How and why do armed groups that become known as “rebels” initially use violence? New datasets show that such violence is often small in scale. Numerous empirical examples indicate that it is also often ambiguous—not easily identified as a precursor to anti-state rebellion. This paper seeks to explain these patterns. We argue that a variety of fledgling nonstate armed groups find small-scale, anonymous anti-state violence useful, despite the risks. Therefore, armed groups that later become distinguishable as “rebels” or “bandits” often initially use this similar repertoire of violence. The resulting ambiguity of this violence—for outsiders from states to scholars—presents an opportunity for aspiring rebels, since states struggle to discern the threat they pose. Ambiguity lessens when aspiring rebels opt to use offensive, larger-scale violence. We illustrate our claims with three historical case studies that enable close examination of early armed group violence, as well as 12 brief case vignettes. Our analyses show the promise of integrating research on rebel origins, criminality, and state formation.

The multi-UAV task allocation problem can be divided into two components: optimising UAV resource allocation and developing an optimal execution plan. Existing single-population algorithms often get trapped in local optima and require improved accuracy. Although multi-population algorithms perform better, they introduce higher complexity, significantly increasing running time. This paper proposes a Two-Stage Multi-Population Wolf Pack Algorithm (2SMPWPA) to address these issues. This algorithm innovatively splits the task allocation problem into two stages: the initial stage focuses on optimising UAV resource utilisation. In contrast, the subsequent stage focuses on optimising the execution plans for the existing UAV resources. Furthermore, the algorithm categorises the population into a leader group and two normal groups, where the leader group consists of elite individuals from the ordinary groups. To ensure the outstanding individuals in the normal groups have adequate computational resources, a population competition mechanism is introduced to dynamically adjust the size of each sub-population based on their average contribution to the optimal solution. To prevent the ‘big eats small’ scenario, the algorithm incorporates population protection and migration mechanisms to maintain diversity. Additionally, a population communication mechanism is implemented to preserve ‘vitality’ during the later iterations, preventing the algorithm from converging to local optima. Comparative experiments demonstrate that the 2SMPWPA significantly outperforms recent algorithms regarding solution accuracy, effectively addressing the trade-off between solution precision and running time.

The intersection of design and narrative plays a crucial role in shaping meaningful experiences. While narrative experience has been explored in product design, its role in service design remains underdeveloped. This study introduces a narrative-driven service design approach, integrating narrative to enhance user experiences. Using a Research through Design methodology, ten digital service prototypes were developed, embedding “stories of moments of joy” as a design foundation. Findings suggest that starting with narratives fosters deeper emotional engagement and enhances service interactions. Participant feedback highlights how this approach provides an alternative to traditional problem-solving models, emphasizing narrative-driven innovation in service design. By positioning narrative as a central design element, this study contributes to advancing service design methodologies.

Recent advancements in machine learning (ML) offer substantial potential for enhancing product development. However, adoption in companies remains limited due to challenges in framing domain-specific problems as ML tasks and selecting suitable ML algorithms, requiring expertise often lacking. This study investigates the use of large language models (LLMs) as recommender systems for facilitating ML implementation. Using a dataset derived from peer-reviewed publications, the LLMs were evaluated for their ability to recommend ML algorithms for product development-related problems. The results indicate moderate success, with GPT-4o achieving the highest accuracy by recommending suitable ML algorithms in 61% of cases. Key limitations include inaccurate recommendations and challenges in identifying multiple sub-problems. Future research will explore prompt engineering to improve performance.

Advertisements play a key role in shaping perceptions of gender identity, which are influenced by biological traits and cultural beliefs. In India, practices like arranged marriages have historically defined gender roles, but younger generations are increasingly challenging these norms, especially through dating apps. This study examines how dating app advertisements address gender dynamics and societal challenges in India. By applying Barthes’ Semiotic theory, we analyzed a popular Bumble ad. The findings reveal how the ad promotes female agency, subverts gender norms, and portrays men as emotionally expressive. By blending modern technology with family values, the ad presents dating as empowering and respectful, challenging rigid societal norms. The study promotes inclusivity and shows how ads reshape gender narratives, and offers insights for creating socially responsible campaigns.

Ponzi schemes are financial frauds that are pervasive throughout the world. Since they cause serious harm to society, it is of interest to study them so that they can be prevented. Typically, a Ponzi scheme is instigated by a promoter who promises above-average investment returns. He uses funds from the early investors to pay his later investors. These scams can occasionally last a long time, but they are ultimately unsustainable. This paper describes some well-known Ponzi schemes and identifies their common characteristics. We also review some of the approaches used to model Ponzi schemes.

Visual-Language (VL) models offer potential for advancing Engineering Design (ED) by integrating text and visuals from technical documents. We review VL applications across ED phases, highlighting three key challenges: (i) understanding how functional and structural information is complementarily expressed by text and images, (ii) creating large-scale multimodal design datasets and (iii) improving VL models’ ability to represent ED knowledge. A dataset of 1.5 million text-image pairs and an evaluation dataset for cross-modal information retrieval were developed using patents. By Fine-tuning and testing the CLIP base model on these datasets, we identified significant limitations in VL models’ capacity to capture fine-grained technical details required for precision-driven ED tasks. Based on these findings, we propose future research directions to advance VL models for ED applications.

Worldbuilding is a concept that has been used to describe the creation of immersive landscapes in fiction and games and is deeply resonant with archaeological knowledge construction. This article argues for worldbuilding in archaeology as a creative intervention that encourages an exploration of archaeological data throughout the process of creation, interpretation and dissemination to generate past worlds, shaped through community storytelling. Through the examples of Çatalhöyük in Second Life, Other Eyes and the Avebury Papers projects, I explore a playful practice that closely interrogates reuse of archaeological data and encourages lateral thinking amongst students and other archaeological storytellers.

To support the transition towards a circular economy in hospitals, this qualitative study aimed at understanding how the adoption of reusable surgical gown can be facilitated. It investigates design features that enhance usability and promote sustained (re)use. A wearing test identified difficulties in wearing reusable gowns. Data collection included observations of 34 surgeries and a survey completed by 73 respondents. Thematic analysis revealed opportunities to improve usability, such as optimising packaging to speed up donning, a wider neck opening to reduce discomfort, and incorporating ‘tearable’ closures to simplify doffing. Innovation strategies relevant to the users involve thermal regulation, monitoring gown performance, and including reusable gowns in custom procedure tray packaging. These findings are discussed in relation to design adjustments and value-chain partners.

CAD tasks require engineering designers to manage cognitive, perceptual, and motor demands while solving complex design problems. Understanding the relationship between workload (WL) and CAD performance is essential for improving design outcomes and processes. However, this relationship, particularly under varying task complexities, remains insufficiently explored. This study investigates WL-performance relationships in two CAD modelling tasks of differing complexity. WL was measured with NASA TLX, including its individual components. CAD performance was evaluated and described through outcomes and processes using multiple metrics. The results revealed significant monotonic relationships between WL and performance, with stronger correlations in the high-complexity task.

After medical marijuana legalization (MML) by U.S. states, firms’ cost of equity (COE) decreases, especially for those with more growth opportunities, higher productivity, or a more skilled workforce. This policy change also reduces firm risk and leads to an increase in labor supply through increased labor force participation, employment, hours worked, and net migration. Further, home prices rise after MML, reflecting increased local housing demand due to a growing supply of workers. These findings align with theoretical models that link asset prices to labor markets and suggest that MML can lower firms’ COE by mitigating labor search frictions.

This article explores the underlying causes of vigilantism, moving beyond existing explanations to propose a novel perspective: state absenteeism. Drawing upon an original dataset collected at the subnational level in Guatemala, the study utilizes police station data as a proxy measure of state presence. This research article sheds light on the intricate dynamics driving vigilantism by analyzing the interplay between state actions, security provision, and the emergence of extralegal justice mechanisms. Empirical findings suggest that existing theories do not fully explain the surge in vigilantism, underscoring the importance of considering state provision of security at the subnational level. This theoretical and empirical contribution highlights the role of uneven state presence in shaping responses to insecurity and calls for more equitable and locally responsive security provision to address the root causes of extralegal justice.

This paper explores the employment implications of integrating service robots in waste management. Using the scenario technique method, 14 critical influencing factors were identified and analyzed to develop a Best-Case, Worst-Case, and Trend scenario. A SWOT analysis was used to identify implications and develop measures. The findings indicate that service robots can enhance working conditions and enable service expansion but pose risks like job displacement without proper education and reskilling. The study underscores the need for regulatory frameworks, workforce adaptation, and education to ensure socially sustainable robotic integration.

A design catalog is a repository of design problems and their solutions, enabling designers to explore and discover applicable solutions for their specific design challenges. Creating such catalogs has depended on human knowledge and implicit judgment, with no systematic approach established. This study aims to develop a systematic method to create a design catalog from patent documents. We utilize a large language model (LLM) to extract problem-solution pairs described in the documents, presenting them as general purpose-means pairs. Subsequently, we create a design catalog by classifying the problems using similarity-based clustering, enhanced by the LLM’s semantic text similarity capabilities. We demonstrate a case study of creating a design catalog for martial arts devices and generating new design concepts based on the catalog to verify the effectiveness of the proposed method.

Extending the lifetime of products is one objective of a Circular Economy. The lifetime of a vehicle is limited not only by wear, but also by declining customer satisfaction. Customer satisfaction is related to the different types of quality. Components aim for different types of quality. That is why modularization is seen as a possible enabler to facilitate both durability and adaptability in the vehicle structure. Additionally, extending their lifetime integrates passenger vehicles into a Circular Economy. This paper aims to define classes of components to support the development of a modular structure for passenger vehicles that is suitable for a Circular Economy. It provides four classes based on the relevance of components to customer satisfaction and their expected lifetime. This enables the targeted development of R-strategies for components.

The EU AI Act (Regulation (EU) 2024/1689) represents a significant departure from the EU’s traditionally restrained regulatory approach to commercial arbitration. The Act classifies certain use cases of AI in arbitration as potentially “high-risk” and introduces stringent compliance obligations for legal tech providers, arbitral institutions and arbitrators. This article argues that the Act’s application to arbitration disrupts the long-standing balance between party autonomy, procedural flexibility and regulatory oversight that has characterised the EU’s treatment of the field. It also highlights the challenges of reconciling its rigid framework with key aspects of arbitration – namely, party autonomy, confidentiality and procedural flexibility. The article concludes by proposing a full or partial carve-out of commercial arbitration from the scope of the AI Act’s high-risk provisions.

Organizational capability is key to achieving strategic goals and adaptability. This study applies the TASKS framework to evaluate taskload, affect, skills, knowledge, and stress using a questionnaire developed through the Environment-Based Design (EBD) methodology. A structured perception-centered evaluation was conducted to assess employees’ perceptions of organizational alignment, with middle managers’ responses serving as a reference. Findings emphasize the need for better communication, leadership engagement, and goal clarity to enhance transformation readiness. The TASKS framework’s perception-centered evaluation assesses organizational capability and identifies role-based misalignments. Future research will expand the framework’s application to validate its effectiveness and refine strategies for enhancing organizational capability.

In the context of volatile markets, characterised by a need for continuous product development involving module-wise product modifications, the importance of flexibility as an attribute of products and their production system has been increasing. This paper presents a methodological approach focusing on the flexibility evaluation of modules regarding their interfaces. The subject encourages engineers and researchers to analyse and rethink the interface design and the location of module boundaries regarding change propagation. The method was validated using the Design Method Validation System (DMVS) to determine its usefulness, applicability and acceptability. The design workshop for validation was applied to a product family of trunk lids by employees of a German car manufacturer.

Computer-aided design (CAD) has become essential for hardware product development in our industrial age. However, increasing complexity, shorter lead times, and cost pressures present new challenges. While generative AI has gained significant attention and transformed various business functions, its application in engineering design with CAD remains underdeveloped. Our research aims to explore why generative AI has not yet reached its potential in CAD, despite its prominence in other fields, by identifying key challenges through case studies and a literature review. These challenges include small datasets, difficulty representing mixed data types, proprietary file formats, and lack of advanced CAD modeling commands. We propose future developments such as high-quality datasets, a vendor-neutral format, novel neural network architectures, and expanded generative methods.