The analysis of thin film flow of non-Newtonian fluid with Vogel model viscosity in cylindrical pipe is considered. Vogel model is introduced to account for the temperature-dependent viscosity. Perturbation technique is employed to solve the dimensionless nonlinear momentum and energy equations. Results indicate that increase in the magnetic field parameter increases both the flow velocity and the temperature of the cylinder and a little increase in the Brinkman number greatly increased the temperature of the plate. Results further show that increase in the parameter M, slightly reduced the flow velocity and increase in the parameter B, drastically reduced the velocity of the fluid flow. It is observed that as B increases, the cylindrical wall temperature is greatly lowered which means that particles loose more thermal energy that would hitherto help in overcoming the attractive forces holding them together. Read more

Inadequate mechanism for efficient patient health monitoring and data aggregation mechanism in healthcare services has posed serious bottleneck to healthcare delivery the world over. Healthcare sensors have become an accessible means for the communication of data from patients to medical personnel. The use of Healthcare sensors is also an invention in medical practice which involves measuring vital physiological parameters of patients with the objectives of detecting disorders to mitigating them and preventing severe complications. In this research, a system architecture for effective tracking of patients’ health by deploying the Internet of Medical things (IoMT) as an approach is developed. The methodology used for this research was the Design Science research methodology. The choice of the methodology was born out of the fact that the methodology involves the construction and evaluation of the prototype (artefacts) that address a considerably acknowledged problem. In this adopted approach, real-time data are sent to a local server through communication channels (Wi-Fi) and then transmitted to the Internet of Things (IoT) server through a designated network route using a Wi-Fi module. For efficient transmission of vital signs to the cloud, a Blynk IoT- server was used as platform. Two types of sensors DS18B20 an AD8232 ECG were deployed in monitoring and tracking body temperature and heart rate respectively, the XAMPP server was used as the local server platform. The outcome of this research include a developed artefacts and a mechanism where real-time numerical data is communicated to the report platform which is further transformed into ordered numbers. The novelty in this work involves collating real-time data from sensors attached to patients in IoMT-enabled environments, thereafter a simple linear regression model was deployed to convert these real-time data to ordered numbers which indicate the level of severity of the vital signals collated. These ordered numbers are converted to visuals to enhance ease of view. To the best of our knowledge, this approach has not been implemented by previous research studies reviewed. Based on our findings, we recommend that: the prototype system be developed and deployed in healthcare facilities and the designed web applications be plugged into an existing web domains of healthcare facilities to enhance timely intervention by the healthcare experts. Read more

This study assessed the effects of climate variability on Maize yield in Wukari Local Government Area of Taraba State between 1999 and 2018. The research design employed in the study was an ex-post facto and analytical design. Hence, the study is a quantitative study that provides statistical data as empirical evidence and produces descriptive and informative sceneries of the topic study. The study employed the use of secondary data, sourced from the Nigeria Metrological Agency (NIMET) and Agriculture Development Program (ADP), Jalingo. Data used in the study were analysed using both descriptive and inferential statistics. The descriptive statistics used in the study were the time series trend analysis, while the inferential statistics employed in the study were the Ordinary Least Square (OLS) regression and correlation analysis. Based on the objectives of this study and data analysis, the results revealed an increasing trend in average annual maximum temperature in the study area, with a 62 percent variability, and a deceasing trend in average annual minimum temperature, with a 44 percent variability. More so, the results revealed an almost uniform but increasing trend in average annual rainfall in the study area between 1999 and 2018, with a 37 percent variability. Furthermore, the results of the trend analysis revealed an increasing trend in maize yield in the study area, with a 50 percent variability. The correlation analysis revealed a non-statistically significant strong positive linear relationship (r = 0.088, sig. 0.712 > 0.05) between the average rainfall and average temperature in the study area. The correlation analysis also revealed a non-statistically significant weak negative relationship (r = -0.072, sig. 0.762 > 0.05) between average rainfall variability and maize yield in the study area. A statistically significant moderate positive linear relationship (r = 0.564, sig. 0.010 < 0.05) was found between average temperature and maize yield in the study area. The linear regression analysis revealed that 33 percent (r-squared: r2 = 0.333) of the variation in maize yield is explained by the variation in rainfall, and temperature between the periods of 1999 and 2018 in Wukari Local Government Area of Taraba State. Hence, rainfall and temperature affect maize yield in the study area by 33 percent. The study thus recommended that farmers should adopt climate change and variability mitigation and adaptive measures. These include the use of resistant and drought tolerant species. In addition, there is need for the Ministry of Agriculture and Taraba State Agricultural Development Programme to educate farmers and farm agents on the realities and effects of climate change and variability, as well as adaptive measures that can be taken. These include better and practicable environmental policies, improved agricultural techniques, and alternative water sources which consist of irrigation farming, and mulching, vis-à-vis creating sustainable food security in the long run. Read more

Plant growth is heavily facilitated by the extent to which many interacting climate variables remain within appropriate conditions. The Ongoing global climatic change can significantly alter conditions for plant growth, in turn upsetting ecological and social systems. While there have been substantial developments in understanding the physical features of climate change, complete studies incorporating climate, biological, and social sciences are less common. This paper used climate projections under alternative mitigation situations to show how changes in environmental variables that limit plant growth could influence ecosystems, research, policies and humans. Results showed multiple climate variables becoming limiting for plant growth, particularly in tropical areas, which resulted in considerable reductions in plant yields. Furthermore, the paper posited that reductions in plant growth due to unsuitable growing days can lead to less suitable condition for plant growth and tree mortality can trigger ecological responses, including changes in plant community composition. The paper concludes by recommending that afforestation programs should be vigorously pursued. Read more

The adsorption of phenol in aqueous solution was investigated using carbonized neem leaves adsorbents. The effect of various factors such as contact time, phenol concentration, pH, temperature, and amount of adsorbent on the adsorption capacity of the adsorbents was determined. The results show that maximum adsorption was obtained at a contact time of 150 minutes, at a phenol concentration of 50 mg/L, optimum adsorption temperature of 45⁰C, pH of 2 and adsorbent dosage of 3 g/L. The equilibrium data was well described by the Freundlich isotherm equation (R2= 0.955 ). This shows a heterogenous adsorption process. The kinetics of the process were explained by the intra-particle diffusion models (R2>0.9). This fitted data indicates a degree of boundary layer control and is not limited to intra-particle diffusion. Read more

The essentiality of electric load forecast for the effective design and management of electric power systems has been achieved in this study. PHEDC may plan for infrastructure construction, resource allocation, and energy management by using accurate long-term load forecasts of this study. In the context of the Woji Estate 11/0.415 kV Feeder in Port Harcourt, Nigeria, we have discussed the use of artificial neural networks (ANNs) for a long-term of ten (10) years load forecasting in this paper starting from January 2020- December 2029. However, curve fitting feed-forward artificial neural network has been employed for the simulation on MATLAB 2020 environment, with six(6) input datasets obtained from Transmission Company of Nigeria(TCN), Oginigba and Port Harcourt electricity distribution Company, and average temperature dataset from NIMET-Abuja all in Nigeria from January, 2015-December, 2019. The regression plot of epoch 11 with training; R=1 and validation of 0.9999 have been achieved which indicates how efficient was the training of the dataset. Levenberg-Marquardt (LM) algorithm is used as an optimization technique in this study. In addition, training, test, validation, and error analysis have been used to examine the effectiveness of the LM algorithm, it has been found optimal with ANN. The general observation shows that ANN provides effective results on long-term electrical load forecasting of the Woji Estate Feeder with a total forecasted value of 29734.4 MWHR and an average value of 24778.67 MWHR at the end of the tenth year. Read more

With the increase in demand for renewable energy, photovoltaic (PV) panels have emerged as a major alternative for harvesting solar energy. However, the efficiency and performance of PV panels are inextricably related to environmental conditions. This study examined the effect of ambient variables on the performance of photovoltaic (PV) panels. Through controlled tests, the researchers investigated critical environmental parameters such as sun irradiance, temperature, wind speed, humidity, and dust deposition. Modern sensors and data-gathering methods were used to monitor how these variables affected PV panel output. Statistical tools were used to determine the relationship between environmental factors and PV panel efficiency. The findings showed a clear relationship between environmental variables and PV panel performance. Solar irradiance was recognized as a major indicator for energy generation, while temperature had complex implications on current output. Wind speed, relative humidity, and dust deposition were discovered to have discernible detrimental effects on panel performance. This study adds to the increasing knowledge about PV systems by highlighting the complex links between ambient conditions and panel efficiency. The findings highlight the importance of site-specific considerations in building and running PV installations to ensure optimal energy output and system longevity. The article's results have practical consequences for both the solar energy sector and researchers, leading to the development of ways to improve PV panel performance and contribute to the sustainable energy landscape. Read more

The research looked into the misconceptions held by Physics students about some selected heat and temperature concepts. The research design employed in this study was a diagnostic-descriptive research design. A sample of 56 Senior High School (SHS) two (2) students, drawn from Berekum Senior School in the Berekum Municipality, Ghana, were used for the study. An instrument known as the Two-Tier Test Instrument of Heat and Temperature Concepts (HTCT) was employed in data gathering. Mean score, standard deviation, frequencies, percentage were used to answer research questions one and two, while one-way multivariate analysis of covariance (MANCOVA) and correlational analysis was used to answer research question three. Results indicated that students used for this study hold misconceptions, lack understanding, and have difficulties explaining the selected heat and temperature concepts used in the HTCT instrument. The results also revealed that misconceptions about heat and temperature concepts non-significantly interact with student age (F .545 = .463, p > .05), but significantly interact with students' gender (F 4.541 = .038, p < .05). Based on the results obtained, the researchers recommended the use of two-tier test instruments and advanced organisers to identify students’ misconceptions of scientific concepts. Read more

The purpose of the current study is to explore the relations between coronavirus disease 2019 (COVID-19) case counts and meteorological factors in two capital cities of Kingdom of Saudi Arabia (K.S.A). Meteorological data on daily average (high, low and mean) barometer (atmospheric pressure) (hpa - hectoPascals), relative humidity (%), temperature (oc), wind gust (km/h), wind speed (km/h) and drew point (oc) during the study period, which was 35 days. The finding showed the person correlation coefficients between Daily number of infected cases of COVID-19 and weather factors in Al Riyad and Mecca cities in K.S.A. The results in Al Riayd and Mecca cities were: Daily number of infected cases of COVID-19 had obvious linear positive correlations significantly (p<0.01) with daily average temperature (r = 0.72; 0.72), daily average high temperature (r = 0.70; 0.65) and daily average low temperature (r = 0.67; 0.71); respectively, and significantly (p<0.01) negative correlations with daily average barometer (r = - 0.72; - 0.50), daily average high barometer (r = - 0.63; - 0.53), daily average low barometer (r = - 0.67; - 0.52); respectively. Also, in Al Riayd city the confirmed COVID-19 correlated significantly (p<0.01) with daily average humidity (r = - 0.59), daily average high humidity (r = - 0.60) and daily average low humidity (r = - 0.52) and in Mecca city was lower significant (p<0.5) with daily average humidity (r = - 0.38). Read more