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A smart city is a potential solution to the problems caused by the unprecedented speed of urbanization. However, the increasing availability of big data is a challenge for transforming a city into a smart one. Conventional statistics and econometric methods may not work well with big data. One promising direction is to leverage advanced machine learning tools in analyzing big data about cities. In this paper, the authors propose a model to learn region embedding. The learned embedding can be used for more accurate prediction by representing discrete variables as continuous vectors that encode the meaning of a region.

The authors use the random walk and skip-gram methods to learn embedding and update the preliminary embedding generated by graph convolutional network (GCN). The authors apply this model to a real-world dataset from Manhattan, New York, and use the learned embedding for crime event prediction.

This study’s results show that the proposed model can learn multi-dimensional city data more accurately. Thus, it facilitates cities to transform themselves into smarter ones that are more sustainable and efficient.

The authors propose an embedding model that can learn multi-dimensional city data for improving predictive analytics and urban operations. This model can learn more dimensions of city data, reduce the amount of computation and leverage distributed computing for smart city development and transformation.

This paper aims to investigate the rise of a Chinese fashion cluster in Lombardy.

Three approaches and descending levels of analysis are integrated: a quantitative analysis based on demographic data to highlight the evolution of the regional distribution of the Chinese community and Chinese entrepreneurship in Lombardy; a literature review to reconstruct the historical development of Chinatown in Milan; and few in-depth interviews and a survey to represent how the Chinese living in Chinatown perceive the changing role of the enclave.

The Chinese in Lombardy are rising as a regional ethnic fashion cluster. This cluster is rising out of three major drivers: ethnic social capital as a source of community-based entrepreneurship; the crisis of traditional industrial districts in the 1990s as a trigger opportunity; and the trans-regionalization of the fashion industry as a main driver of its current development. The rise of this cluster is bottom-up.

The findings are based on a single case study. There are evidences showing that the Chinese are rising as regional and/or inter-regional clusters in other institutional settings. However, this study may benefit from comparisons with other institutional and national contexts.

Chinese entrepreneurship may foster regional growth as a complementary source of cultural variety, internationalization and multi-regional co-specialization.

Entrepreneurship may foster social cohesion and collaboration.

This paper contributes to existing literature by proposing a would-be theory of the evolution of regional ethnic clusters.

This paper aims to explore and demonstrate the ability to integrate entry-level additive manufacturing (AM) techniques with responsive polymers capable of mechanical to chemical energy transduction. This integration signifies the merger of AM and smart materials.

Custom filaments were synthesized comprising covalently incorporated spiropyran moieties. The mechanical activation and chemical response of the spiropyran-containing filaments were demonstrated in materials that were produced via fused filament fabrication techniques.

Custom filaments were successfully produced and printed with complete preservation of the mechanochemical reactivity of the spiropyran units. These smart materials were demonstrated in two key constructs: a center-cracked test specimen and a mechanochromic force sensor. The mechanochromic nature of the filament enables (semi)quantitative assessment of peak loads based on color change, without requiring any external analytical techniques.

This paper describes the first examples of three-dimensional-printed mechanophores, which may be of significant interest to the AM community. The ability to control the chemical response to external mechanical forces, in combination with AM to process the bulk materials, potentiates customizability at the molecular and macroscopic length scales.

Anchorage with concrete bearing pad is commonly used in Iran for stabilization of excavations because of the ease of construction, less costs and less time consumption than the soldier pile method. In this method, a wall facing which includes the concrete bearing pads at the location of the anchors and a shotcrete layer between the bearing pads is constructed parallel to the excavation operation similar to the nailing method.

In this paper, using the finite element software Abaqus, a three-dimensional model of the above-mentioned type of wall is constructed, and the effect of spacing and size of bearing pads on the wall behavior is discussed.

According to the obtained results, the size of the concrete bearing pads has little effect on wall deformations, but the internal forces and bending moments developed in the shotcrete layer between the bearing pads are greatly influenced by the bearing pads dimensions and spacing.

Owing to the discrete elements of the wall facing, the behavior of this system is completely three-dimensional.

Monolithic precast concrete frame structures have been promoted and developed in recent years. Owing to material deterioration and a weaker structural integrity, monolithic precast concrete frame structures may suffer from insufficient seismic capacity as service time increases. A typical joint of monolithic precast concrete frame structure is studied in this paper. The purpose of this paper is to perform numerical modeling of the typical joint subjected to low cyclic load at different ages and analyze the hysteretic behavior reduction with ages under common atmosphere environment.

Existing un-carbonated concrete, carbonated concrete and corroded rebar are all considered as deterioration factors for the typical joint, whose constitutive models are introduced into the finite element model to study. Moreover, time-dependent constitutive model of existing un-carbonated concrete and mechanical model of bond between precast and cast-in-place concrete are established on the basis of existing experimental data. Then, finite element method is used to investigate the seismic property reduction of the typical joint, where nonlinear springs are set to simulate bonding between precast and cast-in-place concrete.

Analyzing the results, the reduction of reaction force from skeleton curves of the joint is significant in the first 30 years of service time, and slows down after 30 years. Besides, the ductility, secant stiffness and equivalent viscous damping coefficient of the typical joint remain almost unchanged in the first decade, but decrease obviously after 10 years.

The originality of the paper consists in the following. The time-dependent constitutive model of existing un-carbonated concrete is established and used in finite element method. Besides, bonding between precast and cast-in-place concrete is considered using nonlinear springs. There is a reference value for the seismic performance assessment of existing monolithic precast concrete frame structures.

This paper aims to focus on the characteristics of a two-tier network featuring internal subject cooperation and external embedded cooperation in the context of regional innovation systems (RISs) and explore the influence of network characteristics on knowledge emergence.

Using social network analysis, a two-tier internal and external cooperation network of a RIS is constructed. A negative binomial regression method is used to explore the effects of the characteristics of these two-tier internal and external networks on knowledge emergence, the moderating effect of the cooperation knowledge base in this context is investigated and grouping and quantile regressions are used to conduct heterogeneity analysis.

The scale of the internal cooperation network has a positive effect on knowledge emergence, and the betweenness centralization of the internal cooperation network has an inverted U-shaped effect on knowledge emergence. The scale and structural holes of the external embedded network have an inverted U-shaped effect on knowledge emergence. Furthermore, the internal cooperation knowledge base weakens the influence of the external embedded network on knowledge emergence.

This research may enlighten policymakers with respect to improving the scale and structure of the RIS cooperation network and matching the embedded network based on the internal cooperation knowledge base to promote knowledge emergence.

This research contributes to the study of knowledge emergence by exploring the influence of a two-tier network structure and scale characteristics on knowledge emergence in RISs. This paper also extends the framework of relevant research by integrating the internal cooperation knowledge base into the analysis of externally embedded cooperation and knowledge emergence.

The technique for hiding confidential data in specific digital media by enhancing the graphical contents is known as watermarking. The dissemination of information over a secure channel is essential for multimedia applications. The purpose of this study is to develop a secure communication approach for OFDM system.

This paper exploits a secure communication in the orthogonal frequency division multiplexing (OFDM) system using wavelet-based video watermarking technique. In this work, the Chronological-MS algorithm is used for securing the data communication in the OFDM system. Here, the secret message is embedded in video frames using wavelet transform for hiding sensitive information and the hidden information is transmitted over the OFDM system. The Chronological-MS algorithm is used for selecting the optimal regions in the video for embedding secret message. In embedding phase, wavelet coefficients are obtained by applying wavelet transform on the frame for embedding the secret message. Meanwhile, in extraction phase, the inverse wavelet transform is applied to extract the secret message.

Considering number of frames, the maximum Peak signal-to-noise ratio (PSNR) value is attained by proposed Wavelet + Chronological MS method for Video 2 with value 73.643 dB, respectively. Meanwhile, the minimum mean squared error (MSE) attained by the proposed Wavelet + Chronological MS method is when considering number of frames with MSE values as 0.001 for both Videos 1 and 2. The minimum bit error rate (BER) value is attained by the proposed method with value 0.00009 considering random noise with Video 1. Thus, the proposed Wavelet + Chronological MS have shown better results than the existing techniques.

This work proposes a wavelet-based watermarking method using Chronological-MS, for initiating secured communication over an OFDM. One of the main advantages of wavelets is that they offer a simultaneous localization in time and frequency domain. Hence, the proposed method offers the highly secured data transmission over the OFDM.

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

The purpose of this study is to explore the heat transfer enhancement in copper–water nanofluid flowing in a diagonally vented rectangular enclosure with four discrete heaters mounted centrally on the sidewalls and a square-shaped embedded heated block in the influence of a static magnetic field.

Four discrete heaters are mounted centrally on each sidewall of the rectangular enclosure that embraces a heated square block. A static transverse magnetic field is acting on the vertical walls. The Navier–Stokes equations of motion and the energy equation are modified by incorporating Lorentz force and basic physical properties of nanofluid. The derived momentum and energy equations are tackled numerically using the successive over-relaxation technique associating with the Gauss–Seidel iteration technique. The effects of physical parameters connected to dynamics of flow and heat convection are explored from streamlines and isotherms graphs and discussed numerically in terms of Nusselt number.

The effect of the embedded heated square block size and its location in the enclosure, nanoparticles volume fraction and the intensity of the magnetic field on flow and heat transfer are computed. Compared with the case when no heated block is embedded in the enclosure, in free convection at Ra = 106, the average local Nusselt number on the wall-mounted heaters is attenuated by 8.25%, 11.24% and 12.75% when the enclosure embraced a heated square block of side length 10% of H, 20% of H and 30% of H, respectively. An increase in Hartmann number suppresses the heat convection.

The enhancement in the convective heat is greater when the buoyancy effect dominates the viscous effects. Placing the embedded heated block near the inlet vent, the lower temperature zone has reduced while the embedded heated block is at the central location of the enclosure, the high-temperature zone has expanded. The external magnetic field can be used as a non-invasive controlling device.

The numerically simulated results for heat convection of water-based copper nanofluid agreed qualitatively with the existing experimental results.

The models could be used in designing a target-oriented heat exchanger.

The paper includes a comparative study for three locations of the embedded heated square. The optimal results for the centrally located heated block are also performed for three different sizes of the embedded block. The numerically simulated results are compared with the published numerical and experimental studies.

In heterogeneously segmented markets, collaborating with product users in product innovation is important for business success. End user innovators and embedded user innovators differ in terms of their prior embeddedness in the target industry. The purpose of this study is twofold. First, the authors empirically compare these two types of user innovators in terms of their diffusion channel selection. Second, the authors analyze how the technological advances of their innovations affect this difference.

Using an online questionnaire survey, this study collected a sample of 237 user-generated innovations in Japan and analyzed several hypotheses using quantitative statistical approaches.

The analysis shows that embedded user innovators are more likely than end user innovators to transfer their innovations to producers rather than peers. As the technological advances of their innovations increase, end user innovators' likelihood of transferring their innovation to producers increases more significantly than that of embedded user innovators.

This is the first paper to investigate the difference between end user innovators and embedded user innovators with respect to their diffusion channel selection as well as the moderating role of technological advances. The findings bring new perspectives to the domains of user–producer collaboration and technology transfer.