NextLabs Cloud Authorization service (CloudAz) is industry first Attribute Based Access Control (ABAC) service in the cloud. CloudAz allows you to centrally control who can access your applications and data and helps prevent and reduce the risk of data breaches,misuse,fraud,and compliance violations

Mphasis Gen AI Consulting & Assessment service enables organizations to navigate their journey of Generative AI services, tools, and implementation roadmap. Whether you are looking to explore Large language models (LLMs), synthetically generating data, identifying business use-cases or a roadmap to Gen AI adoption, our team of AI-ML expert consultants and developers can be your partner to help you get kick-started. We leverage our deep domain expertise and Gen AI Solution IP to help you design & build the best solution for your business needs.

GCMs under CMIP6 have been widely used to investigate climate change impacts and put forward associated adaptation and mitigation strategies. However, the relatively coarse spatial resolutions (usually 100~300km) preclude their direct applications at regional scales, which are exactly where the analysis (e.g., hydrological model simulation) is performed. To bridge this gap, a typical approach is to ‘refine’ the information from GCMs through regional climate downscaling experiments, which can be conducted statistically, dynamically, or a combination thereof. Statistical downscaling establishes relationships between large-scale climate indicators and small-scale climate variables in the reference (historical) period. Subsequently, these relationships are kept unchanged in the future and used to predict the future variables. On the other hand, dynamical downscaling operates based on the physical processes and the associated interactions in the climate systems and thus can produce a f[...]

Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production, among others. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to lack of long-term data records, standardized methods for event identification, geographical inconsistencies and data uncertainties. To tackle these challenges, we develop the first Global Drought and Flood Catalogue (GDFC) [He et al., 2020] for 1950-2016 by merging the latest in situ and remote-sensing datasets with state-of-the-art land surface and hydrodynamic modeling to provide a continuous and consistent estimate of the terrestrial water cycle and its extremes. This GDFC also includes[...]