The novel coronavirus outbreak was first reported in late December 2019 and more than 7 million people were infected with this disease and over 0.40 million worldwide lost their lives. The first case was diagnosed on 30 January 2020 in India and the figure crossed 0.24 million as of 6 June 2020. This paper presents a detailed study of recently developed forecasting models and predicts the number of confirmed, recovered, and death cases in India caused by COVID-19. The correlation coefficients and multiple linear regression applied for prediction and autocorrelation and autoregression have been used to improve the accuracy. The predicted number of cases shows a good agreement with 0.9992 R-squared score to the actual values. The finding suggests that lockdown and social distancing are two important factors that can help to suppress the increasing spread rate of COVID-19.
Aim:The aim of the present study was to compare and evaluate the flexural strength of heat-polymerized Lucitone 199 and SR Ivocap denture base resin materials which uses polymerization techniques of compression molding and injection molding respectively and effect of artificial saliva and distilled water on long-term.
Materials and methods:Ninety specimens each from both the materials measuring 65 × 10 × 3 mm were prepared. After the polymerization, flexural strength was calculated after 24 hours (control group) without immersing in the liquid medium. The test group specimens immersed in saliva and distilled water at 37 0 C was calculated for the flexural strength at 2 weeks, 1 month, 2 months and 4 months. The flexural strength was measured using a universal testing machine. One-way analysis of variance (ANOVA) method was used to analyze the data, pairwise comparisons were done using Bonferroni post hoc test with a probability of less than 0.05 were considered to be statistically significant.
Results:The evaluation showed that, despite the duration of immersion and type of acrylic resin, high flexural strength was seen with specimens immersed in saliva than specimens under distilled water. The higher flexural strength was seen in SR Ivocap compared to that of Lucitone199 with the p value less than 0.05 which showed significant statistically.
Conclusion:From the results, we can conclude that the higher flexural strength was shown in specimens of SR Ivocap fabricated through injection molding technique compared to specimens of Lucitone 199 fabricated through compression molding technique after immersion in artificial saliva and distilled water for long term.
Clinical significance:The homogeneous copolymer beads, the difference in the water sorption and powder to liquid ratios also affect the mechanical properties of the resins other than the type of resin used in the dentures base.
A wireless sensor network (WSN) is a collection of various tiny devices known as sensor nodes, which are also called motes. Due to high-energy consumption, the possibility of hardware, link or node failure, and some malicious attacks, sensor networks are considered error-prone networks. Hence, fault tolerance (FT) in WSN is one of the prominent issues. This paper presents a novel FT approach named nodelink failure fault tolerance model (NLFFT Model) in WSN, to handle the faults that occur either by link or node failure during data transmission from the sensor to the sink or base station. The NLFFT model consists of an improved quadratic minimum spanning tree (Imp-QMST) approach. This approach helps in finding the alternate link whenever it fails due to various situations and also an improved-handoff (Imp-Handoff) algorithm to support the node failure to the fault tolerance. Improved QMST uses the artificial bee colony (ABC) algorithm to find an alternate edge in place of the broken or failed edge in the spanning tree, in order to improve the fault tolerance in WSN. Imp-Handoff suggests a novel way to find the faulty node owing to less battery power and replaces a defective node by an appropriate neighbor to shift the tasks performed by a faulty node in WSN. Simulation results clearly state that as compared to the basic techniques i.e. Q-MST and Handoff algorithm, the proposed NLFFT model improvises the performance of WSN around by 7%. The results prove that the Imp-QMST gives about 6% improved throughput, 5% less end-to-end delay, and 6% less power consumption than the QMST algorithm. Similarly, Imp-Handoff improves about 4% throughput, 6% less end-to-end delay, and utilizes 7% less power consumption.
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