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The purpose of this research is to investigate the usage characteristics and the information propagation patterns of Chinese microblogs in different stages of an epidemic, given that the microblogging in China is different from other parts of the world. In addition, the authors aim to conceptualize the roles of different users and provide insights for using microblogging platforms to disseminate information in this context.

The authors conducted an analysis on Sina Weibo microblogs about the African Swine Fever epidemic from August to October 2018. The authors firstly applied a label propagation algorithm to classify users into government, media, verified users and nonverified users. The authors analyzed several user metrics, traced the information propagation patterns of their microblogs and calculated the average speed of information propagation using computational approaches.

The authors’ findings show that different types of users played different roles, such as supplying information, amplifying information, relaying information and engaging with other users. The microblogs posted by media dominated the propagation in most cases, but general users can propagate information faster. The direction of information propagation is one-way for the majority of microblogs, and few users repost earlier information. Additionally, microblogs attract more attention at the beginning and the middle phases of an epidemic. In the context of managing epidemics, the authors recommend governments and other verified users can work together to use microblogging platforms efficiently.

This research is one of the few studies to investigate information propagation patterns of different user categories on a Chinese microblogging platform during an epidemic. The authors’ work can be used by government agencies and public health authorities for disseminating information efficiently during epidemics or emergencies, especially in the early stages.

This paper aims to propose a system for automatically forming ad hoc networks using mobile phones and battery-powered wireless routers for emergency situations. The system also provides functions to send emergency messages and identify the location of victims based on the network topology information.

Optimized link state routing protocol is used to instantly form an ad hoc emergency network based on WiFi signals from mobile phones of the victims, backup battery-powered wireless routers preinstalled in buildings and mobile devices deployed by search and rescue teams. The proposed system is also designed to recover from partial crash of network and nodes lost.

Experimental results demonstrate the effectiveness of the proposed system in terms of battery life, transmission distance and noises.

A novel message routing schedule is proposed for conserving battery life. A novel function to estimate the location of a mobile device which sent an emergency message is proposed in this paper.

Intensive traffic often occurs in web‐enabled business processes hosted by travel industry and government portals. An extreme case for intensive traffic is flash crowd situations when the number of web users spike within a short time due to unexpected events caused by political unrest or extreme weather conditions. As a result, the servers hosting these business processes can no longer handle overwhelming service requests. To alleviate this problem, process engineers usually analyze audit trail data collected from the application server and reengineer their business processes to withstand unexpected surge in the visitors. However, such analysis can only reveal the performance of the application server from the internal perspective. This paper aims to investigate this issue.

This paper proposes an approach for analyzing key performance indicators of traffic intensive web‐enabled business processes from audit trail data, web server logs, and stress testing logs.

The key performance indicators identified in the study's approach can be used to understand the behavior of traffic intensive web‐enabled business processes and the underlying factors that affect the stability of the web server.

The proposed analysis also provides an internal as well as an external view of the performance. Moreover, the calculated key performance indicators can be used by the process engineers for locating potential bottlenecks, reengineering business processes, and implementing contingency measures for traffic intensive situations.