Journal of Basics and Applied Sciences Research

Physicochemical properties, kinetics, and thermodynamics study of oil extraction from pearl millet bran

2025-04-06T12:02:14+00:00

The increasing demand for edible oils and the need for sustainable waste management have driven research into alternative oil sources such as millet bran. This study focuses on the kinetics and thermodynamics of oil extraction from millet bran using a Soxhlet extractor with n-hexane as the solvent. The maximum oil yield from millet was observed to be 10.78%, 13.23%, and 15.08% at temperatures of 303, 313, and 323 K respectively. The physicochemical properties, including iodine value, ash content, and density, were analyzed alongside FTIR and GC-MS analysis for characterization, confirming the extracted material as oil. The activation energy (Ea) for millet oil extraction was determined to be +7.8990 kJ/mol. Thermodynamic parameters were also assessed, with activation enthalpy (∆H) recorded at +5413 kJ/mol, activation entropy (∆S) at -8.634 kJmol⁻¹K⁻¹, and Gibbs free energy (∆G) values indicating an endothermic and non-spontaneous reaction. These findings contribute to optimizing pearl millet oil extraction and its potential application in edible oil production.

Notes on a modified exponential-Gamma distribution: Its properties and applications

2025-04-06T12:07:09+00:00

Accurate Modeling and analysis of real-world data playa vital role across various fields, enabling better decision-making and predictions. While it is widely acknowledged that “all models are wrong, but some are useful.” Nevertheless, researchers continuously develop, modify, extend, generalize and combine models with other distributionsto enhance accuracy and achieve significant progress. This paper introduces the Exponentiated Generalized new Exponential-Gamma distribution (EGnEG), a novel four parameters univariate continuous lifetime probability distribution that extends the new Exponential-Gamma distribution. The proposed distribution is named the Exponentiated Generalized new Exponential-Gamma distribution (EGnEG). Its survival and hazard rate functions of the distribution were derived and analyzed visually to understand its properties. Graphical representations of the probability density function (PDF), cumulative distribution function (CDF) and hazard rate function illustrate the distribution’s behaviors across different parameter values.Additionally, Entropy measures and order statistic were determined to further assess its characteristics. The parameters of the EGnEG distribution were estimated using three different methods: Maximum Likelihood Method (MLE), Least Squares Estimation (LSE), and Cramer-Von-Mises Estimation (CVME). To assess its Goodness-of-fit, the distribution was applied to a real-life dataset and compared with that of some existing related distributions. The comparison based on the values of , Akaike Information Criteria (AIC) Bayesian Information Criteria (BIC).The results from the dataset indicate that the Exponentiated Generalized New Exponential-Gamma (EGnEG) distribution out performs other competing distributions considered in the study. Therefore, this new distribution is recommended as a valuable alternative for modeling real life datasets, offering improved flexibility and accuracy in statistical modeling.

Dosimetric evaluation of terrestrial Gamma radiation and associated cancer risk in federal university Dutsin-Ma, Nigeria

2025-04-06T12:13:49+00:00

This study evaluates natural radioactivity on FUDMA campuses to ensure radiological safety. Since natural radionuclides are always present in the environment, exposure to terrestrial gamma radiation is unavoidable. The research aimed to measure terrestrial gamma radiation dose rates (TGDR), calculate the annual effective dose (AED), and assess the excess lifetime cancer risk (ELCR). A digital radiation meter was used for measurements, while Microsoft Excel was used for data analysis.At the take-off campus, The highest AED was recorded at the school clinic (TOC-A5) with a value of 2.76 mSv/y, while the lowest was at the school gate (TOC-A1) at 1.02 mSv/y. The average AED across the campus was 1.75 mSv/y. At the main campus, the highest AED was 2.64 mSv/y at the school clinic (MC-A4), and the lowest was 1.14 mSv/y at the Senate Building (MC-A2), with an average of 1.64 mSv/y. These values exceed the ICRP (2007) recommended limit of 1 mSv/y for the general public, indicating potential health risks.For ELCR, the take-off campus recorded the highest value at the school clinic (TOC-A5) with 8.68, while the lowest was at the school gate (TOC-A1) with 3.21, averaging 5.49. At the main campus, the highest ELCR was 8.30 at the school clinic (MC-A4), and the lowest was 3.59 at the Senate Building (MC-A2), with an average of 4.99. These results suggest an increased radiological risk compared to standard safety limits.

Economic impact of maize technology on farmers in some selected local government areas of Katsina state, Nigeria

2025-04-06T12:18:41+00:00

This study evaluates the impact of improved maize technology on land holdings, income, and savings of farmers. The study was conducted using both primary and secondary data. The data were collected between June & December 2023 through a field survey conducted by the researcher and assisted by trained enumerators in two-visit interviews using a pre-tested structured questionnaire. The independent t-test produced a value of 6.50, while the corresponding critical value at a 5% significance level is 1.96 and 2.577 at a 1% significance level. This indicates that the calculated t-value exceeds the critical value. Thus, respondents display a significant increase in their land holdings after adopting improved maize technology. The average income of respondents before adopting improved maize technology N91,824.00, which is lower than the income after adopting improved maize technology, averaging N300,945.00. The results also indicated a t-value of 6.95, which is highly significant at the 1% level. The average savings of respondents before adopting improved maize technology was N76,000, while after utilizing improved maize technology, it was N186,000. The independent t-test resulted in a value of 8.82, with corresponding critical values of 1.960 and 2.576 at the 5% and 1% levels, respectively. This indicates that the calculated t-value (8.82) surpasses the critical t-value at both the 5% and 1% significance levels. Consequently, the respondents' savings after using improved technology are significantly higher than their savings prior to using this technology. It was found that though the technology has been widely adopted, its full potentials may not be realized because most farmers are not disposed to the adoption of major components of the technology.

The benefit-cost model: An alternative approach to project life cycle analysis

2025-04-06T12:23:07+00:00

The benefit-cost model (BCM) is a method for assessing a project or investment. Generally, we denote our measure of costs with the sign C and our measure of benefits with the symbol B. Benefit-cost analysis aims to achieve many goals. First, a project's economic viability can be assessed using BCM. Second, competing projects can be compared using the outcomes of a number of benefit-cost evaluations. Business decisions, the value of public investments, the wisdom of managing natural resources, and the effects of changing environmental circumstances can all be evaluated using BCM. In the end, BCM seeks to investigate possible courses of action with the goal of enhancing social welfare. All benefit-cost studies share a number of characteristics, regardless of their purpose. A BCM starts with an issue that needs to be resolved. For instance, reducing poverty in a region can be a community's objective. Next, a number of projects that could potentially address the specific issue are identified. Alternative initiatives to reduce poverty in a region could, for instance, provide farmers with inputs, agricultural financing, or a successful marketing system. These projects' costs and benefits would be determined, computed, and contrasted. The model emphasizes the spectrum of benefits and cost from the project conceptualization to its „death". It involve five identifiable phases, namely; conception, investment. growth, maturity and decline. Its main advantages over the convectional approach is that it consider the behaviour and magnitude of the project"s gross benefit and cost streams over time, thus provides a basis for cash flow analysis, fund phasing and making realistic projections.

Wave propagation patterns for the (2+1) dimensional modified complex KDV system via new extended direct algebra method

2025-04-06T12:28:09+00:00

In this article, we derive various exact solutions and patterns for the complex modified Korteweg–De Vries system of equation (cmKdV) with a generalized innovative extended direct algebra method. The Korteweg-De Vries system exhibits the scientific dynamics of water particles at the surface and beyond the surface level. The system also has applications in ferromagnetic materials, nonlinear optics, and solitons theory. The innovative direct algebra method is applied to obtain dark, multiple, singular, breather and bright wave patterns. This method also provides staggering wave solutions for the complex modified Kortweg-De Vries system in the form of hyperbolic and trigonometric functions. These recovered solutions for the considered model and are more efficient, concise and general than the extant ones. The wave patterns are properly explained with 2-D and 3-D graphs to elucidate wave behaviour for some selected solutions derived for the system. Lastly, the solutions in this work will greatly advance various fields of application of the Kortweg-De Vries equation like optical fibres, ferromagnetic materials, nonlinear optics, signal processing, water waves, plasma physics, soliton theory, string theory and other contemporary sciences.

Synthesis, characterization and antimicrobial activities of a Schiff base derived from acetyl acetone and 2-aminopyridine and its Cobalt (II) and Nickel (II) complexes

2025-04-06T12:32:58+00:00

A Schiff base derived from condensation reaction of from acetyl acetone and 2-aminopyridine was synthesized. It was refluxed with Co (II) and Ni (II) chlorides which results in the formation of the corresponding metal (II) complexes in good yield.The Schiff base and metal (II) complexes were characterized by using FTIR, Solubility test, Melting point and decomposition temperature, molar conductance and Gravimetric analysis. The Schiff bases were insoluble in H₂O, CCl₄, but slightly soluble in methanol, ethanol, acetone, nitrobenzene, and petroleum ether and completely soluble indimethylsulphoxide (DMSO) and dimethylformamide (DMF). The Co (II) and Ni (II) metals Complexes were soluble in DMSO and DMF and insoluble in water, CCl4, and nitrobenzene and petroleum ether. The IR spectral data revealed azomethine peak of the Schiff base at 1600 cm^-1while for the Co (II) and Ni (II) metals complexes, the peak was found within 1611cm^-1– 1605cm^-1supporting coordination of Schiff base/ligands. Magnetic moment values of the synthesized Co (II) and Ni (II) complexes obtained were in the range of 4.30 – 3.38 BM which suggested the complexesare paramagnetic. Molar conductance values were found to be within the range of 5.2– 6.05 Ω^-1cm²mol –¹revealing that all the complexes are non-electrolytic in nature. The compound obtained were tested for antimicrobial activities against some pathogenic bacteria and fungi: Staphylococcus aureus, Streptococcus pyogens, and Salmonella typhi, Aspergillusformigatus, Rhizopus spp. and Mucor spp. respectively, using paper disc diffusion method.The metal Schiff base complexes exhibit higher antimicrobial activity than the free Schiff base.

Internet of things-based smart fish farming: Application of smart sensors and computer vision to provide real-time monitoring and diagnosis in aquaculture

2025-04-06T12:38:40+00:00

ABSTRACT

The frequent occurrence of disease outbreaks in fish farming presents a significant challenge, leading to substantial economic losses and threatening food security, thus hindering the progress toward sustainable development goals (SDGs). In aquaculture, disease prevention relies on early detection of changes in water quality, abnormal fish behavior, and physical deformities, tasks typically handled by skilled fisheries experts, who are in short supply in Nigeria. Traditional manual disease detection methods are often costly and unreliable. This study proposes a computer vision-based solution utilizing Faster Region-based Convolutional Neural Network (FasterR-CNN) with Detectron2 for improved disease detection in fish farming. A dataset of 500 images was collected, pre-processed, and divided into training (70%), validation (15%), and testing (15%) sets. Three Faster R-CNN models (X101, R100, and R50) were trained and evaluated, with the X101 model achieving the highest accuracy of 98%. The results underscore the potential of deep learning techniques for accurate and efficient disease detection, offering a scalable solution to enhance fish health management. This approach provides a reliable and cost-effective alternative to traditional methods, contributing to the sustainability and growth of the aquaculture industry while addressing the need for timely interventions in fish disease control.

Machine learning-based framework for predicting user satisfaction in e-Learning systems

2025-04-07T22:20:24+00:00

The usability of eLearning systems is of paramount importance in determining the effectiveness and user satisfaction. This study introduces a machine learning-based framework to predict users satisfaction on eLearning System aiming to create user-centered platforms that cater to diverse learners satisfaction. The study employed machine learning models such as Support Vector Machines, Decision Trees and Neural Networks to predict user satisfaction towards usability of eLearning System. OC2 (Optimal Course Content & Online Collaboration Lab) dataset was subject into the three said models to predict user’s satisfaction in eLearning System. The results obtained from these models shows a promising performance and a perfect classification of both satisfied and unsatisfied users in eLearning System. All the three models achieved and accuracy, precision, Recall and FI score of 100% which shows there is no misclassification in the three models. This proves the modes underscore its reliability in predicting users’ satisfaction level. The outstanding accuracy of machine learning models in predicting satisfaction levels demonstrates their effectiveness as dependable tools for assessing usability. This study can be extended by employing diverse dataset with different factors in identifying various usability issues and improving the design and functionality of e-Learning Systems. Also other models apart from Support Vector Machine, Decision Tree, and Neural Network can also be applied to this study to know the performance of the models in predicting the usability scores based on the identified factors on the dataset. Future research can extend this study by utilizing a more diverse dataset with additional factors to further refine the identification of usability issues and improve system design. Additionally, alternative machine learning models beyond Support Vector Machines (SVM), Decision Trees (DT), and Neural Networks (NN) can be explored to assess their effectiveness in predicting usability scores based on the identified factors. Also leveraging Deep learning model will enhance the study to know the stage of user satisfaction on the e-learning system.