The effect of temperature on pressure tolerance of the shallow-water shrimp Palaemon serratus

Tolerance to low temperature and high pressure may allow shallow-water species to extend bathymetric range in response to changing climate, but adaptation to contrasting shallow-water environments may affect tolerance to these factors. The brackish shallow-water shrimp Palaemon varians demonstrates remarkable tolerance to elevated hydrostatic pressure and low temperature, but inhabits a highly variable environment: environmental adaptation may therefore make P. varians tolerances unrepresentative of other shallow-water species. Critical thermal maximum (CTmax), critical hydrostatic pressure maximum (CPmax), and acute respiratory response to hydrostatic pressure were assessed in the shallow-water shrimp Palaemon serratus, which inhabits a more stable intertidal habitat. P. serratus’ CTmax was 22.3°C when acclimated at 10°C, and CPmax was 5.9, 10.1, and 14.1 MPa when acclimated at 5, 10, and 15°C respectively: these critical tolerances were consistently lower than P. varians. Respiratory responses to acute hyperbaric exposures similarly indicated lower tolerance to hydrostatic pressure in P. serratus than in P. varians. Contrasting tolerances likely reflect physiological adaptation to differing environments and reveal that the capacity for depth-range extension may vary among species from different habitats.

Near Real-Time Detection Of Pipe Bursts in Water Distribution Systems

The research work presented in the thesis describes a new methodology for the automated near real-time detection of pipe bursts in Water Distribution Systems (WDSs). The methodology analyses the pressure/flow data gathered by means of SCADA systems in order to extract useful informations that go beyond the simple and usual monitoring type activities and/or regulatory reporting , enabling the water company to proactively manage the WDSs sections. The work has an interdisciplinary nature covering AI techniques and WDSs management processes such as data collection, manipulation and analysis for event detection. Indeed, the methodology makes use of (i) Artificial Neural Network (ANN) for the short-term forecasting of future pressure/flow signal values and (ii) Rule-based Model for bursts detection at sensor and district level. The results of applying the new methodology to a District Metered Area in Emilia- Romagna’s region, Italy have also been reported in the thesis. The results gathered illustrate how the methodology is capable to detect the aforementioned failure events in fast and reliable manner. The methodology guarantees the water companies to save water, energy, money and therefore enhance them to achieve higher levels of operational efficiency, a compliance with the current regulations and, last but not least, an improvement of customer service.