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Bayesian Prediction Model Based on Attribute Weighting and Kernel Density Estimations
Xiang, Zhong-Liang,Yu, Xiang-Ru,Kang, Dae-Ki Hindawi Limited 2015 Mathematical problems in engineering Vol.2015 No.-
<P>Although naïve Bayes learner has been proven to show reasonable performance in machine learning, it often suffers from a few problems with handling real world data. First problem is conditional independence; the second problem is the usage of frequency estimator. Therefore, we have proposed methods to solve these two problems revolving around naïve Bayes algorithms. By using an attribute weighting method, we have been able to handle conditional independence assumption issue, whereas, for the case of the frequency estimators, we have found a way to weaken the negative effects through our proposed smooth kernel method. In this paper, we have proposed a compact Bayes model, in which a smooth kernel augments weights on likelihood estimation. We have also chosen an attribute weighting method which employs mutual information metric to cooperate with the framework. Experiments have been conducted on UCI benchmark datasets and the accuracy of our proposed learner has been compared with that of standard naïve Bayes. The experimental results have demonstrated the effectiveness and efficiency of our proposed learning algorithm.</P>
Granular Effects on Depositional Processes of Debris Avalanches
Yu-xiang Hu,Hai-bo Li,Shun-chao Qi,Gang Fan,Jia-wen Zhou 대한토목학회 2020 KSCE JOURNAL OF CIVIL ENGINEERING Vol.24 No.4
Debris avalanches generally have massive mobility and extensive deposited area, which have strong relation to the granular effect. The granular effect is an outcome from the interaction (based on frictional mechanism) among particles. Discrepancy of grain composition, for instance the difference of particle size and their proportion of particles, is a key factor to influence granular effect. A series of laboratory tests have been conducted to research the granular effects on depositional processes of debris avalanches containing fine- and coarse-grained particles. The velocity of debris front, the morphology characteristics and runout distance of the debris avalanche are considered in detail, and the mass parameter and the size parameter are introduced in analysis for the research of granular effect. The experimental results indicate that the granular effects can decrease the dissipation of momentum and increase the accumulation region, leading to further runout distance and more extensive deposited areas. Further, the velocity, the range of depositions of debris avalanches are larger than that in other conditions when the proportion of fine-grained particles is 30%, especially the materials consist of heterogeneous granular. It means there have a critical mass parameter in experiments which lead to the high efficiency of granular effects, and the granular effect is weakening in homogeneous granular composition.
Lung-Targeting Delivery of Dexamethasone Acetalte Loaded Solid Lipid Nanoparticles
Xiang, Qing-Yu,Wang, Min-Ting,Chen, Fu,Gong, Tao,Jian, Yan-Lin,Zhang, Zhi-Rong,Huang, Yuan 대한약학회 2007 Archives of Pharmacal Research Vol.30 No.4
The objective of the present study was to develop a novel solid lipid nanoparticle (SLN) for the lung-targeting delivery of dexamethasone acetate (DXM) by intravenous administration. DXM loaded SLN colloidal suspensions were prepared by the high pressure homogenization method. The mean particle size, drug loading capacity and drug entrapment efficiency (EE %) of SLNs were investigated. In vitro drug release was also determined. The biodistribution and lung-targeting efficiency of DXM-SLNs and DXM-solutions (DXM-sol) in mice after intravenous administration were studied using reversed-phase high-performance liquid chromatography(HPLC). The results (expressed as mean ${\pm}$ SD) showed that the DXM-SLNs had an average diameter of 552 ${\pm}$ 6.5 nm with a drug loading capacity of 8.79 ${\pm}$ 0.04% and an entrapment efficiency of 92.1 ${\pm}$ 0.41%. The in vitro drug release profile showed that the initial burst release of DXM from DXM-SLNs was about 68% during the first 2 h, and then the remaining drug was released gradually over the following 48 hours. The biodistribution of DXM-SLNs in mice was significantly different from that of DXM-sol. The concentration of DXM in the lung reached a maximum level at 0.5 h post DXM-SLNs injection. A 17.8-fold larger area under the curve of DXM-SLNs was achieved compared to that of DXM-sol. These results indicate that SLN may be promising lung-targeting drug carrier for lipophilic drugs such as DXM.
Xiang Yu,Haizhou Wu,Jianhao Zhang 한국식품과학회 2015 Food Science and Biotechnology Vol.24 No.2
Monascus spp. are fungi, traditionally eaten in Asian countries, that have the the potential to be used as a nitrite substitute in meat-product processing. Effects of using Monascus ruber MJ-1 as a nitrite substitute on color, lipid oxidation and proteolysis of fermented meat mince were investigated in this study. After 4 days of fermentation, a* and L* values for the samples were significantly higher and lower than the values for the control, respectively; however, b* values were significantly lower than that of the control throughout the fermentation process (p<0.05). Furthermore, Monascus ruber MJ-1 fermentation decreased the thiobarbituric acid reactive substances (TBARs) value and increased the myofibril fragmentation index (MFI) value of meat mince significantly (p<0.05). SDS-PAGE results further indicated that myofibrillar proteins were degraded during fermentation, while sarcoplasmic proteins were not. These results suggest prospects for the broad application of Monascus as a nitrite substitute in meat processing.
( Yu-cheng Yang ),( Kai-sheng Tseng ),( Xiang-yu Huang ),( Sheng-hao Chen ),( Joe-air Jiang ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
In this study, a generic intelligent wireless sensing platform with the IoT and wireless transmission technology is established for greenhouse monitoring, which is capable of greenhouse data collection and analysis. The data analyzed by a cloud server would provide important information regarding the efficiency of greenhouse management and cultivation methods. To monitor greenhouse related parameters, an embedded board is placed in a greenhouse as the gateway. Monitoring data and images collected by sensors deployed in the greenhouse are transmitted to the embedded board via an Ethernet or wireless transmission module. The data are eventually sent to a database by wireless AP. The AP connects to the internet by using a 4G USB dongle, which can provide sufficient data transmission speed for real-time data transmission. In wireless hotspots and areas with Wi-Fi, not only can images be transmitted to the cloud, but also can the mobile APP be used to perform remote monitoring, so experimental observations are no longer affected by geographical impacts. Moreover, to ensure the preservation of images and numeric data, the gateway carries a high durability secure digital (SD) memory card to store images and data; if the network encounters some problems, such as unable to connect and a lack of network traffic, the data can be stored in the SD card. Thus, the proposed platform is able to perform heavy information transmission duties and support performing all wireless monitoring tasks. In addition, the analyzed data can be used by agricultural personnel to provide critical cultivation suggestions.
Prediction of NOx Emission in Lean Premixed Gas Turbine Combustor Using a CFD - CRN Approach
Xiang Yu Hou,Seung Han Kim,Truc Huu Nguyen,Jung Kyu Park,Seung Chai Jung,Si Won Yang 대한기계학회 2014 대한기계학회 춘추학술대회 Vol.2014 No.11
This works developed chemical reactor network approach (CRN) with a short time and precise result to predict NOx emissions from a gas turbine rapidly and precisely to amend the defect of the computed time and precision with traditional CFD method for combustor designs. The chemical reactor network with 4 PSRs and 2 PFRs is developed here based on the computational fluid dynamics (CFD). The predictions of exhaust emission in this CRN approach were carried out by using CHEMKIN code and full GRI 3.0 chemical kinetic mechanism. The model combustor tests were conducted at different equivalence ratios. The predicted temperature and NOx emission results closely matched the experimental data. Also NO formation pathway analysis was carried out for the deep understanding of the relative contribution of four NO formation mechanism.
Yu Xiang,Jinyue He,Ruonan Bai,Huorong Gou,Fei Luo,Xuequan Huang,Zehua Zhang 대한척추신경외과학회 2023 Neurospine Vol.20 No.4
Objective: To investigate the value of Hounsfield units (HUs) as an independent predictor of failed percutaneous drainage of spinal tuberculosis paraspinal abscess under computed tomography (CT) guidance. Methods: A retrospective analysis was conducted on 61 patients who underwent CT-guided percutaneous drainage for spinal tuberculosis paraspinal abscess between October 2017 and October 2020. Preoperative CT scans were used to measure the HUs of the abscess. Patients were categorized into successful drainage (n = 49) and failed drainage (n = 12) groups. Statistical analysis involved independent sample t-tests and chi-square tests to compare between the 2 groups. Binary logistic regression was performed to identify independent predictive factors for drainage failure. Receiver operating characteristic (ROC) curves were employed to ascertain risk factor thresholds and diagnostic performance. Results: Among the patients, 49 experienced successful drainage while 12 faced drainage failure. The mean HUs of abscesses in the failed drainage group were significantly higher than those in the successful drainage group (p < 0.001). ROC analysis revealed an area under the curve of 0.897 (95% confidence interval, 0.808–0.986) for predicting drainage failure based on HUs. The optimal HU cutoff value for predicting drainage failure was 22.3, with a sensitivity of 91.7% and specificity of 69.4%. Conclusion: HUs are an independent predictor of failed percutaneous drainage of spinal tuberculosis paraspinal abscess under CT guidance. The HU value of 22.3 can be used as an initial screening threshold for predicting the success or failure of drainage.