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Hydrological extremes occupy a large spatial extent, with a temporal sequence, both of which can be influenced by a range of climatological and geographical phenomena. Understanding the key information in the spatial and temporal domain is essential to make accurate forecasts. The capabilities of deep learning methods can be applied in such instances due to their enhanced ability in learning complex relationships. Given its success in other domains, this study presents a framework that features a long short-term memory deep learning model for spatio temporal hydrological extreme forecasting in the South Pacific region. The data consists of satellite rainfall estimates and sea surface temperature (SST) anomalies. We use the satellite rainfall estimate to calculate the effective drought index (EDI), an indicator of hydrological extreme events. The framework is developed to forecast monthly EDI using three different approaches: (i) univariate (ii) multivariate with neighbouring spatial points (iii) multivariate with neighbouring spatial points and the eigenvector values of SST. Additionally, better identification of extreme wet events is noted with the inclusion of the eigenvector values of SST. By establishing the framework for the multivariate approach in two forms, it is evident that the model accuracy is contingent on understanding the dominant feature which influences precipitation regimes in the Pacific. The framework can be used to better understand linear and non-linear relationships within multi-dimensional data in other study regions, and provide long-term climate outlooks.

Recently, applications of agent-based model (ABM) have been used to understand the interaction between social and hydrological systems. These systems are dynamic and co-evolving, which can be captured through different decision-making entities in an ABM simulation. Therefore, this review aims to better understand the use of ABM for flood risk management and assessment (FRMA). The review comprises a systematic selection of literature using the PRISMA method, which is then assessed using an adapted version of the overview, design, and detail (ODD) protocol to better understand the ABM model development process for FRMA. The review finds that the use of the ODD protocol was only seen in 25% of the studies. The studies which did not explicitly use the ODD had a comprehensive description of the models, albeit done in a non-standardised way. Modellers continue to face the dilemma between parsimony and the breadth of the model as identified from the design component of the ODD. The hydrological component is mainly captured in the sub-model process of the ODD, however, improvements in the definition of the sub-model component may warrant a more comprehensive description of the processes and facilitate comparison across studies. The applications of ABM have shown promise to understand long term flood risks, test the efficacy of policies and better understand the factors that affect warning response during the flood evacuation process. ODD adopted for this review may consequently allow for the adoption and more coherent use of the protocol to document models in FRMA.

Flooding events are set to worsen in rapidly urbanising Pacific Islands.(1) Most Pacific Island cities and towns are in low-lying areas vulnerable to more severe tropical cyclones and rising sea levels. Approaches to disaster response and recovery need to improve. This article uses four principles drawn from area-based approaches (ABAs) – people-centred responses, adaptive processes, multi-sector collaboration and reflective practice – to review urban disaster recovery efforts in Fiji, Solomon Islands and Vanuatu. Information was gathered through key informant interviews and a literature review. The research found positive examples of collaboration, locally tailored social protection mechanisms and community leadership. It also found challenges, including bypassed community structures and traditional adaptation techniques, duplication of efforts and weak coordination. The article concludes with a call to localise ABA approaches further, to strengthen people-centred disaster recovery and locally owned resilience.

This chapter observes that in urban disaster recovery operations, the concept of resilience is mostly situated in pre-disaster mitigation and preparedness policies and actions. There are, however, opportunities to identify and highlight approaches in post-disaster relief and recovery that also build resilience. This chapter discusses three such opportunities. The first is adopting approaches such as area-based approaches (ABAs) that promote a people-centred approach in recovery, in particular using, supporting and strengthening people’s and communities’ assets/capital, in particular social capital. The second is to recognise the complexity and inter-related nature of towns and cities, for which adopting a systems-based approach is helpful. The third is to improve efforts at greater collaboration between the wide range of governmental and non-governmental agencies and organisations involved in disaster recovery, for which again ABAs can be of help.

The classical approach to the creation of assembly work instructions for high value, complex products is time-consuming and prone to error. It requires a process engineer to write the work instructions step-by-step and manually insert specific technical information, using an encompassing document of manufacturing parameters or life cycle management software. The latter offers synchronisation to design changes through updateable parameters, however major design modifications still require significant manual work to modify the text contents and structure of the work instructions. This leaves the work instruction documentation vulnerable to human error, as well as making the process time-consuming to fully synchronise. A methodology was therefore developed to resolve these issues, utilising JavaScript and VBA for Office to create a simple interface for rapid content generation for work instructions including text, MBD extracted parameters, images and formatting. The overall methodology speeds up the creation of assembly work instructions and reduces errors by implementing automatic insertion of parameters from an MBD model. The implementation and effectiveness of the suggested approach is demonstrated on a case study for the assembly of the joined wing configuration of the RACER helicopter, the latest generation of compound helicopters of Airbus Helicopter.

Electrical impedance tomography (EIT) is a non-invasive imaging technique that reconstructs the interior conductivity distribution of samples from a set of voltage measurements performed on the sample boundary. EIT reconstruction is a non-linear and ill-posed inverse problem. Consequently, the non-linearity results in a high computational cost of solution, while regularisation and the most informative measurements must be used to overcome ill-posedness. To build the foundation of future research into EIT applications for 2D materials, such as graphene, we designed and implemented a novel approach to measurement optimisation via a machine learning adaptive electrode selection algorithm (A-ESA). Furthermore, we modified the forward solver of a python-based EIT simulation software, pyEIT, to include the complete electrode model (CEM) and employed it on 2D square samples (Liu B et al 2018 SoftwareX 7 304–8; Somersalo E et al 1992 SIAM J. Appl. Math. 52 1023–40). In addition, the deep D-Bar U-Net convolutional neural network architecture was applied to post-process conductivity map reconstructions from the GREIT algorithm (Hamilton and Hauptmann 2018 IEEE Trans. Med. Imaging 37 2367–77; Adler et al 2009 Physiol. Meas. 30 S35). The A-ESA offered around 20% lower reconstruction losses in fewer measurements than the standard opposite–adjacent electrode selection algorithm, on both simulated data and when applied to a real graphene-based device. The CEM enhanced forward solver achieved a 3% lower loss compared to the original pyEIT forward model. Finally, an experimental evaluation was performed on a graphene laminate film. Overall, this work demonstrates how EIT could be applied to 2D materials and highlights the utility of machine learning in both the experimental and analytical aspects of EIT.

Purpose This paper aims to discuss what people perceive as risks and resilience factors, and how they build everyday resilience. Design/methodology/approach The study focuses on Kampung (literally “village”) Plampitan, a neighbourhood in the inner-city part of Surabaya. The research used field observation, in-depth interviews and workshops during community meetings to collect data. Findings The results show how people respond to daily risks and find the support necessary to survive. The problems and risks revealed in the study include crime and economic difficulties, such as unemployment and insufficient income. Coping strategies identified are classified into place-based adaption, people-based network and political network. These strategies can serve as a starting point for local communities to assess their resilience and assist them in enhancing “everyday” resilience. Originality/value The paper argues that the concept of resilience must go beyond top-down approaches to disaster risk management and integrate bottom-up understanding from the perspective of local people, especially among marginal and disadvantaged communities. The paper develops the emerging and overlooked concept of “everyday resilience” and suggests that it is essential in surviving both “everyday” or small-scale chronic risks and large-scale disasters.

There is growing recognition of the cumulative impact that converging environmental, political, social and economic risks have on the ability of cities to function in times of shocks and stresses. While many frameworks and assessment tools have been developed to assess the technical resilience of the infrastructure of cities, there have been fewer attempts to holistically understand and map all the factors that interact to potentially produce functioning urban systems, including the role of institutions (both formal and informal). Applied integrated research is needed to understand the daily functioning, vulnerability and resilience of these rapidly growing cities amid chronic and acute stresses and shocks; to also understand why and how multiple risks and protective factors converge and interact to constrain or enable cities to fulfil their core functions in times of stability and crisis; and finally to produce operationally relevant recommendations that could inform interventions.

The application of digital twins and artificial intelligence to manufacturing has shown potential in improving system resilience, responsiveness, and productivity. Traditional digital twin approaches are generally applied to single, static systems to enhance a specific process. This paper proposes a framework that applies digital twins and artificial intelligence to manufacturing system reconfiguration, i.e., the layout, process parameters, and operation time of multiple assets, to enable system decision making based on varying demands from the customer or market. A digital twin environment has been developed to simulate the manufacturing process with multiple industrial robots performing various tasks. A data pipeline is built in the digital twin with an API (application programming interface) to enable the integration of artificial intelligence. Artificial intelligence methods are used to optimise the digital twin environment and improve system decision-making. Finally, a multi-agent program approach shows the communication and negotiation status between different agents to determine the optimal configuration for a manufacturing system to solve varying problems. Compared with previous research, this framework combines distributed intelligence, artificial intelligence for decision making, and production line optimisation that can be widely applied in modern reactive manufacturing applications. QC 20221123Part of proceedings: ISBN 978-3-031-18325-6

The manufacturing industries in a number of high-labour-cost economies are undergoing a shift towards increased automation and intelligence typified by the ‘Industry 4.0’ paradigm. Advances from computer science research including digital informatics enables the addition of intelligence and autonomy to the flexible and reconfigurable manufacturing systems developed by manufacturing systems research. One such advance is the development of Data Distribution Services (DDSs) that enable the robust and timely distribution of high-quality data in a scalable manner. This paper describes how a DDS can be used to integrate systems across a production enterprise, including directly on a shop floor rather than using additional specialised shop floor integration components often required by other communications protocols.

Adaptive production systems are a key trend in modern advanced manufacturing. This stems from the requirement for the system to respond to disruption , either in the form of product changes or changes to other operational parameters. The design and reconfig-uration of these systems is therefore a unique challenge for the community. One approach to systems design is based on functional and behavioural modelling, drawn from the field of design theory. Existing approaches suffer from lack of focus on the adaptive properties of the system. While traditional production systems design focusses on the physical system structure and associated processes, new approaches based on functional and behavioural models are particularly suited to addressing the challenges of disruptive production environments resulting from Industry 4.0 and similar trends. We therefore present a Function-Behaviour-Structure (FBS) methodology for Evolvable Assembly Systems (EAS), a class of self-adaptive reconfigurable production systems, comprising an ontology model and design process. The ontology model provides definitions for Function, Structure, and Behaviour of an adaptive production system. This model is used as the input to a functional modelling design process for EAS-like systems , where the design process must be integrated into the system control behaviour. The framework is illustrated with an example taken from a real EAS instan-tiation using industrial hardware.

Part 4: Assembly Cells and Systems; International audience; Aerospace production systems face increasing requirements for flexibility and reconfiguration, along with considerations of cost, utilisation, and efficiency. This drives a need for systems with a small number of automation platforms (e.g. industrial robots) that can make use of a larger number of end effectors that are potentially flexible or multifunctional. This leads to the challenge of ensuring that the configuration and location of each end effector is tracked by the system at all times, even in the face of manual adjustments, to ensure that the correct processes are applied to the product at the right time. We present a solution based on a Data Distribution Service that provides the system with full awareness of the context of its automation platforms and end effectors. The solution is grounded with an example use case from WingLIFT, a research programme led by a large aerospace manufacturer. The WingLIFT project in which this solution was developed builds on the adaptive systems approach from the Evolvable Assembly Systems project, with focus on extending and increasing the aerospace industrial applicability of plug and produce techniques. The design of this software solution is described from multiple perspectives, and accompanied by details of a physical demonstration cell that is in the process of being commissioned.

This project has received funding from the European Research Council (ERC) under the European Union���s Seventh Framework Programme (FP7-2007-2013) (Grant agreement No. 323727).

Current sectoral drivers for the manufacturing of complex products - such as airframe assembly -require new manufacturing system paradigms to meet them. In this paper, we propose a conceptual framework for cyber-physical systems driven by ubiquitous context-awareness by drawing together a unique and coherent vision that merges several extant concepts. This framework leverages recent progress in agent-based systems, exible manufacturing, ubiquitous computing, and metrology-driven robotic assembly in the Evolvable Assembly Systems project. As such, although it is adapted for and grounded in manufacturing facilities for airframe assembly, it is not specifically tailored to that application and is a much more general framework. As well as outlining our conceptual framework, we also provide a vision for assembly grounded in a review of existing research in the area.

Measurement Assisted Assembly is a key concept for the modernisation of aerospace assembly processes, i.e. improving their efficiency while reducing the manufacturing costs. This concept suggests a paradigm shift in the assembly of high-complexity products as it encompasses the development and the use of robotics solutions smartly integrated with innovative measurement technologies. Expected outcomes are, among others, a better positioning accuracy of the components and a significant reduction of the rectification and rework requirements that are usually common with traditional assembly processes, especially but not limited to aerospace manufacturing. To achieve these objectives, a high precision metrology system that automatically inspects and corrects the pose of the robotic manipulators during the assembly operations is of great importance. In this paper, a high-accuracy real-life application of the concept of Measurement Assisted Assembly is presented as a part of the Future Automated Aircraft Assembly Demonstrator developed by the University of Nottingham. Experimentations have shown that, using the production environment described hereafter, a positioning accuracy better than +- 0.1 mm can be achieved for large airframe components.

This paper examines area-based approaches (ABAs) in urban post-disaster contexts. After introducing the main features of ABAs, the paper discusses current practice in humanitarian response, and the need within urban areas to draw lessons from urban development approaches, from which ABAs have emerged. The paper then presents lessons from research concerning the application of ABAs in relation to phases of the project management cycle: assessment and design, implementation, and monitoring, evaluation and learning. The paper ends with a brief discussion. Overall, it argues that for ABAs to be effective, they need to draw on longstanding lessons from urban development, plan for a longer timeframe for their actions than is otherwise often the case in recovery operations, and consider the need to scale up actions for wider city application.

This paper presents a vision for a transformable manufacturing system based on reconfigurable and adaptive intelligent technologies, anchored in the description of a real automation cell demonstrating the assembly of various aircraft structures. This production environment developed by the University of Nottingham is a new reconfigurable production environment for the complete manufacturing of high-accuracy high-complexity low-volume aerospace products. This agile reconfigurable cell can accommodate multiple product variants and families which ensures that the capital invested in fixturing is not tied to one application. By compressing the assembly of an aircraft wing structure into a single automated cell, the Future Automated Aircraft Assembly Demonstrator features a flexible, holistic, and context-aware solution that includes automated positioning, drilling, and fastening processes. The assembly cell is based on industrial robots for the handling of aircraft components while an intelligent metrology and control system monitors the cell and ensures that the operations carried out are safe and adhere to the tight tolerances required. These modules are integrated into a single, standardised interface requiring only one operator to control the cell. Performance analyses have shown that, using the reconfigurable production environment described hereafter, an assembly part location accuracy better than ±0.1 mm can be achieved.

When producing complex and highly customisable products in low volumes (or in ‘batch sizes of one’), automation of production systems is critical for competitiveness and profitability in high labour-cost economies. To facilitate batch-size-of-one production, ‘topology generation’, ‘realisability’, and ‘control’ algorithms have been developed as part of the Evolvable Assembly Systems (EAS) project. The topology generation algorithm computes all the possible sequences of parallel activities that assembly resources can perform on parts and is run offline whenever the layout of the production facility changes, whereas realisability checking and controller generation are performed at run-time to check whether a production facility with a given set of assembly resources can assemble a desired product, and how the product should be assembled, e.g., which resources to use, and when. Generated controllers are output in Business to Manufacturing Markup Language (B2MML). Taken together, the algorithms thus represent a step toward a complete path from the formal specification of an assembly system and the products to be assembled, to the automated synthesis of executable process plans. This paper presents each algorithm in sufficient detail to allow their reimplementation by other researchers. Topology generation is the most expensive step in the approach. A preliminary experimental evaluation of the scalability of topology generation is presented, which suggests that, for small to medium sized production facilities, the time required for recomputing the topology is sufficiently small not to preclude frequent factory transformations, e.g., the addition of new resources.

© 2019 SAE International. All Rights Reserved. Evolvable Assembly Systems is a five year UK research council funded project into flexible and reconfigurable manufacturing systems. The principal goal of the research programme has been to define and validate the vision and support architecture, theoretical models, methods and algorithms for Evolvable Assembly Systems as a new platform for open, adaptable, context-aware and cost effective production. The project is now coming to a close; the concepts developed during the project have been implemented on a variety of demonstrators across a number of manufacturing domains including automotive and aerospace assembly. This paper will show the progression of demonstrators and applications as they increase in complexity, specifically focussing on the Future Automated Aerospace Assembly Phase 1 technology demonstrator (FA3D). The FA3D Phase 1 demonstrated automated assembly of aerospace products using precision robotic processes in conjunction with low-cost reconfigurable fixturing supported by large volume metrology. This was underpinned by novel agent-based control for transformable batch-size-of-one production. The paper will conclude by introducing Phase 2 of the Future Automated Aerospace Assembly Demonstrator - currently in development - that will translate the Evolvable Assembly Systems research to a higher technology readiness level and address the challenges of scalable and transformable manufacturing systems.

The design and reconfiguration of adaptive production systems is a key driver in modern advanced manufacturing. We summarise the use of an approach from the field of functional modelling to capture the function, behaviour, and structure of a system. This model is an integral part of the Evolvable Assembly Systems architecture, allowing the system to adapt its behaviour in response to changing product requirements. The integrated approach is illustrated with an example taken from a real EAS instantiation.

Modern manufacturing companies are under economic pressure to respond more rapidly to a changing production environment, including short product life cycles, individually customisable products, and the introduction of new disruptive manufacturing processes. This paper details a new manufacturing philosophy and software architecture for Evolvable Assembly Systems, based on the principles of decentralization, context-awareness and intelligent resources, that is implemented using intelligent agent technology and a data distribution service. We also detail the Smart Manufacturing and Reconfigurable Technology demonstrator - an instantiation of the Evolvable Assembly Systems architecture - that implements a proof-of-concept abstract custom pharmaceuticals production line.

This paper addresses the issue of organised adaptation in flexible manufacturing systems using multi-agent systems and electronic institutions. We analyse two representative case studies, the first a reconfigurable manufacturing system for intelligent ‘plug and produce’, and the second an evolvable assembly system that changes over time as context and requirements change. We observe that the management of adaptation is typically the responsibility of intelligent agents operating in a multi-agent system, but that dealing with openness, selfdetermination or fulfilment and violation of contracts requires conventional, mutually-agreed and mutable rules. We therefore propose to use the computational framework of self-organising electronic institutions as one way to represent solutions to these problems. However, issues of scale, complexity and uncertainty also need to be addressed, and we discuss how the framework can be developed to address these research challenges. We conclude with some comments on the potential transformative impact of self-organising multi-agent systems on reconfigurable manufacturing and evolvable assembly systems.

The automation and flexibility of production systems is a key step towards improved profitability and competitiveness in high labour cost areas, when producing high-complexity, low-volume products. In the Evolvable Assembly Systems (EAS) project, the 'manufacturability' (or 'realisability') and 'control' algorithms were introduced to accommodate the batch-size-of-one production of highly customisable products. These algorithms enable checking whether a production line can manufacture a given product with its available set of resources, and how the product should be manufactured, e.g. which resources to use, and when. To this end, the authors formally define production recipes, which represent products, and manufacturing resources which make up a manufacturing facility. This paper re-defines these notions in the ISO-standard EBNF (Extended Backus-Naur Form) notation, and adapts the the manufacturability and control algorithms to accommodate the new definitions. The new algorithms and data structures reflect more closely the ones that are used in an implemented software tool. This paper also reports a method by which recipes and resources could be used to generate manufacturing process controllers in the Business to Manufacturing Markup Language (B2MML) standard. In doing so, this paper takes a step toward a complete path from the formal specification of a manufacturing facility and the products to be manufactured, to the automatic generation of executable process plans.

Individuals and organizations responding to humanitarian crises recognize the need to improve urban emergency response and preparedness – including the need to devise better methods for assessing vulnerability within urban populations. This systematic review represents the first ever attempt to systematically search, sort and synthesize the existing evidence in order to consolidate findings on the tools, methods and metrics used to identify and prioritize vulnerable people, households and communities, including those displaced within and to urban areas. It is accompanied by a stand-alone executive summary and evidence brief. It forms part of a series of humanitarian evidence syntheses and systematic reviews commissioned by the Humanitarian Evidence Programme .

Introduction Urbanization has challenged many humanitarian practices given the complexity of cities. Urban humanitarian crises have similarly made identifying vulnerable populations difficult. As humanitarians respond to cities with chronic deficiencies in basic needs stressed by a crisis, identifying and prioritizing the most in need populations with finite resources is critical. Methods The full systematic review applied standard systematic review methodology that was described in detail, peer-reviewed, and published before the research was conducted.Results: While the science of humanitarian practice is still developing, a systematic review of targeting vulnerable populations in urban humanitarian crises shed some light on the evidence base to guide policy and practice. This systematic review, referenced and available online, led to further findings that did not meet the pre-defined inclusion and exclusion criteria for evidence set out in the full review but that the authors, in their expert opinion, believe provide valuable insight nonetheless given their recurrence. Discussion These additional findings that did not meet criteria for evidence and formal inclusion in the full manuscript, but deemed valuable by the subject expert authors, are discussed in this commentary.

This paper examines 10 challenges for making automation a team player (Klein et al., 2004) in the context of Evolvable Assembly Systems (EAS) with the aim of delivering requirements for effective hybrid human-automation decision making. Specific decision making use cases for a demonstrator system were analysed to capture opportunities and requirements for effective human-agent cooperative decision making. These requirements covered agent design, human-machine interface design, context aware computing requirements and human competency. As such, the paper provides concrete examples of how general principles for hybrid decision making can be applied to EAS, and presents a pilot of a method for future requirements elicitation.

Japanese cedar leaves from Iwaki, Fukushima were analyzed for carbon, cesium and iodine isotopic compositions before and after the 2011 nuclear accident. The Δ14C values reflect ambient atmospheric 14C concentrations during the year the leaves were sampled/defoliated, and also previous year(s). The elevated 129I and 134,137Cs concentrations are attributed to direct exposure to the radioactive fallout for the pre-fallout-expended leaves and to internal translocation from older parts of the tree for post-fallout-expended leaves. 134Cs/137Cs and 129I/137Cs activity ratios suggest insignificant isotopic and elemental fractionation during translocation. However, fractionation between radioiodine and radiocesium is significant during transportation from the source.

The January 2001 earthquake that struck the state of Gujarat in India damaged or destroyed some 8,000 villages and 490 towns. In the months and years after the earthquake, many organizations undertook widespread reconstruction programmes. One such collaboration between the NGO CARE India and the Federation of Indian Chambers of Commerce and Industry (FICCI) built 5,554 permanent houses as well as schools and community centres in 23 villages. This paper revisits 10 of the 23 villages that were partially or fully rebuilt by FICCI–CARE, 10 years after the earthquake. It finds that while the houses remain structurally strong and are mostly in use, residents’ levels of satisfaction, perception and usage are mixed. A central theme concerns the initial prioritization of seismic safety, which has sacrificed longer-term considerations of comfort, adaptability and the environment. The paper describes the houses that were built and presents findings according to structural condition, engagement in design, adaptations, house selling and perceptions of safety. The discussion presents four issues that emerge from the findings and wider research. The paper ends by proposing a simple equation for good housing, which places people’s involvement in building processes as the vital component.

Total excavation revealed a later prehistoric multi-ditched and multi-palisaded enclosure succeeded by unenclosed settlement. Despite the obvious investment of time and labour in the construction of the numerous palisades and three large concentric ditches episodic, seasonal use of the site is strongly indicated. Total artefact retrieval produced a cannel coal assemblage which has thrown light on the manufacture of the cannel coal bracelets so typical of later prehistoric sites in west-central Scotland.

This paper(1) looks at some of the winners and losers in the reconstruction efforts following the 2001 earthquake in Gujarat. It reviews some of the consequences of different approaches to reconstruction and these are illustrated by the experiences of three villages. It ends with a discussion of the lessons that are repeatedly ignored after disaster.

Recent natural disasters show that it is almost always the poorest who are worst affected. The rapidly urbanizing cities of Asia, Africa and Latin America present unprecedented concentrations of poverty, and in so doing mark new levels of vulnerability. Increasing urbanization brings new challenges to reducing the threat of disaster. Yet disasters are often ignored until they strike, when the damage has been done and relief is the only response. The situation is compounded by the separation of urban programming from disaster management. In a future context of increased urban growth, however, marked by an inevitable increase in urban disasters, new approaches that ‘mainstream’ disaster mitigation into urban development interventions at all levels are needed. Livelihood methodologies being practised by an increasing number of developmental organizations take account of the threat of disaster as intrinsic to developmental strategies. Livelihoods offer one approach that unifies understandings of both disaster management with development planning. Such an approach has major policy implications for decision makers aiming to make cities truly sustainable.