Concept maps have been used to measure student learning and performance comparisons before and after interventions. However, these concept maps may vary in structure and content. The complexity of the maps are difficult to measure when multiple concepts are embedded within individuals nodes. A systematic coding scheme is evaluated for objectivity and reliability using 22 undergraduate researchers. The findings suggest that the coding scheme is suitable and will allow multiple different researchers to generate similar bi-partite graphs from the notional concept maps generated. Additional work is needed to ensure that the semantic content is not invalidated through the coding scheme.

This paper analyses the amount of design-oriented content in higher education, as well as the extend of activities from product development such as clarification of problem or task, shaping the modules and usage requirements and assurance of fulfilment of requirements. The mechanical engineering study degree program of three universities is analysed by categorizing courses to design-oriented, design-related, basic science and additional expense. These particular courses are then further investigated by assigning the learning hours to certain product development activities regarding the VDI 2221 guideline. The results show that between 14 % and 26 % (bachelor) and from 27 % to 33 % (master) of courses are design-oriented. Most of the time is spent on achieving competencies in shaping modules, e.g. design parts. The other eight activities are treated less than 10 % of the total workload.

The predominant adoption of artificial intelligence (AI) in the design process is constantly evolving with the continuous upgradation of generative AI tools. Current studies emphasised generative AI’s role in individual disciplines, with limited understanding of its use across diverse design disciplines like product, fashion, and UX design. Therefore, the importance of this review is to explore the latest trends in utilisation, commonalities, and differences of generative AI tools and tasks, and AI types across design disciplines. With the assistance of Google Scholar, relevant papers were identified based on alignment with the review’s scope. The study highlights the transformative role of tools like ChatGPT and DALL-E in enhancing creativity, ideation, and decision-making. The outcomes of the review offer insights for future systematic reviews and practical guidance for designers.

Engineering design tasks are cognitively complex and there is a growing interest in understanding the neurocognitive processes involved in design. Consequently, researchers are increasingly using bio-physical markers such as eye tracking to study design neurocognition. However, these studies are largely correlational, and little is understood about the construct validity of eye-tracking metrics such as fixation durations and saccade frequency. Moreover, these studies rarely account for non-design factors such as neurodivergence (e.g., ADHD) on eye-tracking metrics during design. We aim to examine this research gap through a causal-comparative study with designers with and without ADHD, performing divergent and convergent design tasks. Our findings call for a deeper investigation into the construct validity of eye-tracking metrics while considering a broad range of external factors.

The application of Generative Artificial Intelligence (AI) in early-stage design processes has emerged as a promising method for generating innovative solution concepts. However, AI-driven concepts may introduce secondary problems when implemented practically. This study proposes a systematic framework integrating Generative AI (GPT-4o), patent analysis using Retrieval-Augmented Generation (RAG), and Failure Mode and Effects Analysis (FMEA) to predict, evaluate, and mitigate potential risks. Applied to a case study on nickel recovery through froth flotation, the framework significantly enhanced the feasibility, usefulness, and sustainability of solution concepts. The research highlights the scientific contribution and practical benefits of combining Generative AI with structured risk-analysis methods for sustainable innovation.

Developing new factories is effectively a design task. In this paper a case study on barriers to efficient project communication is presented. Preceding research has shown that production systems design projects can be more efficiently executed and that as many as 95% of all problems in collaborations are due to a lack of communication. The study was designed to grasp project communication barriers from three projects and developed a visual planning tool. The findings show that digital planning software supports mainly in the categories of Egocentrism and Mistrust, Equivocality and Ambiguity and less in Interaction Capability, Asynchronisity and Noise and Information-sharing Behaviour. Recommendations for future research is to connect the project communication support to quantitative project performance aswell as the acceptance of technology in production systems design.

Functional decomposition (FD) is essential for simplifying complex systems in engineering design but remains a resource-intensive task reliant on expert knowledge. Despite advances in artificial intelligence, the automation of FD remains underexplored. This study introduces the use of GPT-4o, enhanced with a proposed Monte Carlo tree search for functional decomposition (MCTS-FD) algorithm, to automate FD. The approach is evaluated qualitatively by comparing outputs with those of graduate engineering students and quantitatively by assessing metrics such as structural integrity and semantic accuracy. The results show that GPT-4o, enhanced by MCTS-FD, outperforms smaller models in error rates and graph connectivity, highlighting the potential of large language models to automate FD with human-like accuracy.

Generative AI, guided by inventive heuristics, can systematically and rapidly generate hundreds of ideas for engineering inventive design problems. This paper examines the reliability and effectiveness of AI-powered “idea funnelling,” a process that generates, evaluates, filters, and synthesizes raw ideas into feasible solution concepts. Key challenges include the consistency and objectivity of AI-driven evaluations, the robustness of concept generation, and the collaboration of multiple AI chatbots such as ChatGPT and Gemini. The study explores the integration of human expertise in hybrid problem-solving teams to improve feasibility, contextual relevance, and innovation quality. Through comparative experiments, it provides insights to improve the reliability of AI-driven concept creation and the performance of hybrid AI-human teams in solving complex engineering design problems.

Spermophagus niger L. is a well-known pest of roselle (Hibiscus sabdariffa L.) seeds in West Africa and responsible of mostly damage. This study first reported the presence of S. niger Motschulsky in kenaf (Hibiscus cannabinus L.) seeds stored. Samples of kenaf seeds collected at four locations in Burkina Faso. In the laboratory, the seeds were incubated until adults’ insects emerged. The emerged insects were first identified morphologically and their biodemographic parameters studied under controlled conditions (32°C ± 0.1, 43% ± 1 r.h.). The results showed that S. niger (Coleoptera: Chrysomelidae) was the only pest encountered on H. cannabinus seeds in storage and was able to complete its development cycle there. Over the course of its life, which lasts an average of 7 days, the female laid around 40 eggs, resulting in 24 individuals dominated by females. The embryonic and total development time were average 5 and 26 days, respectively. Spermophagus niger population doubled in 6 days, with an intrinsic rate of natural increase of 0.105. The finite rate of increase and the generation time averaged 1.11 and 31.86 days, respectively. This study pointed out for the first time that S. niger is able to evolve successfully on H. cannabinus seeds in storage conditions and therefore, could be a serious pest of this important crop. The data from this study could therefore be used as a basis for the post-harvest management of H. cannabinus seeds.

Did George Floyd’s murder and its ensuing protests produce a racial reckoning? Conventional social-science accounts, emphasizing the stability of racial attitudes, dismiss this possibility. In contrast, we theorize how these events may have altered Americans’ racial attitudes, in broadly progressive or in potentially countervailing ways across partisan and racial subgroups. An original content analysis of partisan media demonstrates how the information environment framed Black Americans before and after the summer of 2020. Then we examine temporal trends using three different attitude measures: most important problem judgments, explicit favorability towards Whites versus Blacks, and implicit associations. Challenging the conventional wisdom, our analyses demonstrate that racial attitudes changed following George Floyd’s murder, but in ways dependent upon attitude measure and population subgroup.

Need analysis is essential for organisations to design efficient knowledge management (KM) practices, especially in contexts where knowledge is a critical asset and evolving fast. The research explores the application of large language model (LLM)-based agents in automating need analysis for KM practices. A two-layered model using Retrieval-Augmented Generation (RAG) architecture was developed and tested on datasets, including interviews with managers and consultants. The system automates NLP analysis, identifies stakeholder needs, and generates insights comparable to manual methods. Results demonstrate high efficiency and accuracy, with the model aligning with expert conclusions and offering actionable recommendations. This study highlights the potential of LLM-based systems to enhance KM processes, addressing challenges faced by non-technical professionals and optimising workflows.

Hackathons have recently garnered significant research interest. Hackathon teams frequently include developer, business, and designer roles, yet the designer role and experience of design in hackathon teams are poorly understood. In this paper, we present findings from ten interviews with designer hackathon participants. A thematic analysis reveals that the responsibilities of designers at hackathons roughly align with more typical design contexts, although the format of hackathon events forces designers to adapt approaches to design. Hackathon participants value teams with diverse skills, including design skills, yet designers face resistance from peers in developer roles when seeking to use established design methods for validating needs and generating solutions. This tension can make designers feel unwelcome at hackathons, harming efforts to attract a more diverse participant pool.

Teams have been favored due to the diverse knowledge access. However, diversity can also have negative effects, and team outputs can be influenced by many factors, such as psychological safety. While the effects of psychological safety have been studied, its development has received less attention. Prior research in this area has focused either on specific populations or cross-sectional effects. To add to this area, this study examined the longitudinal development of psychological safety in engineering capstone students: how it evolves, and whether this can be influenced by team-related experiences. This study showed that although psychological safety did change meaningfully with time, neither time nor experience alone could capture the change. The results could shed light on the evolution of psychological safety, as well as what factors could potentially influence its development.

The blowgun is a weapon that employs the force of breath for expelling a projectile and has been traditionally used for hunting and (occasionally) war. The use of blowguns extends to ancient times and is advantageous in dense-forest areas of South America and South East Asia. A classification system of blowgun types introduced in 1948 for South America is extended here. We assembled a global database that includes collection data and ethnographic accounts of blowgun types and other related features that were linked to available linguistic information. Our analyses show that geography explains the distribution of blowgun types to some degree, but within regions of the world it is possible to identify cultural connections. Darts are by far the most used projectiles and in combination with toxins (e.g. curare), these weapons reach their highest potential. A case study on the use of blowguns in groups of Austronesian language speakers shows clade-specific preferences across the tree. Our comprehensive database provides a general overview of large-scale patterns and suggests that incorporation of other related data (e.g. sights, mouthpieces, quivers) would enhance the understanding of fine-scale cultural patterns.

In this article, the author develops an Islamic normative legal theoretical framework by using three key Islamic methodological approaches—(1) juridical theory of law (uṣūl), (2) legal maxims (qawāʿid), and (3) purposive-based theory (maqāṣid)—in light of Ronald Dworkin’s notions of rules, principles, and policies, respectively. While uṣūl is used to develop rules, qawāʿid and maqāṣid provide the normative values that govern rulemaking. In addition to presenting a coherent model of Islamic normative legal theory, the author examines legitimacy issues of Islamic law that relate to links of rules to sharīʿa revealed texts and applies the Islamic normative legal theoretical framework to contemporary rulings on the environment, organ transplants, and Islamic finance. The case studies show that using the integrated normative framework would yield more ethical rulings than those that focus on juridical methods (uṣūl) only. The author argues that while the extent of legal legitimacy can vary across different rulings, the application of the Islamic normative legal framework ensures normative legitimacy in all cases, ensuring the moral character of Islamic law.