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Go to Editorial ManagerArtificial intelligence (AI) is rapidly advancing as a valuable tool in oncology for enhancing detection and management of cancer. The integration of AI with PET/CT imaging presents significant scenarios for improving efficiency and accuracy of cancer diagnosis. This study examines the current applications of AI with PET/CT imaging, highlighting its role in diagnosing, differentiating, delineating, staging, assessing therapy response, determining prognosis, and enhancing image quality. A comprehensive literature search was conducted in six data-bases to get the most recent works, use Springer, Scopus, PubMed, Web of Science, IEEE, and Google Scholar in the last five years (2019-2024), identifying 80 studies that met the criteria for inclusion that focused on AI-driven models applied to PET/CT data in various cancers, with lung cancer being the most studied. Other cancers examined include head and neck, breast, lymph nodes, whole body, and others. All studies involved human subjects. The findings indicate that AI holds promise in improving cancer detection, identifying benign from malignant tumors, aiding in segmentation, response evaluation, staging, and determining the prognosis. However, the application of AI-powered models and PET/CT-derived radiomics in clinical practice is limited because of issues of data normalization, reproducibility, and the requirement of large multi-center data sets for improving model generalizability. All these limitations have to be solved to guarantee the dependable and ethical use of AI in day-to-day clinical activities.
Experiments were conducted to study the effect of quenching medium carbon steel in water-based MWCNTs nanofluids at 0.05 % wt. concentration quenchant, a large cylindrical sample with 46 mm diameter and 40 mm length made from medium carbon steel used with three K-type thermocouples with a diameter of 1.5 mm inserted in three locations for sample (center of the sample, mid-point between center and surface and 1 mm from the surface). A time-temperature reading data system was used to read temperature history during cooling stage.The same experiments were simulated using ANSYS Workbench with Thermal Transient Version 19, the cooling curves at three locations for the cylindrical steel sample calculated during quenching in MWCNTs nanofluids. Quench factor analysis was used to predict the hardness results from the calculated and measured cooling curves, and these results compared with the hardness test results conducted in the significant sample from the center to the surface. The results show excellent compatibility when compared between the hardness results from cooling curves, and it also shows a good agreement with the results of the hardness test, especially at the sample surface.
The present investigation looked at whether the Bailey approach to aggregate gradation could be used to construct Superpave HMA blends. It also looked at how this approach influenced the rutting performance associated with these mixes and compared it to mixes of asphalt created by Superpave gradations. The current research included four aggregate gradations: both fine and coarse gradations for the Superpave and Bailey gradation procedures. The repeated loading test was utilized to assess the rutting performance. The findings indicated that temperature, stress level, and aggregate gradation all had a significant impact on rutting performance. In contrast to the other three gradations, the third mixture gradation exhibited the least amount of non-reversible deformation. It translates to pavement that is more resistant to rutting and less susceptible to it.
A proposed modern technique for determination the blood group typing by monitoring the agglutination of red blood cells using acousto-optical technique and digital camera. The method based on analysis the digital image of the agglutination process by MATLAB software._x000D_ We present an overview of two acousto-optic sensing approaches; the first demonstrates the cuvette approach while the second is the microscope slide approach. The cuvette approach digital image analyzing depends on the green channel distribution of the original image and count the brighten pixels, while the microscope slide approach passes through series of algorithms started with grayscale filter and end with edge detection it counts the different color pixels._x000D_ The experimental result shown that it is possible to enhance the determination of blood group typing by using acousto-optical technique in both cases of using isohemagglutinating sera as well as the crossmatch test in a short time and high efficiency compared with the traditional methods.