Science Applications International Corporation (United States)

Viet Nam faces a significant burden of viral hepatitis-associated liver disease, but the contribution of HDV, the most severe form of viral hepatitis, remains underinvestigated. HDV is substantial in the Northern and Central regions, but has not been documented in the South of Viet Nam. To investigate HDV prevalence and its association with severe liver disease in Southern Viet Nam, we used the standardized assay (LIAISON XL Anti-HDV) to detect HDV antibodies (anti-HDV) in 721 HBsAg positive individuals with hepatitis flare (n = 158), liver cirrhosis (LC) (181), hepatocellular carcinoma (HCC) (207), chronic hepatitis B (CHB) (175). Unexpectedly, anti-HDV was rare and only detected in 11/721 participants (1.5%), and not significantly different among groups: 2/158 (1.3%) in flare, 4/181 (2.2%) in LC, and 5/207 (2.4%) in HCC, and 0/175 (0%) in CHB. This suggests that HDV is not one of the major contributors to the high burden of liver disease in Southern Viet Nam. The discrepancy of HDV prevalence between Northern-Central and Southern regions suggests location-specific distribution of HDV, which in turn reflects differences in HDV transmission routes, study populations, and/or study methodologies. Our study underscores the need for tailored, regional screening strategies rather than a single national guideline for HDV infection.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-33268-0.

Abstract Snow reanalyses that combine process based models and remote sensing observations of snow provide estimates of seasonal snow water equivalent (SWE) evolution that surpass the accuracies of traditional modeling approaches. However, snow reanalyses are only available over smaller subregions and sometimes use computationally expensive modeling approaches. We investigate whether 1 km‐resolution and daily SWE from a popular reanalysis could be learned by connecting only trusted meteorological fields (multidecadal precipitation patterns and daily air temperature) and remotely sensed snow cover using a deep learning model. Relative to point observations of SWE evolution in the western United States, the deep learning model was able to reproduce the spatial and temporal evolution estimated by the snow reanalysis. Further, we found that the deep learning model was efficient and could be expanded geographically to estimate SWE evolution in the European Alps, demonstrating a high average coefficient of correlation (0.81) and low peak‐SWE bias (<1%) versus point estimates of SWE in seasonally snowy locations, with no statistical difference between regions at different elevations and with different forest cover. This study demonstrates how deep learning approaches could be used to mine connections between daily SWE evolution, snow cover remote sensing, and limited meteorological information to generate and expand the geographical extent of fine‐resolution historical snow estimates in complex terrains.

Abstract The advent of global river network data products and river routing models (RRMs) has resulted in substantial improvements to our understanding of the quantity and movement of water in Earth's rivers. However, the majority of vector‐based global river networks are derived from digital elevation models (DEMs) that do not allow representation of river network complexities such as bifurcations and divergences. Resulting discharge estimates may therefore not accurately reflect the movement of water. Satellite remote sensing has enabled the development of river networks that represent increased river complexities—albeit with limited topological accuracy—a necessary ingredient for river routing. Here, we describe an algorithm that generates globally consistent topology for a river network with optically‐derived centerlines—producing new and updated attributes—and adapt an existing vector‐based RRM to handle the added complexity in connectivity (i.e., divergences) via discharge partitioning. We apply the adapted RRM to runoff from an ensemble of land surface models for two case study Pfafstetter level‐2 basins (the Amazon and Mackenzie Rivers) to estimate 3‐hourly discharge between 2015 and 2024. We show that the adapted RRM is able to partition discharge at divergences, which results in a mean increase of 0.75 in Kling‐Gupta Efficiency and 0.31 in correlation coefficient at 6 gauges located on a divergence when compared to routing on a traditional DEM‐derived river network (i.e., contains no divergences). By comparing the resulting hydrographs from the optically‐ and DEM‐derived river networks, we find that flow waves travel faster through portions of river networks where divergences are accurately represented.

Restoring populations of native keystone species can increase landscape resilience to global change when those species create or modify ecosystems. The North American beaver (Castor canadensis) is an ecosystem engineer that increases river water storage and residence time, increasing fire resilience at the landscape level. Beaver populations in North America are significantly lower than they were historically, but over the last decade, beavers have been increasingly recognized for their ecosystem services, and reintroduction efforts throughout their historic range have become more prevalent. Here, we modeled potential beaver dam‐building capacity, associated surface water storage, and fire resilience in California's Sierra Nevada, a region at high risk of drought and wildfire. We estimate that 51% of beaver dam‐building capacity remains in this region compared to historical levels, and considerable dam capacity remains in all watersheds. Our conservative estimates suggest that beaver dams have the potential to store a total of 120 million m3 of surface water and create 2200 km2 of fire resilience in high fire risk areas. Additionally, streams where beavers have the potential to create the greatest water and fire benefits due to physical landscape and habitat characteristics are frequently found within watersheds that are at high risk for both drought and fire. Specifically, we identified five priority watersheds that have both high risk for drought and fire impacts, and have high potential to benefit from beaver conservation and restoration. Even in areas where fire and drought are less probable, the reestablishment of beavers will likely provide similar benefits. This unique approach to quantifying potential beaver benefits illustrates that wildlife can increase resilience to global change stressors and suggests that biodiversity and nature‐based climate solutions are intertwined.

An increasing variety of communications services are available to space missions. Yet varying standards between providers hinder adoption due to the complexity of managing configurations for each communication pathway. We present a software framework which alleviates this issue by automatically establishing end-to-end communications during contacts with a provider. The proposed development configures spacecraft protocols at the physical, link, and network layers. The spacecraft remains reachable at the same IP address regardless of the active provider. Underlying protocols and routes are abstracted, allowing the user to simply send data to a destination with the framework ensuring its delivery. We evaluate a full implementation of the framework in laboratory experiments conducted on an emulated communications testbed. These tests demonstrate data delivery across three different services with rapid (<20s) reconfiguration as the spacecraft transitions between providers.

This paper introduces py-guam, an open-source experimentation framework developed for the NASA Generic Urban Air Mobility simulation (GUAM) environment, facilitating the integration and evaluation of advanced artificial intelligence (AI) algorithms. We present a systems approach which enables the seamless incorporation of data-driven models, including off-nominal and failure state detection, into the GUAM’s Cognitive Architecture (CA). The framework supports customizable experimentation parameters, derives Safety Performance Indicators (SPIs) from UL 4600 safety case analyses, and employs rapid UAM simulations to assess AI impacts on flight performance across diverse scenarios. Through comprehensive testing and validation experiments, we demonstrate GUAM’s capability to enhance safety and efficiency in urban air mobility operations. Additionally, the open-source nature of py-guam fosters community collaboration, ensuring continuous improvement and adaptability to evolving technological advancements. This work establishes a robust tool for developing and testing AI-driven urban air mobility (UAM) systems, advancing the safety and reliability of autonomous urban air vehicles.

Through algorithms, scenario-based planning, and integration with SAP S/4HANA, the solution helps businesses make data-driven decisions while ensuring seamless communication between carriers, suppliers, and customers. Additionally, integration with external systems via Electronic Data Interchange (EDI) and APIs has improved visibility across the supply chain, allowing for proactive exception management and automated notifications. By implementing key enhancements, companies have achieved significant cost reductions, improved shipment visibility, and strengthened overall supply chain management. Research significance: Implementing SAP Transportation Management (SAP TM) is critical to modernizing logistics operations by improving transportation efficiency, optimizing freight costs, and ensuring regulatory compliance. The research highlights the need to create actions to initiate shipments, adjust transportation requests, and manage the lifecycle of merchandise, ensuring data integrity, and process automation. These advancements significantly contribute to reducing administrative workload, improving real-time tracking, and reducing transportation costs through optimal route planning and carrier selection. Methodology: Alternative: Express Logistics, Global Freight, Speedy Transport, Safe Way Carriers, Eco Ship Solutions. Evaluation Parameters: Delivery Efficiency, Cost Savings, Transit Time, Delay Rate. Result: These findings reveal that Global Freight received the highest ranking, and Speedy Transport received the lowest ranking. Conclusion: The value of the dataset for SAP Transportation Management Implementation, according to the MOORA method, Global Freight achieves the highest ranking.”