6 research outputs found

    On the competitive facility location problem with a Bayesian spatial interaction model

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    The competitive facility location problem arises when businesses plan to enter a new market or expand their presence. We introduce a Bayesian spatial interaction model which provides probabilistic estimates on location-specific revenues and then formulate a mathematical framework to simultaneously identify the location and design of new facilities that maximise revenue. To solve the allocation optimisation problem, we develop a hierarchical search algorithm and associated sampling techniques that explore geographic regions of varying spatial resolution. We demonstrate the approach by producing optimal facility locations and corresponding designs for two large-scale applications in the supermarket and pub sectors of Greater London

    Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

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    This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management and health monitoring; and durability, life cycle analysis, and circular economy. This paper presents a unique perspective on applications of AI/DL/ML in these domains for the complete building lifecycle, from conceptual stage, design stage, construction stage, operational and maintenance stage until the end of life. Furthermore, data collection strategies using smart vision and sensors, data cleaning methods (post-processing), data storage for developing these models are discussed, and the challenges in model development and strategies to overcome these challenges are elaborated. Future trends in these domains and possible research avenues are also presented

    Analysis of OpenStreetMap data quality at different stages of a participatory mapping process : evidence from slums in Africa and Asia

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    This paper examines OpenStreetMap data quality at different stages of a participatory mapping process in seven slums in Africa and Asia. Data were drawn from an OpenStreetMap-based participatory mapping process developed as part of a research project focusing on understanding inequalities in healthcare access of slum residents in the Global South. Descriptive statistics and qualitative analysis were employed to examine the following research question: What is the spatial data quality of collaborative remote mapping achieved by volunteer mappers in morphologically complex urban areas? Findings show that the completeness achieved by remote mapping largely depends on the morphology and characteristics of slums such as building density and rooftop architecture, varying from 84 in the best case, to zero in the most difficult site. The major scientific contribution of this study is to provide evidence on the spatial data quality of remotely mapped data through volunteer mapping efforts in morphologically complex urban areas such as slums; the results could provide insights into how much fieldwork would be needed in what level of complexity and to what extent the involvement of local volunteers in these efforts is required

    A Bayesian spatial interaction framework for optimal facility location in urban environments

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    The actions of many interacting entities within socio-economic systems proclaim the configurations such as the spatial structure in urban environments. Hence understanding these underlying interactions are important in making the location decisions for growth in urban systems. In this thesis, a Bayesian spatial interaction model, henceforth BSIM, is developed to provide probabilistic predictions about revenues generated by a particular business location, based on its features and the potential customers’ characteristics in a given region. BSIM explicitly accounts for the competition among the facilities through a probability determined by evaluating a store-specific Gaussian distribution at a particular customer location. I propose a scalable variational inference framework that exhibits comparable performance in terms of parameter identification and uncertainty quantification while being significantly faster than competing Markov Chain Monte Carlo inference schemes. The advantages of the introduced BSIM are explored in addressing the competitive facility location problem that typically arises when businesses plan to enter a new market or expand their presence in an environment with existing competitors. A mathematical modelling framework is formulated to simultaneously identify the location and design of new stores in order to maximise the revenue predicted from BSIM in a geographical region. Solving the underlying optimisation problem requires the provision of an exhaustive set of potential sites, which is difficult in practice. Instead, a search algorithm is proposed based on the quadtree method to overcome this challenge by hierarchically exploring geographic regions of varying spatial resolution. This thesis introduces multiple large-scale real-world datasets compiled with open and proprietary data. Finally, demonstrate the proposed framework by producing optimal facility locations and corresponding designs for two case studies in the supermarket and pub sectors in Greater London, providing valuable insights for planning and decision-making under uncertainty

    Phylogenetic Tree Construction Using K-Mer Forest- Based Distance Calculation

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    Phylogenetics is one of the dominant data engineering research disciplines based on biological information. More particularly here, we consider raw DNA sequences and do comparative analysis in order to come up with important conclusions. When representing evolutionary relationships among different organisms in a concise manner, the phylogenetic tree helps significantly. When constructing phylogenetic trees, the elementary step is to calculate the genetic distance among species. Alignment-based sequencing and alignment-free sequencing are the two main distance computation methods that are used to find genetic relatedness of different species. In this paper we propose a novel alignment-free, pairwise, distance calculation method based on k-mers and a state of art machine learning-based phylogenetic tree construction mechanism. With the proposed approach we can convert longer DNA sequences into compendious k-mer forests which gear up the efficiency of comparison. Later we construct the phylogenetic tree based on calculated distances with the help of an algorithm build upon k-medoid clustering, which guaranteed significant efficiency and accuracy compared to traditional phylogenetic tree construction methods

    Phylogenetic Tree Construction Using K-Mer Forest- Based Distance Calculation

    No full text
    Phylogenetics is one of the dominant data engineering research disciplines based on biological information. More particularly here, we consider raw DNA sequences and do comparative analysis in order to come up with important conclusions. When representing evolutionary relationships among different organisms in a concise manner, the phylogenetic tree helps significantly. When constructing phylogenetic trees, the elementary step is to calculate the genetic distance among species. Alignment-based sequencing and alignment-free sequencing are the two main distance computation methods that are used to find genetic relatedness of different species. In this paper we propose a novel alignment-free, pairwise, distance calculation method based on k-mers and a state of art machine learning-based phylogenetic tree construction mechanism. With the proposed approach we can convert longer DNA sequences into compendious k-mer forests which gear up the efficiency of comparison. Later we construct the phylogenetic tree based on calculated distances with the help of an algorithm build upon k-medoid clustering, which guaranteed significant efficiency and accuracy compared to traditional phylogenetic tree construction methods
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