Assessing the knock-on effects of flooding on road transportation (article)

This is the final version. Available on open access from Elsevier via the DOI in this recordThe dataset associated with this article is available in ORE: https://doi.org/10.24378/exe.1384Flooding can affect every aspect of our lives and road transportation is no an exception. However, the interaction between floods and transportation was not investigated closely in the past. As transportation is the lifeline of any economy, it is essential to analyse potential dangers and threads that can lead to network capacity restraints. Considering the potential of flooding to affect large areas for long durations, disruptions to transportation can result in extensive knock-on effects. To examine how flooding can impact road transportation a novel methodology was developed into a software tool which integrates flood and traffic models. The flood is simulated with InfoWorks flood model and the traffic is represented by a detailed microscopic model (SUMO), which simulates individual vehicles and their interactions. The two systems are integrated in a dynamic way, whereby changes in the flood propagation dictates the temporal variation of road network capacity restrictions. Depending on the flood characteristics, a flooded road in the traffic model undergoes either a speed limit reduction or a complete closure. Once a road has been closed for traffic, vehicles that originally pass through it are forced to choose alternative routes to reach their unique destinations. The reroute will put an additional strain on a system that is already suffering reduced network capacity. The most vulnerable roads in the network are identified after a comparison of the traffic conditions under normal and flooded situation. The results indicate that the locations of flooded streets cannot be directly associated with the most severely congested areas emphasising the significance of the knock-on effects when describing flood impacts on road transportation.European Commissio

Methodological Framework for Analysing Cascading Effects from Flood Events: The Case of Sukhumvit Area, Bangkok, Thailand

This is the final version of the article. Available from MDPI via the DOI in this record.Impacts from floods in urban areas can be diverse and wide ranging. These can include the loss of human life, infrastructure and property damages, as well as other kinds of nuisance and inconvenience to urban life. Hence, the ability to identify and quantify wider ranging effects from floods is of the utmost importance to urban flood managers and infrastructure operators. The present work provides a contribution in this direction and describes a methodological framework for analysing cascading effects from floods that has been applied for the Sukhumvit area in Bangkok (Thailand). It demonstrates that the effects from floods can be much broader in their reach and magnitude than the sole impacts incurred from direct and immediate losses. In Sukhumvit, these include loss of critical services, assets and goods, traffic congestion and delays in transportation, loss of business and income, disturbances and discomfort to the residents, and all these can be traced with the careful analysis of cascading effects. The present work explored the use of different visualization options to present the findings. These include a casual loop diagram, a HAZUR resilience map, a tree diagram and GIS maps.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 603663 for the research project PEARL (Preparing for Extreme and Rare events in coastaL regions). The authors are grateful to Opticits for providing the HAZUR software licence, within the collaboration of the EU H2020 research project RESCCUE (RESilience to cope with Climate Change in Urban arEas—a multisectorial approach focusing on water) Grant Agreement 700174

Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA

This is the final version. Available on open access from the American Geophysical Union via the DOI in this recordData Availability Statement: The COVID-19 contagious persons per day data used for generating the COVID-19 hotspot prevalence in the study are available from the Dutch National Institute for Public Health and the Environment (RIVM) at https://data.rivm.nl/covid-19/COVID-19_prevalentie.json with license http://creativecommons.org/publicdomain/mark/1.0/deed.nl.Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically-based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS-CoV-2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time-resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back-tracing of human-excreted biomarkers based on only sampling at the outlet or other key points, but would require high-frequency, contaminant-specific sensor systems that are not available currently

Combining machine learning and spatial data processing techniques for allocation of large-scale nature-based solutions

This is the final version. Available on open access from IWA Publishing via the DOI in this recordData availability statement: All relevant data are available from an online repository or repositories: (https://developers.google.com/earth-engine/datasets; https://search.asf.alaska.edu/#/; https://geo.gob.bo/; https://www.openstreetmap.org/#map=7/52.154/5.295; https://rsis.ramsar.org/; https://www.arcgis.com/apps/mapviewer/index.html?layers=cfcb7609de5f478eb7666240902d4d3d; https://land.copernicus.eu/en/products/corine-land-cover; https://worldcover2020.esa.int/viewer).The escalating impacts of climate change trigger the necessity to deal with hydro-meteorological hazards. Nature-based solutions (NBSs) seem to be a suitable response, integrating the hydrology, geomorphology, hydraulic, and ecological dynamics. While there are some methods and tools for suitability mapping of small-scale NBSs, literature concerning the spatial allocation of large-scale NBSs is still lacking. The present work aims to develop new toolboxes and enhance an existing methodology by developing spatial analysis tools within a geographic information system (GIS) environment to allocate large-scale NBSs based on a multi-criteria algorithm. The methodologies combine machine learning spatial data processing techniques and hydrodynamic modelling for allocation of large-scale NBSs. The case studies concern selected areas in the Netherlands, Serbia, and Bolivia, focusing on three large-scale NBS: rainwater harvesting, wetland restoration, and natural riverbank stabilisation. Information available from the EC H2020 RECONECT project as well as other available data for the specific study areas was used. The research highlights the significance of incorporating machine learning, GIS, and remote sensing techniques for the suitable allocation of large-scale NBSs. The findings may offer new insights for decision-makers and other stakeholders involved in future sustainable environmental planning and climate change adaptation.European Union Horizon 202

Optimization of insect cell based protein production processes - online monitoring, expression systems, scale-up

Due to the increasing use of insect cell based expression systems in research and industrial recombinant protein production, the development of efficient and reproducible production processes remains a challenging task. In this context, the application of online monitoring techniques is intended to ensure high and reproducible product qualities already during the early phases of process development. In the following chapter, the most common transient and stable insect cell based expression systems are briefly introduced. Novel applications of insect cell based expression systems for the production of insect derived antimicrobial peptides/proteins (AMPs) are discussed using the example of G. mellonella derived gloverin. Suitable in situ sensor techniques for insect cell culture monitoring in disposable and common bioreactor systems are outlined with respect to optical and capacitive sensor concepts. Since scale-up of production processes is one of the most critical steps in process development, a conclusive overview is given about scale up aspects for industrial insect cell culture processes

Evaluating the Impact of Nature-Based Solutions: A Handbook for Practitioners

The Handbook aims to provide decision-makers with a comprehensive NBS impact assessment framework, and a robust set of indicators and methodologies to assess impacts of nature-based solutions across 12 societal challenge areas: Climate Resilience; Water Management; Natural and Climate Hazards; Green Space Management; Biodiversity; Air Quality; Place Regeneration; Knowledge and Social Capacity Building for Sustainable Urban Transformation; Participatory Planning and Governance; Social Justice and Social Cohesion; Health and Well-being; New Economic Opportunities and Green Jobs. Indicators have been developed collaboratively by representatives of 17 individual EU-funded NBS projects and collaborating institutions such as the EEA and JRC, as part of the European Taskforce for NBS Impact Assessment, with the four-fold objective of: serving as a reference for relevant EU policies and activities; orient urban practitioners in developing robust impact evaluation frameworks for nature-based solutions at different scales; expand upon the pioneering work of the EKLIPSE framework by providing a comprehensive set of indicators and methodologies; and build the European evidence base regarding NBS impacts. They reflect the state of the art in current scientific research on impacts of nature-based solutions and valid and standardized methods of assessment, as well as the state of play in urban implementation of evaluation frameworks

Response of a CMS HGCAL silicon-pad electromagnetic calorimeter prototype to 20-300 GeV positrons

The Compact Muon Solenoid Collaboration is designing a new high-granularity endcap calorimeter, HGCAL, to be installed later this decade. As part of this development work, a prototype system was built, with an electromagnetic section consisting of 14 double-sided structures, providing 28 sampling layers. Each sampling layer has an hexagonal module, where a multipad large-area silicon sensor is glued between an electronics circuit board and a metal baseplate. The sensor pads of approximately 1 cm$^2$ are wire-bonded to the circuit board and are readout by custom integrated circuits. The prototype was extensively tested with beams at CERN's Super Proton Synchrotron in 2018. Based on the data collected with beams of positrons, with energies ranging from 20 to 300 GeV, measurements of the energy resolution and linearity, the position and angular resolutions, and the shower shapes are presented and compared to a detailed Geant4 simulation

Performance of the CMS High Granularity Calorimeter prototype to charged pion beams of 20−-300 GeV/c

The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based on silicon sensors interspersed between lead and copper (or copper tungsten) absorbers. The hadronic section uses layers of stainless steel as an absorbing medium and silicon sensors as an active medium in the regions of high radiation exposure, and scintillator tiles directly readout by silicon photomultipliers in the remaining regions. As part of the development of the detector and its readout electronic components, a section of a silicon-based HGCAL prototype detector along with a section of the CALICE AHCAL prototype was exposed to muons, electrons and charged pions in beam test experiments at the H2 beamline at the CERN SPS in October 2018. The AHCAL uses the same technology as foreseen for the HGCAL but with much finer longitudinal segmentation. The performance of the calorimeters in terms of energy response and resolution, longitudinal and transverse shower profiles is studied using negatively charged pions, and is compared to GEANT4 predictions. This is the first report summarizing results of hadronic showers measured by the HGCAL prototype using beam test data.Comment: To be submitted to JINS

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels