Network Structure Research Articles

From tweets to tensions: exploring the roots of political polarization in Turkish constitutional referendum

ABSTRACT This study examines between-group and within-group polarization in online social networks to uncover latent factors and ideological changes over time. The study used 2017 Turkish constitutional referendum as a case focusing on Twitter dynamics between the yes-vote and no-vote communities, examining divergence of worldviews and partisan responses. We examined how changes in partisan asymmetries over time shape echo-chambers in opinion, network, and user polarization providing evidence of polarization at group, sub-group, and individual levels. Findings show differences in polarization between groups at different time periods. Before the referendum, yes-voting users are main drivers of polarization and more inward-looking than no-voting users. In contrast, considering the gap between winners and losers of the referendum and associated mood swings, in the days following the referendum no-voting users are main drivers of polarization. This demonstrates fine-grained details of polarization and changes in users’ sentiment as a latent factor behind seasonal behavior differences observed between two groups. Overall, yes-voting community was ideologically more coherent with a relatively denser network structure and more polarized body of users.

Physicochemical properties and in vitro digestive behavior of astaxanthin loaded Pickering emulsion gel regulated by konjac glucomannan and κ-carrageenan

This study aimed to elaborate the combination effect of polysaccharides on physicochemical properties and in vitro digestive behavior of astaxanthin (AST)-loaded Pickering emulsion gel. AST-loaded Pickering emulsion gel was prepared by heating Pickering emulsion with konjac glucomannan (KGM) and κ-carrageenan (CRG). The microstructure revealed that adding the two polysaccharides resulted in Pickering emulsion forming a network structure. It exhibited a denser and more uniform network structure, enhancing its mechanical properties four times and increasing its water-holding capacity by 20 %. In vitro digestion experiments demonstrated that the release of free fatty acids from the Pickering emulsion gel (4.25 %) was notably lower than that from conventional Pickering emulsion (17.19 %), whereas AST bioaccessibility was remarkably low at 0.003 %. It provided a feasible strategy to regulate the bioaccessibility in Pickering emulsion, which has theoretical significance to guide the current eutrophic diet people.

Microplastics and benthic animals reshape the geochemical characteristics of dissolved organic matter by inducing changes in keystone microbes in riparian sediments

Dissolved organic matter (DOM) in riparian sediments plays a vital role in regulating element cycling and pollutant behavior of river ecosystems. Microplastics (MPs) and benthic animals (BAs) have been frequently detected in riparian sediments, influencing the substance transformation in river ecosystems. However, there is still a lack of systematic investigation on the effects of MPs and BAs on sediment DOM. This study investigated the impact of MPs and BAs on the geochemical characteristics of DOM in riparian sediments and their microbial mechanisms. The results showed that MPs and BAs increased sediment DOC concentration by 34.24%∼232.97% and promoted the conversion of macromolecular components to small molecular components, thereby reducing the humification degree of DOM. Mathematical model verified that the changes of keystone microbes composition in sediments were direct factors affecting the characteristics of DOM in riparian sediment. Especially, MPs tolerant microbes, including Planctomicrobium, Rhodobacter, Hirschia and Lautropia, significantly increased DOC concentration and decreased humification degree (P < 0.05). In addition, MPs and BAs could also influence keystone microbes in sediments by altering the structure of microbial network, thereby indirectly affecting DOM characteristics. The study demonstrates the pollution behavior of MPs in river ecosystems and provides a basis for protecting the ecological function of riparian sediments from MPs pollution.

Chitosan-derived N-doped porous carbon with fiber network structure for advanced lithium‑sulfur batteries

The flexible design and construction of advanced carbon-based sulfur hosts contribute to the rapid redox and reaction kinetics of lithium polysulfides (LiPSs) in lithium‑sulfur batteries (LSBs), laying the foundation for the next generation of high-energy density energy storage devices. Herein, chitosan (CS)-derived porous N-doped carbon with fiber network structure was prepared by a simple freeze-drying method combined with sintering process. The effect of inorganic salt additives on the morphology of CS-derived carbon was investigated by SEM. The types and contents of N-doping were investigated through XPS analysis. The influence of morphology and N content on the performance of LSBs has been explored in detail through cycling and CV testing. In addition, the true active sites in the CS-derived carbon were determined by XPS and first principles calculations. As an active species, pyrrolic N exhibits superior adsorption energy and the most suitable degradation energy barrier for LiPSs. As the optimal electrode, the performance of LSBs under different electrolyte to sulfur (E/S) conditions was further investigated to explore the potential application of CS-KOH electrodes in high-energy density batteries. The results unveil that the influence of E/S on Li2S deposition is quite significant, with a limit of 5 μL mg−1. This work provides a new strategy for the preparation of high-performance advanced carbon-based materials with fiber network structures from polysaccharide biomass, and also promotes the application of CS in energy storage.

A natural protein-lipid co-assembly as efficient Pickering emulsifier from egg yolk sphere microgel prepared via high-pressure homogenization

Egg yolk sphere is a kind of natural colloidal histological structure formed by the co-assembly of a large number of proteins and lipids, which has the dual functions of hydrophilic and lipophilic for application in emulsion. In this study, egg yolk sphere microgels (EYSMs) were prepared by high pressure homogenization (HPH) technique at various pressures (0–90 MPa) and used as Pickering emulsifiers for emulsification to obtain oil-in-water emulsions with an oil-water ratio of 11:9. The microscopic morphology, structure, and physicochemical properties of EYSMs were analyzed. The results showed that EYSMs obtained at 60 MPa (60-EYSMs) exhibited superior emulsifying properties with an emulsifying activity index (EAI) of 2.8 m2/g and an emulsifying stability index (ESI) of 356.6 min, resulting in enhanced storage stability of the emulsion for 30 d. The HPH treatment decreased the lipid content in EYSMs while creating a more homogeneous protein-lipid arrangement, which provided EYSMs with balanced hydrophilic and hydrophobic sites to improve emulsifying properties. Furthermore, the free sulfhydryl groups and hydrophobic moieties exposed by HPH promoted -S-S- formation on the EYSMs surface, promoting the increase in surface hydrophobicity and emulsifying ability. The confocal laser scanning microscopy (CLSM) and cryogenic scanning electron microscopy (cryo-SEM) revealed that the EYSMs adsorbed at oil-water interface and formed an intercross-linked spatial three-dimensional network structure, which worked as a physical barrier to the stable emulsion formation. This study introduces a novel research concept and delves into the emulsification mechanism of protein-lipid composite particles, offering a fresh insight for the development and utilization of emulsifiers.

Spatial network analysis and driving forces of urban carbon emission performance: Insights from Guangdong Province

As the primary contributor to carbon emissions, how cities enhance their carbon emission performance and mitigate emissions is crucial for achieving low-carbon urban environments in China. However, existing research often overlooks the spatial interconnectedness of carbon emission performance, neglecting reciprocal influences among cities. This study examines the network structure of carbon emission performance among Guangdong's cities from 1997 to 2019, using a super-efficient SBM model and social network analysis, and measures spatial impacts of network factors with the spatial Durbin model. Findings reveal that: (1) The overall network of carbon emission performance is relatively loose with minimal changes in connectivity and efficiency but shows significant local clustering. (2) Shenzhen, Guangzhou, and Zhuhai have high centrality, dominating carbon emission performance resources and acting as key transmission nodes, while most other regions have low centrality, indicating network polarization and potential vulnerabilities. (3) Enhancing a region's centrality, economic development, industrial structure, openness, and attraction of talent and technology can boost local carbon emission performance, but may also lead to the displacement of emissions to neighboring areas and outflow of low-carbon and innovative elements, negatively affecting surrounding regions through spatial spillover effects. This research advances regional carbon emission reduction strategies by highlighting the interplay between spatial networks and carbon emission performance, fostering synergies in reduction efforts.

Can we predict mixed grain boundaries from their tilt and twist components?

One of the major challenges towards understanding and further utilizing the properties and functional behaviors of grain boundaries (GB) is the complexity of general GBs with mixed tilt and twist character. Here, we report the correlations between mixed GBs and their tilt and twist components in terms of structure, energy and stress field by computationally examining 7440 silicon GBs. Such correlations indicate that low angle mixed GBs are formed through the reconstruction mechanisms between their superposed tilt and twist components, which are revealed as the energetically favorable dissociation, motion and reaction of dislocations and stacking faults. In addition, various complex disconnection network structures are discovered near the conventional twin and structural unit GBs, implying the role of disconnection superposition in forming high angle mixed GBs. By unveiling the energetic correlation, an extended Read-Shockley model that predicts the general trends of GB energy is proposed and confirmed in various GB structures across different lattices. Finally, this work is validated in comparison with experimental observations and first-principles calculations.

O-arylation reaction over lanthanide metal–organic framework: Synthesis, structure and catalytic reaction

Two novel lanthanide based two-dimensional metal-organic framework (MOF) compounds [Tb(NDC)(NO3)(DMA)2]n (MOF-1) and [Ho(NDC)(NO3)(DMA)2]n (MOF-2) [H2NDC=2,6-naphthalenedicarboxylic acid and DMA=N,N-dimethylacetamide] have been synthesized following solvothermal route and structurally characterized. Structural analysis reveals that both the compounds are crystallized in the orthorhombic crystal system with space group Pbca. MOFs feature a “paddle-wheel” (PW) type core building units which are constructed via carboxylato oxygen coordination with lanthanide atoms. 2D lanthanide based MOFs consist of paddle-wheel building block are rare in the literature. MOF-1 and MOF-2 possess an octa-coordinated metal center. Apart from carboxylato groups, oxygen atoms of DMA and NO3− ions are also coordinated to metal centers. Paddle-wheel cores are interconnected to each other to give rise to layered network structure in both the cases. 2D layers are propagated parallel to bc plane and layers are stacked one upon another along the crystallographic a axis. Notably, both MOF-1 and MOF-2 are capable to catalyze O-arylation reaction efficiently under heterogeneous medium. However, amongst them, catalytic efficacy of MOF-1 towards O-arylation reaction is found to be better than that of MOF-2.

Synthesis of hierarchical magnesium diboride-guar gum interfacial Ru nanocomposite electrode for enhanced supercapacitor performance

Herein, we report for the first time a simple strategy to design a hierarchical chemically exfoliated magnesium diboride and guar gum network structure decorated with Ru nanoparticles (eMgB2-GG@Ru) as an electrode to evaluate its electrochemical performance for the application of supercapacitor. The eMgB2 and functionalized eMgB2-GG@Ru materials were thoroughly examined using XRD, TGA, DLS, FE-SEM, STEM, AFM, XPS, and BET techniques. The combined eMgB2-GG@Ru electrode exhibits a network structure morphology with an increased interlayer distance of eMgB2 nanolayers along with a uniform distribution of spherical Ru nanoparticles. The electrochemical performance of eMgB2-GG@Ru and its pristine materials was studied through CV, GCD, and EIS to determine their supercapacitor performance. The eMgB2-GG@Ru electrode demonstrates higher specific capacitance (352 F/g) than its eMgB2@Ru (258.9 F/g), and MgB2 (214.5 F/g) counterparts at a current density of 0.5 A/g in a three-electrode setup using 3 M KOH electrolyte. The hierarchical eMgB2-GG@Ru solid-state symmetric devices maintained higher capacity retention of 89 % even after 7000 cycles, achieving a maximum energy density of 26.12 kW/kg at the power density of 450 W/kg at 0.5 A/g. Therefore, the innovative eMgB2-GG@Ru electrode offers superior electrochemical performance with efficient electrolyte ion mobility for energy storage applications.

Preparation of bacterial cellulose/acrylic acid-based pH-responsive smart dressings by graft copolymerization method

Conventional wound dressings used in trauma treatment have a single function and insufficient adaptability to the wound environment, making it difficult to meet the complex demands of the healing process. Stimuli-responsive hydrogels can respond specifically to the particular environment of the wound area and realize on-demand responsive release by loading active substances, which can effectively promote wound healing. In this paper, BC/PAA-pH responsive hydrogels (BPPRHs) were prepared by graft copolymerization of acrylic acid (AA) to the end of the molecular chain of bacterial cellulose (BC) network structure. Antibacterial pH-responsive ‘smart’ dressings were prepared by loading curcumin (Cur) onto the hydrogels. Surface morphology, chemical groups, crystallinity, rheological, and mechanical properties of BPPRHs were analyzed by different characterization methods. The drug release behavior under different physiological conditions and bacteriostatic properties of BPPRH-Cur dressings were also investigated. The results of structural characterization and performance studies show that the hydrogel has a three-dimensional mesh structure and can respond to wound pH in a ‘smart’ drug release capacity. The drug release behavior of the BPPRH-Cur dressings under different environmental conditions conformed to the logistic and Weibull kinetic models. BPPRH-Cur displayed good antimicrobial activity against common pathogens of wound infections such as E. coli, S. aureus, and P. aeruginosa by destroying the cell membrane and lysing the bacterial cells. This study lays the foundation for the development of new pharmaceutical dressings with positive health, economic and social benefits.

Collaborative truck–drone–motorcycle delivery of emergency supplies for mountain wildfire suppression

This paper studies the transportation of emergency supplies for mountain wildfire suppression by trucks, drones, and motorcycles. The primary objective is to minimize the time of emergency supplies delivery. By examining the structure of mountainous road networks, the involvement of motorcycles and unmanned aerial vehicles (drones) in collaborative delivery operations, the risk associated with fire spread, and the optimal location for supplies distribution, this problem has been formulated as a collaborative truck–drone–motorcycle location-routing robust optimization model. The aim of introducing a robust optimization model is to ensure the feasibility of the routes under uncertain demand conditions. A box uncertainty set is employed to describe the uncertainty of emergent demands. An improved adaptive large neighborhood search (IALNS) algorithm is developed to solve this problem. A delete operator, a collaborative truck–drone delivery strategy, and a location strategy are introduced to handle various constraints involved in the problem, search the optimal location of the supplies distribution point, and compute vehicle routings. Our method has been compared with several existing algorithms, such as genetic algorithm, simulated annealing algorithm, tabu search algorithm, and whale optimization algorithm. Their average gap with IALNS are respectively 50.64%, 21.20%, 32.59%, and 19.13%. The experimental results demonstrate the efficacy and efficiency of the proposed model and algorithm.

Constructing p-n heterojunctions of Cl-Cu2O and PANI on three-dimensional nanofiber networks for enhanced photocatalytic degradation of oxytetracycline

A p-n heterojunction was constructed on a bacterial cellulose (BC) film by combining n-type semiconductor chlorine-doped Cu2O (Cl-Cu2O) and p-type semiconductor polyaniline (PANI), creating a ternary composite flexible film of Cl-Cu2O/PANI/BC. The chemical structure, microscopic morphology, and photoelectrochemical properties of Cl-Cu2O/PANI/BC were characterized and analyzed. Compared with pure Cl-Cu2O, the Cl-Cu2O/PANI/BC composite flexible film exhibited a broader response range to visible and near-infrared light, higher carrier concentration, lower impedance value, and substantial reduction in the recombination probability of photogenerated carriers. The process conditions for the photocatalytic degradation of oxytetracycline (OTC) were optimized using the response surface method, and the degradation rate of OTC caused by the Cl-Cu2O/PANI/BC was 96.3 %. In the degradation process of OTC, superoxide radical is the main active species. It was found that OTC molecules degraded via demethylation, secondary alcohol oxidation, decarboxylation, and deamination reactions, finally being degraded into H2O, CO2, and other small organic molecules. Thirteen major intermediates and possible degradation pathways were speculated. The photocatalytic performance of the Cl-Cu2O/PANI/BC was improved owing to the successful construction of the p-n heterojunction. The built-in electric field generated at the interface between the two semiconductors promoted the diffusion of photogenerated holes from the n-type Cl-Cu2O particles to the PANI coating layer on the BC chain, whereas the photogenerated electrons from PANI diffused toward Cl-Cu2O, accelerating the separation efficiency of photoinduced electrons and holes in the two semiconductors and thereby improving the efficiency of photocatalytic degradation. In addition, the three-dimensional network structure of a composite flexible film can help recover the photocatalyst. After performing seven experiments for recovery and reuse, the photocatalytic degradation rate of OTC still remained above 90 %.

Multi-deformable interpenetrating network thermosensitive hydrogel fluorescent device for real-time and visual detection of nitrite

Functionalized thermosensitive hydrogel materials exhibit excellent properties for the fabrication of sensing devices that enable real-time visual detection of food safety duo to their good plasticity and powerful loading capacity. Here, a ratiometric fluorescent device based on an interpenetrating network (IPN) thermosensitive hydrogel was designed to embed functionalized Au nanoclusters (Au NCs) and Blue Carbon dots (BCDs) composites in a multi-network structure to build a sensitive hazardous material nitrite (NO2-) chemsensor. The hydrogel was utilized poloxamer 407 (P407), lignin and cellulose to form stable IPN structure, which resulted in complementation and synergy, thereby strengthening its porous network structure. The combination of fluorescent nanoprobes with the porous network structure has the potential to enhance stable fluorescence signals and improve sensing sensitivity. Moreover, the thermosensitive liquid-solid transition characteristics of the hydrogel facilitate its preparation into diverse sensing devices following curing at room temperature. The hydrogel device, when combined with a smartphone system, converted image information into data information, thereby enabling the accurate quantification of NO2- with a detection limit of 9.38 nM in 2 s. The designed multi-functional hydrogel device is capable of real-time differentiation of NO2- dosage with the naked eye, offering a high-contrast, rapid-response sensing methodology for visual assessment of food freshness. This research contributes to the expansion of hydrogel materials applications and the detection of hazardous materials in food safety.

Neural quantile optimization for edge–cloud networking

We seek the best traffic allocation scheme for the edge–cloud networking subject to SD-WAN architecture and burstable billing. First, we formulate a family of quantile-based integer programming problems for a fixed network topology with random parameters describing the traffic demands. Then, to overcome the difficulty caused by the discrete feature, we generalize the Gumbel-softmax reparameterization method to induce an unconstrained continuous optimization problem as a regularized continuation of the discrete problem. Finally, we introduce the Gumbel-softmax sampling neural network to solve optimization problems via unsupervised learning. The neural network structure reflects the edge–cloud networking topology and is trained to minimize the expectation of the cost function for unconstrained continuous optimization problems. The trained network works as an efficient traffic allocation scheme sampler, outperforming the random strategy in feasibility and cost value. Besides testing the quality of the output allocation scheme, we examine the generalization property of the network by increasing the time steps and the number of users. We also feed the solution to existing integer optimization solvers as initial conditions and verify the warm-starts can accelerate the short-time iteration process. The framework is general, and the decoupled feature of the random neural networks is adequate for practical implementations.

In-Situ Sulfuration of CoAl Metal-Organic Framework for Enhanced Supercapacitor Properties.

Designing efficient electrode materials is necessary for supercapacitors but remains highly challenging. Herein, cobalt sulfide with crystalline/amorphous heterophase (denoted as Co(Al)S) derived from an Al metal-organic framework was constructed by ion exchange/acid etching and subsequent sulfidation strategy. It was found that rational sulfidation by adjusting the sulfur source concentration to a suitable level was favorable to form a 3D nanosheet-interconnected network architecture with a large specific surface area, which promoted ion/electron transport and charge separation. Benefiting from the features of the unique network structure and heterophase accompanied by aluminum, nitrogen and carbon coordinated in amorphous phase, the optimal Co(Al)S(10) exhibited a high specific capacity (1791.8 C g-1 at 1 A g-1), an outstanding rate capability and an excellent cycling stability. Furthermore, the as-assembled Co(Al)S//AC device afforded an energy density of 72.3 Wh kg-1 at a power density of 750 W kg-1, verifying that the Co(Al)S was a promising material for energy storage devices. The developed scheme is expected to promote the application of MOF-derived electrode materials in electrochemical energy storage and conversion fields.

Differences in networks of alexithymia and cognitive emotion regulation strategies among older adults with different health statuses in eastern China—A network analysis and network comparison

To identify core and bridge nodes in the network structure of alexithymia and cognitive emotion regulation strategies in community-dwelling older adults, and compare network differences among older adults with different health statuses, we recruited 677 participants and network analysis was performed in R 4.2.0. After including the covariates, the nodes Catastrophizing, Difficulty Identifying feelings, and Refocusing on Planning ranked as the top three. The nodes Externally Oriented Thoughts and Difficulty Identifying Feelings were identified as bridge nodes based on bridge strength values. Significant differences were observed between the healthy and comorbidity groups, and also between the single chronic disease and comorbidity groups (p < 0.05). Catastrophizing, Difficulty Identifying Feelings, and Refocusing on Planning were the core nodes, and Externally Oriented Thoughts and Difficulty Identifying Feelings were the key bridge nodes. The network structure of comorbidity in older adults was characterized by stronger ties to non-adaptive cognitive emotion regulation strategies.

A network frame offers a promising transdisciplinary tool for understanding complex health and health care system problems like suicide

As health and health care systems continue to face massive challenges from local to global well-being, understanding the processes that lead to improvement or deterioration in human health has embraced a broad range of forces from genes to national cultures. Despite the many efforts to deploy a common framework that captures diverse drivers at scale, the common missing element is the absence of a flexible mechanism that can guide research within and across levels. This hinders both the cumulation of knowledge and the development of a scientific foundation for multiplex interventions. However, studies across disciplines using a wide variety of methods and measures have converged on "connectedness" as crucial to understanding how factors operate in the health space. More formally, a focus on the critical role of the network structure and content of key elements and how they interact, rather than just on the elements themselves, offers both a generalized theory of active factors within levels and the potential to theorize interactions across levels. One critical contemporary health crisis, suicide, is deployed to illustrate the Network Embedded Symbiome Framework. The wide range of health and health care research where networks have been implicated supports its potential but also cautions against inevitable limits that will require creative theorizing and data harmonization to move forward.

High precision modeling with stacked neural network to predict the mechanical property of aluminum alloy

The compositions and microstructures of aluminum alloys determine their mechanical properties. This study used fourteen machine learning models, including random forest and gradient boosting regression, to predict the ultimate tensile strength (UTS) of aluminum alloys. Due to dataset and model limitations, individual model predictions lacked accuracy. Thus, a novel stacked neural network (SNN) was proposed, utilizing multiple single models as base models. This SNN improves prediction accuracy and generalization by adjusting the network structure. The 31 combinations of base models provide the most effective integration strategy. The SNN’s predicted tensile strength closely matches experimental data, confirming its effectiveness and accuracy.

R&amp;D Alliance and Innovation: The Interaction of Network Structure and the Quality of the Relationship

AbstractMuch research has taken a structural perspective to examine how the R&amp;D alliance network affects innovation. However, little is known about how the quality of the relationship affects firms’ innovation. In this study, I examine the interaction of network structure and the quality of the relationship and explore the impact on innovation. Using a combination of propensity score weighting and difference‐in‐difference estimation strategies to address endogeneity, I find that the quality of the relationship moderates the inverted U‐shaped relationship between network structure and innovation performance. In addition, I present evidence consistent with possible underlying mechanisms: both network structure and the quality of the relationship affect firms’ access to novel information and firms’ recombination capacity to influence innovation performance.

NCNet: Deformable medical image registration network based on neighborhood cross-attention combined with multi-resolution constraints

Objective. Existing registration networks based on cross-attention design usually divide the image pairs to be registered into patches for input. The division and merging operations of a series of patches are difficult to maintain the topology of the deformation field and reduce the interpretability of the network. Therefore, our goal is to develop a new network architecture based on a cross-attention mechanism combined with a multi-resolution strategy to improve the accuracy and interpretability of medical image registration. Approach. We propose a new deformable image registration network NCNet based on neighborhood cross-attention combined with multi-resolution strategy. The network structure mainly consists of a multi-resolution feature encoder, a multi-head neighborhood cross-attention module and a registration decoder. The hierarchical feature extraction capability of our encoder is improved by introducing large kernel parallel convolution blocks; the cross-attention module based on neighborhood calculation is used to reduce the impact on the topology of the deformation field and double normalization is used to reduce its computational complexity. Main result. We performed atlas-based registration and inter-subject registration tasks on the public 3D brain magnetic resonance imaging datasets LPBA40 and IXI respectively. Compared with the popular VoxelMorph method, our method improves the average DSC value by 7.9% and 3.6% on LPBA40 and IXI. Compared with the popular TransMorph method, our method improves the average DSC value by 4.9% and 1.3% on LPBA40 and IXI. Significance. We proved the advantages of the neighborhood attention calculation method compared to the window attention calculation method based on partitioning patches, and analyzed the impact of the pyramid feature encoder and double normalization on network performance. This has made a valuable contribution to promoting the further development of medical image registration methods.