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The purpose of this study is to deal largely with the influence of temperature variation on the measurement accuracy of transit-time ultrasonic flowmeter.

The causes of measurement error due to temperature are qualitatively and quantitatively analyzed, and a mathematical model is established. The experimental data are processed and analyzed, and the temperature compensation coefficient of flow measurement is obtained.

The experimental results show that the flow measurement results by temperature compensation are helpful in improving the measurement accuracy of the ultrasonic flowmeter.

This study has certain application value, which can provide theoretical support for the design of high-precision ultrasonic flowmeters and design guidance.

It is worth emphasizing that there are few research studies on the influence factors of temperature. This paper focuses on the influence of the temperature change on the flowmeter that is modeled, and the high precision flow parameter test system is designed based on the established model.

This paper aims to examine how presence (the social presence of live streaming platforms, of viewers, of live streamers and telepresence) affects consumer trust and flow state, thus inducing impulsive buying behaviors, personal sense of power as moderator.

Drawing on the Stimulus-Organism-Response (S-O-R) framework, the conceptual model covers social presence, telepresence, consumer trust, flow state, personal sense of power and impulsive buying behavior. An online survey was conducted from 405 consumers with the experience of live streaming shopping in China; structural equation modeling (SEM) was used for data analysis.

Results find that three dimensions of social presence (the social presence of live streaming platforms, of viewers, of live streamers) and telepresence have a positive and significant influence on consumer trust and flow state, thus triggering consumers’ impulsive buying behavior. Furthermore, consumers’ sense of power moderates the process from consumer trust, flow state to impulsive buying behavior.

This study will help live streamers and e-retailers to have a further understand on how to stimulate consumers’ buying behavior. Furthermore, it also provides reference for the development of live streaming commerce in other countries.

This research examines the effect of social presence and telepresence on impulsive buying behavior in live streaming commerce, which is inadequately examined in extant literature.

Recently, various Twitter Sentiment Analysis (TSA) techniques have been developed, but little has paid attention to the microblogging feature – emojis, and few works have been conducted on the multi-class sentiment analysis of tweets. The purpose of this paper is to consider the popularity of emojis on Twitter and investigate the feasibility of an emoji training heuristic for multi-class sentiment classification of tweets. Tweets from the “2016 Orlando nightclub shooting” were used as a source of study. Besides, this study also aims to demonstrate how mapping can contribute to interpreting sentiments.

The authors presented a methodological framework to collect, pre-process, analyse and map public Twitter postings related to the shooting. The authors designed and implemented an emoji training heuristic, which automatically prepares the training data set, a feature needed in Big Data research. The authors improved upon the previous framework by advancing the pre-processing techniques, enhancing feature engineering and optimising the classification models. The authors constructed the sentiment model with a logistic regression classifier and selected features. Finally, the authors presented how to visualise citizen sentiments on maps dynamically using Mapbox.

The sentiment model constructed with the automatically annotated training sets using an emoji approach and selected features performs well in classifying tweets into five different sentiment classes, with a macro-averaged F-measure of 0.635, a macro-averaged accuracy of 0.689 and the MAEM of 0.530. Compared to those experimental results in related works, the results are satisfactory, indicating the model is effective and the proposed emoji training heuristic is useful and feasible in multi-class TSA. The maps authors created, provide a much easier-to-understand visual representation of the data, and make it more efficient to monitor citizen sentiments and distributions.

This work appears to be the first to conduct multi-class sentiment classification on Twitter with automatic annotation of training sets using emojis. Little attention has been paid to applying TSA to monitor the public’s attitudes towards terror attacks and country’s gun policies, the authors consider this work to be a pioneering work. Besides, the authors have introduced a new data set of 2016 Orlando Shooting tweets, which will be made available for other researchers to mine the public’s political opinions about gun policies.