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Through this research study, the authors found that digital thread has made significant progress in the life cycle management of the US Air Force. The authors hope that by reviewing similar studies in the aerospace field, the meaning of digital thread can be summarized and applied to a wider range of fields. In addition, theoretically, the definition of digital twin and digital thread are not unified. The authors hope that the comparison of digital thread and digital twin will better enable scholars to distinguish between the two concepts. Besides, the authors are also looking forward that more people will realize the significance of digital thread and carry out future research.

Complete research about digital thread and the relevant concept of the digital twin is conducted. First, by searching in Google Scholar with the keyword “digital thread”, the authors filter results and save literature with high relevance to digital thread. The authors also track these papers’ references for more paper of digital thread and digital twin. After removing the duplicate and low-relevance literature, 72 digital thread-related literature studies are saved and further analyzed from the perspective of time development, application field and research directions.

Digital thread application in industries other than the aviation manufacturing industry is still relatively few, and the research on the application of digital thread in real industrial scenarios is mainly at the stage of framework design and design-side decision optimization. In addition, the digital thread needs a new management mechanism and organizational structure to realize landing. The new management mechanism and the process can adapt to the whole life cycle management process based on the digital thread, manage the data security and data update, and promote the digital thread to play a better effect on the organizational management.

Based on a review of digital thread, future research directions and usage suggestions are given. The fault diagnosis of high-speed train bogie as an example shows the effectiveness of the method and also partially demonstrates the advantages and effects brought by the digital thread connecting the data models at various stages.

This paper first investigates and analyzes the theoretical connotation and research progress of digital thread and gives a complete definition of digital thread from the perspective of the combination of digital thread and digital twins. Next, the research process of digital thread is reviewed, and the application fields, research directions and achievements in recent years are summarized. Finally, taking the fault diagnosis of high-speed train bogie as an example partially demonstrates the advantages and effects brought by the digital thread connecting the data models at various stages.

The paper aims to address the organizational transformation of firms for value creation resulting from cloud computing (CC).

With reference to the theory of organizational fit, we modeled organizational transformation as a function of five aspects of CC practice: functionality, data management, roles and competences of information technology services, control and organizational culture. The output variable was tested against a set of input variables defined with reference to the technology–organization–environment (TOE) and technology acceptance model (TAM). Based on a sample of 487 companies in seven countries in Europe, Asia, and the United States, the authors distinguished two groups of firms: transformational and hyper transformational.

The results highlight the key factors that determine whether a firm falls into one of these two groups, and include perceived usefulness and perceived ease of use, complexity and compatibility of CC technology, and adequacy of resources. Top management support and government policy are found to only play a role for the transformational group while, surprisingly, vendor support had no impact for either group.

This research contributes to the literature on the role of digital transformation in value creation and on digitization of firms and organizational design, notably by considering the contribution of CC to the organizational dimension. To the best of the authors’ knowledge, this is the first study to make the link between TOE and TAM models and organizational fit theory, thereby going beyond the general approach to adoption found in information system research.

In the aerial transportation area, fuel costs are critical to the economic viability of companies, and so urgent measures should be adopted to avoid any unnecessary increase in operational costs. In particular, this paper addresses the case of missed approach manouevres, showing that it is still possible to optimize the usual procedure.

The costs involved in a standard procedure following a missed approach are analysed through a simulation model, and they are compared with the improvements achieved with a fast reinjection scheme proposed in a prior work.

Experimental results show that, for a standard A320 aircraft, fuel savings ranging from 55% to 90% can be achieved through the reinjection method.

To the best of the authors’ knowledge, this work is the first study in the literature addressing the fuel savings benefits obtained by applying a reinjection technique for missed approach manoeuvres.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

The COVID-19 pandemic has forced countries to consider how to reach vulnerable communities with extended outreach services to improve vaccination uptake. The authors created an optimization model to align with decision-makers' objective to maximize immunization coverage within constrained budgets and deploy resources considering empirical data and endogenous demand.

A mixed integer program (MIP) determines the location of outreach sites and the resource deployment across health centers and outreach sites. The authors validated the model and evaluated the approach in consultation with UNICEF using a case study from The Gambia.

Results in The Gambia showed that by opening new outreach sites and optimizing resource allocation and scheduling, the Ministry of Health could increase immunization coverage from 91.0 to 97.1% under the same budget. Case study solutions informed managerial insights to drive gains in vaccine coverage even without the application of sophisticated tools.

The research extended resource constrained LMIC vaccine distribution modeling literature in two ways: first, endogenous calculation of demand as a function of distance to health facility location enabled the effective design of the vaccine network around convenience to the community and second, the model's resource bundle concept more accurately and flexibly represented complex requirements and costs for specific resources, which facilitated buy-in from stakeholders responsible for managing health budgets. The paper also demonstrated how to leverage empirical research and spatial analysis of publicly available demographic and geographic data to effectively represent important contextual factors.

Several online services offer functionalities to access information from “big research graphs” (e.g. Google Scholar, OpenAIRE, Microsoft Academic Graph), which correlate scholarly/scientific communication entities such as publications, authors, datasets, organizations, projects, funders, etc. Depending on the target users, access can vary from search and browse content to the consumption of statistics for monitoring and provision of feedback. Such graphs are populated over time as aggregations of multiple sources and therefore suffer from major entity-duplication problems. Although deduplication of graphs is a known and actual problem, existing solutions are dedicated to specific scenarios, operate on flat collections, local topology-drive challenges and cannot therefore be re-used in other contexts.

This work presents GDup, an integrated, scalable, general-purpose system that can be customized to address deduplication over arbitrary large information graphs. The paper presents its high-level architecture, its implementation as a service used within the OpenAIRE infrastructure system and reports numbers of real-case experiments.

GDup provides the functionalities required to deliver a fully-fledged entity deduplication workflow over a generic input graph. The system offers out-of-the-box Ground Truth management, acquisition of feedback from data curators and algorithms for identifying and merging duplicates, to obtain an output disambiguated graph.

To our knowledge GDup is the only system in the literature that offers an integrated and general-purpose solution for the deduplication graphs, while targeting big data scalability issues. GDup is today one of the key modules of the OpenAIRE infrastructure production system, which monitors Open Science trends on behalf of the European Commission, National funders and institutions.

Cloud Manufacturing (CM) is the manufacturing version of Cloud Computing and aims to increase flexibility in the provision of manufacturing services. On-demand manufacturing services can be requested by users to the cloud and this enables the concept of Manufacturing-as-a-Service (MaaS). Given the considerable number of prototypes and proofs of concept addressed in literature, this work seeks real CM platforms to study them from a business perspective, in order to discover what MaaS concretely means today and how these platforms are operating.

Since the number of real applications of this paradigm is very limited (if the authors exclude prototypes), the research approach is qualitative. The paper presents a multiple-case analysis of 6 different platforms operating in the manufacturing field today. It is based on empirical data and inductively researches differences among them (e.g. stakeholders, operational flows, capabilities offered and scalability level).

MaaS has come true in some contexts, and today it is following two different deployment models: open or closed to the provider side. The open architecture is inspired by a truly open platform which allows any company to be part of the pool of service providers, while the closed architecture is limited to a single service provider of the manufacturing services, as it happens in most cloud computing services.

The research shoots a picture of what MaaS offers today in term of capabilities, what are the deployment models and finally suggests a framework to assess different levels of development of MaaS platforms.

The purpose of this paper is to try to reach the main factors that could put national security at risk as a result of government cloud computing programs.

The paper adopts the analytical approach to first lay foundations of the relation between national security, cybersecurity and cloud computing, then it moves to analyze the main vulnerabilities that could affect national security in cases of government cloud computing usage.

The paper reached several findings such as the relation between cybersecurity and national security as well as a group of factors that may affect national security when governments shift to cloud computing mainly pertaining to storing data over the internet, the involvement of a third party, the lack of clear regulatory frameworks inside and between countries.

Governments are continuously working on developing their digital capacities to meet citizens’ demands. One of the most trending technologies adopted by governments is “cloud computing”, because of the tremendous advantages that the technology provides; such as huge cost-cutting, huge storage and computing capabilities. However, shifting to cloud computing raises a lot of security concerns.

The value of the paper resides in the novelty of the topic, which is a new contribution to the theoretical literature on relations between new technologies and national security. It is empirically important as well to help governments stay safe while enjoying the advantages of cloud computing.