petabytes of data there is a continuing search for faster, more responsive computing platforms capable of supporting computational experimentation. Weather forecast models, for instance, process gigabytes of data to produce regional (mesoscale) predictions on…

Proactive assessment of accident risk to improve safety on a system of freeways : [research brief].

DOT National Transportation Integrated Search

2012-05-01

As traffic safety on freeways continues to be a growing concern, much progress has been made in shifting from reactive (incident detection) to proactive (real-time crash risk assessment) traffic strategies. Reliable models that can take in real-time ...

Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression.

PubMed

Horst, Fabian; Eekhoff, Alexander; Newell, Karl M; Schöllhorn, Wolfgang I

2017-01-01

Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns. Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales. Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of these findings, especially towards more individualized and situational diagnoses and therapy.

Failure Time Analysis of Office System Use.

ERIC Educational Resources Information Center

Cooper, Michael D.

1991-01-01

Develops mathematical models to characterize the probability of continued use of an integrated office automation system and tests these models on longitudinal data collected from 210 individuals using the IBM Professional Office System (PROFS) at the University of California at Berkeley. Analyses using survival functions and proportional hazard…

Precipitation data considerations for evaluating subdaily changes in rainless periods due to climate change

USDA-ARS?s Scientific Manuscript database

Quantifying magnitudes and frequencies of rainless times between storms (TBS), or storm occurrence, is required for generating continuous sequences of precipitation for modeling inputs to small watershed models for conservation studies. Two parameters characterize TBS, minimum TBS (MTBS) and averag...

SIMULATION FROM ENDPOINT-CONDITIONED, CONTINUOUS-TIME MARKOV CHAINS ON A FINITE STATE SPACE, WITH APPLICATIONS TO MOLECULAR EVOLUTION.

PubMed

Hobolth, Asger; Stone, Eric A

2009-09-01

Analyses of serially-sampled data often begin with the assumption that the observations represent discrete samples from a latent continuous-time stochastic process. The continuous-time Markov chain (CTMC) is one such generative model whose popularity extends to a variety of disciplines ranging from computational finance to human genetics and genomics. A common theme among these diverse applications is the need to simulate sample paths of a CTMC conditional on realized data that is discretely observed. Here we present a general solution to this sampling problem when the CTMC is defined on a discrete and finite state space. Specifically, we consider the generation of sample paths, including intermediate states and times of transition, from a CTMC whose beginning and ending states are known across a time interval of length T. We first unify the literature through a discussion of the three predominant approaches: (1) modified rejection sampling, (2) direct sampling, and (3) uniformization. We then give analytical results for the complexity and efficiency of each method in terms of the instantaneous transition rate matrix Q of the CTMC, its beginning and ending states, and the length of sampling time T. In doing so, we show that no method dominates the others across all model specifications, and we give explicit proof of which method prevails for any given Q, T, and endpoints. Finally, we introduce and compare three applications of CTMCs to demonstrate the pitfalls of choosing an inefficient sampler.

Assimilation of remote sensing observations into a continuous distributed hydrological model: impacts on the hydrologic cycle

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Campo, Lorenzo; Cenci, Luca; Silvestro, Francesco; Delogu, Fabio; Boni, Giorgio; Rudari, Roberto

2015-04-01

The reliable estimation of hydrological variables (e.g. soil moisture, evapotranspiration, surface temperature) in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays remote sensing can offer a chance to provide good space-time estimates of several hydrological variables and then improve hydrological model performances especially in environments with scarce in-situ data. This work investigates the impact of the assimilation of different remote sensing products on the hydrological cycle by using a continuous physically based distributed hydrological model. Three soil moisture products derived by ASCAT (Advanced SCATterometer) are used to update the model state variables. The satellite-derived products are assimilated into the hydrological model using different assimilation techniques: a simple nudging and the Ensemble Kalman Filter. Moreover two assimilation strategies are evaluated to assess the impact of assimilating the satellite products at model spatial resolution or at the satellite scale. The experiments are carried out for three Italian catchments on multi year period. The benefits on the model predictions of discharge, LST, evapotranspiration and soil moisture dynamics are tested and discussed.

An Extended EPQ-Based Problem with a Discontinuous Delivery Policy, Scrap Rate, and Random Breakdown

PubMed Central

Song, Ming-Syuan; Chen, Hsin-Mei; Chiu, Yuan-Shyi P.

2015-01-01

In real supply chain environments, the discontinuous multidelivery policy is often used when finished products need to be transported to retailers or customers outside the production units. To address this real-life production-shipment situation, this study extends recent work using an economic production quantity- (EPQ-) based inventory model with a continuous inventory issuing policy, defective items, and machine breakdown by incorporating a multiple delivery policy into the model to replace the continuous policy and investigates the effect on the optimal run time decision for this specific EPQ model. Next, we further expand the scope of the problem to combine the retailer's stock holding cost into our study. This enhanced EPQ-based model can be used to reflect the situation found in contemporary manufacturing firms in which finished products are delivered to the producer's own retail stores and stocked there for sale. A second model is developed and studied. With the help of mathematical modeling and optimization techniques, the optimal run times that minimize the expected total system costs comprising costs incurred in production units, transportation, and retail stores are derived, for both models. Numerical examples are provided to demonstrate the applicability of our research results. PMID:25821853

An extended EPQ-based problem with a discontinuous delivery policy, scrap rate, and random breakdown.

PubMed

Chiu, Singa Wang; Lin, Hong-Dar; Song, Ming-Syuan; Chen, Hsin-Mei; Chiu, Yuan-Shyi P

2015-01-01

In real supply chain environments, the discontinuous multidelivery policy is often used when finished products need to be transported to retailers or customers outside the production units. To address this real-life production-shipment situation, this study extends recent work using an economic production quantity- (EPQ-) based inventory model with a continuous inventory issuing policy, defective items, and machine breakdown by incorporating a multiple delivery policy into the model to replace the continuous policy and investigates the effect on the optimal run time decision for this specific EPQ model. Next, we further expand the scope of the problem to combine the retailer's stock holding cost into our study. This enhanced EPQ-based model can be used to reflect the situation found in contemporary manufacturing firms in which finished products are delivered to the producer's own retail stores and stocked there for sale. A second model is developed and studied. With the help of mathematical modeling and optimization techniques, the optimal run times that minimize the expected total system costs comprising costs incurred in production units, transportation, and retail stores are derived, for both models. Numerical examples are provided to demonstrate the applicability of our research results.

Effects of lesions to the left lateral prefrontal cortex on task-specific top-down biases and response strategies in blocked-cyclic naming.

PubMed

Belke, Eva

Anders, Riès, van Maanen and Alario put forward evidence accumulation modelling of object naming times as an alternative to neural network models of lexical retrieval. The authors exemplify their approach using data from the blocked-cyclic naming paradigm, requiring speakers to repeatedly name small sets of related or unrelated objects. The effects observed with this paradigm are understood reasonably well within the tradition of neural network modelling. However, implemented neural network models do not specify interfaces for task-specific top-down influences and response strategies that are likely to play a role in the blocked-cyclic naming paradigm, distinguishing it from continuous, non-cyclic manipulations of the naming context. I argue that the evidence accumulation approach falls short on this account as well, as it does not specify the potential contribution of task-specific top-down processes and strategic facilitation effects to the response time distributions. Future endeavours to model or fit data from blocked-cyclic naming experiments should strive to do so by simultaneously considering data from continuous context manipulations.

The formation of compact groups of galaxies. I: Optical properties

NASA Technical Reports Server (NTRS)

Diaferio, Antonaldo; Geller, Margaret J.; Ramella, Massimo

1994-01-01

The small crossing time of compact groups of galaxies (t(sub cr)H(sub 0) approximately less than 0.02) makes it hard to understand why they are observable at all. Our dissipationless N-body simulations show that within a single rich collapsing group compact groups of galaxies continually form. The mean lifetime of a particular compact configuration if approximately 1 Gyr. On this time scale, members may merge and/or other galaxies in the loose group may join the compact configuration. In other words, compact configurations are continually replaced by new systems. The frequency of this process explains the observability of compact groups. Our model produces compact configurations (compact groups (CG's) with optical properties remarkably similar to Hickson's (1982) compact groups (HCG's): (1) CG's have a frequency distribution of members similar to that of HCG's; (2) CG's are approximately equals 10 times as dense as loose groups; (3) CG's have dynamical properties remarkably similar to those of HCG's; (4) most of the galaxy members of CG's are not merger remnants. The crucial aspect of the model is the relationship between CG's and the surrounding rich loose group. Our model predicts the frequency of occurrence of CG's. A preliminary analysis of 18 rich loose groups is consistent with the model prediction. We suggest further observational tests of the model.

The Effects of Hearing Aid Compression Parameters on the Short-Term Dynamic Range of Continuous Speech

ERIC Educational Resources Information Center

Henning, Rebecca L. Warner; Bentler, Ruth A.

2008-01-01

Purpose: The purpose of this study was to evaluate and quantitatively model the independent and interactive effects of compression ratio, number of compression channels, and release time on the dynamic range of continuous speech. Method: A CD of the Rainbow Passage (J. E. Bernthal & N. W. Bankson, 1993) was used. The hearing aid was a…

Development of Model Systematic Trilateral Approach to Provide Continuing Education for Nursing Home and Small Hospital Personnel. Final Report.

ERIC Educational Resources Information Center

Schrader, Marvin A.; And Others

The project was designed to determine the feasibility of having a vocational technical adult education (VTAE) district provide continuing education inservice training for health care facilities using videotape equipment so that employees could gain knowledge and skills without leaving the facility or having to involve time outside the normal…

Development of the Nonstationary Incremental Analysis Update Algorithm for Sequential Data Assimilation System

NASA Astrophysics Data System (ADS)

Ham, Yoo-Geun; Song, Hyo-Jong; Jung, Jaehee; Lim, Gyu-Ho

2017-04-01

This study introduces a altered version of the incremental analysis updates (IAU), called the nonstationary IAU (NIAU) method, to enhance the assimilation accuracy of the IAU while retaining the continuity of the analysis. Analogous to the IAU, the NIAU is designed to add analysis increments at every model time step to improve the continuity in the intermittent data assimilation. Still, unlike the IAU, the NIAU method applies time-evolved forcing employing the forward operator as rectifications to the model. The solution of the NIAU is better than that of the IAU, of which analysis is performed at the start of the time window for adding the IAU forcing, in terms of the accuracy of the analysis field. It is because, in the linear systems, the NIAU solution equals that in an intermittent data assimilation method at the end of the assimilation interval. To have the filtering property in the NIAU, a forward operator to propagate the increment is reconstructed with only dominant singular vectors. An illustration of those advantages of the NIAU is given using the simple 40-variable Lorenz model.

Random walks and diffusion on networks

NASA Astrophysics Data System (ADS)

Masuda, Naoki; Porter, Mason A.; Lambiotte, Renaud

2017-11-01

Random walks are ubiquitous in the sciences, and they are interesting from both theoretical and practical perspectives. They are one of the most fundamental types of stochastic processes; can be used to model numerous phenomena, including diffusion, interactions, and opinions among humans and animals; and can be used to extract information about important entities or dense groups of entities in a network. Random walks have been studied for many decades on both regular lattices and (especially in the last couple of decades) on networks with a variety of structures. In the present article, we survey the theory and applications of random walks on networks, restricting ourselves to simple cases of single and non-adaptive random walkers. We distinguish three main types of random walks: discrete-time random walks, node-centric continuous-time random walks, and edge-centric continuous-time random walks. We first briefly survey random walks on a line, and then we consider random walks on various types of networks. We extensively discuss applications of random walks, including ranking of nodes (e.g., PageRank), community detection, respondent-driven sampling, and opinion models such as voter models.

Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study

PubMed Central

Clark, Matthew T.; Calland, James Forrest; Enfield, Kyle B.; Voss, John D.; Lake, Douglas E.; Moorman, J. Randall

2017-01-01

Background Charted vital signs and laboratory results represent intermittent samples of a patient’s dynamic physiologic state and have been used to calculate early warning scores to identify patients at risk of clinical deterioration. We hypothesized that the addition of cardiorespiratory dynamics measured from continuous electrocardiography (ECG) monitoring to intermittently sampled data improves the predictive validity of models trained to detect clinical deterioration prior to intensive care unit (ICU) transfer or unanticipated death. Methods and findings We analyzed 63 patient-years of ECG data from 8,105 acute care patient admissions at a tertiary care academic medical center. We developed models to predict deterioration resulting in ICU transfer or unanticipated death within the next 24 hours using either vital signs, laboratory results, or cardiorespiratory dynamics from continuous ECG monitoring and also evaluated models using all available data sources. We calculated the predictive validity (C-statistic), the net reclassification improvement, and the probability of achieving the difference in likelihood ratio χ2 for the additional degrees of freedom. The primary outcome occurred 755 times in 586 admissions (7%). We analyzed 395 clinical deteriorations with continuous ECG data in the 24 hours prior to an event. Using only continuous ECG measures resulted in a C-statistic of 0.65, similar to models using only laboratory results and vital signs (0.63 and 0.69 respectively). Addition of continuous ECG measures to models using conventional measurements improved the C-statistic by 0.01 and 0.07; a model integrating all data sources had a C-statistic of 0.73 with categorical net reclassification improvement of 0.09 for a change of 1 decile in risk. The difference in likelihood ratio χ2 between integrated models with and without cardiorespiratory dynamics was 2158 (p value: <0.001). Conclusions Cardiorespiratory dynamics from continuous ECG monitoring detect clinical deterioration in acute care patients and improve performance of conventional models that use only laboratory results and vital signs. PMID:28771487

Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study.

PubMed

Moss, Travis J; Clark, Matthew T; Calland, James Forrest; Enfield, Kyle B; Voss, John D; Lake, Douglas E; Moorman, J Randall

2017-01-01

Charted vital signs and laboratory results represent intermittent samples of a patient's dynamic physiologic state and have been used to calculate early warning scores to identify patients at risk of clinical deterioration. We hypothesized that the addition of cardiorespiratory dynamics measured from continuous electrocardiography (ECG) monitoring to intermittently sampled data improves the predictive validity of models trained to detect clinical deterioration prior to intensive care unit (ICU) transfer or unanticipated death. We analyzed 63 patient-years of ECG data from 8,105 acute care patient admissions at a tertiary care academic medical center. We developed models to predict deterioration resulting in ICU transfer or unanticipated death within the next 24 hours using either vital signs, laboratory results, or cardiorespiratory dynamics from continuous ECG monitoring and also evaluated models using all available data sources. We calculated the predictive validity (C-statistic), the net reclassification improvement, and the probability of achieving the difference in likelihood ratio χ2 for the additional degrees of freedom. The primary outcome occurred 755 times in 586 admissions (7%). We analyzed 395 clinical deteriorations with continuous ECG data in the 24 hours prior to an event. Using only continuous ECG measures resulted in a C-statistic of 0.65, similar to models using only laboratory results and vital signs (0.63 and 0.69 respectively). Addition of continuous ECG measures to models using conventional measurements improved the C-statistic by 0.01 and 0.07; a model integrating all data sources had a C-statistic of 0.73 with categorical net reclassification improvement of 0.09 for a change of 1 decile in risk. The difference in likelihood ratio χ2 between integrated models with and without cardiorespiratory dynamics was 2158 (p value: <0.001). Cardiorespiratory dynamics from continuous ECG monitoring detect clinical deterioration in acute care patients and improve performance of conventional models that use only laboratory results and vital signs.

Phylogeography Takes a Relaxed Random Walk in Continuous Space and Time

PubMed Central

Lemey, Philippe; Rambaut, Andrew; Welch, John J.; Suchard, Marc A.

2010-01-01

Research aimed at understanding the geographic context of evolutionary histories is burgeoning across biological disciplines. Recent endeavors attempt to interpret contemporaneous genetic variation in the light of increasingly detailed geographical and environmental observations. Such interest has promoted the development of phylogeographic inference techniques that explicitly aim to integrate such heterogeneous data. One promising development involves reconstructing phylogeographic history on a continuous landscape. Here, we present a Bayesian statistical approach to infer continuous phylogeographic diffusion using random walk models while simultaneously reconstructing the evolutionary history in time from molecular sequence data. Moreover, by accommodating branch-specific variation in dispersal rates, we relax the most restrictive assumption of the standard Brownian diffusion process and demonstrate increased statistical efficiency in spatial reconstructions of overdispersed random walks by analyzing both simulated and real viral genetic data. We further illustrate how drawing inference about summary statistics from a fully specified stochastic process over both sequence evolution and spatial movement reveals important characteristics of a rabies epidemic. Together with recent advances in discrete phylogeographic inference, the continuous model developments furnish a flexible statistical framework for biogeographical reconstructions that is easily expanded upon to accommodate various landscape genetic features. PMID:20203288

Finite time convergent learning law for continuous neural networks.

PubMed

Chairez, Isaac

2014-02-01

This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

In My Own Time: Tuition Fees, Class Time and Student Effort in Non-Formal (Or Continuing) Education

ERIC Educational Resources Information Center

Bolli, Thomas; Johnes, Geraint

2015-01-01

We develop and empirically test a model which examines the impact of changes in class time and tuition fees on student effort in the form of private study. The data come from the European Union's Adult Education Survey, conducted over the period 2005-2008. We find, in line with theoretical predictions, that the time students devote to private…

The Relationships between Individualism, Nationalism, Ethnocentrism, and Authoritarianism in Flanders: A Continuous Time-Structural Equation Modeling Approach.

PubMed

Angraini, Yenni; Toharudin, Toni; Folmer, Henk; Oud, Johan H L

2014-01-01

This article analyzes the relationships among nationalism (N), individualism (I), ethnocentrism (E), and authoritarianism (A) in continuous time (CT), estimated as a structural equation model. The analysis is based on the General Election Study for Flanders, Belgium, for 1991, 1995, and 1999. We find reciprocal effects between A and E and between E and I as well as a unidirectional effect from A on I. We furthermore find relatively small, but significant, effects from both I and E on N but no effect from A on N or from N on any of the other variables. Because of its central role in the N-I-E-A complex, mitigation of authoritarianism has the largest potential to reduce the spread of nationalism, ethnocentrism, and racism in Flanders.

Fast maximum likelihood estimation using continuous-time neural point process models.

PubMed

Lepage, Kyle Q; MacDonald, Christopher J

2015-06-01

A recent report estimates that the number of simultaneously recorded neurons is growing exponentially. A commonly employed statistical paradigm using discrete-time point process models of neural activity involves the computation of a maximum-likelihood estimate. The time to computate this estimate, per neuron, is proportional to the number of bins in a finely spaced discretization of time. By using continuous-time models of neural activity and the optimally efficient Gaussian quadrature, memory requirements and computation times are dramatically decreased in the commonly encountered situation where the number of parameters p is much less than the number of time-bins n. In this regime, with q equal to the quadrature order, memory requirements are decreased from O(np) to O(qp), and the number of floating-point operations are decreased from O(np(2)) to O(qp(2)). Accuracy of the proposed estimates is assessed based upon physiological consideration, error bounds, and mathematical results describing the relation between numerical integration error and numerical error affecting both parameter estimates and the observed Fisher information. A check is provided which is used to adapt the order of numerical integration. The procedure is verified in simulation and for hippocampal recordings. It is found that in 95 % of hippocampal recordings a q of 60 yields numerical error negligible with respect to parameter estimate standard error. Statistical inference using the proposed methodology is a fast and convenient alternative to statistical inference performed using a discrete-time point process model of neural activity. It enables the employment of the statistical methodology available with discrete-time inference, but is faster, uses less memory, and avoids any error due to discretization.

Potential for real-time understanding of coupled hydrologic and biogeochemical processes in stream ecosystems: Future integration of telemetered data with process models for glacial meltwater streams

NASA Astrophysics Data System (ADS)

McKnight, Diane M.; Cozzetto, Karen; Cullis, James D. S.; Gooseff, Michael N.; Jaros, Christopher; Koch, Joshua C.; Lyons, W. Berry; Neupauer, Roseanna; Wlostowski, Adam

2015-08-01

While continuous monitoring of streamflow and temperature has been common for some time, there is great potential to expand continuous monitoring to include water quality parameters such as nutrients, turbidity, oxygen, and dissolved organic material. In many systems, distinguishing between watershed and stream ecosystem controls can be challenging. The usefulness of such monitoring can be enhanced by the application of quantitative models to interpret observed patterns in real time. Examples are discussed primarily from the glacial meltwater streams of the McMurdo Dry Valleys, Antarctica. Although the Dry Valley landscape is barren of plants, many streams harbor thriving cyanobacterial mats. Whereas a daily cycle of streamflow is controlled by the surface energy balance on the glaciers and the temporal pattern of solar exposure, the daily signal for biogeochemical processes controlling water quality is generated along the stream. These features result in an excellent outdoor laboratory for investigating fundamental ecosystem process and the development and validation of process-based models. As part of the McMurdo Dry Valleys Long-Term Ecological Research project, we have conducted field experiments and developed coupled biogeochemical transport models for the role of hyporheic exchange in controlling weathering reactions, microbial nitrogen cycling, and stream temperature regulation. We have adapted modeling approaches from sediment transport to understand mobilization of stream biomass with increasing flows. These models help to elucidate the role of in-stream processes in systems where watershed processes also contribute to observed patterns, and may serve as a test case for applying real-time stream ecosystem models.

Evaluation of ConPrim: A three-part model for continuing education in primary health care.

PubMed

Berggren, Erika; Strang, Peter; Orrevall, Ylva; Ödlund Olin, Ann; Sandelowsky, Hanna; Törnkvist, Lena

2016-11-01

To overcome the gap between existing knowledge and the application of this knowledge in practice, a three-part continuing educational model for primary health care professionals (ConPrim) was developed. It includes a web-based program, a practical exercise and a case seminar. To evaluate professionals' perceptions of the design, pedagogy and adaptation to primary health care of the ConPrim continuing educational model as applied in a subject-specific intervention. A total of 67 professionals (nurses and physicians) completed a computer-based questionnaire evaluating the model's design, pedagogy and adaptation to primary health care one week after the intervention. Descriptive statistics were used. Over 90% found the design of the web-based program and case seminar attractive; 86% found the design of the practical exercise attractive. The professionals agreed that the time spent on two of the three parts was acceptable. The exception was the practical exercise: 32% did not fully agree. Approximately 90% agreed that the contents of all parts were relevant to their work and promoted interactive and interprofessional learning. In response to the statements about the intervention as whole, approximately 90% agreed that the intervention was suitable to primary health care, that it had increased their competence in the subject area, and that they would be able to use what they had learned in their work. ConPrim is a promising model for continuing educational interventions in primary health care. However, the time spent on the practical exercise should be adjusted and the instructions for the exercise clarified. ConPrim should be tested in other subject-specific interventions and its influence on clinical practice should be evaluated. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prediction of flow dynamics using point processes

NASA Astrophysics Data System (ADS)

Hirata, Yoshito; Stemler, Thomas; Eroglu, Deniz; Marwan, Norbert

2018-01-01

Describing a time series parsimoniously is the first step to study the underlying dynamics. For a time-discrete system, a generating partition provides a compact description such that a time series and a symbolic sequence are one-to-one. But, for a time-continuous system, such a compact description does not have a solid basis. Here, we propose to describe a time-continuous time series using a local cross section and the times when the orbit crosses the local cross section. We show that if such a series of crossing times and some past observations are given, we can predict the system's dynamics with fine accuracy. This reconstructability neither depends strongly on the size nor the placement of the local cross section if we have a sufficiently long database. We demonstrate the proposed method using the Lorenz model as well as the actual measurement of wind speed.

Scalar and vector Keldysh models in the time domain

NASA Astrophysics Data System (ADS)

Kiselev, M. N.; Kikoin, K. A.

2009-04-01

The exactly solvable Keldysh model of disordered electron system in a random scattering field with extremely long correlation length is converted to the time-dependent model with extremely long relaxation. The dynamical problem is solved for the ensemble of two-level systems (TLS) with fluctuating well depths having the discrete Z 2 symmetry. It is shown also that the symmetric TLS with fluctuating barrier transparency may be described in terms of the vector Keldysh model with dime-dependent random planar rotations in xy plane having continuous SO(2) symmetry. Application of this model to description of dynamic fluctuations in quantum dots and optical lattices is discussed.

The Volcanism Ontology (VO): a model of the volcanic system

NASA Astrophysics Data System (ADS)

Myer, J.; Babaie, H. A.

2017-12-01

We have modeled a part of the complex material and process entities and properties of the volcanic system in the Volcanism Ontology (VO) applying several top-level ontologies such as Basic Formal Ontology (BFO), SWEET, and Ontology of Physics for Biology (OPB) within a single framework. The continuant concepts in BFO describe features with instances that persist as wholes through time and have qualities (attributes) that may change (e.g., state, composition, and location). In VO, the continuants include lava, volcanic rock, and volcano. The occurrent concepts in BFO include processes, their temporal boundaries, and the spatio-temporal regions within which they occur. In VO, these include eruption (process), the onset of pyroclastic flow (temporal boundary), and the space and time span of the crystallization of lava in a lava tube (spatio-temporal region). These processes can be of physical (e.g., debris flow, crystallization, injection), atmospheric (e.g., vapor emission, ash particles blocking solar radiation), hydrological (e.g., diffusion of water vapor, hot spring), thermal (e.g., cooling of lava) and other types. The properties (predicates) relate continuants to other continuants, occurrents to continuants, and occurrents to occurrents. The ontology also models other concepts such as laboratory and field procedures by volcanologists, sampling by sensors, and the type of instruments applied in monitoring volcanic activity. When deployed on the web, VO will be used to explicitly and formally annotate data and information collected by volcanologists based on domain knowledge. This will enable the integration of global volcanic data and improve the interoperability of software that deal with such data.

On the formation of fold-type oscillation marks in the continuous casting of steel.

PubMed

Vynnycky, M; Saleem, S; Devine, K M; Florio, B J; Mitchell, S L; O'Brien, S B G

2017-06-01

Asymptotic methods are employed to revisit an earlier model for oscillation-mark formation in the continuous casting of steel. A systematic non-dimensionalization of the governing equations, which was not carried out previously, leads to a model with 12 dimensionless parameters. Analysis is provided in the same parameter regime as for the earlier model, and surprisingly simple analytical solutions are found for the oscillation-mark profiles; these are found to agree reasonably well with the numerical solution in the earlier model and very well with fold-type oscillation marks that have been obtained in more recent experimental work. The benefits of this approach, when compared with time-consuming numerical simulations, are discussed in the context of auxiliary models for macrosegregation and thermomechanical stresses and strains.

On the formation of fold-type oscillation marks in the continuous casting of steel

PubMed Central

Saleem, S.; Devine, K. M.; Florio, B. J.; Mitchell, S. L.; O’Brien, S. B. G.

2017-01-01

Asymptotic methods are employed to revisit an earlier model for oscillation-mark formation in the continuous casting of steel. A systematic non-dimensionalization of the governing equations, which was not carried out previously, leads to a model with 12 dimensionless parameters. Analysis is provided in the same parameter regime as for the earlier model, and surprisingly simple analytical solutions are found for the oscillation-mark profiles; these are found to agree reasonably well with the numerical solution in the earlier model and very well with fold-type oscillation marks that have been obtained in more recent experimental work. The benefits of this approach, when compared with time-consuming numerical simulations, are discussed in the context of auxiliary models for macrosegregation and thermomechanical stresses and strains. PMID:28680666

National Centers for Environmental Prediction

Science.gov Websites

Modeling Center continuously monitors its NWP model performance against different performance measures, and AIRCFT GFS SSI and forecast fits to RAOBS for last 7 days spatial bias maps for different regions different regions GFS SSI and forecast fits to RAOBS for calendar months (time series, spatial and vertical

40 CFR 60.2675 - What operating limits must I meet and by when?

Code of Federal Regulations, 2014 CFR

2014-07-01

...) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Emission Limitations... electronic submission of the test report must also include the make and model of the PM CPMS instrument...

40 CFR 60.2800 - Can reporting dates be changed?

Code of Federal Regulations, 2013 CFR

2013-07-01

... (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Recordkeeping and Reporting § 60.2800 Can... dates. See § 60.19(c) for procedures to seek approval to change your reporting date. Model Rule—Title V...

40 CFR 60.2800 - Can reporting dates be changed?

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Recordkeeping and Reporting § 60.2800 Can... dates. See § 60.19(c) for procedures to seek approval to change your reporting date. Model Rule—Title V...

40 CFR 60.2675 - What operating limits must I meet and by when?

Code of Federal Regulations, 2013 CFR

2013-07-01

...) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Emission Limitations... electronic submission of the test report must also include the make and model of the PM CPMS instrument...

42 CFR § 512.307 - Subsequent calculations.

Code of Federal Regulations, 2010 CFR

2017-10-01

... (CONTINUED) HEALTH CARE INFRASTRUCTURE AND MODEL PROGRAMS EPISODE PAYMENT MODEL Pricing and Payment § 512.307... the initial NPRA, using claims data and non-claims-based payment data available at that time, to account for final claims run-out, final changes in non-claims-based payment data, and any additional...

USE OF REMOTE SENSING AIR QUALITY INFORMATION IN REGIONAL SCALE AIR POLLUTION MODELING: CURRENT USE AND REQUIREMENTS

EPA Science Inventory

In recent years the applications of regional air quality models are continuously being extended to address atmospheric pollution phenomenon from local to hemispheric spatial scales over time scales ranging from episodic to annual. The need to represent interactions between physic...

Galerkin v. least-squares Petrov–Galerkin projection in nonlinear model reduction

DOE PAGES

Carlberg, Kevin Thomas; Barone, Matthew F.; Antil, Harbir

2016-10-20

Least-squares Petrov–Galerkin (LSPG) model-reduction techniques such as the Gauss–Newton with Approximated Tensors (GNAT) method have shown promise, as they have generated stable, accurate solutions for large-scale turbulent, compressible flow problems where standard Galerkin techniques have failed. Furthermore, there has been limited comparative analysis of the two approaches. This is due in part to difficulties arising from the fact that Galerkin techniques perform optimal projection associated with residual minimization at the time-continuous level, while LSPG techniques do so at the time-discrete level. This work provides a detailed theoretical and computational comparison of the two techniques for two common classes of timemore » integrators: linear multistep schemes and Runge–Kutta schemes. We present a number of new findings, including conditions under which the LSPG ROM has a time-continuous representation, conditions under which the two techniques are equivalent, and time-discrete error bounds for the two approaches. Perhaps most surprisingly, we demonstrate both theoretically and computationally that decreasing the time step does not necessarily decrease the error for the LSPG ROM; instead, the time step should be ‘matched’ to the spectral content of the reduced basis. In numerical experiments carried out on a turbulent compressible-flow problem with over one million unknowns, we show that increasing the time step to an intermediate value decreases both the error and the simulation time of the LSPG reduced-order model by an order of magnitude.« less

Galerkin v. least-squares Petrov–Galerkin projection in nonlinear model reduction

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

Carlberg, Kevin Thomas; Barone, Matthew F.; Antil, Harbir

Least-squares Petrov–Galerkin (LSPG) model-reduction techniques such as the Gauss–Newton with Approximated Tensors (GNAT) method have shown promise, as they have generated stable, accurate solutions for large-scale turbulent, compressible flow problems where standard Galerkin techniques have failed. Furthermore, there has been limited comparative analysis of the two approaches. This is due in part to difficulties arising from the fact that Galerkin techniques perform optimal projection associated with residual minimization at the time-continuous level, while LSPG techniques do so at the time-discrete level. This work provides a detailed theoretical and computational comparison of the two techniques for two common classes of timemore » integrators: linear multistep schemes and Runge–Kutta schemes. We present a number of new findings, including conditions under which the LSPG ROM has a time-continuous representation, conditions under which the two techniques are equivalent, and time-discrete error bounds for the two approaches. Perhaps most surprisingly, we demonstrate both theoretically and computationally that decreasing the time step does not necessarily decrease the error for the LSPG ROM; instead, the time step should be ‘matched’ to the spectral content of the reduced basis. In numerical experiments carried out on a turbulent compressible-flow problem with over one million unknowns, we show that increasing the time step to an intermediate value decreases both the error and the simulation time of the LSPG reduced-order model by an order of magnitude.« less

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets.

PubMed

Zhang, Jian-Hua; Xia, Jia-Jun; Garibaldi, Jonathan M; Groumpos, Petros P; Wang, Ru-Bin

2017-06-01

In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller. Copyright © 2017 Elsevier B.V. All rights reserved.

40 CFR 60.2645 - How do I obtain my operator qualification?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Operator Training...

40 CFR 60.2650 - How do I maintain my operator qualification?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Operator Training...

40 CFR 60.2650 - How do I maintain my operator qualification?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Operator Training...

40 CFR 60.2645 - How do I obtain my operator qualification?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Operator Training...

A general theory on frequency and time-frequency analysis of irregularly sampled time series based on projection methods - Part 2: Extension to time-frequency analysis

NASA Astrophysics Data System (ADS)

Lenoir, Guillaume; Crucifix, Michel

2018-03-01

Geophysical time series are sometimes sampled irregularly along the time axis. The situation is particularly frequent in palaeoclimatology. Yet, there is so far no general framework for handling the continuous wavelet transform when the time sampling is irregular. Here we provide such a framework. To this end, we define the scalogram as the continuous-wavelet-transform equivalent of the extended Lomb-Scargle periodogram defined in Part 1 of this study (Lenoir and Crucifix, 2018). The signal being analysed is modelled as the sum of a locally periodic component in the time-frequency plane, a polynomial trend, and a background noise. The mother wavelet adopted here is the Morlet wavelet classically used in geophysical applications. The background noise model is a stationary Gaussian continuous autoregressive-moving-average (CARMA) process, which is more general than the traditional Gaussian white and red noise processes. The scalogram is smoothed by averaging over neighbouring times in order to reduce its variance. The Shannon-Nyquist exclusion zone is however defined as the area corrupted by local aliasing issues. The local amplitude in the time-frequency plane is then estimated with least-squares methods. We also derive an approximate formula linking the squared amplitude and the scalogram. Based on this property, we define a new analysis tool: the weighted smoothed scalogram, which we recommend for most analyses. The estimated signal amplitude also gives access to band and ridge filtering. Finally, we design a test of significance for the weighted smoothed scalogram against the stationary Gaussian CARMA background noise, and provide algorithms for computing confidence levels, either analytically or with Monte Carlo Markov chain methods. All the analysis tools presented in this article are available to the reader in the Python package WAVEPAL.

Modelling pH evolution and lactic acid production in the growth medium of a lactic acid bacterium: application to set a biological TTI.

PubMed

Ellouze, M; Pichaud, M; Bonaiti, C; Coroller, L; Couvert, O; Thuault, D; Vaillant, R

2008-11-30

Time temperature integrators or indicators (TTIs) are effective tools making the continuous monitoring of the time temperature history of chilled products possible throughout the cold chain. Their correct setting is of critical importance to ensure food quality. The objective of this study was to develop a model to facilitate accurate settings of the CRYOLOG biological TTI, TRACEO. Experimental designs were used to investigate and model the effects of the temperature, the TTI inoculum size, pH, and water activity on its response time. The modelling process went through several steps addressing growth, acidification and inhibition phenomena in dynamic conditions. The model showed satisfactory results and validations in industrial conditions gave clear evidence that such a model is a valuable tool, not only to predict accurate response times of TRACEO, but also to propose precise settings to manufacture the appropriate TTI to trace a particular food according to a given time temperature scenario.

Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning

NASA Astrophysics Data System (ADS)

Talei, Amin; Chua, Lloyd Hock Chye; Quek, Chai; Jansson, Per-Erik

2013-04-01

SummaryA study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall-runoff modeling application. The local learning model was first tested on three different catchments: an outdoor experimental catchment measuring 25 m2 (Catchment 1), a small urban catchment 5.6 km2 in size (Catchment 2), and a large rural watershed with area of 241.3 km2 (Catchment 3). The results obtained from the local learning model were comparable or better than results obtained from physically-based, i.e. Kinematic Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The local learning algorithm also required a shorter training time compared to a global learning NFS model. The local learning model was next tested in real-time mode, where the model was continuously adapted when presented with current information in real time. The real-time implementation of the local learning model gave better results, without the need for retraining, when compared to a batch NFS model, where it was found that the batch model had to be retrained periodically in order to achieve similar results.

Pathwise upper semi-continuity of random pullback attractors along the time axis

NASA Astrophysics Data System (ADS)

Cui, Hongyong; Kloeden, Peter E.; Wu, Fuke

2018-07-01

The pullback attractor of a non-autonomous random dynamical system is a time-indexed family of random sets, typically having the form {At(ṡ) } t ∈ R with each At(ṡ) a random set. This paper is concerned with the nature of such time-dependence. It is shown that the upper semi-continuity of the mapping t ↦At(ω) for each ω fixed has an equivalence relationship with the uniform compactness of the local union ∪s∈IAs(ω) , where I ⊂ R is compact. Applied to a semi-linear degenerate parabolic equation with additive noise and a wave equation with multiplicative noise we show that, in order to prove the above locally uniform compactness and upper semi-continuity, no additional conditions are required, in which sense the two properties appear to be general properties satisfied by a large number of real models.

Analysis on flood generation processes by means of a continuous simulation model

NASA Astrophysics Data System (ADS)

Fiorentino, M.; Gioia, A.; Iacobellis, V.; Manfreda, S.

2006-03-01

In the present research, we exploited a continuous hydrological simulation to investigate on key variables responsible of flood peak formation. With this purpose, a distributed hydrological model (DREAM) is used in cascade with a rainfall generator (IRP-Iterated Random Pulse) to simulate a large number of extreme events providing insight into the main controls of flood generation mechanisms. Investigated variables are those used in theoretically derived probability distribution of floods based on the concept of partial contributing area (e.g. Iacobellis and Fiorentino, 2000). The continuous simulation model is used to investigate on the hydrological losses occurring during extreme events, the variability of the source area contributing to the flood peak and its lag-time. Results suggest interesting simplification for the theoretical probability distribution of floods according to the different climatic and geomorfologic environments. The study is applied to two basins located in Southern Italy with different climatic characteristics.

Applications of the Theory of Distributed and Real Time Systems to the Development of Large-Scale Timing Based Systems.

DTIC Science & Technology

1996-04-01

time systems . The focus is on the study of ’building-blocks’ for the construction of reliable and efficient systems. Our works falls into three...Members of MIT’s Theory of Distributed Systems group have continued their work on modelling, designing, verifying and analyzing distributed and real

An Alternative Model of Continuing Professional Development for Teachers: Giving Teachers Time

ERIC Educational Resources Information Center

Haydn, Terry; Barton, Roy; Oliver, Ann

2008-01-01

The paper reports on the outcomes of a Department of Culture, Museums and Sport (DCMS) funded project which provided resources for three groups of teachers in different subjects and age phases to have some time where they were freed from their teaching responsibilities, and also given time to meet together with other teachers to share ideas. The…

Transport and Reactive Flow Modelling Using A Particle Tracking Method Based on Continuous Time Random Walks

NASA Astrophysics Data System (ADS)

Oliveira, R.; Bijeljic, B.; Blunt, M. J.; Colbourne, A.; Sederman, A. J.; Mantle, M. D.; Gladden, L. F.

2017-12-01

Mixing and reactive processes have a large impact on the viability of enhanced oil and gas recovery projects that involve acid stimulation and CO2 injection. To achieve a successful design of the injection schemes an accurate understanding of the interplay between pore structure, flow and reactive transport is necessary. Dependent on transport and reactive conditions, this complex coupling can also be dependent on initial rock heterogeneity across a variety of scales. To address these issues, we devise a new method to study transport and reactive flow in porous media at multiple scales. The transport model is based on an efficient Particle Tracking Method based on Continuous Time Random Walks (CTRW-PTM) on a lattice. Transport is modelled using an algorithm described in Rhodes and Blunt (2006) and Srinivasan et al. (2010); this model is expanded to enable for reactive flow predictions in subsurface rock undergoing a first-order fluid/solid chemical reaction. The reaction-induced alteration in fluid/solid interface is accommodated in the model through changes in porosity and flow field, leading to time dependent transport characteristics in the form of transit time distributions which account for rock heterogeneity change. This also enables the study of concentration profiles at the scale of interest. Firstly, we validate transport model by comparing the probability of molecular displacement (propagators) measured by Nuclear Magnetic Resonance (NMR) with our modelled predictions for concentration profiles. The experimental propagators for three different porous media of increasing complexity, a beadpack, a Bentheimer sandstone and a Portland carbonate, show a good agreement with the model. Next, we capture the time evolution of the propagators distribution in a reactive flow experiment, where hydrochloric acid is injected into a limestone rock. We analyse the time-evolving non-Fickian signatures for the transport during reactive flow and observe an increase in transport heterogeneity at latter times, representing the increase in rock heterogeneity. Evolution of transit time distribution is associated with the evolution of concentration profiles, thus highlighting the impact of initial rock structure on the reactive transport for a range of Pe and Da numbers.

Making statistical inferences about software reliability

NASA Technical Reports Server (NTRS)

Miller, Douglas R.

1988-01-01

Failure times of software undergoing random debugging can be modelled as order statistics of independent but nonidentically distributed exponential random variables. Using this model inferences can be made about current reliability and, if debugging continues, future reliability. This model also shows the difficulty inherent in statistical verification of very highly reliable software such as that used by digital avionics in commercial aircraft.

Data on copula modeling of mixed discrete and continuous neural time series.

PubMed

Hu, Meng; Li, Mingyao; Li, Wu; Liang, Hualou

2016-06-01

Copula is an important tool for modeling neural dependence. Recent work on copula has been expanded to jointly model mixed time series in neuroscience ("Hu et al., 2016, Joint Analysis of Spikes and Local Field Potentials using Copula" [1]). Here we present further data for joint analysis of spike and local field potential (LFP) with copula modeling. In particular, the details of different model orders and the influence of possible spike contamination in LFP data from the same and different electrode recordings are presented. To further facilitate the use of our copula model for the analysis of mixed data, we provide the Matlab codes, together with example data.

Empirical study of the dependence of the results of multivariable flexible survival analyses on model selection strategy.

PubMed

Binquet, C; Abrahamowicz, M; Mahboubi, A; Jooste, V; Faivre, J; Bonithon-Kopp, C; Quantin, C

2008-12-30

Flexible survival models, which avoid assumptions about hazards proportionality (PH) or linearity of continuous covariates effects, bring the issues of model selection to a new level of complexity. Each 'candidate covariate' requires inter-dependent decisions regarding (i) its inclusion in the model, and representation of its effects on the log hazard as (ii) either constant over time or time-dependent (TD) and, for continuous covariates, (iii) either loglinear or non-loglinear (NL). Moreover, 'optimal' decisions for one covariate depend on the decisions regarding others. Thus, some efficient model-building strategy is necessary.We carried out an empirical study of the impact of the model selection strategy on the estimates obtained in flexible multivariable survival analyses of prognostic factors for mortality in 273 gastric cancer patients. We used 10 different strategies to select alternative multivariable parametric as well as spline-based models, allowing flexible modeling of non-parametric (TD and/or NL) effects. We employed 5-fold cross-validation to compare the predictive ability of alternative models.All flexible models indicated significant non-linearity and changes over time in the effect of age at diagnosis. Conventional 'parametric' models suggested the lack of period effect, whereas more flexible strategies indicated a significant NL effect. Cross-validation confirmed that flexible models predicted better mortality. The resulting differences in the 'final model' selected by various strategies had also impact on the risk prediction for individual subjects.Overall, our analyses underline (a) the importance of accounting for significant non-parametric effects of covariates and (b) the need for developing accurate model selection strategies for flexible survival analyses. Copyright 2008 John Wiley & Sons, Ltd.

40 CFR 60.2755 - When must I submit my waste management plan?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Recordkeeping and...

40 CFR 60.2580 - When must I complete each increment of progress?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Increments of...

40 CFR 60.2780 - What must I include in the deviation report?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Recordkeeping and...

40 CFR 60.2740 - What records must I keep?

Code of Federal Regulations, 2013 CFR

2013-07-01

... (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Recordkeeping and Reporting § 60.2740...

40 CFR 60.2750 - What reports must I submit?

Code of Federal Regulations, 2013 CFR

2013-07-01

... (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Recordkeeping and Reporting § 60.2750...

40 CFR 60.2755 - When must I submit my waste management plan?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Recordkeeping and...

40 CFR 60.2780 - What must I include in the deviation report?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Recordkeeping and...

40 CFR 60.2625 - When must I submit my waste management plan?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Waste Management...

40 CFR 60.2740 - What records must I keep?

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Recordkeeping and Reporting § 60.2740...

40 CFR 60.2750 - What reports must I submit?

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Recordkeeping and Reporting § 60.2750...

40 CFR 60.2820 - When must I complete each increment of progress?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Air Curtain...

40 CFR 60.2580 - When must I complete each increment of progress?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Increments of...

40 CFR 60.2625 - When must I submit my waste management plan?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Waste Management...

40 CFR 60.2580 - When must I complete each increment of progress?

Code of Federal Regulations, 2012 CFR

2012-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Increments of...

Delayed extubation to nasal continuous positive airway pressure in the immature baboon model of bronchopulmonary dysplasia: lung clinical and pathological findings.

PubMed

Thomson, Merran A; Yoder, Bradley A; Winter, Vicki T; Giavedoni, Luis; Chang, Ling Yi; Coalson, Jacqueline J

2006-11-01

Using the 125-day baboon model of bronchopulmonary dysplasia treated with prenatal steroid and exogenous surfactant, we hypothesized that a delay of extubation from low tidal volume positive pressure ventilation to nasal continuous positive airway pressure at 5 days (delayed nasal continuous positive airway pressure group) would not induce more lung injury when compared with baboons aggressively weaned to nasal continuous positive airway pressure at 24 hours (early nasal continuous positive airway pressure group), because both received positive pressure ventilation. After delivery by cesarean section at 125 days (term: 185 days), infants received 2 doses of Curosurf (Chiesi Farmaceutica S.p.A., Parma, Italy) and daily caffeine citrate. The delay in extubation to 5 days resulted in baboons in the delayed nasal continuous positive airway pressure group having a lower arterial to alveolar oxygen ratio, high PaCO2, and worse respiratory function. The animals in the delayed nasal continuous positive airway pressure group exhibited a poor respiratory drive that contributed to more reintubations and time on mechanical ventilation. A few animals in both groups developed necrotizing enterocolitis and/or sepsis, but infectious pneumonias were not documented. Cellular bronchiolitis and peribronchiolar alveolar wall thickening were more frequently seen in the delayed nasal continuous positive airway pressure group. Bronchoalveolar lavage levels of interleukin-6, interleukin-8, monocyte chemotactic protein-1, macrophage inflammatory protein-1 alpha, and growth-regulated oncogene-alpha were significantly increased in the delayed nasal continuous positive airway pressure group. Standard and digital morphometric analyses showed no significant differences in internal surface area and nodal measurements between the groups. Platelet endothelial cell adhesion molecule vascular staining was not significantly different between the 2 nasal continuous positive airway pressure groups. Volutrauma and/or low-grade colonization of airways secondary to increased reintubations and ventilation times are speculated to play causative roles in the delayed nasal continuous positive airway pressure group findings.

Integrating an incident management system within a continuity of operations programme: case study of the Bank of Canada.

PubMed

Loop, Carole

2013-01-01

Carrying out critical business functions without interruption requires a resilient and robust business continuity framework. By embedding an industry-standard incident management system within its business continuity structure, the Bank of Canada strengthened its response plan by enabling timely response to incidents while maintaining a strong focus on business continuity. A total programme approach, integrating the two disciplines, provided for enhanced recovery capabilities. While the value of an effective and efficient response organisation is clear, as demonstrated by emergency events around the world, incident response structures based on normal operating hierarchy can experience unique challenges. The internationally-recognised Incident Command System (ICS) model addresses these issues and reflects the five primary incident management functions, each contributing to the overall strength and effectiveness of the response organisation. The paper focuses on the Bank of Canada's successful implementation of the ICS model as its incident management and continuity of operations programmes evolved to reflect current best practices.

When continuous observations just won't do: developing accurate and efficient sampling strategies for the laying hen.

PubMed

Daigle, Courtney L; Siegford, Janice M

2014-03-01

Continuous observation is the most accurate way to determine animals' actual time budget and can provide a 'gold standard' representation of resource use, behavior frequency, and duration. Continuous observation is useful for capturing behaviors that are of short duration or occur infrequently. However, collecting continuous data is labor intensive and time consuming, making multiple individual or long-term data collection difficult. Six non-cage laying hens were video recorded for 15 h and behavioral data collected every 2 s were compared with data collected using scan sampling intervals of 5, 10, 15, 30, and 60 min and subsamples of 2 second observations performed for 10 min every 30 min, 15 min every 1 h, 30 min every 1.5 h, and 15 min every 2 h. Three statistical approaches were used to provide a comprehensive analysis to examine the quality of the data obtained via different sampling methods. General linear mixed models identified how the time budget from the sampling techniques differed from continuous observation. Correlation analysis identified how strongly results from the sampling techniques were associated with those from continuous observation. Regression analysis identified how well the results from the sampling techniques were associated with those from continuous observation, changes in magnitude, and whether a sampling technique had bias. Static behaviors were well represented with scan and time sampling techniques, while dynamic behaviors were best represented with time sampling techniques. Methods for identifying an appropriate sampling strategy based upon the type of behavior of interest are outlined and results for non-caged laying hens are presented. Copyright © 2013 Elsevier B.V. All rights reserved.

Bifurcations in a discrete time model composed of Beverton-Holt function and Ricker function.

PubMed

Shang, Jin; Li, Bingtuan; Barnard, Michael R

2015-05-01

We provide rigorous analysis for a discrete-time model composed of the Ricker function and Beverton-Holt function. This model was proposed by Lewis and Li [Bull. Math. Biol. 74 (2012) 2383-2402] in the study of a population in which reproduction occurs at a discrete instant of time whereas death and competition take place continuously during the season. We show analytically that there exists a period-doubling bifurcation curve in the model. The bifurcation curve divides the parameter space into the region of stability and the region of instability. We demonstrate through numerical bifurcation diagrams that the regions of periodic cycles are intermixed with the regions of chaos. We also study the global stability of the model. Copyright © 2015 Elsevier Inc. All rights reserved.

Multivariate normal maximum likelihood with both ordinal and continuous variables, and data missing at random.

PubMed

Pritikin, Joshua N; Brick, Timothy R; Neale, Michael C

2018-04-01

A novel method for the maximum likelihood estimation of structural equation models (SEM) with both ordinal and continuous indicators is introduced using a flexible multivariate probit model for the ordinal indicators. A full information approach ensures unbiased estimates for data missing at random. Exceeding the capability of prior methods, up to 13 ordinal variables can be included before integration time increases beyond 1 s per row. The method relies on the axiom of conditional probability to split apart the distribution of continuous and ordinal variables. Due to the symmetry of the axiom, two similar methods are available. A simulation study provides evidence that the two similar approaches offer equal accuracy. A further simulation is used to develop a heuristic to automatically select the most computationally efficient approach. Joint ordinal continuous SEM is implemented in OpenMx, free and open-source software.

Control-based continuation: Bifurcation and stability analysis for physical experiments

NASA Astrophysics Data System (ADS)

Barton, David A. W.

2017-02-01

Control-based continuation is technique for tracking the solutions and bifurcations of nonlinear experiments. The idea is to apply the method of numerical continuation to a feedback-controlled physical experiment such that the control becomes non-invasive. Since in an experiment it is not (generally) possible to set the state of the system directly, the control target becomes a proxy for the state. Control-based continuation enables the systematic investigation of the bifurcation structure of a physical system, much like if it was numerical model. However, stability information (and hence bifurcation detection and classification) is not readily available due to the presence of stabilising feedback control. This paper uses a periodic auto-regressive model with exogenous inputs (ARX) to approximate the time-varying linearisation of the experiment around a particular periodic orbit, thus providing the missing stability information. This method is demonstrated using a physical nonlinear tuned mass damper.

In-line Raman spectroscopic monitoring and feedback control of a continuous twin-screw pharmaceutical powder blending and tableting process.

PubMed

Nagy, Brigitta; Farkas, Attila; Gyürkés, Martin; Komaromy-Hiller, Szofia; Démuth, Balázs; Szabó, Bence; Nusser, Dávid; Borbás, Enikő; Marosi, György; Nagy, Zsombor Kristóf

2017-09-15

The integration of Process Analytical Technology (PAT) initiative into the continuous production of pharmaceuticals is indispensable for reliable production. The present paper reports the implementation of in-line Raman spectroscopy in a continuous blending and tableting process of a three-component model pharmaceutical system, containing caffeine as model active pharmaceutical ingredient (API), glucose as model excipient and magnesium stearate as lubricant. The real-time analysis of API content, blend homogeneity, and tablet content uniformity was performed using a Partial Least Squares (PLS) quantitative method. The in-line Raman spectroscopic monitoring showed that the continuous blender was capable of producing blends with high homogeneity, and technological malfunctions can be detected by the proposed PAT method. The Raman spectroscopy-based feedback control of the API feeder was also established, creating a 'Process Analytically Controlled Technology' (PACT), which guarantees the required API content in the produced blend. This is, to the best of the authors' knowledge, the first ever application of Raman-spectroscopy in continuous blending and the first Raman-based feedback control in the formulation technology of solid pharmaceuticals. Copyright © 2017 Elsevier B.V. All rights reserved.

The mentoring experiences of new graduate midwives working in midwifery continuity of care models in Australia.

PubMed

Cummins, Allison M; Denney-Wilson, E; Homer, C S E

2017-05-01

The aim of this paper was to explore the mentoring experiences of new graduate midwives working in midwifery continuity of care models in Australia. Most new graduates find employment in hospitals and undertake a new graduate program rotating through different wards. A limited number of new graduate midwives were found to be working in midwifery continuity of care. The new graduate midwives in this study were mentored by more experienced midwives. Mentoring in midwifery has been described as being concerned with confidence building based through a personal relationship. A qualitative descriptive study was undertaken and the data were analysed using continuity of care as a framework. We found having a mentor was important, knowing the mentor made it easier for the new graduate to call their mentor at any time. The new graduate midwives had respect for their mentors and the support helped build their confidence in transitioning from student to midwife. With the expansion of midwifery continuity of care models in Australia mentoring should be provided for transition midwives working in this way. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model

PubMed Central

Abdelnour, A. Farras; Huppert, Theodore

2009-01-01

Near-infrared spectroscopy is a non-invasive neuroimaging method which uses light to measure changes in cerebral blood oxygenation associated with brain activity. In this work, we demonstrate the ability to record and analyze images of brain activity in real-time using a 16-channel continuous wave optical NIRS system. We propose a novel real-time analysis framework using an adaptive Kalman filter and a state–space model based on a canonical general linear model of brain activity. We show that our adaptive model has the ability to estimate single-trial brain activity events as we apply this method to track and classify experimental data acquired during an alternating bilateral self-paced finger tapping task. PMID:19457389

Identification of market trends with string and D2-brane maps

NASA Astrophysics Data System (ADS)

Bartoš, Erik; Pinčák, Richard

2017-08-01

The multidimensional string objects are introduced as a new alternative for an application of string models for time series forecasting in trading on financial markets. The objects are represented by open string with 2-endpoints and D2-brane, which are continuous enhancement of 1-endpoint open string model. We show how new object properties can change the statistics of the predictors, which makes them the candidates for modeling a wide range of time series systems. String angular momentum is proposed as another tool to analyze the stability of currency rates except the historical volatility. To show the reliability of our approach with application of string models for time series forecasting we present the results of real demo simulations for four currency exchange pairs.

Le modèle stochastique SIS pour une épidémie dans un environnement aléatoire.

PubMed

Bacaër, Nicolas

2016-10-01

The stochastic SIS epidemic model in a random environment. In a random environment that is a two-state continuous-time Markov chain, the mean time to extinction of the stochastic SIS epidemic model grows in the supercritical case exponentially with respect to the population size if the two states are favorable, and like a power law if one state is favorable while the other is unfavorable.

Measuring patient-centered medical home access and continuity in clinics with part-time clinicians.

PubMed

Rosland, Ann-Marie; Krein, Sarah L; Kim, Hyunglin Myra; Greenstone, Clinton L; Tremblay, Adam; Ratz, David; Saffar, Darcy; Kerr, Eve A

2015-05-01

Common patient-centered medical home (PCMH) performance measures value access to a single primary care provider (PCP), which may have unintended consequences for clinics that rely on part-time PCPs and team-based care. Retrospective analysis of 110,454 primary care visits from 2 Veterans Health Administration clinics from 2010 to 2012. Multi-level models examined associations between PCP availability in clinic, and performance on access and continuity measures. Patient experiences with access and continuity were compared using 2012 patient survey data (N = 2881). Patients of PCPs with fewer half-day clinic sessions per week were significantly less likely to get a requested same-day appointment with their usual PCP (predicted probability 17% for PCPs with 2 sessions/week, 20% for 5 sessions/week, and 26% for 10 sessions/week). Among requests that did not result in a same-day appointment with the usual PCP, there were no significant differences in same-day access to a different PCP, or access within 2 to 7 days with patients' usual PCP. Overall, patients had >92% continuity with their usual PCP at the hospital-based site regardless of PCP sessions/week. Patients of full-time PCPs reported timely appointments for urgent needs more often than patients of part-time PCPs (82% vs 71%; P < .01), but reported similar experiences with routine access and continuity. Part-time PCP performance appeared worse when using measures focused on same-day access to patients' usual PCP. However, clinic-level same-day access, same-week access to the usual PCP, and overall continuity were similar for patients of part-time and full-time PCPs. Measures of in-person access to a usual PCP do not capture alternate access approaches encouraged by PCMH, and often used by part-time providers, such as team-based or non-face-to-face care.

Effective Hubbard model for Helium atoms adsorbed on a graphite

NASA Astrophysics Data System (ADS)

Motoyama, Yuichi; Masaki-Kato, Akiko; Kawashima, Naoki

Helium atoms adsorbed on a graphite is a two-dimensional strongly correlated quantum system and it has been an attractive subject of research for a long time. A helium atom feels Lennard-Jones like potential (Aziz potential) from another one and corrugated potential from the graphite. Therefore, this system may be described by a hardcore Bose Hubbard model with the nearest neighbor repulsion on the triangular lattice, which is the dual lattice of the honeycomb lattice formed by carbons. A Hubbard model is easier to simulate than the original problem in continuous space, but we need to know the model parameters of the effective model, hopping constant t and interaction V. In this presentation, we will present an estimation of the model parameters from ab initio quantum Monte Carlo calculation in continuous space in addition to results of quantum Monte Carlo simulation for an obtained discrete model.

A novel approach of modeling continuous dark hydrogen fermentation.

PubMed

Alexandropoulou, Maria; Antonopoulou, Georgia; Lyberatos, Gerasimos

2018-02-01

In this study a novel modeling approach for describing fermentative hydrogen production in a continuous stirred tank reactor (CSTR) was developed, using the Aquasim modeling platform. This model accounts for the key metabolic reactions taking place in a fermentative hydrogen producing reactor, using fixed stoichiometry but different reaction rates. Biomass yields are determined based on bioenergetics. The model is capable of describing very well the variation in the distribution of metabolic products for a wide range of hydraulic retention times (HRT). The modeling approach is demonstrated using the experimental data obtained from a CSTR, fed with food industry waste (FIW), operating at different HRTs. The kinetic parameters were estimated through fitting to the experimental results. Hydrogen and total biogas production rates were predicted very well by the model, validating the basic assumptions regarding the implicated stoichiometric biochemical reactions and their kinetic rates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Why Is Past Depression the Best Predictor of Future Depression? Stress Generation as a Mechanism of Depression Continuity in Girls

ERIC Educational Resources Information Center

Rudolph, Karen D.; Flynn, Megan; Abaied, Jamie L.; Groot, Alison; Thompson, Renee

2009-01-01

This study examined whether a transactional interpersonal life stress model helps to explain the continuity in depression over time in girls. Youth (86 girls, 81 boys; M age = 12.41, SD = 1.19) and their caregivers participated in a three-wave longitudinal study. Depression and episodic life stress were assessed with semistructured interviews.…