The current global issue of the COVID-19 pandemic has prompted the push and utilization of all available means to halt its spread. COVID-19 is a highly infectious disease, and continuously monitoring early symptoms could help avert catastrophic devastation. This paper proposes an innovative use of the Internet of Things (IoT) enabled system to efficiently and effectively detect early COVID-19 signs at a relatively low cost. This study adopted an experimental approach in designing and constructing a low-cost hardware system using a Wi-Fi enabled microcontroller, a temperature sensor, and a heart rate sensor for students. The proposed system detected and distinguished normal and abnormal temperature, regular and irregular heartbeat and constantly displayed the student's status in a mobile application. Consistent tests proved that the developed IoT-enabled system was reliable, responsive, and cost-effective. The mass production of this device will aid in the early detection of the disease, thereby mitigating the spread among students, particularly in underdeveloped countries. The paper's merit stems from the microcontroller's intelligence programming and the sensor's operation via the mobile application, which enables low-cost early identification of abnormal temperature and heartbeat irregularities.
The uncertainties and biases associated with Global Circulation Models (GCMs) ascend from global to regional and local scales which delimits the applicability and suitability of GCMs in site-specific impact assessment research. The study downscaled two GCMs to evaluate the effects of climate change (CC) in the Black Volta Basin (BVB) using the Statistical DownScaling Model (SDSM) and 40-year ground station data. The study employed Taylor diagrams, dimensionless, dimensioned, and goodness-of-fit statistics to evaluate model performance. The SDSM produced a good performance in downscaling daily precipitation, maximum, and minimum temperatures in the basin. Future projections of precipitation by HadCM3 and CanESM2 indicated decreasing trends revealed by the delta statistics and Innovative Trend Analysis (ITA) plots. Both models projected near- to far-future increases in temperature and decreases in precipitation by 2.05–23.89%, 5.41–46.35%, and 5.84–35.33% in the near-, mid-, and far-future, respectively. Therefore, the BVB is expected to become hotter and drier by 2100. As such, climate actions to combat detrimental effects on the BVB must be revamped since the basin hosts one of the largest hydropower dams in Ghana. The study is expected to support the integration of CC mitigation into local, national, and international policies, and support knowledge and capacity building to meet CC challenges.
Poor software quality has led to tremendous financial losses, necessitating the goal of this study. This study aimed to find out the major cause of poor quality of software and propose solutions to mitigate the problem. Histogram analysis was conducted using data from software development firms’ online applications used to track all defects and issues for each project, which are logged under a unique project ID. The requirement engineering stage was found to produce the most problems that directly or indirectly impact software quality. The capability maturity model integration, prototyping, ISO 9001, Walkthroughs, and Formal Inspections were proposed as solutions that could be used to mitigate the software quality problems that arise from the requirement engineering stage in the software development life cycle.
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