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Institution

University of Electronic Science and Technology of China

EducationChengdu, China
About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Antenna (radio) & Dielectric. The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.


Papers
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Journal ArticleDOI
TL;DR: Zhang et al. as discussed by the authors proposed a key laboratory for low-grade energy utilization technologies and systems at the National Center for Nanoscience and Technology (NCLT) in China.
Abstract: 1Center for Excellence in Nanoscience (CAS), Key Laboratory of Nanosystem and Hierarchical Fabrication (CAS), National Center for Nanoscience and Technology, Beijing 100190, China 2Key Laboratory of Low-grade Energy Utilization Technologies and Systems (MOE), School of Energy and Power Engineering, Chongqing University, Chongqing 400044, China 3Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha 410083, China 4Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China 5School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China 6Institute for Energy Research, Jiangsu University, Zhenjiang 212013, China 7Key Laboratory of Polar Materials and Devices (MoE), School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China 8College of Chemistry, Sichuan University, Chengdu 610064, China 9School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China

146 citations

Journal ArticleDOI
TL;DR: An expert system that stacks two support vector machine (SVM) models for the effective prediction of HF by proposing a hybrid grid search algorithm (HGSA) that is capable of optimizing the two models simultaneously.
Abstract: About half of the people who develop heart failure (HF) die within five years of diagnosis. Over the years, researchers have developed several machine learning-based models for the early prediction of HF and to help cardiologists to improve the diagnosis process. In this paper, we introduce an expert system that stacks two support vector machine (SVM) models for the effective prediction of HF. The first SVM model is linear and $L_{1}$ regularized. It has the capability to eliminate irrelevant features by shrinking their coefficients to zero. The second SVM model is $L_{2}$ regularized. It is used as a predictive model. To optimize the two models, we propose a hybrid grid search algorithm (HGSA) that is capable of optimizing the two models simultaneously. The effectiveness of the proposed method is evaluated using six different evaluation metrics: accuracy, sensitivity, specificity, the Matthews correlation coefficient (MCC), ROC charts, and area under the curve (AUC). The experimental results confirm that the proposed method improves the performance of a conventional SVM model by 3.3%. Moreover, the proposed method shows better performance compared to the ten previously proposed methods that achieved accuracies in the range of 57.85%–91.83%. In addition, the proposed method also shows better performance than the other state-of-the-art machine learning ensemble models.

146 citations

Journal ArticleDOI
TL;DR: Vanadium and nitrogen co-doped TiO2 nanocrystal (4-5nm) photocatalysts were prepared through direct nitridation of the controlled hydrolysis product of Tetrabutyl titanate in V (IV) ions solution.

145 citations

Journal ArticleDOI
TL;DR: A novel diagnostic system that uses random search algorithm (RSA) for features selection and random forest model for heart failure prediction and shows better performance than five other state of the art machine learning models.
Abstract: Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there is a paucity of human experts and equipments. Hence, different researchers have developed different intelligent systems for automated detection of heart failure. However, most of these methods are facing the problem of overfitting i.e. the recently proposed methods improved heart failure detection accuracy on testing data while compromising heart failure detection accuracy on training data. Consequently, the constructed models overfit to the testing data. In order, to come up with an intelligent system that would show good performance on both training and testing data, in this paper we develop a novel diagnostic system. The proposed diagnostic system uses random search algorithm (RSA) for features selection and random forest model for heart failure prediction. The proposed diagnostic system is optimized using grid search algorithm. Two types of experiments are performed to evaluate the precision of the proposed method. In the first experiment, only random forest model is developed while in the second experiment the proposed RSA based random forest model is developed. Experiments are performed using an online heart failure database namely Cleveland dataset. The proposed method is efficient and less complex than conventional random forest model as it produces 3.3% higher accuracy than conventional random forest model while using only 7 features. Moreover, the proposed method shows better performance than five other state of the art machine learning models. In addition, the proposed method achieved classification accuracy of 93.33% while improving the training accuracy as well. Finally, the proposed method shows better performance than eleven recently proposed methods for heart failure detection.

145 citations

Journal ArticleDOI
TL;DR: This work presents a novel "small-intelligent" strategy to construct mutagenesis libraries that have a minimal gene library size without inherent amino acid biases, stop codons, or rare codons of Escherichia coli by coupling well-designed combinatorial degenerate primers with suitable PCR-basedmutagenesis methods.
Abstract: Site-saturation mutagenesis is a powerful tool for protein optimization due to its efficiency and simplicity. A degenerate codon NNN or NNS (K) is often used to encode the 20 standard amino acids, but this will produce redundant codons and cause uneven distribution of amino acids in the constructed library. Here we present a novel "small-intelligent" strategy to construct mutagenesis libraries that have a minimal gene library size without inherent amino acid biases, stop codons, or rare codons of Escherichia coli by coupling well-designed combinatorial degenerate primers with suitable PCR-based mutagenesis methods. The designed primer mixture contains exactly one codon per amino acid and thus allows the construction of small-intelligent mutagenesis libraries with one gene per protein. In addition, the software tool DC-Analyzer was developed to assist in primer design according to the user-defined randomization scheme for library construction. This small-intelligent strategy was successfully applied to the randomization of halohydrin dehalogenases with one or two randomized sites. With the help of DC-Analyzer, the strategy was proven to be as simple as NNS randomization and could serve as a general tool to efficiently randomize target genes at positions of interest.

145 citations


Authors

Showing all 51090 results

NameH-indexPapersCitations
Gang Chen1673372149819
Frede Blaabjerg1472161112017
Kuo-Chen Chou14348757711
Yi Yang143245692268
Guanrong Chen141165292218
Shuit-Tong Lee138112177112
Lei Zhang135224099365
Rajkumar Buyya133106695164
Lei Zhang130231286950
Bin Wang126222674364
Haiyan Wang119167486091
Bo Wang119290584863
Yi Zhang11643673227
Qiang Yang112111771540
Chun-Sing Lee10997747957
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023159
2022980
20217,384
20207,220
20196,976