Literary Thoughts of Sancharam

This is a work about Nadaswaram artists from Karisal village. The manner in which Nadaswaram artists were oppressed on the basis of caste, the stagnation in the development of Nadaswaram music in the development of technology. The novel also chronically explains the glorious life of the Music Vellalars of Tamil Nadu and the downs in their lives in the course of time.  It illustrates the picture of nadaswaram artists being oppressed and hunted down as criminals in society and organisation. 'It illustrates the picture of nadaswaram artists being oppressed and hunted down as criminals in society and organisation. S. Ra's writings can also be classified as ‘Literary Taste’

A Smart Remote Monitoring System for Prenatal Care in Rural Areas

The complications in maternity especially the women lives in rural sector can be reduced through regular monitoring of their vitals like blood pressure, SpO2 and fetal growth. The internet of things (IoT) is the modern technology bridges the gap between the traditional clinical setting with its consumers as well promotes the telemedicine industry into great levels of accessing proactive healthcare facilities. The predominant aim of this work is to bring a remote monitoring device which assesses the significant health indicators of the pregnant women and their fetus status cost effectively. In order to build such kit, the biosensors like heart rate, SpO2, pressure, temperature and load cell which gives the weight of the fetus are integrated into Arudino board. The sensor readings are processed through ThingSpeak. The timely medical attention is proposed upon observing abnormal physiological vitals of the women which is implemented through a buzzer system in this device. Like such devices in realism help to predict the pregnancy risk and decrease the mortality rate

Digital Companion for Elders in Tracking Health and Intelligent Recommendation Support using Deep Learning

Ambient assisted living (AAL) facilitates the daily routines of elderly people, particularly those who have clinical difficulties or physical limitations. The latest technologies like distributed compuring,internet of things (IoT) and machine learning pave the ground for the creation of an effective automated tracker which aids elder citizens to live independently. The suggested system is attempted to design a wearable that monitors the blood glucose level through sweat. To achieve high accuracy, the proposed system uses ambient sensing and deep learning based techniques. It places a strong emphasis on calculating the health index by taking into account numerous disease-related characteristics or vitals such as heart rate, blood pressure, SpO2, blood glucose level, respiration rate, sweat rate, uric acid, and temperature. From the wearable device designed the vital signs are gathered, further environmental sensors and camera fixed around the person continually monitors the behavioral pattern along with physiological signals. This ensures the improved accuracy of health state prediction from its conventional models in place. The key advantage of this device is that it may be held and operated anyplace without interrupting their day-to-day tasks because the device is to be cheap, reliable and speedy

A Study of Association of High Serum Prolactin Levels with other Biochemical Parameters in Pre Eclampsia

INTRODUCTION: Pre eclampsia is a pregnancy related disorder, which is the leading cause of maternal and fetal mortality and morbidity. It clinically manifest after 20 weeks of gestational age with blood pressure > 140/90 mm of Hg and proteinuria and resolves within 42 days after delivery. It is widely known as the “Disease of theories” as its exact etiopathogenesis is still an enigma. Among the various hypotheses, an imbalance between angiogenic and antiangiogenic property are discussed widely. At this point, the role of prolactin hormone is widely researched. In pre eclampsia, there is upregulation of trophoblastic cathepsin D, which cleaves 23 KDa prolactin into its fragments namely 14 KDa and 16 KDa. This 16 KDa fragment typically exhibits anti angiogenic properties by blocking vascular endothelial growth factor and placental growth factor. AIM OF THE STUDY: To find the roleof serum prolactin and other biochemical parameters such as serum uric acid, lactate dehydrogenase, alkaline phosphatase and 24 hours urine protein in preeclampsia. OBJECTIVES: 1. To estimate the level of serum prolactin, serum uric acid, serum lactate dehydrogenase, serum alkaline phosphatase and 24 hours urine protein in Preeclampsia patients. 2. To evaluate the correlation of elevated levels of serum Prolactin and other biochemical parameters in preeclampsia patients in a tertiary care center. MATERIALS AND METHODS: It is a case control study, conducted in the department of biochemistry, government Coimbatore medical college during the period of march 2018 to march 2019 among 100 antenatal mothers(50 pre eclampsia mothers and 50 normal antenatal mothers). Statistical analysis is done using chi-square test and student’s t-test. OBSERVATIONS AND RESULTS: Among the 50 Pre eclampsia mothers studied, the mean serum prolactin is 1223.45 ng/ml against 12.05 ng/ml in controls, the mean serum lactate dehydrogenase in pre eclampsia is 654.64 IU/L against 243.36 IU/L in normal antenatal mothers. The following parameters like serum prolactin, serum lactate dehydrogenase, serum alkaline phosphatase, 24 hours urine protein are statistically significant as their p value is < 0.05. In this study, Receiver operating characteristic curve of Serum Prolactin has criterion > 147.7 ng/ml as the cut off value for predictingpre eclampsia with sensitivity- 98 % and specificity- 100%. For Serum Lactate Dehydrogenase, Receiver operating characteristic curve has criterion >379.9 IU/L as the cut off value for predicting pre eclampsia with sensitivity- 98 % and specificity- 98%. For Serum Alkaline Phosphatase, Receiver operating characteristic curve has criterion >95 IU/L as the cut off value for predicting pre eclampsia with sensitivity- 92% and specificity- 96%. For Serum Uric Acid, Receiver operating characteristic curve has criterion >4.85 mg/dl as the cut off value for predicting pre eclampsia with sensitivity- 92% and specificity- 96%. CONCLUSION: Pre eclampsia is the leading cause of fetal and maternal morbidity and mortality.To prevent the complications, it is necessary to subclassify pre eclampsia based on biomarkers. Several research activities have analysed different biomarkers in this area of expertise. In this study, among the commonly available biochemical parameters, there is significant association between pre eclampsia and serum prolactin, serum uric acid, serum lactate dehydrogenase and 24 hours urine protein

A Complete Design of Smart Wind Farm Enriched with Novel Anemometer

Renewable energy are endless and environment friendly sources of power production and also it is considered as the alternative of non- sustainable energy sources like coal, fossil fuels and nuclear power. Wind Energy is accounted as one of the fast depleting, pollution free energy source compared to hydro power and thermal power. Internet of Things (IoT), which is a wireless, self configuring network of sensors powered with communication facility promotes the facility of remote monitoring and control activities in smarter way without human intervention. Likewise machine learning is another technological giant offers accurate prediction over the voluminous data and imposes intelligence to the machines kept for operation. The primary objective is to develop an IoT based wind farm module that enables installed capacity identification, structural monitoring and scope for power generation in that locality

Air Watch: An Ample Design of Indoor Air Quality Monitoring System

The environment is getting contaminated drastically by introducing harmful materials into the atmosphere through the excessive activities of human in adding comfort and luxurious style in their living. The pollutant level in air has high impact of healthiness of the person inhaling it. Air not outside as well indoor is infected by various hazardous particles and gases.&nbsp; To assess the air quality in the particular environment, it stimulates the need to monitor the hazardous elements listed. The internet of things (IoT) and artificial intelligence (AI) became the part of human life by adding smartness in their daily routines from facilitating control over appliances to own health factor as well automate operations. The primary objective of the effort is to identify the gases that cause air pollution, measure the air quality, and assess the level of pollution so that we can determine which gases cause pollution and at what place is the air being impacted. An IoT based indoor air quality monitoring system is built through incorporating carbon monoxide (CO), carbon dioxide (CO2), Ozone (O3), particulate matters (PM) and volatile organic components (VOC) sensors into Arduino board. The design ensures a complete air monitor, extends reliable service at low cost. A rule based system is developed to automate events upon the estimated air quality index (AQI) out of the sensory circuit

Insights from Modeling the 3D Structure of New Delhi Metallo-β-Lactamse and Its Binding Interactions with Antibiotic Drugs

New Delhi metallo-beta-lactamase (NDM-1) is an enzyme that makes bacteria resistant to a broad range of beta-lactam antibiotic drugs. This is because it can inactivate most beta-lactam antibiotic drugs by hydrolyzing them. For in-depth understanding of the hydrolysis mechanism, the three-dimensional structure of NDM-1 was developed. With such a structural frame, two enzyme-ligand complexes were derived by respectively docking Imipenem and Meropenem (two typical beta-lactam antibiotic drugs) to the NDM-1 receptor. It was revealed from the NDM-1/Imipenem complex that the antibiotic drug was hydrolyzed while sitting in a binding pocket of NDM-1 formed by nine residues. And for the case of NDM-1/Meropenem complex, the antibiotic drug was hydrolyzed in a binding pocket formed by twelve residues. All these constituent residues of the two binding pockets were explicitly defined and graphically labeled. It is anticipated that the findings reported here may provide useful insights for developing new antibiotic drugs to overcome the resistance problem

Unsupervised record matching with noisy and incomplete data

We consider the problem of duplicate detection in noisy and incomplete data: given a large data set in which each record has multiple entries (attributes), detect which distinct records refer to the same real world entity. This task is complicated by noise (such as misspellings) and missing data, which can lead to records being different, despite referring to the same entity. Our method consists of three main steps: creating a similarity score between records, grouping records together into "unique entities", and refining the groups. We compare various methods for creating similarity scores between noisy records, considering different combinations of string matching, term frequency-inverse document frequency methods, and n-gram techniques. In particular, we introduce a vectorized soft term frequency-inverse document frequency method, with an optional refinement step. We also discuss two methods to deal with missing data in computing similarity scores. We test our method on the Los Angeles Police Department Field Interview Card data set, the Cora Citation Matching data set, and two sets of restaurant review data. The results show that the methods that use words as the basic units are preferable to those that use 3-grams. Moreover, in some (but certainly not all) parameter ranges soft term frequency-inverse document frequency methods can outperform the standard term frequency-inverse document frequency method. The results also confirm that our method for automatically determining the number of groups typically works well in many cases and allows for accurate results in the absence of a priori knowledge of the number of unique entities in the data set