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.

Site-Specific Weed Management (SSWM) provides precise weed control and reduces the use of herbicides, which not only reduces the risk of environmental damage but also improves agricultural productivity. Accurate and efficient weed detection is the foundation for SSWM. However, complex field environments and small-target weeds in fields pose challenges for their detection. To address the above limitation, we developed WeedDETR, a real-time end-to-end detection model specifically designed to enhance the detection of small-target weeds in unmanned aerial vehicle (UAV) imagery. WeedDETR incorporates RepCBNet, a backbone network optimized through structural re-parameterization, to improve fine-grained feature extraction and accelerate inference. In addition, the designed feature complement fusion module (FCFM) was used for multi-scale feature fusion to alleviate the problem of small-target weed information being ignored in the deep network. During training, varifocal loss was used to focus on high-quality weed samples. We experimented on a new dataset, GZWeed, which contains weed imagery captured by an UAV. The experimental results demonstrated that WeedDETR achieves 73.9% and 91.8% AP0.5 (average precision at 0.5 intersection over union threshold) in the weed and Chinese cabbage [Brassica rapa subsp. chinensis (L.) Hanelt] categories, respectively, while achieving an inference speed of 76.28 FPS (frames per second). In comparison to YOLOv5-L, YOLOv6-M, and YOLOv8-L, WeedDETR demonstrated superior accuracy and speed, exhibiting 3.5%, 6.3%, and 3.6% higher AP0.5 for weed categories, while FPS was 14.9%, 12.9%, and 1.4% higher, respectively. The innovative architectural design of WeedDETR significantly enhances the detection accuracy of small-target weeds, enables efficient end-to-end weed detection. The proposed method establishes a solid technological foundation for UAV-based precision weeding systems in field conditions, advancing the development of deep learning-driven intelligent weed management.

In its ruling of 25 January 2025, the Court of Justice of the European Union (CJEU) clarified the scope of the Waste Shipments Regulation in the context of hazardous waste generated during maritime accidents. The Court concluded that “contrary to the circumstances envisaged” in its previous ruling (Conti 11. Container Schiffarts I), the shipment of the MSC Flaminia from Germany to Romania had to be subject to the prior written notification and consent procedure provided for by that Regulation. The difference between the two scenarios lies in the circumstance that, in the case at hand, part of the waste had already been offloaded, preventing the applicability of Article 1 (3) (b) exclusion. The latter, according to the Court, has a temporary nature and should be interpreted restrictively as to ensure compliance with the Union’s obligations under the Basel Convention. The judgment reinforces a precautionary, risk-based approach to hazardous waste shipments in line with supranational sustainability goals.

This research addresses the critical need for designing alternative healthcare monitoring systems that support health benefiting parent-child interactions during hospital stays, especially in neonatal intensive care units (NICUs). We developed a HIPAA-compliant, remote healthcare monitoring system designed to facilitate positive interactions between parents and their infants such as skin-to-skin contact. To evaluate the proposed system, we conducted a proof-of-concept experiment using video and sensor data collection to assess the system’s feasibility and usability with adult participants. Additionally, we examined participants’ subjective experiences through post-interaction surveys and interviews. Overall, the system was perceived as helpful in supporting caregiver-patient interactions. Future improvements can address concerns about continuous monitoring and data management.

This paper presents a hybrid framework that integrates physical and virtual testing to enhance cross-sectional studies in the field of engineering design. The framework addresses the critical challenge that valid inferences in realistic cross-sectional studies are often hampered by the manufacturing constraints of physical prototypes and the limitations of virtual prototypes. Using the example of a snap-fit system, the framework shows how predictive modelling and parametric design enable efficient iterations for building design knowledge. By combining the empirical accuracy of physical testing with the scalability of virtual simulations, the framework reduces iteration times, improves resource efficiency and adapts to different study conditions.

Human data has significant value in Data-Driven Design, offering opportunities for user-centered product and service development. This paper explores how human data, categorized into behavioral, physiological, feedback, and emotional types, contributes to problem framing, iterative refinement, customization, and emotional design. Real-world case studies from academic literature and industry demonstrate how human data enables adaptive, personalized, and emotionally engaging solutions. Ethical challenges, including privacy, bias, and transparency, are explored, highlighting the importance of responsible data practices. The analysis underscores human data’s value in combining technical precision with empathetic design, fostering innovation and enhancing user experiences while promoting ethical use through principles of privacy, consent, and inclusivity.

As a means for both the construction and communication of social identity in diverse human groups worldwide, objects of personal adornment can help to explain some prehistoric lifeways and beliefs. This study examines the materials and manufacture traces of whole and fragmentary pendants found in association with human burials at the Early Period (c. 4200 cal BC–cal AD 250) Ortiz site in south-western Puerto Rico. Using data from microscopy, elemental analysis and petrography, the authors propose that these pendants were a tangible manifestation of group identity, rooted in a sense of localised belonging, which persisted over almost a millennium.

In principle, inaccuracies in the representation of the climate’s internal variability could undermine the measurement of the human contribution to warming. Equally in principle, the success of the measurement practice could provide evidence that our assumptions about internal variability are correct. I argue that neither condition obtains: current measurement practices do not provide evidence for the accuracy of our assumptions precisely because they are not as sensitive to inaccuracy in the representation of internal variability as might be worried. I end by drawing some lessons about stability and “robustness reasoning” more generally.

Reasoning about dynamic systems with a fine-grained temporal and numeric resolution presents significant challenges for logic-based approaches like Answer Set Programming (ASP). To address this, we introduce and elaborate upon a novel temporal and constraint-based extension of the logic of Here-and-There and its nonmonotonic equilibrium extension, representing, to the best of our knowledge, the first approach to nonmonotonic temporal reasoning with constraints specifically tailored for ASP. This expressive system is achieved by a synergistic combination of two foundational ASP extensions: the linear-time logic of Here-and-There, providing robust nonmonotonic temporal reasoning capabilities, and the logic of Here-and-There with constraints, enabling the direct integration and manipulation of numeric constraints, among others. This work establishes the foundational logical framework for tackling complex dynamic systems with high resolution within the ASP paradigm.

In this paper, we explore the relationship between divergent thinking and stakeholder identification on 15 student engineering design teams. We examine the relationship between fluency, originality, flexibility, and elaboration on the Alternate Uses Task (AUT), a common measure of divergent thinking, and in stakeholder identification. We find fluency and originality to be positively and statistically significantly correlated between the AUT and stakeholder identification task. Flexibility was positively correlated and elaboration was negatively correlated; both lacked statistical significance. Our results suggest that divergent thinking and stakeholder identification may be correlated, and leveraging exercises to improve divergent thinking may also help improve stakeholder identification. Future work can continue to explore this relationship with larger sample sizes and additional tasks.