University of Electronic Science and Technology of 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.

iPTM-mLys: identifying multiple lysine PTM sites and their different types

Abstract: MOTIVATION: Post-translational modification, abbreviated as PTM, refers to the change of the amino acid side chains of a protein after its biosynthesis. Owing to its significance for in-depth understanding various biological processes and developing effective drugs, prediction of PTM sites in proteins have currently become a hot topic in bioinformatics. Although many computational methods were established to identify various single-label PTM types and their occurrence sites in proteins, no method has ever been developed for multi-label PTM types. As one of the most frequently observed PTMs, the K-PTM, namely, the modification occurring at lysine (K), can be usually accommodated with many different types, such as 'acetylation', 'crotonylation', 'methylation' and 'succinylation'. Now we are facing an interesting challenge: given an uncharacterized protein sequence containing many K residues, which ones can accommodate two or more types of PTM, which ones only one, and which ones none? RESULTS: To address this problem, a multi-label predictor called IPTM-MLYS: has been developed. It represents the first multi-label PTM predictor ever established. The novel predictor is featured by incorporating the sequence-coupled effects into the general PseAAC, and by fusing an array of basic random forest classifiers into an ensemble system. Rigorous cross-validations via a set of multi-label metrics indicate that the first multi-label PTM predictor is very promising and encouraging.

Flexible piezoelectric pressure sensor based on polydopamine-modified BaTiO3/PVDF composite film for human motion monitoring

Abstract: Flexible pressure sensors based on piezoelectric materials have been intensively investigated for their wide applications in wearable electronics. However, traditional films based on inorganic/organic composite piezoelectric materials face the bottleneck of defects and cracks or poor dispersion, which hinders the performance of pressure sensors. Herein, polydopamine (PDA) was introduced as a surface modification agent to modify barium titanate (BaTiO3, BTO), which was then blended with poly(vinylidene fluoride) (PVDF) matrix in different ratios to form uniform and homogeneous PDA@BTO/PVDF composites. Afterwards, the flexible piezoelectric pressure sensor was fabricated by a facial solution-casting method. This PDA-modification strategy can improve the dispersion of BTO into PVDF matrix, as well as reduce the interface hole defects and cracks between the two components. As a result, the 17 wt% PDA@BTO/PVDF sensor exhibited a fast response of 61 ms and a remarkable piezoelectric output voltage of 9.3 V, which showed obvious improvement as compared to the pristine PVDF and BTO/PVDF composite counterparts. In addition, as an energy supplier, the sensor could produce a maximum power of 0.122 μW/cm2 even with high load resistance of 70 MΩ. This pressure sensor was sensitive to various human motions, showing great potential in the applications of wearable electronics.

Short-Term Solar Power Forecasting Based on Weighted Gaussian Process Regression

Abstract: Photovoltaic (PV) power is volatile in nature and raises the level of uncertainty in power systems. PV power forecasting is an important measure to solve this problem. It helps to improve the reliability and reduces the generation cost. Advances in computer technology and sensors make the numeric modeling methods a hotspot in the field of PV power forecasting. However, data modeling methods strongly rely on the accuracy of measurement data. Unavoidable outliers in the measured meteorological data have an adverse effect on the model due to their heteroscedasticity. Although many studies can be found focusing on outlier detection, only a few have incorporated outlier detection with regression models. In this study, an innovative method employing the weighted Gaussian process regression approach is proposed, such that data samples with higher outlier potential have a low weight. A density-based local outlier detection approach is introduced to compensate the deterioration of Euclidean distance for high-dimensional data. A novel concept of the degree of nonlinear correlation is incorporated to compute the contribution of every individual data attribute. Effectiveness of the proposed method is demonstrated by performing an experimental analysis and making comparisons with other typical data-based approaches, and the results exhibit higher estimation accuracy.

Ultrahigh-Responsivity, Rapid-Recovery, Solar-Blind Photodetector Based on Highly Nonstoichiometric Amorphous Gallium Oxide

Abstract: Recently, Ga2O3-based, solar-blind photodetectors (PDs) have been extensively studied for various commercial and military applications. However, to date, studies have focused only on the crystalline phases, especially β-Ga2O3, and the crystalline quality must be carefully controlled because of its strong impact on device characteristics. Based on previous reports, amorphous-semiconductor-based PDs can also be expected to exhibit excellent photodetection characteristics. In this work, amorphous gallium oxide thin films were deposited by radio frequency (RF) magnetron sputtering, and the metal–semiconductor–metal (MSM) PD was fabricated and compared with a β-Ga2O3 film prepared side-by-side as the control sample. The as-sputtered film possessed a high density of defects, including structural disorders, oxygen vacancies, and likely, dangling bonds, resulting in record-high responsivity (70.26 A/W) for a thin-film-type gallium oxide PD due to a high internal gain and the contribution of extrinsic transitions ...