Search Results (89)

Background/Objectives: The increased accessibility of information has resulted in a rise in patients trying to self-diagnose and opting for self-medication, either as a primary treatment or as a supplement to medical care. Our objective was to evaluate the reliability, comprehensibility, and readability of the responses provided by ChatGPT 4.0 when queried about the most prevalent orthopaedic problems, thus ascertaining the occurrence of misguidance and the necessity for an audit of the disseminated information. Methods: ChatGPT 4.0 was presented with 26 open-ended questions. The responses were evaluated by two observers using a Likert scale in the categories of diagnosis, recommendation, and referral. The scores from the responses were subjected to subgroup analysis according to the area of interest (AoI) and anatomical region. The readability and comprehensibility of the chatbot’s responses were analyzed using the Flesch–Kincaid Reading Ease Score (FRES) and Flesch–Kincaid Grade Level (FKGL). Results: The majority of the responses were rated as either ‘adequate’ or ‘excellent’. However, in the diagnosis category, a significant difference was found in the evaluation made according to the AoI (p = 0.007), which is attributed to trauma-related questions. No significant difference was identified in any other category. The mean FKGL score was 7.8 ± 1.267, and the mean FRES was 52.68 ± 8.6. The average estimated reading level required to understand the text was considered as “high school”. Conclusions: ChatGPT 4.0 facilitates the self-diagnosis and self-treatment tendencies of patients with musculoskeletal disorders. However, it is imperative for patients to have a robust understanding of the limitations of chatbot-generated advice, particularly in trauma-related conditions. Full article

Assessing energy consumption in existing old residential buildings is key for urban energy conservation and decarbonization. Previous studies on old residential building energy assessment face challenges due to data limitations and inadequate prediction methods. This study develops a novel approach integrating building energy simulation and machine learning to predict large-scale old residential building energy use using multi-source datasets. Using Guangzhou as a case study, open-source building data was collected to identify 31,209 old residential buildings based on age thresholds and areas of interest (AOIs). Key building form parameters (i.e., long side, short side, number of floors) were then classified to identify residential archetypes. Building energy consumption data for each prototype was generated using EnergyPlus (V23.2.0) simulations. Furthermore, XGBoost and Random Forest machine learning algorithms were used to predict city-scale old residential building energy consumption. Results indicated that five representative prototypes exhibited cooling energy use ranging from 17.32 to 21.05 kWh/m², while annual electricity consumption ranged from 60.10 to 66.53 kWh/m². The XGBoost model demonstrated strong predictive performance (R² = 0.667). SHAP (Shapley Additive Explanations) analysis identified the Building Shape Coefficient (BSC) as the most significant positive predictor of energy consumption (SHAP value = 0.79). This framework enables city-level energy assessment for old residential buildings, providing critical support for retrofitting strategies in sustainable urban renewal planning. Full article

Background/Objectives: Deficits in emotion recognition abilities have been described as risk factors for intimate partner violence (IPV) perpetration. However, much of this research is based on self-reports or instruments that present limited psychometric properties. While current scientific literature supports the use of eye tracking to assess cognitive and emotional processes, including emotional decoding abilities, there is a gap in the scientific literature when it comes to measuring these processes in IPV perpetrators using eye tracking in an emotional decoding task. Hence, the aim of this study was to examine the association between fixation times via eye tracking and emotional decoding abilities in IPV perpetrators, controlling for potential confounding variables. Methods: To this end, an emotion recognition task was created using an eye tracker in a group of 52 IPV perpetrators. This task consisted of 20 images with people expressing different emotions. For each picture, the facial region was selected as an area of interest (AOI). The fixation times were added to obtain a total gaze fixation time score. Additionally, an ad hoc emotional decoding multiple-choice test about each picture was developed. These instruments were complemented with other self-reports previously designed to measure emotion decoding abilities. Results: The results showed that the longer the total fixation times on the AOI, the better the emotional decoding abilities in IPV perpetrators. Specifically, fixation times explained 20% of the variance in emotional decoding test scores. Additionally, our ad hoc emotional decoding test was significantly correlated with previously designed emotion recognition tools and showed similar reliability to the eyes test. Conclusions: Overall, this pilot study highlights the importance of including eye movement signals to explore attentional processes involved in emotion recognition abilities in IPV perpetrators. This would allow us to adequately specify the therapeutic needs of IPV perpetrators to improve current interventions. Full article

Mobile eye tracking is an important tool in psychology and human-centered interaction design for understanding how people process visual scenes and user interfaces. However, analyzing recordings from head-mounted eye trackers, which typically include an egocentric video of the scene and a gaze signal, is a time-consuming and largely manual process. To address this challenge, we develop eyeNotate, a web-based annotation tool that enables semi-automatic data annotation and learns to improve from corrective user feedback. Users can manually map fixation events to areas of interest (AOIs) in a video-editing-style interface (baseline version). Further, our tool can generate fixation-to-AOI mapping suggestions based on a few-shot image classification model (IML-support version). We conduct an expert study with trained annotators (n = 3) to compare the baseline and IML-support versions. We measure the perceived usability, annotations’ validity and reliability, and efficiency during a data annotation task. We asked our participants to re-annotate data from a single individual using an existing dataset (n = 48). Further, we conducted a semi-structured interview to understand how participants used the provided IML features and assessed our design decisions. In a post hoc experiment, we investigate the performance of three image classification models in annotating data of the remaining 47 individuals. Full article

Large-scale scene rendering faces challenges in managing massive scene data and mitigating rendering latency caused by suboptimal loading sequences. Although current approaches utilize Level of Detail (LOD) for dynamic resource loading, two limitations remain. One is loading priority, which does not adequately consider the factors affecting visual effects such as LOD selection and visible area. The other is the insufficient trade-off between rendering quality and loading latency. To this end, we propose a loading prioritization metric called Vision Degree (VD), derived from LOD selection, loading time, and the trade-off between rendering quality and loading latency. During rendering, VDs are sorted in descending order to achieve an optimized loading and unloading sequence. At the same time, a compensation factor is proposed to further compensate for the visual loss caused by the reduced LOD level and to optimize the rendering effect. Finally, we optimize the initial viewpoint selection by minimizing the average model-to-viewpoint distance, thereby reducing the initial scene loading time. Experimental results demonstrate that our method reduces the rendering latency by 24–29% compared with the existing Area-of-Interest (AOI)-based loading strategy, while maintaining comparable visual quality. Full article

The inequity in the distribution of green spaces in megacities has a detrimental effect on the physical and mental well-being of their inhabitants, highlighting the necessity for careful and strategic urban planning, along with appropriate regulatory interventions. Nevertheless, scholarly articles addressing the equity of access to urban green spaces primarily concentrate on urban parks, with limited studies examining the influence of alternative types of green spaces. This research initially recognized and categorized informal green spaces (IGS) located within the Third Ring Road of Chengdu, utilizing the UGS-1m dataset and area of interest (AOI) data, in accordance with a well-defined classification framework. Then, the G2SFCA method and Gini coefficient were employed to assess the impact of IGS on the green space accessibility, especially scenario analysis of open and shared use of green space. The findings indicate that (1) IGS in the narrow sense constitute 21.2% of the overall green spaces within the study area, resulting in a reduction of the Gini coefficient by 0.103; (2) IGS in the broad sense, including public affiliated green spaces, shows an even more positive effect on improving the equity of green space supply, with a reduction of the Gini coefficient by 0.28; (3) there exists great spatial disparity in accessibility improvement effect by different types of IGS, so public policies must be customized to reflect local circumstances, taking into account the practicality and associated costs of management and maintenance of various IGS as well as accessibility enhancement; (4) certain older residential areas may not be amenable to effective enhancement through the use of IGS alone, and these should then adopt a multidimensional greening strategy such as green-roof. The findings of this research offer valuable insights for the planning and management of green spaces in densely populated urban environments, thereby aiding in the development of more refined models for the development of “Garden Cities”. Full article

Lifelong learning, particularly in adult education, has gained considerable attention due to rapid lifestyle changes, including pandemic-induced lockdowns. This research targets adult learners returning to higher education after gap years, emphasizing their preference for technology with clear, practical benefits. However, many still need help operating digital media. This research aims to identify best practices for sustainably providing digital scientific materials to students by examining respondents’ tendencies in viewing journal article pages and scientific posters, with a focus on layout designs that include both textual and schematic elements. The research questions focus on (1) identifying the characteristics of Areas of Interest (AoI) that effectively attract learners’ attention and (2) determining the preferred characteristics for each learner group. Around 110 respondents were selected during the experiments using web tracking technology. Utilizing this web-based eye-tracking tool, we propose eight activities to detect learners’ perceptions of text-based learning object materials. The fact that first language significantly shapes learners’ attention was confirmed by time-leap analysis and AoI distances showing they focus more on familiar elements. While adult learners exhibit deeper engagement with scientific content and sustained concentration during reading, their unique preferences toward digital learning materials result in varied focus patterns, particularly in initial interest and time spent on tasks. Thus, it is recommended that lecturers deliver digital content for adult learners in a textual format or by placing the important parts of posters in the center. Full article

Recent advancements in Mobile Eye-Tracking (MET) technology have enabled the detailed examination of visitors’ embodied visual behaviors as they navigate exhibition spaces. This study employs MET to investigate visual attention patterns in an archeological museum, with a particular focus on identifying “hotspots” of attention. Through a multi-phase research design, we explore the relationship between visitor gaze behavior and museum learning experiences in a real-world setting. Using three key eye movement metrics—Time to First Fixation (TFF), Average Fixation Duration (AFD), and Total Fixation Duration (TFD), we analyze the distribution of visual attention across predefined Areas of Interest (AOIs). Time to First Fixation varied substantially by element, occurring most rapidly for artifacts and most slowly for labels, while video screens showed the shortest mean latency but greatest inter-individual variability, reflecting sequential exploration and heterogeneous strategies toward dynamic versus static media. Total Fixation Duration was highest for video screens and picture panels, intermediate yet variable for artifacts and text panels, and lowest for labels, indicating that dynamic and pictorial content most effectively sustain attention. Finally, Average Fixation Duration peaked on artifacts and labels, suggesting in-depth processing of descriptive elements, and it was shortest on video screens, consistent with rapid, distributed fixations in response to dynamic media. The results provide novel insights into the spatial and contextual factors that influence visitor engagement and knowledge acquisition in museum environments. Based on these findings, we discuss strategic implications for museum research and propose practical recommendations for optimizing exhibition design to enhance visitor experience and learning outcomes. Full article

Eye-tracking studies in virtual reality (VR) deliver insights into behavioral function. The gold standard of evaluating gaze behavior is based on manual scoring, which is labor-intensive. Previously proposed automated eye-tracking algorithms for VR head mount display (HMD) were not validated against manual scoring, or tested in dynamic areas of interest (AOIs). Our study validates the accuracy of an automated scoring algorithm, which determines temporal fixation behavior on static and dynamic AOIs in VR, against subjective human annotation. The interclass-correlation coefficient (ICC) was calculated for the time of first fixation (TOFF) and total fixation duration (TFD), in ten participants, each presented with 36 static and dynamic AOIs. High ICC values (≥0.982; p < 0.0001) were obtained when comparing the algorithm-generated TOFF and TFD to the raters’ annotations. In sum, our algorithm is accurate in determining temporal parameters related to gaze behavior when using HMD-based VR. Thus, the significant time required for human scoring among numerous raters can be rendered obsolete with a reliable automated scoring system. The algorithm proposed here was designed to sub-serve a separate study that uses TOFF and TFD to differentiate apathy from depression in those suffering from Alzheimer’s dementia. Full article

The European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service (CLMS), provides free pan-European ground motion data to support local and regional ground deformation analyses. To enhance the accessibility and usability of EGMS products, a new webapp, EGMStream, has been developed using Python and JavaScript for downloading and converting EGMS data. This revised and updated version improves the functionality and performance of the original R-based desktop tool, avoiding the need for a standalone software installation. Users can now simply access the webapp with an internet connection. In addition, the web version enhances data processing by leveraging high-performance server-side computing without relying on personal computer resources. The EGMStream webapp offers advanced features, including the parallel processing of large datasets and extraction of converted EGMS data for areas of interest (AoI) in various GIS-compatible formats. The transition from standalone software to a cloud-based system streamlines the integration of EGMS data into existing workflows, broadens user accessibility, and supports large-scale geospatial analysis. Consequently, this shift promotes the dissemination of these relevant and free available measurement data to a wider audience, including non-expert users. Full article

Urban green spaces (UGSs) provide recreational and cultural services to urban residents and play an important role in mental health. This study uses big data mining techniques to analyze 62 urban parks in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXUA) based on data such as points of interest (POIs), areas of interest (AOIs), and user comments from the popular social media platform Dianping. In addition, the authors apply sentiment analysis using perceptual dictionaries combined with geographic information data to identify text emotions. A structural equation model (SEM) was constructed in IBM SPSS AMOS 24.0 software to investigate the relationship between five external features, five types of cultural services, nine landscape elements, four environmental factors, and tourist emotions. The results show that UGS external features, cultural services, landscape elements, and environmental factors all have positive effects on residents’ emotions, with landscape elements having the greatest impact. The other factors show similar effects on residents’ moods. In various UGSs, natural elements such as vegetation and water tend to evoke positive emotions in residents, while artificial elements such as roads, squares, and buildings elicit more varied emotional responses. This research provides science-based support for the design and management of urban parks. Full article

This study compared the efficacy of AI neuroscience tools versus traditional design methods in enhancing viewer engagement with political campaign materials from the Harris–Trump presidential campaigns. Utilising a mixed-methods approach, we integrated quantitative analysis employing AI’s eye-tracking consumer behaviour metrics (Predict, trained on 180,000 screenings) with an AI-LLM neuroscience-based marketing assistant (CoPilot), with 67,429 areas of interest (AOIs). The original flyer, from an Al Jazeera article, served as the baseline. Professional graphic designers created three redesigned versions, and one was done using recommendations from CoPilot. Metrics including total attention, engagement, start attention, end attention, and percentage seen were evaluated across 13–14 areas of interest (AOIs) for each design. Results indicated that human-enhanced Design 1 with AI eye-tracking achieved superior overall performance across multiple metrics. While the AI-enhanced Design 3 demonstrated strengths in optimising specific AOIs, it did not consistently outperform human-touched designs, particularly in text-heavy areas. The study underscores the complex interplay between neuroscience AI algorithms and human-centred design in political campaign branding, offering valuable insights for future research in neuromarketing and design communication strategies. Python, Pandas, Matplotlib, Seaborn, Spearman correlation, and the Kruskal–Wallis H-test were employed for data analysis and visualisation. Full article

Background: Recent research suggests that an athlete’s gaze behavior plays a significant role in expert sport performance. However, there is a lack of studies investigating sex differences in gaze behavior during technical and tactical actions. Objectives: Therefore, the purpose of this study was to analyze the eye movements of elite female and male handball goalkeepers during penalty throws. Methods: In total, 40 handball goalkeepers participated in the study (female: n = 20; male: n = 20). Eye movements were recorded during a series of five penalty throws in real-time conditions. The number of fixations and dwell time, including quiet eye, for selected areas of interest were recorded using a mobile eye-tracking system. Results: Significant differences were found in quiet-eye duration between effective and ineffective goalkeeper interventions (females: mean difference (MD) = 92.26; p = 0.005; males: MD = 122.83; p < 0.001). Significant differences in gaze behavior between female and male handball goalkeepers were observed, specifically in the number of fixations and fixation duration on the selected areas of interest (AOIs). Male goalkeepers primarily observed the throwing upper arm AOI, the throwing forearm (MD = 15.522; p < 0.001), the throwing arm AOI (MD = 6.83; p < 0.001), and the ball (MD = 7.459; z = 3.47; p < 0.001), whereas female goalkeepers mainly observed the torso AOI (MD = 14.264; p < 0.001) and the head AOI (MD = 11.91; p < 0.001) of the throwing player. Conclusions: The results suggest that female goalkeepers’ gaze behavior is based on a relatively constant observation of body areas to recall task-specific information from memory, whilst male goalkeepers mainly observe moving objects in spatio-temporal areas. From a practical perspective, these results can be used to develop perceptual training programs tailored to athletes’ sex. Full article

The liver is a frequent focus in radiology due to its diverse pathology, and artificial intelligence (AI) could improve diagnosis and management. This systematic review aimed to assess and categorize research studies on AI applications in liver radiology from 2018 to 2024, classifying them according to areas of interest (AOIs), AI task and imaging modality used. We excluded reviews and non-liver and non-radiology studies. Using the PRISMA guidelines, we identified 6680 articles from the PubMed/Medline, Scopus and Web of Science databases; 1232 were found to be eligible. A further analysis of a subgroup of 329 studies focused on detection and/or segmentation tasks was performed. Liver lesions were the main AOI and CT was the most popular modality, while classification was the predominant AI task. Most detection and/or segmentation studies (48.02%) used only public datasets, and 27.65% used only one public dataset. Code sharing was practiced by 10.94% of these articles. This review highlights the predominance of classification tasks, especially applied to liver lesion imaging, most often using CT imaging. Detection and/or segmentation tasks relied mostly on public datasets, while external testing and code sharing were lacking. Future research should explore multi-task models and improve dataset availability to enhance AI’s clinical impact in liver imaging. Full article

With the increasing demand for outdoor recreation and fitness, this study aims to assess the connectivity of the outdoor green recreation (OGR) network from the perspective of green travel and propose optimization framework. The Point of Interest (POI) and Area of Interest (AOI) datasets of OGR spots in Zhengzhou were utilized as the primary research materials. A combination of GIS spatial analysis and Graph index calculation is employed to quantify and diagnose the connectivity of the OGR network based on multi-source data (land cover, topography, and road network). The index system for cost surface establishment was improved and proposed, shifting its focus from previous biological migration and ecological network to human green travel and improving the connectivity of the OGR network. The technical optimization process of the OGR network is explored and presented. The results show that: (1) The scale, number, and distribution of OGR spots and the connectivity of the OGR network are significantly different in urban and rural areas. Numerous small-scale OGR spots and short-distance recreational paths are distributed in urban areas, while a limited number of large-scale OGR spots and long-distance recreational paths are situated in rural areas with better natural resources. (2) Compared with driving travel, the connectivity of the OGR network is poor when walking and cycling. Graph indexes of Dg, BC, and dPC can be used to reflect the connection capability, bridging role, and contribution of each spot to overall network connectivity. (3) The current OGR network is optimized through 30 new spots based on the perspective of green travel and land suitability analysis. The network connectivity will improve by 4%, and the number of recreational paths suitable for green travel increased by 41. (4) The methodologies for quantifying and optimizing OGR network connectivity from the perspective of green travel will offer valuable references for future research in this field. Full article