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RISS 인기검색어
- 검색키워드
- 저자 : Wenbo Hao (검색결과 4 건)
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Yinghui Bao,Chunqi Gao,Wenbo Hao,Cheng Ji,Lihong Zhao,Jianyun Zhang,Tao Liu,Qiugang Ma 아세아·태평양축산학회 2015 Animal Bioscience Vol.28 No.10
The present study was conducted to determine the effects of L-carnosine (LC) and/or alpha-lipoic acid (ALA) supplementation on growth performance, blood thyroid hormones and lipid profiles in finishing pigs. A total of 40 (Landrace×Yorkshire) pigs with an initial body weight of 57.93±3.14 kg were randomly allocated to 4 experimental diets using a 2×2 factorial arrangement with 2 LC supplemental levels (0 or 0.1%) and 2 ALA supplemental levels (0 or 0.03%) in basal diets. The results showed that pigs fed LC-supplemented diets increased final live weight, average daily gain, and average daily feed intake compared to those of pigs fed without LC-supplemented diets (p<0.05). Dietary supplementation with ALA did not affect the growth performance and carcass traits of pigs (p>0.05). Additionally, LC supplementation increased serum triiodothyronine, thyroxine levels, and ALA supplementation increased serum triiodothyronine levels (p<0.05). Serum total cholesterol and triglycerides levels were significantly decreased in LC and ALA supplemented groups, respectively (p<0.05). Moreover, serum low density lipoprotein cholesterol levels were lower in the ALAsupplemented groups than those of pigs fed without ALA-supplemented diets (p<0.05). However, no significant LC×ALA interaction effect on growth performance, blood thyroid hormones and lipid profiles was found. This study suggested that dietary supplementation of LC resulted in better growth performance compared to that of ALA supplementation. L-carnosine and/or ALA supplementation positively modified blood lipid profiles, which may have the potential to prevent cardiovascular diseases.
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Bao, Yinghui,Gao, Chunqi,Hao, Wenbo,Ji, Cheng,Zhao, Lihong,Zhang, Jianyun,Liu, Tao,Ma, Qiugang Asian Australasian Association of Animal Productio 2015 Animal Bioscience Vol.28 No.10
The present study was conducted to determine the effects of L-carnosine (LC) and/or alpha-lipoic acid (ALA) supplementation on growth performance, blood thyroid hormones and lipid profiles in finishing pigs. A total of 40 ($Landrace{\times}Yorkshire$) pigs with an initial body weight of $57.93{\pm}3.14kg$ were randomly allocated to 4 experimental diets using a $2{\times}2$ factorial arrangement with 2 LC supplemental levels (0 or 0.1%) and 2 ALA supplemental levels (0 or 0.03%) in basal diets. The results showed that pigs fed LC-supplemented diets increased final live weight, average daily gain, and average daily feed intake compared to those of pigs fed without LC-supplemented diets (p<0.05). Dietary supplementation with ALA did not affect the growth performance and carcass traits of pigs (p>0.05). Additionally, LC supplementation increased serum triiodothyronine, thyroxine levels, and ALA supplementation increased serum triiodothyronine levels (p<0.05). Serum total cholesterol and triglycerides levels were significantly decreased in LC and ALA supplemented groups, respectively (p<0.05). Moreover, serum low density lipoprotein cholesterol levels were lower in the ALA-supplemented groups than those of pigs fed without ALA-supplemented diets (p<0.05). However, no significant $LC{\times}ALA$ interaction effect on growth performance, blood thyroid hormones and lipid profiles was found. This study suggested that dietary supplementation of LC resulted in better growth performance compared to that of ALA supplementation. L-carnosine and/or ALA supplementation positively modified blood lipid profiles, which may have the potential to prevent cardiovascular diseases.
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FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection
( Yongxin Feng ),( Yingyun Kang ),( Hao Zhang ),( Wenbo Zhang ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.1
Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the de-tection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be signifi-cantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.
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