Secure development is an ever-evolving field that has advanced quickly in recent years with initiatives like Secure Development Lifecycle (SDLC), Development Security Operations (DevSecOps), and Model-Based Security Engineering (MBSE). Despite the persistence of the security and design communities to include security in the design process, significant security breaches continue to occur. Our work reviews existing literature to determine the current state of the research at the intersection of these design and cybersecurity fields and ultimately proposes an integrative and systematic approach for developers to generate design principles that incorporate traceable security. This approach integrates security regulations and design principles and activities, encouraging compliance and security considerations at the earliest stages of the design thinking process.

Large Language Models offer a novel approach with low barriers to entry to potentially improve knowledge transfer in product development. After identifying knowledge barriers from literature that are potentially addressable through LLM-based applications, we analyze two GDPR-compliant LLM applications - ChatGPT Enterprise and Langdock - examining their key features: assistants and chatbots for both, and prompt libraries and LLM-based file search for Langdock. Then, we evaluate each feature’s potential to mitigate each barrier. Our findings show that assistants and chatbots provide wide-ranging support across many barriers, whereas prompt libraries and file search deliver targeted solutions for a narrower set of specific challenges. Given the numerous influencing factors and the rapidly evolving field of LLMs, the study concludes with a research agenda to validate the theoretical findings.

Current quantitative methods for estimating product-related environmental emissions face limitations in supporting sustainable design, particularly in second-life product strategies. This paper highlights challenges in accurately assessing emissions and environmental impacts under existing regulations, which often fail to reward designs enabling circularity. Through examples of current practices, it underscores methodological ambiguities and regulatory gaps, proposing a research agenda for improved tools and frameworks. These advancements aim to better support the design, production, and certification of sustainable, second-life-ready solutions, fostering more effective environmental impact reduction. Additionally, the paper emphasizes the need for regulatory adaptation to incentivize circular design practices, ensuring a fair evaluation of products conceived for second-life applications

Generative Design (GD) tools can produce high-performing components with complex geometries that are challenging to conceive via traditional methods. While potentially disruptive, GD tools have yet to achieve widespread use in industry. One reason is that current GD tools are limited to manufacturing methods capable of producing intricate geometries that GD often creates such as 3D printing. To overcome this barrier, this paper quantifies the benefit of altering generatively designed parts to use standardized elements like wire stock and sheet metal via processes such as CNC bending and water jet cutting. Using a parametric cost model, we show that parts incorporating standard components can halve the unit price for production volumes of only 4 parts. Finite Element Analysis (FEA) reveals that replacing up to 60% of part volume has minimal impact on performance. Our findings highlight a gap and opportunity in existing GD research.

This paper explores how creative preservation, affected by a regulatory framework, unfolds in the design of complex systems. Based on a case study of the Boeing 737 aircraft, it focuses on the role of grandfather rights, as part of the regulatory framework of aircraft design, as a precursor for creative preservation. The paper analyzes three design decisions related tot the evolving Boeing 737 aircraft models over a period of six decades and highlight the changing logic of creative preservation in relation to technology maturity, increasing complexity of design decisions, and expanded stakeholder involvement. Overall, the paper demonstrates that the management of design heritage is a ‘living system’ and that foundational practices may slowly become ineffective.

Native to North America, Virginia pepperweed is a winter annual weed in the mustard family (Brassicaceae) found commonly in agricultural crops, roadsides, landscapes, and other undisturbed areas. Known for its peppery taste, Virginia pepperweed has emerged as a troublesome and difficult-to-control weed in and around major row crops in the Mississippi Delta region. Recently, Virginia pepperweed management has become increasingly challenging due to the weed’s ability to survive control measures when applications are made beyond its early rosette stage and high fecundity rates (∼100,000 seeds plant−1). Therefore there is a need to develop effective control measures that could reduce the spread of Virginia pepperweed in crop production systems. Greenhouse experiments were conducted in the 2024 season to evaluate the activity of various burndown herbicides labeled for Virginia pepperweed control in row crops. Virginia pepperweed seed was stratified and germinated in a growth chamber, and seedlings were transplanted into pots and kept in a greenhouse. The herbicides tested at the 1X rate were glyphosate at 1,261 g ai ha−1, glufosinate at 672 g ae ha−1, 2,4-D at 1,065 g ai ha−1, and paraquat at 840 g ai ha−1. Herbicides were sprayed at three growth stages: early rosette, late rosette, and bolting. Virginia pepperweed control was evaluated at 1, 2, 3, and 4 wk after herbicide application (WAA). Shoot dry biomass data were collected at 4 WAA. Application of 2,4-D resulted in 95% to 100% Virginia pepperweed control at all three growth stages. Depending on the growth stage at which herbicides were applied, there was 40% to 50% control with glyphosate, 18% to 47% with glufosinate, and 0% to 71% with paraquat, with 0% biomass reduction at the bolting stage. However, the highest dry biomass reduction (>80%) was observed with 2,4-D applications at the early rosette stage. Herbicide applications at the early rosette stage resulted in maximum Virginia pepperweed control.

Combinatorial optimization (CO) is essential for improving efficiency and performance in engineering applications. Traditional algorithms based on pure mathematical reasoning are limited and incapable to capture the contextual nuances for optimization. This study explores the potential of Large Language Models (LLMs) in solving engineering CO problems by leveraging their reasoning power and contextual knowledge. We propose a novel LLM-based framework that integrates network topology and contextual domain knowledge to optimize the sequencing of Design Structure Matrix (DSM) —a common CO problem. Our experiments on various DSM cases demonstrate that the proposed method achieves faster convergence and higher solution quality than benchmark methods. Moreover, results show that incorporating contextual domain knowledge significantly improves performance despite the choice of LLMs.

Prototyping is an important component of the engineering design process and has become a frequently studied topic in engineering education. The iterative strategy of creating prototypes, where a single design is refined with repeated improvements, is widely taught and considered to be the default approach to prototyping. However, research has shown that a parallel approach to prototyping, where multiple concepts are tested simultaneously, has potential benefits when exploring a complex design space. Recent studies on parallel prototyping in first-year engineering classrooms have shown that students required to use a parallel strategy produced higher performing final designs than students who used an iterative strategy. This work places the parallel and iterative prototyping strategies in a typical classroom setting where first-year engineering students have control over their strategy.

Bolted joints are critical for maintaining structural integrity and reliability. Accurate prediction of parameters is essential for optimal performance. Traditional methods often fail to capture the non-linear behavior or require significant computational resources, limiting accuracy and efficiency. This study addresses these limitations by combining empirical data with a feed-forward neural network. Leveraging experimental data and systematic preprocessing, the model effectively captures nonlinear relationships, including rescaling output variables to address scale discrepancies, achieving 95% predictive accuracy. While limited dataset size restricts generalizability, the findings demonstrate the potential of neural networks as a reliable, efficient alternative for bolted joint design. Future work aims to expand datasets and explore hybrid modeling techniques to enhance applicability.

Although the limits of life under individual extremes have been extensively studied, systematic experiments to quantify how combined extremes set the limits to life are lacking. We investigated the combined effects of extremes in temperature, salinity (NaCl) and pH on the growth limits of the marine bacterium Halomonas hydrothermalis, to test the hypothesis that limits to growth under combinations of the extremes establish a more restricted niche than the individual extremes. We show that the combination of supra-optimal temperature, pH and NaCl act synergistically in defining the limits of growth under multiple extremes. Although at optimal growth temperatures (30°C) maximum growth was achieved at pH 7, the maximum temperature limit of 43°C was achieved at pH 8. Under these conditions, the maximum NaCl concentration limit was 6.58% (wt/vol). Decreasing the temperature to 42 and 41/40°C increased the salinity limit to 7.01 % and 8.24 %, respectively. These data show that multiple extremes restrict the limits to growth of this organism to a greater extent than individual extremes and show how natural environments with extremes of temperature, pH and salinity could have restricted microbial diversity, or be uninhabitable, even when each individual extreme lies within the bounds of known microbial growth. These data imply that ‘maps’ of the limits to the biosphere based on laboratory-derived individual extremes may over-exaggerate growth limits in natural environments, which are rarely subject to single extremes, highlighting the need for multi-parameter analyses.

Developing methodical approaches, from methods and concepts to algorithms and comprehensive methodologies, requires application-specific expertise and a structured procedure to ensure both workflow efficiency and validity. This contribution introduces a conceptual model for assessing the maturity of methodical approaches through ten predefined readiness levels. By achieving level-specific sub-goals, the model aims to systematize the development process while progressively increasing maturity, ultimately yielding more effective approaches. This novel concept not only supports the structured development of methodical approaches, but also facilitates their comparability and evaluation. The necessity of the proposed concept is substantiated through a systematic literature review, while its functionality is critically evaluated and validated by experts and using multiple examples.

Given positive Radon measures, $\mu $ and $\lambda $, on the complex unit circle, we show that absolute continuity of $\mu $ with respect to $\lambda $ is equivalent to their reproducing kernel Hilbert spaces of “analytic Cauchy transforms” in the complex unit disk having dense intersection in the space of $\mu $-Cauchy transforms.

Generating electronic solutions to be integrated into mechatronic prototypes can be challenging for non-experts. Available electronic modules already implement certain functionalities. Selecting the suitable modules and connecting them in the right way can be tricky. This paper presents a method that (1) maps project requirements onto sets of electronic modules and microcontrollers from a database, (2) optimizes module selection and combinations using search algorithms based on graph theory, (3) maintains electrical feasibility, (4) and generates a bill of materials. The result is a blueprint that describes how to connect the selected modules to enable the desired functionalities.

Sensor-integrating, gentelligent components “inherit” data on operational loads from one generation to the next for design optimisations and require an optimal sensor placement (OSP) to make accurate decisions based on this data. The OSP can be very time-consuming, and most studies focus only on one load case. To address this issue, a methodology for OSP for several load cases, based on the region-growing algorithm for FEM simulation data (RGA4FEM) for solution space reduction, is presented. For validation of the methodology’s applicability, a case study is carried out for a boom of a satellite antenna. The results show that region-based approaches are slower to converge but need smaller populations to find global optima with a genetic algorithm. Furthermore, high robustness is achieved for the most demanding parameters on all load cases in a single optimisation.

Publicly available generative AI tools, such as ChatGPT, Midjourney, and DALL-E 3, have the potential to transform product development by accelerating tasks and improving design ideation. Through case studies of scenario management and persona storyboarding, this research explores the strengths and limitations of generative AI (GenAI) tools. The results highlight GenAI's ability to accelerate routine tasks, improve ideation, and support iterative design, but also reveal limitations in contextual understanding and output quality. Key findings show that effective GenAI integration depends on precise prompt design, iterative interaction and critical validation. Despite their potential, GenAI tools cannot replace human expertise for nuanced design tasks. The study provides actionable insights and best practices for leveraging GenAI tools, paving the way for enhanced human-AI collaboration.

This paper investigates the effectiveness of machine learning models in predicting customer-relevant functional attributes of vehicles based on selected design variables, using a limited automobile market dataset. By comparing machine learning algorithms such as Support Vector Regression, k-Nearest Neighbour Regression, and Lasso Regression, the study evaluates the models’ predictive accuracy and their potential application in automotive design. The findings highlight both the opportunities and limitations of these methods, emphasising their capacity to support data-driven decision-making despite constraints posed by dataset size, as encountered in real-world, early-stage automotive platform strategies.

This work develops a method to integrate operational data into system models following MBSE principles. Empirical analysis reveals significant obstacles to data-driven development, including heterogeneous and non-transparent data structures, poor metadata documentation, insufficient data quality, lack of references, and limited data-driven mindset. A method based on the RFLP chain links operating data structures to logical-level elements. Data analyses are aligned with specific requirements or functional/physical elements, enabling systematic data-driven modeling. This method improves efficiency, fosters system knowledge development, and connects technical systems with operational data.

This study explores the role of ChatGPT in the completeness of collaborative computer-aided design (CAD) tasks requiring varying types of engineering knowledge. In the experiment involving 22 pairs of mechanical engineering students, three different collaborative CAD tasks were undertaken with and without ChatGPT support. The findings indicate that ChatGPT support hinders completeness in collaborative CAD-specific tasks reliant on CAD knowledge but demonstrates limited potential in assisting open-ended tasks requiring domain-specific engineering expertise. While ChatGPT mitigates task-specific challenges by providing general engineering knowledge, it fails to improve overall task completeness. The results underscore the complementary role of AI and human knowledge.

Reading experience provides critical input for language learning. This is typically quantified via estimates of print exposure, such as the Author Recognition Test (ART), although it may be unreliable in L2. This study introduces the Author Fluency Task (AFT) as an alternative measure, comparing with ART for assessing knowledge of English discourse connectives and collocations among 60 bilingual French/English speakers, and a comparison sample of 60 L1 English speakers. Participants completed AFT, ART, and LexTALE in both languages. Analysis of L2 measures showed AFT more accurately predicted L2 vocabulary knowledge than ART, even when controlling for proficiency (LexTALE). Conversely, ART was more effective for L1 speakers, showing a striking dissociation between the measures across language groups. Additionally, data showed limited contributions from L1 proficiency and print exposure on L2 vocabulary. These findings recommend AFT as a valuable tool for quantifying the role of L2 print exposure for language learning.