Explore the vast range of titles available.

This research presents a comprehensive framework for personalized design aesthetic preference modeling that integrates variational autoencoders (VAE) with meta-learning approaches to address the limitations of traditional design evaluation methodologies. The proposed system combines multi-modal feature extraction from visual, textual, and behavioral data through attention-based fusion mechanisms, enabling robust capture of individual aesthetic preferences while maintaining generalization across diverse design domains. The VAE-based probabilistic modeling framework captures uncertainty in aesthetic judgments through learned latent representations, while the meta-learning component enables rapid adaptation to individual user preferences with minimal training data. Experimental evaluation across six design datasets demonstrates superior performance, achieving 84.7% accuracy in aesthetic preference prediction and 70% reduction in adaptation requirements compared to conventional transfer learning approaches. The framework successfully addresses the dual challenges of personalization and cross-domain generalization, providing practical foundations for intelligent design tools and automated content curation systems.

In order to improve the precision of clothing development of fast fashion brands, consumers’ sense of experience, and brand loyalty, a design method of clothing pattern is proposed by combining Kansei engineering theory and improved particle swarm optimization (IPSO)–back propagation neural network (BPNN) model. First, based on the theory of Kansei engineering, the perceptual image experiment of clothing patterns was designed, and the mean value of perceptual image evaluation of clothing patterns by young consumers was obtained through an online questionnaire survey. Second, based on the IPSO and the BPNN, the nonlinear correlation mapping model between the design elements of clothing pattern and consumers’ perceptual image is established. Finally, based on the calculation of target image weight by analytic hierarchy process (AHP) method and IPSO-BPNN model, the optimal combination of clothing pattern design elements under the requirement of multi-target image is output. Taking the paper-cut pattern of sweater shirt as an example, the feasibility of this research method is verified. The research not only helped the designer to design a costume pattern that meet the individual emotional needs of consumers, but also provided a clear design index and reference, and made the costume design process more targeted, precise, and intelligent.

The present paper shows the role of industrial design in the process of modernisation of railway vehicles, illustrated through a study case – personal experience. The collaboration with the “Electroputere VFU Pașcani” factory, specialized in reparations, modernization and building railway items, led to a work experience along with engineers, as well as learning the particularities of working in a factory, of turning a design project into reality. Besides from the specific steps of the design process, an important aspect is represented by the communication with the producer, in order to know all technologies and materials which can be used during the fabrication process. The obtained product must offer originality through shape and colour, fully responding to the beneficiary’s needs.

The application of Internet of Things (IoT) technology has become more and more widespread, and the smart home based on IoT technology has penetrated thousands of households and the design of the internal environment of the building is also gradually intelligentized and digitized. This study realizes the personalized and humanized design of IoT technology in the internal environment of buildings through the design of a personalized smart home system based on IoT, which focuses on the use of the Apriori algorithm to mine the association rules of the user’s preferences for the internal environment of the building. Then, it uses the GA-BP neural network algorithm to predict the audience’s preferences and the PMV indexes so as to achieve the personalized and humanized design of the smart home system. And human-centered design. The system is evaluated for its performance and practical application, and the design effect of the building’s interior environment is examined. The network bandwidth bottleneck of the system is 2M, and the absolute error of the GA-BP prediction model is within (−0.04, 0.19), with an average accuracy of 99.245% and an improvement of 20.38% over the BP prediction model. The importance and satisfaction of all the scenario models of this system are within (3,5), the scenario model settings are more reasonable, the user satisfaction is high, and it can meet the user’s personalized and humanized needs. The results of this research can guide the personalized and humanized design of the internal environment of the building and provide certain theoretical references for the systematic study of the design of the internal environment of the building.

Three-dimensional printing has significantly transformed the design and manufacture of robotic prostheses, making these devices more accessible, customized, and functional. This paper examines the historical evolution of prosthetic technology, tracing its development from rudimentary mechanical devices to the integration of advanced technologies, such as 3D printing. This innovation has enabled the production of prostheses at lower costs while enhancing their adaptability and performance. The review highlights how 3D printing has driven a disruptive shift in prosthetic customization, and how emerging technologies—including smart materials and artificial intelligence—have expanded the capabilities of prosthetic devices, offering more adaptive and natural movements. However, challenges persist, particularly regarding the need for standardization and infrastructural expansion to ensure equitable access to these technologies. Future research into novel materials and manufacturing techniques holds the potential to further improve the functionality, affordability, and accessibility of prosthetic devices. In conclusion, while 3D printing has marked a significant milestone in the evolution of robotic prosthetics, overcoming existing challenges is essential to realize its global impact and benefits fully.

Background Intensive longitudinal (IL) designs provide the potential to study symptoms as they evolve in real-time within individuals. This has promising clinical implications, potentially allowing conclusions at the level of specific individuals. The current study aimed to establish the feasibility of IL designs, as indicated by self-rated burden and attrition, in the context of psychiatry. Additionally, we evaluated three core assumptions about the instruments (diary items) used in IL designs. These assumptions are: diary items (1) reflect experiences that change over time within individuals (indicated by item variability), (2) are interpreted consistently over time, and (3) correspond to retrospective assessments of psychopathology. Methods TRAILS TRANS-ID is an add-on IL study in the clinical cohort of the TRAILS study. Daily diaries on psychopathological symptoms for six consecutive months were completed by 134 at risk young adults (age 22.6 ± 0.6 years). At baseline, immediately after the diary period, and one year after the diary period, participants completed a diagnostic interview. Results Excellent compliance (88.5% of the diaries completed), low participant burden (M = 3.21; SD = 1.42; range 1–10), and low attrition (8.2%) supported the feasibility of six-month IL designs. Diary items differed in their variability over time. Evaluation of the consistency of diary item interpretations showed that within-individual variability in scores could not be attributed to changing interpretations over time. Further, daily symptom reports reasonably correlated with retrospective assessments (over a six month period) of psychopathology obtained with the diagnostic interview, suggesting that both measures might complement each other. Conclusion The current study is the first to show that IL designs over extensive periods (i.e., multiple months) in psychiatry are feasible, and meet three core assumptions to study change in psychopathology. This might allow for addressing novel and promising hypotheses in our field, and might substantially alter how we treat and study mental ill-health.

Product customization is a means that effectively caters to personal needs, and as such, has increasingly caught the attention of both consumers and manufacturers. With technological advancements, the customization of products is now being made available through mobile applications. However, mobile apps need to be easy to use and operate, which presents some challenges for mobile app designers. In response, this study proposes an interactive evolutionary design method for mobile apps, based on an interactive genetic algorithm, to help consumers generate high-quality designs and enhance their retail experience by optimizing synthetic fitness and reducing the user’s fatigue from evaluation. Firstly, a human–computer interaction model for mobile interactive evolutionary design was launched to solve the screen space problem and simplify the evaluation process. Secondly, to accelerate the convergence of the algorithm, this paper combines hesitation patterns to obtain accurate individual fitness. Thirdly, an ongoing prediction and replacement mechanism were presented to improve user experience. After addressing these items, the proposed method is applied to a customization system that involves traditional brocade patterns of the Zhuang ethnic group in southwestern China and validated using a conventional interactive evolutionary design system with an interactive genetic algorithm. The experimental results show that the proposed method increases the designs’ efficiency, and can help consumers effectively customize their product purchases on mobile devices.

In order to meet the driving characteristics and needs of different types of drivers and to improve driving comfort and safety, this article designs personalized variable transmission ratio schemes based on the classification results of drivers’ steering characteristics and proposes a switching strategy for selecting variable transmission ratio schemes in response to changes in driver types. First, data collected from driving simulator experiments are used to classify drivers into three categories using the fuzzy C-means clustering algorithm, and the steering characteristics of each category are analyzed. Subsequently, based on the steering characteristics of each type of driver, suitable speed ranges, steering wheel travel, and yaw rate gain values are selected to design the variable transmission ratio, forming personalized variable transmission ratio schemes. Then, a switching strategy for variable transmission ratio schemes is designed, using a support vector machine to build a driver classification and identification model, and a transition scheme for variable transmission ratios is proposed. Finally, simulations are conducted to validate the personalized variable transmission ratio schemes and the transition schemes. The results show that the personalized variable transmission ratio schemes reduce driver burden and improve vehicle handling stability while meeting the driving characteristics and needs of different types of drivers. The switching strategy for selecting variable transmission ratio schemes can smoothly transition between different schemes for different types of drivers, ensuring that the variable transmission ratio schemes better match the driving characteristics and needs of the driver without affecting normal driving.

Custom-tailored designs have attracted increasing attention from both consumers and manufacturers due to increasingly intense market competition. We propose and verify a method for custom designing swimming goggles that is suitable for use on the Internet. Twenty-five points representing head features were first identified, and the relationship between these points and the size of the goggles were confirmed. The correct position for photography was then experimentally determined, and a camera-position corrector was designed and manufactured. A three-dimensional (3D) scanning model was divided into 18 planes based on the feature points, and the contour curve of the surface on each plane was extracted. Secondly a Hermite interpolation curve was then used to describe the contour curve for the head, and a parametric 3D head model was established. The method of using orthographic photographs with patches to obtain 3D data was summarized to determine the size of the user’s head, and a 3D model of the user’s head and the 3D model of the goggles were established. Lastly, we developed an algorithm for eliminating errors in the photographs. We also produced an operational flowchart for an application (APP) following the research approaches and then determined the page structure of the APP based on the flowchart to verify the validity of our proposed method and ultimately to establish an APP for interactively designing swimming goggles. The entire APP operation process was completed using a volunteer as an experimental subject when a model for custom-tailored goggles was obtained. The model was then processed and applied using 3D printing. The volunteer confirmed the model by declaring that the goggles were comfortable to wear and perfectly positioned on his face, thereby verifying the validity of the method.

(1) Background: The three-dimensional printing (3DP) technique has been reported to be of great utility in spine surgery. The purpose of this study is to report the clinical application of personalized preoperative digital planning and a 3DP guidance template in the treatment of severe and complex adult spinal deformity. (2) Methods: eight adult patients with severe rigid kyphoscoliosis were given personalized surgical simulation based on the preoperative radiological data. Guidance templates for screw insertion and osteotomy were designed and manufactured according to the planning protocol and used during the correction surgery. The perioperative, and radiological parameters and complications, including surgery duration, estimated blood loss, pre- and post-operative cobb angle, trunk balance, and precision of osteotomy operation with screw implantation were collected retrospectively and analyzed to evaluate the clinical efficacy and safety of this technique. (3) Results: Of the eight patients, the primary pathology of scoliosis included two adult idiopathic scoliosis (ADIS), four congenital scoliosis (CS), one ankylosing spondylitis (AS), and one tuberculosis (TB). Two patients had a previous history of spinal surgery. Three pedicle subtraction osteotomies (PSOs) and five vertebral column resection (VCR) osteotomies were successfully performed with the application of the guide templates. The main cobb angle was corrected from 99.33° to 34.17°, and the kyphosis was corrected from 110.00° to 42.00°. The ratio of osteotomy execution and simulation was 97.02%. In the cohort, the average screw accuracy was 93.04%. (4) Conclusions: The clinical application of personalized digital surgical planning and precise execution via 3D printing guidance templates in the treatment of severe adult rigid deformity is feasible, effective, and easily generalizable. The preoperative osteotomy simulation was executed with high precision, utilizing personalized designed guidance templates. This technique can be used to reduce the surgical risk and difficulty of screw placement and high-level osteotomy.