Embedded System to Prevent Traffic Congestion by Creating Traffic Light Delays

Increase of vehicles has been created traffic congestion and traffic jams which is a serious threat for the ambulance to reach its destinations in time. We as engineers have to consider this serious problem and duty to make an easy go for the ambulance during emergency. In a solution to solve this threat to prevent further threat to lives we are going to systemize the sensors which optimizes the traffic light operated by microcontroller which is powered by solar panel. This system reduces the traffic jam and congestion up to certain extent. Microcontroller used here is 89S52 which belongs to MCS-51 family. IR Transmitter and Receiver are placed on either side of the road. When vehicle passes in between IR Transmitter and Receiver, IR System is activated. IR System is controlled by microcontroller and t counts the number of vehicle passing on the road and keeps in memory. When vehicle count exceeds the limit microcontroller create the traffic light delays. Based on vehicle count, microcontroller defines different ranges for traffic light delays and update accordingly. At user pre-defined recording interval it records the vehicle count on a real time base. This recorded data is used to analyze traffic conditions in future. This data could be downloaded through communication between the microcontroller and the computer which is done by the computer administrator (access the traffic condition) on a central computer station access the traffic condition and reduce the congestion by creating traffic light delays

Cloning and sequencing of the virulent gene LipL32 of Leptospira interrogans serovar Autumnalis

Aim: To clone the virulent gene LipL32 of Leptospira interrogans serovar Autumnalis and to analyze the sequence with LipL32 gene of other pathogenic serovars of Leptopsira. Materials and Methods: Leptospira interrogans serovar Autumnalis procured from Leptospira research laboratory, Chennai was used in the study. Polymerase chain reaction (PCR) was carried out for amplifying LipL32 gene using the reported primers of Leptospira Kirschnerii. The PCR product was cloned into TA cloning vector and the vector was transformed into E.Coli DH5á cells. The plasmid was isolated from E.Coli and sent for sequencing with universal primers. The sequence was submitted in genbank with accession number JQ861883. Results: The PCR product revealed an amplicon of 790 bp. The LipL32 gene sequence of Leptospira interrogans serovar Autumnalis showed 99 % similarity with most of the pathogenic Leptospires. Conclusions: LipL32 gene of Leptospira is highly conserved in most of the pathogenic Leptospires. The study concludes that this gene could be used as a target for the diagnosis of leptospirosis in animals and humans and could be tested as an important candidate antigen for vaccine production. [Vet World 2013; 6(4.000): 193-195

Natural Language to SQL: Automated Query Formation Using NLP Techniques

In this era of information world, given any topic, we are able to get relevant data or documents at a mouse click. The flexibility that internet provides is the user friendly language or Natural Language to search for required topic. Natural Language Querying and Retrieval has made internet popular. It is implicit for business user to understand what the business data is indicating to find better business opportunities. Querying for required data the business users are using SQL. To effectively Query such systems, the Business users has to master the Language. But many business users may not be aware of the SQL language or may not be aware of the databases and some users feel difficulty to write the long SQL Queries. Therefore, it is equally important to query the database very easily. The work here presents a case study to help the business users to type a query in Natural Language, which then converts into SQL statement and process this SQL query against the Databases and get the expected result. This work proposes QCNER approach to extract SQL properties from Natural Language Query. The proposed approach after the application of SMOTE technique depicts 92.31 accuracy over the existing models.

SARS-CoV-2 seroprevalence among the general population and healthcare workers in India, December 2020–January 2021

Background: Earlier serosurveys in India revealed seroprevalence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) of 0.73% in May–June 2020 and 7.1% in August–September 2020. A third serosurvey was conducted between December 2020 and January 2021 to estimate the seroprevalence of SARS-CoV-2 infection among the general population and healthcare workers (HCWs) in India. Methods: The third serosurvey was conducted in the same 70 districts as the first and second serosurveys. For each district, at least 400 individuals aged ≥10 years from the general population and 100 HCWs from subdistrict-level health facilities were enrolled. Serum samples from the general population were tested for the presence of immunoglobulin G (IgG) antibodies against the nucleocapsid (N) and spike (S1-RBD) proteins of SARS-CoV-2, whereas serum samples from HCWs were tested for anti-S1-RBD. Weighted seroprevalence adjusted for assay characteristics was estimated. Results: Of the 28,598 serum samples from the general population, 4585 (16%) had IgG antibodies against the N protein, 6647 (23.2%) had IgG antibodies against the S1-RBD protein, and 7436 (26%) had IgG antibodies against either the N protein or the S1-RBD protein. Weighted and assay-characteristic-adjusted seroprevalence against either of the antibodies was 24.1% [95% confidence interval (CI) 23.0–25.3%]. Among 7385 HCWs, the seroprevalence of anti-S1-RBD IgG antibodies was 25.6% (95% CI 23.5–27.8%). Conclusions: Nearly one in four individuals aged ≥10 years from the general population as well as HCWs in India had been exposed to SARS-CoV-2 by December 2020