Modeling Climate Change Mitigation Strategies for Coastal Communities Using GIS

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

A Consistent Dark Matter Interpretation For CoGeNT and DAMA/LIBRA

In this paper, we study the recent excess of low energy events observed by the CoGeNT collaboration and the annual modulation reported by the DAMA/LIBRA collaboration, and discuss whether these signals could both be the result of the same elastically scattering dark matter particle. We find that, without channeling but when taking into account uncertainties in the relevant quenching factors, a dark matter candidate with a mass of approximately ~7.0 GeV and a cross section with nucleons of sigma_{DM-N} ~2x10^-4 pb (2x10^-40 cm^2) could account for both of these observations. We also comment on the events recently observed in the oxygen band of the CRESST experiment and point out that these could potentially be explained by such a particle. Lastly, we compare the region of parameter space favored by DAMA/LIBRA and CoGeNT to the constraints from XENON 10, XENON 100, and CDMS (Si) and find that these experiments cannot at this time rule out a dark matter interpretation of these signals.Comment: 8 pages, 6 figure

Denoising Particle Beam Micrographs with Plug-and-Play Methods

In a particle beam microscope, a raster-scanned focused beam of particles interacts with a sample to generate a secondary electron (SE) signal pixel by pixel. Conventionally formed micrographs are noisy because of limitations on acquisition time and dose. Recent work has shown that estimation methods applicable to a time-resolved measurement paradigm can greatly reduce noise, but these methods apply pixel by pixel without exploiting image structure. Raw SE count data can be modeled with a compound Poisson (Neyman Type A) likelihood, which implies data variance that is signal-dependent and greater than the variation in the underlying particle-sample interaction. These statistical properties make methods that assume additive white Gaussian noise ineffective. This paper introduces methods for particle beam micrograph denoising that use the plug-and-play framework to exploit image structure while being applicable to the unusual data likelihoods of this modality. Approximations of the data likelihood that vary in accuracy and computational complexity are combined with denoising by total variation regularization, BM3D, and DnCNN. Methods are provided for both conventional and time-resolved measurements, assuming SE counts are available. In simulations representative of helium ion microscopy and scanning electron microscopy, significant improvements in root mean-squared error (RMSE), structural similarity index measure (SSIM), and qualitative appearance are obtained. Average reductions in RMSE are by factors ranging from 2.24 to 4.11

Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in theWater Column of Freshwater Lakes

Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L−1 for CHLa; R² = 0.75, RMSE = 0.22 m−1 for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L−1 for CHLa; R² = 0.66, RMSE = 0.25 m−1 for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS

Influence of Translocations on Eastern Wild Turkey Population Genetics in Texas

Between 1979 and 2006, over 7,000 eastern wild turkeys (Meleagris gallopavo silvestris) from 16 states were translocated to east Texas in an attempt to restore a stable, huntable population. Although current populations are stable in some areas and a spring male-only hunting season was opened in 1995, turkey density in the region remains low and large areas of apparently suitable habitat are not occupied. The longterm effects of the extensive translocations and current levels of connectivity among various populations are unknown. We used microsatellite DNA analysis to assess the influence of translocations on current genetic structure and gene flow in eastern wild turkeys. The influence of translocations was clearly evident and reflected historical contributions from the Midwest and southeastern United States. The east Texas population consisted of 3 distinct genetic clusters. Despite a lack of clear geographic barriers and nearly contiguous forest cover in much of the east Texas landscape, regional gene flow among clusters appeared to be limited. Diversity in the regional population remains high, but we recommend that regulations reflect the current population structure and that long-term efforts should be made to increase connectivity among wild turkeys in the region

Quantitative Genetic Analyses of Postcanine Morphological Crown Variation

Objectives: This article presents estimates of narrow-sense heritability and bivariate genetic correlation for 14 tooth crown morphological variants scored on permanent premolars, first molars, and second molars. The objective is to inform data collection and analytical practices in dental biodistance and to provide insights on the development of molar crowns as integrated structures. Materials and Methods: African American dental casts from the Menegaz-Bock collection were recorded for the Arizona State University Dental Anthropology System. Estimates of narrow-sense heritability and genetic correlation were generated using SOLAR v.8.1.1, which included assessment of age, sex, and birth year as covariates. Both continuous scale and dichotomized estimates are provided. Results: Heritability estimates were nonsignificant for the majority of variables; however, for variables yielding significant estimates, values were moderate to high in magnitude and comparable to previous studies. Comparing left and right-side heritability estimates suggests directional asymmetry in the expression of environmental variance, something not seen in anterior tooth traits. Genetic correlations were moderate among antimeres and metameres and low for different traits scored on the same tooth crown. Although several negative correlations were noted, few reached statistical significance. Results affirm some of the current data cleaning and analytical practices in dental biodistance, but others are called into question. These include the pooling of males and females and combining left and right-side data into a single dataset. Conclusions: In comparison to anterior tooth crown traits, postcanine heritabilities were more often non-significant; however, those traits with significant heritability also tended to produce higher estimates. Genetic correlations were unremarkable, in part, because they were underpowered. However, M1 results may provide insight into the complex relationship between genes, environment, and development in determining ultimate crown form

Functional Outcome in Spinal Meningioma Surgery and Use of Intraoperative Neurophysiological Monitoring.

Data on intraoperative neurophysiological monitoring (IOM) during spinal meningioma (SM) surgery are scarce. The aim of this study was to assess the role of IOM and its impact on post-operative functional outcome. Eighty-six consecutive surgically treated SM patients were included. We assessed pre and post-operative Modified McCormick Scale (mMCS), radiological and histopathological data and IOM findings. Degree of cord compression was associated with preoperative mMCS and existence of motor or sensory deficits (p < 0.001). IOM was used in 51 (59.3%) patients (IOM-group). Median pre and post-operative mMCS was II and I, respectively (p < 0.001). Fifty-seven (66.3%) patients showed an improvement of at least one grade in the mMCS one year after surgery. In the IOM group, only one patient had worsened neurological status, and this was correctly predicted by alterations in evoked potentials. Analysis of both groups found no significantly better neurological outcome in the IOM group, but IOM led to changes in surgical strategy in complex cases. Resection of SM is safe and leads to improved neurological outcome in most cases. Both complication and tumor recurrence rates were low. We recommend the use of IOM in surgically challenging cases, such as completely ossified or large ventrolateral SM

Non-Line-of-Sight Tracking and Mapping with an Active Corner Camera

The ability to form non-line-of-sight (NLOS) images of changing scenes could be transformative in a variety of fields, including search and rescue, autonomous vehicle navigation, and reconnaissance. Most existing active NLOS methods illuminate the hidden scene using a pulsed laser directed at a relay surface and collect time-resolved measurements of returning light. The prevailing approaches include raster scanning of a rectangular grid on a vertical wall opposite the volume of interest to generate a collection of confocal measurements. These are inherently limited by the need for laser scanning. Methods that avoid laser scanning track the moving parts of the hidden scene as one or two point targets. In this work, based on more complete optical response modeling yet still without multiple illumination positions, we demonstrate accurate reconstructions of objects in motion and a 'map' of the stationary scenery behind them. The ability to count, localize, and characterize the sizes of hidden objects in motion, combined with mapping of the stationary hidden scene, could greatly improve indoor situational awareness in a variety of applications