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.

Engineering systems are represented in a variety of physical, graphical, and virtual ways, supporting decision making about the systems and their operation. As part of a larger research endeavor exploring influences of representation modality, the presented work examines how product identification impacts subsystem clustering behavior. This is achieved through a study using pictorial and functional representations of common household products. Participants were tasked with grouping elements into non-overlapping clusters. Results suggest that correctly identifying a product does not affect clustering behavior regardless of representation modality. This implies that other aspects of the representations are impacting partition convergence. These factors, along with connections to prior work are explored as discussion points and areas of future research.

The possibility of automatic evaluation in online exams offers the advantage of automatic evaluation compared to paper-based exams with manual assessment. Nevertheless, teachers and students have major concerns about digital exams e.g. students are afraid of getting worse grades due to reduced inputs and determined evaluation steps. To analyze these concerns for Engineering Design Education this paper investigates to what extent can be found differences in the results between digital and paper-based examination formats when assessing the same learning outcomes with the same tasks about dimensioning machine elements. The paper contains the transformation of existing paper-based exam tasks into digital automatic evaluable tasks and the data from students participating in a digital exam are compared with data from students with a paper-based exam.

In 1926, Roberto Bartoccini excavated a late-antique tomb at Sirte, Libya. Fifty-three inscriptions in Latin, Greek and Latino-Punic have been recorded and used as evidence of a thriving Christian community. This article reassesses these inscriptions, paying particular attention to the Latino-Punic texts, and discusses the persistence of a Punic identity that can be placed in the context of the wider archaeological landscape.

Design research is highly interdisciplinary, connecting to significant research problems such as the scientificity of design research and blurring boundaries of design disciplines. This paper adopts the perspective of philosophy of technology, regarding design as technical artifact-making activities. It endeavors to identify potential design research approaches based on the evolution of the philosophy of technology, and explain how these approaches have emerged, developed, and evolved. These include: analytic philosophy approach, pragmatism approach, and phenomenology approach. These three research approaches can represent the differentiation rules of design research in both independent and interrelated manners. The clarification can make the philosophical stances of technical artifact-making activities clearer, and provide philosophical references for future design meta-research.

Topology optimization combined with additive manufacturing enables the creation of complex, high-performance products. However, industrial applications often involve numerous and complex requirements, making it challenging to align the design and manufacturing process to meet all demands. A particular challenge is to determine which requirements should be included in the optimization problem statement. This paper presents a procedure model to integrate requirements and feasibility constraints into the design and manufacturing process. It includes two major steps: organizing requirements and constraints in the process and identifying the problem statement. The procedure is applied to the requirements of an engine bracket of AUDI AG, demonstrating its ability to handle numerous requirements and to specify the problem statement.

This study proposed a framework to visualize research trends and create methods to forecast future directions in the design research methodology field from 2018 to 2022. A case study is conducted using a dataset of abstracts from conference proceedings included in the American Society of Mechanical Engineers (ASME) International Design Theory and Methodology Conference track from 2018 to 2022. The proposed method involves extracting keywords from research articles, transforming them into vectors, determining the similarity between keyword pairs to form a keyword network, and constructing a Sankey diagram to show the topic evolution pathways. The resulting Sankey diagrams provide insight into relationships between research topics.

Artificial Intelligence (AI) techniques are increasingly explored to support design activities within the manufacturing context mainly driven by the development of AI technologies. However, few studies were conducted in practice from industrial perspectives. This research aims to understand the opportunities and challenges of AI in design in the real world. A workshop involving twenty-five participants from more than ten manufacturing firms is organised to collect relevant information. The opportunities and challenges identified are categorised by adopting a readily available data-driven design framework. Seven research directions are proposed accordingly to address the industry challenges and opportunities. This research serves as a guide for ensuring future AI in design research and applications are grounded in practice to bridge the gap between academic research and industry practice.