With the continuous development of the catering industry, people have also put forward higher requirements for catering standards and dining experience. As an important field of technological innovation, smart restaurants are gradually becoming a new trend in the restaurant industry. In this study, the Menglong Noodle Restaurant accomplished a full-service design that included vision, equipment, ordering and dining process, staff experience, and take-out packing process by integrating a series of innovative technologies. Menglong Noodle Restaurant allows IOT technology to enter the restaurant industry, reduces labor costs through smart technology, provides faster and more convenient restaurant services, and offers a better dining and management experience for customers and staff. In the future, smart restaurants will become the mainstream of the restaurant industry, providing more comprehensive and attentive services to attract more customers while balancing costs and focusing on efficiency. At the same time, consumers will be able to obtain a richer and more exciting intelligent and interactive dining culture experience.
This paper explores the pathways and strategies for enhancing agricultural product branding through cultural empowerment within the context of generative artificial intelligence. The research proposes using rural cultural digital archives as a foundation, utilizing large language models and text-to-image models to extract and generate brand designs with distinct local cultural characteristics. The study employs the CIPP model for systematic analysis, proposing key steps such as cultural asset digitization, the construction of local cultural databases, and the generation of brand design intelligent agents. The aim is to address the current issue of agricultural product brands lacking authentic and resonant local cultural features in the context of artificial intelligence.
This paper focuses on the degree of transitivity of fuzzy relations. First, we delve into fundamental properties of transitivity degree, along with a-T-transitivity and a-TL-strong transitivity. Subsequently, we propose an alternative approach to characterize the degree of transitivity based on the degree of similarity between relations R and S, where S represents a transitive fuzzy relation on set X. Finally, we explore the relationship between two distinct degrees of transitivity.
The unequal distribution of household chores has emerged as a significant indicator of gender inequality, with women often bearing a disproportionate share of the burden. This study introduces AI agents designed to automate and equalize chore distribution by effectively managing both visible and invisible tasks. The AI agent monitors user preferences and schedules to ensure fair and efficient task allocation. Future developments could include integration with IoT devices and the incorporation of auditory and visual interactions. User testing reveals that the AI agent enhances fairness, reduces conflicts, and fosters household harmony. This research offers a promising solution to addressing gender inequality in domestic labor and paves the way for more intelligent household management systems.
As global aging intensifies, the elderly commonly face issues of decreased hand strength and reduced joint flexibility. Hypertension, a prevalent chronic condition, severely impacts the quality of life for the elderly. Existing hand massage products on the market often feature a single function and fail to meet the comprehensive needs of the elderly. This paper aims to design an age-appropriate intelligent hand product tailored to the physiological characteristics of the elderly population, integrating hand massage, blood pressure monitoring, and medication management functionalities to enhance health management and quality of life for seniors. Through detailed market research and user needs analysis, this paper proposes design principles and carries out prototype design, demonstrating significant advantages in ease of use and functionality integration.
To improve the efficiency and scope of vocabulary and sample collection in traditional Kansei engineering (KE), and to address the issue of poor accuracy in predicting product form imagery, this study introduces a novel research framework that incorporates elements of artificial intelligence, natural language processing, machine learning, and optimization algorithms. Initially, product samples and Kansei vocabulary were collected from e-commerce platforms using Python web crawlers and Word2vec, with representative samples and vocabulary selected. Following this, Kansei prediction models were developed using Back Propagation Neural Network (BPNN), a nonlinear machine learning method, and enhanced with the application of the Seagull Optimization Algorithm (SOA) and Genetic Algorithm (GA) to improve their performance. Lastly, an error comparison of the prediction models was conducted to determine the optimal model for guiding product form design, with hair dryer products used as the case study for validation. The results indicate that the SOA-BPNN model had the smallest prediction error, demonstrating that employing SOA to enhance BPNN's performance in predicting users’ emotional responses to product form is reliable. The SOA-BPNN model also showed greater robustness, making it suitable for predicting form imagery in product design. Moreover, this research framework provides valuable insights for related studies in KE.
With the increasing degree of social aging, eldercare issues have become increasingly prominent. Based on the concept of an elderly companion robot, authors design an Application (APP) by analyzing user needs and habits, and meticulously reviewing design cases of related types of APPs. Ultimately, an APP interface design proposal based on visual symbolism theory is presented. This APP aims to provide users with a comfortable operational experience, establish a bridge of interaction between children and their elderly parents, improve the quality of life for the elderly, and make eldercare more convenient.
This study uses the Macau bus system as a case example to investigate the interaction issues between smartphone navigation and on-site transit signage systems. By applying the FBS model (Function-Behavior-Structure), the research identifies core problems related to navigation accuracy, sign design, and vehicle identification. To address these issues, several improvement measures are proposed, including optimizing the navigation app interface, increasing the size of bus stop signs, and enhancing vehicle identification. These recommendations aim to improve user navigation experiences and the overall efficiency of the bus system, while also offering valuable insights for enhancing public transportation systems in other urban contexts.
This study explores the development of lightweight interactive structures fabricated using 3D-printed polylactic acid (PLA), which can fold and unfold in response to environmental stimuli such as temperature and humidity. To facilitate user interaction and real-time testing of structural behaviors, an online platform was created. This platform allows users to test Ballistic Seed Pods and experience how different climate conditions affect these structures. Additionally, a parametric design tool was developed to simulate the effects of design variables, providing customization options for applications in adaptive architecture, product design, and robotics.
Physical toys like tangrams remain popular for their unique contributions to children’s cognitive development and creativity enhancement. With the advantages of versatility, accessibility, and affordability, however, tangram still lacks essential innovation in interactive methods to meet modern needs for the ultimate gaming experience. To revitalize traditional tangram, this paper proposed an interaction design framework based on Augmented Reality (AR) and developed Tangram Magic, an AR-based tangram system. The usability test’s results suggested that Tangram Magic improved user engagement and ease of learning, offering insights for modernizing traditional toys with AR technology.
Design fixation refers to the phenomenon where designers adhere to a limited set of ideas during the design process, thereby hindering innovation. Utilizing various types of stimulus materials is considered a significant method to alleviate design fixation. With the rapid development of generative artificial intelligence technologies, designers can efficiently access a variety of inspirational stimulus materials. However, research on how to best combine multiple types of stimulus materials to mitigate design fixation is sparse. This study combined two types of stimulus materials—text and virtual models—and experimentally explored the impact of stimulus timing (synchronous, virtual model before text, and text before virtual model) and combination forms (matching and mixed) on design fixation. The results indicate that presenting the physical model before the text in a mixed sequential manner effectively alleviates design fixation.
Conversational artificial intelligence-assisted product design faces challenges such as uncertainty in design quality, interaction barriers, and inefficient processes. To address these issues, this study introduces the role of agile coach within designer-AI collaboration, aiming to improve iteration efficiency and design results’ quality. We developed two implementation schemes for the agile coach, one performed by a human and the other by the AI. Results of the user experiment indicate that the existence of the agile coach significantly enhances the iteration efficiency and the design results in integrity, complexity, and feasibility. Moreover, the AI agile coach outperforms the human agile coach in improving complexity, feasibility, and iteration efficiency. This study expands the application of the agile iteration method in human-AI collaboration and provides new insights into how conversational AI can empower product design.
NFTs enter the horizon in 2017, when cryprokit-ties as a game token became popular. In 2021, it is the meta year of NFT eruption. Not only do the two famous auctions confirm the unique values of NFTs, but a lot of research has also analyzed the features of NFTs and their values. This paper proposes common factors to price the NFTs, inspired by classical asset pricing theory in finance. The common factors are total sale, buyer/seller, transfer and etc. Moreover, we incorporate machine learning methods including regularized regressions, trees, and neural networks. We use the representative cryptopunks as head pictures in social communities, NBA top shot as the real-world reflection in NFT world, autoglyphs, and bored ape yacht club. The empirical results greatly support our proposed factors on NFT pricing.
This paper focuses on the various problems of children's use of drinking fountains in kindergarten scenarios, such as hidden safety hazards, and optimizes and improves the design of existing kindergarten drinking fountains through user surveys and data analysis to solve the problems encountered by young children when they use drinking fountains alone. In the research process, interviews were first conducted with a specific group of kindergarteners, and then based on the results of the interviews, the questionnaires were organized in different dimensions, and then the questionnaires were developed and distributed online. Finally, the survey data were descriptively analyzed through KMO measurements in SPSS computing software to come up with appropriate design strategies and apply appropriate science and technology to create an innovative design for kindergarten water fountains.
Aiming at the problems of traditional butterfly optimization algorithm which is easy to fall into local optimum and slow convergence speed, an improved butterfly optimization algorithm (ICBOA) incorporating multiple strategies is proposed. The initial population of Tent chaotic mapping algorithm is optimized to enhance the population diversity; a nonlinear parameter adjustment mechanism is introduced to balance the global search and local exploitation; Cauchy variation is introduced in the global search stage, and stochastic inertia weights are introduced in the local search stage to improve the search efficiency. The ICBOA algorithm is compared and analyzed with the traditional butterfly optimization algorithm and five other optimization algorithms on nine benchmark test functions, and statistically analyzed by box plots, and the results show that the proposed improved algorithm has higher convergence speed and accuracy, and effectively avoids the problem of falling into the local extremes.
Reading storybooks is one of the primary ways to cultivate children’s creativity and enhance their logical thinking. Deep engagement in storylines helps children interpret the content from multiple perspectives, understand the different standpoints of various characters, and thereby encourages comprehensive thinking to refine their logical reasoning. However, in current family education practices, traditional paper-based storytelling methods are still predominantly used to interact with children. This significantly limits children’s engagement with the story, leading to a reliance on parents’ interpretations rather than developing their own insights and independent thinking. Therefore, this study presents StoryChat, an interactive storybooks reading system based on artificial intelligence technology that allows children to engage in dialogue and interaction with agent characters in the story. A user study involving 12 pairs of children aged 5-7 and their parents showed that StoryChat increased children’s interest in reading, promoted children’s empathy cultivation and stimulated deeper thinking. This research provides new ideas for designing interactive storybooks and offers empirical support and design recommendations for the future application of agent characters in children’s storybooks education.
In the era of digital intelligence, urban block design methods are transitioning from traditional to automated approaches. As society's demand for spatial quality increases, urban block design methods need to meet more complex requirements. Existing methods have room for improvement in terms of result diversity and dynamic parameter response. To address these challenges, our study develops a rule-based urban block generation design method, formulating the generation rules according to urban characteristics and regulations. Our study utilizes cellular automata and multi-agent algorithms to establish an automated process for urban block generation. Additionally, we develop a multi-objective optimization model to enhance the spatial perception of urban block forms. Our findings reveal that this method effectively addresses the constraints of complexity and dynamically adapts to various parameters. It also successfully generates a diverse array of urban block forms. Overall, our research contributes a novel automated design workflow to the field of computational urban block design, offering novel perspectives and advancing the capabilities of urban planning tools.
With the emergence of new lifestyles and values, outdoor sports have developed rapidly, there are fewer types of camping shower products at present, and people's outdoor shower methods are restricted to a certain extent. From the perspective of campers, this paper analyzes the design pain points and user characteristics of the existing outdoor portable shower equipment. Based on the Kano Model analysis, the function attributes of outdoor shower products were refined, the Better-Worse coefficients for different types of needs were calculated, and the design principles of portable shower equipment meeting the camping scene were put forward. This study broadens the individual needs of camping people and provides a new development idea for outdoor product design.
Rapid prototyping has always been a challenge in e-textiles and wearable circuits. This paper introduces a toolkit for rapidly prototyping wearable circuits. The toolkit includes a modified Lilypad Arduino USB board, redesigned sensor and actuator modules, and quick-connect cables. The Lilypad and modules can be quickly attached to the fabric by gluing magnets to their PCB bottoms and using corresponding magnetic fixing pads. O-ring connectors and bolts are used for quick connection. Eleven commonly used sensor and actuator modules for wearable projects were selected. This kit facilitates rapid prototyping by enabling quick attachment and connection. Additionally, several example applications are provided.
In a complex and dynamic supply chain environment, the interaction of various influencing factors often alters the fluctuation trends and patterns of demand, making it difficult for inventory policy to adapt to demand dynamics. This study proposes a deep reinforcement learning approach to multi-echelon inventory optimization, leveraging the dynamic estimations of demand patterns through Bayesian networks. The simulation results demonstrate that the total cost of the multi-echelon system for the proposed method is noticeably lower than the existing demand forecasting based dynamic optimization method while maintaining the similar cycle service levels.
