In home-based elderly care, nursing beds play a crucial role in the daily lives of older adults. However, most existing nursing beds are designed for general patients, neglecting the specific needs of people with disabilities and their caregivers. To enhance user satisfaction with nursing beds, this study proposes a conceptual design approach based on a KANO-AHP integrated model based on behavioral design. First, the needs of caregivers and people with disabilities are identified through behavioral observations and in-depth interviews. The Fogg Behavior Model is then applied to translate these behavioral insights into extractable design elements, which are subsequently classified and prioritized systematically using the Kano model. Subsequently, the Analytic Hierarchy Process is employed to screen the most critical needs from the extracted ones and transform them into key design elements, thereby defining the structural components of the product. This integrated approach enables an accurate mapping from user requirements to design elements, thereby facilitating the development of nursing beds for people with disabilities. This study demonstrates the feasibility and effectiveness of the KANO-AHP model in design research for the aging population, offering valuable guidance and an innovative perspective for nursing bed design.

In the context of the shipping industry’s transition towards low-carbon solutions, hydrogen energy exhibits substantial application potential in marine propulsion systems. Fuel reforming for hydrogen production represents one of the key technologies for efficient hydrogen production in maritime applications. Nevertheless, this process involves complex multi-scale reaction mechanisms, challenges in catalyst design, and difficulties in system optimization. This paper conducts a comprehensive review of the recent progress in the application of machine learning in fuel reforming hydrogen production technology. In the realm of catalysts, machine learning has expedited the design of efficient catalysts via high-throughput screening, performance prediction, and active site regulation. In reaction modeling, machine learning has facilitated the development of multi-scale kinetic models, enhancing the interpretability and predictive accuracy of reaction pathways. Regarding equipment and system optimization, machine learning has enabled innovations in reactor design, collaborative optimization of process parameters, and intelligent system control. This review aims to provide theoretical foundations and practical guidance for the technological development of ship propulsion systems. Moreover, it explores the future directions for the deep integration of machine learning and hydrogen energy technologies, thereby promoting the low-carbon and intelligent transformation of the shipping industry.

Renewable methanol is considered a promising carrier for sustainable hydrogen due to its convenience in storage and transportation. Methanol steam reforming (MSR) using exhaust heat from industrial boilers can further enhance energy efficiency. However, existing methanol reforming systems still face challenges in terms of matching with industrial boilers, heat exchanger compactness, and adaptability to fluctuations in exhaust gas conditions. To address these issues, this study proposes the design of a modular methanol reforming system driven by the exhaust heat of small industrial boilers and develops a three-dimensional multiphysics simulation model to investigate the heat transfer and reaction characteristics within the reactor. The results indicate that, within the ranges of exhaust heat temperature (220–270 °C), flow rate (0.4–1.2 g/s), and channel spacing (60–100 mm), increasing the exhaust heat temperature enhances the endothermic reforming process, while decreasing the channel spacing improves heat transfer and increases methanol conversion. The reactor with a 60 mm channel spacing achieves a conversion ratio of up to 95.3% at a flow rate of 0.4 g/s. Although the hydrogen yield increases with flow rate, the single-pass conversion ratio decreases due to shorter residence time and increased load per unit volume. Compared to traditional fixed-structure reactors, the proposed modular system allows flexible matching of scale and heat exchange capacity through adjustable channel configurations, enhancing adaptability to fluctuations in industrial exhaust temperature and load. This design improves the utilization efficiency of low-grade waste heat and offers a practical engineering solution for sustainable distributed hydrogen production.

This study presents an integrated design methodology for earthquake demolition rescue robots by combining UXMs, Kano, and QFD to improve design rationality and performance in extreme rescue scenarios. It addresses key gaps in existing approaches, particularly the lack of systematic experiential data acquisition, quantitative requirement analysis, and effective design translation. UXMs are applied to reconstruct critical task scenarios and identify high-load nodes and user experience variations. The Kano model is used to prioritise and classify user requirements, which are then translated into engineering characteristics through QFD. Based on this framework, a conceptual robot design is developed using the FBS model and evaluated through process-level simulation and usability assessment. The results demonstrate that the proposed method enables structured requirement transformation and supports traceable design decisions. Simulation indicates the consistency of task workflows and coordination among functional modules at the process level. A System Usability Scale score of 80.22 indicates a relatively high level of perceived usability at the conceptual evaluation stage. The proposed methodology provides a structured and traceable conceptual design framework for earthquake rescue robots. While the current validation is based on conceptual-level evaluation, the methodology offers a traceable design pathway that may be extended to other high-risk emergency equipment with further empirical testing.

This study aims to explore and propose a design-oriented methodology for solar–thermal methanol reforming (ST-MSR) hydrogen production equipment suitable for marine applications. To address key challenges such as the intermittency of solar energy, spatial and environmental constraints on board ships, operational safety, and user experience, a multidisciplinary integrated-design decision-making framework is established. First, the Kano model is employed to systematically analyze the latent needs of target users regarding ST-MSR equipment, while the analytic hierarchy process (AHP) is used to determine the weighting of evaluation criteria. Second, the theory of inventive problem solving (TRIZ) is applied to generate innovative conceptual design solutions. Finally, the technique for order preference by similarity to an ideal solution (TOPSIS) is adopted to conduct a multi-dimensional comprehensive evaluation and optimization-based selection of the conceptual alternatives. The optimal design scheme is thus identified in terms of energy performance, product characteristics, user experience, economic feasibility, and environmental adaptability. The results indicate that the microchannel and phase-change thermal-storage integrated solar–thermal-tracking chemical reactor achieves the highest comprehensive evaluation score among the proposed schemes, demonstrating superior performance in terms of safety, energy efficiency, and adaptability to marine environments. This research provides a systematic industrial design methodology and practical reference for the design and product development of clean energy equipment for ships, contributing to the green and sustainable transformation of the maritime industry.