Minerological characterization studies of archaeological pottery sherds using FT – IR and TG A - DTA

        The Archaeological pottery sherds excavated in Alagankulam, an ancient port city of Tamilnadu, India, have historical significance owing to the heritage and trade link possessed with the Roman Empire. They were examined by employing the analytical techniques Fourier Transform Infra Red (FTIR) and Thermogravimetry – Differential Thermal Analysis (TGA-DTA) with an objective to identify the mineralogical characteristics of the raw materials used for their production. Based on the mineralogical assemblages observed in FTIR, the nature of the clay used, the textural and vitrification structures were inferred.   The reactions associated with the mineral compositions present in the potsherds on controlled heating over the linear temperature ramp from room temperature to 1200°C in an inert atmosphere were realized by TGA-DTA results. The characterization studies were able to indicate the conditions of firing process adopted and firing temperature attained by the artisans at the time of manufacture of the artifacts of the present investigation

A Cloud-Oriented Green Computing Architecture for E-Learning Applications

Cloud computing is a highly scalable and cost-effective infrastructure for running Web applications. E-learning or e-Learning is one of such Web application has increasingly gained popularity in the recent years, as a comprehensive medium of global education system/training systems. The development of e-Learning Application within the cloud computing environment enables users to access diverse software applications, share data, collaborate more easily, and keep their data safely in the infrastructure. However, the growing demand of Cloud infrastructure has drastically increased the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high operational cost, which reduces the profit margin of Cloud providers, but also leads to high carbon emissions which is not environmentally friendly. Hence, energy-efficient solutions are required to minimize the impact of Cloud-Oriented E-Learning on the environment. E-learning methods have drastically changed the educational environment and also reduced the use of papers and ultimately reduce the production of carbon footprint. E-learning methodology is an example of Green computing. Thus, in this paper, it is proposed a Cloud-Oriented Green Computing Architecture for eLearning Applications (COGALA). The e-Learning Applications using COGALA can lower expenses, reduce energy consumption, and help organizations with limited IT resources to deploy and maintain needed software in a timely manner. This paper also discussed the implication of this solution for future research directions to enable Cloud-Oriented Green Computing

VOICE RECOGNIZATION FOR MOUSE CONTROL USING HCI

One  of  the  most  important  research  areas  in  the  field  of  Human -Computer-Interaction (HCI) is gesture  recognition as it provides a natural and intuitive  way  to  communicate  between  people  and  machines.  Voice-based HCI applications range from computer applications to virtual/augmented reality and is recently being explored in other fields. This work proposes the implementation of absolute virtual mouse based on the interpretation of voice reorganization control. The procedure is to control the mouse pointer as for the mouse movement to up/down/left/right, open the file, dragging the file. This  virtual  device  is designed  specifically  as  an  alternative non-contact  pointer  for  people  with mobility impairments in the upper extremities. The implementation of the virtual mouse by voice control is to make HCI simplification for disabled persons especially for the person who are not having the hands and arms, and Alternative mouse cursor positioning system for laptops

ANALYSIS OF OVARIAN DISEASES USING ULTRASOUND IMAGES

Ovarian disease is the most common disease occurring in female reproductive organs. The premature recognition and action needed for diagnosis the disease to avoid infertility or cancer to them. The reason behind infertility is Polycystic Ovary Syndrome (PCOS) and the Ovarian Regenerative Tissues Cells.  PCOS is the kind of endocrine disorder present in female’s ovary and creates problem with Irregular menstrual periods, skin diseases, excess hair growth in body and face, acne and finally infertility. For ovarian regenerative tissues cells histopathology slides can be well stored put away in digitized color image design. One of the well known determination inclinations to recognize ovarian tissues is ultrasound image scanner. In any case, because of various shape, size and color, distinguishing proof of ovarian tissues is a testing assignment for ultrasound scanners as it procedure dark scale images. In order to overcome these diseases, we proposed techniques for ultrasound images. By this techniques follicle in the ovary and the ovarian regenerative tissues cells is automatically detected

Clinical Profile and Risk Factors for Severity and Mortality in Acute Bronchiolitis in Children Less Than 2 Years of Age Attending an Urban Referral Centre

INTRODUCTION: Acute bronchiolitis, an acute infectious disease of the lower respiratory tract, which primarily affects the smaller airways. It is predominantly a viral respiratory disease. It is one of the leading causes of hospitalisation in infants and young children. It occurs usually between one month to 24 months of age with a peak incidence between 3 and 6 months of age. Each year in the United States, approximately two per 100,000 infants die as a result of complications associated with bronchiolitis1. In young children, the clinical diagnosis of this disease may overlap with viral wheezing and an acute viral triggered asthma. AIM OF THIS STUDY: 1. To describe the clinical profile and 2.To study the risk factors for severity and mortality in infants and young children less than 2 years of age presenting with acute bronchiolitis at an urban referral hospital. DISCUSSION: Bronchiolitis is an acute, infectious disease of the upper and lower respiratory tract resulting in obstruction of the smaller airways. Although it may occur in all age groups, as the larger airways of older children and adults better tolerate mucosal edema and severe symptoms are usually only evident in young infants. It usually occurs in children less than two years of age and presents with coughing, wheezing, and shortness of breath often caused by respiratory syncytial virus. In our study, we have included 215 children who were diagnosed as bronchiolitis. Out of them, 167(77.67%) children had mild and moderate disease, whereas 48(22.33%) children had severe disease similar to El Radhi A, et al study56. Among those 48 children presented as severe disease, 4 of them died with a mortality rate of 1.86% similar to Thorburn K52 study, with a mortality rate of 1.7% and other studies showing mortality rate ranging from 0.5 to 7%. This wide range in mortality could be due to varying prevalence of pathogenic organisms in different regions of the world. In our study, 125(58.10%) children were belong to less than 6 months of age which is comparable with Shay DK, et al study in which 57% of cases were less than 6 months of age group54. The mean age group in our study is 4.6 months which is comparable to El-Radhi A, et al study56. Out of 215 children in our study, 63.7% children were males and 36.3% children were females similar to Al-Muhsen SZ, et al study60. Most of these cases (83.3%) were reported between the months of October to January. This seasonal pattern is comparable to Al-Muhsen SZ, et al study60. All children in our study presented with a short duration of upper respiratory illness in the form of cough, cold, sneezing or running nose along with breathing difficulty which similar to other studies51-60. Among the 215 children, fever was documented in 150(69.8%) children in our study, of which 87.3% children had low grade fever, which is comparable to El-Radhi A, et al study56. The duration of hospital stay was ranging from 1- 15 days with a mean hospital stay of 3.52 days, which is similar to El-Radhi A, et al study56, in which the mean duration of hospital stay was 3.3 days, whereas Fjaerli HO, et al study59 showed 4.0 days, as the mean duration of hospital stay. CONCLUSION

Hairy root induction from hypocotyl segments of groundnut (Arachis hypogaea L.)

Hairy roots were induced from hypocotyl explants excised from seven day old aseptically grown seedlings of groundnut using Agrobacterium rhizogenes 15834. The percentage of hairy root induction and number of hairy roots per ex-plant varied with infection period. The suitable co-cultivation period was 48 h. The hairy roots were fast growing, thin, slender and sometimes having branches which varied in their morphological nature. The cefotaxime concentration of 250 mgL-1 was found to be most suitable for hairy root induction in groundnut

An efficient method for callus induction of an important medicinal plant (Sarcostemma brevistigma) from stem segments

The present study was undertaken to evaluate the most suitable concentration of growth regulators i.e., IAA, NAA, 2,4-D with BAP and Kin for callus induction. Stems were proved to be the best explant for culture, which were grown on MS basal medium with different concentration of various growth regulators. The standard plant tissue culture protocol for callus culture was adopted. The highest efficiency of callus formation was observed in the medium containing different concentration of 2, 4-D and BAP. In vitro generated callus can be used as a source for the isolation of secondary metabolites from Sarcostemma brevistigma

Mouth Image Based Person Authentication Using DWLSTM and GRU

Recently several classification methods were introduced to solve mouth based biometric authentication systems. The results of previous investigations into mouth prints are insufficient and produce lesser authentication results. This is mainly due to the difficulties that accompany any analysis of the mouths: mouths are very flexible and pliable, and successive mouth print impressions even those obtained from the same person may significantly differ from one other. The existing machine learning methods, may not achieve higher performance and only few methods are available using deep learning for mouth biometric authentication. The use of deep learning based mouth biometrics authentication gives higher results than usual machine learning methods. The proposed mouth based biometric authentication (MBBA) system is rigorously examined with real world data and challenges with the purpose that could be expected on mouth-based solution deployed on a mobile device. The proposed system has three major steps such as (1) database collection, (2) creating model for authentication, (3) performance evaluation. The database is collected from Annamalai University deep learning laboratory which consists of 5000 video frames belongs to 10 persons. The person authentication model is created using divergence weight long short term memory (DWLSTM) and gated recurrent unit (GRU) to capture the temporal relationship in mouth images of a person. The existing and proposed methods are implemented via the Anaconda with Jupyter notebook. Finally the results of the proposed model are compared against existing methods such as support vector machine (SVM), and Probabilistic Neural Network (PNN) with respect to metrics like precision, recall, F1-score, and accuracy of mouth

DYSMENORRHEA AMONG FEMALE MEDICAL SCIENCES STUDENTS IN MACHS: PREVALENCE, PREDICTORS AND OUTCOME

Objective: This study intended to determine the prevalence, predictors, and outcome of dysmenorrhea among female medical sciences students at Mohammed Al-Mana College for Medical Sciences (MACHS), Dammam, Saudi Arabia. Methods: A cross-sectional study was adopted, and 292 female medical sciences students of MACHS were selected using stratified random sampling. A semi-structured and self- administrated questionnaire was used to collect personal and socio-demographic information from the selected female medical sciences students. The information about the menstrual history, stress, and smoking were also gathered. The data analysis was carried out using the descriptive statistics and Chi-square test. Results: The prevalence of dysmenorrhea was 73.28% among female medical sciences students. Concerning the signs and symptoms of dysmenorrhea, the abdominal pain was predominant symptoms among 73.28% of the respondents, and it was found to be statistically significant (p≤0.05). Sleep disturbance was observed as the prominent outcome of dysmenorrhea, as reported by 64% of the respondents