Submerse: Visualizing Storm Surge Flooding Simulations in Immersive Display Ecologies

We present Submerse, an end-to-end framework for visualizing flooding scenarios on large and immersive display ecologies. Specifically, we reconstruct a surface mesh from input flood simulation data and generate a to-scale 3D virtual scene by incorporating geographical data such as terrain, textures, buildings, and additional scene objects. To optimize computation and memory performance for large simulation datasets, we discretize the data on an adaptive grid using dynamic quadtrees and support level-of-detail based rendering. Moreover, to provide a perception of flooding direction for a time instance, we animate the surface mesh by synthesizing water waves. As interaction is key for effective decision-making and analysis, we introduce two novel techniques for flood visualization in immersive systems: (1) an automatic scene-navigation method using optimal camera viewpoints generated for marked points-of-interest based on the display layout, and (2) an AR-based focus+context technique using an auxiliary display system. Submerse is developed in collaboration between computer scientists and atmospheric scientists. We evaluate the effectiveness of our system and application by conducting workshops with emergency managers, domain experts, and concerned stakeholders in the Stony Brook Reality Deck, an immersive gigapixel facility, to visualize a superstorm flooding scenario in New York City

Diagnostic Accuracy of Ultrasonography for Identification of Elbow Fractures in Children; a Systematic Review and Meta-analysis

Introduction: In spite of the results of previous studies regarding the benefits of ultrasonography for diagnosis of elbow fractures in children, the exact accuracy of this imaging modality is still under debate. Therefore, in this diagnostic systematic review and meta-analysis, we aimed to investigate the accuracy of ultrasonography in this regard. Methods: Two independent reviewers performed systematic search in Web of Science, Embase, PubMed, Cochrane, and Scopus for studies published from inception of these databases to May 2023. Quality assessment of the included studies was performed using Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2). Meta-Disc software version 1.4 and Stata statistical software package version 17.0 were used for statistical analysis. Results: A total of 648 studies with 1000 patients were included in the meta-analysis. The pooled sensitivity and specificity were 0.95 (95% CI: 0.93-0.97) and 0.87 (95% CI: 0.84-0.90), respectively. Pooled positive likelihood ratio (PLR) was 6.71 (95% CI: 3.86-11.67), negative likelihood ratio (NLR) was 0.09 (95% CI: 0.03-0.22), and pooled diagnostic odds ratio (DOR) of ultrasonography in detection of elbow fracture in children was 89.85 (95% CI: 31.56-255.8). The area under the summary receiver operating characteristic (ROC) curve for accuracy of ultrasonography in this regard was 0.93. Egger's and Begg's analyses showed that there is no significant publication bias (P=0.11 and P=0.29, respectively). Conclusion: Our meta-analysis revealed that ultrasonography is a relatively promising diagnostic imaging modality for identification of elbow fractures in children. However, clinicians employing ultrasonography for diagnosis of elbow fractures should be aware that studies included in this meta-analysis had limitations regarding methodological quality and are subject to risk of bias. Future high-quality studies with standardization of ultrasonography examination protocol are required to thoroughly validate ultrasonography for elbow fractures