Results 251 to 260 of about 574,732 (350)

Topological Properties of International Commodity Market: How Uncertainty Affects the Linkages?

Agribusiness, EarlyView.
ABSTRACT The study aims to explore the network topology of the international commodity market by examining the interconnections among 21 commodity futures across various categories, including energy, precious and industrial metals, and agriculture. We analyze the market structure of these commodity futures under both low and high uncertainty conditions
Ibrahim Yagli, Bayram Deviren
wiley   +1 more source

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

Uncovering Structure-Conductivity Relationships in Anion Exchange Membranes (AEMs) Using Interpretable Machine Learning. [PDF]

Membranes (Basel)
Naghshnejad P, Das D, Romagnoli JA, Kumar R, Chen J.   +4 more
europepmc   +1 more source

Approximately convex functions on topological vector spaces

, 2010
Jacek Tabor, Józef Tabor, Marek Żołdak   +2 more
openalex   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, EarlyView.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

Robust ultrahigh-Q resonances in tetramer metasurfaces through centroid symmetry protection and area conservation. [PDF]

Light Sci Appl
Zhou C, Jin R, He H, Huang J, Li G, Huang L.   +5 more
europepmc   +1 more source

On characterizations of s-topological vector spaces

, 2019
Shallu Sharma, Madhu Ram, Sahil Billawria, Tsering Landol   +3 more
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

An Anti-Interference Demultiplexing Method for Electromagnetic Bessel Beams Carrying Orbital Angular Momentum. [PDF]

Sensors (Basel)
Mi C, Huang X, Qiao W, Zhang Y.
europepmc   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Advanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source