Critical infrastructure represents important national assets for producing or distributing continuous flows of essential goods or services. When one aspect of the critical infrastructure shuts down due to an external disruption, other portions of the system that need those goods or services are likely to stop shortly thereafter, thus exacerbating the damage caused by the external disruption. Literature on critical infrastructure interdependency (CII) shows a need for analyzing failure records with time information from different types of critical infrastructure. This paper presents a knowledge discovery process for CII to extract records of frequent patterns of critical infrastructure failure that are directly or indirectly triggered by external disruptions. The knowledge discovery process, including integration of critical infrastructure failure records and their transformation into the data format needed by a data mining algorithm, is described. The paper includes a discussion on a disaster mitigation a...

Using a visualization engine to analyze power meter generated data about energy consumption of a building can provide immediate and informative feedback for energy conservation research. This paper proposed a platform, real-time replay system (RTRS), to provide such a visualized and interactive environment through the Unity 3d game engine. RTRS combines two data sources: Revit, a building information model (BIM) tool, and WiFi power socket sensors into Unity 3d and replays electricity usage events. RTRS users can easily change the scene or jump to the given time frame identified as a wasteful operation, and the system will display potential energy conservation suggestions. In addition, RTRS users can set up event triggering parameters or query conditions against the sensor database inside RTRS. In overall, RTRS can provide an intuitive and easy-to-use platform for managing electricity usage data of a building, reducing the time and efforts for looking up information at the raw data level.

As our society ages, more and more elderly or disabled people live in long-term care (LTC) facilities, which are vulnerable to fires and may result in heavy casualties. Because of the low mobility of LTC residents, firefighters often need to enter the facility to save people. In addition, due to LTC facility management needs, many doors or windows on the passages for a fire rescue operation may be blocked. Thus, firefighters have to employ forcible entry tools such as disk cutters for passing through, which may lengthen the rescue time if an incorrect route or tool is utilized. As new information technologies such as ontology and building information modeling (BIM) have matured, this research aims at proposing a BIM-based ontology model to help firefighters determine better rescue routes instead of using rules of thumb. Factors such as the path length, building components and materials encountered, and forcible entry tools carried are considered in the model. Real LTC fire investigation reports are used for the comparisons between the original routes and the ones generated by the proposed model, and seven experts joined the evaluation workshop to provide further insights. The experts agreed that using the proposed approach can lead to better fire rescue route planning. The proposed BIM-based ontology model could be extended to accommodate additional needs for hospital fire scenes, in the hopes of enhancing the efficiency and effectiveness of firefighters’ rescue operations in such important facilities.

Transit-oriented development (TOD) is a crucial method for sustainable urban development and encouraging the use of public transit, especially for large densely populated cities such as Can Tho City, one of the top five cities in Vietnam. This study is aimed at identifying the factors which have a significant effect on the citizens’ engagement of public transport in the TOD environment. Based on the literature review and expert knowledge, 3 hypotheses related to citizens’ perceptions and citizen’s engagement are developed. A questionnaire was accordingly established using structural equation modeling (SEM). As a result, a total of 8 factors with 31 stems were obtained. The survey questionnaires were distributed to citizens of Can Tho City with 749 responses collected for KMO test Chi-square of 0.905. The analysis shows that all three hypotheses stand: (1) the citizens’ perception of local government’s capacity plays an important role in the citizens’ willingness to engage on public transport; the citizens’ perception of the usefulness of (2) TOD and (3) public transport has a positive influence on the engagement on public transport system. The major contribution of this study is that it can help other cities in developing countries to apply TOD.

In the context of the growing emphasis on energy conservation and carbon reduction, the widespread deployment of smart meters in residential and commercial buildings is instrumental in promoting electricity savings. In Taiwan, local governments are actively promoting the installation of smart meters, empowering residents to monitor their electricity consumption and detect abnormal usage patterns, thus mitigating the risk of electrical fires. This safety-oriented approach is a significant driver behind the adoption of smart meters. However, the analysis of the substantial data generated by these meters necessitates pre-processing to address anomalies. Presently, these data primarily serve billing calculations or the extraction of power-saving patterns through big data analytics. To address these challenges, this study proposes a comprehensive approach that integrates a relational database for storing electricity consumption data with knowledge graphs. This integrated method effectively addresses data scarcity at various time scales and identifies prolonged periods of excessive electricity consumption, enabling timely alerts to residents for specific appliance shutdowns. Deep learning techniques are employed to analyze historical consumption data and real-time smart meter readings, with the goal of identifying and mitigating hazardous usage behavior, consequently reducing the risk of electrical fires. The research includes numerical values and text-based predictions for a comprehensive evaluation, utilizing data from ten Taiwanese households in 2022. The anticipated outcome is an improvement in household electrical safety and enhanced energy efficiency.