Generative modeling for 3D brain MRI is challenged by a trade-off between anatomical fidelity, sample diversity, and computational efficiency. Diffusion-based approaches achieve strong visual quality but typically require hundreds to thousands of sampling steps, while latent-space compression can introduce reconstruction artifacts and degrade fine-grained anatomy. We introduce FlowLet, a conditional generative framework that performs Flow Matching in an invertible 3D wavelet domain. This representation enables multi-scale generation without learned latent compression, while deterministic ODE sampling allows fast inference. Age conditioning is modeled through complementary feature-wise modulation and spatially adaptive cross-attention, enabling explicit control over age-related morphological variation. Across multi-site neuroimaging datasets, FlowLet achieves competitive and, in several settings, superior global fidelity compared to diffusion-based baselines using as few as 10 sampling steps. Region-based evaluation across 95 cortical and subcortical brain regions demonstrates improved local anatomical plausibility beyond what is captured by global similarity metrics alone. In a downstream brain age prediction study, models augmented with FlowLet-generated data consistently reduce prediction error relative to real-only training and other generative baselines. Rather than focusing on a single dominant metric improvement, these results highlight a consistent trade-off between efficiency, controllability, and anatomically meaningful 3D brain MRI generation. The proposed framework is released as open-source to support reproducibility.

Increasing emissions due to urbanization pose a significant threat to human health. Air pollutants such as NO2, PM2.5, and PM10have a well-documented impact on quality of life and mortality risk. Numerous studies are currently investigating the correlation between air pollution and adverse health effects, highlighting the risks associated with exposure to such emissions. In this context, models capable of predicting mortality by leveraging information on pollution and socioeconomic factors are essential for prevention. This work contributes to this field by using Machine Learning (ML) to predict mortality in the Municipality of Taranto (Italy). We propose an eXtreme Gradient Boosting (XGBoost) regression model that uses air pollution levels and socioeconomic data to assess mortality risk over a grid-based map of the examined city. Our study aligns with state-of-the-art findings, further expanding the understanding of the correlation between emissions, economic conditions, and mortality. The proposed model achieves a Root Mean Square Error (RMSE) of 1.61 on the test set, demonstrating the effectiveness of this approach. Additionally, eXplainable Artificial Intelligence (XAI) analysis is conducted using Shapley values to gain insights into the model’s decision-making process and better understand the importance of each feature in predicting mortality risk.

The investigation of neural correlates of mental imagery and perception has been a pivotal area of research in cognitive neuroscience, offering significant insights into how the brain represents both imagined and perceived experiences. While previous studies have successfully identified electrophysiological markers associated with motor and perceptual imagery, the neural signatures of motivational imagery—encompassing desires, needs, and cravings—remain underexplored. This study employs different machine learning classifiers applied to EEG data to classify and compare neural representations of twelve distinct motivational states under perception and imagery conditions. We conducted experiments using 14-channel and 18-channel EEG configurations to capture and analyze the neural responses of participants exposed to various stimuli. Our primary aims were to evaluate classification performance, measured by accuracy, and assess the impact of electrode density on performance. The results indicate that perception conditions generally yield higher accuracy in distinguishing motivational states than imagery conditions. Specifically, primary needs and somatosensory states exhibited strong and clear neural patterns in perception, with a peak accuracy of 88% in the 18-channel setup, while the accuracy for imagined states was more variable. Comparisons between 14-channel and 18-channel configurations revealed that higher electrode density slightly improved performance but was not significantly superior.

Cardiovascular disease (CVD) is a general term referring to several heart or blood vessels abnormality. Heart failure (HF), directly associated to (CVD), is a significant global health problem as well as the leading cause of morbidity and mortality. The early detection of this condition is crucial for patient health. Traditional diagnostic methods for HF, such as history taking and physical examination, are often insufficient and require the use of advanced techniques such as Electrocardiogram (ECG). This study aims to extract temporal and morphological features from (ECG) signals and compare different Machine Learning (ML) classification models to enable rapid diagnosis and provide interpretable predictions. Specifically, we propose a Light Gradient Boosting (LGBM) model that can discriminate Normal Sinus Rhythm (NSR) and Arrhythmia (ARR) with a high accuracy of 0.99, achieving a Precision of 1.00, Recall of 0.99, and f1-score of 0.99 in the (NSR) class, and Precision of 0.99, Recall of 1.00, and f1-score of 0.99 in the (ARR) class, respectively. In addition, eXplainable Artificial Intelligence (XAI) analysis is performed to explain the model predictions.

Brain-computer interfaces are widely used to control machines using Electroencephalography (EEG) signals. Several low-cost electroencephalographs are available on the market that achieves good-quality EEG signals. One of the most intriguing issues for developing biofeedback systems is classifying users' emotional states using EEG signals and Machine Learning (ML) methods. In our study, we propose a novel ML-based biofeedback tool using a BCI to detect two different users' mental states: Focus, and Relaxation. We compared several ML algorithms achieving an average accuracy on the Test Set of 0.90 by using SVM. Finally, we propose a prototype for music generation according to the classification output that could be adopted in neurorehabilitation scenarios.

Synthetic Aperture Radar (SAR) systems are effective tools for many remote sensing applications. In particular, the modeling of the backscatter coefficient under various conditions is of interest in many scientific fields. Seasonal changes can have a considerable effect on the values of the reflectivity of a target, therefore these variations should be estimated to improve the accuracy of the models. In this paper a statistical analysis of radar backscatter coefficient seasonal variations using C-band SAR data is presented.