LAUTECH Journal of Engineering and Technology

DEVELOPMENT OF AN IOT BASED MOBILE ROBOT FOR HAZARDOUS GAS DETECTION

Wasiu Oyediran Adedeji — 2024-04-01

The escalating concerns regarding environmental safety and the potential hazards posed by poisonous gases necessitate innovative approaches for efficient detection and monitoring. This paper introduces a novel solution in the form of a remote-controlled mobile robot equipped with advanced gas-sensing technologies. The robotic system aims to autonomously navigate hazardous environments, identifying and quantifying the presence of poisonous gases in real time. The methodology involves the integration of state-of-the-art gas sensors on the four wheeled mobile robot, enabling it to perform comprehensive gas detection while being remotely controlled for optimal safety. The paper details the design and implementation of the mobile robot sensors and navigational controls, emphasizing its adaptability to various terrains and its ability to transmit real-time data to an Internet of Things (IOT) application. Results from experimental trials demonstrate the efficiency and effectiveness of the proposed system in detecting and mapping poisonous gas concentrations, providing a valuable tool for environmental monitoring and emergency response.

DEVELOPMENT OF A DECENTRALISED MODEL FOR ELECTRONIC EXAMINATION PASS USING BLOCKCHAIN TECHNOLOGY

ADEKOYA TUBOSUN FESTUS — 2024-04-01

Conventional examination pass systems face persistent challenges of security breaches and administrative inefficiencies due to the centralised nature of these systems. There is need to address the problem of centralisation to enhance the security of these system and fortify the integrity of the examination process by mitigating the risks of data manipulation and unauthorized access. This study introduces a decentralised framework, powered by Hyperledger Fabric private blockchain, to revolutionize examination pass management.

ASSESSMENT OF PHYSICAL AND CHEMICAL PROPERTIES OF SOILS IN KWARA STATE POLYTECHNIC FOREST RESERVES

Dauda Kola Abdulkadir — 2024-04-01

The different features of soil greatly affect the flora and vegetative diversity of a forest. The physical and chemical characteristics of soils in the Kwara State Polytechnic Forest Reserve were evaluated to assess the fertility and productivity status of the soils. Three composite soil samples were collected randomly from different locations at the depth of 0-20cm, 20-60cm, and 60-100cm using soil auger. The physical parameters evaluated include: soil texture using hydrometer, soil infiltration rates and capacity by double ring infiltrometer, soil temperature using soil thermometer, and available soil moisture content by digital soil moisture meter. Results of the soil particle size analysis revealed that soil in the study area is sandy loam, using textural classification triangle chart. This indicates that the soil is generally very light-textured with sand percentage averaging more than 80% and loam is 20%. The estimated average infiltration rate of the soil in the study area is 96.9mm/hr and the values of infiltration capacities (K) were generally high and varied from 0.00956cm/s to 0.0104cm/s. The results of moisture contents for the sampling location points (Point A, B, and C) around the study area are; 1.08 %, 1.05%, and 1.09%, respectively. Similarly, the observed soil temperatures are; 6.7^oC, 5.4^oC, and 7.8^oC, respectively. Chemical analysis results revealed that the soil pH was moderately to slightly acidic and it ranged from 5.30 to 6.87. The average organic carbon ranged from 0.142-0.267% of the entire soil nutrients relating to soil fertility. The available phosphorous content of the soil is high which ranged from 20.276 to 28.342mg/l. The sodium status of the soil is generally low which ranged from 0.156 to 0.653me/l. The exchangeable sodium percentage (ESP) value of the soil ranged from 5.90 to 10.0%. The calcium status of the soil is generally moderate which ranged from 4.36 to6.22 me/l. Magnesium been the dominant cation ranged from 1.16 to2.26 me/l. The organic matter of the soil is moderate as the values obtained ranged from 0.133 - 0.165%. The cation exchange capacity (CEC) of the soil ranged from 4.76 to 5.52me/l. Therefore, soil physical and chemical properties were the dominant factors influencing the extent of decomposition process. Thus, the forest reserve serves as protection for the soil as well as promoting the fertility and productivity of the soils to support a flourishing vegetation types in the study area.

CHARACTERIZATION OF PELLETS PRODUCED FROM RICE BRAN AND CORNCOB

Folorunsho Adegboyega OLA — 2024-04-01

Production of pellet from biomass as gained interest of researchers in recent times due to their carbon-neutral characteristics. Problems such as low density and bulkiness inhibit their use as solid biofuels. This study produced and characterized pellets from rice bran (RB) and corncob using a pelleting machine. RB was collected from a rice milling factory in Ilorin, Nigeria and corncob was collected from a farm in Ogbomoso, Nigeria. The corncobs were hammer milled. Then both the hammer milled corncob and RB were sieved with BS 14 sieve and mixed with starch additive at 5% by weight. Pellets were then produced from the starched RB and corncob. The pellets produced from both materials were evaluated for pellet length, bulk density, proximate composition (moisture, ash, volatile matter and fixed carbon contents) and higher heating value. The mean values of pellet length, bulk density and moisture, ash, volatile matter and fixed carbon contents and higher heating values were: (39.90 mm, 0.367 g/cm³, 14.30%db, 32.60%, 18.20% 34.90% and 17.69MJ/kg) and (14.90 mm, 0.166g/cm³, 11.60%db, 72.10%, 14.30%, 2.00% and 16.92 MJ/kg) for RB and corncob pellets, respectively. The results revealed that both materials can be used as raw material for solid biofuels.

EVOLUTION OF A MODEL TO DETERMINE UNSECURED TRANSACTIONS

Patrick Ozoh — 2024-04-01

The widespread presence of fraudulent transactions in financial institutions is of significance in banking operations. Examples of financial instruments that are utilized include credit cards, smart cards, swipe cards, etc. These cards provide important information and enable small costs to be incurred by customers. These small amounts are removed from customer accounts. Banks need to discover the correctness of transactions, thus the introduction of the evaluation of models to determine unsecured transactions. The focus of this research is to contribute to the field of the application of machine learning to banking operations by introducing tools for predicting unsecured transactions in the banking sector. The research objectives include the examination of different methods utilized in machine learning for investigating unsecured transactions about the physical stealing of credit cards and the illegal collection of details on credit cards. To accomplish the aims of this research, information gathering is done using Kaggle. Kaggle is obtainable online. The major focus of this research is to examine cardholders' spending patterns. The method includes using a multilayer perceptron (MLP). This is utilized with training of 70% and testing of 30% subsets. The evaluation of the model is done using a confusion matrix technique. This research is implemented using the Python programming language. The model produces accuracy rates of 93% and 99% respectively. This research can leverage achievements recorded to improve security concerns in financial institutions.

APPLICATION OF MACHINE LEARNING TO TEXT CLASSIFICATION

Patrick Ozoh — 2024-04-01

The information superhighway provides important principles for giving out information to various consultations. Organizations depend on knowing customer observations about products and services. Data can be enormous to process physically. This study investigates a technique applying Python programming to collect datasets instinctively. The use of machine learning models evolves by applying Random Forest and Naïve Bayes algorithms. These techniques are applied to the data collected for text classification purposes. This process distributes data into; positive, negative, slightly negative, slightly positive, or neutral. The results from the study show the Random Forest classifier is more efficient than the Naïve Bayes algorithm, resulting in an accuracy rate of 76.5% about Naïve Bayes (70.01%). This technique enables organizations to receive insights into customer ways of thinking.

EFFECT OF GUM ARABIC BIOPOLYMER ON THE ATTERBERG LIMIT AND CATION EXCHANGE CAPACITY OF LEAD- CONTAMINATED LATERITIC SOIL

kazeem Ishola — 2024-04-01

Laboratory tests and regression analysis were used to assess the liquid limit, plastic limit, and plasticity index, as well as cation-exchange capacity (CEC) of the contaminated lateritic soil treated with up to 25% Gum Arabic biopolymer (GAB). Tests conducted on the soil sample include specific gravity, Atterberg limit, particle size distribution, and cation exchange capacity (CEC). The findings indicate a rise in cation-exchange capacity up to 25% GAB content, while the liquid limit, plastic limit, and plasticity of the lead-contaminated soil decreased with an increase of up to 10% GAB content. However, regression analysis of test results shows a strong correlation between the experimental and predicted values. The consistency indices for use as a subgrade material on lightly traveled roads were improved by a 10% GAB content blend with contaminated lateritic soil.

PROPOSAL OF NEW TWO-PARAMETER ESTIMATOR FOR GAMMA REGRESSION MODEL WITH CORRELATED REGRESSORS

Janet Iyabo Idowu — 2024-04-01

Multicollinearity among the explanatory variables in the gamma regression model, make the usual maximum likelihood estimator (MLE) for estimating regression parameters in the multiple regression analysis inefficient as the variance of MLE is high and unstable. In recent times, some researchers have proposed estimator based on ridge and Liu biasing parameters to handle the problem of multicollinearity. This paper proposes new two parameter estimators in gamma regression models when there is collinearity among the explanatory variables. Conditions under which the proposed Gamma Modified New Two-Parameter (GMNTP) has better performance are established theoretically and simulation studies were also conducted. Both simulation and real life application results show that, GMNTP estimator with shrinkage parameter has better performance than the existing estimators in terms of MSE.

REBOUND RESILIENCE AND HEAT BUILD-UP OF HYBRID CARBON BLACK/SILICA-REINFORCED SOLID TIRE TREAD COMPOUND CONTAINING GROUND TIRE RUBBER OF DIFFERENT PARTICLE SIZES

Reginald Umunakwe — 2024-04-01

The effect of addition of 20 phr ground tire rubber (GTR) of 40, 60, 80 and 100 mesh sizes in hybrid carbon black/silica (CB/SiO₂) reinforced solid tire tread compound on the rebound resilience and heat build-up was investigated. The hybrid filler reinforcement was in the ratio of 50/10. Two-step mixing utilizing an internal mixer and two-roll mill was employed for the mixing, while vulcanization was carried out at 150. Results show that additional GTR lowered the rebound resilience and increased the heat build-up of the vulcanizates with the 100 mesh size GTR impacting the most adverse effect on the vulcanizates. The use of hybrid CB/SiO₂reinforcement helped in the recovery of some of the lost rebound due to the addition of GTR in the matrix. The use of hybrid CB/SiO₂ reinforcement contributed to only a slight reduction in the heat build-up of solid tire tread compound containing 20 phr of GTR.

PERFORMANCE ESTIMATION OF LONG-HAUL OPTICAL TRANSMISSION SYSTEM OVER A COHERENT SYSTEM USING GAUSSIAN NOISE MODEL

Oluwaseun Olayinka Tooki — 2024-04-01

The demand for internet capacity is expected to increase exponentially owing to more implementation of the Internet of Things and 5G which requires high speed and data volume to be delivered to clients. To accommodate this internet demand in the future, fiber optics is the most viable alternative for delivering reliable high-speed Internet access with good quality of service. Optical Transmission systems (OTSs), however, suffer from signal impairments such as nonlinearities in optical fibre and Amplified Spontaneous Emission noise. Signal impairments due to fiber nonlinearities become more significant as the optical power, transmission distance, capacity and the number of channels in the fiber are increased. Hence, to maintain the quality of the transmission as the length of the transmission increases, the Gaussian Noise (GN) model, a reliable tool for performance prediction over a wide range of system scenarios, was used to check for signal distortion. Noise estimation and optical signal-to-noise ratio were used to measure the quality of transmission while the obtained results were also used to evaluate the performance of longer-distance OTS. This paper addresses the nonlinear issue and provides possible solutions to identified challenges.