MOBILE DATA COLLECTOR FOR SECURE TIME SYNCHRONIZATION IN CLUSTERED WIRELESS SENSOR NETWORK

Secure time synchronization is a key requirement for many sophisticated application running on these networks. Most of the existing secure time synchronization protocols incur high communication and storage costs and are subject to a few known security attacks. In wireless sensor network (WSN), lifetime of the network is determined by the amount of energy consumption by the nodes. To improve the lifetime of the network, nodes are organized into clusters, in which the cluster head (CH) collects and aggregates the data. A special node called mobile data collector (MDC) is used to collect the data from the CH and transfer it to the base station (BS) By using proposed method MDC authenticated to CH by computing shared secret keys on the fly. Once the MDC and CH are authenticated, all the sensor nodes in the cluster are synchronized, time synchronization reduce the communication and storage requirements of each CH. Security analysis of this proposed system shows that it is highly robust against different attacks namely compromised CH, reply attack, message manipulation attack as well as pulse delay attack

Pregnancy outcome in patient who had first trimester bleeding in previous pregnancy: a prospective study

Background: Patient who had history of spontaneous abortion in her previous pregnancy is associated with adverse outcome in her present pregnancy.Methods: A total 63 pregnant women attending OPD and admitted in department of obstetrics and gynecology, Yenepoya Medical College, from April 2017 to September 2017, considered and outcome were studied.Results: Out of 63 patient’s majority (57.1%) of patients belong to the age group 21-29 year. Anemia was found to be very severe in 4.3%, severe in 10% and moderate in 30% patients. Maximum patients (45.7%) were with history of previous one abortion followed by previous two abortions (38.6%). The final outcomes were term livebirth 47 (74.3%), abortion 9 (14.3%), preterm delivery 5 (8.6%), and stillbirth 2 (2.8%) caesarean section (23.3%) for various indications. 19.23% had term PROM, 9.09% had PPROM, 5.76% had term IUGR, 3.84% term IUD, preterm IUD accounts for 9.09% and still birth accounted for about 1.92% which was term, pre-eclampsia accounted for 4.76%, malpresentation for 7.93%, total 3 cases of antepartum hemorrhage out of which  placenta previa accounts for about 3.1% and abruption for 1.58%, manual removal of placenta 4.7% and low birth weight 7.6%.Conclusions: Previous history of spontaneous abortion is associated with adverse pregnancy outcome. There is increased risk of abortion, preterm delivery, need for caesarean sections and fetal loss which can be reduced by booking and giving antenatal care

Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets

The COVID-19 pandemic has presented significant challenges to the healthcare industry and society as a whole. With the rapid development of COVID-19 vaccines, social media platforms have become a popular medium for discussions on vaccine-related topics. Identifying vaccine-related tweets and analyzing them can provide valuable insights for public health research-ers and policymakers. However, manual annotation of a large number of tweets is time-consuming and expensive. In this study, we evaluate the usage of Large Language Models, in this case GPT-4 (March 23 version), and weak supervision, to identify COVID-19 vaccine-related tweets, with the purpose of comparing performance against human annotators. We leveraged a manu-ally curated gold-standard dataset and used GPT-4 to provide labels without any additional fine-tuning or instructing, in a single-shot mode (no additional prompting)