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Periodicals

A State-of-the-Art Review on the Integration of Building Information Modeling (BIM) and Geographic Information System (GIS)

The integration of Building Information Modeling (BIM) and Geographic Information System (GIS) has been identified as a promising but challenging topic to transform information towards the generation of knowledge and intelligence. Achievement of integrating these two concepts and enabling technologies will have a significant impact on solving problems in the civil, building and infrastructure sectors. However, since GIS and BIM were originally developed for different purposes, numerous challenges are being encountered for the integration. To better understand these two different domains, this paper reviews the development and dissimilarities of GIS and BIM, the existing integration methods, and investigates their potential in various applications. This study shows that the integration methods are developed for various reasons and aim to solve different problems. The parameters influencing the choice can be summarized and named as “EEEF” criteria: effectiveness, extensibility, effort, and flexibility. Compared with other methods, semantic web technologies provide a promising and generalized integration solution. However, the biggest challenges of this method are the large efforts required at early stage and the isolated development of ontologies within one particular domain. The isolation problem also applies to other methods. Therefore, openness is the key of the success of BIM and GIS integration.

Architecture & Planning• 2017• Multidisciplinary Digital Publishing Institute
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Periodicals

The intense world syndrome – an alternative hypothesis for autism

Autism is a devastating neurodevelopmental disorder with a polygenetic predisposition that seems to be triggered by multiple environmental factors during embryonic and/or early postnatal life. While significant advances have been made in identifying the neuronal structures and cells affected, a unifying theory that could explain the manifold autistic symptoms has still not emerged. Based on recent synaptic, cellular, molecular, microcircuit, and behavioral results obtained with the valproic acid (VPA) rat model of autism, we propose here a unifying hypothesis where the core pathology of the autistic brain is hyper-reactivity and hyper-plasticity of local neuronal circuits. Such excessive neuronal processing in circumscribed circuits is suggested to lead to hyper-perception, hyper-attention, and hyper-memory, which may lie at the heart of most autistic symptoms. In this view, the autistic spectrum are disorders of hyper-functionality, which turns debilitating, as opposed to disorders of hypo-functionality, as is often assumed. We discuss how excessive neuronal processing may render the world painfully intense when the neocortex is affected and even aversive when the amygdala is affected, leading to social and environmental withdrawal. Excessive neuronal learning is also hypothesized to rapidly lock down the individual into a small repertoire of secure behavioral routines that are obsessively repeated. We further discuss the key autistic neuropathologies and several of the main theories of autism and re-interpret them in the light of the hypothesized Intense World Syndrome.

Architecture & Planning• 2007• Frontiers Media
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Periodicals

IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends

Smart agriculture techniques have recently seen widespread interest by farmers. This is driven by several factors, which include the widespread availability of economically-priced, low-powered Internet of Things (IoT) based wireless sensors to remotely monitor and report conditions of the field, climate, and crops. This enables efficient management of resources like minimizing water requirements for irrigation and minimizing the use of toxic pesticides. Furthermore, the recent boom in Artificial Intelligence can enable farmers to deploy autonomous farming machinery and make better predictions of the future based on present and past conditions to minimize crop diseases and pest infestation. Together these two enabling technologies have revolutionized conventional agriculture practices. This survey paper provides: (a) A detailed tutorial on the available advancements in the field of smart agriculture systems through IoT technologies and AI techniques; (b) A critical review of these two available technologies and challenges in their widespread deployment; and (c) An in-depth discussion about the future trends including both technological and social, when smart agriculture systems will be widely adopted by the farmers globally.

Architecture & Planning• 2022• Institute of Electrical and Electronics Engineers
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Periodicals

A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition

Speech is the most significant mode of communication among human beings and a potential method for human-computer interaction (HCI) by using a microphone sensor. Quantifiable emotion recognition using these sensors from speech signals is an emerging area of research in HCI, which applies to multiple applications such as human-reboot interaction, virtual reality, behavior assessment, healthcare, and emergency call centers to determine the speaker's emotional state from an individual's speech. In this paper, we present major contributions for; (i) increasing the accuracy of speech emotion recognition (SER) compared to state of the art and (ii) reducing the computational complexity of the presented SER model. We propose an artificial intelligence-assisted deep stride convolutional neural network (DSCNN) architecture using the plain nets strategy to learn salient and discriminative features from spectrogram of speech signals that are enhanced in prior steps to perform better. Local hidden patterns are learned in convolutional layers with special strides to down-sample the feature maps rather than pooling layer and global discriminative features are learned in fully connected layers. A SoftMax classifier is used for the classification of emotions in speech. The proposed technique is evaluated on Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets to improve accuracy by 7.85% and 4.5%, respectively, with the model size reduced by 34.5 MB. It proves the effectiveness and significance of the proposed SER technique and reveals its applicability in real-world applications.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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Periodicals

Variational LSTM Enhanced Anomaly Detection for Industrial Big Data

With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly discussed topic in digital and intelligent industry field. The security problem existing in the signal processing on large scale of data stream is still a challenge issue in industrial internet of things, especially when dealing with the high-dimensional anomaly detection for intelligent industrial application. In this article, to mitigate the inconsistency between dimensionality reduction and feature retention in imbalanced IBD, we propose a variational long short-term memory (VLSTM) learning model for intelligent anomaly detection based on reconstructed feature representation. An encoder-decoder neural network associated with a variational reparameterization scheme is designed to learn the low-dimensional feature representation from high-dimensional raw data. Three loss functions are defined and quantified to constrain the reconstructed hidden variable into a more explicit and meaningful form. A lightweight estimation network is then fed with the refined feature representation to identify anomalies in IBD. Experiments using a public IBD dataset named UNSW-NB15 demonstrate that the proposed VLSTM model can efficiently cope with imbalance and high-dimensional issues, and significantly improve the accuracy and reduce the false rate in anomaly detection for IBD according to F1, area under curve (AUC), and false alarm rate (FAR).

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Periodicals

A Review on Mixed Reality: Current Trends, Challenges and Prospects

Currently, new technologies have enabled the design of smart applications that are used as decision-making tools in the problems of daily life. The key issue in designing such an application is the increasing level of user interaction. Mixed reality (MR) is an emerging technology that deals with maximum user interaction in the real world compared to other similar technologies. Developing an MR application is complicated, and depends on the different components that have been addressed in previous literature. In addition to the extraction of such components, a comprehensive study that presents a generic framework comprising all components required to develop MR applications needs to be performed. This review studies intensive research to obtain a comprehensive framework for MR applications. The suggested framework comprises five layers: the first layer considers system components; the second and third layers focus on architectural issues for component integration; the fourth layer is the application layer that executes the architecture; and the fifth layer is the user interface layer that enables user interaction. The merits of this study are as follows: this review can act as a proper resource for MR basic concepts, and it introduces MR development steps and analytical models, a simulation toolkit, system types, and architecture types, in addition to practical issues for stakeholders such as considering MR different domains.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Periodicals

A Comprehensive Survey on UAV Communication Channel Modeling

Unmanned aerial vehicles (UAVs) have stroke great interested both by the academic community and the industrial community due to their diverse military applications and civilian applications. Furthermore, UAVs are also envisioned to be part of future airspace traffic. The application functions delivery relies on information exchange among UAVs as well as between UAVs and ground stations (GSs), which further closely depends on aeronautical channels. However, there is a paucity of comprehensive surveys on aeronautical channel modeling in line with the specific aeronautical characteristics and scenarios. To fill this gap, this paper focuses on reviewing the air-to-ground (A2G), ground-to-ground (G2G), and air-to-air (A2A) channel measurements and modeling for UAV communications and aeronautical communications under various scenarios. We also provide the design guideline for managing the link budget of UAV communications taking account of link losses and channel fading effects. Moreover, we also analyze the receive/transmit diversity gain and spatial multiplexing gain achieved by multiple-antenna-aided UAV communications. Finally, we discuss the remaining challenge and open issues for the future development of UAV communication channel modeling.

Architecture & Planning• 2019• Institute of Electrical and Electronics Engineers
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Periodicals

Exosomal DMBT1 from human urine-derived stem cells facilitates diabetic wound repair by promoting angiogenesis

Chronic non-healing wounds represent one of the most common complications of diabetes and need advanced treatment strategies. Exosomes are key mediators of cell paracrine action and can be directly utilized as therapeutic agents for tissue repair and regeneration. Here, we explored the effects of exosomes from human urine-derived stem cells (USC-Exos) on diabetic wound healing and the underlying mechanism. Methods: USCs were characterized by flow cytometry and multipotent differentiation potential analyses. USC-Exos were isolated from the conditioned media of USCs and identified by transmission electron microscopy and flow cytometry. A series of functional assays in vitro were performed to assess the effects of USC-Exos on the activities of wound healing-related cells. Protein profiles in USC-Exos and USCs were examined to screen the candidate molecules that mediate USC-Exos function. The effects of USC-Exos on wound healing in streptozotocin-induced diabetic mice were tested by measuring wound closure rates, histological and immunofluorescence analyses. Meanwhile, the role of the candidate protein in USC-Exos-induced regulation of angiogenic activities of endothelial cells and diabetic wound healing was assessed. Results: USCs were positive for CD29, CD44, CD73 and CD90, but negative for CD34 and CD45. USCs were able to differentiate into osteoblasts, adipocytes and chondrocytes. USC-Exos exhibited a cup-or sphere-shaped morphology with a mean diameter of 51.57 2.93 nm and positive for CD63 and TSG101. USC-Exos could augment the functional properties of wound healing-related cells including the angiogenic activities of endothelial cells. USC-Exos were enriched in the proteins that are involved in regulation of wound healing-related biological processes. Particularly, a pro-angiogenic protein called deleted in malignant brain tumors 1 (DMBT1) was highly expressed in USC-Exos. Further functional assays showed that DMBT1 protein was required for USC-Exos-induced promotion of angiogenic responses of cultured endothelial cells, as well as angiogenesis and wound healing in diabetic mice. Conclusion: Our findings suggest that USC-Exos may represent a promising strategy for diabetic soft tissue wound healing by promoting angiogenesis via transferring DMBT1 protein.

Architecture & Planning• 2018• Ivyspring International Publisher
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Periodicals

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
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Periodicals

The potential for tourism and hospitality experience research in human-robot interactions

Purpose The purpose of this study is to review recent work in the robotics literature and identify future opportunities for consumer/tourist experience research in human-robot interactions (HRIs). Design/methodology/approach The paper begins by covering the framework of robotic agent presence and embodiment that are relevant for HRI. Next, the paper identifies future opportunities for hospitality and tourism scholars to undertake consumer/tourist experience research in HRIs. Findings The result of this study provided potential directions for advancing theoretical, methodological and managerial implications for tourism experience research in HRI. Research limitations/implications Concepts from robotics research are diffusing into a range of disciplines, from engineering to social sciences. These advancements open many unique, yet urgent, opportunities for hospitality and tourism research. Practical implications This paper illustrates the speed at which robotics research is progressing. Moreover, the concepts reviewed in this research on robotic presence and embodiment are relevant for real-world applications in hospitality and tourism. Social implications Developments in robotics research will transform hospitality and tourism experiences in the future. Originality/value This research is one of the early papers in the field to review robotics research and provide innovative directions to broaden the interdisciplinary perspective for future hospitality and tourism research.

Architecture & Planning• 2017• Emerald Publishing Limited
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Periodicals

ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses

This empirical study examines ChatGPT as an educational and learning tool. It investigates the opportunities and chal-lenges that ChatGPT provides to the students and instruc-tors of communication, business writing, and composition courses. It also strives to provide recommendations. After conducting 30 theory-based and application-based ChatGPT tests, it is found that ChatGPT has the potential of replacing search engines as it provides accurate and relia-ble input to students. For opportunities, the study found that ChatGPT provides a platform for students to seek an-swers to theory-based questions and generate ideas for ap-plication-based questions. It also provides a platform for in-structors to integrate technology in classrooms and conduct workshops to discuss and evaluate generated responses. For challenges, the study found that ChatGPT, if unethically used by students, may lead to human unintelligence and un-learning. This may also present a challenge to instructors as the use of ChatGPT negatively affects their ability to dif-ferentiate between meticulous and automaton-dependent students, on the one hand, and measure the achievement of learning outcomes, on the other hand. Based on the out-come of the analysis, this study recommends communica-tion, business writing, and composition instructors to (1) re-frain from making theory-based questions as take-home as-sessments, (2) provide communication and business writing students with detailed case-based and scenario-based as-sessment tasks that call for personalized answers utilizing critical, creative, and imaginative thinking incorporating lec-tures and textbook material, (3) enforce submitting all take-home assessments on plagiarism detection software, espe-cially for composition courses, and (4) integrate ChatGPT generated responses in classes as examples to be discussed in workshops. Remarkably, this study found that ChatGPT skillfully paraphrases regenerated responses in a way that is not detected by similarity detection software. To maintain their effectiveness, similarity detection software providers need to upgrade their software to avoid such incidents from slipping unnoticed.

Architecture & Planning• 2023
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Periodicals

Submergence and Waterlogging Stress in Plants: A Review Highlighting Research Opportunities and Understudied Aspects

Soil flooding creates composite and complex stress in plants known as either submergence or waterlogging stress depending on the depth of the water table. In nature, these stresses are important factors dictating the species composition of the ecosystem. On agricultural land, they cause economic damage associated with long-term social consequences. The understanding of the plant molecular responses to these two stresses has benefited from research studying individual components of the stress, in particular low-oxygen stress. To a lesser extent, other associated stresses and plant responses have been incorporated into the molecular framework, such as ion and ROS signaling, pathogen susceptibility, and organ-specific expression and development. In this review, we aim to highlight known or suspected components of submergence/waterlogging stress that have not yet been thoroughly studied at the molecular level in this context, such as miRNA and retrotransposon expression, the influence of light/dark cycles, protein isoforms, root architecture, sugar sensing and signaling, post-stress molecular events, heavy-metal and salinity stress, and mRNA dynamics (splicing, sequestering, and ribosome loading). Finally, we explore biotechnological strategies that have applied this molecular knowledge to develop cultivars resistant to flooding or to offer alternative uses of flooding-prone soils, like bioethanol and biomass production.

Architecture & Planning• 2019• Frontiers Media
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Periodicals

Strategic Planning Research: Toward a Theory-Driven Agenda

This review incorporates strategic planning research conducted over more than 30 years and ranges from the classical model of strategic planning to recent empirical work on intermediate outcomes, such as the reduction of managers’ position bias and the coordination of subunit activity. Prior reviews have not had the benefit of more socialized perspectives that developed in response to Mintzberg’s critique of planning, including research on planned emergence and strategy-as-practice approaches. To stimulate a resurgence of research interest on strategic planning, this review therefore draws on a diverse body of theory beyond the rational design and contingency approaches that characterized research in this domain until the mid-1990s. We develop a broad conceptualization of strategic planning and identify future research opportunities for improving our understanding of how strategic planning influences organizational outcomes. Our framework incorporates the role of strategic planning practitioners; the underlying routines, norms, and procedures of strategic planning (practices); and the concrete activities of planners (praxis).

Architecture & Planning• 2013• SAGE Publishing
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Periodicals

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Non-Terrestrial Networks in 5G & Beyond: A Survey

Fifth-generation (5G) telecommunication systems are expected to meet the world market demands of accessing and delivering services anywhere and anytime. The Non-Terrestrial Network (NTN) systems are able to satisfy the requests of anywhere and anytime connections by offering wide-area coverage and ensuring service availability, continuity, and scalability. In this work, we review the 3GPP NTN features and their potential for satisfying the user expectations in 5G & beyond networks. The state of the art, current 3GPP research activities, and open issues are summarized to highlight the importance of NTN over the wireless communication landscape. Future research directions are also identified to assess the role of NTN in 5G and beyond systems.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Slums from Space—15 Years of Slum Mapping Using Remote Sensing

The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution (VHR) sensors. This improves the ability to produce information for pro-poor policy development and to build methods capable of supporting systematic global slum monitoring required for international policy development such as the Sustainable Development Goals. This review provides an overview of slum mapping-related remote sensing publications over the period of 2000–2015 regarding four dimensions: contextual factors, physical slum characteristics, data and requirements, and slum extraction methods. The review has shown the following results. First, our contextual knowledge on the diversity of slums across the globe is limited, and slum dynamics are not well captured. Second, a more systematic exploration of physical slum characteristics is required for the development of robust image-based proxies. Third, although the latest commercial sensor technologies provide image data of less than 0.5 m spatial resolution, thereby improving object recognition in slums, the complex and diverse morphology of slums makes extraction through standard methods difficult. Fourth, successful approaches show diversity in terms of extracted information levels (area or object based), implemented indicator sets (single or large sets) and methods employed (e.g., object-based image analysis (OBIA) or machine learning). In the context of a global slum inventory, texture-based methods show good robustness across cities and imagery. Machine-learning algorithms have the highest reported accuracies and allow working with large indicator sets in a computationally efficient manner, while the upscaling of pixel-level information requires further research. For local slum mapping, OBIA approaches show good capabilities of extracting both area- and object-based information. Ultimately, establishing a more systematic relationship between higher-level image elements and slum characteristics is essential to train algorithms able to analyze variations in slum morphologies to facilitate global slum monitoring.

Architecture & Planning• 2016• Multidisciplinary Digital Publishing Institute
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Learning as immersive experiences: Using the four‐dimensional framework for designing and evaluating immersive learning experiences in a virtual world

Abstract Traditional approaches to learning have often focused upon knowledge transfer strategies that have centred on textually‐based engagements with learners, and dialogic methods of interaction with tutors. The use of virtual worlds, with text‐based, voice‐based and a feeling of ‘presence’ naturally is allowing for more complex social interactions and designed learning experiences and role plays, as well as encouraging learner empowerment through increased interactivity. To unpick these complex social interactions and more interactive designed experiences, this paper considers the use of virtual worlds in relation to structured learning activities for college and lifelong learners. This consideration necessarily has implications upon learning theories adopted and practices taken up, with real implications for tutors and learners alike. Alongside this is the notion of learning as an ongoing set of processes mediated via social interactions and experiential learning circumstances within designed virtual and hybrid spaces. This implies the need for new methodologies for evaluating the efficacy, benefits and challenges of learning in these new ways. Towards this aim, this paper proposes an evaluation methodology for supporting the development of specified learning activities in virtual worlds, based upon inductive methods and augmented by the four‐dimensional framework reported in a previous study. The study undertaken aimed to test the efficacy of the proposed evaluation methodology and framework, and to evaluate the broader uses of a virtual world for supporting lifelong learners specifically in their educational choices and career decisions. The paper presents the findings of the study and considers that virtual worlds are reorganising significantly how we relate to the design and delivery of learning. This is opening up a transition in learning predicated upon the notion of learning design through the lens of ‘immersive learning experiences’ rather than sets of knowledge to be transferred between tutor and learner. The challenges that remain for tutors rest with the design and delivery of these activities and experiences. The approach advocated here builds upon an incremental testing and evaluation of virtual world learning experiences.

Architecture & Planning• 2009• Wiley
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Cloud-Assisted IoT-Based SCADA Systems Security: A Review of the State of the Art and Future Challenges

Industrial systems always prefer to reduce their operational expenses. To support such reductions, they need solutions that are capable of providing stability, fault tolerance, and flexibility. One such solution for industrial systems is cyber physical system (CPS) integration with the Internet of Things (IoT) utilizing cloud computing services. These CPSs can be considered as smart industrial systems, with their most prevalent applications in smart transportation, smart grids, smart medical and eHealthcare systems, and many more. These industrial CPSs mostly utilize supervisory control and data acquisition (SCADA) systems to control and monitor their critical infrastructure (CI). For example, WebSCADA is an application used for smart medical technologies, making improved patient monitoring and more timely decisions possible. The focus of the study presented in this paper is to highlight the security challenges that the industrial SCADA systems face in an IoT-cloud environment. Classical SCADA systems are already lacking in proper security measures; however, with the integration of complex new architectures for the future Internet based on the concepts of IoT, cloud computing, mobile wireless sensor networks, and so on, there are large issues at stakes in the security and deployment of these classical systems. Therefore, the integration of these future Internet concepts needs more research effort. This paper, along with highlighting the security challenges of these CI's, also provides the existing best practices and recommendations for improving and maintaining security. Finally, this paper briefly describes future research directions to secure these critical CPSs and help the research community in identifying the research gaps in this regard.

Architecture & Planning• 2016• Institute of Electrical and Electronics Engineers
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