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  • Authors: Ogbonnaya, Ewa;  Advisor: -;  Participants: Rizwan, Ansari Ahmad; Bamidele, Emakoji Ile; Ayuba, Victoria (2022)

  • Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder

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  • Authors: Proença, Maria da Conceição;  Advisor: -;  Participants: Rebelo, Maria Teresa (2022)

  • We used the model x of YOLO v5, keeping the hyperparameters by default, and annotated 24 images of a subset of 81 tiles with one class of objects of interest. Sixteen images were used for train and eight for validation, respecting the recommended 30% with a split 20–10% between train and validation. The train was done once and took a few hours (23.2 h for 448 iterations in a laptop equipped with dual Core Intel i7-10750H processor, 16 GB SDRAM and an NVIDIA GeForce RTX 2060), but the resulting weights can be used to detect the same objects of interest in the future on any similar image (Fig. 1), with a processing time of 2500 ms for each image.

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  • Authors: Ayeni, Temitope Oluwatosin;  Advisor: -;  Participants: Arotupin, Daniel Juwon; Ayo, Oisagah Ezekiel (2022)

  • Table 1 shows counts from three different dilutions (10−5, 10−6 and 10−7). Fungal colonies decrease with increasing dilution. The number of fungal colonies is least in 10−7 and highest in 10−5 for all plates. Hence, the number of fungal colonies decreases with increasing concentration.

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  • Authors: Abdullahi, Mustapha;  Advisor: -;  Participants: Adeniji, Shola Elijah; Arthur, David Ebuka; Musa, Shuaibu (2020)

  • At first, the structure of the compounds was accurately drawn and optimized using the Spartan 14 software at DFT level of theory with B3LYP/6-31G** basis set in a vacuum. The diverse chemometric descriptors were computed from the optimized structures using the PaDEL descriptors software, and the division of the dataset into training and test sets was done based on Kennard-Stone’s algorithm. Five (5) models were generated from the training set using genetic function approximation, and model 1 was chosen as the best due to its robust internal and external validation metrics (R2train = 0.8563, R2adjusted = 0.8185, PRESS = 3.5724, average R¯¯¯¯2m (LOO-train) = 0.6751, Q2cv = 0.7534, R2pred= 0.7543, R2test = 0.6993) which passed the model criteria of acceptability. 6-Bromo-N-(2-(4-bromop...

  • BB


  • Authors: Radwan, M. M.;  Advisor: -;  Participants: Nagi, Shaymaa M. (2022)

  • Evaluation of the final setting time was carried out on the material pastes of normal consistency prepared with distilled water at a liquid/solid ratio of 0.23 ml/g as given in “Setting time” section. The recorded average setting time was 20 min at 37 °C (average of five readings). Figure 3 shows the hardness data of samples cured in the different hydration media at all curing ages. Figure 3 indicates that the hardness values of samples cured in distilled water (DW) increase with the hydration period from one day and up to 14 days, while for samples cured in SBF solution and the Pseudomonas aeruginosa microbe in MHB media there was a decrease in the hardness values at all curing ages. However, the samples immersed in the microbe medium showed slightly higher hardness values than tho...