This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.

Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges.

In this work, some marble types were collected from Egypt, and their shielding characteristics were estimated. Their rigidity, in addition to their elegant shape, led us to consider their use as a protective shield, in addition to making the workplace more beautiful. The mass attenuation coefficient (μ/ρ) was calculated for three types of marble (Breshia, Galala, and Trista) experimentally, using a narrow gamma ray source and high pure germanium (HPGe). The results obtained were compared with the XCOM program and indicated a very good agreement between the two methods. The linear attenuation coefficient (μ) was evaluated to calculate the half and tenth value layers. The maximum μ value of 1.055, 1.041, and 1.024 cm⁻¹ was obtained for Breshia, Galala, and Trista, respectively, at 0.06 MeV. The mean free path for studying the materials was compared with other shielding materials and showed good results at different energy scales. The energy absorption (EABF) and exposure buildup factors (EBF) were determined at different mean free paths. The fast neutron removal cross section Σ_R was calculated and expresses the ability of the marbles to slow down fast neutrons through multiple scattering. This is the ability of the marbles to shield fast neutrons.

Magmatic rocks represent one of the most significant rocks due to their abundance, durability and appearance; they can be used as ornamental stones in the construction of dwellings. The current study is concerned with the detailed petrography and natural radioactivity of seven magmatic rocks. All are commercial granitic rocks and are identified as black Aswan, Nero Aswan, white Halayeb, Karnak, Verdi, red Hurghada and red Aswan. Their respective mineralogical compositions are classified as porpheritic granodiorite, granodiorite, tonalite, monzogranite, syenogranite, monzogranite and syenogranite. A total of nineteen samples were prepared from these seven rock types in order to assess their suitability as ornamental stones. Concentrations of ²²⁶Ra, ²³²Th and ⁴⁰K radionuclides were measured using NaI (Tl) scintillation gamma-ray spectrometry. Among the studied magmatic rocks, white Halayeb had the lowest average values of ²²⁶Ra (15.7 Bq/kg), ²³²Th (4.71 Bq/kg) and ⁴⁰K (~292 Bq/kg), all below the UNSCEAR reported average world values or recommended reference limits. In contrast, the other granitic rocks have higher values than the recommended limit. Except for the absorbed dose rate, other radiological hazard parameters including radium equivalent activity, annual effective dose equivalent, external, and internal hazard indices reflect that the White Halyeb rocks are favorable for use as ornamental stone in the construction of luxurious and high-demand residential buildings.

Palm oil is the main cash crop of tropical Asia, and the implementation of LPWAN (low-power wide-area network) technologies for smart agriculture applications in palm oil plantations will benefit the palm oil industry in terms of making more revenue. This research attempts to characterize the LoRa 433 MHz frequency channels for the available spreading factors (SF7-SF12) and bandwidths (125 kHz, 250 kHz, and 500 kHz) for wireless sensor networks. The LoRa channel modeling in terms of path-loss calculation uses empirical measurements of RSS (received signal strength) in a palm oil plantation located in Selangor, Malaysia. In this research, about 1500 LoS (line-of-sight) and 300 NLoS (non-line-of-sight) propagation measurement data are collected for path-loss prediction modeling. Using the empirical data, a prediction model is constructed. The path-loss exponent for LoS propagation of the proposed prediction model is found to be 2.34 and 2.9 for 125–250 kHz bandwidth and 500 kHz bandwidth, respectively. Again, for the NLoS propagation links, the attenuation per trunk is found to be 7.58 dB, 7.04 dB, 5.35 dB, 5.02 dB, 5.01 dB, and 5 dB for SF7-SF12, and the attenuation per canopy is found to be 9.32 dB, 7.96 dB, 6.2 dB, 5.89 dB, 5.79 dB, and 5.45 dB for SF7-SF12. Moreover, the prediction model is found to be the better choice (mean RMSE 2.74 dB) in comparison to the empirical foliage loss models (Weissberger’s and ITU-R) to predict the path loss in palm oil plantations.