MT Ser, a binary blue subdwarf

We have classified and determined the parameters of the evolved close binary MT Ser. Our moderate-resolution spectra covering various phases of the orbital period were taken with the 6-m telescope of the Special Astrophysical Observatory. The spectra of MT Ser freed from the contribution of the surrounding nebula Abell 41 contained no emission lines due to the reflection effect. The radial velocities measured from lines of different elements showed them to be constant on a time scale corresponding to the orbital period. At the same time, we find effects of broadening for the HeII absorption lines, due to the orbital motion of two hot stars of similar types. As a result, we classify MT Ser as a system with two blue subdwarfs after the common-envelope stage. We estimate the component masses and the distance to the object from the Doppler broadening of the HeII lines. We demonstrate that the age of the ambient nebula, Abell 41, is about 35 000 years.     

Rotational and magnetic corrections to the g-mode frequencies in a one-dimensional MHD model

The central magnetic field and rotation of the solar radiative zone are responsible for corrections to the g-mode frequencies. Magnetogravitational spectra are calculated analytically in a simple one-dimensional MHD model that goes beyond the WKB approximation and avoid any cusp resonances that trap the wave within the radiative zone in the presence of a weak magnetic background. The calculations are compared with spacecraft observations of the 1% frequency shifts for candidate g-modes found in the SOHO GOLF experiment. The magnetic correction is the main contribution for a strong magnetic field satisfying the approximation used. It is shown that a constant magnetic field of 700 kG in the radiative zone provides the required frequency shift for the n = ?10 g-mode. The rotational correction, which is due to the Coriolis force in the one-dimensional model used, is much less than a percent (α_Ω ≤ 0.003).     

Lake bank filtration at Nainital,India: water-quality evaluation

There are different water-supply schemes in Uttarakhand, India to tap the water from streams, rivers and lakes. At Nainital, seven tube-wells (depths 22.6–36.7 m), located at a distance of <100 m from the lake, are being used to abstract (1) lake water after passage through the soil and (2) subsurface water/groundwater flowing towards the lake. Water samples from the lake and tube-wells were analyzed in monsoon and non-monsoon periods from 1997 to 2006. Total dissolved solids, EC, alkalinity and hardness were found to be marginally greater in tube-well waters. The difference in hydrochemistry of tube-well water was mainly due to variation in flow regimes during monsoon and non-monsoon periods. Results clearly indicate that lake water as such is not potable as it contains unacceptable levels of organic matter in terms of COD (~44 mg/L), coliforms (~15.6 × 10⁴ MPN/100 mL) and nutrients. Coliform bacteria and COD have not been detected in any of the tube-well water samples over the years. Lake water, treated by sand filters did not conform to drinking water standards. These investigations have led to the closure of the treatment facility and installation of two tube-wells in addition to the existing five tube-wells.

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Résumé  Il existe divers projets d’alimentation en eau dans l’état d’Uttarakhand, Inde, afin de capter l’eau de ruisseaux, de rivières et de lacs. A Nainital, sept puits tubés (profondeur de 22.6–36.7 m), situés à une distance < à 100 m du lac, sont utilisés pour prélever (1) de l’eau du lac après transit à travers le sol et (2) de l’eau de sub-surface/eau souterraine s’écoulant vers le lac. Des échantillons d’eau du lac et des puits tubés ont été analysés en périodes de mousson et de celles sans mousson 1997 à 2006. Résidu sec, C.E., alcalinité et dureté ont été trouvés marginalement supérieurs dans l’eau des puits tubés. La différence d’hydrochimie de l’eau des puits tubés était surtout due à la variation des régimes d’écoulement pendant les périodes de mousson et de celles sans mousson. Les résultats indiquent clairement que l’eau du lac en tant que telle n’est pas potable car elle contient des teneurs inacceptables de matière organique en termes de COD (~44 mg/L), de coliformes (~15.6 × 10⁴ MPN/100 mL) et d’éléments nutritifs. Des bactéries coliformes et du COD n’ont été détectés dans aucun des échantillons d’eau de puits tubés au fil des années. L’eau du lac traitée par des filtres à sable ne se conformait pas aux normes de l’eau potable. Ces recherches ont conduit à la fermeture de l’installation de traitement et à l’implantation de deux puits tubés en plus des cinq puits existants.

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Resumen  En el estado de Uttarakhand, India, existen diferentes esquemas de abastecimiento de agua que explotan agua de arroyos, ríos y lagos. En Nainital, siete pozos (profundidades entre 22.6–36.7 m), ubicados a una distancia de <100 m del lago, se usan para extraer (1) agua del lago luego de su pasaje a través del suelo y (2) agua superficial y subterránea que fluye hacia el lago. En períodos de monzón y de no monzón de 1997 a 2006 se han analizado muestras de agua del lago y de las captaciones. Se halló que el agua de los pozos es ligeramente mayor en términos del total de sólidos disueltos, la conductividad eléctrica, la alcalinidad y la dureza. La diferencia en la hidroquímica del agua de las perforaciones se debe principalmente a la variación de los regímenes de flujo durante los períodos de monzón y de no monzón. Los resultados claramente indican que el agua del lago no es potable por su contenido inaceptable de materia orgánica medida como demanda de carbono orgánico/oxígeno –DCO- (~44 mg/L), coliformes (~15.6 × 10⁴ NMP/100 mL) y nutrientes. En el período, no se han detectado bacterias coliformes ni DCO en las muestras de agua de las captaciones. El agua del lago, tratada con filtros de arena, no conformó los estándares de agua para bebida. Estas investigaciones han demostrado la necesidad de clausurar las instalaciones de tratamiento y la adición de dos captaciones a las cinco ya existentes.

     

The role of overwash in the evolution of mixing zone morphology within barrier islands

Overwash is a major controlling factor in the morphology of the mixing zone of coastal aquifers. Conceptual models of the mixing zone describe an interface controlled by tidal oscillations, wave run-up, and other factors; however, few describe the influence of large storm events. In August 1993, Hatteras Island, North Carolina, USA, experienced a 3-m storm surge due to Hurricane Emily. Sound-side flooding infiltrated a wellfield, causing a dramatic increase in TDS levels that persisted for more than 3 years. Two-dimensional simulations with SUTRA, the USGS finite-element model, are calibrated to the TDS breakthrough data of this storm to infer model dispersivity values. Simulations using the calibrated dispersivity values, predicted flooding levels, and 54 years of hurricane records to determine the influence of the overwash events suggest that it is rare for the mixing zone to approximate the conceptual morphology. Even during quiescent periods such as between 1965 and 1975, TDS levels do not return to theoretical levels before being elevated by a subsequent storm event. Thus, while tidal oscillations and other factors are important to mixing zone development, basic wind events and more severe storm events may have more influence and lasting effect on the morphology of the mixing zone.     

Nutrient Mass Flux between Florida Bay and the Florida Keys National Marine Sanctuary

There is a net discharge of water and nutrients through Long Key Channel from Florida Bay to the Florida Keys National Marine Sanctuary (FKNMS). There has been speculation that this water and its constituents may be contributing to the loss of coral cover on the Florida Keys Reef tract over the past few decades, as well as speculation that changes in freshwater flow in the upstream Everglades ecosystem associated with the Comprehensive Everglades Restoration Plan may exacerbate this phenomenon. The results of this study indicate that although there is a net export of approximately 3,850 (±404) ton N year^?1 and 63 (±7) ton P year^?1, the concentrations of these nutrients flowing out of Florida Bay are the same as those flowing in. This implies that no significant nutrient enrichment is occurring in the waters of the FKNMS in the vicinity of Long Key Channel. Because of the effect of restricted southwestward water flow through Florida Bay by shallow banks and small islands, the volume of relatively high-nutrient water from central and eastern portions of the bay exiting through the channel is small compared to the average tidal exchange. Nutrient loading of relatively enriched bay waters is mediated by tidal exchange and mixing with more ambient concentrations of the western Florida Bay and Hawk Channel. System-wide budgets indicate that the contribution of Florida Bay waters to the inorganic nitrogen pool of the Keys coral reef is small relative to offshore inputs.     

Automated Kerogen Classification in Microscope Images of Dispersed Kerogen Preparation

We develop the classification part of a system that analyses transmitted light microscope images of dispersed kerogen preparation. The system automatically extracts kerogen pieces from the image and labels each piece as either inertinite or vitrinite. The image pre-processing analysis consists of background removal, identification of kerogen material, object segmentation, object extraction (individual images of pieces of kerogen) and feature calculation for each object. An expert palynologist was asked to label the objects into categories inertinite and vitrinite, which provided the ground truth for the classification experiment. Ten state-of-the-art classifiers and classifier ensembles were compared: Naïve Bayes, decision tree, nearest neighbour, the logistic classifier, multilayered perceptron (MLP), support vector machines (SVM), AdaBoost, Bagging, LogitBoost and Random Forest. The logistic classifier was singled out as the most accurate classifier, with an accuracy greater than 90. Using a 10 times 10-fold cross-validation provided within the Weka software, we found that the logistic classifier was significantly better than five classifiers (p<0.05) and indistinguishable from the other four classifiers. The initial set of 32 features was subsequently reduced to 6 features without compromising the classification accuracy. A further evaluation of the system alerted us to the possible sensitivity of the classification to the ground truth that might vary from one human expert to another. The analysis also revealed that the logistic classifier made most of the correct classifications with a high certainty.