ABUAD Journal of Engineering Research and Development (AJERD)

Efficient Energy Management System using Honey Badger Algorithm for Smart Agriculture

2024-10-08T13:05:15+00:00

Today, optimization is crucial to solving energy crises, especially in smart homes. However, the optimization-based methods for energy management in smart agriculture available globally need further improvement, which motivates this study. To resolve the problem, an efficient scheduling farm energy management system is required. Therefore, this study proposes a Farm Energy Management System (FEMS) for smart agriculture by adopting a honey-badger optimization algorithm. In the proposed system, a multi-objective optimization problem is formulated to find the best solutions for achieving the set of objectives, such as electricity cost, load minimization and peak-to-average ratio minimization, while considering the farmers' comfort. The proposed system considers commercialized agriculture with the integration of Renewable Energy Resources (RES). Also, the proposed system minimizes both load consumption and electricity costs via the scheduling of farm appliances in response to Real-Time Pricing (RTP) and Time-of-Use (ToU) pricing schemes in the electricity market. Extensive experiments are carried out in MATLAB 2018A to determine the efficacy of the proposed system. The proposed FEMS consists of sixteen farm appliances with their respective power ratings, inclusive of RES. The simulation results showed that a system without FEMS has a high electricity cost of 50.69% as compared to 43.04% for FEMS without RES and 6.27% for FEMS with RES when considering the ToU market price. For RTP market price, a system without FEMS has an electricity cost of 42.30%, as compared to 30.64% for FEMS without RES and 27.24% for FEMS with RES. Besides, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 151.40 kW for FEMS without RES and 18.85 kW for FEMS with RES when considering the ToU market price. Also, for the RTP market price, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 186.40 kW for FEMS without RES and 90.68 kW for FEMS with RES. The significance of the study is to propose a conceptualized FEMS based on the honey badger optimization algorithm. The proposed system provides scheduling of farm appliances that alleviates the burden of the electricity grid and is cost-effective for large and small-scale farmers.

Development of an Automatic Phase Selector for Nigerian Power Utility Customers

2024-10-08T13:05:14+00:00

Power utility customers in a developing country like Nigeria have constituted a habit of changing the electricity supply line from an unavailable or unstable phase to the most available or stable phase. The category of customers involved in this character are those on single phase power supply. However, this act is being carried out manually at the meter point using the cut-out fuses. This attitude results in phase unbalances, overheating electrical equipment including feeder pillars, transformer coils, network faults, and overall system instability. Thus, this paper presents the development of an Automatic Phase Selector for Nigerian Power Utility Customers. The device automatically selects an available phase from the three-phase power supply lines. The research comprises designing an automatic phase selector circuit, simulation of the designed circuit, programming code development in C- Language for the microcontroller, construction of the designed circuit, and carrying out tests on completed work done to ascertain the effectiveness of the developed system. The system operation involved a three-phase supply from the closest distribution network of the power utility company which is connected to a three-in-one gang switch while the switching ON and OFF of their static switches represent phase-off in an ideal situation. The operational results of this system are presented in the form of the truth table which indicates that the affected customer would not have a power supply only when the 3-phases are under voltage or overvoltage or unavailable. This implies that one of the three phases that meet the three criteria would be switched ON. A pure sine wave was used as input into the Optocoupler and the output waveform of the rectified pulsating signal is separately displayed. This output waveform is very clean and noiseless. Finally, the system when practically tested with an unbalanced three-phase supply, worked perfectly enhancing the flexibility of operating an Automatic Phase selector and hence avoiding manual switching of the phase selector which has been attributed to changing of cut-out fuses and associated stress as well as having a user-friendly phase selector.

Unlocking the Potential of Palm Kernel Shell and Quarry Dust: A Cost-Driven Approach to Replacing Sand and Gravel in Concrete

2024-10-08T13:05:12+00:00

This research investigates the potential of palm kernel shells (PKS) and quarry dust (QD) as sustainable and cost-effective replacements for sand and gravel in concrete production. The study explores the impact of varying PKS and QD content on workability, density, water absorption, and mechanical properties. While increasing these alternative aggregates decreases workability and density, it improves water absorption and, in some cases, mechanical strength. Response Surface Methodology (RSM) identified a combination of 5% PKS and 20% QD (-1, -1) as the optimal replacement level for achieving a balance between cost and performance. This mix offers a significant cost reduction of 18.2% relative to concrete made with conventional aggregates. The study highlights the potential of PKS and QD as sustainable alternatives for conventional aggregates. Utilizing these readily available waste materials can reduce reliance on natural resources, promote waste management practices, and contribute to a more environmentally friendly construction industry. Additionally, the research suggests that quarry dust alone might be a more suitable replacement material than PKS due to its superior influence on concrete strength. This research provides valuable insights for optimizing concrete mix design with PKS and QD, promoting cost-effective and sustainable construction practices in regions with abundant palm oil production and quarrying activities.

Evaluation of Effective Interfacial Area in a Rotating Packed Bed Equipped with Dual Gas Inlets

2024-10-08T13:05:11+00:00

This study investigates the effective interfacial area in a novel rotating packed bed (RPB) equipped with dual gas inlets instead of the conventional single-gas-inlet RPB. The aim is to enhance the mass transfer efficiency of gas-liquid contacting processes in RPBs by increasing the number of gas inlets to improve the spread of gas supply into the packing. The RPB is a promising gas-liquid contactor configuration known for its intensified mass transfer characteristics. However, the impact of additional gas inlets on the effective interfacial area of the packing remains unexplored. An experimental method assessed the interfacial area under varying operational conditions which include a liquid flow rate of 0.30-0.60 m³/h, a gas flow rate of 100-300 Nm³/h, and a rotation speed of 600-1000 rpm. At operating conditions covering the maximum rotation speed of 1400 rpm, gas flow and liquid flow rates of 300 Nm³/h and 0.60 m³/h respectively, the results showed that on average, 55 to 97% of the 2400m²/m³ specific packing area can be effectively utilized for gas-liquid mass transfer during separation operations using the RPB. Compared to results reported for single-gas-inlet RPBs using similar packings, the RPB with double gas inlet proved to provide higher utilization of the packing. By simply doubling the number of gas inlets, the findings provide valuable insights into optimizing RPB designs and operations which could enhance mass transfer efficiency for various chemical and environmental applications.

Adaptive Radio Access Technology Selection Algorithm for Heterogeneous Wireless Networks

2024-10-08T13:05:10+00:00

In Heterogeneous Wireless Networks (HWNs), Radio Access Technologies (RAT) can only consider the situation of one particular Radio Resource Management (RRM) which is unsuitable for managing multiple RATs. This study deployed an adaptive RAT selection scheme model to allocate users to the best RAT with the use of the cost function variable. The adopted model uses different input criteria like signal strength, network loads, service type and QoS requirement for the best access network selections. The adaptive RAT selection algorithm was executed in different service mixes (voice and data service) to access model suitability for users in Global System for Mobile Communications with Enhanced Data Rates for Global Evolution Radio Access Network (GERAN) and Universal Mobile Telecommunications System Radio Access Network (UTRAN). The proposed algorithm resulted in the call blocking probability reduction by 0.03 for GERAN and 0.14 for UTRAN as validated with the existing algorithm based on load balancing, service-based and priority-based. The drop implied an increased probability of ensuring session stability and high quality of the active service, leading to a high load distribution.

Response Surface Methodology Optimization of Wear Rate Parameters in Metallic Alloys

2024-10-08T13:05:08+00:00

The optimization of wear rate parameters in metallic alloys using Response Surface Methodology (RSM) has been experimentally performed. The wear rate, a critical factor affecting the durability and performance of metallic components, served as the response parameter, while track diameter, sliding speed, and mass difference were considered as independent variables. The Central Composite Design (CCD) experimental method systematically explored the response surface and optimizes the wear rate. A mathematical model was developed, revealing a significant p-value of 0.043 in the ANOVA table, indicating the collective influence of the independent variables on wear rate at a significance level of 0.05. Furthermore, the model demonstrates a substantial explanatory power, with R-squared of 69.45% and adjusted R-squared of 51.95%. The p-value calculated to be 0.60 for the statistical Lack of fit indicated a satisfactory model. These findings highlight the effectiveness of RSM in optimizing the experimental input values and offer valuable insights for enhancing the durability and performance of metallic alloys in various industrial applications. The obtained result addresses the problem of uncertainty inherent in optimal levels of input parameters wear experimentation.

Models Development for Prediction of Blast Efficiency and Total Charge in a Typical Quarry

2024-10-08T13:05:00+00:00

The prediction of blast efficiency is usually achieved by using models; this in turn, gives better and more efficient rock fragmentation. However, the accuracy of the prediction often times relies on the model development validation. In this study, models were developed and compared upon validation for predicting the blast efficiency and total charge required for efficient fragmentation using artificial neural network (ANN). Rock samples were gathered from the study are, and the uniaxial compressive strength (UCS) test was carried out on all the samples based on international standard. The average UCS obtained from the rock samples at the Eminent quarry (EQ) is 153.61 MPa. The dimension of in-situ rock mass considered in the study area is 60 m x 40 m, and the in-situ block sizes obtained vary from 2.02 m² to 3.20 m². The average percentage value of F₅₀ obtained from the Split-Desktop image analyses is approximately 72.44 cm. The various results obtained from the UCS, in-situ block size distribution, image analysis of the blasted rocks and the total charge were used to develop the models for the prediction of blast efficiency. The key issue of concern about these models is that they are mostly site specific and the fact that if they perform well in a location does not guarantee the other. Hence, the validation and suitability of these models on the mine site. The blast efficiency prediction using ANN is compared with measured efficiency and the value of coefficient of determination, R² obtained is 0.9733. The value of the coefficient of determination, R² obtained from ANN by comparing the prediction of the total charge and the measured total charge is 0.9773. The findings showed that, the proposed ANN based mathematical models are suitable and thus, give better prediction to blasting efficiency and the possible total charge.

Enhancing Social Engagement among Online Learners' Using AI-Driven Tools: National Open University of Nigeria Leaners' Perspective

2024-10-08T13:05:06+00:00

The need for online education has increased significantly. People now prefer to work to fulfill the necessities of life and pursue education to advance their skills because of the rising difficulty. This quest increases the demand for distance education thereby raising questions about how distance learning institutions can effectively assist their learners. Employment of Artificial Intelligence (AI) tools will not only provide solutions but also improve and render effective service and support to learners. AI-driven tools such as personalized or adaptive learning and chatbots for learner support have significantly helped to improve efficiency in virtual environments. This research aims to investigate how National Open University of Nigeria (NOUN) students view the contribution of AI tools in enhancing social interaction in their virtual learning environment. The study seeks to determine the requirements, inclinations, and challenges related to social interaction in the online learning space and explore how AI-powered solutions might effectively address these challenges to create a more dynamic and engaging learning environment. A survey was conducted to ascertain the level of awareness among the learners on the use of these tools, the challenges related to social interaction in online space and explore the ways AI-powered tools can effectively address issues in the learning environment to create a more dynamic and engaging learning environment. This study has identified that a greater number of learners in NOUN have little or no knowledge of the availability of these tools as well as how they can effectively use it. The level of awareness of the learners on the use of these tools is low. The study found 27.5% awareness and usage of AI tools provided by the institution. Several platforms were identified by respondents; however, ChatGPT was the most widely used AI platform. The study also discusses the importance of AI tools in enhancing collaboration and social engagement among learners. It identifies the challenges in integrating AI in Education and provides possible solutions to the challenges.

Investigating Internal Heat Exchanger Performance in a VCR System with a CO2 and LPG Refrigerant Mixture

2024-10-08T13:05:02+00:00

In this study, an attempt was made to develop a cooling system with an internal heat exchanger using a mixture of carbon dioxide (CO₂) and liquefied petroleum gas (LPG) as refrigerants to help eliminate the global warming potential and other harmful environmental effects caused by conventional refrigerants'. The CO₂ and LPG refrigeration experimental setup was constructed with varying sizes of capillary tubes, a pressure controller, an evaporator, and a gas hob. The working ranges were initially confirmed through exploratory experiments with low-pressure and high-pressure flow circuits, using and without an internal heat exchanger (IHE). The evaporator temperature helped to determine the proportional changes in the coefficient of performance (COP). The REFPROP software design was used to conduct experiments and determine the important process parameters. A confirmation test was performed to validate the expected results of the REFPROP software technique. The results showed that the experiments conducted using IHE had a COP with greater performance levels as follows: mean of 1.398 and SD of 0.367 which is greater than the value of the experiments undertaken without IHE which had a COP performance levels as follows: mean of 0.67 and SD of 0.19. The Paired Samples T-test found these differences to be significant, at p-value < 0.033. The null hypothesis was rejected, hence there is evidence to suggest that the COP of the experiment with IHE is statistically greater than the COP of the experiment without IHE, with a 95% confidence interval of -1.357 and -0.099

Mitigating the Impact of Climate Change on Vegetable Farming: An Evaluation of Artificial Planting Technique

2024-10-08T13:05:05+00:00

A worldwide issue, global warming results from human activity changing the climate and having a negative impact on people, animals, and plants. However, in terms of plants, the sun provides the primary elements required for healthy growth of photosynthetic plants, which use the energy from the sun to create food for themselves. Light with varying wavelengths that serve distinct functions during the photosynthetic process are the essential elements that are captured from the sun. The wavelength of the ultraviolet (UV) component of sunlight varies, characterized as UV A (315–400 nm) and UV B (280–315 nm) are the primary components that must be precisely proportioned for a profitable farming. In order to lessen the impact of climate change on vegetable farming, this research suggests integrating light emitting diodes (LEDs) in artificial growing machines as well as planned irrigation systems as an alternate source of ultraviolet sunshine. To provide the necessary UV light combination, blue, red and white colours of light-emitting diodes (LEDs) were combined using diffusers. The red, blue, and white LEDs were used for two weeks, each 12 hours a day, to influence the plants growth, with red promoting photosynthesis, white improving it, and blue encouraging stem and leaf growth. An Arduino Uno was used to program both the hardware and software components of the automated growth machine. The outcome of planting varied vegetable plant under LED lights was contrasted with the outcome of planting the identical set of plants under direct sunlight. After the first and second weeks of planting, the plants' performances under both circumstances are comparable.

Elevating Tanzania's Tourism: Integrating GIS, AR and AI for Immersive Exploration and Promotion

2024-10-08T13:05:03+00:00

This study presents a comprehensive examination of the integration of Geographic Information Systems (GIS), Augmented Reality (AR) and Artificial Intelligence (AI) in tourism promotion in Tanzania. The literature review underscores the significance of these technologies in enhancing visitor experiences, destination management, and marketing strategies. The proposed integrated system design combines GIS's spatial mapping capabilities, AI's personalised recommendations, and AR's immersive content delivery to optimise tourist satisfaction and engagement. Key components include the GIS module for spatial data management, the AI recommendation engine for personalised suggestions, and the AR interface for immersive content overlay. Discussions highlight how the proposed system, by addressing critical challenges in the tourism sector, aligns with existing research findings and reassures its effectiveness. Ultimately, the study emphasises the potential of GIS, AR and AI technologies to revolutionise tourism promotion in Tanzania, fostering sustainable growth and cultural appreciation while enhancing visitor experiences.

Sparse FIR Filter Design using Double Generalized Orthogonal Matching Pursuit (DGOMP)

2024-10-08T13:04:58+00:00

In this paper, sparse FIR filter was designed using Double Generalized Orthogonal Matching Pursuit (DGOMP) to reduce memory usage and increasing the speed thereby decreasing computational complexity of the algorithm. Mathematical models were formulated and simulations were conducted to validate the performance of the proposed method. The performance was compared with BOMP and Conventional FIR filter. The results showed that the DGOMP method achieved higher sparsity and a better approximation of an ideal filter. Additionally, the designed sparse FIR filters using DGOMP showed better performance in terms of time of execution when the signal lengths keep increasing, giving a 10% faster execution time when compared to BOMP. The passband and stopband attenuation, as well as ripple values were better, offering the flexibility of parameter adjustment. The results showed that DGOMP is a promising approach for designing sparse FIR filters.

Cybersecurity Assessment and Vulnerability Modelling of Networks and Web Services in Nigerian Colleges of Education

2024-10-08T13:04:56+00:00

Cybersecurity threats are among the most significant risks facing organizations and government today, and administrative boards have now been held accountable. This is an experimental research activity conducted to perform a holistic cybersecurity assessment and vulnerability modelling on the Information and Communication Technology (ICT) infrastructure and services of Colleges of Education in the six geopolitical zones. The study adopts an integrated bi-modal threat modelling and assessment (IBTMA) method by combining assessment and modelling approaches, which involves mixed-methods, along with computer-based experimentation to comprehensively evaluate and model cybersecurity threats, identify vulnerabilities, and propose effective mitigation strategies. Logistic regression data analysis was used to model the relationship between dependent variables (e.g., presence or absence of vulnerabilities or threats) and independent variables (e.g., cybersecurity practices, system configurations, policies, and staff training programs). This cybersecurity assessment provides the initial understanding of the security landscape and practices. The next step involves using the Microsoft Threat Modeling tool on the assets to identify specific threats. These threats are then prioritized based on their potential impact and likelihood. Assessment result of the vulnerability exposure is supported by the threat modelling report, which shows several threats: tampering, elevation of privilege, denial of service, privilege escalation, information disclosure, and spoofing. Findings from the study indicate that colleges face critical network and web vulnerabilities that need holistic solution.

Drying Process of Senna alata Medicinal Leave: Comparative Empirical and Artificial Neural Networks Modelling of Mass Transfer Kinetics with Energy Analysis

2024-10-08T12:39:13+00:00

This study investigated the microwave drying of Senna alata leaves (SAL) for sustainable utilization. The effect of SAL form (un-chopped and chopped) and microwave power (200, 400 and 600 W) on the drying characteristics and energy utilization with comparative semi-empirical and artificial neural network (ANN) modelling was investigated. SAL was dried at the selected drying factors (leaf form and microwave power); and moisture transport characteristics including moisture content, moisture ratio, effective moisture diffusivity, activation energy, energy consumption, specific energy consumption and energy efficiency were determined gravimetrically and empirically. In addition, models were utilized to represent the experimental observations and compared statistically. Results showed that un-chopped SAL had a drying time of 10, 8.87, 7.34 s while chopped SAL had a drying time of 8.34, 5.45, 3.5 s at 200, 400 and 600 W, respectively. The effective moisture diffusivity of un-chopped and chopped SAL ranged between 1.40e-6 - 1.94e-6 m²/s and 1.99e-6 – 3.79e-6 m²/s at 200, 400 and 600 W, respectively; while activation energy was 1.79 and 3.64 W/g, respectively. The un-chopped SAL has energy efficiency of 47.38, 26.71 and 21.52% while chopped SAL has energy efficiency of 56.47, 43.49 and 45.14 KJ/kWs at 200, 400 and 600 W. The range of coefficient of determination (R²) of empirical models was 0.9963 – 0.9994 while R² value of ANN model was 0.9996. It was generally observed that the form of SAL and selected microwave power affected the drying and energy indicators, where size alteration (chopping) and increment in microwave power reduced the drying time and improved the energy indicators. The semi-empirical and ANN models performed well in representing the drying process with ANN having a marginal edge. These results are useful in conservation of SAL, control and commercialization of the microwave drying process.

Comparative Modelling, Sensitivity Analysis and Thermodynamics Study of the Adsorption Characteristics of Dried Nauclea latifolia Medicinal Leaves

2024-10-08T12:39:12+00:00

The adsorption characteristics of Nauclea latifolia medicinal leaves were examined across selected temperatures (30–50°C) and water activity levels (0.044–0.900) to assess its storage stability. Both univariate semi-empirical and multivariate statistical models were comparatively employed to represent and predict the observed adsorption characteristics. Additionally, sensitivity analysis was conducted to evaluate the dependence of the adsorption characteristics (that is, equilibrium moisture content (EMC (g/g d.b.)) on temperature and water activity storage factors. The net isosteric heat and entropy of adsorption were also determined alongside compensation theory values. The results indicated that EMC decreased with increasing temperature and increased with rising water activity. The minimum and maximum EMC values of 0.015 and 0.221 g/g d.b. were observed at 50°C. The safe moisture content for storing dried Nauclea latifolia medicinal leaves was 12.6 (g/g d.b.) at 30 to 40 °C and 9 (g/g d.b.) at 50°C. Amongst the models tested, the Peleg model demonstrated best performance, with its R² values ranging from 0.9897 to 0.994 and RMSE values between 0.0039 and 0.0129. Sensitivity analysis revealed that EMC is more sensitive to water activity than to temperature. The net isosteric heat and entropy of adsorption decreased with increasing EMC, indicating that the process was enthalpy-driven. In conclusion, the findings underscore the importance of environmental management in maintaining the storage properties of Nauclea latifolia medicinal leaves. The results of the models are useful in guiding the optimal storage conditions and the design of tailored storage facilities for Nauclea latifolia medicinal leaves.

Reliability-based Assessment of the Structural Integrity of some Existing Reinforced Concrete Columns

2024-10-08T12:39:10+00:00

Abnormal loading can initiate the progressive collapse of a reinforced concrete building. A progressive collapse may start as a local failure, followed by a sequence of reactions leading to a massive portion failure of an entire structure. Reinforced concrete columns are significant structural elements in ascertaining the integrity of framed buildings. This paper presents the report on the structural integrity of reinforced concrete columns of two selected university buildings labelled A and B investigated by using non-destructive testing techniques. First-Order Reliability Method (FORM) was deployed to process the data from the field in order to generate the implied safety indices for all accessed columns in the two buildings. The computed safety indices decrease as the simulated designed practical axial loads/moments increase for all the assumed steel ratios (0.4%, 1.59% and 6%) based on BS 8110:1997:1. When compared with the target safety level of 3.8 according to BS EN 1990:2002+A1:2005 for 50 years reference period of Class RC2 structural members in the ultimate limit state, almost all the columns passed the reliability test except the columns labelled 71 in building A; and C81A and C85 for building B. The highlighted critical columns show the direction for immediate repairs to forestall the initiation of eventual progressive failure of the buildings.

Comparative Study of the Viscosities and Thermal Conductivities of Groundnut and Coconut Oils Dispersed with Graphene Particles Reinforced with Oleic Acid

2024-10-08T12:39:09+00:00

This study addresses some challenges accrued using mineral oil as cutting fluid and suggest alternatives to suitable, eco-friendly, non-toxic and biodegradable solution using vegetable oil. Oils extracted from vegetables are environmentally friendly, biodegradable, and non-toxic compared with mineral oils. To investigate their optimal use for industrial applications, this study tested base oil's thermal-physical properties (kinematic viscosity and thermal conductivity). Temperatures of 40⁰C and 100⁰C were considered for kinematic viscosity, and it was improved with the infusion of graphene nanoparticles and oleic acid. The thermal conductivities of the base oils at temperatures of 50⁰C, 60⁰C, and 70⁰C were tested against the addition of graphene nanoparticles at the same temperatures with compositions of 0.001%, 0.003%, and 0.005%. Thermal conductivity of the groundnut oil at 50, 60 and 70⁰C were 0.495, 0.320 and 0.225 Wm-¹K-¹. The average of the compositions at 50, 60 and 70⁰C were 0.527, 0.33 and 0.25 Wm-¹K-¹. Compare to coconut oil at 50, 60 and 70⁰C were 0.534, 0.318 and 0.214 Wm-¹K-¹, and the average of the compositions at 50, 60 and 70⁰C were 0.622, 0.36 and 0.24 Wm-¹K-¹. Kinematic viscosity increments of coconut oil performed better than groundnut oil at 0.001wt% with 40⁰C is 7.15% and 3.68% for groundnut oil. Groundnut edged coconut oil at 0.003wt% at 40⁰C 17.98% and 11.83%. Similarly, with 0.005wt% at 100⁰C coconut oil improve with 63.70% compare 59.73% of groundnut oil. Groundnut oil has a higher viscosity index than coconut oil without the addition of nano-lubricant 436.3 and 209. With the infusion of nano-lubricant the average viscosity index for groundnut oil is 535.17 compare to 406.25 of the coconut oils. It can be verified that the infusion of graphene nanoparticles in both oils can be deployed in machining applications to reduce the friction between contacting surfaces and dissipate heat from the cutting zone.

Development of a Particulate Matter and Carbon Monoxide Detector

2024-10-08T12:39:07+00:00

Air pollution is inarguably a common tragedy in the today’s world: a resultant effect of industrialization and civilization. This work considered two of the most common domestic air pollutants – particulate matter (PM) and carbon monoxide (CO). This developed device is able to detect the presence of these two pollutants in the atmosphere, and trigger an alarm when the levels of these pollutants is above the safe level with respect to the World Health Organization (WHO) standards. NOVA SDS011 and MQ135 were used as the particulate matter and carbon monoxide sensors respectively, 20 x 4 Liquid Crystal Display (LCD) was used as the display unit, and a buzzer as the alarm device which is triggered when the pollutant level is high. The device utilizes Arduino Uno R3 as its microcontroller for controlling the operation of the device. The key contribution to knowledge of this work is the design of a low-cost, portable and modern pollutant detector that can be traditionally deployed in either closed or open environments. On testing the device under different conditions for 500 seconds per condition, the indoor PM2.5, P.M10 and CO levels ranged between 16-19 µg/m³, 43-80 µg/m³and 0.6-1.3 parts per million (PPM) respectively. The outdoor PM2.5, PM10 and CO levels were between 17-23 µg/m³, 19-62 µg/m³ and 0.3-0.6 PPM respectively. These levels are considered reasonable enough compared to World Health Organization safe limits of below 25 µg/m³, below 54 µg/m³ and 9 PPM for the PM2.5, PM10 and CO respectively. The device was further exposed to the combustion of fuels and to a dusty environment to read very unsafe limits. This work helped to develop a cost-efficient pollution detector; even as optimal operating efficiency was retained.

Development of an Automatic Scissors Screw Car Jack

2024-10-08T12:39:06+00:00

In this paper, an existing manual scissors screw car jack was converted to an automatic scissors screw car jack by introducing a sleeve coupling between the manual screw jack and a 12V DC electric motor, which is powered through a direct current energy flow directly from the car battery. The DC electric motor provides a turning effect to automatically power the screw jack for the purpose of lifting the desired load. A screw jack (manual or automatic) is a simple machine used in lifting heavy loads. It becomes very useful in vehicles when changing a punctured tire. It is mandatory for each vehicle plying an approved road to carry a jack for fixing a punctured tire. However, the operation of most car jacks is done manually and also requires prolonged bending or squatting positions when using the jack, making it difficult for most women, the disabled, the elderly, etc., and above all, not ergonomically suitable for the human body due to crouching and squatting positions when in use, which may result in health complications. The automatic scissors car jack developed was successfully tested on five different types of vehicles, with different weights. Five different tests measurements were carried out on each vehicle. The time taken to lift the vehicles 0.03 m off the ground was recorded. They ranged from 74 s to 90 s depending on the weight of the vehicle involved. The heavier the vehicle, the longer the time taken to lift the vehicle off the ground to the predetermined height of 0.03 m.

Whale Optimization Technique Based Economic Load Dispatch

2024-10-08T12:39:04+00:00

This study concentrates on optimizing the Economic Load Dispatch (ELD) for three major Nigerian power systems: Sapele, Jebba, and Egbin. These systems, each comprising varying numbers of generating units and facing fluctuating load demands ranging from 300 MW to 1000 MW, necessitate efficient resource allocation to minimize operational expenses. Employing the innovative Whale Optimization Algorithm (WOA), inspired by the cooperative behaviour of humpback whales, this research tackles the intricate non-linear characteristics of the ELD problem. The primary goal is to determine the ideal power generation timetable that reduces total generation costs while fulfilling power demand constraints. Through mathematical modelling, the power systems and their economic aspects are represented. The proposed WOA-based approach is implemented and juxtaposed against optimization methods to gauge its efficacy in achieving cost-effective load dispatch. In addition to the fast convergence characteristics of the optimization technique, the study reveals minimum optimal generation costs of 150,567 Naira/Hr, 189,352 Naira/Hr, and 244,075 Naira/Hr for the Sapele, Jebba, and Egbin power systems, respectively, under various load conditions. Conversely, maximum optimal generation costs reach 480,431 Naira/Hr, 590,871 Naira/Hr, and 750,453 Naira/Hr for the same systems, demonstrating the algorithm's adaptability to diverse load scenarios.

Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach

2024-10-08T12:39:03+00:00

Often, prospective tenants need to know the rental price of an apartment, and homeowners need to know how best to price their apartments. This work aims to predict house rental prices in Lagos, Nigeria, using machine learning by examining the relationship between the rental price and features such as the number of bedrooms, bathrooms, toilets, location and house status(newly built, furnished, and/or serviced). Five machine learning models were trained and evaluated using mean absolute error (MAE), root mean squared error (RMSE) and r-square (R2); the random forest regression model outperformed the other four models with the lowest MAE, RMSE and the highest R2. This study also revealed that the number of bedrooms and the apartment's location are the most significant predictors, confirmed using the feature importance analysis. The developed model can be used to estimate the rental price of a property in Lagos, Nigeria.

Assessment of Air Pollution Levels from a Building Construction Site on Lagos Island

2024-10-08T12:39:01+00:00

The introduction highlights the challenges of air pollution from construction activities on a site in Lagos Island, Nigeria, emphasizing the need for comprehensive studies to assess air pollution levels and evaluate its implications for public health and environmental quality. The methodology outlines the monthly data collection process, using the Earth Sense Zephyr (equipped with electrochemical detectors for gases) to measure CO, NO, NO₂, O₃, and Optical light scattering for particles) to measure PM₂.₅, and PM₁₀, and the ARA n-FRM Sampler for additional data collection on PM₂.₅, and PM₁₀. The study found that CO, NO, and NO₂ levels were influenced by construction activities, vehicle emissions and industrial sources, with notable peaks in CO and NO concentrations during specific months. Ozone levels remained consistently low, likely due to the "titration effect," while particulate matter (PM₂.₅ and PM₁₀) showed significant seasonal variation, peaking during the dry season due to construction dust and dry weather conditions. The findings underscore the need for stringent regulatory measures and effective dust control practices, particularly during periods of increased construction activity and dry weather, to mitigate air pollution and protect public health. In conclusion, the study provides valuable insights into the dynamics of air pollution from a typical construction site in Lagos Island, emphasizing the urgency of sustainable interventions to safeguard public health and environmental integrity. The study proposes enhanced monitoring and surveillance, stringent regulatory measures, promotion of sustainable construction practices, and public awareness and education, to address the challenges associated with construction-related air pollution on Lagos Island.

Recent Advances on the Adsorption of Pollutants from Aqueous Media Using Clay-Based Adsorbents

2024-10-08T12:39:00+00:00

The sequestration of pollutants from wastewater remains an active research topic recently owing to persistent disposal of industrial wastewater to waterbodies without adequate management strategies available especially in the developing countries. Different technologies have been employed in which adsorption has found a wide range of application. Today, various low cost adsorbents have been developed and evaluated for the adsorption processes. Clay mineral is one of the low cost natural adsorbents requiring minimum modification to enhance its adsorptive capacities. To maintain a clean and safe environment the water bodies must be free of contaminants of emerging concern. The availability of potable water is a global effort, as two of the UN's seventeen Sustainable Development Goals (SDG) are centered on water which is not surprising. Goal 6 focuses on clean water and sanitation whereas Goal 14 focuses on life below the water. With this in view, the availability of potable water highlights the significance of this study, which analyzes the potential of clay minerals as a good precursor for water treatment. Therefore, this review focuses on the clay minerals, its availability in Nigeria, classification and modification of the clay adsorbent.

Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models

2024-10-08T12:38:58+00:00

The escalating threat of congestion in wireless networks on a global scale prompts the need for effective detection and management techniques. This study investigates the tracking and detection of congestion in wireless networks, particularly within the banking industry, where digital transactions are rapidly increasing. It addresses the challenge of congestion management through machine learning (ML) models, aiming to enhance network performance and service quality. This research evaluates various ML algorithms, including Support Vector Machines, Decision Trees, and Random Forests, to identify the most effective approach for congestion detection. This research utilizes a dataset sourced from MainOne Limited, which covered August 18th, 20th, 22nd, 23rd, and 24th, 2023, and included banking operation hours from 7 AM to 4 PM each day. Preprocessing of data is conducted to optimize model training. Following training, various performance metrics including accuracy, precision, recall, F1 score, response time, and confusion matrix are assessed. Results demonstrate that Random Forest outperforms other models in accuracy, precision, recall, F1 score, and response time, with an accuracy of 98.90%. This research discusses the importance of continuous innovation in banking network analytics to tackle evolving congestion challenges. Future recommendations include leveraging advanced ML techniques like deep learning and reinforcement learning and exploring ensemble learning methods to enhance congestion detection models further.

Circular Microstrip Antennas in 5G: Evaluating Metamaterial Integration

2024-10-08T12:38:56+00:00

The rapid emergence of Fifth-Generation (5G) technologies necessitate the development of highly efficient antenna systems with compact design that can support Ultra-Wideband (UWB) frequencies. This work presents the design and enhancement of a Circular Microstrip Antenna (CMSA) for 5G UWB applications using metamaterials. The study focuses on the design of CMSA and the integration of a Complementary Split-Ring Resonator (CSRR) into the circular patch of the CMSA. The design is simulated using Computer Simulation Technology (CST) Studio 2023. The system design without metamaterials achieved a gain of 5.28 dBi and a bandwidth of 353.0 MHz. The integration of the CSRR led to an improvement in gain, 5.39 dBi at 3.8 GHz, which is above most of the literature reviewed, although there was a slight reduction in bandwidth to 135.2 MHz. The objectives of achieving a CMSA design with a gain between 5 to 10 dBi while maintaining a compact size were accomplished. Despite the slight reduction in bandwidth observed when integrating the CSRR into the CMSA, the overall results highlight the significant role metamaterials played in enhancing the performance of microstrip antennas for 5G technology applications.

Appraisal of the Flooding Behaviour of Rotating Packed Beds

2024-10-08T12:38:57+00:00

Rotating packed beds (RPBs) enhances mass transfer processes because a centrifugal force which is several -times greater than gravity is used as the driving force. The complexity of fluid flow across RPBs has made predicting and accurately determining their hydrodynamic behaviours difficult. The flooding point as a hydrodynamic characteristic is essential for the accurate design and scale-up of RPBs. However, variations in flooding point definitions and methodologies across the literature highlight the need for standardized approaches in studying RPB flooding phenomena. This study compared four approaches based on pressure drop fluctuations and the volume of liquid ejected from the RPB to determine the onset of flooding in RPBs using experimental results from a pilot-scale counter-current RPB. For rotational speeds of 300 -1500 rpm, gas flow rate of 100-300 Nm³/h, and liquid flow rates of 0.39-0.75 m³/h, the pressure drop varied from 314 to 2,100 Pa. Quantitative comparisons of the results based on different flooding point definitions showed wide variations with the values of the pressure drop at the onset of flooding differing by as much as 325 %. A quantitative approach based on virtual observations and the ejection of 8 % of the total liquid flow rate from the rotor’s eye is proposed as the standard method for identifying the onset of flooding in RPBs.

Formulation Ratio Effectiveness of Green Metal Working Fluid (GMWF) as a Bio Alternative for Green Manufacturing

2024-10-08T12:38:54+00:00

Metalworking fluid (MWF) is essential for ensuring quality products and extended tool life during machining operations. While there are various sources of MWF, the need to minimize health hazards associated with mineral-based metal working fluid now calls for more environmentally friendly green metal working fluid (GMWF) from bio-degradable sources. Also, the effectiveness of vegetable-based GMWF significantly depends on the degree of functionalization. Though some studies considered the issue, the comparative analysis of the effect formulations (variation in concentration) of the constituting elements of the GMWF, especially for the base vegetable oil under consideration; has been grossly underreported. In this study, a GMWF emulsion has been developed from soybeans, palm fruits, and coconut with varying formulation ratios. Physicochemical characterization such as flash point, fire point, pour point, pH, density, and viscosity of the developed GMWF were analyzed. Also, a performance evaluation of the said GMWF was carried out and the investigation has shown that the physicochemical properties of the developed GMWF matched, as a potential substitute for conventional mineral-based MWF. Additionally, a performance evaluation conducted during a mechanical machining operation revealed that the GMWF showed an improved surface roughness of about 10.77% compared to conventional mineral MWF. Observations during the machining operation further revealed that the formulated GMWF demonstrated some level of environmental tolerance as it was not associated with misting or the discharge of fumes. The research outcome will impact green machining science and MWF technology for sustainable mechanical machining and cutting fluid development.

Microstructure Characteristics and Mechanical Properties of Grey Cast Iron at Varied Ferrosilicon Addition

2024-10-08T12:38:53+00:00

Inoculation is an essential metallurgical route for controlling solidification conditions of cast iron, consequently, in this study, the influence of varied percent ferrosilicon (FeSi) addition on microstructure and mechanical properties of grey cast iron (GCI) was investigated. A 50Kg capacity rotary furnace was used to melt the charge (Auto engine block scrap, graphite, ferrosilicon (FeSi) and limestone). The casting was produced in a greensand mold with wooden rectangular pattern of length 50 mm and breadth 30 mm. Chemical compositions and carbon equivalent values (CEVs) of the samples were determined, using Optical emission spectrometry (AR 4 metal analyzer) and the expression respectively. Microstructures of the samples were obtained, using metallurgical microscope (model number NJF-120A). The tensile and hardness properties were measured, using Universal tensile tester and Rockwell hardness tester respectively. From the results, C and Si were the major elements. Other trace elements were Mn, P, S and Al. CEVs of both the control and inoculated samples were less than 4.3%. Microstructure of the control sample was comprised of primary dendrites and graphite flakes, while those of the inoculated samples were characterized by varied amount of more developed primary dendrites, longer graphite flakes and austenite dendrites. Also, a number of small MnS particles were observed in relative amount within the microstructures. Tensile and hardness properties of the FeSi inoculated samples were superior to the control sample. Highest tensile strength and hardness values of 76.62 MPa and 99.89 HRB respectively were obtained at the optimum inoculation of 1.5 wt. % FeSi.

Modelling of Cyber Attack Detection and Response System for 5G Network Using Machine Learning Technique

2024-10-08T13:06:10+00:00

The rapid increase in the adoption of 5G networks has revolutionized communication technologies, enabling high-speed data transmission and connectivity across various domains. However, the advent of 5G technology comes with an increased risk of cyber-attacks and security breaches, necessitating the development of robust defence mechanisms to safeguard network infrastructure and mitigate potential threats. The work presents a novel approach for modelling a cyber-attack response system tailored specifically for 5G networks, leveraging machine learning techniques to enhance threat detection and response capabilities. The study introduced innovative methodologies, including the integration of standard backpropagation and dropout regularization technique. Furthermore, an intelligent cyber threat classification model that proactively detects and mitigates malware threats in 5G networks was developed. Additionally, a comprehensive cyber-attack response model designed to isolate threats from the network infrastructure and mitigate potential security risks was formulated. The result of testing the response algorithm with simulation, and considering quality of service such as throughput, latency and packet loss, showed 80.05%, 24.9ms and 4.09% respectively. During system integration of the model on 5G network with stimulated malware, the throughput reported 71.81%. Also, packet loss reported loss rate of 23.18%, while latency reported 178.98ms. Our findings contribute to the advancement of cybersecurity in 5G environments and lay the foundation for the development of robust cyber defence systems to safeguard critical network infrastructure against emerging threats.

Low and High Gas Tungsten Arc Welding Heat Inputs Influence on Corrosion of Stainless Steel Weldments

2024-10-08T12:38:50+00:00

In this study, influence of low and high heat inputs on corrosion susceptibility of 304L austenitic stainless steel (ASS) in simulated 0.5 molar solution of NaCl was investigated. Gas tungsten arc welding (GTAW) was used to generate low and high levels welding heat input. Microstructures of the weldments were examined, using metallurgical optical microscope (OMM) (Olympus GX51), while the corrosion behaviours were evaluated by potentiodynamic polarization tests, and corrosion data were recorded, using a computer-based data logging system – Autolab PGSTAT 204N. From the results, the evolving microstructures of the weldments before corrosion were characteristically heterogeneous; austenite (γ) was the leading phase, while ferrite (α) grains were dispersed within the γ matrix. Fusion zone (FZ) and heat affected zone (HAZ) microstructures after corrosion were characterised by pits of varying sizes with different alignments. And at GTAW speed, current and voltage of 7.2 mm/s, 200A and 40V, corresponding to low heat inputs, there were few number and size of pits relative to 1.7 mm/s, 200A and 40V, corresponding to high heat input. Shift in corrosion potentials (E_corr) toward less negative direction, that is more nobility was observed at the low heat inputs induced GTAW parameters as compared to the corresponding high heat inputs induced GTAW parameters. In general, corrosion susceptibility of 304L ASS in the simulated 0.5 molar solution of NaCl was heightened at high heat inputs induced GTAW parameters as compared to the corresponding low heat inputs parameters.

Developing and Implementing an Artificial Intelligence (AI)-Driven System For Electricity Theft Detection

2024-10-08T13:06:09+00:00

Electricity theft is a significant challenge for utility companies worldwide, leading to substantial economic losses and inefficiencies in power distribution. Traditional methods of detecting electricity theft, such as manual inspections and routine audits, are often inefficient and ineffective. To address this issue, this study aims to develop and implement an artificial intelligence (AI)-driven system for electricity theft detection. Methodology used are data collection, data analysis, feature selection with Chi-Square, feature transformation with Principal Component Analysis (PCA), Support Vector Machine (SVM) and model for electricity theft detection. To achieve this, a Particle Swarm Optimization Algorithm (PSO) was applied to improve training performance of the SVM, using data of meter recharge information collected from Enugu Electricity Distribution Company (EEDC). The system effectiveness is validated through extensive testing using real-world data from various regions and scenarios, demonstrating its robustness and adaptability. The system result considering FDR reported that 0.11 was achieved for the particle swarm based SVM model. When TPR was considered for analysis, it was observed that particle swarm based SVM attained a score of 0.89. In addition, Particle swarm based SVM attained PPV of 0.895. In terms of accuracy, the particle swarm based SVM reported an accuracy of 0.857. The result showed that the particle swarm based SVM performed better from the system validation achieved through comparative analysis, hence it is recommended for use to develop the new software for energy theft investigation. The implementation of this AI-driven solution offers numerous benefits, including enhanced detection accuracy, reduced operational costs, and improved overall efficiency of power distribution networks. Moreover, it enables utility companies to take proactive measures to prevent theft, ensuring a more reliable and secure electricity supply for consumers.

Design and Finite Element Analysis of a Thresher for Palm Oil (Elaies guineensis) Extraction Plant

2024-10-08T12:38:47+00:00

This study presents the design and Finite Element Analysis (FEA) of a thresher used in palm oil (Elaeis guineensis) extraction plants. The FEA was performed to ensure safe and cost effective of the thresher before fabrication. The analytical design of the threshing shaft and drum of the thresher was validated using SolidWorks (2021) CAD software for static simulation, employing plain carbon steel as the material. For the threshing shaft, forces of and were applied at strategic points, resulting in a maximum bending stress of , significantly below the yield strength of . The shaft's diameter of 50 mm was confirmed as adequate with a factor of safety (FOS) ranging from 3.17 to 142.42, validating the shaft design's safety for fabrication. Similarly, the drum unit, supported by a spider arm and cylindrical bars, was subjected to an equivalent twisting moment of 861.25 Nm and a batch weight of 1226.25 N. The maximum von Mises stress of was well within safe limits, indicating robustness under operational loads. The maximum resultant displacement and equivalent strain were respectively which can be said to be minimal, reinforcing the drum's structural integrity. A minimum FOS of 20.45 further highlighted the drum's durability and resistance to fatigue. These results confirm the reliability and safety of the designed thresher components, ensuring efficient and sustainable palm oil extraction.

Evaluating the Ethical Practices in Developing AI and Ml Systems in Tanzania

2024-10-08T12:38:46+00:00

Artificial Intelligence (AI) and Machine Learning (ML) present transformative opportunities for sectors in developing countries like Tanzania that were previously hindered by manual processes and data inefficiencies. Despite these advancements, the ethical challenges of bias, fairness, transparency, privacy, and accountability are critical during AI and ML system design and deployment. This study explores these ethical dimensions from the perspective of Tanzanian IT professionals, given the country's nascent AI landscape. The research aims to understand and address these challenges using a mixed-method approach, including case studies, a systematic literature review, and critical analysis. Findings reveal significant concerns about algorithm bias, the complexity of ensuring fairness and equity, transparency and explainability, which are crucial for promoting trust and understanding among users, and heightened privacy and security risks. The study underscores the importance of integrating ethical considerations throughout the development lifecycle of AI and ML systems and the necessity of robust regulatory frameworks. Recommendations include developing targeted regulatory guidelines, providing comprehensive training for IT professionals, and fostering public trust through transparency and accountability. This study underscores the importance of ethical AI and ML practices to ensure responsible and equitable technological development in Tanzania.

Carbon Dots Derived from Waste Fish Scale for Enhanced Removal of Levofloxacin Drug: Parametric Optimization, Isotherm and Kinetic Studies

2024-10-08T12:38:43+00:00

The public health and environmental protection have been facing a great challenge for efficient antibiotics' adsorption from aqueous solution. In this work, a carbon dots nanoparticle from biomass (fish scale) was synthesized and employed for antibiotic adsorption. The synthesized fish scale carbon dots (FCD) were characterized by means of the X-ray diffraction (XRD), Scanning Electron Microscopy (SEM) and Fourier transform infrared (FTIR) analyses. Experiments on adsorption were performed to examine the capability of the synthesized adsorbent for adsorption of Levofloxacin. The optimum conditions were ascertained through the use of Response Surface Methodology (RSM) design to increase the effectiveness of levofloxacin removal, and there was 96.03% removal efficiency of 60 minutes contact time, 10 mg/L levofloxacin concentration and FCD dosage of 0.2 g/L. Also, the adsorption experiments indicated that at the lowest concentration of 10 mg/L, at time 45 min and 0.15 mg dosage the adsorption rate was high. For the kinetics data, the pseudo-second order model best fit the data. Furthermore, the Redlich-Peterson model fit isothermal data the best.

Cost-Efficient Automated Intrusion Detection and Reporting System for Homes in Nigeria

2024-10-08T12:38:45+00:00

Substantial investments are made to mitigate the persistent threat to lives and assets posed by various forms of intrusion. Automated security systems have emerged as crucial tools for safeguarding homes against intrusions in recent times. A major advantage of this is its independent ability to report human activities around homes without direct observation. However, the cost of implementing the system is not pocket-friendly to an average Nigerian given the prevailing economic situation. This research therefore seeks to develop and implement an intrusion detection and reporting system using low cost materials while maintaining a balance between quality and cost at the same time. The system architecture employs the passive infrared sensor (PIR) for motion detection, the ultrasonic sensor measures intruders’ location from the home entrance while the NodeMCU ESP8266 microcontroller is responsible for coordinating the reception and relaying of signals within the system. The system was implemented with varying distances of human presence from the security device. Reports of potential intrusion alerts were obtained within reasonable time frames (between 11 seconds and 49 seconds) on the homeowner’s mobile phone via the Blynk application. This performance demonstrates the reliability of the system for home security. The system is equally cost-efficient relative to most similar state-of-the art IoT based home security systems considered in this work. Our work therefore contributes to knowledge by proposing an affordable home security solution for a low budget Nigerian user.

Comparative Analysis of Biodiesel Produced from Blends of Palm Kernel Shell and Cocoa Pods Oils with Conventional Diesel Fuel: Characterizations, FTIR, GC-MS, XRD and SEM Analysis of the Nano Catalyst

2024-10-08T12:38:42+00:00

The uncertainty of predicting the conditions of bio oils for the production of quality biofuels and reusability of catalyst, saving cost of production and time, make characterization of the oils/catalyst imperative. Characterization of bio oils, extracted from palm kernel shell and cocoa pods, the blends, catalyst and biodiesel produced therefrom is investigated. A maximum biodiesel yield of 76.05% was obtained at optimal conditions. Titanuim oxide used proved to be efficient catalyst for converting the oil blends to biodiesel. The established results obtained show kinematic viscosity of 5.65 – 7.78 mm²s^-1 @ 40 ^oC, density of 0.8428 – 0.8642 kg/m³, cloud point of 4.48 – 6.48 ^oC, fire point of 108 – 150 ^oC, cetane index of 37.78 – 30.13, acid value of mg KOH/g, API gravity of 32.89 – 29, anicidine point of 50 – 46 ^oC etc. All the values fell within the recommended ASTM and EN standards. The GC-MS, XRD, EDX, SEM, and FTIR analyses carried out to evaluate the quality of the sample with respect to deterioration, gave an ester percentage of 99.9% for the bio-oil and biodiesel, which is within the minimum standard range of not less than 96.5% recommended. The GC-MS of the blended oil shows that the most prevalent fatty acids identified amongst 13 other distinct compounds were methyl linolenate, methyl palmitate, methyl oleate and methyl eicosadinoate with percentage concentrations of 63.03, 26.9, 8.1 and 2% respectively. The XRD analysis confirmed the titanium oxide anatase structure with a peak of 25.4 degrees. The SEM analysis shows high porosity with high specific surface area of the catalyst at magnification of 80 – 269μm; and the FTIR analysis revealed that the functional groups for the bio-oil and blended biodiesel were in range.

Investigation of the Fitness for Service (FFS) of Cracks in API 5L X70 Pipeline Steel using Failure Assessment Diagram (FAD)

2024-10-08T12:38:40+00:00

In this study, the fitness for service of crack propagation in API 5L X-70 stee1 was investigated using a model called Failure Assessment Diagram (FAD) to determine how fit a crack can be under certain operational pressure. It is a known fact that during the production of pipes, there are tendencies for flaws such as inclusions and cracks to occur in the pipes. When these flaws are subjected to stresses, there are tendencies for failures to occur starting from where the cracks or flaws are located. The failure due to the propagation of the cracks leads to oil spillage causing pollution to the environment which had negatively impacted livelihood of the host communities and aquatic lives. This had resulted in Government spending huge amount of money maintaining the pipelines and remediation. The purpose of this paper is to investigate the fitness for service of crack lengths 12 mm, 17 mm, 22 mm and 27 mm using the Failure Assessment Diagram (FAD) mode1. The material used in this paper is API 5L X 70 steel in the form of a Compact Tension (CT) specimen machined according to ASTM E1820 – 13. API 5L X-70 stee1 is a low-carbon stee1 with a carbon content of as low as 0.04 %. It is used in the production of pipelines for conveying crude oil and natural gas from the place of production to the place of refining or export. In the investigation of the fitness for service of the cracks, a charpy V-notch impact test was carried out to determine the energy required to fracture the steel, which was later inputted numerically into a critical stress intensity factor formula in accordance with BS 7910 – 13 standards to obtain the critical stress intensity factor (K_ICor K_Q). The stress intensity factor (K_I) was obtained from formula also according to BS 7910 – 13 standards.The ratio of K_I to K_Q was used in the FAD analysis. Subsequently, a monotonic tensile test was conducted to obtain the yield stress ( _ys) and the reference stress ( _ref) was obtained numerically according to BS 7910 – 13. The ratio of _ref) to _ys) was also used in the FAD analysis. The FAD analysis was used to determine the fitness for services and fracture behaviour of each crack. Scanning electron microscopy (SEM) was used to confirm the fracture behaviour obtained from the FAD. The results obtained show that the energy from the charpy V-notch impact test was 302.9 J and the critical stress intensity factor (K_Q) correlated numerically according to BS 7910 – 13 was determine as 246.73 MPa . The yield stresses obtained from the monotonic test for crack lengths of 12 mm, 17 mm, 22 mm and 27 mm were 132.51 MPa, 109.10 MPa, 114.36 MPa and 118.21 MPa, respectively. In the FAD analysis, it was observed that the safe operational stress to ensure fitness for service decreases with an increase in crack length. The fracture behaviour shows a ductile fracture behaviour since the FAD lies within the plastic collapse region. This fracture behaviour was confirmed by the image obtained from the scanning electron microscopy (SEM), which showed a cup and cone image suggesting ductile fracture behaviour. This FAD method will ensure that safe operational stresses are maintained for various crack length to prolong the life span of the pipeline. It is a novel method that can also be used to properly schedule the rate of inspection in pipelines alongside Ultrasonic sound, Liquid penetrant, Magnetic particle and radiographic methods of inspection.

Systematic Review of Occupational Injuries and Illnesses in the Nigerian Mining Industry

2024-10-08T12:37:16+00:00

Nigerian mining sector (NMS) has been ranked as second industrial sector with numbers of occupational injuries and illnesses (OII) that pose serious social and economic impacts on workers and society. Meanwhile, little is known about the specific aspects of OII in the mining sector that have been studied in the literature. As a result, this study seeks to review the existing research on workers' OII with the intention of identifying the primary causes, symptoms, and influencing factors that may relate to a persistent rise of injuries and illnesses in the NMS. This is to determine the sector dynamic in relation to OII and provide stakeholders in the sector with lack data that are required for planning, implementation, and monitoring sustainable OII. The study employed widely accessible electronic databases (Science Direct, PubMed, Scopus, Google Scholar, and Web of Science) to perform a systematic review of peer-reviewed articles from 2010 to 2023. The causes, symptoms, and influencing factors of OII in the mining industry were highlighted in the compilation of pertinent data. The study search yielded 210 peer-reviewed publications with 70 publications retained after systematic screening and 20 were retained for final analysis. The study identified eight major causes, four body parts that are most harmed, and five primary symptoms of OII in the NMS. Also, the factors that influencing OII in NMS were classified as sociodemographic, behavioural and organizational/management factors. The study concluded that the use of personal protective equipment, provision of first aid and medical facilities, enactment of safety laws and policies and adequate safety training and supervision are the most common ways that can reduce the high rate of OII in the Nigerian mining sector.

Green Hydrogen Synthesis from Human Urine as Sustainable Bioenergy Resources

2024-10-11T23:15:48+00:00

With the earths cry for help as pollution rate increases, researchers are faced with a common task of tackling pollution resulting from dependence on fossil fuel as well as delivering sustainable energy. Renewable energy resources such as solar, wind, hydro to mention a few are currently finding applications within the world energy mix but some limitations which range from meteorology of locations to expected maximum energy output attainable. Hydrogen, the most abundant element in the world stand chance of abating this problem. However conventional method of its producing, poses severe treat to the atmospheric environment with the release of oxides of carbon hence, referred as blue hydrogen. Contrarily, green hydrogen from human urine stands a more sustainable and environmentally friendly energy resource. This study was aimed to empirically model the synthesis of green hydrogen from urea in human urine by an electrolytic process. The synthesized hydrogen was characterized on physiochemical properties of conductivity, turbidity, pH, specific gravity and colour, while the precursor urine characterized on gender, exposure duration and storage temperature. The synthesis process was modelled using Microsoft excel solver for the overall cell energy or polarization curve model, the Faraday’s efficiency model and gas purity model at electrolyte concentrations of 25 wt./wt., 30 wt./wt. and 35 wt./wt. of potassium oxide (buffer} to urea over a five-temperature interval range of 45 to 85 ^oC. Findings revealed that the gas produced was 99.88% hydrogen at the cathode. Also, hydrogen produced increased with increase in electrolyte concentration and moderate temperature with optimal conditions at 35 w/w electrolyte concentration and 65 ^oC. However, the minimum cell voltage was 2.06 V at 85 ^oC and 35 w/w electrolyte concentration. With an exception of the Faraday’s efficiency model at 30 wt./wt. electrolyte concentration across the system’s operating temperature range yielding an R² value of 0.711, all the models yielded coefficient of determination values in the range of 0.96 and 0.99, indicating good fit for the alkaline urine electrolysis for green hydrogen synthesis from human urine.

Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks

2024-10-01T17:26:50+00:00

The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy.

Early Detection of Congenital Heart Diseases among Infants Using Artificial Neural Network Algorithm

2024-08-24T12:22:34+00:00

Congenital Heart Disease (CHD) detection has continued to witness a consistent increase in research attention. CHD diseases are vast and also span across diverse demographics, without sparing pregnant women, unborn babies, or newly born babies. The aim of this study is to develop a detection model capable of detecting heart disease among infants with high accuracy and to also suggest solutions to manage it. To achieve this, the CHD types were identified to develop a data model that considered infants. The data model was processed through imputation, feature selection, and transformation using the imputation method and Principal Component Analysis (PCA). After the data processing stage, Artificial Neural Network (ANN) algorithm is adopted and trained with the data to generate the model for the detection of the CHD. Comparative analysis was used to evaluate the performance of the models in comparison with other models adopted in other works, considering metrics that defined the success of the detection models. The results showed that the ANN has the best detection outcome with an accuracy of 97.44%. Although the use of Logistic Regression algorithm attained a high level of performance with an accuracy of 95.00%, but it still falls below the proposed ANN algorithm. Another highly performing algorithm is the use of Extreme Gradient Boosting (XGB) which achieved an accuracy of 92.00%.

Efficiency of Ceramic Composite Filter Produced Using Nano Particulate Carbonaceous Material

2024-09-25T22:40:39+00:00

Millions of people have no access to safe water. This has led to continued incidence of waterborne diseases with severe cases of complications and sometimes death. Attempts to use silica sand-based materials for the production of filters by researchers are still evolving. In this study a novel hybrid clay-nanoparticulate agro-waste blend water filter with appropriate pore size and flow rate for contaminant-free water filtration was produced. The composite filters were produced using hydraulic pressing and sintering process. Four different ratios of clay, activated carbon and nanoparticle coconut shell (60:30:10, 60:20:20, 60:10:30, and 60:0:40 wt %) were mixed and fired at 700 °C, 750 °C, 800 °C, 850°C, 900 °C, 950 °C, and 1000 °C respectively at the rate of 75 °C/hr. E. Coli, Total Coliform, and turbidity tests were carried out on the influent water collected from Lagos Lagoon, and effluent water samples were filtered using the produced samples. The result of water tests revealed that all the filters produced removed between 86.81 % to 99% Escherichia Coliform (E. coli), and 81.81% to 93.31%% total coliform in the water sample. The blend of 60:10:30 fired at 850 °C and 900 °C showed improved properties with a flow rate value of 2.83 l/hr and 2.77 l/hr. In conclusion, the study established that synthesis of nanoparticle coconut shell and activated carbon is a suitable material for producing clay composite filters that can purify water to Nigerian Standards for Drinking Water Quality (NSDWQ) acceptable level.

Empirical Mode Decomposition Based Amplify and Forward Technique for Cooperative Cognitive Radio System

2024-10-15T09:01:37+00:00

The rapid growth in the mobile industry due to increase in number of users accessing diverse services causes a high demand for radio spectrum. Nonetheless, the radio spectrum allocated for different wireless communication services is restricted. Cooperative Cognitive Radio (CCR) technique with Amplify and Forward (AF) relaying protocol used to address the problem suffers from noise amplification resulting in poor reception at the destination. Hence, in this paper, Empirical Mode Decomposition (EMD) based AF technique for CCR system was carried out to improve the performance of the existing CCR with AF relaying protocol. The transmitted signal from Primary User (PU) was received at the Secondary User (SU) where SU superimposed its own signal using Exclusive OR (XOR) rule. The combined signal from XOR was made to pass through EMD and amplified using AF by multiplying with the relay gain. The amplified signal was radiated to PU and SU receivers during the second hop transmission. The multiple copies of the receive signal at the SU receiver at different number of path (L = 2, 4) were combined at destination using Maximal Ratio Combiner (MRC). Mathematical expression for Bit Error Rate (BER) and Throughput (TP) were derived using the Probability Density Function (PDF) of the Nakagami-m fading distribution. Extensive simulations using MATLAB R2021a were employed to assess the effectiveness of the proposed technique and evaluated using BER and TP by comparing with the existing AF CCR. The EMD based AF technique proposed gave better performance with 75.2% reduction in BER and 21.86% increase in TP over the existing AF technique for CCR. The proposed technique can be deployed to improve the performance of cooperative cognitive radio system.

Role of Bar and Restaurant Management System: Case of London Lounge, Ubungo Dar-es-Salaam

2024-09-25T07:57:15+00:00

This study aims at revealing the role played by Bar and Restaurant Management System (BRMS) in Tanzania, by studying its functionalities and user perceptions and later providing recommendations that will improve the Food and Beverage (F&B) services. London Lounge was selected as a case study to represent the middle-class bar within the country that makes use of BRMSs. The literature shows that the common design of BRMS include functionalities for accounting, inventory and employee management where the system users had positive perception on the advantages of system usage except for the inexperienced users. Focus group discussion was used as the method for data collection where the study team identified that the appropriate system users are Counter Attendants, Supervisors, Managers and the Director. The findings revealed that the system had enough functionality to exhaust the principal duties for each system user based on their job positions and hence played a big role in managing their operations. The study highlighted some system weaknesses hence provided the recommendations to improve the functionalities of the system and attract more users towards system usage. Future research on design and development of BRMS integrated with other systems to manage multiple businesses under the same ownership is highly recommended in this study.

Review of Blockchain Technology on Data Security and Privacy: Recommendations for Advancing Tanzania's ICT Sector

2024-09-14T14:39:14+00:00

The rapid growth of the ICT sector has led to increased global data exchange and innovation opportunities. The increased use of technology has brought about concerns regarding data security and privacy which triggered a rising interest in blockchain technology as a potential solution to security challenges. This research endeavours to comprehensively examine the influence of blockchain on data security and privacy inside and outside Tanzanian, filling the gap that exists. The blended approach was used to analyse blockchain's effects while focusing on ethical considerations. The review emphasizes the benefits of blockchain in enhancing data security, trust, and transparency, along with its various applications. The research highlights blockchain technology's potential to offer robust data protection and improve transparency while identifying the challenges that must be addressed for successful implementation. The study investigates the role of blockchain in securing information systems in global and Tanzanian settings, focusing on sectors such as healthcare, banking, education, and land registration. The study emphasises the significant potential of blockchain technology to impact various industries in Tanzania profoundly. It offers valuable insights for professionals, policymakers, and researchers, highlighting the need to investigate how blockchain can be applied explicitly within different sectors. Additionally, it suggests practical strategies for seamlessly integrating this technology, considering the unique challenges the Tanzanian ICT sector encounters.

Development of a Smart Lock System using QR Code Technology

2024-11-12T09:15:03+00:00

A smart lock system refers to a modern security solution that enables users to remotely control access to their homes or businesses using a smart device such as smartphone, tablet, or computer. This paper presents the development of a smart lock system using Quick Response (QR) code technology to address the problem of insecurity, inconvenience, and inflexibility in access control. The proposed system eliminates the need for physical keys or complex authentication and verification methods. The system integrates an ESP8266 microcontroller, barcode scanner, 16x2 liquid-crystal display (LCD), solenoid lock, relay, and a 12V power supply. The system allows authorised users to generate unique QR codes representing their access credentials through a user-friendly interface. These QR codes are scanned by the system, which processes the codes, verifies access permissions via a central server and provides real-time feedback through the LCD. Upon successful verification, the system triggers a relay to control the solenoid lock, to grant or deny access. The implementation and testing of the smart lock system demonstrated successful operation. The performance indices include authentication speed, system reliability, accuracy and response time, which were evaluated and found to be satisfactory. The obtained results showed that the proposed system outperformed traditional methods such as keys, and fingerprint authentication, with an access speed of 2.00 seconds, which is significantly faster than the alternatives. Comparative analysis with existing access control techniques demonstrated the developed system's enhanced security features and user-friendly operation, making it applicable for residential, commercial, and industrial use. Additionally, the system's scalability and adaptability allow it to be customised for various environments, offering a versatile solution to diverse security challenges. This work contributes to the field of security technology by providing an efficient and scalable smart lock system with novel algorithms, which enhances the convenience, robustness of access control and seamless user experience.