Models, Entropy and Information of Temporal Social Networks

Science.gov (United States)

Zhao, Kun; Karsai, Márton; Bianconi, Ginestra

Temporal social networks are characterized by heterogeneous duration of contacts, which can either follow a power-law distribution, such as in face-to-face interactions, or a Weibull distribution, such as in mobile-phone communication. Here we model the dynamics of face-to-face interaction and mobile phone communication by a reinforcement dynamics, which explains the data observed in these different types of social interactions. We quantify the information encoded in the dynamics of these networks by the entropy of temporal networks. Finally, we show evidence that human dynamics is able to modulate the information present in social network dynamics when it follows circadian rhythms and when it is interfacing with a new technology such as the mobile-phone communication technology.

DynaPop-X: A population dynamics model applied to spatio-temporal exposure assessment - Implementation aspects from the CRISMA project

Science.gov (United States)

Aubrecht, Christoph; Steinnocher, Klaus; Humer, Heinrich; Huber, Hermann

2014-05-01

In the context of proactive disaster risk as well as immediate situational crisis management knowledge of locational social aspects in terms of spatio-temporal population distribution dynamics is considered among the most important factors for disaster impact minimization (Aubrecht et al., 2013a). This applies to both the pre-event stage for designing appropriate preparedness measures and to acute crisis situations when an event chain actually unfolds for efficient situation-aware response. The presented DynaPop population dynamics model is developed at the interface of those interlinked crisis stages and aims at providing basic input for social impact evaluation and decision support in crisis management. The model provides the starting point for assessing population exposure dynamics - thus here labeled as DynaPop-X - which can either be applied in a sense of illustrating the changing locations and numbers of affected people at different stages during an event or as ex-ante estimations of probable and maximum expected clusters of affected population (Aubrecht et al., 2013b; Freire & Aubrecht, 2012). DynaPop is implemented via a gridded spatial disaggregation approach and integrates previous efforts on spatio-temporal modeling that account for various aspects of population dynamics such as human mobility and activity patterns that are particularly relevant in picturing the highly dynamic daytime situation (Ahola et al., 2007; Bhaduri, 2008; Cockings et al., 2010). We will present ongoing developments particularly focusing on the implementation logic of the model using the emikat software tool, a data management system initially designed for inventorying and analysis of spatially resolved regional air pollutant emission scenarios. This study was performed in the framework of the EU CRISMA project. CRISMA is funded from the European Community's Seventh Framework Programme FP7/2007-2013 under grant agreement no. 284552. REFERENCES Ahola, T., Virrantaus, K., Krisp, J

Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques.

Science.gov (United States)

Chang, Fi-John; Chen, Pin-An; Chang, Li-Chiu; Tsai, Yu-Hsuan

2016-08-15

This study attempts to model the spatio-temporal dynamics of total phosphate (TP) concentrations along a river for effective hydro-environmental management. We propose a systematical modeling scheme (SMS), which is an ingenious modeling process equipped with a dynamic neural network and three refined statistical methods, for reliably predicting the TP concentrations along a river simultaneously. Two different types of artificial neural network (BPNN-static neural network; NARX network-dynamic neural network) are constructed in modeling the dynamic system. The Dahan River in Taiwan is used as a study case, where ten-year seasonal water quality data collected at seven monitoring stations along the river are used for model training and validation. Results demonstrate that the NARX network can suitably capture the important dynamic features and remarkably outperforms the BPNN model, and the SMS can effectively identify key input factors, suitably overcome data scarcity, significantly increase model reliability, satisfactorily estimate site-specific TP concentration at seven monitoring stations simultaneously, and adequately reconstruct seasonal TP data into a monthly scale. The proposed SMS can reliably model the dynamic spatio-temporal water pollution variation in a river system for missing, hazardous or costly data of interest. Copyright © 2016 Elsevier B.V. All rights reserved.

Dynamic perfusion patterns in temporal lobe epilepsy

International Nuclear Information System (INIS)

Dupont, Patrick; Paesschen, Wim van; Zaknun, John J.; Maes, Alex; Tepmongkol, Supatporn; Locharernkul, Chaichon; Vasquez, Silvia; Carpintiero, Silvina; Bal, C.S.; Dondi, Maurizio

2009-01-01

To investigate dynamic ictal perfusion changes during temporal lobe epilepsy (TLE). We investigated 37 patients with TLE by ictal and interictal SPECT. All ictal injections were performed within 60 s of seizure onset. Statistical parametric mapping was used to analyse brain perfusion changes and temporal relationships with injection time and seizure duration as covariates. The analysis revealed significant ictal hyperperfusion in the ipsilateral temporal lobe extending to subcortical regions. Hypoperfusion was observed in large extratemporal areas. There were also significant dynamic changes in several extratemporal regions: ipsilateral orbitofrontal and bilateral superior frontal gyri and the contralateral cerebellum and ipsilateral striatum. The study demonstrated early dynamic perfusion changes in extratemporal regions probably involved in both propagation of epileptic activity and initiation of inhibitory mechanisms. (orig.)

Dynamic perfusion patterns in temporal lobe epilepsy

Energy Technology Data Exchange (ETDEWEB)

Dupont, Patrick; Paesschen, Wim van [KU Leuven/UZ Gasthuisberg, Nuclear Medicine, Medical Imaging Center and Neurology, Leuven (Belgium); Zaknun, John J. [International Atomic Energy Agency (IAEA), Nuclear Medicine Section, Division of Human Health, Wagramer Strasse 5, PO BOX 200, Vienna (Austria); University Hospital of Innsbruck, Department of Nuclear Medicine, Innsbruck (Austria); Maes, Alex [KU Leuven/UZ Gasthuisberg, Nuclear Medicine, Medical Imaging Center and Neurology, Leuven (Belgium); AZ Groeninge, Nuclear Medicine, Kortrijk (Belgium); Tepmongkol, Supatporn; Locharernkul, Chaichon [Chulalongkorn University, Nuclear Medicine and Neurology, Bangkok (Thailand); Vasquez, Silvia; Carpintiero, Silvina [Fleni Instituto de Investigaciones Neurologicas, Nuclear Medicine, Buenos Aires (Argentina); Bal, C.S. [All India Institute of Medical Sciences, Nuclear Medicine, New Delhi (India); Dondi, Maurizio [International Atomic Energy Agency (IAEA), Nuclear Medicine Section, Division of Human Health, Wagramer Strasse 5, PO BOX 200, Vienna (Austria); Ospedale Maggiore, Nuclear Medicine, Bologna (Italy)

2009-05-15

To investigate dynamic ictal perfusion changes during temporal lobe epilepsy (TLE). We investigated 37 patients with TLE by ictal and interictal SPECT. All ictal injections were performed within 60 s of seizure onset. Statistical parametric mapping was used to analyse brain perfusion changes and temporal relationships with injection time and seizure duration as covariates. The analysis revealed significant ictal hyperperfusion in the ipsilateral temporal lobe extending to subcortical regions. Hypoperfusion was observed in large extratemporal areas. There were also significant dynamic changes in several extratemporal regions: ipsilateral orbitofrontal and bilateral superior frontal gyri and the contralateral cerebellum and ipsilateral striatum. The study demonstrated early dynamic perfusion changes in extratemporal regions probably involved in both propagation of epileptic activity and initiation of inhibitory mechanisms. (orig.)

Nonlinear Spatio-Temporal Dynamics and Chaos in Semiconductors

Science.gov (United States)

Schöll, Eckehard

2005-08-01

Nonlinear transport phenomena are an increasingly important aspect of modern semiconductor research. This volume deals with complex nonlinear dynamics, pattern formation, and chaotic behavior in such systems. It bridges the gap between two well-established fields: the theory of dynamic systems and nonlinear charge transport in semiconductors. This unified approach helps reveal important electronic transport instabilities. The initial chapters lay a general framework for the theoretical description of nonlinear self-organized spatio-temporal patterns, such as current filaments, field domains, fronts, and analysis of their stability. Later chapters consider important model systems in detail: impact ionization induced impurity breakdown, Hall instabilities, superlattices, and low-dimensional structures. State-of-the-art results include chaos control, spatio-temporal chaos, multistability, pattern selection, activator-inhibitor kinetics, and global coupling, linking fundamental issues to electronic device applications. This book will be of great value to semiconductor physicists and nonlinear scientists alike.

Estimating spatio-temporal dynamics of size-structured populations

DEFF Research Database (Denmark)

Kristensen, Kasper; Thygesen, Uffe Høgsbro; Andersen, Ken Haste

2014-01-01

with simple stock dynamics, to estimate simultaneously how size distributions and spatial distributions develop in time. We demonstrate the method for a cod population sampled by trawl surveys. Particular attention is paid to correlation between size classes within each trawl haul due to clustering...... of individuals with similar size. The model estimates growth, mortality and reproduction, after which any aspect of size-structure, spatio-temporal population dynamics, as well as the sampling process can be probed. This is illustrated by two applications: 1) tracking the spatial movements of a single cohort...

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences.

Science.gov (United States)

Jung, Minju; Hwang, Jungsik; Tani, Jun

2015-01-01

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns.

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

Science.gov (United States)

Novosad, Philip; Reader, Andrew J.

2016-06-01

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral

Spatio-temporal dynamics of security investments in an interdependent risk environment

Science.gov (United States)

Shafi, Kamran; Bender, Axel; Zhong, Weicai; Abbass, Hussein A.

2012-10-01

In a globalised world where risks spread through contagion, the decision of an entity to invest in securing its premises from stochastic risks no longer depends solely on its own actions but also on the actions of other interacting entities in the system. This phenomenon is commonly seen in many domains including airline, logistics and computer security and is referred to as Interdependent Security (IDS). An IDS game models this decision problem from a game-theoretic perspective and deals with the behavioural dynamics of risk-reduction investments in such settings. This paper enhances this model and investigates the spatio-temporal aspects of the IDS games. The spatio-temporal dynamics are studied using simple replicator dynamics on a variety of network structures and for various security cost tradeoffs that lead to different Nash equilibria in an IDS game. The simulation results show that the neighbourhood configuration has a greater effect on the IDS game dynamics than network structure. An in-depth empirical analysis of game dynamics is carried out on regular graphs, which leads to the articulation of necessary and sufficient conditions for dominance in IDS games under spatial constraints.

Temporal networks

CERN Document Server

Saramäki, Jari

2013-01-01

The concept of temporal networks is an extension of complex networks as a modeling framework to include information on when interactions between nodes happen. Many studies of the last decade examine how the static network structure affect dynamic systems on the network. In this traditional approach  the temporal aspects are pre-encoded in the dynamic system model. Temporal-network methods, on the other hand, lift the temporal information from the level of system dynamics to the mathematical representation of the contact network itself. This framework becomes particularly useful for cases where there is a lot of structure and heterogeneity both in the timings of interaction events and the network topology. The advantage compared to common static network approaches is the ability to design more accurate models in order to explain and predict large-scale dynamic phenomena (such as, e.g., epidemic outbreaks and other spreading phenomena). On the other hand, temporal network methods are mathematically and concept...

Temporal fidelity in dynamic social networks

DEFF Research Database (Denmark)

Stopczynski, Arkadiusz; Sapiezynski, Piotr; Pentland, Alex ‘Sandy’

2015-01-01

of the network dynamics can be used to inform the process of measuring social networks. The details of measurement are of particular importance when considering dynamic processes where minute-to-minute details are important, because collection of physical proximity interactions with high temporal resolution...

Spatio-temporal dynamics of the tropical rain forest

Energy Technology Data Exchange (ETDEWEB)

Chave, J. [CEN Saclay, Gif-sur-Yvette (France). Service de Physique de l' Etat Condense

2000-07-01

Mechanisms which drive the dynamics of forest ecosystems are complex, from seedling establishment to pollination, and seed dispersal by animals, running water or wind. These processes are more complex when the ecosystem shelters a large number of species and of vegetative forms, as it is the case in the tropical rainforest. To take them into account, we must develop and use models. I present a review of the fundamental mechanisms for the of a natural forest dynamics - photosynthesis, tree growth, recruitment and mortality - as well as a description of the past and of the present of tropical rainforests. This information is used to develop a spatially-explicit and individual-based forest model. Simplified models are deduced from it, and they serve to address more specific issues, such as the resilience of the forest to climate disturbances, or savanna-forest dynamics. The last topic is related to the spatio-temporal description of tropical plant biodiversity. A detailed introduction to the problem is provided, and models accounting for the maintenance of diversity are compared. These models include non spatial as well a spatial approaches (branching anihilating random walks and voter model with mutation). (orig.)

Quantifying temporal trends in fisheries abundance using Bayesian dynamic linear models: A case study of riverine Smallmouth Bass populations

Science.gov (United States)

Schall, Megan K.; Blazer, Vicki S.; Lorantas, Robert M.; Smith, Geoffrey; Mullican, John E.; Keplinger, Brandon J.; Wagner, Tyler

2018-01-01

Detecting temporal changes in fish abundance is an essential component of fisheries management. Because of the need to understand short‐term and nonlinear changes in fish abundance, traditional linear models may not provide adequate information for management decisions. This study highlights the utility of Bayesian dynamic linear models (DLMs) as a tool for quantifying temporal dynamics in fish abundance. To achieve this goal, we quantified temporal trends of Smallmouth Bass Micropterus dolomieu catch per effort (CPE) from rivers in the mid‐Atlantic states, and we calculated annual probabilities of decline from the posterior distributions of annual rates of change in CPE. We were interested in annual declines because of recent concerns about fish health in portions of the study area. In general, periods of decline were greatest within the Susquehanna River basin, Pennsylvania. The declines in CPE began in the late 1990s—prior to observations of fish health problems—and began to stabilize toward the end of the time series (2011). In contrast, many of the other rivers investigated did not have the same magnitude or duration of decline in CPE. Bayesian DLMs provide information about annual changes in abundance that can inform management and are easily communicated with managers and stakeholders.

3D City Models with Different Temporal Characteristica

DEFF Research Database (Denmark)

Bodum, Lars

2005-01-01

traditional static city models and those models that are built for realtime applications. The difference between the city models applies both to the spatial modelling and also when using the phenomenon time in the models. If the city models are used in visualizations without any variation in time or when......-built dynamic or a model suitable for visualization in realtime, it is required that modelling is done with level-of-detail and simplification of both the aesthetics and the geometry. If a temporal characteristic is combined with a visual characteristic, the situation can easily be seen as a t/v matrix where t...... is the temporal characteristic or representation and v is the visual characteristic or representation....

A Hybrid Approach Combining the Multi-Temporal Scale Spatio-Temporal Network with the Continuous Triangular Model for Exploring Dynamic Interactions in Movement Data: A Case Study of Football

Directory of Open Access Journals (Sweden)

Pengdong Zhang

2018-01-01

Full Text Available Benefiting from recent advantages in location-aware technologies, movement data are becoming ubiquitous. Hence, numerous research topics with respect to movement data have been undertaken. Yet, the research of dynamic interactions in movement data is still in its infancy. In this paper, we propose a hybrid approach combining the multi-temporal scale spatio-temporal network (MTSSTN and the continuous triangular model (CTM for exploring dynamic interactions in movement data. The approach mainly includes four steps: first, the relative trajectory calculus (RTC is used to derive three types of interaction patterns; second, for each interaction pattern, a corresponding MTSSTN is generated; third, for each MTSSTN, the interaction intensity measures and three centrality measures (i.e., degree, betweenness and closeness are calculated; finally, the results are visualized at multiple temporal scales using the CTM and analyzed based on the generated CTM diagrams. Based on the proposed approach, three distinctive aims can be achieved for each interaction pattern at multiple temporal scales: (1 exploring the interaction intensities between any two individuals; (2 exploring the interaction intensities among multiple individuals, and (3 exploring the importance of each individual and identifying the most important individuals. The movement data obtained from a real football match are used as a case study to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach is useful in exploring dynamic interactions in football movement data and discovering insightful information.

Measuring and modeling the temporal dynamics of nitrogen balance in an experimental-scale paddy field

Science.gov (United States)

Tseng, C.; Lin, Y.

2013-12-01

Nitrogen balance involves many mechanisms and plays an important role to maintain the function of nature. Fertilizer application in agriculture activity is usually seen as a common and significant nitrogen input to environment. Improper fertilizer application on paddy field can result in great amount of various types of nitrogen losses. Hence, it is essential to understand and quantify the nitrogen dynamics in paddy field for fertilizer management and pollution control. In this study, we develop a model which considers major transformation processes of nitrogen (e.g. volatilization, nitrification, denitrification and plant uptake). In addition, we measured different types of nitrogen in plants, soil and water at plant growth stages in an experimental-scale paddy field in Taiwan. The measurement includes total nitrogen in plants and soil, and ammonium-N (NH4+-N), nitrate-N (NO3--N) and organic nitrogen in water. The measured data were used to calibrate the model parameters and validate the model for nitrogen balance simulation. The results showed that the model can accurately estimate the temporal dynamics of nitrogen balance in paddy field during the whole growth stage. This model might be helpful and useful for future fertilizer management and pollution control in paddy field.

A Temporal Domain Decomposition Algorithmic Scheme for Large-Scale Dynamic Traffic Assignment

Directory of Open Access Journals (Sweden)

Eric J. Nava

2012-03-01

This paper presents a temporal decomposition scheme for large spatial- and temporal-scale dynamic traffic assignment, in which the entire analysis period is divided into Epochs. Vehicle assignment is performed sequentially in each Epoch, thus improving the model scalability and confining the peak run-time memory requirement regardless of the total analysis period. A proposed self-turning scheme adaptively searches for the run-time-optimal Epoch setting during iterations regardless of the characteristics of the modeled network. Extensive numerical experiments confirm the promising performance of the proposed algorithmic schemes.

Dynamic CRM occupancy reflects a temporal map of developmental progression.

Science.gov (United States)

Wilczyński, Bartek; Furlong, Eileen E M

2010-06-22

Development is driven by tightly coordinated spatio-temporal patterns of gene expression, which are initiated through the action of transcription factors (TFs) binding to cis-regulatory modules (CRMs). Although many studies have investigated how spatial patterns arise, precise temporal control of gene expression is less well understood. Here, we show that dynamic changes in the timing of CRM occupancy is a prevalent feature common to all TFs examined in a developmental ChIP time course to date. CRMs exhibit complex binding patterns that cannot be explained by the sequence motifs or expression of the TFs themselves. The temporal changes in TF binding are highly correlated with dynamic patterns of target gene expression, which in turn reflect transitions in cellular function during different stages of development. Thus, it is not only the timing of a TF's expression, but also its temporal occupancy in refined time windows, which determines temporal gene expression. Systematic measurement of dynamic CRM occupancy may therefore serve as a powerful method to decode dynamic changes in gene expression driving developmental progression.

Dynamics of temporally localized states in passively mode-locked semiconductor lasers

Science.gov (United States)

Schelte, C.; Javaloyes, J.; Gurevich, S. V.

2018-05-01

We study the emergence and the stability of temporally localized structures in the output of a semiconductor laser passively mode locked by a saturable absorber in the long-cavity regime. For large yet realistic values of the linewidth enhancement factor, we disclose the existence of secondary dynamical instabilities where the pulses develop regular and subsequent irregular temporal oscillations. By a detailed bifurcation analysis we show that additional solution branches that consist of multipulse (molecules) solutions exist. We demonstrate that the various solution curves for the single and multipeak pulses can splice and intersect each other via transcritical bifurcations, leading to a complex web of solutions. Our analysis is based on a generic model of mode locking that consists of a time-delayed dynamical system, but also on a much more numerically efficient, yet approximate, partial differential equation. We compare the results of the bifurcation analysis of both models in order to assess up to which point the two approaches are equivalent. We conclude our analysis by the study of the influence of group velocity dispersion, which is only possible in the framework of the partial differential equation model, and we show that it may have a profound impact on the dynamics of the localized states.

Research on Process-oriented Spatio-temporal Data Model

Directory of Open Access Journals (Sweden)

XUE Cunjin

2016-02-01

Full Text Available According to the analysis of the present status and existing problems of spatio-temporal data models developed in last 20 years,this paper proposes a process-oriented spatio-temporal data model (POSTDM,aiming at representing,organizing and storing continuity and gradual geographical entities. The dynamic geographical entities are graded and abstracted into process objects series from their intrinsic characteristics,which are process objects,process stage objects,process sequence objects and process state objects. The logical relationships among process entities are further studied and the structure of UML models and storage are also designed. In addition,through the mechanisms of continuity and gradual changes impliedly recorded by process objects,and the modes of their procedure interfaces offered by the customized ObjcetStorageTable,the POSTDM can carry out process representation,storage and dynamic analysis of continuity and gradual geographic entities. Taking a process organization and storage of marine data as an example,a prototype system (consisting of an object-relational database and a functional analysis platform is developed for validating and evaluating the model's practicability.

The temporal dynamics of speeded decision making

NARCIS (Netherlands)

Dutilh, G.

2012-01-01

This dissertation sheds light on the temporal dynamics of behavior in speeded decision making. Participants on reaction time (RT) tasks learn, get distracted, speed up, slow down, get confused, get bored, and eventually may start guessing. One can safely say that participants' behavior is dynamic.

Iterative analysis of cerebrovascular reactivity dynamic response by temporal decomposition.

Science.gov (United States)

van Niftrik, Christiaan Hendrik Bas; Piccirelli, Marco; Bozinov, Oliver; Pangalu, Athina; Fisher, Joseph A; Valavanis, Antonios; Luft, Andreas R; Weller, Michael; Regli, Luca; Fierstra, Jorn

2017-09-01

To improve quantitative cerebrovascular reactivity (CVR) measurements and CO 2 arrival times, we present an iterative analysis capable of decomposing different temporal components of the dynamic carbon dioxide- Blood Oxygen-Level Dependent (CO 2 -BOLD) relationship. Decomposition of the dynamic parameters included a redefinition of the voxel-wise CO 2 arrival time, and a separation from the vascular response to a stepwise increase in CO 2 (Delay to signal Plateau - DTP) and a decrease in CO 2 (Delay to signal Baseline -DTB). Twenty-five (normal) datasets, obtained from BOLD MRI combined with a standardized pseudo-square wave CO 2 change, were co-registered to generate reference atlases for the aforementioned dynamic processes to score the voxel-by-voxel deviation probability from normal range. This analysis is further illustrated in two subjects with unilateral carotid artery occlusion using these reference atlases. We have found that our redefined CO 2 arrival time resulted in the best data fit. Additionally, excluding both dynamic BOLD phases (DTP and DTB) resulted in a static CVR, that is maximal response, defined as CVR calculated only over a normocapnic and hypercapnic calibrated plateau. Decomposition and novel iterative modeling of different temporal components of the dynamic CO 2 -BOLD relationship improves quantitative CVR measurements.

A full time-domain approach to spatio-temporal dynamics of semiconductor lasers. II. Spatio-temporal dynamics

Science.gov (United States)

Böhringer, Klaus; Hess, Ortwin

The spatio-temporal dynamics of novel semiconductor lasers is discussed on the basis of a space- and momentum-dependent full time-domain approach. To this means the space-, time-, and momentum-dependent Full-Time Domain Maxwell Semiconductor Bloch equations, derived and discussed in our preceding paper I [K. Böhringer, O. Hess, A full time-domain approach to spatio-temporal dynamics of semiconductor lasers. I. Theoretical formulation], are solved by direct numerical integration. Focussing on the device physics of novel semiconductor lasers that profit, in particular, from recent advances in nanoscience and nanotechnology, we discuss the examples of photonic band edge surface emitting lasers (PBE-SEL) and semiconductor disc lasers (SDLs). It is demonstrated that photonic crystal effects can be obtained for finite crystal structures, and leading to a significant improvement in laser performance such as reduced lasing thresholds. In SDLs, a modern device concept designed to increase the power output of surface-emitters in combination with near-diffraction-limited beam quality, we explore the complex interplay between the intracavity optical fields and the quantum well gain material in SDL structures. Our simulations reveal the dynamical balance between carrier generation due to pumping into high energy states, momentum relaxation of carriers, and stimulated recombination from states near the band edge. Our full time-domain approach is shown to also be an excellent framework for the modelling of the interaction of high-intensity femtosecond and picosecond pulses with semiconductor nanostructures. It is demonstrated that group velocity dispersion, dynamical gain saturation and fast self-phase modulation (SPM) are the main causes for the induced changes and asymmetries in the amplified pulse shape and spectrum of an ultrashort high-intensity pulse. We attest that the time constants of the intraband scattering processes are critical to gain recovery. Moreover, we present

Temporal variability in phosphorus transfers: classifying concentration–discharge event dynamics

Directory of Open Access Journals (Sweden)

P. Haygarth

2004-01-01

Full Text Available The importance of temporal variability in relationships between phosphorus (P concentration (Cp and discharge (Q is linked to a simple means of classifying the circumstances of Cp–Q relationships in terms of functional types of response. New experimental data at the upstream interface of grassland soil and catchment systems at a range of scales (lysimeters to headwaters in England and Australia are used to demonstrate the potential of such an approach. Three types of event are defined as Types 1–3, depending on whether the relative change in Q exceeds the relative change in Cp (Type 1, whether Cp and Q are positively inter-related (Type 2 and whether Cp varies yet Q is unchanged (Type 3. The classification helps to characterise circumstances that can be explained mechanistically in relation to (i the scale of the study (with a tendency towards Type 1 in small scale lysimeters, (ii the form of P with a tendency for Type 1 for soluble (i.e., p–Q relationships that can be developed further to contribute to future models of P transfer and delivery from slope to stream. Studies that evaluate the temporal dynamics of the transfer of P are currently grossly under-represented in comparison with models based on static/spatial factors. Keywords: phosphorus, concentration, discharge, lysimeters, temporal dynamics, overland flow

Modelling spatio-temporal variability of Mytilus edulis (L.) growth by forcing a dynamic energy budget model with satellite-derived environmental data

Science.gov (United States)

Thomas, Yoann; Mazurié, Joseph; Alunno-Bruscia, Marianne; Bacher, Cédric; Bouget, Jean-François; Gohin, Francis; Pouvreau, Stéphane; Struski, Caroline

2011-11-01

In order to assess the potential of various marine ecosystems for shellfish aquaculture and to evaluate their carrying capacities, there is a need to clarify the response of exploited species to environmental variations using robust ecophysiological models and available environmental data. For a large range of applications and comparison purposes, a non-specific approach based on 'generic' individual growth models offers many advantages. In this context, we simulated the response of blue mussel ( Mytilus edulis L.) to the spatio-temporal fluctuations of the environment in Mont Saint-Michel Bay (North Brittany) by forcing a generic growth model based on Dynamic Energy Budgets with satellite-derived environmental data (i.e. temperature and food). After a calibration step based on data from mussel growth surveys, the model was applied over nine years on a large area covering the entire bay. These simulations provide an evaluation of the spatio-temporal variability in mussel growth and also show the ability of the DEB model to integrate satellite-derived data and to predict spatial and temporal growth variability of mussels. Observed seasonal, inter-annual and spatial growth variations are well simulated. The large-scale application highlights the strong link between food and mussel growth. The methodology described in this study may be considered as a suitable approach to account for environmental effects (food and temperature variations) on physiological responses (growth and reproduction) of filter feeders in varying environments. Such physiological responses may then be useful for evaluating the suitability of coastal ecosystems for shellfish aquaculture.

The Financial Accounting Model from a System Dynamics' Perspective

OpenAIRE

Melse, Eric

2006-01-01

This paper explores the foundation of the financial accounting model. We examine the properties of the accounting equation as the principal algorithm for the design and the development of a System Dynamics model. Key to the perspective is the foundational requirement that resolves the temporal conflict that resides in a stock and flow model. Through formal analysis the accounting equation is redefined as a cybernetic model by expressing the temporal and dynamic properties of its terms. Articu...

Spatial and Temporal Dynamics and Value of Nature-Based Recreation, Estimated via Social Media.

Science.gov (United States)

Sonter, Laura J; Watson, Keri B; Wood, Spencer A; Ricketts, Taylor H

2016-01-01

Conserved lands provide multiple ecosystem services, including opportunities for nature-based recreation. Managing this service requires understanding the landscape attributes underpinning its provision, and how changes in land management affect its contribution to human wellbeing over time. However, evidence from both spatially explicit and temporally dynamic analyses is scarce, often due to data limitations. In this study, we investigated nature-based recreation within conserved lands in Vermont, USA. We used geotagged photographs uploaded to the photo-sharing website Flickr to quantify visits by in-state and out-of-state visitors, and we multiplied visits by mean trip expenditures to show that conserved lands contributed US $1.8 billion (US $0.18-20.2 at 95% confidence) to Vermont's tourism industry between 2007 and 2014. We found eight landscape attributes explained the pattern of visits to conserved lands; visits were higher in larger conserved lands, with less forest cover, greater trail density and more opportunities for snow sports. Some of these attributes differed from those found in other locations, but all aligned with our understanding of recreation in Vermont. We also found that using temporally static models to inform conservation decisions may have perverse outcomes for nature-based recreation. For example, static models suggest conserved land with less forest cover receive more visits, but temporally dynamic models suggest clearing forests decreases, rather than increases, visits to these sites. Our results illustrate the importance of understanding both the spatial and temporal dynamics of ecosystem services for conservation decision-making.

Using the relational event model (REM) to investigate the temporal dynamics of animal social networks.

Science.gov (United States)

Tranmer, Mark; Marcum, Christopher Steven; Morton, F Blake; Croft, Darren P; de Kort, Selvino R

2015-03-01

Social dynamics are of fundamental importance in animal societies. Studies on nonhuman animal social systems often aggregate social interaction event data into a single network within a particular time frame. Analysis of the resulting network can provide a useful insight into the overall extent of interaction. However, through aggregation, information is lost about the order in which interactions occurred, and hence the sequences of actions over time. Many research hypotheses relate directly to the sequence of actions, such as the recency or rate of action, rather than to their overall volume or presence. Here, we demonstrate how the temporal structure of social interaction sequences can be quantified from disaggregated event data using the relational event model (REM). We first outline the REM, explaining why it is different from other models for longitudinal data, and how it can be used to model sequences of events unfolding in a network. We then discuss a case study on the European jackdaw, Corvus monedula , in which temporal patterns of persistence and reciprocity of action are of interest, and present and discuss the results of a REM analysis of these data. One of the strengths of a REM analysis is its ability to take into account different ways in which data are collected. Having explained how to take into account the way in which the data were collected for the jackdaw study, we briefly discuss the application of the model to other studies. We provide details of how the models may be fitted in the R statistical software environment and outline some recent extensions to the REM framework.

An investigation of temporal regularization techniques for dynamic PET reconstructions using temporal splines

International Nuclear Information System (INIS)

Verhaeghe, Jeroen; D'Asseler, Yves; Vandenberghe, Stefaan; Staelens, Steven; Lemahieu, Ignace

2007-01-01

The use of a temporal B-spline basis for the reconstruction of dynamic positron emission tomography data was investigated. Maximum likelihood (ML) reconstructions using an expectation maximization framework and maximum A-posteriori (MAP) reconstructions using the generalized expectation maximization framework were evaluated. Different parameters of the B-spline basis of such as order, number of basis functions and knot placing were investigated in a reconstruction task using simulated dynamic list-mode data. We found that a higher order basis reduced both the bias and variance. Using a higher number of basis functions in the modeling of the time activity curves (TACs) allowed the algorithm to model faster changes of the TACs, however, the TACs became noisier. We have compared ML, Gaussian postsmoothed ML and MAP reconstructions. The noise level in the ML reconstructions was controlled by varying the number of basis functions. The MAP algorithm penalized the integrated squared curvature of the reconstructed TAC. The postsmoothed ML was always outperformed in terms of bias and variance properties by the MAP and ML reconstructions. A simple adaptive knot placing strategy was also developed and evaluated. It is based on an arc length redistribution scheme during the reconstruction. The free knot reconstruction allowed a more accurate reconstruction while reducing the noise level especially for fast changing TACs such as blood input functions. Limiting the number of temporal basis functions combined with the adaptive knot placing strategy is in this case advantageous for regularization purposes when compared to the other regularization techniques

Spatial and Temporal Dynamics and Value of Nature-Based Recreation, Estimated via Social Media

Science.gov (United States)

Watson, Keri B.; Wood, Spencer A.; Ricketts, Taylor H.

2016-01-01

Conserved lands provide multiple ecosystem services, including opportunities for nature-based recreation. Managing this service requires understanding the landscape attributes underpinning its provision, and how changes in land management affect its contribution to human wellbeing over time. However, evidence from both spatially explicit and temporally dynamic analyses is scarce, often due to data limitations. In this study, we investigated nature-based recreation within conserved lands in Vermont, USA. We used geotagged photographs uploaded to the photo-sharing website Flickr to quantify visits by in-state and out-of-state visitors, and we multiplied visits by mean trip expenditures to show that conserved lands contributed US $1.8 billion (US $0.18–20.2 at 95% confidence) to Vermont’s tourism industry between 2007 and 2014. We found eight landscape attributes explained the pattern of visits to conserved lands; visits were higher in larger conserved lands, with less forest cover, greater trail density and more opportunities for snow sports. Some of these attributes differed from those found in other locations, but all aligned with our understanding of recreation in Vermont. We also found that using temporally static models to inform conservation decisions may have perverse outcomes for nature-based recreation. For example, static models suggest conserved land with less forest cover receive more visits, but temporally dynamic models suggest clearing forests decreases, rather than increases, visits to these sites. Our results illustrate the importance of understanding both the spatial and temporal dynamics of ecosystem services for conservation decision-making. PMID:27611325

Near equilibrium dynamics and one-dimensional spatial—temporal structures of polar active liquid crystals

International Nuclear Information System (INIS)

Yang Xiao-Gang; Wang Qi; Forest, M. Gregory

2014-01-01

We systematically explore near equilibrium, flow-driven, and flow-activity coupled dynamics of polar active liquid crystals using a continuum model. Firstly, we re-derive the hydrodynamic model to ensure the thermodynamic laws are obeyed and elastic stresses and forces are consistently accounted. We then carry out a linear stability analysis about constant steady states to study near equilibrium dynamics around the steady states, revealing long-wave instability inherent in this model system and how active parameters in the model affect the instability. We then study model predictions for one-dimensional (1D) spatial—temporal structures of active liquid crystals in a channel subject to physical boundary conditions. We discuss the model prediction in two selected regimes, one is the viscous stress dominated regime, also known as the flow-driven regime, while the other is the full regime, in which all active mechanisms are included. In the viscous stress dominated regime, the polarity vector is driven by the prescribed flow field. Dynamics depend sensitively on the physical boundary condition and the type of the driven flow field. Bulk-dominated temporal periodic states and spatially homogeneous states are possible under weak anchoring conditions while spatially inhomogeneous states exist under strong anchoring conditions. In the full model, flow-orientation interaction generates a host of planar as well as out-of-plane spatial—temporal structures related to the spontaneous flows due to the molecular self-propelled motion. These results provide contact with the recent literature on active nematic suspensions. In addition, symmetry breaking patterns emerge as the additional active viscous stress due to the polarity vector is included in the force balance. The inertia effect is found to limit the long-time survival of spatial structures to those with small wave numbers, i.e., an asymptotic coarsening to long wave structures. A rich set of mechanisms for generating

Spatio-temporal patterns in simple models of marine systems

Science.gov (United States)

Feudel, U.; Baurmann, M.; Gross, T.

2009-04-01

Spatio-temporal patterns in marine systems are a result of the interaction of population dynamics with physical transport processes. These physical transport processes can be either diffusion processes in marine sediments or in the water column. We study the dynamics of one population of bacteria and its nutrient in in a simplified model of a marine sediments, taking into account that the considered bacteria possess an active as well as an inactive state, where activation is processed by signal molecules. Furthermore the nutrients are transported actively by bioirrigation and passively by diffusion. It is shown that under certain conditions Turing patterns can occur which yield heterogeneous spatial patterns of the species. The influence of bioirrigation on Turing patterns leads to the emergence of ''hot spots``, i.e. localized regions of enhanced bacterial activity. All obtained patterns fit quite well to observed patterns in laboratory experiments. Spatio-temporal patterns appear in a predator-prey model, used to describe plankton dynamics. These patterns appear due to the simultaneous emergence of Turing patterns and oscillations in the species abundance in the neighborhood of a Turing-Hopf bifurcation. We observe a large variety of different patterns where i) stationary heterogeneous patterns (e.g. hot and cold spots) compete with spatio-temporal patterns ii) slowly moving patterns are embedded in an oscillatory background iii) moving fronts and spiral waves appear.

Spatio-temporal pattern formation, fractals, and chaos in conceptual ecological models as applied to coupled plankton-fish dynamics

International Nuclear Information System (INIS)

Medvinskii, Aleksandr B; Tikhonova, Irina A; Tikhonov, D A; Ivanitskii, Genrikh R; Petrovskii, Sergei V; Li, B.-L.; Venturino, E; Malchow, H

2002-01-01

The current turn-of-the-century period witnesses the intensive use of the bioproducts of the World Ocean while at the same time calling for precautions to preserve its ecological stability. This requires that biophysical processes in aquatic systems be comprehensively explored and new methods for monitoring their dynamics be developed. While aquatic and terrestrial ecosystems have much in common in terms of their mathematical description, there are essential differences between them. For example, the mobility of oceanic plankton is mainly controlled by diffusion processes, whereas terrestrial organisms naturally enough obey totally different laws. This paper is focused on the processes underlying the dynamics of spatially inhomogeneous plankton communities. We demonstrate that conceptual reaction-diffusion mathematical models are an appropriate tool for investigating both complex spatio-temporal plankton dynamics and the fractal properties of planktivorous fish school walks. (reviews of topical problems)

Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.

Science.gov (United States)

Kasabov, Nikola; Dhoble, Kshitij; Nuntalid, Nuttapod; Indiveri, Giacomo

2013-05-01

On-line learning and recognition of spatio- and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as Spike-Timing Dependent Plasticity (STDP) and its variant Spike Driven Synaptic Plasticity (SDSP), can also be used to learn spatio-temporal representations, but they usually require many iterations in an unsupervised or semi-supervised mode of learning. This paper introduces a new class of eSNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode. The paper also introduces a new model called deSNN, that utilises rank-order learning and SDSP spike-time learning in unsupervised, supervised, or semi-supervised modes. The SDSP learning is used to evolve dynamically the network changing connection weights that capture spatio-temporal spike data clusters both during training and during recall. The new deSNN model is first illustrated on simple examples and then applied on two case study applications: (1) moving object recognition using address-event representation (AER) with data collected using a silicon retina device; (2) EEG SSTD recognition for brain-computer interfaces. The deSNN models resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank-order or STDP learning. The reason is that the deSNN makes use of both the information contained in the order of the first input spikes

The Models of Inter-temporal Consume

Directory of Open Access Journals (Sweden)

Nora Mihail

2007-10-01

Full Text Available The articol presents a category of consumption models which shows the manner how theexpenses of consume in an economy are related to the available income achieved by this economy and theinterest rate from the financial market. Since the income as well as the expenses of consume are realizedin time, such dynamic models of consume are also referred to as models of inter-temporal consume,emphasizing therefore the fact that the available income achieved at a certain moment may be used forconsume at a future moment, whereas the decision of consume taken at a current moment may consider theincome that is to be achieved in the future.

Dynamic PET image reconstruction integrating temporal regularization associated with respiratory motion correction for applications in oncology

Science.gov (United States)

Merlin, Thibaut; Visvikis, Dimitris; Fernandez, Philippe; Lamare, Frédéric

2018-02-01

Respiratory motion reduces both the qualitative and quantitative accuracy of PET images in oncology. This impact is more significant for quantitative applications based on kinetic modeling, where dynamic acquisitions are associated with limited statistics due to the necessity of enhanced temporal resolution. The aim of this study is to address these drawbacks, by combining a respiratory motion correction approach with temporal regularization in a unique reconstruction algorithm for dynamic PET imaging. Elastic transformation parameters for the motion correction are estimated from the non-attenuation-corrected PET images. The derived displacement matrices are subsequently used in a list-mode based OSEM reconstruction algorithm integrating a temporal regularization between the 3D dynamic PET frames, based on temporal basis functions. These functions are simultaneously estimated at each iteration, along with their relative coefficients for each image voxel. Quantitative evaluation has been performed using dynamic FDG PET/CT acquisitions of lung cancer patients acquired on a GE DRX system. The performance of the proposed method is compared with that of a standard multi-frame OSEM reconstruction algorithm. The proposed method achieved substantial improvements in terms of noise reduction while accounting for loss of contrast due to respiratory motion. Results on simulated data showed that the proposed 4D algorithms led to bias reduction values up to 40% in both tumor and blood regions for similar standard deviation levels, in comparison with a standard 3D reconstruction. Patlak parameter estimations on reconstructed images with the proposed reconstruction methods resulted in 30% and 40% bias reduction in the tumor and lung region respectively for the Patlak slope, and a 30% bias reduction for the intercept in the tumor region (a similar Patlak intercept was achieved in the lung area). Incorporation of the respiratory motion correction using an elastic model along with a

The temporal-relevance temporal-uncertainty model of prospective duration judgment.

Science.gov (United States)

Zakay, Dan

2015-12-15

A model aimed at explaining prospective duration judgments in real life settings (as well as in the laboratory) is presented. The model is based on the assumption that situational meaning is continuously being extracted by humans' perceptual and cognitive information processing systems. Time is one of the important dimensions of situational meaning. Based on the situational meaning, a value for Temporal Relevance is set. Temporal Relevance reflects the importance of temporal aspects for enabling adaptive behavior in a specific moment in time. When Temporal Relevance is above a certain threshold a prospective duration judgment process is evoked automatically. In addition, a search for relevant temporal information is taking place and its outcomes determine the level of Temporal Uncertainty which reflects the degree of knowledge one has regarding temporal aspects of the task to be performed. The levels of Temporal Relevance and Temporal Uncertainty determine the amount of attentional resources allocated for timing by the executive system. The merit of the model is in connecting timing processes with the ongoing general information processing stream. The model rests on findings in various domains which indicate that cognitive-relevance and self-relevance are powerful determinants of resource allocation policy. The feasibility of the model is demonstrated by analyzing various temporal phenomena. Suggestions for further empirical validation of the model are presented. Copyright © 2015 Elsevier Inc. All rights reserved.

Noise-induced temporal dynamics in Turing systems

KAUST Repository

Schumacher, Linus J.; Woolley, Thomas E.; Baker, Ruth E.

2013-01-01

We examine the ability of intrinsic noise to produce complex temporal dynamics in Turing pattern formation systems, with particular emphasis on the Schnakenberg kinetics. Using power spectral methods, we characterize the behavior of the system using

Modelling estimation and analysis of dynamic processes from image sequences using temporal random closed sets and point processes with application to the cell exocytosis and endocytosis

OpenAIRE

Díaz Fernández, Ester

2010-01-01

In this thesis, new models and methodologies are introduced for the analysis of dynamic processes characterized by image sequences with spatial temporal overlapping. The spatial temporal overlapping exists in many natural phenomena and should be addressed properly in several Science disciplines such as Microscopy, Material Sciences, Biology, Geostatistics or Communication Networks. This work is related to the Point Process and Random Closed Set theories, within Stochastic Ge...

Temporal networks

Science.gov (United States)

Holme, Petter; Saramäki, Jari

2012-10-01

A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names-temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered

Semantics of Temporal Models with Multiple Temporal Dimensions

DEFF Research Database (Denmark)

Kraft, Peter; Sørensen, Jens Otto

ending up with lexical data models. In particular we look upon the representations by sets of normalised tables, by sets of 1NF tables and by sets of N1NF/nested tables. At each translation step we focus on how the temporal semantic is consistently maintained. In this way we recognise the requirements...... for representation of temporal properties in different models and the correspondence between the models. The results rely on the assumptions that the temporal dimensions are interdependent and ordered. Thus for example the valid periods of existences of a property in a mini world are dependent on the transaction...... periods in which the corresponding recordings are valid. This is not the normal way of looking at temporal dimensions and we give arguments supporting our assumption....

A customized light sheet microscope to measure spatio-temporal protein dynamics in small model organisms.

Directory of Open Access Journals (Sweden)

Matthias Rieckher

Full Text Available We describe a customizable and cost-effective light sheet microscopy (LSM platform for rapid three-dimensional imaging of protein dynamics in small model organisms. The system is designed for high acquisition speeds and enables extended time-lapse in vivo experiments when using fluorescently labeled specimens. We demonstrate the capability of the setup to monitor gene expression and protein localization during ageing and upon starvation stress in longitudinal studies in individual or small groups of adult Caenorhabditis elegans nematodes. The system is equipped to readily perform fluorescence recovery after photobleaching (FRAP, which allows monitoring protein recovery and distribution under low photobleaching conditions. Our imaging platform is designed to easily switch between light sheet microscopy and optical projection tomography (OPT modalities. The setup permits monitoring of spatio-temporal expression and localization of ageing biomarkers of subcellular size and can be conveniently adapted to image a wide range of small model organisms and tissue samples.

Modeling dynamic swarms

KAUST Repository

Ghanem, Bernard

2013-01-01

This paper proposes the problem of modeling video sequences of dynamic swarms (DSs). We define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS, we present a probabilistic model that learns both the spatial layout of swarm elements (based on low-level image segmentation) and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally. We assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework, we iterate between learning the spatial layout of the swarm and its dynamics. We learn the swarm transformations using ICM, which iterates between estimating these transformations and updating their distribution in the spatiotemporal neighborhoods. We demonstrate the validity of our method by conducting experiments on real and synthetic video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data. © 2012 Elsevier Inc. All rights reserved.

Bursts and heavy tails in temporal and sequential dynamics of foraging decisions.

Directory of Open Access Journals (Sweden)

Kanghoon Jung

2014-08-01

Full Text Available A fundamental understanding of behavior requires predicting when and what an individual will choose. However, the actual temporal and sequential dynamics of successive choices made among multiple alternatives remain unclear. In the current study, we tested the hypothesis that there is a general bursting property in both the timing and sequential patterns of foraging decisions. We conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period. Regarding when choices were made, we found bursts of rapidly occurring actions, separated by time-varying inactive periods, partially based on a circadian rhythm. Regarding what was chosen, we found sequential dynamics in affective choices characterized by two key features: (a a highly biased choice distribution; and (b preferential attachment, in which the animals were more likely to choose what they had previously chosen. To capture the temporal dynamics, we propose a dual-state model consisting of active and inactive states. We also introduce a satiation-attainment process for bursty activity, and a non-homogeneous Poisson process for longer inactivity between bursts. For the sequential dynamics, we propose a dual-control model consisting of goal-directed and habit systems, based on outcome valuation and choice history, respectively. This study provides insights into how the bursty nature of behavior emerges from the interaction of different underlying systems, leading to heavy tails in the distribution of behavior over time and choices.

Bursts and Heavy Tails in Temporal and Sequential Dynamics of Foraging Decisions

Science.gov (United States)

Jung, Kanghoon; Jang, Hyeran; Kralik, Jerald D.; Jeong, Jaeseung

2014-01-01

A fundamental understanding of behavior requires predicting when and what an individual will choose. However, the actual temporal and sequential dynamics of successive choices made among multiple alternatives remain unclear. In the current study, we tested the hypothesis that there is a general bursting property in both the timing and sequential patterns of foraging decisions. We conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period. Regarding when choices were made, we found bursts of rapidly occurring actions, separated by time-varying inactive periods, partially based on a circadian rhythm. Regarding what was chosen, we found sequential dynamics in affective choices characterized by two key features: (a) a highly biased choice distribution; and (b) preferential attachment, in which the animals were more likely to choose what they had previously chosen. To capture the temporal dynamics, we propose a dual-state model consisting of active and inactive states. We also introduce a satiation-attainment process for bursty activity, and a non-homogeneous Poisson process for longer inactivity between bursts. For the sequential dynamics, we propose a dual-control model consisting of goal-directed and habit systems, based on outcome valuation and choice history, respectively. This study provides insights into how the bursty nature of behavior emerges from the interaction of different underlying systems, leading to heavy tails in the distribution of behavior over time and choices. PMID:25122498

Temporal nonlinear beam dynamics in infiltrated photonic crystal fibers

DEFF Research Database (Denmark)

Bennet, Francis; Rosberg, Christian Romer; Neshev, Dragomir N.

Liquid-infiltrated photonic crystal fibers (PCFs) offer a new way of studying light propagation in periodic and discrete systems. A wide range of available fiber structures combined with the ease of infiltration opens up a range of novel experimental opportunities for optical detection and bio...... the evolution of the fiber output beam in the few micro or milliseconds after the beam is turned on. The characterization of the temporal behavior of the thermal nonlinear response provides important information about the nonlocality associated with heat diffusion inside the fiber, thus enabling studies of long...... and technological potential of liquid-infiltrated PCFs it is important to understand the temporal dynamics of nonlinear beam propagation in such structures. In this work we consider thermally induced spatial nonlinear effects in infiltrated photonic crystal fibers. We experimentally study the temporal dynamics...

An advanced analysis and modelling the air pollutant concentration temporal dynamics in atmosphere of the industrial cities: Odessa city

Science.gov (United States)

Buyadzhi, V. V.; Glushkov, A. V.; Khetselius, O. Yu; Ternovsky, V. B.; Serga, I. N.; Bykowszczenko, N.

2017-10-01

Results of analysis and modelling the air pollutant (dioxide of nitrogen) concentration temporal dynamics in atmosphere of the industrial city Odessa are presented for the first time and based on computing by nonlinear methods of the chaos and dynamical systems theories. A chaotic behaviour is discovered and investigated. To reconstruct the corresponding strange chaotic attractor, the time delay and embedding dimension are computed. The former is determined by the methods of autocorrelation function and average mutual information, and the latter is calculated by means of correlation dimension method and algorithm of false nearest neighbours. It is shown that low-dimensional chaos exists in the nitrogen dioxide concentration time series under investigation. Further, the Lyapunov’s exponents spectrum, Kaplan-Yorke dimension and Kolmogorov entropy are computed.

Dynamic models in research and management of biological invasions.

Science.gov (United States)

Buchadas, Ana; Vaz, Ana Sofia; Honrado, João P; Alagador, Diogo; Bastos, Rita; Cabral, João A; Santos, Mário; Vicente, Joana R

2017-07-01

Invasive species are increasing in number, extent and impact worldwide. Effective invasion management has thus become a core socio-ecological challenge. To tackle this challenge, integrating spatial-temporal dynamics of invasion processes with modelling approaches is a promising approach. The inclusion of dynamic processes in such modelling frameworks (i.e. dynamic or hybrid models, here defined as models that integrate both dynamic and static approaches) adds an explicit temporal dimension to the study and management of invasions, enabling the prediction of invasions and optimisation of multi-scale management and governance. However, the extent to which dynamic approaches have been used for that purpose is under-investigated. Based on a literature review, we examined the extent to which dynamic modelling has been used to address invasions worldwide. We then evaluated how the use of dynamic modelling has evolved through time in the scope of invasive species management. The results suggest that modelling, in particular dynamic modelling, has been increasingly applied to biological invasions, especially to support management decisions at local scales. Also, the combination of dynamic and static modelling approaches (hybrid models with a spatially explicit output) can be especially effective, not only to support management at early invasion stages (from prevention to early detection), but also to improve the monitoring of invasion processes and impact assessment. Further development and testing of such hybrid models may well be regarded as a priority for future research aiming to improve the management of invasions across scales. Copyright © 2017 Elsevier Ltd. All rights reserved.

A model of nematode dynamics in the Westerschelde estuary

NARCIS (Netherlands)

Li, J.; Vincx, M.; Herman, P.M.J.

1996-01-01

We developed a time dynamic model to investigate the temporal dynamics of nematode community in the brackish zone of the Westerschelde Estuary. The biomass of four nematode feeding groups observed from March 1991 to February 1992 is used to calibrate the model. Using environmental data as the input,

Temporal dynamics of all-optical switching in Photonic Crystal Cavity

DEFF Research Database (Denmark)

Colman, Pierre; Heuck, Mikkel; Yu, Yi

2014-01-01

The temporal dynamics of all-optical switching has been investigated in a Photonic Crystal Cavity with a 150fs-40aJ/pulse resolution. This allowed observing for the first time effects like pulse reshaping, pulse delay and intra-cavity Four-Wave-Mixing.......The temporal dynamics of all-optical switching has been investigated in a Photonic Crystal Cavity with a 150fs-40aJ/pulse resolution. This allowed observing for the first time effects like pulse reshaping, pulse delay and intra-cavity Four-Wave-Mixing....

Temporal diagnostic analysis of the SWAT model to detect dominant periods of poor model performance

Science.gov (United States)

Guse, Björn; Reusser, Dominik E.; Fohrer, Nicola

2013-04-01

Hydrological models generally include thresholds and non-linearities, such as snow-rain-temperature thresholds, non-linear reservoirs, infiltration thresholds and the like. When relating observed variables to modelling results, formal methods often calculate performance metrics over long periods, reporting model performance with only few numbers. Such approaches are not well suited to compare dominating processes between reality and model and to better understand when thresholds and non-linearities are driving model results. We present a combination of two temporally resolved model diagnostic tools to answer when a model is performing (not so) well and what the dominant processes are during these periods. We look at the temporal dynamics of parameter sensitivities and model performance to answer this question. For this, the eco-hydrological SWAT model is applied in the Treene lowland catchment in Northern Germany. As a first step, temporal dynamics of parameter sensitivities are analyzed using the Fourier Amplitude Sensitivity test (FAST). The sensitivities of the eight model parameters investigated show strong temporal variations. High sensitivities were detected for two groundwater (GW_DELAY, ALPHA_BF) and one evaporation parameters (ESCO) most of the time. The periods of high parameter sensitivity can be related to different phases of the hydrograph with dominances of the groundwater parameters in the recession phases and of ESCO in baseflow and resaturation periods. Surface runoff parameters show high parameter sensitivities in phases of a precipitation event in combination with high soil water contents. The dominant parameters give indication for the controlling processes during a given period for the hydrological catchment. The second step included the temporal analysis of model performance. For each time step, model performance was characterized with a "finger print" consisting of a large set of performance measures. These finger prints were clustered into

Economic agglomerations and spatio-temporal cycles in a spatial growth model with capital transport cost

Science.gov (United States)

Juchem Neto, J. P.; Claeyssen, J. C. R.; Pôrto Júnior, S. S.

2018-03-01

In this paper we introduce capital transport cost in a unidimensional spatial Solow-Swan model of economic growth with capital-induced labor migration, considered in an unbounded domain. Proceeding with a stability analysis, we show that there is a critical value for the capital transport cost where the dynamic behavior of the economy changes, provided that the intensity of capital-induced labor migration is strong enough. On the one hand, if the capital transport cost is higher than this critical value, the spatially homogeneous equilibrium of coexistence of the model is stable, and the economy converges to this spatially homogeneous state in the long run; on the other hand, if transport cost is lower than this critical value, the equilibrium is unstable, and the economy may develop different spatio-temporal dynamics, including the formation of stable economic agglomerations and spatio-temporal economic cycles, depending on the other parameters in the model. Finally, numerical simulations support the results of the stability analysis, and illustrate the spatio-temporal dynamics generated by the model, suggesting that the economy as a whole benefits from the formation of economic agglomerations and cycles, with a higher capital transport cost reducing this gain.

Emergent dynamics of spatio-temporal chaos in a heterogeneous excitable medium.

Science.gov (United States)

Bittihn, Philip; Berg, Sebastian; Parlitz, Ulrich; Luther, Stefan

2017-09-01

Self-organized activation patterns in excitable media such as spiral waves and spatio-temporal chaos underlie dangerous cardiac arrhythmias. While the interaction of single spiral waves with different types of heterogeneity has been studied extensively, the effect of heterogeneity on fully developed spatio-temporal chaos remains poorly understood. We investigate how the complexity and stability properties of spatio-temporal chaos in the Bär-Eiswirth model of excitable media depend on the heterogeneity of the underlying medium. We employ different measures characterizing the chaoticity of the system and find that the spatial arrangement of multiple discrete lower excitability regions has a strong impact on the complexity of the dynamics. Varying the number, shape, and spatial arrangement of the heterogeneities, we observe strong emergent effects ranging from increases in chaoticity to the complete cessation of chaos, contrasting the expectation from the homogeneous behavior. The implications of our findings for the development and treatment of arrhythmias in the heterogeneous cardiac muscle are discussed.

Effects of dynamic-range compression on temporal acuity

DEFF Research Database (Denmark)

Wiinberg, Alan; Jepsen, Morten Løve; Epp, Bastian

2016-01-01

Some of the challenges that hearing-aid listeners experience with speech perception in complex acoustic environments may originate from limitations in the temporal processing of sounds. To systematically investigate the influence of hearing impairment and hearing-aid signal processing on temporal...... processing, temporal modulation transfer functions (TMTFs) and “supra-threshold” modulation-depth discrimination (MDD) thresholds were obtained in normal-hearing (NH) and hearing-impaired (HI) listeners with and without wide-dynamic range compression (WDRC). The TMTFs were obtained using tonal carriers of 1...... with the physical compression of the modulation depth due to the WDRC. Indications of reduced temporal resolution in the HI listeners were observed in the TMTF patterns for the 5 kHz carrier. Significantly higher MDD thresholds were found for the HI group relative to the NH group. No relationship was found between...

A Computational Model Based on Multi-Regional Calcium Imaging Represents the Spatio-Temporal Dynamics in a Caenorhabditis elegans Sensory Neuron.

Directory of Open Access Journals (Sweden)

Masahiro Kuramochi

Full Text Available Due to the huge number of neuronal cells in the brain and their complex circuit formation, computer simulation of neuronal activity is indispensable to understanding whole brain dynamics. Recently, various computational models have been developed based on whole-brain calcium imaging data. However, these analyses monitor only the activity of neuronal cell bodies and treat the cells as point unit. This point-neuron model is inexpensive in computational costs, but the model is unrealistically simplistic at representing intact neural activities in the brain. Here, we describe a novel three-unit Ordinary Differential Equation (ODE model based on the neuronal responses derived from a Caenorhabditis elegans salt-sensing neuron. We recorded calcium responses in three regions of the ASER neuron using a simple downstep of NaCl concentration. Our simple ODE model generated from a single recording can adequately reproduce and predict the temporal responses of each part of the neuron to various types of NaCl concentration changes. Our strategy which combines a simple recording data and an ODE mathematical model may be extended to realistically understand whole brain dynamics by computational simulation.

Face processing regions are sensitive to distinct aspects of temporal sequence in facial dynamics.

Science.gov (United States)

Reinl, Maren; Bartels, Andreas

2014-11-15

Facial movement conveys important information for social interactions, yet its neural processing is poorly understood. Computational models propose that shape- and temporal sequence sensitive mechanisms interact in processing dynamic faces. While face processing regions are known to respond to facial movement, their sensitivity to particular temporal sequences has barely been studied. Here we used fMRI to examine the sensitivity of human face-processing regions to two aspects of directionality in facial movement trajectories. We presented genuine movie recordings of increasing and decreasing fear expressions, each of which were played in natural or reversed frame order. This two-by-two factorial design matched low-level visual properties, static content and motion energy within each factor, emotion-direction (increasing or decreasing emotion) and timeline (natural versus artificial). The results showed sensitivity for emotion-direction in FFA, which was timeline-dependent as it only occurred within the natural frame order, and sensitivity to timeline in the STS, which was emotion-direction-dependent as it only occurred for decreased fear. The occipital face area (OFA) was sensitive to the factor timeline. These findings reveal interacting temporal sequence sensitive mechanisms that are responsive to both ecological meaning and to prototypical unfolding of facial dynamics. These mechanisms are temporally directional, provide socially relevant information regarding emotional state or naturalness of behavior, and agree with predictions from modeling and predictive coding theory. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Temporal information encoding in dynamic memristive devices

Energy Technology Data Exchange (ETDEWEB)

Ma, Wen; Chen, Lin; Du, Chao; Lu, Wei D., E-mail: wluee@eecs.umich.edu [Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109 (United States)

2015-11-09

We show temporal and frequency information can be effectively encoded in memristive devices with inherent short-term dynamics. Ag/Ag{sub 2}S/Pd based memristive devices with low programming voltage (∼100 mV) were fabricated and tested. At weak programming conditions, the devices exhibit inherent decay due to spontaneous diffusion of the Ag atoms. When the devices were subjected to pulse train inputs emulating different spiking patterns, the switching probability distribution function diverges from the standard Poisson distribution and evolves according to the input pattern. The experimentally observed switching probability distributions and the associated cumulative probability functions can be well-explained using a model accounting for the short-term decay effects. Such devices offer an intriguing opportunity to directly encode neural signals for neural information storage and analysis.

Kronecker-ARX models in identifying (2D) spatial-temporal systems

NARCIS (Netherlands)

Sinquin, B.; Verhaegen, M.H.G.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

2017-01-01

In this paper we address the identification of (2D) spatial-temporal dynamical systems governed by the Vector Auto-Regressive (VAR) form. The coefficient-matrices of the VAR model are parametrized as sums of Kronecker products. When the number of terms in the sum is small compared to the size of

Spatio-temporal dynamics of a pulsed microwave argon plasma: ignition and afterglow

International Nuclear Information System (INIS)

Carbone, Emile; Sadeghi, Nader; Vos, Erik; Hübner, Simon; Van Veldhuizen, Eddie; Van Dijk, Jan; Nijdam, Sander; Kroesen, Gerrit

2015-01-01

In this paper, a detailed investigation of the spatio-temporal dynamics of a pulsed microwave plasma is presented. The plasma is ignited inside a dielectric tube in a repetitively pulsed regime at pressures ranging from 1 up to 100 mbar with pulse repetition frequencies from 200 Hz up to 500 kHz. Various diagnostic techniques are employed to obtain the main plasma parameters both spatially and with high temporal resolution. Thomson scattering is used to obtain the electron density and mean electron energy at fixed positions in the dielectric tube. The temporal evolution of the two resonant and two metastable argon 4s states are measured by laser diode absorption spectroscopy. Nanosecond time-resolved imaging of the discharge allows us to follow the spatio-temporal evolution of the discharge with high temporal and spatial resolution. Finally, the temporal evolution of argon 4p and higher states is measured by optical emission spectroscopy. The combination of these various diagnostics techniques gives deeper insight on the plasma dynamics during pulsed microwave plasma operation from low to high pressure regimes. The effects of the pulse repetition frequency on the plasma ignition dynamics are discussed and the plasma-off time is found to be the relevant parameter for the observed ignition modes. Depending on the delay between two plasma pulses, the dynamics of the ionization front are found to be changing dramatically. This is also reflected in the dynamics of the electron density and temperature and argon line emission from the plasma. On the other hand, the (quasi) steady state properties of the plasma are found to depend only weakly on the pulse repetition frequency and the afterglow kinetics present an uniform spatio-temporal behavior. However, compared to continuous operation, the time-averaged metastable and resonant state 4s densities are found to be significantly larger around a few kHz pulsing frequency. (paper)

A temporal interpolation approach for dynamic reconstruction in perfusion CT

International Nuclear Information System (INIS)

Montes, Pau; Lauritsch, Guenter

2007-01-01

This article presents a dynamic CT reconstruction algorithm for objects with time dependent attenuation coefficient. Projection data acquired over several rotations are interpreted as samples of a continuous signal. Based on this idea, a temporal interpolation approach is proposed which provides the maximum temporal resolution for a given rotational speed of the CT scanner. Interpolation is performed using polynomial splines. The algorithm can be adapted to slow signals, reducing the amount of data acquired and the computational cost. A theoretical analysis of the approximations made by the algorithm is provided. In simulation studies, the temporal interpolation approach is compared with three other dynamic reconstruction algorithms based on linear regression, linear interpolation, and generalized Parker weighting. The presented algorithm exhibits the highest temporal resolution for a given sampling interval. Hence, our approach needs less input data to achieve a certain quality in the reconstruction than the other algorithms discussed or, equivalently, less x-ray exposure and computational complexity. The proposed algorithm additionally allows the possibility of using slow rotating scanners for perfusion imaging purposes

Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models

OpenAIRE

David Ebert

2006-01-01

One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created ar...

Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

Science.gov (United States)

Ma, Xiaoke; Sun, Penggang; Wang, Yu

2018-04-01

Many networks derived from society and nature are temporal and incomplete. The temporal link prediction problem in networks is to predict links at time T + 1 based on a given temporal network from time 1 to T, which is essential to important applications. The current algorithms either predict the temporal links by collapsing the dynamic networks or collapsing features derived from each network, which are criticized for ignoring the connection among slices. to overcome the issue, we propose a novel graph regularized nonnegative matrix factorization algorithm (GrNMF) for the temporal link prediction problem without collapsing the dynamic networks. To obtain the feature for each network from 1 to t, GrNMF factorizes the matrix associated with networks by setting the rest networks as regularization, which provides a better way to characterize the topological information of temporal links. Then, the GrNMF algorithm collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

Temporal dimension in cognitive models

International Nuclear Information System (INIS)

Decortis, F.; Cacciabue, P.C.

1988-01-01

Increased attention has been given to the role of humans in nuclear power plant safety, but one aspect seldom considered is the temporal dimension of human reasoning. Time is recognized as crucial in human reasoning and has been the subject of empirical studies where cognitive mechanisms and strategies to face the temporal dimension have been studied. The present study shows why temporal reasoning is essential in Human Reliability Analysis and how it could be introduced in a human model. Accounting for the time dimension in human behaviour is discussed first, with reference to proven field studies. Then, theoretical modelling of the temporal dimension in human reasoning and its relevance in simulation of cognitive activities of plant operator is discussed. Finally a Time Experience Model is presented

Temporal rainfall estimation using input data reduction and model inversion

Science.gov (United States)

Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.

2016-12-01

Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

Science.gov (United States)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of

Spatial-temporal data model and fractal analysis of transportation network in GIS environment

Science.gov (United States)

Feng, Yongjiu; Tong, Xiaohua; Li, Yangdong

2008-10-01

How to organize transportation data characterized by multi-time, multi-scale, multi-resolution and multi-source is one of the fundamental problems of GIS-T development. A spatial-temporal data model for GIS-T is proposed based on Spatial-temporal- Object Model. Transportation network data is systemically managed using dynamic segmentation technologies. And then a spatial-temporal database is built to integrally store geographical data of multi-time for transportation. Based on the spatial-temporal database, functions of spatial analysis of GIS-T are substantively extended. Fractal module is developed to improve the analyzing in intensity, density, structure and connectivity of transportation network based on the validation and evaluation of topologic relation. Integrated fractal with GIS-T strengthens the functions of spatial analysis and enriches the approaches of data mining and knowledge discovery of transportation network. Finally, the feasibility of the model and methods are tested thorough Guangdong Geographical Information Platform for Highway Project.

A non-parametric hierarchical model to discover behavior dynamics from tracks

NARCIS (Netherlands)

Kooij, J.F.P.; Englebienne, G.; Gavrila, D.M.

2012-01-01

We present a novel non-parametric Bayesian model to jointly discover the dynamics of low-level actions and high-level behaviors of tracked people in open environments. Our model represents behaviors as Markov chains of actions which capture high-level temporal dynamics. Actions may be shared by

UTP and Temporal Logic Model Checking

Science.gov (United States)

Anderson, Hugh; Ciobanu, Gabriel; Freitas, Leo

In this paper we give an additional perspective to the formal verification of programs through temporal logic model checking, which uses Hoare and He Unifying Theories of Programming (UTP). Our perspective emphasizes the use of UTP designs, an alphabetised relational calculus expressed as a pre/post condition pair of relations, to verify state or temporal assertions about programs. The temporal model checking relation is derived from a satisfaction relation between the model and its properties. The contribution of this paper is that it shows a UTP perspective to temporal logic model checking. The approach includes the notion of efficiency found in traditional model checkers, which reduced a state explosion problem through the use of efficient data structures

Integrating Future Land Use Scenarios to Evaluate the Spatio-Temporal Dynamics of Landscape Ecological Security

Directory of Open Access Journals (Sweden)

Yi Lu

2016-11-01

Full Text Available Urban ecological security is the basic principle of national ecological security. However, analyses of the spatial and temporal dynamics of ecological security remain limited, especially those that consider different scenarios of urban development. In this study, an integrated method is proposed that combines the Conversion of Land Use and its Effects (CLUE-S model with the Pressure–State–Response (P-S-R framework to assess landscape ecological security (LES in Huangshan City, China under two scenarios. Our results suggest the following conclusions: (1 the spatial and temporal dynamics of ecological security are closely related to the urbanization process; (2 although the average values of landscape ecological security are similar under different scenarios, the areas of relatively high security levels vary considerably; and (3 spatial heterogeneity in ecological security exists between different districts and counties, and the city center and its vicinity may face relatively serious declines in ecological security in the future. Overall, the proposed method not only illustrates the spatio-temporal dynamics of landscape ecological security under different scenarios but also reveals the anthropogenic effects on ecosystems by differentiating between causes, effects, and human responses at the landscape scale. This information is of great significance to decision-makers for future urban planning and management.

Discrete dynamic modeling of cellular signaling networks.

Science.gov (United States)

Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

Temporal dynamics of hippocampal neurogenesis in chronic neurodegeneration

Science.gov (United States)

Suzzi, Stefano; Vargas-Caballero, Mariana; Fransen, Nina L.; Al-Malki, Hussain; Cebrian-Silla, Arantxa; Garcia-Verdugo, Jose Manuel; Riecken, Kristoffer; Fehse, Boris; Perry, V. Hugh

2014-01-01

The study of neurogenesis during chronic neurodegeneration is crucial in order to understand the intrinsic repair mechanisms of the brain, and key to designing therapeutic strategies. In this study, using an experimental model of progressive chronic neurodegeneration, murine prion disease, we define the temporal dynamics of the generation, maturation and integration of new neurons in the hippocampal dentate gyrus, using dual pulse-chase, multicolour γ-retroviral tracing, transmission electron microscopy and patch-clamp. We found increased neurogenesis during the progression of prion disease, which partially counteracts the effects of chronic neurodegeneration, as evidenced by blocking neurogenesis with cytosine arabinoside, and helps to preserve the hippocampal function. Evidence obtained from human post-mortem samples, of both variant Creutzfeldt-Jakob disease and Alzheimer’s disease patients, also suggests increased neurogenic activity. These results open a new avenue into the exploration of the effects and regulation of neurogenesis during chronic neurodegeneration, and offer a new model to reproduce the changes observed in human neurodegenerative diseases. PMID:24941947

Spatio-temporal statistical models with applications to atmospheric processes

International Nuclear Information System (INIS)

Wikle, C.K.

1996-01-01

This doctoral dissertation is presented as three self-contained papers. An introductory chapter considers traditional spatio-temporal statistical methods used in the atmospheric sciences from a statistical perspective. Although this section is primarily a review, many of the statistical issues considered have not been considered in the context of these methods and several open questions are posed. The first paper attempts to determine a means of characterizing the semiannual oscillation (SAO) spatial variation in the northern hemisphere extratropical height field. It was discovered that the midlatitude SAO in 500hPa geopotential height could be explained almost entirely as a result of spatial and temporal asymmetries in the annual variation of stationary eddies. It was concluded that the mechanism for the SAO in the northern hemisphere is a result of land-sea contrasts. The second paper examines the seasonal variability of mixed Rossby-gravity waves (MRGW) in lower stratospheric over the equatorial Pacific. Advanced cyclostationary time series techniques were used for analysis. It was found that there are significant twice-yearly peaks in MRGW activity. Analyses also suggested a convergence of horizontal momentum flux associated with these waves. In the third paper, a new spatio-temporal statistical model is proposed that attempts to consider the influence of both temporal and spatial variability. This method is mainly concerned with prediction in space and time, and provides a spatially descriptive and temporally dynamic model

A Temporal-Causal Modelling Approach to Integrated Contagion and Network Change in Social Networks

NARCIS (Netherlands)

Blankendaal, Romy; Parinussa, Sarah; Treur, Jan

2016-01-01

This paper introduces an integrated temporal-causal model for dynamics in social networks addressing the contagion principle by which states are affected mutually, and both the homophily principle and the more-becomes-more principle by which connections are adapted over time. The integrated model

The effect of STDP temporal kernel structure on the learning dynamics of single excitatory and inhibitory synapses.

Directory of Open Access Journals (Sweden)

Yotam Luz

Full Text Available Spike-Timing Dependent Plasticity (STDP is characterized by a wide range of temporal kernels. However, much of the theoretical work has focused on a specific kernel - the "temporally asymmetric Hebbian" learning rules. Previous studies linked excitatory STDP to positive feedback that can account for the emergence of response selectivity. Inhibitory plasticity was associated with negative feedback that can balance the excitatory and inhibitory inputs. Here we study the possible computational role of the temporal structure of the STDP. We represent the STDP as a superposition of two processes: potentiation and depression. This allows us to model a wide range of experimentally observed STDP kernels, from Hebbian to anti-Hebbian, by varying a single parameter. We investigate STDP dynamics of a single excitatory or inhibitory synapse in purely feed-forward architecture. We derive a mean-field-Fokker-Planck dynamics for the synaptic weight and analyze the effect of STDP structure on the fixed points of the mean field dynamics. We find a phase transition along the Hebbian to anti-Hebbian parameter from a phase that is characterized by a unimodal distribution of the synaptic weight, in which the STDP dynamics is governed by negative feedback, to a phase with positive feedback characterized by a bimodal distribution. The critical point of this transition depends on general properties of the STDP dynamics and not on the fine details. Namely, the dynamics is affected by the pre-post correlations only via a single number that quantifies its overlap with the STDP kernel. We find that by manipulating the STDP temporal kernel, negative feedback can be induced in excitatory synapses and positive feedback in inhibitory. Moreover, there is an exact symmetry between inhibitory and excitatory plasticity, i.e., for every STDP rule of inhibitory synapse there exists an STDP rule for excitatory synapse, such that their dynamics is identical.

Temporal dynamics of ikaite in experimental sea ice

OpenAIRE

S. Rysgaard; F. Wang; R. J. Galley; R. Grimm; D. Notz; M. Lemes; N.-X. Geilfus; A. Chaulk; A. A. Hare; O. Crabeck; B. G. T. Else; K. Campbell; L. L. Sørensen; J. Sievers; T. Papakyriakou

2014-01-01

Ikaite (CaCO3 · 6H2O) is a metastable phase of calcium carbonate that normally forms in a cold environment and/or under high pressure. Recently, ikaite crystals have been found in sea ice, and it has been suggested that their precipitation may play an important role in air–sea CO2 exchange in ice-covered seas. Little is known, however, of the spatial and temporal dynamics of ikaite in sea ice. Here we present evidence for highly dynamic ikaite precipitation and dissolution i...

Models of neural dynamics in brain information processing - the developments of 'the decade'

International Nuclear Information System (INIS)

Borisyuk, G N; Borisyuk, R M; Kazanovich, Yakov B; Ivanitskii, Genrikh R

2002-01-01

Neural network models are discussed that have been developed during the last decade with the purpose of reproducing spatio-temporal patterns of neural activity in different brain structures. The main goal of the modeling was to test hypotheses of synchronization, temporal and phase relations in brain information processing. The models being considered are those of temporal structure of spike sequences, of neural activity dynamics, and oscillatory models of attention and feature integration. (reviews of topical problems)

Model Checking Discounted Temporal Properties

NARCIS (Netherlands)

de Alfaro, Luca; Faella, Marco; Henzinger, Thomas A.; Majumdar, Rupak; Stoelinga, Mariëlle Ida Antoinette

2005-01-01

Temporal logic is two-valued: a property is either true or false. When applied to the analysis of stochastic systems, or systems with imprecise formal models, temporal logic is therefore fragile: even small changes in the model can lead to opposite truth values for a specification. We present a

Model Checking Discounted Temporal Properties

NARCIS (Netherlands)

de Alfaro, Luca; Faella, Marco; Henzinger, Thomas A.; Majumdar, Rupak; Stoelinga, Mariëlle Ida Antoinette; Jensen, K; Podelski, A.

2004-01-01

Temporal logic is two-valued: a property is either true or false. When applied to the analysis of stochastic systems, or systems with imprecise formal models, temporal logic is therefore fragile: even small changes in the model can lead to opposite truth values for a specification. We present a

Genome-scale modelling of microbial metabolism with temporal and spatial resolution.

Science.gov (United States)

Henson, Michael A

2015-12-01

Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated. © 2015 Authors; published by Portland Press Limited.

Temporal Expectations Guide Dynamic Prioritization in Visual Working Memory through Attenuated α Oscillations.

Science.gov (United States)

van Ede, Freek; Niklaus, Marcel; Nobre, Anna C

2017-01-11

Although working memory is generally considered a highly dynamic mnemonic store, popular laboratory tasks used to understand its psychological and neural mechanisms (such as change detection and continuous reproduction) often remain relatively "static," involving the retention of a set number of items throughout a shared delay interval. In the current study, we investigated visual working memory in a more dynamic setting, and assessed the following: (1) whether internally guided temporal expectations can dynamically and reversibly prioritize individual mnemonic items at specific times at which they are deemed most relevant; and (2) the neural substrates that support such dynamic prioritization. Participants encoded two differently colored oriented bars into visual working memory to retrieve the orientation of one bar with a precision judgment when subsequently probed. To test for the flexible temporal control to access and retrieve remembered items, we manipulated the probability for each of the two bars to be probed over time, and recorded EEG in healthy human volunteers. Temporal expectations had a profound influence on working memory performance, leading to faster access times as well as more accurate orientation reproductions for items that were probed at expected times. Furthermore, this dynamic prioritization was associated with the temporally specific attenuation of contralateral α (8-14 Hz) oscillations that, moreover, predicted working memory access times on a trial-by-trial basis. We conclude that attentional prioritization in working memory can be dynamically steered by internally guided temporal expectations, and is supported by the attenuation of α oscillations in task-relevant sensory brain areas. In dynamic, everyday-like, environments, flexible goal-directed behavior requires that mental representations that are kept in an active (working memory) store are dynamic, too. We investigated working memory in a more dynamic setting than is conventional

Mapping and spatial-temporal modeling of Bromus tectorum invasion in central Utah

Science.gov (United States)

Jin, Zhenyu

Cheatgrass, or Downy Brome, is an exotic winter annual weed native to the Mediterranean region. Since its introduction to the U.S., it has become a significant weed and aggressive invader of sagebrush, pinion-juniper, and other shrub communities, where it can completely out-compete native grasses and shrubs. In this research, remotely sensed data combined with field collected data are used to investigate the distribution of the cheatgrass in Central Utah, to characterize the trend of the NDVI time-series of cheatgrass, and to construct a spatially explicit population-based model to simulate the spatial-temporal dynamics of the cheatgrass. This research proposes a method for mapping the canopy closure of invasive species using remotely sensed data acquired at different dates. Different invasive species have their own distinguished phenologies and the satellite images in different dates could be used to capture the phenology. The results of cheatgrass abundance prediction have a good fit with the field data for both linear regression and regression tree models, although the regression tree model has better performance than the linear regression model. To characterize the trend of NDVI time-series of cheatgrass, a novel smoothing algorithm named RMMEH is presented in this research to overcome some drawbacks of many other algorithms. By comparing the performance of RMMEH in smoothing a 16-day composite of the MODIS NDVI time-series with that of two other methods, which are the 4253EH, twice and the MVI, we have found that RMMEH not only keeps the original valid NDVI points, but also effectively removes the spurious spikes. The reconstructed NDVI time-series of different land covers are of higher quality and have smoother temporal trend. To simulate the spatial-temporal dynamics of cheatgrass, a spatially explicit population-based model is built applying remotely sensed data. The comparison between the model output and the ground truth of cheatgrass closure demonstrates

Demand-supply dynamics in tourism systems: A spatio-temporal GIS analysis. The Alberta ski industry case study

Science.gov (United States)

Bertazzon, Stefania

The present research focuses on the interaction of supply and demand of down-hill ski tourism in the province of Alberta. The main hypothesis is that the demand for skiing depends on the socio-economic and demographic characteristics of the population living in the province and outside it. A second, consequent hypothesis is that the development of ski resorts (supply) is a response to the demand for skiing. From the latter derives the hypothesis of a dynamic interaction between supply (ski resorts) and demand (skiers). Such interaction occurs in space, within a range determined by physical distance and the means available to overcome it. The above hypotheses implicitly define interactions that take place in space and evolve over time. The hypotheses are tested by temporal, spatial, and spatio-temporal regression models, using the best available data and the latest commercially available software. The main purpose of this research is to explore analytical techniques to model spatial, temporal, and spatio-temporal dynamics in the context of regional science. The completion of the present research has produced more significant contributions than was originally expected. Many of the unexpected contributions resulted from theoretical and applied needs arising from the application of spatial regression models. Spatial regression models are a new and largely under-applied technique. The models are fairly complex and a considerable amount of preparatory work is needed, prior to their specification and estimation. Most of this work is specific to the field of application. The originality of the solutions devised is increased by the lack of applications in the field of tourism. The scarcity of applications in other fields adds to their value for other applications. The estimation of spatio-temporal models has been only partially attained in the present research. This apparent limitation is due to the novelty and complexity of the analytical methods applied. This opens new

Dynamic imaging in mild traumatic brain injury: support for the theory of medial temporal vulnerability.

Science.gov (United States)

Umile, Eric M; Sandel, M Elizabeth; Alavi, Abass; Terry, Charles M; Plotkin, Rosette C

2002-11-01

To determine whether patients with mild traumatic brain injury (TBI) and persistent postconcussive symptoms have evidence of temporal lobe injury on dynamic imaging. Case series. An academic medical center. Twenty patients with a clinical diagnosis of mild TBI and persistent postconcussive symptoms were referred for neuropsychologic evaluation and dynamic imaging. Fifteen (75%) had normal magnetic resonance imaging (MRI) and/or computed tomography (CT) scans at the time of injury. Neuropsychologic testing, positron-emission tomography (PET), and single-photon emission-computed tomography (SPECT). Temporal lobe findings on static imaging (MRI, CT) and dynamic imaging (PET, SPECT); neuropsychologic test findings on measures of verbal and visual memory. Testing documented neurobehavioral deficits in 19 patients (95%). Dynamic imaging documented abnormal findings in 18 patients (90%). Fifteen patients (75%) had temporal lobe abnormalities on PET and SPECT (primarily in medial temporal regions); abnormal findings were bilateral in 10 patients (50%) and unilateral in 5 (25%). Six patients (30%) had frontal abnormalities, and 8 (40%) had nonfrontotemporal abnormalities. Correlations between neuropsychologic testing and dynamic imaging could be established but not consistently across the whole group. Patients with mild TBI and persistent postconcussive symptoms have a high incidence of temporal lobe injury (presumably involving the hippocampus and related structures), which may explain the frequent finding of memory disorders in this population. The abnormal temporal lobe findings on PET and SPECT in humans may be analogous to the neuropathologic evidence of medial temporal injury provided by animal studies after mild TBI. Copyright 2002 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation

Modelling temporal networks of human face-to-face contacts with public activity and individual reachability

Science.gov (United States)

Zhang, Yi-Qing; Cui, Jing; Zhang, Shu-Min; Zhang, Qi; Li, Xiang

2016-02-01

Modelling temporal networks of human face-to-face contacts is vital both for understanding the spread of airborne pathogens and word-of-mouth spreading of information. Although many efforts have been devoted to model these temporal networks, there are still two important social features, public activity and individual reachability, have been ignored in these models. Here we present a simple model that captures these two features and other typical properties of empirical face-to-face contact networks. The model describes agents which are characterized by an attractiveness to slow down the motion of nearby people, have event-triggered active probability and perform an activity-dependent biased random walk in a square box with periodic boundary. The model quantitatively reproduces two empirical temporal networks of human face-to-face contacts which are testified by their network properties and the epidemic spread dynamics on them.

Forecasting Temporal Dynamics of Cutaneous Leishmaniasis in Northeast Brazil

Science.gov (United States)

Lewnard, Joseph A.; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R.; Glesby, Marshall J.; Ko, Albert I.; Carvalho, Edgar M.; Schriefer, Albert; Weinberger, Daniel M.

2014-01-01

Introduction Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. Methodology/Principal Findings We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. Significance These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets. PMID:25356734

Temporal dynamics of categorization: Forgetting as the basis of abstraction and generalization

Directory of Open Access Journals (Sweden)

Haley eVlach

2014-09-01

Full Text Available Historically, models of categorization have focused on how learners track frequencies and co-occurrence information to abstract relevant category features for generalization. The current study takes a different approach by examining how the temporal dynamics of categorization affect abstraction and generalization. In the learning phase of the experiment, all relevant category features were presented an equal number of times across category exemplars. However, the relevant features were presented on one of two learning schedules: massed or interleaved. At a series of immediate and delayed tests, learners were asked to generalize to novel exemplars that contained massed features, interleaved features, or all novel features. The results of this experiment revealed that, at an immediate test, learners more readily generalized based upon features presented on a massed schedule. Conversely, at a delayed test, learners more readily generalized based upon features presented on an interleaved schedule, until information was no longer readily retrievable from memory. These findings suggest that forgetting and retrieval processes engendered by the temporal dynamics of learning are used as a basis of abstraction, implicating forgetting as a central mechanism of generalization.

The financial accounting model from a system dynamics' perspective

NARCIS (Netherlands)

Melse, E.

2006-01-01

This paper explores the foundation of the financial accounting model. We examine the properties of the accounting equation as the principal algorithm for the design and the development of a System Dynamics model. Key to the perspective is the foundational requirement that resolves the temporal

Characterizing and modeling the dynamics of online popularity.

Science.gov (United States)

Ratkiewicz, Jacob; Fortunato, Santo; Flammini, Alessandro; Menczer, Filippo; Vespignani, Alessandro

2010-10-08

Online popularity has an enormous impact on opinions, culture, policy, and profits. We provide a quantitative, large scale, temporal analysis of the dynamics of online content popularity in two massive model systems: the Wikipedia and an entire country's Web space. We find that the dynamics of popularity are characterized by bursts, displaying characteristic features of critical systems such as fat-tailed distributions of magnitude and interevent time. We propose a minimal model combining the classic preferential popularity increase mechanism with the occurrence of random popularity shifts due to exogenous factors. The model recovers the critical features observed in the empirical analysis of the systems analyzed here, highlighting the key factors needed in the description of popularity dynamics.

Dynamic Circuit Model for Spintronic Devices

KAUST Repository

Alawein, Meshal

2017-01-09

In this work we propose a finite-difference scheme based circuit model of a general spintronic device and benchmark it with other models proposed for spintronic switching devices. Our model is based on the four-component spin circuit theory and utilizes the widely used coupled stochastic magnetization dynamics/spin transport framework. In addition to the steady-state analysis, this work offers a transient analysis of carrier transport. By discretizing the temporal and spatial derivatives to generate a linear system of equations, we derive new and simple finite-difference conductance matrices that can, to the first order, capture both static and dynamic behaviors of a spintronic device. We also discuss an extension of the spin modified nodal analysis (SMNA) for time-dependent situations based on the proposed scheme.

Dynamic Circuit Model for Spintronic Devices

KAUST Repository

Alawein, Meshal; Fariborzi, Hossein

2017-01-01

In this work we propose a finite-difference scheme based circuit model of a general spintronic device and benchmark it with other models proposed for spintronic switching devices. Our model is based on the four-component spin circuit theory and utilizes the widely used coupled stochastic magnetization dynamics/spin transport framework. In addition to the steady-state analysis, this work offers a transient analysis of carrier transport. By discretizing the temporal and spatial derivatives to generate a linear system of equations, we derive new and simple finite-difference conductance matrices that can, to the first order, capture both static and dynamic behaviors of a spintronic device. We also discuss an extension of the spin modified nodal analysis (SMNA) for time-dependent situations based on the proposed scheme.

Predicting seizures in untreated temporal lobe epilepsy using point-process nonlinear models of heartbeat dynamics.

Science.gov (United States)

Valenza, G; Romigi, A; Citi, L; Placidi, F; Izzi, F; Albanese, M; Scilingo, E P; Marciani, M G; Duggento, A; Guerrisi, M; Toschi, N; Barbieri, R

2016-08-01

Symptoms of temporal lobe epilepsy (TLE) are frequently associated with autonomic dysregulation, whose underlying biological processes are thought to strongly contribute to sudden unexpected death in epilepsy (SUDEP). While abnormal cardiovascular patterns commonly occur during ictal events, putative patterns of autonomic cardiac effects during pre-ictal (PRE) periods (i.e. periods preceding seizures) are still unknown. In this study, we investigated TLE-related heart rate variability (HRV) through instantaneous, nonlinear estimates of cardiovascular oscillations during inter-ictal (INT) and PRE periods. ECG recordings from 12 patients with TLE were processed to extract standard HRV indices, as well as indices of instantaneous HRV complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra) obtained through definition of inhomogeneous point-process nonlinear models, employing Volterra-Laguerre expansions of linear, quadratic, and cubic kernels. Experimental results demonstrate that the best INT vs. PRE classification performance (balanced accuracy: 73.91%) was achieved only when retaining the time-varying, nonlinear, and non-stationary structure of heartbeat dynamical features. The proposed approach opens novel important avenues in predicting ictal events using information gathered from cardiovascular signals exclusively.

Spatial Temporal Modelling of Particulate Matter for Health Effects Studies

Science.gov (United States)

Hamm, N. A. S.

2016-10-01

Epidemiological studies of the health effects of air pollution require estimation of individual exposure. It is not possible to obtain measurements at all relevant locations so it is necessary to predict at these space-time locations, either on the basis of dispersion from emission sources or by interpolating observations. This study used data obtained from a low-cost sensor network of 32 air quality monitoring stations in the Dutch city of Eindhoven, which make up the ILM (innovative air (quality) measurement system). These stations currently provide PM10 and PM2.5 (particulate matter less than 10 and 2.5 m in diameter), aggregated to hourly means. The data provide an unprecedented level of spatial and temporal detail for a city of this size. Despite these benefits the time series of measurements is characterized by missing values and noisy values. In this paper a space-time analysis is presented that is based on a dynamic model for the temporal component and a Gaussian process geostatistical for the spatial component. Spatial-temporal variability was dominated by the temporal component, although the spatial variability was also substantial. The model delivered accurate predictions for both isolated missing values and 24-hour periods of missing values (RMSE = 1.4 μg m-3 and 1.8 μg m-3 respectively). Outliers could be detected by comparison to the 95% prediction interval. The model shows promise for predicting missing values, outlier detection and for mapping to support health impact studies.

Evolution of predator dispersal in relation to spatio-temporal prey dynamics: how not to get stuck in the wrong place!

Directory of Open Access Journals (Sweden)

Justin M J Travis

Full Text Available The eco-evolutionary dynamics of dispersal are recognised as key in determining the responses of populations to environmental changes. Here, by developing a novel modelling approach, we show that predators are likely to have evolved to emigrate more often and become more selective over their destination patch when their prey species exhibit spatio-temporally complex dynamics. We additionally demonstrate that the cost of dispersal can vary substantially across space and time. Perhaps as a consequence of current environmental change, many key prey species are currently exhibiting major shifts in their spatio-temporal dynamics. By exploring similar shifts in silico, we predict that predator populations will be most vulnerable when prey dynamics shift from stable to complex. The more sophisticated dispersal rules, and greater variance therein, that evolve under complex dynamics will enable persistence across a broader range of prey dynamics than the rules which evolve under relatively stable prey conditions.

Temporal dynamics of online petitions.

Science.gov (United States)

Böttcher, Lucas; Woolley-Meza, Olivia; Brockmann, Dirk

2017-01-01

Online petitions are an important avenue for direct political action, yet the dynamics that determine when a petition will be successful are not well understood. Here we analyze the temporal characteristics of online-petition signing behavior in order to identify systematic differences between popular petitions, which receive a high volume of signatures, and unpopular ones. We find that, in line with other temporal characterizations of human activity, the signing process is typically non-Poissonian and non-homogeneous in time. However, this process exhibits anomalously high memory for human activity, possibly indicating that synchronized external influence or contagion play and important role. More interestingly, we find clear differences in the characteristics of the inter-event time distributions depending on the total number of signatures that petitions receive, independently of the total duration of the petitions. Specifically, popular petitions that attract a large volume of signatures exhibit more variance in the distribution of inter-event times than unpopular petitions with only a few signatures, which could be considered an indication that the former are more bursty. However, petitions with large signature volume are less bursty according to measures that consider the time ordering of inter-event times. Our results, therefore, emphasize the importance of accounting for time ordering to characterize human activity.

Impact of Partial Time Delay on Temporal Dynamics of Watts-Strogatz Small-World Neuronal Networks

Science.gov (United States)

Yan, Hao; Sun, Xiaojuan

2017-06-01

In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts-Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay τ and the other is the probability of partial time delay pdelay. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay τ, the probability pdelay could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay τ, temporal coherence and mean firing rate do not have great changes with respect to pdelay. Time delay τ always has great influence on both temporal coherence and mean firing rate no matter what is the value of pdelay. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay τ. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.

Simulation of sensory integration dysfunction in autism with dynamic neural fields model

NARCIS (Netherlands)

Chonnaparamutt, W.; Barakova, E.I.; Rutkowski, L.; Taseusiewicz, R.

2008-01-01

This paper applies dynamic neural fields model [1,23,7] to multimodal interaction of sensory cues obtained from a mobile robot, and shows the impact of different temporal aspects of the integration to the precision of movements. We speculate that temporally uncoordinated sensory integration might be

Practical considerations for high spatial and temporal resolution dynamic transmission electron microscopy

Energy Technology Data Exchange (ETDEWEB)

Armstrong, Michael R. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States)], E-mail: armstrong30@llnl.gov; Boyden, Ken [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Browning, Nigel D. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Department of Chemical Engineering and Materials Science, University of California-Davis, One Shields Avenue, Davis, CA 95616 (United States); Campbell, Geoffrey H.; Colvin, Jeffrey D.; De Hope, William J.; Frank, Alan M. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Gibson, David J.; Hartemann, Fred [N Division, Physics and Advanced Technologies Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-280, Livermore, CA 94550 (United States); Kim, Judy S. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Department of Chemical Engineering and Materials Science, University of California-Davis, One Shields Avenue, Davis, CA 95616 (United States); King, Wayne E.; La Grange, Thomas B.; Pyke, Ben J.; Reed, Bryan W.; Shuttlesworth, Richard M.; Stuart, Brent C.; Torralva, Ben R. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States)

2007-04-15

Although recent years have seen significant advances in the spatial resolution possible in the transmission electron microscope (TEM), the temporal resolution of most microscopes is limited to video rate at best. This lack of temporal resolution means that our understanding of dynamic processes in materials is extremely limited. High temporal resolution in the TEM can be achieved, however, by replacing the normal thermionic or field emission source with a photoemission source. In this case the temporal resolution is limited only by the ability to create a short pulse of photoexcited electrons in the source, and this can be as short as a few femtoseconds. The operation of the photo-emission source and the control of the subsequent pulse of electrons (containing as many as 5x10{sup 7} electrons) create significant challenges for a standard microscope column that is designed to operate with a single electron in the column at any one time. In this paper, the generation and control of electron pulses in the TEM to obtain a temporal resolution <10{sup -6} s will be described and the effect of the pulse duration and current density on the spatial resolution of the instrument will be examined. The potential of these levels of temporal and spatial resolution for the study of dynamic materials processes will also be discussed.

Practical considerations for high spatial and temporal resolution dynamic transmission electron microscopy

International Nuclear Information System (INIS)

Armstrong, Michael R.; Boyden, Ken; Browning, Nigel D.; Campbell, Geoffrey H.; Colvin, Jeffrey D.; De Hope, William J.; Frank, Alan M.; Gibson, David J.; Hartemann, Fred; Kim, Judy S.; King, Wayne E.; La Grange, Thomas B.; Pyke, Ben J.; Reed, Bryan W.; Shuttlesworth, Richard M.; Stuart, Brent C.; Torralva, Ben R.

2007-01-01

Although recent years have seen significant advances in the spatial resolution possible in the transmission electron microscope (TEM), the temporal resolution of most microscopes is limited to video rate at best. This lack of temporal resolution means that our understanding of dynamic processes in materials is extremely limited. High temporal resolution in the TEM can be achieved, however, by replacing the normal thermionic or field emission source with a photoemission source. In this case the temporal resolution is limited only by the ability to create a short pulse of photoexcited electrons in the source, and this can be as short as a few femtoseconds. The operation of the photo-emission source and the control of the subsequent pulse of electrons (containing as many as 5x10 7 electrons) create significant challenges for a standard microscope column that is designed to operate with a single electron in the column at any one time. In this paper, the generation and control of electron pulses in the TEM to obtain a temporal resolution -6 s will be described and the effect of the pulse duration and current density on the spatial resolution of the instrument will be examined. The potential of these levels of temporal and spatial resolution for the study of dynamic materials processes will also be discussed

How Well Do Students in Secondary School Understand Temporal Development of Dynamical Systems?

Science.gov (United States)

Forjan, Matej; Grubelnik, Vladimir

2015-01-01

Despite difficulties understanding the dynamics of complex systems only simple dynamical systems without feedback connections have been taught in secondary school physics. Consequently, students do not have opportunities to develop intuition of temporal development of systems, whose dynamics are conditioned by the influence of feedback processes.…

Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh.

Science.gov (United States)

Rahman, M Tauhid Ur; Tabassum, Faheemah; Rasheduzzaman, Md; Saba, Humayra; Sarkar, Lina; Ferdous, Jannatul; Uddin, Syed Zia; Zahedul Islam, A Z M

2017-10-17

Change analysis of land use and land cover (LULC) is a technique to study the environmental degradation and to control the unplanned development. Analysis of the past changing trend of LULC along with modeling future LULC provides a combined opportunity to evaluate and guide the present and future land use policy. The southwest coastal region of Bangladesh, especially Assasuni Upazila of Satkhira District, is the most vulnerable to natural disasters and has faced notable changes in its LULC due to the combined effects of natural and anthropogenic causes. The objectives of this study are to illustrate the temporal dynamics of LULC change in Assasuni Upazila over the last 27 years (i.e., between 1989 and 2015) and also to predict future land use change using CA-ANN (cellular automata and artificial neural network) model for the year 2028. Temporal dynamics of LULC change was analyzed, employing supervised classification of multi-temporal Landsat images. Then, prediction of future LULC was carried out by CA-ANN model using MOLUSCE plugin of QGIS. The analysis of LULC change revealed that the LULC of Assasuni had changed notably during 1989 to 2015. "Bare lands" decreased by 21% being occupied by other land uses, especially by "shrimp farms." Shrimp farm area increased by 25.9% during this period, indicating a major occupational transformation from agriculture to shrimp aquaculture in the study area during the period under study. Reduction in "settlement" area revealed the trend of migration from the Upazila. The predicted LULC for the year 2028 showed that reduction in bare land area would continue and 1595.97 ha bare land would transform into shrimp farm during 2015 to 2028. Also, the impacts of the changing LULC on the livelihood of local people and migration status of the Upazila were analyzed from the data collected through focus group discussions and questionnaire surveys. The analysis revealed that the changing LULC and the occupational shift from paddy

Retinal Vascular and Oxygen Temporal Dynamic Responses to Light Flicker in Humans.

Science.gov (United States)

Felder, Anthony E; Wanek, Justin; Blair, Norman P; Shahidi, Mahnaz

2017-11-01

To mathematically model the temporal dynamic responses of retinal vessel diameter (D), oxygen saturation (SO2), and inner retinal oxygen extraction fraction (OEF) to light flicker and to describe their responses to its cessation in humans. In 16 healthy subjects (age: 60 ± 12 years), retinal oximetry was performed before, during, and after light flicker stimulation. At each time point, five metrics were measured: retinal arterial and venous D (DA, DV) and SO2 (SO2A, SO2V), and OEF. Intra- and intersubject variability of metrics was assessed by coefficient of variation of measurements before flicker within and among subjects, respectively. Metrics during flicker were modeled by exponential functions to determine the flicker-induced steady state metric values and the time constants of changes. Metrics after the cessation of flicker were compared to those before flicker. Intra- and intersubject variability for all metrics were less than 6% and 16%, respectively. At the flicker-induced steady state, DA and DV increased by 5%, SO2V increased by 7%, and OEF decreased by 13%. The time constants of DA and DV (14, 15 seconds) were twofold smaller than those of SO2V and OEF (39, 34 seconds). Within 26 seconds after the cessation of flicker, all metrics were not significantly different from before flicker values (P ≥ 0.07). Mathematical modeling revealed considerable differences in the time courses of changes among metrics during flicker, indicating flicker duration should be considered separately for each metric. Future application of this method may be useful to elucidate alterations in temporal dynamic responses to light flicker due to retinal diseases.

Models of neural dynamics in brain information processing - the developments of 'the decade'

Energy Technology Data Exchange (ETDEWEB)

Borisyuk, G N; Borisyuk, R M; Kazanovich, Yakov B [Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Pushchino, Moscow region (Russian Federation); Ivanitskii, Genrikh R [Institute for Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, Moscow region (Russian Federation)

2002-10-31

Neural network models are discussed that have been developed during the last decade with the purpose of reproducing spatio-temporal patterns of neural activity in different brain structures. The main goal of the modeling was to test hypotheses of synchronization, temporal and phase relations in brain information processing. The models being considered are those of temporal structure of spike sequences, of neural activity dynamics, and oscillatory models of attention and feature integration. (reviews of topical problems)

Longitudinal structure in temperate stream fish communities: evaluating conceptual models with temporal data

Science.gov (United States)

Roberts, James H.; Hitt, Nathaniel P.

2010-01-01

Five conceptual models of longitudinal fish community organization in streams were examined: (1) niche diversity model (NDM), (2) stream continuum model (SCM), (3) immigrant accessibility model (IAM), (4) environmental stability model (ESM), and (5) adventitious stream model (ASM). We used differences among models in their predictions about temporal species turnover, along with five spatiotemporal fish community data sets, to evaluate model applicability. Models were similar in predicting a positive species richness–stream size relationship and longitudinal species nestedness, but differed in predicting either similar temporal species turnover throughout the stream continuum (NDM, SCM), higher turnover upstream (IAM, ESM), or higher turnover downstream (ASM). We calculated measures of spatial and temporal variation from spatiotemporal fish data in five wadeable streams in central and eastern North America spanning 34–68 years (French Creek [New York], Piasa Creek [Illinois], Spruce Run [Virginia], Little Stony Creek [Virginia], and Sinking Creek [Virginia]). All streams exhibited substantial species turnover (i.e., at least 27% turnover in stream-scale species pools), in contrast to the predictions of the SCM. Furthermore, community change was greater in downstream than upstream reaches in four of five streams. This result is most consistent with the ASM and suggests that downstream communities are strongly influenced by migrants to and from species pools outside the focal stream. In Sinking Creek, which is isolated from external species pools, temporal species turnover (via increased richness) was higher upstream than downstream, which is a pattern most consistent with the IAM or ESM. These results corroborate the hypothesis that temperate stream habitats and fish communities are temporally dynamic and that fish migration and environmental disturbances play fundamental roles in stream fish community organization.

Community ecology in 3D: Tensor decomposition reveals spatio-temporal dynamics of large ecological communities

DEFF Research Database (Denmark)

Frelat, Romain; Lindegren, Martin; Dencker, Tim Spaanheden

2017-01-01

it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered...... by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii...

Integrating real-time and manual monitored data to predict hillslope soil moisture dynamics with high spatio-temporal resolution using linear and non-linear models

Science.gov (United States)

Spatio-temporal variability of soil moisture (') is a challenge that remains to be better understood. A trade-off exists between spatial coverage and temporal resolution when using the manual and real-time ' monitoring methods. This restricted the comprehensive and intensive examination of ' dynamic...

Temporal network epidemiology

CERN Document Server

Holme, Petter

2017-01-01

This book covers recent developments in epidemic process models and related data on temporally varying networks. It is widely recognized that contact networks are indispensable for describing, understanding, and intervening to stop the spread of infectious diseases in human and animal populations; “network epidemiology” is an umbrella term to describe this research field. More recently, contact networks have been recognized as being highly dynamic. This observation, also supported by an increasing amount of new data, has led to research on temporal networks, a rapidly growing area. Changes in network structure are often informed by epidemic (or other) dynamics, in which case they are referred to as adaptive networks. This volume gathers contributions by prominent authors working in temporal and adaptive network epidemiology, a field essential to understanding infectious diseases in real society.

A Temporal Ratio Model of Memory

Science.gov (United States)

Brown, Gordon D. A.; Neath, Ian; Chater, Nick

2007-01-01

A model of memory retrieval is described. The model embodies four main claims: (a) temporal memory--traces of items are represented in memory partly in terms of their temporal distance from the present; (b) scale-similarity--similar mechanisms govern retrieval from memory over many different timescales; (c) local distinctiveness--performance on a…

Modeling Temporal Evolution and Multiscale Structure in Networks

DEFF Research Database (Denmark)

Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

2013-01-01

Many real-world networks exhibit both temporal evolution and multiscale structure. We propose a model for temporally correlated multifurcating hierarchies in complex networks which jointly capture both effects. We use the Gibbs fragmentation tree as prior over multifurcating trees and a change......-point model to account for the temporal evolution of each vertex. We demonstrate that our model is able to infer time-varying multiscale structure in synthetic as well as three real world time-evolving complex networks. Our modeling of the temporal evolution of hierarchies brings new insights...

Dynamic interactions between hydrogeological and exposure parameters in daily dose prediction under uncertainty and temporal variability

Energy Technology Data Exchange (ETDEWEB)

Kumar, Vikas, E-mail: vikas.kumar@urv.cat [Department of Chemical Engineering, Rovira i Virgili University, Tarragona 43007 (Spain); Barros, Felipe P.J. de [Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles 90089, CA (United States); Schuhmacher, Marta [Department of Chemical Engineering, Rovira i Virgili University, Tarragona 43007 (Spain); Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier [Hydrogeology Group, Department of Geotechnical Engineering and Geosciences, University Politècnica de Catalunya-BarcelonaTech, Barcelona 08034 (Spain)

2013-12-15

Highlights: • Dynamic parametric interaction in daily dose prediction under uncertainty. • Importance of temporal dynamics associated with the dose. • Different dose experienced by different population cohorts as a function of time. • Relevance of uncertainty reduction in the input parameters shows temporal dynamism. -- Abstract: We study the time dependent interaction between hydrogeological and exposure parameters in daily dose predictions due to exposure of humans to groundwater contamination. Dose predictions are treated stochastically to account for an incomplete hydrogeological and geochemical field characterization, and an incomplete knowledge of the physiological response. We used a nested Monte Carlo framework to account for uncertainty and variability arising from both hydrogeological and exposure variables. Our interest is in the temporal dynamics of the total dose and their effects on parametric uncertainty reduction. We illustrate the approach to a HCH (lindane) pollution problem at the Ebro River, Spain. The temporal distribution of lindane in the river water can have a strong impact in the evaluation of risk. The total dose displays a non-linear effect on different population cohorts, indicating the need to account for population variability. We then expand the concept of Comparative Information Yield Curves developed earlier (see de Barros et al. [29]) to evaluate parametric uncertainty reduction under temporally variable exposure dose. Results show that the importance of parametric uncertainty reduction varies according to the temporal dynamics of the lindane plume. The approach could be used for any chemical to aid decision makers to better allocate resources towards reducing uncertainty.

Modeling structural change in spatial system dynamics: A Daisyworld example.

Science.gov (United States)

Neuwirth, C; Peck, A; Simonović, S P

2015-03-01

System dynamics (SD) is an effective approach for helping reveal the temporal behavior of complex systems. Although there have been recent developments in expanding SD to include systems' spatial dependencies, most applications have been restricted to the simulation of diffusion processes; this is especially true for models on structural change (e.g. LULC modeling). To address this shortcoming, a Python program is proposed to tightly couple SD software to a Geographic Information System (GIS). The approach provides the required capacities for handling bidirectional and synchronized interactions of operations between SD and GIS. In order to illustrate the concept and the techniques proposed for simulating structural changes, a fictitious environment called Daisyworld has been recreated in a spatial system dynamics (SSD) environment. The comparison of spatial and non-spatial simulations emphasizes the importance of considering spatio-temporal feedbacks. Finally, practical applications of structural change models in agriculture and disaster management are proposed.

Embodiment of intersubjective time: relational dynamics as attractors in the temporal coordination of interpersonal behaviors and experiences.

Science.gov (United States)

Laroche, Julien; Berardi, Anna Maria; Brangier, Eric

2014-01-01

This paper addresses the issue of "being together," and more specifically the issue of "being together in time." We provide with an integrative framework that is inspired by phenomenology, the enactive approach and dynamical systems theories. To do so, we first define embodiment as a living and lived phenomenon that emerges from agent-world coupling. We then show that embodiment is essentially dynamical and therefore we describe experiential, behavioral and brain dynamics. Both lived temporality and the temporality of the living appear to be complex, multiscale phenomena. Next we discuss embodied dynamics in the context of interpersonal interactions, and briefly review the empirical literature on between-persons temporal coordination. Overall, we propose that being together in time emerges from the relational dynamics of embodied interactions and their flexible co-regulation.

Temporal and spatial dynamics of mineral levels of forage, soil and ...

African Journals Online (AJOL)

Temporal and spatial dynamics of mineral levels of forage, soil and cattle blood ... In the plain lands, local variations occurred for soil phosphorus and magnesium. ... Rangeland improvement and supplementation strategies are suggested to ...

A dynamic, climate-driven model of Rift Valley fever

Directory of Open Access Journals (Sweden)

Joseph Leedale

2016-03-01

Full Text Available Outbreaks of Rift Valley fever (RVF in eastern Africa have previously occurred following specific rainfall dynamics and flooding events that appear to support the emergence of large numbers of mosquito vectors. As such, transmission of the virus is considered to be sensitive to environmental conditions and therefore changes in climate can impact the spatiotemporal dynamics of epizootic vulnerability. Epidemiological information describing the methods and parameters of RVF transmission and its dependence on climatic factors are used to develop a new spatio-temporal mathematical model that simulates these dynamics and can predict the impact of changes in climate. The Liverpool RVF (LRVF model is a new dynamic, process-based model driven by climate data that provides a predictive output of geographical changes in RVF outbreak susceptibility as a result of the climate and local livestock immunity. This description of the multi-disciplinary process of model development is accessible to mathematicians, epidemiological modellers and climate scientists, uniting dynamic mathematical modelling, empirical parameterisation and state-of-the-art climate information.

Ananke: temporal clustering reveals ecological dynamics of microbial communities

Directory of Open Access Journals (Sweden)

Michael W. Hall

2017-09-01

Full Text Available Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of clustering based on sequence identity, marker-gene data sets collected over time can be clustered based on temporal correlation to reveal ecologically meaningful associations. We present Ananke, a free and open-source algorithm and software package that complements existing sequence-identity-based clustering approaches by clustering marker-gene data based on time-series profiles and provides interactive visualization of clusters, including highlighting of internal OTU inconsistencies. Ananke is able to cluster distinct temporal patterns from simulations of multiple ecological patterns, such as periodic seasonal dynamics and organism appearances/disappearances. We apply our algorithm to two longitudinal marker gene data sets: faecal communities from the human gut of an individual sampled over one year, and communities from a freshwater lake sampled over eleven years. Within the gut, the segregation of the bacterial community around a food-poisoning event was immediately clear. In the freshwater lake, we found that high sequence identity between marker genes does not guarantee similar temporal dynamics, and Ananke time-series clusters revealed patterns obscured by clustering based on sequence identity or taxonomy. Ananke is free and open-source software available at https://github.com/beiko-lab/ananke.

Internal cycling, not external loading, decides the nutrient limitation in eutrophic lake: A dynamic model with temporal Bayesian hierarchical inference.

Science.gov (United States)

Wu, Zhen; Liu, Yong; Liang, Zhongyao; Wu, Sifeng; Guo, Huaicheng

2017-06-01

Lake eutrophication is associated with excessive anthropogenic nutrients (mainly nitrogen (N) and phosphorus (P)) and unobserved internal nutrient cycling. Despite the advances in understanding the role of external loadings, the contribution of internal nutrient cycling is still an open question. A dynamic mass-balance model was developed to simulate and measure the contributions of internal cycling and external loading. It was based on the temporal Bayesian Hierarchical Framework (BHM), where we explored the seasonal patterns in the dynamics of nutrient cycling processes and the limitation of N and P on phytoplankton growth in hyper-eutrophic Lake Dianchi, China. The dynamic patterns of the five state variables (Chla, TP, ammonia, nitrate and organic N) were simulated based on the model. Five parameters (algae growth rate, sediment exchange rate of N and P, nitrification rate and denitrification rate) were estimated based on BHM. The model provided a good fit to observations. Our model results highlighted the role of internal cycling of N and P in Lake Dianchi. The internal cycling processes contributed more than external loading to the N and P changes in the water column. Further insights into the nutrient limitation analysis indicated that the sediment exchange of P determined the P limitation. Allowing for the contribution of denitrification to N removal, N was the more limiting nutrient in most of the time, however, P was the more important nutrient for eutrophication management. For Lake Dianchi, it would not be possible to recover solely by reducing the external watershed nutrient load; the mechanisms of internal cycling should also be considered as an approach to inhibit the release of sediments and to enhance denitrification. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spatial and temporal dynamics of the microbial community in the Hanford unconfined aquifer

Energy Technology Data Exchange (ETDEWEB)

Lin, Xueju; McKinley, James P.; Resch, Charles T.; Kaluzny, Rachael M.; Lauber, C.; Fredrickson, Jim K.; Knight, Robbie C.; Konopka, Allan

2012-03-29

Pyrosequencing analysis of 16S rRNA genes was used to study temporal dynamics of groundwater Bacteria and Archaea over 10 months within 3 well clusters separated by ~30 m and located 250 m from the Columbia River on the Hanford Site, WA. Each cluster contained 3 wells screened at different depths ranging from 10 to 17 m that differed in hydraulic conductivities. Representative samples were selected for analyses of prokaryotic 16S and eukaryotic 18S rRNA gene copy numbers. Temporal changes in community composition occurred in all 9 wells over the 10 month sampling period. However, there were particularly strong effects near the top of the water table when the seasonal rise in the Columbia River caused river water intrusion at the top of the aquifer. The occurrence and disappearance of some microbial assemblages (such as Actinobacteria ACK-M1) were correlated to river water intrusion. This seasonal impact on microbial community structure was greater in the shallow saturated zone than deeper in the aquifer. Spatial and temporal patterns for several 16S rRNA gene operational taxonomic units associated with particular physiological functions (e.g.methane oxidizers and metal reducers) suggests dynamic changes in fluxes of electron donors and acceptors over an annual cycle. In addition, temporal dynamics in eukaryotic 18S rRNA gene copies and the dominance of protozoa in 18S clone libraries suggest that bacterial community dynamics could be affected not only by the physical and chemical environment, but also by top-down biological control.

Patient specific dynamic geometric models from sequential volumetric time series image data.

Science.gov (United States)

Cameron, B M; Robb, R A

2004-01-01

Generating patient specific dynamic models is complicated by the complexity of the motion intrinsic and extrinsic to the anatomic structures being modeled. Using a physics-based sequentially deforming algorithm, an anatomically accurate dynamic four-dimensional model can be created from a sequence of 3-D volumetric time series data sets. While such algorithms may accurately track the cyclic non-linear motion of the heart, they generally fail to accurately track extrinsic structural and non-cyclic motion. To accurately model these motions, we have modified a physics-based deformation algorithm to use a meta-surface defining the temporal and spatial maxima of the anatomic structure as the base reference surface. A mass-spring physics-based deformable model, which can expand or shrink with the local intrinsic motion, is applied to the metasurface, deforming this base reference surface to the volumetric data at each time point. As the meta-surface encompasses the temporal maxima of the structure, any extrinsic motion is inherently encoded into the base reference surface and allows the computation of the time point surfaces to be performed in parallel. The resultant 4-D model can be interactively transformed and viewed from different angles, showing the spatial and temporal motion of the anatomic structure. Using texture maps and per-vertex coloring, additional data such as physiological and/or biomechanical variables (e.g., mapping electrical activation sequences onto contracting myocardial surfaces) can be associated with the dynamic model, producing a 5-D model. For acquisition systems that may capture only limited time series data (e.g., only images at end-diastole/end-systole or inhalation/exhalation), this algorithm can provide useful interpolated surfaces between the time points. Such models help minimize the number of time points required to usefully depict the motion of anatomic structures for quantitative assessment of regional dynamics.

An Agent Model of Temporal Dynamics in Relapse and Recurrence in Depression

NARCIS (Netherlands)

Aziz, A.A.; Klein, M.C.A.; Treur, J.

2009-01-01

This paper presents a dynamic agent model of recurrences of a depression for an individual. Based on several personal characteristics and a representation of events (i.e. life events or daily hassles) the agent model can simulate whether a human agent that recovered from a depression will fall into

Bio-Inspired Neural Model for Learning Dynamic Models

Science.gov (United States)

Duong, Tuan; Duong, Vu; Suri, Ronald

2009-01-01

A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model.

Science.gov (United States)

Bringmann, Laura F; Ferrer, Emilio; Hamaker, Ellen L; Borsboom, Denny; Tuerlinckx, Francis

2018-01-01

Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.

Temporal variability in phosphorus transfers: classifying concentration-discharge event dynamics

Science.gov (United States)

Haygarth, P.; Turner, B. L.; Fraser, A.; Jarvis, S.; Harrod, T.; Nash, D.; Halliwell, D.; Page, T.; Beven, K.

The importance of temporal variability in relationships between phosphorus (P) concentration (Cp) and discharge (Q) is linked to a simple means of classifying the circumstances of Cp-Q relationships in terms of functional types of response. New experimental data at the upstream interface of grassland soil and catchment systems at a range of scales (lysimeters to headwaters) in England and Australia are used to demonstrate the potential of such an approach. Three types of event are defined as Types 1-3, depending on whether the relative change in Q exceeds the relative change in Cp (Type 1), whether Cp and Q are positively inter-related (Type 2) and whether Cp varies yet Q is unchanged (Type 3). The classification helps to characterise circumstances that can be explained mechanistically in relation to (i) the scale of the study (with a tendency towards Type 1 in small scale lysimeters), (ii) the form of P with a tendency for Type 1 for soluble (i.e., <0.45 μm P forms) and (iii) the sources of P with Type 3 dominant where P availability overrides transport controls. This simple framework provides a basis for development of a more complex and quantitative classification of Cp-Q relationships that can be developed further to contribute to future models of P transfer and delivery from slope to stream. Studies that evaluate the temporal dynamics of the transfer of P are currently grossly under-represented in comparison with models based on static/spatial factors.

3D Printed Pediatric Temporal Bone: A Novel Training Model.

Science.gov (United States)

Longfield, Evan A; Brickman, Todd M; Jeyakumar, Anita

2015-06-01

Temporal bone dissection is a fundamental element of otologic training. Cadaveric temporal bones (CTB) are the gold standard surgical training model; however, many institutions do not have ready access to them and their cost can be significant: $300 to $500. Furthermore, pediatric cadaveric temporal bones are not readily available. Our objective is to develop a pediatric temporal bone model. Temporal bone model. Tertiary Children's Hospital. Pediatric patient model. We describe the novel use of a 3D printer for the generation of a plaster training model from a pediatric high- resolution CT temporal bone scan of a normal pediatric temporal bone. Three models were produced and were evaluated. The models utilized multiple colors (white for bone, yellow for the facial nerve) and were of high quality. Two models were drilled as a proof of concept and found to be an acceptable facsimile of the patient's anatomy, rendering all necessary surgical landmarks accurately. The only negative comments pertaining to the 3D printed temporal bone as a training model were the lack of variation in hardness between cortical and cancellous bone, noting a tactile variation from cadaveric temporal bones. Our novel pediatric 3D temporal bone training model is a viable, low-cost training option for previously inaccessible pediatric temporal bone training. Our hope is that, as 3D printers become commonplace, these models could be rapidly reproduced, allowing for trainees to print models of patients before performing surgery on the living patient.

The absence or temporal offset of visual feedback does not influence adaptation to novel movement dynamics.

Science.gov (United States)

McKenna, Erin; Bray, Laurence C Jayet; Zhou, Weiwei; Joiner, Wilsaan M

2017-10-01

Delays in transmitting and processing sensory information require correctly associating delayed feedback to issued motor commands for accurate error compensation. The flexibility of this alignment between motor signals and feedback has been demonstrated for movement recalibration to visual manipulations, but the alignment dependence for adapting movement dynamics is largely unknown. Here we examined the effect of visual feedback manipulations on force-field adaptation. Three subject groups used a manipulandum while experiencing a lag in the corresponding cursor motion (0, 75, or 150 ms). When the offset was applied at the start of the session (continuous condition), adaptation was not significantly different between groups. However, these similarities may be due to acclimation to the offset before motor adaptation. We tested additional subjects who experienced the same delays concurrent with the introduction of the perturbation (abrupt condition). In this case adaptation was statistically indistinguishable from the continuous condition, indicating that acclimation to feedback delay was not a factor. In addition, end-point errors were not significantly different across the delay or onset conditions, but end-point correction (e.g., deceleration duration) was influenced by the temporal offset. As an additional control, we tested a group of subjects who performed without visual feedback and found comparable movement adaptation results. These results suggest that visual feedback manipulation (absence or temporal misalignment) does not affect adaptation to novel dynamics, independent of both acclimation and perceptual awareness. These findings could have implications for modeling how the motor system adjusts to errors despite concurrent delays in sensory feedback information. NEW & NOTEWORTHY A temporal offset between movement and distorted visual feedback (e.g., visuomotor rotation) influences the subsequent motor recalibration, but the effects of this offset for

The temporal spectrum of adult mosquito population fluctuations: conceptual and modeling implications.

Directory of Open Access Journals (Sweden)

Yun Jian

Full Text Available An improved understanding of mosquito population dynamics under natural environmental forcing requires adequate field observations spanning the full range of temporal scales over which mosquito abundance fluctuates in natural conditions. Here we analyze a 9-year daily time series of uninterrupted observations of adult mosquito abundance for multiple mosquito species in North Carolina to identify characteristic scales of temporal variability, the processes generating them, and the representativeness of observations at different sampling resolutions. We focus in particular on Aedes vexans and Culiseta melanura and, using a combination of spectral analysis and modeling, we find significant population fluctuations with characteristic periodicity between 2 days and several years. Population dynamical modelling suggests that the observed fast fluctuations scales (2 days-weeks are importantly affected by a varying mosquito activity in response to rapid changes in meteorological conditions, a process neglected in most representations of mosquito population dynamics. We further suggest that the range of time scales over which adult mosquito population variability takes place can be divided into three main parts. At small time scales (indicatively 2 days-1 month observed population fluctuations are mainly driven by behavioral responses to rapid changes in weather conditions. At intermediate scales (1 to several month environmentally-forced fluctuations in generation times, mortality rates, and density dependence determine the population characteristic response times. At longer scales (annual to multi-annual mosquito populations follow seasonal and inter-annual environmental changes. We conclude that observations of adult mosquito populations should be based on a sub-weekly sampling frequency and that predictive models of mosquito abundance must include behavioral dynamics to separate the effects of a varying mosquito activity from actual changes in the

Spatial and temporal dynamics of land use pattern response to ...

African Journals Online (AJOL)

Urban settlements account for only two percent of the Earth's land surface. However, over half of the world's population resides in cities (United Nations, 2001). The quantitative evidences presented here showed that there were drastic changes in the temporal and spatial dynamics of land use/land cover. As an overall ...

Erythrocyte orientation and lung conductivity analysis with a high temporal resolution FEM model for bioimpedance measurements

NARCIS (Netherlands)

Ulbrich, M.; Paluchowski, P.; Muehlsteff, J.; Leonhardt, S.

2012-01-01

Impedance cardiography (ICG) is a simple and cheap method to acquirehemodynamic parameters. In this work, the influence of three dynamic physiological sources has been analyzed using a model of the humanthorax with a high temporal resolution. Therefore, simulations havebeen conducted using the

The Temporal Dynamics of Arc Expression Regulate Cognitive Flexibility.

Science.gov (United States)

Wall, Mark J; Collins, Dawn R; Chery, Samantha L; Allen, Zachary D; Pastuzyn, Elissa D; George, Arlene J; Nikolova, Viktoriya D; Moy, Sheryl S; Philpot, Benjamin D; Shepherd, Jason D; Müller, Jürgen; Ehlers, Michael D; Mabb, Angela M; Corrêa, Sonia A L

2018-05-24

Neuronal activity regulates the transcription and translation of the immediate-early gene Arc/Arg3.1, a key mediator of synaptic plasticity. Proteasome-dependent degradation of Arc tightly limits its temporal expression, yet the significance of this regulation remains unknown. We disrupted the temporal control of Arc degradation by creating an Arc knockin mouse (ArcKR) where the predominant Arc ubiquitination sites were mutated. ArcKR mice had intact spatial learning but showed specific deficits in selecting an optimal strategy during reversal learning. This cognitive inflexibility was coupled to changes in Arc mRNA and protein expression resulting in a reduced threshold to induce mGluR-LTD and enhanced mGluR-LTD amplitude. These findings show that the abnormal persistence of Arc protein limits the dynamic range of Arc signaling pathways specifically during reversal learning. Our work illuminates how the precise temporal control of activity-dependent molecules, such as Arc, regulates synaptic plasticity and is crucial for cognition. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Network-Oriented Modeling of Multi-Criteria Homophily and Opinion Dynamics in Social Media

NARCIS (Netherlands)

Kozyreva, Olga; Pechina, Anna; Treur, J.

2018-01-01

In this paper we model the opinion dynamics in social groups in combination with adaptation of the connections based on a multicriteria homophily principle. The adaptive network model has been designed according to a Network-Oriented Modeling approach based on temporal-causal networks. The model has

Conceptual Model of Dynamic Geographic Environment

Directory of Open Access Journals (Sweden)

Martínez-Rosales Miguel Alejandro

2014-04-01

Full Text Available In geographic environments, there are many and different types of geographic entities such as automobiles, trees, persons, buildings, storms, hurricanes, etc. These entities can be classified into two groups: geographic objects and geographic phenomena. By its nature, a geographic environment is dynamic, thus, it’s static modeling is not sufficient. Considering the dynamics of geographic environment, a new type of geographic entity called event is introduced. The primary target is a modeling of geographic environment as an event sequence, because in this case the semantic relations are much richer than in the case of static modeling. In this work, the conceptualization of this model is proposed. It is based on the idea to process each entity apart instead of processing the environment as a whole. After that, the so called history of each entity and its spatial relations to other entities are defined to describe the whole environment. The main goal is to model systems at a conceptual level that make use of spatial and temporal information, so that later it can serve as the semantic engine for such systems.

A neural model of the temporal dynamics of figure-ground segregation in motion perception.

Science.gov (United States)

Raudies, Florian; Neumann, Heiko

2010-03-01

How does the visual system manage to segment a visual scene into surfaces and objects and manage to attend to a target object? Based on psychological and physiological investigations, it has been proposed that the perceptual organization and segmentation of a scene is achieved by the processing at different levels of the visual cortical hierarchy. According to this, motion onset detection, motion-defined shape segregation, and target selection are accomplished by processes which bind together simple features into fragments of increasingly complex configurations at different levels in the processing hierarchy. As an alternative to this hierarchical processing hypothesis, it has been proposed that the processing stages for feature detection and segregation are reflected in different temporal episodes in the response patterns of individual neurons. Such temporal epochs have been observed in the activation pattern of neurons as low as in area V1. Here, we present a neural network model of motion detection, figure-ground segregation and attentive selection which explains these response patterns in an unifying framework. Based on known principles of functional architecture of the visual cortex, we propose that initial motion and motion boundaries are detected at different and hierarchically organized stages in the dorsal pathway. Visual shapes that are defined by boundaries, which were generated from juxtaposed opponent motions, are represented at different stages in the ventral pathway. Model areas in the different pathways interact through feedforward and modulating feedback, while mutual interactions enable the communication between motion and form representations. Selective attention is devoted to shape representations by sending modulating feedback signals from higher levels (working memory) to intermediate levels to enhance their responses. Areas in the motion and form pathway are coupled through top-down feedback with V1 cells at the bottom end of the hierarchy

Time-resolved PIV technique for high temporal resolution measurement of mechanical prosthetic aortic valve fluid dynamics.

Science.gov (United States)

Kaminsky, R; Morbiducci, U; Rossi, M; Scalise, L; Verdonck, P; Grigioni, M

2007-02-01

Prosthetic heart valves (PHVs) have been used to replace diseased native valves for more than five decades. Among these, mechanical PHVs are the most frequently implanted. Unfortunately, these devices still do not achieve ideal behavior and lead to many complications, many of which are related to fluid mechanics. The fluid dynamics of mechanical PHVs are particularly complex and the fine-scale characteristics of such flows call for very accurate experimental techniques. Adequate temporal resolution can be reached by applying time-resolved PIV, a high-resolution dynamic technique which is able to capture detailed chronological changes in the velocity field. The aim of this experimental study is to investigate the evolution of the flow field in a detailed time domain of a commercial bileaflet PHV in a mock-loop mimicking unsteady conditions, by means of time-resolved 2D Particle Image Velocimetry (PIV). The investigated flow field corresponded to the region immediately downstream of the valve plane. Spatial resolution as in "standard" PIV analysis of prosthetic valve fluid dynamics was used. The combination of a Nd:YLF high-repetition-rate double-cavity laser with a high frame rate CMOS camera allowed a detailed, highly temporally resolved acquisition (up to 10000 fps depending on the resolution) of the flow downstream of the PHV. Features that were observed include the non-homogeneity and unsteadiness of the phenomenon and the presence of large-scale vortices within the field, especially in the wake of the valve leaflets. Furthermore, we observed that highly temporally cycle-resolved analysis allowed the different behaviors exhibited by the bileaflet valve at closure to be captured in different acquired cardiac cycles. By accurately capturing hemodynamically relevant time scales of motion, time-resolved PIV characterization can realistically be expected to help designers in improving PHV performance and in furnishing comprehensive validation with experimental data

Three-Dimensional Water and Carbon Cycle Modeling at High Spatial-Temporal Resolutions

Science.gov (United States)

Liao, C.; Zhuang, Q.

2017-12-01

Terrestrial ecosystems in cryosphere are very sensitive to the global climate change due to the presence of snow covers, mountain glaciers and permafrost, especially when the increase in near surface air temperature is almost twice as large as the global average. However, few studies have investigated the water and carbon cycle dynamics using process-based hydrological and biogeochemistry modeling approach. In this study, we used three-dimensional modeling approach at high spatial-temporal resolutions to investigate the water and carbon cycle dynamics for the Tanana Flats Basin in interior Alaska with emphases on dissolved organic carbon (DOC) dynamics. The results have shown that: (1) lateral flow plays an important role in water and carbon cycle, especially in dissolved organic carbon (DOC) dynamics. (2) approximately 2.0 × 104 kg C yr-1 DOC is exported to the hydrological networks and it compromises 1% and 0.01% of total annual gross primary production (GPP) and total organic carbon stored in soil, respectively. This study has established an operational and flexible framework to investigate and predict the water and carbon cycle dynamics under the changing climate.

Temporal dynamics and transcriptional control using single-cell gene expression analysis.

Science.gov (United States)

Kouno, Tsukasa; de Hoon, Michiel; Mar, Jessica C; Tomaru, Yasuhiro; Kawano, Mitsuoki; Carninci, Piero; Suzuki, Harukazu; Hayashizaki, Yoshihide; Shin, Jay W

2013-01-01

Changes in environmental conditions lead to expression variation that manifest at the level of gene regulatory networks. Despite a strong understanding of the role noise plays in synthetic biological systems, it remains unclear how propagation of expression heterogeneity in an endogenous regulatory network is distributed and utilized by cells transitioning through a key developmental event. Here we investigate the temporal dynamics of a single-cell transcriptional network of 45 transcription factors in THP-1 human myeloid monocytic leukemia cells undergoing differentiation to macrophages. We systematically measure temporal regulation of expression and variation by profiling 120 single cells at eight distinct time points, and infer highly controlled regulatory modules through which signaling operates with stochastic effects. This reveals dynamic and specific rewiring as a cellular strategy for differentiation. The integration of both positive and negative co-expression networks further identifies the proto-oncogene MYB as a network hinge to modulate both the pro- and anti-differentiation pathways. Compared to averaged cell populations, temporal single-cell expression profiling provides a much more powerful technique to probe for mechanistic insights underlying cellular differentiation. We believe that our approach will form the basis of novel strategies to study the regulation of transcription at a single-cell level.

Spatial and temporal dynamics of the genetic organization of small mammal populations

International Nuclear Information System (INIS)

Smith, M.H.; Manlove, M.N.; Joule, J.

1978-01-01

A functional population is a group of organisms and their offspring that contributes to a common gene pool within a certain area and time period. It is also the unit of evolution and should be viewed both in quantitative and qualitative terms. Selection, drift, dispersal, and mutation can alter the composition of populations. Spatial heterogeneity in allele frequencies argues for a conceptual model that has a series of relatively small populations semi-isolated from one another. Because of the relatively high levels of genetic variability characteristic of most mammalian species, significant amounts of gene flow between these spatially subdivided populations must occur when longer time periods are considered. Fluctuations in the genetic structure of populations seem to be important in altering the fitness of the individuals within the populations. The interaction of populations through gene flow is important in changing the levels of intrapopulational genetic variability. Populations can be characterized as existing on a continuum from relatively stable to unstable numbers and by other associated changes in their characteristics. Temporal changes in allele frequency occur in a variety of mammals. Conceptually, a species can be viewed as a series of dynamic populations that vary in numbers and quality in both a spatial and temporal context even over short distances and time periods. Short term changes in the quality of individuals in a population can be important in altering the short term dynamics of a population

Reconstruction of tissue dynamics in the compressed breast using multiplexed measurements and temporal basis functions

Science.gov (United States)

Boverman, Gregory; Miller, Eric L.; Brooks, Dana H.; Fang, Qianqian; Carp, S. A.; Selb, J. J.; Boas, David A.

2007-02-01

In the course of our experiments imaging the compressed breast in conjunction with digital tomosynthesis, we have noted that significant changes in tissue optical properties, on the order of 5%, occur during our imaging protocol. These changes seem to consistent with changes both in total Hemoglobin concentration as well as in oxygen saturation, as was the case for our standalone breast compression study, which made use of reflectance measurements. Simulation experiments show the importance of taking into account the temporal dynamics in the image reconstruction, and demonstrate the possibility of imaging the spatio-temporal dynamics of oxygen saturation and total Hemoglobin in the breast. In the image reconstruction, we make use of spatio-temporal basis functions, specifically a voxel basis for spatial imaging, and a cubic spline basis in time, and we reconstruct the spatio-temporal images using the entire data set simultaneously, making use of both absolute and relative measurements in the cost function. We have modified the sequence of sources used in our imaging acquisition protocol to improve our temporal resolution, and preliminary results are shown for normal subjects.

Model-based control of the temporal patterns of intracellular signaling in silico

Science.gov (United States)

Murakami, Yohei; Koyama, Masanori; Oba, Shigeyuki; Kuroda, Shinya; Ishii, Shin

2017-01-01

The functions of intracellular signal transduction systems are determined by the temporal behavior of intracellular molecules and their interactions. Of the many dynamical properties of the system, the relationship between the dynamics of upstream molecules and downstream molecules is particularly important. A useful tool in understanding this relationship is a methodology to control the dynamics of intracellular molecules with an extracellular stimulus. However, this is a difficult task because the relationship between the levels of upstream molecules and those of downstream molecules is often not only stochastic, but also time-inhomogeneous, nonlinear, and not one-to-one. In this paper, we present an easy-to-implement model-based control method that makes the target downstream molecule to trace a desired time course by changing the concentration of a controllable upstream molecule. Our method uses predictions from Monte Carlo simulations of the model to decide the strength of the stimulus, while using a particle-based approach to make inferences regarding unobservable states. We applied our method to in silico control problems of insulin-dependent AKT pathway model and EGF-dependent Akt pathway model with system noise. We show that our method can robustly control the dynamics of the intracellular molecules against unknown system noise of various strengths, even in the absence of complete knowledge of the true model of the target system. PMID:28275530

A temporal modelling environment for internally grounded beliefs, desires and intentions

NARCIS (Netherlands)

Jonker, C.M.; Treur, J.; Wijngaards, W.C.A.

In this paper the internal dynamics of mental states, in particular states based on beliefs, desires and intentions, is formalised using a temporal language. A software environment is presented that can be used to specify, simulate and analyse temporal dependencies between mental states in

Somatic growth dynamics of West Atlantic hawksbill sea turtles: a spatio-temporal perspective

Science.gov (United States)

Bjorndal, Karen A.; Chaloupka, Milani; Saba, Vincent S.; Diez, Carlos E.; van Dam, Robert P.; Krueger, Barry H.; Horrocks, Julia A.; Santos, Armando J.B.; Bellini, Cláudio; Marcovaldi, Maria A.G.; Nava, Mabel; Willis, Sue; Godley, Brendan J.; Gore, Shannon; Hawkes, Lucy A.; McGowan, Andrew; Witt, Matthew J.; Stringell, Thomas B.; Sanghera, Amdeep; Richardson, Peter B.; Broderick, Annette C.; Phillips, Quinton; Calosso, Marta C.; Claydon, John A.B.; Blumenthal, Janice; Moncada, Felix; Nodarse, Gonzalo; Medina, Yosvani; Dunbar, Stephen G.; Wood, Lawrence D.; Lagueux, Cynthia J.; Campbell, Cathi L.; Meylan, Anne B.; Meylan, Peter A.; Burns Perez, Virginia R.; Coleman, Robin A.; Strindberg, Samantha; Guzmán-H, Vicente; Hart, Kristen M.; Cherkiss, Michael S.; Hillis-Starr, Zandy; Lundgren, Ian; Boulon, Ralf H.; Connett, Stephen; Outerbridge, Mark E.; Bolten, Alan B.

2016-01-01

Somatic growth dynamics are an integrated response to environmental conditions. Hawksbill sea turtles (Eretmochelys imbricata) are long-lived, major consumers in coral reef habitats that move over broad geographic areas (hundreds to thousands of kilometers). We evaluated spatio-temporal effects on hawksbill growth dynamics over a 33-yr period and 24 study sites throughout the West Atlantic and explored relationships between growth dynamics and climate indices. We compiled the largest ever data set on somatic growth rates for hawksbills – 3541 growth increments from 1980 to 2013. Using generalized additive mixed model analyses, we evaluated 10 covariates, including spatial and temporal variation, that could affect growth rates. Growth rates throughout the region responded similarly over space and time. The lack of a spatial effect or spatio-temporal interaction and the very strong temporal effect reveal that growth rates in West Atlantic hawksbills are likely driven by region-wide forces. Between 1997 and 2013, mean growth rates declined significantly and steadily by 18%. Regional climate indices have significant relationships with annual growth rates with 0- or 1-yr lags: positive with the Multivariate El Niño Southern Oscillation Index (correlation = 0.99) and negative with Caribbean sea surface temperature (correlation = −0.85). Declines in growth rates between 1997 and 2013 throughout the West Atlantic most likely resulted from warming waters through indirect negative effects on foraging resources of hawksbills. These climatic influences are complex. With increasing temperatures, trajectories of decline of coral cover and availability in reef habitats of major prey species of hawksbills are not parallel. Knowledge of how choice of foraging habitats, prey selection, and prey abundance are affected by warming water temperatures is needed to understand how climate change will affect productivity of consumers that live in association with coral reefs. Main

A Novel Temporal Bone Simulation Model Using 3D Printing Techniques.

Science.gov (United States)

Mowry, Sarah E; Jammal, Hachem; Myer, Charles; Solares, Clementino Arturo; Weinberger, Paul

2015-09-01

An inexpensive temporal bone model for use in a temporal bone dissection laboratory setting can be made using a commercially available, consumer-grade 3D printer. Several models for a simulated temporal bone have been described but use commercial-grade printers and materials to produce these models. The goal of this project was to produce a plastic simulated temporal bone on an inexpensive 3D printer that recreates the visual and haptic experience associated with drilling a human temporal bone. Images from a high-resolution CT of a normal temporal bone were converted into stereolithography files via commercially available software, with image conversion and print settings adjusted to achieve optimal print quality. The temporal bone model was printed using acrylonitrile butadiene styrene (ABS) plastic filament on a MakerBot 2x 3D printer. Simulated temporal bones were drilled by seven expert temporal bone surgeons, assessing the fidelity of the model as compared with a human cadaveric temporal bone. Using a four-point scale, the simulated bones were assessed for haptic experience and recreation of the temporal bone anatomy. The created model was felt to be an accurate representation of a human temporal bone. All raters felt strongly this would be a good training model for junior residents or to simulate difficult surgical anatomy. Material cost for each model was $1.92. A realistic, inexpensive, and easily reproducible temporal bone model can be created on a consumer-grade desktop 3D printer.

A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates

OpenAIRE

Lindström, Johan; Szpiro, Adam A; Sampson, Paul D; Oron, Assaf P; Richards, Mark; Larson, Tim V; Sheppard, Lianne

2013-01-01

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implem...

The Temporal Derivative of Expected Utility: A Neural Mechanism for Dynamic Decision-making

Science.gov (United States)

Zhang, Xian; Hirsch, Joy

2012-01-01

Real world tasks involving moving targets, such as driving a vehicle, are performed based on continuous decisions thought to depend upon the temporal derivative of the expected utility (∂V/∂t), where the expected utility (V) is the effective value of a future reward. However, those neural mechanisms that underlie dynamic decision-making are not well understood. This study investigates human neural correlates of both V and ∂V/∂t using fMRI and a novel experimental paradigm based on a pursuit-evasion game optimized to isolate components of dynamic decision processes. Our behavioral data show that players of the pursuit-evasion game adopt an exponential discounting function, supporting the expected utility theory. The continuous functions of V and ∂V/∂t were derived from the behavioral data and applied as regressors in fMRI analysis, enabling temporal resolution that exceeded the sampling rate of image acquisition, hyper-temporal resolution, by taking advantage of numerous trials that provide rich and independent manipulation of those variables. V and ∂V/∂t were each associated with distinct neural activity. Specifically, ∂V/∂t was associated with anterior and posterior cingulate cortices, superior parietal lobule, and ventral pallidum, whereas V was primarily associated with supplementary motor, pre and post central gyri, cerebellum, and thalamus. The association between the ∂V/∂t and brain regions previously related to decision-making is consistent with the primary role of the temporal derivative of expected utility in dynamic decision-making. PMID:22963852

The temporal derivative of expected utility: a neural mechanism for dynamic decision-making.

Science.gov (United States)

Zhang, Xian; Hirsch, Joy

2013-01-15

Real world tasks involving moving targets, such as driving a vehicle, are performed based on continuous decisions thought to depend upon the temporal derivative of the expected utility (∂V/∂t), where the expected utility (V) is the effective value of a future reward. However, the neural mechanisms that underlie dynamic decision-making are not well understood. This study investigates human neural correlates of both V and ∂V/∂t using fMRI and a novel experimental paradigm based on a pursuit-evasion game optimized to isolate components of dynamic decision processes. Our behavioral data show that players of the pursuit-evasion game adopt an exponential discounting function, supporting the expected utility theory. The continuous functions of V and ∂V/∂t were derived from the behavioral data and applied as regressors in fMRI analysis, enabling temporal resolution that exceeded the sampling rate of image acquisition, hyper-temporal resolution, by taking advantage of numerous trials that provide rich and independent manipulation of those variables. V and ∂V/∂t were each associated with distinct neural activity. Specifically, ∂V/∂t was associated with anterior and posterior cingulate cortices, superior parietal lobule, and ventral pallidum, whereas V was primarily associated with supplementary motor, pre and post central gyri, cerebellum, and thalamus. The association between the ∂V/∂t and brain regions previously related to decision-making is consistent with the primary role of the temporal derivative of expected utility in dynamic decision-making. Copyright © 2012 Elsevier Inc. All rights reserved.

Dynamic sensitivity analysis of long running landslide models through basis set expansion and meta-modelling

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Rohmer, Jeremy

2016-04-01

Predicting the temporal evolution of landslides is typically supported by numerical modelling. Dynamic sensitivity analysis aims at assessing the influence of the landslide properties on the time-dependent predictions (e.g., time series of landslide displacements). Yet two major difficulties arise: 1. Global sensitivity analysis require running the landslide model a high number of times (> 1000), which may become impracticable when the landslide model has a high computation time cost (> several hours); 2. Landslide model outputs are not scalar, but function of time, i.e. they are n-dimensional vectors with n usually ranging from 100 to 1000. In this article, I explore the use of a basis set expansion, such as principal component analysis, to reduce the output dimensionality to a few components, each of them being interpreted as a dominant mode of variation in the overall structure of the temporal evolution. The computationally intensive calculation of the Sobol' indices for each of these components are then achieved through meta-modelling, i.e. by replacing the landslide model by a "costless-to-evaluate" approximation (e.g., a projection pursuit regression model). The methodology combining "basis set expansion - meta-model - Sobol' indices" is then applied to the La Frasse landslide to investigate the dynamic sensitivity analysis of the surface horizontal displacements to the slip surface properties during the pore pressure changes. I show how to extract information on the sensitivity of each main modes of temporal behaviour using a limited number (a few tens) of long running simulations. In particular, I identify the parameters, which trigger the occurrence of a turning point marking a shift between a regime of low values of landslide displacements and one of high values.

Temporal feature integration for music genre classification

DEFF Research Database (Denmark)

Meng, Anders; Ahrendt, Peter; Larsen, Jan

2007-01-01

, but they capture neither the temporal dynamics nor dependencies among the individual feature dimensions. Here, a multivariate autoregressive feature model is proposed to solve this problem for music genre classification. This model gives two different feature sets, the diagonal autoregressive (DAR......) and multivariate autoregressive (MAR) features which are compared against the baseline mean-variance as well as two other temporal feature integration techniques. Reproducibility in performance ranking of temporal feature integration methods were demonstrated using two data sets with five and eleven music genres...

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior

DEFF Research Database (Denmark)

Hansen, Sofie Therese; Hansen, Lars Kai

2016-01-01

the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging......Electroencephalography (EEG) can capture brain dynamics in high temporal resolution. By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding...

Hotspots of Community Change: Temporal Dynamics Are Spatially Variable in Understory Plant Composition of a California Oak Woodland.

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Erica N Spotswood

Full Text Available Community response to external drivers such climate and disturbance can lead to fluctuations in community composition, or to directional change. Temporal dynamics can be influenced by a combination of drivers operating at multiple spatial scales, including external landscape scale drivers, local abiotic conditions, and local species pools. We hypothesized that spatial variation in these factors can create heterogeneity in temporal dynamics within landscapes. We used understory plant species composition from an 11 year dataset from a California oak woodland to compare plots where disturbance was experimentally manipulated with the removal of livestock grazing and a prescribed burn. We quantified three properties of temporal variation: compositional change (reflecting the appearance and disappearance of species, temporal fluctuation, and directional change. Directional change was related most strongly to disturbance type, and was highest at plots where grazing was removed during the study. Temporal fluctuations, compositional change, and directional change were all related to intrinsic abiotic factors, suggesting that some locations are more responsive to external drivers than others. Temporal fluctuations and compositional change were linked to local functional composition, indicating that environmental filters can create subsets of the local species pool that do not respond in the same way to external drivers. Temporal dynamics are often assumed to be relatively static at the landscape scale, provided disturbance and climate are continuous. This study shows that local and landscape scale factors jointly influence temporal dynamics creating hotspots that are particularly responsive to climate and disturbance. Thus, adequate predictions of response to disturbance or to changing climate will only be achieved by considering how factors at multiple spatial scales influence community resilience and recovery.

Hotspots of Community Change: Temporal Dynamics Are Spatially Variable in Understory Plant Composition of a California Oak Woodland.

Science.gov (United States)

Spotswood, Erica N; Bartolome, James W; Allen-Diaz, Barbara

2015-01-01

Community response to external drivers such climate and disturbance can lead to fluctuations in community composition, or to directional change. Temporal dynamics can be influenced by a combination of drivers operating at multiple spatial scales, including external landscape scale drivers, local abiotic conditions, and local species pools. We hypothesized that spatial variation in these factors can create heterogeneity in temporal dynamics within landscapes. We used understory plant species composition from an 11 year dataset from a California oak woodland to compare plots where disturbance was experimentally manipulated with the removal of livestock grazing and a prescribed burn. We quantified three properties of temporal variation: compositional change (reflecting the appearance and disappearance of species), temporal fluctuation, and directional change. Directional change was related most strongly to disturbance type, and was highest at plots where grazing was removed during the study. Temporal fluctuations, compositional change, and directional change were all related to intrinsic abiotic factors, suggesting that some locations are more responsive to external drivers than others. Temporal fluctuations and compositional change were linked to local functional composition, indicating that environmental filters can create subsets of the local species pool that do not respond in the same way to external drivers. Temporal dynamics are often assumed to be relatively static at the landscape scale, provided disturbance and climate are continuous. This study shows that local and landscape scale factors jointly influence temporal dynamics creating hotspots that are particularly responsive to climate and disturbance. Thus, adequate predictions of response to disturbance or to changing climate will only be achieved by considering how factors at multiple spatial scales influence community resilience and recovery.

Using dynamic Brownian bridge movement modelling to measure temporal patterns of habitat selection.

Science.gov (United States)

Byrne, Michael E; Clint McCoy, J; Hinton, Joseph W; Chamberlain, Michael J; Collier, Bret A

2014-09-01

Accurately describing animal space use is vital to understanding how wildlife use habitat. Improvements in GPS technology continue to facilitate collection of telemetry data at high spatial and temporal resolutions. Application of the recently introduced dynamic Brownian bridge movement model (dBBMM) to such data is promising as the method explicitly incorporates the behavioural heterogeneity of a movement path into the estimated utilization distribution (UD). Utilization distributions defining space use are normally estimated for time-scales ranging from weeks to months, obscuring much of the fine-scale information available from high-volume GPS data sets. By accounting for movement heterogeneity, the dBBMM provides a rigorous, behaviourally based estimate of space use between each set of relocations. Focusing on UDs generated between individual sets of locations allows us to quantify fine-scale circadian variation in habitat use. We used the dBBMM to estimate UDs bounding individual time steps for three terrestrial species with different life histories to illustrate how the method can be used to identify fine-scale variations in habitat use. We also demonstrate how dBBMMs can be used to characterize circadian patterns of habitat selection and link fine-scale patterns of habitat use to behaviour. We observed circadian patterns of habitat use that varied seasonally for a white-tailed deer (Odocoileus virginianus) and coyote (Canis latrans). We found seasonal patterns in selection by the white-tailed deer and were able to link use of conifer forests and agricultural fields to behavioural state of the coyote. Additionally, we were able to quantify the date in which a Rio Grande wild turkey (Meleagris gallopavo intermedia) initiated laying as well as when during the day, she was most likely to visit the nest site to deposit eggs. The ability to quantify circadian patterns of habitat use may have important implications for research and management of wildlife

Spatio-temporal dynamics of a fish predator: Density-dependent and hydrographic effects on Baltic Sea cod population.

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Valerio Bartolino

Full Text Available Understanding the mechanisms of spatial population dynamics is crucial for the successful management of exploited species and ecosystems. However, the underlying mechanisms of spatial distribution are generally complex due to the concurrent forcing of both density-dependent species interactions and density-independent environmental factors. Despite the high economic value and central ecological importance of cod in the Baltic Sea, the drivers of its spatio-temporal population dynamics have not been analytically investigated so far. In this paper, we used an extensive trawl survey dataset in combination with environmental data to investigate the spatial dynamics of the distribution of the Eastern Baltic cod during the past three decades using Generalized Additive Models. The results showed that adult cod distribution was mainly affected by cod population size, and to a minor degree by small-scale hydrological factors and the extent of suitable reproductive areas. As population size decreases, the cod population concentrates to the southern part of the Baltic Sea, where the preferred more marine environment conditions are encountered. Using the fitted models, we predicted the Baltic cod distribution back to the 1970s and a temporal index of cod spatial occupation was developed. Our study will contribute to the management and conservation of this important resource and of the ecosystem where it occurs, by showing the forces shaping its spatial distribution and therefore the potential response of the population to future exploitation and environmental changes.

The Temporal Dynamics of Visual Search: Evidence for Parallel Processing in Feature and Conjunction Searches

Science.gov (United States)

McElree, Brian; Carrasco, Marisa

2012-01-01

Feature and conjunction searches have been argued to delineate parallel and serial operations in visual processing. The authors evaluated this claim by examining the temporal dynamics of the detection of features and conjunctions. The 1st experiment used a reaction time (RT) task to replicate standard mean RT patterns and to examine the shapes of the RT distributions. The 2nd experiment used the response-signal speed–accuracy trade-off (SAT) procedure to measure discrimination (asymptotic detection accuracy) and detection speed (processing dynamics). Set size affected discrimination in both feature and conjunction searches but affected detection speed only in the latter. Fits of models to the SAT data that included a serial component overpredicted the magnitude of the observed dynamics differences. The authors concluded that both features and conjunctions are detected in parallel. Implications for the role of attention in visual processing are discussed. PMID:10641310

Modeling the spatio-temporal dynamics of porcine reproductive & respiratory syndrome cases at farm level using geographical distance and pig trade network matrices.

Science.gov (United States)

Amirpour Haredasht, Sara; Polson, Dale; Main, Rodger; Lee, Kyuyoung; Holtkamp, Derald; Martínez-López, Beatriz

2017-06-07

Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically devastating infectious diseases for the swine industry. A better understanding of the disease dynamics and the transmission pathways under diverse epidemiological scenarios is a key for the successful PRRS control and elimination in endemic settings. In this paper we used a two step parameter-driven (PD) Bayesian approach to model the spatio-temporal dynamics of PRRS and predict the PRRS status on farm in subsequent time periods in an endemic setting in the US. For such purpose we used information from a production system with 124 pig sites that reported 237 PRRS cases from 2012 to 2015 and from which the pig trade network and geographical location of farms (i.e., distance was used as a proxy of airborne transmission) was available. We estimated five PD models with different weights namely: (i) geographical distance weight which contains the inverse distance between each pair of farms in kilometers, (ii) pig trade weight (PT ji ) which contains the absolute number of pig movements between each pair of farms, (iii) the product between the distance weight and the standardized relative pig trade weight, (iv) the product between the standardized distance weight and the standardized relative pig trade weight, and (v) the product of the distance weight and the pig trade weight. The model that included the pig trade weight matrix provided the best fit to model the dynamics of PRRS cases on a 6-month basis from 2012 to 2015 and was able to predict PRRS outbreaks in the subsequent time period with an area under the ROC curve (AUC) of 0.88 and the accuracy of 85% (105/124). The result of this study reinforces the importance of pig trade in PRRS transmission in the US. Methods and results of this study may be easily adapted to any production system to characterize the PRRS dynamics under diverse epidemic settings to more timely support decision-making.

Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?

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Shi Chen

Full Text Available Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density, subgroup clustering (modularity, triadic property (transitivity, and dyadic interactions (correlation coefficient from a quadratic assignment procedure at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level or temporal (aggregated at daily level resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc. also changed substantially at different time and locations. There were certain time (feeding and location (hay that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect disease transmission pathways.

Temporal Feature Integration for Music Organisation

DEFF Research Database (Denmark)

Meng, Anders

2006-01-01

This Ph.D. thesis focuses on temporal feature integration for music organisation. Temporal feature integration is the process of combining all the feature vectors of a given time-frame into a single new feature vector in order to capture relevant information in the frame. Several existing methods...... for handling sequences of features are formulated in the temporal feature integration framework. Two datasets for music genre classification have been considered as valid test-beds for music organisation. Human evaluations of these, have been obtained to access the subjectivity on the datasets. Temporal...... ranking' approach is proposed for ranking the short-time features at larger time-scales according to their discriminative power in a music genre classification task. The multivariate AR (MAR) model has been proposed for temporal feature integration. It effectively models local dynamical structure...

The Temporal Dynamics of Spoken Word Recognition in Adverse Listening Conditions

Science.gov (United States)

Brouwer, Susanne; Bradlow, Ann R.

2016-01-01

This study examined the temporal dynamics of spoken word recognition in noise and background speech. In two visual-world experiments, English participants listened to target words while looking at four pictures on the screen: a target (e.g. "candle"), an onset competitor (e.g. "candy"), a rhyme competitor (e.g.…

Evaluation of spatio-temporal Bayesian models for the spread of infectious diseases in oil palm.

Science.gov (United States)

Denis, Marie; Cochard, Benoît; Syahputra, Indra; de Franqueville, Hubert; Tisné, Sébastien

2018-02-01

In the field of epidemiology, studies are often focused on mapping diseases in relation to time and space. Hierarchical modeling is a common flexible and effective tool for modeling problems related to disease spread. In the context of oil palm plantations infected by the fungal pathogen Ganoderma boninense, we propose and compare two spatio-temporal hierarchical Bayesian models addressing the lack of information on propagation modes and transmission vectors. We investigate two alternative process models to study the unobserved mechanism driving the infection process. The models help gain insight into the spatio-temporal dynamic of the infection by identifying a genetic component in the disease spread and by highlighting a spatial component acting at the end of the experiment. In this challenging context, we propose models that provide assumptions on the unobserved mechanism driving the infection process while making short-term predictions using ready-to-use software. Copyright © 2018 Elsevier Ltd. All rights reserved.

High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.

Science.gov (United States)

Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong

2018-08-01

This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests

Science.gov (United States)

He, Yujie; Yang, Jinyan; Zhuang, Qianlai; Harden, Jennifer W.; McGuire, A. David; Liu, Yaling; Wang, Gangsheng; Gu, Lianhong

2015-01-01

Soil carbon dynamics of terrestrial ecosystems play a significant role in the global carbon cycle. Microbial-based decomposition models have seen much growth recently for quantifying this role, yet dormancy as a common strategy used by microorganisms has not usually been represented and tested in these models against field observations. Here we developed an explicit microbial-enzyme decomposition model and examined model performance with and without representation of microbial dormancy at six temperate forest sites of different forest types. We then extrapolated the model to global temperate forest ecosystems to investigate biogeochemical controls on soil heterotrophic respiration and microbial dormancy dynamics at different temporal-spatial scales. The dormancy model consistently produced better match with field-observed heterotrophic soil CO2 efflux (RH) than the no dormancy model. Our regional modeling results further indicated that models with dormancy were able to produce more realistic magnitude of microbial biomass (analysis showed that soil organic carbon content was the dominating factor (correlation coefficient = 0.4–0.6) in the simulated spatial pattern of soil RHwith both models. In contrast to strong temporal and local controls of soil temperature and moisture on microbial dormancy, our modeling results showed that soil carbon-to-nitrogen ratio (C:N) was a major regulating factor at regional scales (correlation coefficient = −0.43 to −0.58), indicating scale-dependent biogeochemical controls on microbial dynamics. Our findings suggest that incorporating microbial dormancy could improve the realism of microbial-based decomposition models and enhance the integration of soil experiments and mechanistically based modeling.

A model relating Eulerian spatial and temporal velocity correlations

Science.gov (United States)

Cholemari, Murali R.; Arakeri, Jaywant H.

2006-03-01

In this paper we propose a model to relate Eulerian spatial and temporal velocity autocorrelations in homogeneous, isotropic and stationary turbulence. We model the decorrelation as the eddies of various scales becoming decorrelated. This enables us to connect the spatial and temporal separations required for a certain decorrelation through the ‘eddy scale’. Given either the spatial or the temporal velocity correlation, we obtain the ‘eddy scale’ and the rate at which the decorrelation proceeds. This leads to a spatial separation from the temporal correlation and a temporal separation from the spatial correlation, at any given value of the correlation relating the two correlations. We test the model using experimental data from a stationary axisymmetric turbulent flow with homogeneity along the axis.

Multi-material 3D Models for Temporal Bone Surgical Simulation.

Science.gov (United States)

Rose, Austin S; Kimbell, Julia S; Webster, Caroline E; Harrysson, Ola L A; Formeister, Eric J; Buchman, Craig A

2015-07-01

A simulated, multicolor, multi-material temporal bone model can be created using 3-dimensional (3D) printing that will prove both safe and beneficial in training for actual temporal bone surgical cases. As the process of additive manufacturing, or 3D printing, has become more practical and affordable, a number of applications for the technology in the field of Otolaryngology-Head and Neck Surgery have been considered. One area of promise is temporal bone surgical simulation. Three-dimensional representations of human temporal bones were created from temporal bone computed tomography (CT) scans using biomedical image processing software. Multi-material models were then printed and dissected in a temporal bone laboratory by attending and resident otolaryngologists. A 5-point Likert scale was used to grade the models for their anatomical accuracy and suitability as a simulation of cadaveric and operative temporal bone drilling. The models produced for this study demonstrate significant anatomic detail and a likeness to human cadaver specimens for drilling and dissection. Simulated temporal bones created by this process have potential benefit in surgical training, preoperative simulation for challenging otologic cases, and the standardized testing of temporal bone surgical skills. © The Author(s) 2015.

Lateralization for dynamic facial expressions in human superior temporal sulcus.

Science.gov (United States)

De Winter, François-Laurent; Zhu, Qi; Van den Stock, Jan; Nelissen, Koen; Peeters, Ronald; de Gelder, Beatrice; Vanduffel, Wim; Vandenbulcke, Mathieu

2015-02-01

Most face processing studies in humans show stronger activation in the right compared to the left hemisphere. Evidence is largely based on studies with static stimuli focusing on the fusiform face area (FFA). Hence, the pattern of lateralization for dynamic faces is less clear. Furthermore, it is unclear whether this property is common to human and non-human primates due to predisposing processing strategies in the right hemisphere or that alternatively left sided specialization for language in humans could be the driving force behind this phenomenon. We aimed to address both issues by studying lateralization for dynamic facial expressions in monkeys and humans. Therefore, we conducted an event-related fMRI experiment in three macaques and twenty right handed humans. We presented human and monkey dynamic facial expressions (chewing and fear) as well as scrambled versions to both species. We studied lateralization in independently defined face-responsive and face-selective regions by calculating a weighted lateralization index (LIwm) using a bootstrapping method. In order to examine if lateralization in humans is related to language, we performed a separate fMRI experiment in ten human volunteers including a 'speech' expression (one syllable non-word) and its scrambled version. Both within face-responsive and selective regions, we found consistent lateralization for dynamic faces (chewing and fear) versus scrambled versions in the right human posterior superior temporal sulcus (pSTS), but not in FFA nor in ventral temporal cortex. Conversely, in monkeys no consistent pattern of lateralization for dynamic facial expressions was observed. Finally, LIwms based on the contrast between different types of dynamic facial expressions (relative to scrambled versions) revealed left-sided lateralization in human pSTS for speech-related expressions compared to chewing and emotional expressions. To conclude, we found consistent laterality effects in human posterior STS but not

Model Based Temporal Reasoning

Science.gov (United States)

Rabin, Marla J.; Spinrad, Paul R.; Fall, Thomas C.

1988-03-01

Systems that assess the real world must cope with evidence that is uncertain, ambiguous, and spread over time. Typically, the most important function of an assessment system is to identify when activities are occurring that are unusual or unanticipated. Model based temporal reasoning addresses both of these requirements. The differences among temporal reasoning schemes lies in the methods used to avoid computational intractability. If we had n pieces of data and we wanted to examine how they were related, the worst case would be where we had to examine every subset of these points to see if that subset satisfied the relations. This would be 2n, which is intractable. Models compress this; if several data points are all compatible with a model, then that model represents all those data points. Data points are then considered related if they lie within the same model or if they lie in models that are related. Models thus address the intractability problem. They also address the problem of determining unusual activities if the data do not agree with models that are indicated by earlier data then something out of the norm is taking place. The models can summarize what we know up to that time, so when they are not predicting correctly, either something unusual is happening or we need to revise our models. The model based reasoner developed at Advanced Decision Systems is thus both intuitive and powerful. It is currently being used on one operational system and several prototype systems. It has enough power to be used in domains spanning the spectrum from manufacturing engineering and project management to low-intensity conflict and strategic assessment.

Temporal dynamics of divided spatial attention.

Science.gov (United States)

Itthipuripat, Sirawaj; Garcia, Javier O; Serences, John T

2013-05-01

In naturalistic settings, observers often have to monitor multiple objects dispersed throughout the visual scene. However, the degree to which spatial attention can be divided across spatially noncontiguous objects has long been debated, particularly when those objects are in close proximity. Moreover, the temporal dynamics of divided attention are unclear: is the process of dividing spatial attention gradual and continuous, or does it onset in a discrete manner? To address these issues, we recorded steady-state visual evoked potentials (SSVEPs) as subjects covertly monitored two flickering targets while ignoring an intervening distractor that flickered at a different frequency. All three stimuli were clustered within either the lower left or the lower right quadrant, and our dependent measure was SSVEP power at the target and distractor frequencies measured over time. In two experiments, we observed a temporally discrete increase in power for target- vs. distractor-evoked SSVEPs extending from ∼350 to 150 ms prior to correct (but not incorrect) responses. The divergence in SSVEP power immediately prior to a correct response suggests that spatial attention can be divided across noncontiguous locations, even when the targets are closely spaced within a single quadrant. In addition, the division of spatial attention appears to be relatively discrete, as opposed to slow and continuous. Finally, the predictive relationship between SSVEP power and behavior demonstrates that these neurophysiological measures of divided attention are meaningfully related to cognitive function.

Spatio-temporal cell dynamics in tumour spheroid irradiation

International Nuclear Information System (INIS)

Kempf, H.; Bleicher, M.; Meyer-Hermann, M.; Kempf, H.; Bleicher, M.; Kempf, H.; Meyer-Hermann, M.

2010-01-01

Multicellular tumour spheroids are realistic in vitro systems in radiation research that integrate cell-cell interaction and cell cycle control by factors in the medium. The dynamic reaction inside a tumour spheroid triggered by radiation is not well understood. Of special interest is the amount of cell cycle synchronization which could be triggered by irradiation, since this would allow follow-up irradiations to exploit the increased sensitivity of certain cell cycle phases. In order to investigate these questions we need to support irradiation experiments with mathematical models. In this article a new model is introduced combining the dynamics of tumour growth and irradiation treatments. The tumour spheroid growth is modelled using an agent-based Delaunay/Voronoi hybrid model in which the cells are represented by weighted dynamic vertices. Cell properties like full cell cycle dynamics are included. In order to be able to distinguish between different cell reactions in response to irradiation quality we introduce a probabilistic model for damage dynamics. The overall cell survival from this model is in agreement with predictions from the linear-quadratic model. Our model can describe the growth of avascular tumour spheroids in agreement to experimental results. Using the probabilistic model for irradiation damage dynamics the classic 'four Rs' of radiotherapy can be studied in silico. We found a pronounced reactivation of the tumour spheroid in response to irradiation. A majority of the surviving cells is synchronized in their cell cycle progression after irradiation. The cell synchronization could be actively triggered and should be exploited in an advanced fractionation scheme. Thus it has been demonstrated that our model could be used to understand the dynamics of tumour growth after irradiation and to propose optimized fractionation schemes in cooperation with experimental investigations. (authors)

The interplay between perceptual organization and object recognition: Temporal dynamics and neuropsychology

OpenAIRE

Torfs, Katrien

2012-01-01

The ease and efficiency with which we perceive objects in daily life masks the complexity of the processes involved. The main goal of my doctoral research was to enhance our understanding of the complex interplay between perceptual organization and object recognition. To this end, we investigated the dynamic interplay between different component processes of object recognition, and their temporal dynamics. In the first part of this thesis, I present three behavioral studies focusing on the ro...

Backtracking and Mixing Rate of Diffusion on Uncorrelated Temporal Networks

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Martin Gueuning

2017-10-01

Full Text Available We consider the problem of diffusion on temporal networks, where the dynamics of each edge is modelled by an independent renewal process. Despite the apparent simplicity of the model, the trajectories of a random walker exhibit non-trivial properties. Here, we quantify the walker’s tendency to backtrack at each step (return where he/she comes from, as well as the resulting effect on the mixing rate of the process. As we show through empirical data, non-Poisson dynamics may significantly slow down diffusion due to backtracking, by a mechanism intrinsically different from the standard bus paradox and related temporal mechanisms. We conclude by discussing the implications of our work for the interpretation of results generated by null models of temporal networks.

The Temporal Dynamics Model of Emotional Memory Processing: A Synthesis on the Neurobiological Basis of Stress-Induced Amnesia, Flashbulb and Traumatic Memories, and the Yerkes-Dodson Law

OpenAIRE

Diamond, David M.; Campbell, Adam M.; Park, Collin R.; Halonen, Joshua; Zoladz, Phillip R.

2007-01-01

We have reviewed research on the effects of stress on LTP in the hippocampus, amygdala and prefrontal cortex (PFC) and present new findings which provide insight into how the attention and memory-related functions of these structures are influenced by strong emotionality. We have incorporated the stress-LTP findings into our “temporal dynamics†model, which provides a framework for understanding the neurobiological basis of flashbulb and traumatic memories, as well as stress-induced ...

Temporal dynamics of attentional selection in adult male carriers of the fragile X premutation allele and adult controls

Directory of Open Access Journals (Sweden)

Ling Mei Wong

2015-02-01

Full Text Available Carriers of the fragile X premutation allele (fXPCs have an expanded CGG trinucleotide repeat size within the emph{FMR1} gene and are at increased risk of developing Fragile X-associated Tremor Ataxia Syndrome (FXTAS. Previous research has shown that male fXPCs with FXTAS exhibit cognitive decline, predominantly in executive functions such as inhibitory control and working memory. Recent evidence suggests fXPCs may also exhibit impairments in processing temporal information. The attentional blink (AB task is often used to examine the dynamics of attentional selection, but disagreements exist as to whether the AB is due to excessive or insufficient attentional control. In this study, we used a variant of the AB task and neuropsychological testing to explore the dynamics of attentional selection, relate AB performance to attentional control, and determine whether fXPCs exhibited temporal and/or attentional control impairments. Participants were adult male fXPCs, aged 18--48 years and asymptomatic for FXTAS (emph{n} = 19 and age-matched male controls (emph{n} = 20. We found that fXPCs did not differ from controls in the AB task, indicating that the temporal dynamics of attentional selection were intact. However, they were impaired in the letter-number sequencing task, a test of attentional control. In the combined fXPC and control group, letter-number sequencing performance correlated positively with AB magnitude. This finding supports models that posit the AB is due to excess attentional control. In our two-pronged analysis approach, we contribute to the theoretical literature in controls by extending the AB literature, and we enhance our understanding of fXPCs by demonstrating that at least some aspects of temporal processing may be spared.

Attention increases the temporal precision of conscious perception: verifying the Neural-ST Model.

Directory of Open Access Journals (Sweden)

Srivas Chennu

2009-11-01

Full Text Available What role does attention play in ensuring the temporal precision of visual perception? Behavioural studies have investigated feature selection and binding in time using fleeting sequences of stimuli in the Rapid Serial Visual Presentation (RSVP paradigm, and found that temporal accuracy is reduced when attentional control is diminished. To reduce the efficacy of attentional deployment, these studies have employed the Attentional Blink (AB phenomenon. In this article, we use electroencephalography (EEG to directly investigate the temporal dynamics of conscious perception. Specifically, employing a combination of experimental analysis and neural network modelling, we test the hypothesis that the availability of attention reduces temporal jitter in the latency between a target's visual onset and its consolidation into working memory. We perform time-frequency analysis on data from an AB study to compare the EEG trials underlying the P3 ERPs (Event-related Potential evoked by targets seen outside vs. inside the AB time window. We find visual differences in phase-sorted ERPimages and statistical differences in the variance of the P3 phase distributions. These results argue for increased variation in the latency of conscious perception during the AB. This experimental analysis is complemented by a theoretical exploration of temporal attention and target processing. Using activation traces from the Neural-ST(2 model, we generate virtual ERPs and virtual ERPimages. These are compared to their human counterparts to propose an explanation of how target consolidation in the context of the AB influences the temporal variability of selective attention. The AB provides us with a suitable phenomenon with which to investigate the interplay between attention and perception. The combination of experimental and theoretical elucidation in this article contributes to converging evidence for the notion that the AB reflects a reduction in the temporal acuity of

The Role of Inhibition in a Computational Model of an Auditory Cortical Neuron during the Encoding of Temporal Information

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Bendor, Daniel

2015-01-01

In auditory cortex, temporal information within a sound is represented by two complementary neural codes: a temporal representation based on stimulus-locked firing and a rate representation, where discharge rate co-varies with the timing between acoustic events but lacks a stimulus-synchronized response. Using a computational neuronal model, we find that stimulus-locked responses are generated when sound-evoked excitation is combined with strong, delayed inhibition. In contrast to this, a non-synchronized rate representation is generated when the net excitation evoked by the sound is weak, which occurs when excitation is coincident and balanced with inhibition. Using single-unit recordings from awake marmosets (Callithrix jacchus), we validate several model predictions, including differences in the temporal fidelity, discharge rates and temporal dynamics of stimulus-evoked responses between neurons with rate and temporal representations. Together these data suggest that feedforward inhibition provides a parsimonious explanation of the neural coding dichotomy observed in auditory cortex. PMID:25879843

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

DEFF Research Database (Denmark)

Fraccaro, Marco; Kamronn, Simon Due; Paquet, Ulrich

2017-01-01

This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non-linear dynamics of the objects in its world. We introduce the Kalman variational auto-encoder, a framework...... for unsupervised learning of sequential data that disentangles two latent representations: an object’s representation, coming from a recognition model, and a latent state describing its dynamics. As a result, the evolution of the world can be imagined and missing data imputed, both without the need to generate...

Temporal dynamics of a subtropical urban forest in San Juan, Puerto Rico, 2001-2010

Science.gov (United States)

J. M. Tucker Lima; C. L. Staudhammer; T. J. Brandeis; F. J. Escobedo; W. Zipperer

2013-01-01

Several studies report urban tree growth and mortality rates as well as species composition, structural dynamics, and other characteristics of urban forests in mostly temperate, inland urban areas. Temporal dynamics of urban forests in subtropical and tropical forest regions are, until now, little explored and represent a new and important direction for study and...

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.

Science.gov (United States)

Gordonov, Simon; Hwang, Mun Kyung; Wells, Alan; Gertler, Frank B; Lauffenburger, Douglas A; Bathe, Mark

2016-01-01

Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.

Temporal dynamics of ikaite in experimental sea ice

DEFF Research Database (Denmark)

Rysgaard, Søren; Wang, F.; Galley, R.J.

2014-01-01

Ikaite (CaCO3·6H2O) is a metastable phase of calcium carbonate that normally forms in a cold environment and/or under high pressure. Recently, ikaite crystals have been found in sea ice, and it has been suggested that their precipitation may play an important role in air–sea CO2 exchange in ice......-covered seas. Little is known, however, of the spatial and temporal dynamics of ikaite in sea ice. Here we present evidence for highly dynamic ikaite precipitation and dissolution in sea ice grown at an outdoor pool of the Sea-ice Environmental Research Facility (SERF) in Manitoba, Canada. During...... the experiment, ikaite precipitated in sea ice when temperatures were below −4 C, creating three distinct zones of ikaite concentrations: (1) a millimeter-to-centimeter-thin surface layer containing frost flowers and brine skim with bulk ikaite concentrations of > 2000 μmol kg−1, (2) an internal layer...

Temporal dynamics of motor cortex excitability during perception of natural emotional scenes

NARCIS (Netherlands)

Borgomaneri, Sara; Gazzola, Valeria; Avenanti, Alessio

2014-01-01

Although it is widely assumed that emotions prime the body for action, the effects of visual perception of natural emotional scenes on the temporal dynamics of the human motor system have scarcely been investigated. Here, we used single-pulse transcranial magnetic stimulation (TMS) to assess motor

Noise-induced temporal dynamics in Turing systems

KAUST Repository

Schumacher, Linus J.

2013-04-25

We examine the ability of intrinsic noise to produce complex temporal dynamics in Turing pattern formation systems, with particular emphasis on the Schnakenberg kinetics. Using power spectral methods, we characterize the behavior of the system using stochastic simulations at a wide range of points in parameter space and compare with analytical approximations. Specifically, we investigate whether polarity switching of stochastic patterns occurs at a defined frequency. We find that it can do so in individual realizations of a stochastic simulation, but that the frequency is not defined consistently across realizations in our samples of parameter space. Further, we examine the effect of noise on deterministically predicted traveling waves and find them increased in amplitude and decreased in speed. © 2013 American Physical Society.

Model Checking Temporal Logic Formulas Using Sticker Automata

Science.gov (United States)

Feng, Changwei; Wu, Huanmei

2017-01-01

As an important complex problem, the temporal logic model checking problem is still far from being fully resolved under the circumstance of DNA computing, especially Computation Tree Logic (CTL), Interval Temporal Logic (ITL), and Projection Temporal Logic (PTL), because there is still a lack of approaches for DNA model checking. To address this challenge, a model checking method is proposed for checking the basic formulas in the above three temporal logic types with DNA molecules. First, one-type single-stranded DNA molecules are employed to encode the Finite State Automaton (FSA) model of the given basic formula so that a sticker automaton is obtained. On the other hand, other single-stranded DNA molecules are employed to encode the given system model so that the input strings of the sticker automaton are obtained. Next, a series of biochemical reactions are conducted between the above two types of single-stranded DNA molecules. It can then be decided whether the system satisfies the formula or not. As a result, we have developed a DNA-based approach for checking all the basic formulas of CTL, ITL, and PTL. The simulated results demonstrate the effectiveness of the new method. PMID:29119114

Towards understanding temporal and spatial dynamics of seagrass landscapes using time-series remote sensing

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Lyons, Mitchell B.; Roelfsema, Chris M.; Phinn, Stuart R.

2013-03-01

The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales, however, the link to large spatial extent landscape and population dynamics is still unresolved in seagrass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consistency to appropriately address this issue. This study uses two high temporal resolution time-series of thematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter- and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authors developed an object-based approach to map seagrass cover level distribution from a long term archive of Landsat TM and ETM+ images on the Eastern Banks (≈200 km2), Moreton Bay, Australia. In this work a range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual maps from 1988 to 2010 and a time-series of 16 monthly maps during 2008-2010. Significant new insight was presented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrass cover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels over time. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks, but there was a significant decline in seagrass cover level condition. A case study of two smaller communities within the Eastern Banks that experienced a decline in both overall seagrass area and condition are examined in detail, highlighting possible differences in environmental and process drivers. We demonstrate how trend and time-series analysis enabled seagrass distribution to be appropriately assessed in context of its spatial and temporal history and provides the ability to not only quantify change, but also describe the type of change. We also demonstrate the potential use of time-series analysis products to investigate seagrass growth and

Threshold model of cascades in empirical temporal networks

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Karimi, Fariba; Holme, Petter

2013-08-01

Threshold models try to explain the consequences of social influence like the spread of fads and opinions. Along with models of epidemics, they constitute a major theoretical framework of social spreading processes. In threshold models on static networks, an individual changes her state if a certain fraction of her neighbors has done the same. When there are strong correlations in the temporal aspects of contact patterns, it is useful to represent the system as a temporal network. In such a system, not only contacts but also the time of the contacts are represented explicitly. In many cases, bursty temporal patterns slow down disease spreading. However, as we will see, this is not a universal truth for threshold models. In this work we propose an extension of Watts’s classic threshold model to temporal networks. We do this by assuming that an agent is influenced by contacts which lie a certain time into the past. I.e., the individuals are affected by contacts within a time window. In addition to thresholds in the fraction of contacts, we also investigate the number of contacts within the time window as a basis for influence. To elucidate the model’s behavior, we run the model on real and randomized empirical contact datasets.

Different relationships between temporal phylogenetic turnover and phylogenetic similarity and in two forests were detected by a new null model.

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Huang, Jian-Xiong; Zhang, Jian; Shen, Yong; Lian, Ju-yu; Cao, Hong-lin; Ye, Wan-hui; Wu, Lin-fang; Bin, Yue

2014-01-01

Ecologists have been monitoring community dynamics with the purpose of understanding the rates and causes of community change. However, there is a lack of monitoring of community dynamics from the perspective of phylogeny. We attempted to understand temporal phylogenetic turnover in a 50 ha tropical forest (Barro Colorado Island, BCI) and a 20 ha subtropical forest (Dinghushan in southern China, DHS). To obtain temporal phylogenetic turnover under random conditions, two null models were used. The first shuffled names of species that are widely used in community phylogenetic analyses. The second simulated demographic processes with careful consideration on the variation in dispersal ability among species and the variations in mortality both among species and among size classes. With the two models, we tested the relationships between temporal phylogenetic turnover and phylogenetic similarity at different spatial scales in the two forests. Results were more consistent with previous findings using the second null model suggesting that the second null model is more appropriate for our purposes. With the second null model, a significantly positive relationship was detected between phylogenetic turnover and phylogenetic similarity in BCI at a 10 m×10 m scale, potentially indicating phylogenetic density dependence. This relationship in DHS was significantly negative at three of five spatial scales. This could indicate abiotic filtering processes for community assembly. Using variation partitioning, we found phylogenetic similarity contributed to variation in temporal phylogenetic turnover in the DHS plot but not in BCI plot. The mechanisms for community assembly in BCI and DHS vary from phylogenetic perspective. Only the second null model detected this difference indicating the importance of choosing a proper null model.

Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

Science.gov (United States)

Ruiz-Pérez, Guiomar; Koch, Julian; Manfreda, Salvatore; Caylor, Kelly; Francés, Félix

2017-12-01

Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment - the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

A Spatio-Temporal Enhanced Metadata Model for Interdisciplinary Instant Point Observations in Smart Cities

Directory of Open Access Journals (Sweden)

Nengcheng Chen

2017-02-01

Full Text Available Due to the incomprehensive and inconsistent description of spatial and temporal information for city data observed by sensors in various fields, it is a great challenge to share the massive, multi-source and heterogeneous interdisciplinary instant point observation data resources. In this paper, a spatio-temporal enhanced metadata model for point observation data sharing was proposed. The proposed Data Meta-Model (DMM focused on the spatio-temporal characteristics and formulated a ten-tuple information description structure to provide a unified and spatio-temporal enhanced description of the point observation data. To verify the feasibility of the point observation data sharing based on DMM, a prototype system was established, and the performance improvement of Sensor Observation Service (SOS for the instant access and insertion of point observation data was realized through the proposed MongoSOS, which is a Not Only SQL (NoSQL SOS based on the MongoDB database and has the capability of distributed storage. For example, the response time of the access and insertion for navigation and positioning data can be realized at the millisecond level. Case studies were conducted, including the gas concentrations monitoring for the gas leak emergency response and the smart city public vehicle monitoring based on BeiDou Navigation Satellite System (BDS used for recording the dynamic observation information. The results demonstrated the versatility and extensibility of the DMM, and the spatio-temporal enhanced sharing for interdisciplinary instant point observations in smart cities.

Temporal dynamics of ikaite in experimental sea ice

Science.gov (United States)

Rysgaard, S.; Wang, F.; Galley, R. J.; Grimm, R.; Notz, D.; Lemes, M.; Geilfus, N.-X.; Chaulk, A.; Hare, A. A.; Crabeck, O.; Else, B. G. T.; Campbell, K.; Sørensen, L. L.; Sievers, J.; Papakyriakou, T.

2014-08-01

Ikaite (CaCO3 · 6H2O) is a metastable phase of calcium carbonate that normally forms in a cold environment and/or under high pressure. Recently, ikaite crystals have been found in sea ice, and it has been suggested that their precipitation may play an important role in air-sea CO2 exchange in ice-covered seas. Little is known, however, of the spatial and temporal dynamics of ikaite in sea ice. Here we present evidence for highly dynamic ikaite precipitation and dissolution in sea ice grown at an outdoor pool of the Sea-ice Environmental Research Facility (SERF) in Manitoba, Canada. During the experiment, ikaite precipitated in sea ice when temperatures were below -4 °C, creating three distinct zones of ikaite concentrations: (1) a millimeter-to-centimeter-thin surface layer containing frost flowers and brine skim with bulk ikaite concentrations of >2000 μmol kg-1, (2) an internal layer with ikaite concentrations of 200-400 μmol kg-1, and (3) a bottom layer with ikaite concentrations of ikaite crystals to dissolve. Manual removal of the snow cover allowed the sea ice to cool and brine salinities to increase, resulting in rapid ikaite precipitation. The observed ikaite concentrations were on the same order of magnitude as modeled by FREZCHEM, which further supports the notion that ikaite concentration in sea ice increases with decreasing temperature. Thus, varying snow conditions may play a key role in ikaite precipitation and dissolution in sea ice. This could have a major implication for CO2 exchange with the atmosphere and ocean that has not been accounted for previously.

Bayesian state space models for dynamic genetic network construction across multiple tissues.

Science.gov (United States)

Liang, Yulan; Kelemen, Arpad

2016-08-01

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Synthesizing Dynamic Programming Algorithms from Linear Temporal Logic Formulae

Science.gov (United States)

Rosu, Grigore; Havelund, Klaus

2001-01-01

The problem of testing a linear temporal logic (LTL) formula on a finite execution trace of events, generated by an executing program, occurs naturally in runtime analysis of software. We present an algorithm which takes an LTL formula and generates an efficient dynamic programming algorithm. The generated algorithm tests whether the LTL formula is satisfied by a finite trace of events given as input. The generated algorithm runs in linear time, its constant depending on the size of the LTL formula. The memory needed is constant, also depending on the size of the formula.

Spatio-temporal dynamics and laterality effects of face inversion, feature presence and configuration, and face outline

Directory of Open Access Journals (Sweden)

Ksenija eMarinkovic

2014-11-01

Full Text Available Although a crucial role of the fusiform gyrus in face processing has been demonstrated with a variety of methods, converging evidence suggests that face processing involves an interactive and overlapping processing cascade in distributed brain areas. Here we examine the spatio-temporal stages and their functional tuning to face inversion, presence and configuration of inner features, and face contour in healthy subjects during passive viewing. Anatomically-constrained magnetoencephalography (aMEG combines high-density whole-head MEG recordings and distributed source modeling with high-resolution structural MRI. Each person's reconstructed cortical surface served to constrain noise-normalized minimum norm inverse source estimates. The earliest activity was estimated to the occipital cortex at ~100 ms after stimulus onset and was sensitive to an initial coarse level visual analysis. Activity in the right-lateralized ventral temporal area (inclusive of the fusiform gyrus peaked at ~160ms and was largest to inverted faces. Images containing facial features in the veridical and rearranged configuration irrespective of the facial outline elicited intermediate level activity. The M160 stage may provide structural representations necessary for downstream distributed areas to process identity and emotional expression. However, inverted faces additionally engaged the left ventral temporal area at ~180 ms and were uniquely subserved by bilateral processing. This observation is consistent with the dual route model and spared processing of inverted faces in prosopagnosia. The subsequent deflection, peaking at ~240ms in the anterior temporal areas bilaterally, was largest to normal, upright faces. It may reflect initial engagement of the distributed network subserving individuation and familiarity. These results support dynamic models suggesting that processing of unfamiliar faces in the absence of a cognitive task is subserved by a distributed and

Separating temporal and topological effects in walk-based network centrality.

Science.gov (United States)

Colman, Ewan R; Charlton, Nathaniel

2016-07-01

The recently introduced concept of dynamic communicability is a valuable tool for ranking the importance of nodes in a temporal network. Two metrics, broadcast score and receive score, were introduced to measure the centrality of a node with respect to a model of contagion based on time-respecting walks. This article examines the temporal and structural factors influencing these metrics by considering a versatile stochastic temporal network model. We analytically derive formulas to accurately predict the expectation of the broadcast and receive scores when one or more columns in a temporal edge-list are shuffled. These methods are then applied to two publicly available data sets and we quantify how much the centrality of each individual depends on structural or temporal influences. From our analysis, we highlight two practical contributions: a way to control for temporal variation when computing dynamic communicability and the conclusion that the broadcast and receive scores can, under a range of circumstances, be replaced by the row and column sums of the matrix exponential of a weighted adjacency matrix given by the data.

An investigation into the effects of temporal resolution on hepatic dynamic contrast-enhanced MRI in volunteers and in patients with hepatocellular carcinoma

International Nuclear Information System (INIS)

Gill, Andrew B; Graves, Martin J; Lomas, David J; Black, Richard T; Bowden, David J; Priest, Andrew N

2014-01-01

This study investigated the effect of temporal resolution on the dual-input pharmacokinetic (PK) modelling of dynamic contrast-enhanced MRI (DCE-MRI) data from normal volunteer livers and from patients with hepatocellular carcinoma. Eleven volunteers and five patients were examined at 3 T. Two sections, one optimized for the vascular input functions (VIF) and one for the tissue, were imaged within a single heart-beat (HB) using a saturation-recovery fast gradient echo sequence. The data was analysed using a dual-input single-compartment PK model. The VIFs and/or uptake curves were then temporally sub-sampled (at interval ▵t = [2–20] s) before being subject to the same PK analysis. Statistical comparisons of tumour and normal tissue PK parameter values using a 5% significance level gave rise to the same study results when temporally sub-sampling the VIFs to HB < ▵t <4 s. However, sub-sampling to ▵t > 4 s did adversely affect the statistical comparisons. Temporal sub-sampling of just the liver/tumour tissue uptake curves at ▵t ≤ 20 s, whilst using high temporal resolution VIFs, did not substantially affect PK parameter statistical comparisons. In conclusion, there is no practical advantage to be gained from acquiring very high temporal resolution hepatic DCE-MRI data. Instead the high temporal resolution could be usefully traded for increased spatial resolution or SNR. (paper)

Multiresolution Network Temporal and Spatial Scheduling Model of Scenic Spot

Directory of Open Access Journals (Sweden)

Peng Ge

2013-01-01

Full Text Available Tourism is one of pillar industries of the world economy. Low-carbon tourism will be the mainstream direction of the scenic spots' development, and the ω path of low-carbon tourism development is to develop economy and protect environment simultaneously. However, as the tourists' quantity is increasing, the loads of scenic spots are out of control. And the instantaneous overload in some spots caused the image phenomenon of full capacity of the whole scenic spot. Therefore, realizing the real-time schedule becomes the primary purpose of scenic spot’s management. This paper divides the tourism distribution system into several logically related subsystems and constructs a temporal and spatial multiresolution network scheduling model according to the regularity of scenic spots’ overload phenomenon in time and space. It also defines dynamic distribution probability and equivalent dynamic demand to realize the real-time prediction. We define gravitational function between fields and takes it as the utility of schedule, after resolving the transportation model of each resolution, it achieves hierarchical balance between demand and capacity of the system. The last part of the paper analyzes the time complexity of constructing a multiresolution distribution system.

Global sensitivity analysis of a dynamic model for gene expression in Drosophila embryos

Science.gov (United States)

McCarthy, Gregory D.; Drewell, Robert A.

2015-01-01

It is well known that gene regulation is a tightly controlled process in early organismal development. However, the roles of key processes involved in this regulation, such as transcription and translation, are less well understood, and mathematical modeling approaches in this field are still in their infancy. In recent studies, biologists have taken precise measurements of protein and mRNA abundance to determine the relative contributions of key factors involved in regulating protein levels in mammalian cells. We now approach this question from a mathematical modeling perspective. In this study, we use a simple dynamic mathematical model that incorporates terms representing transcription, translation, mRNA and protein decay, and diffusion in an early Drosophila embryo. We perform global sensitivity analyses on this model using various different initial conditions and spatial and temporal outputs. Our results indicate that transcription and translation are often the key parameters to determine protein abundance. This observation is in close agreement with the experimental results from mammalian cells for various initial conditions at particular time points, suggesting that a simple dynamic model can capture the qualitative behavior of a gene. Additionally, we find that parameter sensitivites are temporally dynamic, illustrating the importance of conducting a thorough global sensitivity analysis across multiple time points when analyzing mathematical models of gene regulation. PMID:26157608

Effective Connectivity between Ventral Occipito-Temporal and Ventral Inferior Frontal Cortex during Lexico-Semantic Processing. A Dynamic Causal Modeling Study

Directory of Open Access Journals (Sweden)

Marcela Perrone-Bertolotti

2017-06-01

Full Text Available It has been suggested that dorsal and ventral pathways support distinct aspects of language processing. Yet, the full extent of their involvement and their inter-regional connectivity in visual word recognition is still unknown. Studies suggest that they might reflect the dual-route model of reading, with the dorsal pathway more involved in grapho-phonological conversion during phonological tasks, and the ventral pathway performing lexico-semantic access during semantic tasks. Furthermore, this subdivision is also suggested at the level of the inferior frontal cortex, involving ventral and dorsal parts for lexico-semantic and phonological processing, respectively. In the present study, we assessed inter-regional brain connectivity and task-induced modulations of brain activity during a phoneme detection and semantic categorization tasks, using fMRI in healthy subject. We used a dynamic causal modeling approach to assess inter-regional connectivity and task demand modulation within the dorsal and ventral pathways, including the following network components: the ventral occipito-temporal cortex (vOTC; dorsal and ventral, the superior temporal gyrus (STG; dorsal, the dorsal inferior frontal gyrus (dIFG; dorsal, and the ventral IFG (vIFG; ventral. We report three distinct inter-regional interactions supporting orthographic information transfer from vOTC to other language regions (vOTC -> STG, vOTC -> vIFG and vOTC -> dIFG regardless of task demands. Moreover, we found that (a during semantic processing (direct ventral pathway the vOTC -> vIFG connection strength specifically increased and (b a lack of modulation of the vOTC -> dIFG connection strength by the task that could suggest a more general involvement of the dorsal pathway during visual word recognition. Results are discussed in terms of anatomo-functional connectivity of visual word recognition network.

Data Driven Approach for High Resolution Population Distribution and Dynamics Models

Energy Technology Data Exchange (ETDEWEB)

Bhaduri, Budhendra L [ORNL; Bright, Eddie A [ORNL; Rose, Amy N [ORNL; Liu, Cheng [ORNL; Urban, Marie L [ORNL; Stewart, Robert N [ORNL

2014-01-01

High resolution population distribution data are vital for successfully addressing critical issues ranging from energy and socio-environmental research to public health to human security. Commonly available population data from Census is constrained both in space and time and does not capture population dynamics as functions of space and time. This imposes a significant limitation on the fidelity of event-based simulation models with sensitive space-time resolution. This paper describes ongoing development of high-resolution population distribution and dynamics models, at Oak Ridge National Laboratory, through spatial data integration and modeling with behavioral or activity-based mobility datasets for representing temporal dynamics of population. The model is resolved at 1 km resolution globally and describes the U.S. population for nighttime and daytime at 90m. Integration of such population data provides the opportunity to develop simulations and applications in critical infrastructure management from local to global scales.

Spatio-temporal modeling of nonlinear distributed parameter systems

CERN Document Server

Li, Han-Xiong

2011-01-01

The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein s

Spatial and Temporal Dynamics in Air Pollution Exposure Assessment

Science.gov (United States)

Dias, Daniela; Tchepel, Oxana

2018-01-01

Analyzing individual exposure in urban areas offers several challenges where both the individual’s activities and air pollution levels demonstrate a large degree of spatial and temporal dynamics. This review article discusses the concepts, key elements, current developments in assessing personal exposure to urban air pollution (seventy-two studies reviewed) and respective advantages and disadvantages. A new conceptual structure to organize personal exposure assessment methods is proposed according to two classification criteria: (i) spatial-temporal variations of individuals’ activities (point-fixed or trajectory based) and (ii) characterization of air quality (variable or uniform). This review suggests that the spatial and temporal variability of urban air pollution levels in combination with indoor exposures and individual’s time-activity patterns are key elements of personal exposure assessment. In the literature review, the majority of revised studies (44 studies) indicate that the trajectory based with variable air quality approach provides a promising framework for tackling the important question of inter- and intra-variability of individual exposure. However, future quantitative comparison between the different approaches should be performed, and the selection of the most appropriate approach for exposure quantification should take into account the purpose of the health study. This review provides a structured basis for the intercomparing of different methodologies and to make their advantages and limitations more transparent in addressing specific research objectives. PMID:29558426

Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests

Energy Technology Data Exchange (ETDEWEB)

He, Yujie [Purdue Univ., West Lafayette, IN (United States). Dept. of Earth, Atmospheric, and Planetary Sciences; Yang, Jinyan [Univ. of Georgia, Athens, GA (United States). Warnell School of Forestry and Natural Resources; Northeast Forestry Univ., Harbin (China). Center for Ecological Research; Zhuang, Qianlai [Purdue Univ., West Lafayette, IN (United States). Dept. of Earth, Atmospheric, and Planetary Sciences; Purdue Univ., West Lafayette, IN (United States). Dept. of Agronomy; Harden, Jennifer W. [U.S. Geological Survey, Menlo Park, CA (United States); McGuire, Anthony D. [Alaska Cooperative Fish and Wildlife Research Unit, U.S. Geological Survey, Univ. of Alaska, Fairbanks, AK (United States). U.S. Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit; Liu, Yaling [Purdue Univ., West Lafayette, IN (United States). Dept. of Earth, Atmospheric, and Planetary Sciences; Wang, Gangsheng [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Climate Change Science Inst. and Environmental Sciences Division; Gu, Lianhong [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division

2015-11-20

Soil carbon dynamics of terrestrial ecosystems play a significant role in the global carbon cycle. Microbial-based decomposition models have seen much growth recently for quantifying this role, yet dormancy as a common strategy used by microorganisms has not usually been represented and tested in these models against field observations. Here in this study we developed an explicit microbial-enzyme decomposition model and examined model performance with and without representation of microbial dormancy at six temperate forest sites of different forest types. We then extrapolated the model to global temperate forest ecosystems to investigate biogeochemical controls on soil heterotrophic respiration and microbial dormancy dynamics at different temporal-spatial scales. The dormancy model consistently produced better match with field-observed heterotrophic soil CO₂ efflux (R_H) than the no dormancy model. Our regional modeling results further indicated that models with dormancy were able to produce more realistic magnitude of microbial biomass (<2% of soil organic carbon) and soil R_H (7.5 ± 2.4 PgCyr^-1). Spatial correlation analysis showed that soil organic carbon content was the dominating factor (correlation coefficient = 0.4-0.6) in the simulated spatial pattern of soil R_H with both models. In contrast to strong temporal and local controls of soil temperature and moisture on microbial dormancy, our modeling results showed that soil carbon-to-nitrogen ratio (C:N) was a major regulating factor at regional scales (correlation coefficient = -0.43 to -0.58), indicating scale-dependent biogeochemical controls on microbial dynamics. Our findings suggest that incorporating microbial dormancy could improve the realism of microbial-based decomposition models and enhance the integration of soil experiments and mechanistically based modeling.

Spatio-temporal models of mental processes from fMRI.

Science.gov (United States)

Janoos, Firdaus; Machiraju, Raghu; Singh, Shantanu; Morocz, Istvan Ákos

2011-07-15

Understanding the highly complex, spatially distributed and temporally organized phenomena entailed by mental processes using functional MRI is an important research problem in cognitive and clinical neuroscience. Conventional analysis methods focus on the spatial dimension of the data discarding the information about brain function contained in the temporal dimension. This paper presents a fully spatio-temporal multivariate analysis method using a state-space model (SSM) for brain function that yields not only spatial maps of activity but also its temporal structure along with spatially varying estimates of the hemodynamic response. Efficient algorithms for estimating the parameters along with quantitative validations are given. A novel low-dimensional feature-space for representing the data, based on a formal definition of functional similarity, is derived. Quantitative validation of the model and the estimation algorithms is provided with a simulation study. Using a real fMRI study for mental arithmetic, the ability of this neurophysiologically inspired model to represent the spatio-temporal information corresponding to mental processes is demonstrated. Moreover, by comparing the models across multiple subjects, natural patterns in mental processes organized according to different mental abilities are revealed. Copyright © 2011 Elsevier Inc. All rights reserved.

Dynamic Textures Modeling via Joint Video Dictionary Learning.

Science.gov (United States)

Wei, Xian; Li, Yuanxiang; Shen, Hao; Chen, Fang; Kleinsteuber, Martin; Wang, Zhongfeng

2017-04-06

Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DT) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named joint video dictionary learning (JVDL), to model a video adaptively. By treating the sparse coefficients of image frames over a learned dictionary as the underlying "states", we learn an efficient and robust linear transition matrix between two adjacent frames of sparse events in time series. Hence, a dynamic scene sequence is represented by an appropriate transition matrix associated with a dictionary. In order to ensure the stability of JVDL, we impose several constraints on such transition matrix and dictionary. The developed framework is able to capture the dynamics of a moving scene by exploring both sparse properties and the temporal correlations of consecutive video frames. Moreover, such learned JVDL parameters can be used for various DT applications, such as DT synthesis and recognition. Experimental results demonstrate the strong competitiveness of the proposed JVDL approach in comparison with state-of-the-art video representation methods. Especially, it performs significantly better in dealing with DT synthesis and recognition on heavily corrupted data.

A Model of the Spatio-temporal Dynamics of Drosophila Eye Disc Development.

Science.gov (United States)

Fried, Patrick; Sánchez-Aragón, Máximo; Aguilar-Hidalgo, Daniel; Lehtinen, Birgitta; Casares, Fernando; Iber, Dagmar

2016-09-01

Patterning and growth are linked during early development and have to be tightly controlled to result in a functional tissue or organ. During the development of the Drosophila eye, this linkage is particularly clear: the growth of the eye primordium mainly results from proliferating cells ahead of the morphogenetic furrow (MF), a moving signaling wave that sweeps across the tissue from the posterior to the anterior side, that induces proliferating cells anterior to it to differentiate and become cell cycle quiescent in its wake. Therefore, final eye disc size depends on the proliferation rate of undifferentiated cells and on the speed with which the MF sweeps across the eye disc. We developed a spatio-temporal model of the growing eye disc based on the regulatory interactions controlled by the signals Decapentaplegic (Dpp), Hedgehog (Hh) and the transcription factor Homothorax (Hth) and explored how the signaling patterns affect the movement of the MF and impact on eye disc growth. We used published and new quantitative data to parameterize the model. In particular, two crucial parameter values, the degradation rate of Hth and the diffusion coefficient of Hh, were measured. The model is able to reproduce the linear movement of the MF and the termination of growth of the primordium. We further show that the model can explain several mutant phenotypes, but fails to reproduce the previously observed scaling of the Dpp gradient in the anterior compartment.

A Model of the Spatio-temporal Dynamics of Drosophila Eye Disc Development.

Directory of Open Access Journals (Sweden)

Patrick Fried

2016-09-01

Full Text Available Patterning and growth are linked during early development and have to be tightly controlled to result in a functional tissue or organ. During the development of the Drosophila eye, this linkage is particularly clear: the growth of the eye primordium mainly results from proliferating cells ahead of the morphogenetic furrow (MF, a moving signaling wave that sweeps across the tissue from the posterior to the anterior side, that induces proliferating cells anterior to it to differentiate and become cell cycle quiescent in its wake. Therefore, final eye disc size depends on the proliferation rate of undifferentiated cells and on the speed with which the MF sweeps across the eye disc. We developed a spatio-temporal model of the growing eye disc based on the regulatory interactions controlled by the signals Decapentaplegic (Dpp, Hedgehog (Hh and the transcription factor Homothorax (Hth and explored how the signaling patterns affect the movement of the MF and impact on eye disc growth. We used published and new quantitative data to parameterize the model. In particular, two crucial parameter values, the degradation rate of Hth and the diffusion coefficient of Hh, were measured. The model is able to reproduce the linear movement of the MF and the termination of growth of the primordium. We further show that the model can explain several mutant phenotypes, but fails to reproduce the previously observed scaling of the Dpp gradient in the anterior compartment.

DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models

Science.gov (United States)

Ullrich, Paul A.; Jablonowski, Christiane; Kent, James; Lauritzen, Peter H.; Nair, Ramachandran; Reed, Kevin A.; Zarzycki, Colin M.; Hall, David M.; Dazlich, Don; Heikes, Ross; Konor, Celal; Randall, David; Dubos, Thomas; Meurdesoif, Yann; Chen, Xi; Harris, Lucas; Kühnlein, Christian; Lee, Vivian; Qaddouri, Abdessamad; Girard, Claude; Giorgetta, Marco; Reinert, Daniel; Klemp, Joseph; Park, Sang-Hun; Skamarock, William; Miura, Hiroaki; Ohno, Tomoki; Yoshida, Ryuji; Walko, Robert; Reinecke, Alex; Viner, Kevin

2017-12-01

Atmospheric dynamical cores are a fundamental component of global atmospheric modeling systems and are responsible for capturing the dynamical behavior of the Earth's atmosphere via numerical integration of the Navier-Stokes equations. These systems have existed in one form or another for over half of a century, with the earliest discretizations having now evolved into a complex ecosystem of algorithms and computational strategies. In essence, no two dynamical cores are alike, and their individual successes suggest that no perfect model exists. To better understand modern dynamical cores, this paper aims to provide a comprehensive review of 11 non-hydrostatic dynamical cores, drawn from modeling centers and groups that participated in the 2016 Dynamical Core Model Intercomparison Project (DCMIP) workshop and summer school. This review includes a choice of model grid, variable placement, vertical coordinate, prognostic equations, temporal discretization, and the diffusion, stabilization, filters, and fixers employed by each system.

A Dynamic Approach to Modeling Dependence Between Human Failure Events

Energy Technology Data Exchange (ETDEWEB)

Boring, Ronald Laurids [Idaho National Laboratory

2015-09-01

In practice, most HRA methods use direct dependence from THERP—the notion that error be- gets error, and one human failure event (HFE) may increase the likelihood of subsequent HFEs. In this paper, we approach dependence from a simulation perspective in which the effects of human errors are dynamically modeled. There are three key concepts that play into this modeling: (1) Errors are driven by performance shaping factors (PSFs). In this context, the error propagation is not a result of the presence of an HFE yielding overall increases in subsequent HFEs. Rather, it is shared PSFs that cause dependence. (2) PSFs have qualities of lag and latency. These two qualities are not currently considered in HRA methods that use PSFs. Yet, to model the effects of PSFs, it is not simply a matter of identifying the discrete effects of a particular PSF on performance. The effects of PSFs must be considered temporally, as the PSFs will have a range of effects across the event sequence. (3) Finally, there is the concept of error spilling. When PSFs are activated, they not only have temporal effects but also lateral effects on other PSFs, leading to emergent errors. This paper presents the framework for tying together these dynamic dependence concepts.

Modeling the coupled return-spread high frequency dynamics of large tick assets

Science.gov (United States)

Curato, Gianbiagio; Lillo, Fabrizio

2015-01-01

Large tick assets, i.e. assets where one tick movement is a significant fraction of the price and bid-ask spread is almost always equal to one tick, display a dynamics in which price changes and spread are strongly coupled. We present an approach based on the hidden Markov model, also known in econometrics as the Markov switching model, for the dynamics of price changes, where the latent Markov process is described by the transitions between spreads. We then use a finite Markov mixture of logit regressions on past squared price changes to describe temporal dependencies in the dynamics of price changes. The model can thus be seen as a double chain Markov model. We show that the model describes the shape of the price change distribution at different time scales, volatility clustering, and the anomalous decrease of kurtosis. We calibrate our models based on Nasdaq stocks and we show that this model reproduces remarkably well the statistical properties of real data.

Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

KAUST Repository

Zhang, Bohai

2014-01-01

Various continuously-indexed spatio-temporal process models have been constructed to characterize spatio-temporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset.

Translation of a High-Level Temporal Model into Lower Level Models: Impact of Modelling at Different Description Levels

DEFF Research Database (Denmark)

Kraft, Peter; Sørensen, Jens Otto

2001-01-01

given types of properties, and examine how descriptions on higher levels translate into descriptions on lower levels. Our example looks at temporal properties where the information is concerned with the existence in time. In a high level temporal model with information kept in a three-dimensional space...... the existences in time can be mapped precisely and consistently securing a consistent handling of the temporal properties. We translate the high level temporal model into an entity-relationship model, with the information in a two-dimensional graph, and finally we look at the translations into relational...... and other textual models. We also consider the aptness of models that include procedural mechanisms such as active and object databases...

The Temporal Signature of Memories: Identification of a General Mechanism for Dynamic Memory Replay in Humans

Science.gov (United States)

Michelmann, Sebastian; Bowman, Howard; Hanslmayr, Simon

2016-01-01

Reinstatement of dynamic memories requires the replay of neural patterns that unfold over time in a similar manner as during perception. However, little is known about the mechanisms that guide such a temporally structured replay in humans, because previous studies used either unsuitable methods or paradigms to address this question. Here, we overcome these limitations by developing a new analysis method to detect the replay of temporal patterns in a paradigm that requires participants to mentally replay short sound or video clips. We show that memory reinstatement is accompanied by a decrease of low-frequency (8 Hz) power, which carries a temporal phase signature of the replayed stimulus. These replay effects were evident in the visual as well as in the auditory domain and were localized to sensory-specific regions. These results suggest low-frequency phase to be a domain-general mechanism that orchestrates dynamic memory replay in humans. PMID:27494601

Insights into soil carbon dynamics across climatic and geologic gradients from temporally-resolved radiocarbon measurements

Science.gov (United States)

van der Voort, T. S.; Hagedorn, F.; Mannu, U.; Walthert, L.; McIntyre, C.; Eglinton, T. I.

2016-12-01

Soil carbon constitutes the largest terrestrial reservoir of organic carbon, and therefore quantifying soil organic matter dynamics (carbon turnover, stocks and fluxes) across spatial gradients is essential for an understanding of the carbon cycle and the impacts of global change. In particular, links between soil carbon dynamics and different climatic and compositional factors remains poorly understood. Radiocarbon constitutes a powerful tool for unraveling soil carbon dynamics. Temporally-resolved radiocarbon measurements, which take advantage of "bomb-radiocarbon"-driven changes in atmospheric 14C, enable further constraints to be placed on C turnover times. These in turn can yield more precise flux estimates for both upper and deeper soil horizons. This project combines bulk radiocarbon measurements on a suite of soil profiles spanning strong climatic (MAT 1.3-9.2°C, MAP 600 to 2100 mm m-2y-1) and geologic gradients with a more in-depth approach for a subset of locations. For this subset, temporal and carbon-fraction specific radiocarbon data has been acquired for both topsoil and deeper soils. These well-studied sites are part of the Long-Term Forest Ecosystem Research (LWF) program of the Swiss Federal Institute for Forest, Snow and Landscape research (WSL). Resulting temporally-resolved turnover estimates are coupled to carbon stocks, fluxes across this wide range of forest ecosystems and are examined in the context of environmental drivers (temperature, precipitation, primary production and soil moisture) as well as composition (sand, silt and clay content). Statistical analysis on the region-scale - correlating radiocarbon signature with climatic variables such as temperature, precipitation, primary production and elevation - indicates that composition rather than climate is a key driver of ­­Δ14C signatures. Estimates of carbon turnover, stocks and fluxes derived from temporally-resolved measurements highlight the pivotal role of soil moisture as a

Reliability and Minimum Detectable Change of Temporal-Spatial, Kinematic, and Dynamic Stability Measures during Perturbed Gait.

Directory of Open Access Journals (Sweden)

Christopher A Rábago

Full Text Available Temporal-spatial, kinematic variability, and dynamic stability measures collected during perturbation-based assessment paradigms are often used to identify dysfunction associated with gait instability. However, it remains unclear which measures are most reliable for detecting and tracking responses to perturbations. This study systematically determined the between-session reliability and minimum detectable change values of temporal-spatial, kinematic variability, and dynamic stability measures during three types of perturbed gait. Twenty young healthy adults completed two identical testing sessions two weeks apart, comprised of an unperturbed and three perturbed (cognitive, physical, and visual walking conditions in a virtual reality environment. Within each session, perturbation responses were compared to unperturbed walking using paired t-tests. Between-session reliability and minimum detectable change values were also calculated for each measure and condition. All temporal-spatial, kinematic variability and dynamic stability measures demonstrated fair to excellent between-session reliability. Minimal detectable change values, normalized to mean values ranged from 1-50%. Step width mean and variability measures demonstrated the greatest response to perturbations with excellent between-session reliability and low minimum detectable change values. Orbital stability measures demonstrated specificity to perturbation direction and sensitivity with excellent between-session reliability and low minimum detectable change values. We observed substantially greater between-session reliability and lower minimum detectable change values for local stability measures than previously described which may be the result of averaging across trials within a session and using velocity versus acceleration data for reconstruction of state spaces. Across all perturbation types, temporal-spatial, orbital and local measures were the most reliable measures with the

Dynamic density functional theory of solid tumor growth: Preliminary models

Directory of Open Access Journals (Sweden)

Arnaud Chauviere

2012-03-01

Full Text Available Cancer is a disease that can be seen as a complex system whose dynamics and growth result from nonlinear processes coupled across wide ranges of spatio-temporal scales. The current mathematical modeling literature addresses issues at various scales but the development of theoretical methodologies capable of bridging gaps across scales needs further study. We present a new theoretical framework based on Dynamic Density Functional Theory (DDFT extended, for the first time, to the dynamics of living tissues by accounting for cell density correlations, different cell types, phenotypes and cell birth/death processes, in order to provide a biophysically consistent description of processes across the scales. We present an application of this approach to tumor growth.

Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach

Directory of Open Access Journals (Sweden)

Meng Zhou

2016-12-01

Full Text Available Spatial structure is a fundamental characteristic of cities that influences the urban functioning to a large extent. While administrative partitioning is generally done in the form of static spatial division, understanding a more temporally dynamic structure of the urban space would benefit urban planning and management immensely. This study makes use of a large-scale mobile phone positioning dataset to characterize the diurnal dynamics of the interaction-based urban spatial structure. To extract the temporally vibrant structure, spatial interaction networks at different times are constructed based on the movement connections of individuals between geographical units. Complex network community detection technique is applied to identify the spatial divisions as well as to quantify their temporal dynamics. Empirical analysis is conducted using data containing all user positions on a typical weekday in Shenzhen, China. Results are compared with official zoning and planned structure and indicate a certain degree of expansion in urban central areas and fragmentation in industrial suburban areas. A high level of variability in spatial divisions at different times of day is detected with some distinct temporal features. Peak and pre-/post-peak hours witness the most prominent fluctuation in spatial division indicating significant change in the characteristics of movements and activities during these periods of time. Findings of this study demonstrate great potential of large-scale mobility data in supporting intelligent spatial decision making and providing valuable knowledge to the urban planning sectors.

Temporal scaling in information propagation

Science.gov (United States)

Huang, Junming; Li, Chao; Wang, Wen-Qiang; Shen, Hua-Wei; Li, Guojie; Cheng, Xue-Qi

2014-06-01

For the study of information propagation, one fundamental problem is uncovering universal laws governing the dynamics of information propagation. This problem, from the microscopic perspective, is formulated as estimating the propagation probability that a piece of information propagates from one individual to another. Such a propagation probability generally depends on two major classes of factors: the intrinsic attractiveness of information and the interactions between individuals. Despite the fact that the temporal effect of attractiveness is widely studied, temporal laws underlying individual interactions remain unclear, causing inaccurate prediction of information propagation on evolving social networks. In this report, we empirically study the dynamics of information propagation, using the dataset from a population-scale social media website. We discover a temporal scaling in information propagation: the probability a message propagates between two individuals decays with the length of time latency since their latest interaction, obeying a power-law rule. Leveraging the scaling law, we further propose a temporal model to estimate future propagation probabilities between individuals, reducing the error rate of information propagation prediction from 6.7% to 2.6% and improving viral marketing with 9.7% incremental customers.

DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models

Directory of Open Access Journals (Sweden)

P. A. Ullrich

2017-12-01

Full Text Available Atmospheric dynamical cores are a fundamental component of global atmospheric modeling systems and are responsible for capturing the dynamical behavior of the Earth's atmosphere via numerical integration of the Navier–Stokes equations. These systems have existed in one form or another for over half of a century, with the earliest discretizations having now evolved into a complex ecosystem of algorithms and computational strategies. In essence, no two dynamical cores are alike, and their individual successes suggest that no perfect model exists. To better understand modern dynamical cores, this paper aims to provide a comprehensive review of 11 non-hydrostatic dynamical cores, drawn from modeling centers and groups that participated in the 2016 Dynamical Core Model Intercomparison Project (DCMIP workshop and summer school. This review includes a choice of model grid, variable placement, vertical coordinate, prognostic equations, temporal discretization, and the diffusion, stabilization, filters, and fixers employed by each system.

Amnesia, rehearsal, and temporal distinctiveness models of recall.

Science.gov (United States)

Brown, Gordon D A; Della Sala, Sergio; Foster, Jonathan K; Vousden, Janet I

2007-04-01

Classical amnesia involves selective memory impairment for temporally distant items in free recall (impaired primacy) together with relative preservation of memory for recency items. This abnormal serial position curve is traditionally taken as evidence for a distinction between different memory processes, with amnesia being associated with selectively impaired long-term memory. However recent accounts of normal serial position curves have emphasized the importance of rehearsal processes in giving rise to primacy effects and have suggested that a single temporal distinctiveness mechanism can account for both primacy and recency effects when rehearsal is considered. Here we explore the pattern of strategic rehearsal in a patient with very severe amnesia. When the patient's rehearsal pattern is taken into account, a temporal distinctiveness model can account for the serial position curve in both amnesic and control free recall. The results are taken as consistent with temporal distinctiveness models of free recall, and they motivate an emphasis on rehearsal patterns in understanding amnesic deficits in free recall.

Characterizing Phase Transitions in a Model of Neutral Evolutionary Dynamics

Science.gov (United States)

Scott, Adam; King, Dawn; Bahar, Sonya

2013-03-01

An evolutionary model was recently introduced for sympatric, phenotypic evolution over a variable fitness landscape with assortative mating (Dees & Bahar 2010). Organisms in the model are described by coordinates in a two-dimensional phenotype space, born at random coordinates with limited variation from their parents as determined by a mutation parameter, mutability. The model has been extended to include both neutral evolution and asexual reproduction in Scott et al (submitted). It has been demonstrated that a second order, non-equilibrium phase transition occurs for the temporal dynamics as the mutability is varied, for both the original model and for neutral conditions. This transition likely belongs to the directed percolation universality class. In contrast, the spatial dynamics of the model shows characteristics of an ordinary percolation phase transition. Here, we characterize the phase transitions exhibited by this model by determining critical exponents for the relaxation times, characteristic lengths, and cluster (species) mass distributions. Missouri Research Board; J.S. McDonnell Foundation

The Voronoi spatio-temporal data structure

Science.gov (United States)

Mioc, Darka

2002-04-01

Current GIS models cannot integrate the temporal dimension of spatial data easily. Indeed, current GISs do not support incremental (local) addition and deletion of spatial objects, and they can not support the temporal evolution of spatial data. Spatio-temporal facilities would be very useful in many GIS applications: harvesting and forest planning, cadastre, urban and regional planning, and emergency planning. The spatio-temporal model that can overcome these problems is based on a topological model---the Voronoi data structure. Voronoi diagrams are irregular tessellations of space, that adapt to spatial objects and therefore they are a synthesis of raster and vector spatial data models. The main advantage of the Voronoi data structure is its local and sequential map updates, which allows us to automatically record each event and performed map updates within the system. These map updates are executed through map construction commands that are composed of atomic actions (geometric algorithms for addition, deletion, and motion of spatial objects) on the dynamic Voronoi data structure. The formalization of map commands led to the development of a spatial language comprising a set of atomic operations or constructs on spatial primitives (points and lines), powerful enough to define the complex operations. This resulted in a new formal model for spatio-temporal change representation, where each update is uniquely characterized by the numbers of newly created and inactivated Voronoi regions. This is used for the extension of the model towards the hierarchical Voronoi data structure. In this model, spatio-temporal changes induced by map updates are preserved in a hierarchical data structure that combines events and corresponding changes in topology. This hierarchical Voronoi data structure has an implicit time ordering of events visible through changes in topology, and it is equivalent to an event structure that can support temporal data without precise temporal

4D-PET reconstruction using a spline-residue model with spatial and temporal roughness penalties

Science.gov (United States)

Ralli, George P.; Chappell, Michael A.; McGowan, Daniel R.; Sharma, Ricky A.; Higgins, Geoff S.; Fenwick, John D.

2018-05-01

4D reconstruction of dynamic positron emission tomography (dPET) data can improve the signal-to-noise ratio in reconstructed image sequences by fitting smooth temporal functions to the voxel time-activity-curves (TACs) during the reconstruction, though the optimal choice of function remains an open question. We propose a spline-residue model, which describes TACs as weighted sums of convolutions of the arterial input function with cubic B-spline basis functions. Convolution with the input function constrains the spline-residue model at early time-points, potentially enhancing noise suppression in early time-frames, while still allowing a wide range of TAC descriptions over the entire imaged time-course, thus limiting bias. Spline-residue based 4D-reconstruction is compared to that of a conventional (non-4D) maximum a posteriori (MAP) algorithm, and to 4D-reconstructions based on adaptive-knot cubic B-splines, the spectral model and an irreversible two-tissue compartment (‘2C3K’) model. 4D reconstructions were carried out using a nested-MAP algorithm including spatial and temporal roughness penalties. The algorithms were tested using Monte-Carlo simulated scanner data, generated for a digital thoracic phantom with uptake kinetics based on a dynamic [18F]-Fluromisonidazole scan of a non-small cell lung cancer patient. For every algorithm, parametric maps were calculated by fitting each voxel TAC within a sub-region of the reconstructed images with the 2C3K model. Compared to conventional MAP reconstruction, spline-residue-based 4D reconstruction achieved  >50% improvements for five of the eight combinations of the four kinetics parameters for which parametric maps were created with the bias and noise measures used to analyse them, and produced better results for 5/8 combinations than any of the other reconstruction algorithms studied, while spectral model-based 4D reconstruction produced the best results for 2/8. 2C3K model-based 4D reconstruction generated

The Temporal Signature of Memories: Identification of a General Mechanism for Dynamic Memory Replay in Humans.

Directory of Open Access Journals (Sweden)

Sebastian Michelmann

2016-08-01

Full Text Available Reinstatement of dynamic memories requires the replay of neural patterns that unfold over time in a similar manner as during perception. However, little is known about the mechanisms that guide such a temporally structured replay in humans, because previous studies used either unsuitable methods or paradigms to address this question. Here, we overcome these limitations by developing a new analysis method to detect the replay of temporal patterns in a paradigm that requires participants to mentally replay short sound or video clips. We show that memory reinstatement is accompanied by a decrease of low-frequency (8 Hz power, which carries a temporal phase signature of the replayed stimulus. These replay effects were evident in the visual as well as in the auditory domain and were localized to sensory-specific regions. These results suggest low-frequency phase to be a domain-general mechanism that orchestrates dynamic memory replay in humans.

Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times

DEFF Research Database (Denmark)

Rasmussen, Jakob Gulddahl; Møller, Jesper

2007-01-01

Summary. We consider statistical and computational aspects of simulation-based Bayesian inference for a spatial-temporal model based on a multivariate point process which is only observed at sparsely distributed times. The point processes are indexed by the sites of a spatial lattice......, and they exhibit spatial interaction. For specificity we consider a particular dynamical spatial lattice data set which has previously been analysed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared...... with discrete time processes in the setting of the present paper as well as other spatial-temporal situations....

Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method

International Nuclear Information System (INIS)

Nasser, Hassan; Cessac, Bruno; Marre, Olivier

2013-01-01

Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main statistics of the data is required. In the first part, we present a review on recent results dealing with spike train statistics analysis using maximum entropy models (MaxEnt). Most of these studies have focused on modelling synchronous spike patterns, leaving aside the temporal dynamics of the neural activity. However, the maximum entropy principle can be generalized to the temporal case, leading to Markovian models where memory effects and time correlations in the dynamics are properly taken into account. In the second part, we present a new method based on Monte Carlo sampling which is suited for the fitting of large-scale spatio-temporal MaxEnt models. The formalism and the tools presented here will be essential to fit MaxEnt spatio-temporal models to large neural ensembles. (paper)

Dynamic-stochastic modeling of snow cover formation on the European territory of Russia

OpenAIRE

A. N. Gelfan; V. M. Moreido

2014-01-01

A dynamic-stochastic model, which combines a deterministic model of snow cover formation with a stochastic weather generator, has been developed. The deterministic snow model describes temporal change of the snow depth, content of ice and liquid water, snow density, snowmelt, sublimation, re-freezing of melt water, and snow metamorphism. The model has been calibrated and validated against the long-term data of snow measurements over the territory of the European Russia. The model showed good ...

Temporal ecology in the Anthropocene.

Science.gov (United States)

Wolkovich, E M; Cook, B I; McLauchlan, K K; Davies, T J

2014-11-01

Two fundamental axes - space and time - shape ecological systems. Over the last 30 years spatial ecology has developed as an integrative, multidisciplinary science that has improved our understanding of the ecological consequences of habitat fragmentation and loss. We argue that accelerating climate change - the effective manipulation of time by humans - has generated a current need to build an equivalent framework for temporal ecology. Climate change has at once pressed ecologists to understand and predict ecological dynamics in non-stationary environments, while also challenged fundamental assumptions of many concepts, models and approaches. However, similarities between space and time, especially related issues of scaling, provide an outline for improving ecological models and forecasting of temporal dynamics, while the unique attributes of time, particularly its emphasis on events and its singular direction, highlight where new approaches are needed. We emphasise how a renewed, interdisciplinary focus on time would coalesce related concepts, help develop new theories and methods and guide further data collection. The next challenge will be to unite predictive frameworks from spatial and temporal ecology to build robust forecasts of when and where environmental change will pose the largest threats to species and ecosystems, as well as identifying the best opportunities for conservation. © 2014 John Wiley & Sons Ltd/CNRS.

Structured spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires

DEFF Research Database (Denmark)

Møller, Jesper; Diaz-Avalos, Carlos

Spatio-temporal Cox point process models with a multiplicative structure for the driving random intensity, incorporating covariate information into temporal and spatial components, and with a residual term modelled by a shot-noise process, are considered. Such models are flexible and tractable fo...... dataset consisting of 2796 days and 5834 spatial locations of fires. The model is compared with a spatio-temporal log-Gaussian Cox point process model, and likelihood-based methods are discussed to some extent....

Structured Spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires

DEFF Research Database (Denmark)

Møller, Jesper; Diaz-Avalos, Carlos

2010-01-01

Spatio-temporal Cox point process models with a multiplicative structure for the driving random intensity, incorporating covariate information into temporal and spatial components, and with a residual term modelled by a shot-noise process, are considered. Such models are flexible and tractable fo...... data set consisting of 2796 days and 5834 spatial locations of fires. The model is compared with a spatio-temporal log-Gaussian Cox point process model, and likelihood-based methods are discussed to some extent....

Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease.

Science.gov (United States)

Jie, Biao; Liu, Mingxia; Shen, Dinggang

2018-07-01

Functional connectivity networks (FCNs) using resting-state functional magnetic resonance imaging (rs-fMRI) have been applied to the analysis and diagnosis of brain disease, such as Alzheimer's disease (AD) and its prodrome, i.e., mild cognitive impairment (MCI). Different from conventional studies focusing on static descriptions on functional connectivity (FC) between brain regions in rs-fMRI, recent studies have resorted to dynamic connectivity networks (DCNs) to characterize the dynamic changes of FC, since dynamic changes of FC may indicate changes in macroscopic neural activity patterns in cognitive and behavioral aspects. However, most of the existing studies only investigate the temporal properties of DCNs (e.g., temporal variability of FC between specific brain regions), ignoring the important spatial properties of the network (e.g., spatial variability of FC associated with a specific brain region). Also, emerging evidence on FCNs has suggested that, besides temporal variability, there is significant spatial variability of activity foci over time. Hence, integrating both temporal and spatial properties of DCNs can intuitively promote the performance of connectivity-network-based learning methods. In this paper, we first define a new measure to characterize the spatial variability of DCNs, and then propose a novel learning framework to integrate both temporal and spatial variabilities of DCNs for automatic brain disease diagnosis. Specifically, we first construct DCNs from the rs-fMRI time series at successive non-overlapping time windows. Then, we characterize the spatial variability of a specific brain region by computing the correlation of functional sequences (i.e., the changing profile of FC between a pair of brain regions within all time windows) associated with this region. Furthermore, we extract both temporal variabilities and spatial variabilities from DCNs as features, and integrate them for classification by using manifold regularized multi

The effects of spatial and temporal heterogeneity on the population dynamics of four animal species in a Danish landscape

Directory of Open Access Journals (Sweden)

Forchhammer Mads C

2009-06-01

Full Text Available Abstract Background Variation in carrying capacity and population return rates is generally ignored in traditional studies of population dynamics. Variation is hard to study in the field because of difficulties controlling the environment in order to obtain statistical replicates, and because of the scale and expense of experimenting on populations. There may also be ethical issues. To circumvent these problems we used detailed simulations of the simultaneous behaviours of interacting animals in an accurate facsimile of a real Danish landscape. The models incorporate as much as possible of the behaviour and ecology of skylarks Alauda arvensis, voles Microtus agrestis, a ground beetle Bembidion lampros and a linyphiid spider Erigone atra. This allows us to quantify and evaluate the importance of spatial and temporal heterogeneity on the population dynamics of the four species. Results Both spatial and temporal heterogeneity affected the relationship between population growth rate and population density in all four species. Spatial heterogeneity accounted for 23–30% of the variance in population growth rate after accounting for the effects of density, reflecting big differences in local carrying capacity associated with the landscape features important to individual species. Temporal heterogeneity accounted for 3–13% of the variance in vole, skylark and spider, but 43% in beetles. The associated temporal variation in carrying capacity would be problematic in traditional analyses of density dependence. Return rates were less than one in all species and essentially invariant in skylarks, spiders and beetles. Return rates varied over the landscape in voles, being slower where there were larger fluctuations in local population sizes. Conclusion Our analyses estimated the traditional parameters of carrying capacities and return rates, but these are now seen as varying continuously over the landscape depending on habitat quality and the mechanisms

Temporal dynamics of top predators interactions in the Barents Sea.

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Durant, Joël M; Skern-Mauritzen, Mette; Krasnov, Yuri V; Nikolaeva, Natalia G; Lindstrøm, Ulf; Dolgov, Andrey

2014-01-01

The Barents Sea system is often depicted as a simple food web in terms of number of dominant feeding links. The most conspicuous feeding link is between the Northeast Arctic cod Gadus morhua, the world's largest cod stock which is presently at a historical high level, and capelin Mallotus villosus. The system also holds diverse seabird and marine mammal communities. Previous diet studies may suggest that these top predators (cod, bird and sea mammals) compete for food particularly with respect to pelagic fish such as capelin and juvenile herring (Clupea harengus), and krill. In this paper we explored the diet of some Barents Sea top predators (cod, Black-legged kittiwake Rissa tridactyla, Common guillemot Uria aalge, and Minke whale Balaenoptera acutorostrata). We developed a GAM modelling approach to analyse the temporal variation diet composition within and between predators, to explore intra- and inter-specific interactions. The GAM models demonstrated that the seabird diet is temperature dependent while the diet of Minke whale and cod is prey dependent; Minke whale and cod diets depend on the abundance of herring and capelin, respectively. There was significant diet overlap between cod and Minke whale, and between kittiwake and guillemot. In general, the diet overlap between predators increased with changes in herring and krill abundances. The diet overlap models developed in this study may help to identify inter-specific interactions and their dynamics that potentially affect the stocks targeted by fisheries.

Innovations in individual feature history management - The significance of feature-based temporal model

Science.gov (United States)

Choi, J.; Seong, J.C.; Kim, B.; Usery, E.L.

2008-01-01

A feature relies on three dimensions (space, theme, and time) for its representation. Even though spatiotemporal models have been proposed, they have principally focused on the spatial changes of a feature. In this paper, a feature-based temporal model is proposed to represent the changes of both space and theme independently. The proposed model modifies the ISO's temporal schema and adds new explicit temporal relationship structure that stores temporal topological relationship with the ISO's temporal primitives of a feature in order to keep track feature history. The explicit temporal relationship can enhance query performance on feature history by removing topological comparison during query process. Further, a prototype system has been developed to test a proposed feature-based temporal model by querying land parcel history in Athens, Georgia. The result of temporal query on individual feature history shows the efficiency of the explicit temporal relationship structure. ?? Springer Science+Business Media, LLC 2007.

Interstitial diffusion and the relationship between compartment modelling and multi-scale spatial-temporal modelling of (18)F-FLT tumour uptake dynamics.

Science.gov (United States)

Liu, Dan; Chalkidou, Anastasia; Landau, David B; Marsden, Paul K; Fenwick, John D

2014-09-07

Tumour cell proliferation can be imaged via positron emission tomography of the radiotracer 3'-deoxy-3'-18F-fluorothymidine (18F-FLT). Conceptually, the number of proliferating cells might be expected to correlate more closely with the kinetics of 18F-FLT uptake than with uptake at a fixed time. Radiotracer uptake kinetics are standardly visualized using parametric maps of compartment model fits to time-activity-curves (TACs) of individual voxels. However the relationship between the underlying spatiotemporal accumulation of FLT and the kinetics described by compartment models has not yet been explored. In this work tumour tracer uptake is simulated using a mechanistic spatial-temporal model based on a convection-diffusion-reaction equation solved via the finite difference method. The model describes a chain of processes: the flow of FLT between the spatially heterogeneous tumour vasculature and interstitium; diffusion and convection of FLT within the interstitium; transport of FLT into cells; and intracellular phosphorylation. Using values of model parameters estimated from the biological literature, simulated FLT TACs are generated with shapes and magnitudes similar to those seen clinically. Results show that the kinetics of the spatial-temporal model can be recovered accurately by fitting a 3-tissue compartment model to FLT TACs simulated for those tumours or tumour sub-volumes that can be viewed as approximately closed, for which tracer diffusion throughout the interstitium makes only a small fractional change to the quantity of FLT they contain. For a single PET voxel of width 2.5-5 mm we show that this condition is roughly equivalent to requiring that the relative difference in tracer uptake between the voxel and its neighbours is much less than one.

A dynamic appearance descriptor approach to facial actions temporal modeling

NARCIS (Netherlands)

Jiang, Bihan; Valstar, Michel; Martinez, Brais; Pantic, Maja

Both the configuration and the dynamics of facial expressions are crucial for the interpretation of human facial behavior. Yet to date, the vast majority of reported efforts in the field either do not take the dynamics of facial expressions into account, or focus only on prototypic facial

Using temporal information to construct, update, and retrieve situation models of narratives

NARCIS (Netherlands)

Rinck, M.; Hähnel, A.; Becker, G.

2001-01-01

Four experiments explored how readers use temporal information to construct and update situation models and retrieve them from memory. In Experiment 1, readers spontaneously constructed temporal and spatial situation models of single sentences. In Experiment 2, temporal inconsistencies caused

Simultaneous temporally resolved DPIV and pressure measurements of symmetric oscillations in a scaled-up vocal fold model

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Ringenberg, Hunter; Rogers, Dylan; Wei, Nathaniel; Krane, Michael; Wei, Timothy

2017-11-01

The objective of this study is to apply experimental data to theoretical framework of Krane (2013) in which the principal aeroacoustic source is expressed in terms of vocal fold drag, glottal jet dynamic head, and glottal exit volume flow, reconciling formal theoretical aeroacoustic descriptions of phonation with more traditional lumped-element descriptions. These quantities appear in the integral equations of motion for phonatory flow. In this way time resolved velocity field measurements can be used to compute time-resolved estimates of the relevant terms in the integral equations of motion, including phonation aeroacoustic source strength. A simplified 10x scale vocal fold model from Krane, et al. (2007) was used to examine symmetric, i.e. `healthy', oscillatory motion of the vocal folds. By using water as the working fluid, very high spatial and temporal resolution was achieved. Temporal variation of transglottal pressure was simultaneously measured with flow on the vocal fold model mid-height. Experiments were dynamically scaled to examine a range of frequencies corresponding to male and female voice. The simultaneity of the pressure and flow provides new insights into the aeroacoustics associated with vocal fold oscillations. Supported by NIH Grant No. 2R01 DC005642-11.

Temporal dynamics of glyoxalase 1 in secondary neuronal injury.

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Philipp Pieroh

Full Text Available BACKGROUND: Enhanced glycolysis leads to elevated levels of the toxic metabolite methylglyoxal which contributes to loss of protein-function, metabolic imbalance and cell death. Neurons were shown being highly susceptible to methylglyoxal toxicity. Glyoxalase 1 as an ubiquitous enzyme reflects the main detoxifying enzyme of methylglyoxal and underlies changes during aging and neurodegeneration. However, little is known about dynamics of Glyoxalase 1 following neuronal lesions so far. METHODS: To determine a possible involvement of Glyoxalase 1 in acute brain injury, we analysed the temporal dynamics of Glyoxalase 1 distribution and expression by immunohistochemistry and Western Blot analysis. Organotypic hippocampal slice cultures were excitotoxically (N-methyl-D-aspartate, 50 µM for 4 hours lesioned in vitro (5 minutes to 72 hours. Additionally, permanent middle cerebral artery occlusion was performed (75 minutes to 60 days. RESULTS: We found (i a predominant localisation of Glyoxalase 1 in endothelial cells in non-lesioned brains (ii a time-dependent up-regulation and re-distribution of Glyoxalase 1 in neurons and astrocytes and (iii a strong increase in Glyoxalase 1 dimers after neuronal injury (24 hours to 72 hours when compared to monomers of the protein. CONCLUSIONS: The high dynamics of Glyoxalase 1 expression and distribution following neuronal injury may indicate a novel role of Glyoxalase 1.

Bioluminescence in a complex coastal environment: 1. Temporal dynamics of nighttime water-leaving radiance

Science.gov (United States)

Moline, Mark A.; Oliver, Matthew J.; Mobley, Curtis D.; Sundman, Lydia; Bensky, Thomas; Bergmann, Trisha; Bissett, W. Paul; Case, James; Raymond, Erika H.; Schofield, Oscar M. E.

2007-11-01

Nighttime water-leaving radiance is a function of the depth-dependent distribution of both the in situ bioluminescence emissions and the absorption and scattering properties of the water. The vertical distributions of these parameters were used as inputs for a modified one-dimensional radiative transfer model to solve for spectral bioluminescence water-leaving radiance from prescribed depths of the water column. Variation in the water-leaving radiance was consistent with local episodic physical forcing events, with tidal forcing, terrestrial runoff, particulate accumulation, and biological responses influencing the shorter timescale dynamics. There was a >90 nm shift in the peak water-leaving radiance from blue (˜474 nm) to green as light propagated to the surface. In addition to clues in ecosystem responses to physical forcing, the temporal dynamics in intensity and spectral quality of water-leaving radiance provide suitable ranges for assessing detection. This may provide the information needed to estimate the depth of internal light sources in the ocean, which is discussed in part 2 of this paper.

Groundwater-fed irrigation impacts spatially distributed temporal scaling behavior of the natural system: a spatio-temporal framework for understanding water management impacts

International Nuclear Information System (INIS)

Condon, Laura E; Maxwell, Reed M

2014-01-01

Regional scale water management analysis increasingly relies on integrated modeling tools. Much recent work has focused on groundwater–surface water interactions and feedbacks. However, to our knowledge, no study has explicitly considered impacts of management operations on the temporal dynamics of the natural system. Here, we simulate twenty years of hourly moisture dependent, groundwater-fed irrigation using a three-dimensional, fully integrated, hydrologic model (ParFlow-CLM). Results highlight interconnections between irrigation demand, groundwater oscillation frequency and latent heat flux variability not previously demonstrated. Additionally, the three-dimensional model used allows for novel consideration of spatial patterns in temporal dynamics. Latent heat flux and water table depth both display spatial organization in temporal scaling, an important finding given the spatial homogeneity and weak scaling observed in atmospheric forcings. Pumping and irrigation amplify high frequency (sub-annual) variability while attenuating low frequency (inter-annual) variability. Irrigation also intensifies scaling within irrigated areas, essentially increasing temporal memory in both the surface and the subsurface. These findings demonstrate management impacts that extend beyond traditional water balance considerations to the fundamental behavior of the system itself. This is an important step to better understanding groundwater’s role as a buffer for natural variability and the impact that water management has on this capacity. (paper)

Approximate Entropy as a measure of complexity in sap flow temporal dynamics of two tropical tree species under water deficit

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Gustavo M. Souza

2004-09-01

Full Text Available Approximate Entropy (ApEn, a model-independent statistics to quantify serial irregularities, was used to evaluate changes in sap flow temporal dynamics of two tropical species of trees subjected to water deficit. Water deficit induced a decrease in sap flow of G. ulmifolia, whereas C. legalis held stable their sap flow levels. Slight increases in time series complexity were observed in both species under drought condition. This study showed that ApEn could be used as a helpful tool to assess slight changes in temporal dynamics of physiological data, and to uncover some patterns of plant physiological responses to environmental stimuli.Entropia Aproximada (ApEn, um modelo estatístico independente para quantificar irregularidade em séries temporais, foi utilizada para avaliar alterações na dinâmica temporal do fluxo de seiva em duas espécies arbóreas tropicais submetidas à deficiência hídrica. A deficiência hídrica induziu uma grande redução no fluxo de seiva em G. ulmifolia, enquanto que na espécie C. legalis manteve-se estável. A complexidade das séries temporais foi levemente aumentada sob deficiência hídrica. O estudo mostrou que ApEn pode ser usada como um método para detectar pequenas alterações na dinâmica temporal de dados fisiológicos, e revelar alguns padrões de respostas fisiológicas a estímulos ambientais.

Temporal dynamics of spectral bioindicators evidence biological and ecological differences among functional types in a cork oak open woodland

Science.gov (United States)

Cerasoli, Sofia; Costa e Silva, Filipe; Silva, João M. N.

2016-06-01

The application of spectral vegetation indices for the purpose of vegetation monitoring and modeling increased largely in recent years. Nonetheless, the interpretation of biophysical properties of vegetation through their spectral signature is still a challenging task. This is particularly true in Mediterranean oak forest characterized by a high spatial and temporal heterogeneity. In this study, the temporal dynamics of vegetation indices expected to be related with green biomass and photosynthetic efficiency were compared for the canopy of trees, the herbaceous layer, and two shrub species: cistus ( Cistus salviifolius) and ulex ( Ulex airensis). coexisting in a cork oak woodland. All indices were calculated from in situ measurements with a FieldSpec3 spectroradiometer (ASD Inc., Boulder, USA). Large differences emerged in the temporal trends and in the correlation between climate and vegetation indices. The relationship between spectral indices and temperature, radiation, and vapor pressure deficit for cork oak was opposite to that observed for the herbaceous layer and cistus. No correlation was observed between rainfall and vegetation indices in cork oak and ulex, but in the herbaceous layer and in the cistus, significant correlations were found. The analysis of spectral vegetation indices with fraction of absorbed PAR (fPAR) and quantum yield of chlorophyll fluorescence ( ΔF/ Fm') evidenced strongest relationships with the indices Normalized Difference Water Index (NDWI) and Photochemical Reflectance Index (PRI)512, respectively. Our results, while confirms the ability of spectral vegetation indices to represent temporal dynamics of biophysical properties of vegetation, evidence the importance to consider ecosystem composition for a correct ecological interpretation of results when the spatial resolution of observations includes different plant functional types.

Spatial and temporal dynamics of multidimensional well-being, livelihoods and ecosystem services in coastal Bangladesh

Science.gov (United States)

Adams, Helen; Adger, W. Neil; Ahmad, Sate; Ahmed, Ali; Begum, Dilruba; Lázár, Attila N.; Matthews, Zoe; Rahman, Mohammed Mofizur; Streatfield, Peter Kim

2016-01-01

Populations in resource dependent economies gain well-being from the natural environment, in highly spatially and temporally variable patterns. To collect information on this, we designed and implemented a 1586-household quantitative survey in the southwest coastal zone of Bangladesh. Data were collected on material, subjective and health dimensions of well-being in the context of natural resource use, particularly agriculture, aquaculture, mangroves and fisheries. The questionnaire included questions on factors that mediate poverty outcomes: mobility and remittances; loans and micro-credit; environmental perceptions; shocks; and women’s empowerment. The data are stratified by social-ecological system to take into account spatial dynamics and the survey was repeated with the same respondents three times within a year to incorporate seasonal dynamics. The dataset includes blood pressure measurements and height and weight of men, women and children. In addition, the household listing includes basic data on livelihoods and income for approximately 10,000 households. The dataset facilitates interdisciplinary research on spatial and temporal dynamics of well-being in the context of natural resource dependence in low income countries. PMID:27824340

Spatio-temporal data analytics for wind energy integration

CERN Document Server

Yang, Lei; Zhang, Junshan

2014-01-01

This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable energy generation into bulk power grids. The operational challenges of wind, and its variability are carefully examined. A spatio-temporal analysis approach enables the authors to develop Markov-chain-based short-term forecasts of wind farm power generation. To deal with the wind ramp dynamics, a support vector machine enhanced Markov model is introduced. The stochastic optimization of economic di

Oscillatory dynamics in a model of vascular tumour growth - implications for chemotherapy

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Maini PK

2010-04-01

Full Text Available Abstract Background Investigations of solid tumours suggest that vessel occlusion may occur when increased pressure from the tumour mass is exerted on the vessel walls. Since immature vessels are frequently found in tumours and may be particularly sensitive, such occlusion may impair tumour blood flow and have a negative impact on therapeutic outcome. In order to study the effects that occlusion may have on tumour growth patterns and therapeutic response, in this paper we develop and investigate a continuum model of vascular tumour growth. Results By analysing a spatially uniform submodel, we identify regions of parameter space in which the combination of tumour cell proliferation and vessel occlusion give rise to sustained temporal oscillations in the tumour cell population and in the vessel density. Alternatively, if the vessels are assumed to be less prone to collapse, stable steady state solutions are observed. When spatial effects are considered, the pattern of tumour invasion depends on the dynamics of the spatially uniform submodel. If the submodel predicts a stable steady state, then steady travelling waves are observed in the full model, and the system evolves to the same stable steady state behind the invading front. When the submodel yields oscillatory behaviour, the full model produces periodic travelling waves. The stability of the waves (which can be predicted by approximating the system as one of λ-ω type dictates whether the waves develop into regular or irregular spatio-temporal oscillations. Simulations of chemotherapy reveal that treatment outcome depends crucially on the underlying tumour growth dynamics. In particular, if the dynamics are oscillatory, then therapeutic efficacy is difficult to assess since the fluctuations in the size of the tumour cell population are enhanced, compared to untreated controls. Conclusions We have developed a mathematical model of vascular tumour growth formulated as a system of partial

Temporal Glare

DEFF Research Database (Denmark)

Ritschel, Tobias; Ihrke, Matthias; Frisvad, Jeppe Revall

2009-01-01

Glare is a consequence of light scattered within the human eye when looking at bright light sources. This effect can be exploited for tone mapping since adding glare to the depiction of high-dynamic range (HDR) imagery on a low-dynamic range (LDR) medium can dramatically increase perceived contra...... to initially static HDR images. By conducting psychophysical studies, we validate that our method improves perceived brightness and that dynamic glare-renderings are often perceived as more attractive depending on the chosen scene.......Glare is a consequence of light scattered within the human eye when looking at bright light sources. This effect can be exploited for tone mapping since adding glare to the depiction of high-dynamic range (HDR) imagery on a low-dynamic range (LDR) medium can dramatically increase perceived contrast....... Even though most, if not all, subjects report perceiving glare as a bright pattern that fluctuates in time, up to now it has only been modeled as a static phenomenon. We argue that the temporal properties of glare are a strong means to increase perceived brightness and to produce realistic...

Bi-temporal 3D Active Appearance Modelling

DEFF Research Database (Denmark)

Stegmann, Mikkel Bille

2005-01-01

in fourdimensional MRI. The theoretical foundation of our work is the generative two-dimensional Active Appearance Models by Cootes et al., here extended to bi-temporal, three-dimensional models. Further issues treated include correction of respiratory induced slice displacements, systole detection, and a texture...

Laminar and Temporal Expression Dynamics of Coding and Noncoding RNAs in the Mouse Neocortex

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Sofia Fertuzinhos

2014-03-01

Full Text Available The hallmark of the cerebral neocortex is its organization into six layers, each containing a characteristic set of cell types and synaptic connections. The transcriptional events involved in laminar development and function still remain elusive. Here, we employed deep sequencing of mRNA and small RNA species to gain insights into transcriptional differences among layers and their temporal dynamics during postnatal development of the mouse primary somatosensory neocortex. We identify a number of coding and noncoding transcripts with specific spatiotemporal expression and splicing patterns. We also identify signature trajectories and gene coexpression networks associated with distinct biological processes and transcriptional overlap between these processes. Finally, we provide data that allow the study of potential miRNA and mRNA interactions. Overall, this study provides an integrated view of the laminar and temporal expression dynamics of coding and noncoding transcripts in the mouse neocortex and a resource for studies of neurodevelopment and transcriptome.

Predictive spatio-temporal model for spatially sparse global solar radiation data

International Nuclear Information System (INIS)

André, Maïna; Dabo-Niang, Sophie; Soubdhan, Ted; Ould-Baba, Hanany

2016-01-01

This paper introduces a new approach for the forecasting of solar radiation series at a located station for very short time scale. We built a multivariate model in using few stations (3 stations) separated with irregular distances from 26 km to 56 km. The proposed model is a spatio temporal vector autoregressive VAR model specifically designed for the analysis of spatially sparse spatio-temporal data. This model differs from classic linear models in using spatial and temporal parameters where the available predictors are the lagged values at each station. A spatial structure of stations is defined by the sequential introduction of predictors in the model. Moreover, an iterative strategy in the process of our model will select the necessary stations removing the uninteresting predictors and also selecting the optimal p-order. We studied the performance of this model. The metric error, the relative root mean squared error (rRMSE), is presented at different short time scales. Moreover, we compared the results of our model to simple and well known persistence model and those found in literature. - Highlights: • A spatio-temporal VAR forecast model is used for spatially sparse data solar. • Lags and locations are selected by an optimization strategy. • Definition of spatial ordering of predictors influences forecasting results. • The model shows a better performance predictive at 30 min ahead in our context. • Benchmarking study shows a more accurate forecast at 1 h ahead with spatio-temporal VAR.

Building spatio-temporal database model based on ontological approach using relational database environment

International Nuclear Information System (INIS)

Mahmood, N.; Burney, S.M.A.

2017-01-01

Everything in this world is encapsulated by space and time fence. Our daily life activities are utterly linked and related with other objects in vicinity. Therefore, a strong relationship exist with our current location, time (including past, present and future) and event through with we are moving as an object also affect our activities in life. Ontology development and its integration with database are vital for the true understanding of the complex systems involving both spatial and temporal dimensions. In this paper we propose a conceptual framework for building spatio-temporal database model based on ontological approach. We have used relational data model for modelling spatio-temporal data content and present our methodology with spatio-temporal ontological accepts and its transformation into spatio-temporal database model. We illustrate the implementation of our conceptual model through a case study related to cultivated land parcel used for agriculture to exhibit the spatio-temporal behaviour of agricultural land and related entities. Moreover, it provides a generic approach for designing spatiotemporal databases based on ontology. The proposed model is capable to understand the ontological and somehow epistemological commitments and to build spatio-temporal ontology and transform it into a spatio-temporal data model. Finally, we highlight the existing and future research challenges. (author)

Kalman filter techniques for accelerated Cartesian dynamic cardiac imaging.

Science.gov (United States)

Feng, Xue; Salerno, Michael; Kramer, Christopher M; Meyer, Craig H

2013-05-01

In dynamic MRI, spatial and temporal parallel imaging can be exploited to reduce scan time. Real-time reconstruction enables immediate visualization during the scan. Commonly used view-sharing techniques suffer from limited temporal resolution, and many of the more advanced reconstruction methods are either retrospective, time-consuming, or both. A Kalman filter model capable of real-time reconstruction can be used to increase the spatial and temporal resolution in dynamic MRI reconstruction. The original study describing the use of the Kalman filter in dynamic MRI was limited to non-Cartesian trajectories because of a limitation intrinsic to the dynamic model used in that study. Here the limitation is overcome, and the model is applied to the more commonly used Cartesian trajectory with fast reconstruction. Furthermore, a combination of the Kalman filter model with Cartesian parallel imaging is presented to further increase the spatial and temporal resolution and signal-to-noise ratio. Simulations and experiments were conducted to demonstrate that the Kalman filter model can increase the temporal resolution of the image series compared with view-sharing techniques and decrease the spatial aliasing compared with TGRAPPA. The method requires relatively little computation, and thus is suitable for real-time reconstruction. Copyright © 2012 Wiley Periodicals, Inc.

Dynamic profiling of different ready-to-drink fermented dairy products: A comparative study using Temporal Check-All-That-Apply (TCATA), Temporal Dominance of Sensations (TDS) and Progressive Profile (PP).

Science.gov (United States)

Esmerino, Erick A; Castura, John C; Ferraz, Juliana P; Tavares Filho, Elson R; Silva, Ramon; Cruz, Adriano G; Freitas, Mônica Q; Bolini, Helena M A

2017-11-01

Despite the several differences in ingredients, processes and nutritional values, dairy foods as yogurts, fermented milks and milk beverages are widely accepted worldwide, and although they have their sensory profiling normally covered by descriptive analyses, the temporal perception involved during the consumption are rarely considered. In this sense, the present work aimed to assess the dynamic sensory profile of three categories of fermented dairy products using different temporal methodologies: Temporal Dominance of Sensations (TDS), Progressive Profiling (PP), Temporal CATA (TCATA), and compare the results obtained. The findings showed that the different sensory characteristics among the products are basically related to their commercial identity. Regarding the methods, all of them collected the variations between samples with great correlation between data. In addition, to detect differences in intensities, TCATA showed to be the most sensitive method in detecting textural changes. When using PP, a balanced experimental design considering the number of attributes, time intervals, and food matrix must be weighed. The findings are of interest to guide sensory and consumer practitioners involved in the dairy production to formulate/reformulate their products and help them choosing the most suitable dynamic method to temporally evaluate them. Copyright © 2017 Elsevier Ltd. All rights reserved.

Developing Baltic cod recruitment models I : Resolving spatial and temporal dynamics of spawning stock and recruitment for cod, herring, and sprat

DEFF Research Database (Denmark)

Köster, Fritz; Möllmann, C.; Neuenfeldt, Stefan

2001-01-01

The Baltic Sea comprises a heterogeneous oceanographic environment influencing the spatial and temporal potential for reproductive success of cod (Gadus morhua) and sprat (Sprattus sprattus) in the different spawning basins. Hence, to quantify stock and recruitment dynamics, it is necessary......-disaggregated multispecies virtual population analyses (MSVPA) were performed for interacting species cod, herring (Clupea harengus), and sprat in the different subdivisions of the Central Baltic. The MSVPA runs revealed distinct spatial trends in population abundance, spawning biomass, recruitment, and predation...

Model-Based Speech Signal Coding Using Optimized Temporal Decomposition for Storage and Broadcasting Applications

Science.gov (United States)

Athaudage, Chandranath R. N.; Bradley, Alan B.; Lech, Margaret

2003-12-01

A dynamic programming-based optimization strategy for a temporal decomposition (TD) model of speech and its application to low-rate speech coding in storage and broadcasting is presented. In previous work with the spectral stability-based event localizing (SBEL) TD algorithm, the event localization was performed based on a spectral stability criterion. Although this approach gave reasonably good results, there was no assurance on the optimality of the event locations. In the present work, we have optimized the event localizing task using a dynamic programming-based optimization strategy. Simulation results show that an improved TD model accuracy can be achieved. A methodology of incorporating the optimized TD algorithm within the standard MELP speech coder for the efficient compression of speech spectral information is also presented. The performance evaluation results revealed that the proposed speech coding scheme achieves 50%-60% compression of speech spectral information with negligible degradation in the decoded speech quality.

Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches

Science.gov (United States)

Scott L. Powell; Warren B. Cohen; Sean P. Healey; Robert E. Kennedy; Gretchen G. Moisen; Kenneth B. Pierce; Janet L. Ohmann

2010-01-01

Spatially and temporally explicit knowledge of biomass dynamics at broad scales is critical to understanding how forest disturbance and regrowth processes influence carbon dynamics. We modeled live, aboveground tree biomass using Forest Inventory and Analysis (FIA) field data and applied the models to 20+ year time-series of Landsat satellite imagery to...

The use of spatio-temporal correlation to forecast critical transitions

Science.gov (United States)

Karssenberg, Derek; Bierkens, Marc F. P.

2010-05-01

Complex dynamical systems may have critical thresholds at which the system shifts abruptly from one state to another. Such critical transitions have been observed in systems ranging from the human body system to financial markets and the Earth system. Forecasting the timing of critical transitions before they are reached is of paramount importance because critical transitions are associated with a large shift in dynamical regime of the system under consideration. However, it is hard to forecast critical transitions, because the state of the system shows relatively little change before the threshold is reached. Recently, it was shown that increased spatio-temporal autocorrelation and variance can serve as alternative early warning signal for critical transitions. However, thus far these second order statistics have not been used for forecasting in a data assimilation framework. Here we show that the use of spatio-temporal autocorrelation and variance in the state of the system reduces the uncertainty in the predicted timing of critical transitions compared to classical approaches that use the value of the system state only. This is shown by assimilating observed spatio-temporal autocorrelation and variance into a dynamical system model using a Particle Filter. We adapt a well-studied distributed model of a logistically growing resource with a fixed grazing rate. The model describes the transition from an underexploited system with high resource biomass to overexploitation as grazing pressure crosses the critical threshold, which is a fold bifurcation. To represent limited prior information, we use a large variance in the prior probability distributions of model parameters and the system driver (grazing rate). First, we show that the rate of increase in spatio-temporal autocorrelation and variance prior to reaching the critical threshold is relatively consistent across the uncertainty range of the driver and parameter values used. This indicates that an increase in

Temporal expression-based analysis of metabolism.

Directory of Open Access Journals (Sweden)

Sara B Collins

Full Text Available Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM. We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such "history-dependent" sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.

Assimilation of remote sensing observations into a sediment transport model of China's largest freshwater lake: spatial and temporal effects.

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Zhang, Peng; Chen, Xiaoling; Lu, Jianzhong; Zhang, Wei

2015-12-01

Numerical models are important tools that are used in studies of sediment dynamics in inland and coastal waters, and these models can now benefit from the use of integrated remote sensing observations. This study explores a scheme for assimilating remotely sensed suspended sediment (from charge-coupled device (CCD) images obtained from the Huanjing (HJ) satellite) into a two-dimensional sediment transport model of Poyang Lake, the largest freshwater lake in China. Optimal interpolation is used as the assimilation method, and model predictions are obtained by combining four remote sensing images. The parameters for optimal interpolation are determined through a series of assimilation experiments evaluating the sediment predictions based on field measurements. The model with assimilation of remotely sensed sediment reduces the root-mean-square error of the predicted sediment concentrations by 39.4% relative to the model without assimilation, demonstrating the effectiveness of the assimilation scheme. The spatial effect of assimilation is explored by comparing model predictions with remotely sensed sediment, revealing that the model with assimilation generates reasonable spatial distribution patterns of suspended sediment. The temporal effect of assimilation on the model's predictive capabilities varies spatially, with an average temporal effect of approximately 10.8 days. The current velocities which dominate the rate and direction of sediment transport most likely result in spatial differences in the temporal effect of assimilation on model predictions.

A Mixed Land Cover Spatio-temporal Data Model Based on Object-oriented and Snapshot

Directory of Open Access Journals (Sweden)

LI Yinchao

2016-07-01

Full Text Available Spatio-temporal data model (STDM is one of the hot topics in the domains of spatio-temporal database and data analysis. There is a common view that a universal STDM is always of high complexity due to the various situation of spatio-temporal data. In this article, a mixed STDM is proposed based on object-oriented and snapshot models for modelling and analyzing landcover change (LCC. This model uses the object-oriented STDM to describe the spatio-temporal processes of land cover patches and organize their spatial and attributive properties. In the meantime, it uses the snapshot STDM to present the spatio-temporal distribution of LCC on the whole via snapshot images. The two types of models are spatially and temporally combined into a mixed version. In addition to presenting the spatio-temporal events themselves, this model could express the transformation events between different classes of spatio-temporal objects. It can be used to create database for historical data of LCC, do spatio-temporal statistics, simulation and data mining with the data. In this article, the LCC data in Heilongjiang province is used for case study to validate spatio-temporal data management and analysis abilities of mixed STDM, including creating database, spatio-temporal query, global evolution analysis and patches spatio-temporal process expression.

Temporal bird community dynamics are strongly affected by landscape fragmentation in a Central American tropical forest region

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Blandón, A.C.; Perelman, S.B.; Ramírez, M.; López, A.; Javier, O.; Robbins, Chandler S.

2016-01-01

Habitat loss and fragmentation are considered the main causes of species extinctions, particularly in tropical ecosystems. The objective of this work was to evaluate the temporal dynamics of tropical bird communities in landscapes with different levels of fragmentation in eastern Guatemala. We evaluated five bird community dynamic parameters for forest specialists and generalists: (1) species extinction, (2) species turnover, (3) number of colonizing species, (4) relative species richness, and (5) a homogeneity index. For each of 24 landscapes, community dynamic parameters were estimated from bird point count data, for the 1998–1999 and 2008–2009 periods, accounting for species’ detection probability. Forest specialists had higher extinction rates and a smaller number of colonizing species in landscapes with higher fragmentation, thus having lower species richness in both time periods. Alternatively, forest generalists elicited a completely different pattern, showing a curvilinear association to forest fragmentation for most parameters. Thus, greater community dynamism for forest generalists was shown in landscapes with intermediate levels of fragmentation. Our study supports general theory regarding the expected negative effects of habitat loss and fragmentation on the temporal dynamics of biotic communities, particularly for forest specialists, providing strong evidence from understudied tropical bird communities.

Fire, humans, and climate: modeling distribution dynamics of boreal forest waterbirds.

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Börger, Luca; Nudds, Thomas D

2014-01-01

Understanding the effects of landscape change and environmental variability on ecological processes is important for evaluating resource management policies, such as the emulation of natural forest disturbances. We analyzed time series of detection/nondetection data using hierarchical models in a Bayesian multi-model inference framework to decompose the dynamics of species distributions into responses to environmental variability, spatial variation in habitat conditions, and population dynamics and interspecific interactions, while correcting for observation errors and variation in sampling regimes. We modeled distribution dynamics of 14 waterbird species (broadly defined, including wetland and riparian species) using data from two different breeding bird surveys collected in the Boreal Shield ecozone within Ontario, Canada. Temporal variation in species occupancy (2000-2006) was primarily driven by climatic variability. Only two species showed evidence of consistent temporal trends in distribution: Ring-necked Duck (Aythya collaris) decreased, and Red-winged Blackbird (Agelaius phoeniceus) increased. The models had good predictive ability on independent data over time (1997-1999). Spatial variation in species occupancy was strongly related to the distribution of specific land cover types and habitat disturbance: Fire and forest harvesting influenced occupancy more than did roads, settlements, or mines. Bioclimatic and habitat heterogeneity indices and geographic coordinates exerted negligible influence on most species distributions. Estimated habitat suitability indices had good predictive ability on spatially independent data (Hudson Bay Lowlands ecozone). Additionally, we detected effects of interspecific interactions. Species responses to fire and forest harvesting were similar for 13 of 14 species; thus, forest-harvesting practices in Ontario generally appeared to emulate the effects of fire for waterbirds over timescales of 10-20 years. Extrapolating to all

Spatio-temporal modeling for residential burglary

NARCIS (Netherlands)

Mahfoud, M.; Bhulai, Sandjai; van der Mei, R.D.; Bhulai, Sandjai; Kardaras, Dimitris

2017-01-01

Spatio-temporal modeling is widely recognized as a promising means for predicting crime patterns. Despite their enormous potential, the available methods are still in their infancy. A lot of research focuses on crime hotspot detection and geographic crime clusters, while a systematic approach to

Sensitivity analysis of complex models: Coping with dynamic and static inputs

International Nuclear Information System (INIS)

Anstett-Collin, F.; Goffart, J.; Mara, T.; Denis-Vidal, L.

2015-01-01

In this paper, we address the issue of conducting a sensitivity analysis of complex models with both static and dynamic uncertain inputs. While several approaches have been proposed to compute the sensitivity indices of the static inputs (i.e. parameters), the one of the dynamic inputs (i.e. stochastic fields) have been rarely addressed. For this purpose, we first treat each dynamic as a Gaussian process. Then, the truncated Karhunen–Loève expansion of each dynamic input is performed. Such an expansion allows to generate independent Gaussian processes from a finite number of independent random variables. Given that a dynamic input is represented by a finite number of random variables, its variance-based sensitivity index is defined by the sensitivity index of this group of variables. Besides, an efficient sampling-based strategy is described to estimate the first-order indices of all the input factors by only using two input samples. The approach is applied to a building energy model, in order to assess the impact of the uncertainties of the material properties (static inputs) and the weather data (dynamic inputs) on the energy performance of a real low energy consumption house. - Highlights: • Sensitivity analysis of models with uncertain static and dynamic inputs is performed. • Karhunen–Loève (KL) decomposition of the spatio/temporal inputs is performed. • The influence of the dynamic inputs is studied through the modes of the KL expansion. • The proposed approach is applied to a building energy model. • Impact of weather data and material properties on performance of real house is given

Modeling spatial-temporal operations with context-dependent associative memories.

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Mizraji, Eduardo; Lin, Juan

2015-10-01

We organize our behavior and store structured information with many procedures that require the coding of spatial and temporal order in specific neural modules. In the simplest cases, spatial and temporal relations are condensed in prepositions like "below" and "above", "behind" and "in front of", or "before" and "after", etc. Neural operators lie beneath these words, sharing some similarities with logical gates that compute spatial and temporal asymmetric relations. We show how these operators can be modeled by means of neural matrix memories acting on Kronecker tensor products of vectors. The complexity of these memories is further enhanced by their ability to store episodes unfolding in space and time. How does the brain scale up from the raw plasticity of contingent episodic memories to the apparent stable connectivity of large neural networks? We clarify this transition by analyzing a model that flexibly codes episodic spatial and temporal structures into contextual markers capable of linking different memory modules.

Large scale stochastic spatio-temporal modelling with PCRaster

NARCIS (Netherlands)

Karssenberg, D.J.; Drost, N.; Schmitz, O.; Jong, K. de; Bierkens, M.F.P.

2013-01-01

PCRaster is a software framework for building spatio-temporal models of land surface processes (http://www.pcraster.eu). Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations are available to model

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking.

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Pârvu, Ovidiu; Gilbert, David

2016-01-01

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour

Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration

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Tsvetkova, Milena; García-Gavilanes, Ruth; Yasseri, Taha

2016-11-01

Disagreement and conflict are a fact of social life. However, negative interactions are rarely explicitly declared and recorded and this makes them hard for scientists to study. In an attempt to understand the structural and temporal features of negative interactions in the community, we use complex network methods to analyze patterns in the timing and configuration of reverts of article edits to Wikipedia. We investigate how often and how fast pairs of reverts occur compared to a null model in order to control for patterns that are natural to the content production or are due to the internal rules of Wikipedia. Our results suggest that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. We further relate these interactions to the status of the involved editors. Even though the individual reverts might not necessarily be negative social interactions, our analysis points to the existence of certain patterns of negative social dynamics within the community of editors. Some of these patterns have not been previously explored and carry implications for the knowledge collection practice conducted on Wikipedia. Our method can be applied to other large-scale temporal collaboration networks to identify the existence of negative social interactions and other social processes.

Improvement of Lambert-Beer law dynamic range by the use of temporal gates on transmitted light pulse through a scattering medium

International Nuclear Information System (INIS)

Yoshino, Hironori; Wada, Kenji; Horinaka, Hiromichi; Cho, Yoshio; Umeda, Tokuo; Osawa, Masahiko.

1995-01-01

The Lambert-Beer law holding for pulsed lights transmitted through a scattering medium was examined using a streak camera. The Lambert-Beer law dynamic range is found to be limited by floor levels that are caused by scattered photons and are controllable by the use of a temporal gate on the transmitted pulse. The dynamic range improvement obtained for a scattering medium of 2.8 cm -1 scattering coefficient of a thickness of 80 mm by a temporal gate of 60 ps was as much as 50 dB and the Lambert-Beer law dynamic rang reached to 140 dB. (author)

Bridge Deterioration Prediction Model Based On Hybrid Markov-System Dynamic

Directory of Open Access Journals (Sweden)

Widodo Soetjipto Jojok

2017-01-01

Full Text Available Instantaneous bridge failure tends to increase in Indonesia. To mitigate this condition, Indonesia’s Bridge Management System (I-BMS has been applied to continuously monitor the condition of bridges. However, I-BMS only implements visual inspection for maintenance priority of the bridge structure component instead of bridge structure system. This paper proposes a new bridge failure prediction model based on hybrid Markov-System Dynamic (MSD. System dynamic is used to represent the correlation among bridge structure components while Markov chain is used to calculate temporal probability of the bridge failure. Around 235 data of bridges in Indonesia were collected from Directorate of Bridge the Ministry of Public Works and Housing for calculating transition probability of the model. To validate the model, a medium span concrete bridge was used as a case study. The result shows that the proposed model can accurately predict the bridge condition. Besides predicting the probability of the bridge failure, this model can also be used as an early warning system for bridge monitoring activity.

Encoding model of temporal processing in human visual cortex.

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Stigliani, Anthony; Jeska, Brianna; Grill-Spector, Kalanit

2017-12-19

How is temporal information processed in human visual cortex? Visual input is relayed to V1 through segregated transient and sustained channels in the retina and lateral geniculate nucleus (LGN). However, there is intense debate as to how sustained and transient temporal channels contribute to visual processing beyond V1. The prevailing view associates transient processing predominately with motion-sensitive regions and sustained processing with ventral stream regions, while the opposing view suggests that both temporal channels contribute to neural processing beyond V1. Using fMRI, we measured cortical responses to time-varying stimuli and then implemented a two temporal channel-encoding model to evaluate the contributions of each channel. Different from the general linear model of fMRI that predicts responses directly from the stimulus, the encoding approach first models neural responses to the stimulus from which fMRI responses are derived. This encoding approach not only predicts cortical responses to time-varying stimuli from milliseconds to seconds but also, reveals differential contributions of temporal channels across visual cortex. Consistent with the prevailing view, motion-sensitive regions and adjacent lateral occipitotemporal regions are dominated by transient responses. However, ventral occipitotemporal regions are driven by both sustained and transient channels, with transient responses exceeding the sustained. These findings propose a rethinking of temporal processing in the ventral stream and suggest that transient processing may contribute to rapid extraction of the content of the visual input. Importantly, our encoding approach has vast implications, because it can be applied with fMRI to decipher neural computations in millisecond resolution in any part of the brain. Copyright © 2017 the Author(s). Published by PNAS.

Femtosecond laser spectroscopy of spins: Magnetization dynamics in thin magnetic films with spatio-temporal resolution

International Nuclear Information System (INIS)

Carpene, E.; Mancini, E.; Dallera, C.; Puppin, E.; De Silvestri, S.

2010-01-01

Based on the Magneto-Optical Kerr Effect (MOKE), we have developed an experimental set-up that allows us to fully characterize the magnetization dynamics in thin magnetic films by measuring all three real space components of the magnetization vector M. By means of the pump-probe technique it is possible to extract the time dependence of each individual projection with sub-picosecond resolution. This method has been exploited to investigate the temporal evolution of the magnetization (modulus and orientation) induced by an ultrashort laser pulse in thin epitaxial iron films. According to our results, we deduced that the initial, sub-picosecond demagnetization is established at the electronic level through electron-magnon excitations. The subsequent dynamics is characterized by a precessional motion on the 100 ps time scale, around an effective, time-dependent magnetic field. Following the full dynamics of M, the temporal evolution of the magneto-crystalline anisotropy constant can be unambiguously determined, providing the experimental evidence that the precession is triggered by the rapid, optically-induced misalignment between the magnetization vector and the effective magnetic field. These results suggest a possible pathway toward the ultrarapid switching of the magnetization.

Spatio-temporal dynamics of ocean conditions and forage taxa reveal regional structuring of seabird–prey relationships.

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Santora, Jarrod A; Schroeder, Isaac D; Field, John C; Wells, Brian K; Sydeman, William J

Studies of predator–prey demographic responses and the physical drivers of such relationships are rare, yet essential for predicting future changes in the structure and dynamics of marine ecosystems. Here, we hypothesize that predator–prey relationships vary spatially in association with underlying physical ocean conditions, leading to observable changes in demographic rates, such as reproduction. To test this hypothesis, we quantified spatio-temporal variability in hydrographic conditions, krill, and forage fish to model predator (seabird) demographic responses over 18 years (1990–2007). We used principal component analysis and spatial correlation maps to assess coherence among ocean conditions, krill, and forage fish, and generalized additive models to quantify interannual variability in seabird breeding success relative to prey abundance. The first principal component of four hydrographic measurements yielded an index that partitioned “warm/weak upwelling” and “cool/strong upwelling” years. Partitioning of krill and forage fish time series among shelf and oceanic regions yielded spatially explicit indicators of prey availability. Krill abundance within the oceanic region was remarkably consistent between years, whereas krill over the shelf showed marked interannual fluctuations in relation to ocean conditions. Anchovy abundance varied on the shelf, and was greater in years of strong stratification, weak upwelling and warmer temperatures. Spatio-temporal variability of juvenile forage fish co-varied strongly with each other and with krill, but was weakly correlated with hydrographic conditions. Demographic responses between seabirds and prey availability revealed spatially variable associations indicative of the dynamic nature of “predator–habitat” relationships. Quantification of spatially explicit demographic responses, and their variability through time, demonstrate the possibility of delineating specific critical areas where the

Spatiotemporal neural network dynamics for the processing of dynamic facial expressions

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Sato, Wataru; Kochiyama, Takanori; Uono, Shota

2015-01-01

The dynamic facial expressions of emotion automatically elicit multifaceted psychological activities; however, the temporal profiles and dynamic interaction patterns of brain activities remain unknown. We investigated these issues using magnetoencephalography. Participants passively observed dynamic facial expressions of fear and happiness, or dynamic mosaics. Source-reconstruction analyses utilizing functional magnetic-resonance imaging data revealed higher activation in broad regions of the bilateral occipital and temporal cortices in response to dynamic facial expressions than in response to dynamic mosaics at 150–200 ms and some later time points. The right inferior frontal gyrus exhibited higher activity for dynamic faces versus mosaics at 300–350 ms. Dynamic causal-modeling analyses revealed that dynamic faces activated the dual visual routes and visual–motor route. Superior influences of feedforward and feedback connections were identified before and after 200 ms, respectively. These results indicate that hierarchical, bidirectional neural network dynamics within a few hundred milliseconds implement the processing of dynamic facial expressions. PMID:26206708

Spatiotemporal neural network dynamics for the processing of dynamic facial expressions.

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Sato, Wataru; Kochiyama, Takanori; Uono, Shota

2015-07-24

The dynamic facial expressions of emotion automatically elicit multifaceted psychological activities; however, the temporal profiles and dynamic interaction patterns of brain activities remain unknown. We investigated these issues using magnetoencephalography. Participants passively observed dynamic facial expressions of fear and happiness, or dynamic mosaics. Source-reconstruction analyses utilizing functional magnetic-resonance imaging data revealed higher activation in broad regions of the bilateral occipital and temporal cortices in response to dynamic facial expressions than in response to dynamic mosaics at 150-200 ms and some later time points. The right inferior frontal gyrus exhibited higher activity for dynamic faces versus mosaics at 300-350 ms. Dynamic causal-modeling analyses revealed that dynamic faces activated the dual visual routes and visual-motor route. Superior influences of feedforward and feedback connections were identified before and after 200 ms, respectively. These results indicate that hierarchical, bidirectional neural network dynamics within a few hundred milliseconds implement the processing of dynamic facial expressions.

Fluid consumption and taste novelty determines transcription temporal dynamics in the gustatory cortex.

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Inberg, Sharon; Jacob, Eyal; Elkobi, Alina; Edry, Efrat; Rappaport, Akiva; Simpson, T Ian; Armstrong, J Douglas; Shomron, Noam; Pasmanik-Chor, Metsada; Rosenblum, Kobi

2016-02-09

Novel taste memories, critical for animal survival, are consolidated to form long term memories which are dependent on translation regulation in the gustatory cortex (GC) hours following acquisition. However, the role of transcription regulation in the process is unknown. Here, we report that transcription in the GC is necessary for taste learning in rats, and that drinking and its consequences, as well as the novel taste experience, affect transcription in the GC during taste memory consolidation. We show differential effects of learning on temporal dynamics in set of genes in the GC, including Arc/Arg3.1, known to regulate the homeostasis of excitatory synapses. We demonstrate that in taste learning, transcription programs were activated following the physiological responses (i.e., fluid consumption following a water restriction regime, reward, arousal of the animal, etc.) and the specific information about a given taste (i.e., taste novelty). Moreover, the cortical differential prolonged kinetics of mRNA following novel versus familiar taste learning may represent additional novelty related molecular response, where not only the total amount, but also the temporal dynamics of transcription is modulated by sensory experience of novel information.

A Relational Encoding of a Conceptual Model with Multiple Temporal Dimensions

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Gubiani, Donatella; Montanari, Angelo

The theoretical interest and the practical relevance of a systematic treatment of multiple temporal dimensions is widely recognized in the database and information system communities. Nevertheless, most relational databases have no temporal support at all. A few of them provide a limited support, in terms of temporal data types and predicates, constructors, and functions for the management of time values (borrowed from the SQL standard). One (resp., two) temporal dimensions are supported by historical and transaction-time (resp., bitemporal) databases only. In this paper, we provide a relational encoding of a conceptual model featuring four temporal dimensions, namely, the classical valid and transaction times, plus the event and availability times. We focus our attention on the distinctive technical features of the proposed temporal extension of the relation model. In the last part of the paper, we briefly show how to implement it in a standard DBMS.

Temporal dynamics of soil organic carbon after land-use change in the temperate zone – carbon response functions as a model approach

DEFF Research Database (Denmark)

Poeplau, Christopher; Don, Axel; Vesterdal, Lars

2011-01-01

Land-use change (LUC) is a major driving factor for the balance of soil organic carbon (SOC) stocks and the global carbon cycle. The temporal dynamic of SOC after LUC is especially important in temperate systems with a long reaction time. On the basis of 95 compiled studies covering 322 sites...... approach, the developed CRFs provide an easily applicable tool to estimate SOC stock changes after LUC to improve greenhouse gas reporting in the framework of UNFCCC....

Modeling Soil Carbon Dynamics in Northern Forests: Effects of Spatial and Temporal Aggregation of Climatic Input Data.

Science.gov (United States)

Dalsgaard, Lise; Astrup, Rasmus; Antón-Fernández, Clara; Borgen, Signe Kynding; Breidenbach, Johannes; Lange, Holger; Lehtonen, Aleksi; Liski, Jari

2016-01-01

Boreal forests contain 30% of the global forest carbon with the majority residing in soils. While challenging to quantify, soil carbon changes comprise a significant, and potentially increasing, part of the terrestrial carbon cycle. Thus, their estimation is important when designing forest-based climate change mitigation strategies and soil carbon change estimates are required for the reporting of greenhouse gas emissions. Organic matter decomposition varies with climate in complex nonlinear ways, rendering data aggregation nontrivial. Here, we explored the effects of temporal and spatial aggregation of climatic and litter input data on regional estimates of soil organic carbon stocks and changes for upland forests. We used the soil carbon and decomposition model Yasso07 with input from the Norwegian National Forest Inventory (11275 plots, 1960-2012). Estimates were produced at three spatial and three temporal scales. Results showed that a national level average soil carbon stock estimate varied by 10% depending on the applied spatial and temporal scale of aggregation. Higher stocks were found when applying plot-level input compared to country-level input and when long-term climate was used as compared to annual or 5-year mean values. A national level estimate for soil carbon change was similar across spatial scales, but was considerably (60-70%) lower when applying annual or 5-year mean climate compared to long-term mean climate reflecting the recent climatic changes in Norway. This was particularly evident for the forest-dominated districts in the southeastern and central parts of Norway and in the far north. We concluded that the sensitivity of model estimates to spatial aggregation will depend on the region of interest. Further, that using long-term climate averages during periods with strong climatic trends results in large differences in soil carbon estimates. The largest differences in this study were observed in central and northern regions with strongly

Population responses to environmental change in a tropical ant: the interaction of spatial and temporal dynamics.

Science.gov (United States)

Jackson, Doug; Vandermeer, John; Perfecto, Ivette; Philpott, Stacy M

2014-01-01

Spatial structure can have a profound, but often underappreciated, effect on the temporal dynamics of ecosystems. Here we report on a counterintuitive increase in the population of a tree-nesting ant, Azteca sericeasur, in response to a drastic reduction in the number of potential nesting sites. This surprising result is comprehensible when viewed in the context of the self-organized spatial dynamics of the ants and their effect on the ants' dispersal-limited natural enemies. Approximately 30% of the trees in the study site, a coffee agroecosystem in southern Mexico, were pruned or felled over a two-year period, and yet the abundance of the ant nests more than doubled over the seven-year study. Throughout the transition, the spatial distribution of the ants maintained a power-law distribution - a signal of spatial self organization - but the local clustering of the nests was reduced post-pruning. A cellular automata model incorporating the changed spatial structure of the ants and the resulting partial escape from antagonists reproduced the observed increase in abundance, highlighting how self-organized spatial dynamics can profoundly influence the responses of ecosystems to perturbations.

Population responses to environmental change in a tropical ant: the interaction of spatial and temporal dynamics.

Directory of Open Access Journals (Sweden)

Doug Jackson

Full Text Available Spatial structure can have a profound, but often underappreciated, effect on the temporal dynamics of ecosystems. Here we report on a counterintuitive increase in the population of a tree-nesting ant, Azteca sericeasur, in response to a drastic reduction in the number of potential nesting sites. This surprising result is comprehensible when viewed in the context of the self-organized spatial dynamics of the ants and their effect on the ants' dispersal-limited natural enemies. Approximately 30% of the trees in the study site, a coffee agroecosystem in southern Mexico, were pruned or felled over a two-year period, and yet the abundance of the ant nests more than doubled over the seven-year study. Throughout the transition, the spatial distribution of the ants maintained a power-law distribution - a signal of spatial self organization - but the local clustering of the nests was reduced post-pruning. A cellular automata model incorporating the changed spatial structure of the ants and the resulting partial escape from antagonists reproduced the observed increase in abundance, highlighting how self-organized spatial dynamics can profoundly influence the responses of ecosystems to perturbations.

Spatio-temporal and stochastic modelling of severe acute respiratory syndrome

Directory of Open Access Journals (Sweden)

Poh-Chin Lai

2013-11-01

Full Text Available This study describes the development of a spatio-temporal disease model based on the episodes of severe acute respiratory syndrome (SARS that took place in Hong Kong in 2003. In contrast to conventional, deterministic modelling approaches, the model described here is predominantly spatial. It incorporates stochastic processing of environmental and social variables that interact in space and time to affect the patterns of disease transmission in a community. The model was validated through a comparative assessment between actual and modelled distribution of diseased locations. Our study shows that the inclusion of location-specific characteristics satisfactorily replicates the spatial dynamics of an infectious disease. The Pearson’s correlation coefficients for five trials based on 3-day aggregation of disease counts for 1-3, 4-6 and 7-9 day forecasts were 0.57- 0.95, 0.54-0.86 and 0.57-0.82, respectively, while the correlation based on 5-day aggregation for the 1-5 day forecast was 0.55- 0.94 and 0.58-0.81 for the 6-10 day forecast. The significant and strong relationship between actual results and forecast is encouraging for the potential development of an early warning system for detecting this type of disease outbreaks.

Equivalent Dynamic Models.

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Molenaar, Peter C M

2017-01-01

Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.

Building a bridge into the future: dynamic connectionist modeling as an integrative tool for research on intertemporal choice.

Science.gov (United States)

Scherbaum, Stefan; Dshemuchadse, Maja; Goschke, Thomas

2012-01-01

Temporal discounting denotes the fact that individuals prefer smaller rewards delivered sooner over larger rewards delivered later, often to a higher extent than suggested by normative economical theories. In this article, we identify three lines of research studying this phenomenon which aim (i) to describe temporal discounting mathematically, (ii) to explain observed choice behavior psychologically, and (iii) to predict the influence of specific factors on intertemporal decisions. We then opt for an approach integrating postulated mechanisms and empirical findings from these three lines of research. Our approach focuses on the dynamical properties of decision processes and is based on computational modeling. We present a dynamic connectionist model of intertemporal choice focusing on the role of self-control and time framing as two central factors determining choice behavior. Results of our simulations indicate that the two influences interact with each other, and we present experimental data supporting this prediction. We conclude that computational modeling of the decision process dynamics can advance the integration of different strands of research in intertemporal choice.

Building a bridge into the future: Dynamic connectionist modeling as an integrative tool for research on intertemporal choice

Directory of Open Access Journals (Sweden)

Stefan eScherbaum

2012-11-01

Full Text Available Temporal discounting denotes the fact that individuals prefer smaller rewards delivered sooner over larger rewards delivered later, often to a higher extent than suggested by normative economical theories. In this article, we identify three lines of research studying this phenomenon which aim (i to describe temporal discounting mathematically, (ii to explain observed choice behavior psychologically, and (iii to predict the influence of specific factors on intertemporal decisions. We then opt for an approach integrating postulated mechanisms and empirical findings from these three lines of research. Our approach focuses on the dynamical properties of decision processes and is based on computational modeling. We present a dynamic connectionist model of intertemporal choice focusing on the role of self-control and time framing as two central factors determining choice behavior. Results of our simulations indicate that the two influences interact with each other, and we present experimental data supporting this prediction. We conclude that computational modeling of the decision process dynamics can advance the integration of different strands of research in intertemporal choice.

Dynamic Systems and Control Engineering

International Nuclear Information System (INIS)

Kim, Jong Seok

1994-02-01

This book deals with introduction of dynamic system and control engineering, frequency domain modeling of dynamic system, temporal modeling of dynamic system, typical dynamic system and automatic control device, performance and stability of control system, root locus analysis, analysis of frequency domain dynamic system, design of frequency domain dynamic system, design and analysis of space, space of control system and digital control system such as control system design of direct digital and digitalization of consecutive control system.

Dynamic Systems and Control Engineering

Energy Technology Data Exchange (ETDEWEB)

Kim, Jong Seok

1994-02-15

This book deals with introduction of dynamic system and control engineering, frequency domain modeling of dynamic system, temporal modeling of dynamic system, typical dynamic system and automatic control device, performance and stability of control system, root locus analysis, analysis of frequency domain dynamic system, design of frequency domain dynamic system, design and analysis of space, space of control system and digital control system such as control system design of direct digital and digitalization of consecutive control system.

Dynamic response and transfer function of social systems: A neuro-inspired model of collective human activity patterns.

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Lymperopoulos, Ilias N

2017-10-01

The interaction of social networks with the external environment gives rise to non-stationary activity patterns reflecting the temporal structure and strength of exogenous influences that drive social dynamical processes far from an equilibrium state. Following a neuro-inspired approach, based on the dynamics of a passive neuronal membrane, and the firing rate dynamics of single neurons and neuronal populations, we build a state-of-the-art model of the collective social response to exogenous interventions. In this regard, we analyze online activity patterns with a view to determining the transfer function of social systems, that is, the dynamic relationship between external influences and the resulting activity. To this end, first we estimate the impulse response (Green's function) of collective activity, and then we show that the convolution of the impulse response with a time-varying external influence field accurately reproduces empirical activity patterns. To capture the dynamics of collective activity when the generating process is in a state of statistical equilibrium, we incorporate into the model a noisy input convolved with the impulse response function, thus precisely reproducing the fluctuations of stationary collective activity around a resting value. The outstanding goodness-of-fit of the model results to empirical observations, indicates that the model explains human activity patterns generated by time-dependent external influences in various socio-economic contexts. The proposed model can be used for inferring the temporal structure and strength of external influences, as well as the inertia of collective social activity. Furthermore, it can potentially predict social activity patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dynamical principles in neuroscience

International Nuclear Information System (INIS)

Rabinovich, Mikhail I.; Varona, Pablo; Selverston, Allen I.; Abarbanel, Henry D. I.

2006-01-01

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?

Dynamical principles in neuroscience

Science.gov (United States)

Rabinovich, Mikhail I.; Varona, Pablo; Selverston, Allen I.; Abarbanel, Henry D. I.

2006-10-01

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?

Revisiting Temporal Markov Chains for Continuum modeling of Transport in Porous Media

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Delgoshaie, A. H.; Jenny, P.; Tchelepi, H.

2017-12-01

The transport of fluids in porous media is dominated by flow­-field heterogeneity resulting from the underlying permeability field. Due to the high uncertainty in the permeability field, many realizations of the reference geological model are used to describe the statistics of the transport phenomena in a Monte Carlo (MC) framework. There has been strong interest in working with stochastic formulations of the transport that are different from the standard MC approach. Several stochastic models based on a velocity process for tracer particle trajectories have been proposed. Previous studies have shown that for high variances of the log-conductivity, the stochastic models need to account for correlations between consecutive velocity transitions to predict dispersion accurately. The correlated velocity models proposed in the literature can be divided into two general classes of temporal and spatial Markov models. Temporal Markov models have been applied successfully to tracer transport in both the longitudinal and transverse directions. These temporal models are Stochastic Differential Equations (SDEs) with very specific drift and diffusion terms tailored for a specific permeability correlation structure. The drift and diffusion functions devised for a certain setup would not necessarily be suitable for a different scenario, (e.g., a different permeability correlation structure). The spatial Markov models are simple discrete Markov chains that do not require case specific assumptions. However, transverse spreading of contaminant plumes has not been successfully modeled with the available correlated spatial models. Here, we propose a temporal discrete Markov chain to model both the longitudinal and transverse dispersion in a two-dimensional domain. We demonstrate that these temporal Markov models are valid for different correlation structures without modification. Similar to the temporal SDEs, the proposed model respects the limited asymptotic transverse spreading of

Machine Learning-based discovery of closures for reduced models of dynamical systems

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Pan, Shaowu; Duraisamy, Karthik

2017-11-01

Despite the successful application of machine learning (ML) in fields such as image processing and speech recognition, only a few attempts has been made toward employing ML to represent the dynamics of complex physical systems. Previous attempts mostly focus on parameter calibration or data-driven augmentation of existing models. In this work we present a ML framework to discover closure terms in reduced models of dynamical systems and provide insights into potential problems associated with data-driven modeling. Based on exact closure models for linear system, we propose a general linear closure framework from viewpoint of optimization. The framework is based on trapezoidal approximation of convolution term. Hyperparameters that need to be determined include temporal length of memory effect, number of sampling points, and dimensions of hidden states. To circumvent the explicit specification of memory effect, a general framework inspired from neural networks is also proposed. We conduct both a priori and posteriori evaluations of the resulting model on a number of non-linear dynamical systems. This work was supported in part by AFOSR under the project ``LES Modeling of Non-local effects using Statistical Coarse-graining'' with Dr. Jean-Luc Cambier as the technical monitor.

Frontiers in Fluctuation Spectroscopy: Measuring protein dynamics and protein spatio-temporal connectivity

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Digman, Michelle

Fluorescence fluctuation spectroscopy has evolved from single point detection of molecular diffusion to a family of microscopy imaging correlation tools (i.e. ICS, RICS, STICS, and kICS) useful in deriving spatial-temporal dynamics of proteins in living cells The advantage of the imaging techniques is the simultaneous measurement of all points in an image with a frame rate that is increasingly becoming faster with better sensitivity cameras and new microscopy modalities such as the sheet illumination technique. A new frontier in this area is now emerging towards a high level of mapping diffusion rates and protein dynamics in the 2 and 3 dimensions. In this talk, I will discuss the evolution of fluctuation analysis from the single point source to mapping diffusion in whole cells and the technology behind this technique. In particular, new methods of analysis exploit correlation of molecular fluctuations originating from measurement of fluctuation correlations at distant points (pair correlation analysis) and methods that exploit spatial averaging of fluctuations in small regions (iMSD). For example the pair correlation fluctuation (pCF) analyses done between adjacent pixels in all possible radial directions provide a window into anisotropic molecular diffusion. Similar to the connectivity atlas of neuronal connections from the MRI diffusion tensor imaging these new tools will be used to map the connectome of protein diffusion in living cells. For biological reaction-diffusion systems, live single cell spatial-temporal analysis of protein dynamics provides a mean to observe stochastic biochemical signaling in the context of the intracellular environment which may lead to better understanding of cancer cell invasion, stem cell differentiation and other fundamental biological processes. National Institutes of Health Grant P41-RRO3155.

Improvement of temporal and dynamic subtraction images on abdominal CT using 3D global image matching and nonlinear image warping techniques

International Nuclear Information System (INIS)

Okumura, E; Sanada, S; Suzuki, M; Takemura, A; Matsui, O

2007-01-01

Accurate registration of the corresponding non-enhanced and arterial-phase CT images is necessary to create temporal and dynamic subtraction images for the enhancement of subtle abnormalities. However, respiratory movement causes misregistration at the periphery of the liver. To reduce these misregistration errors, we developed a temporal and dynamic subtraction technique to enhance small HCC by 3D global matching and nonlinear image warping techniques. The study population consisted of 21 patients with HCC. Using the 3D global matching and nonlinear image warping technique, we registered current and previous arterial-phase CT images or current non-enhanced and arterial-phase CT images obtained in the same position. The temporal subtraction image was obtained by subtracting the previous arterial-phase CT image from the warped current arterial-phase CT image. The dynamic subtraction image was obtained by the subtraction of the current non-enhanced CT image from the warped current arterial-phase CT image. The percentage of fair or superior temporal subtraction images increased from 52.4% to 95.2% using the new technique, while on the dynamic subtraction images, the percentage increased from 66.6% to 95.2%. The new subtraction technique may facilitate the diagnosis of subtle HCC based on the superior ability of these subtraction images to show nodular and/or ring enhancement

Selective 4D modelling framework for spatial-temporal land information management system

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Doulamis, Anastasios; Soile, Sofia; Doulamis, Nikolaos; Chrisouli, Christina; Grammalidis, Nikos; Dimitropoulos, Kosmas; Manesis, Charalambos; Potsiou, Chryssy; Ioannidis, Charalabos

2015-06-01

This paper introduces a predictive (selective) 4D modelling framework where only the spatial 3D differences are modelled at the forthcoming time instances, while regions of no significant spatial-temporal alterations remain intact. To accomplish this, initially spatial-temporal analysis is applied between 3D digital models captured at different time instances. So, the creation of dynamic change history maps is made. Change history maps indicate spatial probabilities of regions needed further 3D modelling at forthcoming instances. Thus, change history maps are good examples for a predictive assessment, that is, to localize surfaces within the objects where a high accuracy reconstruction process needs to be activated at the forthcoming time instances. The proposed 4D Land Information Management System (LIMS) is implemented using open interoperable standards based on the CityGML framework. CityGML allows the description of the semantic metadata information and the rights of the land resources. Visualization aspects are also supported to allow easy manipulation, interaction and representation of the 4D LIMS digital parcels and the respective semantic information. The open source 3DCityDB incorporating a PostgreSQL geo-database is used to manage and manipulate 3D data and their semantics. An application is made to detect the change through time of a 3D block of plots in an urban area of Athens, Greece. Starting with an accurate 3D model of the buildings in 1983, a change history map is created using automated dense image matching on aerial photos of 2010. For both time instances meshes are created and through their comparison the changes are detected.

Exploring spatial-temporal dynamics of fire regime features in mainland Spain

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Jiménez-Ruano, Adrián; Rodrigues Mimbrero, Marcos; de la Riva Fernández, Juan

2017-10-01

This paper explores spatial-temporal dynamics in fire regime features, such as fire frequency, burnt area, large fires and natural- and human-caused fires, as an essential part of fire regime characterization. Changes in fire features are analysed at different spatial - regional and provincial/NUTS3 - levels, together with summer and winter temporal scales, using historical fire data from Spain for the period 1974-2013. Temporal shifts in fire features are investigated by means of change point detection procedures - Pettitt test, AMOC (at most one change), PELT (pruned exact linear time) and BinSeg (binary segmentation) - at a regional level to identify changes in the time series of the features. A trend analysis was conducted using the Mann-Kendall and Sen's slope tests at both the regional and NUTS3 level. Finally, we applied a principal component analysis (PCA) and varimax rotation to trend outputs - mainly Sen's slope values - to summarize overall temporal behaviour and to explore potential links in the evolution of fire features. Our results suggest that most fire features show remarkable shifts between the late 1980s and the first half of the 1990s. Mann-Kendall outputs revealed negative trends in the Mediterranean region. Results from Sen's slope suggest high spatial and intra-annual variability across the study area. Fire activity related to human sources seems to be experiencing an overall decrease in the northwestern provinces, particularly pronounced during summer. Similarly, the Hinterland and the Mediterranean coast are gradually becoming less fire affected. Finally, PCA enabled trends to be synthesized into four main components: winter fire frequency (PC1), summer burnt area (PC2), large fires (PC3) and natural fires (PC4).

Temporal dynamics of biogeochemical processes at the Norman Landfill site

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Arora, Bhavna; Mohanty, Binayak P.; McGuire, Jennifer T.; Cozzarelli, Isabelle M.

2013-01-01

The temporal variability observed in redox sensitive species in groundwater can be attributed to coupled hydrological, geochemical, and microbial processes. These controlling processes are typically nonstationary, and distributed across various time scales. Therefore, the purpose of this study is to investigate biogeochemical data sets from a municipal landfill site to identify the dominant modes of variation and determine the physical controls that become significant at different time scales. Data on hydraulic head, specific conductance, δ2H, chloride, sulfate, nitrate, and nonvolatile dissolved organic carbon were collected between 1998 and 2000 at three wells at the Norman Landfill site in Norman, OK. Wavelet analysis on this geochemical data set indicates that variations in concentrations of reactive and conservative solutes are strongly coupled to hydrologic variability (water table elevation and precipitation) at 8 month scales, and to individual eco-hydrogeologic framework (such as seasonality of vegetation, surface-groundwater dynamics) at 16 month scales. Apart from hydrologic variations, temporal variability in sulfate concentrations can be associated with different sources (FeS cycling, recharge events) and sinks (uptake by vegetation) depending on the well location and proximity to the leachate plume. Results suggest that nitrate concentrations show multiscale behavior across temporal scales for different well locations, and dominant variability in dissolved organic carbon for a closed municipal landfill can be larger than 2 years due to its decomposition and changing content. A conceptual framework that explains the variability in chemical concentrations at different time scales as a function of hydrologic processes, site-specific interactions, and/or coupled biogeochemical effects is also presented.

Left or right? Lateralizing temporal lobe epilepsy by dynamic amygdala fMRI.

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Ives-Deliperi, Victoria; Butler, James Thomas; Jokeit, Hennric

2017-05-01

In this case series, the findings of 85 functional MRI studies employing a dynamic fearful face paradigm are reported. Previous findings have shown the paradigm to generate bilateral amygdala activations in healthy subjects and unilateral activations in patients with MTLE, in the contralateral hemisphere to seizure origin. Such findings suggest ipsilateral limbic pathology and offer collateral evidence in lateralizing MTLE. The series includes 60 patients with TLE, 12 patients with extra-temporal lobe epilepsy, and 13 healthy controls. Functional MRI studies using a 1.5T scanner were conducted over a three-year period at a single epilepsy center and individual results were compared with EEG findings. In the cohort of unilateral TLE patients, lateralized activations of the amygdala were concordant with EEG findings in 76% of patients (77% lTLE, 74% rTLE). The differences in the mean lateralized indices of the lTLE, rTLE, and healthy control groups were all statistically significant. Lateralized amygdala activations were concordant with EEG findings in only 31% of the 12 patients with extra-temporal lobe epilepsy and bilateral amygdala activations were generated in all but one of the healthy control subjects. This case series further endorses the utility of the dynamic fearful face functional MRI paradigm using the widely available 1.5T as an adjunctive investigation to lateralize TLE. Copyright © 2017 Elsevier Inc. All rights reserved.

Quantization of systems with temporally varying discretization. I. Evolving Hilbert spaces

International Nuclear Information System (INIS)

Höhn, Philipp A.

2014-01-01

A temporally varying discretization often features in discrete gravitational systems and appears in lattice field theory models subject to a coarse graining or refining dynamics. To better understand such discretization changing dynamics in the quantum theory, an according formalism for constrained variational discrete systems is constructed. While this paper focuses on global evolution moves and, for simplicity, restricts to flat configuration spaces R N , a Paper II [P. A. Höhn, “Quantization of systems with temporally varying discretization. II. Local evolution moves,” J. Math. Phys., e-print http://arxiv.org/abs/arXiv:1401.7731 [gr-qc].] discusses local evolution moves. In order to link the covariant and canonical picture, the dynamics of the quantum states is generated by propagators which satisfy the canonical constraints and are constructed using the action and group averaging projectors. This projector formalism offers a systematic method for tracing and regularizing divergences in the resulting state sums. Non-trivial coarse graining evolution moves lead to non-unitary, and thus irreversible, projections of physical Hilbert spaces and Dirac observables such that these concepts become evolution move dependent on temporally varying discretizations. The formalism is illustrated in a toy model mimicking a “creation from nothing.” Subtleties arising when applying such a formalism to quantum gravity models are discussed

Temporal node centrality in complex networks

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Kim, Hyoungshick; Anderson, Ross

2012-02-01

Many networks are dynamic in that their topology changes rapidly—on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered graph, which reduces a dynamic network to a static network with directed flows. This enables us to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case. We then demonstrate how our model applies to a number of interesting edge cases, such as where the network connectivity depends on a small number of highly mobile vertices or edges, and show that our centrality definition allows us to track the evolution of connectivity. Finally we apply our model and techniques to two real-world dynamic graphs of human contact networks and then discuss the implication of temporal centrality metrics in the real world.

A complex systems approach to dynamic spatial simulation modeling: LandUse and LandCover change in the Ecuadorian Amazon

Science.gov (United States)

Messina, Joseph Paul

The Ecuadorian Amazon, lying in the headwaters of the Napo and Aguarico River valleys, is experiencing rapid change in LandUse and LandCover (LULC) conditions and regional landscape diversity uniquely tied to spontaneous agricultural colonization and oil exploration. Beginning in the early 1970s, spontaneous colonization occurred on squattered lands located adjacent to oil company roads and in government development sectors composed of multiple 50 ha land parcels organized into "piano key" shaped family farms or fincas. Since fincas are managed at the household level as spatially discrete, temporally independent units, land conversion at the finca-level is recognized as the chief proximate cause of deforestation within the region. Focusing on the spatial and temporal dynamics of deforestation, agricultural extensification, and plant succession at the finca-level, and urbanization at the community-level, cell-based morphogenetic models of LandUse and LandCover Change (LULCC) were developed as the foundation for predictive models of regional LULCC dynamics and landscape diversity. Two cellular automata models were developed and used to integrate biophysical, geographical, and social variables to characterize temporally dynamic landscapes. The human, geographical, and biophysical dimensions of land use and land cover change were examined, specifically deforestation, anthropogenic extensification, and reforestation. Remotely-sensed data ranging temporally from the 1970s through 1999, combined with thematic map coverages of biophysical gradients and geographical accessibility, were linked to household and community survey data collected in 1990 and 1999. Image processing techniques for LULC characterization and spatial analyses of landscape structure were used to assess the rate and nature of LULCC throughout the time-series. In addition, LULC and LULCC associated with secondary plant succession and agricultural extensification were assessed and simulated for specific

Brain Dynamics An Introduction to Models and Simualtions

CERN Document Server

Haken, Hermann

2008-01-01

Brain Dynamics serves to introduce graduate students and nonspecialists from various backgrounds to the field of mathematical and computational neurosciences. Some of the advanced chapters will also be of interest to the specialists. The book approaches the subject through pulse-coupled neural networks, with at their core the lighthouse and integrate-and-fire models, which allow for the highly flexible modelling of realistic synaptic activity, synchronization and spatio-temporal pattern formation. Topics also include pulse-averaged equations and their application to movement coordination. The book closes with a short analysis of models versus the real neurophysiological system. The second edition has been thoroughly updated and augmented by two extensive chapters that discuss the interplay between pattern recognition and synchronization. Further, to enhance the usefulness as textbook and for self-study, the detailed solutions for all 34 exercises throughout the text have been added.

Combating Weapons of Mass Destruction: Models, Complexity, and Algorithms in Complex Dynamic and Evolving Networks

Science.gov (United States)

2015-11-01

Gholamreza, and Ester, Martin. “Modeling the Temporal Dynamics of Social Rating Networks Using Bidirectional Effects of Social Relations and Rating...1.1.2 β-disruptor Problems Besides the homogeneous network model consisting of uniform nodes and bidirectional links, the heterogeneous network model... neural and metabolic networks .” Biological Cybernetics 90 (2004): 311–317. 10.1007/s00422-004-0479-1. URL http://dx.doi.org/10.1007/s00422-004-0479-1 [51

Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

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Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

2016-11-01

Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

Parameterizing Coefficients of a POD-Based Dynamical System

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Kalb, Virginia L.

2010-01-01

A method of parameterizing the coefficients of a dynamical system based of a proper orthogonal decomposition (POD) representing the flow dynamics of a viscous fluid has been introduced. (A brief description of POD is presented in the immediately preceding article.) The present parameterization method is intended to enable construction of the dynamical system to accurately represent the temporal evolution of the flow dynamics over a range of Reynolds numbers. The need for this or a similar method arises as follows: A procedure that includes direct numerical simulation followed by POD, followed by Galerkin projection to a dynamical system has been proven to enable representation of flow dynamics by a low-dimensional model at the Reynolds number of the simulation. However, a more difficult task is to obtain models that are valid over a range of Reynolds numbers. Extrapolation of low-dimensional models by use of straightforward Reynolds-number-based parameter continuation has proven to be inadequate for successful prediction of flows. A key part of the problem of constructing a dynamical system to accurately represent the temporal evolution of the flow dynamics over a range of Reynolds numbers is the problem of understanding and providing for the variation of the coefficients of the dynamical system with the Reynolds number. Prior methods do not enable capture of temporal dynamics over ranges of Reynolds numbers in low-dimensional models, and are not even satisfactory when large numbers of modes are used. The basic idea of the present method is to solve the problem through a suitable parameterization of the coefficients of the dynamical system. The parameterization computations involve utilization of the transfer of kinetic energy between modes as a function of Reynolds number. The thus-parameterized dynamical system accurately predicts the flow dynamics and is applicable to a range of flow problems in the dynamical regime around the Hopf bifurcation. Parameter

Representation of spatial and temporal variability of daily wind speed and of intense wind events over the Mediterranean Sea using dynamical downscaling: impact of the regional climate model configuration

Directory of Open Access Journals (Sweden)

M. Herrmann

2011-07-01

Full Text Available Atmospheric datasets coming from long term reanalyzes of low spatial resolution are used for different purposes. Wind over the sea is, for example, a major ingredient of oceanic simulations. However, the shortcomings of those datasets prevent them from being used without an adequate corrective preliminary treatment. Using a regional climate model (RCM to perform a dynamical downscaling of those large scale reanalyzes is one of the methods used in order to produce fields that realistically reproduce atmospheric chronology and where those shortcomings are corrected. Here we assess the influence of the configuration of the RCM used in this framework on the representation of wind speed spatial and temporal variability and intense wind events on a daily timescale. Our RCM is ALADIN-Climate, the reanalysis is ERA-40, and the studied area is the Mediterranean Sea.

First, the dynamical downscaling significantly reduces the underestimation of daily wind speed, in average by 9 % over the whole Mediterranean. This underestimation has been corrected both globally and locally, and for the whole wind speed spectrum. The correction is the strongest for periods and regions of strong winds. The representation of spatial variability has also been significantly improved. On the other hand, the temporal correlation between the downscaled field and the observations decreases all the more that one moves eastwards, i.e. further from the atmospheric flux entry. Nonetheless, it remains ~0.7, the downscaled dataset reproduces therefore satisfactorily the real chronology.

Second, the influence of the choice of the RCM configuration has an influence one order of magnitude smaller than the improvement induced by the initial downscaling. The use of spectral nudging or of a smaller domain helps to improve the realism of the temporal chronology. Increasing the resolution very locally (both spatially and temporally improves the representation of spatial

Interplay between the temporal dynamics of the vaginal microbiota and human papillomavirus detection.

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Brotman, Rebecca M; Shardell, Michelle D; Gajer, Pawel; Tracy, J Kathleen; Zenilman, Jonathan M; Ravel, Jacques; Gravitt, Patti E

2014-12-01

We sought to describe the temporal relationship between vaginal microbiota and human papillomavirus (HPV) detection. Thirty-two reproductive-age women self-collected midvaginal swabs twice weekly for 16 weeks (937 samples). Vaginal bacterial communities were characterized by pyrosequencing of barcoded 16S rRNA genes and clustered into 6 community state types (CSTs). Each swab was tested for 37 HPV types. The effects of CSTs on the rate of transition between HPV-negative and HPV-positive states were assessed using continuous-time Markov models. Participants had an average of 29 samples, with HPV point prevalence between 58%-77%. CST was associated with changes in HPV status (PVaginal microbiota dominated by L. gasseri was associated with increased clearance of detectable HPV. Frequent longitudinal sampling is necessary for evaluation of the association between HPV detection and dynamic microbiota. © The Author 2014. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Directory of Open Access Journals (Sweden)

Cécile Cazals

2016-07-01

Full Text Available In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to monitor hydrological dynamics of the Poitevin marshland in western France. We analyze a time series of 14 radar images acquired in VV and HV polarizations from December 2014 to May 2015 with a 12-day time step. Both polarizations are used with a hysteresis thresholding algorithm which uses both spatial and temporal information to distinguish open water, flooded vegetation and non-flooded grassland. Classification results are compared to in situ piezometric measurements combined with a Digital Terrain Model derived from LiDAR data. Results reveal that open water is successfully detected, whereas flooded grasslands with emergent vegetation and fine-grained patterns are detected with moderate accuracy. Five hydrological regimes are derived from the flood duration and mapped. Analysis of time steps in the time series shows that decreased temporal repetitivity induces significant differences in estimates of flood duration. These results illustrate the great potential to monitor variations in seasonal floods with the high temporal frequency of Sentinel-1A acquisitions.

Temporal adaptation enhances efficient contrast gain control on natural images.

Directory of Open Access Journals (Sweden)

Fabian Sinz

Full Text Available Divisive normalization in primary visual cortex has been linked to adaptation to natural image statistics in accordance to Barlow's redundancy reduction hypothesis. Using recent advances in natural image modeling, we show that the previously studied static model of divisive normalization is rather inefficient in reducing local contrast correlations, but that a simple temporal contrast adaptation mechanism of the half-saturation constant can substantially increase its efficiency. Our findings reveal the experimentally observed temporal dynamics of divisive normalization to be critical for redundancy reduction.

Dynamic Raman imaging system with high spatial and temporal resolution

Science.gov (United States)

Wang, Lei; Dai, Yinzhen; He, Hao; Lv, Ruiqi; Zong, Cheng; Ren, Bin

2017-09-01

There is an increasing need to study dynamic changing systems with significantly high spatial and temporal resolutions. In this work, we integrated point-scanning, line-scanning, and wide-field Raman imaging techniques into a single system. By using an Electron Multiplying CCD (EMCCD) with a high gain and high frame rate, we significantly reduced the time required for wide-field imaging, making it possible to monitor the electrochemical reactions in situ. The highest frame rate of EMCDD was ˜50 fps, and the Raman images for a specific Raman peak can be obtained by passing the signal from the sample through the Liquid Crystal Tunable Filter. The spatial resolutions of scanning imaging and wide-field imaging with a 100× objective (NA = 0.9) are 0.5 × 0.5 μm2 and 0.36 × 0.36 μm2, respectively. The system was used to study the surface plasmon resonance of Au nanorods, the surface-enhanced Raman scattering signal distribution for Au Nanoparticle aggregates, and dynamic Raman imaging of an electrochemical reacting system.

Spatial and Temporal Flood Risk Assessment for Decision Making Approach

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Azizat, Nazirah; Omar, Wan-Mohd-Sabki Wan

2018-03-01

Heavy rainfall, adversely impacting inundation areas, depends on the magnitude of the flood. Significantly, location of settlements, infrastructure and facilities in floodplains result in many regions facing flooding risks. A problem faced by the decision maker in an assessment of flood vulnerability and evaluation of adaptation measures is recurrent flooding in the same areas. Identification of recurrent flooding areas and frequency of floods should be priorities for flood risk management. However, spatial and temporal variability become major factors of uncertainty in flood risk management. Therefore, dynamic and spatial characteristics of these changes in flood impact assessment are important in making decisions about the future of infrastructure development and community life. System dynamics (SD) simulation and hydrodynamic modelling are presented as tools for modelling the dynamic characteristics of flood risk and spatial variability. This paper discusses the integration between spatial and temporal information that is required by the decision maker for the identification of multi-criteria decision problems involving multiple stakeholders.

Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs

International Nuclear Information System (INIS)

Kowalska-Pyzalska, Anna; Maciejowska, Katarzyna; Suszczyński, Karol; Sznajd-Weron, Katarzyna; Weron, Rafał

2014-01-01

Using an agent-based modeling approach we study the temporal dynamics of consumer opinions regarding switching to dynamic electricity tariffs and the actual decisions to switch. We assume that the decision to switch is based on the unanimity of τ past opinions. The resulting model offers a hypothetical, yet plausible explanation of why there is such a big discrepancy between consumer opinions, as measured by market surveys, and the actual participation in pilot programs and the adoption of dynamic tariffs. We argue that due to the high indifference level in today's retail electricity markets, customer opinions are very unstable and change frequently. The conducted simulation study shows that reducing the indifference level can result in narrowing the intention–behavior gap. A similar effect can be achieved by decreasing the decision time that a consumer takes to make a decision. - Highlights: • We propose an agent-based model to study the adoption of dynamic electricity tariffs. • The decision to change the tariff is based on the unanimity of τ past opinions. • The model explains why the empirically observed intention–behavior gap exists. • The adoption of dynamic tariffs is impossible due to the high level of indifference in today's societies. • Reducing the indifference level or decreasing the decision time can result in narrowing the gap

Content Validity of Temporal Bone Models Printed Via Inexpensive Methods and Materials.

Science.gov (United States)

Bone, T Michael; Mowry, Sarah E

2016-09-01

Computed tomographic (CT) scans of the 3-D printed temporal bone models will be within 15% accuracy of the CT scans of the cadaveric temporal bones. Previous studies have evaluated the face validity of 3-D-printed temporal bone models designed to train otolaryngology residents. The purpose of the study was to determine the content validity of temporal bone models printed using inexpensive printers and materials. Four cadaveric temporal bones were randomly selected and clinical temporal bone CT scans were obtained. Models were generated using previously described methods in acrylonitrile butadiene styrene (ABS) plastic using the Makerbot Replicator 2× and Hyrel printers. Models were radiographically scanned using the same protocol as the cadaveric bones. Four images from each cadaveric CT series and four corresponding images from the model CT series were selected, and voxel values were normalized to black or white. Scan slices were compared using PixelDiff software. Gross anatomic structures were evaluated in the model scans by four board certified otolaryngologists on a 4-point scale. Mean pixel difference between the cadaver and model scans was 14.25 ± 2.30% at the four selected CT slices. Mean cortical bone width difference and mean external auditory canal width difference were 0.58 ± 0.66 mm and 0.55 ± 0.46 mm, respectively. Expert raters felt the mastoid air cells were well represented (2.5 ± 0.5), while middle ear and otic capsule structures were not accurately rendered (all averaged bones for training residents in cortical mastoidectomies, but less effective for middle ear procedures.

Temporal dynamics of European bat Lyssavirus type 1 and survival of Myotis myotis bats in natural colonies.

Science.gov (United States)

Amengual, Blanca; Bourhy, Hervé; López-Roig, Marc; Serra-Cobo, Jordi

2007-06-27

Many emerging RNA viruses of public health concern have recently been detected in bats. However, the dynamics of these viruses in natural bat colonies is presently unknown. Consequently, prediction of the spread of these viruses and the establishment of appropriate control measures are hindered by a lack of information. To this aim, we collected epidemiological, virological and ecological data during a twelve-year longitudinal study in two colonies of insectivorous bats (Myotis myotis) located in Spain and infected by the most common bat lyssavirus found in Europe, the European bat lyssavirus subtype 1 (EBLV-1). This active survey demonstrates that cyclic lyssavirus infections occurred with periodic oscillations in the number of susceptible, immune and infected bats. Persistence of immunity for more than one year was detected in some individuals. These data were further used to feed models to analyze the temporal dynamics of EBLV-1 and the survival rate of bats. According to these models, the infection is characterized by a predicted low basic reproductive rate (R(0) = 1.706) and a short infectious period (D = 5.1 days). In contrast to observations in most non-flying animals infected with rabies, the survival model shows no variation in mortality after EBLV-1 infection of M. myotis. These findings have considerable public health implications in terms of management of colonies where lyssavirus-positive bats have been recorded and confirm the potential risk of rabies transmission to humans. A greater understanding of the dynamics of lyssavirus in bat colonies also provides a model to study how bats contribute to the maintenance and transmission of other viruses of public health concern.

Temporal dynamics of European bat Lyssavirus type 1 and survival of Myotis myotis bats in natural colonies.

Directory of Open Access Journals (Sweden)

Blanca Amengual

Full Text Available Many emerging RNA viruses of public health concern have recently been detected in bats. However, the dynamics of these viruses in natural bat colonies is presently unknown. Consequently, prediction of the spread of these viruses and the establishment of appropriate control measures are hindered by a lack of information. To this aim, we collected epidemiological, virological and ecological data during a twelve-year longitudinal study in two colonies of insectivorous bats (Myotis myotis located in Spain and infected by the most common bat lyssavirus found in Europe, the European bat lyssavirus subtype 1 (EBLV-1. This active survey demonstrates that cyclic lyssavirus infections occurred with periodic oscillations in the number of susceptible, immune and infected bats. Persistence of immunity for more than one year was detected in some individuals. These data were further used to feed models to analyze the temporal dynamics of EBLV-1 and the survival rate of bats. According to these models, the infection is characterized by a predicted low basic reproductive rate (R(0 = 1.706 and a short infectious period (D = 5.1 days. In contrast to observations in most non-flying animals infected with rabies, the survival model shows no variation in mortality after EBLV-1 infection of M. myotis. These findings have considerable public health implications in terms of management of colonies where lyssavirus-positive bats have been recorded and confirm the potential risk of rabies transmission to humans. A greater understanding of the dynamics of lyssavirus in bat colonies also provides a model to study how bats contribute to the maintenance and transmission of other viruses of public health concern.

A statistical rain attenuation prediction model with application to the advanced communication technology satellite project. Part 2: Theoretical development of a dynamic model and application to rain fade durations and tolerable control delays for fade countermeasures

Science.gov (United States)

Manning, Robert M.

1987-01-01

A dynamic rain attenuation prediction model is developed for use in obtaining the temporal characteristics, on time scales of minutes or hours, of satellite communication link availability. Analagous to the associated static rain attenuation model, which yields yearly attenuation predictions, this dynamic model is applicable at any location in the world that is characterized by the static rain attenuation statistics peculiar to the geometry of the satellite link and the rain statistics of the location. Such statistics are calculated by employing the formalism of Part I of this report. In fact, the dynamic model presented here is an extension of the static model and reduces to the static model in the appropriate limit. By assuming that rain attenuation is dynamically described by a first-order stochastic differential equation in time and that this random attenuation process is a Markov process, an expression for the associated transition probability is obtained by solving the related forward Kolmogorov equation. This transition probability is then used to obtain such temporal rain attenuation statistics as attenuation durations and allowable attenuation margins versus control system delay.

Subliminal semantic priming changes the dynamic causal influence between the left frontal and temporal cortex.

Science.gov (United States)

Matsumoto, Atsushi; Kakigi, Ryusuke

2014-01-01

Recent neuroimaging experiments have revealed that subliminal priming of a target stimulus leads to the reduction of neural activity in specific regions concerned with processing the target. Such findings lead to questions about the degree to which the subliminal priming effect is based only on decreased activity in specific local brain regions, as opposed to the influence of neural mechanisms that regulate communication between brain regions. To address this question, this study recorded EEG during performance of a subliminal semantic priming task. We adopted an information-based approach that used independent component analysis and multivariate autoregressive modeling. Results indicated that subliminal semantic priming caused significant modulation of alpha band activity in the left inferior frontal cortex and modulation of gamma band activity in the left inferior temporal regions. The multivariate autoregressive approach confirmed significant increases in information flow from the inferior frontal cortex to inferior temporal regions in the early time window that was induced by subliminal priming. In the later time window, significant enhancement of bidirectional causal flow between these two regions underlying subliminal priming was observed. Results suggest that unconscious processing of words influences not only local activity of individual brain regions but also the dynamics of neural communication between those regions.

Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics.

Science.gov (United States)

Ling, Hong; Samarasinghe, Sandhya; Kulasiri, Don

2013-12-01

Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary differential equations (ODE). However, critical limitations of ODE models are difficulty in kinetic parameter estimation and numerical solution of a large number of equations, making them more suited to smaller systems. In this article, we introduce a novel recurrent artificial neural network (RNN) that addresses above limitations and produces a continuous model that easily estimates parameters from data, can handle a large number of molecular interactions and quantifies temporal dynamics and emergent systems properties. This RNN is based on a system of ODEs representing molecular interactions in a signalling network. Each neuron represents concentration change of one molecule represented by an ODE. Weights of the RNN correspond to kinetic parameters in the system and can be adjusted incrementally during network training. The method is applied to the p53-Mdm2 oscillation system - a crucial component of the DNA damage response pathways activated by a damage signal. Simulation results indicate that the proposed RNN can successfully represent the behaviour of the p53-Mdm2 oscillation system and solve the parameter estimation problem with high accuracy. Furthermore, we presented a modified form of the RNN that estimates parameters and captures systems dynamics from sparse data collected over relatively large time steps. We also investigate the robustness of the p53-Mdm2 system using the trained RNN under various levels of parameter perturbation to gain a greater understanding of the control of the p53-Mdm2 system. Its outcomes on robustness are consistent with the current biological knowledge of this system. As more

Natural image sequences constrain dynamic receptive fields and imply a sparse code.

Science.gov (United States)

Häusler, Chris; Susemihl, Alex; Nawrot, Martin P

2013-11-06

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Exploring spatial–temporal dynamics of fire regime features in mainland Spain

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A. Jiménez-Ruano

2017-10-01

Full Text Available This paper explores spatial–temporal dynamics in fire regime features, such as fire frequency, burnt area, large fires and natural- and human-caused fires, as an essential part of fire regime characterization. Changes in fire features are analysed at different spatial – regional and provincial/NUTS3 – levels, together with summer and winter temporal scales, using historical fire data from Spain for the period 1974–2013. Temporal shifts in fire features are investigated by means of change point detection procedures – Pettitt test, AMOC (at most one change, PELT (pruned exact linear time and BinSeg (binary segmentation – at a regional level to identify changes in the time series of the features. A trend analysis was conducted using the Mann–Kendall and Sen's slope tests at both the regional and NUTS3 level. Finally, we applied a principal component analysis (PCA and varimax rotation to trend outputs – mainly Sen's slope values – to summarize overall temporal behaviour and to explore potential links in the evolution of fire features. Our results suggest that most fire features show remarkable shifts between the late 1980s and the first half of the 1990s. Mann–Kendall outputs revealed negative trends in the Mediterranean region. Results from Sen's slope suggest high spatial and intra-annual variability across the study area. Fire activity related to human sources seems to be experiencing an overall decrease in the northwestern provinces, particularly pronounced during summer. Similarly, the Hinterland and the Mediterranean coast are gradually becoming less fire affected. Finally, PCA enabled trends to be synthesized into four main components: winter fire frequency (PC1, summer burnt area (PC2, large fires (PC3 and natural fires (PC4.

Temporal microbiota changes of high-protein diet intake in a rat model.

Science.gov (United States)

Mu, Chunlong; Yang, Yuxiang; Luo, Zhen; Zhu, Weiyun

2017-10-01

Alterations of specific microbes serve as important indicators that link gut health with specific diet intake. Although a six-week high-protein diet (45% protein) upregulates the pro-inflammatory response and oxidative stress in colon of rats, the dynamic alteration of gut microbiota remains unclear. To dissect temporal changes of microbiota, dynamic analyses of fecal microbiota were conducted using a rat model. Adult rats were fed a normal-protein diet or an HPD for 6 weeks, and feces collected at different weeks were used for microbiota and metabolite analysis. The structural alteration of fecal microbiota was observed after 4 weeks, especially for the decreased appearance of bands related to Akkermansia species. HPD increased numbers of Escherichia coli while decreased Akkermansia muciniphila, Bifidobacterium, Prevotella, Ruminococcus bromii, and Roseburia/Eubacterium rectale (P protein diet. HPD also decreased the copies of genes encoding butyryl-CoA:acetate CoA-transferase and Prevotella-associated methylmalonyl-CoA decarboxylase α-subunit (P high-protein diet. Copyright © 2017 Elsevier Ltd. All rights reserved.

Investigating fine-scale spatio-temporal predator-prey patterns in dynamic marine ecosystems: a functional data analysis approach

NARCIS (Netherlands)

Embling, C.B.; Illian, J.; Armstrong, E.; van der Kooij, J.; Sharples, J.; Camphuysen, K.C.J.; Scott, B.E.

2012-01-01

1. Spatial management of marine ecosystems requires detailed knowledge of spatio-temporal mechanisms linking physical and biological processes. Tidal currents, the main driver of ecosystem dynamics in temperate coastal ecosystems, influence predator foraging ecology by affecting prey distribution

The consequences of time averaging for measuring temporal species turnover in the fossil record

Science.gov (United States)

Tomašových, Adam; Kidwell, Susan

2010-05-01

Modeling time averaging effects with simple simulations allows us to evaluate the magnitude of change in temporal species turnover that is expected to occur in long (paleoecological) time series with fossil assemblages. Distinguishing different modes of metacommunity dynamics (such as neutral, density-dependent, or trade-off dynamics) with time-averaged fossil assemblages requires scaling-up time-averaging effects because the decrease in temporal resolution and the decrease in temporal inter-sample separation (i.e., the two main effects of time averaging) substantially increase community stability relative to assemblages without or with weak time averaging. Large changes in temporal scale that cover centuries to millennia can lead to unprecedented effects on temporal rate of change in species composition. Temporal variation in species composition monotonically decreases with increasing duration of time-averaging in simulated fossil assemblages. Time averaging is also associated with the reduction of species dominance owing to the temporal switching in the identity of dominant species. High degrees of time averaging can cause that community parameters of local fossil assemblages converge to parameters of metacommunity rather that to parameters of individual local non-averaged communities. We find that the low variation in species composition observed among mollusk and ostracod subfossil assemblages can be explained by time averaging alone, and low temporal resolution and reduced temporal separation among assemblages in time series can thus explain a substantial part of the reduced variation in species composition relative to unscaled predictions of neutral model (i.e., species do not differ in birth, death, and immigration rates on per capita basis). The structure of time-averaged assemblages can thus provide important insights into processes that act over larger temporal scales, such as evolution of niches and dispersal, range-limit dynamics, taxon cycles, and

Learning predictive statistics from temporal sequences: Dynamics and strategies.

Science.gov (United States)

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-10-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

INFORMATION MINING OF SPATIO-TEMPORAL EVOLUTION OF LAKES BASED ON MULTIPLE DYNAMIC MEASUREMENTS

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W. Feng

2017-09-01

Full Text Available Lakes are important water resources and integral parts of the natural ecosystem, and it is of great significance to study the evolution of lakes. The area of each lake increased and decreased at the same time in natural condition, only but the net change of lakes’ area is the result of the bidirectional evolution of lakes. In this paper, considering the effects of net fragmentation, net attenuation, swap change and spatial invariant part in lake evolution, a comprehensive evaluation indexes of lake dynamic evolution were defined,. Such degree contains three levels of measurement: 1 the swap dynamic degree (SDD reflects the space activity of lakes in the study period. 2 the attenuation dynamic degree (ADD reflects the net attenuation of lakes into non-lake areas. 3 the fragmentation dynamic degree (FDD reflects the trend of lakes to be divided and broken into smaller lakes. Three levels of dynamic measurement constitute the three-dimensional "Swap - attenuation – fragmentation" dynamic evolution measurement system of lakes. To show its effectiveness, the dynamic measurement was applied to lakes in Jianghan Plain, the middle Yangtze region of China for a more detailed analysis of lakes from 1984 to 2014. In combination with spatial-temporal location characteristics of lakes, the hidden information in lake evolution in the past 30 years can be revealed.

Use of soil moisture dynamics and patterns at different spatio-temporal scales for the investigation of subsurface flow processes

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T. Blume

2009-07-01

Full Text Available Spatial patterns as well as temporal dynamics of soil moisture have a major influence on runoff generation. The investigation of these dynamics and patterns can thus yield valuable information on hydrological processes, especially in data scarce or previously ungauged catchments. The combination of spatially scarce but temporally high resolution soil moisture profiles with episodic and thus temporally scarce moisture profiles at additional locations provides information on spatial as well as temporal patterns of soil moisture at the hillslope transect scale. This approach is better suited to difficult terrain (dense forest, steep slopes than geophysical techniques and at the same time less cost-intensive than a high resolution grid of continuously measuring sensors. Rainfall simulation experiments with dye tracers while continuously monitoring soil moisture response allows for visualization of flow processes in the unsaturated zone at these locations. Data was analyzed at different spacio-temporal scales using various graphical methods, such as space-time colour maps (for the event and plot scale and binary indicator maps (for the long-term and hillslope scale. Annual dynamics of soil moisture and decimeter-scale variability were also investigated. The proposed approach proved to be successful in the investigation of flow processes in the unsaturated zone and showed the importance of preferential flow in the Malalcahuello Catchment, a data-scarce catchment in the Andes of Southern Chile. Fast response times of stream flow indicate that preferential flow observed at the plot scale might also be of importance at the hillslope or catchment scale. Flow patterns were highly variable in space but persistent in time. The most likely explanation for preferential flow in this catchment is a combination of hydrophobicity, small scale heterogeneity in rainfall due to redistribution in the canopy and strong gradients in unsaturated conductivities leading to

A time-domain binaural detection model and its predictions temporal-resolution data

NARCIS (Netherlands)

Breebaart, D.J.; Par, van de S.L.J.D.E.; Kohlrausch, A.G.

2002-01-01

This paper discusses the application of a time-domain binaural signal-detection model in the context of estimates of the temporal resolution of the binaural auditory system. It is demonstrated that the optimal detector which is present in the model is crucial to account for specific temporal

Understanding onsets of rainfall in Southern Africa using temporal probabilistic modelling

CSIR Research Space (South Africa)

Cheruiyot, D

2010-12-01

Full Text Available This research investigates an alternative approach to automatically evolve the hidden temporal distribution of onset of rainfall directly from multivariate time series (MTS) data in the absence of domain experts. Temporal probabilistic modelling...

Creative-Dynamics Approach To Neural Intelligence

Science.gov (United States)

Zak, Michail A.

1992-01-01

Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

Temporal dynamics of distinct CA1 cell populations during unconscious state induced by ketamine.

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Hui Kuang

2010-12-01

Full Text Available Ketamine is a widely used dissociative anesthetic which can induce some psychotic-like symptoms and memory deficits in some patients during the post-operative period. To understand its effects on neural population dynamics in the brain, we employed large-scale in vivo ensemble recording techniques to monitor the activity patterns of simultaneously recorded hippocampal CA1 pyramidal cells and various interneurons during several conscious and unconscious states such as awake rest, running, slow wave sleep, and ketamine-induced anesthesia. Our analyses reveal that ketamine induces distinct oscillatory dynamics not only in pyramidal cells but also in at least seven different types of CA1 interneurons including putative basket cells, chandelier cells, bistratified cells, and O-LM cells. These emergent unique oscillatory dynamics may very well reflect the intrinsic temporal relationships within the CA1 circuit. It is conceivable that systematic characterization of network dynamics may eventually lead to better understanding of how ketamine induces unconsciousness and consequently alters the conscious mind.

Environmental and management impacts on temporal variability of soil hydraulic properties

Science.gov (United States)

Bodner, G.; Scholl, P.; Loiskandl, W.; Kaul, H.-P.

2012-04-01

cm) showed a similar time course as a moving average of rainfall. Drying induced a decrease in conductivity while wetting of the soil resulted in higher conductivity values. Approaching saturation however, the drying phase showed a different behaviour with increasing values of hydraulic conductivity. This may be explained probably by formation of cracks acting as large macropores. We concluded that aggregate coalescence as a function of capillary forces and soil rheologic properties (cf. Or et al., 2002) are a main predictor of temporal dynamics of near saturated soil hydraulic properties while different plant covers only had a minor effect on the observed system dynamics. Or, D., Ghezzehei, T.A. 2002. Modeling post-tillage soil structural dynamics. a review. Soil Till Res. 64, 41-59.

A hierarchical Bayesian spatio-temporal model for extreme precipitation events

KAUST Repository

Ghosh, Souparno; Mallick, Bani K.

2011-01-01

We propose a new approach to model a sequence of spatially distributed time series of extreme values. Unlike common practice, we incorporate spatial dependence directly in the likelihood and allow the temporal component to be captured at the second level of hierarchy. Inferences about the parameters and spatio-temporal predictions are obtained via MCMC technique. The model is fitted to a gridded precipitation data set collected over 99 years across the continental U.S. © 2010 John Wiley & Sons, Ltd..

A hierarchical Bayesian spatio-temporal model for extreme precipitation events

KAUST Repository

Ghosh, Souparno

2011-03-01

We propose a new approach to model a sequence of spatially distributed time series of extreme values. Unlike common practice, we incorporate spatial dependence directly in the likelihood and allow the temporal component to be captured at the second level of hierarchy. Inferences about the parameters and spatio-temporal predictions are obtained via MCMC technique. The model is fitted to a gridded precipitation data set collected over 99 years across the continental U.S. © 2010 John Wiley & Sons, Ltd..

Envelope detection using temporal magnetization dynamics of resonantly interacting spin-torque oscillator

Science.gov (United States)

Nakamura, Y.; Nishikawa, M.; Osawa, H.; Okamoto, Y.; Kanao, T.; Sato, R.

2018-05-01

In this article, we propose the detection method of the recorded data pattern by the envelope of the temporal magnetization dynamics of resonantly interacting spin-torque oscillator on the microwave assisted magnetic recording for three-dimensional magnetic recording. We simulate the envelope of the waveform from recorded dots with the staggered magnetization configuration, which are calculated by using a micromagnetic simulation. We study the data detection methods for the envelope and propose a soft-output Viterbi algorithm (SOVA) for partial response (PR) system as a signal processing system for three dimensional magnetic recording.

Statistical Mechanics of Temporal and Interacting Networks

Science.gov (United States)

Zhao, Kun

In the last ten years important breakthroughs in the understanding of the topology of complexity have been made in the framework of network science. Indeed it has been found that many networks belong to the universality classes called small-world networks or scale-free networks. Moreover it was found that the complex architecture of real world networks strongly affects the critical phenomena defined on these structures. Nevertheless the main focus of the research has been the characterization of single and static networks. Recently, temporal networks and interacting networks have attracted large interest. Indeed many networks are interacting or formed by a multilayer structure. Example of these networks are found in social networks where an individual might be at the same time part of different social networks, in economic and financial networks, in physiology or in infrastructure systems. Moreover, many networks are temporal, i.e. the links appear and disappear on the fast time scale. Examples of these networks are social networks of contacts such as face-to-face interactions or mobile-phone communication, the time-dependent correlations in the brain activity and etc. Understanding the evolution of temporal and multilayer networks and characterizing critical phenomena in these systems is crucial if we want to describe, predict and control the dynamics of complex system. In this thesis, we investigate several statistical mechanics models of temporal and interacting networks, to shed light on the dynamics of this new generation of complex networks. First, we investigate a model of temporal social networks aimed at characterizing human social interactions such as face-to-face interactions and phone-call communication. Indeed thanks to the availability of data on these interactions, we are now in the position to compare the proposed model to the real data finding good agreement. Second, we investigate the entropy of temporal networks and growing networks , to provide

QUAL-NET, a high temporal-resolution eutrophication model for large hydrographic networks

Science.gov (United States)

Minaudo, Camille; Curie, Florence; Jullian, Yann; Gassama, Nathalie; Moatar, Florentina

2018-04-01

To allow climate change impact assessment of water quality in river systems, the scientific community lacks efficient deterministic models able to simulate hydrological and biogeochemical processes in drainage networks at the regional scale, with high temporal resolution and water temperature explicitly determined. The model QUALity-NETwork (QUAL-NET) was developed and tested on the Middle Loire River Corridor, a sub-catchment of the Loire River in France, prone to eutrophication. Hourly variations computed efficiently by the model helped disentangle the complex interactions existing between hydrological and biological processes across different timescales. Phosphorus (P) availability was the most constraining factor for phytoplankton development in the Loire River, but simulating bacterial dynamics in QUAL-NET surprisingly evidenced large amounts of organic matter recycled within the water column through the microbial loop, which delivered significant fluxes of available P and enhanced phytoplankton growth. This explained why severe blooms still occur in the Loire River despite large P input reductions since 1990. QUAL-NET could be used to study past evolutions or predict future trajectories under climate change and land use scenarios.

QUAL-NET, a high temporal-resolution eutrophication model for large hydrographic networks

Directory of Open Access Journals (Sweden)

C. Minaudo

2018-04-01

Full Text Available To allow climate change impact assessment of water quality in river systems, the scientific community lacks efficient deterministic models able to simulate hydrological and biogeochemical processes in drainage networks at the regional scale, with high temporal resolution and water temperature explicitly determined. The model QUALity-NETwork (QUAL-NET was developed and tested on the Middle Loire River Corridor, a sub-catchment of the Loire River in France, prone to eutrophication. Hourly variations computed efficiently by the model helped disentangle the complex interactions existing between hydrological and biological processes across different timescales. Phosphorus (P availability was the most constraining factor for phytoplankton development in the Loire River, but simulating bacterial dynamics in QUAL-NET surprisingly evidenced large amounts of organic matter recycled within the water column through the microbial loop, which delivered significant fluxes of available P and enhanced phytoplankton growth. This explained why severe blooms still occur in the Loire River despite large P input reductions since 1990. QUAL-NET could be used to study past evolutions or predict future trajectories under climate change and land use scenarios.

Human Plague Risk: Spatial-Temporal Models

Science.gov (United States)

Pinzon, Jorge E.

2010-01-01

This chpater reviews the use of spatial-temporal models in identifying potential risks of plague outbreaks into the human population. Using earth observations by satellites remote sensing there has been a systematic analysis and mapping of the close coupling between the vectors of the disease and climate variability. The overall result is that incidence of plague is correlated to positive El Nino/Southem Oscillation (ENSO).

Comparison of Ocean Dynamics with a Regional Circulation Model and Improved Altimetry in the North-Western Mediterranean

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Jérôme Bouffard

2008-01-01

Full Text Available The spatial and temporal resolution of satellite altimetry is usually sufficient for monitoring the changes of sea surface topography in the open ocean. However, coastal ocean dynamics are much more complex, being characterized by smaller spatial and temporal scales of variability. The quality and availability of satellite-derived products along the coasts have to be improved, with a strategy optimized for coastal targets. Therefore a coastal multi-satellite altimetry dataset (TOPEX/Poseidon, Jason-1; Envisat; GFO at a 10 - 20 Hz sampling rate has been derived from routine geophysical data products using a new processing software dedicated to coastal zone applications. Improved along-track sea level variations with fine space scales are available in the North-western Mediterranean Sea from 2001 to 2003, and are compared with high-resolution numerical model elevations from the eddy-resolving model SYMPHONIE. This preparatory work emphasizes the potential of improved multi-satellite altimetry for validating coastal hydro-dynamical models and could contribute in the future to a better tuning of the boundary conditions of the simulations.

The Emergence of Visual Awareness: Temporal Dynamics in Relation to Task and Mask Type

Science.gov (United States)

Kiefer, Markus; Kammer, Thomas

2017-01-01

One aspect of consciousness phenomena, the temporal emergence of visual awareness, has been subject of a controversial debate. How can visual awareness, that is the experiential quality of visual stimuli, be characterized best? Is there a sharp discontinuous or dichotomous transition between unaware and fully aware states, or does awareness emerge gradually encompassing intermediate states? Previous studies yielded conflicting results and supported both dichotomous and gradual views. It is well conceivable that these conflicting results are more than noise, but reflect the dynamic nature of the temporal emergence of visual awareness. Using a psychophysical approach, the present research tested whether the emergence of visual awareness is context-dependent with a temporal two-alternative forced choice task. During backward masking of word targets, it was assessed whether the relative temporal sequence of stimulus thresholds is modulated by the task (stimulus presence, letter case, lexical decision, and semantic category) and by mask type. Four masks with different similarity to the target features were created. Psychophysical functions were then fitted to the accuracy data in the different task conditions as a function of the stimulus mask SOA in order to determine the inflection point (conscious threshold of each feature) and slope of the psychophysical function (transition from unaware to aware within each feature). Depending on feature-mask similarity, thresholds in the different tasks were highly dispersed suggesting a graded transition from unawareness to awareness or had less differentiated thresholds indicating that clusters of features probed by the tasks quite simultaneously contribute to the percept. The latter observation, although not compatible with the notion of a sharp all-or-none transition between unaware and aware states, suggests a less gradual or more discontinuous emergence of awareness. Analyses of slopes of the fitted psychophysical functions

Simulation-Based Dynamic Passenger Flow Assignment Modelling for a Schedule-Based Transit Network

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Xiangming Yao

2017-01-01

Full Text Available The online operation management and the offline policy evaluation in complex transit networks require an effective dynamic traffic assignment (DTA method that can capture the temporal-spatial nature of traffic flows. The objective of this work is to propose a simulation-based dynamic passenger assignment framework and models for such applications in the context of schedule-based rail transit systems. In the simulation framework, travellers are regarded as individual agents who are able to obtain complete information on the current traffic conditions. A combined route selection model integrated with pretrip route selection and entrip route switch is established for achieving the dynamic network flow equilibrium status. The train agent is operated strictly with the timetable and its capacity limitation is considered. A continuous time-driven simulator based on the proposed framework and models is developed, whose performance is illustrated through a large-scale network of Beijing subway. The results indicate that more than 0.8 million individual passengers and thousands of trains can be simulated simultaneously at a speed ten times faster than real time. This study provides an efficient approach to analyze the dynamic demand-supply relationship for large schedule-based transit networks.

Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks.

Science.gov (United States)

Vestergaard, Christian L; Génois, Mathieu

2015-10-01

Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling.

Application of dynamic susceptibility contrast-enhanced perfusion in temporal lobe epilepsy

Energy Technology Data Exchange (ETDEWEB)

Xing, Wu; Wang, Xiaoyi; Xie, Fangfang; Liao, Weihua [Dept. of Radiology, Xiangya Hospital of Central South Univ., Changsha (China)], e-mail: doctoring@sina.com

2013-02-15

Background: Accurately locatithe epileptogenic focus in temporal lobe epilepsy (TLE) is important in clinical practice. Single-photon emission computed tomography (SPECT) and positron-emission tomography (PET) have been widely used in the lateralization of TLE, but both have limitations. Magnetic resonance perfusion imaging can accurately and reliably reflect differences in cerebral blood flow and volume. Purpose: To investigate the diagnostic value of dynamic susceptibility contrast-enhanced (DSC) perfusion magnetic resonance imaging (MRI) in the lateralization of the epileptogenic focus in TLE. Material and Methods: Conventional MRI and DSC-MRI scanning was performed in 20 interictal cases of TLE and 20 healthy volunteers. The relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) of the bilateral mesial temporal lobes of the TLE cases and healthy control groups were calculated. The differences in the perfusion asymmetry indices (AIs), derived from the rCBV and rCBF of the bilateral mesial temporal lobes, were pared between the two groups. Results: In the control group, there were no statistically significant differences between the left and right sides in terms of rCBV (left 1.55 {+-} 0.32, right 1.57 {+-} 0.28) or rCBF (left 99.00 {+-} 24.61, right 100.38 {+-} 23.46) of the bilateral mesial temporal lobes. However, in the case group the ipsilateral rCBV and rCBF values (1.75 {+-} 0.64 and 96.35 {+-} 22.63, respectively) were markedly lower than those of the contralateral side (2.01 {+-} 0.79 and 108.56 {+-} 26.92; P < 0.05). Both the AI of the rCBV (AIrCBV; 13.03 {+-} 10.33) and the AI of the rCBF (AIrCBF; 11.24 {+-} 8.70) of the case group were significantly higher than that of the control group (AIrCBV 5.55 {+-} 3.74, AIrCBF 5.12 {+-} 3.48; P < 0.05). The epileptogenic foci of nine patients were correctly lateralized using the 95th percentile of the AIrCBV and AIrCBF of the control group as the normal upper limits. Conclusion: In

Temporal Microbial Community Dynamics in Microbial Electrolysis Cells – Influence of Acetate and Propionate Concentration

KAUST Repository

Rao, Hari Ananda

2017-07-20

Microbial electrolysis cells (MECs) are widely considered as a next generation wastewater treatment system. However, fundamental insight on the temporal dynamics of microbial communities associated with MEC performance under different organic types with varied loading concentrations is still unknown, nevertheless this knowledge is essential for optimizing this technology for real-scale applications. Here, the temporal dynamics of anodic microbial communities associated with MEC performance was examined at low (0.5 g COD/L) and high (4 g COD/L) concentrations of acetate or propionate, which are important intermediates of fermentation of municipal wastewaters and sludge. The results showed that acetate-fed reactors exhibited higher performance in terms of maximum current density (I: 4.25 ± 0.23 A/m), coulombic efficiency (CE: 95 ± 8%), and substrate degradation rate (98.8 ± 1.2%) than propionate-fed reactors (I: 2.7 ± 0.28 A/m; CE: 68 ± 9.5%; substrate degradation rate: 84 ± 13%) irrespective of the concentrations tested. Despite of the repeated sampling of the anodic biofilm over time, the high-concentration reactors demonstrated lower and stable performance in terms of current density (I: 1.1 ± 0.14 to 4.2 ± 0.21 A/m), coulombic efficiency (CE: 44 ± 4.1 to 103 ± 7.2%) and substrate degradation rate (64.9 ± 6.3 to 99.7 ± 0.5%), while the low-concentration reactors produced higher and dynamic performance (I: 1.1 ± 0.12 to 4.6 ± 0.1 A/m; CE: 52 ± 2.5 to 105 ± 2.7%; substrate degradation rate: 87.2 ± 0.2 to 99.9 ± 0.06%) with the different substrates tested. Correlating reactor\\'s performance with temporal dynamics of microbial communities showed that relatively similar anodic microbial community composition but with varying relative abundances was observed in all the reactors despite differences in the substrate and concentrations tested. Particularly, Geobacter was the predominant bacteria on the anode biofilm of all MECs over time suggesting its

Modelling of the nonlinear soliton dynamics in the ring fibre cavity

Science.gov (United States)

Razukov, Vadim A.; Melnikov, Leonid A.

2018-04-01

Using the cabaret method numerical realization, long-time spatio-temporal dynamics of the electromagnetic field in a nonlinear ring fibre cavity with dispersion is investigated during the hundreds of round trips. Formation of both the temporal cavity solitons and irregular pulse trains is demonstrated and discussed.

Process-based distributed modeling approach for analysis of sediment dynamics in a river basin

Directory of Open Access Journals (Sweden)

M. A. Kabir

2011-04-01

Full Text Available Modeling of sediment dynamics for developing best management practices of reducing soil erosion and of sediment control has become essential for sustainable management of watersheds. Precise estimation of sediment dynamics is very important since soils are a major component of enormous environmental processes and sediment transport controls lake and river pollution extensively. Different hydrological processes govern sediment dynamics in a river basin, which are highly variable in spatial and temporal scales. This paper presents a process-based distributed modeling approach for analysis of sediment dynamics at river basin scale by integrating sediment processes (soil erosion, sediment transport and deposition with an existing process-based distributed hydrological model. In this modeling approach, the watershed is divided into an array of homogeneous grids to capture the catchment spatial heterogeneity. Hillslope and river sediment dynamic processes have been modeled separately and linked to each other consistently. Water flow and sediment transport at different land grids and river nodes are modeled using one dimensional kinematic wave approximation of Saint-Venant equations. The mechanics of sediment dynamics are integrated into the model using representative physical equations after a comprehensive review. The model has been tested on river basins in two different hydro climatic areas, the Abukuma River Basin, Japan and Latrobe River Basin, Australia. Sediment transport and deposition are modeled using Govers transport capacity equation. All spatial datasets, such as, Digital Elevation Model (DEM, land use and soil classification data, etc., have been prepared using raster "Geographic Information System (GIS" tools. The results of relevant statistical checks (Nash-Sutcliffe efficiency and R–squared value indicate that the model simulates basin hydrology and its associated sediment dynamics reasonably well. This paper presents the

Research of Cadastral Data Modelling and Database Updating Based on Spatio-temporal Process

Directory of Open Access Journals (Sweden)

ZHANG Feng

2016-02-01

Full Text Available The core of modern cadastre management is to renew the cadastre database and keep its currentness,topology consistency and integrity.This paper analyzed the changes and their linkage of various cadastral objects in the update process.Combined object-oriented modeling technique with spatio-temporal objects' evolution express,the paper proposed a cadastral data updating model based on the spatio-temporal process according to people's thought.Change rules based on the spatio-temporal topological relations of evolution cadastral spatio-temporal objects are drafted and further more cascade updating and history back trace of cadastral features,land use and buildings are realized.This model implemented in cadastral management system-ReGIS.Achieved cascade changes are triggered by the direct driving force or perceived external events.The system records spatio-temporal objects' evolution process to facilitate the reconstruction of history,change tracking,analysis and forecasting future changes.

Turbulent swirling flow in a dynamic model of a uniflow-scavenged two-stroke engine

DEFF Research Database (Denmark)

Ingvorsen, Kristian Mark; Meyer, Knud Erik; Walther, Jens Honore

2014-01-01

turbulence models. In the present work, the flow in a dynamic scale model of a uniflowscavenged cylinder is investigated experimentally. The model has a transparent cylinder and a moving piston driven by a linear motor. The flow is investigated using phase-locked stereoscopic particle image velocimetry (PIV...... cannot be assumed to be quasi-steady. The temporal development of the swirl strength is investigated by computing the angular momentum. The swirl strength shows an exponential decay from scavenge port closing to scavenge port opening corresponding to a reduction of 34 %, which is in good agreement...

Design and implementation of segment oriented spatio-temporal model in urban panoramic maps

Science.gov (United States)

Li, Haiting; Fei, Lifan; Peng, Qingshan; Li, Yanhong

2009-10-01

Object-oriented spatio-temporal model is directed by human cognition that each object has what/where/when attributes. The precise and flexible structure of such models supports multi-semantics of space and time. This paper reviews current research of spatio-temporal models using object-oriented approach and proposed a new spatio-temporal model based on segmentation in order to resolve the updating problem of some special GIS system by taking advantages of object-oriented spatio-temporal model and adopting category theory. Category theory can be used as a unifying framework for specifying complex systems and it provides rules on how objects may be joined. It characterizes the segments of object through mappings between them. The segment-oriented spatio-temporal model designed for urban panoramic maps is described and implemented. We take points and polylines as objects in this model in the management of panoramic map data. For the randomness of routes which transportation vehicle adopts each time, road objects in this model are split into some segments by crossing points. The segments still remains polyline type, but the splitting makes it easier to update the panoramic data when new photos are captured. This model is capable of eliminating redundant data and accelerating data access when panoramas are unchanged. For evaluation purpose, the data types and operations are designed and implemented in PostgreSQL and the results of experiments come out to prove that this model is efficient and expedient in the application of urban panoramic maps.

Improvement of a three-dimensional atmospheric dynamic model and examination of its performance over complex terrain

International Nuclear Information System (INIS)

Nagai, Haruyasu; Yamazawa, Hiromi

1994-11-01

A three-dimensional atmospheric dynamic model (PHYSIC) was improved and its performance was examined using the meteorological data observed at a coastal area with a complex terrain. To introduce synoptic meteorological conditions into the model, the initial and boundary conditions were improved. By this improvement, the model can predict the temporal change of wind field for more than 24 hours. Moreover, the model successfully simulates the land and sea breeze observed at Shimokita area in the summer of 1992. (author)

Evaluating complementary networks of restoration plantings for landscape-scale occurrence of temporally dynamic species.

Science.gov (United States)

Ikin, Karen; Tulloch, Ayesha; Gibbons, Philip; Ansell, Dean; Seddon, Julian; Lindenmayer, David

2016-10-01

Multibillion dollar investments in land restoration make it critical that conservation goals are achieved cost-effectively. Approaches developed for systematic conservation planning offer opportunities to evaluate landscape-scale, temporally dynamic biodiversity outcomes from restoration and improve on traditional approaches that focus on the most species-rich plantings. We investigated whether it is possible to apply a complementarity-based approach to evaluate the extent to which an existing network of restoration plantings meets representation targets. Using a case study of woodland birds of conservation concern in southeastern Australia, we compared complementarity-based selections of plantings based on temporally dynamic species occurrences with selections based on static species occurrences and selections based on ranking plantings by species richness. The dynamic complementarity approach, which incorporated species occurrences over 5 years, resulted in higher species occurrences and proportion of targets met compared with the static complementarity approach, in which species occurrences were taken at a single point in time. For equivalent cost, the dynamic complementarity approach also always resulted in higher average minimum percent occurrence of species maintained through time and a higher proportion of the bird community meeting representation targets compared with the species-richness approach. Plantings selected under the complementarity approaches represented the full range of planting attributes, whereas those selected under the species-richness approach were larger in size. Our results suggest that future restoration policy should not attempt to achieve all conservation goals within individual plantings, but should instead capitalize on restoration opportunities as they arise to achieve collective value of multiple plantings across the landscape. Networks of restoration plantings with complementary attributes of age, size, vegetation structure, and

Spatial-temporal modeling of malware propagation in networks.

Science.gov (United States)

Chen, Zesheng; Ji, Chuanyi

2005-09-01

Network security is an important task of network management. One threat to network security is malware (malicious software) propagation. One type of malware is called topological scanning that spreads based on topology information. The focus of this work is on modeling the spread of topological malwares, which is important for understanding their potential damages, and for developing countermeasures to protect the network infrastructure. Our model is motivated by probabilistic graphs, which have been widely investigated in machine learning. We first use a graphical representation to abstract the propagation of malwares that employ different scanning methods. We then use a spatial-temporal random process to describe the statistical dependence of malware propagation in arbitrary topologies. As the spatial dependence is particularly difficult to characterize, the problem becomes how to use simple (i.e., biased) models to approximate the spatially dependent process. In particular, we propose the independent model and the Markov model as simple approximations. We conduct both theoretical analysis and extensive simulations on large networks using both real measurements and synthesized topologies to test the performance of the proposed models. Our results show that the independent model can capture temporal dependence and detailed topology information and, thus, outperforms the previous models, whereas the Markov model incorporates a certain spatial dependence and, thus, achieves a greater accuracy in characterizing both transient and equilibrium behaviors of malware propagation.

Spatial patterns and temporal dynamics of global scale climate-groundwater interactions

Science.gov (United States)

Cuthbert, M. O.; Gleeson, T. P.; Moosdorf, N.; Schneider, A. C.; Hartmann, J.; Befus, K. M.; Lehner, B.

2017-12-01

The interactions between groundwater and climate are important to resolve in both space and time as they influence mass and energy transfers at Earth's land surface. Despite the significance of these processes, little is known about the spatio-temporal distribution of such interactions globally, and many large-scale climate, hydrological and land surface models oversimplify groundwater or exclude it completely. In this study we bring together diverse global geomatic data sets to map spatial patterns in the sensitivity and degree of connectedness between the water table and the land surface, and use the output from a global groundwater model to assess the locations where the lateral import or export of groundwater is significant. We also quantify the groundwater response time, the characteristic time for groundwater systems to respond to a change in boundary conditions, and map its distribution globally to assess the likely dynamics of groundwater's interaction with climate. We find that more than half of the global land surface significantly exports or imports groundwater laterally. Nearly 40% of Earth's landmass has water tables that are strongly coupled to topography with water tables shallow enough to enable a bi-directional exchange of moisture with the climate system. However, only a small proportion (around 12%) of such regions have groundwater response times of 100 years or less and have groundwater fluxes that would significantly respond to rapid environmental changes over this timescale. We last explore fundamental relationships between aridity, groundwater response times and groundwater turnover times. Our results have wide ranging implications for understanding and modelling changes in Earth's water and energy balance and for informing robust future water management and security decisions.

Sensitivity of modeled estuarine circulation to spatial and temporal resolution of input meteorological forcing of a cold frontal passage

Science.gov (United States)

Weaver, Robert J.; Taeb, Peyman; Lazarus, Steven; Splitt, Michael; Holman, Bryan P.; Colvin, Jeffrey

2016-12-01

In this study, a four member ensemble of meteorological forcing is generated using the Weather Research and Forecasting (WRF) model in order to simulate a frontal passage event that impacted the Indian River Lagoon (IRL) during March 2015. The WRF model is run to provide high and low, spatial (0.005° and 0.1°) and temporal (30 min and 6 h) input wind and pressure fields. The four member ensemble is used to force the Advanced Circulation model (ADCIRC) coupled with Simulating Waves Nearshore (SWAN) and compute the hydrodynamic and wave response. Results indicate that increasing the spatial resolution of the meteorological forcing has a greater impact on the results than increasing the temporal resolution in coastal systems like the IRL where the length scales are smaller than the resolution of the operational meteorological model being used to generate the forecast. Changes in predicted water elevations are due in part to the upwind and downwind behavior of the input wind forcing. The significant wave height is more sensitive to the meteorological forcing, exhibited by greater ensemble spread throughout the simulation. It is important that the land mask, seen by the meteorological model, is representative of the geography of the coastal estuary as resolved by the hydrodynamic model. As long as the temporal resolution of the wind field captures the bulk characteristics of the frontal passage, computational resources should be focused so as to ensure that the meteorological model resolves the spatial complexities, such as the land-water interface, that drive the land use responsible for dynamic downscaling of the winds.

Development of a model forecasting Dermanyssus gallinae's population dynamics for advancing Integrated Pest Management in laying hen facilities.

Science.gov (United States)

Mul, Monique F; van Riel, Johan W; Roy, Lise; Zoons, Johan; André, Geert; George, David R; Meerburg, Bastiaan G; Dicke, Marcel; van Mourik, Simon; Groot Koerkamp, Peter W G

2017-10-15

The poultry red mite, Dermanyssus gallinae, is the most significant pest of egg laying hens in many parts of the world. Control of D. gallinae could be greatly improved with advanced Integrated Pest Management (IPM) for D. gallinae in laying hen facilities. The development of a model forecasting the pests' population dynamics in laying hen facilities without and post-treatment will contribute to this advanced IPM and could consequently improve implementation of IPM by farmers. The current work describes the development and demonstration of a model which can follow and forecast the population dynamics of D. gallinae in laying hen facilities given the variation of the population growth of D. gallinae within and between flocks. This high variation could partly be explained by house temperature, flock age, treatment, and hen house. The total population growth variation within and between flocks, however, was in part explained by temporal variation. For a substantial part this variation was unexplained. A dynamic adaptive model (DAP) was consequently developed, as models of this type are able to handle such temporal variations. The developed DAP model can forecast the population dynamics of D. gallinae, requiring only current flock population monitoring data, temperature data and information of the dates of any D. gallinae treatment. Importantly, the DAP model forecasted treatment effects, while compensating for location and time specific interactions, handling the variability of these parameters. The characteristics of this DAP model, and its compatibility with different mite monitoring methods, represent progression from existing approaches for forecasting D. gallinae that could contribute to advancing improved Integrated Pest Management (IPM) for D. gallinae in laying hen facilities. Copyright © 2017 Elsevier B.V. All rights reserved.

Monitor-Based Statistical Model Checking for Weighted Metric Temporal Logic

DEFF Research Database (Denmark)

Bulychev, Petr; David, Alexandre; Larsen, Kim Guldstrand

2012-01-01

We present a novel approach and implementation for ana- lysing weighted timed automata (WTA) with respect to the weighted metric temporal logic (WMTL≤ ). Based on a stochastic semantics of WTAs, we apply statistical model checking (SMC) to estimate and test probabilities of satisfaction with desi......We present a novel approach and implementation for ana- lysing weighted timed automata (WTA) with respect to the weighted metric temporal logic (WMTL≤ ). Based on a stochastic semantics of WTAs, we apply statistical model checking (SMC) to estimate and test probabilities of satisfaction...

Temporal dynamics of selective attention and conflict resolution during cross-dimensional go-nogo decisions

Directory of Open Access Journals (Sweden)

Moschner Carsten

2007-08-01

Full Text Available Abstract Background Decision-making is a fundamental capacity which is crucial to many higher-order psychological functions. We recorded event-related potentials (ERPs during a visual target-identification task that required go-nogo choices. Targets were identified on the basis of cross-dimensional conjunctions of particular colors and forms. Color discriminability was manipulated in three conditions to determine the effects of color distinctiveness on component processes of decision-making. Results Target identification was accompanied by the emergence of prefrontal P2a and P3b. Selection negativity (SN revealed that target-compatible features captured attention more than target-incompatible features, suggesting that intra-dimensional attentional capture was goal-contingent. No changes of cross-dimensional selection priorities were measurable when color discriminability was altered. Peak latencies of the color-related SN provided a chronometric measure of the duration of attention-related neural processing. ERPs recorded over the frontocentral scalp (N2c, P3a revealed that color-overlap distractors, more than form-overlap distractors, required additional late selection. The need for additional response selection induced by color-overlap distractors was severely reduced when color discriminability decreased. Conclusion We propose a simple model of cross-dimensional perceptual decision-making. The temporal synchrony of separate color-related and form-related choices determines whether or not distractor processing includes post-perceptual stages. ERP measures contribute to a comprehensive explanation of the temporal dynamics of component processes of perceptual decision-making.

From static to temporal network theory: Applications to functional brain connectivity

Directory of Open Access Journals (Sweden)

William Hedley Thompson

2017-06-01

Full Text Available Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto. Temporal network theory is a subfield of network theory that has had limited application to date within network neuroscience. The aims of this work are to introduce temporal network theory, define the metrics relevant to the context of network neuroscience, and illustrate their potential by analyzing a resting-state fMRI dataset. We found both between-subjects and between-task differences that illustrate the potential for these tools to be applied in a wider context. Our tools for analyzing temporal networks have been released in a Python package called Teneto.

MODELING SPECTRAL AND TEMPORAL MASKING IN THE HUMAN AUDITORY SYSTEM

DEFF Research Database (Denmark)

Dau, Torsten; Jepsen, Morten Løve; Ewert, Stephan D.

2007-01-01

An auditory signal processing model is presented that simulates psychoacoustical data from a large variety of experimental conditions related to spectral and temporal masking. The model is based on the modulation filterbank model by Dau et al. [J. Acoust. Soc. Am. 102, 2892-2905 (1997)] but inclu......An auditory signal processing model is presented that simulates psychoacoustical data from a large variety of experimental conditions related to spectral and temporal masking. The model is based on the modulation filterbank model by Dau et al. [J. Acoust. Soc. Am. 102, 2892-2905 (1997...... was tested in conditions of tone-in-noise masking, intensity discrimination, spectral masking with tones and narrowband noises, forward masking with (on- and off-frequency) noise- and pure-tone maskers, and amplitude modulation detection using different noise carrier bandwidths. One of the key properties...

Models for inference in dynamic metacommunity systems

Science.gov (United States)

Dorazio, Robert M.; Kery, Marc; Royle, J. Andrew; Plattner, Matthias

2010-01-01

A variety of processes are thought to be involved in the formation and dynamics of species assemblages. For example, various metacommunity theories are based on differences in the relative contributions of dispersal of species among local communities and interactions of species within local communities. Interestingly, metacommunity theories continue to be advanced without much empirical validation. Part of the problem is that statistical models used to analyze typical survey data either fail to specify ecological processes with sufficient complexity or they fail to account for errors in detection of species during sampling. In this paper, we describe a statistical modeling framework for the analysis of metacommunity dynamics that is based on the idea of adopting a unified approach, multispecies occupancy modeling, for computing inferences about individual species, local communities of species, or the entire metacommunity of species. This approach accounts for errors in detection of species during sampling and also allows different metacommunity paradigms to be specified in terms of species- and location-specific probabilities of occurrence, extinction, and colonization: all of which are estimable. In addition, this approach can be used to address inference problems that arise in conservation ecology, such as predicting temporal and spatial changes in biodiversity for use in making conservation decisions. To illustrate, we estimate changes in species composition associated with the species-specific phenologies of flight patterns of butterflies in Switzerland for the purpose of estimating regional differences in biodiversity.

Improvement of a mesoscale atmospheric dynamic model PHYSIC. Utilization of output from synoptic numerical prediction model for initial and boundary condition

International Nuclear Information System (INIS)

Nagai, Haruyasu; Yamazawa, Hiromi

1995-03-01

This report describes the improvement of the mesoscale atmospheric dynamic model which is a part of the atmospheric dispersion calculation model PHYSIC. To introduce large-scale meteorological changes into the mesoscale atmospheric dynamic model, it is necessary to make the initial and boundary conditions of the model by using GPV (Grid Point Value) which is the output of the numerical weather prediction model of JMA (Japan Meteorological Agency). Therefore, the program which preprocesses the GPV data to make a input file to PHYSIC was developed and the input process and the methods of spatial and temporal interpolation were improved to correspond to the file. Moreover, the methods of calculating the cloud amount and ground surface moisture from GPV data were developed and added to the model code. As the example of calculation by the improved model, the wind field simulations of a north-west monsoon in winter and a sea breeze in summer in the Tokai area were also presented. (author)

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

Science.gov (United States)

García-Calvo, Raúl; Guisado, JL; Diaz-del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent

Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks.

Science.gov (United States)

García-Calvo, Raúl; Guisado, J L; Diaz-Del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent

A Compact Synchronous Cellular Model of Nonlinear Calcium Dynamics: Simulation and FPGA Synthesis Results.

Science.gov (United States)

Soleimani, Hamid; Drakakis, Emmanuel M

2017-06-01

Recent studies have demonstrated that calcium is a widespread intracellular ion that controls a wide range of temporal dynamics in the mammalian body. The simulation and validation of such studies using experimental data would benefit from a fast large scale simulation and modelling tool. This paper presents a compact and fully reconfigurable cellular calcium model capable of mimicking Hopf bifurcation phenomenon and various nonlinear responses of the biological calcium dynamics. The proposed cellular model is synthesized on a digital platform for a single unit and a network model. Hardware synthesis, physical implementation on FPGA, and theoretical analysis confirm that the proposed cellular model can mimic the biological calcium behaviors with considerably low hardware overhead. The approach has the potential to speed up large-scale simulations of slow intracellular dynamics by sharing more cellular units in real-time. To this end, various networks constructed by pipelining 10 k to 40 k cellular calcium units are compared with an equivalent simulation run on a standard PC workstation. Results show that the cellular hardware model is, on average, 83 times faster than the CPU version.

Spatio-temporal dynamics of action-effect associations in oculomotor control.

Science.gov (United States)

Riechelmann, Eva; Pieczykolan, Aleksandra; Horstmann, Gernot; Herwig, Arvid; Huestegge, Lynn

2017-10-01

While there is ample evidence that actions are guided by anticipating their effects (ideomotor control) in the manual domain, much less is known about the underlying characteristics and dynamics of effect-based oculomotor control. Here, we address three open issues. 1) Is action-effect anticipation in oculomotor control reflected in corresponding spatial saccade characteristics in inanimate environments? 2) Does the previously reported dependency of action latency on the temporal effect delay (action-effect interval) also occur in the oculomotor domain? 3) Which temporal effect delay is optimally suited to develop strong action-effect associations over time in the oculomotor domain? Participants executed left or right free-choice saccades to peripheral traffic lights, causing an (immediate or delayed) action-contingent light switch in the upper vs. lower part of the traffic light. Results indicated that saccades were spatially shifted toward the location of the upcoming change, indicating anticipation of the effect (location). Saccade latency was affected by effect delay, suggesting that corresponding time information is integrated into event representations. Finally, delayed (vs. immediate) effects were more effective in strengthening action-effect associations over the course of the experiment, likely due to greater saliency of perceptual changes occurring during target fixation as opposed to changes during saccades (saccadic suppression). Overall, basic principles underlying ideomotor control appear to generalize to the oculomotor domain. Copyright © 2017 Elsevier B.V. All rights reserved.

Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam's Window.

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Onorante, Luca; Raftery, Adrian E

2016-01-01

Bayesian model averaging has become a widely used approach to accounting for uncertainty about the structural form of the model generating the data. When data arrive sequentially and the generating model can change over time, Dynamic Model Averaging (DMA) extends model averaging to deal with this situation. Often in macroeconomics, however, many candidate explanatory variables are available and the number of possible models becomes too large for DMA to be applied in its original form. We propose a new method for this situation which allows us to perform DMA without considering the whole model space, but using a subset of models and dynamically optimizing the choice of models at each point in time. This yields a dynamic form of Occam's window. We evaluate the method in the context of the problem of nowcasting GDP in the Euro area. We find that its forecasting performance compares well with that of other methods.

Pre-operative simulation of pediatric mastoid surgery with 3D-printed temporal bone models.

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Rose, Austin S; Webster, Caroline E; Harrysson, Ola L A; Formeister, Eric J; Rawal, Rounak B; Iseli, Claire E

2015-05-01

As the process of additive manufacturing, or three-dimensional (3D) printing, has become more practical and affordable, a number of applications for the technology in the field of pediatric otolaryngology have been considered. One area of promise is temporal bone surgical simulation. Having previously developed a model for temporal bone surgical training using 3D printing, we sought to produce a patient-specific model for pre-operative simulation in pediatric otologic surgery. Our hypothesis was that the creation and pre-operative dissection of such a model was possible, and would demonstrate potential benefits in cases of abnormal temporal bone anatomy. In the case presented, an 11-year-old boy underwent a planned canal-wall-down (CWD) tympano-mastoidectomy for recurrent cholesteatoma preceded by a pre-operative surgical simulation using 3D-printed models of the temporal bone. The models were based on the child's pre-operative clinical CT scan and printed using multiple materials to simulate both bone and soft tissue structures. To help confirm the models as accurate representations of the child's anatomy, distances between various anatomic landmarks were measured and compared to the temporal bone CT scan and the 3D model. The simulation allowed the surgical team to appreciate the child's unusual temporal bone anatomy as well as any challenges that might arise in the safety of the temporal bone laboratory, prior to actual surgery in the operating room (OR). There was minimal variability, in terms of absolute distance (mm) and relative distance (%), in measurements between anatomic landmarks obtained from the patient intra-operatively, the pre-operative CT scan and the 3D-printed models. Accurate 3D temporal bone models can be rapidly produced based on clinical CT scans for pre-operative simulation of specific challenging otologic cases in children, potentially reducing medical errors and improving patient safety. Copyright © 2015 Elsevier Ireland Ltd. All rights

Locating the source of spreading in temporal networks

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Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Yi, Dongyun

2017-02-01

The topological structure of many real networks changes with time. Thus, locating the sources of a temporal network is a creative and challenging problem, as the enormous size of many real networks makes it unfeasible to observe the state of all nodes. In this paper, we propose an algorithm to solve this problem, named the backward temporal diffusion process. The proposed algorithm calculates the shortest temporal distance to locate the transmission source. We assume that the spreading process can be modeled as a simple diffusion process and by consensus dynamics. To improve the location accuracy, we also adopt four strategies to select which nodes should be observed by ranking their importance in the temporal network. Our paper proposes a highly accurate method for locating the source in temporal networks and is, to the best of our knowledge, a frontier work in this field. Moreover, our framework has important significance for controlling the transmission of diseases or rumors and formulating immediate immunization strategies.