Snapshot Data Research Articles

A new Automated Behavioural Response system to integrate playback experiments into camera trap studies

Summary How animals respond to anthropogenic disturbances is a core component of conservation biology and how they respond to predators and competitors is equally of central importance to wildlife ecology. Camera traps have rapidly become a critical tool in wildlife research by providing a fully automated means of observing animals without needing an observer present, permitting data to be collected on rare or elusive species and infrequent events. Snapshots from camera traps revealing a species’ presence have been the principal data used to date to gauge behaviour; but, lacking experimental controls, such data permit only correlational analyses potentially open to confounding effects. Playback experiments provide a powerful means to directly test the behavioural responses of animals, enabling strong inferences and rigorous conclusions not subject to the potential confounds affecting the interpretation of snapshot data; the principal factor to date limiting the use of playback experiments being the need to have an observer present. We developed an Automated Behavioural Response system (ABR) comprising a custom‐built motion‐sensitive speaker system that can be paired with any commercially available camera trap, providing the means to conduct playback experiments directly testing the behavioural responses of any species that can be ‘caught’ on a camera trap. We describe field tests in Uganda, Canada and the USA, experimentally testing the effects of anthropogenic disturbances and interactions among large carnivores, in species as diverse as elephants, black bears, chimpanzees and cougars; experiments that would be completely infeasible without the ABR. We evaluate factors affecting the rate of successful data collection in the experiments in Uganda and Canada, and detail how we maximized the system's performance in the USA experiment. By integrating the power playback experiments provide to directly and rigorously test behavioural responses with the capacity camera trapping affords to study virtually any animal anywhere, the ABR can both greatly expand the range of research questions addressed by conservation biologists and wildlife ecologists and qualitatively improve the rigour of the resulting conclusions. We discuss various ways to optimize the ABR's performance in any circumstance, and the many novel research opportunities made available by this new methodology.

Using the Heart Foundation Heart Failure Toolkit to Design a Model of Care for Patients Admitted with Heart Failure

Background: Heart failure (HF) admissions have increased at the Alfred Hospital over the last three years. The Victorian Cardiac Clinical Network provided funding for a project to improve the HF model of care. Method: We used a five-step redesign process. The Heart Foundation HF Toolkit was used to inform the redesign process. Alfred Health redesign service assisted. Results: We brought together interested stakeholders to discuss views and define the problems. An executive sponsor was secured. A project brief established the aims and scope of the project. VAED and VCOR HF SNAPSHOT data were used. The patient journey was mapped from community, through hospital, and back to community with multi-disciplinary teams from acute care, outpatient clinics and community services. The HF Toolkit was used to inform a gap analysis and potential solutions according to the three pillars outlined in the toolkit, with HF specialty input. The main themes for change were extracted from these solutions. The HF model of care redesign included HF care bundles, HF education, improved communications, and a transitional safety net. Outcome measures were agreed upon. It is now being implemented. Conclusion: A model of care was designed for the Alfred Hospital to maximise time HF patients spend well in the community by delivering best practice guidelines to reduce variation in care and improve overall outcomes.

Temporal Summary Images: An Approach to Narrative Visualization via Interactive Annotation Generation and Placement.

Visualization is a powerful technique for analysis and communication of complex, multidimensional, and time-varying data. However, it can be difficult to manually synthesize a coherent narrative in a chart or graph due to the quantity of visualized attributes, a variety of salient features, and the awareness required to interpret points of interest (POls). We present Temporal Summary Images (TSIs) as an approach for both exploring this data and creating stories from it. As a visualization, a TSI is composed of three common components: (1) a temporal layout, (2) comic strip-style data snapshots, and (3) textual annotations. To augment user analysis and exploration, we have developed a number of interactive techniques that recommend relevant data features and design choices, including an automatic annotations workflow. As the analysis and visual design processes converge, the resultant image becomes appropriate for data storytelling. For validation, we use a prototype implementation for TSIs to conduct two case studies with large-scale, scientific simulation datasets.

System specification for dynamic configuration management of networked reconfigurable industrial products

The survey research presented in this paper, highlighted an urgent need of the machine industry to find a secure and safe solution to digitise and manage as-maintained configuration data of remote and configurable products in the field. Two new concepts to improve configuration management of networked industrial products were identified: 1) utilisation of mobile computing technology as a safe and secure configuration data carrier and an execution platform to conduct configuration management tasks and to keep configuration data in sync, especially in cases of no internet connection in the field; 2) incorporate machine operational and condition measurement data snapshots as part of configuration data to track and enrich insight into machine performance for warranty purposes. The research resulted a design foundation for the desired solution - a system context with the most important stakeholders and interfaces to external systems, and 20 system requirements for the system functions.

Generalized method of moments for estimating parameters of stochastic reaction networks.

BackgroundDiscrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks.ResultsWe propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies.ConclusionsThe generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0342-8) contains supplementary material, which is available to authorized users.

Análise da Imagem dos Turistas quanto ao Desenvolvimento do Turismo em Perobas, Touros-RN

O presente artigo tem como objetivo avaliar a imagem dos turistas quanto ao desenvolvimento do turismo nos recifes de corais de Perobas. A comunidade de Perobas situa-se no município de Touros-RN, distante aproximadamente 90 km da Capital do Estado - Natal, o qual faz parte da área de influência direta da Área de Proteção Ambiental dos Recifes de Corais juntamente com os municípios de Maxaranguape e Rio do Fogo. Os processos metodológicos consistiram inicialmente em revisão da literatura, depois uso de questionários como instrumento de coleta de dados questionários, visita in loco e capturas de imagens. Os resultados obtidos mostraram que os principais destinos de origens dos turistas são São Paulo e Rio de Janeiro, que o principal motivo para visitar Perobas é o mergulho, e as primeiras informações do local são dadas pelas agências de Natal. Além disso, mostraram o interesse dos turistas em visitar um centro de informações do local, já quanto à verificação da satisfação dos passeios observou-seque eles manifestaram-se relativamente satisfeitos. No entanto, também verificou práticas inadequadas por parte dos turistas, como pisoteio de corais, alimentação artificial aos peixes, coletas de corais, devido à falta de fiscalização e a não obtenção de informações sobre as normas do local.

Identification and characterization of coherent structures in gasoline injector nozzle flow using proper orthogonal decomposition

Proper orthogonal decomposition (POD) methodology is used to identify and characterize coherent structures in the gasoline nozzle flow. A correlation matrix is generated from the fluctuating velocity data snapshots produced by in-house Large Eddy simulations (LES) of an internal flow of a gasoline nozzle at a pressure of 1.1 MPa (11 bar). The modes obtained from the solution of correlation matrix identify the most energetic and dominant recirculation zones along with strong axial and radial pulsations. Initial modes show big vortical structures and inter-mode coupling but later modes identify less energetic, smaller and uncoupled structures. The dominant modes demonstrate the key role of highly energetic large coherent structures in the flow organization at the exit of the injector. The frequencies obtained from Fast Fourier transform (FFT) analysis of axial and radial fluctuating velocities give an additional insight to the characteristics of important coherent structures that govern the nozzle flow.

Performance Analysis of Static and Dynamic State Estimation Incorporating Synchro Phasor Measurements

Objective: State estimation is used as a necessary tool for online monitoring, analysis and control of power systems. The main objective of this paper is to deal with static state estimation using the snap shot data and dynamic state estimation which accounts for the time varying behaviour of power system states in to consideration. Methods/Analysis: Effects of inclusion of PMU measurements along with the available metered data have been explored using the weighted least square state estimation technique in this paper. A comparative analysis of static state estimation with and without bad data has been carried out and the bad data has been identified and eliminated by using largest normalized residue test. Findings: To investigate the time varying nature of the system states linear state estimator with second order approximation and kalman filter techniques has been proposed in this paper. Case studies are conducted on IEEE 14 bus test system and the test results obtained from non linear, linear first order, second order and kalman filter techniques have been compared. Application/Improvements: Correction of state variables are obtained using linear state estimation with first order and second order approximation and kalman filter techniques has been compared to get the better state estimation algorithm.

Cluster-Based Reduced-Order Modeling of Lithium-Ion Battery

The Lithium-ion batteries are certainly the battery of choice today because of their high voltage platform, low maintenance, high energy and power density, good cycle performance, and low self-discharge. Developments in lithium-ion battery technology make them inexpensive, small and extremely light-weight. These batteries are capable of providing the great power needs of today’s high-tech industries, ranging from small portable devices, e.g. smart phones, to full/hybrid electric vehicles and also alternative energy storage requirements of renewable power generation. The lithium-ion battery performance plays a significant role in the system energy management. As a consequence, battery real-time monitoring and accurate state-of-charge (SOC) estimation are necessary. However, full order electrochemical models are not suitable for this purpose due to high computational cost. Reduced order model (ROM) is one of the promising models which is computationally cost effective while ensuring accurate results. Using data clustering are one of attractive ways to make reduced order model. The capability of cluster-based reduced order modeling (CROM) has been proven in fluid mechanics engineering fields. This approach, starts with a snapshot data set. The observations are clustered based on their similarity and then the means of these clusters (centroids) are used to constitute the reduced order basis. In the present study, cluster-based reduced order modeling is applied to electrochemical transport governing equations in order to simulate a lithium-ion battery. For this purpose, the governing equations including conservation of species and charge are solved all together using finite volume method (FVM) by which the snapshot data are provided. The data are clustered by k-means clustering algorithm in order to obtain the centroids which are optimal low-dimensional basis for representing an ensemble of high-dimensional simulation data. This low-dimensional basis are used to formulate reduced order models of complex fields of battery. The obtained numerical results show that not only the CROM of lithium-ion battery significantly decreases the computational time but also there is an excellent agreement with the results of computational fluid dynamics (CFD) models, consequently is suitable for deployment in real-time monitoring and battery management systems.

SPITZER IRAC SPARSELY SAMPLED PHASE CURVE OF THE EXOPLANET WASP-14B

ABSTRACT Motivated by a high Spitzer IRAC oversubscription rate, we present a new technique of randomly and sparsely sampling the phase curves of hot Jupiters. Snapshot phase curves are enabled by technical advances in precision pointing as well as careful characterization of a portion of the central pixel on the array. This method allows for observations which are a factor of approximately two more efficient than full phase curve observations, and are furthermore easier to insert into the Spitzer observing schedule. We present our pilot study from this program using the exoplanet WASP-14b. Data of this system were taken both as a sparsely sampled phase curve as well as a staring-mode phase curve. Both data sets, as well as snapshot-style observations of a calibration star, are used to validate this technique. By fitting our WASP-14b phase snapshot data set, we successfully recover physical parameters for the transit and eclipse depths as well as the amplitude and maximum and minimum of the phase curve shape of this slightly eccentric hot Jupiter. We place a limit on the potential phase to phase variation of these parameters since our data are taken over many phases over the course of a year. We see no evidence for eclipse depth variations compared to other published WASP-14b eclipse depths over a 3.5 year baseline.

Application of Uncorrelated Leaning from Low-rank Dictionary in Blind Source Separation

This paper proposes a kind of method about signal BOA estimation from the aspect of sparse decomposition. The whole interested space is divided into several potential angles of arrival to establish a over-complete directory to convert the estimation problem of signal DOA to sparse representation problem. A MMV array is formed by data received from multiple snapshots, then using optimization method of joint sparse constraint to solve the problem. First, make singular value decomposition on received data array to connect the each snapshot data, then using the sparse representation problem of bounded to solve the problem. To improve the anti-noise performance of algorithm, the paper applies similar Sigmoid function of two parameters to approximate norm. This method applies to the DOA estimation of narrow-band and broad-band signal. shall be used for solving MMV problem, which achieves joint sparse constraint of all frequency of reception matrix of broadband signal, to make array elements spacing break through the limitation of half wavelength and improve resolution of DOA signal.

Streamwise-varying steady transpiration control in turbulent pipe flow

The effect of streamwise-varying steady transpiration on turbulent pipe flow is examined using direct numerical simulation at fixed friction Reynolds number $\mathit{Re}_{{\it\tau}}=314$. The streamwise momentum equation reveals three physical mechanisms caused by transpiration acting in the flow: modification of Reynolds shear stress, steady streaming and generation of non-zero mean streamwise gradients. The influence of these mechanisms has been examined by means of a parameter sweep involving transpiration amplitude and wavelength. The observed trends have permitted identification of wall transpiration configurations able to reduce or increase the overall flow rate $-36.1\,\%$ and $19.3\,\%$, respectively. Energetics associated with these modifications are presented. A novel resolvent formulation has been developed to investigate the dynamics of pipe flows with a constant cross-section but with time-mean spatial periodicity induced by changes in boundary conditions. This formulation, based on a triple decomposition, paves the way for understanding turbulence in such flows using only the mean velocity profile. Resolvent analysis based on the time-mean flow and dynamic mode decomposition based on simulation data snapshots have both been used to obtain a description of the reorganization of the flow structures caused by the transpiration. We show that the pipe flows dynamics are dominated by a critical-layer mechanism and the waviness induced in the flow structures plays a role on the streamwise momentum balance by generating additional terms.

Risky writing: Working with a heteroglossic pedagogy to deepen pre-service teachers’ learning

This article explores how course design and assessment in a first year pre-service teacher education course for English students can be imbued with academic depth and rigour in ways that enable students to take intellectual and textual risks. We argue for a conceptualisation of risky writing in which we open up student critical engagement with sociolinguistic issues by juxtaposing academic and creative genres in curriculum course material and assessment. Academic writing in its current form is problematised and questions are raised about the extent to which academic courses provide students with an apprenticeship into compliance, conformity and silence. We present the possibilities of using a heteroglossic pedagogy (Blackledge & Creese, 2014) for learning, teaching and writing. The principles underpinning the course (linguistic diversity as a resource, the value of lived experience and the interrelation of epistemological access and academic rigour) constitute a heteroglossic pedagogy. We illustrate these principles using two examples, one from student performance during the course and the second from independent writing for an assignment. Together, the two data snapshots illustrate the pedagogic possibilities of fluid movements between distantiation and appropriation using flexible genres, which ultimately facilitate deeper student engagement and understanding of disciplinary knowledge. The two data snapshots are not mere descriptions or anecdotes detached from principles (Slonimsky & Shalem, 2004: 92). They facilitate academic depth and rigour because of the carefully staged moves between the strange and the familiar in a context that encourages students to take creative and intellectual risks.

Whole-Genome Sequencing for Routine Pathogen Surveillance in Public Health: a Population Snapshot of Invasive Staphylococcus aureus in Europe.

ABSTRACTThe implementation of routine whole-genome sequencing (WGS) promises to transform our ability to monitor the emergence and spread of bacterial pathogens. Here we combined WGS data from 308 invasive Staphylococcus aureus isolates corresponding to a pan-European population snapshot, with epidemiological and resistance data. Geospatial visualization of the data is made possible by a generic software tool designed for public health purposes that is available at the project URL (http://www.microreact.org/project/EkUvg9uY?tt=rc). Our analysis demonstrates that high-risk clones can be identified on the basis of population level properties such as clonal relatedness, abundance, and spatial structuring and by inferring virulence and resistance properties on the basis of gene content. We also show that in silico predictions of antibiotic resistance profiles are at least as reliable as phenotypic testing. We argue that this work provides a comprehensive road map illustrating the three vital components for future molecular epidemiological surveillance: (i) large-scale structured surveys, (ii) WGS, and (iii) community-oriented database infrastructure and analysis tools.

Parallel data-driven decomposition algorithm for large-scale datasets: with application to transitional boundary layers

Many fluid flows of engineering interest, though very complex in appearance, can be approximated by low-order models governed by a few modes, able to capture the dominant behavior (dynamics) of the system. This feature has fueled the development of various methodologies aimed at extracting dominant coherent structures from the flow. Some of the more general techniques are based on data-driven decompositions, most of which rely on performing a singular value decomposition (SVD) on a formulated snapshot (data) matrix. The amount of experimentally or numerically generated data expands as more detailed experimental measurements and increased computational resources become readily available. Consequently, the data matrix to be processed will consist of far more rows than columns, resulting in a so-called tall-and-skinny (TS) matrix. Ultimately, the SVD of such a TS data matrix can no longer be performed on a single processor, and parallel algorithms are necessary. The present study employs the parallel TSQR algorithm of (Demmel et al. in SIAM J Sci Comput 34(1):206–239, 2012), which is further used as a basis of the underlying parallel SVD. This algorithm is shown to scale well on machines with a large number of processors and, therefore, allows the decomposition of very large datasets. In addition, the simplicity of its implementation and the minimum required communication makes it suitable for integration in existing numerical solvers and data decomposition techniques. Examples that demonstrate the capabilities of highly parallel data decomposition algorithms include transitional processes in compressible boundary layers without and with induced flow separation.

Full-field pressure from snapshot and time-resolved volumetric PIV

This paper deals with pressure estimation from snapshot and time-resolved three-component (3C) volumetric PIV data using Taylor’s hypothesis, an Eulerian and a pseudo-Lagrangian approach. The Taylor’s hypothesis approach has been shown to provide accurate results for pressure in the case of 3C planar PIV data with an appropriate choice of convection velocity (de Kat and Ganapathisubramani 2013), and here we extend its use on 3C volumetric velocity snapshots. Application of the techniques to synthetic data shows that the Taylor’s hypothesis approach performs best using the streamwise mean as the convection velocity and is affected the least by noise, while the Eulerian approach suffers the most. In terms of resolution, the pseudo-Lagrangian approach is the most sensitive. Its accuracy can be improved by increasing the frame time-separation when computing the material derivative, at the expense of volume loss from fluid parcels leaving the FOV. Comparison of the techniques on turbulent boundary layer data with DNS supports these observations and shows that the Taylor’s hypothesis approach is the only way we can get pressure when time information is not present.

Approximate partitioned method of snapshots for POD

The proper orthogonal decomposition (POD) method has been widely used to construct efficient numerical surrogate models for computationally intensive applications in control and optimization. An inherent challenge with this method is that POD basis generation can be computationally expensive due to the huge size of the input snapshot data obtained from typical high-fidelity, large-scale dynamic system simulations. However, if the process can be distributed into much smaller tasks over multiple processors in parallel, computational time can be drastically reduced. In this paper, we put forth a novel partitioned method for generating the POD basis from snapshot data. This method preserves the distributed nature of the data and takes advantage of parallelism for computation. Additionally, it greatly reduces subtask communication volume. Two numerical examples are presented that demonstrate the effectiveness of the new method.

Probability distribution of multiple-target data snapshots applied to large-aperture array processing

We consider sonar array target detection and azimuth estimation in experimental scenarios consisting of large-aperture horizontal line arrays of hydrophones in the water column. In such scenarios, successful distinction of quiet targets from the background noise strongly depends on having sufficient independent data snapshots available for computation of adaptive beamforming weights. In this work, the discrete Fourier transform matrix is used for projecting the array data into a sparse domain, from which the theoretical probability density function of each (sparse-domain) datum can be obtained. It is shown that the sparse data exhibits a scaled chi-squared distributed magnitude, with a scale factor that depends on signal-to-background noise ratio and array size. Using this theoretical distribution and Monte Carlo simulations, minimum experimental requirements for target detection are quantified and highlight the tradeoff between data sample size, array aperture, and signal-to-noise ratio. In addition, a s...

HORIZON RUN 4 SIMULATION: COUPLED EVOLUTION OF GALAXIES AND LARGE-SCALE STRUCTURES OF THE UNIVERSE

The Horizon Run 4 is a cosmological $N$-body simulation designed for the study of coupled evolution between galaxies and large-scale structures of the Universe, and for the test of galaxy formation models. Using $6300^3$ gravitating particles in a cubic box of $L_{\rm box} = 3150 ~h^{-1}{\rm Mpc}$, we build a dense forest of halo merger trees to trace the halo merger history with a halo mass resolution scale down to $M_s = 2.7 \times 10^{11} h^{-1}{\rm M_\odot}$. We build a set of particle and halo data, which can serve as testbeds for comparison of cosmological models and gravitational theories with observations. We find that the FoF halo mass function shows a substantial deviation from the universal form with tangible redshift evolution of amplitude and shape. At higher redshifts, the amplitude of the mass function is lower, and the functional form is shifted toward larger values of $\ln (1/\sigma)$. We also find that the baryonic acoustic oscillation feature in the two-point correlation function of mock galaxies becomes broader with a peak position moving to smaller scales and the peak amplitude decreasing for increasing directional cosine $\mu$ compared to the linear predictions. From the halo merger trees built from halo data at 75 redshifts, we measure the half-mass epoch of halos and find that less massive halos tend to reach half of their current mass at higher redshifts. Simulation outputs including snapshot data, past lightcone space data, and halo merger data are available at http://sdss.kias.re.kr/astro/Horizon-Run4/.

Storm event representation and analysis based on a directed spatiotemporal graph model

Large amounts of time series of spatial snapshot data have been collected or generated for the monitoring and modeling of environmental systems. Those time series of data also provide the opportunity to study the movements and dynamics of many different natural phenomena. While the snapshot organization is conceptually simple and straightforward, it does not directly capture or represent the dynamic characteristics of the phenomena. This study presents computational methods to identify dynamic events from time series of spatial snapshots. Events are represented as directed spatiotemporal graphs to characterize their initiation, development, movement, and cessation. Graph-based algorithms are used to analyze the dynamics of the events. The method is applied to time series of high-resolution radar reflectivity images during one of the deadliest storm outbreaks that impacted 15 states of southeastern United States between 23 and 29 April 2011. As shown in this case study, convective storm events identified using our methods are consistent with previous studies, and our analysis confirms that the left split/merger occurs more than right split/merger in those convective storm events, which confirms theory, numerical simulations, and other observed case studies. While this study does not differentiate between storm modes, the method shows potential for capturing a more detailed climatology of precipitation characteristics.