Battle of Postdisaster Response and Restoration

[EN] The paper presents the results of the Battle of Postdisaster Response and Restoration (BPDRR) presented in a special session at the first International water distribution systems analysis & computing and control in the water industry (WDSA/CCWI) Joint Conference, held in Kingston, Ontario, Canada, in July 2018. The BPDRR problem focused on how to respond and restore water service after the occurrence of five earthquake scenarios that cause structural damage in a water distribution system. Participants were required to propose a prioritization schedule to fix the damages of each scenario while following restrictions on visibility/nonvisibility of damages. Each team/approach was evaluated against six performance criteria: (1) time without supply for hospital/firefighting, (2) rapidity of recovery, (3) resilience loss, (4) average time of no user service, (5) number of users without service for eight consecutive hours, and (6) water loss. Three main types of approaches were identified from the submissions: (1) general-purpose metaheuristic algorithms, (2) greedy algorithms, and (3) ranking-based prioritizations. All three approaches showed potential to solve the challenge efficiently. The results of the participants showed that for this network, the impact of a large-diameter pipe failure on the network is more significant than several smaller pipes failures. The location of isolation valves and the size of hydraulic segments influenced the resilience of the system during emergencies. On average, the interruptions to water supply (hospitals and firefighting) varied considerably among solutions and emergency scenarios, highlighting the importance of private water storage for emergencies. The effects of damages and repair work were more noticeable during the peak demand periods (morning and noontime) than during the low-flow periods; and tank storage helped to preserve functionality of the network in the first few hours after a simulated event. (C) 2020 American Society of Civil Engineers.Paez, D.; Filion, Y.; Castro-Gama, M.; Quintiliani, C.; Santopietro, S.; Sweetapple, C.; Meng, F.... (2020). Battle of Postdisaster Response and Restoration. Journal of Water Resources Planning and Management. 146(8):1-13. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001239S1131468Balut A. R. Brodziak J. Bylka and P. Zakrzewski. 2018. “Battle of post-disaster response and restauration (BPDRR).” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Bibok A. 2018. “Near-optimal restoration scheduling of damaged drinking water distribution systems using machine learning.” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Castro-Gama M. C. Quintiliani and S. Santopietro. 2018. “After earthquake post-disaster response using a many-objective approach a greedy and engineering interventions.” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Cimellaro, G. P., Tinebra, A., Renschler, C., & Fragiadakis, M. (2016). New Resilience Index for Urban Water Distribution Networks. Journal of Structural Engineering, 142(8). doi:10.1061/(asce)st.1943-541x.0001433Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. doi:10.1109/tit.1967.1053964Creaco, E., Franchini, M., & Alvisi, S. (2010). Optimal Placement of Isolation Valves in Water Distribution Systems Based on Valve Cost and Weighted Average Demand Shortfall. Water Resources Management, 24(15), 4317-4338. doi:10.1007/s11269-010-9661-5Deb, K., Mohan, M., & Mishra, S. (2005). Evaluating the ε-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. Evolutionary Computation, 13(4), 501-525. doi:10.1162/106365605774666895Deuerlein J. D. Gilbert E. Abraham and O. Piller. 2018. “A greedy scheduling of post-disaster response and restoration using pressure-driven models and graph segment analysis.” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Deuerlein, J. W. (2008). Decomposition Model of a General Water Supply Network Graph. Journal of Hydraulic Engineering, 134(6), 822-832. doi:10.1061/(asce)0733-9429(2008)134:6(822)Di Nardo, A., Di Natale, M., Giudicianni, C., Santonastaso, G. F., & Savic, D. (2018). Simplified Approach to Water Distribution System Management via Identification of a Primary Network. Journal of Water Resources Planning and Management, 144(2), 04017089. doi:10.1061/(asce)wr.1943-5452.0000885Eliades D. G. M. Kyriakou S. Vrachimis and M. M. Polycarpou. 2016. “EPANET-MATLAB toolkit: An open-source software for interfacing EPANET with MATLAB.” In Proc. 14th Int. Conf. on Computing and Control for the Water Industry (CCWI) 8. The Hague The Netherlands: International Water Conferences. https://doi.org/10.5281/zenodo.831493.Fragiadakis, M., Christodoulou, S. E., & Vamvatsikos, D. (2013). Reliability Assessment of Urban Water Distribution Networks Under Seismic Loads. Water Resources Management, 27(10), 3739-3764. doi:10.1007/s11269-013-0378-0Gilbert, D., Abraham, E., Montalvo, I., & Piller, O. (2017). Iterative Multistage Method for a Large Water Network Sectorization into DMAs under Multiple Design Objectives. Journal of Water Resources Planning and Management, 143(11), 04017067. doi:10.1061/(asce)wr.1943-5452.0000835Hill, D., Kerkez, B., Rasekh, A., Ostfeld, A., Minsker, B., & Banks, M. K. (2014). Sensing and Cyberinfrastructure for Smarter Water Management: The Promise and Challenge of Ubiquity. Journal of Water Resources Planning and Management, 140(7), 01814002. doi:10.1061/(asce)wr.1943-5452.0000449Hwang, H. H. M., Lin, H., & Shinozuka, M. (1998). Seismic Performance Assessment of Water Delivery Systems. Journal of Infrastructure Systems, 4(3), 118-125. doi:10.1061/(asce)1076-0342(1998)4:3(118)Li Y. J. Gao C. Jian C. Ou and S. Hu. 2018. “A two-stage post-disaster response and restoration method for the water distribution system.” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Liu, W., Zhao, Y., & Li, J. (2014). Seismic functional reliability analysis of water distribution networks. Structure and Infrastructure Engineering, 11(3), 363-375. doi:10.1080/15732479.2014.887121Luong, H. T., & Nagarur, N. N. (2005). Optimal Maintenance Policy and Fund Allocation in Water Distribution Networks. Journal of Water Resources Planning and Management, 131(4), 299-306. doi:10.1061/(asce)0733-9496(2005)131:4(299)MacQueen J. B. 1967. “Some methods for classification and analysis of multivariate observations.” In Vol. 1 of Proc. 5th Berkeley Symp. on Mathematical Statistics and Probability 281–297. Berkeley: University of California Press.Mahmoud, H. A., Kapelan, Z., & Savić, D. (2018). Real-Time Operational Response Methodology for Reducing Failure Impacts in Water Distribution Systems. Journal of Water Resources Planning and Management, 144(7), 04018029. doi:10.1061/(asce)wr.1943-5452.0000956Meng, F., Fu, G., Farmani, R., Sweetapple, C., & Butler, D. (2018). Topological attributes of network resilience: A study in water distribution systems. Water Research, 143, 376-386. doi:10.1016/j.watres.2018.06.048Ostfeld, A., Uber, J. G., Salomons, E., Berry, J. W., Hart, W. E., Phillips, C. A., … Walski, T. (2008). The Battle of the Water Sensor Networks (BWSN): A Design Challenge for Engineers and Algorithms. Journal of Water Resources Planning and Management, 134(6), 556-568. doi:10.1061/(asce)0733-9496(2008)134:6(556)Paez D. Y. Filion and M. Hulley. 2018a. “Battle of post-disaster response and restoration (BPDRR)—Problem description and rules.” Accessed June 14 2019. https://www.queensu.ca/wdsa-ccwi2018/problem-description-and-files.Paez, D., Suribabu, C. R., & Filion, Y. (2018). Method for Extended Period Simulation of Water Distribution Networks with Pressure Driven Demands. Water Resources Management, 32(8), 2837-2846. doi:10.1007/s11269-018-1961-1Salcedo C. A. Aguilar P. Cuero S. Gonzalez S. Muñoz J. Pérez A. Posada J. Robles and K. Vargas. 2018. “Determination of the hydraulic restoration capacity of b-city involving a multi-criteria decision support model.” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Santonastaso G. F. E. Creaco A. Di Nardo and M. Di Natale. 2018. “Post-disaster response and restauration of B-town network based on primary network.” In Vol. 1 of Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. Kingston Canada: Open Journal Systems.Sophocleous S. E. Nikoloudi H. A. Mahmoud K. Woodward and M. Romano. 2018. “Simulation-based framework for the restoration of earthquake-damaged water distribution networks using a genetic algorithm.” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Sweetapple C. F. Meng R. Farmani G. Fu and D. Butler. 2018. “A heuristic approach to water network post-disaster response and restoration.” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Tabucchi, T., Davidson, R., & Brink, S. (2010). Simulation of post-earthquake water supply system restoration. Civil Engineering and Environmental Systems, 27(4), 263-279. doi:10.1080/10286600902862615Taormina, R., Galelli, S., Tippenhauer, N. O., Salomons, E., Ostfeld, A., Eliades, D. G., … Ohar, Z. (2018). Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks. Journal of Water Resources Planning and Management, 144(8), 04018048. doi:10.1061/(asce)wr.1943-5452.0000969Walski, T. M. (1993). Water distribution valve topology for reliability analysis. Reliability Engineering & System Safety, 42(1), 21-27. doi:10.1016/0951-8320(93)90051-yWang, Y., Au, S.-K., & Fu, Q. (2010). Seismic Risk Assessment and Mitigation of Water Supply Systems. Earthquake Spectra, 26(1), 257-274. doi:10.1193/1.3276900Yoo, D. G., Kang, D., & Kim, J. H. (2016). Optimal design of water supply networks for enhancing seismic reliability. Reliability Engineering & System Safety, 146, 79-88. doi:10.1016/j.ress.2015.10.001Zhang Q. F. Zheng K. Diao B. Ulanicki and Y. Huang. 2018. “Solving the battle of post-disaster response and restauration (BPDRR) problem with the aid of multi-phase optimization framework.” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems

Elemental composition of vegetables cultivated over coal-mining waste

ABSTRACT We assessed elemental composition of the liver in mice subjected to one-time or chronic consumption of the juice of vegetables cultivated in a vegetable garden built over deposits of coal waste. Lactuca sativa L. (lettuce), Beta vulgaris L. (beet), Brassica oleracea L. var. italica (broccoli) and Brassica oleracea L. var. acephala (kale) were collected from the coal-mining area and from a certified organic farm (control). Elemental composition was analyzed by particle-induced X-ray emission (PIXE) method. Concentrations of Mg, S, and Ca of mice subjected to one-time consumption of broccoli and concentrations of these same elements plus Si of mice receiving kale were higher in the coal-mining area. Concentrations of P, K, and Cu were increase after chronic consumption of lettuce from the coal-mining area, whereas the levels of Si, P, K, Fe, and Zn were higher in the group consuming kale from the coal-mining area. Our data suggests that people consuming vegetables grown over coal wastes may ingest significant amounts of chemical elements that pose a risk to health, since these plants contain both essential and toxic metals in a wide range of concentrations, which can do more harm than good

Association between breakfast frequency and physical activity and sedentary time : a cross-sectional study in children from 12 countries

BackgroundExisting research has documented inconsistent findings for the associations among breakfast frequency, physical activity (PA), and sedentary time in children. The primary aim of this study was to examine the associations among breakfast frequency and objectively-measured PA and sedentary time in a sample of children from 12 countries representing a wide range of human development, economic development and inequality. The secondary aim was to examine interactions of these associations between study sites.MethodsThis multinational, cross-sectional study included 6228 children aged 9-11years from the 12 International Study of Childhood Obesity, Lifestyle and the Environment sites. Multilevel statistical models were used to examine associations between self-reported habitual breakfast frequency defined using three categories (breakfast consumed 0 to 2days/week [rare], 3 to 5days/week [occasional] or 6 to 7days/week [frequent]) or two categories (breakfast consumed less than daily or daily) and accelerometry-derived PA and sedentary time during the morning (wake time to 1200h) and afternoon (1200h to bed time) with study site included as an interaction term. Model covariates included age, sex, highest parental education, body mass index z-score, and accelerometer waking wear time.ResultsParticipants averaged 60 (s.d. 25) min/day in moderate-to-vigorous PA (MVPA), 315 (s.d. 53) min/day in light PA and 513 (s.d. 69) min/day sedentary. Controlling for covariates, breakfast frequency was not significantly associated with total daily or afternoon PA and sedentary time. For the morning, frequent breakfast consumption was associated witha higher proportion of time in MVPA (0.3%), higher proportion of time in light PA (1.0%) and lower min/day and proportion of time sedentary (3.4min/day and 1.3%) than rare breakfast consumption (all p0.05). No significant associations were found when comparing occasional with rare or frequent breakfast consumption, or daily with less than daily breakfast consumption. Very few significant interactions with study site were found.ConclusionsIn this multinational sample of children, frequent breakfast consumption was associated with higher MVPA and light PA time and lower sedentary time in the morning when compared with rare breakfast consumption, although the small magnitude of the associations may lack clinical relevance.Trial registrationThe International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) is registered at(Identifier NCT01722500).Peer reviewe