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At the end of 2019, a novel Corona Virus surfaced as a pathogen and eventually resulted in a pandemic situation. The infection rate unleashed havoc worldwide firstly affecting European nations then the US. So, in this paper we proposed a software application which intakes user data to calculate the threat based on the merits of reconducted survey which prompts the user to check if they are undergoing any of the medically proven symptoms and sends it to the local municipal clinic. This in turn will minimize the need for medical workers to visit door to door constantly at risk of getting infected. This software is equipped with features like GPS tracking, threat level predictor, contamination graph it also determines medicinal urgency etc. Basically, our system is supposed to assist the day to day workers and visit places in scenarios of maximum urgency. This proposed system is used to prevent allowing likely symptomatic as well as asymptomatic users to enter in the hotspot regions’ public places such as malls and markets. The application will generate a unique QR code for respective users which will depict their merit based on mandatory survey that whether that person is affected or not. The QR code can be used in order enter the public area places where scanners are placed.

Hybrid composites are those composites that have a combination of two or more reinforcement fibres and could be used for the superior properties that are unachievable by any monolithic material. The objective of the present study is to design the aluminium-epoxy hybrid composite of varying number of fiber strands. The fibres are kept in 0°/90° orientation and the width of the fibre strands is varied in the analysis keeping the length and the height of the matrix constant. A 100 N load is applied on the hybrid composite block and the corresponding mechanical behaviour along with the material properties is obtained using finite element analysis (FEA). The results observed in the 0/90 orientation of the hybrid composite include the assembly stress distribution and assembly displacement. The parameters are varied to obtain the corresponding set of results and the optimized structure is suggested using FEA.

Age-hardenable aluminium alloys have higher strength compared non-heat-treatable alloys due to strengthening by the precipitates. Increasing the toughness of such alloys require improved balance between strength and ductility. To improve both ductility and strength through modification of composition and heat-treatment parameters of age-hardenable Al alloys (2XXX, 6XXX and 7XXX) an artificial intelligence based computational design approach is employed. Published data on the alloys are used for developing the data-driven models for tensile strength, yield strength, and %elongation. Fuzzy C means clustering is used to cluster the variables in the database into different levels and to generate fuzzy rule correlating those variables. Adaptive neuro-fuzzy inference system (ANFIS) used the fuzzy rules to develop the data-driven fuzzy predictive models for the said properties of the alloys. This models in turn played the role of objective functions for the multi-objective optimization using genetic algorithm (GA) for handling the conflicting objectives of improving ductility as well as strength to design alloys. The generated Pareto solutions are analyzed for finding suitable composition and process parameters fulfilling the purpose.