Unmanned aerial vehicles (UAVs) have gained increasing importance due to their expanding application areas and operational flexibility. Selecting the most suitable UAV, however, represents a complex multi-criteria decision-making (MCDM) problem that involves numerous technical and performance-related factors. This study addresses the UAV selection problem by employing four distinct MCDM approaches: Evidential fuzzy MCDM based on Belief Entropy, Intuitionistic Fuzzy Dempster-Shafer Theory (DST), Spherical Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Type-2 Neutrosophic Fuzzy CRITIC-MABAC. Each method incorporates different fuzzy set theories, while a common seven-point linguistic scale is utilized to ensure consistency across models. The evaluation criteria were determined through a comprehensive literature review, and expert opinions were collected from experienced UAV pilots and technical personnel. The analysis identified the most suitable UAV alternative among the considered options. Sensitivity analyses were conducted to assess the robustness of the obtained results. The findings demonstrate that the proposed framework enables a simultaneous comparison of different fuzzy set environments on a unified linguistic scale. Overall, the results are consistent, reliable, and practically applicable, offering valuable insights and methodological contributions to the field of UAV selection and fuzzy MCDM applications.

Traditional public health disinfection tasks relying on fixed-area coverage often suffer from resource waste, delayed intervention, and low response efficiency. This study proposes a case-density-driven closed-loop intelligent strategy for air-ground-human collaborative disinfection, establishing an end-to-end framework from case perception to task scheduling. Firstly, a spatiotemporal risk field is constructed based on reported case data and population mobility information, and high-risk areas are adaptively identified and prioritized through dynamic evaluation. Secondly, for coordinated execution by unmanned aerial vehicles (UAVs), ground vehicles, and personnel, a multi-objective coupled optimization model is designed, targeting coverage efficiency, suppression timeliness, path conflicts, and resource cost to generate executable collaborative schedules. Furthermore, a closed-loop execution mechanism is developed, enabling real-time rolling re-planning and adaptive strategy correction in response to task feedback, unexpected disturbances (area lockdown, equipment failure, chemical shortage), and risk field updates. Experimental results demonstrate that the proposed closed-loop approach significantly improves coverage, suppression time, and resource utilization compared with traditional static scheduling and single-entity planning methods across multiple scenarios, and exhibits robustness against environmental uncertainties and resource disturbances. This framework provides a feasible theoretical and methodological foundation for intelligent, precise, and resilient public health disinfection operations.

Prediction of product life cycle (PLC) at the early stage is important to support the planning of production, inventory control, launch timing, and decisions of product renewal. However, newly introduced batik products often have little historical sales data; therefore, it is difficult to apply conventional forecasting methods. This study introduced the Diffusion Bubble Model for Product Diffusion (DBM-PD) as a low-data diffusion model for predicting cumulative product adoption in batik fashion small and medium enterprises (SMEs). The DBM-PD adapts the bubble-decomposition logic into a product diffusion context, where each adoption bubble represents a possible market response at a specific stage of the PLC. To assess the model performance, this study compared the DBM-PD with three benchmark models: the Bass Diffusion Model, Triangle Model, and Circle Model. Four datasets of PLC from batik fashion products were used as empirical cases. Each dataset was transformed into a cumulative adoption ratio to ensure comparable model evaluation. The results showed that the DBM-PD achieved the lowest prediction error across all four datasets. The Bass Diffusion Model ranked second, indicating that innovation and imitation effects remain relevant in batik product adoption. The Triangle and Circle Models were useful as simple geometric benchmarks, but their assumptions were less able to capture irregular adoption patterns. These findings revealed that the DBM-PD could establish a more flexible prediction structure for estimating PLC at the early stage when only limited data were available. A parametersensitivity check discovered that the number and width of bubbles affected in-sample accuracy: additional bubbles could reduce errors but also increased model complexity, while the seven-bubble, $\sigma=0.08$ configuration offered a transparent balance between accuracy and parsimony. The root mean square error (RMSE) rankings were identical across the four datasets, and a Friedman test indicated a significant overall difference among the models $\left(\chi^2(3)=12.00, p=0.007\right)$. This study contributes to the management of technology-based PLC by offering a practical framework of diffusion modelling for batik fashion SMEs.

Human-robot interaction has become a fundamental component of Industry 5.0. Although recent advances in generative artificial intelligence have substantially improved contextual reasoning and interactive capabilities, existing human-robot interaction decision-making frameworks remain predominantly task-oriented and rarely account for human emotional responses during collaborative decision processes. To address this limitation, a regret-based human-robot interaction framework driven by generative artificial intelligence was proposed for adaptive decision-making in Industry 5.0 environments. A dual-regret mechanism was introduced by jointly modeling human regret $\left(R_h\right)$ and robot task regret $\left(R_{t r}\right)$. Contextual knowledge was dynamically retrieved through a retrievalaugmented generation architecture, enabling interaction strategies to be generated according to task conditions, historical user preferences, and evolving human emotional states. Personalized regret profiles were constructed, while explainable counterfactual action generation was incorporated. Furthermore, real-time reinforcement learning was employed to continuously minimize cumulative regret. Experimental evaluations conducted under multiple interaction scenarios demonstrated that the proposed framework effectively improved decision quality, enhanced user satisfaction, and increased task execution efficiency. Compared with conventional human-robot interaction decision-making approaches, cumulative interaction regret was reduced by approximately 20%, while greater robustness and adaptability were achieved under dynamically changing operational conditions. These findings suggest that the proposed framework provides an effective paradigm for integrating emotional intelligence, generative reasoning, and adaptive optimization into human-centered robotic systems, thereby offering a promising decision-making architecture for next-generation collaborative robotics in Industry 5.0.