Results 161 to 170 of about 749,938 (278)

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

Nutritional risk screening and analysis of factors influencing nutritional risk in patients with stable chronic obstructive pulmonary disease. [PDF]

Pak J Med Sci
Cui N, Cui Y, Wu Q, Wang S, Wang W.
europepmc   +1 more source

Accelerating Surface Composition Characterization of Thin‐Film Materials Libraries Using Multi‐Output Gaussian Process Regression

Advanced Intelligent Discovery, EarlyView.
To integrate surface analysis into materials discovery workflows, Gaussian process regression is used to accurately predict surface compositions from rapidly acquired volume composition data (obtained by energy‐dispersive X‐ray spectroscopy), drastically reducing the number of required surface measurements on thin‐film materials libraries.
Felix Thelen, Florian Lourens, Alfred Ludwig   +2 more
wiley   +1 more source

A Quasi-Monte Carlo Method Based on Neural Autoregressive Flow. [PDF]

Entropy (Basel)
Wei Y, Xi W.
europepmc   +1 more source

Inverse Design of Alloys via Generative Algorithms: Optimization and Diffusion within Learned Latent Space

Advanced Intelligent Discovery, EarlyView.
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla, Ellis Crabtree, Fredrick Michael, Yayue Pan, Jida Huang   +4 more
wiley   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Clinical Retrospective Study on the Role of Spleen-Invigorating and Qi-Supporting Therapy in Non-Small Cell Lung Cancer Immunotherapy and Construction of Survival Prediction Model. [PDF]

Biol Proced Online
Chen Z, Huang J, Shen C, Li Y, Wen Z, Sun Y, Liu H, Wang L.   +7 more
europepmc   +1 more source

Clustering Multivariate Normal Distributions [PDF]

, 2009
Frank Nielsen, Richard Nock
openaire   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Nomogram for predicting cognitive impairment in middle-aged and elderly individuals with self-reported hearing loss: Insights from the longitudinal CHARLS cohort. [PDF]

Braz J Otorhinolaryngol
Li C, Mei Y, Li W, Liu D.
europepmc   +1 more source