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Background: With 30,000 estimated maternal deaths in a year across the country, high MMR remains a matter of grave concern, and thus it is paramount to ensure quality ANC to each pregnant woman, identify the high risk pregnancies (HRPs) and track these for counselling, management birth preparedness and referral till the outcome to close the loop. Pradhan Mantri Surakshit Matritva Abhiyan (PMSMA) was launched in 2016, as a fixed-day, assured, comprehensive quality ANC service on the 9th of every month to every pregnant woman across the country. Screening, identification, line listing and appropriate management of high-risk pregnancies by OBGY/CEmOC/ BEmOCspecialist and referral to appropriate higher facilities are some of the fundamental elements of PMSMA Objective: To determine the level of satisfaction among beneficiaries under PMSMA scheme Methodology: A cross-sectional study was done among 300 pregnant women who were delivered within 6 months from the date of the study and who had registered & availed the Ante-Natal Care (ANC) services under Rural field Practice area of Guntur Medical college. Guntur. Andhra Pradesh. The data thus collected were entered into Microsoft-Excel spreadsheet and was analyzed using IBM SPSS software Version 24.0 Results: Out of 300 beneficiaries, 237(79%) were satisfied with the cleanliness of wards & toilets followed by 278(92.69%) with Adequate privacy During Examination, 287(97.67%) with basic laboratory investigation, 166(55.34%) with transport facilities during discharge. The majority of study participants were in the age group of 20-30 years i.e. 269 (89.70%) and most of their educational status above intermediate i.e. 207(69%) Conclusion: If every pregnant woman in India is examined by a Medical Officer/Specialist and Investigated appropriately at least once during the PMSMA, the Abhiyan can play a crucial role in reducing the number of maternal deaths & High-Risk Pregnancy (HRP) in our country

Sustainability in any production emphasizes green-manufacturing techniques, improvement in quality with energy-efficient techniques, and environment-friendly processes. Titanium machining productivity is greatly influenced by speed, as high cutting velocity raises the temperatures in the shear zone and heat, owing to its low thermal conductivity. Hence in this work, an attempt is made to increase productivity by exploring the efficacy at transition speed for titanium alloy machining. Water-soluble lubricant is mist-sprayed as aerosols at a near-zero temperature in minor quantity, to minimize the temperatures generated during the cutting process at increased speed. Besides, an optimal decision variable vector optimizes multi-goals of machining Titanium grade 5 alloys under Minimum quantity cooling lubrication explored in this study in transitional speed zones. The response goals are the optimization of “vibration, surface quality, tool wear rate, and Material removal rate.” Multi goal optimization achieved by hybrid Taguchi coupled with Data Envelopment Analysis based Ranking (DEAR). The tool wear is very rapid at velocities of 200 mm/min. DEAR technique uses computed Multi performance rank index (MPRI) to predict the best data set at: (velocity, feed, doc) at (120 mm/min, 0.2 mm/rev, 1.0 mm). In this setting, the responses are compared in dry, flood, and MQL environment. It is observed a 30%, 60%, 40% improvement in surface finish, tool life, and vibrations compared to a dry environment and 13% and 3% of roughness and tool wear rate compared to a flood environment. Thus MQCL can be adopted for Ti6Al4V at transitional speeds.

A total of 150 soil samples were collected from 0-30 cm depth and 58 groundwater samples from Bapatla and Karla Palem mandals of Gunutr district, Andhra Pradesh for assessment of properties of soil and quality of groundwater. The textura classes of soils were sandy, loamy sand and sandy loam. Correlation coefficients of soil properties were worked out between ionic composition and physico-chemical properties of soil. The ionic composition of soil and groundwater were predominant with Na+, Cl-, Mg2+, Ca2+ and HCO3 ions, around 44 per cent of soils the study area is under moderate to severe salinity. The soils were grouped based on soil reaction (pH), electrical conductivity (ECe) and exchangeable sodium percentage (ESP), soils under different cropping systems were analysed for salinity and alkalinity. As per the groundwater quality for irrigation 34.48% samples were good, 25.86% samples were marginally saline, 8.62% samples were high SAR saline, 22.41% samples alkali, 8.62% were high SAR alkali. [ABSTRACT FROM AUTHOR]

• The 3D MRI images are mainly considered for detecting the brain tumor. • The input dataset are initially taken to pre-processing where Average Mass Elimination Algorithm (AMEA) is applied. • AMEA is to remove the noisy pixel form the images. • The obtained weight helps to improve the performance of the CNN model to fetch most effective results. The 3D MRI images are mainly considered for detecting the brain tumor. The deep learning approaches are highly effective for detecting the disease in its early stage. However, the detection and classification is achieved though the highly enhanced deep learning approaches that provides various classes. However, the considerable limitation in this field is detecting the significant features. In order to handle these issues, A highly enhanced deep learning approach is considered that is based on Convolutional Neural Network (CNN) with mass correlation analysis. Here, the input dataset is initially taken to pre-processing where Average Mass Elimination Algorithm (AMEA) is applied. AMEA is to remove the noisy pixel form the images. The significant features are fetched using Median values of white mass. Then the extracted features are trained using the CNN model based on Mass Correlation Analysis (MCA) that helps to assign the weight measure. The obtained weight helps to improve the performance of the CNN model to fetch most effective results. [ABSTRACT FROM AUTHOR]

Present research discussed the utilization of aminated pumpkin seed powder (APSP) as an adsorbent for methyl orange (MO) removal from aqueous solution. Batch sorption experiments were carried to evaluate the influence of pH, initial dye concentration, contact time, and temperature. The APSP was characterized by using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM). The experimental equilibrium adsorption data were fitted using two two-parameter models (Langmuir and Freundlich) and two three-parameter models (Sips and Toth). Langmuir and Sips isotherms provided the best model for MO adsorption data. The maximum monolayer sorption capacity was found to be 200.3 mg/g based on the Langmuir isotherm model. The pseudo-first-order and pseudo-second-order model equations were used to analyze the kinetic data of the adsorption process and the data was fitted well with the pseudo-second-order kinetic model ( R 2 >0.97). The calculated thermodynamic parameters such as Δ G 0 , Δ H 0 and Δ S 0 from experimental data showed that the sorption of MO onto APSP was feasible, spontaneous and endothermic in the temperature range 298–318 K. The FTIR results revealed that amine and carboxyl functional groups present on the surface of APSP. The SEM results show that APSP has an irregular and porous surface which is adequate morphology for dye adsorption. Desorption experiments were carried to explore the feasibility of adsorbent regeneration and the adsorbed MO from APSP was desorbed using 0.1 M NaOH with an efficiency of 93.5%. Findings of the present study indicated that APSP can be successfully used for removal of MO from aqueous solution. [ABSTRACT FROM AUTHOR]