Search Results (3,315)

Background: The study aimed to assess the diagnostic and therapeutic management of infectious keratitis at a tertiary referral center, focusing on how microbiological analysis influences clinical outcomes. Methods: A retrospective review was conducted on 220 patients (221 eyes) with infectious keratitis treated between November 2021 and January 2025. Data collected included clinical presentation, microbiological findings, treatment approaches, and outcomes. Statistical analyses examined the relationships between microbiological results, improvements in visual acuity, and the need for rescue surgery. Results: Bacterial keratitis accounted for 64% of cases, followed by viral (20%), fungal (13%), and Acanthamoeba (3%). Microbiological testing was performed in 107 cases, with a culture positivity rate of 75.7%. Positive microbiological findings were significantly associated with better visual acuity (p = 0.019) and a reduced, though not statistically significant, need for rescue surgery. Use of contact lenses and ocular trauma were independent risk factors for culture positivity. Delayed referral (more than 15 days) was linked to longer treatment durations and a higher likelihood of surgical intervention (p < 0.001). Microbiological diagnosis correlates with improved visual outcomes and a decreased need for surgical procedures. Conclusion: Early referral and targeted therapy are essential for optimizing prognosis. The use of contact lenses and cases of ocular trauma should prompt early diagnostic sampling. Full article

Spatial reasoning ability plays a critical role in predicting academic outcomes, particularly in STEM (science, technology, engineering, and mathematics) education. According to the Cattell–Horn–Carroll (CHC) theory of human intelligence, spatial reasoning is a general ability including various specific attributes. However, most spatial assessments focus on testing one specific spatial attribute or a limited set (e.g., visualization, rotation, etc.), rather than general spatial ability. To address this limitation, we created a mixed spatial test that includes mental rotation, object assembly, and isometric perception subtests to evaluate both general spatial ability and specific attributes. To understand the complex relationship between general spatial ability and mastery of specific attributes, we used a higher-order linear logistic model (HO-LLM), which is designed to simultaneously estimate high-order ability and sub-attributes. Additionally, this study compares four spatial ability classification frameworks using each to construct Q-matrices that define the relationships between test items and spatial reasoning attributes within the HO-LLM framework. Our findings indicate that HO-LLMs improve model fit and show distinct patterns of attribute mastery, highlighting which spatial attributes contribute most to general spatial ability. The results suggest that higher-order LLMs can offer a deeper and more interpretable assessment of spatial ability and support tailored training by identifying areas of strength and weakness in individual learners. Full article

The crystallization behavior of polymers affects the structure of aggregated states, which influences the properties of materials. Atomic force microscopy (AFM) is a helpful characterization tool with high spatial resolution at the nanometer-to-micrometer scale and low-destruction imaging capabilities, making it an important means of studying polymer crystallography. This review is intended for scientists in polymer materials and physics, aiming to inspire how the rich applications of AFM can be harnessed to address fundamental scientific questions in polymer crystallization. This paper reviews recent advances in polymer crystallization characterization based on AFM, focusing on its applications in visualizing hierarchical polymer crystal structures (single crystals, spherulites, dendritic crystals, and shish kebab crystals), investigating crystallization kinetics (in situ monitoring of crystal growth), and analyzing structure–property relationships (structural changes under temperature and stress). Finally, we introduce the application of the latest AFM technology in addressing key issues in polymer crystallization, such as single-molecule force spectroscopy (SMFS) and atomic force microscopy–infrared spectroscopy (AFM-IR). As AFM technology advances toward higher precision, greater efficiency, and increased functionality, it is expected to deliver more exciting developments in the field of polymer crystallization. Full article

Water pumping stations are essential components of national water infrastructure, yet their construction involves complex, high-risk processes, and traditional risk management approaches often show significant limitations in practice. To address this challenge, this study proposes a Building Information Modeling (BIM)-based approach that integrates structured risk information into an interactive nD BIM environment. We first developed an extended Risk Breakdown Matrix (eRBM), which systematically organizes risk factors, assessment levels, and causal relationships. This is linked to the BIM model through a customized BIM–risk integration framework. Subsequently, the framework is further implemented and quantitatively validated via a Navisworks plug-in. The system incorporates three core components: (1) a structured risk information model, (2) a visualization mechanism for dynamic, spatiotemporal risk representation and (3) risk influence path analysis using the Decision-Making Trial and Evaluation Laboratory–Interpretive Structural Modeling (DEMATEL–ISM) method. The plug-in allows users to access risk information on demand and monitor its evolution over time and space during the construction process. This study makes contributions by innovatively integrating risk information with BIM and developing a data-driven visualization tool for decision support, thereby enhancing project managers’ ability to anticipate, prioritize, and mitigate risks throughout the construction lifecycle of water pumping station projects. Full article

Background/Objectives: Understanding the relationship between plasma connecting peptide (C-peptide) levels and health-related quality of life (HRQoL) can inform diabetes management strategies. This study aimed to assess plasma C-peptide levels, HRQoL, and their association in patients with prediabetes, type 1 diabetes (T1D), and type 2 diabetes (T2D) attending outpatient departments (OPDs) in tertiary care hospitals. Methods: A cross-sectional survey was conducted between 1 January and 30 June 2023, using the EuroQoL Five Dimensions (EQ-5D-5L) instrument. Participants with prediabetes, T1D, or T2D were recruited from OPDs in diabetology, endocrinology, general practice, and family medicine at Sheikh Zayed Hospital (SZH) and Mayo Hospital (MH) in Pakistan. Plasma C-peptide levels were measured and HRQoL was assessed using EQ-5D-5L and the EQ Visual Analog Scale (VAS). Results: A total of 301 patients were included: 42 with prediabetes (14%), 70 with T1D (23.2%), and 189 with T2D (62.8%). The median C-peptide level was 0.46 nmol/L (IQR 0.13–0.85), the HRQoL score was 78.5% (IQR 63.2–100%), and the EQ VAS score was 85% (IQR 70–90%). C-peptide levels were significantly correlated with HRQoL scores (r = 0.14, p < 0.02) and differed across mobility, daily activity, pain/discomfort, and anxiety/depression domains (all p < 0.02). HRQoL scores significantly varied among the three groups (p < 0.0001), particularly in the aforementioned domains. Conclusions: C-peptide levels and HRQoL differ significantly across diabetes types, with lower C-peptide associated with reduced mobility, increased pain, and mental health issues. These findings underscore the importance of targeting C-peptide regulation to enhance HRQOL in diabetic populations. Full article

Mobile commerce has transformed the retail landscape, yet the determinants of impulse buying behavior in this environment remain understudied, particularly in emerging markets. This research investigates the factors influencing impulse buying in mobile commerce in Chile using the Stimulus–Organism–Response framework. A quantitative cross-sectional study collected data from 451 mobile shoppers via an online survey. Structural equation modeling with PLS-SEM revealed that eight of the thirteen hypothesized relationships were significant. Mobile application factors (visual appeal and portability) positively influenced hedonic and utilitarian values. Among personal factors, economic well-being, family influence, and credit card use directly impacted impulse buying, while time availability did not. Hedonic value strongly influenced impulse buying behavior, but utilitarian value showed no significant effect. Contrary to expectations, the COVID-19 pandemic negatively impacted impulse buying. These findings extend theoretical understanding of mobile impulse buying determinants and provide practical insights for mobile commerce developers and marketers to enhance their platforms and strategies. Full article

Within the context of urban sustainability, the renewal and activation of communities have received growing attention. Public art, as a common approach to community revitalization, has long been regarded as an effective means of addressing urban and community issues. Basic human senses serve as a bridge between residents and community spaces, offering an effective entry point for creating human-oriented spaces. This study addresses the challenge of insufficient spatial vitality in community spaces by examining how sensory interventions can enhance residents’ participation in public art and thereby contribute to the revitalization of communities. To guide this inquiry, a theoretical framework was constructed based on sensory marketing theory and the Stimulus–Organism–Response (SOR) model, focusing on three core dimensions: sensory stimuli, perceptual responses, and behavioral intention. The study further investigated the relationship between public art and residents’ willingness to participate through five types of sensory stimuli, using a measurement scale and Structural Equation Modeling (SEM), with eight public art installations in Shanghai serving as case references. It also assessed the relative strength of each effect. Participant interviews and non-participatory observations were subsequently conducted for validation and supplementary analysis. The results show that residents’ participation willingness in community public art is directly influenced by perceptual responses (emotional fluctuations, cognitive memory, and physiological responses), and indirectly influenced by different sensory stimuli. Cognitive memory, shaped mainly by olfactory and visual stimuli, emerged as the most important factor in encouraging participation. Participation willingness also varies across generations, and different sensory stimuli are associated with distinct participation patterns. Based on empirical data from Shanghai’s community activation practices, the study proposes implementation strategies guided by the Theory of Planned Behavior (TPB) to enhance spatial vitality, promote community activation, and support sustainable development. Full article

Background: Glaucoma is a multifactorial optic neuropathy and the leading cause of irreversible blindness worldwide. Vascular mechanisms, including impaired perfusion of the optic nerve head, are increasingly recognized as contributors to disease progression. Optical coherence tomography angiography (OCTA) enables non-invasive assessment of retinal and choroidal microvasculature, including peripapillary microvasculature dropout (MvD), which may serve as a marker of glaucomatous damage. Methods: A cross-sectional case–control study was conducted, including patients with primary open-angle glaucoma (OAG) and healthy controls. All participants underwent a comprehensive ophthalmic evaluation and OCTA imaging using the PLEX Elite 9000 system. Peripapillary vessel density (pVD), flow index (pFI), peripapillary choroidal thickness (PCT), β-zone parapapillary atrophy (β-PPA), and choroidal vascular indices were measured. MvD was defined as the complete absence of microvasculature within the β-PPA boundary. Statistical analyses included univariate and multivariate regression models to examine variables associated with PCT and to assess the association between MvD and visual field mean defect (MD), as well as other glaucoma characteristics. ROC curve analysis was performed to evaluate the ability of MvD to discriminate between different levels of visual field defects. Results: A total of 87 eyes (41 glaucomatous, 46 controls) were analyzed. Glaucoma patients exhibited significantly lower pVD, pFI, PCT, and choroidal vascular indices compared to the controls. MvD was detected in 10 glaucomatous eyes and was associated with a larger β-PPA area, smaller choroidal luminal and stromal areas, and worse mean deviation (MD) values. Multivariate regression showed that the number of ocular hypotensive treatments and StructureIndex variables were significantly associated with PCT (adjusted R² = 0.14). Logistic regression analysis identified MD, MD slope, and β-PPA area as variables significantly associated with the presence of MvD. ROC analysis showed that the presence of MvD had good discriminatory ability for visual field mean defects (MDs) (AUC = 0.77, 95% CI: 0.69–0.87; p = 0.005). Conclusions: Peripapillary MvD detected by OCTA is associated with reduced choroidal vascularity, increased β-PPA, and greater visual field deterioration in glaucoma patients. MvD may serve as a structural marker associated with functional deterioration in glaucoma patients. Full article

Background/Objectives: Integrating 3D visualization technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), into dental education may enhance students’ understanding of facial anatomy and clinical procedures. This study aimed to assess dental students’ perceptions of using MR for three-dimensional visualizations of impacted teeth. Methods: Cone-beam computed tomography (CBCT) scans of patients with impacted teeth were retrospectively selected from a university clinic database. The CBCT images were processed to adjust contrast for optimal visualization before being uploaded to MR goggles (HoloLens 2). A total of 114 final-year dental students participated, each manipulating the 3D images in space using the goggles. Following this, they completed a seven-question survey on a five-point Likert scale (1 = strongly agree, 5 = strongly disagree), evaluating image quality and the usefulness of 3D visualization. Results: The study group consisted of 29 males and 85 females (mean age = 24.11 years, SD = 1.48). The most favorable responses were for enhanced visualization of the impacted tooth’s position relative to adjacent structures and the inclusion of 3D image visualization as a teaching aid, which benefited students while learning and allowed them to better understand the course of the procedure for exposure/extraction of the impacted tooth, with median scores of 1, indicating a highly favorable opinion. A statistically significant relationship was found between the responses of females and males regarding the quality of the presented image using HoloLens 2 goggles. No significant correlation was found between participants with and without prior experience using VR/MR/AR. No significant correlation was found between age and responses. Conclusions: Students reported an improved understanding of the relationships between impacted teeth and adjacent structures, as well as potential benefits for clinical training. These findings demonstrate a high level of acceptance of MR technology among students; however, further research is required to objectively assess its effectiveness in enhancing learning outcomes. Full article

Understanding spatial relationships between objects in images is crucial for robotic navigation, augmented reality systems, and autonomous driving applications, among others. However, existing vision-language benchmarks often overlook explicit spatial reasoning, limiting progress in this area. We attribute this limitation in part to existing open datasets and evaluation metrics, which tend to overlook spatial details. To address this gap, we make three contributions: First, we greatly extend the COCO dataset with annotations of spatial relations, providing a resource for spatially aware image captioning and visual question answering. Second, we propose a new evaluation framework encompassing metrics that assess image captions’ spatial accuracy at both the sentence and dataset levels. And third, we conduct a benchmark study of various vision encoder–text decoder transformer architectures for image captioning using the introduced dataset and metrics. Results reveal that current models capture spatial information only partially, underscoring the challenges of spatially grounded caption generation. Full article

Objective: Vitamin D deficiency (VDD) is common in pregnancy and may affect lipid metabolism. The underlying mechanisms are multifactorial, but most evidence so far comes from non-pregnant populations. This study aims to identify metabolites and metabolic patterns associated with VDD in early pregnancy and to evaluate their relationships with maternal lipid profiles. Methods: A nested case–control research was carried out in the Zhoushan Pregnant Women Cohort (ZPWC). Cases were defined as women with VDD (25(OH)D < 20 ng/mL), and controls (≥20 ng/mL) were matched 1:1 using propensity scores based on age, pre-pregnancy BMI, gestational week, and calendar year at blood sampling. The untargeted metabolomics of first-trimester maternal plasma were measured. Metabolic profiles were analyzed using partial least squares-discriminant analysis (PLS-DA). Principal component analysis (PCA) was applied to visualize group separation, and metabolite set enrichment analysis (MSEA) was performed to reveal biologically relevant metabolic patterns. Associations between VDD-related metabolite components in early pregnancy and lipid levels in mid-pregnancy were assessed using linear regression models. Results: 44 cases and 44 controls were selected for the study. There were 60 metabolites identified as being connected to VDD. Among these, 26 metabolites, primarily glycerophospholipids and fatty acyls, exhibited decreased levels in the VDD group. In contrast, 34 metabolites showed increased levels, mainly comprising benzene derivatives, carboxylic acids, and organooxygen compounds. PCA based on these metabolites explained 52.8% of the total variance (R²X = 0.528) across the first six principal components (PC1: 16.4%, PC2: 10.6%, PC3: 9.2%, PC4: 6.3%, PC5: 5.7%, PC6: 4.6%). PC2, dominated by lineolic acids and derivatives, was negatively associated with total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) (all p < 0.01). PC3, dominated by glycerophosphocholines, was negatively associated with TC, TG, and high-density lipoprotein cholesterol (HDL-C) (all p < 0.05). MSEA revealed significant enrichment of the pantothenate and CoA biosynthesis pathway after multiple testing correction (FDR < 0.05). Conclusions: This study reveals distinct metabolic alterations linked to VDD and suggests potential mechanisms underlying its association with maternal lipid metabolism in early pregnancy. Full article

Information geometry provides a data-informed geometric lens for understanding data or physical systems, treating data or physical states as points on statistical manifolds endowed with information metrics, such as the Fisher information. Building on this foundation, we develop a robust mathematical framework for analyzing data residing on Riemannian manifolds, integrating geometric insights into information-theoretic principles to reveal how information is structured by curvature and nonlinear manifold geometry. Central to our approach are tools that respect intrinsic geometry: gradient flow lines, exponential and logarithmic maps, and kernel-based principal component analysis. These ingredients enable faithful, low-dimensional representations and insightful visualization of complex data, capturing both local and global relationships that are critical for interpreting physical phenomena, ranging from microscopic to cosmological scales. This framework may elucidate how information manifests in physical systems and how informational principles may constrain or shape dynamical laws. Ultimately, this could lead to groundbreaking discoveries and significant advancements that reshape our understanding of reality itself. Full article

Vibrotactile stimulation has applications in a variety of fields, including medicine, virtual reality, and human–computer interaction. Eccentric Rotating Mass (ERM) vibrating motors are widely used in wearable haptic devices owing to their small size, low cost, and low-energy features. User experience with vibrotactile stimulation is an important factor in ergonomic design for these applications. The effects of ERM motor vibrations on upper-extremity sensation and perception, which are important in the design of better wearable haptic devices, have not been thoroughly studied previously. Our study focuses on the relationship between user sensation and perception and on different vibration parameters, including frequency, location, and number of motors. We conducted experiments with vibrotactile stimulation on 15 healthy participants while the subjects were both at rest and in motion to capture different use cases of haptic devices. Eight motors were placed on a consistent set of muscles in the subjects’ upper extremities, and one motor was placed on their index fingers. We found a significant correlation between voltage and sensation intensity (r = 0.39). This finding is important in the design and safety of customized haptic devices. However, we did not find a significant aggregate-level correlation with the perceived pleasantness of the simulation. The sensation intensity varied based on the location of the vibration on the upper extremities (with the lowest intensities on the triceps brachii and brachialis) and slightly decreased (5.9 ± 2.9%) when the participants performed reaching movements. When a single motor was vibrating, the participants’ accuracy in identifying the motor without visual feedback increased as the voltage increased, reaching up to 81.4 ± 14.2%. When we stimulated three muscles simultaneously, we found that most participants were able to identify only two out of three vibrating motors (41.7 ± 32.3%). Our findings can help identify stimulation parameters for the ergonomic design of haptic devices. Full article

Changes in rainfall patterns and irregular air temperature have become essential issues in analyzing hydrometeorological trends in Jakarta. This study aims to select the best copula of the stationary and non-stationary copula models and visualize and explore the relationship between rainfall and air temperature to predict hydrometeorological trends. The methods used include combining univariate Lognormal and Generalized Extreme Value (GEV) distributions with Clayton, Gumbel, and Frank copulas, as well as parameter estimation using the fminsearch algorithm, Markov Chain Monte Carlo (MCMC) simulation, and a combination of both. The results show that the best model is the non-stationary Clayton copula estimated using MCMC simulation, which has the lowest Akaike Information Criterion (AIC) value. This model effectively captures extreme dependence in the lower tail of the distribution, indicating a potential increase in extreme low events such as cold droughts. Visualization of the best model through contour plots shows a shifting center of the distribution over time. This study contributes to developing dynamic hydrometeorological models for adaptation planning of changing hydrometeorological trends in Indonesia. Full article

Border surveillance, as a critical component of national security, places increasingly stringent demands on the target perception capabilities of video monitoring systems, especially in wide-area and complex environments. To address the limitations of existing systems in low-confidence target detection and multi-camera collaboration, this paper proposes a novel visual enhancement method for cooperative control of multiple PTZ (Pan–Tilt–Zoom) cameras based on hierarchical reinforcement learning. The proposed approach establishes a hierarchical framework composed of a Global Planner Agent (GPA) and multiple Local Executor Agents (LEAs). The GPA is responsible for global target assignment, while the LEAs perform fine-grained visual enhancement operations based on the assigned targets. To effectively model the spatial relationships among multiple targets and the perceptual topology of the cameras, a graph-based joint state space is constructed. Furthermore, a graph neural network is employed to extract high-level features, enabling efficient information sharing and collaborative decision-making among cameras. Experimental results in simulation environments demonstrate the superiority of the proposed method in terms of target coverage and visual enhancement performance. Hardware experiments further validate the feasibility and robustness of the approach in real-world scenarios. This study provides an effective solution for multi-camera cooperative surveillance in complex environments. Full article