Collaboration on risk management: the governance of a non-potable water reuse scheme in London

Ageing water infrastructure and population growth, issues that are characteristic of megacities, are likely to exacerbate water supply deficits in London. To address this threat, wastewater reclamation and non-potable reuse can potentially close the supply-demand gap without impacting on environmental water bodies. There is a need to understand the types of challenges that diverse stakeholders face in relation to the governance of NPR schemes, and how those challenges might be addressed in a megacity context. A case study is used to explore these challenges for an operational sewer mining scheme in London, where reclaimed non-potable water is used for irrigation and toilet flushing at the site of the London 2012 Olympic Park. Through qualitative analysis of interview and document data, the results highlight that collaboration and learning opportunities are perceived as necessary to improve scheme governance. The findings indicate that formal and informal engagement activities centred on risk management can support the development of common understandings, build important inter-stakeholder relationships and help maintain trust. Non-potable reuse can contribute to the resilience of megacities through infrastructure diversification, but its feasibility will depend on the willingness of stakeholders to participate and continually negotiate new risk management practices

FabSim3: An automation toolkit for verified simulations using high performance computing

A common feature of computational modelling and simulation research is the need to perform many tasks in complex sequences to achieve a usable result. This will typically involve tasks such as preparing input data, pre-processing, running simulations on a local or remote machine, post-processing, and performing coupling communications, validations and/or optimisations. Tasks like these can involve manual steps which are time and effort intensive, especially when it involves the management of large ensemble runs. Additionally, human errors become more likely and numerous as the research work becomes more complex, increasing the risk of damaging the credibility of simulation results. Automation tools can help ensure the credibility of simulation results by reducing the manual time and effort required to perform these research tasks, by making more rigorous procedures tractable, and by reducing the probability of human error due to a reduced number of manual actions. In addition, efficiency gained through automation can help researchers to perform more research within the budget and effort constraints imposed by their projects. This paper presents the main software release of FabSim3, and explains how our automation toolkit can improve and simplify a range of tasks for researchers and application developers. FabSim3 helps to prepare, submit, execute, retrieve, and analyze simulation workflows. By providing a suitable level of abstraction, FabSim3 reduces the complexity of setting up and managing a large-scale simulation scenario, while still providing transparent access to the underlying layers for effective debugging. The tool also facilitates job submission and management (including staging and curation of files and environments) for a range of different supercomputing environments. Although FabSim3 itself is application-agnostic, it supports a provably extensible plugin system where users automate simulation and analysis workflows for their own application domains. To highlight this, we briefly describe a selection of these plugins and we demonstrate the efficiency of the toolkit in handling large ensemble workflows.EPSRC under grant agreement EP/W007711/1, as well as by the VECMA and HiDALGO projects, which have received funding from the European Union Horizon 2020 research and innovation programme under grant agreement nos 800925 and 824115. In addition, FabFlee was supported by the ITFLOWS project and FabCovid19 by the STAMINA project, both of which have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 882986 and No 883441 respectivel

Modularity for Large Virtual Reality Applications

International audienceThis paper focuses on the design of high performance VR applications. These applications usually involve various I/O devices and complex simulations. A parallel architecture or grid infrastructure is required to provide the necessary I/O and processing capabilities. Developing such applications faces several difficulties, two important ones being software engineering and performance issues. We argue that application modularity is a key concept to help the developer handle the complexity of these applications. We discuss how various approaches borrowed from other existing works can be combined to significantly improve the modularity of VR applications. This led to the development of the FlowVR middleware that associates a data-flow model with a hierarchical component model. Different case studies are presented to discuss the benefits of the approach proposed

The ESCAPE project : Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) and ALADIN (Aire Limitee Adaptation Dynamique Developpement International); and COSMO-EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU-GPU arrangements

Development and Characterization of Aloe vera Mucilaginous-Based Hydrogels for Psoriasis Treatment

The Aloe vera (L.) Burman f. pulp extract (AE), obtained from the inner parts of Aloe vera leaves, is rich in polysaccharides, including glucomannans, acemannans, pectic compounds, cellulose, and hemicelluloses; acemannan and glucomannan are considered the two main components responsible for most of the plant’s therapeutical properties. Besides having anti-inflammatory activity, these polysaccharides accelerate wound healing and promote skin regeneration, thus they can be utilized in healing products. The objective of this study was to develop Aloe vera mucilaginous-based hydrogels for topical use in psoriasis treatment. The hydrogels were prepared with 80% w/w of A. vera mucilaginous gel, evaluating two distinct polymers as the gelling agent: 1% carbopol 940 (FC1 and FC2) or 2% hydroxyethylcellulose (FH3 and FH4). FC1, FC2, FH3 and FH4 were evaluated for their organoleptic characteristics, rheological properties, pH and glucomannan content. Polysaccharide fractions (PFs) were extracted from the AE and used as a group of chemical markers and characterized by infrared (IR) spectroscopy and 1H nuclear magnetic resonance (H NMR). The quantification of these markers in the raw material (AE) and in the hydrogels was carried out using spectrophotometric techniques in the UV-VIS region. The hydrogels-based hydroxyethylcellulose (FH3 and FH4) had glucomannan contents of 6.76 and 4.01 mg/g, respectively. Formulations with carbopol, FC1 and FC2, had glucomannan contents of 8.69 and 9.17 mg/g, respectively, an ideal pH for application on psoriasis, in addition to good spreadability and pseudoplastic and thixotropic behavior. Considering these results, hydrogel FC1 was evaluated for its keratolytic activity in a murine model of hyperkeratinization. For that, 0.5 mL of test formulations FC1 and FPC (0.05% clobetasol propionate cream) were topically applied to the proximal region of adult rats daily for 13 days. After euthanasia, approximately 2.5 cm of the proximal portion of each animal’s tail was cut and placed in 10% buffered formalin. Then, each tail fragment was processed and stained with hematoxylin and eosin (HE), and the results obtained from the histological sections indicated a 61% reduction in stratum corneum for animals treated with the A. vera hydrogel (FC1G) and 66% for animals treated with clobetasol propionate (PCG), compared to the group of animals that did not receive treatment (WTG). This study led to the conclusion that compared to the classic treatment (clobetasol propionate), the 80% A. vera hydrogel showed no significant difference, being effective in controlling hyperkeratinization

FabSim3: An automation toolkit for verified simulations using high performance computing

A common feature of computational modelling and simulation research is the need to perform many tasks in complex sequences to achieve a usable result. This will typically involve tasks such as preparing input data, pre-processing, running simulations on a local or remote machine, post-processing, and performing coupling communications, validations and/or optimisations. Tasks like these can involve manual steps which are time and effort intensive, especially when it involves the management of large ensemble runs. Additionally, human errors become more likely and numerous as the research work becomes more complex, increasing the risk of damaging the credibility of simulation results. Automation tools can help ensure the credibility of simulation results by reducing the manual time and effort required to perform these research tasks, by making more rigorous procedures tractable, and by reducing the probability of human error due to a reduced number of manual actions. In addition, efficiency gained through automation can help researchers to perform more research within the budget and effort constraints imposed by their projects. This paper presents the main software release of FabSim3, and explains how our automation toolkit can improve and simplify a range of tasks for researchers and application developers. FabSim3 helps to prepare, submit, execute, retrieve, and analyze simulation workflows. By providing a suitable level of abstraction, FabSim3 reduces the complexity of setting up and managing a large-scale simulation scenario, while still providing transparent access to the underlying layers for effective debugging. The tool also facilitates job submission and management (including staging and curation of files and environments) for a range of different supercomputing environments. Although FabSim3 itself is application-agnostic, it supports a provably extensible plugin system where users automate simulation and analysis workflows for their own application domains. To highlight this, we briefly describe a selection of these plugins and we demonstrate the efficiency of the toolkit in handling large ensemble workflows

Absolute Flux Density Calibration of the Greenland Telescope Data for Event Horizon Telescope Observations

Starting from the observing campaign in April 2018, the Greenland Telescope (GLT) has been added as a new station of the Event Horizon Telescope (EHT) array. Visibilities on baselines to the GLT, particularly in the North-South direction, potentially provide valuable new constraints for the modeling and imaging of sources such as M87*. The GLT's location at high Northern latitudes adds unique challenges to its calibration strategies. Additionally, the performance of the GLT was not optimal during the 2018 observations due to it being only partially commissioned at the time. This document describes the steps taken to estimate the various parameters (and their uncertainties) required for the absolute flux calibration of the GLT data as part of the EHT. In particular, we consider the non-optimized status of the GLT in 2018, as well as its improved performance during the 2021 EHT campaign.Comment: 17 pages, 4 figures, EHT Memo Series 2023-L1-0