Keynote Speakers/主讲嘉宾

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Prof. Luan De-yu

Qingdao University of science and technology,  China 

Research Area: Chaotic mixing of fluid and numerical simulation of flow field

Title: Determination of cavern boundary with pseudoplastic fluid based on apparent viscosity method


The cavern characteristics of xanthan gum solutions stirred by impeller of perturbed six-bent- bladed turbine (6PBT) were researched numerically using CFD with laminar flow model. The apparent viscosity method was proposed to determine the boundary of the cavern. Results showed that the cavern sizes predicted by apparent viscosity were in good agreement with the calculated results all the time, and the apparent viscosity method was not influenced by speed and impeller configuration. However, the predicted results of cavern by the traditional speed method usually appeared the big deviation, especially in high speed. So it is feasible to determine the cavern boundary with the apparent viscosity, i.e. 0.25 times yield viscosity of pseudoplastic fluid, as the unified standard.



Assoc. Prof. Sunil Kumar Jha

Nanjing University of Information Science and Technology, China

Research Area: Data Mining, Artificial Intelligence Applications, Chemical Sensing, Nano-Informatics,Renewable Energy

Title: Data Fusion Approaches in Human Body Odor Data Mining


The odor is the characteristic and alarming aroma of the human body. It is a significant information source of an individual's unique characteristic and physical condition in biometric, forensic and medical applications. Due to a complex combination of VOCs, the identification of individuals on the basis of body odor by conventional instruments is a tough task. The objective of the present research is to search for an optimal subset of VOCs in body odor, which can produce differentiation in an individual by using the combination of analytical methods and chemometric analysis. Specifically, the implementation of data fusion approaches to search discriminating biomarker volatile organic chemicals (VOCs) in body odor for individual differentiation has been demonstrated. Also, some novel approaches to decision level data fusion have been discussed in human body odor mining. Gas chromatography–mass spectrometry (GC– MS) characterized human body odor samples have been used in analysis and validation.

Important Dates

截稿日期|Submission Deadline:

February 27, 2020  April 6, 2020


录用通知|Notification Date:

About 1-2 weeks after the submission


会议时间|Conference Date:

April 10-12, 2020

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