Risk assessment in gas processing plants plays a critical role in preventing hazardous events that may escalate into catastrophic accidents and severe economic losses. Conventional approaches, such as ...

Abstract: The multiple joint Linz-Donawitz converter gas (LDG) holder systems are usually employed to alleviate the LDG fluctuation in steel enterprises. A dynamic modeling method based on ...

Abstract: Awareness of the impact of component-level radiation response on the system is challenging. This article discusses the radiation response of a power supply system by combining the power ...

Dynamic Graph Neural Networks (Dynamic GNNs) have emerged as powerful tools for modeling real-world networks with evolving topologies and node attributes over time. A survey by Professors Zhewei Wei, ...

Type to search articles, cases, and authors. Press ↵ to view all results. Because a state lacks the power to confer immunity from federal causes of action, the Louisiana Court of Appeal’s judgment ...

ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...

As a classical basic model for causal inference, Bayesian networks are of vital importance both in artificial intelligence with uncertainty and interpretability. The significant status of Bayesian ...

Bayesian networks offer a powerful way to handle uncertainty in complex systems. By modeling probabilistic relationships, they reveal how variables influence one another, even when data is incomplete.

Artificial intelligence has come a long way—from rule-based systems to deep learning and, more recently, generative AI. With breakthroughs like DeepSeek, GPT-4 Turbo, and multimodal AI models, we are ...