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Concrete, as the most widely used construction material, is inextricably connected with human development. Despite conceptual and methodological progress in concrete science, concrete formulation for target properties remains a challenging task due to the ever-increasing complexity of cementitious systems. With the ability to tackle complex tasks autonomously, machine learning (ML) has demonstrated its transformative potential in concrete research. Given the rapid adoption of ML for concrete mixture design, there is a need to understand methodological limitations and formulate best practices in this emerging computational field. Here, we review the areas in which ML has positively impacted concrete science, followed by a comprehensive discussion of the implementation, application, and interpretation of ML algorithms. We conclude by outlining future directions for the concrete community to fully exploit the capabilities of ML models.

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Multiple primary cancer is defined as the multiple occurrence of malignant neoplasms in the same individual. Due to the development of new diagnostic techniques and the rise in long-term survival of cancer, reports of multiple primary cancers have gradually increased. Herein, we describe the case of a 68-year-old female patient with quadruple primary cancer of the breast, rectum, ovary, and endometrium. For its great rarity, we report this case with a review of the literature.