In SSc, digital lesions are represented by DPS, DU, calcinosis and gangrene, and provide an evidence-based DU subsetting according to their origin and main characteristics. Subsetting may be helpful for a precise DU evaluation and staging, and in randomized controlled trials for a precise identification of those DUs that are to be included in therapeutic studies.

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US is a reliable tool giving reproducible results, and is able to detect digital dermal thickening in SSc.

This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different data sources: urban water demand (SCADA data) and individual customer water consumption (AMR data). In the first case, reliable forecasting can be used to optimize operations, particularly the pumping schedule, in order to reduce energy-related costs, while in the second case, the comparison between forecast and actual values may support the online detection of anomalies, such as smart meter faults, fraud or possible cyber-physical attacks. Results are presented for a real case: the water distribution network in Milan.