Search

Pulsed-field gel electrophoresis (PFGE) remains the tool for transmission and outbreak tracing. However, inter-laboratory comparison of PFGE could not be made due to the lack of a standard for interpretation and congruence profiles assignment. This study aims to overcome the shortcoming of PFGE profiling, as well as human performance in data analysis by developing a web-based tool for monitoring multidrug-resistant organisms (MDRO) and tracking the transmission based on PFGE. A web-based MDRO monitoring and transmission tracking system was developed based on three main subsystems. To build the database, banding patterns of PFGE were converted into string data using the box-based fragmentation method. The data could be visualized based on different categories and the transmission tracing could be achieved by the coexistence of the similar profile. This system could be used for monitoring the genotypic and phenotypic trends of MDRO and to trace potential outbreaks in hospitals.

Traditionally, picture-based password systems employ password objects (pictures/icons/symbols) as input during an authentication session, thus making them vulnerable to “shoulder-surfing” attack because the visual interface by function is easily observed by others. Recent software-based approaches attempt to minimize this threat by requiring users to enter their passwords indirectly by performing certain mental tasks to derive the indirect password, thus concealing the user’s actual password. However, weaknesses in the positioning of distracter and password objects introduce usability and security issues. In this paper, a new method, which conceals information about the password objects as much as possible, is proposed. Besides concealing the password objects and the number of password objects, the proposed method allows both password and distracter objects to be used as the challenge set’s input. The correctly entered password appears to be random and can only be derived with the knowledge of the full set of password objects. Therefore, it would be difficult for a shoulder-surfing adversary to identify the user’s actual password. Simulation results indicate that the correct input object and its location are random for each challenge set, thus preventing frequency of occurrence analysis attack. User study results show that the proposed method is able to prevent shoulder-surfing attack.