10,318 research outputs found
Descriptive oceanography during the Frontal Air‐Sea Interaction Experiment: Medium‐ to large‐scale variability
Medium‐ and large‐scale oceanographic variability in the Sargasso Sea is examined during the Frontal Air‐Sea Interaction Experiment (FASINEX), focusing primarily on processes that influence the formation of subtropical fronts. From Fall to Spring the mean meridional gradient of meridional Ekman transport in the Subtropical Convergence Zone (STCZ) enhances the meridional sea surface temperature (Ts) gradients between 26° and 32°N. In the presence of this enhanced mean gradient, baroclinic eddies with zonal wavelengths of ≈800 km and periods of ≈200 days exert the dominant influence on the formation of subtropical fronts at medium and large scales. These eddies generate westward propagating Ts anomaly features with the same dominant wavelengths and periods. They are confined between 26° and 32°N and have amplitudes that occasionally exceed ±1°C. Ts fronts tend to be found within bands ≈200 km wide that roughly follow the periphery of these anomaly features. Deformation in the horizontal eddy current field is primarily responsible for the existence of these frontal bands. The migration of the strong front originally bracketed by the FASINEX moored array was related to the westward propagation of the larger‐scale eddy/anomaly/frontal‐band pattern. The moored array was located within a warm‐anomaly feature during most of the experiment, which produced exceptionally warm conditions in the upper ocean. These anomalies are confined between 26° and 32°N, not only because the relatively large seasonal mean Tsy there allows horizontal eddy currents to force strong anomalies, but also because the baroclinic eddies with wavelengths of ≈800 km and periods of ≈200 days are confined to the STCZ. Large meridional variability exists in many properties of the eddy field, much of which can be traced to the influence of the Sargasso Sea mean current field on eddy variability
Improving animal monitoring using small unmanned aircraft systems (sUAS) and deep learning networks
In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and whitetailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals
Detection Rates of Northern Bobwhite Coveys Using a Small Unmanned Aerial System-Mounted Thermal Camera
The northern bobwhite (Colinus virginianus; hereafter, bobwhite) requires intensive monitoring to evaluate management efforts and determine harvest rates. However, traditional monitoring techniques (e.g., covey-call surveys) are labor-intensive and imprecise. Small unmanned aerial systems (sUASs) mounted with thermal cameras have demonstrated promise for monitoring multiple avian species and could provide a less intensive and more effective approach to monitoring bobwhite coveys, assuming coveys produce a recognizable heat signature. To assess sUAS monitoring, we evaluated the influence of bobwhite covey size (3, 6, and 12) and cover type (grass, shrub, and forest) on covey detectability by a sUAS equipped with a thermal camera. We hypothesized that forest would have the lowest covey detection due to trees obstructing detection of the thermal signature and that larger covey size would improve covey detection due to the formation of larger, more visibly distinct thermal signatures. We placed groups of known-size, pen-reared bobwhites in steel mesh cages (3, 6, and 12 individuals/cage) in 3 vegetation types (grass, shrub, and forest) among predetermined locations on a private farm in Clay County, Mississippi, USA (3 replicates, 27 total cages). At civil twilight on 5 March 2020, the sUAS flew a systematic route over the cage area at 30 m above ground level, capturing thermal infrared photographs every 5 seconds. To assess detection, we distributed 57 photographs to 31 volunteers and asked them to assign a binary value for detection (1, 0) regarding covey presence in each photograph. Overall true positive rate was 0.551 but improved with increasing covey size. By vegetation type, simulated coveys in grass had the lowest true positive rate by photograph (0.403), followed by forest (0.562) and shrub (0.605). Results indicate that sUASs and thermal camera technology could be a viable method for surveying intact bobwhite coveys, especially if detection of smaller groups and those in denser vegetation improves. As this technology advances, we recommend that future research focus on evaluating the efficacy of this novel methodology through assessing the influence of weather conditions, camera specifications, flight speed, and altitude, as well as assessing machine learning for processing photos
Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys
Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We classified visible and thermal images separately and compared them with the results of image fusion. Fused images provided minimal improvement for cows and horses compared to visible images alone, likely because the size, shape, and color of these species made them conspicuous against the background. For white-tailed deer, which were typically cryptic against their backgrounds and often in shadows in visible images, the added information from thermal images improved detection and classification in fusion methods from 15 to 85%. Our results suggest that image fusion is ideal for surveying animals inconspicuous from their backgrounds, and our approach uses few image pairs to train compared to typical machine-learning methods. We discuss computational and field considerations to improve drone surveys using our fusion approach.
Supplemental files attached below
A Chandra and Spitzer census of the young star cluster in the reflection nebula NGC 7129
The reflection nebula NGC 7129 has long been known to be a site of recent
star formation as evidenced, e.g., by the presence of deeply embedded
protostars and HH objects. However, studies of the stellar population produced
in the star formation process have remained rudimentary. At a presumed age of
~3 Myr, NGC7129 is in the critical range where disks around young stars
disappear. We make use of Chandra X-ray and Spitzer and 2MASS IR imaging
observations to identify the pre-main sequence stars in NGC7129. We define a
sample of Young Stellar Objects based on color-color diagrams composed from IR
photometry between 1.6 and 8 mu, from 2MASS and Spitzer, and based on X-ray
detected sources from a Chandra observation. This sample is composed of 26
Class II and 25 Class III candidates. The sample is estimated to be complete
down to ~ 0.5 solar masses. The most restricted and least biased sub-sample of
pre-main sequence stars is composed of lightly absorbed (A_V < 5 mag) stars in
the cluster core. This sample comprises 7 Class II and 14 Class III sources, it
has a disk fraction of 33^{+24}_{-19} %, and a median X-ray luminosity of log
(L_x) [erg/s] = 30.3. Despite the various uncertainties related to the sample
selection, absorption, mass distribution, distance and, consequently, the
computation of disk fraction and X-ray luminosities, the data yield consistent
results. In particular, we confirm the age of ~3 Myr for the NGC7129 cluster.
The derived disk fraction is similar to that of sigma Orionis, smaller than
that of Cha I (~2 Myr), and larger than that of Upper Sco (5 Myr). The X-ray
luminosity function is similar to that of NGC 2264 (2 Myr) but fainter than
that of the Orion Nebula Cluster (1 Myr).Comment: accepted for publication in Astronomy & Astrophysic
An appraisal of analytical tools used in predicting clinical outcomes following radiation therapy treatment of men with prostate cancer: a systematic review
Background: Prostate cancer can be treated with several different modalities, including radiation treatment. Various prognostic tools have been developed to aid decision making by providing estimates of the probability of different outcomes. Such tools have been demonstrated to have better prognostic accuracy than clinical judgment alone. Methods: A systematic review was undertaken to identify papers relating to the prediction of clinical outcomes (biochemical failure, metastasis, survival) in patients with prostate cancer who received radiation treatment, with the particular aim of identifying whether published tools are adequately developed, validated, and provide accurate predictions. PubMed and EMBASE were searched from July 2007. Title and abstract screening, full text review, and critical appraisal were conducted by two reviewers. A review protocol was published in advance of commencing literature searches. Results: The search strategy resulted in 165 potential articles, of which 72 were selected for full text review and 47 ultimately included. These papers described 66 models which were newly developed and 31 which were external validations of already published predictive tools. The included studies represented a total of 60,457 patients, recruited between 1984 and 2009. Sixty five percent of models were not externally validated, 57% did not report accuracy and 31% included variables which are not readily accessible in existing datasets. Most models (72, 74%) related to external beam radiation therapy with the remainder relating to brachytherapy (alone or in combination with external beam radiation therapy). Conclusions: A large number of prognostic models (97) have been described in the recent literature, representing a rapid increase since previous reviews (17 papers, 1966–2007). Most models described were not validated and a third utilised variables which are not readily accessible in existing data collections. Where validation had occurred, it was often limited to data taken from single institutes in the US. While validated and accurate models are available to predict prostate cancer specific mortality following external beam radiation therapy, there is a scarcity of such tools relating to brachytherapy. This review provides an accessible catalogue of predictive tools for current use and which should be prioritised for future validation.Elspeth Raymond, Michael E. O’Callaghan, Jared Campbell, Andrew D. Vincent, Kerri Beckmann, David Roder, Sue Evans, John McNeil, Jeremy Millar, John Zalcberg, Martin Borg and Kim Morett
3-[2-(6-Bromo-2-phenyl-3H-imidazo[4,5-b]pyridin-3-yl)ethyl]-1,3-oxazolidin-2-one
In the title molecule, C17H15BrN4O2, the fused-ring system is essentially planar, the largest deviation from the mean plane being 0.015 (2) Å, and forms dihedral angles of 37.8 (2) and 35.5 (2)° with the phenyl and oxazolidine rings, respectively. The conformation adopted by the molecule is stabilized by an intramolecular π⋯π interaction [centroid–centroid distance = 3.855(2) Å] between oxazolidine and phenyl rings. The crystal packing features intermolecular C—H⋯N and C—H⋯O interactions
Global generalized solutions for Maxwell-alpha and Euler-alpha equations
We study initial-boundary value problems for the Lagrangian averaged alpha
models for the equations of motion for the corotational Maxwell and inviscid
fluids in 2D and 3D. We show existence of (global in time) dissipative
solutions to these problems. We also discuss the idea of dissipative solution
in an abstract Hilbert space framework.Comment: 27 pages, to appear in Nonlinearit
- …