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All Journal Mikailalsys Journal of Mathematics and Statistics ASEAN Journal for Science and Engineering in Materials
Adamu, A.
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Phytochemical Assessment of The Extracts of Stem (Bark) and Leaves of Theobroma Cocoa Materials: Experimental Procedure and Its Comparison to Literature Abulude, Francis Olawale; Ogunkoya, Mary Omofolarin; Adenibuyan, Grace Bamise; Arifalo, Kikelomo Mabinuola; Akinusotu, Akinyinka; Samuel, Ademola; Adamu, A.; Kenni, Amoke Monisola; Bello, Lateef Johnson
ASEAN Journal for Science and Engineering in Materials Vol 1, No 2 (2022): AJSEM: Volume 1, Issue 2, September 2022
Publisher : Bumi Publikasi Nusantara

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Abstract

Plants had already long been utilized as medicines to treat illnesses. Theobroma cocoa is a plant with antifungal, antibacterial, antioxidant, antiemetic, larvicidal, hepatoprotective, anti-diarrheal, anti-inflammatory, antimalarial, anthelmintic, antiarthritic, wound healing, and anticarcinogenic potential. The purpose of this study was to look into the phytochemical compositions of crude extracts of Theobroma cocoa leaves and stem (bark) materials. Carbohydrates, saponins, and phlobatannins were found in higher concentrations in both the stem (bark) and the leaves, whereas tannins, glycosides, resins, and alkaloids were found in lower concentrations in both samples. Depending on the solvent used for extraction, different phytochemical compositions are obtained in each part of the tree. However, for the majority of the phytochemicals, water extraction yielded higher concentrations.
Methods and Applications of Point Estimation in Inferential Statistics: A Case Study of Energy Consumption Data at ATBU Yakubu, J.; Bishir, A.; Jibril, J.; Adamu, A.; James, K. Y.; Ibrahim, A. I.
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 3 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i3.7467

Abstract

This study employs established point estimation techniques in inferential statistics—including Ordinary Least Squares (OLS), Maximum Likelihood Estimation (MLE), Ridge regression, and Lasso regression—to analyze a 30-month dataset on energy consumption, billing, and revenue collection from Abubakar Tafawa Balewa University (ATBU), Bauchi. The primary objective is to assess the accuracy and efficiency of parameter estimation methods for predicting revenue based on energy billed. Using regression-based models, the study evaluates performance across two sites: the Main Campus and the Permanent Site. Empirical findings demonstrate strong model explanatory power, with R² values of approximately 0.90 and 0.80, respectively, indicating a high degree of reliability in the predictive capacity of the models. OLS is shown to provide unbiased estimates, while regularization techniques such as Ridge and Lasso improve model robustness by addressing multicollinearity and overfitting. The results highlight the practical applicability of statistical modeling in energy revenue forecasting and offer valuable insights for institutional energy management. The study concludes by recommending the integration of regularized regression techniques for more resilient forecasting frameworks in similar energy consumption environments.
Co-Authors Abulude, Francis Olawale Adenibuyan, Grace Bamise Akinusotu, Akinyinka Arifalo, Kikelomo Mabinuola Bello, Lateef Johnson Bishir, A. Ibrahim, A. I. James, K. Y. Jibril, J. Kenni, Amoke Monisola Ogunkoya, Mary Omofolarin Samuel, Ademola Yakubu, J.