Synthesizing the latest Configurational Contents of International Marketing

This study is an effort to attempt and identify the most recent configurational contents developed during 1990 - 2012. Literature of this particular duration is assessed to explore attention captured by each individual configurational content by researchers, publication outlets and in terms of their application in a particular industry. This literature synthesis 1834 research articles published in particular time frame 1990 - 2012, which yield 57 different configurational contents of international marketing with application in 32 different industries. These 57 configurational contents are categorized under 7 derived clusters. Review also focuses on attention of publication outlets in context of specialized volumes published in each research stream of international marketing. This synthesis find that international marketing has made substantial progress in context of concepts, their application. All the configurational contents and research streams successfully captured the attention of researchers and publication outlets. Keywords: International marketing, Configurational Contents, Research Stream

Detection Of Fetal Electrocardiogram from Multivariate Abdominal Recordings by using Wavelets and Neuro-Fuzzy Systems

The fetal electrocardiogram (FECG) signal reflects the electrical activity of the fetal heart. It contains  information  on  the health  status of the fetus and therefore, an early diagnosis of any cardiac defects before delivery (Specially in case of  labour pain) increases the effectiveness of the appropriate treatment. In this paper we consider one signal from the thoracic and another from abdomen of the mother. The artificial neural network fuzzy inference system (ANFIS) is used for estimating the FECG component from one abdominal ECG recording and one reference thoracic maternal electrocardiogram (MECG) signal. The obtained FECG is being enhanced by using wavelet transform. Key words: ECG, MECG, FECG, Neural network , Fuzzy logic, Membership function and Wavelet transform

A Comparative Strategy Using PI & Fuzzy Controller for Optimization of Power Quality Control

This paper explores the analytical study and simulation of fuzzy logic controller and PI controller, to control the dc output voltage of shunt active power filter for harmonic reduction and power quality improvement in case of nonlinear load. Here we have exercised an effort to design and evaluate a converter to compensate the harmonics for 1-phase AC to DC bridge rectifier which is working as the main converter in unregulated mode. The work depends on the scheme where an ancillary converter is linked in shunt with the main or primary converter whose turn on and turn off time is controlled by an appropriate controller (pi/fuzzy logic) for harmonic compensation of the primary converter which is working as nonlinear load. The Model of converter is proposed on MATLAB\SIMULINK Software and the results are analyzed satisfactorily.

Influence of defect pairs in Ga-based ordered defect compounds: a hybrid density functional study

In the present paper, density functional theory (DFT) based calculations have been performed to predict the stability, electronic, and optical properties of Ga-rich ordered defect compounds (ODCs). The calculated lattice constants, bulk modulus, their pressure derivatives, and optical constants show good agreement with available experimental data. The hybrid exchange correlations functional have been considered to calculate ground state total energy and energy band gap of the material. The calculated formation energy of ODCs comes smaller than pure CuGaSe2 (CGS). Our calculated optical absorption coefficients indicate that the energy band gap of ODCs can be tuned by changing the number of donor-acceptor defect pairs (2V(cu)(-), + Ga-cu(2+)). The band offset has been calculated to understand the reason of band gap alteration while the number of defect pair changes. Our results may be helpful for other experiments to further improve the performance of ODCs

Correlation between WOMAC score and hyalrunoic acid levels in knee osteoarthritis

Background: Osteoarthritis, a whole organ disease is diagnosed on clinical and radiological features, but plain radiographs show changes only in moderate to advanced stage of disease. Biochemical marker such as Hyaluronic Acid (HA) is used as a diagnostic tool in early stages. Hyaluronic acid level estimation has limited use in developing world due to cost and availability.Methods: A case-control study was done to correlate role of WOMAC score and serum Hyaluronic acid levels in knee osteoarthritis. All subjects were asked to fill the WOMAC questionnaire and were subjected to knee radiography. Blood samples of all subjects were tested for serum levels of Hyaluronic acid by Enzyme Linked Immuno-Sorbent Assay (ELISA). The assessment of severity was done by K-L grading of the radiographs.Results: The mean age in case group was 51.28 ± 7.93 years and in control group was 46.08 ± 4.81 years (P 60 is independently associated with the outcome.Conclusion: WOMAC scores are significantly associated with knee osteoarthritis and can play a crucial role in identification, gradation and management of patients with knee osteoarthritis and can be used singly along with clinical features in situations where treatment cost and assessment of serum HA levels is of concern.

Investigating the seasonal variability in source contribution to PM(2.5)and PM(10)using different receptor models during 2013-2016 in Delhi, India

The present work deals with the seasonal variations in the contribution of sources to PM(2.5)and PM(10)in Delhi, India. Samples of PM(2.5)and PM(10)were collected from January 2013 to December 2016 at an urban site of Delhi, India, and analyzed to evaluate their chemical components [organic carbon (OC), elemental carbon (EC), water-soluble inorganic components (WSICs), and major and trace elements]. The average concentrations of PM(2.5)and PM(10)were 131 +/- 79 mu g m(-3)and 238 +/- 106 mu g m(-3), respectively during the entire sampling period. The analyzed and seasonally segregated data sets of both PM(2.5)and PM(10)were used as input in the three different receptor models, i.e., principal component analysis-absolute principal component score (PCA-APCS), UNMIX, and positive matrix factorization (PMF), to achieve conjointly corroborated results. The present study deals with the implementation and comparison of results of three different multivariate receptor models (PCA-APCS, UNMIX, and PMF) on the same data sets that allowed a better understanding of the probable sources of PM(2.5)and PM(10)as well as the comportment of these sources with respect to different seasons. PCA-APCS, UNMIX, and PMF extracted similar sources but in different contributions to PM(2.5)and PM10. All the three models extracted 7 similar sources while mutually confirmed the 4 major sources over Delhi, i.e., secondary aerosols, vehicular emissions, biomass burning, and soil dust, although the contribution of these sources varies seasonally. PCA-APCS and UNMIX analysis identified a less number of sources (besides mixed type) as compared to the PMF, which may cause erroneous interpretation of seasonal implications on source contribution to the PM mass concentration

Metabolic fingerprinting of joint tissue of collagen-induced arthritis (CIA) rat

Rheumatoid arthritis (RA) is a systemic autoimmune disease whose major characteristics persistent joint inflammation that results in joint destruction and failure of the function. Collagen-induced arthritis (CIA) rat is an autoimmune disease model and in many ways shares features with RA. The CIA is associated with systemic manifestations, including alterations in the metabolism. Nuclear magnetic resonance (NMR) spectroscopy-based metabolomics has been successfully applied to the perchloric acid extract of the joint tissue of CIA rat and control rat for the analysis of aqueous metabolites. GPC (Glycerophosphocholine), carnitine, acetate, and creatinine were important discriminators of CIA rats as compared to control rats. Level of lactate (significance; p = 0.004), alanine (p = 0.025), BCA (Branched-chain amino acids) (p = 0.006) and creatinine (p = 0.023) was significantly higher in CIA rats as compared to control rats. Choline (p = 0.038) and GPC (p = 0.009) were significantly reduced in CIA rats as compared to control rats. Choline to GPC correlation was good and negative (Pearson correlation = -0.63) for CIA rats as well as for control rats (Pearson correlation = -0.79). All these analyses collectively considered as metabolic fingerprinting of the joint tissue of CIA rat as compared to control rat. The metabolic fingerprinting of joint tissue of CIA rats was different as compared to control rats. The metabolic fingerprinting reflects inflammatory disease activity in CIA rats with synovitis, demonstrating that underlying inflammatory process drives significant changes in metabolism that can be measured in the joint tissue. Therefore, the outcome of this study may be helpful for understanding the mechanism of metabolic processes in RA. This may be also helpful for the development of advanced diagnostic methods and therapy for RA

Molecular timetrees using relaxed clocks and uncertain phylogenies

A common practice in molecular systematics is to infer phylogeny and then scale it to time by using a relaxed clock method and calibrations. This sequential analysis practice ignores the effect of phylogenetic uncertainty on divergence time estimates and their confidence/credibility intervals. An alternative is to infer phylogeny and times jointly to incorporate phylogenetic errors into molecular dating. We compared the performance of these two alternatives in reconstructing evolutionary timetrees using computer-simulated and empirical datasets. We found sequential and joint analyses to produce similar divergence times and phylogenetic relationships, except for some nodes in particular cases. The joint inference performed better when the phylogeny was not well resolved, situations in which the joint inference should be preferred. However, joint inference can be infeasible for large datasets because available Bayesian methods are computationally burdensome. We present an alternative approach for joint inference that combines the bag of little bootstraps, maximum likelihood, and RelTime approaches for simultaneously inferring evolutionary relationships, divergence times, and confidence intervals, incorporating phylogeny uncertainty. The new method alleviates the high computational burden imposed by Bayesian methods while achieving a similar result