The Gulf Stream has been characterized as either a barrier or blender to fluid transfer, a duality relevant to gyre‐scale climate adjustment. However, previous characterization depended on relatively sparse, Lagrangian in situ observations. The finite‐time Lyapunov exponent (FTLE) is calculated from satellite altimetry to identify Lagrangian coherent structures (LCS) in the Gulf Stream region. These LCS provide dense sampling of flow and capture distinct regions associated with mixing. Independent observations of ocean color contain similar flow‐dependent structures, providing verification of the method and highlighting transport and mixing processes that influence sea surface temperature and chlorophyll, among other water properties. Diagnosed LCS support the existing Bower kinematic model of the Gulf Stream, but also highlight novel behavior of comparable importance. These include vortex pinch‐off and formation of spiral eddies, clearly identified by LCS and which may be explained by considering changes to flow topology and the dynamics of shear‐flow instability at both small and large Rossby number. Such processes, seen though LCS, may further enable validation of climate models. The spatial distribution of these intermittent processes is characterized in terms of the criticality of jet dynamics with respect to Rossby wave propagation, and whether the jet is in an unstable or wave‐maker regime. The generation and connectivity of hyperbolic trajectories in the flow appears to play an important role in governing large‐scale transport and mixing across the Gulf Stream.

In 2019, COVID-19 quickly spread across the world, infecting billions of people and disrupting the normal lives of citizens in every country. Governments, organizations, and research institutions all over the world are dedicating vast resources to research effective strategies to fight this rapidly propagating virus. With virus testing, most countries publish the number of confirmed cases, dead cases, recovered cases, and locations routinely through various channels and forms. This important data source has enabled researchers worldwide to perform different COVID-19 scientific studies, such as modeling this virus’s spreading patterns, developing prevention strategies, and studying the impact of COVID-19 on other aspects of society. However, one major challenge is that there is no standardized, updated, and high-quality data product that covers COVID-19 cases data internationally. This is because different countries may publish their data in unique channels, formats, and time intervals, which hinders researchers from fetching necessary COVID-19 datasets effectively, especially for fine-scale studies. Although existing solutions such as John’s Hopkins COVID-19 Dashboard and 1point3acres COVID-19 tracker are widely used, it is difficult for users to access their original dataset and customize those data to meet specific requirements in categories, data structure, and data source selection. To address this challenge, we developed a toolset using cloud-based web scraping to extract, refine, unify, and store COVID-19 cases data at multiple scales for all available countries around the world automatically. The toolset then publishes the data for public access in an effective manner, which could offer users a real time COVID-19 dynamic dataset with a global view. Two case studies are presented about how to utilize the datasets. This toolset can also be easily extended to fulfill other purposes with its open-source nature.

This paper introduces JAX-BTE, a GPU-accelerated, differentiable solver for the phonon Boltzmann Transport Equation (BTE) based on differentiable programming. JAX-BTE enables accurate, efficient and differentiable multiscale thermal modeling by leveraging high-performance GPU computing and automatic differentiation. The solver efficiently addresses the high-dimensional and complex integro-differential nature of the phonon BTE, facilitating both forward simulations and data-augmented inverse simulations through end-to-end optimization. Validation is performed across a range of 1D to 3D simulations, including complex FinFET structures, in both forward and inverse settings, demonstrating excellent performance and reliability. JAX-BTE significantly outperforms state-of-the-art BTE solvers in forward simulations and uniquely enables inverse simulations, making it a powerful tool for multiscale thermal analysis and design for semiconductor devices.

Under the global health crisis of COVID-19, timely, and accurate epidemic data are important for observation, monitoring, analyzing, modeling, predicting, and mitigating impacts. Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic. Current COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data structures. To address this shortcoming, a multi-scale spatiotemporal data product is proposed as a public repository platform, based on a spatiotemporal cube, and allows the integration of different data sources by adopting various data standards. Within the spatiotemporal cube, a comprehensive data processing workflow gathers disparate COVID-19 epidemic datasets at the global, national, provincial/state, county, and city levels. This proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time step. This rapid-response dataset allows the integration of other relevant socio-economic and environmental factors for spatiotemporal analysis. The data is available in Harvard Dataverse platform (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN) and GitHub open source repository (https://github.com/stccenter/COVID-19-Data).

Compressible direct numerical simulations are employed to elucidate the low-wavenumber behaviour of wall-pressure fluctuations in turbulent channel flow and the effect of flow Mach number in the nearly incompressible regime. Simulations are conducted at bulk Mach numbers 0.4, 0.2 and 0.1, and friction Reynolds number 180. In addition to the convective ridge that is virtually Mach-number-independent, acoustic ridges, whose magnitudes are orders of magnitude lower, are identified in the two-dimensional wavenumber–frequency spectrum. At lower frequencies, the acoustic ridges represent propagating longitudinal and oblique waves that match the theoretical predictions of two-dimensional duct modes with a uniform mean flow. They decay with decreasing Mach number but remain distinctly identifiable even at Mach 0.1. At high frequencies, in contrast, no propagating waves are found, and the spectral level in the supersonic wavenumber range is broadly elevated and increases with decreasing Mach number.