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Research backbone networks like GÉANT2 and the national research and education networks are used by a variety of scientists and research projects. These users and the network engineers operating the networks would like to get access to network performance metrics to optimise their use of the network and to troubleshoot performance degradations when they happen. A variety of tools for performing network measurements already exist, and the perfSONAR architecture developed in the Joint Research Activity 1 (JRA1) of GÉANT2 aims at integrating them into a coherent framework. However, a harmonised definition of the most interesting metrics and how measurements must be carried out is still lacking.

This paper suggests some of the elementary metrics which are relevant for assessing network performance, along with an indication about how to post‐process (or “transform”, or “compose”) them in order to obtain derived summary values that can quickly and intuitively give an indication of network performance. Methods to perform the composition are presented, together with constraints which have to be taken into account to get accurate results. In particular, delay measurements are the most delicate ones to compose.

The authors carried out a series of experiments for proofing the validity of the composition of delay metrics, and briefly present some preliminary results, that confirm the applicability of the proposed methodology.

Future work needs to confirm the paper's findings on other data sets, possibly collected in different network locations.

The practical implication of the findings is that it is possible for a network operator to accurately predict high percentiles of delays on an end‐to‐end path starting from independent delay measurements on subsequent path sections.

The main original contribution of this paper is the application to a real data set of a post‐processing procedure that is derived from simple statistics theory.

This study introduces a new approach, called the social structure approach, for ranking academic journals by focusing on hospitality and tourism journals; and a hybrid metric, including the combination of the journal impact factor via citations and a social network metric, called the journal knowledge domain index (JKDI).

Twenty-five hospitality and tourism journals were selected to test this approach. Collaboration-based metrics, productivity-based metrics, and network-based metrics are considered components of the social structure approach. Additionally, a hybrid metric, including the combination of the journal impact factor via citations and a social network metric, JKDI, is developed.

The study’s findings show that top or leading journals have a weaker position in some social structure approach metrics compared to other (or follower) journals. However, according to the JKDI, leading journals have remained constant with the other ranking studies.

The ranking of academic journals is vital for the stakeholders of academia. Consequently, the findings of this study may help stakeholders to design an optimal ranking system and formulate and implement effective research strategies for knowledge creation and dissemination.

As one of the first in the journal-ranking literature, this study has significant implications, as it introduces a new ranking approach.

The purpose of this paper is to develop and operationalize a process for prioritizing supply chain risks that is capable of capturing the value at risk (VaR), the maximum loss expected at a given confidence level for a specified timeframe associated with risks within a network setting.

The proposed “Worst Expected Best” method is theoretically grounded in the framework of Bayesian Belief Networks (BBNs), which is considered an effective technique for modeling interdependency across uncertain variables. An algorithm is developed to operationalize the proposed method, which is demonstrated using a simulation model.

Point estimate-based methods used for aggregating the network expected loss for a given supply chain risk network are unable to project the realistic risk exposure associated with a supply chain. The proposed method helps in establishing the expected network-wide loss for a given confidence level. The vulnerability and resilience-based risk prioritization schemes for the model considered in this paper have a very weak correlation.

This paper introduces a new “Worst Expected Best” method to the literature on supply chain risk management that helps in assessing the probabilistic network expected VaR for a given supply chain risk network. Further, new risk metrics are proposed to prioritize risks relative to a specific VaR that reflects the decision-maker's risk appetite.

Despite the systemic nature of circular economy (CE), theorisation that draws from a supply network perspective is only incipient. Moreover, the operations and supply chain management (OSCM) field has engaged in little dialogue with circularity. This study explores social network analysis (SNA) to depict how the shift from linear to circular not only leads to higher rates of resource economy, repair and recycle but also reshapes governance dynamics and network structure of supply networks.

The study departs from a systematic review of the literature and draws from core concepts in OSCM, CE and SNA to offer theoretical propositions that articulate how social network metrics can depict supply network circularity. The framework is illustrated with examples from fashion and electronics industries.

Four theoretical propositions enlighten how betweenness centrality, eigenvector centrality and network density can explain the shift from linear to circular supply networks across the three CE strategies of narrowing, slowing and closing.

The combination of biomimicry, CE, the push–pull dichotomy and social network metrics offer a theory-driven framework for supply network circularity.

To better understand the success of an open source software (OSS) project, this study aims to examine the role of social dependency networks (i.e. social and technical dependencies) in online communities.

This study focuses on dependencies using three network metrics – degree centrality, betweenness centrality and closeness centrality – in developer and module networks. A longitudinal analysis from the projects hosted at Sourceforge.net is conducted to examine the effects of social and technical networks on the success of OSS projects. To address our research questions, we have constructed research models to investigate the social network effects in developer networks, the technical network effects in module networks, and the social-technical network effects in both types of networks.

The results reveal nonlinear relationships between degree centrality in both social and technical networks and OSS success, highlighting the importance of a moderate level of degree centrality in team structure and software architecture. Meanwhile, a moderate level of betweenness centrality and a lower level of closeness centrality between developers lead to a higher chance of OSS project success.

This study is the first attempt to consider the network metrics in both module networks of the technical sub-system and developer networks of the social sub-system to better understand their influences on project success.

The aim of this study is o examine the advantages and disadvantages of different existing scoring systems in the cybersecurity domain and their applicability to the AEC industry and to systematically apply a scoring system to determine scores for some of the most significant construction participants.

This study proposes a methodology that uses the Common Vulnerability Scoring System (CVSS) to calculate scores and the likelihood of occurrence based on communication frequencies to ultimately determine risk categories for different paths in a construction network. As a proof of concept, the proposed methodology is implemented in a construction network from a real project found in the literature.

Results show that the proposed methodology could provide valuable information to assist project participants to assess the overall cybersecurity vulnerability of construction and assist during the vulnerability-management processes. For example, a project owner can use this information to get a better understanding of what to do to limit its vulnerability, which will lead to the overall improvement of the security of the construction network.

It has to be noted that the scoring systems, the scores and categories adopted in the study need not necessarily be an exact representation of all the construction participants or networks. Therefore, caution should be exercised to avoid generalizing the results of this study.

The proposed methodology can provide valuable information and assist project participants to assess the overall cyber-vulnerability of construction projects and support the vulnerability-management processes. For example, a project owner can use this approach to get a better understanding of what to do to limit its cyber-vulnerability exposure, which will ultimately lead to the overall improvement of the construction network's security. This study will also help raise more awareness about the cybersecurity implications of the digitalization and automation of the AEC industry among practitioners and construction researchers.

Given the amount of digitized services and tools used in the AEC industry, cybersecurity is increasingly becoming critical for society in general. In some cases, (e.g. critical infrastructure) incidents could have significant economic and societal or public safety implications. Therefore, proper consideration and action from the AEC research community and industry are needed.

To the authors' knowledge, this is the first attempt to measure and assess the cybersecurity of individual participants and the construction network as a whole by using the Common Vulnerability Scoring System.

Cross-laminated timber (CLT) is an innovative construction material that provides a balanced mix of structural stiffness, fabrication flexibility and sustainability. CLT development and innovation diffusion require close collaborations between its supply chain architectural, engineering, construction and manufacturing (AECM) stakeholders. As such, the purpose of this study is to provide a preliminary understanding of the knowledge diffusion and innovation process of CLT construction.

The study implemented a longitudinal social network analysis of the AECM companies involved in 100 CLT projects in the UK. The project data were acquired from an industry publication and decoded in the form of a multimode project-company network, which was projected into a single-mode company collaborative network. This complete network was filtered into a four-phase network to allow the longitudinal analysis of the CLT collaborations over time. A set of network and node social network analysis metrics was used to characterize the topology patters of the network and the centrality of the companies.

The study highlighted the scale-free structure of the CLT collaborative network that depends on the influential hubs of timber manufacturers, engineers and contractors to accelerate the innovation diffusion. However, such CLT supply collaborative network structure is more vulnerable to disruptions due to its dependence on these few prominent hubs. Also, the industry collaborative network’s decreased modularity confirms the maturity of the CLT technology and the formation of cohesive clusters of innovation partners. The macro analysis approach of the study highlighted the critical role of supply chain upstream stakeholders due to their higher centralities in the collaborative network. Stronger collaborations were found between the supply chain upstream stakeholders (timber manufacturers) and downstream stakeholders (architects and main contractors).

The study contributes to the field of industrialized and CLT construction by characterizing the collaborative networks between CLT supply chain stakeholders that are critical to propose governmental policies and industry initiatives to advance this sustainable construction material.

Applications of comprehensive Quality Function Deployment (QFD) – or QFD in the broad sense – to strategic management have been known for some time, and its results have been discussed within the international community of QFD specialists. It is therefore tempting to investigate the contribution of combinatory metrics to strategy deployment. Combinatory metrics are constructed upon the capability of QFD to evaluate the deployment topics’ contribution to customers’ needs. They provide a practical means to explain business strategy by “local” metrics that are easily understood and applied by responsible people. Combinatory metrics also point to foundations of QFD that explain how to apply QFD for very complicated environments. This foundation provides techniques and means to work with various influencing factors and conflicting topics. This paper explains the theory as needed for strategy deployment and presents a sample case from a software company.

The purpose of this paper is to review the different interpretations of four key performance indicators of water distribution networks (WDNs): reliability, resilience, redundancy and robustness. It then addresses a range of metrics which have been developed to assess the performance of critical infrastructures, in particular WDNs.

The paper provides a comprehensive review and categorization of performance indicators of WDNs. The main focus is on papers addressing performance indicators of water distribution systems, additionally papers on application of complex system approach to critical infrastructures are also included.

Due to this complexity, a wide range of interpretation of WDNs performance indicators exists in the literature. This represents a significant impediment toward universally accepted interpretation of these indicators Accurate assessment of WDNs’ performance depends on clear definition of system performance indicators as well as accurate quantifying of these indicators. The application of 18 metrics as a basis for assessing the system performance have been reviewed in this paper and none are particularly significant as standalone values. Combination of these indicators are required to accurately indicate the performance of WDNs.

The authors believe that this paper can be a valuable source of information for academic researchers and practitioners and suggests a roadmap for future works.