Author：Zheng, YL;Liu, Q;Poon, C

Abstract:Cuffless and continuous blood pressure (BP) measurement using wearable devices is of great clinical value and health monitoring importance. Pulse arrival time (PAT) based technique was considered as one of the most promising methods for this purpose. Considering the dynamic and nonlinear relationship between BP, PAT and other cardiovascular variables, this paper proposes for the first time to use nonlinear autoregressive models with extra inputs (ARX) for BP estimation. The models were first trained by the baseline data of all 25 subjects to determine the model structure and then trained by individual data to obtain the personalized model parameters. To assess the effects of the dynamic and nonlinear factors, the data during water drinking and the first 5 minutes of recovery after drinking were used to validate the four models: linear regression, linear ARX, nonlinear regression and nonlinear ARX. The reference BP, which were measured by Finometer, were increased by 36.7 +/- 10.5 mmHg for SBP and 28.4 +/- 7.7 mmHg for DBP. This BP changes were best modelled by the nonlinear ARX, with Mean +/- SD differences of 5.6 +/- 8.8 mmHg for SBP and 3.8 +/- 5.8 mmHg for DBP. The study also showed that nonlinear factor significantly reduced the root mean square error (RSME) by about 50%%, i.e., from 20.4 to 10.7 mmHg for SBP and 13.3 to 7.3 mmHg for DBP during drinking. While the effects of dynamic factors were not as significant as nonlinear factors, especially after introducing nonlinear factors.

Author：Xinan Chen ; Ruibin Bai ; Rong Qu ; Haibo Dong ; Jianjun Chen

Abstract:International and domestic maritime trade has been expanding dramatically in the last few decades, seaborne container transportation has become an indispensable part of maritime trade efficient and easy-to-use containers. As an important hub of container transport, container terminals use a range of metrics to measure their efficiency, among which the hourly container throughput (i.e., the number of twentyfoot equivalent unit containers, or TEUs) is the most important objective to improve. This paper proposes a genetic programming approach to build a dynamic truck dispatching system trained on real-world stochastic operations data. The experimental results demonstrated the superiority of this dynamic approach and the potential for practical applications.

Author：Chen, H;Xiong, WQ;Xiong, PC;Zhao, JY

Abstract:The problem of dishonest transactions in two-sided markets is increasingly prominent, and its governance mechanism needs to be improved. Pricing strategy is an effective means of platform governance, which can restrain the negative network externalities caused by dishonest transactions. By using the inter-temporal analysis method, this paper analyzes the influence of network externalities in different periods on platform pricing and regulatory costs. The research shows that: from the perspective of platform profit maximization, (1) The greater the positive network externalities of merchants, the lower the platform charges merchants; (2) The greater the negative network externalities caused by dishonest transactions, the higher the platform charges merchants; (3) The greater the network externalities of consumers to merchants, the higher the platform charges merchants. Finally, the influence of cross network externality on platform pricing, platform supervision cost, number of both sides of the platform and platform profit is analyzed by simulation. This provides suggestions for the platform to control merchants' dishonest transaction behavior through pricing.

Author：Wang, CY;Huang, DY;Su, JL;Yu, LM;Ma, F

Abstract:Computer-aided image classification has achieved start-of-the-art performance since Convolutional Neural Network structures were employed. Classical neural networks such as AlexNet and VGG-Net inspired several rules of designing network models. Besides benchmark datasets such as MNIST, CIFAR and ImageNet, classification performance of medical images such as chromosome karyotyping images also improved via Convolutional Neural Network. However, there are few studies on generalization among different datasets. In this paper, we designed a neural network with nine layers, and achieved classification accuracy of 0.984, 0.816 and 0.921 on the dataset of MNIST, CIFAR and chromosome karyotype images. We also visualized the output of several layers of the model and explained that smooth output between neural network layers may induce lower accuracy on classification.