The importance of automating pavement maintenance tasks for highway systems has garnered interest from both industry and academia. Despite significant research efforts and promising demonstrations being devoted to reaching a level of semi-automation featuring digital sensing and inspection, site maintenance work still requires manual processes using special vehicles and equipment, reflecting a clear gap to transition to fully autonomous maintenance. This paper reviews the current progress in pavement maintenance automation in terms of inspection and repair operations, followed by a discussion of three key technical challenges related to robotic sensing, control, and actuation. To address these challenges, we propose a conceptual solution we term Autonomous Maintenance Plant (AMP), mainly consisting of five modules for sensing, actuation, control, power supply, and mobility. This AMP concept is part of the “Digital Roads” project’s cyber-physical platform where a road digital twin (DT) is created based on its physical counterpart to enable real-time condition monitoring, sensory data processing, maintenance decision making, and repair operation execution. In this platform, the AMP conducts high-resolution survey and autonomous repair operations enabled (instructed) by the road DT. This process is unmanned and completely autonomous with an expectation to create a fully robotized highway pavement maintenance system.

It’s less than a year since OpenAI’s board voted to fire Sam Altman as CEO, in a palace coup that lasted just a weekend before Altman was reinstated. That weekend and subsequent events in OpenAI’s storyline provide all the ingredients for a soap opera. So, just in case Netflix is interested, here’s a stab at a synopsis of what might be just the first of many seasons of ‘The Generative AI Wars’.

This article outlines a human-centered approach to developing digital patient stories, for sharing their experiences in health care, while preserving patient and others’ privacy. Employing a research-through-design approach, the study proposes a design solution using visualization and digital storytelling to document patients’ and families’ experiences and emotions, as well as their interactions with healthcare professionals in the postnatal unit. By transforming selected observational data into animated stories, this approach has the potential to elicit empathy, stimulate stakeholder engagement, and serve as a practical training tool for clinicians. This work was conducted as part of a broader study that aims to contribute to the existing knowledge base by advancing our understanding of stakeholder needs in birthing facilities and through postpartum discharge. This study primarily focuses on strategies for the development of digital stories and summarizes the factors that contributed to the production of digital stories within the context of sensitive data. It may serve as a valuable resource for students, researchers and practitioners interested in utilizing digital stories to encourage discussions, education and ultimately to enhance systems of health care for respect, equity and support.

Data mining and techniques for analyzing big data play a crucial role in various practical fields, including financial markets. However, only a few quantitative studies have been focused on predicting daily stock market returns. The data mining methods used in previous studies are either incomplete or inefficient. This study used the FPC clustering algorithm and prominent clustering algorithms such as K-means, IPC, FDPC, and GOPC for clustering stock market data. The stock market data utilized in this study comprise data from cement companies listed on the Tehran Stock Exchange. These data concerning capital returns and price fluctuations will be examined and analyzed to guide investment decisions. The analysis process involves extracting the stock market data of these companies over the past two years. Subsequently, these companies are categorized based on two criteria: profitability percentage and short-term and long-term price fluctuations, using the FPC clustering algorithm and the classification above algorithms. Then, the results of these clustering analyses are compared against each other using standard and recognized evaluation criteria to assess the quality of the clustering analysis. The findings of this investigation indicate that the FPC algorithm provides more favorable results than other algorithms. Based on the results, companies demonstrating profitability, stability, and loss within short-term (weekly and monthly) and long-term (three-month, six-month, and one-year) time frames will be placed within their respective clusters and introduced accordingly.

Under the umbrella concepts of upscaling and emerging technology, a wide variety of phenomena related to technology development and deployment in society are examined to meet societal imperatives (e.g., environment, safety, social justice). The design literature does not provide an explicit common theoretical and practical framework to clarify the assessment method to handle “an” upscaling. In this nebulous context, designers are struggling to identify the characteristics to anticipate the consequences of emerging technology upscaling. This article therefore first proposes a structuring framework to analyze the literature in a wide range of industrial sectors (energy, chemistry, building, etc.). This characterization brought to light five prevalent archetypes clarifying the concepts of upscaling and emerging technology. Then, a synthesis of invariants and methodological requirements for designers is proposed to deal with upscaling assessment according to each archetype, based on a literature review of existing design methods. This literature review process showed a disparity in treatment for some archetypes, regarding the industrial sector. A discussion is consequently proposed in the conclusion to guide design practices.

To improve understanding of prototyping practice at the fuzzy front end of the design process, this article presents an analysis of a prototyping dataset captured during the IDEA challenge – a 4-day virtually hosted hackathon – using Pro2booth, a web-based prototype capture tool. The dataset comprised 203 prototypes created by four independent teams working in university labs across Europe supported by interviews carried out with each of the teams after the event. The results of the study provide nine key findings about prototyping at hackathons. These include elucidation of the purposes of prototypes in physical, digital and sketch domains and characterisation of teams’ prototyping practices and strategies. The most successful strategy focused on learning about the problem or solution space, often via physical prototypes rather than following more prescriptive ‘theoretical’ methodologies. Recommendations on prototyping strategies in hackathons or similar scenarios are provided, highlighting the importance of practical strategies that prioritise learning and adaptation. The results of this study raise the broader question to the wider research community of how design research and teaching should balance high-level strategic approaches with more hands-on ‘operational’ prototyping.

While governments have long discussed the promise of delegating important decisions to machines, actual use often lags. Consequently, we know little about the variation in the deployment of such delegations in large numbers of similar governmental organizations. Using data from crime laboratories in the United States, we examine the uneven distribution over time of a specific, well-known expert system for ballistics imaging for a large sample of local and regional public agencies; an expert system is an inference engine joined with a knowledge base. Our statistical model is informed by the push-pull-capability theory of innovation in the public sector. We test hypotheses about the probability of deployment and provide evidence that the use of this expert system varies with the pull of agency task environments and the enabling support of organizational resources—and that the impacts of those factors have changed over time. Within this context, we also present evidence that general knowledge of the use of expert systems has supported the use of this specific expert system in many agencies. This empirical case and this theory of innovation provide broad evidence about the historical utilization of expert systems as algorithms in public sector applications.

Within Holocaust studies, there has been an increasingly uncritical acceptance that by engaging with social media, Holocaust memory has shifted from the ‘era of the witness’ to the ‘era of the user’ (Hogervorst 2020). This paper starts by problematising this proposition. This claim to a paradigmatic shift implies that (1) the user somehow replaces the witness as an authority of memory, which neglects the wealth of digital recordings of witnesses now circulating in digital spaces and (2) agency online is solely human-centric, a position that ignores the complex negotiations between corporations, individuals, and computational logics that shape our digital experiences. This article proposes instead that we take a posthumanist approach to understanding Holocaust memory on, and with, social media. Adapting Barad's (2007) work on entanglement to memory studies, we analyse two case studies on TikTok: the #WeRemember campaign and the docuseries How To: Never Forget to demonstrate: (1) the usefulness of reading Holocaust memory on social media through the lens of entanglement which offers a methodology that accounts for the complex network of human and non-human actants involved in the production of this phenomenon which are simultaneously being shaped by it. (2) That professional memory institutions and organisations are increasingly acknowledging the use of social media for the sake of Holocaust memory. Nevertheless, we observe that in practice the significance of technical actancy is still undervalued in this context.

Design occurs in complex socio-technical contexts with conflicting stakeholder goals, requirements and other constraints. These limit solution options and create trade-offs where improvements relative to one goal come at the expense of performance on another. Little is known about how the design context influences trade-offs, or how designers interact with context to manage trade-offs. This article reports on an exploratory qualitative interview study investigating design trade-offs in relation to their socio-technical context. We identified nine themes reflecting engineering designers’ perceptions of the influence of the design context on their ability to resolve trade-offs. Findings suggest that the design context is both a source of trade-offs, and of knowledge and information that helps designers clarify ambiguous requirements to navigate and resolve trade-offs. The results provide insight into how designers interact with the design context to learn about the structure of their design problems and the degrees of freedom available to resolve trade-offs. The findings also contribute to understanding the effects of path dependencies in trade-off situations, and how the sequential distribution of design decisions over time constrains trade-off resolution. We discuss some of the goals and challenges of conducting rigorous qualitative research in design and identify potential directions for further research.