How bandwidth selection algorithms impact exploratory data analysis using kernel density estimation.

PubMed

Harpole, Jared K; Woods, Carol M; Rodebaugh, Thomas L; Levinson, Cheri A; Lenze, Eric J

2014-09-01

Exploratory data analysis (EDA) can reveal important features of underlying distributions, and these features often have an impact on inferences and conclusions drawn from data. Graphical analysis is central to EDA, and graphical representations of distributions often benefit from smoothing. A viable method of estimating and graphing the underlying density in EDA is kernel density estimation (KDE). This article provides an introduction to KDE and examines alternative methods for specifying the smoothing bandwidth in terms of their ability to recover the true density. We also illustrate the comparison and use of KDE methods with 2 empirical examples. Simulations were carried out in which we compared 8 bandwidth selection methods (Sheather-Jones plug-in [SJDP], normal rule of thumb, Silverman's rule of thumb, least squares cross-validation, biased cross-validation, and 3 adaptive kernel estimators) using 5 true density shapes (standard normal, positively skewed, bimodal, skewed bimodal, and standard lognormal) and 9 sample sizes (15, 25, 50, 75, 100, 250, 500, 1,000, 2,000). Results indicate that, overall, SJDP outperformed all methods. However, for smaller sample sizes (25 to 100) either biased cross-validation or Silverman's rule of thumb was recommended, and for larger sample sizes the adaptive kernel estimator with SJDP was recommended. Information is provided about implementing the recommendations in the R computing language. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

PubMed Central

2012-01-01

Background Myocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome in electrocardiogram (ECG) more accurately and automatically can prevent it from developing into a catastrophic disease. To this end, we propose a new method, which employs wavelets and simple feature selection. Methods For training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in 90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method based on the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used for differentiating ST episodes from normal: 1) the area between QRS offset and T-peak points, 2) the normalized and signed sum from QRS offset to effective zero voltage point, and 3) the slope from QRS onset to offset point. We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiers to those features. Results We evaluated the algorithm by kernel density estimation (KDE) and support vector machine (SVM) methods. Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemic ST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively. The SVM classifier detects 355 ischemic ST episodes. Conclusions We proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removing baseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and feature extraction from morphology of ECG waveforms explicitly. It was shown that the number of selected features were sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposed KDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require any numerical values of the parameters

Development of Voltage Regulation Plan by Composing Subsystem with the SFES for DC On-line Electric Vehicle

NASA Astrophysics Data System (ADS)

Jung, S.; Lee, J. H.; Yoon, M.; Lee, H.; Jang, G.

The study of the application process of the relatively small size 'Superconducting Flywheel Energy Storage (SFES)' system is conducted to regulate voltage fluctuation of the DC On-Line Electric Vehicle (OLEV) system, which is designed by using DC power system network. It is recommended to construct the power conversion system nearby the substation because the charging system is under the low voltage. But as the system is usually built around urban area and it makes hard to construct the subsystems at every station, voltage drop can occur in power supply inverter that is some distance from the substation. As the alternative of this issue, DC distribution system is recently introduced and has possibility to solve the above issue. In this paper, SFES is introduced to solve the voltage drop under the low voltage distribution system by using the concept of the proposed DC OLEV which results in building the longer distance power supply system. The simulation to design the SFES by using DC power flow analysis is carried out and it is verified in this paper.

Speckle Interferometry at the U.S. Naval Observatory. 20th

DTIC Science & Technology

2015-10-06

W. 1936, MNRAS, 96, 266 [Mlb1936] Muterspaugh, M. W., Hartkopf, W. I., Lane, B. F., et al. 2010, AJ, 140, 1623 [Mut2010b] Novakovic , B. 2006, IAU...C26 Circ., 158 [Nov2006] Novakovic , B. 2008, Obs, 128, 56 [Nov2008b] Novakovic , B., & Todorovic, N. 2006, Serbian AJ, 172, 21 [Nov2006e] Olevic, D. 2002

Extreme lymphocytosis with myelomonocytic morphology in a horse with diffuse large B-cell lymphoma.

PubMed

Meichner, Kristina; Kraszeski, Blaire H; Durrant, Jessica R; Grindem, Carol B; Breuhaus, Babetta A; Moore, Peter F; Neel, Jennifer A; Linder, Keith E; Borst, Luke B; Fogle, Jonathan E; Tarigo, Jaime L

2017-03-01

An 11-year-old, 443-kg Haflinger mare was presented to the North Carolina State University Veterinary Teaching Hospital with a 2-week history of lethargy and a 3-day duration of anorexia, pyrexia, tachycardia, and ventral edema. Severe pitting edema, peripheral lymphadenopathy, and a caudal abdominal mass were noted on physical examination. An extreme leukocytosis (154.3 × 10 3 /μL) and microscopic hematologic findings suggestive of myelomonocytic leukemia were observed. Serum protein electrophoresis revealed a monoclonal gammopathy and urine protein electrophoresis revealed a monoclonal light chain proteinuria. Necropsy and histopathology confirmed widespread neoplastic infiltration in many organs with a heterogenous population of cells; there was no apparent evidence of bone marrow involvement. Immunohistochemistry confirmed presence of a majority of B cells with a limited antigen expression, admixed with a lower number of T cells. Molecular clonality analysis of IgH2, IgH3, and kappa-deleting element (KDE, B cell) on whole blood and KDE on infiltrated tissues revealed clonal rearrangements, and the KDE intron clones that amplified in blood and in infiltrated tissue were identical. In contrast, the clonality analysis of T-cell receptor γ revealed no clonality on blood cells and infiltrated tissues. In conjunction with the histopathologic changes, the lesion was interpreted to be composed of neoplastic B cells with a reactive T-cell population. Polymerase chain reaction testing for equine herpes virus 5 was negative. The final diagnosis was diffuse large B-cell lymphoma with a marked hematogenous component. © 2016 American Society for Veterinary Clinical Pathology.

SOMKE: kernel density estimation over data streams by sequences of self-organizing maps.

PubMed

Cao, Yuan; He, Haibo; Man, Hong

2012-08-01

In this paper, we propose a novel method SOMKE, for kernel density estimation (KDE) over data streams based on sequences of self-organizing map (SOM). In many stream data mining applications, the traditional KDE methods are infeasible because of the high computational cost, processing time, and memory requirement. To reduce the time and space complexity, we propose a SOM structure in this paper to obtain well-defined data clusters to estimate the underlying probability distributions of incoming data streams. The main idea of this paper is to build a series of SOMs over the data streams via two operations, that is, creating and merging the SOM sequences. The creation phase produces the SOM sequence entries for windows of the data, which obtains clustering information of the incoming data streams. The size of the SOM sequences can be further reduced by combining the consecutive entries in the sequence based on the measure of Kullback-Leibler divergence. Finally, the probability density functions over arbitrary time periods along the data streams can be estimated using such SOM sequences. We compare SOMKE with two other KDE methods for data streams, the M-kernel approach and the cluster kernel approach, in terms of accuracy and processing time for various stationary data streams. Furthermore, we also investigate the use of SOMKE over nonstationary (evolving) data streams, including a synthetic nonstationary data stream, a real-world financial data stream and a group of network traffic data streams. The simulation results illustrate the effectiveness and efficiency of the proposed approach.

Understanding the Requirements for Open Source Software

DTIC Science & Technology

2009-06-17

GNOME and K Development Environment ( KDE ) for end-user interfaces, the Eclipse and NetBeans interactive development environments for Java-based Web...17 4.1. Informal Post-hoc Assertion of OSS Requirements vs . Requirements Elicitation...18 4.2. Requirements Reading, Sense-making, and Accountability vs . Requirements Analysis

Three-dimensional holoscopic image coding scheme using high-efficiency video coding with kernel-based minimum mean-square-error estimation

NASA Astrophysics Data System (ADS)

Liu, Deyang; An, Ping; Ma, Ran; Yang, Chao; Shen, Liquan; Li, Kai

2016-07-01

Three-dimensional (3-D) holoscopic imaging, also known as integral imaging, light field imaging, or plenoptic imaging, can provide natural and fatigue-free 3-D visualization. However, a large amount of data is required to represent the 3-D holoscopic content. Therefore, efficient coding schemes for this particular type of image are needed. A 3-D holoscopic image coding scheme with kernel-based minimum mean square error (MMSE) estimation is proposed. In the proposed scheme, the coding block is predicted by an MMSE estimator under statistical modeling. In order to obtain the signal statistical behavior, kernel density estimation (KDE) is utilized to estimate the probability density function of the statistical modeling. As bandwidth estimation (BE) is a key issue in the KDE problem, we also propose a BE method based on kernel trick. The experimental results demonstrate that the proposed scheme can achieve a better rate-distortion performance and a better visual rendering quality.

Factor associated variations in the home range of a resident Adriatic common bottlenose dolphin population.

PubMed

Rako-Gospić, Nikolina; Radulović, Marko; Vučur, Tihana; Pleslić, Grgur; Holcer, Draško; Mackelworth, Peter

2017-11-15

This study investigates the influence of the most dominant factors (association patterns, gender, natal philopatry and anthropogenic pressure) on the home range size of the 44 most resident common bottlenose dolphins (Tursiops truncatus) inhabiting the waters of the Cres-Lošinj archipelago (north Adriatic Sea, Croatia), a recently declared NATURA 2000 SCI. Results show that variations in home range patterns (MCP, 95% KDE and 50% KDE home range size) among the individual resident dolphins are primarily related to differences in gender and reflect the way in which different genders respond to external stressors. In addition, results confirm the seasonal influence of nautical tourism on both female and male dolphins through changes in their home range sizes. The overall results improve current knowledge of the main anthropogenic threats that should be taken into consideration when developing conservation measures to be applied to this Cres and Lošinj SCI. Copyright © 2017 Elsevier Ltd. All rights reserved.

Kernel Density Surface Modelling as a Means to Identify Significant Concentrations of Vulnerable Marine Ecosystem Indicators

PubMed Central

Kenchington, Ellen; Murillo, Francisco Javier; Lirette, Camille; Sacau, Mar; Koen-Alonso, Mariano; Kenny, Andrew; Ollerhead, Neil; Wareham, Vonda; Beazley, Lindsay

2014-01-01

The United Nations General Assembly Resolution 61/105, concerning sustainable fisheries in the marine ecosystem, calls for the protection of vulnerable marine ecosystems (VME) from destructive fishing practices. Subsequently, the Food and Agriculture Organization (FAO) produced guidelines for identification of VME indicator species/taxa to assist in the implementation of the resolution, but recommended the development of case-specific operational definitions for their application. We applied kernel density estimation (KDE) to research vessel trawl survey data from inside the fishing footprint of the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area in the high seas of the northwest Atlantic to create biomass density surfaces for four VME indicator taxa: large-sized sponges, sea pens, small and large gorgonian corals. These VME indicator taxa were identified previously by NAFO using the fragility, life history characteristics and structural complexity criteria presented by FAO, along with an evaluation of their recovery trajectories. KDE, a non-parametric neighbour-based smoothing function, has been used previously in ecology to identify hotspots, that is, areas of relatively high biomass/abundance. We present a novel approach of examining relative changes in area under polygons created from encircling successive biomass categories on the KDE surface to identify “significant concentrations” of biomass, which we equate to VMEs. This allows identification of the VMEs from the broader distribution of the species in the study area. We provide independent assessments of the VMEs so identified using underwater images, benthic sampling with other gear types (dredges, cores), and/or published species distribution models of probability of occurrence, as available. For each VME indicator taxon we provide a brief review of their ecological function which will be important in future assessments of significant adverse impact on these habitats here and

Using GIS Mapping to Target Public Health Interventions: Examining Birth Outcomes Across GIS Techniques.

PubMed

MacQuillan, E L; Curtis, A B; Baker, K M; Paul, R; Back, Y O

2017-08-01

With advances in spatial analysis techniques, there has been a trend in recent public health research to assess the contribution of area-level factors to health disparity for a number of outcomes, including births. Although it is widely accepted that health disparity is best addressed by targeted, evidence-based and data-driven community efforts, and despite national and local focus in the U.S. to reduce infant mortality and improve maternal-child health, there is little work exploring how choice of scale and specific GIS visualization technique may alter the perception of analyses focused on health disparity in birth outcomes. Retrospective cohort study. Spatial analysis of individual-level vital records data for low birthweight and preterm births born to black women from 2007 to 2012 in one mid-sized Midwest city using different geographic information systems (GIS) visualization techniques [geocoded address records were aggregated at two levels of scale and additionally mapped using kernel density estimation (KDE)]. GIS analyses in this study support our hypothesis that choice of geographic scale (neighborhood or census tract) for aggregated birth data can alter programmatic decision-making. Results indicate that the relative merits of aggregated visualization or the use of KDE technique depend on the scale of intervention. The KDE map proved useful in targeting specific areas for interventions in cities with smaller populations and larger census tracts, where they allow for greater specificity in identifying intervention areas. When public health programmers seek to inform intervention placement in highly populated areas, however, aggregated data at the census tract level may be preferred, since it requires lower investments in terms of time and cartographic skill and, unlike neighborhood, census tracts are standardized in that they become smaller as the population density of an area increases.

Kernel density surface modelling as a means to identify significant concentrations of vulnerable marine ecosystem indicators.

PubMed

Kenchington, Ellen; Murillo, Francisco Javier; Lirette, Camille; Sacau, Mar; Koen-Alonso, Mariano; Kenny, Andrew; Ollerhead, Neil; Wareham, Vonda; Beazley, Lindsay

2014-01-01

The United Nations General Assembly Resolution 61/105, concerning sustainable fisheries in the marine ecosystem, calls for the protection of vulnerable marine ecosystems (VME) from destructive fishing practices. Subsequently, the Food and Agriculture Organization (FAO) produced guidelines for identification of VME indicator species/taxa to assist in the implementation of the resolution, but recommended the development of case-specific operational definitions for their application. We applied kernel density estimation (KDE) to research vessel trawl survey data from inside the fishing footprint of the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area in the high seas of the northwest Atlantic to create biomass density surfaces for four VME indicator taxa: large-sized sponges, sea pens, small and large gorgonian corals. These VME indicator taxa were identified previously by NAFO using the fragility, life history characteristics and structural complexity criteria presented by FAO, along with an evaluation of their recovery trajectories. KDE, a non-parametric neighbour-based smoothing function, has been used previously in ecology to identify hotspots, that is, areas of relatively high biomass/abundance. We present a novel approach of examining relative changes in area under polygons created from encircling successive biomass categories on the KDE surface to identify "significant concentrations" of biomass, which we equate to VMEs. This allows identification of the VMEs from the broader distribution of the species in the study area. We provide independent assessments of the VMEs so identified using underwater images, benthic sampling with other gear types (dredges, cores), and/or published species distribution models of probability of occurrence, as available. For each VME indicator taxon we provide a brief review of their ecological function which will be important in future assessments of significant adverse impact on these habitats here and elsewhere.

Free-ranging farm cats: home range size and predation on a livestock unit in Northwest Georgia.

PubMed

Kitts-Morgan, Susanna E; Caires, Kyle C; Bohannon, Lisa A; Parsons, Elizabeth I; Hilburn, Katharine A

2015-01-01

This study's objective was to determine seasonal and diurnal vs. nocturnal home range size, as well as predation for free-ranging farm cats at a livestock unit in Northwest Georgia. Seven adult cats were tracked with attached GPS units for up to two weeks for one spring and two summer seasons from May 2010 through August 2011. Three and five cats were tracked for up to two weeks during the fall and winter seasons, respectively. Feline scat was collected during this entire period. Cats were fed a commercial cat food daily. There was no seasonal effect (P > 0.05) on overall (95% KDE and 90% KDE) or core home range size (50% KDE). Male cats tended (P = 0.08) to have larger diurnal and nocturnal core home ranges (1.09 ha) compared to female cats (0.64 ha). Reproductively intact cats (n = 2) had larger (P < 0.0001) diurnal and nocturnal home ranges as compared to altered cats. Feline scat processing separated scat into prey parts, and of the 210 feline scats collected during the study, 75.24% contained hair. Of these 158 scat samples, 86 contained non-cat hair and 72 contained only cat hair. Other prey components included fragments of bone in 21.43% of scat and teeth in 12.86% of scat. Teeth were used to identify mammalian prey hunted by these cats, of which the Hispid cotton rat (Sigmodon hispidus) was the primary rodent. Other targeted mammals were Peromyscus sp., Sylvilagus sp. and Microtus sp. Invertebrates and birds were less important as prey, but all mammalian prey identified in this study consisted of native animals. While the free-ranging farm cats in this study did not adjust their home range seasonally, sex and reproductive status did increase diurnal and nocturnal home range size. Ultimately, larger home ranges of free-ranging cats could negatively impact native wildlife.

Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters.

PubMed

Papantonopoulos, Georgios; Takahashi, Keiso; Bountis, Tasos; Loos, Bruno G

2014-01-01

There is neither a single clinical, microbiological, histopathological or genetic test, nor combinations of them, to discriminate aggressive periodontitis (AgP) from chronic periodontitis (CP) patients. We aimed to estimate probability density functions of clinical and immunologic datasets derived from periodontitis patients and construct artificial neural networks (ANNs) to correctly classify patients into AgP or CP class. The fit of probability distributions on the datasets was tested by the Akaike information criterion (AIC). ANNs were trained by cross entropy (CE) values estimated between probabilities of showing certain levels of immunologic parameters and a reference mode probability proposed by kernel density estimation (KDE). The weight decay regularization parameter of the ANNs was determined by 10-fold cross-validation. Possible evidence for 2 clusters of patients on cross-sectional and longitudinal bone loss measurements were revealed by KDE. Two to 7 clusters were shown on datasets of CD4/CD8 ratio, CD3, monocyte, eosinophil, neutrophil and lymphocyte counts, IL-1, IL-2, IL-4, INF-γ and TNF-α level from monocytes, antibody levels against A. actinomycetemcomitans (A.a.) and P.gingivalis (P.g.). ANNs gave 90%-98% accuracy in classifying patients into either AgP or CP. The best overall prediction was given by an ANN with CE of monocyte, eosinophil, neutrophil counts and CD4/CD8 ratio as inputs. ANNs can be powerful in classifying periodontitis patients into AgP or CP, when fed by CE values based on KDE. Therefore ANNs can be employed for accurate diagnosis of AgP or CP by using relatively simple and conveniently obtained parameters, like leukocyte counts in peripheral blood. This will allow clinicians to better adapt specific treatment protocols for their AgP and CP patients.

GPU Acceleration of Mean Free Path Based Kernel Density Estimators for Monte Carlo Neutronics Simulations

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

Burke, TImothy P.; Kiedrowski, Brian C.; Martin, William R.

Kernel Density Estimators (KDEs) are a non-parametric density estimation technique that has recently been applied to Monte Carlo radiation transport simulations. Kernel density estimators are an alternative to histogram tallies for obtaining global solutions in Monte Carlo tallies. With KDEs, a single event, either a collision or particle track, can contribute to the score at multiple tally points with the uncertainty at those points being independent of the desired resolution of the solution. Thus, KDEs show potential for obtaining estimates of a global solution with reduced variance when compared to a histogram. Previously, KDEs have been applied to neutronics formore » one-group reactor physics problems and fixed source shielding applications. However, little work was done to obtain reaction rates using KDEs. This paper introduces a new form of the MFP KDE that is capable of handling general geometries. Furthermore, extending the MFP KDE to 2-D problems in continuous energy introduces inaccuracies to the solution. An ad-hoc solution to these inaccuracies is introduced that produces errors smaller than 4% at material interfaces.« less

Nowcasting Cloud Fields for U.S. Air Force Special Operations

DTIC Science & Technology

2017-03-01

application of Bayes’ Rule offers many advantages over Kernel Density Estimation (KDE) and other commonly used statistical post-processing methods...reflectance and probability of cloud. A statistical post-processing technique is applied using Bayesian estimation to train the system from a set of past...nowcasting, low cloud forecasting, cloud reflectance, ISR, Bayesian estimation, statistical post-processing, machine learning 15. NUMBER OF PAGES

Speckle Interferometry at Soar in 2010 and 2011: Measures, Orbits, and Rectilinear Fits

DTIC Science & Technology

2012-02-01

Astron. Nachr., 331, 304 18 The Astronomical Journal, 143:42 (19pp), 2012 February Hartkopf, Tokovinin, & Mason Cvetkovic, Z., & Novakovic , B. 2006...Serb. AJ, 173, 73 Cvetkovic, Z., Novakovic , B., & Todorovic, N. 2008, New Astron., 13, 125 Davis, J., Mendez, A., Seneta, E. B., et al. 2005, MNRAS...J., et al. 2004, A&A, 418, 989 Novakovic , B. 2006, Inf. Circ., 158, 1 Novakovic , B. 2007, Chin. J. Astron. Astrophys., 7, 415 Olevic, D. 2002a, Inf

The chronology of reindeer hunting on Norway's highest ice patches

PubMed Central

Pilø, Lars; Finstad, Espen; Ramsey, Christopher Bronk; Martinsen, Julian Robert Post; Nesje, Atle; Solli, Brit; Wangen, Vivian; Callanan, Martin

2018-01-01

The melting of perennial ice patches globally is uncovering a fragile record of alpine activity, especially hunting and the use of mountain passes. When rescued by systematic fieldwork (glacial archaeology), this evidence opens an unprecedented window on the chronology of high-elevation activity. Recent research in Jotunheimen and surrounding mountain areas of Norway has recovered over 2000 finds—many associated with reindeer hunting (e.g. arrows). We report the radiocarbon dates of 153 objects and use a kernel density estimation (KDE) method to determine the distribution of dated events from ca 4000 BCE to the present. Interpreted in light of shifting environmental, preservation and socio-economic factors, these new data show counterintuitive trends in the intensity of reindeer hunting and other high-elevation activity. Cold temperatures may sometimes have kept humans from Norway's highest elevations, as expected based on accessibility, exposure and reindeer distributions. In times of increasing demand for mountain resources, however, activity probably continued in the face of adverse or variable climatic conditions. The use of KDE modelling makes it possible to observe this patterning without the spurious effects of noise introduced by the discrete nature of the finds and the radiocarbon calibration process. PMID:29410869

Integrating a Trusted Computing Base Extension Server and Secure Session Server into the LINUX Operating System

DTIC Science & Technology

2001-09-01

Readily Available Linux has been copyrighted under the terms of the GNU General Public 5 License (GPL)1. This is a license written by the Free...GNOME and KDE . d. Portability Linux is highly compatible with many common operating systems. For...using suitable libraries, Linux is able to run programs written for other operating systems. [Ref. 8] 1 The GNU Project is coordinated by the

Free-Ranging Farm Cats: Home Range Size and Predation on a Livestock Unit In Northwest Georgia

PubMed Central

Kitts-Morgan, Susanna E.; Caires, Kyle C.; Bohannon, Lisa A.; Parsons, Elizabeth I.; Hilburn, Katharine A.

2015-01-01

This study’s objective was to determine seasonal and diurnal vs. nocturnal home range size, as well as predation for free-ranging farm cats at a livestock unit in Northwest Georgia. Seven adult cats were tracked with attached GPS units for up to two weeks for one spring and two summer seasons from May 2010 through August 2011. Three and five cats were tracked for up to two weeks during the fall and winter seasons, respectively. Feline scat was collected during this entire period. Cats were fed a commercial cat food daily. There was no seasonal effect (P > 0.05) on overall (95% KDE and 90% KDE) or core home range size (50% KDE). Male cats tended (P = 0.08) to have larger diurnal and nocturnal core home ranges (1.09 ha) compared to female cats (0.64 ha). Reproductively intact cats (n = 2) had larger (P < 0.0001) diurnal and nocturnal home ranges as compared to altered cats. Feline scat processing separated scat into prey parts, and of the 210 feline scats collected during the study, 75.24% contained hair. Of these 158 scat samples, 86 contained non-cat hair and 72 contained only cat hair. Other prey components included fragments of bone in 21.43% of scat and teeth in 12.86% of scat. Teeth were used to identify mammalian prey hunted by these cats, of which the Hispid cotton rat (Sigmodon hispidus) was the primary rodent. Other targeted mammals were Peromyscus sp., Sylvilagus sp. and Microtus sp. Invertebrates and birds were less important as prey, but all mammalian prey identified in this study consisted of native animals. While the free-ranging farm cats in this study did not adjust their home range seasonally, sex and reproductive status did increase diurnal and nocturnal home range size. Ultimately, larger home ranges of free-ranging cats could negatively impact native wildlife. PMID:25894078

Probability Distribution Extraction from TEC Estimates based on Kernel Density Estimation

NASA Astrophysics Data System (ADS)

Demir, Uygar; Toker, Cenk; Çenet, Duygu

2016-07-01

Statistical analysis of the ionosphere, specifically the Total Electron Content (TEC), may reveal important information about its temporal and spatial characteristics. One of the core metrics that express the statistical properties of a stochastic process is its Probability Density Function (pdf). Furthermore, statistical parameters such as mean, variance and kurtosis, which can be derived from the pdf, may provide information about the spatial uniformity or clustering of the electron content. For example, the variance differentiates between a quiet ionosphere and a disturbed one, whereas kurtosis differentiates between a geomagnetic storm and an earthquake. Therefore, valuable information about the state of the ionosphere (and the natural phenomena that cause the disturbance) can be obtained by looking at the statistical parameters. In the literature, there are publications which try to fit the histogram of TEC estimates to some well-known pdf.s such as Gaussian, Exponential, etc. However, constraining a histogram to fit to a function with a fixed shape will increase estimation error, and all the information extracted from such pdf will continue to contain this error. In such techniques, it is highly likely to observe some artificial characteristics in the estimated pdf which is not present in the original data. In the present study, we use the Kernel Density Estimation (KDE) technique to estimate the pdf of the TEC. KDE is a non-parametric approach which does not impose a specific form on the TEC. As a result, better pdf estimates that almost perfectly fit to the observed TEC values can be obtained as compared to the techniques mentioned above. KDE is particularly good at representing the tail probabilities, and outliers. We also calculate the mean, variance and kurtosis of the measured TEC values. The technique is applied to the ionosphere over Turkey where the TEC values are estimated from the GNSS measurement from the TNPGN-Active (Turkish National Permanent

Towards the Ubiquitous Deployment of DNSSEC

DTIC Science & Technology

2016-01-01

with other deployment partners around the world, there is now a significant and growing number of TLDs that have been signed, and a number of...as Google Earth, the Blackberry 10 operating system, and the entire set of K Desktop Environment (KDE) windowing system applications are based on...differentiate between transient errors and legitimate DNS spoofing attacks is likely going to be very important as deployment grows . The importance of

A Trusted Path Design and Implementation for Security Enhanced Linux

DTIC Science & Technology

2004-09-01

functionality by a member of the team? Witten, et al., [21] provides an excellent discussion of some aspects of the subject. Ultimately, open vs ...terminal window is a program like gnome - terminal that provides a TTY-like environment as a window inside an X Windows session. The phrase computer...Editors selected No sound or video No graphics Check all development boxes except KDE Administrative tools System tools No printing support

Visual Motion Perception

DTIC Science & Technology

1991-08-15

Conversely, displays Atr con- past experience to the experimental stimuli. structed %xith normal density- controlled KDE cues but %ith 5. Excluding...frame. This 3Ndisplays, gray background is displayed’ on ail introduces 50% -scintillation (density control lion even frames (labelled 1:0). Other non ...video tapes were prepared, each of whsich contained all the experimental ASL signs but distributed 1 2 3 4 into dliffereint. filter groups . Eight

Space Use and Movement of a Neotropical Top Predator: The Endangered Jaguar

PubMed Central

Stabach, Jared A.; Fleming, Chris H.; Calabrese, Justin M.; De Paula, Rogério C.; Ferraz, Kátia M. P. M.; Kantek, Daniel L. Z.; Miyazaki, Selma S.; Pereira, Thadeu D. C.; Araujo, Gediendson R.; Paviolo, Agustin; De Angelo, Carlos; Di Bitetti, Mario S.; Cruz, Paula; Lima, Fernando; Cullen, Laury; Sana, Denis A.; Ramalho, Emiliano E.; Carvalho, Marina M.; Soares, Fábio H. S.; Zimbres, Barbara; Silva, Marina X.; Moraes, Marcela D. F.; Vogliotti, Alexandre; May, Joares A.; Haberfeld, Mario; Rampim, Lilian; Sartorello, Leonardo; Ribeiro, Milton C.; Leimgruber, Peter

2016-01-01

Accurately estimating home range and understanding movement behavior can provide important information on ecological processes. Advances in data collection and analysis have improved our ability to estimate home range and movement parameters, both of which have the potential to impact species conservation. Fitting continuous-time movement model to data and incorporating the autocorrelated kernel density estimator (AKDE), we investigated range residency of forty-four jaguars fit with GPS collars across five biomes in Brazil and Argentina. We assessed home range and movement parameters of range resident animals and compared AKDE estimates with kernel density estimates (KDE). We accounted for differential space use and movement among individuals, sex, region, and habitat quality. Thirty-three (80%) of collared jaguars were range resident. Home range estimates using AKDE were 1.02 to 4.80 times larger than KDE estimates that did not consider autocorrelation. Males exhibited larger home ranges, more directional movement paths, and a trend towards larger distances traveled per day. Jaguars with the largest home ranges occupied the Atlantic Forest, a biome with high levels of deforestation and high human population density. Our results fill a gap in the knowledge of the species’ ecology with an aim towards better conservation of this endangered/critically endangered carnivore—the top predator in the Neotropics. PMID:28030568

Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection

USGS Publications Warehouse

Jarnevich, Catherine S.; Talbert, Marian; Morisette, Jeffrey T.; Aldridge, Cameron L.; Brown, Cynthia; Kumar, Sunil; Manier, Daniel; Talbert, Colin; Holcombe, Tracy R.

2017-01-01

Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.

Space Use and Movement of a Neotropical Top Predator: The Endangered Jaguar.

PubMed

Morato, Ronaldo G; Stabach, Jared A; Fleming, Chris H; Calabrese, Justin M; De Paula, Rogério C; Ferraz, Kátia M P M; Kantek, Daniel L Z; Miyazaki, Selma S; Pereira, Thadeu D C; Araujo, Gediendson R; Paviolo, Agustin; De Angelo, Carlos; Di Bitetti, Mario S; Cruz, Paula; Lima, Fernando; Cullen, Laury; Sana, Denis A; Ramalho, Emiliano E; Carvalho, Marina M; Soares, Fábio H S; Zimbres, Barbara; Silva, Marina X; Moraes, Marcela D F; Vogliotti, Alexandre; May, Joares A; Haberfeld, Mario; Rampim, Lilian; Sartorello, Leonardo; Ribeiro, Milton C; Leimgruber, Peter

2016-01-01

Accurately estimating home range and understanding movement behavior can provide important information on ecological processes. Advances in data collection and analysis have improved our ability to estimate home range and movement parameters, both of which have the potential to impact species conservation. Fitting continuous-time movement model to data and incorporating the autocorrelated kernel density estimator (AKDE), we investigated range residency of forty-four jaguars fit with GPS collars across five biomes in Brazil and Argentina. We assessed home range and movement parameters of range resident animals and compared AKDE estimates with kernel density estimates (KDE). We accounted for differential space use and movement among individuals, sex, region, and habitat quality. Thirty-three (80%) of collared jaguars were range resident. Home range estimates using AKDE were 1.02 to 4.80 times larger than KDE estimates that did not consider autocorrelation. Males exhibited larger home ranges, more directional movement paths, and a trend towards larger distances traveled per day. Jaguars with the largest home ranges occupied the Atlantic Forest, a biome with high levels of deforestation and high human population density. Our results fill a gap in the knowledge of the species' ecology with an aim towards better conservation of this endangered/critically endangered carnivore-the top predator in the Neotropics.

The Ubuntu Chat Corpus for Multiparticipant Chat Analysis

DTIC Science & Technology

2013-03-01

Intelligence (www.aaai.org). All rights reserved. the # LINUX corpus (Elsner and Charniak 2010), and the #IPHONE/#PHYSICS/#PYTHON corpus (Adams 2008). For many...made publicly available, making it difficult to comparatively evaluate dif- ferent techniques. Corpus Description Ubuntu, a Linux -based operating...Kubuntu (Ubuntu with KDE ) support #ubuntu-devel 2 112 074 12 140 53.7 2004-10-01 Developmental team coordination #ubuntu+1 1 621 680 26 805 52.6 2007-04-04

MVC for Content Management on the Cloud

DTIC Science & Technology

2011-09-01

Windows, Linux , MacOS, PalmOS and other customized ones (Qiu). Figure 20 illustrates implementation of MVC architecture. Qiu examines a “universal...Listing of Unzipped Text Document (From O’Reilly & Associates, Inc, 2005) Figure 37 shows the results of unzipping this file in Linux . The contents of the...ODF Adoption TC, and the ODF Alliance include members from Adobe, BBC, Bristol City Council, Bull, Corel, EDS, EMC, GNOME, IBM, Intel, KDE , MySQL

Exact solutions for STO and (3+1)-dimensional KdV-ZK equations using (G‧/G2) -expansion method

NASA Astrophysics Data System (ADS)

Bibi, Sadaf; Mohyud-Din, Syed Tauseef; Ullah, Rahmat; Ahmed, Naveed; Khan, Umar

This article deals with finding some exact solutions of nonlinear fractional differential equations (NLFDEs) by applying a relatively new method known as (G‧/G2) -expansion method. Solutions of space-time fractional Sharma-Tasso-Olever (STO) equation of fractional order and (3+1)-dimensional KdV-Zakharov Kuznetsov (KdV-ZK) equation of fractional order are reckoned to demonstrate the validity of this method. The fractional derivative version of modified Riemann-Liouville, linked with Fractional complex transform is employed to transform fractional differential equations into the corresponding ordinary differential equations.

Open Radio Communications Architecture Core Framework V1.1.0 Volume 1 Software Users Manual

DTIC Science & Technology

2005-02-01

on a PC utilizing the KDE desktop that comes with Red Hat Linux . The default desktop for most Red Hat Linux installations is the GNOME desktop. The...SCA) v2.2. The software was designed for a desktop computer running the Linux operating system (OS). It was developed in C++, uses ACE/TAO for CORBA...middleware, Xerces for the XML parser, and Red Hat Linux for the Operating System. The software is referred to as, Open Radio Communication

The 2-degree Field Lensing Survey: photometric redshifts from a large new training sample to r < 19.5

NASA Astrophysics Data System (ADS)

Wolf, C.; Johnson, A. S.; Bilicki, M.; Blake, C.; Amon, A.; Erben, T.; Glazebrook, K.; Heymans, C.; Hildebrandt, H.; Joudaki, S.; Klaes, D.; Kuijken, K.; Lidman, C.; Marin, F.; Parkinson, D.; Poole, G.

2017-04-01

We present a new training set for estimating empirical photometric redshifts of galaxies, which was created as part of the 2-degree Field Lensing Survey project. This training set is located in a ˜700 deg2 area of the Kilo-Degree-Survey South field and is randomly selected and nearly complete at r < 19.5. We investigate the photometric redshift performance obtained with ugriz photometry from VST-ATLAS and W1/W2 from WISE, based on several empirical and template methods. The best redshift errors are obtained with kernel-density estimation (KDE), as are the lowest biases, which are consistent with zero within statistical noise. The 68th percentiles of the redshift scatter for magnitude-limited samples at r < (15.5, 17.5, 19.5) are (0.014, 0.017, 0.028). In this magnitude range, there are no known ambiguities in the colour-redshift map, consistent with a small rate of redshift outliers. In the fainter regime, the KDE method produces p(z) estimates per galaxy that represent unbiased and accurate redshift frequency expectations. The p(z) sum over any subsample is consistent with the true redshift frequency plus Poisson noise. Further improvements in redshift precision at r < 20 would mostly be expected from filter sets with narrower passbands to increase the sensitivity of colours to small changes in redshift.

A comparative study of outlier detection for large-scale traffic data by one-class SVM and kernel density estimation

NASA Astrophysics Data System (ADS)

Ngan, Henry Y. T.; Yung, Nelson H. C.; Yeh, Anthony G. O.

2015-02-01

This paper aims at presenting a comparative study of outlier detection (OD) for large-scale traffic data. The traffic data nowadays are massive in scale and collected in every second throughout any modern city. In this research, the traffic flow dynamic is collected from one of the busiest 4-armed junction in Hong Kong in a 31-day sampling period (with 764,027 vehicles in total). The traffic flow dynamic is expressed in a high dimension spatial-temporal (ST) signal format (i.e. 80 cycles) which has a high degree of similarities among the same signal and across different signals in one direction. A total of 19 traffic directions are identified in this junction and lots of ST signals are collected in the 31-day period (i.e. 874 signals). In order to reduce its dimension, the ST signals are firstly undergone a principal component analysis (PCA) to represent as (x,y)-coordinates. Then, these PCA (x,y)-coordinates are assumed to be conformed as Gaussian distributed. With this assumption, the data points are further to be evaluated by (a) a correlation study with three variant coefficients, (b) one-class support vector machine (SVM) and (c) kernel density estimation (KDE). The correlation study could not give any explicit OD result while the one-class SVM and KDE provide average 59.61% and 95.20% DSRs, respectively.

An evaluation of potential sampling locations in a reservoir with emphasis on conserved spatial correlation structure.

PubMed

Yenilmez, Firdes; Düzgün, Sebnem; Aksoy, Aysegül

2015-01-01

In this study, kernel density estimation (KDE) was coupled with ordinary two-dimensional kriging (OK) to reduce the number of sampling locations in measurement and kriging of dissolved oxygen (DO) concentrations in Porsuk Dam Reservoir (PDR). Conservation of the spatial correlation structure in the DO distribution was a target. KDE was used as a tool to aid in identification of the sampling locations that would be removed from the sampling network in order to decrease the total number of samples. Accordingly, several networks were generated in which sampling locations were reduced from 65 to 10 in increments of 4 or 5 points at a time based on kernel density maps. DO variograms were constructed, and DO values in PDR were kriged. Performance of the networks in DO estimations were evaluated through various error metrics, standard error maps (SEM), and whether the spatial correlation structure was conserved or not. Results indicated that smaller number of sampling points resulted in loss of information in regard to spatial correlation structure in DO. The minimum representative sampling points for PDR was 35. Efficacy of the sampling location selection method was tested against the networks generated by experts. It was shown that the evaluation approach proposed in this study provided a better sampling network design in which the spatial correlation structure of DO was sustained for kriging.

Makef15: An ADCIRC Model Fort.15 Input File Creation GUI for Parameter Specification and Periodic Boundary Forcing

DTIC Science & Technology

2007-12-07

is shown in the sequence of Figures 1 through 4, which were generated on a Linux platform (Fedora Core 3 and Core 6) using the Gnome (version 2.8.0...and KDE (version 3.5.7) desktop environments. Each of these figures presents a view of the GUI as it is scrolled downward one screen at a time with...number of tidal constituents desired vs . the number of selected constituents, see the error display in Figure 18). Several examples were discussed in

The Computational Science Environment (CSE)

DTIC Science & Technology

2009-08-01

supported CSE platforms. Developers can also build against different versions of a particular package (e.g., Python-2.4 vs . Python-2.5) via a...8.2.1 TK Testing Error and Workaround It has been found that TK tends to produces more testing errors when using KDE , and in some instances, the test...suite freezes when reaching the TK select test. These issues have not been seen when using Gnome . 8.2.2 VTK Testing Error and Workaround VTK test

Iterative dataset optimization in automated planning: Implementation for breast and rectal cancer radiotherapy.

PubMed

Fan, Jiawei; Wang, Jiazhou; Zhang, Zhen; Hu, Weigang

2017-06-01

To develop a new automated treatment planning solution for breast and rectal cancer radiotherapy. The automated treatment planning solution developed in this study includes selection of the iterative optimized training dataset, dose volume histogram (DVH) prediction for the organs at risk (OARs), and automatic generation of clinically acceptable treatment plans. The iterative optimized training dataset is selected by an iterative optimization from 40 treatment plans for left-breast and rectal cancer patients who received radiation therapy. A two-dimensional kernel density estimation algorithm (noted as two parameters KDE) which incorporated two predictive features was implemented to produce the predicted DVHs. Finally, 10 additional new left-breast treatment plans are re-planned using the Pinnacle 3 Auto-Planning (AP) module (version 9.10, Philips Medical Systems) with the objective functions derived from the predicted DVH curves. Automatically generated re-optimized treatment plans are compared with the original manually optimized plans. By combining the iterative optimized training dataset methodology and two parameters KDE prediction algorithm, our proposed automated planning strategy improves the accuracy of the DVH prediction. The automatically generated treatment plans using the dose derived from the predicted DVHs can achieve better dose sparing for some OARs without compromising other metrics of plan quality. The proposed new automated treatment planning solution can be used to efficiently evaluate and improve the quality and consistency of the treatment plans for intensity-modulated breast and rectal cancer radiation therapy. © 2017 American Association of Physicists in Medicine.

Multinuclear NMR of CaSiO(3) glass: simulation from first-principles.

PubMed

Pedone, Alfonso; Charpentier, Thibault; Menziani, Maria Cristina

2010-06-21

An integrated computational method which couples classical molecular dynamics simulations with density functional theory calculations is used to simulate the solid-state NMR spectra of amorphous CaSiO(3). Two CaSiO(3) glass models are obtained by shell-model molecular dynamics simulations, successively relaxed at the GGA-PBE level of theory. The calculation of the NMR parameters (chemical shielding and quadrupolar parameters), which are then used to simulate solid-state 1D and 2D-NMR spectra of silicon-29, oxygen-17 and calcium-43, is achieved by the gauge including projector augmented-wave (GIPAW) and the projector augmented-wave (PAW) methods. It is shown that the limitations due to the finite size of the MD models can be overcome using a Kernel Estimation Density (KDE) approach to simulate the spectra since it better accounts for the disorder effects on the NMR parameter distribution. KDE allows reconstructing a smoothed NMR parameter distribution from the MD/GIPAW data. Simulated NMR spectra calculated with the present approach are found to be in excellent agreement with the experimental data. This further validates the CaSiO(3) structural model obtained by MD simulations allowing the inference of relationships between structural data and NMR response. The methods used to simulate 1D and 2D-NMR spectra from MD GIPAW data have been integrated in a package (called fpNMR) freely available on request.

Systematic analysis of hot Yb* isotopes using the energy density formalism

NASA Astrophysics Data System (ADS)

Jain, Deepika; Sharma, Manoj K.; Rajni; Kumar, Raj; Gupta, Raj K.

2014-10-01

A systematic study of the spin-orbit density interaction potential is carried out, with spherical as well as deformed choices of nuclei, for a variety of near-symmetric and asymmetric colliding nuclei leading to various isotopes of the compound nucleus Yb*, using the semiclassical extended Thomas-Fermi formulation (ETF) of the Skyrme energy density formalism (SEDF). We observe that the spin-orbit density interaction barrier height ( and barrier position ( increase systematically with the increase in number of neutrons in both the projectile and target, for spherical systems. On allowing deformation effects with optimum orientations, the barrier-height increases by a large order of magnitude, as compared to the spherical case, in going from 156Yb* to 172Yb* nuclear systems formed via near-symmetric Ni+Mo or asymmetric O+Sm colliding nuclei, except that for the oblate-shaped nuclei, the is the highest and shifts towards a smaller (compact) interaction radius. The temperature does not change the behavior of spin-orbit density dependent ( and independent ( interaction potentials, except for some minor changes in the magnitude. The orientation degree of freedom also plays an important role in modifying the barrier characteristics and hence produces a large effect on the fusion cross section. The fusion excitation function of the compound nuclei 160, 164Yb* formed in different incoming channels, show clearly that the new forces GSkI and KDE0v1 respond better than the old SIII force. Among the first two, KDE0v1 seems to perform better. The fusion cross-sections are also predicted for a few other isotopes of Yb*.

Clustering on very small scales from a large sample of confirmed quasar pairs: does quasar clustering track from Mpc to kpc scales?

NASA Astrophysics Data System (ADS)

Eftekharzadeh, S.; Myers, A. D.; Hennawi, J. F.; Djorgovski, S. G.; Richards, G. T.; Mahabal, A. A.; Graham, M. J.

2017-06-01

We present the most precise estimate to date of the clustering of quasars on very small scales, based on a sample of 47 binary quasars with magnitudes of g < 20.85 and proper transverse separations of ˜25 h-1 kpc. Our sample of binary quasars, which is about six times larger than any previous spectroscopically confirmed sample on these scales, is targeted using a kernel density estimation (KDE) technique applied to Sloan Digital Sky Survey (SDSS) imaging over most of the SDSS area. Our sample is 'complete' in that all of the KDE target pairs with 17.0 ≲ R ≲ 36.2 h-1 kpc in our area of interest have been spectroscopically confirmed from a combination of previous surveys and our own long-slit observational campaign. We catalogue 230 candidate quasar pairs with angular separations of <8 arcsec, from which our binary quasars were identified. We determine the projected correlation function of quasars (\\bar{W}_p) in four bins of proper transverse scale over the range 17.0 ≲ R ≲ 36.2 h-1 kpc. The implied small-scale quasar clustering amplitude from the projected correlation function, integrated across our entire redshift range, is A = 24.1 ± 3.6 at ˜26.6 h-1 kpc. Our sample is the first spectroscopically confirmed sample of quasar pairs that is sufficiently large to study how quasar clustering evolves with redshift at ˜25 h-1 kpc. We find that empirical descriptions of how quasar clustering evolves with redshift at ˜25 h-1 Mpc also adequately describe the evolution of quasar clustering at ˜25 h-1 kpc.

Satellite tracking reveals habitat use by juvenile green sea turtles Chelonia mydas in the Everglades, Florida, USA

USGS Publications Warehouse

Hart, Kristen M.; Fujisaki, Ikuko

2010-01-01

We tracked the movements of 6 juvenile green sea turtles captured in coastal areas of southwest Florida within Everglades National Park (ENP) using satellite transmitters for periods of 27 to 62 d in 2007 and 2008 (mean ± SD: 47.7 ± 12.9 d). Turtles ranged in size from 33.4 to 67.5 cm straight carapace length (45.7 ± 12.9 cm) and 4.4 to 40.8 kg in mass (16.0 ± 13.8 kg). These data represent the first satellite tracking data gathered on juveniles of this endangered species at this remote study site, which may represent an important developmental habitat and foraging ground. Satellite tracking results suggested that these immature turtles were resident for several months very close to capture and release sites, in waters from 0 to 10 m in depth. Mean home range for this springtime tracking period as represented by minimum convex polygon (MCP) was 1004.9 ± 618.8 km2 (range 374.1 to 2060.1 km2), with 4 of 6 individuals spending a significant proportion of time within the ENP boundaries in 2008 in areas with dense patches of marine algae. Core use areas determined by 50% kernel density estimates (KDE) ranged from 5.0 to 54.4 km2, with a mean of 22.5 ± 22.1 km2. Overlap of 50% KDE plots for 6 turtles confirmed use of shallow-water nearshore habitats =0.6 m deep within the park boundary. Delineating specific habitats used by juvenile green turtles in this and other remote coastal areas with protected status will help conservation managers to prioritize their efforts and increase efficacy in protecting endangered species.

Spatial variation in mortality risk for haematological malignancies near a petrochemical refinery: a population-based case-control study

PubMed Central

Di Salvo, Francesca; Meneghini, Elisabetta; Vieira, Veronica; Baili, Paolo; Mariottini, Mauro; Baldini, Marco; Micheli, Andrea; Sant, Milena

2015-01-01

Introduction The study investigated the geographic variation of mortality risk for hematological malignancies (HMs) in order to identify potential high-risk areas near an Italian petrochemical refinery. Material and methods A population-based case-control study was conducted and residential histories for 171 cases and 338 sex- and age-matched controls were collected. Confounding factors were obtained from interviews with consenting relatives for 109 HM deaths and 267 controls. To produce risk mortality maps, two different approaches were applied. We mapped (1) adptive kernel density relative risk estimation (KDE) for case-control studies which estimates a spatial relative risk function using the ratio between cases and controls’ densities, and (2) estimated odds ratios for case-control study data using generalized additive models (GAMs) to smooth the effect of location, a proxy for exposure, while adjusting for confounding variables. Results No high-risk areas for HM mortality were identified among all subjects (men and women combined), by applying both approaches. Using the adaptive KDE approach, we found a significant increase in death risk only among women in a large area 2–6 km southeast of the refinery and the application of GAMs also identified a similarly-located significant high-risk area among women only (global p-value<0.025). Potential confounding risk factors we considered in the GAM did not alter the results. Conclusion Both approaches identified a high-risk area close to the refinery among women only. Those spatial methods are useful tools for public policy management to determine priority areas for intervention. Our findings suggest several directions for further research in order to identify other potential environmental exposures that may be assessed in forthcoming studies based on detailed exposure modeling. PMID:26073202

A tool for the estimation of the distribution of landslide area in R

NASA Astrophysics Data System (ADS)

Rossi, M.; Cardinali, M.; Fiorucci, F.; Marchesini, I.; Mondini, A. C.; Santangelo, M.; Ghosh, S.; Riguer, D. E. L.; Lahousse, T.; Chang, K. T.; Guzzetti, F.

2012-04-01

We have developed a tool in R (the free software environment for statistical computing, http://www.r-project.org/) to estimate the probability density and the frequency density of landslide area. The tool implements parametric and non-parametric approaches to the estimation of the probability density and the frequency density of landslide area, including: (i) Histogram Density Estimation (HDE), (ii) Kernel Density Estimation (KDE), and (iii) Maximum Likelihood Estimation (MLE). The tool is available as a standard Open Geospatial Consortium (OGC) Web Processing Service (WPS), and is accessible through the web using different GIS software clients. We tested the tool to compare Double Pareto and Inverse Gamma models for the probability density of landslide area in different geological, morphological and climatological settings, and to compare landslides shown in inventory maps prepared using different mapping techniques, including (i) field mapping, (ii) visual interpretation of monoscopic and stereoscopic aerial photographs, (iii) visual interpretation of monoscopic and stereoscopic VHR satellite images and (iv) semi-automatic detection and mapping from VHR satellite images. Results show that both models are applicable in different geomorphological settings. In most cases the two models provided very similar results. Non-parametric estimation methods (i.e., HDE and KDE) provided reasonable results for all the tested landslide datasets. For some of the datasets, MLE failed to provide a result, for convergence problems. The two tested models (Double Pareto and Inverse Gamma) resulted in very similar results for large and very large datasets (> 150 samples). Differences in the modeling results were observed for small datasets affected by systematic biases. A distinct rollover was observed in all analyzed landslide datasets, except for a few datasets obtained from landslide inventories prepared through field mapping or by semi-automatic mapping from VHR satellite imagery

A KDE-Based Random Walk Method for Modeling Reactive Transport With Complex Kinetics in Porous Media

NASA Astrophysics Data System (ADS)

Sole-Mari, Guillem; Fernà ndez-Garcia, Daniel; Rodríguez-Escales, Paula; Sanchez-Vila, Xavier

2017-11-01

In recent years, a large body of the literature has been devoted to study reactive transport of solutes in porous media based on pure Lagrangian formulations. Such approaches have also been extended to accommodate second-order bimolecular reactions, in which the reaction rate is proportional to the concentrations of the reactants. Rather, in some cases, chemical reactions involving two reactants follow more complicated rate laws. Some examples are (1) reaction rate laws written in terms of powers of concentrations, (2) redox reactions incorporating a limiting term (e.g., Michaelis-Menten), or (3) any reaction where the activity coefficients vary with the concentration of the reactants, just to name a few. We provide a methodology to account for complex kinetic bimolecular reactions in a fully Lagrangian framework where each particle represents a fraction of the total mass of a specific solute. The method, built as an extension to the second-order case, is based on the concept of optimal Kernel Density Estimator, which allows the concentrations to be written in terms of particle locations, hence transferring the concept of reaction rate to that of particle location distribution. By doing so, we can update the probability of particles reacting without the need to fully reconstruct the concentration maps. The performance and convergence of the method is tested for several illustrative examples that simulate the Advection-Dispersion-Reaction Equation in a 1-D homogeneous column. Finally, a 2-D application example is presented evaluating the need of fully describing non-bilinear chemical kinetics in a randomly heterogeneous porous medium.

Using kernel density estimation to understand the influence of neighbourhood destinations on BMI

PubMed Central

King, Tania L; Bentley, Rebecca J; Thornton, Lukar E; Kavanagh, Anne M

2016-01-01

Objectives Little is known about how the distribution of destinations in the local neighbourhood is related to body mass index (BMI). Kernel density estimation (KDE) is a spatial analysis technique that accounts for the location of features relative to each other. Using KDE, this study investigated whether individuals living near destinations (shops and service facilities) that are more intensely distributed rather than dispersed, have lower BMIs. Study design and setting A cross-sectional study of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Methods Destinations were geocoded, and kernel density estimates of destination intensity were created using kernels of 400, 800 and 1200 m. Using multilevel linear regression, the association between destination intensity (classified in quintiles Q1(least)–Q5(most)) and BMI was estimated in models that adjusted for the following confounders: age, sex, country of birth, education, dominant household occupation, household type, disability/injury and area disadvantage. Separate models included a physical activity variable. Results For kernels of 800 and 1200 m, there was an inverse relationship between BMI and more intensely distributed destinations (compared to areas with least destination intensity). Effects were significant at 1200 m: Q4, β −0.86, 95% CI −1.58 to −0.13, p=0.022; Q5, β −1.03 95% CI −1.65 to −0.41, p=0.001. Inclusion of physical activity in the models attenuated effects, although effects remained marginally significant for Q5 at 1200 m: β −0.77 95% CI −1.52, −0.02, p=0.045. Conclusions This study conducted within urban Melbourne, Australia, found that participants living in areas of greater destination intensity within 1200 m of home had lower BMIs. Effects were partly explained by physical activity. The results suggest that increasing the intensity of destination distribution could reduce BMI levels by encouraging higher levels of physical activity

SU-F-T-450: The Investigation of Radiotherapy Quality Assurance and Automatic Treatment Planning Based On the Kernel Density Estimation Method

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

Fan, J; Fan, J; Hu, W

Purpose: To develop a fast automatic algorithm based on the two dimensional kernel density estimation (2D KDE) to predict the dose-volume histogram (DVH) which can be employed for the investigation of radiotherapy quality assurance and automatic treatment planning. Methods: We propose a machine learning method that uses previous treatment plans to predict the DVH. The key to the approach is the framing of DVH in a probabilistic setting. The training consists of estimating, from the patients in the training set, the joint probability distribution of the dose and the predictive features. The joint distribution provides an estimation of the conditionalmore » probability of the dose given the values of the predictive features. For the new patient, the prediction consists of estimating the distribution of the predictive features and marginalizing the conditional probability from the training over this. Integrating the resulting probability distribution for the dose yields an estimation of the DVH. The 2D KDE is implemented to predict the joint probability distribution of the training set and the distribution of the predictive features for the new patient. Two variables, including the signed minimal distance from each OAR (organs at risk) voxel to the target boundary and its opening angle with respect to the origin of voxel coordinate, are considered as the predictive features to represent the OAR-target spatial relationship. The feasibility of our method has been demonstrated with the rectum, breast and head-and-neck cancer cases by comparing the predicted DVHs with the planned ones. Results: The consistent result has been found between these two DVHs for each cancer and the average of relative point-wise differences is about 5% within the clinical acceptable extent. Conclusion: According to the result of this study, our method can be used to predict the clinical acceptable DVH and has ability to evaluate the quality and consistency of the treatment planning.« less

Amphibian and reptile road-kills on tertiary roads in relation to landscape structure: using a citizen science approach with open-access land cover data.

PubMed

Heigl, Florian; Horvath, Kathrin; Laaha, Gregor; Zaller, Johann G

2017-06-26

Amphibians and reptiles are among the most endangered vertebrate species worldwide. However, little is known how they are affected by road-kills on tertiary roads and whether the surrounding landscape structure can explain road-kill patterns. The aim of our study was to examine the applicability of open-access remote sensing data for a large-scale citizen science approach to describe spatial patterns of road-killed amphibians and reptiles on tertiary roads. Using a citizen science app we monitored road-kills of amphibians and reptiles along 97.5 km of tertiary roads covering agricultural, municipal and interurban roads as well as cycling paths in eastern Austria over two seasons. Surrounding landscape was assessed using open access land cover classes for the region (Coordination of Information on the Environment, CORINE). Hotspot analysis was performed using kernel density estimation (KDE+). Relations between land cover classes and amphibian and reptile road-kills were analysed with conditional probabilities and general linear models (GLM). We also estimated the potential cost-efficiency of a large scale citizen science monitoring project. We recorded 180 amphibian and 72 reptile road-kills comprising eight species mainly occurring on agricultural roads. KDE+ analyses revealed a significant clustering of road-killed amphibians and reptiles, which is an important information for authorities aiming to mitigate road-kills. Overall, hotspots of amphibian and reptile road-kills were next to the land cover classes arable land, suburban areas and vineyards. Conditional probabilities and GLMs identified road-kills especially next to preferred habitats of green toad, common toad and grass snake, the most often found road-killed species. A citizen science approach appeared to be more cost-efficient than monitoring by professional researchers only when more than 400 km of road are monitored. Our findings showed that freely available remote sensing data in combination with a

A new license plate extraction framework based on fast mean shift

NASA Astrophysics Data System (ADS)

Pan, Luning; Li, Shuguang

2010-08-01

License plate extraction is considered to be the most crucial step of Automatic license plate recognition (ALPR) system. In this paper, a region-based license plate hybrid detection method is proposed to solve practical problems under complex background in which existing large quantity of disturbing information. In this method, coarse license plate location is carried out firstly to get the head part of a vehicle. Then a new Fast Mean Shift method based on random sampling of Kernel Density Estimate (KDE) is adopted to segment the color vehicle images, in order to get candidate license plate regions. The remarkable speed-up it brings makes Mean Shift segmentation more suitable for this application. Feature extraction and classification is used to accurately separate license plate from other candidate regions. At last, tilted license plate regulation is used for future recognition steps.

α versus non-α cluster decays of the excited compound nucleus *124Ce using various formulations of the nuclear proximity potential

NASA Astrophysics Data System (ADS)

Kaur, Arshdeep; Chopra, Sahila; Gupta, Raj K.

2015-06-01

The earlier study of *124Ce formed in the 32S+92Mo reaction at an above barrier beam energy of 150 MeV, using the pocket formula of Blocki et al. for the nuclear proximity potential in the dynamical cluster-decay model (DCM), is extended to the use of other nuclear interaction potentials derived from the Skyrme energy density functional (SEDF) based on the semiclassical extended Thomas Fermi (ETF) approach under the frozen density approximation. The Skyrme forces used are the old SII, SIII, SIV, SKa, SkM, and SLy4 and new GSkI and KDE0(v1), given for both normal and isospin-rich nuclei. It is found that the α -nucleus structure, over the non-α nucleus structure, is preferred for only two Skyrme forces, the SIII and KDE0(v1). An extended intermediate mass fragments (IMFs) window, along with the new decay region of heavy mass fragments (HMFs) and the near-symmetric and symmetric fission fragments which, on adding the complementary heavy fragments, corresponds to (A /2 )±12 mass region of the fusion-fission (ff) process, are predicted by considering cross sections of orders observed in the experiment under study. For the predicted (total) fusion cross section, the survival probability Psurv of the compound nucleus (CN) against fission is shown to be very small because of the very large predicted ff component. On the other hand, the CN formation probability PCN is found to be nearly equal to 1, and hence the decay under study is a pure CN decay for all the nuclear potentials considered, since the estimated noncompound nucleus (nCN) content is almost negligible. We have also applied the extended-Wong model of Gupta and collaborators, and find that the ℓmax values and total fusion cross sections are of the same order as for the DCM. Thus, the extended-Wong model, which describes only the total fusion cross section in terms of the barrier characteristics of the entrance channel nuclei, could be useful for initial experimental studies to be fully treated using the DCM

Nonparametric Density Estimation Based on Self-Organizing Incremental Neural Network for Large Noisy Data.

PubMed

Nakamura, Yoshihiro; Hasegawa, Osamu

2017-01-01

With the ongoing development and expansion of communication networks and sensors, massive amounts of data are continuously generated in real time from real environments. Beforehand, prediction of a distribution underlying such data is difficult; furthermore, the data include substantial amounts of noise. These factors make it difficult to estimate probability densities. To handle these issues and massive amounts of data, we propose a nonparametric density estimator that rapidly learns data online and has high robustness. Our approach is an extension of both kernel density estimation (KDE) and a self-organizing incremental neural network (SOINN); therefore, we call our approach KDESOINN. An SOINN provides a clustering method that learns about the given data as networks of prototype of data; more specifically, an SOINN can learn the distribution underlying the given data. Using this information, KDESOINN estimates the probability density function. The results of our experiments show that KDESOINN outperforms or achieves performance comparable to the current state-of-the-art approaches in terms of robustness, learning time, and accuracy.

Statistically advanced, self-similar, radial probability density functions of atmospheric and under-expanded hydrogen jets

NASA Astrophysics Data System (ADS)

Ruggles, Adam J.

2015-11-01

agreement. This was attributed to the high quality of the measurements which reduced the width of the correctly identified, noise-affected pure air distribution, with respect to the turbulent mixing distribution. The ignitability of the atmospheric jet is determined using the flammability factor calculated from both kernel density estimated (KDE) PDFs and PDFs generated using the newly proposed model. Agreement between contours from both approaches is excellent. Ignitability of the under-expanded jet is also calculated using KDE PDFs. Contours are compared with those calculated by applying the atmospheric model to the under-expanded jet. Once again, agreement is excellent. This work demonstrates that self-similar scalar mixing statistics and ignitability of atmospheric jets can be accurately described by the proposed model. This description can be applied with confidence to under-expanded jets, which are more realistic of leak and fuel injection scenarios.

Statistical Examination of Tornado Report and Warning Near-Storm Environments

NASA Astrophysics Data System (ADS)

Anderson-Frey, Alexandra K.

This study makes use of a 13-year dataset of 14,814 tornado events and 44,961 tornado warnings in the continental United States, along with near-storm environmental data associated with each of those tornado events and warnings, to build a methodology that can be used to create nuanced climatologies of near-storm environmental data. Two key parameter spaces are identified as being particularly useful in this endeavor: mixed-layer convective available potential energy (MLCAPE) versus 0-6-km vector shear magnitude (SHR6) and mixed-layer lifting condensation level (MLLCL) versus 0-1-km storm-relative helicity (SRH1). In addition, the Significant Tornado Parameter (STP) is identified as a useful composite parameter that can highlight near-storm environments that are particularly favorable for the development of significant tornadoes. Two particular statistical methods for the analysis and characterization of near-storm environments are described and applied: Kernel Density Estimation (KDE), which is applied to bulk (proximity soundinglike) parameter values associated with each event or warning, and Self-Organizing Maps (SOMs), which are applied to fully two-dimensional plots of STP in an area surrounding each event or warning. The KDE approach characterizes and identifies differences in the environments of tornadoes forming in quasi-linear convective systems versus those forming in right-moving supercells; specific environmental traits are also identified for different geographical regions, seasons, and times of day. Tornado warning performance is found to be best in environments with particularly large values of MLCAPE and SHR6. The early evening transition (EET) period is of particular interest: MLCAPE and MLLCL heights are in the process of falling, and SHR6 and SRH1 are in the process of increasing. Accordingly, tornadoes rated 2 or greater on the enhanced Fujita scale (EF2+) are also most common during the EET, probability of detection (POD) is relatively high

Active Learning for Directed Exploration of Complex Systems

NASA Technical Reports Server (NTRS)

Burl, Michael C.; Wang, Esther

2009-01-01

Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.

Second-order motions contribute to vection.

PubMed

Gurnsey, R; Fleet, D; Potechin, C

1998-09-01

First- and second-order motions differ in their ability to induce motion aftereffects (MAEs) and the kinetic depth effect (KDE). To test whether second-order stimuli support computations relating to motion-in-depth we examined the vection illusion (illusory self motion induced by image flow) using a vection stimulus (V, expanding concentric rings) that depicted a linear path through a circular tunnel. The set of vection stimuli contained differing amounts of first- and second-order motion energy (ME). Subjects reported the duration of the perceived MAEs and the duration of their vection percept. In Experiment 1 both MAEs and vection durations were longest when the first-order (Fourier) components of V were present in the stimulus. In Experiment 2, V was multiplicatively combined with static noise carriers having different check sizes. The amount of first-order ME associated with V increases with check size. MAEs were found to increase with check size but vection durations were unaffected. In general MAEs depend on the amount of first-order ME present in the signal. Vection, on the other hand, appears to depend on a representation of image flow that combines first- and second-order ME.

Clustering on very small scales from a large, complete sample of confirmed quasar pairs

NASA Astrophysics Data System (ADS)

Eftekharzadeh, Sarah; Myers, Adam D.; Djorgovski, Stanislav G.; Graham, Matthew J.; Hennawi, Joseph F.; Mahabal, Ashish A.; Richards, Gordon T.

2016-06-01

We present by far the largest sample of spectroscopically confirmed binaryquasars with proper transverse separations of 17.0 ≤ Rprop ≤ 36.6 h-1 kpc. Our sample, whichis an order-of-magnitude larger than previous samples, is selected from Sloan Digital Sky Survey (SDSS) imaging over an area corresponding to the SDSS 6th data release (DR6). Our quasars are targeted using a Kernel Density Estimation technique (KDE), and confirmed using long-slit spectroscopy on a range of facilities.Our most complete sub-sample of 44 binary quasars with g<20.85, extends across angular scales of 2.9" < Δθ < 6.3", and is targeted from a parent sample that would be equivalent to a full spectroscopic survey of nearly 300,000 quasars.We determine the projected correlation function of quasars (\\bar Wp) over proper transverse scales of 17.0 ≤ Rprop ≤ 36.6 h-1 kpc, and also in 4 bins of scale within this complete range.To investigate the redshift evolution of quasar clustering on small scales, we make the first self-consistent measurement of the projected quasar correlation function in 4 bins of redshift over 0.4 ≤ z ≤ 2.3.

LiDAR observations of an Earth magmatic plumbing system as an analog for Venus and Mars distributed volcanism

NASA Astrophysics Data System (ADS)

Richardson, Jacob; Connor, Charles; Malservisi, Rocco; Bleacher, Jacob; Connor, Laura

2014-05-01

similar density. The extrapolated reconstruction and conduit mapping provide a first-order estimate of the final intrustive/extrusive volume ratio for the now eroded volcanic field. Earth, Venus and Mars clusters are compared using Kernel Density Estimation (KDE) , which objectively compares cluster area, complexity, and vent density per sq. km. We show that Martian clusters are less dense than Venus clusters, which in turn are less dense than those on Earth. KDE and previous models of intrusive morphology for Mars and Venus are here used to calibrate the San Rafael plumbing system model to clusters on the two planets. The results from the calibrated Mars and Venus plumbing system models can be compared to previous estimates of magma budget and intrusive/extrusive ratios on Venus and Mars.

Multi-loci diagnosis of acute lymphoblastic leukaemia with high-throughput sequencing and bioinformatics analysis.

PubMed

Ferret, Yann; Caillault, Aurélie; Sebda, Shéhérazade; Duez, Marc; Grardel, Nathalie; Duployez, Nicolas; Villenet, Céline; Figeac, Martin; Preudhomme, Claude; Salson, Mikaël; Giraud, Mathieu

2016-05-01

High-throughput sequencing (HTS) is considered a technical revolution that has improved our knowledge of lymphoid and autoimmune diseases, changing our approach to leukaemia both at diagnosis and during follow-up. As part of an immunoglobulin/T cell receptor-based minimal residual disease (MRD) assessment of acute lymphoblastic leukaemia patients, we assessed the performance and feasibility of the replacement of the first steps of the approach based on DNA isolation and Sanger sequencing, using a HTS protocol combined with bioinformatics analysis and visualization using the Vidjil software. We prospectively analysed the diagnostic and relapse samples of 34 paediatric patients, thus identifying 125 leukaemic clones with recombinations on multiple loci (TRG, TRD, IGH and IGK), including Dd2/Dd3 and Intron/KDE rearrangements. Sequencing failures were halved (14% vs. 34%, P = 0.0007), enabling more patients to be monitored. Furthermore, more markers per patient could be monitored, reducing the probability of false negative MRD results. The whole analysis, from sample receipt to clinical validation, was shorter than our current diagnostic protocol, with equal resources. V(D)J recombination was successfully assigned by the software, even for unusual recombinations. This study emphasizes the progress that HTS with adapted bioinformatics tools can bring to the diagnosis of leukaemia patients. © 2016 John Wiley & Sons Ltd.

Evaluation of portable near-infrared spectroscopy for organic milk authentication.

PubMed

Liu, Ningjing; Parra, Hector Aya; Pustjens, Annemieke; Hettinga, Kasper; Mongondry, Philippe; van Ruth, Saskia M

2018-07-01

Organic products are vulnerable to fraud due to their premium price. Analytical methodology helps to manage the risk of fraud and due to the miniaturization of equipment, tests may nowadays even be rapidly applied on-site. The current study aimed to evaluate portable near infrared spectroscopy (NIRS) in combination with chemometrics to distinguish organic milk from other types of milk, and compare its performance with benchtop NIRS and fatty acid profiling by gas chromatography. The sample set included 37 organic retail milks and 50 non-organic retail milks (of which 36 conventional and 14 green 'pasture' milks). Partial least squares discriminant analysis was performed to build classification models and kernel density estimation (KDE) functions were calculated to generate non-parametric distributions for samples' class probabilities. These distributions showed that portable NIRS was successful to distinguish organic milks from conventional milks, and so were benchtop NIRS and fatty acid profiling procedures. However, it was less successful when 'pasture' milks were considered too, since their patterns occasionally resembled those of the organic milk group. Fatty acid profiling was capable of distinguishing organic milks from both non-organic milks though, including the 'pasture' milks. This comparative study revealed that the classification performance of the portable NIRS for this application was similar to that of the benchtop NIRS. Copyright © 2018 Elsevier B.V. All rights reserved.

Estimation of the climate change impact on a catchment water balance using an ensemble of GCMs

NASA Astrophysics Data System (ADS)

Reshmidevi, T. V.; Nagesh Kumar, D.; Mehrotra, R.; Sharma, A.

2018-01-01

This work evaluates the impact of climate change on the water balance of a catchment in India. Rainfall and hydro-meteorological variables for current (20C3M scenario, 1981-2000) and two future time periods: mid of the 21st century (2046-2065) and end of the century (2081-2100) are simulated using Modified Markov Model-Kernel Density Estimation (MMM-KDE) and k-nearest neighbor downscaling models. Climate projections from an ensemble of 5 GCMs (MPI-ECHAM5, BCCR-BCM2.0, CSIRO-mk3.5, IPSL-CM4, and MRI-CGCM2) are used in this study. Hydrologic simulations for the current as well as future climate scenarios are carried out using Soil and Water Assessment Tool (SWAT) integrated with ArcGIS (ArcSWAT v.2009). The results show marginal reduction in runoff ratio, annual streamflow and groundwater recharge towards the end of the century. Increased temperature and evapotranspiration project an increase in the irrigation demand towards the end of the century. Rainfall projections for the future shows marginal increase in the annual average rainfall. Short and moderate wet spells are projected to decrease, whereas short and moderate dry spells are projected to increase in the future. Projected reduction in streamflow and groundwater recharge along with the increase in irrigation demand is likely to aggravate the water stress in the region under the future scenario.

Oxidation of 2,4-dichlorophenoxyacetic acid by ionizing radiation: degradation, detoxification and mineralization

NASA Astrophysics Data System (ADS)

Zona, Robert; Solar, Sonja

2003-02-01

The gamma-radiation-induced degradation of 2,4-dichlorophenoxyacetic acid (2,4-D) was studied in aerated (A) and in during irradiation air saturated (AS) solutions. Whereas the decomposition rates were not influenced by AS, chloride elimination, detoxification as well as mineralization were significantly enhanced. In the range 50-500 μmol dm -3 2,4-D, degradation showed proportionality to concentration, while chloride formation was successively retarded. The ratios of the pseudo first-order rate constants for degradation and chloride formation, kde/ kCl, increase in AS solutions from 1.4 (50 μmol dm -3) to 2.7 (500 μmol dm -3) and in A solutions from 1.4 to 3.3. In AS for total chloride release 0.7 kGy (50 μmol dm -3) to 10 kGy (500 μmol dm -3) were required, the reduction of organic carbon at 10 kGy was 95% (50 μmol dm -3) and 50% (500 μmol dm -3). Increase and decrease of toxicity during irradiation correlated well with formation and degradation of intermediate phenolic products. The doses for detoxification corresponded to those of total dehalogenation. The oxygen uptake was ˜1.1 ppm 100 Gy -1. The presence of the inorganic components of Vienna drinking water affect the degradation parameters insignificantly.

Identification and classification of failure modes in laminated composites by using a multivariate statistical analysis of wavelet coefficients

NASA Astrophysics Data System (ADS)

Baccar, D.; Söffker, D.

2017-11-01

Acoustic Emission (AE) is a suitable method to monitor the health of composite structures in real-time. However, AE-based failure mode identification and classification are still complex to apply due to the fact that AE waves are generally released simultaneously from all AE-emitting damage sources. Hence, the use of advanced signal processing techniques in combination with pattern recognition approaches is required. In this paper, AE signals generated from laminated carbon fiber reinforced polymer (CFRP) subjected to indentation test are examined and analyzed. A new pattern recognition approach involving a number of processing steps able to be implemented in real-time is developed. Unlike common classification approaches, here only CWT coefficients are extracted as relevant features. Firstly, Continuous Wavelet Transform (CWT) is applied to the AE signals. Furthermore, dimensionality reduction process using Principal Component Analysis (PCA) is carried out on the coefficient matrices. The PCA-based feature distribution is analyzed using Kernel Density Estimation (KDE) allowing the determination of a specific pattern for each fault-specific AE signal. Moreover, waveform and frequency content of AE signals are in depth examined and compared with fundamental assumptions reported in this field. A correlation between the identified patterns and failure modes is achieved. The introduced method improves the damage classification and can be used as a non-destructive evaluation tool.

Incremental Refinement of FAÇADE Models with Attribute Grammar from 3d Point Clouds

NASA Astrophysics Data System (ADS)

Dehbi, Y.; Staat, C.; Mandtler, L.; Pl¨umer, L.

2016-06-01

Data acquisition using unmanned aerial vehicles (UAVs) has gotten more and more attention over the last years. Especially in the field of building reconstruction the incremental interpretation of such data is a demanding task. In this context formal grammars play an important role for the top-down identification and reconstruction of building objects. Up to now, the available approaches expect offline data in order to parse an a-priori known grammar. For mapping on demand an on the fly reconstruction based on UAV data is required. An incremental interpretation of the data stream is inevitable. This paper presents an incremental parser of grammar rules for an automatic 3D building reconstruction. The parser enables a model refinement based on new observations with respect to a weighted attribute context-free grammar (WACFG). The falsification or rejection of hypotheses is supported as well. The parser can deal with and adapt available parse trees acquired from previous interpretations or predictions. Parse trees derived so far are updated in an iterative way using transformation rules. A diagnostic step searches for mismatches between current and new nodes. Prior knowledge on façades is incorporated. It is given by probability densities as well as architectural patterns. Since we cannot always assume normal distributions, the derivation of location and shape parameters of building objects is based on a kernel density estimation (KDE). While the level of detail is continuously improved, the geometrical, semantic and topological consistency is ensured.

Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China.

PubMed

Wu, Chao; Ye, Xinyue; Ren, Fu; Wan, You; Ning, Pengfei; Du, Qingyun

2016-01-01

Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord [Formula: see text] method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method's ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree.

Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China

PubMed Central

Ye, Xinyue; Ren, Fu; Wan, You; Ning, Pengfei; Du, Qingyun

2016-01-01

Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord Gi* method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method’s ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree. PMID:27783645

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing

PubMed Central

Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

2017-01-01

Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive. PMID:28604641

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

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

Zhou, Ping; Wang, Chenyu; Li, Mingjie

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) can not fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First,more » the modeling error PDF by the tradional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. Furthermore, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

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

Zhou, Ping; Wang, Chenyu; Li, Mingjie

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) cannot fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, themore » modeling error PDF by the traditional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. However, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

DOE PAGES

Zhou, Ping; Wang, Chenyu; Li, Mingjie; ...

2018-01-31

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) cannot fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, themore » modeling error PDF by the traditional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. However, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Clinical applicability and prognostic significance of molecular response assessed by fluorescent-PCR of immunoglobulin genes in multiple myeloma. Results from a GEM/PETHEMA study.

PubMed

Martinez-Lopez, Joaquin; Fernández-Redondo, Elena; García-Sánz, Ramón; Montalbán, María Angeles; Martínez-Sánchez, Pilar; Pavia, Bruno; Mateos, María Victoria; Rosiñol, Laura; Martín, Marisa; Ayala, Rosa; Martínez, Rafael; Blanchard, María Jesus; Alegre, Adrian; Besalduch, Joan; Bargay, Joan; Hernandez, Miguel T; Sarasquete, María Eugenia; Sanchez-Godoy, Pedro; Fernández, Manuela; Blade, Joan; San Miguel, Jesús F; Lahuerta, Juan Jose

2013-12-01

Minimal residual disease monitoring is becoming increasingly important in multiple myeloma (MM), but multiparameter flow cytometry (MFC) and allele-specific oligonucleotide polymerase chain reaction (ASO-PCR) techniques are not routinely available. This study investigated the prognostic influence of achieving molecular response assessed by fluorescent-PCR (F-PCR) in 130 newly diagnosed MM patients from Grupo Español Multidisciplinar de Melanoma (GEM)2000/GEM05 trials (NCT00560053, NCT00443235, NCT00464217) who achieved almost very good partial response after induction therapy. As a reference, we used the results observed with simultaneous MFC. F-PCR at diagnosis was performed on DNA using three different multiplex PCRs: IGH D-J, IGK V-J and KDE rearrangements. The applicability of F-PCR was 91·5%. After induction therapy, 64 patients achieved molecular response and 66 non-molecular response; median progression-free survival (PFS) was 61 versus 36 months, respectively (P = 0·001). Median overall survival (OS) was not reached (NR) in molecular response patients (5-year survival: 75%) versus 66 months in the non-molecular response group (P = 0·03). The corresponding PFS and OS values for patients with immunophenotypic versus non-immunophenotypic response were 67 versus 42 months (P = 0·005) and NR (5-year survival: 95%) versus 69 months (P = 0·004), respectively. F-PCR analysis is a rapid, affordable, and easily performable technique that, in some circumstances, may be a valid approach for minimal residual disease investigations in MM. © 2013 John Wiley & Sons Ltd.

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing.

PubMed

Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

2017-06-12

Remote sensing technologies have been widely applied in urban environments' monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the "salt and pepper" phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

Contribution of first-principles calculations to multinuclear NMR analysis of borosilicate glasses.

PubMed

Soleilhavoup, Anne; Delaye, Jean-Marc; Angeli, Frédéric; Caurant, Daniel; Charpentier, Thibault

2010-12-01

Boron-11 and silicon-29 NMR spectra of xSiO(2)-(1-x)B(2)O(3) glasses (x=0.40, 0.80 and 0.83) have been calculated using a combination of molecular dynamics (MD) simulations with density functional theory (DFT) calculations of NMR parameters. Structure models of 200 atoms have been generated using classical force fields and subsequently relaxed at the PBE-GGAlevel of DFT theory. The gauge including projector augmented wave (GIPAW) method is then employed for computing the shielding and electric field gradient tensors for each silicon and boron atom. Silicon-29 MAS and boron-11 MQMAS NMR spectra of two glasses (x=0.40 and 0.80) have been acquired and theoretical spectra are found to well agree with the experimental data. For boron-11, the NMR parameter distributions have been analysed using a Kernel density estimation (KDE) approach which is shown to highlight its main features. Accordingly, a new analytical model that incorporates the observed correlations between the NMR parameters is introduced. It significantly improves the fit of the (11)B MQMAS spectra and yields, therefore, more reliable NMR parameter distributions. A new analytical model for a quantitative description of the dependence of the silicon-29 and boron-11 isotropic chemical shift upon the bond angles is proposed, which incorporates possibly the effect of SiO(2)-B(2)O(3) intermixing. Combining all the above procedures, we show how distributions of Si-O-T and B-O-T (T=Si, B) bond angles can be estimated from the distribution of isotropic chemical shift of silicon-29 and boron-11, respectively. Copyright © 2010 John Wiley & Sons, Ltd.

Modeling reactive transport with particle tracking and kernel estimators

NASA Astrophysics Data System (ADS)

Rahbaralam, Maryam; Fernandez-Garcia, Daniel; Sanchez-Vila, Xavier

2015-04-01

Groundwater reactive transport models are useful to assess and quantify the fate and transport of contaminants in subsurface media and are an essential tool for the analysis of coupled physical, chemical, and biological processes in Earth Systems. Particle Tracking Method (PTM) provides a computationally efficient and adaptable approach to solve the solute transport partial differential equation. On a molecular level, chemical reactions are the result of collisions, combinations, and/or decay of different species. For a well-mixed system, the chem- ical reactions are controlled by the classical thermodynamic rate coefficient. Each of these actions occurs with some probability that is a function of solute concentrations. PTM is based on considering that each particle actually represents a group of molecules. To properly simulate this system, an infinite number of particles is required, which is computationally unfeasible. On the other hand, a finite number of particles lead to a poor-mixed system which is limited by diffusion. Recent works have used this effect to actually model incomplete mix- ing in naturally occurring porous media. In this work, we demonstrate that this effect in most cases should be attributed to a defficient estimation of the concentrations and not to the occurrence of true incomplete mixing processes in porous media. To illustrate this, we show that a Kernel Density Estimation (KDE) of the concentrations can approach the well-mixed solution with a limited number of particles. KDEs provide weighting functions of each particle mass that expands its region of influence, hence providing a wider region for chemical reactions with time. Simulation results show that KDEs are powerful tools to improve state-of-the-art simulations of chemical reactions and indicates that incomplete mixing in diluted systems should be modeled based on alternative conceptual models and not on a limited number of particles.

Movements and Habitat-Use of Loggerhead Sea Turtles in the Northern Gulf of Mexico during the Reproductive Period

PubMed Central

Hart, Kristen M.; Lamont, Margaret M.; Sartain, Autumn R.; Fujisaki, Ikuko; Stephens, Brail S.

2013-01-01

Nesting strategies and use of important in-water habitats for far-ranging marine turtles can be determined using satellite telemetry. Because of a lack of information on habitat-use by marine turtles in the northern Gulf of Mexico, we used satellite transmitters in 2010 through 2012 to track movements of 39 adult female breeding loggerhead turtles (Caretta caretta) tagged on nesting beaches at three sites in Florida and Alabama. During the nesting season, recaptured turtles emerged to nest 1 to 5 times, with mean distance between emergences of 27.5 km; however, several turtles nested on beaches separated by ∼250 km within a single season. Mean total distances traveled throughout inter-nesting periods for all turtles was 1422.0±930.8 km. In-water inter-nesting sites, delineated using 50% kernel density estimation (KDE), were located a mean distance of 33.0 km from land, in water with mean depth of −31.6 m; other in-water inter-nesting sites, delineated using minimum convex polygon (MCP) approach, were located a mean 13.8 km from land and in water with a mean depth of −15.8 m. Mean size of in-water inter-nesting habitats were 61.9 km2 (50% KDEs, n = 10) and 741.4 km2 (MCPs, n = 30); these areas overlapped significantly with trawling and oil and gas extraction activities. Abundance estimates for this nesting subpopulation may be inaccurate in light of how much spread there is between nests of the same individual. Further, our results also have consequences for critical habitat designations for northern Gulf loggerheads, as protection of one nesting beach would not encompass the entire range used by turtles during breeding seasons. PMID:23843971

Movements and habitat-use of loggerhead sea turtles in the northern Gulf of Mexico during the reproductive period

USGS Publications Warehouse

Hart, Kristen M.; Lamont, Margaret M.; Sartain-Iverson, Autumn R.; Fujisaki, Ikuko; Stephens, Brail S.

2013-01-01

Nesting strategies and use of important in-water habitats for far-ranging marine turtles can be determined using satellite telemetry. Because of a lack of information on habitat-use by marine turtles in the northern Gulf of Mexico, we used satellite transmitters in 2010 through 2012 to track movements of 39 adult female breeding loggerhead turtles (Caretta caretta) tagged on nesting beaches at three sites in Florida and Alabama. During the nesting season, recaptured turtles emerged to nest 1 to 5 times, with mean distance between emergences of 27.5 km; however, several turtles nested on beaches separated by ~250 km within a single season. Mean total distances traveled throughout inter-nesting periods for all turtles was 1422.0±930.8 km. In-water inter-nesting sites, delineated using 50% kernel density estimation (KDE), were located a mean distance of 33.0 km from land, in water with mean depth of −31.6 m; other in-water inter-nesting sites, delineated using minimum convex polygon (MCP) approach, were located a mean 13.8 km from land and in water with a mean depth of −15.8 m. Mean size of in-water inter-nesting habitats were 61.9 km2 (50% KDEs, n = 10) and 741.4 km2 (MCPs, n = 30); these areas overlapped significantly with trawling and oil and gas extraction activities. Abundance estimates for this nesting subpopulation may be inaccurate in light of how much spread there is between nests of the same individual. Further, our results also have consequences for critical habitat designations for northern Gulf loggerheads, as protection of one nesting beach would not encompass the entire range used by turtles during breeding seasons.

Does Plan B work? Home range estimations from stored on board and transmitted data sets produced by GPS-telemetry in the Colombian Amazon.

PubMed

Cabrera, Jaime A; Molina, Eduardo; González, Tania; Armenteras, Dolors

2016-12-01

Telemetry based on Global Positioning Systems (GPS) makes possible to gather large quantities of information in a very fine scale and work with species that were impossible to study in the past. When working with GPS telemetry, the option of storing data on board could be more desirable than the sole satellite transmitted data, due to the increase in the amount of locations available for analysis. Nonetheless, the uncertainty in the retrieving of the collar unit makes satellite-transmitted technologies something to take into account. Therefore, differences between store-on-board (SoB) and satellite-transmitted (IT) data sets need to be considered. Differences between SoB and IT data collected from two lowland tapirs (Tapirus terrestris), were explored by means of the calculation of home range areas by three different methods: the Minimum Convex Polygon (MCP), the Fixed Kernel Density Estimator (KDE) and the Brownian Bridges (BB). Results showed that SoB and IT data sets for the same individual were similar, with fix ranging from 63 % to 85 % respectively, and 16 m to 17 m horizontal errors. Depending on the total number of locations available for each individual, the home ranges estimated showed differences between 2.7 % and 79.3 %, for the 50 % probability contour and between 9.9 % and 61.8 % for the 95 % probability contour. These differences imply variations in the spatial coincidence of the estimated home ranges. We concluded that the use of IT data is not a good option for the estimation of home range areas if the collar settings have not been designed specifically for this use. Nonetheless, geographical representations of the IT based estimators could be of great help to identify areas of use, besides its assistance to locate the collar for its retrieval at the end of the field season and as a proximate backup when collars disappear.

Movements and habitat-use of loggerhead sea turtles in the northern Gulf of Mexico during the reproductive period.

PubMed

Hart, Kristen M; Lamont, Margaret M; Sartain, Autumn R; Fujisaki, Ikuko; Stephens, Brail S

2013-01-01

Nesting strategies and use of important in-water habitats for far-ranging marine turtles can be determined using satellite telemetry. Because of a lack of information on habitat-use by marine turtles in the northern Gulf of Mexico, we used satellite transmitters in 2010 through 2012 to track movements of 39 adult female breeding loggerhead turtles (Caretta caretta) tagged on nesting beaches at three sites in Florida and Alabama. During the nesting season, recaptured turtles emerged to nest 1 to 5 times, with mean distance between emergences of 27.5 km; however, several turtles nested on beaches separated by ~250 km within a single season. Mean total distances traveled throughout inter-nesting periods for all turtles was 1422.0 ± 930.8 km. In-water inter-nesting sites, delineated using 50% kernel density estimation (KDE), were located a mean distance of 33.0 km from land, in water with mean depth of -31.6 m; other in-water inter-nesting sites, delineated using minimum convex polygon (MCP) approach, were located a mean 13.8 km from land and in water with a mean depth of -15.8 m. Mean size of in-water inter-nesting habitats were 61.9 km(2) (50% KDEs, n = 10) and 741.4 km(2) (MCPs, n = 30); these areas overlapped significantly with trawling and oil and gas extraction activities. Abundance estimates for this nesting subpopulation may be inaccurate in light of how much spread there is between nests of the same individual. Further, our results also have consequences for critical habitat designations for northern Gulf loggerheads, as protection of one nesting beach would not encompass the entire range used by turtles during breeding seasons.

Influence of pedestrian age and gender on spatial and temporal distribution of pedestrian crashes.

PubMed

Toran Pour, Alireza; Moridpour, Sara; Tay, Richard; Rajabifard, Abbas

2018-01-02

Every year, about 1.24 million people are killed in traffic crashes worldwide and more than 22% of these deaths are pedestrians. Therefore, pedestrian safety has become a significant traffic safety issue worldwide. In order to develop effective and targeted safety programs, the location- and time-specific influences on vehicle-pedestrian crashes must be assessed. The main purpose of this research is to explore the influence of pedestrian age and gender on the temporal and spatial distribution of vehicle-pedestrian crashes to identify the hotspots and hot times. Data for all vehicle-pedestrian crashes on public roadways in the Melbourne metropolitan area from 2004 to 2013 are used in this research. Spatial autocorrelation is applied in examining the vehicle-pedestrian crashes in geographic information systems (GIS) to identify any dependency between time and location of these crashes. Spider plots and kernel density estimation (KDE) are then used to determine the temporal and spatial patterns of vehicle-pedestrian crashes for different age groups and genders. Temporal analysis shows that pedestrian age has a significant influence on the temporal distribution of vehicle-pedestrian crashes. Furthermore, men and women have different crash patterns. In addition, results of the spatial analysis shows that areas with high risk of vehicle-pedestrian crashes can vary during different times of the day for different age groups and genders. For example, for those between ages 18 and 65, most vehicle-pedestrian crashes occur in the central business district (CBD) during the day, but between 7:00 p.m. and 6:00 a.m., crashes among this age group occur mostly around hotels, clubs, and bars. This research reveals that temporal and spatial distributions of vehicle-pedestrian crashes vary for different pedestrian age groups and genders. Therefore, specific safety measures should be in place during high crash times at different locations for different age groups and genders to

Improvement of the performance of animal crossing warning signs.

PubMed

Khalilikhah, Majid; Heaslip, Kevin

2017-09-01

Animal-vehicle collisions (AVCs) can result in serious injury and death to drivers, animals' death, and significant economic costs. However, the cost effectiveness of the majority of AVC mitigation measures is a significant issue. A mobile-based data collection effort was deployed to measure signs under the Utah Department of Transportation's (UDOT) jurisdiction. The crash data were obtained from the UDOT risk management database. ArcGIS was employed to link these two data sets and extract animal-related crashes and signs. An algorithm was developed to process the data and identify AVCs that occurred within sign recognition distance. Kernel density estimation (KDE) technique was applied to identify potential crash hotspots. Only 2% of AVCs occurred within the recognition distance of animal crossing signs. Almost 58% of animal-related crashes took place on the Interstate and U.S. highways, wherein only 30% of animal crossing signs were installed. State routes with a higher average number of signs experienced a lower number of AVCs per mile. The differences between AVCs that occurred within versus outside of sign recognition distance were not statistically significant regarding crash severity, time of crash, weather condition, driver age, vehicle speed, and type of animal. It is more likely that drivers become accustomed to deer crossing signs than cow signs. Based on the historical crash data and landscape structure, with attention given to the low cost safety improvement methods, a combination of different types of AVC mitigation measures can be developed to reduce the number of animal-related crashes. After an in-depth analysis of AVC data, warning traffic signs, coupled with other low cost mitigation countermeasures can be successfully placed in areas with higher priority or in critical areas. Practical applications: The findings of this study assist transportation agencies in developing more efficient mitigation measures against AVCs. Copyright © 2017 National

Skyrme forces and decay of the Rf266*104 nucleus synthesized via different incoming channels

NASA Astrophysics Data System (ADS)

Niyti, Deep, Aman; Kharab, Rajesh; Chopra, Sahila; Gupta, Raj K.

2017-03-01

The excitation functions for the production of 262Rf, 261Rf, and 260Rf isotopes via 4 n -, 5 n -, and 6 n -decay channels from the *266Rf compound nucleus are studied within the dynamical cluster-decay model (DCM), including deformations β2 i and so-called hot-optimum orientations θi which support symmetric fission, in agreement with experiments. The data are available for 18O+248Cm and 22Ne+244Pu reactions, respectively, at the energy ranges of Elab=88.2 to 101.3 and 109.0 to 124.8 MeV. For the nuclear interaction potentials, we use the Skyrme energy density functional (SEDF) based on semiclassical extended Thomas Fermi (ETF) approach, which means an extension of the earlier study of excitation functions of *266Rf formed in 18O+248Cm reaction, based on the DCM using the pocket formula for nuclear proximity potential, showing interaction dependence. The Skyrme forces used here are the old SIII and SIV and new GSkI and KDE0(v1) given for both normal and isospin-rich nuclei, with densities added in frozen density approximation. Interestingly, the DCM gives an excellent fit to the measured data on fusion evaporation residue (ER) for both the incoming channels (18O+248Cm and 22Ne+244Pu ) at the energy range Elab=88.2 to 124.8 MeV, independent of the entrance channel and Skyrme force used. The possible fusion-fission (ff) and quasifission (qf) mass regions of fragments on DCM are also predicted. The DCM with Skyrme forces is further used to look for all the possible target-projectile (t-p) combinations forming the cold compound nucleus (CN) *266Rf at the CN excitation energy of Elab for hot compact configurations. The fusion evaporation residue cross sections, for the proposed new reactions in synthesizing the CN *266Rf, are also estimated for the future experiments, and role of mass asymmetry of nuclei is indicated.

Do we really need a large number of particles to simulate bimolecular reactive transport with random walk methods? A kernel density estimation approach

NASA Astrophysics Data System (ADS)

Rahbaralam, Maryam; Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier

2015-12-01

Random walk particle tracking methods are a computationally efficient family of methods to solve reactive transport problems. While the number of particles in most realistic applications is in the order of 106-109, the number of reactive molecules even in diluted systems might be in the order of fractions of the Avogadro number. Thus, each particle actually represents a group of potentially reactive molecules. The use of a low number of particles may result not only in loss of accuracy, but also may lead to an improper reproduction of the mixing process, limited by diffusion. Recent works have used this effect as a proxy to model incomplete mixing in porous media. In this work, we propose using a Kernel Density Estimation (KDE) of the concentrations that allows getting the expected results for a well-mixed solution with a limited number of particles. The idea consists of treating each particle as a sample drawn from the pool of molecules that it represents; this way, the actual location of a tracked particle is seen as a sample drawn from the density function of the location of molecules represented by that given particle, rigorously represented by a kernel density function. The probability of reaction can be obtained by combining the kernels associated to two potentially reactive particles. We demonstrate that the observed deviation in the reaction vs time curves in numerical experiments reported in the literature could be attributed to the statistical method used to reconstruct concentrations (fixed particle support) from discrete particle distributions, and not to the occurrence of true incomplete mixing. We further explore the evolution of the kernel size with time, linking it to the diffusion process. Our results show that KDEs are powerful tools to improve computational efficiency and robustness in reactive transport simulations, and indicates that incomplete mixing in diluted systems should be modeled based on alternative mechanistic models and not on a

Rapid development of image analysis research tools: Bridging the gap between researcher and clinician with pyOsiriX.

PubMed

Blackledge, Matthew D; Collins, David J; Koh, Dow-Mu; Leach, Martin O

2016-02-01

We present pyOsiriX, a plugin built for the already popular dicom viewer OsiriX that provides users the ability to extend the functionality of OsiriX through simple Python scripts. This approach allows users to integrate the many cutting-edge scientific/image-processing libraries created for Python into a powerful DICOM visualisation package that is intuitive to use and already familiar to many clinical researchers. Using pyOsiriX we hope to bridge the apparent gap between basic imaging scientists and clinical practice in a research setting and thus accelerate the development of advanced clinical image processing. We provide arguments for the use of Python as a robust scripting language for incorporation into larger software solutions, outline the structure of pyOsiriX and how it may be used to extend the functionality of OsiriX, and we provide three case studies that exemplify its utility. For our first case study we use pyOsiriX to provide a tool for smooth histogram display of voxel values within a user-defined region of interest (ROI) in OsiriX. We used a kernel density estimation (KDE) method available in Python using the scikit-learn library, where the total number of lines of Python code required to generate this tool was 22. Our second example presents a scheme for segmentation of the skeleton from CT datasets. We have demonstrated that good segmentation can be achieved for two example CT studies by using a combination of Python libraries including scikit-learn, scikit-image, SimpleITK and matplotlib. Furthermore, this segmentation method was incorporated into an automatic analysis of quantitative PET-CT in a patient with bone metastases from primary prostate cancer. This enabled repeatable statistical evaluation of PET uptake values for each lesion, before and after treatment, providing estaimes maximum and median standardised uptake values (SUVmax and SUVmed respectively). Following treatment we observed a reduction in lesion volume, SUVmax and SUVmed for

TH-AB-202-04: Auto-Adaptive Margin Generation for MLC-Tracked Radiotherapy

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

Glitzner, M; Lagendijk, J; Raaymakers, B

Purpose: To develop an auto-adaptive margin generator for MLC tracking. The generator is able to estimate errors arising in image guided radiotherapy, particularly on an MR-Linac, which depend on the latencies of machine and image processing, as well as on patient motion characteristics. From the estimated error distribution, a segment margin is generated, able to compensate errors up to a user-defined confidence. Method: In every tracking control cycle (TCC, 40ms), the desired aperture D(t) is compared to the actual aperture A(t), a delayed and imperfect representation of D(t). Thus an error e(t)=A(T)-D(T) is measured every TCC. Applying kernel-density-estimation (KDE), themore » cumulative distribution (CDF) of e(t) is estimated. With CDF-confidence limits, upper and lower error limits are extracted for motion axes along and perpendicular leaf-travel direction and applied as margins. To test the dosimetric impact, two representative motion traces were extracted from fast liver-MRI (10Hz). The traces were applied onto a 4D-motion platform and continuously tracked by an Elekta Agility 160 MLC using an artificially imposed tracking delay. Gafchromic film was used to detect dose exposition for static, tracked, and error-compensated tracking cases. The margin generator was parameterized to cover 90% of all tracking errors. Dosimetric impact was rated by calculating the ratio between underexposed points (>5% underdosage) to the total number of points inside FWHM of static exposure. Results: Without imposing adaptive margins, tracking experiments showed a ratio of underexposed points of 17.5% and 14.3% for two motion cases with imaging delays of 200ms and 300ms, respectively. Activating the margin generated yielded total suppression (<1%) of underdosed points. Conclusion: We showed that auto-adaptive error compensation using machine error statistics is possible for MLC tracking. The error compensation margins are calculated on-line, without the need of assuming motion

Noncompound nucleus decay contribution in the 12C+93Nb reaction using various formulations of nuclear proximity potential

NASA Astrophysics Data System (ADS)

Chopra, Sahila; Kaur, Arshdeep; Gupta, Raj K.

2015-01-01

The earlier study of excitation functions of *105Ag, formed in the 12C+93Nb reaction, based on the dynamical cluster-decay model (DCM), using the pocket formula for nuclear proximity potential is extended to the use of other nuclear interaction potentials derived from the Skyrme energy density functional (SEDF) based on the semiclassical extended Thomas Fermi (ETF) approach and to the use of the extended-Wong model of Gupta and collaborators. The Skyrme forces used are the old SIII and SIV and the new SSk, GSkI, and KDE0(v1) given for both normal and isospin-rich nuclei, with densities added in the frozen-density approximation. Taking advantage of the fact that different Skyrme forces provide different barrier characteristics, we look for the "barrier modification" effects in terms of choosing an appropriate force and hence for the existence or nonexistence of noncompound nucleus (nCN) effects in this reaction. Interestingly, independent of the choice of Skyrme or proximity force, the extended-Wong model fits the experimental data nicely, without any barrier modification and hence no nCN component in the measured fusion cross section, which consists of light-particle evaporation residue (ER) and intermediate-mass fragments (IMFs) up to mass 13, i.e., σfusionExpt .=σER+σIMFs . However, the predicted fusion cross section due to the extended-Wong model is much larger, possibly because of the so-far missing fusion-fission (ff) component in the data. On the other hand, in agreement with the earlier work using the pocket proximity potential, the DCM fits only some data (mainly IMFs) for only some Skyrme forces, and hence it presents the chosen reaction as a case of a large nCN component, whose empirically estimated content is fitted for use of the DCM with a fragment preformation factor taken equal to one, i.e., using DCM (P0=1 ), by introducing "barrier modification" through changing the neck-length parameter Δ R for a best fit to the empirical nCN data in each (ER

EV Charging Through Wireless Power Transfer: Analysis of Efficiency Optimization and Technology Trends

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

Miller, John M; Rakouth, Heri; Suh, In-Soo

This paper is aimed at reviewing the technology trends for wireless power transfer (WPT) for electric vehicles (EV). It also analyzes the factors affecting its efficiency and describes the techniques currently used for its optimization. The review of the technology trends encompasses both stationary and moving vehicle charging systems. The study of the stationary vehicle charging technology is based on current implementations and on-going developments at WiTricity and Oak Ridge National Lab (ORNL). The moving vehicle charging technology is primarily described through the results achieved by the Korean Advanced Institute of Technology (KAIST) along with on-going efforts at Stanford University.more » The factors affecting the efficiency are determined through the analysis of the equivalent circuit of magnetic resonant coupling. The air gap between both transmitting and receiving coils along with the magnetic field distribution and the relative impedance mismatch between the related circuits are the primary factors affecting the WPT efficiency. Currently the industry is looking at an air gap of 25 cm or below. To control the magnetic field distribution, Kaist has recently developed the Shaped Magnetic Field In Resonance (SMFIR) technology that uses conveniently shaped ferrite material to provide low reluctance path. The efficiency can be further increased by means of impedance matching. As a result, Delphi's implementation of the WiTricity's technology exhibits a WPT efficiency above 90% for stationary charging while KAIST has demonstrated a maximum efficiency of 83% for moving vehicle with its On Line Vehicle (OLEV) project. This study is restricted to near-field applications (short and mid-range) and does not address long-range technology such as microwave power transfer that has low efficiency as it is based on radiating electromagnetic waves. This paper exemplifies Delphi's work in powertrain electrification as part of its innovation for the real world program

Summary Statistics for Homemade ?Play Dough? -- Data Acquired at LLNL

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

Kallman, J S; Morales, K E; Whipple, R E

Using x-ray computerized tomography (CT), we have characterized the x-ray linear attenuation coefficients (LAC) of a homemade Play Dough{trademark}-like material, designated as PDA. Table 1 gives the first-order statistics for each of four CT measurements, estimated with a Gaussian kernel density estimator (KDE) analysis. The mean values of the LAC range from a high of about 2700 LMHU{sub D} 100kVp to a low of about 1200 LMHUD at 300kVp. The standard deviation of each measurement is around 10% to 15% of the mean. The entropy covers the range from 6.0 to 7.4. Ordinarily, we would model the LAC of themore » material and compare the modeled values to the measured values. In this case, however, we did not have the detailed chemical composition of the material and therefore did not model the LAC. Using a method recently proposed by Lawrence Livermore National Laboratory (LLNL), we estimate the value of the effective atomic number, Z{sub eff}, to be near 10. LLNL prepared about 50mL of the homemade 'Play Dough' in a polypropylene vial and firmly compressed it immediately prior to the x-ray measurements. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the data capture and image reconstruction are given in this report. Additional details may be found in the experimental SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an 80% central-core segment of volume elements ('voxels') lying completely within the specimen, away from the walls of the polypropylene vial. All of the voxels within this central core, including those comprised of voids and inclusions, are included in the statistics. We then calculated the mean value, standard deviation and entropy for (a) the four image segments and for (b) their digital gradient images. (A digital gradient image of a given image was obtained by taking the absolute value of the difference between the

Summary Statistics for Fun Dough Data Acquired at LLNL

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

Kallman, J S; Morales, K E; Whipple, R E

Using x-ray computerized tomography (CT), we have characterized the x-ray linear attenuation coefficients (LAC) of a Play Dough{trademark}-like product, Fun Dough{trademark}, designated as PD. Table 1 gives the first-order statistics for each of four CT measurements, estimated with a Gaussian kernel density estimator (KDE) analysis. The mean values of the LAC range from a high of about 2100 LMHU{sub D} at 100kVp to a low of about 1100 LMHU{sub D} at 300kVp. The standard deviation of each measurement is around 1% of the mean. The entropy covers the range from 3.9 to 4.6. Ordinarily, we would model the LAC ofmore » the material and compare the modeled values to the measured values. In this case, however, we did not have the composition of the material and therefore did not model the LAC. Using a method recently proposed by Lawrence Livermore National Laboratory (LLNL), we estimate the value of the effective atomic number, Z{sub eff}, to be near 8.5. LLNL prepared about 50mL of the Fun Dough{trademark} in a polypropylene vial and firmly compressed it immediately prior to the x-ray measurements. Still, layers can plainly be seen in the reconstructed images, indicating that the bulk density of the material in the container is affected by voids and bubbles. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the data capture and image reconstruction are given in this report. Additional details may be found in the experimental SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an 80% central-core segment of volume elements ('voxels') lying completely within the specimen, away from the walls of the polypropylene vial. All of the voxels within this central core, including those comprised of voids and inclusions, are included in the statistics. We then calculated the mean value, standard deviation and entropy for (a) the four image segments and

The abundant extrachromosomal DNA content of the Spiroplasma citri GII3-3X genome

PubMed Central

Saillard, Colette; Carle, Patricia; Duret-Nurbel, Sybille; Henri, Raphaël; Killiny, Nabil; Carrère, Sébastien; Gouzy, Jérome; Bové, Joseph-Marie; Renaudin, Joël; Foissac, Xavier

2008-01-01

Background Spiroplama citri, the causal agent of citrus stubborn disease, is a bacterium of the class Mollicutes and is transmitted by phloem-feeding leafhopper vectors. In order to characterize candidate genes potentially involved in spiroplasma transmission and pathogenicity, the genome of S. citri strain GII3-3X is currently being deciphered. Results Assembling 20,000 sequencing reads generated seven circular contigs, none of which fit the 1.8 Mb chromosome map or carried chromosomal markers. These contigs correspond to seven plasmids: pSci1 to pSci6, with sizes ranging from 12.9 to 35.3 kbp and pSciA of 7.8 kbp. Plasmids pSci were detected as multiple copies in strain GII3-3X. Plasmid copy numbers of pSci1-6, as deduced from sequencing coverage, were estimated at 10 to 14 copies per spiroplasma cell, representing 1.6 Mb of extrachromosomal DNA. Genes encoding proteins of the TrsE-TraE, Mob, TraD-TraG, and Soj-ParA protein families were predicted in most of the pSci sequences, in addition to members of 14 protein families of unknown function. Plasmid pSci6 encodes protein P32, a marker of insect transmissibility. Plasmids pSci1-5 code for eight different S. citri adhesion-related proteins (ScARPs) that are homologous to the previously described protein P89 and the S. kunkelii SkARP1. Conserved signal peptides and C-terminal transmembrane alpha helices were predicted in all ScARPs. The predicted surface-exposed N-terminal region possesses the following elements: (i) 6 to 8 repeats of 39 to 42 amino acids each (sarpin repeats), (ii) a central conserved region of 330 amino acids followed by (iii) a more variable domain of about 110 amino acids. The C-terminus, predicted to be cytoplasmic, consists of a 27 amino acid stretch enriched in arginine and lysine (KR) and an optional 23 amino acid stretch enriched in lysine, aspartate and glutamate (KDE). Plasmids pSci mainly present a linear increase of cumulative GC skew except in regions presenting conserved hairpin

Two-component mantle melting-mixing model for the generation of mid-ocean ridge basalts: Implications for the volatile content of the Pacific upper mantle

NASA Astrophysics Data System (ADS)

Shimizu, Kei; Saal, Alberto E.; Myers, Corinne E.; Nagle, Ashley N.; Hauri, Erik H.; Forsyth, Donald W.; Kamenetsky, Vadim S.; Niu, Yaoling

2016-03-01

We report major, trace, and volatile element (CO2, H2O, F, Cl, S) contents and Sr, Nd, and Pb isotopes of mid-ocean ridge basalt (MORB) glasses from the Northern East Pacific Rise (NEPR) off-axis seamounts, the Quebrada-Discovery-GoFar (QDG) transform fault system, and the Macquarie Island. The incompatible trace element (ITE) contents of the samples range from highly depleted (DMORB, Th/La ⩽ 0.035) to enriched (EMORB, Th/La ⩾ 0.07), and the isotopic composition spans the entire range observed in EPR MORB. Our data suggest that at the time of melt generation, the source that generated the EMORB was essentially peridotitic, and that the composition of NMORB might not represent melting of a single upper mantle source (DMM), but rather mixing of melts from a two-component mantle (depleted and enriched DMM or D-DMM and E-DMM, respectively). After filtering the volatile element data for secondary processes (degassing, sulfide saturation, assimilation of seawater-derived component, and fractional crystallization), we use the volatiles to ITE ratios of our samples and a two-component mantle melting-mixing model to estimate the volatile content of the D-DMM (CO2 = 22 ppm, H2O = 59 ppm, F = 8 ppm, Cl = 0.4 ppm, and S = 100 ppm) and the E-DMM (CO2 = 990 ppm, H2O = 660 ppm, F = 31 ppm, Cl = 22 ppm, and S = 165 ppm). Our two-component mantle melting-mixing model reproduces the kernel density estimates (KDE) of Th/La and 143Nd/144Nd ratios for our samples and for EPR axial MORB compiled from the literature. This model suggests that: (1) 78% of the Pacific upper mantle is highly depleted (D-DMM) while 22% is enriched (E-DMM) in volatile and refractory ITE, (2) the melts produced during variable degrees of melting of the E-DMM controls most of the MORB geochemical variation, and (3) a fraction (∼65% to 80%) of the low degree EMORB melts (produced by ∼1.3% melting) may escape melt aggregation by freezing at the base of the oceanic lithosphere, significantly enriching it in

Decay of super-heavy particles: user guide of the SHdecay program

NASA Astrophysics Data System (ADS)

Barbot, C.

2004-02-01

I give here a detailed user guide for the C++ program SHdecay, which has been developed for computing the final spectra of stable particles (protons, photons, LSPs, electrons, neutrinos of the three species and their antiparticles) arising from the decay of a super-heavy X particle. It allows to compute in great detail the complete decay cascade for any given decay mode into particles of the Minimal Supersymmetric Standard Model (MSSM). In particular, it takes into account all interactions of the MSSM during the perturbative cascade (including not only QCD, but also the electroweak and 3rd generation Yukawa interactions), and includes a detailed treatment of the SUSY decay cascade (for a given set of parameters) and of the non-perturbative hadronization process. All these features allow us to ensure energy conservation over the whole cascade up to a numerical accuracy of a few per mille. Yet, this program also allows to restrict the computation to QCD or SUSY-QCD frameworks. I detail the input and output files, describe the role of each part of the program, and include some advice for using it best. Program summaryTitle of program: SHdecay Catalogue identifier:ADSL Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADSL Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer and operating system: Program tested on PC running Linux KDE and Suse 8.1 Programming language used: C with STL C++ library and using the standard gnu g++ compiler No. lines in distributed program: 14 955 No. of bytes in distributed program, including test data, etc.: 624 487 Distribution format: tar gzip file Keywords: Super-heavy particles, fragmentation functions, DGLAP equations, supersymmetry, MSSM, UHECR Nature of physical problem: Obtaining the energy spectra of the final stable decay products (protons, photons, electrons, the three species of neutrinos and the LSPs) of a decaying super-heavy X particle, within the framework of the Minimal

Mapping young stellar populations toward Orion with Gaia DR1

NASA Astrophysics Data System (ADS)

Zari, E.; Brown, A. G. A.; de Bruijne, J.; Manara, C. F.; de Zeeuw, P. T.

2017-12-01

In this work we use the first data release of the Gaia mission to explore the three-dimensional arrangement and age ordering of the many stellar groups toward the Orion OB association, aiming at a new classification and characterization of the stellar population not embedded in the Orion A and B molecular clouds. We make use of the parallaxes and proper motions provided in the Tycho Gaia Astrometric Solution (TGAS) subset of the Gaia Data Release 1 (DR1) catalog and of the combination of Gaia DR1 and 2MASS photometry. In TGAS, we find evidence for the presence of a young population at a parallax ϖ 2.65 mas, which is loosely distributed around the following known clusters: 25 Ori, ɛ Ori, and σ Ori, and NGC 1980 (ι Ori) and the Orion Nebula Cluster (ONC). The low mass counterpart of this population is visible in the color magnitude diagrams constructed by combining Gaia DR1 G-band photometry and 2MASS. We study the density distribution of the young sources in the sky using a kernel density estimation (KDE). We find the same groups as in TGAS and also some other density enhancements that might be related to the recently discovered Orion X group, Orion dust ring, and λ Ori complex. The maps also suggest that the 25 Ori group presents a northern elongation. We estimated the ages of this population using a Bayesian isochronal fitting procedure assuming a unique parallax value for all the sources, and we inferred the presence of an age gradient going from 25 Ori (13-15 Myr) to the ONC (1-2 Myr). We confirmed this age ordering by repeating the Bayesian fit using the Pan-STARRS1 data. Intriguingly, the estimated ages toward the NGC 1980 cluster span a broad range of values. This can either be due to the presence of two populations coming from two different episodes of star formation or to a large spread along the line of sight of the same population. Some confusion might arise from the presence of unresolved binaries, which are not modeled in the fit, and usually mimic

Using Open data in analyzing urban growth: urban density and change detection

NASA Astrophysics Data System (ADS)

murgante, Beniamino; Nolè, Gabriele; Lasaponara, Rosa; Lanorte, Antonio

2013-04-01

In recent years a great attention has been paid to the evolution and the use of spatial data. Internet technologies accelerated such a process, allowing more direct access to spatial information. It is estimated that more than 600 million people have been connected to the Internet at least once to display maps on the web. Consequently, there is an irreversible process which considers geographical dimension as a fundamental attribute for the management of information flows. Furthermore, the great activity produced by open data movement leads to an easier and clearer access to geospatial information. This trend concerns, in a less evident way, also satellite data, which are increasingly accessible through the web. Spatial planning, geography and other regional sciences find it difficult to build knowledge related to spatial transformation. These problems can be significantly reduced due to a large data availability, producing significant opportunities to capture knowledge useful for a better territorial governance. This study has been developed in a heavily anthropized area in southern Italy, Apulia region, using free spatial data and free multispectral and multitemporal satellite data (Apulia region was one of the first regions in Italy to adopt open data policies). The analysis concerns urban growth, which, in recent decades, showed a rapid increase. In a first step the evolution in time and change detection of urban areas has been analyzed paying particular attention to soil consumption. In the second step Kernel Density has been adopted in order to assess development pressures. KDE (Kernel Density Estimation) function is a technique that provides the density of a phenomenon based on point data. A mobile three dimensional surface has been produced from a set of points distributed over a region of space, which weighs the events within its sphere of influence, depending on their distance from the point from which intensity is estimated. It produces, considering as

Spatial Associations and Network Dynamics Between the Vaccine Exemption Dicsussion in Twitter and the Corresponding Geographic Space

NASA Astrophysics Data System (ADS)

Coronado, Alejandra

Recent outbreaks of vaccine-preventable diseases in the United States have drawn attention to the phenomena of vaccine hesitancy and refusal. Hesitancy is seen through the increasing use of exemptions from state vaccine mandates and the recent use of social media for expressing opinions and perspectives related to vaccination. This research places the vaccination narrative into a geographic context and seeks to understand the relationship between vaccine refusal in physical space and the vaccine discussion in cyberspace. Vaccines have long been considered an effective means of eradicating diseases. Recently, however, California has experienced a decline in vaccination rates and an increase in vaccine exemptions. Until the passing of Senate Bill 277 (SB277) in 2015, children were allowed by California law to skip immunizations if a parent submitted a personal beliefs exemption (PBEs). Under SB277, children who are not vaccinated cannot attend school. Some children are still allowed to skip immunizations by submitting a medical exemption (PMEs) at enrollment. Other children are conditionally admitted to school on the 'condition' that they complete any remaining vaccinations when due. This research analyzed the spatial distribution of vaccine exemptions in kindergarten schools in California using the 2015-2016 school immunization data. The two methods used for analysis included Kernel Density Estimation (KDE) and choropleth maps using data aggregated by county. The results from the choropleth maps show that personal belief exemptions for public, private, and charter kindergarten schools are highly concentrated in northern and rural counties. Aggregating vaccine exemptions at the county level and normalizing by school enrollment showed that counties with high ratios of vaccine exemptions vary across public, private, and charter schools. This research also explored the diffusion networks of the vaccine exemption topic in Twitter. Twitter messages related to the