Citation network analysis for supporting continuous improvement in higher education

Continuous improvement in Higher Education can be supported by effective literature reviews to unveil contemporary and current educational needs and lay the foundations of programmes of study. As no discipline remains static, the aim of this paper is to present a methodology for conducting literature reviews that can complement traditional content-based reviews by revealing the dynamic evolution of a discipline. This methodology is represented by citation network analysis (CNA), a collection of tools that help to detect the dynamics of a field through computer-based systematic analyses of its bibliographic data. Notwithstanding its potential, CNA has been seldom adopted to conduct literature reviews. In this paper, CNA was applied to the evolving field of logistics and supply chain management education. Results provide evidence of the benefits of CNA for the identification of key issues, trends, and evolutionary trajectories supporting continuous improvement in Higher Education in a more scientific and objective way

Information sharing in supply chains: a review of risks and opportunities using the Systematic Literature Network Analysis (SLNA)

Purpose – The purpose of this paper is to identify and discuss the most important research areas on information sharing in supply chains and related risks, taking into account their evolution over time. This paper sheds light on what is happening today and what the trajectories for the future are, with particular respect to the implications for supply chain management. Design/Methodology/Approach – The dynamic literature review method called Systematic Literature Network Analysis (SLNA) was adopted. It combines the Systematic Literature Review approach and bibliographic network analyses, and it relies on objective measures and algorithms to perform quantitative literature-based detection of emerging topics. Findings-The focus of the literature seems to be on threats internal to the extended supply chain rather than external attacks, such as viruses, traditionally related to information technology (IT). The main arising risk appears to be the intentional or non-intentional leakage of information. Also, papers analyse the implications for information sharing coming from " soft " factors such as trust and collaboration among supply chain partners. Opportunities are also highlighted and include how information sharing can be leveraged to confront disruptions and increase resilience. Research limitations/implications – The adopted methodology allows providing an original perspective on the investigated topic, i.e. how information sharing in supply chains and related risks are evolving over time due to the turbulent advances in technology. Practical implications-Emergent and highly critical risks related to information sharing are highlighted to support the design of supply chain risks strategies. Also, critical areas to the development of " beyond-the-dyad " initiatives to manage information sharing risks emerge. Opportunities coming from information sharing that are less known and exploited by companies are provided. Originality/value – This study focuses on the supply chain perspective rather than the traditional IT-based view of information sharing. According to this perspective, this study provides a dynamic representation of the literature on the investigated topic. This is an important contribution to the topic of information sharing in supply chains, which is continuously evolving and shaping new supply chain models

Forecasting high waters at Venice Lagoon using chaotic time series analysis and nonlinear neural networks

Time series analysis using nonlinear dynamics systems theory and multilayer neural networks models have been applied to the time sequence of water level data recorded every hour at 'Punta della Salute' from Venice Lagoon during the years 1980–1994. The first method is based on the reconstruction of the state space attractor using time delay embedding vectors and on the characterisation of invariant properties which define its dynamics. The results suggest the existence of a low dimensional chaotic attractor with a Lyapunov dimension, DL, of around 6.6 and a predictability between 8 and 13 hours ahead. Furthermore, once the attractor has been reconstructed it is possible to make predictions by mapping local-neighbourhood to local-neighbourhood in the reconstructed phase space. To compare the prediction results with another nonlinear method, two nonlinear autoregressive models (NAR) based on multilayer feedforward neural networks have been developed. From the study, it can be observed that nonlinear forecasting produces adequate results for the 'normal' dynamic behaviour of the water level of Venice Lagoon, outperforming linear algorithms, however, both methods fail to forecast the 'high water' phenomenon more than 2–3 hours ahead

From Complex Networks to Time Series Analysis and Viceversa: Application to Metabolic Networks

In this work we present a simple and fast approach to generate network structures based on time series recurrence plots and viceversa. In addition, we discuss the application of the different analysis techniques developed in both fields, i.e. complex networks and time series analysis. Concerning the transformation from time series to networks, we propose a deterministic growth procedure which produces a new types of complex network structures that have some interesting features. This simple and fast approach is able to generate deterministic network structures based on time series recurrence plots. The generated networks contain several properties of the original time series. In this case, networks generated from chaotic attractors display interesting features from the point of view of robustness which could help in designing systems with high tolerance against errors and transfer of information. Chaotic networks based on the Lorenz attractor show that they are highly tolerant against attacks and they have a high ability for the transfer of information or on the contrary they are able to transmit infections faster. It is still necessary to investigate if such chaotic networks exist already in natural or man-made systems or, if possible, to construct such networks and test their properties. On the other hand, the transformation from networks to time series presents some problems concerning the selection of the initial time or in our case the initial node and the way in which the nodes are visited. If a network has been generated following a certain growth law it seems logical to choose the first node as the origin and then proceed following the network growth pattern. However, the situation is not so clear for example with metabolic networks, where it is difficult to select which is the first metabolite. Similar concerns would apply to other types of biological networks. In this case several alternatives could be considered, e.g. ordering using the number of connections. However, we have still to find if there are some invariant/preserved properties in the generated time series from the same network. We have found that rescaled range analysis does not preserve the fractal structure in the time series. In any case, if time series parameters would be invariant against the initial node selection, then they could be used to analyze the networks that have generated said time series. Our future work will continue along these lines.JRC.I.6-Systems toxicolog

Digital twin-enabled smart industrial systems: a bibliometric review

The aim of this study is to investigate the body of literature on digital twins, exploring, in particular, their role in enabling smart industrial systems. This review adopts a dynamic and quantitative bibliometric method including works citations, keywords co-occurrence networks and keywords burst detection with the aim of clarifying the main contributions to this research area and highlighting prevalent topics and trends over time. The analysis performed on citations traces the backbone of contributions to the topic, visible within the main path. Keywords co-occurrence networks depict the prevalent issues addressed, tools implemented and application areas. The burst detection completes the analysis identifying the trends and most recent research areas characterizing research on the digital twin topic. Decision-making, process design and life cycle as well as the enabling role in the adoption of the latest industrial paradigms emerge as the prevalent issues addressed by the body of literature on digital twins. In particular, the up-to-date issues of real-time systems and industry 4.0 technologies, closely related to the concept of smart industrial systems, characterize the latest research trajectories identified in the literature on digital twins. In this context, the digital twin can find new opportunities for application in manufacturing, control and services

Literature review on the ‘Smart Factory’ concept using bibliometric tools

The objective of this paper is to depict a landscape of the scientific literature on the concept of the ‘Smart Factory’, which in recent years is gaining more and more attention from academics and practitioners because of significant innovations in the production systems within the manufacturing sector. To achieve this objective, a dynamic methodology called "Systematic Literature Network Analysis (SLNA)" has been applied. This methodology combines the Systematic Literature Review approach with the analysis of bibliographic networks. The adopted methodology allows complementing traditional content-based literature reviews by extracting quantitative information from bibliographic networks to detect emerging topics, and by revealing the dynamic evolution of the scientific production of a discipline. This dynamic analysis allowed highlighting research directions and critical areas for the development of the "Smart Factory". At the same time, it offers insights on the fields on which companies, associations, politicians and technology providers need to focus in order to allow a real transition towards the implementation of large-scale Smart Factory

Liuc papers

In questo lavoro abbiamo applicato la teoria dell'analisi di serie temporali nonlineari allo studio dei cambi intragiornalieri HFDF96 forniti da Olsen & Associated. Le serie temporali studiate sono costituite dai cambi tra il dollaro e 18 altre monete sia della zona Euro che non. Il nostro obiettivo è stato quello di classificare tali serie e di analizzare il loro comportamento dinamico al fine di stabilire correlazioni tra i cambi. La non stazionarietà presente nelle serie considerate ci ha portato ad applicare la Recurrence Quantification Analysis (RQA). Tale metodo è basato sulla definizione di diversi parametri che permettono la quantificazione del Recurrence Plot (RP). L'analisi dei cambi intragiornalieri ci ha permesso di sviluppare una nuova misura di correlazione non-lineare tra le diverse serie. I risultati mostrano, come previsto, un'alta correlazione tra le monete della zona euro (a conferma del metodo), ma anche una correlazione marcata tra lo Yen Giapponese, il dollaro Canadese e la Sterlina Inglese. Alcune fatti storici, non evidenti dal'evoluzione delle serie temporali dei cambi, si possono ricostruire dall'analisi dei parametri dell'RQA.In this work we have applied non-linear time series analysis to high frequency currency exchange data from the HFDF96 data set provided by Olsen & Associated. The time series studied are the exchange rates between the US dollar and 18 other foreign currencies from within and without the Euro zone. Our goal was to determine if their dynamical behaviors were in some way correlated. The non-existence of stationarity called for the application of Recurrence Quantification Analysis (RQA) as a tool for this analysis, and is based on the definition of several parameters that allow for the quantification of Recurrence Plots (RP). The method was checked using the European Monetary System currency exchanges. The results show, as expected, the high correlation between the currencies that are part of the Euro, but also a strong correlation between the Japanese Yen, the Canadian dollar and the British Pound. Singularities of the series are also demonstrated taking into account historical events, in 1996, in the Euro zone