Scielo RSS <![CDATA[R&D Journal]]> vol. 36 num. lang. en <![CDATA[SciELO Logo]]> <![CDATA[<b>Self-Defence Ammunition Comparison between .22, 9 mmP, .40 & .45 Projectiles</b>]]> This paper presents a comparison of different .22, 9 mm Parabellum (9 mmP), .40 and .45 calibre ammunition. The different projectiles are analysed by performance, considering penetration depth into ballistic gel, while compared with the penetration depth after some clothing, such as a material used for jackets, are penetrated first. The velocity, kinetic energy, and kinetic energy per cross-sectional area of the projectile are analysed, to identify which calibre and projectile has the most impact force on a threat. A cost comparison of the different ammunition is shown, while an analysis is done of the cost per kinetic energy per cross sectional area, for different ammunition.. <![CDATA[<b>Numerical Investigation of Pressure Recovery for an Induced Draught Fan Arrangement</b>]]> This study investigates the potential gain in operating volume flow rate and static efficiency for an induced draught fan arrangement by reducing the outlet kinetic energy loss. The reduction is achieved through pressure recovery, which is the conversion of dynamic pressure into static pressure. Downstream diffusers, stator blade rows, or a combination of these can recover pressure. Six different discharge configurations are tested for a fan. An annular diffuser with equiangular walls at an angle of 22° from the axial direction and a length equal to the fan diameter recovers the most pressure over a range of volume flow rates. The diffuser causes the operating volume flow rate and static efficiency to increase by 6.3 % (relative) and 20 % (absolute), respectively, compared to the initial design point of the particular fan. <![CDATA[<b>Fall Detection System using XGBoost and IoT</b>]]> This project aims to design and implement a fall detection system for the elders using machine learning techniques and Internet-of-Things (IoT). The main issue with fall detection systems is false alarms and hence incorporating machine learning in the fall detection algorithm can tackle this problem. Therefore, choosing the right machine learning algorithm for the given problem is essential and several factors need to be considered in making that choice. For this project, the XGBoost algorithm is used and the machine learning model is trained on the Sisfall dataset. A wearable device that is worn on the waist is designed using an accelerometer, a microcontroller, a Global Positioning System (GPS) module and a buzzer. The acceleration data obtained is converted into features and fed into the machine learning model which will then make a prediction. If a fall event has occurred, the buzzer is activated and emergency contacts of the victim are notified immediately using IoT and Global System for Mobile Communications (GSM). This allows the fall victim to be attended quickly, thus reducing the negative consequences of the fall. The details of the fall are stored on the cloud so that they can be easily accessed by healthcare professionals. Testing the system concluded that the XGBoost machine learning algorithm is well suited for this problem due to the small percentage error obtained.