Stochastic analysis of epidemics on adaptive time varying networks

Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an ``adaptive threshold,'' i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.

Radiatively inefficient accretion flow simulations with cooling: implications for black hole transients

We study the effects of optically thin radiative cooling on the structure of radiatively inefficient accretion flows (RIAFs). The flow structure is geometrically thick, and independent of the gas density and cooling, if the cooling time is longer than the viscous time-scale (i.e. t(cool) greater than or similar to t(visc)). For higher densities, the gas can cool before it can accrete and forms the standard geometrically thin, optically thick Shakura-Sunyaev disc. For usual cooling processes (such as bremsstrahlung), we expect an inner hot flow and an outer thin disc. For a short cooling time the accretion flow separates into two phases: a radiatively inefficient hot coronal phase and a cold thin disc. We argue that there is an upper limit on the density of the hot corona corresponding to a critical value of t(cool)/t(ff)( similar to 10-100), the ratio of the cooling time and the free-fall time. Based on our simulations, we have developed a model for transients observed in black hole X-ray binaries (XRBs). An XRB in a quiescent hot RIAF state can transition to a cold blackbody-dominated state because of an increase in the mass accretion rate. The transition from a thin disc to a RIAF happens because of mass exhaustion due to accretion; the transition happens when the cooling time becomes longer than the viscous time at inner radii. Since the viscous time-scale for a geometrically thin disc is quite long, the high-soft state is expected to be long-lived. The different time-scales in black hole transients correspond to different physical processes such as viscous evolution, cooling and free fall. Our model captures the overall features of observed state transitions in XRBs.

Evaluation of TRMM PR Sampling Error Over a Subtropical Basin Using Bootstrap Technique

Quantitative use of satellite-derived rainfall products for various scientific applications often requires them to be accompanied with an error estimate. Rainfall estimates inferred from low earth orbiting satellites like the Tropical Rainfall Measuring Mission (TRMM) will be subjected to sampling errors of nonnegligible proportions owing to the narrow swath of satellite sensors coupled with a lack of continuous coverage due to infrequent satellite visits. The authors investigate sampling uncertainty of seasonal rainfall estimates from the active sensor of TRMM, namely, Precipitation Radar (PR), based on 11 years of PR 2A25 data product over the Indian subcontinent. In this paper, a statistical bootstrap technique is investigated to estimate the relative sampling errors using the PR data themselves. Results verify power law scaling characteristics of relative sampling errors with respect to space-time scale of measurement. Sampling uncertainty estimates for mean seasonal rainfall were found to exhibit seasonal variations. To give a practical example of the implications of the bootstrap technique, PR relative sampling errors over a subtropical river basin of Mahanadi, India, are examined. Results reveal that the bootstrap technique incurs relative sampling errors < 33% (for the 2 degrees grid), < 36% (for the 1 degrees grid), < 45% (for the 0.5 degrees grid), and < 57% (for the 0.25 degrees grid). With respect to rainfall type, overall sampling uncertainty was found to be dominated by sampling uncertainty due to stratiform rainfall over the basin. The study compares resulting error estimates to those obtained from latin hypercube sampling. Based on this study, the authors conclude that the bootstrap approach can be successfully used for ascertaining relative sampling errors offered by TRMM-like satellites over gauged or ungauged basins lacking in situ validation data. This technique has wider implications for decision making before incorporating microwave orbital data products in basin-scale hydrologic modeling.

Use of Data Driven Techniques for Short Lead Time Streamflow Forecasting in Mahanadi basin

Streamflow forecasts at daily time scale are necessary for effective management of water resources systems. Typical applications include flood control, water quality management, water supply to multiple stakeholders, hydropower and irrigation systems. Conventionally physically based conceptual models and data-driven models are used for forecasting streamflows. Conceptual models require detailed understanding of physical processes governing the system being modeled. Major constraints in developing effective conceptual models are sparse hydrometric gauge network and short historical records that limit our understanding of physical processes. On the other hand, data-driven models rely solely on previous hydrological and meteorological data without directly taking into account the underlying physical processes. Among various data driven models Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs) are most widely used techniques. The present study assesses performance of ARIMA and ANNs methods in arriving at one-to seven-day ahead forecast of daily streamflows at Basantpur streamgauge site that is situated at upstream of Hirakud Dam in Mahanadi river basin, India. The ANNs considered include Feed-Forward back propagation Neural Network (FFNN) and Radial Basis Neural Network (RBNN). Daily streamflow forecasts at Basantpur site find use in management of water from Hirakud reservoir. (C) 2015 The Authors. Published by Elsevier B.V.

Pulse transit time differential measurement by fiber Bragg grating pulse recorder

The present study reports a noninvasive technique for the measurement of the pulse transit time differential (PTTD) from the pulse pressure waveforms obtained at the carotid artery and radial artery using fiber Bragg grating pulse recorders (FBGPR). PTTD is defined as the time difference between the arrivals of a pulse pressure waveform at the carotid and radial arterial sites. The PTTD is investigated as an indicator of variation in the systolic blood pressure. The results are validated against blood pressure variation obtained from a Mindray Patient Monitor. Furthermore, the pulse wave velocity computed from the obtained PTTD is compared with the pulse wave velocity obtained from the color Doppler ultrasound system and is found to be in good agreement. The major advantage of the PTTD measurement via FBGPRs is that the data acquisition system employed can simultaneously acquire pulse pressure waveforms from both FBGPRs placed at carotid and radial arterial sites with a single time scale, which eliminates time synchronization complexity. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

Time scales of the stick-slip dynamics of the peeling of an adhesive tape

The stick-slip dynamics of the peeling of an adhesive tape is characterized by bifurcations that have been experimentally well studied. In this work, we investigate the time scale in which the the stick-slips happen leading to the bifurcations. This is fundamental to understanding the triboluminescence and acoustic emissions associated with the bifurcations. We establish a relationship between the time scale of the bifurcations and the inherent mathematical structure of the peeling dynamics by studying a characteristic time quantity associated with the dynamics.

Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.

Burst event detection in wall turbulence by WVITA method

Wavelet Variable Interval Time Average (WVITA) is introduced as a method incorporating burst event detection in wall turbulence. Wavelet transform is performed to unfold the longitudinal fluctuating velocity time series measured in the near wall region of a turbulent boundary layer using hot-film anemometer. This unfolding is both in time and in space simultaneously. The splitted kinetic of the longitudinal fluctuating velocity time series among different scales is obtained by integrating the square of wavelet coefficient modulus over temporal space. The time scale that related to burst events in wall turbulence passing through the fixed probe is ascertained by maximum criterion of the kinetic energy evolution across scales. Wavelet transformed localized variance of the fluctuating velocity time series at the maximum kinetic scale is put forward instead of localized short time average variance in Variable Interval Time Average (VITA) scheme. The burst event detection result shows that WVITA scheme can avoid erroneous judgement and solve the grouping problem more effectively which is caused by VITA scheme itself and can not be avoided by adjusting the threshold level or changing the short time average interval.

Molecular Dynamics Simulation of Barnacle Cement

Barnacle cement is an underwater adhesive that is used for permanent settlement. Its main components are insoluble protein complexes that have not been fully studied. In present article, we chose two proteins of barnacle cement for study, 36-KD protein and Mrcp-100K protein. In order to investigate the characteristic of above two proteins, we introduced the method of molecular modeling. And the simulation package GROMACS was used to simulate the behavior of these proteins. In this article, before the simulations, we introduce some theories to predict the time scale for polymer relaxation. During the simulation, we mainly focus on two properties of these two proteins: structural stability and adhesive force to substrate. First, we simulate the structural stability of two proteins in water, and then the stability of 36-KD protein in seawater environment is investigated.We find that the stability varies in the different environments. Next, to study adhesive ability of two proteins, we simulate the process of peeling the two proteins from the substrate (graphite). Then, we analyze the main reasons of these results. We find that hydrogen bonds in proteins play an important role in the protein stability. In the process of the peeling, we use Lennard–Jones 12-6 potential to calculate the van der Waals interactions between proteins and substrate.

Effects Of Subgrid-Scale Modeling On Lagrangian Statistics In Large-Eddy Simulation

The application of large-eddy simulation (LES) to turbulent transport processes requires accurate prediction of the Lagrangian statistics of flow fields. However, in most existing SGS models, no explicit consideration is given to Lagrangian statistics. In this paper, we focus on the effects of SGS modeling on Lagrangian statistics in LES ranging from statistics determining single-particle dispersion to those of pair dispersion and multiparticle dispersion. Lagrangian statistics in homogeneous isotropic turbulence are extracted from direct numerical simulation (DNS) and the LES with a spectral eddy-viscosity model. For the case of longtime single-particle dispersion, it is shown that, compared to DNS, LES overpredicts the time scale of the Lagrangian velocity correlation but underpredicts the Lagrangian velocity fluctuation. These two effects tend to cancel one another leading to an accurate prediction of the longtime turbulent dispersion coefficient. Unlike the single-particle dispersion, LES tends to underestimate significantly the rate of relative dispersion of particle pairs and multiple-particles, when initial separation distances are less than the minimum resolved scale due to the lack of subgrid fluctuations. The overprediction of LES on the time scale of the Lagrangian velocity correlation is further confirmed by a theoretical analysis using a turbulence closure theory.

Two-time scales inner solutions and motion of a geostrophic vortex

Based on the authors' previous work, in this paper the systematical analyses on the motion and the inner solutions of a geostrophic vortex have been presented by means of thematched asymptotic expansion method with multiple time scales (S/gh001/2 and α S/gh001/2) and space scales. It has been shown that the leading inner solutions to the core structure in two-time scales analyses are identified with the results in normal one-time scale analyses. The time averages of the first-order solutions on short time variable τ are the same as the first-order solutions obtained in one normal time scale analyses. The geostrophic vortex induces an oscillatory motion in addition to moving with the background flow. The period, amplitude andthe deviation from the mean trajectory depend on the core structure and the initial conditions. The velocity of the motion of vortex center varies periodically and the time average of the velocity on short time variable τ is equal to the value of the local mean velocity.

Molecular Dynamics Simulation of Barnacle Cement

Barnacle cement is an underwater adhesive that is used for permanent settlement. Its main components are insoluble protein complexes that have not been fully studied. In present article, we chose two proteins of barnacle cement for study, 36-KD protein and Mrcp-100K protein. In order to investigate the characteristic of above two proteins, we introduced the method of molecular modeling. And the simulation package GROMACS was used to simulate the behavior of these proteins. In this article, before the simulations, we introduce some theories to predict the time scale for polymer relaxation. During the simulation, we mainly focus on two properties of these two proteins: structural stability and adhesive force to substrate. First, we simulate the structural stability of two proteins in water, and then the stability of 36-KD protein in seawater environment is investigated. We find that the stability varies in the different environments. Next, to study adhesive ability of two proteins, we simulate the process of peeling the two proteins from the substrate (graphite). Then, we analyze the main reasons of these results. We find that hydrogen bonds in proteins play an important role in the protein stability. In the process of the peeling, we use Lennard-Jones 12-6 potential to calculate the van der Waals interactions between proteins and substrate.

“Deborah Numbers”, Coupling Multiple Space and Time Scales and Governing Damage Evolution to Failure

Two different spatial levels are involved concerning damage accumulation to eventual failure. nucleation and growth rates of microdamage nN* and V*. It is found that the trans-scale length ratio c*/L does not directly affect the process. Instead, two independent dimensionless numbers: the trans-scale one * * ( V*)including the * **5 * N c V including mesoscopic parameters only, play the key role in the process of damage accumulation to failure. The above implies that there are three time scales involved in the process: the macroscopic imposed time scale tim = /a and two meso-scopic time scales, nucleation and growth of damage, (* *4) N N t =1 n c and tV=c*/V*. Clearly, the dimensionless number De*=tV/tim refers to the ratio of microdamage growth time scale over the macroscopically imposed time scale. So, analogous to the definition of Deborah number as the ratio of relaxation time over external one in rheology. Let De be the imposed Deborah number while De represents the competition and coupling between the microdamage growth and the macroscopically imposed wave loading. In stress-wave induced tensile failure (spallation) De* < 1, this means that microdamage has enough time to grow during the macroscopic wave loading. Thus, the microdamage growth appears to be the predominate mechanism governing the failure. Moreover, the dimensionless number D* = tV/tN characterizes the ratio of two intrinsic mesoscopic time scales: growth over nucleation. Similarly let D be the “intrinsic Deborah number”. Both time scales are relevant to intrinsic relaxation rather than imposed one. Furthermore, the intrinsic Deborah number D* implies a certain characteristic damage. In particular, it is derived that D* is a proper indicator of macroscopic critical damage to damage localization, like D* ∼ (10–3~10–2) in spallation. More importantly, we found that this small intrinsic Deborah number D* indicates the energy partition of microdamage dissipation over bulk plastic work. This explains why spallation can not be formulated by macroscopic energy criterion and must be treated by multi-scale analysis.

Science-based restoration monitoring of coastal habitats, Volume One: A framework for monitoring plans under the Estuaries and Clean Waters Act of 2000 (Public Law 160-457)

Executive Summary: The Estuary Restoration Act of 2000 (ERA), Title I of the Estuaries and Clean Waters Act of 2000, was created to promote the restoration of habitats along the coast of the United States (including the US protectorates and the Great Lakes). The NOAA National Centers for Coastal Ocean Science was charged with the development of a guidance manual for monitoring plans under this Act. This guidance manual, titled Science-Based Restoration Monitoring of Coastal Habitats, is written in two volumes. It provides technical assistance, outlines necessary steps, and provides useful tools for the development and implementation of sound scientific monitoring of coastal restoration efforts. In addition, this manual offers a means to detect early warnings that the restoration is on track or not, to gauge how well a restoration site is functioning, to coordinate projects and efforts for consistent and successful restoration, and to evaluate the ecological health of specific coastal habitats both before and after project completion (Galatowitsch et al. 1998). The following habitats have been selected for discussion in this manual: water column, rock bottom, coral reefs, oyster reefs, soft bottom, kelp and other macroalgae, rocky shoreline, soft shoreline, submerged aquatic vegetation, marshes, mangrove swamps, deepwater swamps, and riverine forests. The classification of habitats used in this document is generally based on that of Cowardin et al. (1979) in their Classification of Wetlands and Deepwater Habitats of the United States, as called for in the ERA Estuary Habitat Restoration Strategy. This manual is not intended to be a restoration monitoring “cookbook” that provides templates of monitoring plans for specific habitats. The interdependence of a large number of site-specific factors causes habitat types to vary in physical and biological structure within and between regions and geographic locations (Kusler and Kentula 1990). Monitoring approaches used should be tailored to these differences. However, even with the diversity of habitats that may need to be restored and the extreme geographic range across which these habitats occur, there are consistent principles and approaches that form a common basis for effective monitoring. Volume One, titled A Framework for Monitoring Plans under the Estuaries and Clean Waters Act of 2000, begins with definitions and background information. Topics such as restoration, restoration monitoring, estuaries, and the role of socioeconomics in restoration are discussed. In addition, the habitats selected for discussion in this manual are briefly described. (PDF contains 116 pages)