5,459 research outputs found

    Graduate Catalog of Studies, 2023-2024

    Get PDF

    Digitalization and Development

    Get PDF
    This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents. The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term. This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Soundscape in Urban Forests

    Get PDF
    This Special Issue of Forests explores the role of soundscapes in urban forested areas. It is comprised of 11 papers involving soundscape studies conducted in urban forests from Asia and Africa. This collection contains six research fields: (1) the ecological patterns and processes of forest soundscapes; (2) the boundary effects and perceptual topology; (3) natural soundscapes and human health; (4) the experience of multi-sensory interactions; (5) environmental behavior and cognitive disposition; and (6) soundscape resource management in forests

    Deep learning architecture for UAV traffic-density prediction

    Get PDF
    The research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network and encoder-decoder LSTM framework to integrate air traffic flow prediction with the intrinsic complexity metric. This adapted complexity metric takes into account the important differences between ATM and UTM operations, such as dynamic flow structures and airspace density. Additionally, the proposed methodology has been evaluated and verified in a simulation scenario environment, in which a drone delivery system that is considered essential in the delivery of COVID-19 sample tests, package delivery services from multiple post offices, an inspection of the railway infrastructure and fire-surveillance tasks. Moreover, the prediction model also considers the impacts of other significant factors, including emergency UTM operations, static no-fly zones (NFZs), and variations in weather conditions. The results show that the proposed model achieves the smallest RMSE value in all scenarios compared to other approaches. Specifically, the prediction error of the proposed model is 8.34% lower than the shallow neural network (on average) and 19.87% lower than the regression model on average

    Systemic Circular Economy Solutions for Fiber Reinforced Composites

    Get PDF
    This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials

    Tradition and Innovation in Construction Project Management

    Get PDF
    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

    Get PDF
    Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness

    Science and Innovations for Food Systems Transformation

    Get PDF
    This Open Access book compiles the findings of the Scientific Group of the United Nations Food Systems Summit 2021 and its research partners. The Scientific Group was an independent group of 28 food systems scientists from all over the world with a mandate from the Deputy Secretary-General of the United Nations. The chapters provide science- and research-based, state-of-the-art, solution-oriented knowledge and evidence to inform the transformation of contemporary food systems in order to achieve more sustainable, equitable and resilient systems
    • …
    corecore