Systemic Design for the innovation of home appliances The meaningfulness of data in designing sustainable systems

This work addressed the domestic environment considering this context as a complex system characterised by significant impacts in terms of resource consumption. Within the theoretical framework of Systemic Design (SD), this thesis focused on home appliances, in order to understand how to reduce the impact directly attributable to them, while optimising and simplifying daily tasks for the user. A design methodology towards environmental sustainability has been structured, by focusing on the use of data for design purposes and on creating value for the user through meaningful products. It considers the user, the product and the environment as central topics, by giving them the same relevance and the literature review is structured accordingly, investigating needs and requirements, ethical issues, but also current products and future scenarios. During my experience at TU Delft, I spent six months in the Department of Internet of Things at the Faculty of Industrial Design Engineering. Together with computer scientists, we developed a prototype to collect some missing data, establishing the importance of grounding the decision-making on reliable information. IoT and data gathering open a variety of possibilities in monitoring, accessing more precise knowledge of products and households useful for design purposes, up to understand how to fill the gap perceived by the user between needs and solutions. It considered the potential benefits of using IoT indicators to collect missing information about both the product, its use and its operating environment to address critical aspects in the design stage, thus extending products’ lifetime. This thesis highlighted the importance of building multidisciplinary design teams to investigate different classes of requirements, and the need for flexible tools to cope with complex and evolving requirements, the co-evolution of problem and solutions and investigating open-ended questions. This approach leaves room for addressing every step of the traditional life-cycle in a more circular way, shifting the focus from the life-cycle centrality of the previous century to a more complex vision about the product

Silver Linings (Volume 1, Issue 2, 2023-2024)

https://scholarcommons.towerhealth.org/silver_linings/1000/thumbnail.jp

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding

Why are Heritage Interpreters Voiceless at the Trowel's Edge? A Plea for Rewriting the Archaeological Workflow

'Heritage interpretation' is generally conceived as the development and presentation of knowledge about the past for public audiences. Most obviously evidenced in descriptive signs, guides and related media installed on archaeological and cultural sites, heritage interpretation has more than a half-century of theory and applied practice behind it, yet it continues to sit uncomfortably within the typical archaeological workflow. While the concept can be criticized on many fronts, of concern is the lack of recognition that it is of equal relevance to *both* non-expert and expert audiences (as opposed to non-expert audiences alone). Our profession appears to rest on an assumption that archaeologists do their own kind of interpretation—and, separately, non-experts require a special approach that heritage interpreters must facilitate, but that field specialists have no need for—or from which little obvious expert benefit can be derived. For this reason, it is rare to find heritage interpreters embedded in primary fieldwork teams. Here I call for a rethinking of the traditional workflow, with a view to integrating the heritage interpretation toolkit and heritage interpreters themselves into our basic field methodologies. Their direct involvement in disciplinary process from the outset has the potential to transform archaeological interpretation overall

Managing artificial intelligence projects: Key insights from an AI consulting firm

While organisations are increasingly interested in artificial intelligence (AI), many AI projects encounter significant issues or even fail. To gain a deeper understanding of the issues that arise during these projects and the practices that contribute to addressing them, we study the case of Consult, a North American AI consulting firm that helps organisations leverage the power of AI by providing custom solutions. The management of AI projects at Consult is a multi-method approach that draws on elements from traditional project management, agile practices, and AI workflow practices. While the combination of these elements enables Consult to be effective in delivering AI projects to their customers, our analysis reveals that managing AI projects in this way draw upon three core logics, that is, commonly shared norms, values, and prescribed behaviours which influence actors\u27 understanding of how work should be done. We identify that the simultaneous presence of these three logics—a traditional project management logic, an agile logic, and an AI workflow logic—gives rise to conflicts and issues in managing AI projects at Consult, and successfully managing these AI projects involves resolving conflicts that arise between them. From our case findings, we derive four strategies to help organisations better manage their AI projects

The Impact of Social Business Process Management on Policy-making in e-Government

The combination of social media and Business Process Management (BPM) has given rise to the emerging field of “social BPM”. The new devel-opment of social BPM is expected to provide bene-fits like flexibility for knowledge-intensive pro-cesses, like policy-making. The goal of this paper is to understand the impact of social BPM on poli-cy-making. We first present a literature survey showing that social BPM is a new and emerging research area and limited attention has been giv-en to social BPM in e-government. The literature reviews showed a lack of empirical research into the accomplished benefits of social BPM. To bridge this gap, a comprehensive case study in a Dutch government social BPM platform was con-ducted. While not all the benefits suggested in the literature were identified in the case study, nega-tive impact of social BPM were also found. A ten-sion was found between accomplishing flexibility and accountability and user efficiency

Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness

In this modern era, land transports are increasing dramatically. Moreover, self-driven car or the Advanced Driving Assistance System (ADAS) is now the public demand. For these types of cars, road conditions detection is mandatory. On the other hand, compared to the number of vehicles, to increase the number of roads is not possible. Software is the only alternative solution. Road Conditions Detection system will help to solve the issues. For solving this problem, Image processing, and machine learning have been applied to develop a project namely, Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness. Many issues could be considered for road conditions but the main focus will be on the detection of potholes, Maintenance sings and lane. Image processing and machine learning have been combined for our system for detecting in real-time. Machine learning has been applied to maintains signs detection. Image processing has been applied for detecting lanes and potholes. The detection system will provide a lane mark with colored lines, the pothole will be a marker with a red rectangular box and for a road Maintenance sign, the system will also provide information of aintenance sign as maintenance sing is detected. By observing all these scenarios, the driver will realize the road condition. On the other hand situation awareness is the ability to perceive information from it’s surrounding, takes decisions based on perceived information and it makes decision based on prediction

On State-Level Architecture of Digital Government Ecosystems: From ICT-Driven to Data-Centric

The \digital transformation" is perceived as the key enabler for increasing wealth and well-being by politics, media and the citizens alike. In the same vein, digital government steadily receives more and more attention. Digital government gives rise to complex, large-scale state-level system landscapes consisting of many players and technological systems { and we call such system landscapes digital government ecosystems. In this paper, we systematically approach the state-level architecture of digital government ecosystems.We will discover the primacy of the state's institutional design in the architecture of digital government ecosystems, where Williamson's institutional analysis framework supports our considerations as theoretical background. Based on that insight, we will establish the notion of data governance architecture, which links data assets with accountable organizations. Our investigation results into a digital government architecture framework that can help in large-scale digital government design e_orts through (i) separation of concerns in terms of appropriate categories, and (ii) a better assessment of the feasibility of envisioned digital transformations. With its focus on data, the proposed framework perfectly _ts the current discussion on moving from ICT-driven to data-centric digital government