Hasil untuk "Mechanical drawing. Engineering graphics"

Shaw-Chiang Wong, Bai Ying

‘Eight Treasures Tea’ (NETT), a unique herbal tea from Ningxia province, China, is renowned for its distinctive recipe, medicinal properties, and long history, yet public awareness of its cultural and nutritional value remains limited. Preliminary observations suggest that the current packaging design lacks the necessary visual and verbal elements to effectively convey NETT's cultural values (CVs). This study aimed to address this gap by translating NETT’s CVs into creative packaging using a practice-based research approach, conducted in three phases. In the first phase, a visual analysis of 12 existing NETT gift packages revealed limited differentiation in terms of colour, graphics, and typography, with minimal communication of CVs. The second phase involved semi-structured interviews with five experts, identifying eight sub-themes related to NETT’s historical, health, artistic, and spiritual dimensions: historical origin, historical context, health concept, dietary habits, brewing process, drinking method, emotional expression, and cultural interpretation. Findings highlighted the Yellow River as a key symbol of NETT’s historical significance, the artistic merit tied to making and tasting the tea, and the spiritual value shaped by Ningxia’s inclusive and diverse ethnic culture. In the third phase, creative and reflective practice was employed to explore and reconstruct the visual and verbal elements of NETT packaging, representing its multidimensional CVs. The study evoked the interconnectedness of research and practice, highlighting the critical role of the practitioner as a researcher. The creative process and outcomes offered new insights into packaging design, enhancing the communication of NETT’s cultural values to potential consumers.

Mairieli Wessel, Daniel Feitosa, Sangeeth Kochanthara

Rising publication pressure and the routine use of generative AI tools are reshaping how software engineering research is produced, assessed, and taught. While these developments promise efficiency, they also raise concerns about skill degradation, responsibility, and trust in scholarly outputs. This vision paper employs Design Fiction as a methodological lens to examine how such concerns might materialise if current practices persist. Drawing on themes reported in a recent community survey, we construct a speculative artifact situated in a near future research setting. The fiction is used as an analytical device rather than a forecast, enabling reflection on how automated assistance might impede domain knowledge competence, verification, and mentoring practices. By presenting an intentionally unsettling scenario, the paper invites discussion on how the software engineering research community in the future will define proficiency, allocate responsibility, and support learning.

Javier Munguia

Technical drawing is a core skill for any Mechanical Engineering program, intended to equip students with the fundamentals of technical design communication while assimilating key concepts such as views, perspectives, dimensioning, tolerancing and materials selection. Technical drawings are manually-graded by the lead instructor by assessing the drawing's proficiency and quality based on a set of standards and marking criteria. For large student cohorts, this becomes a time-consuming activity, potentially leading to ‘marking fatigue’, usually producing highly variable grades and feedback. By using a dataset of 32 student drawings and a five-criterion rubric, we compare AI-generated grades with historic human-marker scores through error analysis, ANOVA and Kruskal–Wallis tests. Results suggest there is no significant statistical difference between the marks/grades awarded by AI and a human marker. This, however, will depend on the prompting engineering techniques applied, together with additional practices such as role-setting and context-setting. The study also identifies limitations—such as OCR-induced hallucinations, variability between LLM platforms, and lack of batch-processing capabilities—that currently constrain full automation.

Danyang Qiu, Yaoxin Huang, Xinru Li et al.

Piezoelectric metamaterials, serving as critical functional media in high-end equipment, face significant design challenges due to the mesh bottlenecks of traditional finite element methods and the interpretability shortcomings of purely data-driven models. Physical Information Neural Networks (PINNs) establish a robust scientific machine learning paradigm by embedding physical equations, offering an innovative solution to these predicaments. This paper systematically reviews recent advancements of PINNs in piezoelectric metamaterial analysis and design: drawing upon multiscale modelling theory, it elucidates PINNs' mesh-free advantages in handling high-dimensional parameters and their exceptional capability in solving small-sample inverse problems; subsequently, it explores their application paradigms in constructing high-fidelity forward surrogate models and accelerating efficient topology optimisation. Finally, this paper summarises key computational challenges in multi-physics coupling scenarios and outlines potential pathways towards achieving high-fidelity intelligent design, aiming to bridge the existing gap between theoretical modelling and engineering practice in piezoelectric metamaterials.

Han Yang, Ya Fang, Jiaming Cui et al.

سعیده رفیعی, پیمان ولی پور, امید اعلایی et al.

این پژوهش به بررسی قابلیت استفاده از عصاره میوه اوپنتیا به عنوان یک رنگزای طبیعی در رنگرزی نخ‌های پشمی فرش دستباف می‌پردازد. در این مطالعه با هدف ارائه رویکردی پایدار و زیست‌محیطی برای رنگرزی منسوجات، تأثیر عوامل مهم شامل غلظت رنگزا (30-100 درصد)، غلظت دندانه (0-10 درصد)، دما (°C 100-30)، زمان (20-100 دقیقه) و pH (11-3) بر ویژگی‌های رنگی و پایداری رنگ ارزیابی شد. عصاره‌گیری به روش خیساندن انجام گرفت و حضور ترکیبات موثره بتالائین ((Betalain با روش‌های طیف‌سنجی تأیید گردید. تحلیل‌ها نشان داد که عصاره اوپنتیا می‌تواند طیف رنگی متنوعی از قرمز تا قهوه‌ای ایجاد کند. بهترین نتایج در دمای °C 80 ، زمان 90 دقیقه و 5/0 ± 5/5 pH حاصل شد. علاوه بر این، اثر دندانه‌های فلزی مختلف بر ثبات رنگ بررسی گردید؛ دندانه آهن بهترین ثبات شستشویی و دندانه مس بیشترین ثبات نوری را فراهم کرد. مطالعات آماری تأثیر معنادار تمامی متغیرهای فرایند را بر شاخص‌های رنگی، شامل قدرت رنگی (K/S)، خلوص رنگی (C*)، زاویه فام h°)) و ثبات شستشویی و نوری تأیید کردند ( 001/0p < ). با این حال، میزان تأثیر متغیرها بر هر عامل متفاوت بود؛ به عنوان مثال، تغییرات دما بیشترین تأثیر را بر خلوص رنگ داشت، در حالی که دندانه در بهبود زاویه فام و ثبات نقش کلیدی ایفا کرد. از طرفی دیگر نتایج نشان داد که انتخاب شرایط بهینه رنگرزی مناسب می‌تواند ضمن دستیابی به خواص رنگی مطلوب، ویژگی‌های مکانیکی مناسب نخ پشمی را نیز حفظ کند. این پژوهش نشان داد که عصاره اوپنتیا به عنوان یک رنگزای طبیعی پتانسیل بالایی در ایجاد سایه‌های رنگی متنوع با ثبات مطلوب دارد و استفاده از آن می‌تواند رویکردی کارآمد برای توسعه فرایندهای رنگرزی پایدار و کاهش اثرات زیست‌محیطی باشد.

Stefan Behringer, Roman V. Belavkin

This paper explores the application of the <i>Value of Information</i>, <i>(VoI)</i>, based on the Claude Shannon/Ruslan Stratonovich framework within economic contexts. Unlike previous studies that examine circular settings and strategic interactions, we focus on a non-strategic linear setting. We employ standard economically motivated utility functions, including linear, quadratic, constant absolute risk aversion (CARA), and constant relative risk aversion (CRRA), across various priors of the stochastic environment, and analyse the resulting specific <i>VoI</i> forms. The curvature of these <i>VoI</i> functions play a decisive role in determining whether acquiring additional costly information enhances the efficiency of the decision making process. We also outline potential implications for broader decision-making frameworks.

Lucilla Grossi, Luca Guerrini

Metaverses and 3D virtual worlds host numerous exhibition spaces that often replicate physical environments skeuomorphically. This study analyzes 26 digital exhibitions—ranging from institutional virtual twins and fully digital museums to independent artist showcases—to investigate approaches to 3D spatial design for the exhibition of digital art pieces. Findings show that only a minority (10/26) adopt speculative spatial strategies, while the majority embraces reality-mimicking, showing a clear tendency to replicate real-world spaces. We investigated the technological processes behind the creation of these spaces to understand how the intrinsic characteristics of the tools used potentially influence design choices, embedding presets that favor replication. Despite the widespread use of similar technological frameworks, some exhibitions stand out as speculative cases, demonstrating the possibility of experimental and critical spatial innovation. This contrast highlights the fact that while technology shapes and sometimes constrains design, it does not determine it entirely; creative appropriation can push the bound- aries of virtual exhibition design beyond mere copies of the physical world.

Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt

The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.

Daniel Mendez, Paris Avgeriou, Marcos Kalinowski et al.

Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.

Xin Zhang, Lissette Iturburu, Juan Nicolas Villamizar et al.

Structural drawings are widely used in many fields, e.g., mechanical engineering, civil engineering, etc. In civil engineering, structural drawings serve as the main communication tool between architects, engineers, and builders to avoid conflicts, act as legal documentation, and provide a reference for future maintenance or evaluation needs. They are often organized using key elements such as title/subtitle blocks, scales, plan views, elevation view, sections, and detailed sections, which are annotated with standardized symbols and line types for interpretation by engineers and contractors. Despite advances in software capabilities, the task of generating a structural drawing remains labor-intensive and time-consuming for structural engineers. Here we introduce a novel generative AI-based method for generating structural drawings employing a large language model (LLM) agent. The method incorporates a retrieval-augmented generation (RAG) technique using externally-sourced facts to enhance the accuracy and reliability of the language model. This method is capable of understanding varied natural language descriptions, processing these to extract necessary information, and generating code to produce the desired structural drawing in AutoCAD. The approach developed, demonstrated and evaluated herein enables the efficient and direct conversion of a structural drawing's natural language description into an AutoCAD drawing, significantly reducing the workload compared to current working process associated with manual drawing production, facilitating the typical iterative process of engineers for expressing design ideas in a simplified way.

Kevin Hermann, Sven Peldszus, Jan-Philipp Steghöfer et al.

Software security is of utmost importance for most software systems. Developers must systematically select, plan, design, implement, and especially, maintain and evolve security features -- functionalities to mitigate attacks or protect personal data such as cryptography or access control -- to ensure the security of their software. Although security features are usually available in libraries, integrating security features requires writing and maintaining additional security-critical code. While there have been studies on the use of such libraries, surprisingly little is known about how developers engineer security features, how they select what security features to implement and which ones may require custom implementation, and the implications for maintenance. As a result, we currently rely on assumptions that are largely based on common sense or individual examples. However, to provide them with effective solutions, researchers need hard empirical data to understand what practitioners need and how they view security -- data that we currently lack. To fill this gap, we contribute an exploratory study with 26 knowledgeable industrial participants. We study how security features of software systems are selected and engineered in practice, what their code-level characteristics are, and what challenges practitioners face. Based on the empirical data gathered, we provide insights into engineering practices and validate four common assumptions.

Xueliang Gao, Sitan Wu, Hongbo Zhang et al.

Yuan Yuan, Songtao Wang, Kun Liu et al.

Yuanping Xu, Chaolong Zhang, Zhijie Xu et al.

Prashant Goswami

Snow and ice animation methods are becoming increasingly popular in the field of computer graphics (CG). The applications of snow and ice in CG are varied, ranging from generating realistic background landscapes to avalanches and physical interaction with objects in movies, games, etc. Over the past two decades, several methods have been proposed to capture the time‐evolving physical appearance or simulation of snow and ice using different models at different scales. This state‐of‐the‐art report aims to identify existing animation methods in the field, provide an up‐to‐date summary of the research in CG, and identify gaps for promising future work. Furthermore, we also attempt to identify the primarily related work done on snow and ice in some other disciplines, such as civil or mechanical engineering, and draw a parallel with the similarities and differences in CG.

Bruno Oro, Subinay Malhotra

This paper explores the critical gap in integrating Universal Design (UD) into U.S. design education, where sustainability is often prioritized while inclusivity remains elective. Through an ongoing study of 60 universities, initial findings reveal that only two briefly mention UD, and none fully integrate it into their core design programmes. This highlights a disconnect between inclusive values and educational structures. UD, like sustainability, offers broad social and commercial benefits by reducing stigma, expanding markets, and fostering equity. By embedding UD into foundational courses, design education can better prepare students to address environmental and social challenges. This paper argues for UD’s structural inclusion, not as an add-on, but as a key framework for shaping equitable and sustainable futures in design.

Alejandra Amenábar Álamos

Design pedagogy is crucial for transferring project methodologies and fostering creative skills. However, the technological and social transformations of this century have challenged traditional educational canons and the very definition of creativity. This article proposes that this challenge can be analyzed through non-traditional educational paradigms to address this challenge from a design methodology perspective. It first reviews the existing literature on creativity training and presents a case study on training methodologies. This case was incorporated into an undergraduate design course, providing insight into design pedagogy itself. Then, the article discusses the future challenges and projections of Design education and the cultivation of creative skills for the 21st century. Finally, by examining alternative educational approaches, this research contributes to the current discourse on design pedagogy and its adaptation to the changing needs of contemporary society.

Camilo Ayala-Garcia, Matteo Scalabrini, Nitzan Cohen

In times of multiple crises, embracing alternative and sustainable solutions for materials, products, and manufacturing systems becomes increasingly crucial and urgent. Constant improvements and innovations in technology and science, together with consumer demand for the new, encourage an ever-growing production of electronic goods, consequently leading to a broad and multi-levelled scope of problems. While incremental growth follows unsustainable production patterns, the democratisation of technology on the contrary gives rise to contrasting paradigms and ways of production that could shift the power back to the consumer. Starting from the principles of transparency and openness, the Do-It-Yourself-Revolution (DIYR) project aims for a more sustainable product world, where environmentally conscious production raises the value and care for things while at the same time formulating a new contemporary product language. This project aims to create novel products and product ecosystems with a localised production focus, wherein users are motivated to transition into proDusers (producer-users). This transformation is facilitated through instruction, acquiring additional knowledge, and cultivating skills via immersion in the worlds of making, crafting, and design.

Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh

Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.