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.     

Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.     

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.