Evaluation of Doppler Cerebroplacental Ratio as a Predictor of Adverse Perinatal Outcome in High Risk Pregnancy

AIM OF THE STUDY: To determine the screening efficacy of Doppler cerebroplacental ratio, Pulsatility index of umbilical artery, Pulsatility index of middle cerebral artery as a predictor of adverse perinatal outcome in high risk pregnancy. METHOD: Prospective observational study was conducted in Institute of obstetric and gynaecology from September 2015 to august 2016. It included 210 antenatal patients with high risk pregnancy between 32 and 40 weeks from obstetric OP. They were subjected to thorough history taking, clinical examination and Doppler study. PI UA, PI MCA, Cerebroplacental ratio determined. They were under serial Doppler surveillance until delivery. Values obtained just a week prior to delivery were taken for calculation. Patients were delivered according to the protocol of the institution. Maternal and perinatal outcome were compared in terms of modified biophysical profile, fetal heart tracings during labour, mode of onset of labour, meconium staining of liquor during labour, mode of delivery, birth weight, apgar score, neonatal complications and neonatal death. RESULTS: The mean age of the study group was 24.9. GHT and preeclampsia constituted nearly 40.9% of the patients included in our study. The mean CPR 1.05 with a standard deviation 0.248 had statistically significant (p value 0.002) association with hypertension complicating pregnancy. There was no statistical significant mean of difference between CPR in those with anaemia ,diabetes in pregnancy, RHD, hypothyroid, BOH and those without such complications. The mean gestational age of delivery was 35 weeks. Cerebroplacental ratio </= 1.08 had a sensitivity of 67.1% and specificity of 98% in predicting neonatal complications which was statistically significant with a p value < 0.0001,whereas the sensitivity specificity of PI UA and PI MCA were 55%, 85% and 68%, 91% respectively. Cerebroplacental ratio with a mean of 0.8775 and a standard deviation of 0.09223 had a significant mean of association with neonatal death with a p value 0.016. Kaplan meier survival propability curve showed decreased survival probability of the fetuses with cerebroplacental ratio < 0.99 with a significant p value 0.02. 98 out of 105 cases of CPR</= 1.08 were induced. Totally there were 91 LSCS in our group .Out of which 48 with CPR</=1.08 had Fetal Distress as Indication .Cerebroplacental ratio with a mean 1.08 has significant associated mean of difference with those with operative deliveries (p 0.002), preterm deliveries (p 0.000), IUGR (p 0.000). CPR shows good correlation and logistic regression with birth weight and apgar with p value 0.001 and 0.001.There is no statistical significant mean of difference between CPR and non reassuring intrapartum CTG, meconium staining of liquor and amniotic fluid index

Educational Ideals Emphasized in the Naladiyar

The Keelkanaku books which appeared after the Sangam Literature are known as Neethi Literature. Tirukkural, contained among them, is an emperor in the world justice books. Being as the book by Kural Venba got its name ‘Kural,’ which was made up of four feet of by Naladi Venba also named as ‘Naladi’ it is referred to as Naladiyar, Naladi Nanuru, Velanvedham. Education for man is in this birth, which is said to be the benefit of this birth, unlike the wealth that gives him tangible things, it does not lack to give, it grows more and more lower than makes him richer. These are the art of words of Jain sages in these Venbakkal however this research paper reveals the information about the ability of both men and women to remove their ignorance and improving knowledge in the fourteenth Chapter “Education” which are based on Naladiar's work, which has ethical thoughts emphasizing the life of the ancient Tamils

Prediction of Default Customer in Banking Sector using Artificial Neural Network

The aim of this article is to present perdition and risk accuracy analysis of default customer in the banking sector. The neural network is a learning model inspired by biological neuron it is used to estimate and predict that can depend on a large number of inputs. The bank customer dataset from UCI repository, used for data analysis method to extract informative data set from a large volume of the dataset. This dataset is used in the neural network for training data and testing data. In a training of data, the data set is iterated till the desired output. This training data is cross check with test data. This paper focuses on predicting default customer by using deep learning neural network (DNN) algorithm

LaQshya- an uphill climb: a review of implementation of LaQshya programme at a tertiary centre in Chennai

LaQshya- labour room quality improvement initiative, a National Quality Assurance Programme was launched by the National Health Mission, Government of India in 2017 for improving the quality of services provided at the time of delivery and immediate post-partum period. The programme has been implemented at the Institute of Social Obstetrics, Government Kasturba Gandhi Hospital for women and children from the year 2019. A plethora of changes have been brought about at the legendary institution since then. A retrospective programme review of the changes brought about at the Institute of Social Obstetrics, Government Kasturba Gandhi Hospital for Women and Children in the dimensions described under the LaQshya program i.e.; structural improvement and process improvement and henceforth a comparison of the various outcome as key performance indicators before and after the implementation of the programme. The quality of Institute of Social Obstetrics Government Kasturba Gandhi Hospital started at the bottom with 40%, under the guidance of LaQshya has improved to an astounding 93% making us the proud bearers of the prestigious platinum badge which was evident with the obvious improvement in various outcome indicators.  Despite the implementation, LaQshya was an uphill trudge, to break old habits and restrain into new norms and guidelines, the results as mentioned proved to be a beautiful view at the end of the climb. LaQshya is indeed a boon not only to the mothers benefiting from it but also to the service provider as a tool to be a better health care personnel

Estimation of Soil Moisture for Different Crops Using SAR Polarimetric Data

Soil moisture is an essential factor that influences agricultural productivity and hydrological processes. Soil moisture estimation using field detection methods takes time and is challenging. However, using Remote Sensing (RS) and Geographic Information System (GIS) technology, soil moisture parameters become easier to detect. In microwave remote sensing, synthetic aperture radar (SAR) data helps to retrieve soil moisture from more considerable depths because of its high penetration capability and the illumination power of its light source. This study aims to process the SAR Sentinel-1A data and estimate soil moisture using the Water Cloud Model (WCM). Many physical and empirical models have been developed to determine soil moisture from microwave remote sensing platforms. However, the Water Cloud Model gives more accurate results. In this study, the WCM model is used for mixed crop types. The experimental soil moisture was determined from in-situ soil samples collected from various agricultural areas. The soil backscattering values corresponding to the different soil sampling locations were derived from Sentinel SAR data. Using linear regression analysis, the laboratory's soil moisture results and soil backscattering values were correlated to arrive at a model. The model was validated using a secondary set of in-situ moisture content values taken during the same period. The R2 and RMSE of the model were observed to be 0.825 and 0.0274, respectively, proving a strong correlation between the experimental soil moisture and satellite-derived soil moisture for mixed crop field types. This paper explains the methodology for arriving at a model for soil moisture estimation. This model helps to recommend suitable crop types in large, complex areas based on predicted moisture content. Doi: 10.28991/CEJ-2023-09-06-08 Full Text: PD

A Range Query Algorithm to Process KNN Queries in Cloud Computing

In Public Cloud environment, security and data confidentiality is the major problem facing by all the data controller. The service user can take the service from the cloud by getting authorization from the service provider and he can only pay for the service by using the server, for that the service provider lose the control so there may have chances of leaking the information. For that reason the data controller does not want the data to shift to the Cloud. There may have only chance of storing the data is providing the privacy gurantee to the Cloud. The requirement to building privacy is based on CPEL criteria which is confidentiality, privacy, efficiency, low in-house processing cost. By satisfying these requirement will increase the difficulty to store data in the Cloud. In order to eliminate this problem, We are using Random Space Perturbation method for providing the security and efficiency for processing the data. This method is used to building a practical query services in the Cloud. This approach will balance all the requirements by using range query and KNN query services.It provides multifaceted distances , which allows actual listing techniques to increase distances processing. DOI: 10.17762/ijritcc2321-8169.150311

Eosin Yellowish Dye-Sensitized ZnO Nanostructure-Based Solar Cells Employing Solid PEO Redox Couple Electrolyte

ZnO nanostructures are synthesized by low-temperature methods, and they possess polycrystalline hexagonal wurtzite structure with preferential c-axial growth. Morphological study by SEM shows the presence of ~30 nm sized spherical-shaped ZnO nanoparticle, the branched flower-like ZnO composed of many nanorods (length: 1.2 to 4.2 μm and diameter: 0.3 to 0.4 μm), and ~50 nm diameter of individual ZnO nanorods. Reduction in photoemission intensity of nanorods infers the decrease in electron-hole recombination rate, which offers better photovoltaic performance. The dye-sensitized solar cell (DSSC) based on ZnO nanorods sensitized with Eosin yellowish dye exhibits a maximum optimal energy conversion efficiency of 0.163% compared to that of nanoparticles and nanoflowers, due to better dye loading and direct conduction pathway for electron transport