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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.
Plastic pollution represents a significant global environmental challenge, with millions of tons of plastic waste accumulated annually in landfills and natural ecosystems. The COVID-19 pandemic intensified this issue due to the widespread use of single-use personal protective equipment. As a response to this problem, this study examines the use of recycled polyethylene terephthalate and single-use face masks as additives in porous asphalt mixtures to improve the mixtures’ properties and promote sustainability in pavement engineering. The research aimed to evaluate the combined effect of incorporating recycled polymers into a hybrid system, rather than separately modified mixtures of polyethylene terephthalate and single-use face mask fibers. Laboratory tests indicated that the hybrid mixture exhibited optimal performance, achieving a 34% increase in Marshall stability, a 19% decrease in flow, and a 20% reduction in air-void content. Additionally, permeability decreased by 36%, while remaining within the accepted limit, improving moisture susceptibility, and cantabro abrasion losses decreased by up to 11%, suggesting improvements in cohesion, structure, and durability. The findings demonstrate that hybrid polymer modification offers an environmentally sustainable and high-performance approach for future porous asphalt pavements.
In this work, waste glass powder from broken windows and plastic fibers from waste polyethylene terephthalate bottles are utilized to produce an economical self-compact concrete. Fresh properties (slump flow diameter, slump Flow T50, V. Funnel, L–Box), mechanical properties (Compressive strength and Flexural strength) and impact resistance of self-compact concrete are investigated. 15% waste glass powder as a partial replacement of cement with five percentages of polyethylene terephthalate plastic waste were adopted: 0% (reference), 0.5%, 0.75%, 1%, 1.25% and 1.5% by volume. It seems that the flow ability of self-compact concrete decreases with the increasing of the amount of plastic fibers. The compressive strength was increased slightly with plastic fiber content up to (0.75%), about 4.6% For more than (0.75%) plastic fiber. The compressive strength began to decrease about 15.2%. The results showed an improvement in flexural strength and an impact on the resistance in all tested specimens’ content of the plastic fibers, especially at (1.5%) fibers.