QCD challenges from pp to AA collisions: 4th edition.

This paper is a write-up of the ideas that were presented, developed and discussed at the fourth International Workshop on QCD Challenges from pp to AA, which took place in February 2023 in Padua, Italy. The goal of the workshop was to focus on some of the open questions in the field of high-energy heavy-ion physics and to stimulate the formulation of concrete suggestions for making progresses on both the experimental and theoretical sides. The paper gives a brief introduction to each topic and then summarizes the primary results.

Increasing Annual Cancer Incidence in Patients Age 20-49 Years: A Real-Data Study.

Purpose: Data from population-based studies have shown an increased incidence of certain types of neoplasms in patients younger than 50 years (early-onset cancer [EOC]); however, little information is derived from other real-world data sources. In a nonpopulation registry, we analyzed changes in the incidence of several neoplasms in successive generations.

Methods: This cross-sectional study included all patients with a cancer diagnosis registered in one university hospital in Málaga, Spain, between 1998 and 2021, and 18 neoplasms were analyzed.

Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data.

Background: Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions or jurisdictions is challenging because of complex and varying privacy and regulatory requirements.

Deep learning for automatic organ and tumor segmentation in nanomedicine pharmacokinetics.

: Multimodal imaging provides important pharmacokinetic and dosimetry information during nanomedicine development and optimization. However, accurate quantitation is time-consuming, resource intensive, and requires anatomical expertise. : We present NanoMASK: a 3D U-Net adapted deep learning tool capable of rapid, automatic organ segmentation of multimodal imaging data that can output key clinical dosimetry metrics without manual intervention.

Multi-Sensor HR Mass Data Models toward Multi-Temporal-Layered Digital Twins: Maintenance, Design and XR Informed Tour of the Multi-Stratified Appian Way (PAAA).

The article provides an overview of the digitisation project conducted by the Parco Archeologico dell'Appia Antica (PAAA) in Rome, focusing on an 11.7 km section of the Appian Way. This effort is part of the "Appia Regina Viarum" project, supporting the UNESCO heritage site candidacy of the Appian Way.