Secular Behavior of Exoplanets: Self-Consistency and Comparisons with the Planet-Planet Scattering Hypothesis

If mutual gravitational scattering among exoplanets occurs, then it may produce unique orbital properties. For example, two-planet systems that lie near the boundary between circulation and libration of their periapses could result if planet-planet scattering ejected a former third planet quickly, leaving one planet on an eccentric orbit and the other on a circular orbit. We first improve upon previous work that examined the apsidal behavior of known multiplanet systems by doubling the sample size and including observational uncertainties. This analysis recovers previous results that demonstrated that many systems lay on the apsidal boundary between libration and circulation. We then performed over 12,000 three-dimensional N-body simulations of hypothetical three-body systems that are unstable, but stabilize to two-body systems after an ejection. Using these synthetic two-planet systems, we test the planet-planet scattering hypothesis by comparing their apsidal behavior, over a range of viewing angles, to that of the observed systems and find that they are statistically consistent regardless of the multiplicity of the observed systems. Finally, we combine our results with previous studies to show that, from the sampled cases, the most likely planetary mass function prior to planet-planet scattering follows a power law with index -1.1. We find that this pre-scattering mass function predicts a mutual inclination frequency distribution that follows an exponential function with an index between -0.06 and -0.1.Comment: 29 pages, 3 figures, accepted for publication in A

“User Satisfaction on Library Resoures and Serviecs in St.Claret Degree College Library, Bangalore-A Study”

The main motive of this study was to examine and analyze the users’ satisfaction with library resources and services among the faculty members and students of St. Claret Degree College, Bangalore. The present study demonstrates the satisfaction levels of users towards various library resources and services provided by the college library. The result of the study found that a large number of respondents were satisfied with library resources and services. It also finds that the books had become a most widely used resources and circulation services was emerged most preferred service. Some suggestions have been given by the respondents to make about the library resources and services more effective and efficient manner

Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation

Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) based methods for cardiac magnetic resonance (CMR) image segmentation rely on supervision with pixel-level annotations, and may not generalize well to images from a different domain. These methods are typically sensitive to variations in imaging protocols and data acquisition. Since annotating multi-sequence CMR images is tedious and subject to inter- and intra-observer variations, developing methods that can automatically adapt from one domain to the target domain is of great interest. In this paper, we propose an approach for domain adaptation in multi-sequence CMR segmentation task using transfer learning that combines multi-source image information. We first train an encoder-decoder CNN on T2-weighted and balanced-Steady State Free Precession (bSSFP) MR images with pixel-level annotation and fine-tune the same network with a limited number of Late Gadolinium Enhanced-MR (LGE-MR) subjects, to adapt the domain features. The domain-adapted network was trained with just four LGE-MR training samples and obtained an average Dice score of ∼∼85.0% on the test set comprises of 40 LGE-MR subjects. The proposed method significantly outperformed a network without adaptation trained from scratch on the same set of LGE-MR training data

Usage of Fym and Its Impact on Rice Productivity: Empirical Evidence from Tamil Nadu, India

This paper explores the usage of farmyard manure (FYM) and its impact on paddy yield under different soil conditions in Tamil Nadu, using farming households’ three-year rotating panel data from 1993 to 2003. Estimated yield functions reveal that, direct impact of FYM application did not exists in paddy cultivation. Meanwhile, an indirect impact through an increase in the marginal product of chemical fertilizer is observed especially under low inherent soil fertility status. Reflecting the existence of the benefit of FYM application, our factor demand estimation showed that farmers react to FYM price change actively. This means that, reduction in FYM price contributed to the productivity improvement. Key words: Rice, Farm Yard Manure (FYM), Productivity

Screening of seaweed extracts against antibiotic resistant post operative infectious pathogens

Fifty five seaweed extracts belonging to 11 species of seaweeds were tested against post operative infectious drug resistant bacteria viz., E. coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Streptococcus pyogens, Staphylococcus aureus. Among the seaweed extracts, the acetone extracts of Caulerpa cupressoides shows maximum inhibtory activity against E. coli and propanol extracts of Gracilaria edulis shows maximum inhibitory effect against K. pneumoniae. Acetone extracts of Padina tetrastromatica and Laurencia cruciata show maximum inhibitory activity against P. aeruginosa, butanol extracts of Hypnea musciformis, Caulerpa cupressoides and Chaetomorpha linoides show maximum inhibitory effect against S. aureus

Dry sliding friction and wear behavior of hybrid glass - carbon fiber reinforced PA66/PTFE composites

The tribological response and the frictional effects in dry sliding wear behaviour of hybrid Glass –Carbon composites under the action of sliding load and sliding velocity was studied. The material systems considered for the investigation were PA66/PTFE blend (80/20 wt. %), Blend(PA66/PTFE)/10 wt.% short glass fiber (SGF), Blend (PA66/PTFE)/10 wt.% short carbon fiber (SCF) and Blend (PA66/PTFE)/10 wt.% SGF/10 wt.% SCF (GC).These composites were produced using melt mixing method through extrusion and followed by injection molding. The experimentation was conducted as per ASTM G99 method. The experimentation data revealed that the significant wear resistance was exhibited by Glass-Carbon hybrid composites under the action of all the test parameters. This is attributed to the hybrid effect of fibres which may restrict the early reaching of softening point of polymers thereby preventing melting wear. Further, the formation of uniform and defined transfer polymer substrate on the steel disc surface reduced the frictional effects. Further, Blend/SCF composites were better than Blend/SGF composites. The composites studied were sensitive to applied normal load compared to velocity. The combined matrix and fiber wear were credited to the critical wear volume loss. Fiber misalignment, matrix deformation, melting wear and fiber peeling were some of the failure mechanisms observed in the morphological study of hybrid composites through SEM images

Evaluation of the Fresh and Hardened Properties of Steel Fibre Reinforced Self-Compacting Concrete Using Recycled Aggregates as a Replacement Material

In this world of rapid urbanization the demand for natural construction materials is increasing day by day which has created a necessity for alternative construction materials. Recycling of materials is a possible way of eradicating the acute shortage of materials. Considerable work has been done in the area of self-compacting concrete by partial replacement of coarse aggregates (CA) with recycled coarse aggregates (RCA) obtained from construction and demolition debris. The present study has been done by adding steel fibers to concrete in a view of improving the mechanical properties of SCC so that it can be applied in beam column joints. An ideal mix proportion was arrived at, as a result of repeated trials and specimens that were cast and cured. The compression, tensile, and flexural strength parameters were determined and the result has been presented. The results obtained reveal that brick bats in combination with steel fibres may be used extensively in SCC

A Multi-Resolution t-Mixture Model Approach to Robust Group-wise Alignment of Shapes

A novel probabilistic, group-wise rigid registration framework is proposed in this study, to robustly align and establish correspondence across anatomical shapes represented as unstructured point sets. Student’s t-mixture model (TMM) is employed to exploit their inherent robustness to outliers. The primary application for such a framework is the automatic construction of statistical shape models (SSMs) of anatomical structures, from medical images. Tools used for automatic segmentation and landmarking of medical images often result in segmentations with varying proportions of outliers. The proposed approach is able to robustly align shapes and establish valid correspondences in the presence of considerable outliers and large variations in shape. A multi-resolution registration (mrTMM) framework is also formulated, to further improve the performance of the proposed TMM-based registration method. Comparisons with a state-of-the art approach using clinical data show that the mrTMM method in particular, achieves higher alignment accuracy and yields SSMs that generalise better to unseen shapes

Phytoplankton Community Composition in Gowrikere Tank Anandapura, Sagara, Shivamogga, Karnataka, India

No Abstract

A Divide-and-Conquer Approach Towards Understanding Deep Networks

Deep neural networks have achieved tremendous success in various fields including medical image segmentation. However, they have long been criticized for being a black-box, in that interpretation, understanding and correcting architectures is difficult as there is no general theory for deep neural network design. Previously, precision learning was proposed to fuse deep architectures and traditional approaches. Deep networks constructed in this way benefit from the original known operator, have fewer parameters, and improved interpretability. However, they do not yield state-of-the-art performance in all applications. In this paper, we propose to analyze deep networks using known operators, by adopting a divide-and-conquer strategy to replace network components, whilst retaining networks performance. The task of retinal vessel segmentation is investigated for this purpose. We start with a high-performance U-Net and show by step-by-step conversion that we are able to divide the network into modules of known operators. The results indicate that a combination of a trainable guided filter and a trainable version of the Frangi filter yields a performance at the level of U-Net (AUC 0.974 vs. 0.972) with a tremendous reduction in parameters (111, 536 vs. 9, 575). In addition, the trained layers can be mapped back into their original algorithmic interpretation and analyzed using standard tools of signal processing