This paper analyses the notch effect in the fracture behaviour of acrylonitrile–styrene–acrylate (ASA) material manufactured by fused filament fabrication (FFF). The research is performed on 72 single-edge-notch bending (SENB) specimens containing U-notches with nominal notch radii varying from 0 mm (crack-like defects) up to 2.0 mm, and fabricated with three different raster orientations (0/90, 45/−45, 30/−60). Apparent fracture toughness values are obtained for the different conditions and the resulting notch effect is analysed through the Theory of Critical Distances. A fractographic analysis is also performed using Scanning Electron Microscopy (SEM) in order to justify the fracture (macroscopic) behaviour from the observed fracture micromechanisms. The notch effect observed in the three ASA raster orientations is very similar, and lower than that observed in other FFF polymeric alternatives (ABS, PLA).

Within the context of the increasing use of additive manufacturing techniques and the corresponding need to understand the behavior of 3D-printed materials, this paper analyzes the fracture behavior of additively manufactured carbon fiber reinforced (10 wt.%) acrylonitrile-styrene-acrylate (ASA) with three different raster orientations (90/0, 45/−45, 30/−60). The analyzed material (ASA-CF10) combines the remarkable resistance to weathering agents typical of ASA with the enhanced mechanical properties resulting from the inclusion of carbon fiber reinforcement. The analysis is performed on single-edge-notched bending (SENB) specimens containing different types of defects, from cracks to U-notches with notch radii of 0.5 mm, 1 mm and 2 mm. When compared to non-reinforced ASA, the fracture resistance is noticeably higher (nearly double) for the reinforced material in all raster orientations. The notch effect, defined as the increase in the fracture resistance when the notch radius increases, is analyzed through the Theory of Critical Distances (TCD), and it is mostly higher in the reinforced material than in the pristine polymer. These observations are supported by Scanning Electron Microscopy analyses.

This study presents Care-MOVE, a smartphone-based application designed for continuous, passive, and unobtrusive monitoring of mobility, environmental exposure, and cognitive status in older adults within a telemedicine framework. The system integrates movement-related data collected through smartphone sensors (GPS, activity recognition, and caloric expenditure estimation) with contextual air quality information and standardized neuropsychological assessments, resulting in a comprehensive multimodal dataset (Care-MOVE Dataset). An exploratory proof-of-concept study was conducted on a subsample of 53 participants aged over 65, each monitored continuously for five days, contributing on average more than 30,000 longitudinal records. To investigate whether daily motor behavior can serve as a digital biomarker of cognitive functioning, several Machine Learning and Deep Learning models were evaluated using a Leave-One-User-Out (LOUO) cross-validation strategy. The comparative analysis included traditional classifiers (Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, and Support Vector Machines) as well as temporal deep learning architectures (1D CNN, LSTM, GRU, and Transformer). Among all of the evaluated approaches, the Support Vector Machine with RBF kernel achieved the best performance, reaching an accuracy of 98.1%, a balanced accuracy of 0.988, and an F1-score of 0.981, demonstrating robust generalization across unseen subjects. For this reason, the study was designed and presented as an exploratory proof-of-concept rather than a definitive clinical validation. This integrated approach not only enables the collection of detailed and contextualized data but also opens new perspectives for proactive digital healthcare, focused on risk prevention, improving quality of life, and promoting autonomy in elderly patients.