Results 241 to 250 of about 6,436,916 (343)

A novel synthetic 3,4,5-tri-feruloylquinic acid enhances learning and memory via neurotrophin signaling in an aging model senescence-accelerated prone 8 mice [PDF]

Hongyu Lin, Kazunori Sasaki, Farhana Ferdousi, Shinji Kondo, Hiroko Isoda   +4 more
openalex   +1 more source

CTRNet: a lightweight and efficient deep learning model for field maize whorl identification. [PDF]

Sci Rep
Tian X, Zhang J, Li Y.
europepmc   +1 more source

Stretching the Printability Metric in Direct‐Ink Writing with Highly Extensible Yield‐Stress Fluids

Advanced Functional Materials, EarlyView.
This study introduces “drawability” as a new metric for assessing printability in direct‐ink writing, focusing on gap‐spanning performance and speed robustness. By designing yield‐stress fluids with high extensibility, we demonstrate that extensional strain‐to‐break significantly enhances printability.
Chaimongkol Saengow, Samya Sen, Joaquin Yus, Eliza E. Lovrich, Amanda G. Hoika, Kelly M. Chang, Arielle A. Pfeil, Nellie Haug, Amy J. Wagoner Johnson, Randy H. Ewoldt   +9 more
wiley   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

A Lightweight Radar-Camera Fusion Deep Learning Model for Human Activity Recognition. [PDF]

Sensors (Basel)
Jeon M, Woo S.
europepmc   +1 more source

Selective Benzene Capture by Metal‐Organic Frameworks

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) hold significant potential for capturing benzene from air emissions and hydrocarbon mixtures in liquid phases. This capability stems from their precisely engineered structures, versatile chemistries, and diverse binding interactions.
Zongsu Han, Ankit Mondal, Jiatong Huo, Yihao Yang, Hong‐Cai Zhou   +4 more
wiley   +1 more source

Identification of Predictive Biomarkers for the Progression to Severe COVID-19 Caused by Omicron Variants Using Metabolomics and Machine Learning Models

Shuaijie Zhang, Chunhua Wang, Lu Zhang, Ye Fan, Xiaogang Mao, Ying Xia, Junjie Lu, Tingyu Yu, Jianzhong Zhao, Yan‐Long Jia, Mengjun Tang, Lijuan Yin, Yang Yang, Hui Xing, Wenjie Tan, Liang Shen, Fengsheng Cao   +16 more
openalex   +1 more source

SHAP-explained machine-learning model for high-risk gastric cancer identification. [PDF]

Front Oncol
Oh HJ, Kim CH, Jun JK, Suh M, Choi KS, Choi IJ, Park B.   +6 more
europepmc   +1 more source

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +1 more source