Context engineering is the discipline of managing the language model's context window. Unlike prompt engineering, which focuses on crafting effective instructions, context engineering addresses the ...
The TotalEnergies Integrated Multi-Energy Model is building business resilience TotalEnergies is building business resilience and value chain sustainability during a time of market turbulence, through ...
Burlington, Canada, 27th Nov 2025 – Pillars of Wellness announced the introduction of an integrated care model focused on women’s health. The initiative establishes a coordinated framework designed to ...
Neural tissue engineering aims to mimic the brain's complex environment, the extracellular matrix, which supports nerve cell growth, development, and proper connectivity. This environment is carefully ...
A few days ago, Google finally explained why its best AI image generation model is called Nano Banana, confirming speculation that the moniker was just a placeholder that stuck after the model went ...
Even as concern and skepticism grows over U.S. AI startup OpenAI's buildout strategy and high spending commitments, Chinese open source AI providers are escalating their competition and one has even ...
Should you have feedback on this article, please complete the fields below. Please indicate if your feedback is in the form of a letter to the editor that you wish to have published. If so, please be ...
Graduate Institute of Environmental Engineering, College of Engineering, National Taiwan University, 71, Chou-Shan Road, Da’an Dist., Taipei 106, Taiwan Graduate Institute of Cancer Biology and Drug ...
Abstract: The rapid development of Artificial Intelligence (AI) has had a significant impact on many sectors, including aerospace engineering. This paper proposes a case-based AI education framework ...
The University of Michigan recently announced their new Integrated Business and Engineering Program, a collaboration between the Ross School of Business and the College of Engineering to prepare ...
Abstract: Deep reinforcement learning (DRL) is a promising way to develop autonomous driving decision-making models. However, poor driving decisions and low sample efficiency for multiple DRL coupled ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results