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Background Rhinoplasty and rhinoseptoplasty are very common and complex surgical procedures whereas the nose plays an important functional role in breathing and an aesthetic role in the mid face. Methods Modified Heidemann spatula is characterized by a hand piece and two faces in the end of the instrument, both of them blunt and each one with a different slant; we have modified the ends of the instrument edging them. Results The advantages of modified Heidemann spatula are the easy detachment of the mucosa and the submucosal septal up to the thin caudal perichondrium starting from the columellar region, easily identifying the cleavage plane and then practicing the perichondral dissection of the septum bilaterally without damaging the mucosa. Conclusion The modified Heidemann spatula, with its two cutting parts, becomes a very valid surgical instrument in septoplasty and rhinoseptoplasty surgery, providing multiple advantages.

A widespread adoption of predictive maintenance procedures using automated Fault Detection and Diagnostics (FDD) methods could be used to reduce the energy waste of poorly maintained and/or improperly controlled Air-Handling Units (AHUs). A detailed dynamic simulation model of the AHU (nominal cooling/heating capacity of 5.0 kW) serving the SENS-i Lab of the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Italy) has been developed, calibrated and validated based on several experimental tests by means of the dynamic simulation software TRNSYS. In this paper, the model has been used to analyze the performance of the AHU while serving a typical Italian building office. In greater detail, the simulations have been carried out with and without the occurrence of typical faults of AHUs, such as the positive and negative offsets of both room temperature sensor and relative humidity sensor. The results allowed to investigate the dynamic variations of key-parameters associated to fault operation, identify simplified rules for detection of any non-optimal states of HVAC and implementation of effective FDD procedures, evaluate the impact of faults on thermo-hygrometric comfort conditions and electric energy consumption, assess the potential energy and economic benefits of automated FDD methods.

Automated Fault Detection and Diagnostics (FDD) could provide a cornerstone for predictive maintenance of Air-Handling Units (AHUs). An innovative multi-sensorial laboratory, called SENS-i Lab, has been set-up at the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Italy). The laboratory is equipped with an AHU (nominal cooling/heating capacity of 5.0/5.0 kW) aiming to control the thermo-hygrometric comfort inside a 4.0x4.0x3.6 m test room; the AHU is fully instrumented in order to monitor and control its operation. Several experiments for assessing the performance of the AHU have been carried out; a detailed dynamic simulation model has been developed by means of the software TRNSYS and then calibrated and validated with respect to the measured data. The results demonstrated a very good agreement between the predicted outputs and the experimental observations, highlighting the suitability of the developed model to be used in combination with FFD methods for implementing HVAC predictive maintenance programs.