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The Precambrian formations of south-central Rajasthan host the unique deposits of pegmatite popularly known as ‘Alaska white’. These deposits are located mainly in Jawaja and Rajgarh areas of Ajmer, Gangapur and Jahazpur areas of Bhilwara and Amet and Deogarh areas of Rajsamand districts, Rajasthan. Field and the mineralogical study indicate that these deposits represent a compact and massive-type of pegmatite composed mainly of microcline, quartz and muscovite with subordinate amounts of albite and tourmaline. These pegmatite deposits occur as rounded to sub-rounded lenticular bodies emplaced concordantly within the rock formations of Bhilwara and Delhi Supergroups. These deposits are being developed and utilized by open-cast, fully-mechanized block-benching methods of mining for their extensive use as decorative and dimensional stones. Nearly 90% of the recovered resource is being exported mainly to U.S.A. and China in the form of well-dressed blocks of suitable sizes.

As the world is engulfed with COVID-19 pandemic and the glimpse of vaccine is still a distant dream, taking precautions and maintaining the norms suggested by WHO will keep us safe. With this, we present in this paper a solution that would help travelers induce confidence in traveling while keeping in mind the guidelines that must be followed. The solution focuses on an end to end service that will not only help the travelers to make informed and safe decisions but also allow the hospitality industry to monetize from this application. This paper is focused on a detailed analysis of the solution that is being presented to tackle the problems faced by various industries and their fear of resuming the work. A software-based approach is taken for providing a simple and engaging user experience to the user along with an AI approach to detect and predict the COVID trend in various cities. Along with this, a system that detects if people are wearing a mask or not will also be verified by thealgorithm.

Vehicular traffic is endlessly increasing everywhere within the world and may cause terrible traffic jams at intersections. Traffic congestion and tidal flow are major facts that cause delays to emergency vehicles. Fire brigade officials, ambulances, and police officers often get delayed due to such congestion and traffic. With the use of the right technology, like Artificial Intelligence and real-time monitoring of the traffic, such a predicament can be moderated thereby saving the lives of the needy. The solution presented allows emergency Vehicle (EV) drivers to select the route of commute on the mobile application. Modules on that route selected are activated for the adaptive traffic control system. The driver’s GPS is dynamically updated on the cloud and fetched by the Raspberry Pi module. As the vehicle enters a given radius, the module checks for other EVs in the vicinity for priority assignment. The module starts traffic density detection and changes the traffic light states to clear the traffic if the density is above a certain threshold. After the EV comes closer to the signal, the light turns green irrespective of the traffic density. The camera cross validates if the EV has passed. Once it does, traffic signals switch to regular operation. This will allow the emergency vehicles to reach the destination on time and save the lives of those in need without delay. The entire solution is robust and reliable and can manage traffic efficiently keeping in mind the imperativeness of the emergency vehicles.

Since the invention of the first motor vehicle, the automobile industry has been advancing by leaps and bounds. Two-wheelers are no exception, but the safety of the driver is still a precarious situation. Due to the very built and design of the two-wheeler, the rider is more vulnerable to accidents as compared to the traditional four-wheeler vehicle. Therefore, in this paper we propose a holistic solution for the safety of the driver of a two-wheeler. The proposed solution aims to perform three main tasks. Firstly, alcohol detection which would be required to detect whether the rider is under the influence of alcohol. Secondly, a fall detection system which would be required to send notifications to the emergency contacts in case of an accident or a serious fall from the two-wheeler. Lastly, a navigation system to enhance the user experience. Our solution consists of a synchronous working of both hardware and software. For both the alcohol and fall detection we would use sensors which would feed the data to the microcontroller, which in turn would compare the data with the threshold values in real time. By pushing notifications to the relevant emergency contacts, we have managed to make use of the Internet of Things which always helps the system to remain connected to the Internet. The details of the implementation have been elaborated in this paper. In addition, the implementation and observations have also been highlighted to solidify our proposed solution. We believe such a system can not only help secure the life of the rider of the two-wheeler but also improve the experience one has while driving.

The proliferation of data usage and insights gained from it has seen its applications manifold in the recent decades. One such application of data available is through analysis of the effect of news and historical data on the stock market. The paper has analysed the effect of historical prices and news on the Stock market using various models. Since news and historical data can gravely impact the Stock market, the paper uses Natural Language Processing (NLP) to analyse the news and several predictive analysis models to make use of the historical data to make predictions concerning the stock prices. The NLP algorithm utilized makes use of the text as input and tokenizes each sentence into words to make the process of analysis easier. Google’s Bidirectional Encoder Representations from Transformers (BERT) model is used for the analysis of the news. For the historical data analysis, the two models namely Auto-regressive Integrated Moving Average (ARIMA) and Long short-term memory (LSTM) have been compared and post evaluation it has been found that LSTM Recurrent Neural Networks (RNN) is a superior model. The data for these models, has been procured from the Internet to ensure relevancy and credibility. Both the models used in the analysis of news and historical data have been cross-validated several times which has yielded adequate accuracy.

This paper presents an application of optical character recognition(OCR) which can extract information from images of bills and receipts; store it as machine-processable text; in an organized manner for ease of access. It can do this efficiently even in the presence of watermarks on the bills or any shadows in the images of the bills. In developing this application, OpenCV has been used for the processing of the images and the Tesseract OCR engine has been used for optical character recognition and text extraction. The image is first processed using OpenCV for the removal of any shadows or watermarks present in it. For longer invoices, by employing the image bifurcation process, the data can be easily extracted which was not possible earlier. Furthermore, the processed image is passed on to the Tesseract OCR engine for the retrieving of text present in it. The text is then searched for important information, such as the total amount spent and the date on the receipt, using string processing.

L’Inde a pris recemment conscience que la menace du terrorisme islamiste etait aussi une realite nationale, d’autant plus que la communaute musulmane en Inde est relativement importante, obligeant a reflechir a l’impact de la propagande islamiste notamment via les reseaux sociaux.

Background: Enterococci are common commensal organism of enteric tract and act as opportunistic pathogen and may cause infection in community as well as in hospitalised individuals. In present study association of several types of virulence factors like haemolysin, gelatinase and biofilm formation have been studied among HLAR and Vancomycin resistant Enterococci (VRE) isolates of enterococci among UTI patients.Methods: The samples were collected from all hospitalized and OPD patients of MBS Hospital, JK Lone Hospital and NMC Hospital. Government Medical College, Kota, Rajasthan, India. A total of 360 isolates of enterococcus were collected during the period of 2 years from April 2016 to April 2018 in microbiology laboratory, Department of Microbiology, Government Medical College, Kota, Rajasthan, India. All virulence factors were detected by phenotypic methods and MIC values were detected for high level gentamicin and vancomycin.Results: Among all enterococcal isolates most common factor was biofilm production 191 (53.05%) followed by haemolysin 131 (36.38%) and gelatinase production 72 (20%). Total resistant (MIC> 500 µg/ml) isolates for gentamicin was 194 (89.4%). In agar dilution 14 (11.2%) isolates were found sensitive, 61 (48.8%) isolates were found intermediate and 50 (40%) isolates were found to be resistant for vancomycin. HLAR and VRE was maximum associated with haemolysin + bio-film followed by gelatinase+biofilm, haemolysin+gelatinase+bio- film and least with haemolysin + gelatinase.Conclusions: In present study enterococcus show significant production of biofilm and other virulence factors. With production of biofilm they become more resistant to routinely used concentration of antibiotics posing threat for treatment failure. A continuous monitoring is needed particularly for resistance to aminoglycoside and vancomycin to stop their institutional spread. Judicial use of antibiotics should be encouraged both in community as well as in institutions.