Recent years have witnessed an increasing interest in air pollutants and their effects on human health. More generally, it has become evident how human, animal and environmental health are deeply interconnected within a One Health framework. Ground level air monitoring stations are sparse and thus have limited coverage due to high costs. Satellite and reanalysis data represent an alternative with high spatio-temporal resolution. The idea of this work is to build an Artificial Intelligence model for the estimation of surface-level daily concentrations of air pollutants over the entire Italian territory using satellite, climate reanalysis, geographical and social data. As ground truth we use data from the monitoring stations of the Regional Environmental Protection Agency (ARPA) covering the period 2019–2022 at municipal level. The analysis compares different models and applies an Explainable Artificial Intelligence approach to evaluate the role of individual features in the model. The best model reaches an average $R^2$ of 0.84 ± 0.01 and MAE of 5.00 ± 0.01 $\mathsf{\mu }$g/m³ across all pollutants which compare well with the body of literature. The XAI analysis highlights the pivotal role of satellite and climate reanalysis data. Our work can facilitate One Health surveys and help researchers and policy makers.

Air pollution remains a major environmental challenge, with severe impacts on human health and ecosystems. Recent advances in satellite technology have transformed air quality monitoring by enabling global, continuous observations of atmospheric pollutants. However, satellite data often lack the precision of ground-based stations. This study aims to develop a machine learning model to predict daily surface concentrations of key air pollutants (NO₂, O₃, PM₁₀, and PM_2.5) at high spatial resolution (300 m) in the Apulia region. Using Regional Environmental Protection Agency (ARPA) station data from 2019 to 2022 and meteorological, geographic, land-use, and temporal variables, we trained an XGBoost model on a 300 m grid. Model performance, assessed by repeated cross-validation, showed an average $R^2$ of 0.71, with values of 0.77 for NO₂, 0.78 for O₃, 0.67 for PM_2.5, and 0.64 for PM₁₀. eXplainable AI (XAI) methods confirmed strong alignment with established scientific knowledge, enhancing model reliability and offering insights into pollutant distribution drivers.

Beam shaping is considered a reliable technology capable of enhancing mechanical properties, stability, and productivity in Laser Powder Bed Fusion processes of metals (PBF-LB/M). However, transferring optimal parameter sets between different beam shapes is still a major challenge, and systematic and interpretable assessments are essential to separate beam-shape effects from the influence of process parameters. In this work, we present a novel approach based on Machine Learning (ML) and eXplainable Artificial Intelligence (XAI), which can potentially support defect prediction across the process window and reduce reliance on lengthy trial-and-error DoE campaigns. Different programmable ring beam configurations are integrated as explicit features, and their impact is quantified via SHapley Additive exPlanations (SHAP). Ti-6Al-4V cubes were produced over a broad processing window and analysed by cross-sectional metallography and image-based defect quantification. An XGBoost classifier was trained to discriminate low- and high-defect regimes (1% threshold), achieving 93.5% accuracy with 95% sensitivity, 83% specificity, and 97% precision. The principal outcome is that, while conventional comparisons at process conditions with the same Volumetric Energy Density (VED) but different beam shapes suggest broadly comparable behaviour between ring shapes, SHAP consistently separates them into two distinct responses. A more balanced core–ring splitting behaves as a neutral-to-beneficial alternative to the Gaussian beam shape, whereas a ring-dominated mode increases the probability of exceeding the 1% defect within the investigated VED range. These results provide an interpretable basis to assess parameter transferability across beam shapes, and demonstrate the value of the proposed ML–XAI strategy as a novel strategy to quantify beam-shape effects beyond VED-only re-parameterisation.

Objective. Higher-order statistical dependencies can encode cooperative structure in complex systems, but are often missed by pairwise analyses and supervised pipelines optimized for discrimination. We aim to develop an unsupervised feature ranking framework that prioritizes genes by higher-order synergy within transcriptomic communities. Approach. We combine PCA derived latent components with O-information to quantify gene level synergy within network communities. Using hepatocellular carcinoma (HCC) microarray data (GSE102079), we start from gene communities identified in our previous community detection workflow and analyze them without supervised pre filtering. For each community, we select latent components through permutation based criteria, compute O-information between each gene and the retained components, assess significance using surrogate testing, and rank genes by synergy. Top ranked genes are selected using knee based criteria on ranked synergy profiles. Main results. Synergy based ranking consistently highlights genes associated with functions related to HCC, such as metabolic remodeling, extracellular matrix dynamics, EMT, angiogenesis, immune modulation and oxidative stress responses. Several high-synergy genes supported by literature were previously discarded by Boruta, indicating that synergy prioritizes cooperative module roles rather than solely discriminative markers. Significance. The proposed method provides a reproducible, fully unsupervised tool for feature ranking based on higher-order interactions, complementing supervised feature selection approaches and offering a general domain agnostic tool applicable to any samples by features dataset.

Air pollution remains a major environmental challenge, with severe impacts on human health and ecosystems. Recent advances in satellite technology have transformed air quality monitoring by enabling global, continuous observations of atmospheric pollutants. However, satellite data often lack the precision of ground-based stations. This study aims to develop a machine learning model to predict daily surface concentrations of key air pollutants (NO2, O3, PM10, PM2.5) at high spatial resolution (300 m) in the Apulia region. Using Regional Environmental Protection Agency (ARPA) station data from 2019 to 2022 and meteorological, geographic, land-use, and temporal variables, we trained an XGBoost model on a 300 m grid. Model performance, assessed by repeated cross-validation, showed an average R^2 of 0.71, with values of 0.77 for NO2, 0.78 for O3, 0.67 for PM2.5, and 0.64 for PM10. eXplainable AI (XAI) methods confirmed strong alignment with established scientific knowledge, enhancing model reliability and offering insights into pollutant distribution drivers.