Network Size Research Articles

Exploiting the determinant factors on the available flexibility area of ADNs at TSO‐DSO interface

AbstractActive distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil‐fuel‐based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two‐step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69‐bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs.

A Multi-Agent Reinforcement Learning Blockchain Framework for Improving Vehicular Internet of Things Cybersecurity

The Vehicular Internet of Things (VIoT) is a novel idea in the field of connected transportation systems that defines a new paradigm. Nevertheless, even the most modern and complex system will require additional and more powerful layers to protect the conversation from interception and the data from leakage. The centralized models have problems like trust problems and that there might be vulnerabilities and that is why there are attempts to integrate decentralization in operation. The first challenge in the VIoT networks is that the security and openness of such a connection and data transfer are still not well developed. Another issue is the security and dependability of the interaction between the vehicles and the infrastructure, although this issue is magnified by the size of the VIoT network. This research is a blockchain and game theory base research that uses Multi-Agent Reinforcement Learning (MARL) to improve the security and efficiency of the VIoT ecosystem. The technology of blockchain gives a distributed ledger where data cannot be altered or erased. Moreover, the MARL architecture allows for the realisation of better decisions for each of the members of the network. To this, the set that was made up of the smart contracts, Vehicle Units (VUs) and the decentralized servers that form the proposed architecture would be added to allow for the right flow and processing of the data. The blockchain’s decentralized nature provides a guarantee for all secure, immutable data transfers and transparent transactions throughout the network of the VIoT. MARL enables agents to learn and acquire the best strategies as they pass through time, which leads to secure and effective communication among entities. Besides, the implementation of lightweight cryptography techniques and strategic selections according to game theory help to protect and improve the performance of the security system of the VIoT ecosystem.

Shortest path counting in complex networks based on powers of the adjacency matrix.

Complex networks describe a broad range of systems in nature and society. As a fundamental concept of graph theory, the path connecting nodes and edges plays a crucial role in network science, where the computation of shortest path lengths and numbers has garnered substantial focus. It is well known that powers of the adjacency matrix can calculate the number of walks, specifying their corresponding lengths. However, developing methodologies to quantify both the number and length of shortest paths through the adjacency matrix remains a challenge. Here, we extend powers of the adjacency matrix from walks to shortest paths. We address the all-pairs shortest path count problem and propose a fast algorithm based on powers of the adjacency matrix that counts both the number and the length of all shortest paths. Numerous experiments on synthetic and real-world networks demonstrate that our algorithm is significantly faster than the classical algorithms across various network types and sizes. Moreover, we verified that the time complexity of our proposed algorithm significantly surpasses that of the current state-of-the-art algorithms. The superior property of the algorithm allows for rapid calculation of all shortest paths within large-scale networks, offering significant potential applications in traffic flow optimization and social network analysis.

Social and Religious Networks with Respect to the Health Behaviors of African American Women: A Systematic Review.

The aim of this systematic mixed studies review was to examine how social networks influence physical activity and dietary patterns among African American women. We searched PUBMED, CINAHL, JSTOR, SocioIndex, Medline, Cochrane Library, sportDISCUS, and PsycINFO to review articles published between Jan 1, 2007, and July 1, 2021. Only 23 studies met the inclusion criteria. All studies were conducted in the US. Social support received from family members, friends, and churches was found to positively influence physical activity and/or dietary patterns among African American women. An inconsistent relationship was found between social network size, health behavior characteristics of social networks, and physical activity and/or dietary patterns among African American women. Future research should incorporate structural and functional social network strategies to promote physical activity and healthy eating behaviors effectively and sustainably among African American women.

Social network size, empathy, and white matter: A diffusion tensor imaging (DTI) study.

Social networks are fundamental for social interactions, with the social brain hypothesis positing that the size of the neocortex evolved to meet social demands. However, the role of fractional anisotropy (FA) in white matter (WM) tracts relevant to mentalizing, empathy, and social networks remains unclear. In this study, we investigated the relationships between FA in brain regions associated with social cognition (superior longitudinal fasciculus (SLF), cingulum (CING), uncinate fasciculus, inferior fronto-occipital fasciculus), social network characteristics (diversity, size, complexity), and empathy (cognitive, affective). We employed diffusion tensor imaging, tract-based spatial statistics, and mediation analyses to examine these associations. Our findings revealed that increased social network size was positively correlated with FA in the left SLF. Further, our mediation analysis showed that lower FA in left CING was associated with increased social network size, mediated by cognitive empathy. In summary, our findings suggest that WM tracts involved in social cognition play distinct roles in social network size and empathy, potentially implicating affective brain regions. In conclusion, our findings offer new perspectives on the cognitive mechanisms involved in understanding others' mental states and experiencing empathy within supportive social networks, with potential implications for understanding individual differences in social behavior and mental health.

Migration spillovers within families: Evidence from Thailand

When a person migrates, are their family members more likely to migrate too? I estimate the causal impact of family migrant network size on migration decisions using household survey data from rural Thailand. Large but temporary labor demand shocks in a nearby city—originating from a national infrastructure program—provide plausibly exogenous variation in family members' migration decisions based on their ages at the time of the program. Among those too young to be directly impacted by the program, I find that each older family migrant increases their migration probability by about 5 percentage points. Further analysis suggests a role for better information about the destination in driving this impact. My findings imply that the short-run benefits of relieving migration constraints can underestimate the long-run benefits due to spillovers within the household.

Network Robustness Prediction: Influence of Training Data Distributions.

Network robustness refers to the ability of a network to continue its functioning against malicious attacks, which is critical for various natural and industrial networks. Network robustness can be quantitatively measured by a sequence of values that record the remaining functionality after a sequential node- or edge-removal attacks. Robustness evaluations are traditionally determined by attack simulations, which are computationally very time-consuming and sometimes practically infeasible. The convolutional neural network (CNN)-based prediction provides a cost-efficient approach to fast evaluating the network robustness. In this article, the prediction performances of the learning feature representation-based CNN (LFR-CNN) and PATCHY-SAN methods are compared through extensively empirical experiments. Specifically, three distributions of network size in the training data are investigated, including the uniform, Gaussian, and extra distributions. The relationship between the CNN input size and the dimension of the evaluated network is studied. Extensive experimental results reveal that compared to the training data of uniform distribution, the Gaussian and extra distributions can significantly improve both the prediction performance and the generalizability, for both LFR-CNN and PATCHY-SAN, and for various functionality robustness. The extension ability of LFR-CNN is significantly better than PATCHY-SAN, verified by extensive comparisons on predicting the robustness of unseen networks. In general, LFR-CNN outperforms PATCHY-SAN, and thus LFR-CNN is recommended over PATCHY-SAN. However, since both LFR-CNN and PATCHY-SAN have advantages for different scenarios, the optimal settings of the input size of CNN are recommended under different configurations.

The international dissemination of China’s English academic journals on Twitter: Structure, theme and impact

Objective/Significance: To measure the international communication capacity of China’s English academic journal papers based on social media platforms. Method/Process: Social network analysis, content analysis and emotion analysis were used to describe the diffusion structure of China’s English academic journal papers on Twitter from the aspects of network structure and identify important node users, explore the identity characteristics and emotion characteristics of users as well as diffusion capability of journals of different disciplines. Conclusion/Discovery: Journal articles in the fields of natural sciences and engineering as well as humanities and social sciences exhibit a strong positive correlation between their diffusion structure and network size. Networks with a mixed diffusion structure typically have larger network sizes. The users occupying key positions in the diffusion network typically have profile information that is academically relevant. The retweets and comments are mainly statements without emotional inclination, and the comments are mainly summary descriptions of the papers. The distribution of diffusion width, diffusion strength and diffusion speed of each discipline is significantly different. There is a significant positive correlation between the diffusion structure of papers and their diffusion capabilities. Innovation/Value: To reveal the diffusion structure and characteristics of China’s English academic journal papers on social media platforms, so as to provide reference for expanding the visibility of China’s English academic journals and improving the international dissemination capacity of China’s English journals.

Quantized kernel recursive q-Rényi-like algorithm

This paper introduces the kernel recursive q-Rényi-like (KRqRL) algorithm, based on the q-Rényi kernel function and the kernel recursive least squares (KRLS) algorithm. To reduce the computational complexity and memory requirements of the KRqRL algorithm, an online vector quantization (VQ) method is employed to quantize the network size to a codebook size, resulting in the quantized KRqRL (QKRqRL) algorithm. This paper provides a detailed analysis of the convergence and computational complexity of the QKRqRL algorithm. In the simulation experiments, the network size of each algorithm is reduced to 25% of its original size. The performance of the QKRqRL algorithm is evaluated in terms of convergence speed, prediction error, and computation time under non-Gaussian noise conditions. Finally, the QKRqRL algorithm is further validated using sunspot data, demonstrating its superior stability and online prediction performance.

A hierarchy of metabolite exchanges in metabolic models of microbial species and communities.

The metabolic network of an organism can be analyzed as a constraint-based model. This analysis can be biased, optimizing an objective such as growth rate, or unbiased, aiming to describe the full feasible space of metabolic fluxes through pathway analysis or random flux sampling. In particular, pathway analysis can decompose the flux space into fundamental and formally defined metabolic pathways. Unbiased methods scale poorly with network size due to combinatorial explosion, but a promising approach to improve scalability is to focus on metabolic subnetworks, e.g., cells' metabolite exchanges with each other and the environment, rather than the full metabolic networks. Here, we applied pathway enumeration and flux sampling to metabolite exchanges in microbial species and a microbial community, using models ranging from central carbon metabolism to genome-scale and focusing on pathway definitions that allow direct targeting of subnetworks such as metabolite exchanges (elementary conversion modes, elementary flux patterns, and minimal pathways). Enumerating growth-supporting metabolite exchanges, we found that metabolite exchanges from different pathway definitions were related through a hierarchy, and we show that this hierarchical relationship between pathways holds for metabolic networks and subnetworks more generally. Metabolite exchange frequencies, defined as the fraction of pathways in which each metabolite was exchanged, were similar across pathway definitions, with a few specific exchanges explaining large differences in pathway counts. This indicates that biological interpretation of predicted metabolite exchanges is robust to the choice of pathway definition, and it suggests strategies for more scalable pathway analysis. Our results also signal wider biological implications, facilitating detailed and interpretable analysis of metabolite exchanges and other subnetworks in fields such as metabolic engineering and synthetic biology.

A two-stage location model covering COVID-19 sampling, transport and DNA diagnosis: design of a national scheme for infection control.

During the COVID-19 pandemic, a system was established in China that required testing of all residents for COVID-19. It consisted of sampling stations, laboratories capable of carrying out DNA investigations and vehicles carrying out immediate transfer of all samples from the former to the latter. Using Beilin District, Xi'an City, Shaanxi Province, China as example, we designed a genetic algorithm based on a two-stage location coverage model for the location of the sampling stations with regard to existing residencies as well as the transfer between the sampling stations and the laboratories. The aim was to estimate the minimum transportation costs between these units. In the first stage, the model considered demands for testing in residential areas, with the objective of minimizing the costs related to travel between residencies and sampling stations. In the second stage, this approach was extended to cover the location of the laboratories doing the DNAinvestigation, with the aim of minimizing the transportation costs between them and the sampling stations as well as the estimating the number of laboratories needed. Solutions were based on sampling stations and laboratories existing in 2022, with the results visualized by geographic information systems (GIS). The results show that the genetic algorithm designed in this paper had a better solution speed than the Gurobi algorithm. The convergence was better and the larger the network size, the more efficient the genetic algorithm solution time.

Synchronization on complex dynamical networks via intermittently sampled-data pinning control

Each specific control strategy has a unique advantage, and combining multiple control strategies can harness the advantages of these strategies. Designing new control strategies by integrating different control strategies is an interesting and challenging topic. This paper introduces an intermittently sampled-data pinning (ISP) control strategy, which merges intermittent control, sampled-data control and pinning control, to study synchronization on complex dynamical networks. The ISP control strategy is proposed to solve three difficulties: first, the controllers transmitting feedback signals may be discontinuous; second, the controllers often cannot operate continuously in practical applications; third, it is usually hard to control all nodes in a dynamical network as the network size is huge. Sufficient conditions are obtained for realizing synchronization on dynamical networks. Furthermore, time delays are incorporated into the proposed control strategy to address the untimely reception of feedback signals and achieve the synchronization conditions on dynamical networks. Finally, two numerical examples demonstrate the effectiveness of the proposed control method.

Social networks and COVID-19 vaccination intention in Dutch middle-aged and older adults in 2020: Insights into individual, interpersonal, community, and societal determinants – The SaNAE study

BackgroundSocial networks, our social relationships, influence the spread of infectious diseases and preventive behaviors such as vaccination. Here, we aimed to assess which individual, interpersonal (social network characteristics), community and societal factors are associated with coronavirus disease 2019 (COVID-19) vaccination intention during the second wave of the COVID-19 pandemic in 2020, prior to vaccine availability. MethodsThis cross-sectional study collected primary data from 5,001 community-dwelling adults aged 40 years and older in the Netherlands, using an online questionnaire from August and November 2020. COVID-19 vaccination intention was measured by assessing whether respondents were willing to receive a COVID-19 vaccination if the vaccines became available. Associations between individual (sociodemographic variables, health, health concerns), interpersonal (social network characteristics including structure, function, and quality), community (social and labor participation) and societal factors (degree of urbanization), and the outcome variables COVID-19 vaccination intention (yes vs no, yes vs unsure, unsure vs no) were assessed in stepwise multivariable logistic regression analyses. p-values < 0.05 indicated statistical significance. ResultsAmong participants (N = 3,396), 59 % reported a positive intention to vaccinate, 35 % were unsure, and 6 % had no intention. Men, individuals of older age, those with a college or university degree, those concerned about their personal and family health, and knowledge about protecting oneself from the virus were more likely to have the intention to vaccinate (versus no intention). Interpersonal factors associated included having a larger network size (social network structure) and a larger proportion of informational supporters (social network function). Living outside of urban areas, a societal factor, was associated with vaccination intention (versus no intention). ConclusionThis study identified key factors influencing COVID-19 vaccination intention. Health promotion efforts should address not only individual factors but also incorporate the social environment. Our findings highlight the importance of organizing social networks to mobilize social support for pandemic preparedness.

Identification of malfunctioning quantum devices

We consider the problem of correctly identifying a malfunctioning quantum device that forms part of a network of N such devices, which can be considered as the quantum analog of classical anomaly detection. In the case where the devices in question are sources assumed to prepare identical quantum pure states, with the faulty source producing a different anomalous pure state, we show that the optimal probability of successful identification requires a global quantum measurement. We also put forth several local measurement strategies—both adaptive and nonadaptive—that achieve the same optimal probability of success in the limit where the number of devices to be checked is large. In the case where the faulty device performs a known unitary operation, we show that the use of entangled probes provides an improvement that even allows perfect identification for values of the unitary parameter that surpass a certain threshold. Finally, if the faulty device implements a known qubit channel, we find that the optimal probability for detecting the position of rank-one and rank-two Pauli channels can be achieved by product state inputs and separable measurements for any size of network, whereas for rank-three and general amplitude damping channels, optimal identification requires entanglement with N qubit ancillas. Published by the American Physical Society 2024

Mapping the Landscape of Those Left Behind When a Person With Dementia Dies: Roles of Race and Ethnicity.

People with dementia can have many family and friends who might be affected by their deaths. Pursuing the long-term aim of understanding how dementia deaths affect close family and friends, this project lays groundwork through estimates of who those close family and friends are, with special attention to race and ethnicity. Regression models estimated associations between dementia, race/ethnicity, and close family and friend network size, controlling for age, sex, education, marital status, and household wealth for 1386 deceased people with dementia from the Health and Retirement Study (2004 to 2018). Persons with dementia had an average of 9.4 close family and friends at death. But patterns of close family and friends were different among non-Latino Black (10.8), Latino (9.9), and non-Latino White (9.2) people with dementia at death. Notably, non-Latino White persons with dementia had the fewest close family (3.7), followed by non-Latino Black (5.1), and Latino (7.7) persons with dementia. Knowing who might be affected by dementia deaths is the first step to explore how dementia-related deaths impact close family and friends. Future work can now sample bereaved family and friends of people with dementia to explore their experiences and develop culturally appropriate supports.

Greater social network complexity mitigates pandemic-related negativity

Having strong, diverse social relationships is tightly linked to myriad physical and mental health benefits by mitigating the negative impacts of adverse events. The COVID-19 pandemic brought increased uncertainty and changes to our social lives that reduced the spread of COVID-19 (e.g., social distancing), but also likely impacted emotional well-being. We investigated how one of these changes – changing social networks – relates to emotional bias (a proxy for well-being), specifically one’s tendency to interpret ambiguous stimuli as positive or negative (i.e., valence bias). Participants ( N = 614) categorized the valence of clearly (e.g., angry and happy expressions) and ambiguously valenced stimuli (e.g., surprised expressions), and were asked about their social network before and during the pandemic. Compared to before the pandemic, participants reported a decrease in overall network size ( p &lt; .001) and number of embedded networks (i.e., a measure of network complexity represented by number of areas of social activity; p &lt; .001). In a model with all three social network dimensions (size, complexity, diversity), network complexity uniquely predicted valence bias ( p = .02). Specifically, participants with greater social network complexity during the pandemic showed a more positive bias. Further, participants that did not experience a pandemic-related reduction in network complexity were protected from pandemic-related increases in negativity ( p &lt; .001). In other words, having a more complex social network could act as a protective factor against adverse outcomes in times of uncertainty.

Which type of information’s quality in live streaming commerce captures consumers’ attention the most? Considering the moderating role of network size

PurposeThis study aims to identify which types of information quality influence purchase intentions the most in live streaming commerce and to examine the role of network size in this context.Design/methodology/approachWe propose a model to investigate the correlation among the quality of different information in live streaming commerce, consumer trust, network size and purchase intention. An empirical analysis of 505 questionnaires was conducted by constructing a structural equation model.FindingsThe empirical findings indicate that information quality can directly enhance purchase intention and exert an indirect influence through the mediating factors of trust in products and streamers. Perceived network size positively moderates the relationship between information quality and trust in products. Of the five types of information, the quality of bullet-screen comments information is most important to consumers.Originality/valueThis study represents the first systematic analysis of how the quality of multiple types of information in live streaming commerce influences consumer trust and purchase intention, integrated within a unified framework. It uniquely introduces network size as a moderating variable, offering both theoretical insights and practical guidance for balancing information quality with network size in live streaming commerce environments.

Social Network Density Mediates the Association Between Problematic Social Media Use and Depressive Symptoms

Previous studies have indicated that problematic social media use (PSMU) is positively related to depressive symptoms. However, the potential mechanisms underlying this relationship are not well understood. For example, prior research found that depressive symptoms are related to one’s social network structure, such as network size and density. Therefore, we examined whether one’s social network size and density mediate the relationship between PSMU and depressive symptoms. We conducted an in-person survey to collect measures of PSMU, social network size and density, and depressive symptoms. Our analysis showed that there was a positive relationship between PSMU and depressive symptoms, which was mediated by social network density but not size. Specifically, the greater one’s PSMU, the less dense their social network, and the greater their depressive symptoms. Our research suggests that PSMU may affect how individuals maintain their social connections with others, which may affect mental health. Implications were discussed.

Anonymous group structure algorithm based on community structure.

A social network is a platform that users can share data through the internet. With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. To mitigate this problem, an anonymous group structure algorithm based on community structure is proposed in this article. At first, a privacy protection scheme model is designed, which can be adjusted dynamically according to the network size and user demand. Secondly, based on the community characteristics, the concept of fuzzy subordinate degree is introduced, then three kinds of community structure mining algorithms are designed: the fuzzy subordinate degree-based algorithm, the improved Kernighan-Lin algorithm, and the enhanced label propagation algorithm. At last, according to the level of privacy, different anonymous graph construction algorithms based on community structure are designed. Furthermore, the simulation experiments show that the three methods of community division can divide the network community effectively. They can be utilized at different privacy levels. In addition, the scheme can satisfy the privacy requirement with minor changes.

Optimized design of horseshoe-and-spur filterless networks leveraging point-to-multipoint coherent pluggable transceivers

Cutting-edge network architectures and solutions are needed to empower operators to address capacity demands in metro and access networks efficiently. The horseshoe topology, along with similar topologies, is commonly employed in metro-aggregation segments due to its compatibility with the hub-and-spoke traffic pattern present in these networks and the survivability that they can provide. A filterless architecture can enhance cost-effectiveness by replacing active elements with passive components. Moreover, supporting coherent-based point-to-multipoint transceivers—enabled by digital subcarrier multiplexing (DSCM)—can yield additional cost savings. It is noteworthy that telecommunication network topologies often evolve to accommodate more end (leaf) nodes, extending the original horseshoe with spurs or small and short trees. This paper targets these types of networks and proposes combining the utilization of coherent pluggable transceivers leveraging DSCM to guarantee transparent communication between the hub and leaf nodes while adopting different filterless node architectures with selective amplifier deployment. Moreover, it discusses the potential advantages of the architecture using an exact optimization framework tailored to various network sizes and scenarios, which accounts for the amplifiers’ placement and the available types of power splitters/combiners/couplers. The results demonstrate that strategically deploying add/drop and express amplifiers, along with optimizing coupler ratios, can effectively meet design requirements while minimizing the number of optical amplifiers required.