Data-driven Smart Research Articles

The Metaverse as a virtual form of data-driven smart urbanism: platformization and its underlying processes, institutional dimensions, and disruptive impacts

The emerging phenomenon of platformization has given rise to what has been termed "platform society,“ a digitally connected world where platforms have penetrated the heart of urban societies—transforming social practices, disrupting social interactions and market relations, and affecting democratic processes. One of the recent manifestations of platformization is the Metaverse, a global platform whose data infrastructures, governance models, and economic processes are predicted to penetrate different urban sectors and spheres of urban life. The Metaverse is an idea of a hypothetical set of “parallel virtual worlds” that incarnate ways of living in believably virtual cities as an alternative to future data-driven smart cities. However, this idea has already raised concerns over what constitutes the global architecture of computer mediation underlying the Metaverse with regard to different forms of social life as well as social order. This study analyzes the core emerging trends enabling and driving data-driven smart cities and uses the outcome to devise a novel framework for the digital and computing processes underlying the Metaverse as a virtual form of data-driven smart cities. Further, it examines and discusses the risks and impacts of the Metaverse, paying particular attention to: platformization; the COVID-19 crisis and the ensuing non-spontaneous "normality" of social order; corporate-led technocratic governance; governmentality; privacy, security, and trust; and data governance. A thematic analysis approach is adopted to cope with the vast body of literature of various disciplinarities. The analysis identifies five digital and computing processes related to data-driven smart cities: digital instrumentation, digital hyper-connectivity, datafication, algorithmization, and platformization. The novelty of the framework derived based on thematic analysis lies in its essential processual digital and computing components and the way in which these are structured and integrated given their clear synergies as to enabling the functioning of the Metaverse towards potentially virtual cities. This study highlights how and why the identified digital and computing processes—as intricately interwoven with the entirety of urban ways of living—arouse contentions and controversies pertaining to society’ public values. As such, it provides new insights into understanding the complex interplay between the Metaverse as a form of science and technology and the other dimensions of society. Accordingly, it contributes to the scholarly debates in the field of Science, Technology, and Society (STS) by highlighting the societal and ethical implications of the platformization of urban societies through the Metaverse.

Special Issue on “Sensing Technologies of Plant Eco-physiological Information Contributing to Data-driven Smart Agriculture”

Special Issue on “Sensing Technologies of Plant Eco-physiological Information Contributing to Data-driven Smart Agriculture”

Surface roughness prediction in milling using long-short term memory modelling

Artificial Intelligence (AI), presently vogue in technology, energizes many researchers to address complex issues. As a result of advances in machine learning and data analytics, the manufacturing cycle has become more efficient. Optimizing machining parameters is vital to ensure better surface quality. To enable proactive actions in manufacturing, incipient surface roughness prediction is quite essential. The challenge of capturing non-linear dynamics is becoming more complex as the data grows. Traditional machine learning techniques are unable to express the sequential features extracted. The recent approach, Long Short-Term Memory (LSTM), can handle a variety of data lengths and extract long-term series features. In this paper, the LSTM approach is utilized to forecast surface roughness during milling of the S45C steel dataset. On the test data set, the Root Mean Square Error (RMSE) loss is computed using the LSTM model, yielding an RMSE loss of 0.1097. In data-driven smart manufacturing, the LSTM model demonstrates its capabilities in surface roughness decision-making.

Supply Chain Management 4.0: Looking Backward, Looking Forward

About ten years ago, a bold vision with a prospect of largely autonomous factories of the future with digitized processes, data-driven smart applications, and seamlessly integrated supply chains was born and quickly became known as Industry 4.0 or aka the 4th Industrial Revolution. A true game-changer, Industry 4.0 has revolutionized the possibilities of advanced manufacturing across the manufacturing sector at large. However, the advancement of supply chains and supply chain management that are essential elements of the wider production engineering process has not yet reached the same level of maturity. The purpose of this paper is to review the state-of-art of supply chain management within the context of Industry 4, from both an academic and industry perspective. The authors will discuss the key elements of typical supply chains in terms of what has already been achieved and identify important research challenges to be addressed over the course of the next 5-10 years. The content obtained was done through a combination of a supply chain management workshop at the University of Liverpool in 2020 and an extensive literature review. The unique contribution of this work is finding how each identified supply chain management element e.g., procurement has progressed within the context of I4 in both academia and industry, focusing on State-of-Art and future challenges.

Talking About Big Data-Driven Smart Urban Economy at the 6th Edition of ICESBA International Conference on Sciences and Business Administration

Talking About Big Data-Driven Smart Urban Economy at the 6th Edition of ICESBA International Conference on Sciences and Business Administration

Intelligent tool wear monitoring and multi-step prediction based on deep learning model

In modern manufacturing industry, tool wear monitoring plays a significant role in ensuring product quality and machining efficiency. Numerous data-driven models based on deep learning have been developed to improve the accuracy of tool wear monitoring. However, tool wear monitoring under variable working conditions is rarely investigated. More importantly, for data-driven smart manufacturing, it is more meaningful and challenging to simultaneously achieve tool wear monitoring and multi-step prediction. To address the aforementioned issue, a novel framework based on feature normalization, attention mechanism, and deep learning algorithms was proposed for tool wear monitoring and multi-step prediction. Feature normalization was introduced to eliminate the dependence of local features on cutting conditions, and the attention mechanism was applied to enhance valuable information and weaken redundant information. Then a parallel convolutional neural network (parallel CNN) structure with different layers followed by Bi-directional long short term memory (BiLSTM) was developed for tool condition monitoring. Finally, based on the monitored tool wear values, a new tool condition prediction model based on the dense residual neural network (ResNetD) was proposed for short-term and long-term prediction of tool wear. Tool wear experiments under different combinations of cutting parameters were conducted to verify the proposed model, and the results showed that the proposed model has great advantages in efficiency and robustness compared with other data-driven models.

Eco-districts and data-driven smart eco-cities: Emerging approaches to strategic planning by design and spatial scaling and evaluation by technology

In recent years, the world has witnessed significant progress in implementing numerous eco-district and eco-city projects, which are under the banner of experimentation or seen as sites of innovation. Indeed, the rise of such initiatives serves as a sign of renewed attempts to experiment or innovate in designing urban futures in an increasingly urbanized and datafied society. In light of this, a number of alternative models of eco-urbanism have been proposed by scholars and promoted by policy makers. The prominent among these models are sustainable urban districts and data-driven smart eco-cities. At the core of these models is a range of compact and ecological design strategies and static and dynamic conceptions of spatial scaling. These are intended to produce and boost the benefits of sustainability on the basis of a set of integrated approaches—increasingly supported by big data technologies—as a way to overcome new challenges and introduce new solutions. Based on two case studies conducted on three of the ecologically and technologically leading cities in Europe—Stockholm, Malmö, and Barcelona, this paper analyses and discusses the role and relevance of integrating density, mixed land use, greening, and low-energy buildings to the sustainability of emerging eco-districts; the new conceptions of cities and their spatial scales in the context of data-driven smart eco-cities; and the opportunities and challenges of smart urban metabolism with respect to the evaluation of eco-districts. This study shows that combining compact and ecological design strategies improves the performance of eco-districts with respect to the three dimensions of sustainability, as well as paves the way for their balanced integration for producing synergistic effects. Also, this study highlights the innovative potential and enabling role of urban computing and intelligence in transforming the spatial scaling of data-driven smart eco-cities through generating the kind of designs that increase the effects of sustainability as outcomes of processes. Moreover, this study reveals the advantages of the data-driven approach to the analysis of the flows of resources in urban environments in relation to the evaluation of sustainable urban district development. This paper concludes that data-driven smart planning and evaluation holds great potential for facilitating progress towards achieving the goals of sustainability thanks to the proven role and untapped potential of big data technologies in monitoring, understanding, and analyzing urban processes and systems in real-time.

The underlying components of data-driven smart sustainable cities of the future: a case study approach to an applied theoretical framework

The increased pressure on cities has led to a stronger need to build sustainable cities that can last. Planning sustainable cities of the future, educated by the lessons of the past and anticipating the challenges of the future, entails articulating a multi-scalar vision that, by further interplaying with major societal trends and paradigm shifts in science and technology, produce new opportunities towards reaching the goals of sustainability. Enabled by big data science and analytics, the ongoing transformative processes within sustainable cities are motivated by the need to address and overcome the challenges hampering progress towards sustainability. This means that sustainable cities should be understood, analyzed, planned, designed, and managed in new and innovative ways in order to improve and advance their contribution to sustainability. Therefore, sustainable cities are increasingly embracing and leveraging what smart cities have to offer in terms of data-driven technologies and applied solutions so as to optimize, enhance, and maintain their performance and thus achieve the desired outcomes of sustainability—under what has been termed “data-driven smart sustainable cities.” Based on a case study analysis, this paper develops an applied theoretical framework for strategic sustainable urban development planning. This entails identifying and integrating the underlying components of data-driven smart sustainable cities of the future in terms of the dimensions, strategies, and solutions of the leading global paradigms of sustainable urbanism and smart urbanism. The novelty of the proposed framework lies in combining compact urban design strategies, eco-city design strategies and technology solutions; data-driven smart city technologies, competences, and solutions for sustainability; and environmentally data-driven smart sustainable city solutions and strategies. These combined have great potential to improve and advance the contribution of sustainable cities to the goals of sustainability through harnessing its synergistic effects and balancing the integration of its dimensions. The main contribution of this work lies in providing new insights into guiding the development of various types of strategic planning processes of transformative change towards sustainability, as well as to stimulate and inspire future research endeavors in this direction. This study informs policymakers and planners about the opportunity of attaining important advances in sustainability by integrating the established models of sustainable urbanism and the emerging models of smart urbanism thanks to the proven role and untapped potential of data-driven technologies in catalyzing sustainable development and thus boosting sustainability benefits.

Data-Driven Smart Eco-Cities of the Future: An Empirically Informed Integrated Model for Strategic Sustainable Urban Development

The eco-city is seenas a major force in the overall transition toward a more sustainable urban development paradigm, acting as a set of exemplary techno-environmental solutions for social transformation. However, the scale and rate of urbanization fundamentally disrupt the challenges that eco-cities must contend with. This is coupled with the increased need to understand, plan, and manage eco-cities in new and innovative ways. On this account, transformative processes and practices within eco-cities are being highly responsive to a form of data-driven urbanism as a more effective way to address and overcome the kinds of challenges that eco-cities are designed to solve. That is, eco-cities are increasingly embracing and leveraging what smart cities have to offer in terms of data-driven technologies and applied solutions in an effort to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart eco-cities.” The aim of this paper is to develop an integrated model for strategic sustainable urban development based on empirical research in the form of case studies. This model combines and integrates the leading global paradigms of urbanism—namely eco-cities, data–driven smart cities, and environmentally data-driven smart sustainable cities—in terms of their strategies and solutions. The main contribution of this study lies in providing an innovative way of building future models for sustainable urban development, as well as practical insights into developing strategic planning processes of transformative change toward sustainability based on integrated approaches. The proposed model serves to facilitate progress toward achieving the long-term goals of sustainability for those cities that are badging or regenerating themselves as eco-cities, or manifestly planning to be or become smart eco-cities in the era of big data.

Intrinsic value of food chain data

Intrinsic value of food chain data

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective.

The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on “Data Science” including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Big Data Dreams and Reality in Shenzhen: An Investigation of Smart City Implementation in China

Chinese cities are increasingly using digital technologies to address urban problems and govern society. However, little is known about how this digital transition has been implemented. This study explores the introduction of digital governance in Shenzhen, one of China's most advanced smart cities. We show that, at the local level, the successful implementation of digital systems faces numerous hurdles in long-standing data management and bureaucratic practices that are at least as challenging as the technical problems. Furthermore, the study finds that the digital systems in Shenzhen entail a creeping centralisation of data that potentially turns lower administrative government units into mere users of the city-level smart platforms rather than being in control of their own data resources. Smart city development and big data ambitions thereby imply shifting stakeholder relations at the local level and also pull non-governmental stakeholders, such as information technology companies and research institutions, closer to new data flows and smart governance systems. The findings add to the discussion of big data-driven smart systems and their implications for governance processes in an authoritarian context.

Integrating Data-Based Strategies and Advanced Technologies with Efficient Air Pollution Management in Smart Cities

The COVID-19 pandemic has demonstrated that creative leadership based on data and citizen volunteers is more significant than vaccines themselves, so this study focuses on the collaboration of sophisticated technologies and human potential to monitor air pollution. Air pollution contributes to critical environmental problems in various towns and cities. With the emergence of the smart city concept, appropriate methods to curb exposure to pollutants must be part of an appropriate urban development policy. This study presents a technologically driven air quality solution for smart cities that advertises energy-efficient and cleaner sequestration in these areas. It attempts to explore how to incorporate data-driven approaches and citizen participation into effective public sector pollution management in smart cities as a major component of the smart city definition. The smart city idea was developed as cities became more widespread through communication devices. This study addresses the technical criteria for implementing a framework that public administration can use to prepare for renovation of public buildings, minimizing energy use and costs and linking smart police stations to monitor air pollution as a part of an integrated city. Such a digital transition in resource management will increase public governance energy performance and provide a higher standard for operations and a healthier environment. The study results indicate that complex processes lead to efficient and sustainable smart cities. This research discovered an interpretive pattern in how public agencies, private enterprises, and community members think and what they do in these regional contexts. It concludes that economic and social benefits could be realized by exploiting data-driven smart city development for its social and spatial complexities.

Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning

Sustainable cities are quintessential complex systems—dynamically changing environments and developed through a multitude of individual and collective decisions from the bottom up to the top down. As such, they are full of contestations, conflicts, and contingencies that are not easily captured, steered, and predicted respectively. In short, they are characterized by wicked problems. Therefore, they are increasingly embracing and leveraging what smart cities have to offer as to big data technologies and their novel applications in a bid to effectively tackle the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities.” This paper analyzes and discusses the enabling role and innovative potential of urban computing and intelligence in the strategic, short-term, and joined-up planning of data-driven smart sustainable cities of the future. Further, it devises an innovative framework for urban intelligence and planning functions as an advanced form of decision support. This study expands on prior work done to develop a novel model for data-driven smart sustainable cities of the future. I argue that the fast-flowing torrent of urban data, coupled with its analytical power, is of crucial importance to the effective planning and efficient design of this integrated model of urbanism. This is enabled by the kind of data-driven and model-driven decision support systems associated with urban computing and intelligence. The novelty of the proposed framework lies in its essential technological and scientific components and the way in which these are coordinated and integrated given their clear synergies to enable urban intelligence and planning functions. These utilize, integrate, and harness complexity science, urban complexity theories, sustainability science, urban sustainability theories, urban science, data science, and data-intensive science in order to fashion powerful new forms of simulation models and optimization methods. These in turn generate optimal designs and solutions that improve sustainability, efficiency, resilience, equity, and life quality. This study contributes to understanding and highlighting the value of big data in regard to the planning and design of sustainable cities of the future.

Data Consistency for Data-Driven Smart Energy Assessment.

In the smart grid era, the number of data available for different applications has increased considerably. However, data could not perfectly represent the phenomenon or process under analysis, so their usability requires a preliminary validation carried out by experts of the specific domain. The process of data gathering and transmission over the communication channels has to be verified to ensure that data are provided in a useful format, and that no external effect has impacted on the correct data to be received. Consistency of the data coming from different sources (in terms of timings and data resolution) has to be ensured and managed appropriately. Suitable procedures are needed for transforming data into knowledge in an effective way. This contribution addresses the previous aspects by highlighting a number of potential issues and the solutions in place in different power and energy system, including the generation, grid and user sides. Recent references, as well as selected historical references, are listed to support the illustration of the conceptual aspects.

Data-Driven Smart Sustainable Cities of the Future: New Conceptions of and Approaches to the Spatial Scaling of Urban Form

There is a growing interest in urban form as the spatial concretization of urban sustainability. At the core of sustainable urban form is the spatial pattern of the different types of the physical objects characterizing the built-up areas of settlements at different spatial scales. The spatial features of urban design is key to producing the benefits of sustainability and enacting its effects as outcomes of processes. However, it has been difficult to evaluate how and to what extent sustainable urban forms contribute to sustainability and to judge whether or not a certain sustainable urban form is actually sustainable. Therefore, there is a need to find more effective ways to address and implement the spatial scaling of sustainable urban form in an attempt to increase the positive outcomes of sustainability. This relates to the emerging model of sustainable cities, which is increasingly being enabled by urban computing and intelligence in terms of planning and design under what has been termed “data-driven smart sustainable cities”. This paper analyzes and discusses the emerging conceptions of and approaches to spatial scales that should be considered in the planning and design of data-driven smart sustainable cities of the future. In doing so, it highlights the innovative potential of urban computing and intelligence for enhancing and transforming the spatial scaling of sustainable urban form. I argue that data-driven technologies allow sustainable urban forms to monitor, understand, and analyze the different aspects of their spatial scaling for generating the kind of designs that improve sustainability. Conceiving spatial scales as outcomes of processes and planning accordingly hold great potential for attaining the goals of sustainability. Sustainability outcomes are multi-scalar in nature, which justifies the need to integrate spatial scales that have clear synergies in their management and planning and need to be coupled. This synergic integration produces combined effects that are greater than the sum of the separate effects of spatial scales with respect to sustainability benefits.

A novel model for data-driven smart sustainable cities of the future: the institutional transformations required for balancing and advancing the three goals of\xa0sustainability

In recent years, it has become increasingly feasible to achieve important improvements of sustainability by integrating sustainable urbanism with smart urbanism thanks to the proven role and synergic potential of data-driven technologies. Indeed, the processes and practices of both of these approaches to urban planning and development are becoming highly responsive to a form of data-driven urbanism, giving rise to a new phenomenon known as “data-driven smart sustainable urbanism.” Underlying this emerging approach is the idea of combining and integrating the strengths of sustainable cities and smart cities and harnessing the synergies of their strategies and solutions in ways that enable sustainable cities to optimize, enhance, and maintain their performance on the basis of the innovative data-driven technologies offered by smart cities. These strengths and synergies can be clearly demonstrated by combining the advantages of sustainable urbanism and smart urbanism. To enable such combination, major institutional transformations are required in terms of enhanced and new practices and competences. Based on case study research, this paper identifies, distills, and enumerates the key benefits, potentials, and opportunities of sustainable cities and smart cities with respect to the three dimensions of sustainability, as well as the key institutional transformations needed to support the balancing of these dimensions and to enable the introduction of data-driven technology and the adoption of applied data-driven solutions in city operational management and development planning. This paper is an integral part of a futures study that aims to analyze, investigate, and develop a novel model for data-driven smart sustainable cities of the future. I argue that the emerging data-driven technologies for sustainability as innovative niches are reconfiguring the socio-technical landscape of institutions, as well as providing insights to policymakers into pathways for strengthening existing institutionalized practices and competences and developing and establishing new ones. This is necessary for balancing and advancing the goals of sustainability and thus achieving a desirable future.

A Novel Model for Data-Driven Smart Sustainable Cities of the Future: A Strategic Roadmap to Transformational Change in the Era of Big Data

The conscious push for sustainable cities to be smarter and thus more sustainable in the era of big data is due to the problematicity surrounding their development planning approaches and operational management mechanisms, as well as the fragmentation of their designs and technologies. This has a clear bearing on their performance with respect to the contribution to and balancing of the goals of sustainability. This situation is compounded by the negative consequences of the expansion of urbanization, an irreversible global trend involving a multitude of environmental, social, economic, and spatial conditions that pose unprecedented challenges to policymakers and planners. The underlying argument is that more innovative solutions and sophisticated methods are needed to enable sustainable cities to tackle the kind of problems and complexities they embody. This in turn brings us to the question related to the weak connection between sustainable cities and smart cities as approaches as well as their extreme fragmentation as landscapes, both at the technical and policy levels. Therefore, sustainable cities need to embrace and leverage what smart cities have to offer so that they can optimize, enhance, and maintain their performance and thus achieve the desired outcomes of sustainability. This paper aims to develop a novel model for data-driven smart sustainable cities of the future, and in doing so, it provides a strategic planning process of transformative change towards sustainability. This model combines and integrates the prevailing paradigms of sustainable urbanism and the emerging paradigms of smart urbanism —based on the outcomes of the four case studies conducted on compact cities, eco-cities, data–driven smart cities, and environmentally data-driven smart sustainable cities. As the core of this aggregate model is how to bring about the different forms of infrastructural transformations needed to reach a vision of a sustainable future in the era of big data. Especially, it has become feasible to attain important improvements and advancements of sustainability by amalgamating sustainable cities and smart cities thanks to the proven role of advanced ICT and the untapped potential of data-driven technologies.

Smart Data-Driven Policy on Unmanned Aircraft Systems (UAS): Analysis of Drone Users in U.S. Cities

Realizing the benefits of drones while minimizing public concerns requires development and implementation of drone use policies that are grounded in an understanding of drone users and their behavior. This study aims to contribute to data-driven smart cities by filling our gap in knowledge about city drone users and their compliance behavior. The literature review has identified the main factors affecting drone policy compliance. This study collects data via a national survey of adults on drone behavior and focuses on city drone users. The results show that city drone users are younger with more dispersed educational backgrounds and income distribution than those in the general population. Moreover, civic duty, trust in government, and knowledge about regulatory requirements are motivators for drone users to comply with drone regulation.

Data-driven smart sustainable cities of the future: An evidence synthesis approach to a comprehensive state-of-the-art literature review

Abstract Sustainable cities are currently undergoing unprecedented transformative changes in light of the recent paradigm shift in science and technology brought on by big data science and analytics. These marked changes are motivated by the increased need to tackle the problematicity surrounding sustainable cities as quintessential complex systems in terms of their development planning, operational management, and fragmentary design strategies and technology solutions. That is to say, sustainable cities are increasingly embracing and leveraging what smart cities have to offer in terms of big data technologies and their novel applications in an attempt to effectively deal with the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities.” This new area is a significant gap in and of itself—as it is still in its infancy—that this paper seeks to fill together with to what extent the integration of sustainable urbanism and smart urbanism is addressed and what directions and forms it takes. Using a compelling evidence synthesis approach, this paper provides a comprehensive state-of-the-art literature review of the flourishing field of data-driven smart sustainable cities. This study corroborates that big data technologies will change sustainable urbanism in fundamental and irreversible ways, bringing new and innovative ways of monitoring, understanding, analyzing, planning, and managing sustainable cities. It reveals that the evolving development planning approaches and operational management mechanisms enabled by data-driven technologies are of crucial importance to increase and maintain the contribution of sustainable cities to the goals of sustainability in the face of urbanization. However, what smart urbanism entails and the way it functions raises several critical questions, including whether the policy and governance of data-driven smart sustainable cities of the future will become too technocentric and technocratic respectively, but also with regard to other aspects of social and environmental sustainability. Addressing these important contemporary concerns is of equal importance in achieving the desired outcomes of sustainability. This review and critique of the existing work on the prevailing and emerging paradigms of urbanism provides a valuable reference for scholars and practitioners and the necessary material to inform them of the latest developments in the burgeoning field of data-driven smart sustainable cities. In addition, by shedding light on the increasing adoption and uptake of big data technologies in sustainable urbanism, this study seeks to help policymakers and planners assess the pros and cons of smart urbanism when effectuating sustainable urban transformations in the era of big data, as well as to stimulate prospective research and further critical debates on the topic.