رنگهای متالیک به دلیل جذابیت دیداری و کاربردهای گسترده در صنایع مختلف، بهویژه خودروسازی، همواره مورد توجه بودهاند. با این حال، رفتار نوری پیچیده و وابسته به زاویه این پوششها، ارزیابی خواص آنها را با چالش همراه میسازد. در این پژوهش، ۳۰۸ نمونه رنگ متالیک سامانه پنتون با استفاده از ابزارهای مختلف شامل طیفسنجی، طیفسنجی گونیو، گونیوفوتومتر و براقیتسنج در زوایای گوناگون مورد بررسی قرار گرفتند. نتایج نشان داد که براقیت آینهای و بازتابه مشخصه بیشترین تأثیر را بر روشنایی نمونهها دارند، در حالیکه وضوح تصویر در نمونههای نیمهمات همبستگی قابل توجهی با روشنایی نشان نداد. یافتهها بیانگر آن است که ترکیب دادههای هندسی گونیوفوتومتری با شاخصهای روشنایی میتواند ابزار مناسبی برای درک و پیشبینی ظاهر رنگهای متالیک در کاربردهای صنعتی فراهم کند.
AbstractIn the vast landscape of visualization research, Dimensionality Reduction (DR) and graph analysis are two popular subfields, often essential to most visual data analytics setups. DR aims to create representations to support neighborhood and similarity analysis on complex, large datasets. Graph analysis focuses on identifying the salient topological properties and key actors within network data, with specialized research investigating how such features could be presented to users to ease the comprehension of the underlying structure. Although these two disciplines are typically regarded as disjoint subfields, we argue that both fields share strong similarities and synergies that can potentially benefit both. Therefore, this paper discusses and introduces a unifying framework to help bridge the gap between DR and graph (drawing) theory. Our goal is to use the strongly math‐grounded graph theory to improve the overall process of creating DR visual representations. We propose how to break the DR process into well‐defined stages, discuss how to match some of the DR state‐of‐the‐art techniques to this framework, and present ideas on how graph drawing, topology features, and some popular algorithms and strategies used in graph analysis can be employed to improve DR topology extraction, embedding generation, and result validation. We also discuss the challenges and identify opportunities for implementing and using our framework, opening directions for future visualization research.
Quantum mechanics, the fundamental theory that governs the behaviour of matter and energy at microscopic scales, forms the foundation of quantum computing and quantum information science. As quantum technologies progress, software engineers must develop a conceptual understanding of quantum mechanics to grasp its implications for computing. This article focuses on fundamental quantum mechanics principles for software engineers, including wave-particle duality, superposition, entanglement, quantum states, and quantum measurement. Unlike traditional physics-oriented discussions, this article focuses on computational perspectives, assisting software professionals in bridging the gap between classical computing and emerging quantum paradigms.
Hashini Gunatilake, John Grundy, Rashina Hoda
et al.
Empathy plays a critical role in software engineering (SE), influencing collaboration, communication, and user-centred design. Although SE research has increasingly recognised empathy as a key human aspect, there remains no validated instrument specifically designed to measure it within the unique socio-technical contexts of SE. Existing generic empathy scales, while well-established in psychology and healthcare, often rely on language, scenarios, and assumptions that are not meaningful or interpretable for software practitioners. These scales fail to account for the diverse, role-specific, and domain-bound expressions of empathy in SE, such as understanding a non-technical user's frustrations or another practitioner's technical constraints, which differ substantially from empathy in clinical or everyday contexts. To address this gap, we developed and validated two domain-specific empathy scales: EmpathiSEr-P, assessing empathy among practitioners, and EmpathiSEr-U, capturing practitioner empathy towards users. Grounded in a practitioner-informed conceptual framework, the scales encompass three dimensions of empathy: cognitive empathy, affective empathy, and empathic responses. We followed a rigorous, multi-phase methodology, including expert evaluation, cognitive interviews, and two practitioner surveys. The resulting instruments represent the first psychometrically validated empathy scales tailored to SE, offering researchers and practitioners a tool for assessing empathy and designing empathy-enhancing interventions in software teams and user interactions.
Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey
et al.
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.
The automotive industry generates vast amounts of data from sensors, telemetry, diagnostics, and real-time operations. Efficient data engineering is critical to handle challenges of latency, scalability, and consistency. Modern data lakehouse formats Delta Parquet, Apache Iceberg, and Apache Hudi offer features such as ACID transactions, schema enforcement, and real-time ingestion, combining the strengths of data lakes and warehouses to support complex use cases. This study presents a comparative analysis of Delta Parquet, Iceberg, and Hudi using real-world time-series automotive telemetry data with fields such as vehicle ID, timestamp, location, and event metrics. The evaluation considers modeling strategies, partitioning, CDC support, query performance, scalability, data consistency, and ecosystem maturity. Key findings show Delta Parquet provides strong ML readiness and governance, Iceberg delivers high performance for batch analytics and cloud-native workloads, while Hudi is optimized for real-time ingestion and incremental processing. Each format exhibits tradeoffs in query efficiency, time-travel, and update semantics. The study offers insights for selecting or combining formats to support fleet management, predictive maintenance, and route optimization. Using structured datasets and realistic queries, the results provide practical guidance for scaling data pipelines and integrating machine learning models in automotive applications.
Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.
Aalok Gangopadhyay, Prajwal Singh, Ashish Tiwari
et al.
Shadow art is an exciting form of sculptural art that produces captivating artistic effects through the 2D shadows cast by 3D shapes. Hand shadows, also known as shadow puppetry or shadowgraphy, involve creating various shapes and figures using your hands and fingers to cast meaningful shadows on a wall. In this work, we propose a differentiable rendering-based approach to deform hand models such that they cast a shadow consistent with a desired target image and the associated lighting configuration. We showcase the results of shadows cast by a pair of two hands and the interpolation of hand poses between two desired shadow images. We believe that this work will be a useful tool for the graphics community.
Communication has great potential: it connects people and ideas, inspires action and influences thinking. However, its potential also depends on its quality and its ability to be accurate, truthful and inclusive. Communication Design takes up the ethical responsibility of this potential, systemizing the knowledge and the skills of the discipline, transforming them into methodologies and techniques for constructing a message through the code of visual language.
In this contribution, through a matrix analysis of case studies, useful communication strategies will be identified and described as powerful tools to raise awareness towards the achievement of some of the SDGs. After outlining the status of the literature on the ethical dimension that has characterised communication design as a lever for sustainable development, the research provides a systematic review of selected case studies relating to the discipline, which experience and bear witness to the systemic transition for sustainable development, encouraging social inclusion, awareness, and environmental sustainability.
The European educational system stands in a unique position at the crossroads of training, research and innovation, in shaping sustainable and resilient economies, and in making its territories greener, more inclusive and more digital through culture and creativity. This paper is the result of a one-year collaboration between the Alma Mater Studiorum — Università di Bologna and the Jagiellonian University, under the umbrella of the EIT Culture & Creativity (EIT C&C), in the interim co-chairing of the Direction linked to the Action Program 1 Talent Scaler, to define activities and strategies for the operationalization of the Strategic Objective 1 about Education. This involvement opened up the possibility to activate a fruitful international cooperation with all the members of Una Europa, the alliance of Europe’s eleven largest leading research universities that aim to create a truly European inter-university environment, a University of the Future.
Valentina Gianfrate, Lígia Lopes, Margherita Ascari
et al.
Intersectionality is increasingly suggested as an innovative framework with the potential to advance the understanding and the action towards contrasting inequalities, by highlighting processes of stigmatisation and by encouraging a critical reflection to move beyond singular categories. The contribution explore the relation between the intersectional approach and design cultures and practices by presenting the main outcomes of Track 2 “Intersectional Design for an Accessible and Empowering World” in the frame of the 8th International Forum of Design as a Process. The contributions collected in this frame represent a wealth of practices, methods and applications that show how the theoretical contribution linked to the topic of intersectionality can be applied to the co-creation of innovation in design-driven practices in diversified geographies.
Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan
et al.
Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output including potentially harmful information, racist or sexist language, and hallucinations. Alignment methods are designed to reduce such undesirable outputs via techniques such as fine-tuning, prompt engineering, and representation engineering. However, existing methods face several challenges: some require costly fine-tuning for every alignment task; some do not adequately remove undesirable concepts, failing alignment; some remove benign concepts, lowering the linguistic capabilities of LLMs. To address these issues, we propose Parsimonious Concept Engineering (PaCE), a novel activation engineering framework for alignment. First, to sufficiently model the concepts, we construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept. Given any alignment task, we instruct a concept partitioner to efficiently annotate the concepts as benign or undesirable. Then, at inference time, we decompose the LLM activations along the concept dictionary via sparse coding, to accurately represent the activations as linear combinations of benign and undesirable components. By removing the latter ones from the activations, we reorient the behavior of the LLM towards the alignment goal. We conduct experiments on tasks such as response detoxification, faithfulness enhancement, and sentiment revising, and show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities.
Building up competencies in working with data and tools of Artificial Intelligence (AI) is becoming more relevant across disciplinary engineering fields. While the adoption of tools for teaching and learning, such as ChatGPT, is garnering significant attention, integration of AI knowledge, competencies, and skills within engineering education is lacking. Building upon existing curriculum change research, this practice paper introduces a systems perspective on integrating AI education within engineering through the lens of a change model. In particular, it identifies core aspects that shape AI adoption on a program level as well as internal and external influences using existing literature and a practical case study. Overall, the paper provides an analysis frame to enhance the understanding of change initiatives and builds the basis for generalizing insights from different initiatives in the adoption of AI in engineering education.
This study is a pioneering endeavor to investigate the capabilities of Large Language Models (LLMs) in addressing conceptual questions within the domain of mechanical engineering with a focus on mechanics. Our examination involves a manually crafted exam encompassing 126 multiple-choice questions, spanning various aspects of mechanics courses, including Fluid Mechanics, Mechanical Vibration, Engineering Statics and Dynamics, Mechanics of Materials, Theory of Elasticity, and Continuum Mechanics. Three LLMs, including ChatGPT (GPT-3.5), ChatGPT (GPT-4), and Claude (Claude-2.1), were subjected to evaluation against engineering faculties and students with or without mechanical engineering background. The findings reveal GPT-4's superior performance over the other two LLMs and human cohorts in answering questions across various mechanics topics, except for Continuum Mechanics. This signals the potential future improvements for GPT models in handling symbolic calculations and tensor analyses. The performances of LLMs were all significantly improved with explanations prompted prior to direct responses, underscoring the crucial role of prompt engineering. Interestingly, GPT-3.5 demonstrates improved performance with prompts covering a broader domain, while GPT-4 excels with prompts focusing on specific subjects. Finally, GPT-4 exhibits notable advancements in mitigating input bias, as evidenced by guessing preferences for humans. This study unveils the substantial potential of LLMs as highly knowledgeable assistants in both mechanical pedagogy and scientific research.
Emerging immersive display technologies efficiently utilize resources with perceptual graphics methods such as foveated rendering and denoising. Running multiple perceptual graphics methods challenges devices with limited power and computational resources. We propose a computationally-lightweight learned multitasking perceptual graphics model. Given RGB images and text-prompts, our model performs text-described perceptual tasks in a single inference step. Simply daisy-chaining multiple models or training dedicated models can lead to model management issues and exhaust computational resources. In contrast, our flexible method unlocks consistent high quality perceptual effects with reasonable compute, supporting various permutations at varied intensities using adjectives in text prompts (e.g. mildly, lightly). Text-guidance provides ease of use for dynamic requirements such as creative processes. To train our model, we propose a dataset containing source and perceptually enhanced images with corresponding text prompts. We evaluate our model on desktop and embedded platforms and validate perceptual quality through a user study.
Three-dimensional (3D) solid models of mechanical machine parts are widely used in modern mechanical engineering. One expected approach in creating 3D models is automatically reconstructing them from 2D engineering drawings. This work expands the previous automatic reconstruction methods by adding principles, algorithms, and coding to reconstruct oblique planes on the part. The proposed method uses only two views as the input to reconstruct the 3D solid part, including oblique planes. The proposed method has been implemented by a program written in the ARX 2018 language running on the AutoCAD 2021 platform to reconstruct multiple 3D parts from their two views. Experimental test results on many samples confirmed that the proposed method is reliable, absolutely accurate, and achieves a high reconstruction speed. The output 3D model has also been tested and confirmed for compatibility with CAD/CAM software such as Solid Works, Inventor, and PTC Creo.
Engineering technology students are expected to focus more on applied and hands-on learning, gaining practical knowledge that aligns with industry needs. The industry seeks graduates who are prepared for day-to-day work and well-versed in applied engineering and industry applications. It is beneficial for technology students to engage in problem-solving in machine design using an integrated approach, which exposes them to real-time circumstances in the industry. While students are taught how to design mechanical elements such as shafts, gears, bearings, etc., individually, it is equally essential for them to understand how to integrate these elements into systems or sub-systems. Example problems will be discussed to illustrate how each machine element interacts with other machine elements, aiding students in designing a cohesive system. In an integrated design approach, the output of one machine element serves as input to another, creating a continuous flow of information. An example problem will be presented with minimal inputs, requiring students to devise solutions by selecting appropriate factors that align with industry requirements to solve the problem. They will be encouraged to articulate their thought process behind these selections. Since the industry relies heavily on adhering to industrial standards, students must cite relevant standards in each design decision, drawing from what they learned during their classroom exercises. This approach requires a significant amount of critical thinking, as students must justify their assumptions when addressing various aspects such as the selection of driven machinery, duty cycles, and work environment conditions. Considerations such as stress concentration factors, bearing span and lifespan, interference between machine elements, and selecting appropriate tolerances are crucial. This paper focuses on the integrated problem-solving method and highlights the essential critical thinking requirement in engineering technology education.