Science and Technology Corporation (United States)

Abstract. The MISR Research Aerosol retrieval algorithm (RA) was developed in parallel with the MISR standard operational algorithm (SA), to explore innovative retrieval ideas for possible implementation in the SA, and to provide higher-spatial-resolution results with enhanced sensitivity to particle properties, where possible. Whereas the SA was designed to run automatically on the entire MISR dataset, the RA, a research code, had to be run on a case-by-case basis, with considerable user involvement. We present here a version of the RA, the MISR Research Product Algorithm (MRPA), that has been streamlined and automated for wider use, along with validation results for this algorithm. The compromises required to automate the RA result in somewhat diminished particle-type sensitivity. However, the MRPA provides 1.1 km pixel resolution (compared to 4.4 km for the SA), and comparing the AOD data statistically with AERONET, it offers about half the statistical RMSE, significantly higher correlation coefficient, though somewhat higher bias, for mid-visible AOD compared to the SA over land, along with better Ångström exponent statistics, especially at higher AOD. Statistical validation of particle shape and single-scattering albedo is more difficult, but for sufficiently high AOD, retrievals in smoke-dominated regions show a preponderance of small-medium, spherical, light-absorbing particles, whereas dust-dominated regions tend to have larger, weakly absorbing, non-spherical particles.

Abstract. The Hunga Tonga–Hunga Ha'apai Model–Observation Comparison (HTHH–MOC) project aims to comprehensively investigate the evolution of volcanic water vapor and sulfur emissions and their subsequent atmospheric impacts and underlying response mechanisms using state-of-the-art global climate models. This study evaluates multi-model ensemble simulations participating in the HTHH–MOC free-run experiment with climate projections for 10 years (2022–2032). Model results are evaluated against satellite observations to assess their ability to reproduce the observed evolution of stratospheric water vapor, aerosols, temperature, and ozone from 2022 to 2024. The participating models accurately capture the observed distribution patterns and associated upper atmospheric responses, providing confidence for their future projections. Model simulations suggest that the Hunga eruption-induced stratospheric water vapor anomaly lasts 4–7 years, with a water vapor e-folding time of 31–43 months. This prolonged water vapor perturbation leads to significant stratospheric and mesospheric cooling, resulting in significant ozone loss in the upper stratosphere and lower mesosphere for 7–10 years. Comparisons between simulations with both SO2 and H2O emissions and those with H2O-only emissions indicate that the pronounced dipole response with upper-stratospheric cooling and lower-stratospheric warming is driven by the combined effects of SO2 and H2O injections. These results highlight the prolonged atmospheric impacts of the Hunga eruption and the potential critical role of stratospheric water vapor in modulating long-term atmospheric chemistry and dynamics.

ABSTRACT A new method to improve the identification of gases or vapors in the environment is presented. We construct physically based nonlinear transformations of raw measurement variables from passive infrared remote sensing data and use them as inputs to classification algorithms such as linear discriminant analysis (LDA) and support vector machines (SVM). The physically based transformations are based on the concepts of differential absorption radiometry and contrast ratio from infrared remote sensing. The transformed data significantly improves the classification performance of the classifiers. We show that LDA with transformed input data outperforms an SVM trained on the original measurement variables (overall accuracy improved by up to 23%). The SVM also benefits from using the physically transformed data (up to 20% increase in overall accuracy). Both LDA and SVM give similar performance when the physically transformed data is used, hence the user can use the simpler and more interpretable LDA model rather than the more complex SVM model. These physically based transformations may also be effective in other problems for which one can utilize physical insight of the scenario to build meaningful physical transformations. This new approach has the promise of improving the accuracy of classification algorithms, which may allow using simpler, more interpretable models over more complicated black‐box approaches.

Abstract Aerosols affect radiation, cloud properties, convection, air temperature, and large-scale circulation, yet their influence on precipitation distribution over the Maritime Continent (MC), a complex tropical region composed of islands interspersed with shallow seas, remains poorly understood. Using high-resolution cloud-system resolving model simulations, satellite observations, and reanalysis data, we demonstrate that rising aerosol concentrations amplify oceanic precipitation more than they suppress land precipitation, thereby increasing the sea-to-land precipitation ratio over the MC. This shift is supported by observations and contrasts with the land-enhanced precipitation distribution seen in pristine simulations or those without aerosol radiative effects. Our results underscore that aerosol-induced radiative cooling stabilizes the lower troposphere more over land than over the ocean, enhancing low-level convergence and convection over the sea. Moreover, high aerosol concentrations delay the diurnal precipitation peak over land from late afternoon to midnight, driven by diminished daytime heating and subsequent nighttime increases in moist static energy—an interesting pattern evident in some observed high-aerosol days.

Healthcare institutions frequently encounter serious cyberthreats, and data breaches persist despite regulatory frameworks such as the NIST Cybersecurity Framework and the Health Insurance Portability and Accountability Act (HIPAA). The issue highlights the discrepancy between the criteria for compliance and their implementation in the day-to-day operations of health institutions, making protected health information (PHI) susceptible. A qualitative examination of data breaches from January 2023 to August 2025 from the US Department of Health and Human Services' (HHS) Office for Civil Rights (OCR) Breach Portal is used in this study. The dataset was examined to determine the kind of breach, where it occurred, and how many people were impacted. With network servers as the most frequent point of exposure, the results demonstrate that hacking and IT incidents are both numerous and large enough to dominate healthcare breaches. As a result, the severity of the breach has grown over time, with a huge incident being held accountable for the most impacted individuals. According to the study's conclusion, proactive governance of the healthcare sector requires compliance with paperwork. Enhancing healthcare cybersecurity resilience can be measured using a suggested methodology that includes automation, ongoing monitoring, and employee training.

Abstract Material injected to the stratosphere by volcanoes and pyrocumulonimbus clouds (pyroCbs) is observed to have different lifetimes depending on the altitude, latitude, season of the injection, and removal processes. We adopt a framework that describes the stratospheric lifetime of injected material as the sum of lag and decay timescales and compute these quantities in tracer simulations by injecting trajectory parcels and tracking them over 8 years. We simulate the evolution of the water vapor plume from the January 2022 Hunga eruption. The simulation suggests a lag time of 1.4 years and the decay time 2.35 ± 0.05 years, producing a stratospheric lifetime of 3.75 ± 0.05 years. From Microwave Limb Sounder observations, we estimate the Hunga water vapor plume decay time to be 2.6 ± 0.75 years and the lifetime to be 4.0 ± 0.75 years which is in good agreement with our model calculations. Overall, we find that injected material lifetime increases with altitude and decreases with the latitude. If polar stratospheric cloud formation is a loss process, the lifetime is shortened. Aerosol gravitational settling also shortens the lifetime and should be included especially for aerosols with >0.5 μm radius. We use the observed lifetime of the Hunga aerosol plume and gravitational settling rate to estimate a particle median radius of ∼0.3 µm in agreement with other estimates. Our calculations are also relevant to geoengineering plans for modifying the stratospheric albedo, where sustained stratospheric aerosol concentrations are envisioned.

An essential component of contemporary software development is quality assurance (QA) automation, which guarantees program dependability, effectiveness, and user pleasure. Traditional QA techniques, on the other hand, frequently have trouble finding flaws early in the software development lifecycle, which raises expenses and delays releases. By predicting possible flaws before they appear, predictive analytics which is fueled by machine learning (ML) and artificial intelligence (AI) offers a revolutionary approach to QA automation. This study examines how predictive analytics might improve software quality and expedite testing procedures, hence redefining defect prevention for American businesses. This study uses a systematic methodology that combines machine learning-based defect prediction with real-world case studies, analyzing defect trends and evaluating the effectiveness of predictive models. The results show that enterprises leveraging predictive analytics in QA automation experience higher defect detection rates reduced testing overhead, and faster release cycles. The study identifies key machine learning models, such as Random Forests, Support Vector Machines (SVM), and Neural Networks, which have demonstrated significant accuracy in defect prediction. It also discusses the integration of predictive analytics within DevOps and CI/CD pipelines, enabling continuous monitoring and proactive defect prevention. Defect prediction skills will be significantly improved in the future by developments in Explainable AI (XAI), deep learning models, and Natural Language Processing (NLP). In addition to supporting data-driven decision-making, model transparency, and continuous learning frameworks, this article offers important advice for businesses looking to integrate predictive analytics into their QA procedures. U.S. businesses may go from reactive to proactive QA approaches by adopting predictive analytics, which will guarantee better software quality, lower expenses, and an enhanced user experience

This paper examines emerging solutions for efficient medical Artificial Intelligence (AI) computing, exploring the convergence of AI with innovative technologies like silicon photonics, edge AI, distributed machine learning, and parallelism techniques