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This paper aims to identify the different system approach using Building Information Modelling (BIM) technology that is equipped with decision making processes. Maintenance planning and management are integral components of the construction sector, serving the broader purpose of post-construction activities and processes. However, as Precast Concrete (PC) construction projects increase in scale and complexity, the interconnections among these activities and processes become apparent, leading to planning and performance management challenges. These challenges specifically affect the monitoring of façade components for corrective and preventive maintenance actions.

The concept of maintenance planning for façades, along with the main features of information and communication technology tools and techniques using building information modeling technology, is grounded in the analysis of numerous literature reviews in PC building scenarios.

This research focuses on an integrated system designed to analyze information and support decision-making in maintenance planning for PC buildings. It is based on robust data collection regarding concrete façades' failures and causes. The system aims to provide appropriate planning decisions and minimize the risk of façade failures throughout the building's lifetime.

The study concludes that implementing a research framework to develop such a system can significantly enhance the effectiveness of maintenance planning for façade design, construction and maintenance operations.

Modern subway transportation systems need to satisfy increasing safety demands to rapidly evacuate passengers under hazardous emergency circumstances, such as fires, accidents or terrorist attacks, to reduce passenger injuries or life losses. The emergency evacuation capacity (EEC) of a subway station needs to be revised timely, in case passenger demand increases or the evacuation route changes in the future. However, traditional ways of estimating EEC, e.g. fire drills are time- and resource-consuming and are difficult to revise from time to time. The purpose of this study is to establish an intuitive modelling approach to increase the EEC of subway stations in a stepwised manner.

This study develops an approach to combine agent-based evacuation modelling and building information modelling (BIM) technology to estimate the total evacuation time of a subway station.

Evacuation time can be saved (33% in the studied case) from iterative improvements including stopping escalators running against the evacuation flow and modifying the geometry around escalator exits. Such iterative improvements rely on integrating agent-based modelling and BIM.

The agent-based model can provide a more realistic simulation of intelligent individual movements under emergency circumstances and provides precise feedback on locations of evacuation bottlenecks. This study also examined the effectiveness of two rounds of stepwise improvements in terms of operation or design to increase the EEC of the station.

In recent years, terrorist attacks have gradually become one of the important factors endangering social security. In this context, this research aims to propose methods and principles which can be utilized to make effective evacuation plans to reduce casualties in terrorist attacks.

By analyzing the statistical data of terrorist attack videos, this paper proposes an extended cellular automaton (CA) model and simulates the panic evacuation of the pedestrians in the terrorist attack.

The main findings are as follows. (1) The panic movement of pedestrians leads to the dispersal of the crowd and the increase in evacuation time. (2) Most deaths occur in the early stage of crowd evacuation while pedestrians gather without perceiving the risk. (3) There is a trade-off between escaping from the room and avoidance of attackers for pedestrians. Appropriate panic contagion enables pedestrians to respond more quickly to risks. (4) Casualties are mainly concentrated in complex terrains, e.g. walls, corners, obstacles, exits, etc. (5) The initial position of the attackers has a significant effect on the crowd evacuation. The evacuation efficiency should be reduced if the attacker starts the attack from the exit or corners.

In this research, the concept of “focus region” is proposed to depict the different reactions of pedestrians to danger and the effects of the attacker’s motion (especially the attack strategies of attackers) are classified. Additionally, the influences on pedestrians by direct and indirect panic sources are studied.

Because of the global increase of climate change effects, floods are becoming more frequent and severer, especially in urban areas of coastal cities and islands where floodplains have turned into buildings because of rapid urbanisation, leading to a higher risk of damages. Urban heritage areas should be highly considered in the time of evacuation because of the vulnerability of streets and buildings and limitations on taking counteractions. Given these limitations, this study aims to propose a network of potential evacuation routes based on spatial configuration analysis of the heritage areas.

Penang Island's heritage site, namely, George Town, located on the northwest coast of Malaysia, is chosen as the case study. By using an approach of spatial configuration analysis using space syntax techniques in addition to considering the potential starting points for evacuation and flood risk map of the area, this study analysed the area's street network values for evacuation function during flood crisis time.

Potential evacuation routes were identified for flood disasters in the George Town heritage area. Furthermore, the proposed evacuation routes were evaluated in terms of time for evacuation by metric step-depth analysis of space syntax.

A few studies have focused on practical guidelines for evacuation routes based on spatial configuration analysis, an important yet neglected approach in this regard, especially concerning urban island areas. This study can contribute to providing strategies to reduce vulnerability and casualties in urban heritage areas.

Tourists with different cultural backgrounds think and behave differently. Accurately capturing and correctly understanding cultural differences will help tourist destinations in product/service planning, marketing communication and attracting and retaining tourists. This research employs Hofstede's cultural dimensions theory to analyse the variations in destination image perceptions of Chinese-speaking and English-speaking tourists to Xiamen, a prominent tourist attraction in China.

The evaluation utilizes a two-stage approach, incorporating LDA and BERT-BILSTM models. By leveraging text mining, sentiment analysis and t-tests, this research investigates the variations in tourists' perceptions of Xiamen across different cultures.

The results reveal that cultural disparities significantly impact tourists' perceived image of Xiamen, particularly regarding their preferences for renowned tourist destinations and the factors influencing their travel experience.

This research pioneers applying natural language processing methods and machine learning techniques to affirm the substantial differences in the perceptions of tourist destinations among Chinese-speaking and English-speaking tourists based on Hofstede's cultural theory. The findings furnish theoretical insights for destination marketing organizations to target diverse cultural tourists through precise marketing strategies and illuminate the practical application of Hofstede's cultural theory in tourism and hospitality.

In the mid-2000s, the operator of New York City’s mass transit network committed more than a half-billion dollars to military contractor Lockheed Martin for a security technology capable, in part, of inferring threats based on analysis of data streams, of developing response strategies, and taking automated action toward alerts and calamities in light of evolving circumstances. The project was a failure. This chapter explores the conceptualization and development of this technology – rooted in cybernetics – and compares its conceptual underpinnings with some situated problems of awareness, communication, coordination, and action in emergencies as they unfold in one of the busiest transport systems in the world, the New York subway. The author shows how the technology, with all the theatrical trappings of a “legitimate” security solution, was apparently conceived without a grounded understanding of actual use-cases, and the degree to which the complex interactions which give rise to subway emergency can be anticipated in – and therefore managed through – a technological system. As a case-study, the chapter illustrates the pitfalls of deploying technology against problems which are not well-defined in the first place, to the neglect of investments against much more fundamental problems – such as inadequate communication systems, and unstable relationships with emergency response agencies – which might offer guaranteed benefits, and indeed lay a firm groundwork for future deployment of more ambitious technology.

This article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.

Robots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.

One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

Unplanned urban mobility causes negative effects on the population and the environment. This study aims to understand how higher education institutions (HEIs) are managing the transportation issues related to their activities and how they are implementing actions towards more sustainable practices in this regard.

A systematic review and bibliometric analysis were performed using the Proknow-C method, and the bibliographic portfolio was evaluated to answer guiding questions about distribution of articles over the years and around the world, the most relevant and frequent topics, the travel behavior of university life and the main methodologies used. The meta-analysis was modeled using a programming language in R to execute Bibliometrix package.

Sharing systems, active transport, public transport, urban planning, car parking management and travel behavior are the most relevant topics related to sustainable mobility in HEIs. Different strategies to reduce car use are adopted, and the geographic location of the university and the availability of housing and shopping services in close proximity directly influence travel patterns. As a result, seven guidelines and strategic actions associated were proposed to promote the engagement of institutions in the development of sustainable transport and guide future studies about new solutions to promote sustainable university commutes.

This paper presents a new perspective by performing a critical literature review based on the experiences reported by several isolated studies on the subject. Initiatives of sustainable transport guidelines can be used by academics, urban planners, higher education administrators and other stakeholders to make universities more environmentally friendly, inclusive and accessible.

The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.

The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.

The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.

The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.