Cyprus maintains a population-based cancer registry that allows for in-depth study of cancer in a culturally and environmentally unique setting. Using 11 years of collected data (1998-2008), we present the first comprehensive analysis of cancer in Cyprus. We calculated gender-specific, world age-adjusted incidence rates and time trends for the 26 most incident cancers. This study revealed that overall world age-standardized rates among men increased from 195.4 cases per 100,000 in 1998-2002 to 239.0 cases per 100,000 in 2006-2008. For the entire 11-year period, prostate, lung, colorectal and bladder cancers were the most incident cancers among men. Among women, the overall world age-standardized rate increased from 180.6 cases per 100,000 in 1998-2002 to 217.1 cases per 100,000 in 2006-2008. Over the entire period, breast, colorectal, uterine and thyroid cancers were the most incident cancers in women. There were 16 sex-specific cancers showing statistically significantly increasing incidence trends over the study period and no types for which the rate was significantly decreasing. Thyroid cancer illustrated rapid increases in rates. Results were compared to other Mediterranean European registries using the Cancer Incidence in 5 Continents reports for 1997-2002. Overall cancer incidence in Cyprus is lower than that of many southern Mediterranean countries, and given the known environmental risk factors in Cyprus, the low rate of lung cancer is especially interesting. The epidemiologic patterns reported in this study open the door for future etiologic studies to elucidate the role of environmental and lifestyle factors in this population and highlight opportunities for cancer prevention and control.

Monitoring the water usage of different appliances and informing consumers about it has been shown to have an impact on their behavior toward drinking water conservation. The most practical and cost-effective way to accomplish this is through a non-intrusive approach, that locally analyzes data received from a flow sensor at the main water supply pipe of a household. In this work, we present two different methods addressing the challenges of disaggregating end-use consumption and classifying consumption events. The first method is model-based (MB) and uses a combination of dynamic time wrapping and statistical bounds to analyze four water end-use characteristics. The second, learning-based (LB) method is data-driven and formulates the problem as a time-series classification problem without relying on a priori identification of events. We perform an extensive computational study that includes a comparison between an MB and an LB method, as well as an experimental study to demonstrate the application of the LB method on an edge computing device. Both methods achieve similar F1 scores (LB: 71.73%, MB: 71.04%) with the LB being more precise. The embedded LB method achieves a slightly higher score (72.01%) while enhancing on-site real-time processing, improving security and privacy and enabling cost savings.

Emergencies and disasters (such as earthquakes and floods), may contaminate drinking water systems with pathogens, that can affect the health of both First Responders and Citizens. As part of the Horizon 2020 “Pathogen Contamination Emergency Response Technologies” (PathoCERT) project, we are developing a Digital Twin tool (PathoINVEST) to assist First Responders and Water Authorities in investigating and responding efficiently to drinking water contamination events. In this paper, we present preliminary work on PathoINVEST, its architecture, and how it operates with the PathoCERT ecosystem of technologies. Moreover, using an illustrative case study, we demonstrate how PathoINVEST will process data and produce useful insights for the First Responders during a realistic contamination event. This work demonstrates how different research results can be integrated into a holistic water contamination emergency management system, in accordance with the needs of First Responders who need to make decisions within a limited time frame and to reduce the impact of a contamination event.

Drinking water disinfection by water utilities aims to ensure the safety and high quality of the provided water; however, it can pose a threat to human health due to the formation of disinfection byproducts (DBPs). The prediction and modeling of DBPs are challenging tasks due to the complex reactions within water distribution networks (WDN). To address this challenge, we introduce a virtual testbed based on a realistic WDN in Cyprus that utilizes the EPANET and EPANET-MSX engines to model multi-species reactions for the execution of simulation experiments under various conditions regarding the formation and fate of two families of DBPs within WDNs.

Non-intrusive water usage classification considering limited training data