Evaluation of an App-Based Mobile Triage System for Mass Casualty Incidents: Within-Subjects Experimental Study.

Background: Digitalization in disaster medicine holds significant potential to accelerate rescue operations and ultimately save lives. Mass casualty incidents demand rapid and accurate information management to coordinate effective responses. Currently, first responders manually record triage results on patient cards, and brief information is communicated to the command post via radio communication.

Thermodynamic assessment of evaporation during molten steel testing onboard the International Space Station.

Evaporation control is a critical facility resource during solidification experiments that limits processing time and must be tracked to ensure facility health. A thermodynamic analysis was performed on a ternary FeCrNi sample processed onboard the International Space Station (ISS) using ESA Electromagnetic Levitation (EML) facility in a microgravity environment. A non-ideal solution-based mathematical model was applied for the overall sample mass loss prediction during this study.

Machine learning predicts emergency physician specialties from treatment strategies for patients suspected of myocardial infarction.

Background: Our investigation aimed to determine how the diverse backgrounds and medical specialties of emergency physicians (Eps) influence the accuracy of diagnoses and the subsequent treatment pathways for patients presenting preclinically with MI symptoms. By scrutinizing the relationships between EPs' specialties and their approaches to patient care, we aimed to unveil potential variances in diagnostic accuracy and treatment choices.

Methods: In this retrospective, monocenter cohort study, we leveraged machine learning techniques to analyze a comprehensive dataset of 2328 patients with suspected MI, encompassing preclinical diagnoses, electrocardiogram (ECG) interpretations, and subsequent treatment strategies by attending EPs.

Drivers of coastal benthic communities in a complex environmental setting.

Analyzing the environmental factors affecting benthic communities in coastal areas is crucial for uncovering key factors that require conservation action. Here, we collected benthic and environmental (physical-chemical-historical and land-based) data for 433 transects in Taiwan. Using a k-means approach, five communities dominated by crustose coralline algae, turfs, stony corals, digitate, or bushy octocorals were first delineated.