scholarly journals Estimating animal density in three dimensions using capture-frequency data from remote detectors

2019 ◽  
Author(s):  
Juan S. Vargas Soto ◽  
Rowshyra A. Castañeda ◽  
Nicholas E. Mandrak ◽  
Péter K. Molnár
Keyword(s):  
Population Density ◽  
Animal Movement ◽  
Camera Trapping ◽  
Three Dimensions ◽  
Detection Methods ◽  
Parameter Estimates ◽  
List Type ◽  
Detection Zone ◽  
Detection Frequency ◽  
Wide Range

AbstractRemote detectors are being used increasingly often to study aquatic and aerial species, for which movement is significantly different from terrestrial species. While terrestrial camera-trapping studies have shown that capture frequency, along with the species’ movement speed and detector specifications can be used to estimate absolute densities, the approach has not yet been adapted to cases where movement occurs in three dimensions. Frameworks based on animal movement patterns allow estimating population density from camera-trapping data when animals are not individually distinguishable.Here we adapt one such framework to three-dimensional movement to characterize the relationship between population density, animal speed, characteristics of a remote sensor’s detection zone, and detection frequency. The derivation involves defining the detection zone mathematically and calculating the mean area of the profile it presents to approaching individuals.We developed two variants of the model – one assuming random movement of all individuals, and one allowing for different probabilities for each approach direction (e.g. that animals more often swim/fly horizontally than vertically). We used computer simulations to evaluate model performance for a wide range of animal and detector densities. Simulations show that in ideal conditions the method approximates true density well, and that estimates become increasingly accurate using more detectors, or sampling for longer. Moreover, the method is robust to invalidation of assumptions, accuracy is decreased only in extreme cases where all detectors are facing the same way.We provide equations for estimating population density from detection frequency and outline how to estimate the necessary parameters. We discuss how environmental variables and species-specific characteristics affect parameter estimates and how to account for these differences in density estimations.Our method can be applied to common remote detection methods (cameras and acoustic detectors), which are currently being used to study a diversity of species and environments. Therefore, our work may significantly expand the number and diversity of species for which density can be estimated.

scholarly journals Semblance of heterogeneity in collective cell migration

2017 ◽  
Author(s):  
Linus J. Schumacher ◽  
Philip K. Maini ◽  
Ruth E. Baker
Keyword(s):  
Cell Migration ◽  
Cell Population ◽  
Cell Tracking ◽  
Animal Movement ◽  
Population Heterogeneity ◽  
Three Dimensions ◽  
Biological Research ◽  
Collective Cell Migration ◽  
List Type ◽  
Tracking Data

AbstractCell population heterogeneity is increasingly a focus of inquiry in biological research. For example, cell migration studies have investigated the heterogeneity of invasiveness and taxis in development, wound healing, and cancer. However, relatively little effort has been devoted to explore when heterogeneity is mechanistically relevant and how to reliably measure it. Statistical methods from the animal movement literature offer the potential to analyse heterogeneity in collections of cell tracking data. A popular measure of heterogeneity, which we use here as an example, is the distribution of delays in directional cross-correlation. Employing a suitably generic, yet minimal, model of collective cell movement in three dimensions, we show how using such measures to quantify heterogeneity in tracking data can result in the inference of heterogeneity where there is none. Our study highlights a potential pitfall in the statistical analysis of cell population heterogeneity, and we argue this can be mitigated by the appropriate choice of null models.Highlightsgroups of identical cells appear heterogeneous due to limited sampling and experimental repeatabilityheterogeneity bias increases with attraction/repulsion between cellsmovement in confined environments decreases apparent heterogeneityhypothetical applications in neural crest and in vitro cancer systemsIn BriefWe use a mathematical model to show how cell populations can appear heterogeneous in their migratory characteristics, even though they are made up of identically-behaving individual cells. This has important consequences for the study of collective cell migration in areas such as embryo development or cancer invasion.

scholarly journals A New Method for Calculating the Population Density of Terrestrial Animals Using Camera Traps with Assessment of Roe Deer (Capreolus pygargus Pallas, 1771) (Cervidae, Mammalia) Population Density in the Khingan Nature Reserve as an Example

2020 ◽  
pp. 307-317
Author(s):  
V. A. Kastrikin ◽  
S. A. Podol'skii ◽  
M. S. Babykina
Keyword(s):  
Population Density ◽  
Nature Reserve ◽  
Animal Movement ◽  
Roe Deer ◽  
Camera Traps ◽  
New Method ◽  
Individual Identification ◽  
Entire Study ◽  
Wide Range ◽  
Significant Difference

A new method for calculating the population density of terrestrial animals, which are not amenable to individual identification, using photos or video images obtained by automatic cameras is proposed for discussion. The method is based on the continuous registration of animals on sites formed by the detection zones of camera traps with subsequent extrapolation of the results to the entire study area. A much simpler mathematical apparatus is a significant difference between our proposed method and other methods of accounting by camera traps, which allows it to be applied by a wide range of users. Both the positional measures and the scattering measures necessary for subsequent statistical analysis are calculated quite easily. Also, one of our method’s advantages is no need to know the animal movement speed, the most difficult parameter to calculate, especially in the snowless period of the year. An example of using the bootstrap method is given for the case when the input data distribution parameters do not correspond to the normal one. Using the de Moivre–Laplace theorem, the probability that the animals resting on their beds would get into the detection zone of the camera trap matrices is estimated, which is necessary for the correct use of the proposed method. Solutions are proposed for cases when this probability is low. The problems of our proposed method and their possible solutions are described. An example of calculating the density of roe deer in the open oak forest of the Khingan Nature Reserve is given on the basis of our data obtained from four camera traps.

Comparison of Deterministic and Linear Cosine Spatial Filtering of Transmission Electron Micrographs

1972 ◽  
Vol 30 ◽  
pp. 596-597
Author(s):  
David A. Ansley
Keyword(s):  
Spherical Aberration ◽  
Focal Length ◽  
Chromatic Aberration ◽  
Spatial Filter ◽  
Operating Conditions ◽  
Objective Lens ◽  
List Type ◽  
Spatial Filters ◽  
Transmission Electron ◽  
Wide Range

The coherence of the electron flux of a transmission electron microscope (TEM) limits the direct application of deconvolution techniques which have been used successfully on unmanned spacecraft programs. The theory assumes noncoherent illumination. Deconvolution of a TEM micrograph will, therefore, in general produce spurious detail rather than improved resolution.A primary goal of our research is to study the performance of several types of linear spatial filters as a function of specimen contrast, phase, and coherence. We have, therefore, developed a one-dimensional analysis and plotting program to simulate a wide 'range of operating conditions of the TEM, including adjustment of the:(1) Specimen amplitude, phase, and separation(2) Illumination wavelength, half-angle, and tilt(3) Objective lens focal length and aperture width(4) Spherical aberration, defocus, and chromatic aberration focus shift(5) Detector gamma, additive, and multiplicative noise constants(6) Type of spatial filter: linear cosine, linear sine, or deterministic

Triply-periodic nanostructures in surfactant, block copolymer, and biomembrane systems, and simulation of TEMs

1993 ◽  
Vol 51 ◽  
pp. 884-885
Author(s):  
David M. Anderson ◽  
Tomas Landh
Keyword(s):  
Liquid Crystalline ◽  
Diblock Copolymers ◽  
Cubic Phase ◽  
List Type ◽  
Interpenetrating Networks ◽  
Triply Periodic ◽  
Wide Range ◽  
Bicontinuous Cubic ◽  
Phase Liquid ◽  
Dividing Surface

First discovered in surfactant-water liquid crystalline systems, so-called ‘bicontinuous cubic phases’ have the property that hydropnilic and lipophilic microdomains form interpenetrating networks conforming to cubic lattices on the scale of nanometers. Later these same structures were found in star diblock copolymers, where the simultaneous continuity of elastomeric and glassy domains gives rise to unique physical properties. Today it is well-established that the symmetry and topology of such a morphology are accurately described by one of several triply-periodic minimal surfaces, and that the interface between hydrophilic and hydrophobic, or immiscible polymer, domains is described by a triply-periodic surface of constant, nonzero mean curvature. One example of such a dividing surface is shown in figure 5.The study of these structures has become of increasing importance in the past five years for two reasons:1)Bicontinuous cubic phase liquid crystals are now being polymerized to create microporous materials with monodispersed pores and readily functionalizable porewalls; figure 3 shows a TEM from a polymerized surfactant / methylmethacrylate / water cubic phase; and2)Compelling evidence has been found that these same morphologies describe biomembrane systems in a wide range of cells.

Extension of the Spatial PDI Model to Three Dimensions

1997 ◽  
Vol 84 (1) ◽  
pp. 176-178
Author(s):  
Frank O'Brien
Keyword(s):  
Population Density ◽  
Dimensional Space ◽  
Finite Lattice ◽  
Three Dimensional ◽  
Upper Bounds ◽  
Three Dimensions ◽  
Lower And Upper Bounds ◽  
Density Index ◽  
Three Dimensional Space

The author's population density index ( PDI) model is extended to three-dimensional distributions. A derived formula is presented that allows for the calculation of the lower and upper bounds of density in three-dimensional space for any finite lattice.

scholarly journals Detectivity optimization to detect of ultraweak light fluxes with an EM-CCD as binary photon counter array

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ibtissame Khaoua ◽  
Guillaume Graciani ◽  
Andrey Kim ◽  
François Amblard
Keyword(s):  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Photon Counting ◽  
Detection Methods ◽  
Strong Nonlinearity ◽  
Wide Range ◽  
Depth Analysis ◽  
Spatially Extended ◽  
Extended Sources ◽  
Photon Counting Mode

AbstractFor a wide range of purposes, one faces the challenge to detect light from extremely faint and spatially extended sources. In such cases, detector noises dominate over the photon noise of the source, and quantum detectors in photon counting mode are generally the best option. Here, we combine a statistical model with an in-depth analysis of detector noises and calibration experiments, and we show that visible light can be detected with an electron-multiplying charge-coupled devices (EM-CCD) with a signal-to-noise ratio (SNR) of 3 for fluxes less than $$30\,{\text{photon}}\,{\text{s}}^{ - 1} \,{\text{cm}}^{ - 2}$$ 30 photon s - 1 cm - 2 . For green photons, this corresponds to 12 aW $${\text{cm}}^{ - 2}$$ cm - 2 ≈ $$9{ } \times 10^{ - 11}$$ 9 × 10 - 11 lux, i.e. 15 orders of magnitude less than typical daylight. The strong nonlinearity of the SNR with the sampling time leads to a dynamic range of detection of 4 orders of magnitude. To detect possibly varying light fluxes, we operate in conditions of maximal detectivity $${\mathcal{D}}$$ D rather than maximal SNR. Given the quantum efficiency $$QE\left( \lambda \right)$$ Q E λ of the detector, we find $${ \mathcal{D}} = 0.015\,{\text{photon}}^{ - 1} \,{\text{s}}^{1/2} \,{\text{cm}}$$ D = 0.015 photon - 1 s 1 / 2 cm , and a non-negligible sensitivity to blackbody radiation for T > 50 °C. This work should help design highly sensitive luminescence detection methods and develop experiments to explore dynamic phenomena involving ultra-weak luminescence in biology, chemistry, and material sciences.

scholarly journals A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
R. Sekhar ◽  
K. Sasirekha ◽  
P. S. Raja ◽  
K. Thangavel
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Radial Basis Function Network ◽  
Detection Methods ◽  
Support Vector ◽  
Parameter Method ◽  
List Type ◽  
Fuzzy C Means

Abstract Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

scholarly journals 332 The diagnostic role of shunt series radiographs (SSR) in children presenting to the children’s emergency department (CED) with suspected ventriculoperitoneal (VP) shunt failure

2020 ◽  
Vol 37 (12) ◽  
pp. 852.3-853
Author(s):  
Angharad Griffiths ◽  
Ikechukwu Okafor ◽  
Thomas Beattie
Keyword(s):  
Clinical Outcome ◽  
Sensitivity And Specificity ◽  
Clinical Information ◽  
Flow Diagram ◽  
List Type ◽  
Shunt Failure ◽  
Vp Shunt ◽  
Wide Range ◽  
Diagnostic Role

Aims/Objectives/BackgroundVP shunts are used to drain CSF from the cranial vault because of a wide range of pathologies and, like any piece of hardware, can fail. Traditionally investigations include SSR and CT. This project examines the role of SSR in evaluating children with suspected VP shunt failure.Primary outcome: Sensitivity and specificity of SSR in children presenting to the CED with concern for shunt failure.Methods/DesignConducted in a single centre, tertiary CED of the national Irish Neurosurgical(NS) referral centre (ED attendance:>50,000 patients/year). 100 sequential SSR requested by the CED were reviewed. Clinical information was extracted from electronic requests. Shunt failure was defined by the need for NS intervention(Revision).Abstract 332 Figure 1Abstract 332 Figure 2Results/ConclusionsSensitivity and specificity is presented in figure 1 (two by two table).100 radiographs performed in 84 children.22% shunts revised (see flow diagram).7 SSR’s were abnormal.85% (n=6) shunts revised. [5 following abnormal CT].Of the normal SSR’s; 16 had abnormal CT and revised.85/100 received CT.64 of 85 CT’s (75%) were normal.□6 of the 64 had focal shunt concern.SSR’s shouldn’t be used in isolation. NPV&PPV, Sensitivity&Specificity is low.SSR’s are beneficial where there’s concern over focal shunt problems (injury/pain/swelling) or following abnormal CT.VP shunt failure is not well investigated with SSR alone.SSR’s could be omitted where there is no focal shunt concern/after normal CT (without impacting clinical outcome) reducing radiation exposure and reduce impact on CED’s.59 SSR’s could have been avoided without adverse clinical outcome.

scholarly journals Defects and uncertainties of adhesively bonded composite joints

2021 ◽  
Vol 3 (9) ◽  
Author(s):  
Sadik Omairey ◽  
Nithin Jayasree ◽  
Mihalis Kazilas
Keyword(s):  
Composite Materials ◽  
Composite Structures ◽  
Production Efficiency ◽  
Adhesive Bonding ◽  
Detection Methods ◽  
Bonded Joints ◽  
Adhesively Bonded ◽  
Adhesively Bonded Joints ◽  
Wide Range ◽  
Bonded Composite

AbstractThe increasing use of fibre reinforced polymer composite materials in a wide range of applications increases the use of similar and dissimilar joints. Traditional joining methods such as welding, mechanical fastening and riveting are challenging in composites due to their material properties, heterogeneous nature, and layup configuration. Adhesive bonding allows flexibility in materials selection and offers improved production efficiency from product design and manufacture to final assembly, enabling cost reduction. However, the performance of adhesively bonded composite structures cannot be fully verified by inspection and testing due to the unforeseen nature of defects and manufacturing uncertainties presented in this joining method. These uncertainties can manifest as kissing bonds, porosity and voids in the adhesive. As a result, the use of adhesively bonded joints is often constrained by conservative certification requirements, limiting the potential of composite materials in weight reduction, cost-saving, and performance. There is a need to identify these uncertainties and understand their effect when designing these adhesively bonded joints. This article aims to report and categorise these uncertainties, offering the reader a reliable and inclusive source to conduct further research, such as the development of probabilistic reliability-based design optimisation, sensitivity analysis, defect detection methods and process development.

scholarly journals Author Correction: Spatial variance-mass allometry of population density in felids from camera-trapping studies worldwide

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stefano Anile ◽  
Sébastien Devillard
Keyword(s):  
Population Density ◽  
Camera Trapping ◽  
Spatial Variance

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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