The evidence review report from SAPEA presents the latest scientific evidence on the subject of crisis management, and evidence-based policy options for action. The European Union is confronted with an increasing number of crises with growing complexity. Their effects can cascade and ripple to all parts of society, the economy, and the environment. The effects of such crises are not limited to specific geographical regions or sectors of society, and many crises continue indefinitely. Because they last, they overlap and amplify each other. Decisions made during crises are not only urgent, but have long-lasting consequences. Therefore, strategic crisis management needs to be aligned with broader policy objectives. Using digital tools, guidance and standards can be set for strategic foresight and risk monitoring, to ensure that a longer-term perspective is integrated. Better data preparedness can ensure that information can be shared effectively while respecting privacy and security when a crisis hits. To address the growing complexity of crises, we need a vision that integrates both risk management and crisis management. Different activities and organisations also need to coordinate: responses on multiple geographical and temporal scales need to be aligned, rather than aiming at the one ‘right’ level. Here, the EU could play an important coordinating role. The EU can also play a role in training decision- and policy-makers to coordinate and manage crises. Crises amplify existing inequalities, hitting the most vulnerable the hardest — and this can erode trust across society at exactly the time when trust is most needed. New technologies and forms of communication offer ways for citizens to be engaged and empowered. Involving civil society can bring many advantages, producing a better informed, tailored, and localised response.@en

Malicious software, widely known as malware, is one of the biggest threats to our interconnected society. Cybercriminals can utilize malware to carry out their nefarious tasks. To address this issue, analysts have developed systems that can prevent malware from successfully infecting a machine. Unfortunately, these systems come with two significant limitations. First, they frequently target one specific platform/architecture, and thus, they cannot be ubiquitous. Second, code obfuscation techniques used by malware authors can negatively influence their performance. In this paper, we design and implement HawkEye, a control-flow-graph-based cross-platform malware detection system, to tackle the problems mentioned above. In more detail, HawkEye utilizes a graph neural network to convert the control flow graphs of executable to vectors with the trainable instruction embedding and then uses a machine-learning-based classifier to create a malware detection system. We evaluate HawkEye by testing real samples on different platforms and operating systems, including Linux (x86, x64, and ARM-32), Windows (x86 and x64), and Android. The results outperform most of the existing works with an accuracy of 96.82% on Linux, 93.39% on Windows, and 99.6% on Android. To the best of our knowledge, HawkEye is the first approach to consider graph neural networks in the malware detection field, utilizing natural language processing.

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Android is the most popular smartphone operating system. At the same time, miscreants have already created malicious apps to find new victims and infect them. Unfortunately, existing anti-malware procedures have become obsolete, and thus novel Android malware techniques are in high demand. In this paper, we present Falcon, an Android malware detection and categorization framework. More specifically, we treat the network traffic classification task as a 2D image sequence classification and handle each network packet as a 2D image. Furthermore, we use a bidirectional LSTM network to process the converted 2D images to obtain the network vectors. We then utilize those converted vectors to detect and categorize the malware. Our results reveal that Falcon could be an accurate and viable solution as we get 97.16% accuracy on average for the malware detection and 88.32% accuracy for the malware categorization.

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Android is the most dominant operating system in the mobile ecosystem. As expected, this trend did not go unnoticed by miscreants, and quickly enough, it became their favorite platform for discovering new victims through malicious apps. These apps have become so sophisticated that they can bypass anti-malware measures implemented to protect the users. Therefore, it is safe to admit that traditional anti-malware techniques have become cumbersome, sparking the urge to come up with an efficient way to detect Android malware. In this paper, we present a novel Natural Language Processing (NLP) inspired Android malware detection and categorization technique based on Function Call Graph Embedding. We design a graph neural network (graph embedding) based approach to convert the whole graph structure of an Android app to a vector. We then utilize the graphs' vectors to detect and categorize the malware families. Our results reveal that graph embedding yields better results as we get 99.6% accuracy on average for the malware detection and 98.7% accuracy for the malware categorization.

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Android malicious applications have become so sophisticated that they can bypass endpoint protection measures. Therefore, it is safe to admit that traditional anti-malware techniques have become cumbersome, thereby raising the need to develop efficient ways to detect Android malware. In this paper, we present Hybroid, a hybrid Android malware detection and categorization solution that utilizes program code structures as static behavioral features and network traffic as dynamic behavioral features for detection (binary classification) and categorization (multi-label classification). For static analysis, we introduce a natural-language-processing-inspired technique based on function call graph embeddings and design a graph-neural-network-based approach to convert the whole graph structure of an Android app to a vector. For dynamic analysis, we extract network flow features from the raw network traffic by capturing each application’s network flow. Finally, Hybroid utilizes the network flow features combined with the graphs’ vectors to detect and categorize the malware. Our solution demonstrates 97.0% accuracy on average for malware detection and 94.0% accuracy for malware categorization. Also, we report remarkable results in different performance metrics such as F1-score, precision, recall, and AUC.

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In order to ensure successful product development processes, manifold modelling approaches have been developed, which cover a wide range of aspects such as responsibilities, duration of activities and dependencies. Still, an industry standard does not exist. Users of process modelling approaches are driven by different targets depending on the respective role. Currently, practitioners need to evaluate strengths and weaknesses of each approach by themselves and find little guidance for the selection. As a consequence, users might select unsuitable approaches and do not get the expected result. Thus, the intended applications of the model such as analyses or an optimization of the process are hampered. This could heavily affect companies´ success by product or project failures. The paper shows the concept of a recommendation tool that enables a suitable and effective selection of process modelling approaches. Key element is the description of relevant use cases and personas that represent the various needs of both different company types as well as different roles within such as process modellers and users. By identifying the most relevant case, each practitioner will be successfully guided to the most suitable modelling approach.@en

In many engineering design contexts models are indispensable. They offer decision support and help tackle complex and interconnected design projects, capturing the underlying structure of development processes or resulting products. Because managers and engineers base many decisions on models, it is crucial to understand their properties and how these might influence their behaviour. The level of detail, or granularity, of a model is a key attribute that results from how reality is abstracted in the modelling process. Despite the direct impact granularity has on the use of a model, the general topic has so far only received limited attention and is therefore not well understood or documented. This article provides background on model theory, explores relevant terminology from a range of fields and discusses the implications for engineering design. Based on this, a classification framework is synthesised, which outlines the main manifestations of model granularity. This research contributes to theory by scrutinising the nature of model granularity. It also illustrates how this may manifest in engineering design models, using Design Structure Matrices as an example, and discusses associated challenges to provide a resource for modellers navigating decisions regarding granularity.

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Models of products and design processes are key to interacting with engineering designs and managing the processes by which they are developed. In practice, companies maintain networks of many interrelated models which need to be synthesised in the minds of their users when considering issues that cut across them. This article considers how information from product and design process models can be integrated with a view to help manage these complex interrelationships. A framework highlighting key issues surrounding model integration is introduced and terminology for describing these issues is developed. To illustrate the framework and terminology, selected modelling approaches that integrate product and process information are discussed and organised according to their levels and forms of integration. Opportunities for further work to advance integrated modelling in engineering design research and practice are discussed.

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The design of many products is incremental, based on a prior architecture and may be thought of as a series of changes to an existing design. Nonetheless, design changes and their propagation complicate design process planning. They are a major source of rework and lead to frequent rescheduling and reprioritisation of design tasks. Few methods exist to estimate the impact of such planning decisions with regard to both the design process and the product. Process simulation may provide support in this context when it is based on an appropriate description of the product. However, it can be hard to determine a suitable level of description, or granularity, for the product model. This article explores, by reference to a diesel engine, the implications of product model granularity for simulating design change propagation, and design process planning.

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While product models and process models have a long standing transiting, there are few models that integrate the two type of models. Those that exist are research systems, which even if validated in industry do not have a broad uptake to date. This paper develops an integrated framework for product and process models based on the purpose the models are put it building on a model of Browning and Ramasesh. Selected integrated model are classified according to the framework. This revealed the no model to date gives equal weight to product and process models.

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The authors use simulation to analyse the resource-driven dependencies between concurrent processes used to create customised products in a company. Such processes are uncertain and unique according to the design changes required. However, they have similar structures. For simulation, a level of abstraction is chosen such that all possible processes are represented by the same activity network. Differences between processes are determined by the customisations that they implement. The approach is illustrated through application to a small business that creates customised fashion products. We suggest that similar techniques could be applied to study intertwined design processes in more complex domains.@en

Bringing successful innovative products to the market is in many cases a balance between targeted innovation and the reuse of existing technology. If this integration fails designers also need to look for innovative solutions at the last minute to integrate new and old parts. To avoid this 'emergency' innovation, designers need to understand how different parts of a product are connected to each other and how changes to one component affect other components. This paper reports on a method to predict how change propagates through existing products to assess the risk of innovation. A product is represented as a square matrix and the pair wise dependencies between components are captured with likelihood and impact values, such that indirect risk can be calculated. Using various graphic representations of the structure of the product, designers can explore dependencies between components and avoid design choices that might force them to change highly connected parts.@en

Many aerospace companies are currently making the transition to providing fully-integrated product-service offerings in which their products are designed from the outset with life-cycle considerations in mind. Based on a case study at Rolls-Royce, Civil Aerospace, this paper demonstrates how an interactive approach to process simulation can be used to support the redesign of existing design processes in order to incorporate life-cycle engineering (LCE) considerations. The case study provides insights into the problems of redesigning the conceptual stages of a complex, concurrent engineering design process and the practical value of process simulation as a tool to support the specification of process changes in the context of engineering design. The paper also illustrates how development of a simulation model can provide significant benefit to companies through the understanding of process behaviour that is gained through validating the behaviour of the model using different design and iteration scenarios. Keywords: jet engine design; life-cycle engineering; LCE; process change; design process simulation; applied signposting model; ASM.@en

Engineering change has grown steadily in prominence both as an important issue for industry and as an active academic research area. This paper provides a categorised overview and perspective on the published academic literature on engineering change. The aim is to give new researchers an understanding of the field's breadth and depth, as well as pointers towards additional information, and established researchers a non-dogmatic summary perspective on the work accomplished in this area. Change is defined as an alteration made to parts, drawings or software that have already been released during the product design process and life cycle, regardless of the scale or the type of the change. A change may encompass any modification to the form, fit and/or function.

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Hospitals are facing a triple challenge - meeting mandatory climate change targets and refurbishing aging infrastructure while simultaneously providing quality of care. With the potential of more frequent disruptive weather events, a UK government-funded project was launched in 2009 to investigate practical strategies for the National Health Service to increase its resilience to climate change. This paper presents the process of defining resilience by using the Delphi method and demonstrates its applicability within healthcare design. A Delphi survey is nearing completion which has determined the significant resilience issues and temperature ranges for ideal and critical conditions. Our preliminary findings identified six priorities that lead towards increasing resilience. Using the Delphi method can be a useful tool in clarifying the focus for healthcare design considerations.@en

Efficient planning of design processes is of critical importance to meet tight deadlines and budgets; and the development of process planning tools is a lively research area. This paper describes current planning practice in industry and the challenges associated with it. In industry, a multitude of plans are used in parallel each focussing on a different aspect. The units of planning and their resulting plans roughly fall into product plans considering cost, bill of materials and procurement considerations; process plans including different milestone, lead-times, task and activity plans; and quality plans. Over the course of a project, the same plan can serve as a prescriptive plan defining steps in the process, a target plan against which process is measured, and a record of the process. This paper argues that organisations work because individuals use more than one plan and have a tacit understanding of the relationships between these plans. Variations between different companies are discussed before the paper concludes with a reflection on implication for planning support.@en

Adapting a design to the needs of a specific customer can be seen as a process in which an existing product is changed to incorporate desired properties and to exclude undesired ones. In a multi-project environment, many such projects are typically in execution at any given time and must compete for limited resources. Various strategies and approaches may be taken to organise such multi-project systems in order to maximise their effectiveness. This paper identifies and discusses several such strategies and shows how simulation can be used to explore their effectiveness in different contexts. Based on a case study of a simple, yet real customisation environment, we outline a discrete-event Monte-Carlo simulation model that we developed and which we argue can be used to explore some of the main issues. Although based on a simple example, we propose that the basic structure of our model could be used to derive insights for multi-project customisation environments in complex engineering domains.@en

Connectivity models are useful aids to support design reviews but building the models is an extremely effort intensive process. Connectivity models help to minimise incidents of unexpected rework by drawing attention to vital component interfaces and dependencies. The uptake of connectivity models can be enhanced if effort burden associated model building is significantly reduced. This paper describes an investigation into the use of a distributed approach to building connectivity models as a means of effort reduction. The Design Structure Matrix is broken down into sub-sections which are L-shaped or cross-shaped. These sub-sections are referred to as modules of the connectivity model. The effort required for building a model is compartmentalised within the modules. In other words, the minimisation of model building effort is attained by distributing the modules such that each individual's effort contribution is limited to within a single module.@en