The paper describes the use of immersive technologies and Digital Twins in the architecture, engineering, and construction (AEC) industry. Immersive technologies have immense potential to improve interdisciplinary collaboration as they utilize a visual representation of virtual objects in their real-world perspective. This immersion facilitates effective communication between all parties involved, e.g. to identify errors during the planning and construction of buildings. A specific example of such interdisciplinary collaboration is kitchen planning. The architect draws the house and room plans, the kitchen planner plans the kitchen according to the homeowner’s wishes and the craftsmen then carry out their installations according to the plans for electricity, water, and heating. All plans are based on flat floors and perpendicular walls. However, this is rarely the case in a real building. The challenge with the exact visual representation of virtual objects in the real world using AR therefore lies in the positioning of objects such as a cupboard in relation to the non-orthogonal coordinate systems that result for each room from the real floors and walls. The paper presents an assistance system that integrates immersive technologies and Digital Twins. It is a solution approach for the transformation of object properties based on orthogonal coordinate systems into the non-orthogonal coordinate systems prevalent in reality. With the assistance system, the kitchen planner can create his plans using virtual reality (VR), customers can experience their kitchen virtually and the craftsmen can then be guided using augmented reality (AR), e.g. by precisely localising drilling positions for cabinets.
This study introduces a human-centered and transdisciplinary engineering-centered “4D” design model, comprising Discovering Service Touchpoints, Defining Service Sore Points, Depicting User Experience, and Design Idea Development phases, which are based on service encounter discovery and peal-end rule. The systematic travel experiment involving 30 volunteers across six city tours was conducted to analyze user behaviors and needs. Emotional change curves are utilized to optimize peak and end of travel experiences. The findings validate the 4D model for redefining service encounters and leveraging the peak-end rule for the improvement of public transport services.
Snowmaking and snow storing are increasingly used as climate adaptation strategies in ski resorts all over the world, including in the Arctic. While the decrease of the number of snow cover days is slower than in the Alps, snow security is decreasing particularly at the beginning of the skiing season in October-November. As there is up to 30-times difference between minimum and optimal conditions in the energy and water consumption in snowmaking, it makes sense to optimize the timing of snowmaking to ensure that snowmaking will not turn into maladaptation. Climate services are user-friendly ways of providing relevant climate information for end-users. Our team co-designed a climate service prototype for winter tourism centers in Northern Finland in 2017-2020 by a transdisciplinary co-design process involving climate science, modelling, tourism research, and practitioners including snowmaking professionals and environmental experts from a pilot enterprise. The versatile co-design methods utilized included e.g. visual methods and workshops, and co-evaluation of the prototype. The resulting climate service prototype SnowApp provides a reliable 4-week forecast on snowmaking conditions and hence it is a decision-support tool for ski resort management. The prototype is applicable in other geographical locations, too, and for other snow dependent businesses.
Predicting product quality is a crucial element in smart manufacturing. Then, the development of robust learning models is essential based on historical data relevant to their respective products and production processes. The challenge of quality prediction models lies in the limited availability of production batches and their associated historical data in highly customized products like large power transformers, precision machine tools, and other industrial equipment. Despite the small dataset, ensuring the quality of the final product remains paramount. This study introduces an innovative transfer learning approach integrating adaptive machine learning and non-linear regression. The goal is to accurately predict the quality of highly customized products using datasets limited by the constraints of the primary suppliers with smaller production scales. The research employs a case study and dataset from the production of large power transformers. The input data for training and testing the predictive model include key power transformer parameters, such as core loss values and power loss. The approach utilizes transfer learning that transfers knowledge gained from one task (e.g., predicting the quality outcome of 50 kVA transformers) to a similar task (e.g., predicting the quality outcomes of 25 kVA and 100 kVA transformers). The proposed method enhances the model’s performance and generalization capabilities. Subsequently, the model is fine-tuned rapidly without compromising accuracy. This paper contributes to a comparative analysis with previous research, demonstrating the effectiveness and superiority of the proposed method. Manufacturers can leverage this approach to predict the quality of complex, small-batch, and highly customized industrial products. Ultimately, this method aids in improving production quality and reducing costs.
The dominant transport mode for school journeys is safe, comfortable and affordable private automobiles. The transport system consists of well-engineered roads. Carers know that the car is usually the safest and most convenient option for the school run. Traffic congestion, air pollution, loss of exercise and increased risk to pedestrians and cyclists are thoroughly researched externality costs of the automobile school run. Net zero emission targets impose additional tensions for schools and local authorities to make rapid changes. Travel demand management research has been aiming to keep motorised vehicles away from schools, and to encourage physically active modes for the school run. But changing the infrastructure, travel behaviour and culture of a school community presents a wicked problem that requires transdisciplinary systems approaches from engineering and social science. This article reports on the Transition Engineering co-design process with incumbent stakeholders. The result is a novel learning and teaching programme which also achieved safe and sustainable school transport. The programme enables students to engineer the changes they require, communicate their needs to decision makers, and achieve net zero with fair provision. The school culture shifted to finding their own way to net zero school transport, contributing to wider community travel demand management efforts. The protocol of the design process, the co-design results, and a reflection on the preliminary stakeholder feedback are presented. The article addresses the wider challenge of how transdisciplinary transition engineering can deliver safety and sustainability of incumbent engineered systems, while navigating real-world social and economic dynamics.
At present, only around 10% of the heat pumps required to reach our critical 2050 climate goals are being installed in the UK. The government has set ambitious targets to phase out gas boilers by 2035, replacing them with heat pumps. This paper argues that instead of viewing the low carbon heating transition as a simple techno-economic issue, solved by a technology swap, we need a transdisciplinary systems approach to address this complex socio-technical challenge. Drawing on previous research and the literature we identify the current level of heat pump uptake and consider some of the barriers to the low carbon heating transition including technical aspects, installers skill shortages, financial barriers and informational challenges. We find that these barriers are mostly addressed in silos without considering the interrelationship between different aspects. Heat pumps should be considered in the context of a whole house approach to retrofit and barriers need to be overcome to make the technology more attractive to households. In this paper we call for a systemic, transdisciplinary approach to the low carbon heating transition to accelerate uptake: combining an understanding of social, engineering and policy perspectives. Key to this are systems-based methods and transdisciplinary approaches that enable engineering and engineers to be part of the solution. We present the benefits of this approach and suggest some principles for further research.
This study explores transdisciplinary collaborations in smartwatch technology, with focus on health-related smart watch innovations. Among other leading brands, Apple Watch is a key player in this fast-growing market. Apple Watch has secured a remarkable position, boasting its market share to 37%. Specifically, it commands a 45% market share in the High-Level Operating System (HLOS) smartwatch sector, where advanced healthcare functions are focused. Utilizing advanced patent analysis techniques, including their technological clustering, maturity analysis, and technology function matrix (TFM), this research depicts technological innovations of smart watch, emphasizing on main features of mobile payment, watch bands, and physiological symptom monitoring based on robust domain ontology schema map. The study underscores the roles of transdisciplinary engineering in societal impact on disease care, reflecting on societal implications of smartwatch’s functional innovations. Considerations include issues of accessibility, sustainability, and popularization of health-related smart watch technologies. Through these transdisciplinary innovations, we explore opportunities for positive cares of societal changes (e.g., aging populations, elderlies live alone, and insufficient healthcare professionals per capita). Furthermore, the study discusses collaborative strategies between smartwatch and healthcare industries, highlighting roles of smaller companies in promoting diversity and innovation through strategic alliances. By mapping technological landscape and patent portfolios, this study identifies partnerships enhancing Apple Watch and its alliance’s competitiveness as a case study. This paper contributes to a comprehensive understanding of strategic positioning and alliances of technological innovations, particularly for the advanced of health-related digital smart solutions.
There are numerous initiatives and lighthouse projects around Gaia-X to create the basics of open, trustworthy data ecosystems. One of the challenges in building these data ecosystems is to convince everyone involved of the advantages of multilateral data sharing. A prerequisite is also a common understanding of the rights of use and access to the data. Data sovereignty means that the data provider decides which data with which usage and access rights he wants to make accessible to which user group. Standardised digital representations by, for instance, the Asset Administration Shell (AAS enable creation of such data ecosystems, interlinking the physical and digital space. In the paper at hand, a conceptual approach named Decide4Eco is presented with the aim to enable systematic and flexible decision-making support for product planning and development concerning the sustainability of a product and the entire value chain. Methods of sustainability assessment are expanded to include predictive AI-based effects analyses.
E-participation platforms have emerged as digital tool to facilitate citizen engagement through online deliberation, voting and oversight processes. The digital add-on for participatory democracy (Carole Pateman) can be found in different countries around the world. In Asia, the rollout of two platforms in Taiwan, namely iVoting and Join, has captured the attention of Western media outlets. However, there is little literature on the exact content debated and agreed on these platforms up to this point. Utilising recent advancements in NLP, we explore the content of the proposals that were made. In our study, we combine new approaches of text mining with political analysis on Taiwan’s e-participation platforms. The dataset, which includes 14,118 proposals from 2015 to 2022, has resulted in a distinct topic model being constructed for each platform. With the help of our method, we were able to cluster the proposals thematically and show which concerns were articulated and with how much approval. Based on a random sampling of 110 proposals, we were able to determine that our method assigns 81.82% of the proposals to the corresponding cluster. This can also significantly overcome language barriers, as we employed a translation pipeline within the text-mining process from Chinese into English. Our method is adaptable to e-participation platforms in various languages, providing decision-makers with a more comprehensive tool to understand citizens’ needs and enabling the formulation of more informed and effective policies.
Universities, while central to knowledge dissemination, face challenges in integrating their academic expertise into operational processes, often showing resistance to new technologies. This results in technological debt and inefficiencies in management systems, inadequately addressing the demands of digitalisation and affecting the quality of education and support services. There is a gap in the academic literature between theoretical models and their practical application in university administration, with existing methods not meeting the needs of modern educational environments. The contribution of this paper is the development of a transdisciplinary management framework designed to enhance the administration of academic institutions. This framework, derived from a comprehensive literature review, critiques traditional approaches, introduces a novel data model, explores its practical applications, and integrates digital strategies. A distinctive aspect of this review is the conceptualisation of academic quality as a quantitative construct, positioning it as both a deliverable and a critical axis for a digital transformation in Higher Education Institutions. This framework aims to standardise and modernise administrative processes in higher education, ensuring they align with the expectations of all stakeholders, including university staff, faculty, students, and the broader society. By implementing it, universities can make significant strides towards more effective and efficient administration, indirectly contributing to societal advancement. It focuses on improving institutional management and suggests a pathway towards a more accessible and high-quality educational landscape. This initiative exemplifies the potential of technological innovation in administrative practices, leveraging engineering principles to impact society positively.
The Digital Transformation demands constant societal updating, accompanied by technological evolution to develop new projects, products, and services. Organizations, aware of the need to adapt to these changes, seek to understand emerging technologies’ meanings, concepts, and premises. However, this process is challenging, given the complexity of reconciling the external environment with the internal conditions of organizations and understanding, assimilating, and adopting innovations. Studies and scientific papers have addressed technical issues and explored critical success factors, barriers, limitations, and restrictions, proposing tools, models, frameworks, and methodologies to overcome organizational gaps. However, the lack of a direct connection between technologies and people’s skills and capabilities can hinder the digital transformation journey in organizations. In this context, this paper proposes a multi-criteria approach to correlating the evaluation criteria of Industry X.0 enabling technologies with human resources skills in the organizational environment. This research is applied to a Brazilian Electronics Manufacturing Industry case using the AHP method and TOPSIS Multi-Criteria Decision Making (MCDM). The findings show the possibility of a technical/social correlation, which can help managers better allocate internal resources according to the digital transformation technologies to be implemented in their products.
In the last four decades of reform, China’s economy has achieved historic success, propelled by rapid industrialization. However, this growth has adversely affected the environment. Addressing this, China is actively pursuing a balanced model of development that harmonizes economic and environmental interests. This study adopts transdisciplinary engineering approach. We first construct the green technology innovation efficiency indicators based on literature review and then adopt the super-efficiency SBM-DEA model considering undesirable outputs to measure the industrial green technology innovation efficiency values of 30 provincial administrative regions in China from 2008 to 2020. Sub-regionally, the eastern, central, and western areas differ significantly from one another; developed coastal provinces and certain central and western provinces have greater levels of efficiency.
Intensified competition, heightened customer demands, and mounting pressure for cost savings and carbon footprint reduction characterise the dynamic landscape of contemporary business. Thus, the efficiency of distribution channels plays a pivotal role in meeting the evolving needs of diverse stakeholders. Management’s concern for the seamless flow of products to customers is underscored by imperatives to lower costs, enhance customer satisfaction, adhere to policies and regulations, and sustain personnel wellbeing and motivation. Operational research initiatives such as the Vehicle Routing Problem (VRP) have partially addressed these challenges. However, a comprehensive solution must transcend mathematical optimisation techniques to incorporate the intricate interplay between human resources, sales, customer service, and logistics. Going beyond traditional quantitative solutions, the contribution of this research is twofold: 1) a transdisciplinary framework designed to address the optimisation of distribution channels holistically by recognising the importance of negotiation among key stakeholders. Thus, the VRP solution, while indispensable, serves as a facilitative tool for negotiation, and 2) an industrial case is included to illustrate the application of the transdisciplinary framework, offering practical insights and managerial recommendations for implementing optimised routing strategies for the benefit of stakeholders. This research promotes social change by fostering a collaborative environment beyond mathematical efficiency by integrating environmental and human elements into the decision-making process.
Future energy scenarios usually show pathways to green energy futures are possible. However, since the 2015 Paris Agreement, scientific scenarios show human activity is accelerating toward catastrophic failures and loss. A group of transdisciplinary thinkers discussed the history of sustainability and contemplated how a disruptive shift could occur in time for energy decarbonisation and climate stabilisation. How have transitions occurred in the past, particularly those that involved corrective transdisciplines like fire safety, emergency management, food safety, or waste management? After man-made disasters, engineering and operations fundamentally change through duty of care. Corrective shifts in economic, policy and cultural paradigms seem to follow the evolution of engineering practice. Over time, the prevention of harm and loss is manifested in technological enterprise, infrastructures, energies, and behaviour. The only way the whole-system transition changes the trajectory from danger of catastrophic failure to survivable and thrive-able future is that a corrective transdiscipline evolves now. We followed a logic process, framing an argument, developing a supporting theory, and brainstorming the methods involved. The argument is that since 1970 millions of people have gained awareness of future risks, and a sufficient number have focused their working careers on sustainability. The sustainability-active people are not having sufficient impact to cause a corrective transition, because they have become a diaspora. Our reasoning follows that just transition will eventuate when the diaspora converges to a corrective transdiscipline and create training and research programmes which are valued by industry and policy.
As autonomous driving system (ADS) continues to evolve, adopting Society of Automotive Engineers (SAE) levels 4∼5 concepts, the advances of driving automation/autonomy technologies include to fulfill modern vehicle market demands. ADS solutions require large integrations of multiple engineering disciplines, such as mechanical engineering, electrical engineering, computer science, and cognitive science for ADS’ human-machine interactions. Many ADS developers are actively engaged in R&D in interdisciplinary technologies, such as the domains of perception systems, communication systems, automatic route/path planning, and autonomous driving safety systems. Despite the rapid progress in ADS, there are challenges in various ADS domains to allow its popular- and safe-adoptions on the road! Therefore, this research conducts a comprehensive patent analysis for ADS domains. We first define the domain ontology schema (based on ADS literatures and experts’ verifications). Then, advanced patent search strategies (combing semantic and keyword Boolean search methods) and macro- and micro-levels patent analyses are conducted to present global ADS patent landscape. Overall, this study integrates multiple ADS transdisciplinary knowledge domains and patent analytical methods with various viewpoints. The analytical result provides a viable technological direction(s) for global ADS R&D teams to accelerate high-level autonomous vehicle development. Finally, based on the patent data resources, we also zoom in the ADS patenting landscape of Taiwan’s automotive companies and their main suppliers in ITC components. This research provides evident-based recommendations on the innovation and patenting strategies to increase their competitiveness in the ADS solution.
Given their obvious need, why is it so hard for new pro-poor, engineering-based inclusive innovation (EII) to become more mainstream? Can new trajectories emerge that are more inclusive and environmentally sustainable? Those interested in these questions have studied the role of science and engineering in development but have faced a range of constraints. These include the poverty of those who might benefit most but also institutional barriers to the inclusion of some actors with knowledge and experience of scaling innovations towards the mainstream. This paper presents new theories and a set of case studies of attempts to scale and mainstream innovations. We have gathered data from and analysed scaling up case studies from different sectors and geographies. The paper advocates for the advantages of evolutionary approaches to development engineering that take account of institutional variety over static, neo-classical and one-size-fits-all approaches. We show that one size fits all does not apply to scaling up for engineering-based inclusive innovations. We illustrate that engineering innovations not replicated on a large scale have not necessarily failed. We conclude by arguing that it is possible to go beyond market failure approaches towards a more agile framework for the delivery of innovations and suggest that our results resonate with broader changes in the greening of the global economy.
Today’s industry needs to be able to produce customized products in small batches with agility, low cost, and acceptable quality levels. The constant introduction of new products requires a rapid response to changes in the market and the implementation of innovative strategies. Manufacturing systems have evolved to meet market demands, considering crucial variables such as complexity, innovation, and responsiveness. Automation, using intelligent devices and various types of control, proposes models and solutions to make industrial operations more flexible. Petri Net modelling, a graphical technique that provides a clear and concise visual representation of complex systems, may intuitively map production line processes. Integrating world-class manufacturing (WCM) principles enhances the framework’s effectiveness. In this context, this article proposes a conceptual framework that combines reconfigurable manufacturing systems (RMS) concepts modelled by Petri Nets, the assignment of weights inspired by WCM, and simulation using the Digital Twin (DT). The framework offers a new horizon for achieving operational efficiency and continuous adaptation. Using simulation in a virtual environment it took a proactive approach to validate the framework’s effectiveness. This crucial step makes it possible to compare the results obtained virtually with the actual processes on the production lines. Applying this simulation verifies the model’s robustness and provides crucial insights for ongoing adjustments.
Renewable energy, especially offshore wind energy is one of the big focuses in aiming zero-carbon society in the world and in Japan, as well. In progressing such projects, social acceptance of the local community is one of the important problems. Recently, it is said that conventional NIMBY (not in my backyard) model is not enough in explaining resident’s perceptions and promote social acceptance. Even in ongoing offshore wind farm projects, in fact, there are small number of opponents and large number of uninterested. This paper tried to know why people oppose, agree or have no interests on the projects, and also tried to quantify the correlations of perceptions on offshore wind energy versus other social issues such as climate change, resource depletion etc. Since such information will show the concerns of people who are opposing to or having no interests on wind energies, it would be helpful in explaining the significance of the projects to the local communities. The authors carried out an internet survey to investigate people’s perceptions regarding offshore wind energy. Throughout the survey, the correlations between the other social issues and the attitude against offshore wind energy have been clarified. For most of the problems, residents’ perceptions and non-residents’ perceptions had similarities. However, for other social problems such as “aging population” etc., and other energy issues such as “energy price” also had different influence on residents and non-residents. The information can be the fundamental data in order to know what are the key points to be explained and how to build win-win relations between business stakeholders and local communities. The authors would like to contribute in establishing wide social acceptance to progress the project and achieve carbon neutral society.
In order to cope with the increased demand for air transportation, air traffic system modernization projects have been undertaken in many countries. In future air traffic, the concept of time-based management (TBM) is being considered to improve the efficiency of air traffic flow. However, TBM is currently only a concept and it is not clear how it should be implemented in practice. This study therefore identifies the potential needs of stakeholders for time-based management. We created a stakeholder value network (SVN) for the stakeholders involved in the next generation air traffic management system and identified the primary stakeholders. We also conducted systemized interviews based on the Kano model with the primary stakeholders to identify their needs. By analyzing the SVN and interview results, the discussion was conducted on the potential needs of the primary as well as secondary stakeholders for a future control system to realize TBM.
Traffic control systems, encompassing both air traffic control (ATC) and vessel traffic service (VTS), play a pivotal role in ensuring the safety and efficiency of transportation across aviation and maritime domains. High situation awareness (SA), which includes the ability to perceive information (Level 1 SA), comprehend the meaning (Level 2 SA), and predict future states (Level 3 SA), is crucial for traffic controllers to manage their respective traffic environments effectively under various workload conditions. Eye-tracking (ET) is a prominent physiological method to quantitatively assess operators’ cognitive workload and situation awareness as eye movements are closely related to one’s attention and information-processing mechanisms. This study aims to investigate the effects of SA levels and workload conditions on traffic controllers’ eye movement patterns using ET technology. Using ATC as a case study, experiments were conducted to collect eye movement data of 26 participants while they monitored aircraft on a simulated radar screen and answered freeze-probe queries of different SA levels (Level 1 and Level 2). Workload conditions were varied by the number of aircraft participants needed to monitor. Two-way repeated measures ANOVA showed significant main effects and interaction effects of SA and workload levels on certain ET metrics (e.g., total fixation duration, average fixation duration). This study contributes insights that could enhance the existing knowledge of SA levels in traffic control tasks and reinforce the evidence base linking eye movements and cognitive processes. Applying these insights to maintain optimal SA could reduce human error and elevate safety standards across different transportation sectors.
