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| 1 | Peri-implantation hormonal milieu:elucidating mechanisms of abnormal placentation and fetal growth显示文摘 | Mainigi MA Olalere D Burd I | 2014 | Biol Reprod2014，90，： | 1 |
| 2 | Evaluation of Some Centrifugal Impaction Devices for Shelling Bambara Groundnut显示文摘 | FOluwole A Abdulrahim K Olalere | | AgriculturalEngineering0，，： | 1 |
| 3 | Problems and Prospects of Open and Distance Education in Nigeria 显示文摘 | Yusuf Mudasim Olalere | 2014 | Turkish Online Journal of Distance Education2014，，1： | 1 |
| 4 | Biosorptive removal of Pb 2+ and Cd 2+ onto novel biosorbent: Defatted Carica papaya seeds显示文摘 | U. Adie Gilbert I. Unuabonah Emmanuel A. Adeyemo Adebanjo G. Adeyemi Olalere | 2011 | Biomass and Bioenergy2011，，7： | 1 |
| 5 | The formation bulk density prediction for intact and fractured siliciclastic rocks显示文摘The formation bulk density is one of the most important rock properties required for reservoir evaluation and geomechanical analysis.In intervals where the formation bulk density logs are not acquired,the industry practice is to estimate the formation bulk density from the compressional-wave velocity using empirical relationships.The major problems with the existing empirical relationships are:(1)they were developed primarily for specific lithologies(in most cases clean formations)and have failed to produce reasonable estimates when applied over a lithological column that consists of several stratigraphic units;(2)they are not applicable to rocks that contain microcracks/fractures.In this paper,a new formation bulk density prediction method that can be applied to a wide range of intact and fractured siliciclastic rocks is being proposed based on experimental data.The model is then validated using wireline log data acquired from an onshore well in the tertiary deltaic system of the Niger Delta basin.In the new model,the formation bulk density is expressed as a function of sonic velocity difference and shale volume factor.In general,an excellent agreement exists between the predicted and measured formation bulk density using the new technique.The statistical analysis shows that the new formation bulk density prediction model outperforms the most widely used empirical relationships with the least-root-mean square errors and least residual values. | Babatunde Yusuf Olalere Oloruntobi Stephen Butt | 2019 | Geodesy and Geodynamics2019，10，6： | 0 |
| 6 | Comparative Analysis of Polyphenolic and Antioxidant Constituents in Dried Seedlings and Seedless Acacia nilotica Fruits显示文摘The phenolic and antioxidant constituents in Acacia nilotica fruits have become an important source of medicinal and thera-peutic benefit with powerful biological properties.This study investigated the phenolic content and antioxidant capacity of powdered Acacia fruits with seeds and without seeds.The phenolic content and antioxidant capacities in them were deter-mined using Folin-Ciocalteu and DPPH free radical-scavenging assays.The total phenolic and antioxidants of A.nilotica with seeds were spectrophotometrically determined to be 47.61 and 6.18%greater than when the seeds were removed from the dried fruits,respectively.The LC-MS/QTOF analysis shows the presence of 282 and 214 phenolic compounds in the methanol extracts of A.nilotica with seeds and without seeds,respectively.The present study,therefore,revealed that dried A.nilotica fruits with seeds have higher total phenolic content,antioxidant capacity,and bioactive constituents,which indi-cated that they have more medicinal value than fruits without seeds. | Amani AbdErahman Olalere Olusegun Abayomi AbdElhafiz Eltahir Ahmed Abdurahman Hamid Nour Rosli bin Mohd Yunus Ghada Mohamed Ibrahim Nassereldeen Ahmed Kabbashi | 2018 | Journal of Analysis and Testing2018，2，4： | 0 |
| 7 | Student Performance Prediction Using A Cascaded Bi-level Feature Selection Approach显示文摘Features in educational data are ambiguous which leads to noisy features and curse of dimensionality problems.These problems are solved via feature selection.There are existing models for features selection.These models were created using either a single-level embedded,wrapper-based or filter-based methods.However single-level filter-based methods ignore feature dependencies and ignore the interaction with the classifier.The embedded and wrapper based feature selection methods interact with the classifier,but they can only select the optimal subset for a particular classifier.So their selected features may be worse for other classifiers.Hence this research proposes a robust Cascade Bi-Level(CBL)feature selection technique for student performance prediction that will minimize the limitations of using a single-level technique.The proposed CBL feature selection technique consists of the Relief technique at first-level and the Particle Swarm Optimization(PSO)at the second-level.The proposed technique was evaluated using the UCI student performance dataset.In comparison with the performance of the single-level feature selection technique the proposed technique achieved an accuracy of 94.94%which was better than the values achieved by the single-level PSO with an accuracy of 93.67%for the binary classification task.These results show that CBL can effectively predict student performance. | Wokili Abdullahi Mary Ogbuka Kenneth Morufu Olalere | 2021 | Journal of Computer Science Research2021，3，3： | 0 |
| 8 | TOWARDS EVOLVING NIGERIA'S CRIMINAL POLICY: THE CHALLENGES OF TRADITIONAL AND MODERN CONCEPTS显示文摘 | Adeniyi Olatubosun Zacchaeus Olalere Alayinde | 2013 | US-China Law Review2013，10，3： | 0 |
| 9 | Combining hydropyrolysis and compound specific stable isotope measurements to identify sources of biodegrade PAHs in soils and sediments显示文摘 | Chenggong SUN Gbolagade Olalere Wisdom Ivwurie Mick Cooper Christopher H. Vane Colin Snape | 2006 | Chinese Journal Of Geochemistry2006，25，B08： | 0 |
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