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We discuss how sampling design, units, the observation mechanism and other basic statistical notions figure into modern network data analysis. These considerations pose several new challenges that cannot be adequately addressed by merely extending or generalizing classical methods. Such challenges stem from fundamental differences between the domains in which network data emerge and those for which classical tools were developed. By revisiting these basic statistical considerations, we suggest a framework in which to develop theory and methods for network analysis in a way that accounts for both conceptual and practical challenges of network science. We then discuss how some well-known model classes fit within this framework.

Tackling the questions that systems designers care about, this book brings queueing theory decisively back to computer science. The book is written with computer scientists and engineers in mind and is full of examples from computer systems, as well as manufacturing and operations research. Fun and readable, the book is highly approachable, even for undergraduates, while still being thoroughly rigorous and also covering a much wider span of topics than many queueing books. Readers benefit from a lively mix of motivation and intuition, with illustrations, examples and more than 300 exercises – all while acquiring the skills needed to model, analyze and design large-scale systems with good performance and low cost. The exercises are an important feature, teaching research-level counterintuitive lessons in the design of computer systems. The goal is to train readers not only to customize existing analyses but also to invent their own.

Considering the socio-economic importance of Incheon International Airport, this study explored the changes in its aeronautical and non-aeronautical efficiency between 2001 and 2021. The study was conducted to measure and observe the changes in efficiency during the pre- and post-pandemic era of COVID-19. We employed a two-stage analytical approach to obtain the results using a set of desirable and undesirable variables. For the first stage, we employed a novel network data envelopment analysis–window analysis model to find the efficiency measures; for the second stage, we applied the Tobit regression analysis to observe the impact of some control variables on efficiency levels. The empirical results from the efficiency analysis stage revealed that, although the pandemic negatively affected the efficiency of this airport, the gain from appropriate strategies mitigated the excessive efficiency decline. Moreover, aeronautical activities showed better efficiency than non-aeronautical activities during the study period. In addition, further investigation of the second-stage analysis implied that an outbreak of pandemic diseases such as COVID-19 would dramatically impact international hubs such as Incheon International Airport; however, focusing on the import and export activities, in addition to increasing the connectivity with other airports, would improve the efficiency.

\"This book brings together the insights and practical experience of some of the most experienced Data Plane Development Kit (DPDK) technical experts, detailing the trend of DPDK, data packet processing, hardware acceleration, packet processing and virtualization, as well as the practical application of DPDK in the fields af SDN, NFV, and network storage. The book also devotes many chunks to exploring various core software algorithms, the advanced optimization methods adopted in DPDK, detailed practical experience, and the guides on how to use DPDK\"-- Provided by publisher.

Social network interventions are an effective approach to promote physical activity. These interventions are traditionally designed using self-reported peer nomination network data to represent social connections. However, there is unexplored potential in communication data exchanged through web-based messaging apps or social platforms, given the availability of these data, the developments in artificial intelligence to analyze these data, and the shift of personal communication to the web sphere. The implications of using web-based versus offline social networks on the effectiveness of social network interventions remain largely unexplored. This study aims to investigate the differences in the impact of social network interventions on physical activity levels (PALs) between networks derived from web-based communication and peer nomination data. We used the data on sociometric questionnaires, messages from a web-based communication app, and PAL (number of steps per day) of 408 participants in 21 school classes. We applied social network analysis to identify influential peers and agent-based modeling to simulate the diffusion of PAL and explore the impact of social network interventions on PAL among adolescents in school classes. Influential peers (n=63) were selected based on centrality measures (ie, in-degree, closeness, and betweenness) to spread the intervention. They received health education, which increased their PAL by 17%. In sensitivity analyses, we tested the impact of a 5%, 10%, and 20% increase in PAL among influential peers. There was a 24%-27% overlap in selected influential peers between the 2 network representations. In general, the simulations showed that interventions could increase PAL by 5.0%-5.8% within 2 months. However, the predicted median impact on PAL was slightly higher in networks based on web-based communication data than peer nomination data for in-degree (5.7%, IQR 5.5%-6.1% vs 5.5%, IQR 5.2%-5.8%; P=.002), betweenness (5.6%, IQR 5.4%-5.9% vs 5.0%, IQR 4.7%-5.3%; P<.001), and closeness centrality (5.8%, IQR 5.6%-6.1% vs 5.3%, IQR 5.0%-5.6%; P<.001). A large variation in impact was observed between school classes (range 1.5%-17.5%). Lowering the effectiveness of health education from 17% to 5% would reduce the overall impact of the social network intervention by 3-fold in both networks. Our findings showed that network interventions based on web-based communication data could increase PAL. Web-based communication data may therefore be a valuable addition to peer nomination data for future social network intervention design. Artificial intelligence methods, including agent-based modeling, can help to design these network interventions and provide insights into the role of network characteristics in their effectiveness.

Purpose The literature argues on ambiguous impacts of different types of the common agricultural policy (CAP) subsidies on farm technical efficiency (TE). The purpose of this paper is to estimate and analyse the TE and the impact of the CAP subsidies on the TE of wine farms in Hungary using the farm accountancy data network data set in the period 2013–2019. Design/methodology/approach The authors use stochastic frontiers analysis (SFA) models to estimate the TE scores for the Hungarian wine farms with four wine farm-level inputs in terms of agricultural land, labour, capital and intermediate consumption. The TE scores are explained by the CAP subsidies and economic wine farm size. The different SFA models were applied with robustness tests to investigate the drivers of the TE values of wineries. Findings Like for Hungarian farms in general, the distribution of the wine farm structure is a dual with a greater number of smaller wine farms and a smaller number of bigger wine farms. The agricultural land, capital and intermediate consumption are significantly positively associated with the wine farm TE. With higher capital intensity wine farm TE increase. The results imply that the CAP subsidies decrease the TE of the Hungarian wine farms, whereas economic farm size increase. Originality/value To the best of the authors’ knowledge, this is one of the first specific efficiency studies on the wine sector in the Central and Eastern European region and the first one for Hungary to evaluate the TE at wine farm level and to assess the impact of CAP subsidies and economic farm size on wine farm (in)efficiency to apply production technologies and use farm resources. This study is among the first that applied the fixed-effects stochastic frontier model at the wine farm level to measure the drivers of the TE scores.