Human faces are considered an important type of stimuli integral to social interaction. Faces occupy a substantial share of digital content, and their appearance can meaningfully impact how they are perceived and evaluated. In particular, past work has shown that facial color appearance can directly influence such perceptions. However, little is known regarding the perception of facial gloss and its influence on facial skin color appearance. The current work investigates how skin roughness influences perceived facial gloss and how these in turn affect facial color appearance for 3D rendered faces. Here, “roughness” refers to a parameter of the microfacet function modeling the microscopic surface. Two psychophysical experiments were conducted to model the interaction among skin roughness, perceived facial gloss, and perceived facial color appearance using varied facial skin tones. The results indicated an exponential relationship between skin roughness and perceived facial gloss, which was consistent across different skin tones. Additionally, gloss appearance influenced the perceived lightness of faces, a pattern not observed to the same extent among non-face objects included in the experiment. We expect that these results might partially be explained by discounting specular components for surface color perception to infer color attributes and by simultaneous contrast induced by a concentrated specular highlight. The current findings provide guidance for predicting visual appearance of face and non-face objects and will be useful for gloss and color reproduction of rendered digital faces.
Visual content has the ability to convey and impact human emotions. It is crucial to understanding the emotions being communicated and the ways in which they are implied by the visual elements in images. This study evaluates the aesthetic emotion of portrait art generated by our Generative AI Portraiture System. Using the Visual Aesthetic Wheel of Emotion (VAWE), aesthetic responses were documented and subsequently analyzed using heatmaps and circular histograms with the aim of identifying the emotions evoked by the generated portrait art. The data from 160 participants were used to categorize and validate VAWE’s 20 emotions with selected AI portrait styles. The data were then used in a smaller self-portrait qualitative study to validate the developed prototype for an Emotionally Aware Portrait System, capable of generating a personalized stylization of a user’s self-portrait, expressing a particular aesthetic emotional state from VAWE. The findings bring forth a new vision towards blending affective computing with computational creativity and enabling generative systems with awareness in terms of the emotions they wish their output to elicit.
A design challenge in virtual reality (VR) is balancing users’ freedom to explore the virtual environment with the constraints of a guidance interface that focuses their attention without breaking the sense of immersion or encroaching on their freedom. In virtual exhibitions in which users may explore and engage with content freely, the design of guidance cues plays a critical role. This research explored the effectiveness of three different attention guidance cues in a scavenger-hunt-style multiple visual search task: an extended field of view through a rearview mirror (passive guidance), audio alerts (active guidance), and haptic alerts (active guidance) as well as a fourth control condition with no guidance. Participants were tasked with visually searching for seven specific paintings in a virtual rendering of the Louvre Museum. Performance was evaluated through qualitative surveys and two quantitative metrics: the frequency with which users checked the task list of seven paintings and the total time to complete the task. The results indicated that haptic and audio cues were significantly more effective at reducing the frequency of checking the task list when compared to the control condition while the rearview mirror was the least effective. Unexpectedly, none of the cues significantly reduced the task-completion time. The insights from this research provide VR designers with guidelines for constructing more responsive virtual exhibitions using seamless attentional guidance systems that enhance user experience and interaction in VR environments.
In recent years, with the rise of printed electronics technology, printed flexible sensors have garnered widespread attention. To predict the performance of sensors before actual fabrication, in this paper, the bending strain and resistance response of the sensor’s functional layer were simulated using COMSOL Multiphysics 6.0 software, and the effects of different carbon black/graphene fill ratios on the sensor’s bending performance were explored. The simulation results indicated that resistance increases with the bending angle during the bending process, and the bending-resistance characteristics are better when the carbon black to graphene mass ratio is 2:1. Subsequently, flexible strain sensors were fabricated using screen-printing technology and their bending performance was tested. The experimental results demonstrated that the sensors have good linearity (R2 = 0.9851), favorable response and recovery times (approximately 1 and 2 s, respectively), low hysteresis (4.88%), and better cyclic stability when repeatedly bent at 0∘−20∘ compared to 0∘−90∘. The experimental results were consistent with the simulation results. This study provides a new perspective on the design of flexible strain sensors through synchronized experiments and simulations, which is expected to significantly reduce the cost of prototype development.
The problem of bacterial adhesion, bacterial transmission, and bacterial infection in food packaging has serious implications for the global economy and human health. Therefore, in recent decades, research specialists have worked to develop antibacterial materials which inhibit the growth of bacteria or achieve effective sterilization. To address the issues attributed to bacteria, this study created a superhydrophobic and antibacterial paper by adsorbing a layer of tea polyphenols and the FeCl3 reaction product on the surface of kraft paper, immersing it in AgNO3 solution for the in situ reduction of nanosilver, repeating the process four times, and then treating the surface with palm wax to reduce surface energy. The paper created showed excellent antibacterial properties (S. aureus inhibition rate of 99.963%, E. coli inhibition rate of 99.997%), superhydrophobicity (contact angle of water >155∘, sliding angle <5∘), stability, moisture-resistant performance, and reusability. This superhydrophobic antibacterial paper is extremely suitable for application in food packaging.
Aggregation-induced emission (AIE) polymers have attracted increasing attention due to good processability, film-forming ability, and ease of functionalization. In this study, a series of epoxide polymers doped with AIEgen tetraphenylethene (TPE) are achieved through free-radical-promoted cationic photopolymerization. Free radical photoinitiator acts as a sensitizer for the cationic photopolymerization of epoxides, and the photocuring performance of the TPE-doped photocurable resin is evaluated. UV-Vis absorption and photoluminescence spectra are characterized to investigate the AIE behavior of TPE molecules doped in the epoxy polymer. This research demonstrates that the TPE-doped cationic photocurable resin is capable of generating luminescent film and pattern fabrication, which has significant potential in anti-counterfeiting inks and other optical-functional applications.
Image style transfer, which involves remapping the content of a specified image with a style image, represents a current research focus in the field of artificial intelligence and computer vision. The proliferation of image datasets and the development of various deep learning models have led to the introduction of numerous models and algorithms for image style transfer. Despite the notable successes of deep learning based style transfer in many areas, it faces significant challenges, notably high computational costs and limited generalization capabilities. In this paper, we present a simple yet effective method to address these challenges. The essence of our approach lies in the integration of wavelet transforms into whitening and coloring processes within an image reconstruction network (WTN). The WTN directly aligns the feature covariance of the content image with that of the style image. We demonstrate the effectiveness of our algorithm through examples, generating high-quality stylized images, and conduct comparisons with several recent methods.
The study focuses on the fresh-keeping effect of clove packaging carton on strawberries as well as the preservation and physical properties of the carton. The extraction conditions were optimized by orthogonal test method, and the components of clove extract were analyzed by gas chromatography-mass spectrometry (GC-MS). The physical properties of packaging cartons and the preservation performance of strawberries were studied by comparative experiments. With an increase in the coating amount of clove extract, the empty carton’s compressive strength, bursting strength and edge compressive strength of the carton show a downward trend as a whole, but remain above 85% of the original carton strength, which can meet the needs of use. When the coating amount of clove extract is 150 mL/m2, the preservation performance is the best. At this level, the weight loss rate of strawberry is 34.96%, the soluble solid content is 8.0%, and the titratable acid content is 25%. According to the physical properties and preservation properties of clove extract packaging cartons, the carton with a coating amount of 150 mL/m2 is best suited for meeting food industry’s needs.
Currently, there is a relative insufficiency of research on the feature extraction of braided rivers and the river-scale water system evolution pattern under long time series in China. Therefore, continuous monitoring of surface water and analysis of its evolution process for the Yellow River Delta region have great application value to supplement and improve related knowledge and realize sustainable water resource management. The satellite remote sensing image is an important medium for obtaining surface water change data. Since free high-quality, long time-series high-resolution images are difficult to obtain, this paper selects the Landsat series of image data, which has a longer time span and better consistency, as the data source for the relevant research. In recent years, deep learning models have been gradually applied to the task of extracting surface water bodies from remote sensing images. However, deep learning methods usually have problems such as difficulty in capturing the fine contours of water bodies and poor extraction ability for fine water bodies. Based on this problem, this study proposes an Efficient Local Strip Convolutional Attention model for water system extraction and evolution analysis in the Yellow River Delta region. The experimental results show that the proposed model not only performs best in terms of overall accuracy but also obtains smoother water body boundaries and more complete extraction of small and medium-sized rivers, compared with the water body index methods MNDWI and AWEIsh, the machine learning method SVM, and the semantic segmentation models U-Net, SR-SegNet v2, and FWENet.