Search Results (1,138)

In an era where online interactions increasingly shape social dynamics, the pervasive issue of cyberbullying poses a significant threat to the well-being of individuals, particularly among vulnerable groups. Despite extensive research on text-based cyberbullying detection, the rise of visual content on social media platforms necessitates new approaches to address cyberbullying using images. This domain has been largely overlooked. In this paper, we present a novel dataset specifically designed for the detection of visual cyberbullying, encompassing four distinct classes: abuse, curse, discourage, and threat. The initial prepared dataset (cyberbullying visual indicators dataset (CVID)) comprised 664 samples for training and validation, expanded through data augmentation techniques to ensure balanced and accurate results across all classes. We analyzed this dataset using several advanced deep learning models, including VGG16, VGG19, MobileNetV2, and Vision Transformer. The proposed model, based on DenseNet201, achieved the highest test accuracy of 99%, demonstrating its efficacy in identifying the visual cues associated with cyberbullying. To prove the proposed model’s generalizability, the 5-fold stratified K-fold was also considered, and the model achieved an average test accuracy of 99%. This work introduces a dataset and highlights the potential of leveraging deep learning models to address the multifaceted challenges of detecting cyberbullying in visual content. Full article

In this study the objective was to morphologically characterize fruits, seeds and seedlings of Cereus fernambucensis Lem., as well as evaluate the seed germination and phenological dynamics of these columnar cacti, native to Brazil, which occur in restinga ecosystems. Biometric and morphological determinations were performed using 100 fruits, describing seed morphology in external and internal aspects and considering five stages of development for the characterization of seedlings. In the study of seed germination, two light conditions (12 h photoperiod and complete darkness) were tested under 25 °C, in a completely randomized design with four replicates of 50 seeds each. In the estimation of reproductive phenology, information was collected from herbarium specimens on the SpeciesLink online platform, and the exsiccatae were analyzed for the notes on their labels to evaluate reproductive aspects. Fruits showed an average mass of 21.11 g, length of 44.76 mm, diameter of 28.77 mm and about 336 seeds per fruit. Seeds behave as positive photoblastic, with a high percentage of germination under controlled conditions (94%). Germination is epigeal and phanerocotylar, with slow growth and, at 30 days after sowing, the seedling measures approximately 2 cm, which makes it possible to visualize the appearance of the epicotyl and the first spines. The species blooms and bears fruit throughout the year, with peaks of flowering and fruiting in January and March, respectively. The various characteristics make C. fernambucensis a key species for maintaining the biodiversity of restingas. Full article

Efficient detection of road surface defects is vital for timely maintenance and traffic safety. This study introduces a novel AI-powered web framework, TriRoad AI, that integrates multiple versions of the You Only Look Once (YOLO) object detection algorithms—specifically YOLOv8 and YOLOv11—for automated detection of potholes and cracks. A user-friendly browser interface was developed to enable real-time image analysis, confidence-based prediction filtering, and severity-based geolocation mapping using OpenStreetMap. Experimental evaluation was conducted using two datasets: one from online sources and another from field-collected images in Ankara, Turkey. YOLOv8 achieved a mean accuracy of 88.43% on internet-sourced images, while YOLOv11-B demonstrated higher robustness in challenging field environments with a detection accuracy of 46.15%, and YOLOv8 followed closely with 44.92% on mixed field images. The Gemini AI model, although highly effective in controlled environments (97.64% detection accuracy), exhibited a significant performance drop of up to 80% in complex field scenarios, with its accuracy falling to 18.50%. The proposed platform’s uniqueness lies in its fully integrated, browser-based design, requiring no device-specific installation, and its incorporation of severity classification with interactive geospatial visualization. These contributions address current gaps in generalization, accessibility, and practical deployment, offering a scalable solution for smart infrastructure monitoring and preventive maintenance planning in urban environments. Full article

To address the issue of coating accumulation model failure in unstable environments, this paper proposes a dynamic control method based on visual differential feedback. An image difference model is constructed through online image data modeling and real-time reference image feedback, enabling real-time correction of the coating accumulation model. Firstly, by combining the Arrhenius equation and the Hagen–Poiseuille equation, it is demonstrated that pressure regulation and temperature changes are equivalent under dataset establishment conditions, thereby reducing data collection costs. Secondly, online paint mist image acquisition and processing technology enables real-time modeling, overcoming the limitations of traditional offline methods. This approach reduces modeling time to less than 4 min, enhancing real-time parameter adjustability. Thirdly, an image difference model employing a CNN + MLP structure, combined with feature fusion and optimization strategies, achieved high prediction accuracy: R² > 0.999, RMSE < 0.79 kPa, and σ_e < 0.74 kPa on the test set for paint A; and R² > 0.997, RMSE < 0.67 kPa, and σ_e < 0.66 kPa on the test set for aviation paint B. The results show that the model can achieve good dynamic regulation for both types of typical aviation paint used in the experiment: high-viscosity polyurethane enamel (paint A, viscosity 22 s at 25 °C) and epoxy polyamide primer (paint B, viscosity 18 s at 25 °C). In summary, the image difference model can achieve dynamic regulation of the coating accumulation model in unstable environments, ensuring the stability of the coating accumulation model. This technology can be widely applied in industrial spraying scenarios with high requirements for coating uniformity and stability, especially in occasions with significant fluctuations in environmental parameters or complex process conditions, and has important engineering application value. Full article

Online reviews play a critical role in shaping consumer decisions and providing valuable insights to enhance the products and services for businesses. As visual content becomes increasingly prevalent in reviews, it is essential to understand how image complexity influences review helpfulness. Despite the growing importance of images, the impact of color diversity and texture homogeneity on review helpfulness remains underexplored. Grounded in Information Diagnosticity Theory and Dual Coding Theory, this study investigates the relationship between image complexity and review helpfulness, as well as the moderating role of review text readability. Using a large-scale dataset from the hotel and travel sectors, the findings reveal that color diversity has a positive effect on review helpfulness, while texture homogeneity follows an inverted U-shaped relationship with helpfulness. Furthermore, text readability strengthens the positive impact of texture homogeneity, making moderately homogeneous images more effective when paired with clear and well-structured text. Heterogeneity analysis demonstrates that these effects vary across product categories. The results advance the understanding of multimodal information processing in online reviews, providing actionable guidance for platforms and businesses to refine the review systems. Full article

The performance of visual SLAM in complex, real-world scenarios is often compromised by unreliable feature extraction and matching when using handcrafted features. Although deep learning-based local features excel at capturing high-level information and perform well on matching benchmarks, they struggle with generalization in continuous motion scenes, adversely affecting loop detection accuracy. Our system employs a Model-Agnostic Meta-Learning (MAML) strategy to optimize the training of keypoint extraction networks, enhancing their adaptability to diverse environments. Additionally, we introduce a coarse-to-fine feature tracking mechanism for learned keypoints. It begins with a direct method to approximate the relative pose between consecutive frames, followed by a feature matching method for refined pose estimation. To mitigate cumulative positioning errors, DK-SLAM incorporates a novel online learning module that utilizes binary features for loop closure detection. This module dynamically identifies loop nodes within a sequence, ensuring accurate and efficient localization. Experimental evaluations on publicly available datasets demonstrate that DK-SLAM outperforms leading traditional and learning-based SLAM systems, such as ORB-SLAM3 and LIFT-SLAM. DK-SLAM achieves 17.7% better translation accuracy and 24.2% better rotation accuracy than ORB-SLAM3 on KITTI and 34.2% better translation accuracy on EuRoC. These results underscore the efficacy and robustness of our DK-SLAM in varied and challenging real-world environments. Full article

Objectives: To evaluate the reproducibility of measurements of breech progression angle (BPA) by transperineal ultrasound (US) before and after its standardization by applying an image-based checklist. Methods: Eighteen 3-dimensional (3D) volumes of transperineal US from women at 36–40 weeks of gestation with a singleton fetus in breech presentation were provided to eight operators from four maternity units in Spain and Italy. All operators measured the BPA using 3D US volume processing software, and interobserver reproducibility was evaluated using the intraclass correlation coefficient (ICC). Following an online live review of all measurements by the operators, and the identification of sources of disagreement, an image-based scoring system for BPA measurement was collaboratively developed. The checklist included the following: (1) acquisition in the midsagittal plane, avoiding the posterior shadow of the pubic ramus; (2) visualization of the complete “almond-shaped” pubic symphysis; (3) drawing a first line along the longitudinal axis of the symphysis, dividing it equally; (4) extending this line to the inferior edge of the bone; and (5) drawing a second line tangentially from the lower edge of the symphysis to the lowest recognizable fetal part. The BPA measurements were then repeated using this checklist, and reproducibility was reassessed. Results: Eighteen volumes were analyzed by the eight operators, achieving a moderate reproducibility (ICC: 0.70, 95% confidence interval (CI): 0.48 to 0.86). A score was developed to include a series of landmarks for the appropriate assessment of BPA. Subsequently, the same eighteen volumes were reassessed using the new score, resulting in improved reproducibility (ICC: 0.81, 95% CI: 0.66 to 0.92). Conclusions: The measurement of BPA is feasible and reproducible when using a standardized image-based score. Full article

Introduction: Visual impairment impacts millions of people around the world, with the vast majority of problems being treatable. Disadvantaged communities are unable to utilize the same resources to treat these problems due to a lack of knowledge or resources, in addition to the presence of barriers preventing access. The objective of this paper is to assess eye health awareness and evaluate the barriers for individuals from disadvantaged communities in order to inform future interventions and increase access to care. Methods: This is a pilot study utilizing an online anonymous questionnaire designed to assess the demographics, eye health awareness, and access to eye care of community-based patients. A comprehensive literature review was also conducted using PubMed, Scopus, and Google Scholar to evaluate barriers to eye care and methods to improve community health outcomes. The primary goal was to improve understanding of eye health awareness and access in order to inform future strategies that can help in improving eye health awareness and service availability. Results: The results indicated that 61.2% of respondents believed that eye exams are very important, and only 7.7% of participants believed that regular eye exams are not important. The majority of participants (75%) agree that regular eye exams help prevent serious eye conditions and 84.5% believe that eye health can affect quality of life. 35.6% of participants reported they had their eyes checked by a healthcare professional within the last year, while 21.2% reported never having an eye exam. Although the majority of participants found access to eye care services in their community somewhat or very easy, 8.6% and 9.5% of participants found access difficult and very difficult, respectively. Even though 45.6% of participants reported not facing any barriers regarding access to eye care, the cost of services, long waiting times, and lack of nearby eye care providers were often cited as barriers from the remainder of the participants. Moving forward, local interventions such as mobile eye clinics, public health workshops, and telehealth are viable options to obtain an understanding of the community’s health status in addition to creating opportunities to educate and provide health screenings. Conclusion: The results indicate that although there is awareness of the importance of eye health for the majority of participants, there is still a sizable minority who have insufficient understanding. Barriers to healthcare such as cost, waiting times, and proximity to providers are common problems that are preventing many from seeking eye care. Future interventions should be created to increase access and literacy amongst the community through telehealth, mobile eye clinics, and public health workshops. Additional efforts should be taken by healthcare stakeholders to enhance care delivery, implement policies, and improve awareness. Full article

Objective: This study aims to investigate the feasibility of an online music therapy protocol for individuals previously diagnosed with COVID-19, focusing on their perceptions of their respiratory symptoms and the intervention’s impact on psychosocial measures. Methods: A within-subject experimental design was applied to examine an eight-week weekly online music therapy protocol, including singing, wind instrument playing, and music visualizations. All self-report data were collected bi-weekly throughout the 16-weeks study period, including baseline and post-tests. The measures for respiratory symptoms included the Medical Research Council’s Dyspnea Scale (MRC Dyspnea), Chronic Respiratory Questionnaire-Mastery Scores (CRQ Mastery), and Visual Analogue Scale for breathlessness. The measures for the secondary psychosocial outcomes were the Beck Depression Inventory-Short Form, the Generalized Anxiety Disorder 7-item, the Hospital Anxiety and Depression Scale, the Fatigue Severity Scale, the Epworth Sleepiness Scale, the EuroQol 5-Dimension 5-Level, and the Connor-Davidson Resilience Scale. Results: Twenty-four participants were enrolled. The participants perceived a reduction in respiratory symptoms, and shortness of breath (MRC Dyspnea). Planned comparisons showed significant decreases in MRC from baseline to post-treatment (p = 0.008). The mixed-effects model, including pre-baseline and post-treatment, was significant (p < 0.001). Significant changes in Breathing VAS were consistent with improvements in MRC Dyspnea, showing a significant baseline-to-post difference (p = 0.01). The CRQ Mastery showed significant improvements from baseline to Week 12 (p < 0.001). No significant changes were observed in other secondary measures. Conclusions: Our preliminary findings suggest that this protocol is feasible, and as a result, may help individuals previously diagnosed with COVID-19 to cope with lasting respiratory symptoms and improve their perception of shortness of breath. Live music-making, including playing accessible wind instruments and singing, may contribute to an increase sense of control over breathing. As this was a feasibility study, we conducted multiple uncorrected statistical comparisons to explore potential effects. While this approach may increase the risk of Type I error, the findings are intended to inform hypotheses for future confirmatory studies rather than to draw definitive conclusions. Full article

Background/Objectives: Neuropsychological assessment tools adapted for digital formats are crucial to expanding access and improving cognitive evaluation in post-stroke patients. This study aimed to examine the reliability, convergent validity, and criterion-related validity (concurrent and known-groups) of TRIACOG-Online, a computerized cognitive screening tool designed to assess multiple domains in post-stroke adults in person or remotely. Methods: 98 participants (47 neurologically healthy adults and 51 post-stroke patients) completed a sociodemographic questionnaire, the Mini-Mental State Examination—MMSE, G-38—Nonverbal Intelligence Test, and the TRIACOG-Online assessment. Evaluations were conducted in person, computer mediated. Results: TRIACOG-Online demonstrated high internal consistency (Cronbach’s α = 0.872; McDonald’s ω = 0.923). Statistically significant differences were found between groups in episodic memory, attention, executive functions, and numerical processing, with healthy individuals outperforming post-stroke participants. Effect sizes were medium to large in several domains, especially for visual memory. Validity evidence based on the relationship with external variables was supported by negative correlations with age and positive correlations with education and reading and writing habits, particularly in the clinical group. Educational level showed stronger associations with verbal memory and language, suggesting a protective role in post-stroke cognitive performance. TRIACOG-Online scores demonstrated evidence of convergent validity with MMSE and G-38. Conclusions: TRIACOG-Online shows strong psychometric properties for the cognitive assessment of post-stroke adults. Its computerized format represents a promising tool for clinical and research use in neuropsychology, especially for bedside applications. Full article

Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability for residential development in Jihlava, Czechia. Using five raster-based data layers derived from a multi-criteria evaluation (Urban Planner methodology) across three time horizons (2023, 2028, 2033), the visualizations were implemented in ArcGIS Online and assessed by 19 domain experts via a structured questionnaire. The evaluation focused on clarity, usability, and accuracy in interpreting land suitability values, with the methods being rated on a five-point scale. Results show that the Horizontal Planes method was rated highest in terms of interpretability and user satisfaction, while 3D Surface and Vertical Planes were considered the least effective. The study demonstrates that visualization methods employing visual variables (e.g., color and transparency) are better suited for land suitability communication. The methodological contribution lies in systematically comparing 3D visualization techniques for thematic spatial data, providing guidance for their application in planning practice. The results are primarily intended for urban planners, designers, and local government representatives as supportive tools for efficient planning of future built-up area development. Full article

Accessibility in web systems is essential to ensure everyone can obtain information equally. Based on the Web Content Accessibility Guidelines (WCAGs), the Electronic Government Accessibility Model (eMAG) was established in Brazil to guide the accessibility of federal government web systems. Based on these guidelines, this research sought to understand the reasons behind the persistent gaps in web accessibility in Brazil, even after 20 years of eMAG. To this end, the accessibility of the Integrated Academic Activities Management System (SIGAA), used by 39 higher education institutions in Brazil, was evaluated. The living lab methodology was used to carry out accessibility and usability tests based on students’ experiences with visual impairments during interaction with the system. Furthermore, IT professionals’ knowledge of eMAG/WCAG guidelines, the use of accessibility tools, and their beliefs about accessible systems were investigated through an online questionnaire. Additionally, the syllabuses of training courses for IT professionals at 20 universities were analyzed through document analysis. The research confirmed non-compliance with the guidelines in the software researched, gaps in the knowledge of IT professionals regarding software accessibility practices, and inadequacy of accessibility content within training courses. It is concluded, therefore, that universities should incorporate mandatory courses related to software accessibility into the training programs for IT professionals and that organizations should provide continuous training for IT professionals in software accessibility practices. Furthermore, the current accessibility legislation should be updated, and its compliance should be required within all organizations, whether public or private. Full article

Background: Traumatic brain injury frequently leads to visual dysfunction, affecting up to 75% of patients. These visual issues, if unrecognized, can significantly impair daily functioning. Optometrists are well-positioned to identify and manage such conditions, yet their level of preparedness is not well understood. Objective: This study aimed to assess optometrists’ knowledge, awareness, and management practices regarding TBI-related visual disorders in Saudi Arabia. Methods: A cross-sectional survey was distributed online to 411 licensed optometrists in Saudi Arabia. The 16-item questionnaire assessed demographics, awareness, confidence, knowledge, and management practices related to TBI-associated visual disorders. Results: Only 26.3% of the respondents reported receiving formal education on TBI-related visual disorders. While most recognized common symptoms, such as blurred vision and light sensitivity, comprehensive knowledge of complex visual disorders was limited. A majority (82.5%) recommended referral to other healthcare providers; however, only 16.8% demonstrated high management competency, and 31.5% fell into the low-competency category. Referral patterns and clinical decision-making were significantly associated with experience and formal training. Conclusion: The findings reveal notable gaps in optometrists’ knowledge and preparedness to manage TBI-related visual dysfunctions. Structured educational initiatives and standardized clinical protocols are essential to improve optometric care for individuals with TBI. Full article

Advances in information technology have enabled virtual museums to transcend traditional physical boundaries and become important tools in education. Despite their growing use, the factors influencing the effectiveness of virtual museums in enhancing students’ learning motivation remain underexplored. This study investigates key factors that promote learning motivation among secondary school students using the National Museum of Nature’s Online Virtual Exhibition as a case study. Grounded in the Stimulus–Organism–Response (S-O-R) theoretical framework, a conceptual model was developed and empirically tested using Structural Equation Modeling (SEM) to examine relationships among stimulus variables, psychological states, and learning motivation. Results reveal that affective involvement, cognitive engagement, and perceived presence significantly enhance learning motivation, while immersion shows no significant effect. Among the stimulus factors, perceived enjoyment strongly promotes affective involvement, perceived interactivity enhances cognitive engagement, and content quality primarily supports cognitive processing. Visual aesthetics contribute notably to immersion, affective involvement, and perceived presence. These findings elucidate the multidimensional mechanisms through which user experience in virtual museums influences learning motivation. The study provides theoretical and practical implications for designing effective and engaging virtual museum educational environments, thereby supporting sustainable digital learning practices. Full article

As artificial intelligence agents become integral to immersive virtual reality environments, their inherent opacity presents a significant challenge to transparent human–agent communication. This study aims to determine if a virtual agent can effectively communicate its learning state to a user through facial expressions, and to empirically validate a set of designed expressions for this purpose. We designed three animated facial expression sequences for a stylized three-dimensional avatar, each corresponding to a distinct learning outcome: clear success (Case A), mixed performance (Case B), and moderate success (Case C). An initial online survey ($n=93$) first confirmed the general interpretability of these expressions, followed by a main experiment in virtual reality ($n=30$), where participants identified the agent’s state based solely on these visual cues. The results strongly supported our primary hypothesis (H1), with participants achieving a high overall recognition accuracy of approximately 91%. While user background factors did not yield statistically significant differences, observable trends suggest they may be worthy of future investigation. These findings demonstrate that designed facial expressions serve as an effective and intuitive channel for real-time, affective explainable artificial intelligence (affective XAI), contributing a practical, human-centric method for enhancing agent transparency in collaborative virtual environments. Full article