Macro Network Research Articles

Decoding acceptance and reappraisal strategies from resting state macro networks

Acceptance and reappraisal are considered adaptive emotion regulation strategies. While previous studies have explored the neural underpinnings of these strategies using task-based fMRI and sMRI, a gap exists in the literature concerning resting-state functional brain networks’ contributions to these abilities, especially regarding acceptance. Another intriguing question is whether these strategies rely on similar or different neural mechanisms. Building on the well-known improved emotion regulation and increased cognitive flexibility of individuals who rely on acceptance, we expected to find decreased activity inside the affective network and increased activity inside the executive and sensorimotor networks to be predictive of acceptance. We also expect that these networks may be associated at least in part with reappraisal, indicating a common mechanism behind different strategies. To test these hypotheses, we conducted a functional connectivity analysis of resting-state data from 134 individuals (95 females; mean age: 30.09 ± 12.87 years, mean education: 12.62 ± 1.41 years). To assess acceptance and reappraisal abilities, we used the Cognitive Emotion Regulation Questionnaire (CERQ) and a group-ICA unsupervised machine learning approach to identify resting-state networks. Subsequently, we conducted backward regression to predict acceptance and reappraisal abilities. As expected, results indicated that acceptance was predicted by decreased affective, and executive, and increased sensorimotor networks, while reappraisal was predicted by an increase in the sensorimotor network only. Notably, these findings suggest both distinct and overlapping brain contributions to acceptance and reappraisal strategies, with the sensorimotor network potentially serving as a core common mechanism. These results not only align with previous findings but also expand upon them, illustrating the complex interplay of cognitive, affective, and sensory abilities in emotion regulation.

Micro Effects on Macro Structure in Social Networks

How do individuals’ network selection decisions create unique network structures? Despite broad sociological interest in the micro-level social interactions that create macro-level network structure, few methods are available to statistically evaluate micro-macro relationships in social networks. This study introduces a general methodological framework for testing the effect of (micro) network selection processes, such as homophily, reciprocity, or preferential attachment, on unique (macro) network structures, such as segregation, clustering, or brokerage. The approach uses estimates from a statistical network model to decompose the contributions of each parameter to a node, subgraph, or global network statistic specified by the researcher. A flexible parametric algorithm is introduced to estimate variances, confidence intervals, and p values. Prior micro-macro network methods can be regarded as special cases of the general framework. Extensions to hypothetical network interventions, joint parameter tests, and longitudinal and multilevel network data are discussed. An example is provided analyzing the micro foundations of political segregation in a crime policy collaboration network.

A Dynamic Network Loading Model for Hub Station Pedestrian Flow Collection and Distribution

A macro network loading model for multi-flow lines, time varying, and pedestrian congestion is proposed. The station hub is abstracted as a network of different types of nodes, and the flow of passengers at each node is calculated in real time for the purpose of simulating the hub’s collection and distribution process. For correct transmission of passenger flow on heterogeneous networks, three types of indexes are proposed to distinguish the nodes, and the corresponding fundamental diagrams are then matched. This paper divides the update process of the dynamic network loading model into multiple processes by flow lines, and improves the computational speed of the DNL model. The proposed model is applied to the simulation of passenger flow collection and distribution in an actual hub station with multi-flow lines. The analysis results illustrate that the model can accurately reflect the realistic congestion facilities and explain the formation process of high-density areas. A rolling passenger flow control model based on optimal control theory is proposed. The effectiveness of the control model is verified based on simulation data.

A bi-level planning approach of logistics unmanned aerial vehicle route network

The rapid growth of e-commerce in urban areas has put incremental pressure on delivery. Unmanned Aerial Vehicle (UAV) has great potential in the logistics field with the aggravation of ground congestion. This study proposes a logistics UAV route network planning approach based on the bi-level program, which from the perspectives of network managers and users. The upper model focuses on the macro network configuration, while the lower model focuses on the UAV traffic flow distribution. The two-layer Simulated Annealing (SA) is presented to deal with the iterative process of network topology and traffic flow balance allocation. A case study is carried out using a region from Nanjing, to introduce the application of this bi-level approach. The results suggest that there is a positive correlation between the network overall length and the flow entropy, a negative correlation between the network overall length and the nonlinear coefficient. When the maximum transit time of UAV is 3, the comprehensive performance of network operation is optimal. This study provides a new way for logistics UAV network planning in urban areas, which is significant to regulate the operation of large-scale UAVs.

Mapping the Multitudes, Discovering the Margins: Feminist-Focused Macro Network Analysis and a Visualization-Based Digital Archive as Coevolving Digital Humanities Tools

Abstract: In a post-pandemic world, digital humanities is facing a new moment of opportunity to move beyond the roadblocks of the past—such as resistance to the crossover between feminist and technological projects and a tendency to construct digital tools without specific interpretive intent—and find a blended purpose in developing technological and methodological tools that not only make cultural contributions but also encourage the serendipitous discoveries that are crucial to the work of feminist scholars. Building on such an interconnected developmental relationship, this essay argues that digital tools and analytic methodologies can work together to inform and transform one another.

An Enhanced Dynamic Spectrum Allocation Method on Throughput Maximization in Urban 5G FBMC Heterogeneous Network

Reports have shown that the demand for data managed by wireless systems is expected to grow by more than 500 exabytes by 2025 and beyond. 5G networks are predicted to meet these demands, provided that the spectrum resources are well managed. In this paper, an enhanced dynamic spectrum allocation (E-DSA) method is proposed, which incorporates a cooperative type of game theory called the Nash bargaining solution (NBS). It was assumed that there is one primary user (PU) and two secondary users (SU) in the network and their spectrum allocation was analyzed by testing the validity of the algorithm itself by using price weight factors to control the costs of the spectrum sharing. The solution was established by combining a proposed multiplexing method called the Filter Bank Multicarrier (FBMC) for 5G configuration, with the E-DSA algorithm to maximize the throughput of a heterogeneous 5G network. It was shown that the throughputs for 5G with E-DSA implementation were always higher than those of the ones without E-DSA. The simulation was done using the LabVIEW communication software and was analyzed based on a 5G urban macro and micro network configuration to validate the heterogeneity of the network.

Spatiotemporal Dynamics and Topological Evolution of the Global Crude Oil Trade Network

The high separation of crude oil supply and demand markets has led to the formation of a global crude oil trading system. This paper constructs global crude oil trade networks, integrates macro, meso, and micro network analysis methods, combines geospatial visualization techniques, and then portrays the spatiotemporal patterns and topological evolution of the global crude oil trade networks. Thus, it attempts to dig deeper into the world crude oil competition and cooperation links and evolution laws and provides a scientific reference for a comprehensive understanding of the global crude oil market dynamics. The results show that: (1) After three fluctuations of increase and decrease since 2000, the global crude oil trade volume is entering the adjustment period, and the scale of the crude oil market is rising slowly. (2) The international crude oil trade has formed trade network patterns with complex structures, clear hierarchy and unbalanced distribution. The “rich club” phenomenon is significant, with large trading countries dominating the trade network. (3) The scale and density of the global crude oil trade network show a trend of increasing and then decreasing, the network agglomeration pattern becoming more obvious, the inter-nodal links continuously strengthening, and the network connectivity improving. (4) The global crude oil trade networks are characterized by core–periphery structures, and the polarization effect is significant. The US, Russia, China, Japan, the Netherlands, and South Korea hold the core positions in the crude oil trade network, and the major importing countries have become the dominant forces in the trade network. In addition, we present policy suggestions for different types of countries for energy transformation and security in the global trade market system, which can be used as a reference for policymakers.

Borderline shades: Morphometric features predict borderline personality traits but not histrionic traits

Borderline personality disorder (BPD) is one of the most diagnosed disorders in clinical settings. Besides the fully diagnosed disorder, borderline personality traits (BPT) are quite common in the general population. Prior studies have investigated the neural correlates of BPD but not of BPT. This paper investigates the neural correlates of BPT in a subclinical population using a supervised machine learning method known as Kernel Ridge Regression (KRR) to build predictive models. Additionally, we want to determine whether the same brain areas involved in BPD are also involved in subclinical BPT. Recent attempts to characterize the specific role of resting state-derived macro networks in BPD have highlighted the role of the default mode network. However, it is not known if this extends to the subclinical population. Finally, we wanted to test the hypothesis that the same circuitry that predicts BPT can also predict histrionic personality traits. Histrionic personality is sometimes considered a milder form of BPD, and making a differential diagnosis between the two may be difficult. For the first time KRR was applied to structural images of 135 individuals to predict BPT, based on the whole brain, on a circuit previously found to correctly classify BPD, and on the five macro-networks. At a whole brain level, results show that frontal and parietal regions, as well as the Heschl’s area, the thalamus, the cingulum, and the insula, are able to predict borderline traits. BPT predictions increase when considering only the regions limited to the brain circuit derived from a study on BPD, confirming a certain overlap in brain structure between subclinical and clinical samples. Of all the five macro networks, only the DMN successfully predicts BPD, confirming previous observations on its role in the BPD. Histrionic traits could not be predicted by the BPT circuit. The results have implications for the diagnosis of BPD and a dimensional model of personality.

Optimization of Cognitive Femtocell Network via Oppositional Beetle Swarm Optimization Algorithm

In past decades, cellular networks have raised the usage of spectrum resources due to the victory of mobile broadband services. Mobile devices create massive data than ever before, facing the way cellular networks are installed presently for satisfying the increased traffic requirements. The development of a new exclusive spectrum offered to meet up the traffic requirements is challenging as spectrum resources are limited, hence costly. Cognitive radio technology is presented to increase the pool of existing spectrum resources for mobile users via Femtocells, placed on the top of the available macrocell network for sharing the same spectrum. Nevertheless, the concurrent reuse of spectrum resources from Femto networks poses destructive interference on macro networks. To resolve this issue, this paper introduces an optimal channel allocation model using the Oppositional Beetle Swarm Optimization Algorithm (OBSOA) to allocate the channel with interference avoidance. A new OBSOA is derived in this paper by the inclusion of opposition-based learning (OBL) in BSOA. This algorithm allocates the channels used by PUs (PUs) to the secondary users (SUs) in such a way that interference is minimized. This proposed approach is implemented in the MATrix LABoratory (MATLAB) platform. The performance of this proposed approach is evaluated in terms of several measures and the experimental outcome verified the superior nature of the OBSOA-based channel allocation model. OBSOA model has resulted in a maximum signal-to-interference-plus-noise ratio value of 86.42 dB.

Unlicensed band sharing based on bargaining game for heterogeneous networks

Currently, limited licensed frequency bands cannot meet the increasing demands for various wireless communication applications any more. It is necessary to extend wireless communication networks to unlicensed spectrum. In this paper, we propose a new bargaining framework for unlicensed band access to achieve high spectrum efficiency, where one radio access technology (RAT) (such as macro cellular network) "competes" the unlicensed bands with multiple other RATs (such as small cellular networks or Wi-Fi) virtually. Considering that macro cell can share unlicensed frequencies with multiple small cells which are in the same coverage area for more freedom, we use bargaining game theory to fairly and effectively share the unlicensed spectrum between macro and multiple heterogeneous small cell networks, where bargaining loss and time dissipation loss for virtual "price" of unlicensed bands are mainly considered. In the one-to-many bargaining process, we also develop a multiple RAT alliance game strategy to reduce transmission loss in a joint manner. Simulation results show that the proposed unlicensed band sharing algorithm significantly improves the spectrum efficiency performance compared with the other practical schemes for heterogeneous networks.

Disruptive innovation in the economic organization of China and the West

AbstractWe explore how macro and micro networks influence the diffusion of technological innovation and cultural/social behavior. Across the historical regimes in China and Europe, dynastic lordship's macro networks afforded different advantages in technological innovation. A network particular to Europe, the Roman Church, extended deep into local parishes with ethical norms prescribing fairness to strangers, and these cultural foundations helped guilds, trade associations, merchant courts, and universities operate cooperatively far beyond kinship. In contrast, Chinese emperors relied on ancient Confucian moral codes and system-spanning Confucian-educated officialdom; but fiscal limitations compelled officials to defer to local lineage orders, resulting in an enduring cultural pattern of guanxi and a polity whose institutional problem-solving capacity falter beyond the local level. Yet the civil service system has enabled China to outperform similar lineage-dependent regimes. Probing network topologies, we find that system-spanning networks can facilitate technological diffusion, but local networks influence cultural and behavioral change.

SSGNN: A Macro and Microfacial Expression Recognition Graph Neural Network Combining Spatial and Spectral Domain Features

Emotion recognition from macroexpression and microexpression has been widely used in applications such as human–computer interaction, learning status evaluation, and mental disorder diagnosis. However, due to the complexity of human macroexpressions, recognizing macroexpressions with high accuracy is a challenging task. Moreover, the short duration and low movement intensity of microexpressions make its recognition more difficult. For MM-FER (macro and microfacial expression recognition), the key information can be more efficiently expressed by a graph. In this article, a novel framework based on graph neural network named SSGNN (spatial and spectral domain features based on a graph neural network) is designed to extract spatial and spectral domain features from facial images for MM-FER, which can efficiently recognize both macroexpressions and microexpressions under the same model. SSGNN consists of two parts, SPAGNN and SPEGNN, which are used to extract spectral and spatial domain features, respectively. Experiments proved that jointly using the spectral and spatial information extracted by SSGNN can largely improve the performance of MM-FER when the training sample is limited. First, the influences of different neighbors and samples to the model performance was analyzed. Then, the contribution of SPAGNN and SPEGNN were evaluated. It was discovered that fusing the result of SPAGNN and SPEGNN at decision level further improved the performance of MM-FER. Experiment proved that SSGNN can recognize microexpression acquired by various sensors with higher accuracy under different image resolutions and image formats than the compared state-of-the-art methods in most cases. A cross-dataset experiment demonstrated the generalization ability of SSGNN.

Appropriating Relational Value from Collaborative Networks for Sustainability

Our study elucidates relational value creation and appropriation in collaborative networks for sustainability (CNfS) that focus on grand societal challenges and include a multiplicity and diversity of actors. Using a relational view lens, we conducted a longitudinal, multiple case, field study of collaborative networks for sustainability in the circular textile and fashion industry, unpacking the interplay between value creation from relational interdependence, relational-specific assets and material output and the multilevel appropriation of that value. Our findings show that value appropriation is contingent on the perception of relational use value and cascades through individual, organizational and network levels. The ability of actors to capture cascading value on different levels has a direct influence on the sustained continuity of value creation and to achieving the shared societal goals of CNfS. We developed a model of value appropriation in CNfS to illustrate the cascading flow of value at micro (individual), meso (organizational) and macro (network) levels. Our study makes novel contributions to the literatures on strategic alliances, cross-sector partnerships, and open innovation networks.

CaO/CeO2 nanocomposite dispersed macro-/meso-porous polymer monoliths as new generation visible light heterogeneous photocatalysts

This work highlights the synthesis of CaO doped CeO2 nanocomposites (NCs) prepared by a sonochemical-assisted temperature-controlled hydrothermal method. Considering the toxicology and recovery issues with nano-particles/composites, we have attempted a novel approach by homogeneously dispersing CaO/CeO2 NCs onto a 3D network of macro-/meso-porous monolithic polymer templates. The structural and morphological properties of the synthesized photocatalyst materials have been characterized by p-XRD, FE-SEM-EDAX, HR-TEM-SAED, XPS, FT-IR, UV–Vis-DRS, PLS, and BET analysis. The visible-light photocatalytic performance has been observed using varying stoichiometric ratios of CaO/CeO2 NCs. The CaO/CeO2 NCs dispersed porous polymer monolith reveals superior photocatalytic activity than bare CaO/CeO2 NCs that tend to agglomerate in aqueous solutions, reducing their efficiency. The CaO/CeO2 NCs dispersed polymer monolith exhibits excellent porosity and surface area for the ultra-fast dissipation of organic pollutants. A fluoroquinolone-based antimicrobial drug, namely moxifloxacin, has been chosen as the target pollutant, considering the growing bacterial resistance due to the uncontrolled discharge/discarding of pharmaceutical medications. The best results were achieved using the monolithic photocatalyst dispersed with 20:80 CaO/CeO2 NC, for the complete dissipation of moxifloxacin drug molecules (≥99% degradation), at a pH of 6.0, using 100 mg of photocatalyst to dissipate 15 ppm of moxifloxacin drug, within 2 h of visible-light irradiation. The proposed photocatalyst is cost-effective, durable, reusable, and efficient for the real-time dissipation of environmental pollutants.

Does the “Platform Society” Mean the End of the “Network Society?” Reflections on Platforms and the Structure and Dynamics of Networks

Scholars and journalistic accounts have devoted growing interest to the centralizing trends characterizing platforms and the “platform society.” They often oppose this model to the alleged openness, horizontality, and “equality” they attach to “networks.” Such depictions seem willing to give up on a thorough consideration of network structure, which appears nowadays less fundamental to reflect on the digital world (and society) than it was 25 years ago. Or even question whether the network society model, as proposed by Castells, is unsuitable for describing contemporary society. In our opinion, the dichotomy opposing the (alleged) openness and egalitarian nature of networks (and of the network society), to the current centralization trends characterizing the platform society, as well as the subsequent assumption that networks are an outdated heuristic tool, derive from a misunderstanding of networks’ structure and dynamics. Scholars have shown that the structure of most complex networks can be defined as “scale-free,” following a power-law distribution. Complex networks, indeed, show the tendency for some nodes to become more interconnected than others (thus becoming “hubs”). In this, the understanding of network structure proposed in Castells (1996), contrary to the rhetoric considering networks intrinsically as “egalitarian,” is still a conceptual and analytical tool of the utmost importance for understanding the so-called “platform society.” This paper focuses on networks, network models, and the network society, reviewing what was proposed in Castells (1996). We argue that the social and platform ecosystem we are witnessing today can be understood from the perspective of scale-free networks and is, indeed, consistent with the premises provided in 1996. When observing networks, we address both structure and agency, and both the macro (network morphology) and micro (networked individualism and sociability) levels

Numerical mesoscale tissue model of electrochemotherapy in liver based on histological findings

Electrochemotherapy (ECT) and irreversible electroporation (IRE) are being investigated for treatment of hepatic tumours. The liver is a highly heterogeneous organ, permeated with a network of macro- and microvasculature, biliary tracts and connective tissue. The success of ECT and IRE depends on sufficient electric field established in whole target tissue; therefore, tissue heterogeneity may affect the treatment outcome. In this study, we investigate electroporation in the liver using a numerical mesoscale tissue model. We numerically reconstructed four ECT experiments in healthy porcine liver and computed the electric field distribution using our treatment planning framework. We compared the computed results with histopathological changes identified on microscopic images after treatment. The mean electric field threshold that best fitted the zone of coagulation necrosis was 1225 V/cm, while the mean threshold that best fitted the zone of partially damaged liver parenchyma attributed to IRE was 805 V/cm. We evaluated how the liver macro- and microstructures affect the electric field distribution. Our results show that the liver microstructure does not significantly affect the electric field distribution on the level needed for treatment planning. However, major hepatic vessels and portal spaces significantly affect the electric field distribution, and should be considered when planning treatments.

Spatial Conflict Risk Measurement and Early Warning Based on Network Information Processing

The contradiction of land use is becoming more and more fierce in China, which poses a serious threat to the structural integrity and quantity of ecological land, the uncontrolled expansion of urban construction land, the imbalance between the spatial structure of agricultural land and ecological land, and the unreasonable spatial structure of various types of land within the city. This study mainly discusses the risk measurement and early warning of spatial conflict based on network information processing. In this study, GIS technology is used to deal with the risk of spatial conflict. Spatial conflict, in essence, is an objective geographical phenomenon arising from the competition for spatial resources in the process of human-land relations and is the result of the interaction between natural geographical factors and human factors, and natural geographical processes and human processes, and is a key factor affecting the sustainable development of the region. The logical network attack path composed of IP sequence captured by the space conflict network early warning and emergency response system is transformed into the attack path composed of a corresponding physical address sequence, which is directly displayed on the electronic map. At the same time, the attributes related to the spatial conflict events in the research area are also displayed in the form of a list. When the macro network early warning and emergency response system detect the occurrence of space conflict events in the study area, its emergency response subsystem queries the strategy database and outputs the emergency strategy in real-time by inputting the warning information analyzed by the early warning information analysis subsystem. Finally, the system realizes the functions of network node information visualization processing, network early warning and emergency response information visualization processing, and electronic layer basic operation. It is found that the industrial added value of the study area of ABC urban agglomeration has reached 283.140 billion yuan in recent three years, accounting for 60.25% of the industrial added value of the three cities and 30.98% of the whole province. The results show that the improper spatial regional combination has an important impact on the spatial conflict, and the improper spatial regional combination will significantly improve the level of spatial conflict.

Traffic Load-Based Spectrum Trading between Two Mobile Network Operators in HetNets Using Interference Management Technique

The pursuit of achieving higher data rates in 5G and beyond has triggered numerous technological advancements. One of these is spectrum sharing between Mobile Network Operators (MNOs). In this work, we investigate the spectrum trading between two MNOs with disparate traffic loads in heterogeneous networks. A radio resource sharing technique is proposed that exploits resource block allocation with Interference Range (RBIR) to reduce interoperator interference prompted by spectrum trading. In addition, an algorithm for cooperative load-based spectrum trading at resource block level is proposed for two MNOs with asymmetric traffic load. Two main approaches for spectrum trading, i.e., spectrum sharing (Load-Based Shared Spectrum Pool (LSSP)) and spectrum leasing (Load-Based Leased Spectrum Pool (LLSP)) are analysed, and a heterogeneous network is modeled such that both operators have a dedicated spectrum for their macro and small cell networks. Prior to spectrum trading, a conventional resource block allocation technique is used in which resource blocks from a shared or leased spectrum pool are assigned so that each user experiences the least amount of interference. The performance of LSSP and LLSP is evaluated while taking into account the constraints of both operators, such as net throughput and user satisfaction. Moreover, we evaluate the network performance with increasing interference range as small cells of different transmission/interference range that are present in the network. The performance evaluation demonstrates that when the interference is lesser, as represented by the radius of the interference range, the spectrum sharing is more advantageous in terms of delivering higher average data rates. However, when the interference is higher, then leasing is more beneficial.

The impact of editorial policies and journal models on the number of articles on social humanities and communication studies related to Covid-19; Evidence from international and Indonesian scholarly work

This study examines scientific publications during a pandemic through bibliometric network analysis. We explored three different journal databases to map COVID-19 research in humanities and social sciences, then zoom in to communication studies. Government policy, e-learning, anxiety, economic impact are popular keywords in international and Indonesian articles on Social and Humanities, while disinformation, health communication, behavior change, and literacy are more prominent in communication articles. The researcher chose the keyword occurrence analysis as the basis for mapping the research theme. The bibliographic network was deployed in three strategies to obtain keyword data co-occurrence from research abstracts, keywords from researchers, and coder's approval keywords. Lastly, Vos viewer is used to creating macro and detailed perspective networks for interpretation. The results show that journal policies and models affect the number of COVID-19 publications in the journal. Finally, this study provides an overview that normative theory and behaviorism play a role in social and communication research.

Robust Resource Allocation for Two-Tier HetNets: An Interference-Efficiency Perspective

The heterogeneous network (HetNet) is a promising network for beyond 5G to improve network capacity and spectral efficiency by developing low-power small cells within macro networks. Therefore, appropriate resource allocation schemes should be designed to balance between data rates and the cross-tier interference among different cells. To improve system robustness and also reduce the harmful interference to macrocell users (MUs), in this paper, we maximize the total interference efficiency (IE) (i.e., the ratio of the sum data rates of femtocell users (FUs) to the total cross-tier interference from femtocell base stations (FBSs) to MUs) with imperfect channel state information in a two-tier orthogonal frequency division multiple access based HetNet by jointly optimizing the transmit power of the FBS and the subcarrier allocation factor. Meanwhile, the outage rate constraint of each FU, the outage interference constraint of each MU, the maximum transmit power constraint of the FBS, and the subcarrier assignment constraint are considered simultaneously. The considered problem belongs to a mixed-integer non-linear programming problem and thus is NP-hard. The original problem with uncertain parameters is transformed into a deterministic and convex one solved by using the quadratic transformation approach, the variable relaxation approach, and Lagrange dual theory. The feasible region analysis and robust sensitivity are provided. Simulation results demonstrate that the proposed scheme has a lower interference to the MU and higher IE by comparing it with the traditional schemes.