Problem statement: The shipment of goods from manufacturer to the consumer is a focal point of distribution logistics. In reality, the demand of consumers is not known a priori. This kind of distribution is dealt by Stochastic Vehicle Routing Problem (SVRP) which is a NP-hard problem. In this proposed work, VRP with stochastic demand is considered. A probability distribution is considered as a random variable for stochastic demand of a customer. Approach: In this study, VRPSD is resolved using Meta heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Hybrid PSO (HPSO). Dynamic Programming (DP) is used to find the expected cost of each route generated by GA, PSO and HPSO. Results: The objective is to minimize the total expected cost of a priori route. The fitness value of a priori route is calculated using DP. In proposed HPSO, the initial particles are generated based Nearest Neighbor Heuristic (NNH). Elitism is used in HPSO for updating the particles. The algorithm is implemented using MATLAB7.0 and tested with problems having different number of customers. The results obtained are competitive in terms of execution time and memory usage. Conclusion: The computational time is reduced as polynomial time as O(nKQ) time and the memory required is O(nQ). The ANOVA test is performed to compare the proposed HPSO with other heuristic algorithms.

IntroductionThe year 2020 saw the emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which became a great threat to public health worldwide. The exponential spread of the disease with millions of lives lost worldwide saw the emergence of an accelerated vaccine development with emergency approval from well-known regulatory bodies such as the US Food and Drug Administration, followed by widespread vaccine deployment despite a paucity in safety profile data. This issue becomes even more pronounced when it involves expectant mothers considering the possible undesirable effect toward the unborn child.MethodThis was a retrospective cohort study which was conducted at six general hospitals in the state of Penang, Malaysia. All the pregnant employees who have consented to take the mRNA COVID-19 vaccine and participate in this study were monitored from the time of their first vaccination and up to 28 days after they delivered their babies.ResultsAll the participants had adequate maximum vertical pocket (MVP) and no obvious anomalies or detection of intrauterine growth restriction (IUGR) were detected during the second trimester. However, one subject was reported to have miscarried during the second trimester. The reported mean neonate birth weight was 3.0 kg with the mean Apgar score of 8.8 and 9.8 at 1 and 5 min, respectively. Approximately seven (5.8%) neonates were reported to be small for their gestational age. Another three (2.5%) neonates were reported to have anomalies.ConclusionAs a whole, the inference that can be made from this study is that mRNA COVID-19 vaccine appears to be safe in pregnant women regardless of the trimester as the findings did not show obvious safety warning signs.

COVID-19 and the worldwide lockdown left life in worse condition in which people struggle to meet their livelihood. People are concerned about their lives, jobs, savings, and investments. This paper had analyzed the changes in the mutual fund industry in India due to the virus outbreak from December 2019 to May 2020. A sample of 25 equity-oriented direct growth funds has been considered for this study to analyze their performance along with the sector-wise differences. The results show that the value of majority of the funds had plunged, while some funds had rebounded during the period.