3,228 research outputs found

    Using Analytical Information for Digital Business Transformation through DataOps: A Review and Conceptual Framework

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    Organisations are increasingly practising business analytics to generate actionable insights that can guide their digital business transformation. Transforming business digitally using business analytics is an ongoing process that requires an integrated and disciplined approach to leveraging analytics and promoting collaboration. An emerging business analytics practice, Data Operations (DataOps), provides a disciplined approach for organisations to collaborate using analytical information for digital business transformation. We propose a conceptual framework by reviewing the literature on business analytics, DataOps and organisational information processing theory (OIPT). This conceptual framework explains how organisations can employ DataOps as an integrated and disciplined approach for developing the analytical information processing capability and facilitating boundary-spanning activities required for digital business transformation. This research (a) extends current knowledge on digital transformation by linking it with business analytics from the perspective of OIPT and boundary-spanning activities, and (b) presents DataOps as a novel approach for using analytical information for digital business transformation

    The Potential of Electrospinning to Enable the Realization of Energy-Autonomous Wearable Sensing Systems

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    The market for wearable electronic devices is experiencing significant growth and increasing potential for the future. Researchers worldwide are actively working to improve these devices, particularly in developing wearable electronics with balanced functionality and wearability for commercialization. Electrospinning, a technology that creates nano/microfiber-based membranes with high surface area, porosity, and favorable mechanical properties for human in vitro and in vivo applications using a broad range of materials, is proving to be a promising approach. Wearable electronic devices can use mechanical, thermal, evaporative and solar energy harvesting technologies to generate power for future energy needs, providing more options than traditional sources. This review offers a comprehensive analysis of how electrospinning technology can be used in energy-autonomous wearable wireless sensing systems. It provides an overview of the electrospinning technology, fundamental mechanisms, and applications in energy scavenging, human physiological signal sensing, energy storage, and antenna for data transmission. The review discusses combining wearable electronic technology and textile engineering to create superior wearable devices and increase future collaboration opportunities. Additionally, the challenges related to conducting appropriate testing for market-ready products using these devices are also discussed

    Challenges and potential of monotowns: a systematic literature review

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    Relevance. Monotowns, or single-industry settlements, are pivotal in the economic and social landscapes of former Soviet countries. This systematic literature review explores monotowns, examining their evolution, challenges and implications of these unique urban formations from the 18th century to the present day. The study’s relevance lies in its comprehensive analysis of these towns, which have been significant yet underexplored in academic discourse.Research objective. The objective of this review is to critically examine the multifaceted nature of monotowns, focusing on their socio-economic dynamics, challenges, and potential transformation. The review aims to elucidate the complex interplay of economic, social, and political factors that shape these towns and to identify unexplored areas in this field of study.Data and Methods. To achieve these objectives, the study employs a systematic review protocol comprising four phases: search query, document type, publication stage, and language. This approach involves analyzing a wide range of sources from international and regional databases, ensuring a holistic view of monotowns that captures both historical and contemporary perspectives.Results. Monotowns exhibit considerable resilience amidst economic fluctuations and confront distinct socio-economic challenges, including issues specific to mining monotowns and issues related to public health. The findings underscore the importance of adaptive strategies, corporate social responsibility, and proactive government intervention in addressing these challenges.Conclusion. This review provides a comprehensive exploration of monotowns, contributing valuable insights for future research, particularly in understanding their socio-economic dynamics and the challenges they face. The study is significant for scholars in economic development of the territories and business studies, offering a foundation for further exploration of these unique urban formations

    Organizing sustainable development

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    The role and meaning of sustainable development have been recognized in the scientific literature for decades. However, there has recently been a dynamic increase in interest in the subject, which results in numerous, in-depth scientific research and publications with an interdisciplinary dimension. This edited volume is a compendium of theoretical knowledge on sustainable development. The context analysed in the publication includes a multi-level and multi-aspect analysis starting from the historical and legal conditions, through elements of the macro level and the micro level, inside the organization. Organizing Sustainable Development offers a systematic and comprehensive theoretical analysis of sustainable development supplemented with practical examples, which will allow obtaining comprehensive knowledge about the meaning and its multi-context application in practice. It shows the latest state of knowledge on the topic and will be of interest to students at an advanced level, academics and reflective practitioners in the fields of sustainable development, management studies, organizational studies and corporate social responsibility

    Dataflow Programming and Acceleration of Computationally-Intensive Algorithms

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    The volume of unstructured textual information continues to grow due to recent technological advancements. This resulted in an exponential growth of information generated in various formats, including blogs, posts, social networking, and enterprise documents. Numerous Enterprise Architecture (EA) documents are also created daily, such as reports, contracts, agreements, frameworks, architecture requirements, designs, and operational guides. The processing and computation of this massive amount of unstructured information necessitate substantial computing capabilities and the implementation of new techniques. It is critical to manage this unstructured information through a centralized knowledge management platform. Knowledge management is the process of managing information within an organization. This involves creating, collecting, organizing, and storing information in a way that makes it easily accessible and usable. The research involved the development textual knowledge management system, and two use cases were considered for extracting textual knowledge from documents. The first case study focused on the safety-critical documents of a railway enterprise. Safety is of paramount importance in the railway industry. There are several EA documents including manuals, operational procedures, and technical guidelines that contain critical information. Digitalization of these documents is essential for analysing vast amounts of textual knowledge that exist in these documents to improve the safety and security of railway operations. A case study was conducted between the University of Huddersfield and the Railway Safety Standard Board (RSSB) to analyse EA safety documents using Natural language processing (NLP). A graphical user interface was developed that includes various document processing features such as semantic search, document mapping, text summarization, and visualization of key trends. For the second case study, open-source data was utilized, and textual knowledge was extracted. Several features were also developed, including kernel distribution, analysis offkey trends, and sentiment analysis of words (such as unique, positive, and negative) within the documents. Additionally, a heterogeneous framework was designed using CPU/GPU and FPGAs to analyse the computational performance of document mapping

    Strategy Tripod Perspective on the Determinants of Airline Efficiency in A Global Context: An Application of DEA and Tobit Analysis

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    The airline industry is vital to contemporary civilization since it is a key player in the globalization process: linking regions, fostering global commerce, promoting tourism and aiding economic and social progress. However, there has been little study on the link between the operational environment and airline efficiency. Investigating the amalgamation of institutions, organisations and strategic decisions is critical to understanding how airlines operate efficiently. This research aims to employ the strategy tripod perspective to investigate the efficiency of a global airline sample using a non-parametric linear programming method (data envelopment analysis [DEA]). Using a Tobit regression, the bootstrapped DEA efficiency change scores are further regressed to determine the drivers of efficiency. The strategy tripod is employed to assess the impact of institutions, industry and resources on airline efficiency. Institutions are measured by global indices of destination attractiveness; industry, including competition, jet fuel and business model; and finally, resources, such as the number of full-time employees, alliances, ownership and connectivity. The first part of the study uses panel data from 35 major airlines, collected from their annual reports for the period 2011 to 2018, and country attractiveness indices from global indicators. The second part of the research involves a qualitative data collection approach and semi-structured interviews with experts in the field to evaluate the impact of COVID-19 on the first part’s significant findings. The main findings reveal that airlines operate at a highly competitive level regardless of their competition intensity or origin. Furthermore, the unpredictability of the environment complicates airline operations. The efficiency drivers of an airline are partially determined by its type of business model, its degree of cooperation and how fuel cost is managed. Trade openness has a negative influence on airline efficiency. COVID-19 has toppled the airline industry, forcing airlines to reconsider their business model and continuously increase cooperation. Human resources, sustainability and alternative fuel sources are critical to airline survival. Finally, this study provides some evidence for the practicality of the strategy tripod and hints at the need for a broader approach in the study of international strategies

    "It's not a career": Platform work among young people aged 16-19

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    In the online gig economy, or platform work as it is sometimes known, work can be organised through websites and smartphone apps. People can drive for Uber or Deliveroo, sell items on eBay or Etsy, or rent their properties on Airbnb. This research examines the views of young people between the ages of 16 and 19 in the United Kingdom to see whether they knew about the online gig economy, whether they were using it already to earn money, and whether they expected to use it for their careers. It discovers careers professionals’ levels of knowledge, and their ability (and desire) to include the gig economy in their professional practice. This research contributes to discussions about what constitutes decent work, and whether it can be found within the online gig economy. The results point to ways in which careers practice could include platform work as a means of extending young people’s knowledge about alternative forms of work. This study also makes a theoretical contribution to literature, bringing together elements of careership, cognitive schema theory, and motivational theory and psychology of working theory, in a novel combination, to explain how young people were thinking about platform work in the context of their careers

    Spatial epidemiology of a highly transmissible disease in urban neighbourhoods: Using COVID-19 outbreaks in Toronto as a case study

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    The emergence of infectious diseases in an urban area involves a complex interaction between the socioecological processes in the neighbourhood and urbanization. As a result, such an urban environment can be the incubator of new epidemics and spread diseases more rapidly in densely populated areas than elsewhere. Most recently, the Coronavirus-19 (COVID-19) pandemic has brought unprecedented challenges around the world. Toronto, the capital city of Ontario, Canada, has been severely impacted by COVID-19. Understanding the spatiotemporal patterns and the key drivers of such patterns is imperative for designing and implementing an effective public health program to control the spread of the pandemic. This dissertation was designed to contribute to the global research effort on the COVID-19 pandemic by conducting spatial epidemiological studies to enhance our understanding of the disease's epidemiology in a spatial context to guide enhancing the public health strategies in controlling the disease. Comprised of three original research manuscripts, this dissertation focuses on the spatial epidemiology of COVID-19 at a neighbourhood scale in Toronto. Each manuscript makes scientific contributions and enhances our knowledge of how interactions between different socioecological processes in the neighbourhood and urbanization can influence spatial spread and patterns of COVID-19 in Toronto with the application of novel and advanced methodological approaches. The findings of the outcomes of the analyses are intended to contribute to the public health policy that informs neighbourhood-based disease intervention initiatives by the public health authorities, local government, and policymakers. The first manuscript analyzes the globally and locally variable socioeconomic drivers of COVID-19 incidence and examines how these relationships vary across different neighbourhoods. In the global model, lower levels of education and the percentage of immigrants were found to have a positive association with increased risk for COVID-19. This study provides the methodological framework for identifying the local variations in the association between risk for COVID-19 and socioeconomic factors in an urban environment by applying a local multiscale geographically weighted regression (MGWR) modelling approach. The MGWR model is an improvement over the methods used in earlier studies of COVID-19 in identifying local variations of COVID-19 by incorporating a correction factor for the multiple testing problem in the geographically weighted regression models. The second manuscript quantifies the associations between COVID-19 cases and urban socioeconomic and land surface temperature (LST) at the neighbourhood scale in Toronto. Four spatiotemporal Bayesian hierarchical models with spatial, temporal, and varying space-time interaction terms are compared. The results of this study identified the seasonal trends of COVID-19 risk, where the spatiotemporal trends show increasing, decreasing, or stable patterns, and identified area-specific spatial risk for targeted interventions. Educational level and high land surface temperature are shown to have a positive association with the risk for COVID-19. In this study, high spatial and temporal resolution satellite images were used to extract LST, and atmospheric corrections methods were applied to these images by adopting a land surface emissivity (LSE) model, which provided a high estimation accuracy. The methodological approach of this work will help researchers understand how to acquire long time-series data of LST at a spatial scale from satellite images, develop methodological approaches for atmospheric correction and create the environmental data with a high estimation accuracy to fit into modelling disease. Applying to policy, the findings of this study can inform the design and implementation of urban planning strategies and programs to control disease risks. The third manuscript developed a novel approach for visualization of the spread of infectious disease outbreaks by incorporating neighbourhood networks and the time-series data of the disease at the neighbourhood level. The findings of the model provide an understanding of the direction and magnitude of spatial risk for the outbreak and guide for the importance of early intervention in order to stop the spread of the outbreak. The manuscript also identified hotspots using incidence rate and disease persistence, the findings of which may inform public health planners to develop priority-based intervention plans in a resource constraint situation

    Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality

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    As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach. Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties. Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel

    Design-thinking skill enhancement in virtual reality: A literature study

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    As a methodology, design thinking involves practicing “a way of thinking” that non-designers can use as a source of inspiration instead being limited to a group of professional designers. This methodology has gained research attention because of the growing demands for social innovation and sustainability. The general public is expected to gain design-thinking skills through training or by applying design-thinking tools. Virtual reality (VR) is considered a potential tool to help accelerate augmenting design-thinking skills because it allows users to have embodied and immersive experiences. This study reviews existing literature on how VR has been used to enhance design-thinking skills. The general features of the publications such as the year of publication, design-thinking stages, VR types, targeted participants, and publication fields are analyzed for determining the latest trends and scenarios under this research topic. Further, a thematic analysis that follows creative enhancement structures is conducted to understand the role of VR in enhancing design-thinking skills, and future research directions are discussed based on the results. The review concludes that VR has the potential to enhance creativity in many aspects. Moreover, it highlights the need of gaining deeper understanding about 1) art, humanities, and societal perspectives; 2) cognition processes in VR; 3) emphasizing and defining stages in the design-thinking process; 4) technological improvements combined with the Metaverse; and 5) hybrid of the virtual and real worlds
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