Exploring Structural Patterns Across Evolved and Designed Systems: A Network Perspective

Electric power has become an essential part of daily life: we plug our electronic devices in, switch our lights on, and expect to have power. As the availability of power is usually taken for granted in modern societies, we mostly feel annoyed at its absence and perceive the importance of power during outages which have severe effects on the public order. Blackouts have had disastrous consequences for many countries and they continue to occur frequently. Such examples demonstrate the necessity for careful analysis and planning of power grids, to ultimately increase the reliability of power grids. The power grids have evolved due to economic, environmental and human-caused factors. In addition to the contingency analysis, nowadays, the operation and planning of power grids are facing many other challenges (such as demand growth, targeted attacks, cascading failures, and renewable energy integration). Thus, many questions arise, including: which buses (nodes) to connect with a new line (link)? What are the impacts of malicious attacks on power grids? How may an initial failure result in a cascade of failures? How to prepare for the integration of renewable energy? Answering such questions requires developing new concepts and tools for analysing and planning of power grids. Power grids are one of the largest and the most complex man-made systems on earth. The complex nature of power grids and its underlying structure make it possible to analyse power grids relying on network science. The applications of network science on power grids have shown the promising potential to capture the interdependencies between components and to understand the collective emergent behaviour of complex power grids. This thesis is motivated by the increasing need of reliable power grids and the merits of network science on the investigation of power grids. In this context, relying on network science, we model and analyse the power grid and its near-future challenges in terms of line removals/additions, malicious attacks, cascading failures, and renewable integration.

Whether knowledge can be transferred smoothly between the knowledge units determines the performance of collaborative innovation.This paper is aimed at find out the knowledge transfer influence factor in the process of University-Industry collaborative innovation to promote the performance of collaborative innovation.In the situation of University-Industry collaborative innovation, knowledge transfer shows many characteristics which are different from the traditional one.Based on the analysis of the characteristics and process of University-Industry knowledge transfer, this paper put forward a University-Industry knowledge transfer influence factor conceptual model, combined with the perspective of information network and social network.It divided the influence factor into four respects: the factors of knowledge subjects' characteristics, the factors of collaborative innovation network characteristics, the factors of collaborative innovation situation, and the factors of external environment factors in collaborative innovation.Finally, the strategy to promote the knowledge transfer was given based on the analysis results.

The security function has taken on a dramatically increased importance around the world with the events in New York and Washington in September 2001. As we search for ways to address the altered realities of today's society, the tendency is to create “different” or “unique” approaches to current problems. In the information security (or INFOSEC) realm, Common Criteria (CC) (ISO 15408) documents an international approach to formulating information security requirements. The CC structured methodology is discussed to show its potential application to other Systems Engineering applications. This provides a means to develop consistent and coherent requirement sets. The CC has also established a set of domain specific terms. This paper analyzes the CC terminology to show that CC utilizes the same guiding principles and program milestones as other Systems Engineering disciplines. In the process, we provide a mapping of the CC terminology to that commonly used in non‐security Systems Engineering domains.

ABSTRACTPurpose: The impact of agricultural knowledge transfer (KT) is related to the access to and the quality of services available. Within this context, the allocation of resources in terms of KT offices and the number of advisers are important considerations for understanding KT impact. This quantitative study evaluates the impact of KT resources on farm profitability for clients in Ireland during the recessionary period 2008–2014.Design/Methodology: Teagasc, the public KT service provider in Ireland, experienced significant office closures (43%) and a reduction in advisers (38%) during the economic crisis, yet client numbers declined only slightly (4.5%). Administrative data are merged with a panel data set on farm-level performance to evaluate the impact through Random Effects estimation.Findings: The results show that clients gained a 12.3% benefit to their margin per hectare over the period. However, there was a negative effect of 0.2% for each additional client assigned to the adviser which averaged at 9.6%.Practical Implications: The quantitative findings provide a measure of impact that represents the value for money for the KT service. The key implication is that the client ratio for advisers should be considered when allocating resources and lower ratios would positively impact client margins.Theoretical Implications: This article outlines the value of quantitative studies to estimate impact in a clear translatable manner which can aid the policy discussion around resource deployment.Originality/Value: This study evaluates the impact of KT during a recessionary period when resources were constrained, and uses client ratios to examine the spatial effects.

PurposeWhile numerous studies have studied knowledge transfer (KT) and endeavored to address factors influencing KT, little effort has been made to integrate the findings of prior studies. This paper aims to classify the literature on KT through a detailed exploration of different perspectives of KT inter and intra organizations.Design/methodology/approachUsing actor–network theory (ANT) as the baseline, we conducted a systematic review of KT research to summarize prior KT studies and classify the influential factors on KT. The review covered 115 empirical articles published between 1987 and 2017.FindingsDrawing on the review and ANT guidelines, the authors proposed a conceptual model to categorize KT constitutes into objects including those related to (1) knowledge, (2) knowledge exchange and (3) technology, as well as actants including those related to (4) organization, (5) team/business unit and (6) knowledge sender/receiver.Research limitations/implicationsAdopting a holistic synthesized approach based on ANT, this research puts forward a valid theoretical foundation on further understanding of KT and its antecedents. Indeed, this paper investigates KT inter and intra organizations to recognize and locate the key antecedents of KT, which is of substantial applicability in today’s knowledge-driven economy.Practical implicationsThe findings advance managers and practitioners’ understanding of the important role of actants and objects and their interplay in KT practices.Originality/valueWhile most studies on KT have a narrow focus, this research contributes to holistic understanding of motivational, behavioral, technological and organizational issues related to KT. It also offers a thorough and context-free literature review on KT, which synthesizes the findings of prior studies on KT.

The literature on “open innovation” so far focuses almost exclusively on strategic issues. In this largely conceptual paper we propose behavioral foundations for knowledge exchange and knowledge sharing to address this gap in the literature. Innovation and knowledge development that result from knowledge transfer, is an uncertain and cumulative process that typically involves a number of parties. Knowledge transfer between people and firms has been fruitfully studied from a structural or network perspective. The social network literature however, faces an “action problem”. Focusing on structural elements such as an agent’s position in a network and the types of relations entertained cannot explain why actors actually do share knowledge. The exchange of knowledge is elusive and is a discretionary act for the people involved, certainly in the case of open innovation (OI). It is argued here that social network analysis is to be complemented by the concept of gift exchange, drawing on social exchange literature. Gift exchange – following Mauss’ dictum to “give, receive and reciprocate”– establishes obligations between people especially under circumstances of ambiguity, which explain why and how knowledge exchange relations are established, persist, and may also end. Relationships in a social network and the social capital that inheres in these cannot be drawn on at will to exchange knowledge. These obligations established by gift exchange between individuals who share a connection explain why agents exchange knowledge with each other even in the absence of markets or hierarchy.

Network science has been a promising tool for characterizing and understanding complex systems. A challenging problem in network science is to uncover the community structure of the network. Community structure generally presents the partition of the nodes in the network into several groups based on various structural properties or dynamic behavior. In this paper, we analyze the community structure of Chinese airport network based on Stochastic Block Models (SBM). Different from exisiting studies, the Chinese Airport Delay Correlation Network (CADCN) is constructed with airports as nodes and the correlations between hourly delay time series of airport pairs as edges. To analyze the temporal patterns of community structures, we employ spectral clustering method and classify Chinese airports into 6 different communities. Airports within each community have closer relationships to each other on the delay propagation. A similar investigation to the traditional Chinese airport network (CAN) is carried out based on SBM as well. By comparing the results of two networks, we find that the CADCN has the advantage in revealing the implicit delay correlation than the directed flights connection between airports. Our findings have potential meanings to understand the spread of flight delays and to develop relevant management and control strategies.

Scientific networks play an increasingly important role in facilitating knowledge and technique diffusion. In such networks, highly influential nodes (scientists or literatures) are prone to stimulate other researchers in the generation of innovative ideas. The objective of this study is to detect topic-level influencers from a large collection of links between nodes and textual contents in scientific networks. For this purpose, we propose a sparse link topic model (SLTM) that introduces a “Spike and Slab” prior to achieve sparsity in node-topic distribution. Compared with previous approaches, our model assumes that a node usually focuses on several salient topics instead of a wide range of topics, which is useful in learning topic-level influencers in scientific networks. In addition, a collapsed variational Bayesian (CVB) inference algorithm is designed for large-scale applications. Our experiments are conducted on a large scientific collaboration network. The results reveal that the proposed model significantly improves the precision of topic-level detection. Our analysis also reflects that SLTM can explicitly model the sparse topical structure of each node in the network.

Understanding knowledge transfer in M&As: An integration of resource orchestration and social capital theories and evidence from UK acquiring firms

The mining and exploitation of graph structural information have been the focal points in the study of complex networks. Traditional structural measures in Network Science focus on the analysis and modelling of complex networks from the perspective of network structure, such as the centrality measures, the clustering coefficient, and motifs and graphlets, and they have become basic tools for studying and understanding graphs. In comparison, graph neural networks, especially graph convolutional networks (GCNs), are particularly effective at integrating node features into graph structures via neighbourhood aggregation and message passing, and have been shown to significantly improve the performances in a variety of learning tasks. These two classes of methods are, however, typically treated separately with limited references to each other. In this work, aiming to establish relationships between them, we provide a network science perspective of GCNs. Our novel taxonomy classifies GCNs from three structural information angles, i.e., the layer-wise message aggregation scope, the message content, and the overall learning scope. Moreover, as a prerequisite for reviewing GCNs via a network science perspective, we also summarise traditional structural measures and propose a new taxonomy for them. Finally and most importantly, we draw connections between traditional structural approaches and graph convolutional networks, and discuss potential directions for future research.

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

Effect of transactive memory systems on team performance mediated by knowledge transfer

<p><strong>This study employs computational methodologies to understand the dynamics underpinning the interfaces between scientific evidence and policy making, using New Zealand's COVID-19 response as a case study. My research motivated by governmental rhetoric emphasising "following the science" amid the COVID-19 pandemic response and long-standing theoretical quandaries surrounding the role and uptake of research in policy making, I apply Natural Language Processing (NLP) and network science techniques to furnish innovative quantitative insights into two-communities theory, timeliness and relevance barriers, and the knowledge dissemination process.</strong></p><p>The research employs the exploratory approaches to examine the process of science-based policymaking through network analysis, while also investigating and providing explanatory insights into patterns related to the two-communities theory and barriers to research application.</p><p>Linguistic analyses displayed contrasts in lexical preferences between the scientific and policy spheres. Academic publications exhibit predilections for disciplinary jargon while policy documents orient towards more accessible cross-disciplinary and stakeholder-centric language, potentially hindering knowledge transfer. However, there are also common languages that exist between two communities, which provide the bridge for communication.</p><p>Contrary to self-reported findings, my research did not observe the perceived time pressures on policymakers from the linguistic perspective.</p><p>In terms of barriers for the application of research in policy making, my analysis indicates that, in fact, scientific publications ahead of government policy decisions on interventions do provide timely information—potentially actionable by policymakers. However, content similarity between research outputs and policy documents addressing common intervention topics was modest.</p><p>To elaborate the details of this process by how knowledge flows from science into policy, we develop three species of networked representations capturing the interactions between: (1) scientific publications and policy documents as knowledge products; (2) actors and stakeholders represented by scientists, policymakers, institutions, government agencies, and parliamentary members; and (3) disciplinary knowledge domains embodied in the disseminated information. These multi-faceted, dynamic network analyses identify key actors and pivotal documents in ways instrumental to delineating how scientific knowledge is transformed and re-contextualized to policy formulation.</p><p>Unexpectedly, the study found limited direct citations of scientific literature in key policy documents and an unanticipated prominence of non-COVID-19 topics in central network positions. Analysis of parliamentary debates revealed that scientific knowledge was often transformed into political and governance issues rather than directly informing policy. These findings challenge conventional assumptions about the straightforward application of scientific evidence in policy making, especially during crises.</p><p>The research also uncovered an asynchronous relationship between global scientific output and local policy development, particularly evident from December 2020 to February 2021. This observation highlights the complex, non-linear nature of science-policy interactions and the importance of considering local context in knowledge dissemination processes.</p><p>The computational approach application in the science-policy interface area as well as the quantitative evidence in my research complementing and expanding findings from prior qualitative studies. This thesis provides nuanced insights into the complexities underlying evidence-informed policymaking processes. By revealing unexpected patterns and gaps in knowledge dissemination, it contributes to a more sophisticated understanding of science-policy interactions. The methodological approach developed here offers a framework for future investigations into knowledge transfer dynamics in various policymaking contexts, potentially leading to more effective integration of scientific evidence in policy decisions.</p>

Purpose: The ability to disseminate knowledge in the presence of innovation and capability in businesses creates an advantage over other businesses. Most SMEs do not implement formal tools to protect and share needed knowledge, which makes knowledge transfer difficult. Design/Method/Approach: Quantitative research was adopted. With over eleven (11) thousand registered SMEs in Lagos State and using Yamane’s formula, a total of 386 respondents formed the sample size for this study. Findings: The study established a positive significance of social networking, mentorship and research and development for innovation performance. However, training revealed no impact on innovation performance which further established that most SMEs operators in Nigeria usually improve business skills through apprenticeship instead of an organized training to transfer knowledge. Theoretical Implications: The absorptive capacity theory was adopted to explain the importance of enriching organizational learning by the acquisition of diverse knowledge from different sources to enhance capabilities. It also depicts the impact of technological innovation performance through general abilities of an individual. Practical Implications: The study emphasized that the survival and competitiveness of any business entity in an evolving global environment is dependent on its implementation of continuous innovative improvements through knowledge transfer, but of all knowledge transfer methods, training is the least used method by business owners. Originality/Value: This paper assesses the significance of knowledge transfer as a driver of innovation performance in Nigerian SMEs using such methods as training, mentorship, research and development and social networking employed by SMEs. Research Limitations/Future Research: One major limitation to the study was the inability and unwillingness of SMEs owners and managers to fill in a questionnaire. Paper Type: Empirical JEL Classification: M10, M13

Goal: The motivation of this paper is to forge and foster an insightful and comprehensive understanding of social media potential and its possible impact on Ghana Commercial Bank employees’ performance. Design / Methodology / Approach: Five cities were strategically sampled using convenient sampling technique to represent the various diversifications in corporate compositions. Data was collected from 249 workers of the Ghana Commercial Bank. The study utilized the structural equation modeling technique to construct and evaluate the proposed research model. Results: Social media usage had a significant positive relationship with employee engagement and knowledge transfer. Knowledge transfer had no significant relationship with work performance and employee engagement had a significant positive relationship with work performance. Experience with social media had a significant moderating effect on relationship between knowledge transfer and work performance. Research limitations/implications: The study covered employees from one bank and this has implications on generalization. The study used cross sectional data and employed quantitative analysis. Mixed methods would have added some insights and longitudinal data could further unravel causal relationships among overtime. Practical implications: Social media platform managers and policy makers within the banking sector should proactively harness social media platform and utilize to benefit corporate bodies. Banking authorities and allied stakeholders should train workers to ensure positive inclination to social media usage. Originality/Value: Contributes to theory by validating and establishing the theoretical model. The findings provide insights with respect to development and subsequent deployment of practical guidelines for successful integration of social media in enterprise activities.