Antioxidant Enzyme Activity and Chilling Injury during Low-temperature Storage of Kiwifruit cv. Hongyang Exposed to Gradual Postharvest Cooling

Qingzhen Yang,Jingping Rao,Shunchao Yi,Kun Meng,Jianfeng Wu,Yali Hou 한국원예학회 2012 Horticulture, Environment, and Biotechnology Vol.53 No.6

Kiwifruits (Actinidia chinensis cv. Hongyang) were treated by direct cooling and gradual cooling to investigate the effect of cooling treatment on chilling injury. The direct cooling fruits were immediately cooled at 0℃ after harvest. The gradual cooling fruits were held for 3 days at 5℃ (from 5℃ to 0℃), or for 7 days at 2℃(from 2℃ to 0℃), or decreased in temperature from 15℃ to 5℃ by 5℃ at 1 days intervals and then maintained at 5℃ for 3 days plus a subsequent period of of 7 days at 2℃ (from 15℃ to 0℃). After the above treatments, then those fruit were stored at 0 ± 0.5℃, 90% to 95% RH for 80 days. Gradual cooling (from 15℃ to 0℃) significantly maintained higher percentage of accepted fruit and lower chilling injury index and chilling injury incidence of fruit compared with the direct cooling. Some attributes were then assayed in the fruits treated with gradual cooling (from 15℃ to 0℃). Gradual cooling (from 15℃ to 0℃) inhibited increases in membrane permeability, malondialdehyde content, superoxide anion production rate, and H2O2 content. At the same time, fruit cooled gradually (from 15℃ to 0℃) exhibited higher superoxide dismutase, catalase, ascorbate peroxidase, and peroxidase activities than those treated by direct cooling during storage. The present study indicated that enhancement in antioxidant enzyme activity may be attributed to the reduction in CI symptoms by the gradual cooling treatment (from 15℃ to 0℃).

A Novel Model of Stock Data Mining with M/G/1 Queue for Evaluation of Stock Crash

Qingzhen Xu,Feifei Zhang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.5

Data mining is the process of searching the information from a large amount of data. In order to evaluate the stock crash this paper proposes general decrementing service M/G/1 queue system with multiple adaptive vacations to find information related to stock crash in data about Shanghai Composite Index. We use the probability generating function (P.G.F.) of stationary queue length and LST of waiting time, and their stochastic decomposition to calculate Existing money flow. Existing Money flow calculation model is improved based on the stationary queue length and LST of waiting time. We program to achieve the stock of existing money flow algorithm, and get the number of existing money flow. The improved algorithm can early warn the stock market crash. The empirical result shows that: There will be a rise in price before the Stock Market Crash, and the stock of existing money inflow begin to decrease. The stock market crash fell for at least six months. The stock market crash fell by at least fifty-five percent. Most of the stock market crash fell by over seventy-percent. The stock market crash down time is inversely proportional to the magnitude of the decline. If the down time is short, the magnitude of the decline is large. If the down time is long, the magnitude of the decline is small. The stock market crash is great harm to investors.

Fuzzy Risk Measures and its Application to Portfolio Optimization

Xiaoxian Ma,Qingzhen Zhao,Fangai Liu 한국전산응용수학회 2009 Journal of applied mathematics & informatics Vol.27 No.3

In possibility framework, we propose two risk measures named Fuzzy Value-at-Risk and Fuzzy Conditional Value-at-Risk, based on Credibility measure. Two portfolio optimization models for fuzzy portfolio selection problems are formulated. Then a chaos genetic algorithm based on fuzzy simulation is designed, and finally computational results show that the two risk measures can play a role in possibility space similar to Value-at-Risk and Conditional Value-at-Risk in probability space. In possibility framework, we propose two risk measures named Fuzzy Value-at-Risk and Fuzzy Conditional Value-at-Risk, based on Credibility measure. Two portfolio optimization models for fuzzy portfolio selection problems are formulated. Then a chaos genetic algorithm based on fuzzy simulation is designed, and finally computational results show that the two risk measures can play a role in possibility space similar to Value-at-Risk and Conditional Value-at-Risk in probability space.

PLI-VIO: Real-time Monocular Visual-inertial Odometry Using Point and Line Interrelated Features

Jiahui Zhang,Qingzhen Shang,Jinfu Yang,Mingai Li 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.6

As a popular technology, visual-inertial odometry (VIO) has been widely applied in many fields such as autonomous robots and unmanned aerial vehicle (UAV). However, the trade-off between localization accuracy and real-time performance still needs to be optimized. This paper presents a real-time tightly-coupled monocular VIO system using point and line interrelated features (PLI-VIO) under the sliding window optimization framework. In line feature extraction part of PLI-VIO, a line segment extraction and coalescence algorithm based on EDlines is proposed, which extracts line features in real-time without concession on feature quality. At the same time, in order to get efficient and robust line tracking effect, PLI-VIO presents a line-to-point tracking method that fully utilizes the interrelation between point and line. Specifically, line features are divided as a group of points and tracked by pyramidal implementation of Lucas Kanade feature tracker. The proposed line feature tracking method can effectively reduce time consumption on tracking process in a robust way. Extensive evaluations on Euroc and TUM-VI public datasets are performed to demonstrate the preferable performance of our proposed system, and the results show that PLI-VIO obtains better localization accuracy with less computation cost compared against other state-of-the-art VIO algorithms.

Two-Stream Convolutional Neural Network for Video Action Recognition

( Han Qiao ),( Shuang Liu ),( Qingzhen Xu ),( Shouqiang Liu ),( Wanggan Yang ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.10

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What’s more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

FUZZY RISK MEASURES AND ITS APPLICATION TO PORTFOLIO OPTIMIZATION

Ma, Xiaoxian,Zhao, Qingzhen,Liu, Fangai The Korean Society for Computational and Applied M 2009 Journal of applied mathematics & informatics Vol.27 No.3

In possibility framework, we propose two risk measures named Fuzzy Value-at-Risk and Fuzzy Conditional Value-at-Risk, based on Credibility measure. Two portfolio optimization models for fuzzy portfolio selection problems are formulated. Then a chaos genetic algorithm based on fuzzy simulation is designed, and finally computational results show that the two risk measures can play a role in possibility space similar to Value-at-Risk and Conditional Value-at-Risk in probability space.

Seasonal Variations in Voluntary Intake and Apparent Digestibility of Forages in Goats Grazing on Introduced Leymus chinensis Pasture

Sun, Zewei,Wang, Zaisen,Zhong, Qingzhen,Zhou, Daowei Asian Australasian Association of Animal Productio 2014 Animal Bioscience Vol.27 No.6

The nutrient composition of pasture, voluntary intake and digestibility of diet ingested by goats grazing on an introduced Leymus chinensis pasture were measured across spring (May), summer (July), autumn (October) and winter (March). In each season, 12 Inner Mongolian Cashmere goats (6 wethers and 6 does with an average live weight of $22.2{\pm}1.3$ kg and $19.5{\pm}0.8$ kg, respectively) were used to graze on a 2 hectares size paddock. Diet selection was observed and the plant parts selected by grazing goats and whole plant L. chinensis were sampled simultaneously. The alkane pair $C_{32}:C_{33}$ and $C_{36}$ were used to estimate intake and digestibility, respectively. The results showed that the plant parts selected by goats had higher crude protein (CP) and lower acid detergent fiber (ADF) and neutral detergent fiber (NDF) than the whole plant, especially in the autumn and winter. The voluntary intake of dry matter (DM), CP, ADF, NDF, and metabolizable energy (ME) by goats was highest in summer (p<0.05). The goats ingested more CP, ME, and less ADF in spring than in autumn (p<0.05). The intakes of DM, CP, and ME were lowest in winter (p<0.05). There were significant differences in nutrient intake between wethers and does in each season, except for the ADF and ME intake per metabolic weight ($LW^{0.75}$). The nutrient digestibilities were higher in spring and summer, and decreased significantly during the autumn and winter (p<0.05). Goats, especially wethers, had a relative constant NDF digestibility across seasons, however, the apparent digestibility of CP in both wethers and does, decreased to negative values in winter. The grazing goats experienced relatively sufficient nutrients supply in spring and summer, and a severe deficiency of CP and ME in winter.

Biochemical Characterization of Exoribonuclease Encoded by SARS Coronavirus

Chen, Ping,Jiang, Miao,Hu, Tao,Liu, Qingzhen,Chen, Xiaojiang S.,Guo, Deyin Korean Society for Biochemistry and Molecular Biol 2007 Journal of biochemistry and molecular biology Vol.40 No.5

The nsp14 protein is an exoribonuclease that is encoded by severe acute respiratory syndrome coronavirus (SARS-CoV). We have cloned and expressed the nsp14 protein in Escherichia coli, and characterized the nature and the role(s) of the metal ions in the reaction chemistry. The purified recombinant nsp14 protein digested a 5'-labeled RNA molecule, but failed to digest the RNA substrate that is modified with fluorescein group at the 3'-hydroxyl group, suggesting a 3'-to-5' exoribonuclease activity. The exoribonuclease activity requires $Mg^{2+}$ as a cofactor. Isothermal titration calorimetry (ITC) analysis indicated a two-metal binding mode for divalent cations by nsp14. Endogenous tryptophan fluorescence and circular dichroism (CD) spectra measurements showed that there was a structural change of nsp14 when binding with metal ions. We propose that the conformational change induced by metal ions may be a prerequisite for catalytic activity by correctly positioning the side chains of the residues located in the active site of the enzyme.

Deep sequencing identifies conserved and novel microRNAs from antlers cartilage of Chinese red deer (Cervus elaphus)

Yanxia Chen,Xuedong Liu,Xiaoguang Yang,Yuhui Liu,Xiaomeng Pi,Qingzhen Liu,Dong Zheng 한국유전학회 2015 Genes & Genomics Vol.37 No.5

Deer antlers are the only mammalian appendages subject to an annual cycle of epimorphic regeneration. Within the rapid-growth stage, they display the fastest cartilage development in the animal kingdom. To identify microRNA (miRNA) profiling in red deer (Cervus elaphus) antler cartilage, we applied deep sequencing technology to a small RNA library constructed from pooled cartilage (three antlers from three individuals). We generated 9,520,645 mappable reads with a size distribution of between 15 and 30 nucleotides (miRNAs of 18 nucleotides were the most abundant group: 31 %). Bioinformatics data mining revealed 399 miRNAs in antler cartilage, of which 345 were highly conserved and expressed in 25 other mammals, including the cattle (Bos taurus) and in humans (Homo sapiens). The remaining 54 miRNAs we identified were novel and likely to be antler-cartilage specific, but were expressed at low levels. The identification of these known and newly identified miRNAs in antler cartilage significantly enhances our understanding of the miRNA profiling of regenerating antler cartilage. Further studies are necessary to better understand miRNAs-regulated antlerogenesis.

Multiferroicity in doped hexagonalLuFeO3

Disseler, Steven M.,Luo, Xuan,Gao, Bin,Oh, Yoon Seok,Hu, Rongwei,Wang, Yazhong,Quintana, Dylan,Zhang, Alexander,Huang, Qingzhen,Lau, June,Paul, Rick,Lynn, Jeffrey W.,Cheong, Sang-Wook,Ratcliff, Willia American Physical Society 2015 Physical review. B, Condensed matter and materials Vol.92 No.5