When AAA Means B: The State of Credit Rating in India

As in many other countries, India five year old credit rating industry has grown rapidly amidst persistent doubts about the quality of the rating service. This paper evaluates the ratings given by India leading credit rating agency, CRISIL. We find that CRISIL ratings are not only too liberal by international standards but also internally inconsistent. We argue that to improve the quality of credit rating in India, there must be more competition; credit rating must be opened up to the private sector; and raters must provide unsolicited ratings.

Characterization And Properties Of Polypropylene Recycled Acrylonitrile Butadiene Rubber Rice Husk Powder Composites

Komposit elastomer termoplastik bagi adunan polipropilena (PP)/ getah kitar semula akrilonitril butadiene (NBRr)/ serbuk sekam padi (RHP) telah dikaji. Kesemua sampel ujian disediakan dengan menggunakan pencampur dalaman Haake Rheomix Polydrive R600/ 610 pada suhu 180°C dan kelajuan rotor 50 rpm. Sampel tersebut kemudian dibentuk menggunakan pengacuanan mampatan pada suhu 180°C. Saiz partikel bagi RHP dan NBRr yang digunakan di dalam kajian ini adalah 150 - 300 μm. Perekatan di antara pengisi dan matriks telah ditingkatkan dengan menggunakan asetik anhidrida (Ac) sebagai agen penserasian dan ɤ-APS sebagai agen pengkupel. Sifat-sifat mekanikal dan terma bagi kesemua komposit yang dirawat telah meningkat. Rawatan Ac telah menambahkan ciri-ciri hidrofobik RHP dan meningkatkan lekatannya dengan matrik tak berpolar PP/NBRr. Peningkatan sifat-sifat seperti tensil, kestabilan terma dan penghabluran komposit yang lebih baik ditunjukkan oleh komposit yang dirawat dengan Ac berbanding pengkupelan ɤ-APS. Teknik penserasian matrik dengan menggunakan Eter Diglycidal Bisphenol A (DGEBA) dan maleik anhidrida tercantum polipropilena (PPMAH) juga telah dikaji. Perekatan di antara pengisi dan matrik telah ditingkatkan oleh DGEBA dan PPMAH. DGEBA didapati lebih berkesan dalam meningkatkan perekatan antara pengisi dan matrix. Radiasi alur elektron turut dikaji bagi menghasilkan sambung silang di dalam komposit. Radiasi alur elektron pada dos 40 kGray dengan kehadiran TMPTA (promoter sambung silang) telah digunakan. Keputusan menunjukkan komposit mempunyai sifat-sifat tensil, kandungan gel, kestabilan terma dan kehabluran komposit yang paling tinggi tetapi penyerapan air yang paling rendah. ____________________________________________________________________________________________________________________ Themoplastic elastomer composite of polypropylene (PP)/ recycled acrylonitrile butadiene rubber (NBRr)/ rice husk powder (RHP) was investigated. All test samples were prepared using Haake Rheomix Polydrive R600/ 610 internal mixer at temperature of 180°C and rotor speed of 50 rpm. The samples were later moulded using compression moulding at temperature of 180°C. The particle sizes of the RHP and NBRr used in this research were 150 - 300 μm. The composites properties were enhanced by treating the RHP using Acetic Anhydride (Ac) and ɤ-APS as a coupling agent. Mechanical and thermal properties of all treated composites were improved. The treatment with Ac increased the hydrophobic characteristic of RHP and improves its adhesion with non-polar PP/NBRr matrixes. Better properties such as tensile, thermal stability and crystallinity of composites showed by Ac treatment compared with ɤ-APS coupling agent. Matrix compatibilization techniques were also investigated by using Diglycidal ether of bisphenol A (DGEBA) and Polypropylene grafted maleic anhydride (PPMAH). The adhesion between filler and matrix was improved for both DGEBA and PPMAH. DGEBA was found to be more effective in improving the adhesion between filer and matrix. Electron beam irradiation was also evaluated to introduce cross-linking in the composites. Electron beam irradiation at 40 kGray in the presence of TMPTA (irradiation crosslink promoter) was used. Results showed that composites have highest tensile properties, gel content, thermal stability and crystallinity but lowest percentage of water absorption

Stereological analysis of glial cell subtypes in the primary visual cortex across the life span of rhesus monkeys

The normal aging process is accompanied by mild declines in visual function in both humans and non-human primates, independent of ocular deficiencies, indicating an involvement of structures in the central visual pathway. Studies examining loss of cortical neurons as a potential explanation have concluded that neuron numbers are largely preserved with age both in visual cortex and as well as other cortices. In contrast to the stability of neuron numbers with age, a significant increase in the total number of glia was found in the infragranular layers of the primary visual cortex, in rhesus monkeys (Giannaris and Rosene, 2012). Unpublished data indicates that increase in glial density is correlated with decreased visual function assessed by the behavioral performance metric. In order to understand the basis of glial increase with age in the rhesus monkey, we used immunohistochemistry to parcellate the total number of glia in primary visual cortex into three subtypes: microglia with Iba1, astrocytes with GFAP and oligodendrocytes with Olig2. These were then quantified using unbiased stereology in a subset of 12 animals whose ages ranged from young to old (6 male and 6 female), from the original study of 26 monkeys. Adding together all three subtypes in the current subset of animals showed a modest but non-significant trend toward the increase observed in the larger sample of 26 animals. In this study, examining the three subtypes showed no significant increase and the total number of glial cells was found to be unchanged with age. A definitive answer to how the different subtypes contribute to the overall increase in glia will require analyzing the remainder of the full data set

Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks

Link Prediction (LP) in social networks (SN) is referred to as predicting the likelihood of a link formation in SNs in the near future. There are several types of SNs that are available such as human interaction network, biological network, protein-to-protein interaction network, and so on. Earlier LP researches used heuristics methods, including Common Neighbors, Resource Allocation, and many other similarity score methods. Even though heuristics methods perform better in some types of SNs, their performance is limited in other types of SNs. Finding the best heuristics for a given type of SN is a trial and error process. Recent state-of-the-art research, WLNM and SEAL showed that with deep learning techniques and subgraphing, the heuristics selection could be automated and increase the accuracy of LP. However, WLNM and SEAL have some limitations and still having performance lack in some types of SNs. The objective of this paper is to introduce a novel framework that overcomes the limitations of state-of-the-art methods and improves the accuracy of LP over various types of social networks. We propose a Link Prediction framework called PLACN that analyzes common neighbors based subgraphs using deep learning technique to predict links. PLACN is equipped with two new algorithms that are a subgraph extraction algorithm that efficiently extracts common neighbors of targeted nodes and a proposed new node labeling algorithm based on hop number and average path weight that creates consistent node orders over subgraphs. In addition to the algorithms, we derived a formula based on network properties to find an optimal number node for a given SN. PLACN converts the LP problem into an Image Classification problem and utilizes a Convolutional Neural Network to classify the links. We tested the proposed PLACN on seven different types of real-work networks and compared the performance against heuristics, latent methods, and state-of-the-art methods. Our results show that PLACN outperformed the compared Link Prediction methods while reaching above 96% AUC in tested benchmark social networks

Empirical Equations for Activity and Osmotic Coefficients

A system of equations for fitting the experimental activity and osmotic coefficients of single and mixed electrolytes in aqueous solutions has been empirically developed in the present research. The results obtained through the equations developed here are comparable to the Pitzer equations in terms of accuracy and range of fitting. The equation for activity coefficient developed in the present research compared to the Pitzer activity coefficient equation has a form which is conveinient for computational purposes. The equation for activity coefficients is ln g = -╎ZmZx╎AD[I1/2/(1 + bI1/2)] + E I ln(I) + J1I + J2I3/2 where, b is fixed parameter having a value of 1.8; E, J1, and J2 are floating parameters. The corresponding equation for osmotic coefficients is obtained through the Gibbs-Duhem equation. The parameters have been evaluated by a nonlinear least squares computer program. This program weights all the data points equally. Parameters for both the coefficients are presented. In most of the cases data recommended by Robinson and Stokes is used. In the case of the 2-2 electrolytes one additional parameter is included to obtain acceptable results instead of two by Pitzer. Representing the 2-2 electrolytes in this manner ignores association constants, and thereby simplifying the treatment of these electrolytes at higher solution concentrations. Treatment of mixed electrolytes involves, in addition to pure electrolyte terms, parameters to account for the mixing effects is utilised. Only a few mixed electrolytes involving osmotic coefficients as experimental data has been treated here, and the results obtained are comparable to Pitzer\u27s