Dense Network Research Articles

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.

Оценка развитости узлов и тесноты связи креативной сети в региональной локализации

The relevance of the study is due to the need to obtain comprehensive ideas about the specifics of regional localization of creative industries, the functioning of which is based on close connections between organizations that form a creative product and organizations that promote the distribution of a creative product. The article aims to assess the development of nodes and the connectivity density of a creative network within a regional localization context. The research methodology includes the use of statistical data processing methods, as well as comparative and correlation analysis. By examining the connectivity between nodes in the creative network of organizations operating in the performing arts sector and those in the field of artistic creation, the study concludes that the connections are predominantly strong. This is attributed to the high degree of regional localization among organizations producing and promoting creative products. These findings highlight the significant role of regional development tools in fostering this creative network, particularly through the implementation of organizational and financial support measures.

Formation of Conjugated Polymer Monolayer Networks on Water Surface and Nonlinear Charge Transport

AbstractMaterial‐networked conduction paths provide nonlinear electronic properties, which are essential components of computing and physically mimic the brain. In this study, the formation of conjugated polymer monolayer networks and their nonlinear charge transport is demonstrated. Poly(3‐hexylthiophene) (P3HT) monolayer networks doped with 2,3,5,6‐tetrafluoro‐7,7,8,8‐tetracyanoquinodimethane (F4TCNQ) is fabricated using the co‐spread method with an amphiphilic liquid crystal molecule at the air–water interface. Atomic force microscopy and Ultraviolet–visible–near‐infrared absorption spectroscopy measurements reveal the network surface morphologies and doped electronic states. The correlation between the nonlinear electronic characteristics and network structures of the P3HT/F4TCNQ monolayer networks is further systematically investigated through current–voltage and voltage–time measurements for various doping levels, network densities, and numbers of transferred layers. The current–voltage characteristics of the P3HT/F4TCNQ monolayer network device with a simple two‐terminal structure exhibit nonlinear and ohmic conduction behavior, which depend strongly on the network density and geometric dimension (number of transferred layers). It is concluded that the nonlinear properties arise from the limited and unique network of 2D conduction passes. This study highlights the unique features of conducting polymer monolayer networks, paving the way for neuromorphic device applications including conjugated semiconducting polymer‐based material reservoirs with controllable nanostructures.

Mouse polyomavirus infection induces lamin reorganisation.

The nuclear lamina is a dense network of intermediate filaments beneath the inner nuclear membrane. Composed of A-type lamins (lamin A/C) and B-type lamins (lamins B1 and B2), the nuclear lamina provides a scaffold for the nuclear envelope and chromatin, thereby maintaining the structural integrity of the nucleus. A-type lamins are also found inside the nucleus where they interact with chromatin and participate in gene regulation. Viruses replicating in the cell nucleus have to overcome the nuclear envelope during the initial phase of infection and during the nuclear egress of viral progeny. Here, we focused on the role of lamins in the replication cycle of a dsDNA virus, mouse polyomavirus. We detected accumulation of the major capsid protein VP1 at the nuclear periphery, defects in nuclear lamina staining and different lamin A/C phosphorylation patterns in the late phase of mouse polyomavirus infection, but the nuclear envelope remained intact. An absence of lamin A/C did not affect the formation of replication complexes but did slow virus propagation. Based on our findings, we propose that the nuclear lamina is a scaffold for replication complex formation and that lamin A/C has a crucial role in the early phases of infection with mouse polyomavirus.

Neural network architecture of a mammalian brain

Connectomics research is making rapid advances, although models revealing general principles of connectional architecture are far from complete. Our analysis of 106 published connection reports indicates that the adult rat brain interregional connectome has about 76,940 of a possible 623,310 axonal connections between its 790 gray matter regions mapped in a reference atlas, equating to a network density of 12.3%. We examined the sexually dimorphic network using multiresolution consensus clustering that generated a nested hierarchy of interconnected modules/subsystems with three first-order modules and 157 terminal modules in females. Top-down hierarchy analysis suggests a mirror-image primary module pair in the central nervous system's rostral sector (forebrain-midbrain) associated with behavior control, and a single primary module in the intermediate sector (rhombicbrain) associated with behavior execution; the implications of these results are considered in relation to brain development and evolution. Bottom-up hierarchy analysis reveals known and unfamiliar modules suggesting strong experimentally testable hypotheses. Global network analyses indicate that all hubs are in the rostral module pair, a rich club extends through all three primary modules, and the network exhibits small-world attributes. Simulated lesions of all regions individually enabled ranking their impact on global network organization, and the visual path from the retina was used as a specific example, including the effects of cyclic connection weight changes from the endogenous circadian rhythm generator, suprachiasmatic nucleus. This study elucidates principles of interregional neuronal network architecture for a mammalian brain and suggests a strategy for modeling dynamic structural connectivity.

Neural network reconstruction of the left atrium using sparse catheter paths.

Catheter-based radiofrequency ablation for pulmonary vein isolation has become the first line of treatment for atrial fibrillation in recent years. This requires a rather accurate map of the left atrial sub-endocardial surface including the ostia of the pulmonary veins, which requires dense sampling of the surface and currently takes more than 10min. The focus of this work is to provide left atrial visualization early in the procedure to ease procedure complexity and enable further workflows, such as using catheters that have difficulty sampling the surface. We propose a dense encoder-decoder network with a novel regularization term to reconstruct the shape of the left atrium from partial data which is derived from simple catheter maneuvers. To train the network, we acquire a large dataset of 3D atria shapes and generate corresponding catheter trajectories, from which traversed point clouds are obtained. Once trained, we show that the suggested network can sufficiently approximate the atrium shape based on a given trajectory. We compare several network solutions for the 3D atrium reconstruction. We demonstrate that the solution proposed produces realistic visualization using partial acquisition within a 3-min time interval using human clinical cases.

A Method by using predefined windows to improve the InSAR atmospheric correction model based on GNSS ZTD and its horizontal delay gradient

ABSTRACT Correcting the signal delay caused by electromagnetic waves passing through the atmosphere is crucial in InSAR. Atmospheric delays are significant enough to obscure the true deformation signal, making it difficult for InSAR to detect deformation signals at the millimetre scale. Therefore, it is imperative to mitigate atmospheric interference appropriately. To interpolate sparse GNSS networks and capture small-scale atmospheric delays, this study presents an enhanced method for constructing an atmospheric correction model based on GNSS Zenith Total Delay (ZTD) and its horizontal gradient, which is integrated within a predefined window. To validate the effectiveness of the improved model, we applied it to 27 InSAR short-time and short-spatial baseline unwrapped interferograms. These interferograms are composed of images observed by the Sentinel-1A satellite from January to December 2022 in Southern California. The corrective impact of the enhanced model is also compared against that of the initial inversion model and GACOS to confirm the viability of the approach. Results of the evaluation, considering root mean square error (RMSE), standard deviation (STD), and the correlation between phase and elevation, indicate that our enhanced method reduces correction errors by 35.10%, 20.05%, and 63.52%, respectively, compared to GACOS. Compared to the initial model, these reductions are 58.19%, 57.23%, and 4.34%, respectively. By integrating the outcomes from all three evaluation methods and analysing the corrected images, our enhanced model effectively resolves the inconsistency in correction effects resulting from the spatial variability of atmospheric delay. Moreover, incorporating the horizontal gradient in the inversion method has proven effective in capturing small-scale atmospheric delay signals. This highlights the feasibility and benefits of our enhanced model.

Nano-enhancing polymer as a fluid loss additive for ultra-high temperature cementing

The exploitation of ultra-deep oil and gas wells is accompanied by technical problems of ultra-high temperature, especially in the cementing process, which will directly deactivate many admixtures. By in-situ polymerization of the monomers and Nano-SiO2 to improve the temperature resistance, the resulting high temperature resistant composite polymer slurry system can show high efficiency in reducing fluid loss under the high temperature environment of 240℃. The synthesis of the high temperature resistant polymer was characterized by IR and 1H NMR, and its physicochemical properties were analyzed by DLS, GPC, TEM and Cryo-SEM. The synergistic mechanism of Nano-SiO2 on the high temperature resistance of the polymer was revealed by TGA. Using DLS, SEM and EDS, a series of cement slurry systems were used to analyze and compare the effect on fluid loss reduction of different adding methods of Nano-SiO2 in cement slurry at high temperature. The results showed that the grafted Nano-SiO2 polymer maintained effective adsorption capacity at high temperature without degradation, and its fluid loss reduction ability was significantly improved at high temperature. The high temperature resistant composite polymer is strongly adsorbed between cement particles to form a dense and robust spatial network structure. The CSH gel structure is optimized by the particle size gradation of the system, so as to obtain a dense cement cake, which is expected to be applied in high temperature cementing.

Myosin-I synergizes with Arp2/3 complex to enhance the pushing forces of branched actin networks.

Class I myosins (myosin-Is) colocalize with Arp2/3 complex-nucleated actin networks at sites of membrane protrusion and invagination, but the mechanisms by which myosin-I motor activity coordinates with branched actin assembly to generate force are unknown. We mimicked the interplay of these proteins using the "comet tail" bead motility assay, where branched actin networks are nucleated by the Arp2/3 complex on the surface of beads coated with myosin-I and nucleation-promoting factor. We observed that myosin-I increased bead movement efficiency by thinning actin networks without affecting growth rates. Myosin-I triggered symmetry breaking and comet tail formation in dense networks resistant to spontaneous fracturing. Even with arrested actin assembly, myosin-I alone could break the network. Computational modeling recapitulated these observations, suggesting myosin-I acts as a repulsive force shaping the network's architecture and boosting its force-generating capacity. We propose that myosin-I leverages its power stroke to amplify the forces generated by Arp2/3 complex-nucleated actin networks.

Thermally Conductive Polydimethylsiloxane-Based Composite with a Three-Dimensional Vertically Aligned Thermal Network Incorporating Hexagonal Boron Nitride Nanosheets and Nanodiamonds.

Thermal interface materials play a pivotal role in efficiently transferring heat from heating devices to thermal management components, thereby reducing the risk of component degradation due to overheating. In this study, we propose a strategy for enhancing the out-of-plane thermal conductivity (TC) of composite materials by fabricating a three-dimensional (3D) thermal network within a polydimethylsiloxane (PDMS) matrix. Specifically, the composite material was designed to incorporate a dense thermal network comprising hexagonal boron nitride nanosheets (BNNSs) and nanodiamonds (NDs). The fabrication process commenced with the preparation of BNNSs through liquid-phase exfoliation, followed by the creation of a 3D BNNSs-NDs/polyimide aerogel thermal framework using a unidirectional solidification ice templating method and subsequent heat treatment. Vacuum impregnation and curing were then employed to finalize the production of the 3D BNNSs-NDs/PDMS composite material. Characterization analyses indicated that the addition of NDs filled the voids between BNNSs, leading to the densification of the thermal framework pore walls and the establishment of additional thermal pathways. Impressively, with concentrations of BNNSs and NDs of 17.99 and 7.71 wt %, respectively, the out-of-plane TC of the 3D BNNSs-NDs/PDMS composite material reached 1.623 W m-1 K-1, marking notable enhancements of 754.21% and 256.70% compared to those of pure PDMS and composites prepared via direct blending with randomly distributed BNNSs and NDs, respectively. Furthermore, the 3D BNNSs-NDs thermal framework improved the compressive strength and the dimensional stability of the composite material. Finite element simulations additionally confirmed the synergistic improvement of the TC achieved through the combination of BNNSs and NDs, demonstrating that the 3D BNNSs-NDs/PDMS composite material displayed superior heat conduction and a greater density of thermal pathways compared to those of its counterparts, including 3D BNNSs/PDMS and 3D NDs/PDMS composite materials. In summary, this work presents a strategy for enhancing the out-of-plane TC of polymer-based composite materials by incorporating vertically aligned thermal networks.

SRARDA: A lightweight adaptive residual dense attention generative adversarial network for image super-resolution

Image super-resolution (SR) is the task of inferring a high resolution (HR) image from one/multiple single low resolution (LR) input(s). Traditional networks are evaluated by pixel-level metrics such as Peak-Signal-to-Noise Ratio (PSNR) etc., which do not always align with human perception of image quality. They often produce excessively smooth images that lack high-frequency texture and appear unnatural. Therefore, in this paper, we propose a lightweight adaptive residual dense attention generative adversarial network (SRARDA) for image SR. Firstly, our generator adopts the residual in residual (RIR) structure but redesigns the basic module. By using dynamic residual connection (ARC) to dynamically adjust the importance of residual and main paths, we design a novel adaptive residual dense attention block (ARDAB) that enhances the feature extraction capability of the generator. In addition, we build a high-frequency filtering unit (HFU) to extract more high-frequency features from the LR space. Finally, to fully utilize the discriminator, we use WGAN to compute the difference between the HR image and the reconstructed image. Experiments demonstrate that SRARDA effectively addresses the issue of excessive smoothing in reconstructed images, while also enhancing visual quality.

Sodium alginate supramolecular nanofibers in synergy with surface crack engineering to prepare tough and highly sensitive hydrogels

Soft and wet hydrogels often struggle to achieve both toughness and high sensitivity simultaneously, limiting their usefulness in flexible devices. To tackle this challenge, we devised a strategy that combines supramolecular sodium alginate nanofibers, utilizing Zr4+ as physical crosslinkers, with surface crack engineering via the micro-phase separation of polyaniline, to create a physically and chemically dual crosslinked polyacrylamide (PAM)/sodium alginate (SA)/polyaniline (PANI) hydrogel with exceptional toughness and high sensitivity. Owing to the supramolecular sodium alginate nanofibers, the dual crosslinked hydrogel exhibited a tensile strength of 0.391 MPa, an elongation at break of 568.9 %, and a toughness of 1.020 MJ/m3. The in-situ polymerized polyaniline layer, confined within the dense network, introduced micro-cracks onto the hydrogel surface, resulting in a high gauge factor of 11.4 for the fabricated hydrogel. Furthermore, integrating this hydrogel into a triboelectric nanogenerator transformed it into self-powered sensors capable of detecting external forces and generating various signals without power supply. These findings suggest that the developed hydrogel held great potential in diverse fields, including human motion detection, human-machine interaction, and wearable electronic devices.

Natural Language Processing to assess structure and complexity of system requirements

AbstractThe development process of a system is shaped by numerous variables that influence its progress and outcome. As a result, complexity can increase throughout the development process, potentially leading to negative consequences, which makes the management of complexity critical. Most development processes begin with the definition of needs and requirements. In this paper, the authors present a novel approach that enables the automated extraction of structure from requirements specifications. The approach uses Natural Language Processing to elicit three structural layers from a set of requirements, which are subsequently analyzed using metrics to assess complexity. In a case study, the approach is demonstrated using a set of 79 requirements, within which 246 individual entities are identified. These entities and the requirements are structured and analyzed using network density and spectral entropy. The metrics allow for interpretation and insight generation, such as identifying an increase in the number of potentially problematic loops. The approach achieved a detection and structural accuracy of over 98% in the given case study and is planned to be expanded with future cases.

Multilateral collaborative governance of Arctic shipping safety: Examining the impact of the Arctic Council via collaborative network analysis

Global climate change has dramatically reduced Arctic sea ice, opening new potential shipping routes, which has garnered international attention. This study explores the role of the Arctic Council in the collaborative governance network focused on Arctic shipping safety. For this purpose, a collection of 49 collaborative events initiated by international organisations and the Arctic Council was used as the data sample. These events are divided into two groups, one containing all the events and the other excluding those initiated by the Arctic Council. As a result, two collaborative networks are developed and evaluated based on network density, average path length, network transitivity, centrality indices (degree, closeness, betweenness), and clustering analysis. The results demonstrate that the Arctic Council significantly enhances the network density and transitivity of the international collaboration network, and reshapes core-periphery dynamics and cohesive subgroup structures. For instance, under the influence of the Arctic Council, Canada, France, and Japan surpass the United Kingdom in centrality (degree, closeness, betweenness). Results of the clustering coefficients reveal the Arctic Council's impact on connections among states, leading to shifts in collaboration patterns. Additionally, subgroup cohesion and density coefficients confirm the Arctic Council's role in promoting collaboration among states and restructuring governance networks.

GIAE-DTI: Predicting Drug-Target Interactions Based on Heterogeneous Network and GIN-based Graph Autoencoder.

Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery and repurposing. However, the sparsity of DTI data limits the effectiveness of existing computational methods, which primarily focus on sparse DTI networks and have poor performance in aggregating information from neighboring nodes and representing isolated nodes within the network. In this study, we propose a novel deep learning framework, named GIAE-DTI, which considers cross-modal similarity of drugs and targets and constructs a heterogeneous network for DTI prediction. Firstly, the model calculates the cross-modal similarity of drugs and proteins from the relationships among drugs, proteins, diseases, and side effects, and performs similarity integration by taking the average. Then, a drug-target heterogeneous network is constructed, including drug-drug interactions, protein-protein interactions, and drug-target interactions processed by weighted K nearest known neighbors. In the heterogeneous network, a graph autoencoder based on a graph isomorphism network is employed for feature extraction, while a dual decoder is utilized to achieve better self-supervised learning, resulting in latent feature representations for drugs and targets. Finally, a deep neural network is employed to predict DTIs. The experimental results indicate that on the benchmark dataset, GIAE-DTI achieves AUC and AUPR scores of 0.9533 and 0.9619, respectively, in DTI prediction, outperforming the current state-of-the-art methods. Additionally, case studies on four 5-hydroxytryptamine receptor-related targets and five drugs related to mental diseases show the great potential of the proposed method in practical applications.

A Novel Topology Adaptation Strategy for Dynamic Sparse Training in Deep Reinforcement Learning.

Deep reinforcement learning (DRL) has been widely adopted in various applications, yet it faces practical limitations due to high storage and computational demands. Dynamic sparse training (DST) has recently emerged as a prominent approach to reduce these demands during training and inference phases, but existing DST methods achieve high sparsity levels by sacrificing policy performance as they rely on the absolute magnitude of connections for pruning and randomly generating connections. Addressing this, our study presents a generic method that can be seamlessly integrated into existing DST methods in DRL to enhance their policy performance while preserving their sparsity levels. Specifically, we develop a novel method for calculating the importance of connections within the model. Subsequently, we dynamically adjust the sparse network topology by dropping existing connections and introducing new connections based on their respective importance values. Through validation on eight widely used simulation tasks, our method improves two state-of-the-art (SOTA) DST approaches by up to 70% in episode return and average return across all episodes under various sparsity levels.

Efficient computation of Katz centrality for very dense networks via negative parameter Katz

Abstract Katz centrality (and its limiting case, eigenvector centrality) is a frequently used tool to measure the importance of a node in a network, and to rank the nodes accordingly. One reason for its popularity is that Katz centrality can be computed very efficiently when the network is sparse, ie having only O(n) edges between its n nodes. While sparsity is common in practice, in some applications one faces the opposite situation of a very dense network, where only O(n) potential edges are missing with respect to a complete graph. We explain why and how, even for very dense networks, it is possible to efficiently compute the ranking stemming from Katz centrality for unweighted graphs, possibly directed and possibly with loops, by working on the complement graph. Our approach also provides an interpretation, regardless of sparsity, of ‘Katz centrality with negative parameter’ as usual Katz centrality on the complement graph. For weighted graphs, we provide instead an approximation method that is based on removing sufficiently many edges from the network (or from its complement), and we give sufficient conditions for this approximation to provide the correct ranking. We include numerical experiments to illustrate the advantages of the proposed approach.

Complex network analysis of cryptocurrency market during crashes

This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017–20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density (ρD), average path length (l̄), and average clustering coefficient (cc¯) are estimated from these networks. We find that both ρD and cc¯ are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although ρD and cc¯ decrease in the post-crash period, they remain higher than pre-crash levels for the 2017–18 and 2018–19 crashes suggesting a market attempt to return to normalcy. We get l̄ is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019–20 crash, the values of ρD, cc¯, and l̄ did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes.

Unraveling cereal physical barriers composed of cell walls and protein matrix: Insights from structural changes and starch digestion

Physical barriers composed of cell walls and protein matrix in cereals, as well as their cooking changes, play important roles in starch digestion. In this study, the physical barriers of native and cooked highland barley (HB), brown rice (BR), and oats (OA) kernels and their contribution to starch digestion were investigated. The resistant starch content was similar in cereal flours, but varied among cooked kernels (HB > BR > OA: 45.05 %, 10.30 %, and 24.71 %). The water adsorption, gelatinization enthalpy, and decrease in hardness of HB kernels were lower than those of OA and BR kernels. Microstructural observations of native kernels showed that HB had the thickest cell walls. After cooking, the lowest cell wall deformation and a dense continuous network developed from the protein matrix were observed in HB kernels. During digestion, undigested starch granules encapsulated by the stable cell walls and strong protein network were observed in HB kernels, but not in BR or OA kernels. Furthermore, the heavily milled HB kernels still had more resistant starch than the intact OA and BR kernels. Therefore, the physical barriers of HB kernels exhibited stronger inhibition of starch gelatinization and digestion. Differences in cereal physical barriers led to various inhibitory effects.

Tracking aquatic non-native macroinvertebrate species in Germany using long-term data

Biological invasions pose a global challenge, threatening both biodiversity and human well-being. Projections suggest that as invasions increase, the financial costs associated with management and the ecological harm they cause will also escalate. Here, we examined whether long-term biomonitoring strategies were adequate to identify and track benthic aquatic non-native macroinvertebrate species by using the German subset (151 time series; 129 of which reported non-native species) of the currently most comprehensive European long-term dataset of 1816 macroinvertebrate community time series from 22 European countries. The detection of aquatic non-native species was directly linked to the availability of long-term sites and thus, monitoring effort, having identified the spatio-temporal occurrence of 32 non-native species. The available long-term monitoring site data were mostly concentrated in the western part of Germany, predominantly covering the Rhine River and its tributaries. The spatially biased network of long-term monitoring sites, therefore, naturally skews the detection and reporting of aquatic non-native species toward this area and underestimates Eastern and Southern regions, impeding the comprehension of invasion dynamics. However, based on the available data, we found that the absolute number of non-native species increased and the proportion of non-native species relative to native species decreased over time. This indicates complex ecological interactions between native and non-native species and underlines the value of long-term data for investigating invasion dynamics. Considering the value of comprehensive monitoring networks, a spatially biased network delays the application of management and mitigation plans, possibly worsening the ecological and economic effects of biological invasions in Germany. The results provided here indicate the disadvantages of biased datasets, but simultaneously underline the enormous potential of a dense network of long-term monitoring. Our results also highlight the urgent need to increase and diversify long-term biomonitoring efforts throughout Germany to cover the main freshwater resources and their connections where the introduction risk of non-native species is the highest. Centrally collating such data would provide a profound basis for the monitoring of spreading aquatic non-native species and could serve the implementation of national biosecurity efforts.