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In the present world of constant connectivity, the barrage system, as a system of real-time dynamic comments coupled with video content, has become a popular interactive system technology for video sharing platforms. This study investigates how barrage system fluctuation characteristics, namely, barrage fluctuation amplitude and frequency, impact user interaction.

The research model was estimated with a fixed-effects regression applied to a longitudinal panel dataset collected from one of the most popular video sharing platforms in China (Bilibili.com).

Barrage fluctuation frequency has positive effects on users' real-time (synchronous) barrage interaction and the traditional (asynchronous) comment interaction. Barrage fluctuation amplitude has a positive effect on users' real-time (synchronous) barrage interaction but a negative effect on traditional (asynchronous) comment interaction. In addition, the interaction effects of the barrage fluctuation frequency and the barrage fluctuation amplitude on user interaction show adverse effects.

The results revealed the impact of different barrage fluctuation characteristics on different forms of interaction and provide important theoretical contributions and managerial implications in terms of user interaction on video sharing platforms.

This study aims to investigate the noise-inducing characteristics during the start-up process of a mixed-flow pump and the impact of different start-up schemes on pump noise.

This study conducted numerical simulations on the mixed-flow pump under different start-up schemes and investigated the flow characteristics and noise distribution under these schemes.

The results reveal that the dipole noise is mainly caused by pressure fluctuations, while the quadrupole noise is mainly generated by the generation, development and breakdown of vortices. Additionally, the noise evolution characteristics during the start-up process of the mixed-flow pump can be divided into the initial stage, stable growth stage, impulse stage and stable operation stage.

The findings of this study can provide a theoretical basis for the selection of start-up schemes for mixed-flow pumps, reducing flow noise and improving the operational stability of mixed-flow pumps.

Explores possibilities for simulating the effects of continuous disruptions to an economy using a multi‐sector social accounting model. The underlying thesis for the model is that disruptions (due to events ranging from potholes to earthquakes) are a constant and unavoidable aspect of development and that all institutions and production activities are structured and adapt over time so as to balance performance and protection. The first sections explain the role of input‐output tables, especially social accounts, as the basic framework for evaluating systemic vulnerability to disaster. The next sections explain the underlying behavioral components of the model: how the profile of protection versus disruption and costs of protection are determined, and how adaptation of the protection profile to changing events and societal discounting affects protection. In the final sections, these elements are integrated into a multi‐sector social accounting model of the Niagara Frontier region of New York State – affected by industrial decline and currency fluctuations is dependent on a major hydro‐electric power facility that is considered vulnerable to a variety of unscheduled events. Results focus on how disruptions and responses to them propagate over time and between actors.

As a consequence of the huge loss and damage caused by natural disasters all over the world, an impressive amount of attention is currently being given to a holistic approach in disaster risk management (McEntire, Fuller, Johnston, & Weber, 2002). The world experiences more and more natural disaster impacts in spite of numerous efforts, advancing sciences, and more powerful technologies. Indeed, current disasters are more complex, and climate change poses a greater potential for adverse impacts (Aalst & Burton 2002). Hence, there is a need to reassess the existing disaster risk reduction approaches due to problems in the existing risk management approaches, and new risks brought by climate change and by environment degradation.

Many studies have shown that the intensity and the number of hurricanes are likely to increase. This paper aims to look at the immediate effects of hurricanes on the time on the market, share of houses sold and percentage of houses with price cuts in the housing market using the metropolitan statistical area-level data in Florida.

Using a difference-in-difference method, the authors estimate the impact that a hurricane has on the housing markets.

The authors find that a hurricane has a positive and significant effect on the time on the market. A hurricane leads to a delay of the sale of a typical house in Florida by five days. The authors test for within-year seasonality and show that these effects change with seasonality of the housing market. Markets with seasonal housing prices tend to be affected more by hurricanes than those where housing prices are not seasonal. The authors also show that effects of a hurricane are transient and fade away in a few months. The results remain significant as the hurricane intensity changes.

This is the first study to look at the short-term effects of the hurricanes and how their effects vary based on seasonality of the markets.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

IT is important to open this editorial with an affirmation of faith. It is this:

ACCORDING to a leading London evening paper, outside “efficiency experts” are parasites. In an Evening News leading article these words appear:

IN recent issues we have had contributionsion the future of Work Study as seen by Council members of the Institute of Industrial Technicians, the Society of Industrial Engineers and the Work Study Society.