System Engineering evangelists Larry Peterson and Bruce Davie analyze the system approach behind the Internet in their new ...

Our analysis will include both empirical and non-empirical studies, including study designs that are quantitative, qualitative and mixed methods. We will also include all types of data synthesis ...

The infrastructure behind AI agents isn't static—it’s a living, evolving system. Designing effective data pipelines means ...

Circuit Retrieval and Optimization with Parameter Guidance using LLMs” was published by researchers at Duke University and ...

Though the process of designing a chip using open-source tools may seem daunting at first, it’s an invaluable learning ...

In Part 1, we explored the challenges of implementing machine learning and real-time analytics in semiconductor ...

In an enterprise world drowning in dashboards, one truth keeps surfacing: Data isn’t the problem—product thinking is.

The panelists demystify AI agents and LLMs. They define agentic AI, detail architectural components, and share real-world use cases and limitations. The panel explores how AI transforms the SDLC, ...

Concurrent with the rapidly evolving digital landscape, enterprise data management is poised for transformative change in the coming years. At the same time, organizations driven by exponential data ...

Exposure to alcohol in utero can have enduring health effects across the lifespan, including increased risks related to ...

This paper presents a three-phase power-flow algorithm, in the sequence-component frame, for the microgrid (μgrid) and active distribution system (ADS) applications. The developed algorithm ...