A search for planets transiting the M-dwarf debris disc host, AU Microscopii

We present high-cadence, high-precision multiband photometry of the young, M1Ve, debris disc star, AU Microscopii. The data were obtained in three continuum filters spanning a wavelength range from 4500 to 6600 Å, plus Hα, over 28 nights in 2005. The light curves show intrinsic stellar variability due to star-spots with an amplitude in the blue band of 0.051 mag and a period of 4.847 d. In addition, three large flares were detected in the data which all occur near the minimum brightness of the star. We remove the intrinsic stellar variability and combine the light curves of all the filters in order to search for transits by possible planetary companions orbiting in the plane of the nearly edge-on debris disc. The combined final light curve has a sampling of 0.35 min and a standard deviation of 6.8 mmag. We performed Monte Carlo simulations by adding fake transits to the observed light curve and find with 95 per cent significance that there are no Jupiter mass planets orbiting in the plane of the debris disc on circular orbits with periods,   P ≤ 5  d. In addition, there are no young Neptune like planets (with radii 2.5 times smaller than the young Jupiter) on circular orbits with periods,   P ≤ 3  d.     

Parent stars of extrasolar planets – VIII. Chemical abundances for 18 elements in 31 stars

We present the results of detailed spectroscopic abundance analyses for 18 elements in 31 nearby stars with planets (SWPs). The resulting abundances are combined with other similar studies of nearby SWPs and compared to a sample of nearby stars without detected planets. We find some evidence for abundance differences between these two samples for Al, Si and Ti. Some of our results are in conflict with a recent study of SWPs in the SPOCS data base. We encourage continued study of the abundance patterns of SWPs to resolve these discrepancies.     

Evolution of protoplanetary discs driven by the MRI, self-gravity and hydrodynamical turbulence

We study the viscous evolution of protoplanetary discs driven by the combined action of magnetohydrodynamic turbulence, resulting from the magneto-rotational instability (MRI), self-gravity torques, parametrized in terms of an effective viscosity and an additional viscous agent of unspecified origin. The distribution of torques driving the evolution of the disc is calculated by analysing where in the disc the MRI develops and, to incorporate the effect of self-gravity, calculating the Toomre parameter. We find that, generally, discs rapidly evolve towards a configuration where the intermediate regions, from a fraction of an au to a few au, are stable against the MRI due to their low-ionization degree. As an additional source of viscosity is assumed to operate in those regions, subsequent evolution of the disc is eruptive. Brief episodes of high mass accretion ensue as the criterion for the development of the MRI is met in the low-ionization region. The radial distribution of mass and temperature in the disc differs considerably from disc models with constant α parameter or layered accretion models, with potentially important consequences on the process of planet formation.     

A geostatistical model for estimating edge effects and cumulative human disturbance in wetlands and coastal waters

ABSTRACT

The modification of landscapes for agriculture, residential housing, and other human uses has the potential to alter ecosystem function, reduce native biodiversity, and to diminish the capacity of natural systems to provide essential goods and services. Resultantly, methods aimed at quantifying disturbance intensity at the intersection of anthropogenic and natural lands are important to applied aspects of landscape planning and natural resource management, as well as theoretical facets of spatial ecology. Tools to appraise human disturbance in wetlands and coastal waters are especially needed because the topographic settings of surface waters make them particularly susceptible to pollutant accumulation, hydrologic alteration, and other influences from the surrounding landscape. Assuming a water resources focus, we developed a novel geostatistical method to quantify and visualize edge effects and cumulative human disturbance for all wetlands and coastal waters in the State of Florida (USA). Model validation revealed that estimated disturbance intensity was strongly correlated to species richness, levels of heavy metal contamination, and persistent organic pollutant concentration. The presented modeling framework offers a flexible means to assess human disturbance across spatial scales and could be readily applied to prioritize conservation land acquisition and to anticipate future impacts from the proposed development.     

Hydrological alterations from water infrastructure development in the Mekong floodplains

The Mekong floodplains, which encompasses the region from Kratie Township in Central Cambodia to the Vietnamese East Sea, is a region of globally renown agricultural productivity and biodiversity. The construction of 135 dams across the Mekong basin and the development of delta‐based flood prevention systems have caused public concern given possible threats on the stability of agricultural and ecological systems in the floodplains. Mekong dams store water upstream and regulate flow seasonality, while in situ flood prevention systems re‐distribute water retention capacity in the floodplains. The main aim of this paper is to evaluate possible impacts of the recent development of both hydropower dams and flood prevention systems on hydrological regimes in the Mekong floodplains. An analysis of measured daily and hourly water level data for key stations in the Mekong floodplains from Kratie to the river mouth in Vietnam was conducted. Hydropower dam information was obtained from the hydropower database managed by the Mekong River Commission, and the MODIS satellite imagery was used to detect changes in flooding extent related to the operation of flood prevention systems in the Vietnam Mekong Delta. Results indicate that the upper part of the floodplains, the Cambodian floodplains, may buffer upstream dam impacts to the Vietnam Mekong Delta. Flood prevention up to date has had the greatest effect on the natural hydrological regime of the Mekong floodplains, evidenced by a significant increase of water level rise and fall rates in the upper delta and causing water levels in the middle delta to increase. The development of flood prevention systems has also effected spatial distribution of flooding as indicated via a time series analysis of satellite imagery. While this development leads to increase localized agricultural productivity, our historical data analysis indicates that development of one region detrimentally affects other regions within the delta, which could increase the risk of future conflicts among regions, economic sectors and the ecological value of these important floodplains. Copyright © 2016 John Wiley & Sons, Ltd.     

Modeling of tensile strength of rocks materials based on support vector machines approaches

In the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS‐SVM) provides a computational advantage over SVM by converting quadratic optimization problem into a system of linear equations, LS‐SVM method is also tried in study. The main purpose of this study is to examine the capability of these two SVM algorithms for the prediction of tensile strength of rock materials and to compare its performance with ANN and linear regression (MLR) models. Total porosity, sonic velocity, slake durability index and aggregate impact value were used as input in modeling applications. Favorite performance evaluation measures were employed to assess developed models. The results determined in study indicate that the SVM, LS‐SVM and ANN methods are successful tools for prediction of tensile strength variable and can give good prediction performances than MLR model. Although these three methods are powerful artificial intelligence techniques, LS‐SVM makes the running time considerably faster with the higher accuracy. In terms of accuracy, the LS‐SVM model resulted in error reductions relative to that of the other models. Copyright © 2012 John Wiley & Sons, Ltd.     

Determination of liquefaction susceptibility of soil: a least square support vector machine approach

This article adopts least square support vector machine (LSSVM) for determination of liquefactions susceptibility of soil based on standard penetration test data. Two models (Models I and II) have been developed. For Model I, input variables are cyclic stress ratio and standard penetration test value (N). Model II employs peak ground acceleration and N as input variables. The developed LSSVM models (Models I and II) give equations for determination of liquefaction susceptibility of soil. The performances of Models I and II are the same. The developed LSSVM gives probabilistic output. The results of LSSVM have been compared with the artificial neural network model. This article shows that N and the peak ground acceleration are sufficient input parameters for determination of liquefaction susceptibility of soil. Copyright © 2012 John Wiley & Sons, Ltd.     

Area-level risk factors for heat-related illness in rural and urban locations across North Carolina,USA

An improved understanding of heat vulnerable populations and locations is needed, especially in rural communities. The objective of this study was to identify area-level risk factors for heat-related illness (HRI) at the ZIP code level for urban and rural locations. We aggregated ZIP code-level emergency department visits into rural and urban locations based on population density. Area-level risk factors included previously established heat health risk factors (e.g. poverty, minority) and unexamined area-level risk factors common to rural locations (e.g. mobile homes, agriculture). Due to high spatial autocorrelation, a spatial error regression model was applied to identify risk factors with a significant relationship with HRI. Our results suggest that rural locations are also heat vulnerable, with greater rates of HRI compared to urban locations. Previously unexamined heat-health risk factors, including the number of mobile homes, non-citizens, and the labor-intensity of the agriculture, were all associated with increases of HRI in rural locations. In urban locations, previously established risk factors for heat-related mortality, such as decreased vegetation, living in poverty, and low education attainment were associated with increases in HRI.