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As cities and towns across the world continue to grow to accommodate most of the world’s population increase, so too are they increasingly and often disproportionately exposed to the threat of natural hazards—including those worsened by climate change—such as floods, earthquakes, windstorms, and fires. Efforts that aim to enhance and safeguard urban health are those that seek to build the resilience of people and systems before, during, and after disasters. Yet where these efforts fail or fail to exist, components of vulnerability and urban diversity inform disaster risk. Taking a systems approach is especially essential to recognize the interconnected, complex, and dynamic issues that include and impact on the spectrum of urban health.

Inferences have to be made about likely structures and their effects on fluid flow in a geothermal reservoir at the pre-drilling stage. This is the case for the potential geothermal reservoir in Variscan metasedimentary rocks that are expected to occur in the subsurface at Göttingen. Simple mechanical modelling, using reasonable ranges of values for rock properties, stresses and fluid pressures, is used here to predict the range of possible structures that are likely to exist in the sub-surface and that may be generated during thermal and hydraulic stimulation. Mohr diagrams are a useful way for predicting and illustrating how rocks can go from a stable stress state (i.e., no fracturing occurs) to an unstable stress state (i.e., fracturing occurs). This transition can occur if there are changes in: (1) the failure envelope; (2) the stresses; and/or (3) fluid pressure. Mohr diagrams are used to show under what fluid pressures and tectonic stresses different types and orientations of fractures are likely to be reactivated or generated. The approach enables the effects of parameters to be modelled individually, and for the types and orientations of fractures to be considered. This modelling is useful for helping geoscientists consider, model and predict the ranges of mechanical properties of rock, stresses, fluid pressures and the resultant fractures that are likely to occur in the sub-surface.The Mesozoic rocks of the Somerset coast, UK, are used to illustrate how Mohr diagrams can help understand the history of fracturing. Such understanding is useful for predicting which fractures are likely to occur in the subsurface, which is important for predicting reservoir behaviour.

In this work we have been investigating the luminescence properties of plant opal phytoliths to assess their suitability for determination of age and/or thermometric information from soil and sediment sequences. Opal phytoliths, or bio-opal, form when monosilicic acid from soil-waters is taken up by plants and chemically altered to silica, producing intra- or extra- cellular structures that give grasses and stems their strength. Opal phytoliths are usually considered to be non-crystalline and referred to as silica mineraloid structures, with ~4-9% water

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.

PurposeThe purpose of this paper is to explore the challenges of transitional shelter provision for internally displaced persons (IDPs) in urban informal settlements. While there has been considerable research on postdisaster transitional shelters, less is known on shelters for IDPs in urban informal settlements.Design/methodology/approachA case study is presented based on field research at the communities in Olaria and City in São Paulo, Brazil. Collection of data included personal communication with the dwellers, surveys and interviews with representatives from Techo, one of the few organizations that provide transitional shelters for IDPs. A review of documents regarding the construction, design and users' adaptation of transitional shelter was also undertaken. Data was analyzed using thematic analysis.FindingsThe study found that Techo's program impacts positively on the nonphysical aspects of shelter, such as physical and mental health, security, household responsibilities and community participation. However, Techo's transitional shelter for IDPs in urban informal settlements and most postdisaster transitional shelters share similar problems. These programs respond to the needs of a small proportion of the affected populations with shelters of minimum quality and low durability.Research limitations/implicationsThis paper is limited to a single case study of the NGO Techo and further case study research is recommended to further develop and validate the study results in other urban development contexts.Originality/valueThis research contributes to describing and understanding the context and the provision of transitional shelters to IDPs in urban informal settlements and impacts in the community. With increasing numbers of IDPs settling in urban informal settlements in many parts of the world, this paper is valuable to policymakers, NGOs and researchers operating in this field to facilitate community development.

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 frequency and severity of floods is increasing in many countries, and hospitals play an essential role in protecting the community from their potentially devastating impacts. However, many hospitals are vulnerable to flooding having been designed and located without these risks in mind. In such situations, evacuation of vulnerable patients to safer alternative healthcare facilities might be the most reasonable response. However, there has been little research into hospital evacuation, and to address this research gap, this paper presents an innovative hospital evacuation model that can facilitate the optimal scheduling and sequencing of hospital evacuation under indeterministic flooding conditions. The proposed model combines a flood simulator and a mathematical model. Assuming the high levels of complexity of a realistic hospital evacuation system, a BendersDecomposition approach is developed to cope with large size problems. A case study in New South Wales (NSW), Australia, is used to illustrate the proposed model, and results confirm that the model can generate effective plans to evacuate hospitals in the shortest time possible in flood emergency conditions. The findings of this study contribute new theoretical and practical insights to a growing body of knowledge about disaster response strategies in hospitals and have implications for health service management and health policymakers in countries where health infrastructures are vulnerable to increasing climate change risks.

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.