Results 131 to 140 of about 51,628 (218)

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

open access: yesAdvanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju   +5 more
wiley   +1 more source

Study of Free‐Space Optical Quantum Network: Review and Prospectives

open access: yesAdvanced Science, EarlyView.
Free from the constraints of fiber connections, free‐space quantum network enables longer and more flexible quantum network connections. This review summarizes and comparatively analyzes free‐space quantum network experiments based on ground stations, satellites, and mobile platforms.
Hua‐Ying Liu, Zhenda Xie, Shining Zhu
wiley   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

open access: yesAdvanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu   +4 more
wiley   +1 more source

Enhancing CAR‐T Cell Efficacy in Solid Tumors by Inhibiting CCL5/VEGF‐Mediated Angiogenesis

open access: yesAdvanced Science, EarlyView.
This study reveals that CAR‐T cells in solid tumors produce CCL5, which paradoxically induces VEGF and angiogenesis to promote tumor growth. Blocking CCL5/VEGF signaling—through gene knockout, or the CCR5 inhibitor maraviroc—significantly enhances the antitumor efficacy of CAR‑T therapy (the diagram was created in Biorender).
Shishuo Sun   +15 more
wiley   +1 more source

The Importance of Metal‐Organic Framework Linker Atoms for CO2 Reduction: A DFT Study

open access: yesAdvanced Science, EarlyView.
Using DFT, we examine the role of linker atoms in CO2 reduction on copper‐based metal organic frameworks (Cu MOFs). Our calculations reveal that linker atoms may serve as both CO2 and H‐shuttling sites and suggest linker electrostatics as a descriptor for linker activity. ABSTRACT Although the metal within the secondary building unit of a metal‐organic
Ugochukwu Nwosu, Samira Siahrostami
wiley   +1 more source

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