Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks

Danger- and non-danger-based stressors and their relations to posttraumatic deprecation or growth in Norwegian veterans deployed to Afghanistan

Objective: This study aimed to explore how exposure to danger-based and non-danger-based stressors may influence personal changes in veterans (N = 4053) after deployment to Afghanistan. Method: Twelve war zone related traumatic events were used to form two stressor categories. The non-danger-based category included two stressor types: Moral Challenges and Witnessing, and the danger-based category included one type: Personal Threat. Thus, three stressor types were explored in relation to self-reported personal changes after war zone stressor exposure, e.g. negative changes labelled posttraumatic deprecation, positive changes labelled posttraumatic growth or no major change. Furthermore, the relationship between the stressor types and reported levels of distress were explored. Results: The two non-danger-based stressor types, Moral Challenges (p < .001) and Witnessing (p < .001), were both significantly more associated with deprecation rather than growth, when compared to Personal Threat. Moreover, the non-danger-based stressors were significantly associated with a rise in posttraumatic stress symptoms, as well as a rise in symptoms of depression, anxiety and insomnia (p < .001). In contrast, exposure to the danger-based stressor was only significantly associated with a rise in the posttraumatic stress symptoms in the current model (p < .001). Reports of no-change were significantly associated with low degrees of exposure to all the three stressor types (p < .001). Conclusion: The current study highlights the special adverse effects of non-danger-based stressors. Our findings show that they are more associated with posttraumatic deprecation rather than with growth. This underscores the heterogeneity of responses to traumatic events and adds to the current knowledge about the impact of various stressor types

Caring for Coronavirus Healthcare Workers: Lessons Learned from Long-Term Monitoring of Military Peacekeepers

Background: The current outbreak of the coronavirus disease (COVID-19) is of unprecedented proportions in several regards. Recent reports suggest that many frontline healthcare workers (HCWs) suffer from mental health problems, including posttraumatic stress symptoms (PTSS). Previous studies have identified several key factors associated with short-term PTSS in pandemic HCWs, yet limited data is available on factors associated with long-term PTSS. Understanding the psychological impact of the pandemic on HCWs is important in planning for future outbreaks of emerging infectious diseases. In the current study, we look to findings from a highly relevant subsection of the trauma field, the military domain. Objective: Pandemic HCWs and military peacekeepers may experience similar stressors in the line of duty. This study investigated whether factors linked to short-term PTSS in pandemic HCWs were also associated with long-term PTSS in military peacekeepers. Materials and Methods: Peacekeepers who reported pandemic-relevant stressors during deployment to a UN peacekeeping mission were included in the study (N = 1,627). PTSS was self-reported using the Posttraumatic Stress Disorder Checklist – Military Version. Descriptive instruments were used to assess possible factors associated with PTSS. A multiple linear regression analysis was performed to explore associations between these factors and PTSS. Results: Our model accounted for 50% of the variance in PTSS, F(1503,11) = 139.00, p < 0.001. Age, relationship and employment status, preparedness, working environment, social support after deployment, barriers to disclose, recognition, and loneliness were all significantly associated with PTSS on average 30 years after deployment. The most important risk factors of long-term PTSS were personal barriers to disclose one’s experiences and current unemployment. Conclusion: Several factors linked to short-term PTSS in pandemic HCWs were associated with long-term PTSS in peacekeepers. We discuss how these findings may be used to prevent long-term PTSS in HCWs involved in the current COVID-19 outbreak