Tensile Bond Between Substrate Concrete and Normal Repairing Mortar under Freeze–Thaw Cycles

Concrete patch repair has long been used to repair the damaged concrete structures. In cold regions, freeze– thaw cycle is one of the major damage factors. Not only the material itself is damaged by freeze–thaw cycles, but also the adhesive interface, which is regarded as the weakest part of composite system, degrades under freeze–thaw cycles. Air entraining agent has long been used to increase the freeze–thaw resistance of concrete materials. However, the effect of air entraining agent on the adhesive interface under freeze–thaw cycles has not been explored. The degradation mechanism and failure mode of concrete repair system have not been studied, either. In this study, to investigate the effects of water–cement ratio of substrate concrete and air entraining agent in substrate concrete and repairing mortars, three kinds of substrate concrete were casted and repaired by two kinds of ordinary Portland cement mortar. With certain number of freeze–thaw cycles up to 150 cycles, through splitting prism test, the splitting tensile strength and failure mode of composite specimens were experimented. The relative dynamic elastic modulus and splitting tensile strength of substrate concretes and repairing mortars were obtained as well. Results showed that air entraining agent in the repairing mortar greatly influenced the adhesive tensile strength under freeze–thaw cycles. The water–cement ratio and air entraining agent of substrate concrete insignificantly affected the adhesive interface, but affected the splitting tensile strength and the freeze– thaw resistance of substrate concrete, and thus affected the failure mode of composite specimens

The Impact of Positive Online Review Tags on Snacks Sales: A Case of Bestore in Tmall

Customers’ reviews in e-commerce sites play a significant role in influencing potential customers’ purchasing decisions which ultimately affects products sales. Chinese e-commerce sites like Tmall, Taobao and JD.com contain a collection of aspect tags that group reviews with similar comments tags to help customers browse reviews and evaluate products more conveniently. To validate whether these tags are useful and actually playing a role in promoting future sales, we collected data including product information and review tags on a regular basis for consecutive 8 weeks from Bestore, a snack seller on Tmall. We classified the collected review tags into 9 types based on their semantic meanings. Finally, we analyzed and performed generalized estimating equations (GEE) modeling on the data set consisting of 234 products with a total of 734 tags. The results show that most of the aspect tags are related to immediate period sales volume and certain tags are more capable of nowcasting next immediate sales

Insights into matrix compressibility of coals by mercury intrusion porosimetry and N2 adsorption

This research was funded by the National Natural Science Fund (grant nos. 41830427, 41602170 and 41772160), the National Major Research Program for Science and Technology of China (grant no. 2016ZX05043-001), the Key Research and DevelopmentProjects of the Xinjiang Uygur Autonomous Region (grant no. 2017B03019-01) and the Research Program for Excellent Doctoral Dissertation Supervisor of Beijing (grant no. YB20101141501).Peer reviewedPostprin

Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach

The application of predicting bus travel time with real-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effective to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genetic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model performance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various combinations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC

A Distribution Separation Method Using Irrelevance Feedback Data for Information Retrieval

In many research and application areas, such as information retrieval and machine learning, we often encounter dealing with a probability distribution which is mixed by one distribution that is relevant to our task in hand and the other that is irrelevant and we want to get rid of. Thus, it is an essential problem to separate the irrelevant distribution from the mixture distribution. This paper is focused on the application in Information Retrieval, where relevance feedback is a widely used technique to build a refined query model based on a set of feedback documents. However, in practice, the relevance feedback set, even provided by users explicitly or implicitly, is often a mixture of relevant and irrelevant documents. Consequently, the resultant query model (typically a term distribution) is often a mixture rather than a true relevance term distribution, leading to a negative impact on the retrieval performance. To tackle this problem, we recently proposed a Distribution Separation Method (DSM), which aims to approximate the true relevance distribution by separating a seed irrelevance distribution from the mixture one. While it achieved a promising performance in an empirical evaluation with simulated explicit irrelevance feedback data, it has not been deployed in the scenario where one should automatically obtain the irrelevance feedback data. In this article, we propose a substantial extension of the basic DSM from two perspectives: developing a further regularization framework and deploying DSM in the automatic irrelevance feedback scenario. Specifically, in order to avoid the output distribution of DSM drifting away from the true relevance distribution when the quality of seed irrelevant distribution (as the input to DSM) is not guaranteed, we propose a DSM regularization framework to constrain the estimation for the relevance distribution. This regularization framework includes three algorithms, each corresponding to a regularization strategy incorporated in the objective function of DSM. In addition, we exploit DSM in automatic (i.e., pseudo) irrelevance feedback, by automatically detecting the seed irrelevant documents via three different document re-ranking methods. We have carried out extensive experiments based on various TREC data sets, in order to systematically evaluate the proposed methods. The experimental results demonstrate the effectiveness of our proposed approaches in comparison with various strong baselines