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.

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.

Design by Analogy (DbA) is a powerful method for fostering innovation by transferring knowledge from a source domain to solve problems in a target domain. However, traditional DbA approaches face significant challenges, including resource-intensive database management, linguistic and representational differences across domains, and the complexity of access and mapping processes. These limitations hinder scalability and efficiency, particularly for cross-domain analogies. Recent advancements in Artificial Intelligence (AI), especially Large Language Models (LLMs), offer promising solutions by facilitating efficient knowledge retrieval, bridging linguistic gaps, and enhancing semantic reasoning. This paper explores the potential of AI technologies to address these challenges, proposing a framework for analogical reasoning.

This article challenges a false dichotomy between subjectivity and objectivity in understanding the nature of human social relationships. I argue that social relationships are composed of both subjective and objective components, which are inherently interdependent. They are influenced by biological properties and subject to evolutionary processes, yet they cannot be reduced to them. I use emerging research on kinship as an example that showcases the appeal of this integrated approach. This article takes a step in the direction of a unified account of sociality, contributing to a more comprehensive understanding of human social behavior.

Given the rise of Generative AI and Large Language Models (LLMs), there is a high interest in their use also in engineering design domain. Current research approaches lack to leverage LLM's new orchestration capabilities and use the LLMs in ways that expose their inherent weaknesses. We present a conceptual model to visualize the contribution of LLMs to design tasks and distribute ownership in the design activities: the triangle of design responsibility. A literature review on the design engineering field presents its current uses in this community. The understanding of the model is validated with industry via survey. We identify future research directions in the field of complex product design. We hope that this model helps design automation developers, researchers and industry practitioners to position and assign responsibility effectively in their design automation implementation.

Uncertainty in coping with sustainability demands poses a challenge to decision makers concerned with manufacturing companies’ product engineering. Therefore, our paper reports on a newly developed guide to address their uncertainty and support them in initiating targeted sustainability action. The guide, based on an interview study (n = 25; 4 company cases and 1 consultancy) and a systematic literature review, addresses decision makers in product engineering and beyond. It was initially applied and evaluated in company workshops. The guide provides success criteria and reflection questions for each step toward targeted sustainability action: understanding, operationalizing, and implementing. This paper outlines the main concepts behind the guide and contributes to the literature by suggesting a novel approach to sustainability action in product engineering by addressing uncertainty.

While performing design tasks, engineers rely heavily on their knowledge. However, the expanding knowledge space makes it impractical to perform the design tasks without external inputs. This study explores how AI can bridge the knowledge space expansion gap in design. The study introduces the AICED framework implemented as a web tool Pro-Explora, leveraging advanced multi-agent LLM technology to accelerate early-stage design tasks. Pro-Explora generates professional problem definitions, PDS documents, and unique solution concept images within five minutes, maintaining creative flow. Its effectiveness was validated in a real-life project, with outputs deemed highly relevant by experienced designers. The study highlights the AICED framework’s industry implications, addressing required knowledge. This pioneering study opens new avenues for specific LLM applications in engineering design.

The evolving needs of customers and stakeholders necessitate the collaboration of diverse system elements within a cyber-physical, socio-technical network. Such Sociotechnical systems are characterized by numerous complex interdependencies as well as by endogenous and exogenous influences. A key issue that developers must address is the mitigation of data and information uncertainties. This paper introduces an ontological approach to facilitate the identification and mitigation of uncertainties in data and information within a model-based methodology for satellite development projects. The work outlines the results of preliminary studies forming the foundation for this ontological concept. The proposed approach comprises an overarching General Ontology, complemented by a Uncertainty and Structure Ontology, creating a framework for uncertainty management in satellite development.

Increased interest in space exploration demands a shift in the design and manufacturing of space systems. Traditionally, space structures are limited by constraints associated with launch systems that affect cost, volume, and mass. The concept of Factory in Space (FIS) proposes the fabrication of systems in space to circumvent the launch constraints. FIS offers a transition to a circular economy in space by minimizing resource consumption and creating a self-sustaining factory ecosystem. This paper evaluates the role of circular design in FIS. Circular design in FIS leads to a reduction in design complexity and modular designs that could enhance space exploration. Material selection, modular design, design for robustness, and lifecycle thinking are highlighted as factors that influence design for circularity in FIS. Finally, the challenges associated with circularity in FIS are presented

The development of smart Product-Service Systems (smart PSS) introduces unique challenges for requirements management due to their dynamic, data-driven, and multidisciplinary nature. This paper investigates methods and principles for data-driven requirements management, emphasizing lifecycle alignment, data utilization, multidisciplinary collaboration, and customer-centricity. A systematic literature review forms the basis for assessing 16 existing frameworks, focusing on their suitability for the data-driven design of smart PSS. Key gaps are identified in areas such as data planning, lifecycle integration, and the handling of system-level requirements. To address these challenges, this study proposes principles for data-driven requirements management that leverage real-time data, ensure traceability, foster interdisciplinary alignment, and adapt to contextual variables.

The process of gathering needs and generating requirements for design for individuals with special needs can be particularly challenging, and the intended solutions are increasingly evolving into Cyber-Physical-Social Systems (CPSS) further complicating the task. Co-design is the preferred approach but when the primary users are children, the challenges are compounded since they are unable to partner in the design process making the task of eliciting needs further difficult. This paper presents an empirical attempt to collate a master list of requirements for design for children with special needs to aid the design process. The study revealed several lacunae in comprehensibility of Requirements and Criteria, and mapping of the two, prompting further investigation into the hindrances to developing a robust and comprehensive resource for designers by designers.

This study applies Artificial Intelligence-Generated Content (AIGC) to design cultural products inspired by Sanxingdui, an ancient Chinese civilization famed for mystical bronze artifacts. Addressing the challenge of merging tradition with modernity, an AIGC framework automates cultural element extraction, generates design concepts, and optimizes aesthetics using generative models. Comparative analysis via Quality Function Deployment (QFD) shows AIGC products achieve higher user satisfaction in aesthetics, symbolism, and engagement. The research highlights the significance of AI in enhancing creativity, efficiency, and cultural preservation, despite algorithmic limitations. It provides actionable strategies for integrating AI into cultural industries, bridging heritage and technology to drive sustainable innovation.

The amount of electronic waste worldwide is increasing every year and the often incorrect handling of it has a major impact on the ecosystem. As electronics are also gaining more share in the automotive sector, the industry has to find a suitable way of dealing with them at the vehicle’s end-of-life stage. For this reason, this work introduces an approach consisting of the Physical Component Mapping (PCM) for the interface representation of automotive electronics, alongside the Eco-Sensitivity Framework (ESF) as guidance for circular automotive electronics design. A case study shows how the approach accompanies the product development process and supports identifying suitable strategies that are potentially possible or can be made available through design changes. This helps car makers and suppliers of vehicle electronics to accelerate their transition to a circular economy.