Displays最新文献

{"title":"The effect of VR visual training device on accommodative lag in myopic adolescents","authors":"Li-Te Yin ,&nbsp;Guan-Cheng Lin ,&nbsp;Pei-Chi Su ,&nbsp;Ching-Yung Chen","doi":"10.1016/j.displa.2025.103162","DOIUrl":"10.1016/j.displa.2025.103162","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate whether virtual reality (VR) visual training device can effectively reduce accommodative lag.</div></div><div><h3>Methods</h3><div>This study recruited 20 myopic adolescents (mean age 16.45 ± 0.95 years) and randomly assigned them to either a treatment group (n = 10) or a control group (n = 10). The treatment group underwent 8 weeks of VR visual training, while the control group received no training. Refractive error, accommodative lag, facility, and amplitude were measured before and after the 8-week period. Independent sample t-tests, repeated measures ANOVA, and paired sample t-tests were used to analyzing data.</div></div><div><h3>Results</h3><div>Baseline comparisons showed no significant differences between the treatment and control groups in accommodative lag, facility, amplitude, and refractive error (p &gt; 0.05). Repeated measures ANOVA revealed significant interaction effects between time and group for all three accommodative functions (p ≤ 0.05). Further within-group analysis indicated that the treatment group exhibited significant improvements in accommodative functions after training compared to baseline (p ≤ 0.05), whereas no significant changes were observed in the control group (p &gt; 0.05). In addition, no significant changes in refractive error were observed in either group over the 8-week period (p &gt; 0.05).</div></div><div><h3>Conclusion</h3><div>The VR visual training device effectively reduces accommodative lag and improves accommodative function in myopic adolescents. However, whether it can be applied to myopia control remains unclear and requires further investigation.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103162"},"PeriodicalIF":3.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PcPreT-Net: Predicting classification of decline rate in prostate-specific antigen using graph neural network","authors":"Chongzhe Yan ,&nbsp;Feng Liu ,&nbsp;Ying Cao ,&nbsp;Huijuan Tu ,&nbsp;Zi Xu ,&nbsp;Wuchao Li ,&nbsp;Pinhao Li ,&nbsp;Zhiyang Xing ,&nbsp;Yi Chen ,&nbsp;Zhi-Cheng Li ,&nbsp;Yuanshen Zhao ,&nbsp;Bo Gao ,&nbsp;Rongpin Wang","doi":"10.1016/j.displa.2025.103164","DOIUrl":"10.1016/j.displa.2025.103164","url":null,"abstract":"<div><div>Prostate cancer (PCa) is one of the most common cause of cancer-related deaths among men worldwide, with prostate-specific antigen (PSA) serving as a widely accepted biomarker for the diagnosis, treatment monitoring, and prognosis of PCa. Accurate assessment of PSA dynamics is therefore essential for evaluating therapeutic efficacy and disease progression. Magnetic resonance imaging (MRI) is widely recognized for its accuracy and non-invasive nature in managing PCa, plays a key role in PCa management. We aim to establish a predictive association between MRI data and PSA decline to enable individualized treatment assessment. This study proposes a hybrid classification model combing convolutional neural network (CNN) and graph convolutional network (GCN) to predict PSA decline rate. The graph nodes are constructed from multiparametric MRI (mp-MRI) images with highlighting tumor regions. The CNN, pretrained to classify Gleason score risk levels, serves as an image feature extractor that extracts semantic features and encodes inter-node relationships. Based on these features, a mapping relationship between mp-MRI and PSA decline rate categories was then developed. Ablation experiments validated the effectiveness of the designed feature extraction framework. Comparative tests showed that our model outperformed traditional radiomics, CNN, and vision transformer (ViT) models, achieving an accuracy of 0.870, precision of 0.881, recall of 0.858, and F1-score of 0.872.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103164"},"PeriodicalIF":3.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation method of design elements for scoliosis orthoses based on three-dimensional perspective tracking data","authors":"Shuo Zhang,&nbsp;Jiangnan Li,&nbsp;Li Sun,&nbsp;Jiantao Wu,&nbsp;Manpo Li","doi":"10.1016/j.displa.2025.103159","DOIUrl":"10.1016/j.displa.2025.103159","url":null,"abstract":"<div><div>Product kansei image evaluation is crucial for optimizing product design elements. Developing methods for evaluating kansei image will improve user satisfaction. Traditional methods of assessing kansei image primarily use two-dimensional images, limited by their single perspective and one-sided interaction. To overcome these limitations, this paper proposes a product kansei image evaluation method incorporating three-dimensional perspective tracking data. This study used scoliosis orthosis as an experimental sample and focused on the kansei image word “hidden.” Through kansei image evaluation, we identified the product design elements that most influence the perception of “hidden”, optimizing the design to increase the product’s hiddenness. First, we created an interactive three-dimensional simulation space and placed a three-dimensional model of a scoliosis orthosis inside it. Through this setup, participants could interact in three dimensions with the product, enabling the collection of three-dimensional perspective data. Meanwhile, we divided the scoliosis orthosis into Areas of Interest (AOI) based on the product’s functional regions and acquired eye-tracking data as participants interacted with the product model in the three-dimensional simulation space. The eye-tracking and three-dimensional perspective data were matched with the AOI regions. Finally, we weighted the eye-tracking data with the three-dimensional perspective tracking data to calculate the weighted value of each design element of the scoliosis orthosis associated with the kansei image word “hidden,” thus optimizing the design based on their priority. The results demonstrate that evaluating kansei images with three-dimensional perspective tracking data is more accurate than with eye tracking alone. Further analysis reveals that: (1) Constructing a three-dimensional simulation space to display the product model and enable human–computer interaction provides more accurate experimental data than traditional single-perspective two-dimensional images, better reflecting real user-product interactions. (2) Regarding the weight calculation of design elements, three-dimensional perspective tracking data is incorporated based on eye-tracking data, and a weighted method is used for the calculation. This includes visual data obtained by observing the product from different fixation points, providing more information and improving the results’ authenticity. (3) Eye-tracking data combined with three-dimensional perspective tracking enables designers to make quick decisions on design elements that need to be optimised. Consequently, designers can adjust scoliosis orthoses promptly, improving patient compliance and orthopedic effectiveness. In this study, we propose a new quantitative method for evaluating kansei images in products and provide new insights into the perceptual design of scoliosis orthoses.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103159"},"PeriodicalIF":3.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of the flicker of AMOLED pixel by trap-induced LTPS-TFT current fluctuation model","authors":"Haolin Li,&nbsp;Zheng Zhou,&nbsp;Xiaoyan Liu","doi":"10.1016/j.displa.2025.103155","DOIUrl":"10.1016/j.displa.2025.103155","url":null,"abstract":"<div><div>First frame drop (FFD), low-frequency flicker and various refresh rate (VRR) flicker of 7T1C active-matrix organic light emitting diode (AMOLED) are simulated in real time. By modeling the time-dependent trap capture/emission behavior, hysteresis and the current fluctuation of low-temperature polysilicon thin film transistors (LTPS-TFTs) are simulated. Then the proposed model is applied to the simulation of 7T1C AMOLED pixel. Three forms of flickers are simulated by the proposed trap-induced current fluctuation model and its dependence on frequency and trap properties are also evaluated. Our work provides a physical insight for the circuit transient analysis and a guideline for AMOLED pixel design regarding the reliability issue.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103155"},"PeriodicalIF":3.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implicit feature compression for efficient cloud–edge holographic display","authors":"Mi Zhou ,&nbsp;Hao Zhang ,&nbsp;Mu Ku Chen ,&nbsp;Zihan Geng","doi":"10.1016/j.displa.2025.103151","DOIUrl":"10.1016/j.displa.2025.103151","url":null,"abstract":"<div><div>Holographic displays, with their ability to vividly reconstruct object wavefronts, stand as promising candidates for future immersive display technologies. However, delivering such immersive experiences demands large volumes of holographic data. Compressing holographic data with high compression ratios remains challenging due to the substantial high-frequency content in holograms. To overcome this challenge, we propose an implicit feature compression-based cloud–edge system for efficient holographic display. The distinctive aspect of our approach lies in compressing the implicit features learned during hologram generation into an encoded stream, rather than compressing the hologram itself. This methodology integrates a joint design of a cloud-side encoder and edge-side decoder, with both components performing mixed hologram generation and data compression/decompression. Our results on 1,000 augmented DIV2K test images demonstrate that our approach remarkably reduces the original data volume by 99.8% on average, and the experiments validate our approach. This research establishes a technological foundation for the large-scale commercialization of holographic displays.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103151"},"PeriodicalIF":3.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of commanders’ situation awareness: a study based on SEEV, QN-ACTR, and cognitive load","authors":"Yunfeng Chen ,&nbsp;Beiyuan Guo ,&nbsp;Lei Ouyang ,&nbsp;Weining Fang","doi":"10.1016/j.displa.2025.103158","DOIUrl":"10.1016/j.displa.2025.103158","url":null,"abstract":"<div><div>In modern naval ship command environment, commanders must collaborate with the Command and Control System (C2S) in various scenarios to accomplish complex tasks. Commanders’ effective Situational Awareness (SA) is crucial for effective human–computer collaborative decision-making. To improve the assessment of SA during the commanders’ interaction with the C2S and enhance collaborative efficiency, this paper proposes an improved SA model. This model builds upon a previously developed SA model based on attention allocation and integrates the Queueing Network Adaptive Control of Thought-Rational (QN-ACTR) framework to quantitatively analyze the commanders’ cognitive processes. Furthermore, cognitive load theory is introduced to account for individual differences among commanders. To validate the improved SA model, we conducted simulation experiments involving 23 participants across four different interaction schemes. The subjective scores and eye-tracking data collected during the experiments validate the efficacy of the proposed SA model. The results showed that the proposed SA model can predict the SA of commanders with varying anti-fatigue levels and perform better than the previous model. Furthermore, the findings also indicate that cognitive load and suboptimal interface designs can negatively impact the commanders’ perception and comprehension, reducing the commanders’ SA level. This study provides a new tool for the quantitative measurement of commanders’ SA, which will aid in the development of more effective C2S for naval ships and optimize human–computer interaction design.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103158"},"PeriodicalIF":3.7,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Primary screening model for temporomandibular joint disc displacement based on triple-view face images","authors":"Hui Li ,&nbsp;Qin Zhou ,&nbsp;Xiaoyu Yang ,&nbsp;Ying Tao ,&nbsp;Chi Yang ,&nbsp;Jun Zhou","doi":"10.1016/j.displa.2025.103150","DOIUrl":"10.1016/j.displa.2025.103150","url":null,"abstract":"<div><div>Temporomandibular joint (TMJ) disc displacement (DD) is a common clinical condition characterized by early onset and a high incidence rate. Its pathological changes impact facial contours, modify facial appearance features, and disrupt normal physiological activities of the face. Currently, MRI and CT are the most commonly utilized methods for TMJ examination, but unsuitable for primary DD screening in large sample populations. The utilization of deep learning techniques for primary screening and recognition of DD based on face images holds significant practical value. A total of 714 samples Triple-view Face-Image DD dataset (TvFID<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>) was established for this study, comprising 415 samples of disc displacement without reduction (DDwoR), 180 samples of disc displacement with reduction (DDwR), and 119 samples of normal TMJ (NOR). Each sample includes a series of triple-view face images encompassing frontal, left-side, and right-side views. Based on this facial DD dataset, the paper introduces a classification model for DD based on triple-view face images. All three-view face images undergo facial key-point and position recognition, followed by cropping and down-sizing, and finally face alignment. In this study, we focus on the automated discrimination of TMJ disc displacement disorders, specifically differentiating between DDwoR and DDwR subtypes. For model development, to identify DD cases within the general population, we trained a two-class (DDwoR+DDwR and NOR) classifier that achieved an accuracy of 86.3%. Subsequently, we deployed a secondary two-class (DDwoR and DDwR+NOR) classifier targeting DDwoR cases necessitating active clinical intervention in screening pipelines, which attained 90.4% classification accuracy. Our two-class (DDwoR and DDwR+NOR) discrimination system demonstrates strong potential for clinical application in primary screening of DD, offering reliable differentiation between pathological and normal cases as well as between different displacement subtypes. The DD discrimination model online (<span><span>https://itmjtech.medi7.cn</span><svg><path></path></svg></span> (DDwoR and DDwR+NOR) offers a cost-effective, efficient, and convenient solution for the primary screening of TMJ DD.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103150"},"PeriodicalIF":3.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-sample feature interaction enhancement for few-shot fine-grained classification","authors":"Kaiyang Liao ,&nbsp;Yunfei Tan ,&nbsp;Yuanlin Zheng ,&nbsp;Dingwen Song","doi":"10.1016/j.displa.2025.103157","DOIUrl":"10.1016/j.displa.2025.103157","url":null,"abstract":"<div><div>Few-shot fine-grained image classification faces challenges due to high inter-class feature similarity, significant intra-class variations, and limited data, leading to insufficient model generalization. To address the challenges of category aggregation and separation in few-shot fine-grained scenarios, this paper proposes a classification network enhanced by cross-sample feature interaction. The cross-sample feature interaction enhancement process includes three core components: the Cross-domain Feature Attention Network (CFAN), which enhances feature consistency between support and query sets through channel and spatial attention mechanisms, focusing on capturing critical detail regions; the Global Dependency Augmentation Module (GDAM), which explicitly models dependencies between distant pixels and integrates local and global information; and the Cross-class Interaction Module (CCIM), which uses a bidirectional self-attention mechanism to align local features of query and support samples through complementary and interactive cross-sample features. Additionally, dynamic generation of inter-class feature contrast relationships improves inter-class feature discrimination. Experimental results demonstrate that the proposed method achieves superior performance compared to mainstream methods on multiple public few-shot fine-grained classification datasets, with particularly remarkable results under extremely few-shot conditions. The proposed method significantly improves the accuracy and robustness of few-shot fine-grained classification through an efficient feature interaction mechanism.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103157"},"PeriodicalIF":3.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The art of dish: What makes cooked food visually appealing?","authors":"Jiayu Shu ,&nbsp;Lik-Hang Lee ,&nbsp;Yusen Sun ,&nbsp;Pearl Pu ,&nbsp;Pan Hui","doi":"10.1016/j.displa.2025.103138","DOIUrl":"10.1016/j.displa.2025.103138","url":null,"abstract":"<div><div>People’s liking for cooked food is affected by a number of factors, including appearance, taste, smell, and eating habits. Among all these factors, appearance plays a vital role, especially in some situations where only the appearance of food is available on mobile displays. However, previous research on the effects of appearance on people’s liking for cooked food is limited in dimension and scale. In this paper, we investigate the relationship between three major visual aspects of cooked food and their visual appeal. We propose and extract several visual features in terms of color, texture, and layout, based on images collected from a large online food community. We also train classifiers using proposed visual features to predict the visual appeal of cooked foods. The results show that we can achieve about 77% prediction accuracy, and we find people prefer cooked food with bright and warm colors, and a smooth surface.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103138"},"PeriodicalIF":3.7,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking implicit motion for evaluating image complexity","authors":"Yixiao Li ,&nbsp;Xiaoyuan Yang ,&nbsp;Yuqing Luo ,&nbsp;Hadi Amirpour ,&nbsp;Hantao Liu ,&nbsp;Wei Zhou","doi":"10.1016/j.displa.2025.103131","DOIUrl":"10.1016/j.displa.2025.103131","url":null,"abstract":"<div><div>Image complexity (IC) plays a critical role in both cognitive science and multimedia computing, influencing visual aesthetics, emotional responses, and tasks such as image classification and enhancement. However, defining and quantifying IC remains challenging due to its multifaceted nature, which encompasses both objective attributes (e.g., detail, structure) and subjective human perception. While traditional methods rely on entropy-based or multidimensional approaches, and recent advances employ machine learning and shallow neural networks, these techniques often fail to fully capture the subjective aspects of IC. Inspired by the fact that the human visual system inherently perceives implicit motion in static images, we propose a novel approach to address this gap by explicitly incorporating hidden motion into IC assessment. We introduce the motion-inspired image complexity assessment metric (MICM) as a new framework for this purpose. MICM introduces a dual-branch architecture: One branch extracts spatial features from static images, while the other generates short video sequences to analyze latent motion dynamics. To ensure meaningful motion representation, we design a hierarchical loss function that aligns video features with text prompts derived from image-to-text models, refining motion semantics at both local (i.e., frame and word) and global levels. Experiments on three public image complexity assessment (ICA) databases demonstrate that our approach, MICM, significantly outperforms state-of-the-art methods, validating its effectiveness. The code will be publicly available upon acceptance of the paper.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103131"},"PeriodicalIF":3.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}