Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation

Photoacoustic computed tomography with compressed sensing (CS-PACT) is a commonly used imaging strategy for sparse-sampling PACT. However, it is very time-consuming because of the iterative process involved in the image reconstruction. In this paper, we present a graphics processing unit (GPU)-based parallel computation framework for total-variation-based CS-PACT and adapted into a custom-made PACT system. Specifically, five compute-intensive operators are extracted from the iteration algorithm and are redesigned for parallel performance on a GPU. We achieved an image reconstruction speed 24–31 times faster than the CPU performance. We performed in vivo experiments on human hands to verify the feasibility of our developed method

Attribute-based intervention development for increasing resilience of urban drainage systems

© IWA Publishing 2018. Resilience building commonly focuses on attributes such as redundancy. Whilst this may be effective in some cases, provision of specific attributes does not guarantee resilient performance and research is required to determine the suitability of such approaches. This study uses 250 combined sewer system virtual case studies to explore the effects of two attribute-based interventions (increasing distributed storage and reducing imperviousness) on performance-based resilience measures. These are found to provide improvement in performance under system failure in the majority of case studies, but it is also shown that attribute-based intervention development can result in reduced resilience

A model-based engineering methodology and architecture for resilience in systems-of-systems: a case of water supply resilience to flooding

There is a clear and evident requirement for a conscious effort to be made towards a resilient water system-of-systems (SoS) within the UK, in terms of both supply and flooding. The impact of flooding goes beyond the immediately obvious socio-aspects of disruption, cascading and affecting a wide range of connected systems. The issues caused by flooding need to be treated in a fashion which adopts an SoS approach to evaluate the risks associated with interconnected systems and to assess resilience against flooding from various perspectives. Changes in climate result in deviations in frequency and intensity of precipitation; variations in annual patterns make planning and management for resilience more challenging. This article presents a verified model-based system engineering methodology for decision-makers in the water sector to holistically, and systematically implement resilience within the water context, specifically focusing on effects of flooding on water supply. A novel resilience viewpoint has been created which is solely focused on the resilience aspects of architecture that is presented within this paper. Systems architecture modelling forms the basis of the methodology and includes an innovative resilience viewpoint to help evaluate current SoS resilience, and to design for future resilient states. Architecting for resilience, and subsequently simulating designs, is seen as the solution to successfully ensuring system performance does not suffer, and systems continue to function at the desired levels of operability. The case study presented within this paper demonstrates the application of the SoS resilience methodology on water supply networks in times of flooding, highlighting how such a methodology can be used for approaching resilience in the water sector from an SoS perspective. The methodology highlights where resilience improvements are necessary and also provides a process where architecture solutions can be proposed and testedThe authors would like to thank the EPSRC for the funding on BRIM (EP/N010329/1)

Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation

Photoacoustic computed tomography with compressed sensing (CS-PACT) is a commonly used imaging strategy for sparse-sampling PACT. However, it is very time-consuming because of the iterative process involved in the image reconstruction. In this paper, we present a graphics processing unit (GPU)-based parallel computation framework for total-variation-based CS-PACT and adapted into a custom-made PACT system. Specifically, five compute-intensive operators are extracted from the iteration algorithm and are redesigned for parallel performance on a GPU. We achieved an image reconstruction speed 24–31 times faster than the CPU performance. We performed in vivo experiments on human hands to verify the feasibility of our developed method

Use of artificial intelligence to improve resilience and preparedness against adverse flood events

The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilience

BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems

The field of recommender systems has rapidly evolved over the last few years, with significant advances made due to the in-flux of deep learning techniques. However, as a result of this rapid progress, escalating barriers-to-entry for new researchers is emerging. In particular, state-of-the-art approaches have fragmented into a large number of code-bases, often requiring different input formats, pre-processing stages and evaluating with different metric packages. Hence, it is time-consuming for new researchers to reach the point of having both an effective baseline set and a sound comparative environment. As a step towards elevating this problem, we have developed BETA-Rec, an open source project for Building, Evaluating and Tuning Automated Recommender Systems. BETA-Rec aims to provide a practical data toolkit for building end-to-end recommendation systems in a standardized way. It provides means for dataset preparation and splitting using common strategies, a generalized model engine for implementing recommender models using Pytorch with 9 models available out-of-the-box, as well as a unified training, validation, tuning and testing pipeline. Furthermore, BETA-Rec is designed to be both modular and extensible, enabling new models to be quickly added to the framework. It is deployable in a wide range of environments via pre-built docker containers and supports distributed parameter tuning using Ray. In this demo, we will illustrate the deployment and use of BETA-Rec for researchers and practitioners on a number of standard recommendation datasets. The source code of the project is available at github: https://github.com/beta-team/beta-recsys

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. 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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

Study of structure-function relationships in globulin from Phaseolus angularis (red bean) seeds

published_or_final_versionBotanyDoctoralDoctor of Philosoph