This paper utilizes a transdisciplinary framework that integrates systems thinking, design thinking, and futuristic thinking to address the intertwined challenges of designing sustainable complex systems. Through a workshop with PhD students, we explore how this framework can foster innovative, sustainable, and socially just solutions for complex systems and how the blended approach challenges the traditional practices. Our findings demonstrate the potential of this approach for promoting holistic design considerations, user-centered sustainability, and anticipatory thinking in complex systems development. This work contributes to the growing body of research at the intersection of transdisciplinary design methods, sustainability, and critical computing, offering insights for educators and practitioners in the field of human-computer interaction and complex systems design.

The rapid advancement of digital artificial intelligence has created unrealistic expectations about its transfer to physical industrial systems. This commentary critically examines the fundamental misalignment between digital AI capabilities and the complex requirements of industrial cyber-physical systems. While digital AI excels in pattern recognition and virtual environments, physical intelligence demands understanding of mechanics, materials, energy, and real-world constraints that current AI paradigms inadequately address. The commentary argues that achieving genuine physical intelligence in industrial settings requires a fundamental reorientation toward information integration as the enabling foundation, rather than pursuing ever-larger foundation models. Industrial information integration frameworks must bridge cyber-physical boundaries, handle temporal characteristics properly, represent uncertainty explicitly, and enable human-AI collaboration. This perspective aims to redirect research efforts toward the critical challenges of industrial information integration that will ultimately enable meaningful progress in physical AI for cyber-physical systems.

This article explores strategies for cultivating a paradigm shift in the engineering design of intelligent machines, moving from conventional anthropocentric priorities toward a more ecocentric orientation aligned with ecological ethics. Through a comprehensive literature review, historical patterns of anthropomorphism in intelligent systems are traced, revealing how human-centered considerations have persistently dominated machine design across eras. To disrupt this enduring lineage and integrally reorient technology innovation toward ecological balance, four targeted strategies are proposed: (1) adopting question-seeking and systems thinking; (2) designing integrated ecosystems rather than discrete networks; (3) balancing exploitation with exploration; and (4) infusing diverse perspectives through transdisciplinarity. By reforming engineering education and culture from the ground up, the creation of intelligent machines can be realigned to enrich our shared environment rather than simply maximize human primacy. This paper offers a constructive roadmap to transform the promise of increasingly autonomous technologies in the 21st century and beyond.

The history of machines traces back to the dawn of human civilization, reflecting the intertwined journey of exploration, invention, and discoveries. From ancient simple machines to remarkable innovations like the Antikythera mechanism and automatons, the evolution of machines has been a testament to human ingenuity. In the 21st century, a new era of machines emerges, characterized by intelligence, connectivity, and autonomy.The purpose of the article is two-fold. Firstly, it provides a historical view of the evolution of machines and aims to characterize their future functional attributes in a rapidly changing, technology-driven 21st-century society. Secondly, it discusses the need to prioritize the eco-centered and sustainable design of these systems as opposed to traditional performance-centered practices. To this end, the article starts by presenting a literature review on the evolution and the characteristics of future intelligent machines. We argue that there is a need to reconsider the purpose of the future intelligent machines in the anthropocentrism–ecocentrism continuum. To support this argument, the current approaches to designing these systems and the sustainability concerns related to future characteristics of them are discussed. The article concludes with remarks on different strategies aiming to provide a roadmap to aid the creators of the future connected, intelligent, and autonomous systems to maximize their positive impact and ensure continuous growth of technological development while protecting the environment and addressing societal needs.

Model-based systems engineering (MBSE) is considered an important approach for understanding multidomain fields and is widely used in complex systems such as aerospace. In this article, a detailed survey of MBSE literature was conducted from its commencement to the present trends through bibliometric analysis. Some bibliometric tools were used to implement a visual network analysis of MBSE-related manuscripts. The results of the bibliometric study revealed the interrelationship and distribution of researchers in multidomain fields. The authorized sources of MBSE papers were also assorted. The current practices of MBSE were analyzed. The future directions for MBSE based on the current practices were discussed. We found that MBSE’s research has been conducted by many research teams with distinctive characteristics, and the top publishing sources in this field have emerged. Research on MBSE focuses on system engineering, languages, system of systems, and digitalization. The development of new technologies such as next-generation modeling languages is improving current practical problems. The findings of this study may help researchers gain a faster and more comprehensive understanding of the current and future developments in MBSE.

When combined with information and communication technologies and powerful data analytic algorithms such as artificial intelligence, digital twins enable organisations to conserve physical resources. This applies both during the design phase and when performing diagnostic and predictive analyses during operations. These abilities bring significant opportunities to the infrastructure industry to develop new ways of designing, constructing, operating and monitoring infrastructure at a time when much of the world's civil infrastructure is ageing and showing signs of deterioration. This study aims to find out how digital twins can help the infrastructure industry to deliver and operate sustainable and smart infrastructure assets. This paper presents an overview of digital twin definitions, current practices, benefits and challenges through a series of semi-structured expert interviews with executives from the UK infrastructure industry. Additionally, it suggests a series of strategies to aid digital transformation and digital twin adoption in the industry. Results from the interviews illustrated that the executives involved in digital transformation in the infrastructure industry are very well aware of the definitions, benefits and challenges of digital twins. In general, they understand the value of digital transformation and specifically digital twins. They know the reasons behind the need for transforming the industry and adopting datadriven concepts such as digital twins. Moreover, the executives interviewed as part of this study mentioned common challenges across different infrastructure domains. The strategies presented are focused on addressing these three main challenges identified and agreed upon by the participants - culture, technology adoption and lack of a skilled workforce. The three main strategies, addressing digital transformation (1), cultural transformation (2) and bridging the skills gap (3), are explained later in this paper. The article concludes by underlining the importance of creating equal opportunities for the current workforce to improve their digital fluency and skillset by providing information about the benefits of digital twins throughout the sector and organisations to improve adoption and the realisation of benefits.

This year, 2023, will probably be remembered as the year of generative AI. It is still an open question whether generative AI will change our lives for the better. One thing is certain, though: New artificial-intelligence tools are being unveiled rapidly and will continue for some time to come. And engineers have much to gain from experimenting with them and incorporating them into their design process. • That's already happening in certain spheres. For Aston Martin's DBR22 concept car, designers relied on AI that's integrated into Divergent Technologies' digital 3D software to optimize the shape and layout of the rear subframe components. The rear subframe has an organic, skeletal look, enabled by the AI exploration of forms. The actual components were produced through additive manufacturing. Aston Martin says that this method substantially reduced the weight of the components while maintaining their rigidity. The company plans to use this same design and manufacturing process in upcoming low-volume vehicle models. • Other examples of AI-aided design can be found in NASA's space hardware, including planetary instruments, space telescopes, and the Mars Sample Return mission. NASA engineer Ryan McClelland says that the new AI-generated designs may “look somewhat alien and weird,” but they tolerate higher structural loads while weighing less than conventional components do. Also, they take a fraction of the time to design compared to traditional components. McClelland calls these new designs “evolved structures.” The phrase refers to how the AI software iterates through design mutations and converges on high-performing designs.

In this study, we reviewed aircraft accidents in order to understand how autonomy and safety has been managed in the aviation industry, with the aim of transferring our findings to autonomous cyber-physical systems (CPSs) in general. Through the qualitative analysis of 26 reports of aircraft accidents that took place from 2016 to 2022, we identified the most common contributing factors and the actors involved in aircraft accidents. We found that accidents were rarely the result of a single event or actor, with the most common contributing factor being non-adherence to standard operating procedures (SOPs). Considering that the aviation industry has had decades to perfect their SOPs, it is important for CPSs not only to consider the actors and causes that may contribute to safety-related issues, but also to consider well-defined reporting practices, as well as the different levels of mechanisms checked by diverse stakeholders, in order to minimise the cascading nature of such events to improve safety. In addition to proposing a new definition of safety, in this study we suggest reviewing high-reliability organisations to offer further insights as part of future research on CPS safety.

There is a critical need to make infrastructure systems more efficient, resilient, and sustainable. Infrastructure systems provide the basis for everyday life and enable the flow of goods, information, and services within urban and regional settings. Providing data-centric solutions to improve this flow is essential. This can only be achieved if we manage to transform passive infrastructure assets into cyber-physical systems. Digital twins bring the opportunity to turn passive infrastructure assets into data-centric systems of systems. This article aims to provide a summary of existing digital twin architectures and exemplify a digital twin design and implementation. To this end, a literature review of digital twin architecture is presented in addition to a case study of a digital twin implementation in smart infrastructure. The case study focuses on a digital twin implementation of a bridge and describes in detail the physical, cyber, integration, and service layers of this implementation. Later in the article, we discuss the learnings from this case study under three main categories - systems perspective, information perspective, and organisational perspective. The findings show the importance of acquiring a systems perspective when designing digital twins today to enable interoperable systems of systems in the future. Furthermore, the findings highlight the vital necessity of data and information management while also considering the multidisciplinary aspects of digital twin design and implementation.

Cyber-physical systems (CPS), such as collaborative robots, smart cities, and autonomous vehicles, are seen as decisive contributions to addressing many societal challenges. These systems have the power to provide solutions to cope with an aging population, address climate change, and improve issues of health, public safety and mobility. As a product of the fourth industrial revolution, these systems are currently inviting much interest. However, there are barriers that need to be considered and understood to be able to optimise the potential of these systems to support a more sustainable future. To this end, transdisciplinary skills and a combination of different mindsets are needed to be able to ask the right questions at the right time.There are several approaches that can help us to initiate constructing innovative, transformative, future-oriented and systematic ideas and questions. The three important approaches that are suggested in this article are systems mindset, design mindset and futuristic mindset. These mindsets combined with the transdisciplinary perspective - where different disciplines work jointly to create sustainable solutions not only for today but also for tomorrow - have the power to change the world. This article underlines the importance of transdisciplinarity, presents the three mindsets and illustrates a hypothetical use case on how to blend these three mindsets to enable creative work in the future's transdisciplinary world for human-centred and sustainable future.

A key aspect of cyber-physical systems (CPS) is their potential for integrating information technologies with embedded control systems and physical systems to form new or improved functionalities. CPS thus draws upon advances in many areas. This positioning provides unprecedented opportunities for innovation, both within and across existing domains. However, at the same time, it is commonly understood that we are already stretching the limits of existing methodologies. In embarking towards CPS with such unprecedented capabilities, it becomes essential to improve our understanding of CPS complexity and how we can deal with it. Complexity has many facets, including complexity of the CPS itself, of the environments in which the CPS acts, and in terms of the organizations and supporting tools that develop, operate, and maintain CPS. This book is a result of a journal Special Issue, with the objective of providing a forum for researchers and practitioners to exchange their latest achievements and to identify critical issues, challenges, opportunities, and future directions for how to deal with the complexity of future CPS. The contributions include 10 papers on the following topics: (I) Systems and Societal Aspects Related to CPS and Their Complexity; (II) Model-Based Development Methods for CPS; (III) CPS Resource Management and Evolving Computing Platforms; and (IV) Architectures for CPS.

Model-based systems engineering (MBSE) has been accepted as an extremely important approach to understanding the multi-domain research field comprehensively. In this paper, a bibliometric analysis is used to conduct a comprehensive survey of model-based systems engineering literature of the last five years. The VOSViewer is used to implement visual network analysis for co-authorship, citation, and co-occurrence of the MBSE related papers. The results of the bibliometric study, firstly, reveal the influential research teams and sources. Secondly, research hotspots in the current MBSE domain are identified. The findings of this study aim to help researchers to gain a faster and deeper understanding of the current literature on MBSE.

Smart infrastructure has the potential to revolutionise how infrastructure is delivered, managed and automatically controlled. Data and digital twins offer an opportunity to enable this revolution and secure sustainable future smart infrastructure. In this article, we discuss data as an engineering tool and propose to use data throughout the asset’s whole life cycle from identifying the need, planning and designing to construction, operation, integration and maintenance. This requires systems thinking where focus is not limited to the problems but rather constructs a systemic perspective to understand the interrelationships between components and systems. Future infrastructure is connected, intelligent and data-driven. To enable more sustainable decision-making, we should not only consider how to integrate different infrastructure elements but also use data to monitor, learn from and inform decisions. To this end, we present a case study where several assets, such as bridges, railways and transport systems are integrated, and data are curated for the purpose of aiding climate-conscious, sustainable decision-making. An example systems architecture for integration of different digital twins is explained and benefits of this data-driven, systemic perspective are discussed.

High quality, trustworthy data can help organisations build strategies, capture value, increase the potential of automation and enable insightful and fast decision-making. Data could change the cities we inhabit through real-time solutions to challenges such as traffic congestion, air quality, energy distribution and monitoring. Only the collection, curation and whole-life accessibility of high-quality data can help us to optimise the performance and maintenance of our existing infrastructure, including roads, railways, bridges, buildings and undergrounds. This could make a big difference in a country where we add only 0.5 per cent annually to the capital values of our inherited assets. Vitally, by enabling us to use our infrastructure efficiently and for longer, a sustainable, carbon-free world is within reach. However, despite this potential, research shows that as few as 10 per cent of companies are attempting to put data and artificial intelligence to work across their businesses. Some industries such as telecommunications, automotive and financial services are doing relatively well catching up with the level of maturity seen in information and communication technologies, while others such as health care, education, government, and construction are still not close to realising the full potential of data. Adopting data-oriented approaches is a destination, yet one cannot reach that point without taking the journey. This journey requires companies to curate, collect, assess, operationalise, analyse, visualise and algorithmise data. The process can be long, and new skill sets and perspectives are necessary – as well as investment – for a successful application. However, the opportunities are as limitless as the change is inevitable. Data is often referred to as “the new oil”. I always found this metaphor at once exciting and scary. Today we are, on one hand, grateful for the changes that oil fuelled. Yet on the other hand, one of the world’s biggest struggles today is waste from this revolution. Now that we are at the beginning of a new era, which many call the fourth industrial revolution, it is vital to understand how data-related decisions of today can affect the future and minimise waste from the start. Therefore, it is essential to acquire the fundamentals of data, know how it will be useful for our industry and learn the lessons of other industries to avoid repeating their mistakes. To this end, our strategy should be not only collecting data but collecting the right amount of data for the right purpose, instead of collecting data without a well-defined objective. This requires companies to ask important questions, put initial data management plans in action and continuously check the quality of the data. To enable sustainable, optimised decisions we need not only our data but also data from others. Thus, discussions on how to integrate and share data are more important than ever. If the traditional companies which could benefit most from data and artificial intelligence want to be able to compete, profit and help to build a sustainable world, the decision makers must start embracing data, hire the right people and put in place the required policies to gather the correct data, make it accessible and assess its quality. Only in this way will our industry be in a position to truly take advantage of the next industrial revolution.

We are living through a convergence of crises. In 2020, when the ongoing Covid-19 global pandemic spread across the world, it brought economic instability in its wake. Sectors of the built environment (BE), among others, were hit hard by a public health crisis followed swiftly by an unprecedented economic downturn. In 2018, the Intergovernmental Panel on Climate Change (IPCC) report highlighted the need for rapid and drastic action on climate change by 2030 to prevent the disastrous effects of a world warmed by more than 1.5 degrees C above preindustrial levels, raising the urgency of achieving the United Nations (UN) Sustainable Development Goals (SDGs) adopted by Member States in 2015. Even rapid decarbonisation, the report warned, would likely not be sufficient to address the intertwined problems of poverty, mass migration, politics and ecological collapse that the SDGs seek to address. Digital technology offers an opportunity to better understand and model solutions to these complex crises, but it is unclear how digital technology should be harnessed in the face of an uncertain future. Written at the beginning of this critical decade leading up to 2030, this paper looks ahead 20 years in the future to better understand the resources, technology, economy, governance, infrastructure, mobility and social factors that may shape the development of Britain's digital BE. By exploring four scenarios around the variables of i) the UK's compliance with the interconnected targets of the SDGs and ii) the size of the workforce relative to the dependent population (dependency), this paper concludes with the identification of key strategies that can lead to the sustainable development of the BE sectors, outlining a number of actions that should be combined with the path for recovery from Covid-19 and are based on digital technology and a green information economy that ensures a future better for everyone in a digital built Britain.

Cyber-Physical Systems (CPS) are a result of highly cross-disciplinary processes and are evolving to perform increasingly challenging tasks in dynamically changing environments. This leads to an increasing CPS complexity and therefore the management of uncertainty to ensure the trustworthiness of these systems is needed. Our paper focuses on uncertainty management (UM) both in general and more specifically in the context of CPS situation awareness (SA). The motivation behind this is the important role of SA and its many inherent uncertainties. To this end, firstly, a literature review is conducted to acquire the state of the art of UM. Later, we present findings and observations from the literature review, with two main challenges identified - inconsistent understanding and terminology among a multitude of uncertainty perspectives, and a lack of collaboration among different communities. On this basis, lastly, two case studies are conducted to exemplify the challenges and provide brief ideas on how to deal with them. The whole investigation in the paper suggests an urgent strengthening of common understanding through enhanced collaboration and regulations.

Big data and analytics played an important role in open innovation during the pandemic.Sharing data and transferring knowledge between governments, laboratories and research centres helped us to understand the unpredictable spread of COVID-19.This article firstly explores corporate and public responses to the pandemic, presents different cases and discusses how open innovation, worldwide collaboration and data shaped this response.Having data practises in focus, this article raises concerns and underlines issues related to the applications during responses to COVID-19 at collaborative open innovation projects.

Future cyber-physical systems (CPS), such as smart cities, collaborative robots, autonomous vehicles or intelligent transport systems, are expected to be highly intelligent, electrified, and connected. This study explores a focal question about how these new characteristics may affect the education and research related to CPS in 2030, the date identified by the United Nations to achieve the Agenda for Sustainable Development. To this end, first, we have conducted a trend spotting activity, seeking to identify possible influencing factors that may have a great impact on the future of CPS education and research. These factors were clustered in a total of 12 trends - four certainties; namely connectivity, electrification, data and automation - and eight uncertainties; namely intelligence, data ethics, labour market, lifelong learning, higher education, trust in technology, technological development speed, and sustainable development goals. After that, two of the eight uncertainties are identified and used to construct a scenario matrix, which includes four scenarios. These two uncertainties - the so-called strategic uncertainties - are: fulfilment of sustainable development goals and the nature of the technological development, respectively. These two important uncertainties are considered to build the scenarios due to their potential impact on the research and education of CPS. For instance, sustainable development goals are significant targets for many initiatives, organisations and countries. While 2030 is the deadline to achieve these goals, the relationship between the sustainable development goals related to CPS research and education is not studied well. Similarly, the speed of technological development is seen as a driving force behind future CPS. However, the effect of this speed to CPS research and education environment is not known. Different outcomes of the chosen two uncertainties are, then, combined with the remaining trends and uncertainties. Consequently, four scenarios are derived. The Terminator scenario illustrates a dystopian future where profit is the driving force behind technological progress and sustainable development goals are not accomplished. In contrast, The Iron Giant scenario represents the successful implementation of the sustainable development goals where technological development is the force behind the accomplishment of these goals. The scenario called Slow Progress represents a future where gradual technological improvements are present, but sustainability is still not seen as concerning the issue. The Humanist scenario illustrates a future where slow technological development is happening yet sustainable development goals are successfully implemented. Finally, the scenarios are used to initiate discussions by illustrating what the future of research and education could look like and a list of strategies for future CPS research and education environments is proposed. To this end, we invite educators, researchers, institutions and governments to develop the necessary strategies to enable data-orientated, continuous, interdisciplinary, collaborative, ethical, and sustainable research and education by improving digital fluency, advancing digital equality, contributing to new ways of teaching complex thinking, expanding access to learning platforms and preparing next generations to adapt for a rapidly changing future of work conditions.