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The heat from wildfires volatilizes soil’s organic compounds which form a waxy layer when condensed on cooler soil particles causing soil to repel water. Timely assessment of soil water repellency (SWR) is critical for prediction and prevention of detrimental impacts of hydrophobic soils such as soil erosion, reduced availability of water to plants, and water runoff after rainfalls leading to floods. The Water Drop Penetration Time (WDPT), i.e., the time elapsed from a drop landing on the soil surface to its complete absorption is commonly used to assess the SWR level. Its manual measurements have variability based on the used instruments and subjective observations. The goal of this work is to design an automated system to perform standardized WDPT tests and assess the SWR levels. It consists of an electronically controlled mechanism to release a water drop, and a video camera to record the water penetration process. The latter is modeled as an “action” in video and Temporal Action Localization (TAL) analytics is used for predicting the WDPT and assessing the SWR level.

Western armies have relied on surgical field manuals to help physicians deal with unfamiliar combat medical conditions from the Napoleonic wars to the present day, but there has been little discussion of whether these handbooks have been used or improved outcomes. Recent research shows that the Union Army’s American Civil War (1861–1865) surgical case fatality rates improved as the war progressed, much like the US Army’s experience in later wars, and were generally superior to comparable European results. These positive Civil War outcomes have been attributed to field experience, adoption of best practices, and enhanced communication, without consideration of New York surgeon Stephen Smith’s widely-used Hand-Book of Surgical Operations. The Army of the Potomac added Smith’s pocket-sized Hand-Book to its supply table in 1862. Northern medical journals applauded it, and Smith’s contemporaries documented its wide use. Smith’s handbook explained and demonstrated surgical techniques that were adopted as the war progressed, such as the use of general anesthesia and limb-sparing resection (debridement) of gunshot wounds. It offered pithy, well-documented advice from recognized experts along with numerous illustrations, which allowed untrained physicians to visualize anatomical relationships and see contemporary best practices in a way that no other wartime publication provided, making it an exemplar of a mass-media surgical improvement change agent.

Wildfires are a common and devastating event that impacts ecosystems and communities yearly. Fire experts rely on simulations to better understand how to mitigate the damage they cause and respond to live threats. Many available fire simulation tools and libraries do not produce simulation results fast enough to be used with multiple parameter sets during an active fire and are difficult to integrate with other applications. To address this gap, we propose a GPU-based fire simulation and visualization tool: vFirelib. Using a GPGPU (General Purpose Graphics Processing Unit) framework, we can parallelize the fire spread computations and achieve a 20X computation speedup over a sequential implementation of a fire spread using a widely used fire spread model. To facilitate the integration with other applications, we implemented a wrapper including RESTful APIs to provide fire simulation as a service. In this paper, two examples are illustrated how to simulate wildfire scenarios and visualize results: a web-based application, and a 3D virtual reality application.

Stephen Smith launched a 40-year effort to bring trees to New York City streets in 1872, the year he founded the American Public Health Association (APHA). Smith argued that street trees would mitigate the adverse health effects of Manhattan's summer heat and help purify the air. The young APHA endorsed Smith's position and gave trees a prominent role in urban sanitation, but public health turned away from trees and urban reform movements as it adopted a biomedical public health model in the late 19th century. Nevertheless, Smith wrote and campaigned for a successful 1902 law requiring the New York City Parks Department to assume management of street trees in the name of public health. He then led a 1914 campaign to force the department to uphold his law. New York's street tree program has had an erratic trajectory, but it now generally follows Smith's vision. Public health could play a bigger role in creating greener cities and mitigating climate change with more field research and the health in all policies approach that Smith used to bring trees to Manhattan's streets in 1914. (Am J Public Health. 2025;115(1):66–74. https://doi.org/10.2105/AJPH.2024.307856) [ABSTRACT FROM AUTHOR]

William Halsted wrote to aging surgeon, Stephen Smith, in 1919, that he remembered the lessons Smith had taught him, "when I walked with you through the wards of Bellevue Hospital." Smith was an early advocate of Joseph Lister's antiseptic method, and because of his public health work, he was also an early advocate of environmental hygiene and microbial control based on the unproved germ theory. Although Lister's work at the time emphasized germ killing around the operative site with carbolic acid (antisepsis), Smith adopted and encouraged surgical practices at Bellevue that would be hallmarks of the germ-preventing (asepsis) surgical approach that fully developed after German bacteriologic discoveries in the mid-1880s, and with which Halsted is historically identified. Some physicians and historians have emphasized temporal and conceptual differences between Lister's antisepsis and German asepsis, but Smith and Halsted's experiences argue that surgical asepsis was the evolutionary outcome of germ theory-based surgical changes that began well before scientific proof arrived. [ABSTRACT FROM AUTHOR]

The article discusses the factors that contributed to the success of the Western University during the world war I. Topics mentioned include the financial struggles faced by the faculty, the origins of Western University, the educational contribution of Western University before the war and the vocational training program offered by the U.S. government.

This paper studies how to improve the accuracy of hydrologic models using machine-learning models as post-processors and presents possibilities to reduce the workload to create an accurate hydrologic model by removing the calibration step. It is often challenging to develop an accurate hydrologic model due to the time-consuming model calibration procedure and the nonstationarity of hydrologic data. Our findings show that the errors of hydrologic models are correlated with model inputs. Thus motivated, we propose a modeling-error-learning-based post-processor framework by leveraging this correlation to improve the accuracy of a hydrologic model. The key idea is to predict the differences (errors) between the observed values and the hydrologic model predictions by using machine-learning techniques. To tackle the nonstationarity issue of hydrologic data, a moving-window-based machine-learning approach is proposed to enhance the machine-learning error predictions by identifying the local stationarity of the data using a stationarity measure developed based on the Hilbert–Huang transform. Two hydrologic models, the Precipitation–Runoff Modeling System (PRMS) and the Hydrologic Modeling System (HEC-HMS), are used to evaluate the proposed framework. Two case studies are provided to exhibit the improved performance over the original model using multiple statistical metrics.