Traditional research on ceramic colors relies heavily on qualitative descriptions and subjective judgments. This approach limits the ability to process large-scale cultural heritage datasets and extracts reproducible design knowledge difficult. To bridge this gap, this paper introduces an interactive visual analytics system that merges design principles with computational innovation. Employing computer vision and network science, the system transforms unstructured image data into structured color networks. Key techniques include color quantization for hue extraction, the construction of “ceramic-color” bipartite networks to model co-occurrence patterns, and centrality analysis to identify core color hierarchies. This approach allows researchers to visually explore color associations with cultural attributes (such as dynasties and vessel types) across large collections. Furthermore, it empowers designers to accurately extract historical color schemes for contemporary application. Ultimately, this work establishes a data-driven methodology that effectively integrates digital humanities analytics with modern design practices.

Functional configuration in smart beauty products may become disconnected from actual task processes, while continuous function accumulation can lead to redundancy on the physical device and limited flexibility for subsequent adjustment. To address these issues, this study develops an integrated HTA–Kano–RANCOM–FCE framework for sustainable design decision-making from the perspectives of functional moderation and long-term adaptability. Using daily commuting makeup as the application scenario, Hierarchical Task Analysis (HTA) was employed to decompose the makeup process into eight task stages, while behavioral observations, user interviews, and questionnaire surveys were conducted to elicit user requirements. The Kano model was then applied to identify requirement attributes, and Ranking Comparison (RANCOM) was used to determine requirement weights at each task stage. The applicability of RANCOM was further examined through comparisons with AHP, BWM, and an independent user-consensus ranking. Cross-stage frequency, immediacy, and extensibility were subsequently integrated with requirement importance to guide function allocation between the smart mirror and a companion mini program, and the resulting design was evaluated using Fuzzy Comprehensive Evaluation (FCE). The results revealed clear differences in requirement attributes and weights across task stages. Intelligent multi-source lighting, high-definition detail visualization and magnification, and makeup problem identification and prompts were identified as priority functions for the smart mirror, whereas extensible functions such as skin quality analysis, virtual makeup try-on, and makeup learning were assigned to the mini program for on demand provision. The proposed design achieved an overall FCE score of 4.48/5, providing preliminary evidence that participants positively perceived its task stage compatibility and functional allocation rationale. In the eye makeup comparison case, RANCOM showed a strong correlation with the independent user-consensus ranking (Spearman’s ρ = 0.983) and had a mean completion time of 3.89 min. Under the specific comparison conditions, RANCOM produced a ranking closely aligned with independent user judgments and required a shorter mean completion time than AHP and BWM. By combining requirement prioritization with differentiated physical digital allocation, the proposed framework provides a front-end design approach for limiting unnecessary permanent function integration and retaining flexibility for subsequent updates. These outcomes represent sustainability oriented design potential rather than directly verified environmental, economic, or product lifetime benefits.

Traditional architectural decorative patterns are increasingly reused in contemporary design, yet the link between object selection and design generation often remains experience-driven: public perceptual differences are rarely formalized, and evaluation outcomes seldom constrain generative decisions. This study proposes a perceptual demand-driven layered filtering and design response model (PD–LFDR) that treats traditional architectural decorative patterns as comparable and traceable design resources. Perceptual inputs from multiple stakeholders are converged via Kansei-based semantic aggregation into four core dimensions—symbolism, heritage authenticity, recognition and regionality—and are organized as a perceptual evaluation matrix. Grey relational analysis (GRA) is then applied using an expected perceptual level as the reference sequence to identify representative pattern samples suitable for design intervention. An empirical study on decorative patterns from Shaanxi vernacular dwellings demonstrates a closed-loop workflow: (i) first-round GRA filters representative theme samples, (ii) a second-round GRA selects operable minimal gene units, and, under a unified parametric rule set and a traceable two-layer parameter basis (parameter domain definition and parameter selection), (iii) multiple alternatives are generated and re-evaluated through a third-round GRA to support scheme selection. Robustness checks indicate stable rankings under moderate parameter and weight variation, improving interpretability, reproducibility, and decision efficiency for the computational translation of regional cultural visual resources.

Young novice drivers are prone to negative emotions in high-risk situations. These emotions consume limited cognitive resources and raise crash risk. Existing research has not systematically clarified the relationships among driving scenarios, emotion types, emotion intensity, and multi-channel cognitive workload. Accordingly, this study used a driving-simulator experiment to analyze these linkages. We recruited 144 Chinese young novice drivers and used pre-validated video clips to induce neutral, anger, fear, anxiety. Data were collected via the Self-Assessment Manikin (SAM), the Visual-Auditory-Cognitive-Psychomotor (VACP) workload model, and semi-structured interviews. The results showed that: (1) Negative emotions significantly increased cognitive workload in young novice drivers. Anger and fear causd significant instantaneous workload fluctuations, whereas anxiety yielded the highest mean workload. (2) Distinct negative emotions were triggered by specific driving scenarios, which have different stressors (such as security threat, time pressure and environmental complexity). The potential outcome brought by these situational stressors affect the intensity of emotion. (3) Emotion intensity was positively associated with workload level. High-arousal emotions more likely to increase demands on visual, cognitive, and psychomotor resources. Within a unified paradigm, this study delineates the pathway linking driving scenarios, emotion types, emotion intensity, and multi-channel workload. The findings provide evidence for in-vehicle emotion monitoring and environmental-adaptive interventions.

PURPOSE:To identify the optimal planning strategy for pencil beam scanning carbon-ion radiotherapy (CIRT) in stage I peripheral non-small cell lung cancer (NSCLC), including ground-glass opacity (GGO) and solid lesions. METHODS AND MATERIALS:Thirty patients (15 GGO, 15 solid) receiving 8 Gy (RBE) × 9 fractions were analyzed. The clinical target volume (CTV) was defined as a 7 mm isotropic expansion of the internal gross tumor volume. Eight plans per patient were generated by combining three strategies: margin expansion (ME, CTV+7 mm), density override (DO), and robust optimization (RO). The accumulated deformed dose was calculated from two 4-dimensional quality assurance CTs (QACTs, pre- and mid-CIRT, weighted 5:4). Target coverage was assessed by the percentage volume of CTV receiving ≥ 95% prescription dose (CTV V95%); ipsilateral lung (excluding internal gross tumor volume) dose by mean lung dose (MLD) and the percentage of the lung volume receiving ≥ x Gy (RBE) (Vx, V5-V70). Range and setup uncertainties were assessed by applying ±3.5% stopping power variations and 3-mm translational shifts in six cardinal directions, respectively, with accumulated doses generated using the same workflow. RESULTS:All single strategies improved CTV V95% but increased lung dose. DO increased CTV V95% by 3.3-4.1% with minimal MLD increase of 0.15-0.92 Gy (RBE). RO increased CTV V95% by 4.8-5.1% with a moderate MLD increase of 1.77-1.79 Gy (RBE). ME yielded similar CTV gains but the highest MLD increase of 3.78-4.86 Gy (RBE). For GGOs, CTV+RO provided adequate coverage with the lowest lung dose. For solid tumors, CTV+RO+DO achieved near-complete coverage with improved consistency (range: 92.8-100% vs 85.2-100%) and a minimal additional MLD of ∼0.15 Gy (RBE). With range and setup uncertainties, results remained consistent with nominal conditions when RO was applied. In the absence of RO, the addition of DO to CTV+ME reduced the risk of target underdosage for both GGO and solid tumors at a cost of increased MLD of (-0.08)-0.53 Gy (RBE). CONCLUSION:This study provides quantitative data for evidence-based planning strategy selection in stage I peripheral NSCLC receiving CIRT. Without RO, larger margin (CTV+ME) is recommended; DO is advised for solid tumors and may also benefit GGOs under uncertainty. With RO, a smaller margin (CTV) is sufficient, and DO remains valuable for solid tumors.

Driving anxiety is a major issue that compromises the safety and experience of driving. It has been demonstrated that negative emotions with a high arousal factor like anxiety are manifested in body posture and sitting behavior. This paper will investigate one of the ways of identifying anxiety by assessing the pressure distribution in the sitting posture, and discuss driving situations that have a strong correlation with causing anxiety. Thirty people were recruited through a campus social media platform. The experimental design was a one-factor within-subject experimental design in which the researcher used standardized audio materials and a digital countdown task as a means (or inducement) of achieving calm (baseline) and anxiety, respectively. The induction effects were validated using the Self-Assessment Measure (SAM). Also, a pathway accommodating eight driving conditions was established to address the depth of pressure distribution in each condition by means of pressure mats to examine the behavior of the subjects in the relaxed and anxious conditions. The evaluation of both subjective and objective data was performed using the Wilcoxon signed-rank test, and at the same time, we explored the relationships that existed among the driving situations and anxiety levels. The research findings reveal the following: (1) Compared to baseline emotional state, anxiety-induced conditions exhibit heightened pressure distribution and increased volatility in the thigh, hip, and lower back regions, accompanied by greater anterior-posterior center-of-gravity sway. (2) The study identified 40 significant features distinguishing anxiety from calmness, including aTHR_Max and rCOPBTL_Std, primarily distributed across the left leg, right hip, and lower back regions. (3) Through baseline correction and cosine similarity analysis, scenarios prone to triggering anxiety were identified as those involving high uncertainty and high interactivity (e.g., traffic congestion and entering roundabouts); scenarios characterized by continuity and high predictability (e.g., consecutive turns and parking) showed weaker associations with anxiety. This study provides new data support and design rationale for in-vehicle emotion recognition systems and emotion-intervention-based human-machine interaction design.

The development of scientifically rigorous evaluation methods is essential to overcome three persistent challenges in public navigation interfaces: inadequate guidance, low usability, and suboptimal user experience. Focusing on intelligent medical guidance systems, this study establishes a dual-dimensional analytical framework encompassing layout aesthetics (spatial composition principles) and visual cognition (information processing patterns). We propose an enhanced grey H-convex correlation model integrating Bayesian Best Worst Method (BBWM) and modified CRITIC with reference point (M-CRITIC-RP) to address weight determination limitations in existing models. Our experimental analysis reveals two key findings: First, the synergistic integration of layout aesthetics (e.g., visual hierarchy balance) and visual cognition characteristics (e.g., attention distribution patterns) significantly improves interface usability for medical service navigation. Second, the proposed BBWM-M-CRITIC-RP hybrid model demonstrates superior performance in quantifying aesthetic-cognition relationships, achieving 88% prediction accuracy compared to conventional methods. In a word, our research provides a new theoretical method for traditional visual display design and a new evaluation criterion for interface design, aiming at improving the user experience.

During the cloud-based product form design process, traditional collaborative-filtering recommendation methods fail to effectively calculate similarity metrics or generate relevant knowledge recommendations for newly joined designers, due to their lack of historical knowledge scores, resulting in inefficient knowledge acquisition. Since designers show a clear tendency of professional trust in the knowledge adoption process, they are more inclined to accept knowledge resources recommended by people with similar professional backgrounds to theirs or by authorities in their fields. Therefore, this paper proposes a knowledge recommendation method for product form design integrating crowd-intelligence context similarity and trust relationships in cloud environments. The method first constructs an ontology model and a product form design knowledge ontology, containing task context, designer’s context, and computational context to facilitate the acquisition, storage, processing, and invocation of contextual information and knowledge. Second, the neighboring set of target designers is determined by calculating the multidimensional contextual similarity and trust relationship between designers. Finally, the missing knowledge score of the target designer is predicted by the knowledge evaluation of the neighboring designers, and the recommendation list is generated. The method’s effectiveness and feasibility are confirmed through a case study of coffee machine product form design.

Built environment elements management involving heritage buildings requires a nuanced approach that balances cultural preservation, planning efficiency, and resource optimization. Conventional evaluation methods frequently neglect public perception, leading to misaligned priorities and ineffective heritage resource deployment. To address this gap, this study proposes a Sandglass Tiered Model that integrates public perception into the value assessment process of culturally significant buildings. By integrating multi-source perception data and cultural ontology through a structured, tiered data collection mechanism, the model translates subjective views into four quantifiable indices: symbolizability, authenticity, readability, and regionality. These indices form the basis of an AHP-GRA–driven assessment framework, facilitating value-based prioritization and spatial zoning of heritage elements within construction projects. The model was empirically validated in the Yiling Cultural Heritage Area, where it effectively facilitated differentiated building strategies, optimized resource sequencing, and improved alignment between project goals and stakeholder expectations. Importantly, the model provides a transferable framework that embeds cultural awareness into the lifecycle of heritage building projects—from pre-design evaluation to renovation and adaptive reuse. By integrating public perception into construction workflows, this approach provides a dynamic and participatory framework for managing complex heritage assets within urban development contexts. It improves the precision, responsiveness, and cultural sensitivity of construction planning, offering practical insights for policymakers, architects, and construction managers working in resource-intensive or culturally rich environments.

To address the issue of user empathy throughout the emotional experience process, this study presents a method to evaluate the efficacy of cultural empathy evoked based on fuzzy-FMEA. The method focuses on symbolic culture and creative products, constructing an evaluation index system and decision-making framework in terms of cultural empathic evoking. It utilizes thematic analysis to discover and categorize the factors that influence cultural empathy, as well as an evaluation index system to improve the Failure Mode and Effects Analysis framework. It effectively solves the limitations of traditional FMEA, such as single weighting and uncertainty. According to the assessment report, cognitive association failure and scenario restoration failure are significant risk factors for cultural empathy-evoking failure. This study’s findings provide designers with realistic proposals for thematic symbolic imagery and serialized design forms, as well as scientific assessment tools and decision-making resources for cultural industries and policymakers.

To address the challenges posed by the vast and complex knowledge information in cultural heritage design, such as low knowledge retrieval efficiency and limited visualization, this study proposes a method for knowledge extraction and knowledge graph construction based on graph attention neural networks (GAT). Using Tang Dynasty gold and silver artifacts as samples, we establish a joint knowledge extraction model based on GAT. The model employs the BERT pretraining model to encode collected textual knowledge data, conducts sentence dependency analysis, and utilizes GAT to allocate weights among entities, thereby enhancing the identification of target entities and their relationships. Comparative experiments on public datasets demonstrate that this model significantly outperforms baseline models in extraction effectiveness. Finally, the proposed method is applied to the construction of a knowledge graph for Tang Dynasty gold and silver artifacts. Taking the Gilded Musician Pattern Silver Cup as an example, this method provides designers with a visualized and interconnected knowledge collection structure.

A scientific method for evaluating the design of interfaces is proposed to address the unique characteristics and user needs of infrequent-contact public service interfaces. This method is significant for enhancing service efficiency and promoting the sustainable development of public services. Current interface evaluation methods are limited in scope and often fail to meet actual user needs. To address this, this study focuses on virtual museums, examining users’ aesthetic psychology and cognitive behavior in terms of layout aesthetics and visual cognitive characteristics, aiming to explore the relationship between the two. Interface layout aesthetic values and user visual cognitive measurements were obtained by using computational aesthetics methods and eye-tracking experiments. These served as input data for a new model. An improved gray H-convex correlation model utilizing the ICRITIC method is proposed to examine the mapping relationship between interface layout aesthetics and visual cognitive features. The results demonstrate that our new model achieves over 90% accuracy, outperforming existing models. For virtual museum interfaces, symmetry and dominance significantly influence user visual cognition, with the most notable correlations found between density and gaze shift frequency, simplicity and mean pupil diameter, and order and gaze shift frequency. Additionally, fixation duration, fixation count, and mean pupil diameter were inversely correlated with interface layout aesthetics, whereas gaze shift frequency and gaze time percentage were positively correlated.

In the traditional decision-making process for product form design, designers and experts often prioritize schemes based on their own knowledge and experience. This approach can lead to an oversight of user preferences, ultimately affecting decision outcomes. In contrast, crowd-intelligence-driven, multi-attribute decision-making for product form design in the cloud environment builds upon traditional approaches by leveraging the vast and diverse expertise of individuals on cloud platforms, engaging participants from various fields and roles in the decision-making process to enhance comprehensiveness and accuracy. To address the issue of a single decision-maker and limited user participation in the decision-making process for product form design schemes in the cloud environment, a multi-attribute decision-making method integrating expert knowledge and user preferences is proposed. This method aims to select a product form design scheme that optimally balances expert and user satisfaction. Initially, the Pythagorean Hesitant Fuzzy Set (PHFS) is used to quantify qualitative product attributes and to establish a comprehensive multi-attribute evaluation system. In the aspect of expert decision-making, a gray correlation coefficient decision matrix based on expert knowledge is established and the overall score of the base alternative is calculated by the ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method and the Improved Osculating Value method. In terms of user decision-making, weights are determined by calculating the similarity between user evaluation matrices, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to calculate scores for product form designs based on user preferences. Ultimately, optimal selection is achieved by aggregating the aforementioned expert evaluation values and user preference values. The method’s effectiveness and feasibility are confirmed through a case study of coffee machine product form design schemes.

To address current product styling design issues, such as ignoring the joint effects of multiple styling elements when constructing perceptual imagery fitting models and thus failing to effectively identify the relationships between styling elements, a product styling design method based on fuzzy set qualitative comparative analysis (fsQCA) is proposed. This method first uses semantic differential and statistical methods to obtain users’ evaluative vocabulary for the product’s perceptual imagery. Then, morphological analysis and cluster analysis are employed to establish typical product samples and extract styling elements to create a styling feature library. Perceptual imagery ratings of these styling features are obtained through expert evaluation. fsQCA is then used to analyze the different grouping relationships between styling elements and their influence on product styling imagery, aiming to match user intentions through different element combination paths. The results show that this method achieves a consistency value of 0.9 for the most optimal styling configurations, demonstrating that fsQCA can effectively identify the multiple paths of product styling elements that meet users’ needs. The contributions of this study to the related fields are: (1) providing a new perspective on the relationship between user perceptual imagery and predicted product styling elements, and (2) advancing the theoretical basis for studying multiple paths of product styling elements. The research results demonstrate that using the fsQCA-based product styling design method can accurately portray the multiple paths of product styling elements that meet users’ needs, thereby effectively improving design efficiency. Finally, a teapot styling design study is used as an example to further verify the method’s feasibility.

With the development of smart technology and the increasing variety of everyday products, factors influencing product service touchpoint design have become more diverse and complex. Existing service touchpoint design methods and models often focus narrowly on user research, co-design, and risk analyses, lacking a systematic approach. Consequently, they struggle to deliver solutions that align with user needs. This misalignment may result in issues such as increased cognitive load during product use, a diminished user experience, and lower evaluations of the product. In response, this paper proposes a service touchpoint design model, the “BEDFITA” model. It starts with user behavior and follows a structured, systematic process that includes understanding user behavior, recording user emotions, matching user needs, designing product functions, planning interaction experiences, designing service touchpoints, and analyzing failure risks. The Kano model is employed in the user requirement identification phase to provide more precise user requirement parameters, while FMEA is employed in the failure risk analysis phase to generate more accurate failure risk assessments. This ensures that the final service touchpoint design meets user needs and offers reliability and robustness. Finally, the feasibility and effectiveness of the proposed model are validated through a case study on the service touchpoint design of a smart desk.

In the promotion of sustainable modes of transport, especially public transport, reasonable failure risk assessment at the critical moment in the process of service provider touch with users can improve the service quality to a certain extent. This study presents a product service touch point evaluation approach based on the importance–performance analysis (IPA) of user and failure mode and effect analysis (FMEA). Firstly, the authors capture service product service touch points in the process of user interaction with the product by observing the user behavior in a speculative design experiment, and perform the correlation analysis of the service product service touch point. Second, the authors use the IPA analysis method to evaluate and classify the product service touch points and identify the key product service touch points. Thirdly, the authors propose to analyze the failure of key product service touch points based on user-perceived affective interaction and clarify the priority of each key touch point. Finally, reluctant interpersonal communication, as the key failure caused by high risk, is derived according to the evaluation report, which leads to establishing new product service touch points and improving the overall user experience to promote sustainable transports with similar forms and characteristics.

Because the image quality processed in the traditional art design system can not meet the requirements, so it is necessary to solve this problem to improve the intelligence of the art design system. Analyze the art design system, establish a perfect system architecture, and study each template. The image processing technology is more commonly used in edge detection, so it is necessary to use the zero crossover operator, Robert operator and Gaussian Laplace operator to realize edge detection. After the final system performance test can also prove that the intelligence of the image processing art design system has been effectively improved. It not only improves the quality of the image, but also improves the signal to noise ratio of the image. However, in the subsequent research, the theme is to strengthen the stability and safety of the system.

To investigate the effect of a multimodal interaction balance model on improving the emotional driving experience when users perform a high load driving task in a negative driving emotion context. The questionnaire analysis was used to obtain the main negative emotions and the corresponding driving situations. Combined with the STAR interview method to understand the user's interaction task in specific contexts, the user's cognitive load was assessed by the SWAT subjective load assessment technique to obtain a high cognitive load task, and the VACP model was used to establish a balanced model of interaction task and interaction modality. Simulated multimodal interaction physiological experiments were conducted to analyze the impact of the multimodal balance model on participants' physiological data when they performed high cognitive load tasks with different emotions, and the physiological data were analyzed to assess participants' emotional experience. The emotional experience design of an intelligent vehicle robot is used as an example to validate the method. The results show that the multimodal interaction balance model can effectively reduce the user's cognitive load and improve the pleasantness of the interaction experience, and find a breakthrough for the development of intelligent vehicle-mounted robots in emotional experience.

Adapting to working from home caused physical and psychological difficulties, leading to work–family imbalance and lower employee performance during the COVID-19 pandemic. This study intends to identify the relationship between variables affecting telecommuting experience and improve employees’ perceived organizational support by constructing a balanced model of telecommuting experience. An online questionnaire survey was conducted with 142 employees from different organizations telecommuting during the epidemic in Xi’an. The NASA-TLX scale was used to quantitatively evaluate the cognitive load of employees working from home, and the Analytic Hierarchy Process method was applied to map negative experience factors with cognitive load to obtain the weight value of each factor. Finally, a balanced model of telecommuting experience was constructed through a system map. The results show that mental demand was the key factor affecting employees’ telecommuting experience. A good telecollaboration system could effectively manage work tasks and reduce the psychological load of employees. Frustration and temporal demand also significantly affected employees’ telecommuting experience, mainly due to work–family conflict. Adopting flexible work hours and organizing online sharing activities could reshape employees’ social relationships with their families and colleagues, effectively improving the telecommuting experience. The empirical study validated the effectiveness of the telecommuting experience balance model.

Objective: This research examines the effects of educational materials, delivered with "take-home and cook-with-friends" meal kits, on college students' food agency. Participants: In the spring of 2021, 186 students were recruited at a US public university and randomly allocated into either an intervention group that received meal kits and educational materials or a control group that received only meal kits. Methods: Meal kits containing local ingredients were distributed weekly to the participants and surveys were conducted to measure participants' food agency, using the Cooking and Food Provisioning Action Scale (CAFPAS). Hypothesis tests and regression analysis were then conducted to examine the educational intervention's effects on the CAFPAS scores. Results: The educational intervention had a positive and statistically significant effect on students' CAFPAS scores. Conclusions: Educational interventions hold promise in enhancing college students' food agency, at least in the short term.