3,558 research outputs found

    Mobile heritage practices. Implications for scholarly research, user experience design, and evaluation methods using mobile apps.

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    Mobile heritage apps have become one of the most popular means for audience engagement and curation of museum collections and heritage contexts. This raises practical and ethical questions for both researchers and practitioners, such as: what kind of audience engagement can be built using mobile apps? what are the current approaches? how can audience engagement with these experience be evaluated? how can those experiences be made more resilient, and in turn sustainable? In this thesis I explore experience design scholarships together with personal professional insights to analyse digital heritage practices with a view to accelerating thinking about and critique of mobile apps in particular. As a result, the chapters that follow here look at the evolution of digital heritage practices, examining the cultural, societal, and technological contexts in which mobile heritage apps are developed by the creative media industry, the academic institutions, and how these forces are shaping the user experience design methods. Drawing from studies in digital (critical) heritage, Human-Computer Interaction (HCI), and design thinking, this thesis provides a critical analysis of the development and use of mobile practices for the heritage. Furthermore, through an empirical and embedded approach to research, the thesis also presents auto-ethnographic case studies in order to show evidence that mobile experiences conceptualised by more organic design approaches, can result in more resilient and sustainable heritage practices. By doing so, this thesis encourages a renewed understanding of the pivotal role of these practices in the broader sociocultural, political and environmental changes.AHRC REAC

    Sustainable Built Environment and Its Implications on Real Estate Development: A Comprehensive Analysis

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    The construction and real estate sectors exert significant environmental, economic, and social impacts. The implementation of environmentally friendly practices in construction offers numerous advantages. Sustainable building practices provide a morally and economically viable solution to address the issues of excessive resource consumption and adverse environmental effects. This chapter investigates the intricate interplay between eco-friendly construction and property development, exploring how integrating urban planning, architectural design, and sustainability principles can shape sustainable building practices, market trends, and future development strategies. Sustainable architecture aims to enhance individuals’ quality of life while minimizing harm to the natural world. The influence of such practices on real estate development manifests in cost savings, increased property values, and a growing demand from buyers, as extensively examined in this article. Furthermore, potential regulations, financing, and technology obstacles are thoroughly analyzed. The report substantiates its claims by presenting real-world examples of sustainable techniques applied in real estate markets. Drawing from existing patterns and emerging methodologies, the paper also forecasts the future implications of sustainable built environments on real estate development. In conclusion, the chapter emphasizes that real estate developers must adapt to evolving sustainability requirements to fulfill their environmental responsibilities and meet consumer expectations

    Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs

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    The way we travel is changing rapidly and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this premise, this paper introduces an envisaged Cybersecurity Centre of Excellence (CSCE) designed to bolster researching, testing, and evaluating the cybersecurity of C-ITSs. We explore the design, functionality, and challenges of CSCE's testing facilities, outlining the technological, security, and societal requirements. Through a thorough survey and analysis, we assess the effectiveness of these systems in detecting and mitigating potential threats, highlighting their flexibility to adapt to future C-ITSs. Finally, we identify current unresolved challenges in various C-ITS domains, with the aim of motivating further research into the cybersecurity of C-ITSs

    A Coupled SFM-ASCRIBE Model To Investigate the Influence of Emotions and Collective Behavior in Homogeneous and Heterogeneous Crowds

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    The understanding of crowd behavior dynamics holds immense significance in ensuring public safety across a range of situations, including emergency evacuations and large-scale events. Our research focuses on two primary objectives: investigating the impact of emotions on crowd movement and gaining valuable insights into collective behavior within crowds. To achieve this, we present a coupled model, incorporating an enhanced ASCRIBE model with an agent displacement model. We introduce heterogeneity into our model by incorporating specific mobility laws for different categories of panicked crowds, considering the influence of emotions on both speed and direction. Through numerical simulations, we analyze the model's parameters, observe the behavior of uniform crowds, and explore the collective dynamics within diverse crowds. By conducting comprehensive simulations and analyses, the findings from this study can contribute to the development of more effective crowd management strategies and emergency evacuation protocols

    Metro systems : Construction, operation and impacts

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    Peer reviewedPublisher PD

    Using the IoT Sustainability Assessment Test to Assess Urban Sustainability

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    Using the IoT Sustainability Assessment Test, the effects of sustainable transportation on urban development are thoroughly investigated in this study. In order to provide a comprehensive picture of urban sustainability across diverse metropolitan regions, the research combines data from many urban sustainability indicators, IoT sensor data, sustainability evaluation scores, and demographic data. The results highlight the need for customized urban planning approaches to meet the particular traits and difficulties of each zone, highlighting the critical role that sustainable mobility plays in promoting environmental stewardship and raising the standard of living in urban areas. Data-driven insights are provided to policymakers, enabling them to formulate fair and efficient urban policies by taking cues from high-scoring regions to encourage sustainability in lower-scoring areas. In the end, the study adds to the current conversation on urban sustainability and provides a road map for developing more livable and sustainable urban settings

    Developing a framework leveraging building information modelling to validate fire emergency evacuation

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    In fire emergency management, a delayed execution will cause a significant number of casualties. Conventional fire drills typically only identify a certain percentage of evacuation bottlenecks after the building has been constructed, which is hard to improve. This paper proposes an innovative framework to validate fire emergency evacuation at the early design stage. According to the experience and knowledge of fire emergency evacuation design, the proposed framework also introduces a seamless two-way information channel to embed fire emergency evacuation simulations into a BIM-based design environment. Several critical factors for fire evacuation have been reviewed in relevant domain knowledge, which is used to build virtual characters to test in experimental scenarios. The results are analyzed to validate fire emergency evacuation factors, and the feedback knowledge is stored as a knowledge model for further applications

    Machine learning applications in search algorithms for gravitational waves from compact binary mergers

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    Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe. However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing. In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software. Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals

    COMPUTATIONAL DESIGN FOR ARCHITECTURAL SPACE PLANNING OF COMMERCIAL EXHIBITIONS - A FRAMEWORK FOR VISITORS INTERACTION USING PARAMETRIC DESIGN AND AGENT-BASED MODELING

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    Using computational tools for evaluating spatial layouts of commercial exhibitions provides an opportunity for assessment of performance before execution. However, most evaluation techniques take into consideration only the physical qualities of the built environment, excluding important factors such as crowds. Crowds are essentially dynamic obstacles that hinder visibility and can induce flight response, but they are also a sign of good exposure when in reasonable amounts. This is mostly due to the challenge of quantifying spatial qualities such as users’ interaction and movement for computational representations. This paper proposes a framework using agent-based modeling for simulating user interaction in commercial exhibition spaces combined with a parametric representation of the built environment. The framework is then evaluated by applying it to a case-study of three layout scenarios in a generic exhibition hall. The simulation results show that layouts with vertical aisles, and less horizontal aisles have better footfall distribution

    Investigating the Impact of Covid-19 on Mobility Condition.

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    Having large number of vehicles operating in the freeways of Houston daily, the mobility concern is high as some of the freeways in Houston are among the most congested freeways in United States. During the COVID-19 pandemic, the less congested freeways led to over speeding resulting in various crashes and even fatality. This resulted in changing of drivers; and ultimately the mobility patterns were changed during the study years of 2019, 2020 and 2021. To better understand how this mobility pattern changed over the three years, this research used Machine Learning algorithms to examine the mobility of freeways in Houston during that time. For this purpose, a model was developed using python coding which considered operating speed and other independent variables to understand the change of the traffic mobility. Several methods were used in the study to check the effectiveness of Artificial Intelligence modeling. To check how the mobility was impacted over the years, Violin Plots were also plotted to illustrate the change of operating speed from year 2019 to 2021. The results of this research demonstrated that there are eight factors that have significant effects on the vehicular mobility. Among them, annual average daily traffic is the most influencing in traffic mobility study whereas K-factor is the least effective among the selected variables. Relative countermeasures were recommended according to the influencing factors that were identified
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