An Analysis Of Business Process Outsourcing Strategies Of Public And Private Sector Banks In India

This paper is a study of the recent trends of Business Process Outsourcing (BPO) strategies and practices among banking institutions in India.  The study attempts to analyze BPOs used by private and public banks using four dimensional descriptive conceptual dimensions of outsourcing:  1) shoring model (vendor location/service creation), 2) sourcing model (vendor type), 3) engagement model (number of vendors engaged), and 4) duration of the engagement (contract period).  The comparison of results reveals similar trends of outsourcing for public and private banks. However, public banks are more regulated, and thus are restricted from outsourcing of certain processes to avoid excessive risks of privacy of data and information related to customers.  From a financial strategic point of view, in the long run, the underlying profit margins of a public bank might have adverse effects. &nbsp

Volume change behavior of phosphogypsum treated clayey soils contaminated with inorganic acids – a micro level study

Soils exhibit undesirable volume changes when exposed to high concentrations of acids, which is manifested most frequently in the beds of foundations of industrial establishments associated with their production or use. However, control of this phenomenon has received less attention than it deserves. This paper aims to investigate the mineralogical and micro-structural changes occurred during the volume change behavior of phosphogypsum treated clayey soils contaminated with sulfuric acid and phosphoric acid solutions. Oedometer test results showed high swelling and low compressibility for acid contaminated soils than that of water. The change in microstructure towards flocculated fabric along with mineralogical transformations are responsible for the volume changes in soils. The mineralogical changes that affected the volume change behavior are discussed with FT-IR, XRD and SEM analysis. Phosphogypsum treatment was found to be effective in controlling volume changes in soils with phosphoric acid, whereas in the case of sulfuric acid found to be futile

Techno-economic Study by Teaching Learning-based Optimization Algorithm for Optimal Placement of DG Units in Distribution Systems

A significant improvement in system performance can be achieved by placing Distributed Generator (DG) units of the optimal size in optimum network of radial distribution locations. In order to maximize the economic and technological benefits, it is necessary to reduce yearly economic losses. These losses include expenditures associated with installation and operation of the buses as well as power loss and voltage difference between buses. In view of these multi-objective frameworks, the current problem is assessed and the best compromise solution also referred as the Pareto-optimal solution is provided. In the framework of the multi-objective optimization problem, specific equality as well as inequality constraints is investigated. It is shown in this study that a Multi-Objective Teaching-Learning Based Optimization (MOTLBO) algorithm has been proposed to solve the multi-objective problem. For the purpose of evaluating its performance, the proposed method is being deployed on IEEE-33 and IEEE-69 System of radial bus distribution. A comparison with other recent multi-objective algorithms such as OCDE, KHA and LSFSA is also included in this study. It has been revealed that the algorithm proposed can offer superior outcomes concerning power loss, annual economic loss mitigation and voltage profile enhancement

Rapid Node Cardinality Estimation in Heterogeneous Machine-to-Machine Networks

Machine-to-Machine (M2M) networks are an emerging technology with applications in various fields, including smart grids, healthcare, vehicular telematics and smart cities. Heterogeneous M2M networks contain different types of nodes, e.g., nodes that send emergency, periodic, and normal type data. An important problem is to rapidly estimate the number of active nodes of each node type in every time frame in such a network. In this paper, we design two schemes for estimating the active node cardinalities of each node type in a heterogeneous M2M network with $T$ types of nodes, where $T \ge 2$ is an arbitrary integer. Our schemes consist of two phases-- in phase 1, coarse estimates are computed, and in phase 2, these estimates are used to compute the final estimates to the required accuracy. We analytically derive a condition for one of our schemes that can be used to decide as to which of two possible approaches should be used in phase 2 to minimize its execution time. The expected number of time slots required to execute and the expected energy consumption of each active node under one of our schemes are analysed. Using simulations, we show that our proposed schemes require significantly fewer time slots to execute compared to estimation schemes designed for a heterogeneous M2M network in prior work, and also, compared to separately executing a well-known estimation protocol designed for a homogeneous network in prior work $T$ times to estimate the cardinalities of the $T$ node types, even though all these schemes obtain estimates with the same accuracy.Comment: 14 pages, 21 figure

Bioactive Compounds and Health Benefits ofArtemisiaSpecies

Artemisia L. is a genus of small herbs and shrubs found in northern temperate regions. It belongs to the important family Asteraceae, one of the most numerous plant groupings, which comprises about 1000 genera and over 20000 species. Artemisia has a broad spectrum of bioactivity, owing to the presence of several active ingredients or secondary metabolites, which work through various modes of action. It has widespread pharmacological activities and has been used as traditional medicine since ancient times as an anthelmintic, antispasmodic, antirheumatic, and antibacterial agent and for the treatment of malaria, hepatitis, cancer, inflammation, and menstrual-related disorders. This review comprises the updated information about the ethnomedical uses and health benefits of various Artemisia spp. and general information about bioactive compounds and free radicals

A chickpea genetic variation map based on the sequencing of 3,366 genomes

Zero hunger and good health could be realized by 2030 through effective conservation, characterization and utilization of germplasm resources1 . So far, few chickpea (Cicerarietinum) germplasm accessions have been characterized at the genome sequence level2 . Here we present a detailed map of variation in 3,171 cultivated and 195 wild accessions to provide publicly available resources for chickpea genomics research and breeding. We constructed a chickpea pan-genome to describe genomic diversity across cultivated chickpea and its wild progenitor accessions. A divergence tree using genes present in around 80% of individuals in one species allowed us to estimate the divergence of Cicer over the last 21 million years. Our analysis found chromosomal segments and genes that show signatures of selection during domestication, migration and improvement. The chromosomal locations of deleterious mutations responsible for limited genetic diversity and decreased fitness were identified in elite germplasm. We identified superior haplotypes for improvement-related traits in landraces that can be introgressed into elite breeding lines through haplotype-based breeding, and found targets for purging deleterious alleles through genomics-assisted breeding and/or gene editing. Finally, we propose three crop breeding strategies based on genomic prediction to enhance crop productivity for 16 traits while avoiding the erosion of genetic diversity through optimal contribution selection (OCS)-based pre-breeding. The predicted performance for 100-seed weight, an important yield-related trait, increased by up to 23% and 12% with OCS- and haplotype-based genomic approaches, respectively. On the basis of WGS of 3,366 chickpea germplasm accessions, we report here a rich map of the genetic variation in chickpea. We provide a chickpea pan-genome and offer insights into species divergence, the migration of the cultigen (C. arietinum), rare allele burden and fitness loss in chickpea. We propose three genomic breeding approaches— haplotype-based breeding, genomic prediction and OCS—for developing tailor-made high-yielding and climate-resilient chickpea varieties. We sequenced 3,366 chickpea germplasm lines, including 3,171 cultivated and 195 wild accessions at an average coverage of around 12× (Methods, Extended Data Fig. 1, Supplementary Data 1 Tables 1, 2). Alignment of WGS data to the CDC Frontier reference genome11 identified 3.94 million and 19.57 million single-nucleotide polymorphisms (SNPs) in 3,171 cultivated and 195 wild accessions, respectively (Extended Data Table 1, Supplementary Data 1 Tables 3–7, Supplementary Notes). This SNP dataset was used to assess linkage disequilibrium (LD) decay (Supplementary Data 2 Tables 1, 2, Extended Data Fig. 2, Supplementary Notes) and identify private and population-enriched SNPs (Supplementary Data 3 Tables 1–4, Supplementary Notes). These private and population-enriched SNPs suggest rapid adaptation and can enhance the genetic foundation in the elite gene pool

ON THE ESTIMATION OF POLARIMETRIC PARAMETERS FOR OIL SLICK FEATURE DETECTION FROM HYBRID POL AND DERIVED PSEUDO QUAD POL SAR DATA

Oil spills in oceans have a significant long term effect on the marine ecosystem and are of prime concern for maritime economy. In order to locate and estimate the oil spread area and for quantitative damage assessment, it is required to continually monitor the affected area on the sea and its surroundings and space based remote sensing makes this technically viable. Synthetic Aperture Radar SAR with its high sensitivity to target dielectric constant, look angle and polarization-dependent target backscatter has become a potential tool for oil-spill observation and maritime monitoring. From conventional single-channel SAR (single-pol, HH or VV) to multi-channel SAR – (Dual/Quad-polarization) and more recently compact polarimetric (Hybrid/Slant Linear) SAR systems have been widely used for oil-spill detection in the seas. Various polarimetric features have been proposed to classify oil spills using full, dual and compact polarimetric SAR. RISAT-1 is a C-band SAR with Circular Transmit and Linear Receive (CTLR) hybrid polarimetric imaging capability.This study is aimed at the polarimetric processing of RISAT-1 hybrid pol single look complex (SLC) data for derivation of the decisive polarimetric parameters which can be used to identify oil spills in oceans and their discrimination from look-alike signatures. In order to understand ocean–oil spill signatures from full-quad pol SAR, pseudo-quad pol covariance matrix is constructed from RISAT-1 hybrid pol using polarimetric scattering models .Then polarimetric processing is carried out over pseudo-quad pol data for oil slick detection. In-house developed software is used for carrying out the above oil-spill study