Li Sun

To design an asymmetric supercapacitor device with high capacitance, high energy density, and excellent cycling stability, a NiCo₂O₄@NiWO₄/MXene ternary composite cathode and a hydrophilic carbon nanotube (H-CNTs) anode were constructed and matched for device assembly. The NiCo₂O₄@NiWO₄/MXene cathode with a “nanowire@nanosheet” core-shell hierarchical structure was prepared through a two-step hydrothermal method combined with high-temperature annealing, while hydrophilic carbon nanotubes were obtained by concentrated nitric acid modification. The microstructure, crystal structure, elemental valence states, and pore characteristics of the electrode materials were systematically characterized by SEM, TEM, XRD, XPS, and nitrogen adsorption–desorption measurements. The results showed that the introduction of Ti₃C₂T_x MXene formed an effective conductive network, while the core-shell hierarchical structure provided abundant electrochemically active sites and increased the specific surface area of the ternary composite to 156.4 m²/g. In a three-electrode system, the NiCo₂O₄@NiWO₄/MXene electrode exhibited a specific capacitance of 1830 F/g at 1 A/g and retained 983 F/g at 15 A/g, while the H-CNTs anode delivered a specific capacitance of 774 F/g at 1 A/g. Based on the working potential windows, specific capacitances, and charge balance relationship of the positive and negative electrodes, the active material mass ratio was optimized to approximately 1:2.8. An asymmetric supercapacitor was subsequently designed using a “cathode–separator–anode” laminated configuration with NiCo₂O₄@NiWO₄/MXene as the cathode, H-CNTs as the anode, glass fiber as the separator, and 6 M KOH as the electrolyte. The assembled NiCo₂O₄@NiWO₄/MXene//H-CNTs device operated stably within a voltage window of 0–1.6 V and achieved a specific capacitance of 256 F/g at 1 A/g. The device delivered an energy density of 91.0 Wh/kg at a power density of 800 W/kg and maintained 97.3% of its initial capacitance after 12,000 charge–discharge cycles. These results demonstrate that the proposed electrode matching and laminated structural design can effectively improve the capacitive performance, energy storage capability, and cycling stability of the asymmetric supercapacitor, providing a useful basis for the design and performance optimization of high-performance supercapacitor devices.

Adolescent lower-limb postural abnormalities encompass physiological alignment variation, impaired dynamic motor control, and structural deformity, which require different intervention goals and standards of evidence. This review classifies flexible wearable lower-limb systems into motor-cable, pneumatic artificial muscle, elastic-element, and smart-material technologies, and compares the assisted joints, mass boundaries, power supply, sensing and control, and human-subject findings. Direct evidence in children and adolescents is distinguished from mixed-age evidence and technology transferred from adult populations. Current studies mainly support gait assistance, improved walking economy, and rehabilitation training. A small number of systems have been tested in children with cerebral palsy, but the evidence is insufficient to show that flexible external traction can produce sustained correction of the osseous alignment associated with adolescent genu varum or genu valgum. Motor-cable systems facilitate force-feedback control, pneumatic systems are constrained by their air supply and response characteristics, elastic assistance depends on stiffness and gait-phase matching, and smart materials still present thermal, electrical, and durability challenges. Future adolescent-oriented systems should prioritize growth-adjustable sizing, low distal mass, measurable interaction forces, and fail-safe unloading. Their effectiveness should be evaluated through off-device retention of functional gains and long-term follow-up.

To address the limited integration of affective expectations into transfer-assist nursing robot design, this study proposes a semantic–kinematic affective design framework and applies it to the development of a transfer-assist nursing robot. The framework first constructs a dynamic semantic lexicon through semantic descriptor collection and KJ clustering, thereby identifying users’ core affective expectations for robot-assisted holding and transfer motions. Second, OpenCV-based keyframe extraction, semantic differential evaluation, and imagery transition lines are integrated to establish the matching relationship between core semantic descriptors and representative motion patterns. Third, OpenPose is used to extract upper-limb keypoints, including the shoulders, elbows, and wrists, and two-dimensional motion contours are generated through temporal superposition to provide references for robot form design. Finally, the analytic hierarchy process is applied to determine the weights of design elements, including color, form, and function. Taking a transfer-assist nursing robot as an application case, the proposed process involved nine core dynamic semantic descriptors, 140 keyframes, and multi-level design-element weights. Finite element analysis and a preliminary user evaluation of the resulting robot concept indicated structural feasibility and generally positive perceived qualities. These evaluations provide preliminary evidence regarding the feasibility and perceived characteristics of the resulting design rather than comparative validation of the framework. The study provides a traceable hybrid quantitative–qualitative design pathway for incorporating user-perceived motion semantics into the early-stage design of assistive robotic systems.

This paper proposes an innovative strategy that integrates fine-grained semantic feature decoding with Low-Rank Adaptation (LoRA) fine-tuning model training to significantly improve the performance of text-to-image technology, addressing the limitations of current Generative Artificial Intelligence (GAI) in product conceptual image design. Firstly, semantic information pertinent to product design is collected, and the E-Prime software is utilized to conduct a semantic priming task for extracting key semantic words. Subsequently, the DeepSeek prompt engineering method is employed to decode the fine-grained features of semantic words sequentially from abstract to concrete based on the three dimensions of mental image, functional image, and physical image. Semantic feature prompts are derived by expert evaluation and clustering methods. Finally, the LoRA technique is employed to train the dataset independently based on the semantic feature prompts, achieving the optimal model configuration. Taking the intelligent pulse diagnostic instrument as an example, the application of this strategy in product conceptual design is demonstrated. Furthermore, multi-dimensional assessments of text-to-image outcomes are conducted through comparative experiments, verifying the potential and efficacy of the proposed strategy, which provides a solution for the controlled generation of large models in product design applications.

Fine-grained feature extraction and affective semantic mapping remain significant challenges in product form analysis. To address these issues, this study proposes a contrastive learning-based cross-modal fusion approach for product form imagery recognition, using the front-end design of new energy vehicles (NEVs) as a case study. The proposed method first employs the Biterm Topic Model (BTM) and Analytic Hierarchy Process (AHP) to extract thematic patterns and compute weight distributions from consumer review texts, thereby identifying key imagery style labels. These labels are then leveraged for image annotation, facilitating the construction of a multimodal dataset. Next, ResNet-50 and Transformer architectures serve as the image and text encoders, respectively, to extract and represent multimodal features. To ensure effective alignment and deep fusion of textual and visual representations in a shared embedding space, a contrastive learning mechanism is introduced, optimizing cosine similarity between positive and negative sample pairs. Finally, a fully connected multilayer network is integrated at the output of the Transformer and ResNet with Contrastive Learning (TRCL) model to enhance classification accuracy and reliability. Comparative experiments against various deep convolutional neural networks (DCNNs) demonstrate that the TRCL model effectively integrates semantic and visual information, significantly improving the accuracy and robustness of complex product form imagery recognition. These findings suggest that the proposed method holds substantial potential for large-scale product appearance evaluation and affective cognition research. Moreover, this data-driven fusion underpins sustainable product form design by streamlining evaluation and optimizing resource use.

With the rapid expansion of the Electric Sport Utility Vehicle (ESUV) market, capturing consumer aesthetic preferences and emotional needs through front-end styling has become a key issue in automotive design. However, traditional Kansei Engineering (KE) approaches suffer from limited timeliness, subjectivity, and low predictive accuracy when extracting affective vocabulary and modeling the nonlinear relationship between product form and Kansei imagery. To address these challenges, this study proposes an improved KE-based ESUV styling framework that integrates data mining, machine learning, and generative AI. First, real consumer reviews and front-end styling samples are collected via Python-based web scraping. Next, the Biterm Topic Model (BTM) and Analytic Hierarchy Process (AHP) are used to extract representative Kansei vocabulary. Subsequently, the Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models are constructed and optimized using the Seagull Optimization Algorithm (SOA) and Particle Swarm Optimization (PSO). Experimental results show that SOA-BPNN achieves superior predictive accuracy. Finally, Stable Diffusion is applied to generate ESUV design schemes, and the optimal model is employed to evaluate their Kansei imagery. The proposed framework offers a systematic and data-driven approach for predicting consumer affective responses in the conceptual styling stage, effectively addressing the limitations of conventional experience-based design. Thus, this study offers both methodological innovation and practical guidance for integrating affective modeling into ESUV styling design.

User preferences serve as a crucial dimension in describing the product style and its psychological experience. To assist designers in rapidly producing wheel designs that align with user preferences and enhance design efficiency by improving design transparency, this research establishes a user preference-based Automotive Wheel Hub Style Characterization Model (AWSCM). The model primarily consists of a style space, a design element space, and a matching relationship space. The style space is used to capture user preferences and product styles, the design element space for sample clustering and design element extraction, and the matching relationship space for calculating and analyzing the mathematical relationships between product styles and design elements. The model explores and reveals the logical and mathematical quantitative relationships among user preferences, product styles, and design elements. The results of controlled experiments indicate that the AWSCM has significant positive impacts on the design time and design quality. Particularly for non-professional designers, the AWSCM can help reduce the design time and enhance design comprehension, thereby improving design efficiency. This suggests that the AWSCM is beneficial for aiding designers in quickly generating reasonable designs in their design practice.

The increasing demand for efficiency, brand consistency, and sustainability in automotive design has led to the exploration of innovative methods. This study investigated the impact of the V-shaped Dynamic Morphology Curve (VDMC) on design efficiency, brand consistency, and sustainability outcomes in automobile wheel design. A total of 24 designers took part, divided into an experimental group using VDMC and a control group using traditional CAD methods. VDMC uses parametric modeling to accelerate design iterations while maintaining brand identity. The experimental group completed the design task 31.5% faster, achieved significantly higher brand consistency (9.1/10 vs. 7.8/10), and reduced the number of design iterations by 53.2% compared to the control group. Furthermore, the experimental group made 50.9% fewer design changes, indicating higher design stability. These results show that VDMC significantly improves design efficiency and sustainability by reducing both time and resource consumption while ensuring greater alignment with brand guidelines. This study highlights the potential of VDMC to transform traditional design practices and offers notable benefits for both creative processes and environmental impact. The results suggest that integrating VDMC into design workflows could lead to significant improvements in efficiency and sustainability in the automotive industry and beyond.

In order to solve the problem of insufficient design applicability in the field of lower limb rehabilitation, such as interaction, experience comfort, and modeling color, a biological excitation function system was used to guide the solution of the functional scheme of lower limb rehabilitation products, and the transformation of lower limb rehabilitation products in functional interaction, experience, and morphological color design driven by biological information-driven cross-domain mapping was improved. We used patent knowledge mining to study the product functional requirements of lower limb rehabilitation products. The results were used to screen the required biological prototypes, and the biological incentives were used to guide the design problems. According to the principle of analogy and similarity calculation, the similarity matrix was obtained, and then the strategy was analyzed. Through the analogy of functional system–product technology engineering systems, the engineering relationship between multi-biological and multi-design elements was determined. We realized the biological replacement and upgrading of product functions under biological stimulation to guide the design of lower limb rehabilitation products. The accurate quantitative biological information of multi-biological analogy fit has the significance of optimizing the training effect, improving the operation efficiency, and improving the morphology and modeling of the lower limb rehabilitation product engineering transformation and design. The acquisition rate of the functional design requirements of lower limb rehabilitation products based on text mining reached 95%, and the accuracy of the biological design prototype obtained through similarity calculation was higher than 79%, which verified the feasibility of the accurate bioinformatics design method and improved the rigor of the bioinformatics biomimetic design method.

The ability of AI to process vast datasets can enhance creativity, but its rigid knowledge base and lack of reflective thinking limit sustainable design. Generative Design Thinking (GDT) integrates human cognition and machine learning to enhance design automation. This study aims to explore the cognitive mechanisms underlying GDT and their impact on design efficiency. Using behavioral coding and quantitative analysis, we developed a three-tier cognitive model comprising a macro-cycle (knowledge acquisition and expression), meso-cycle (creative generation, intelligent evaluation, and feedback adjustment), and micro-cycle (knowledge base and model optimization). The findings reveal that increased task complexity elevates cognitive load, supporting the hypothesis that designers need to allocate more cognitive resources for complex problems. Knowledge base optimization significantly impacts design efficiency more than generative model refinement. Moreover, creative generation, evaluation, and feedback adjustment are interdependent, highlighting the importance of a dynamic knowledge base for creativity. This study challenges traditional design automation approaches by advocating for an adaptive framework that balances cognitive processes and machine capabilities. The results suggest that improving knowledge management and reducing cognitive load can enhance design outcomes. Future research should focus on developing flexible, real-time knowledge repositories and optimizing generative models for interdisciplinary and sustainable design contexts.