Nursing

Ajibola O.A., Ogbolumani O.A.

With over 33% of the world's 2.01 billion tons of annual solid waste managed poorly and projections of 3.40 billion tons by 2050, the global waste crisis is escalating. As daily per capita waste ranges from 0.11 to 4.54 kilograms, low-income countries face a tripling of waste production by 2050. Traditional waste management systems are inefficient and contribute to environmental pollution and operational costs. To address these challenges, integrating smart battery storage with IoT-powered solar waste management offers a sustainable alternative. By harnessing solar energy and storing it in smart batteries, these systems ensure the continuous operation of IoT devices, sensors, and waste collection vehicles, reducing reliance on grid power and carbon emissions. A prototype system, designed, developed, and simulated using Proteus circuit simulation, incorporates IoT sensors, solar panels, and smart battery storage to optimise energy use and improve waste collection efficiency. The research findings contribute to advancing sustainable waste management practices and inform the development of similar IoT-based systems for various urban applications.

AGBOOLA M., HASSAN K.A., AJEWOLE T.O.

This study assesses the effectiveness of an electric microgrid wind energy conversion system using both traditional techniques and contemporary embedded systems, such as artificial neural network-based control mechanisms and fuzzy logic control. The text compares and lists the advantages and disadvantages of various types of wind turbines (WTs). Moreover, this control falls into one of two groups: conventional power control or non-traditional power control. On the other side, conventional control describes methods of control such as manually controlling the turbine rotor's rotation speed and using computational analysis. The current work, in contrast, investigates and evaluates contemporary embedded control techniques used in wind energy conversion systems (WECS), including maximum power point tracking, Artificially intelligent control systems, in relation to the control mechanism, provide complete control over the pitch angle, power coefficient, and tip speed ratio for the best possible wind energy extraction. This makes a direct comparison possible. Nonetheless, there are a few drawbacks and difficulties with the two widely utilized contemporary techniques for power quality extractions: artificially intelligent neural networks and their embedded control systems. However, combining contemporary technology with integrated artificial intelligence controllers may be a workable strategy to lessen and even eliminate these difficulties, as well as advantageous for upcoming studies. 

MATLAKALA M.E., MONA S.T.

Large swaths of rural communities in South Africa are faced with water shortages. The Eastern Cape province is one of the most hard-hit areas in the country with very unskilled water administrators and old infrastructure exacerbating the problem. Water shortage is due to the mismanagement of resources that are meant to install, operate, and improve water infrastructures in the province. An increase in population also contributes significantly to the challenge of water shortages. Available water is not potable enough for consumption, as a result, the province is under the stress of access to potable water. The purpose of this study was to model the water supply chain and to determine the nature and causes of water delivery challenges. The model will be used as a decision-support tool to help achieve sustainable water management in the province. System dynamics is an appropriate methodology used to understand cause and effect relationships, policy decisions, and feedback and has been successfully used in solving water management problems in particular. A System dynamics model is developed using the Vensim PLE 8.0.9 (Double Precision) to determine opportunities for improvement in water management, water access, and water supply. The systems dynamics modeling is discussed herein.  

BAKER A.T., OJO O.I., DINKA M.O., RWANGA S.

Poultry farming is vital to Nigeria’s economy and provides a key protein source, yet many farms struggle with slow growth and high mortality in broiler chickens. This study aimed to assess the impact of magnetised water on broiler growth. 40 broiler chickens were housed in four groups (T0-T3) at the National Integrated Farm Project in Ilorin, Nigeria. The water treatment involved a setup with neodymium magnets and varied exposure times: T0 (control, no magnetisation), T1 (55 s), T2 (110 s), and T3 (165 s). Each group had 10 birds, and weekly weight was recorded over seven weeks. A paired t-test was used to analyze growth differences between treatments. Average weekly weight gains for T0, T1, T2, and T3 increased progressively, with T3 showing the highest gain. Paired t-test results indicated significant differences in growth between magnetised and non-magnetised groups, with calculated values above the threshold (ttab = 2.969). This study established that magnetised water increased the performance and growth rate of broiler chicken. It is therefore recommended that magnetised water can be used in producing broiler chicken for high income from poultry farming.

OSMAN O.A., OCHIENG G.M., RWANGA S.

Groundwater contamination from municipal solid waste landfills is a global issue, including South Africa. The Robinson Deep landfill (RDL) in Johannesburg lacks necessary leachate collection and handling facilities, has a shallow groundwater table, and no groundwater quality forecast tool. This situation poses a risk of groundwater contamination. This study aimed to construct groundwater flow and contaminant transport models to predict contamination from leachate migration at RDL. Visual MODFLOW Flex software was used for model construction and verification. Heavy metal concentration observations (Al, Cd, Mn, and Pb) from boreholes BH-1, BH-2, BH-3, and BH-H near the RDL were used to calibrate and validate the contaminant transport model (CTM). The result of the CTM predictive simulations for 2030 show Mn and Pb concentrations in the BH-H groundwater could reach 4.28 mg/L and 6.85 mg/L, respectively, exceeding permissible limits of 0.01 mg/L for Pb and 0.4 mg/L for Mn. The simulations indicate that the RDL threatens groundwater quality, especially in the northern areas of the landfill. Based on these findings, a recommendation is made for future studies on assessment and modelling of groundwater quality to focus on areas where increased concentrations of Pb and Mn are predicted. Further, it is recommended that precautionary preventive measures be implemented to mitigate possible contamination of groundwater in the northern areas of the landfill.

Udom I., Nta S., Usoh G., Kamai M., Ugwuishiwu B.

The non-predictability of rainfall-groundwater table relationships in the ungauged Obio Akpa watershed has rubbished remedial measures against seasonal crop losses.    This study utilizes ten years (2011-2020) climate data obtained from the study area to predict the seasonal rainfall-groundwater table variations and simulate the effects on crop root zone using Upflow model. Twelve wells monitored monthly, characterized the seasonal behaviour of the Groundwater Table (GT). Gravimetric direct measurements of soil water contents were the inputs to UPFLOW model used to simulate the seasonal responses of GT to rainfall. Correlation-regression analysis was used to obtain a prediction equation of GT responses to rainfall. Results showed that in the month of April, the mean rainfall of 230mm induced GT rise of 1.35m (35%) from 3.85m in March to 2.50m in April. The effect on the soil water properties included a corresponding increase of 1.8 vol.% (10.7%) in field capacity at equilibrium with the water table in the preceding month. Mean soil water condition remained optimal at 15.9 vol.% with the GT at 1.5m below the root zone. In September and October when 1859mm and 2129mm of the mean annual rainfall was received, the water table increased by 72% and 74% respectively, and field capacity in equilibrium with the GT increased to 34.9 vol.%. causing GT inundation of the root zone (RZ) and deficit aeration in 74% of the RZ respectively. In November, with reduced rainfall amount and decreased input to groundwater, only 16% of the RZ was in deficit aeration. UPFLOW simulations of the annual soil water regime showed the months of September, October and November as high GT months suitable for planting and harvesting of only shallow rooted, short maturity crops. This knowledge will guide effective water resources and crop management in the watershed.

SALU O., Ogundari I., ANIH S., AKINBAMI J.

The study determined optimum roof tilt and building azimuth for rooftop-based distributed photovoltaic (PV) energy generation in Ikeja, Lagos State, Nigeria as strategic technological input to alternative power generation and new housing development in Metropolitan Lagos. The energy planning & foresight analysis methodology was used. The data were obtained via literature, Global Positioning System (GPS) systems, energy models and site visits; and comprised manual and satellite imagery, dimensions, roof angles and coordinates of the building structures in the study area (Rows between 0 – 60o, Columns between 0 – 180o; System sizes: 533 kW, 110 kW and 0.547 kW). The results showed solar array yield varied from 768,944 kWh per annum for a 500 kVA system to 780 kWh per annum for a 500 VA system. A five tier rooftop generation template of 245 MWp was consequently developed. Optimal roof tilt and building azimuth angles for the systems were estimated to be 5o and 180o respectively though combinations of azimuth and tilt angles not exceeding 30o tilt and 90o azimuth gave acceptable yields. The study concluded that the optimum roof tilt and building azimuth for rooftop-based distributed photovoltaic (PV) energy generation in the study area were strategic policy intelligence inputs for the renewable power generation and new housing development programmes of the Federal and Lagos State Governments.

Adebisi J., Srinu S., Mitonga V.

Recently, every aspect of human existence has been affected by modern technologies including agriculture. The broad range of crops has witnessed setbacks in different capacities due to climate change among other factors, hence leading to diseases and infections; thereby leading to negatively impacted nutrition. This work uses a deep learning algorithm to investigate the classification of potato diseases as a case study which can be leveraged on by other agricultural products for reference.  Potatoes play a vital role in global agriculture, constituting a significant portion of fresh produce consumption. In southern Africa, potatoes hold particular importance, comprising 39% of total fresh produce consumption. However, the industry faces challenges from diseases such as early blight and late blight, resulting in substantial yield losses. Early detection is crucial, prompting the exploration of Convolutional Neural Networks (CNNs) for their disease classification capabilities. CNNs have shown excellent results in plant disease classification based on image data set. This study proposes the potential of aligning existing software-based Central Processing Units (CPUs) and Graphic Processing Units (GPUs) with FPGA-based potato disease classification using CNNs. To this end, five CNN models were trained on the Plant Village dataset for analysis. The models were compared using various performance metrics such as sensitivity, F1-Score, precision and recall. Based on the analysis the most suitable CNN model for FPGA implementation was selected. The chosen model was optimized for size using quantization technique. Subsequently the model was converted to Hardware Description Language (HDL) using Vitis High level synthesis (HLS) tool performance in terms of resources required to implement the model in System on Chip (SoC) was analysed.

Udekwu C.C., Francis U.C., Ojetunde M.M., Okakpu J.C., Awah F.M., Awe O.

The discovery of the first artificial plastic in 1869 (earlier called celluloid) by John Hyatt was a bid to replace the use of Ivory for ornaments and artistry, which endangered animals like the elephant. John Hyatt discovered plastics by dissolving camphor in nitrocellulose and alcohol in heat.  While this invention piqued the interest of scientists to have considered it a groundbreaking scientific discovery, little attention was paid to the aftermath effects of its usage. Hence, as the use of plastics continued to burgeon over the past decades, plastics have now begun to pose a serious threat to marine and land inhabitants. This threat encompasses the competition for space, water contamination, and environmental toxins release, contributing to the pollution of inhabitable lands. Unfortunately, third-world countries are consistently impacted worse as the global demand, production, and distribution of new plastics are increasingly becoming disproportionate to the rate of degradation and recycling. Consequently, many disciplines and research institutes have constantly invested time and resources in developing new technologies to combat non-biodegradable plastic waste. However, since the conventional methods of degrading plastics have been found to be less eco-friendly, there has been a paradigm shift towards using biological techniques to serve as better alternatives. In this study, an overview of the conventional and modern biological and non-biological pathways for degrading plastics are elucidated with concluding views on the microbial degradative pathway as a viable means for developing countries.

Ogundari I., SALU O., ILESANMI O., BAKARE O.

The decentralization of electricity authority in Nigeria has made it expedient for the South-West Geopolitcal Zone to develop strategic power generation infrastructure. This study assessed the techno-economic viability of the liquefied petroleum gas-powered alternative electricity infrastructure option in Southwestern Nigeria as a mitigation strategy to endemic inadequate grid power supply under the region's integrated electric power programmes. An energy project planning and foresight analysis methodology was used. The study showed that a 25 MW stand-alone LPG-based CHP power plant would require 8.567 acres of land, consume approximately 15.459 million kg of Liquefied Petroleum Gas (LPG) annually, while generating 210,240 MWh of electricity annually. This power plant initiative was considered to have acceptable risk (Payback Period = 9 years 3 months; Return on Investment = 10.73%) and viability at the minimum tariff of $0.11/kWh. It also had considerable annual CO2 emissions and fuel cost savings (255.75 metric kilotonnes and $137.58 million respectively) relative to diesel-based alternative power generation. The study recommended that the viability of the power plant be improved by reductions in operations costs. The study concluded that the LPG plant initiative was technically feasible, economically viable and environmentally friendly, and suitable for deployment in the study area.

Asolo E., Gil-Ozoudeh I., Ejimuda C.

This paper introduces an innovative method for sustainable agriculture whereby an AI-powered decision support system (DSS) is developed that makes use of an AI chatbot solution. Using machine learning algorithms and data analytics, such as the ones that support the AI-DSS allows for real-time insights and advice or suggestions to be given out on best farming methods, and crop management among other things. Farmers can talk about the system's comfortability and take advice that is personalized with the AI chatbot interface. The project seeks to boost agricultural productivity, cut down environmental negative influences, and advocate sustainable methods of farming. This study intends to link AI technology with farming, to make our future world more sustainable and food secure.

Jiya J.Y., Mu'azu Abubakar, Adekunle Joseph I., C. Egwim E., Obanimomo K.

This work investigated a novel study of the effect of bleaching and cross-linking cassava starch film. Native cassava starch was bleached with hydrogen peroxide (H2O2), cross-linking was carried out with oxidized sucrose while glycerol was added to enhance the plasticity of the film. Operating temperature and time of 90 oC and 10 Minutes respectively with and addition of 0.5 ml of glycerol gave the best bleached, cross-linked cassava starch film. UV-visible spectrophotometer analysis revealed that the cassava starch film produced at the above reaction conditions retained 88.2 % of its transparency at 96 hours water immersion. The water solubility test shows that the film experienced 52.02 % weight loss after 96 hours immersion in water. The thermo-gravimetric analysis (TGA) shows a significant improvement on the thermal stability with Temperature peak (Tp) of 420.75 oC, compared to 374.13 oC Tp of the control sample (unbleached, uncross-linked) of the cassava starch film.   

Omondi A.N., Ouma Y., Mburu S.N., Achisa C.M.

Inland water bodies are critical ecosystems that serve several functions including the provision of freshwater, regulation of climate and hydrological flows, and pollution control. Therefore, effective monitoring and management of these water resources is critical for sustainable water supply systems. This study evaluated the possible use of satellite data to estimate water quality parameters (WQPs) in an inland water body. The study also used artificial neural network (ANN) models in addition to the satellite data to determine the optimum coagulant dose for water treatment. The use of earth observations and machine learning methods has not been done extensively in developing countries, specifically, in water quality monitoring and management. The study utilized empirical multivariate regression modelling (EMRM) of the spectral reflectances from satellite data for the retrieval of Chla-a, Turbidity, and total suspended solids (TSS) concentrations in an inland water body. Using MLP-ANN modelling, the extracted spectral reflectance values from the selected sampling points in the reservoir were used as model inputs for the prediction of treated WQPs. A second MLP-ANN model was developed to predict the optimum coagulant dose required for raw water treatment. The R2 values achieved with AN model 1 were 0.81, 0.76, and 0.81 respectively for TSS, turbidity, and Chl-a, and 0.99 for the optimum coagulant dose. The study concluded that spectral reflectance from medium resolution satellite data products can be used to estimate WQPs from inland water bodies. Further, the ANN models demonstrate that extracted water quality data from satellite images can be used to inform ANN models for water quality predictions, and for the optimization of water treatment plant operations.

Mathetsa S., Du Plooy C., Tayob K.

Despite their inextricable interlinkage commonly known as Water-Energy-Food nexus, the current disconnect in policy development and management of the water, energy, and food resources threatens their security of supply. The security of these basic human needs is aggravated by the cross-cutting role of climate change which impacts their availability.  These apprehensions suggest that contemporary methods are required to improve and enhance integrated approaches and systems in the management of the food, water, and energy sectors within the discourse of climate change. This study applied Systems Thinking methodologies to foster collaboration amongst key stakeholders within Eskom, electricity generating sector in South Africa. This was done through several simulation workshops held amongst these value chain sectors. The workshops have demonstrated the ability of these systems to enable stakeholders to apply the “nexus thinking” approach in managing the sectors of water, food, energy, and climate change within the power utility. The study concluded by recommending application of this simulation within the policy development and other key sectors to enable a broader application of nexus thinking.

Oyim A., Akerekan O., Ogbonna N.

This study investigates the feasibility of integrating wind power generation at Lagos State University of Science and Technology, specifically within the Mechanical Engineering Department, to address persistent electricity supply challenges in Nigeria. The research focuses on the application of a five-bladed horizontal axis wind turbine (HAWT) and emphasizes the urgent need for sustainable energy solutions due to the depletion of conventional sources and the environmental impact of fossil-fuel-powered generators. The methodology involves mathematical modelling to analyze wind shear exponent, Weibull distribution, maximum power available, and capacity factor. Utilizing the FZ-3000 five-bladed HAWT, data is collected using a wireless wind anemometer. The results reveal a calculated wind shear exponent aligned with terrain characteristics, emphasizing the impact of obstructions on wind speed. Analysis of average wind speed throughout the day demonstrates the potential for continuous power generation at the Mechanical Engineering Department of the Lagos State University of Science and Technology. Examination of wind speed at different heights underscores the significance of elevated turbine towers for capturing higher wind speeds and optimizing power output in the specific context of Lagos State University of Science and Technology. The calculated capacity factor of 44.17% suggests the viability of large wind turbine installations within the Mechanical Engineering Department, with room for improvement through routine maintenance. This research contributes valuable insights into harnessing wind power at Lagos State University of Science and Technology, particularly within the Mechanical Engineering Department, addressing technical and environmental challenges for sustainable energy development.