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Even after scavenging the existence of mankind for the past year, the wrath of CoVID-19 is yet to die down. Countries like India are still getting haunted by the devastating conundrum, with coronavirus ripping through its citizens in the concurrent second wave. The surge of cases has prompted rapid intervention, with medical authorities pushing it to the limit to curve a roadblock to its aggressive growth. But, even after effortless work, human intervention remains slow and insufficient. Furthermore, relevant testing methodologies have shown weakness while detecting threats, with the recent growth of post-Covid complexities, thereby leaving a painful mark. This as such created a major requirement for technological advancements, which can cater to the mass. The growth of computational prowess in the past decade made the field of Deep Learning a major contributor in curving out algorithms to solve this. Adding to the excellent foundation of Deep Learning, this paper, proposes a novel CoWarriorNet model for rapid detection of CoVID-19, via chest X-ray images, which adds in an extra layer of precision and confirmation in the detection of cases in both pre-Covid and post-Covid conditions. The proposed classification model curves out an excellent accuracy of 97.8%, with the major eye-candy being the sensitivity rate of 0.99 when detecting CoVID-19 cases. This model introduces a new concept of Alpha Trimmed Average Pooling, which along with the novel architecture adds a subtle touch to its high efficiency, thereby giving a much-needed solution to the medical experts. The two-mouthed architecture provides the added benefit of a confidence score, deducing human aid in case of discrepancy. [ABSTRACT FROM AUTHOR]

A systematic correlation is established between the axial load-carrying capacity of the socketed piles and empirical methods. The axial load-carrying capabilities were calculated with the help of pile load tests for ten single piles in Navi Mumbai, India. Pile load tests were carried out to evaluate the skin friction and base resistance in overlying soil and rock. The test results of axially loaded bored piles socketed into the rock are mentioned in this paper. The pile socket involved rocks of various strengths such as gypsum, limestone, and basalt. In reality, axial capacity is calculated from skin friction of the pile, which occurs owing to a very small settlement in the rock socket. The unconfined compressive strength of the rock is collected from the laboratory test. The load-bearing capability of the socketed piles is determined using several empirical methods, which are usually evaluated by a back-assessment of the in-situ pile load test. However, the significant load-carrying capacity values derived from empirical correlations are found to be reasonably good compared with those estimated from pile load tests. [ABSTRACT FROM AUTHOR]