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This paper aims to analyze the direction and balance of transnational information flows and look at how nonpriced digital information exchanges related to international trade in goods and services.

The authors obtained quantitative data about Web-related data flows between countries and regions using Telegeography data on “Server Location as a Percentage of Top Websites.” They then explore how those flows are correlated to trade in goods.

Web traffic is highly transnational. More than half of the top 100 websites in 9 of the world’s 13 sub-regions are hosted in the USA. More than 15 per cent of the top 100 websites in 9 of the 13 subregions are hosted in Western Europe. East Asia has the largest negative balance in the relationship between incoming and outgoing Web requests. The authors found a very strong negative correlation (−0.878) between Web traffic balances and the balance of trade in goods across all subregions. A similarly strong positive correlation was found with services trade; however, the incompleteness of the data does not allow for strong conclusions yet.

Further research is needed to correlate Web traffic flows with capital flows. The authors also do not have a well-developed theory to explain the strong negative correlation between information flows and goods trade.

The data and analysis have useful implications for digital economy policy. It indicates that digital protectionism of the sort practice by China may succeed in increasing domestic producers’ share of Web requests, but does not make them globally competitive. The strong negative correlation between the balance of unpriced Web information and the balance of trade in goods indicates interdependence rather than domination, challenging narratives that information flow imbalances are caused by market power of the big platforms.

The paper demonstrates the degree to which unpriced digital exchanges are transnational and how various countries are more or less globally competitive in the supply of information that the rest of the world finds attractive.

No other published papers have used the data on website traffic data, and previous research has not explored empirically the correlation between information flows and goods trade.

In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn impacting the potential fuel burn reduction of the aircraft. Usually, in the preliminary design stage of a BLI propulsion system, it is essential to assess the impact of fuselage boundary layer fluids on fan aerodynamic performances under various flight conditions. However, the hub region flow loss is one of the major loss sources in a fan and would greatly influence the fan performances. Moreover, the inflow distortion also results in a complex and highly nonlinear mapping relation between loss and local physical parameters. It will diminish the prediction accuracy of the commonly used low-fidelity computational approaches which often incorporate traditional physics-based loss models, reducing the reliability of these approaches in evaluating fan performances. Meanwhile, the high-fidelity full-annulus unsteady Reynolds-averaged Navier–Stokes (URANS) approach, even though it can give rather accurate loss predictions, is extremely time-consuming. This study aims to develop a fast and accurate hub loss prediction method for a BLI fan under distorted inflow conditions.

This paper develops a data-driven hub loss prediction method for a BLI fan under distorted inflows. To improve the prediction accuracy and applicability, physical understandings of hub flow features are integrated into the modeling process. Then, the key physical parameters related to flow loss are screened by conducting a sensitivity analysis of influencing parameters. Next, a quasi-steady assumption of flow is made to generate a training sample database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters and the output loss.

The data-driven hub loss model shows higher prediction accuracy than the traditional physics-based loss models. It can accurately capture the circumferentially and radially nonuniform variation trends of the losses and the associated absolute magnitudes in a BLI fan under different blade load, inlet distortion intensity and rotating speed conditions. Compared with the high-fidelity full-annulus URANS results, the averaged relative prediction errors of the data-driven hub loss model are kept less than 10%.

The originality of this paper lies in developing a new method for predicting flow loss in a BLI fan rotor blade hub region. This method offers higher prediction accuracy than the traditional loss models and lower computational time cost than the full-annulus URANS approach, which could realize fast evaluations of fan aerodynamic performances and provide technical support for designing high-performance BLI fans.

This paper aims to explore how omni-channel data flows should be integrated by specifying what data, omni-channel agents and information and digital technologies (IDTs) should be considered and connected.

A multiple case study method is used with 17 British companies. The studies are supported by 68 interviews with the case companies and their consumers, 5 site visits, 4 focus group meetings and the companies’ archival data and documentations.

This paper provides novel frameworks for omni-channel data flow integration from consumer and business perspectives. The frameworks consist of omni-channel agents, their data transactions and their supporting IDTs. Relatedly, this paper formalizes the omni-channel data flow integration in the forms of horizontal, vertical and total integrations and explores their contributions to the adaptability of omni-channel, as a complex adaptive system (CAS). It also discusses that how inter-organizational governance mechanisms can support data flow integration and their relevant IDT implementations.

The breadth and depth of the required IDTs for omni-channel integration prove the necessity for omni-channel systems to move toward total integration. Therefore, supported by CAS and inter-organizational governance theories, this research indicates how data flow integration and IDT can transform the omni-channel through self-organization and autonomy capability enhancement.

This research’s recommended frameworks provide a robust platform to formalize data flow integration as the omni-channel's core driver. Accordingly, it moves the literature from a basic description of “what omni-channel is” and provides a novel and significant debate on what specific data should be shared at what levels between which agents of the omni-channel, and with what type of relationship governance mechanism, to assure omni-channel horizontal, vertical and total integrations.

The paper aims to explore the national security implications of a potential for a World Trade Organization (WTO) dispute on data flow restrictions. It proposes a basic conceptual framework to assess data flows’ restrictions under General Agreement on Trade in Services (GATS) security exception.

If a case were to be brought before the WTO dispute settlement, the defender could support its case by invoking the security exception. This paper analyzes three main arguments that could be brought up: protection from cyber espionage, protection from cyberattacks on critical infrastructure and access to data needed to prevent terrorist threats. These three cases are analyzed both legally and technically to assess the relevance of restrictions on data flows under GATS security exception. This analysis can, more generally, inform the debate on the protection of national security in the digital era.

In the three cases, restrictions on data considered critical for national security might raise the cost of certain attacks. However, the risks would remain pervasive and national security would not be significantly enhanced both legally and technically. The implementation of good security standards and encryption techniques appears to be a more effective way to ensure a better response to cyber threats. All in all, it will be important to investigate on a case by case basis whether the scope of the measure (sectors and data covered) is considered proportionate and whether the measure in question in practice reduces the exposure of the country to cyber espionage, cyberattacks and terrorist threats.

This paper represents a contribution to the literature because it is the first paper to address systematically the issue of data flows and national security in the context of a GATS dispute and because it provides a unique perspective that looks both at legal and technical arguments.

Job and worker flows in the Finnish business sector are studied during a deep recession in the early 1990s. The data set covers effectively the whole work force. The gross job and worker flow rates are fairly high. The evidence suggests that the adjustment of labor input has happened through a reduced hiring rate rather than through an increased separation rate. However, during the recession the group of declining plants included more and larger plants than before, which led to reduced employment. Excess worker turnover (churning) and excess job reallocation have been low during the recession. The evidence of the countercyclicality of job reallocation is mixed. The flows are calculated both for the whole business sector, and for seven main industries. Services have clearly higher flow rates than manufacturing, but the cyclical changes in the flows are fairly similar in all industries. To test the sensitivity of the results to data sources, job flows are calculated from three different statistics.

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.

Companies, governments and individuals are using data to create new services such as apps, artificial intelligence (AI) and the Internet of Things (IoT). These data-driven services rely on large pools of data and a relatively unhindered flow of data across borders (few market access or governance barriers). The current approach to governing cross-border data flows through trade agreements and has not led to binding, universal or interoperable rules governing the use of data. The purpose of this article is to explain the new role of data in trade and to explain why data in trade is different from trade in other goods and services. We then suggest a new approach at the national and international levels.

The author uses a mixed methods approach to examine what the literature says about data as a traded good and or service, examines metaphors regarding the role of data in the economy, and then examines whether or not data is really “traded.”

Many countries do not know how to regulate data driven services. There is no consensus on what the appropriate regulatory environment looks like, nor is there a consensus on what are the barriers to cross-border data flows and what constitutes legitimate domestic regulation.

This is the first article to explain both the unique nature of data and the ineffectiveness of the trade system to address that distinctiveness.

Most literature on workflow (WF) adaptation considered the control flow correctness like absence of dead lock, live-lock, etc. during adaptation. The data aspect of WF adaptation like data flow, database schema changes and their correctness are less studied. When the WF schema is modified, their data flow and the database schema changes. The existing approaches used for adapting these data changes in the underlying database schema are time consuming and/or affect the old data persistence. The purpose of this paper is to concern the dynamic adaptation of the WF schema and implementing its data changes in the existing database schema.

A conceptual framework developed to adapt on-the-fly, the concomitant data changes during WF adaptation. The framework consists a set of data schema compliance criteria (DSC) which identify the data changes that can be directly accommodated in the existing database schema. Data adaptation algorithm (DAA) is developed to handle the data changes that does not conform to the DSC in the existing database schema.

In this approach the existing database schema is dynamically evolved without re-creating it, after WF schema adaptation. Therefore the WF schema changes can be implemented on-the-fly without stopping the running system. It also ensures the persistence of old data residing in the existing database.

A novel approach developed to adapt the data changes in the existing database schema, without requiring recreation or migration the data. This automated consistency checking of data attribute changes in the database schema and implement them dynamically.

Due to the economic and strategic importance of telecommunication services, as well as projected growth rates in the information industries, countries have adopted policy restrictions on transborder data flows (TBDF) both into and out of their countries. The reasons for these measures include privacy concerns, national security and the competitiveness of national industries. The short‐term effects on multinational industries have been an increase in data processing costs and a loss of efficiency. Although trade has not yet been restricted in the long‐term, it is possible that trade, particularly in services, will be severely affected. The evolution and types of barriers to transborder data flow are described and their immediate impact on the telecommunications industry and possible ramifications for world trade in general are assessed.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.