Background and Aim: Sleep-disordered breathing (SDB) is an extremely common disorder with a high impact on morbidity and mortality. The purpose of this study was to compare overlap syndrome (OS) and obesity hypoventilation syndrome (OHS) and to highlight and understand the differences between them. Material and Methods: The study was conducted retrospectively on 132 subjects selected by consecutive sampling from those attending our unit for suspected SDB. After clinical evaluation as well as functional and sleep investigations, the population was divided according to diagnosis in OS and OHS; then, the clinical parameters of two groups were compared with different statistical analysis. Results: The subjects with OHS were younger and reported higher rated daytime sleepiness (p = 0.005). In addition, they presented more nocturnal respiratory events (apnea-hypopnea index (AHI) 63.61 ± 22.79 events·h−1 vs. AHIOS 42.21 ± 22.91 events·h−1, p < 0.0001) at the sleep investigation as worse gas exchange during sleep leading to a higher percentage of nocturnal hypoxemia (p < 0.0001). In contrast, subjects with OS had more an impaired respiratory function. With regard to night-time ventilatory therapy, more subjects with OS were effectively treated with continuous positive airway pressure (CPAP) (p = 0.011), while more OHS were treated with auto-adjusting PAP (APAP) (14% vs. 1%, p = 0.008). Conclusions: The present study tried to establish a framework for OS and OHS because proper management of the two disorders would reduce their burden on healthcare.

The quite recent availability of satellite stereo pairs allows users to extract three-dimensional data that can be used in different domain of applications, such as urban planning, energy, emergency management, etc. This research paper aims to extract digital surface models (DSM) from satellite stereo pairs acquired by three different satellites (Deimos-2, Pléiades-1 and WorldView-3) over the area of the city of Turin (Italy). The results are then assessed in term of geometric accuracy comparing them with a cadastral point height dataset, used as benchmark. The comparison, in terms of difference height values (between the DSM and the benchmark), is calculated on a set of sample points. Just two of the generated DSM guaranteed a high height accuracy level useful for the domain of application, such as existing cartography update, emergency management, building damage assessment, roof slope and solar incoming radiation assessment. Further developments will investigate different blending techniques and software that could provide more accurate results.

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