Additional file 8: Diagnoses for hybridizations, tab separated. (TXT 13 KB)

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

The systems that operate the infrastructure of cities have evolved in a fragmented fashion across several generations of technology, causing city utilities and services to operate sub-optimally and limiting the creation of new value-added services and restrict opportunities for cost-saving. The integration of cross-domain city data offers a new wave of opportunities to mitigate some of these impacts and enables city systems to draw effectively on interoperable data that will be used to deliver smarter cities. Despite the considerable potential of city data, current smart cities initiatives have mainly addressed the problem of data management from a technology perspective, and have disregarded stakeholders and data needs. As a consequence, such initiatives are susceptible to failure from inadequate stakeholder input, requirements neglecting, and information fragmentation and overload. They are also likely to be limited in terms of both scalability and future proofing against technological, commercial and legislative change. This paper proposes a systematic business-modeldriven framework to guide the design of large and highly interconnected data infrastructures which are provided and supported by multiple stakeholders. The framework is used to model, elicit and reason about the requirements of the service, technology, organization, value, and governance aspects of smart cities. The requirements serve as an input to a closed-loop supply chain model, which is designed and managed to explicitly consider the activities and processes that enables the stakeholders of smart cities to efficiently leverage their collective knowledge. We demonstrate how our approach can be used to design data infrastructures by examining a series of exemplary scenarios and by demonstrating how our approach handles the holistic design of a data infrastructure and informs the decision making process.

This paper uses a software development environment construction case study as a framework for a critical analysis of software process modelling. It outlines a research agenda based on this analysis.

This book describes how smart cities can be designed with data at their heart, moving from a broad vision to a consistent city-wide collaborative configuration of activities. The authors present a comprehensive framework of techniques to help decision makers in cities analyse their business strategies, design data infrastructures to support these activities, understand stakeholders' expectations, and translate this analysis into a competitive strategy for creating a smart city data infrastructure. Readers can take advantage of unprecedented insights into how cities and infrastructures function and be ready to overcome complex challenges. The framework presented in this book has guided the design of several urban platforms in the European Union and the design of the City Data Strategy of the Mayor of London, UK.

The assessment of the business models viability is performed by systematically clustering the critical design issues (CDIs) and using them to assess and balance the requirement specifications elicited in the business models analysis. In the case any requirement specification negatively impacts a critical design issue, a requirements trade-off analysis must be carried out. For instance, consider the simplistic example in which a data infrastructure must support the federation of data from external data sources and at the same time satisfy pre-defined CDIs, such as Target Users (service), User engagement (service), Interoperability (technology), and Broaden Partnership (organization). On the one hand, increased content targets engage users with the data platform as well as increase the partnership with external data providers (contributing partners). On the other hand, federating data from other datasets significantly compromises data interoperability and requires the implementation of several mechanisms to mitigate semantic mismatch. Based on such arguments, this requirement should not be satisfied at the moment and revisited at a later stage when circumstances change. The article provides examples of how to trade off requirements against the CDIs, and how to validate business models against the pre-defined critical success factors (CSFs).

In the article we discuss the relevance of service innovation in smart cities. We also discuss trends and drivers that stimulate the development of data infrastructure and define our core concepts. Our focus is on the design of data infrastructures for smart cities and their underlying business models. At the moment, there is no common framework for data infrastructure design nor is there a common framework for designing a common reference architecture for data infrastructure and their business models. We have tried to develop the first framework part of the SMARTify approach. We discuss the theoretical foundation for our business model approach, defining the business model concept as well as the core concepts and specifying the relevant concepts in the business models domains. We look at the interdependencies within as well as between the domains and discuss the core concepts from a design perspective, as well as the CDIs (critical design issues). We also explain in detail the viability and feasibility of business models by looking into CSFs (critical success factors).

Modern economies depend on innovation in services for their future growth. Service innovation increasingly depends on information technology and digitization of information processes. Designing new services is a complex matter, since collaboration with other companies and organizations is necessary. Service innovation is directly related to business models that support these services, i.e. services can only be successful in the long run with a viable business model that creates value for its customers and providers. This book presents a theoretically grounded yet practical approach to designing viable business models for electronic services, including mobile ones, i.e. the STOF model and based on it the STOF method. The STOF model provides a holistic view on business models with four interrelated perspectives, i.e., Service, Technology, Organization and Finance. It elaborates on critical design issues that ultimately shape the business model and drive its viability.

Work on synthetic biology has largely used a component-based metaphor for system construction. While this paradigm has been successful for the construction of numerous systems, the incorporation of contextual design issueseither compositional, host or environmentalwill be key to realising more complex applications. Here, we present a design framework that radically steps away from a purely parts-based paradigm by using aspect-oriented software engineering concepts. We believe that the notion of concerns is a powerful and biologically credible way of thinking about system synthesis. By adopting this approach, we can separate core concerns, which represent modular aims of the design, from cross-cutting concerns, which represent system-wide attributes. The explicit handling of cross-cutting concerns allows for contextual information to enter the design process in a modular way. As a proof-of-principle, we implemented the aspect-oriented approach in the Python tool, SynBioWeaver, which enables the combination, or weaving, of core and cross-cutting concerns. The power and flexibility of this framework is demonstrated through a number of examples covering the inclusion of part context, combining circuit designs in a context dependent manner, and the generation of rule, logic and reaction models from synthetic circuit designs.

The requirements and goals of the platform for smart cities highlight that the supply chain must enable a data environment in which all stakeholders involved are able to co-exist and compete among them. Therefore, the closed-loop supply chain (CLSC) model is designed and managed to explicitly consider activities along both the forward and the reverse flow chain. Data can be produced, re-used, re-manufactured or even disposed. The interesting point here is to facilitate the `knowledge' created in smart cities to be infused back onto the system, and users are able to be part of the data processing and improve the platform through collaborative networking. The value chain analysis explicitly recognizes the interdependencies and profits cost efficiencies from the exploitation of linkages between value activities of a city. For instance, changes in the standard formats (one value activity) may significantly influence the activities involved in operations and outbound logistics (another value activity). These activities must be well co-ordinated if the change in standards is to be accomplished. Establishing new data standards without the platform having means to support it will affect the reliability of the platform. Another issue to consider is a service (an activity value) not be real-time available-to-promise/capable-to-promise and fulfilment or users' needs. Time-sensitive processes in a city, such as fraudulent operation, traffic flows, infrastructure monitoring, emergencies or tragedies, need high data/service availability in order to detect any problem before it actually happens. Value chain analysis and exploration provides a powerful tool for strategic thinking for creating a smart city with a sustainable platform.

Understanding of smart cities and their business models are important in studying how one can best provide value to its stakeholders. Our approach provided through a data infrastructure proposes to improve innovation, development, user engagement and stakeholders'collaboration of smart cities services. Service innovation is directly related to the business models that support these services. We argue that a one-size-fits-all approach to city data management transformation and simplistic approaches to engage stakeholders of city data are unlikely to work. Rather, we focus on the enabling processes and activities by which innovative use of technology and data in the context use of a city's inhabitants, alongside governance strategies supported by a strong value network of partners, can help deliver the various visions of data strategies for cities in more efficient, aligned and effective ways. Embracing the technology and non-technology components of data infrastructures will ensure that standards are adhered to, interoperability is guaranteed, smart governance is in place, a strong value network of partners are built, and feedback is facilitated. The use of our framework will help transform current data management practices and facilitate the creation of a data marketplace within both the public and private sectors facilitating the exploitation of city data in smart cities.

Software engineers should have the ability to abstract the complexity of a whole system composed of products, demands and suppliers emerging from an interconnected network termed a software ecosystem (SECO). Since software suppliers resort to virtual integration, software-consuming organizations face difficulties performing IT management activities and analyzing what application or technology enter their SECO. In this context, the 'silent' effects of nontechnical factors give rise to serious long-term problems, e.g., low productivity, investment loss, financial crisis, or bankruptcy. This paper presents an investigation of SECO effects on software-consuming organizations performing IT management activities in real settings. IT management teams have regular meetings to deliberate on acquisition decisions which they base on experience and IT market recommendations, including spreadsheets and distributed documents. Analysis of the decision space, business objective synergy, and technology/supplier dependency are identified as the most critical health indicators for SECO platform monitoring in IT management activities. This highlights the critical role acquisition preparation plays in the SECO context.

Cities find it tremendously difficult to specialize in all the competencies involved in designing, building and maintaining data infrastructures. However, cities have neither the incentive nor the means of bringing external partners round to the necessary supply chain networks. The consequence of not doing so is that complexity has often overtaken data infrastructures development. Often, it has become longer than a simple ICT project, and as a consequence, more expensive and difficult to design and maintain. A platform-centric approach for the provision of city data in smart cities enables pooling of multiple organizations' knowledge bases - especially cross-sectorial domains - that are more valuable in combination than in isolation. Building an ecosystem of stakeholders who complements the capabilities of a data infrastructure can potentially bring insights about specialized domains, different application markets, lower costs and shorten the time to market for the development of new services. We give an overview of the theory of platforms and their main characteristics. In an effort to move beyond this confusion, a growing literature on open data and data platforms has emerged, though what practical guidance it offers to governments is often unclear. Examining the prevailing strategy of data catalogues or platforms design is an essential starting point in understanding why a new approach is needed to integrating both technology and non-technology components more effectively into data management and business models.

Context: App stores provide a software development space and a market place that are both different from those to which we have become accustomed for traditional software development: The granularity is finer and there is a far greater source of information available for research and analysis. Information is available on price, customer rating and, through the data mining approach presented in this paper, the features claimed by app developers. These attributes make app stores ideal for empirical software engineering analysis.Objective: This paper(1) exploits App Store Analysis to understand the rich interplay between app customers and their developers.Method: We use data mining to extract app descriptions, price, rating, and popularity information from the Blackberry World App Store, and natural language processing to elicit each apps' claimed features from its description.Results: The findings reveal that there are strong correlations between customer rating and popularity (rank of app downloads). We found evidence for a mild correlation between app price and the number of features claimed for the app and also found that higher priced features tended to be lower rated by their users. We also found that free apps have significantly (p-value < 0.001) higher ratings than non free apps, with a moderately high effect size (<(A)over cap>(12) = 0.68). All data from our experiments and analysis are made available on-line to support further investigations. (C) 2017 The Authors. Published by Elsevier B.V.

In this short position paper I consider the contributions that software engineering as a discipline can make to the development and implementation of government policy. It is intended to support the growing body of knowledge on scientific advice in government and to encourage software engineers to engage with policy and the policy community.

App stores are not merely disrupting traditional software deployment practice, but also offer considerable potential benefit to scientific research. Software engineering researchers have never had available, a more rich, wide and varied source of information about software products. There is some source code availability, supporting scientific investigation as it does with more traditional open source systems. However, what is important and different about app stores, is the other data available. Researchers can access user perceptions, expressed in rating and review data. Information is also available on app popularity (typically expressed as the number or rank of downloads). For more traditional applications, this data would simply be too commercially sensitive for public release. Pricing information is also partially available, though at the time of writing, this is sadly submerging beneath a more opaque layer of in-app purchasing. This talk will review research trends in the nascent field of App Store Analysis, presenting results from the UCL app Analysis Group (UCLappA) and others, and will give some directions for future work.

Applying IT in an appropriate and timely way, in harmony with strategies, goals and needs, is important for business performance success. However, because ensuring that IT will support the achievement of business strategies entails a sequence of key decisions in a variety of related IT areas, knowledge that is relevant for effective decision-making tends to be dispersed among multiple business and IT stakeholders, who represent an organisation's diverse and conflicting interests. Consequently, inappropriately assigning decision rights for critical IT decisions increases the risk of misalignment. We study the context in which decision rights are managed as part of governance in software development for business process support so that organisations can achieve high-level alignment between software systems and business processes during software development. In this paper we establish the key components of a system for governance design that links software development governance arrangements, business strategy and performance. We position business process executives and managers as roles responsible for software development governance. Their responsibilities include defining the software system's strategic aims, providing the leadership to put them into effect, and satisfying themselves that an appropriate governance structure is, and remains in place during software development. We also position business process and software developer stakeholders as roles responsible for implementing software systems that help realise strategic aims. We propose four key software development decisions: functional and non-functional requirements, and system architecture and deliverables.

Model checking is an established method for verifying behavioral properties of system models. But model checkers tend to support low-level modeling languages that require intricate models to represent even the simplest systems. Modeling complexity arises in part from the need to encode domain knowledge at relatively low levels of abstraction. In this paper, we demonstrate that formalized domain knowledge can be reused to raise the abstraction level of model and property specifications, and hence the effectiveness of model checking. We describe a novel method for domain-specific model checking called cascading verification that uses composite reasoning over high-level system specifications and formalized domain knowledge to synthesize both low-level system models and their behavioral properties for verification. In particular, model builders use a high-level domain-specific language (DSL) based on YAML to express system specifications that can be verified with probabilistic model checking. Domain knowledge is encoded in the Web Ontology Language (OWL), the Semantic Web Rule Language (SWRL) and Prolog, which are used in combination to overcome their individual limitations. A compiler then synthesizes models and properties for verification by the probabilistic model checker PRISM. We illustrate cascading verification for the domain of uninhabited aerial vehicles (UAVs), for which we have constructed a prototype implementation. An evaluation of this prototype reveals nontrivial reductions in the size and complexity of input specifications compared to the artifacts synthesized for PRISM.