The level of Indoor Air Quality (IAQ) has become a big topic of research, and improving it using passive ventilation methods is imperative due to the cost saving potentials. Designing lecture buildings to use less energy or Zero Energy (ZE) has become more important, and analysing buildings before construction can save money in design changes. This research analyses the performance (thermal comfort [TC]) of a lecture room, investigate the use of passive ventilation methods and determine the energy-saving potential of the proposed passive ventilation method using Computational Fluid Dynamics (CFD). Results obtained showed that air change per hour at a wind velocity of 0.05 m/s was 3.10, which was below standards. Therefore, the lecture hall needs external passive ventilation systems (Solar Chimney [SC]) for improved indoor air quality at minimum cost. Also, it was observed that the proposed passive ventilation (SC) system with the size between 1 and 100 m 3 , made an improvement upon the natural ventilation in the room. There was a 66.69% increase after 10 years in the saving of energy and cost using Solar Chimney as compared to Fans, which depicts that truly energy and cost were saved using passive ventilation systems rather than mechanical ventilation systems.

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Gunshot detection technologies are more applicable in many industries for the security enhancement of public places like the Federal Republic Territory FCT Abuja in Nigeria. Many factors affect the accuracy of the gun detection algorithm. This paper describes an audio-based video surveillance system in an auto pilot situational awareness to detect gunshots in Federal Polytechnic Offa. The Time Difference of Arrival (TDOA) of Shock Wave and Muzzle Blast is integrated to estimate the shooter location in the study are. The proposed design and algorithm was validated and shooter origin was resolute that was very close to theoretical values. The video camera is steering regarding the initial position to localize the acoustic source's position. Implementing an auto pilot situational awareness system is an experimental procedure with a gunshot detection algorithm in a localized environment. In the direction of the weapon, the distance between firearms, types of ammunition, types of study environment, and diffraction of audio, the standard feature for gunshot recognition are Mel frequency cepstral coefficients in terms of uniform gamma-tone filters linearly spaced over the whole frequency range from 0KHZ to 16KHZ. Experiments show that our system can detect gunshots with a precision of 93% at a false rejection rate of 5% when the SNR is 10db while proving the estimate of the source direction of the gunshot with an accuracy of one degree. The outcomes reveal that the data generated by the system can be leveraged by the firefighting department to quickly locate the whereabouts of the indoor fires, and the VR gamification scenarios can expedite the development of situational awareness for the trainees. The research recommends a real-time system implementation for protecting the Federal Polytechnic Offa against any form of treats.