In an era characterized by unprecedented volatility, complexity, and systemic disruption, traditional models of organizational change based on linear planning and reactive adaptation have become insufficient. This paper introduces the concept of mutuation as a novel paradigm of enterprise transformation. Unlike mutation—which implies passive, stochastic adaptation—mutuation denotes a relational, anticipatory, and design-enabled mode of change that aligns internal transformation with external co-evolution. Drawing on a transdisciplinary synthesis of dynamic capabilities, futures literacy, corporate foresight, and systemic design, the study positions design as a critical infrastructure for orchestrating collective anticipation and distributed execution. Through the empirical lens of the SFIDA project, conducted within the Made in Italy Circolare e Sostenibile (MICS) partnership, the paper explores how design routines and speculative artifacts can build anticipatory capacity, foster inter-organizational collaboration, and catalyze strategic coherence in SMEs. The findings illustrate how a Distributed, Open, and Continuous (DOC) model of execution—enabled by design—can foster shared imagination, institutional learning, and sustained innovation. Ultimately, the paper argues for repositioning design as a reflexive and infrastructural agent of mutuation, capable of mediating meaning across human, organizational, and technological systems in the pursuit of futures-driven transformation.
Nidhal Selmi, Jean-michel Bruel, Sébastien Mosser
et al.
Decision-making is a core engineering design activity that conveys the engineer's knowledge and translates it into courses of action. Capturing this form of knowledge can reap potential benefits for the engineering teams and enhance development efficiency. Despite its clear value, traditional decision capture often requires a significant amount of effort and still falls short of capturing the necessary context for reuse. Model-based systems engineering (MBSE) can be a promising solution to address these challenges by embedding decisions directly within system models, which can reduce the capture workload while maintaining explicit links to requirements, behaviors, and architectural elements. This article discusses a lightweight framework for integrating decision capture into MBSE workflows by representing decision alternatives as system model slices. Using a simplified industry example from aircraft architecture, we discuss the main challenges associated with decision capture and propose preliminary solutions to address these challenges.
Giuseppe Boi, Barbara Camocini, Laura Daglio
et al.
This contribution presents an ongoing experimental participatory design activity aimed at exploring the regeneration of the historic village of Oliena in Sardinia, which suffered from depopulation and abandonment due to economic and social changes tightened by its territorial features. The activity was implemented through a reiterative design-driven process based on multidisciplinary, research-by-design, and community co-design approach. The research team guided the five annual event editions, involving young researchers from worldwide and collaborating with local associations, institutions, and experts. These contents stimulated community storytelling and narrative-building, increasing a sense of cultural identity, helping envision future urban regeneration scenarios, and going beyond the village borders to activate mutual connections with the territorial assets. The new awareness encouraged the establishment of the Oliena Centro Association and led to the reassignment of use and value to abandoned buildings.
3D graphics editing is crucial in applications like movie production and game design, yet it remains a time-consuming process that demands highly specialized domain expertise. Automating this process is challenging because graphical editing requires performing a variety of tasks, each requiring distinct skill sets. Recently, vision-language models (VLMs) have emerged as a powerful framework for automating the editing process, but their development and evaluation are bottlenecked by the lack of a comprehensive benchmark that requires human-level perception and presents real-world editing complexity. In this work, we present BlenderGym, the first comprehensive VLM system benchmark for 3D graphics editing. BlenderGym evaluates VLM systems through code-based 3D reconstruction tasks. We evaluate closed- and open-source VLM systems and observe that even the state-of-the-art VLM system struggles with tasks relatively easy for human Blender users. Enabled by BlenderGym, we study how inference scaling techniques impact VLM's performance on graphics editing tasks. Notably, our findings reveal that the verifier used to guide the scaling of generation can itself be improved through inference scaling, complementing recent insights on inference scaling of LLM generation in coding and math tasks. We further show that inference compute is not uniformly effective and can be optimized by strategically distributing it between generation and verification.
Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.
Laura Jamieson, Carlos Francisco Moreno-García, E. Elyan
This paper presents a review of deep learning on engineering drawings and diagrams. These are typically complex diagrams, that contain a large number of different shapes, such as text annotations, symbols, and connectivity information (largely lines). Digitising these diagrams essentially means the automatic recognition of all these shapes. Initial digitisation methods were based on traditional approaches, which proved to be challenging as these methods rely heavily on hand-crafted features and heuristics. In the past five years, however, there has been a significant increase in the number of deep learning-based methods proposed for engineering diagram digitalisation. We present a comprehensive and critical evaluation of existing literature that has used deep learning-based methods to automatically process and analyse engineering drawings. Key aspects of the digitisation process such as symbol recognition, text extraction, and connectivity information detection, are presented and thoroughly discussed. The review is presented in the context of a wide range of applications across different industry sectors, such as Oil and Gas, Architectural, Mechanical sectors, amongst others. The paper also outlines several key challenges, namely the lack of datasets, data annotation, evaluation and class imbalance. Finally, the latest development in digitalising engineering drawings are summarised, conclusions are drawn, and future interesting research directions to accelerate research and development in this area are outlined.
The article describes a systematic approach that is used at the Department of Engineering Graphics at Bauman Moscow State Technical University to teach students the possibilities of modern computer-aided design systems. A methodology for teaching the basics of creating solid-state geometric models of parts and drawings in the Compass-3D system based on a systematic approach is presented. The structure and content of classes are described. The educational materials are developed on the basis of the students' knowledge of stereometry acquired at school and the knowledge gained during the parallel study of the engineering graphics section "Projection drawing". Examples of practical tasks for students' independent work are given. The training begins with the construction of a flat contour, which is used as basis of a three-dimensional model. When constructing a contour, students practice to create the basic primitives that make up the contour, and superimposing geometric and dimensional dependencies on the primitives. The construction of solid-state models begins with the modeling of simple geometric bodies: a prism, a pyramid, a body of rotation. This allows students to focus on practicing shaping operations, and to better navigate the toolbars. Then, students consolidate their knowledge and skills in building models of more complex shapes, similar in design to mechanical engineering parts of two classes: "body of rotation" and "non-body of rotation". Students create electronic drawings based on the constructed models. At the same time, the following is being worked out: creating images in the drawing – views, sections, sections; applying dimensions while maintaining an associative connection with the part model; using drawing design elements. Examples of control tasks for editing created models and drawings are given, which are necessary for students to acquire skills important for engineering practice. It is noted that the acquired knowledge and skills can be used by students in further education. The proposed methodology can be recommended for use in the system of secondary and secondary vocational education.
Kenza Bouchaâla, Khaoula Qaissi, Mustapha Faqir
et al.
Deep drawing is a manufacturing process used to produce billions of metal containers, crucial for the aerospace industry in forming thin-walled components efficiently. However, wrinkling due to compressive instability remains a significant challenge in sheet metal forming. This study investigates plastic wrinkling in sheet metal forming to enhance the final drawn shape. A 3D finite element model using ABAQUS/Explicit was developed to examine the effects of mesh topology, blank holder force, and sheet thickness on wrinkling in AA2090 Al–Li alloy. The finding indicates that that an increase in blank holder force increases wrinkle number and amplitude. Additionally, it was found that a 2000 N blank holder force is necessary to prevent wrinkling.
Efficient waste management is crucial in the healthcare system due to the complex composition and associated risks to workers, patients, and the environment. While there is growing awareness of the need to change the system, there is still an urgent need for a sustainable healthcare system. Sustainability concepts can guide designers in taking action, from considering waste reduction in the choice of materials to optimizing management systems through information and staff training. Systemic design methods can help involve business teams in the development of sustainable practices and strategies for production, use, and disposal. Research aimed to identify the types of disposables produced according to their polymers and weight, and to detect associated problems. The quantification of results led to the development of strategies aimed at reducing the number of polymer types used in product manufacturing with A case study on the Neonatal Nasal Mask, a consumable device was conducted to apply sustainable design strategies.
Issues concerning integration and the relations between different ethnic groups within a changing contemporary society are becoming increasingly prominent in the social and scientific debate. Design skills can be employed as a tool to systematise this multiplicity, promoting innovation that respects diversity, comparison, and interaction between people and cultures.
The purpose of this paper is to provide a synthesis of a research experience, the development of a new Islamic Cultural Centre in the city of Turin (Italy), in which the intercultural design approach constitutes the founding theoretical and methodological basis for the development of the project.
Starting from the analysis of experiences developed in the field of national and international research, the authors developed a methodological approach to the theme of designing to promote intercultural dialogue, furthermore establishing collaboration agreements with local authorities for the development of the Cultural Centre.
سارینا دوستی, بهاءالدین محمودی, مهران بیجاری
et al.
فرایندهای کاتالیزور نوری با استفاده از انرژی خورشید، در زمره روشهای تصفیه شیمیایی نوین در تصفیه آلایندههای آلی رنگی میباشد که در حال حاضر توجه بسیاری را به خود جلب نموده است. کاتالیزور نوری کربن نیترید گرافیتی (g-C3N4) با استفاده از روش بسپارش حرارتی و با بهرهگیری (تکی و یا ترکیبی) از پیشمادههای ملامین، تیوره و کلرید آمونیم سنتز شد. کاتالیزورهای نوری سنتز شده برپایه g-C3N4 برای بررسی ساختار، ریختشناسی و خواص آنها با استفاده از آنالیزهای XRD، FT-IR، SEM، EDX، BET، PL، و DRS مورد بررسی قرار گرفتند و به منظور تخریب ماده رنگزای کاتیونی رودامین بی (RhB) استفاده شدند. نتایج آزمایشات نشان داد که کربن نیترید گرافیتی سنتز شده با پیشماده ترکیبی ملامین، تیوره و کلرید آمونیم (MTA) با دوز 0.2 گرم بر لیتر و غلظت ماده رنگزای 50 میلیگرم بر لیتر بیشترین عملکرد را بر تخریب RhB داشته و در بازه زمانی120 دقیقه (2 ساعت) سبب رنگزدایی 100 درصدی از محلول رنگزا شد. همچنین کارایی کاتالیزوری نوری MTA در تخریب ماده رنگزای RhB در پنج چرخه متوالی تقریباً ثابت بوده است. علاوه بر این، طبق نتایج آزمایشات به دام انداختن گونههای فعال رادیکال سوپر اکسید (•O2-) گونه غالب برای فرآیند تخریب RhB بود. از این رو، MTA را میتوان به دلیل مزایایی مانند سنتز یک مرحلهای، عملکرد کاتالیزور نوری موثر و قابلیت استفاده مجدد، یک کاتالیزور کارآمد در نظر گرفت.
Aline de Campos, Jorge Melegati, Nicolas Nascimento
et al.
Generative Artificial Intelligence (GenAI) has become an emerging technology with the availability of several tools that could impact Software Engineering (SE) activities. As any other disruptive technology, GenAI led to the speculation that its full potential can deeply change SE. However, an overfocus on improving activities for which GenAI is more suitable could negligent other relevant areas of the process. In this paper, we aim to explore which SE activities are not expected to be profoundly changed by GenAI. To achieve this goal, we performed a survey with SE practitioners to identify their expectations regarding GenAI in SE, including impacts, challenges, ethical issues, and aspects they do not expect to change. We compared our results with previous roadmaps proposed in SE literature. Our results show that although practitioners expect an increase in productivity, coding, and process quality, they envision that some aspects will not change, such as the need for human expertise, creativity, and project management. Our results point to SE areas for which GenAI is probably not so useful, and future research could tackle them to improve SE practice.
Creating and understanding art has long been a hallmark of human ability. When presented with finished digital artwork, professional graphic artists can intuitively deconstruct and replicate it using various drawing tools, such as the line tool, paint bucket, and layer features, including opacity and blending modes. While most recent research in this field has focused on art generation, proposing a range of methods, these often rely on the concept of artwork being represented as a final image. To bridge the gap between pixel-level results and the actual drawing process, we present an approach that treats a set of drawing tools as executable programs. This method predicts a sequence of steps to achieve the final image, allowing for understandable and resolution-independent reproductions under the usage of a set of drawing commands. Our experiments demonstrate that our program synthesizer, Art2Prog, can comprehensively understand complex input images and reproduce them using high-quality executable programs. The experimental results evidence the potential of machines to grasp higher-level information from images and generate compact program-level descriptions.
Elicitation interviews are the most common requirements elicitation technique, and proficiency in conducting these interviews is crucial for requirements elicitation. Traditional training methods, typically limited to textbook learning, may not sufficiently address the practical complexities of interviewing techniques. Practical training with various interview scenarios is important for understanding how to apply theoretical knowledge in real-world contexts. However, there is a shortage of educational interview material, as creating interview scripts requires both technical expertise and creativity. To address this issue, we develop a specialized GPT agent for auto-generating interview scripts. The GPT agent is equipped with a dedicated knowledge base tailored to the guidelines and best practices of requirements elicitation interview procedures. We employ a prompt chaining approach to mitigate the output length constraint of GPT to be able to generate thorough and detailed interview scripts. This involves dividing the interview into sections and crafting distinct prompts for each, allowing for the generation of complete content for each section. The generated scripts are assessed through standard natural language generation evaluation metrics and an expert judgment study, confirming their applicability in requirements engineering training.
Olga Guţan, Shreya Hegde, Erick Jimenez Berumen
et al.
AbstractState‐of‐the‐art methods for line drawing vectorization rely on generated frame fields for robust direction disambiguation, with each of the two axes aligning to different intersecting curve tangents around junctions. However, a common source of topological error for such methods are frame field singularities. To remedy this, we introduce the first frame field optimization framework guaranteed to produce singularity‐free fields aligned to a line drawing. We first perform a convex solve for a roughly‐aligned orthogonal frame field (cross field), and then comb away its internal singularities with an optimal transport–based matching. The resulting topology of the field is strictly maintained with the machinery of discrete trivial connections in a final, non‐convex optimization that allows non‐orthogonality of the field, improving smoothness and tangent alignment. Our frame fields can serve as a drop‐in replacement for frame field optimizations used in previous work, improving the quality of the final vectorizations.
One of the objectives of bone tissue engineering is to produce scaffolds that are biocompatible, osteoinductive, and mechanically equivalent to the natural extracellular matrix of bone in terms of structure and function. Reconstructing the osteoconductive bone microenvironment into a scaffold can attract native mesenchymal stem cells and differentiate them into osteoblasts at the defect site. The symbiotic relationship between cell biology and biomaterial engineering could result in composite polymers containing the necessary signals to recreate tissue- and organ-specific differentiation. In the current work, drawing inspiration from the natural stem cell niche to govern stem cell fate, the cell-instructive hydrogel platforms were constructed by engineering the mineralized microenvironment. This work employed two different hydroxyapatite delivery strategies to create a mineralized microenvironment in an alginate-PEGDA interpenetrating network (IPN) hydrogel. The first approach involved coating of nano-hydroxyapatite (nHAp) on poly(lactide-co-glycolide) microspheres and then encapsulating the coated microspheres in an IPN hydrogel for a sustained release of nHAp, whereas the second approach involved directly loading nHAp into the IPN hydrogel. This study demonstrate that both direct encapsulation and a sustained release approach showed enhanced osteogenesis in target-encapsulated cells; however, direct loading of nHAp into the IPN hydrogel increased the mechanical strength and swelling ratio of the scaffold by 4.6-fold and 1.14-fold, respectively. In addition, the biochemical and molecular studies revealed improved osteoinductive and osteoconductive potential of encapsulated target cells. Being less expensive and simple to perform, this approach could be beneficial in clinical settings.