This paper develops an agenda for underrepresented populations in consumer research, highlighting both opportunities and challenges as well as ways to address them. Based on survey responses collected among attendees at the 16th Pangborn Symposium in 2025 together with data obtained through targeted LinkedIn posts in relevant communities (N = 104), we present preliminary evidence that matters linked to greater generalisability are deemed important on a wide array of parameters, including those linked to pressing societal issues, practical relevance and research practices, especially in applied relative to basic research. Adapted study designs, methods and analyses emerged as a key initiative to enable more research on underrepresented populations, while editorial policies that put a stronger emphasis on generalisability and sample diversity turned out to be the least popular initiative in this context. When asked to state which specific underrepresented populations participants would prioritise from a sensory and consumer research perspective, low-income populations were, by far, the most frequently selected category. Grounded in these results, we introduce the PLATEAU acronym, which describes the strong focus on People from Literate, Affluent, Technological, Educated, Anglicised and Urban settings, characterised by Western customs, consumption norms and food culture. We end the paper with several strategies and concrete examples on how to mitigate the present PLATEAU priority and inspire more research on underrepresented populations, while acknowledging complex challenges, including those related to systemic biases embedded in artificial intelligence.
Abstract Owing to the drastic environmental change and frequently occurring disasters like bushfires in Australia and New South Wales (NSW) in particular, this research study aims to develop an unmanned aerial vehicles (UAVs) assisted bushfire detection and prevention system. A rule-based verification framework is developed to identify critical infrastructure impacted by bushfires using a metaheuristic optimization algorithm, with reliance on real-time data. Moreover, the affected area coverage of UAVs is maximized by considering limited resources and energy constraints for better precision of results. Finally, a mathematical optimization model is developed and tested for path planning of different UAV populations in affected bushfire regions. Within the realm of competing techniques for UAVs path planning, this research study extensively scrutinized and assessed four distinct optimization algorithms, including whale optimization algorithm (WOA), grey wolf optimizer (GWO), and particle swarm optimization (PSO). Results indicated that our proposed model, efficient particle swarm optimization (EPSO), consistently outperforms other techniques across all cases. However, the degree of its superiority varies among different scenarios. In Case 3, EPSO exhibits the most dominance, presenting notable improvements with approximately 18.35% to 54.47% reductions in cost and time compared with WOA, GWO, and PSO.
Long-term animal re-identification (ReID) must remain robust to gradual morphological evolution and seasonal appearance shifts. Although recent vision-language models provide strong pretrained visual representations, adapting them to longitudinal ecological settings remains challenging, particularly under identity and temporal distribution shifts. We present a parameter-efficient CLIP adaptation framework for animal ReID and introduce a continuous metadata-conditioning mechanism that incorporates numerical attributes directly into the prompt representation during training. While low-rank visual adaptation, prompt-based supervision, and cross-modal alignment provide the adaptation framework, the proposed metadata-conditioning strategy constitutes the primary methodological contribution. By preserving the continuous structure of numerical metadata rather than discretizing it into textual categories, the proposed approach enables smooth modulation of the embedding space during training while maintaining a purely visual inference pipeline. Experiments on a seven-year longitudinal fish dataset and multiple wildlife benchmarks demonstrate improved performance under closed-set, open-set, and time-aware evaluation protocols. The results demonstrate that continuous metadata conditioning improves robustness to longitudinal appearance variation and temporal distribution shifts, while parameter-efficient adaptation enables a purely visual inference pipeline without requiring metadata at test time. Code and evaluation splits can be found at: https://github.com/AnilOsmanTur/MetaPrompt-ReID.
In the context of Industry 5.0, scheduling heterogeneous resources, such as humans and robots, has become increasingly critical. Task allocation must balance human comfort, ergonomics, and trust with productivity and responsiveness to customer demands. This review explores recent advances and prospects in the automatic generation of schedules and action plans, particularly Behavior Trees (BTs), to improve human–robot collaboration. We examine the application of artificial intelligence techniques to classical production management problems, such as Job Shop Scheduling Problem (JSSP) and Assembly Line Balancing Problems (ALBP), for autonomous task scheduling and robotic behavior design. This includes highlighting innovative scheduling approaches and the advantages of Behavior Trees over traditional models such as Hierarchical Task Networks (HTN) and Finite-State Machines (FSM). Behavior Trees offer a modular and reactive programming structure essential for executing complex tasks assigned to robots. The review also discusses human operators’ perception of robotic actions and identifies best practices for implementing collaborative solutions that prioritize both efficiency and safety.
The social care sector across the globe is increasingly witnessing customer mistreatment incidents that pose significant personal and work-related challenges for frontline employees. Extant research suggests that human resource management (HRM) can be instrumental in extending support to the frontline to overcome the adversities of such mistreatment. In this study, we first develop a comprehensive account of the types of customer mistreatment prevalent in social care. Then, drawing on psychological contract theory, we outline the frontline's expectations of support from HRM practices to deal with such incidents. We further elaborate on gaps in social care that inhibit extending such support through HRM practices to the frontline and establish how the HR co-creation approach can be utilized to plug these gaps. In this multistage qualitative study, we collect data in three stages from 51 frontline employees and 15 HR professionals working in the social care sector who have witnessed customer mistreatment. Based on our study's findings, we propose a novel framework called "FRESH against mistreatment" (Frontline Expectations for Support from HRM), which can aid HR professionals in identifying expectations, gaps, and possibilities for extending support through HRM practices to the frontline in social care to handle incidents of customer mistreatment.
