Zobrazeno 1 - 10
of 1 743
pro vyhledávání: '"HU Jianjun"'
Autor:
Saddam Hussain, Hu Jianjun, Chen Yong, Asad Ali, Haiyan Song, Decong Zheng, Muhammad Usman Farid, Abdul Ghafoor, Mukhtar Ahmed
Publikováno v:
Heliyon, Vol 10, Iss 5, Pp e27180- (2024)
Buckwheat is a globally recognized, nutritionally rich crop with robust adaptability, serving as a multi-purpose plant for its health benefits. Achieving precise and mechanized plot seed harvesting is a critical step in obtaining accurate results in
Externí odkaz:
https://doaj.org/article/83f01919028a46669faf0314a3f64931
Searching for technologically promising crystalline materials with desired thermal transport properties requires an electronic level comprehension of interatomic interactions and chemical intuition to uncover the hidden structure-property relationshi
Externí odkaz:
http://arxiv.org/abs/2410.16066
The accurate prediction of material properties is crucial in a wide range of scientific and engineering disciplines. Machine learning (ML) has advanced the state of the art in this field, enabling scientists to discover novel materials and design mat
Externí odkaz:
http://arxiv.org/abs/2408.09297
Autor:
HE Yutong, REN Meng, HU Jianjun, CHEN Shuohua, GAO Wei, WANG Jing, XIA Changjin, LIANG Di, SHI Jin, SHAN Baoen
Publikováno v:
Zhongliu Fangzhi Yanjiu, Vol 47, Iss 9, Pp 688-693 (2020)
Objective To analyze the screening results of colorectal cancer in urban area of Hebei Province from 2018 to 2019. Methods According to the screening process of early diagnosis and treatment of colorectal cancer in urban area of Hebei Province, we en
Externí odkaz:
https://doaj.org/article/b199a1685b254d4e8fec438566f83569
Deep learning (DL) models have been widely used in materials property prediction with great success, especially for properties with large datasets. However, the out-of-distribution (OOD) performances of such models are questionable, especially when t
Externí odkaz:
http://arxiv.org/abs/2407.15214
Autor:
Wei, Lai, Omee, Sadman Sadeed, Dong, Rongzhi, Fu, Nihang, Song, Yuqi, Siriwardane, Edirisuriya M. D., Xu, Meiling, Wolverton, Chris, Hu, Jianjun
Crystal structure prediction (CSP) is now increasingly used in discovering novel materials with applications in diverse industries. However, despite decades of developments and significant progress in this area, there lacks a set of well-defined benc
Externí odkaz:
http://arxiv.org/abs/2407.00733
Computational prediction of stable crystal structures has a profound impact on the large-scale discovery of novel functional materials. However, predicting the crystal structure solely from a material's composition or formula is a promising yet chall
Externí odkaz:
http://arxiv.org/abs/2404.04810
Deep learning (DL) models have now been widely used for high-performance material property prediction for properties such as formation energy and band gap. However, training such DL models usually requires a large amount of labeled data, which is usu
Externí odkaz:
http://arxiv.org/abs/2401.05223
Photon counting radiation detectors have become an integral part of medical imaging modalities such as Positron Emission Tomography or Computed Tomography. One of the most promising detectors is the wide bandgap room temperature semiconductor detecto
Externí odkaz:
http://arxiv.org/abs/2311.00682
Due to the vast chemical space, discovering materials with a specific function is challenging. Chemical formulas are obligated to conform to a set of exacting criteria such as charge neutrality, balanced electronegativity, synthesizability, and mecha
Externí odkaz:
http://arxiv.org/abs/2310.00475