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Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

Influenced by the advances in data and computing, the scientific practice increasingly involves machine learning and artificial intelligence driven methods which requires specialized capabilities at the system-, science- and service-level in addition to the conventional large-capacity supercomputing approaches. The latest distributed architectures built around the composability of data-centric applications led to the emergence of a new ecosystem for container coordination and integration. However, there is still a divide between the application development pipelines of existing supercomputing environments, and these new dynamic environments that disaggregate fluid resource pools through accessible, portable and re-programmable interfaces. New approaches for dynamic composability of heterogeneous systems are needed to further advance the data-driven scientific practice for the purpose of more efficient computing and usable tools for specific scientific domains. In this paper, we present a novel approach for using composable systems in the intersection between scientific computing, artificial intelligence (AI), and remote sensing domain. We describe the architecture of a first working example of a composable infrastructure that federates Expanse, an NSF-funded supercomputer, with Nautilus, a Kubernetes- based GPU geo-distributed cluster. We also summarize a case study in wildfire modeling, that demonstrates the application of this new infrastructure in scientific workflows: a composed system that bridges the insights from edge sensing, AI and computing capabilities with a physics-driven simulation., None

In Spain, PCR is the tool of choice for the diagnosis of congenital Chagas disease (CD) and serology for diagnosing chronic CD. A loop-mediated isothermal amplification test for DNA detection showed good analytical performance and ease of use. In Spain, PCR is the tool of choice for the diagnosis of congenital Chagas disease (CD) and serology for diagnosing chronic CD. A loop-mediated isothermal amplification test for DNA detection showed good analytical performance and ease of use. We aimed to evaluate the performance of the Loopamp detection kit (Eiken Chemical Co. Ltd., Japan) (Tcruzi -LAMP) for congenital and chronic CD diagnosis using well-characterized samples. We included samples from 39 congenital and 174 chronic CD cases and from 48 uninfected children born to infected mothers and 34 nonchagasic individuals. The sensitivity, specificity, and accuracy of Tcruzi -LAMP were estimated using standard case definitions for congenital CD (positive result by parasitological or PCR tests or serology after 9 months of age) and chronic CD (positive serology by at least two tests). The Tcruzi -LAMP results were read by visual examination and a real-time fluorimeter. For congenital CD, Tcruzi -LAMP sensitivity was 97% for both types of reading; specificity was 92% by visual examination and 94% by fluorimeter. For chronic CD, sensitivity was 47% and specificity 100%. The accuracy in congenital CD was >94% versus 56% in chronic CD. The agreement of Tcruzi -LAMP with PCR tests was better in congenital CD (kappa, 0.86 to 0.91) than in chronic CD (kappa, 0.67 to 0.83). The Loopamp detection kit showed good performance for the diagnosis of congenital CD. Tcruzi -LAMP, like PCR, can be useful for the screening and early diagnosis of congenital infection.

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Se analizaron mediante espectroscopía de plasma inducido por láser (LIBS), espectroscopía Raman y difracción de rayos x (XRD) muestras seleccionadas de diversas zonas de Tenerife con el objetivo de identificar su composición química elemental y mineralógica. Los resultados mostraron los elementos mayoritarios siguientes: O, F, Na, K, Mg, Al, Si, Ca, Ti y Fe. La identificación de las muestras mediante espectroscopía Raman y XRD mostró una mineralogía de tipo basáltica coincidente con los resultados de composición elemental LIBS. Los resultados de los análisis con instrumentación portátil han demostrado la aplicabilidad de la espectroscopía LIBS y, en especial, en combinación con la espectroscopía Raman, para su utilización en la detección mineralógica-química en las zonas de extracción de basaltos y picón para la construcción en Tenerife. Basaltic samples selected from different areas of Tenerife were analyzed by applying laser induced breakdown spectroscopy (LIBS), Raman spectroscopy and X Ray Diffraction (XRD) in order to identify the basic chemical composition and mineralogy. The basic composition obtained from the analysis was: O, F, Na, K, Mg, Al Si, Ca, Ti and Fe. Raman spectroscopic and XRD analyses indicated a basaltic mineralogy which is consistent with the basic composition results obtained from LIBS. The results of the analyses carried out using portable instrumentation proved the suitability of the LIBS, specially combined with the Raman spectroscopy for their application in the mineralogical-chemical identification in the areas where basalts and lapilli are extracted for construction works in Tenerife. Fil: De La Viuda Perez, Ismael. Instituto Nacional de Técnica Aeroespacial; España Fil: Navarro Azor, Rafael. Centro de Astrobiología; España. Consejo Superior de Investigaciones Científicas; España Fil: Lalla, Emmanuel. Centro de Astrobiología; España. Consejo Superior de Investigaciones Científicas; España Fil: Rodriguez Losada, Jose. Universidad de La Laguna; España Fil: Aquilano, Roberto Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Física de Rosario. Universidad Nacional de Rosario. Instituto de Física de Rosario; Argentina Fil: Medina, Jesus. Centro de Astrobiología; España. Consejo Superior de Investigaciones Científicas; España Fil: Rull Perez, Fernando. Centro de Astrobiología; España. Consejo Superior de Investigaciones Científicas; España