As AI workloads push data center power density and network complexity beyond physical capacity, virtual replicas are becoming ...

Ligand-based drug design combines AI and QSAR modeling to prioritize drug candidates, minimizing preclinical failures and ...

No single standard addresses every security risk. The core challenge for AI data center operators is the lack of integration ...

​Improving data delivery is not just an engineering challenge; it is a leadership responsibility. It requires rethinking how ...

In the search for new drugs, artificial intelligence in the form of diffusion models is being used in drug design. What ...

The Data Science and Modeling for Green Chemistry award aims to recognize the research and development of computational tools that guide the design of sustainable chemical processes and the execution ...

Demands for privacy and sovereignty expose limits of architectures built for centralized and borderless data flows ...

Model-based systems engineering (MBSE) has been around for a while, but it continues to gain ground in engineering projects to shorten development cycles and reduces errors. MathWorks has been ...

Modern biology is awash in data. Scientists can sequence DNA, track gene activity cell-by-cell, map proteins in space, and image tissues at microscopic resolution. However, it is a struggle to put all ...

The transition toward sustainable and low-carbon energy systems is driving the rapid development of emerging infrastructures based, for example, on ...

Food insecurity identification modeling for Medicare can establish a reliable method of prioritizing members at risk of food ...

Anthropic is reportedly preparing Claude Opus 4.7 as it accelerates its AI release cycle, alongside new tools that could reshape design and content creation.