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The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism.

Data-driven culture is considered to bring business-oriented cultural transformation to a firm. It is considered to provide substantial dividends to the firms’ product and process innovations. Recently, several firms have been using different advanced technology-embedded business analytics (BA) tools to improve their business performance. Again, advancement of information and communication technology has helped firms to explore the option to use BA tools with artificial intelligence. This has brought radical change in the business-oriented cultural landscape of the firms to arrive at accurate decision-making to improve their innovation and performance. In this perspective, the aim of this study is to show how a firm’s data-driven culture impacts its product and process innovation, which in turn improves its performance and provides better competitive advantage in the current business environment. With the help of background study, a resource-based view model and different theories, a conceptual model has been developed. The conceptual model has been validated with 456 usable responses from the employees of different firms using different business analytics tools. The study highlights that data-driven culture highly influences both product and process innovation, making the firm more competitive in the industry. In this study, leadership support and data-driven culture have been taken as moderators, whereas firm size, firm age and industry type have been taken as control variables.

Machine Learning (ML) is having a profound impact in the domain of Weather and Climate Prediction. A recent development in this area has been the emergence of fully data‐driven ML prediction models which routinely claim superior performance to that of traditional physics‐based models. We examine some aspects of the forecasts produced by three of the leading current ML models, Pangu‐Weather, FourCastNet and GraphCast, with a focus on their fidelity and physical consistency. The main conclusion is that these ML models are not able to properly reproduce sub‐synoptic and mesoscale weather phenomena and lack the fidelity and physical consistency of physics‐based models and this has impacts on the interpretation of their forecasts and their perceived skill. Balancing forecast skill and physical realism will be an important consideration for future ML models. Plain Language Summary The last few years have seen the emergence of a new type of weather forecasting models completely based on ML technologies. These models do not codify the physical laws governing atmospheric dynamics but learn to produce forecasts from historical reanalysis data sets of the Earth system like the ECMWF ERA5. In this work we show that the forecasts produced by some of the leading ML models are physically inconsistent and should be better considered as post‐processing algorithms rather than realistic simulators of the atmosphere. The challenge for next generation of ML models for weather forecasting will be to improve their fidelity while maintaining forecast skill. Key Points Forecasts from Machine Learning (ML) models have energy spectra notably different from those of their training reanalysis fields and Numerical Weather Prediction models This results in overly smooth predictions and weather phenomena at spatial scales shorter than 300–400 km are not properly represented Fundamental physical balances and derived quantities are not realistically represented in the forecasts of the ML models

The availability of an ever‐expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi‐dimensional parameter space and massive data sets involved is emblematic of complex, resource‐intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data‐driven approach and subset of artificial intelligence, is a potential game‐changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine‐assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area. The family of 2D materials is an unprecedented platform for materials by design, thanks to their ever‐expanding material portfolio with rich internal degrees of freedom. The study provides a comprehensive overview of the recent progress, challenges and emerging opportunities in a frontier research area that exploits machine learning—a very powerful data‐driven approach and subset of artificial intelligence—for 2D materials.

Digital transformation and the possibility to collect large amount of data, the so-called big data, from different sources can lead to the redefinition of business processes, models and infrastructures. In this respect, data-driven decision making (DDDM) emphasizes the need to rethink management practices to view data as a driving force that can improve the effectiveness of decisions and nurture innovation. Over time, data-driven principles have been reframed as the foundations for the development of a new mind-set that considers data as a key asset that redesigns entrepreneurship and encompasses orientation, culture and human resources management to help entrepreneurs catch, evaluate and launch entrepreneurial opportunities that can improve technology and knowledge by stimulating innovation. For this reason, the study aims at reconceptualizing entrepreneurship according to a multi-levelled perspective based on the integration of cultural, human, knowledge-based and technological dimension to assess the impact of data-driven management on entrepreneurial opportunities creation and on the development of different kinds of innovation. To assess these goals, empirical research based on constructivist grounded theory and on the administration of semi-structured interviews is performed through the investigation of an Italian public–private Consortium specialized in big data. The findings allow the elaboration of a conceptual framework which classifies the activities of data-driven entrepreneurial processes, the phases of opportunity creation and the different kinds of innovation enabled in the Consortium by guiding entrepreneurs and managers in the elaboration of effective data analysis strategies.

Data-driven modeling directly utilizes experimental data with machine learning techniques to predict a material’s response without the necessity of using phenomenological constitutive models. Although data-driven modeling presents a promising new approach, it has yet to be extended to the modeling of large-deformation biological tissues. Herein, we extend our recent local convexity data-driven (LCDD) framework (He and Chen, 2020) to model the mechanical response of a porcine heart mitral valve posterior leaflet. The predictability of the LCDD framework by using various combinations of biaxial and pure shear training protocols are investigated, and its effectiveness is compared with a full structural, phenomenological model modified from Zhang et al. (2016) and a continuum phenomenological Fung-type model (Tong and Fung, 1976). We show that the predictivity of the proposed LCDD nonlinear solver is generally less sensitive to the type of loading protocols (biaxial and pure shear) used in the data set, while more sensitive to the insufficient coverage of the experimental data when compared to the predictivity of the two selected phenomenological models. While no pre-defined functional form in the material model is necessary in LCDD, this study reinstates the importance of having sufficiently rich data coverage in the date-driven and machine learning type of approaches. It is also shown that the proposed LCDD method is an enhancement over the earlier distance-minimization data-driven (DMDD) against noisy data. This study demonstrates that when sufficient data is available, data-driven computing can be an alternative method for modeling complex biological materials.

Data-driven school improvement has been proposed to improve and support educational practices, and more studies are emerging describing data-driven practices in schools and the effects of data-driven interventions. This paper reports on a study that has taken place within a national program where 15 schools from 6 different municipalities and organizations are working at classroom, school and municipality levels to improve educational practices using data-driven methods. The study aimed at understanding what educational problems teachers, principals and administrative staff in the project aimed to address through the utilization of data-driven methods and the challenges they face in doing so. Using a mixed-methods design, we identified four thematic areas that reflect the focused problem areas of the participants in the project, namely didactics, democracy, assessment and planning, and mental health. All development groups identified problems that can be solved with data-driven methods. Along with this, we also identified five challenges faced by the participants: time and resources, competence, ethics, digital systems and common language. We conclude that the main challenge faced by the participants is data literacy, and that professional development is needed to support effective and successful data-driven practices in schools.

Computational complexity has been the bottleneck for applying physically based simulations in large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessment. To overcome the issue of long computational time and accelerate the prediction process, this paper proposes that the prediction of maximum water depth can be considered an image‐to‐image translation problem in which water depth rasters are generated using the information learned from data instead of by conducting simulations. The proposed data‐driven urban pluvial flood approach is based on a deep convolutional neural network trained using flood simulation data obtained from three catchments and 18 hyetographs. Multiple tests to assess the accuracy and validity of the proposed approach were conducted with both design and real hyetographs. The results show that flood prediction based on neural networks use only 0.5% of the time compared with that of physically based models, with promising accuracy and generalizability. The proposed neural network can also potentially be applied to different but relevant problems, including flood analysis for flood‐safe urban layout planning.

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future may have a possible dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers may need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Many learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, many of these workers are shooting themselves in the feet. In this paper, we propose Human-in-the-loop Artificial Intelligence (HitAI) as a fairer paradigm for AI systems. Recognizing that any AI system has humans in the loop, HitAI will reward these aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Merry Men, HitAI researchers should fight for a fairer Robin Hood Artificial Intelligence that gives back what it steals. This article is part of the special track on AI and Society.

Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time‐consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data‐driven approaches are gaining traction in materials science. In this paper, recent advances in data‐driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data‐driven processes. It is demonstrated how data‐driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data‐driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data‐driven materials R&D for functional coatings, shedding light on its potential and future developments. Functional coatings play a vital role in various industries for their protective and functional properties. However, its design often involves time‐consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data‐driven approaches are gaining traction in materials science. This review provides an overview of data‐driven materials R&D for functional coatings, shedding light on its potential and future developments.