Homogeneous Networks Research Articles

Green, tough, and heat-resistant: A GDL-induced strategy for starch-alginate hydrogels

Hydrogels fabricated by non-covalent interaction garnered significant attention for their eco-friendly and robust mechanical attributes, and are often used in food, medicine and other fields. Although starch-alginate hydrogels exhibit high adhesion and are environmentally sustainable, their applications are limited due to their low elasticity and hardness. Addressing this challenge, we introduce a solvent-induced strategy using glucolactone (GDL) to fabricate hydrogels with enhanced strength and thermal resilience. Utilizing corn starch with varying amylose contents, sodium alginate and calcium carbonate to prepare a double network structure. This GDL-induced hydrogel outperforms most previous starch-based hydrogels in mechanical robustness and thermal stability. Typical starch-alginate hydrogel had a homogeneous network structure and exhibited a high tensile stress of 407.57 KPa, and a high enthalpy value of 1857.67 J/g. This investigation furnishes a facile yet effective method for the synthesis of hydrogels with superior mechanical and thermal properties, thereby broadening the design landscape for starch-based hydrogels.

Mechanically strengthened polybenzimidazole membrane via a two-step crosslinking strategy for high-temperature proton exchange membrane fuel cell

To simultaneously increase the mechanical strength and proton conductivity of Phosphoric acid-doped polybenzimidazole (PA-PBI), a facile two-step crosslinking approach is proposed to prepare the imidazole-rich crosslinked polybenzimidazole membranes for high-temperature proton exchange membrane fuel cells (HT-PEMFCs) in this work. Unlike the classic crosslinking approaches that usually enhance mechanical strength of membrane but lead to a decrease of proton conductivity, the mechanical strength of two-step crosslinked membrane improves from 3.93 MPa to 9.62 MPa and proton conductivity increases from 91 mS cm−1 to 183 mS cm−1 at 200 °C compared with pristine OPBI membrane. The promotion should be ascribed to the introduction of PA-affinity sites on crosslinkers and the formation of homogeneous crosslinking networks. Meanwhile, due to the crosslinked networks, the oxidative and dimensional stability of crosslinked membranes are also promoted. In addition, the two-step cross-linking method can avoid the self-reaction between cross-linking agents, thereby obtaining a more uniform cross-linking network, which is of great significance for the use of fuel cells. When the membranes are used in a fuel cells, they exhibited a maximum power density of 448.8 mW cm−2 at 160 °C (H2/Air, without humidification.).

Identifying the Influences on Network Formation in Structural Isomers of Multifunctional Epoxies Using Near-Infrared Spectroscopy.

The network formation of four epoxy-rich formulations of the structural isomers of triglycidyl aminophenol and diaminodiphenyl sulfone has been monitored by using two complementary techniques, near-infrared spectroscopy and resin temperature monitoring. The differences between these networks have been described using the concentration of epoxide, primary amine, secondary amine, and tertiary amine functional groups and the actual temperature of the resin compared to the oven temperature during the cure schedule. It was found that initially, the 3,3'-diaminodiphenyl sulfone (33'DDS) formulations were more reactive and primary amines were completely consumed before the 4,4'-diaminodiphenyl sulfone (44'DDS) formulations. Secondary amines were formed more quickly in 33'DDS formulations compared to 44'DDS. The triglycidyl-meta-aminophenol (TGmAP) formulations consumed secondary amines and produced tertiary amines faster than the triglycidyl-para-aminophenol (TGpAP) formulations, indicating higher levels of cross-linking occurred earlier in the curing reaction. Etherification occurred much earlier in the TGpAP formulations than in the TGmAP formulations. Results suggest that internal cyclization occurs in the three meta isomer-containing formulations, and a corresponding lack of this effect in TGpAP/44'DDS results in a more homogeneous cross-linked network.

Effect of Highly Crosslinked Domains on the Dimensions of Network Polymers

AbstractThe effect of spatial inhomogeneity on the dimensions of network polymers is investigated by using model networks containing highly crosslinked domains. It is found that the dimensions of network architecture consisting of densely crosslinked domains connected by long chains are larger than those of loosely crosslinked domains connected by short chains, given the cycle rank is the same. The cases with the domains connected by the domains are also investigated. In all cases, the dimensions are larger than the corresponding randomly crosslinked homogeneous networks. This is because the loosely crosslinked regions dominate the dimensions of network polymers. The master curve relationship found for the statistical networks is applicable also for the present types of spatial inhomogeneous network polymers when the cycle rank is increased to make the network well‐developed in a homologous series of networks.

Effect of salting and dehydration treatments on the physicochemical and gel properties of hen and duck egg yolks, plasma and granules.

Salted hen egg yolks are less oily and less flavorful than salted duck egg yolks. However, hen eggs have a more adequate market supply and have a broader application prospect than duck eggs. In the present study, egg yolks, plasma, and granules were dehydrated by adding 1% NaCl to simulate traditional curing process of salted egg yolk. The changes in the pickling process of hen egg yolks (HEY) and duck egg yolks (DEY) plasma and granules were compared to reveal the gelation mechanism and the underlying causes of quality differences in salted HEY and DEY. Salted HEY can be compared with the changes in DEY during the pickling process to provide a theoretical basis for the quality improvement of salted HEY to salted DEY. The results showed that both plasma and granules were involved in gel formation, but exhibited different aggregation behaviors. Based on the intermolecular forces, the HEY proteins achieved aggregation mainly through hydrophobic interactions and DEY proteins mainly through covalent binding. According to spin-spin relaxation time, HEY gels immobilized a large amount of lipid and interacted strongly with lipids. DEY gels showed much free lipid and had weak interaction with lipid. The microstructure showed that HEY proteins were easily unfolded to form a homogeneous three-dimensional gel network structure after salting, whereas heterogeneous aggregates were formed to hinder the gel development in DEY. Changes in protein secondary structure content showed that pickling can promote the transformation of the α-helices to β-sheets structure in HEY gels, whereas more α-helices structure was formed in DEY gels. The present study has demonstrated that different gelation behaviors of hen and duck egg yolk proteins (especially in plasma) through salting treatment led to the difference in the quality of salted HEY and DEY. © 2024 Society of Chemical Industry.

Effect of chitooligosaccharides in inhibiting textural deterioration of chicken myofibril protein gel induced by high temperature

In this study, it was found that the addition of chitooligosaccharides (CO) significantly inhibited the textural deterioration of chicken myofibril protein (MP) gel induced by high temperature (95 °C) treatment. Moreover, this treatment also resulted in MP gel with lower cooking loss (P < 0.05) and increased water holding capacity. This was because the addition of CO weakened the hydrophobic interactions between MP, thus significantly inhibiting the excessive aggregation of MP at high temperature. Moreover, this treatment enhanced the hydrogen bonding between MP or MP and CO, promoted the formation of disulfide bond between MP, then the intermolecular forces between MP were changed to a more balanced state, thus contributing to a more homogeneous and much denser gel network structure. Besides, CD spectroscopy indicated that the incorporation of CO resulted in MP gel with increased content of β-sheet and decreased content of α-helix, thus contributing to the formation of a more ordered gel network. So this study provided a promising way to improve the quality of meat products subjected to high temperature treatment.

Adversarial Socialbots Modeling Based on Structural Information Principles

The importance of effective detection is underscored by the fact that socialbots imitate human behavior to propagate misinformation, leading to an ongoing competition between socialbots and detectors. Despite the rapid advancement of reactive detectors, the exploration of adversarial socialbot modeling remains incomplete, significantly hindering the development of proactive detectors. To address this issue, we propose a mathematical Structural Information principles-based Adversarial Socialbots Modeling framework, namely SIASM, to enable more accurate and effective modeling of adversarial behaviors. First, a heterogeneous graph is presented to integrate various users and rich activities in the original social network and measure its dynamic uncertainty as structural entropy. By minimizing the high-dimensional structural entropy, a hierarchical community structure of the social network is generated and referred to as the optimal encoding tree. Secondly, a novel method is designed to quantify influence by utilizing the assigned structural entropy, which helps reduce the computational cost of SIASM by filtering out uninfluential users. Besides, a new conditional structural entropy is defined between the socialbot and other users to guide the follower selection for network influence maximization. Extensive and comparative experiments on both homogeneous and heterogeneous social networks demonstrate that, compared with state-of-the-art baselines, the proposed SIASM framework yields substantial performance improvements in terms of network influence (up to 16.32%) and sustainable stealthiness (up to 16.29%) when evaluated against a robust detector with 90% accuracy.

Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks

The classic problem of node importance estimation has been conventionally studied with homogeneous network topology analysis. To deal with practical network heterogeneity, a few recent methods employ graph neural models to automatically learn diverse sources of information. However, the major concern revolves around that their fully adaptive learning process may lead to insufficient information exploration, thereby formulating the problem as the isolated node value prediction with underperformance and less interpretability. In this work, we propose a novel learning framework namely SKES. Different from previous automatic learning designs, SKES exploits heterogeneous structural knowledge to enrich the informativeness of node representations. Then based on a sufficiently uninformative reference, SKES estimates the importance value for any input node, by quantifying its informativeness disparity against the reference. This establishes an interpretable node importance computation paradigm. Furthermore, SKES dives deep into the understanding that "nodes with similar characteristics are prone to have similar importance values" whilst guaranteeing that such informativeness disparity between any different nodes is orderly reflected by the embedding distance of their associated latent features. Extensive experiments on three widely-evaluated benchmarks demonstrate the performance superiority of SKES over several recent competing methods.

Reversible adhesives from epoxy-based transesterification-induced vitrimers

Vitrimers are a class of materials that combine the excellent mechanical performance of thermosetting polymers with the malleability of thermoplastic polymers. One of the applications that most benefits from these features is structural adhesives. In this work, epoxy-based vitrimers from diglycidyl ether of bisphenol A, succinic anhydride, and trimethylolpropane are used as reversible adhesives. These materials show glass transition temperatures up to 80 °C as well as a homogeneous network structure. They also show high lap-shear stress values of up to 26 MPa. These adhesives are suitable for reversible adhesion thanks to their excellent vitrimeric behavior, with characteristic relaxation times that are under 10 min at 180 °C. This allows for the re-utilization of the same joint through re-adhesion after failure, the dismantling of adhesive joints with posterior re-bonding, and the application of the adhesive as a coating to make the joint in a different stage. The re-adhesion reaches values up to 96% with respect to the values of the first adhesion. Finally, chemical recycling is achieved through alcoholysis in ethyleneglycol, and this methodology is used to remove adhesive remains from adherend plates, so that they can be used for any other purpose. In summary, these vitrimer adhesives not only show high adhesion shear stress, suitable for structural applications, but also allow re-joining the bond in case of failure, the dismantling of the joint at the end of the bond lifetime, thermally activate the adhesive joint, and recover the adherends to be used for another purpose.

Meta-path-based key node identification in heterogeneous networks

Identifying key nodes in complex networks remains challenging. Whereas previous studies focused on homogeneous networks, real-world systems comprise multiple node and edge types. We propose a meta-path-based key node identification (MKNI) method in heterogeneous networks to better capture complex interconnectivity. Considering that existing studies ignore the differences in propagation probabilities between nodes, MKNI leverages meta-paths to extract semantics and perform node embeddings. Trust probabilities reflecting propagation likelihoods are derived by calculating embedding similarities. Node importance is calculated by using metrics incorporating direct and indirect influence based on trust. The experimental results on three real-world network datasets, DBLP, ACM and Yelp, show that the key nodes identified by MKNI exhibit better information propagation in the Susceptible Infected (SI) and susceptibility-influence model (SIR) model compared to other methods. The proposed method provides a reliable tool for revealing the topological structure and functional mechanisms of the network, which can guide more effective regulation and utilization of the network.

Dual‐Wavelength Vat Photopolymerization 3D Printing with Hybrid Acrylate‐Epoxy Resins: Influence of Resin Composition on Microstructure and Mechanical Properties

Dual‐wavelength vat photopolymerization 3D printing represents a convenient technology for the fabrication of objects with heterogeneous and locally controlled mechanical properties. By using two λ‐orthogonal cross‐linking reactions, it is possible to produce soft and stiff photopolymers with a single resin vat by switching the light source. Herein, hybrid acrylate‐epoxy resins are selectively cured by using either visible or UV light. At 405 nm, a free radical curing of the acrylate monomers is induced while irradiation with 365 nm triggers an additional cationic ring opening reaction of the epoxy monomer yielding interpenetrating photopolymer networks. In a comprehensive approach, the influence of the resin composition and the applied wavelength on cure kinetics, film morphology, (thermo)mechanical properties, and printability are studied. Fully separated as well as homogenous network morphologies are obtained depending on the ratio between acrylate and epoxy monomers, cure rate and applied light source (405 vs 365 nm). In general, glass transition temperature, stiffness, and tensile strength of the photopolymers increase with rising epoxy content. In contrast, a higher epoxy concentration in combination with a higher amount of the cationic photoinitiator compromises on the system's orthogonality, giving rise to the important role of the resin composition in dual‐wavelength vat photopolymerization 3D printing.

Size and ethnic homogeneity of extended social networks in the Netherlands: Differences between migrant groups and migrant generations

AbstractExtended social networks encompass both weak and strong ties to provide social support and resources. Hence, it is important to study what explains variation in these networks. This paper addresses this and examines the size and ethnic homogeneity of extended social networks, and group differences therein, and it aims to explain these differences based on a preference–opportunities approach through a decomposition analysis. We apply state‐of‐the‐art NSUM methods to measure the extended networks for different ethnic‐majority and minoritized groups, also considering migrant generation differences, in the Netherlands. Results show that group differences in network size reflect first‐generation minority citizens having smaller networks and majority citizens having more ethnically homogeneous networks. More positive out‐group attitudes among the Moroccan– and Turkish–Dutch partly explain why these groups have less homogeneous networks than Dutch majority members. Differences in the ethnic composition of neighbourhoods also contribute to explaining the homogeneity gap between Dutch majority and Turkish‐Dutch.

Power control in LTE based on heuristic game theory for interference management

In the conventional LTE homogeneous network, sufficient transmit power of user equipment (UE) is determined by open-loop power control (OL-PC) and closed-loop power control (CL-PC) schemes. However, in a Het-Net environment, setting the UE’s transmit power requires delicate responsiveness to handle the severe and complicated uplink interference. In this paper, an interference-aware uplink power control mechanism based on Heuristic game theory approach is proposed for devices coexisting in a heterogeneous wireless network. Various wireless constraints like channel response, path loss, fading, shadowing, interference and metrics like SNR, SINR, throughput and bit rates are considered. Uplink power is controlled by suitably selecting the penalization factor (β) based on a simple Heuristic game theory approach considering the possible wireless constraints of each user depending on its location in the cell under consideration. The algorithm is framed in such a way to reduce inter-cell interference, limit transmit power, enhance bit rates and throughput of users. A significant improvement of 5.2% in the user coverage/distribution is achieved as a result of interference management compared to conventional power control scheme and power control with convex pricing.

Scrolling in Supramolecular Gels: A Designer's Guide.

Gelation by small molecules is a topic of enormous importance in catalysis, nanomaterials, drug delivery, and pharmaceutical crystallization. The mechanism by which gelators self-organize into a fibrous gel network is poorly understood. Herein, we describe the crystal structures and gelation properties of a library of bis(urea) compounds and show, via molecular dynamics simulations, how gelator aggregation progresses from a continuous pattern of supramolecular motifs to a homogeneous fiber network. Our model suggests that lamellae with asymmetric surfaces scroll into uniform unbranched fibrils, while sheets with symmetric surfaces undergo stacking to form crystals. The self-assembly of asymmetric lamellae is associated with specific molecular features, such as the presence of narrow and flexible end groups with high packing densities, and likely represents a general mechanism for the formation of small-molecule gels.

Leveraging the Activated Monomer Mechanism to Create Grafted Polymer Networks in Epoxide–Acrylate Hybrid Photopolymerizations

Hybrid epoxide–acrylate photopolymerization enables the temporal structuring of polymer networks for advanced material properties. The ability to design polymer network architectures and to tune mechanical properties can be realized through the control of the cationic active center propagation reaction (active chain end mechanism) relative to the cationic chain transfer reaction (activated monomer mechanism). Grafted polymer networks (GPNs) can be developed through the covalent bonding of epoxide chains to acrylate chains through hydroxyl substituents, making hydroxyl-containing acrylates a promising class of chain transfer agents. This work demonstrates the formation of these GPNs and explores the physical properties obtained through the control of hydroxyl content and hybrid formulation composition. The GPNs exhibit a lower glass transition temperature than the neat epoxide network and result in a more homogeneous network. Further investigations of hydroxyl-containing acrylates as chain transfer agents will generate a wider range of physical property options for photopolymerized hybrid coatings, sealants, and adhesives.

Dynamical Analysis of an Improved Bidirectional Immunization SIR Model in Complex Network.

In order to investigate the impact of two immunization strategies-vaccination targeting susceptible individuals to reduce their infection rate and clinical medical interventions targeting infected individuals to enhance their recovery rate-on the spread of infectious diseases in complex networks, this study proposes a bilinear SIR infectious disease model that considers bidirectional immunization. By analyzing the conditions for the existence of endemic equilibrium points, we derive the basic reproduction numbers and outbreak thresholds for both homogeneous and heterogeneous networks. The epidemic model is then reconstructed and extensively analyzed using continuous-time Markov chain (CTMC) methods. This analysis includes the investigation of transition probabilities, transition rate matrices, steady-state distributions, and the transition probability matrix based on the embedded chain. In numerical simulations, a notable concordance exists between the outcomes of CTMC and mean-field (MF) simulations, thereby substantiating the efficacy of the CTMC model. Moreover, the CTMC-based model adeptly captures the inherent stochastic fluctuation in the disease transmission, which is consistent with the mathematical properties of Markov chains. We further analyze the relationship between the system's steady-state infection density and the immunization rate through MCS. The results suggest that the infection density decreases with an increase in the immunization rate among susceptible individuals. The current research results will enhance our understanding of infectious disease transmission patterns in real-world scenarios, providing valuable theoretical insights for the development of epidemic prevention and control strategies.

Robust network of globally coupled heterogeneous limit cycle oscillators due to inertia

We investigate the phase transition from macroscopic oscillatory state to stable homogeneous steady state in a heterogeneous network of globally coupled Stuart–Landau limit cycle oscillators in the presence of the inertial effect. The phase transition, known as aging transition, onsets above a critical fraction of inactive constituents in the mixed population of active and inactive units. We show that even a feeble increase in the inertial strength increases the critical fraction of inactive units significantly for the onset of the phase transition to the macroscopic steady state thereby resulting in a more robust network, in general. In contrast, a large coupling strength, in the case of a homogeneous network, facilitates the manifestation of the phase transition even for a small fraction of inactive oscillators leading to a more fragile network. Nevertheless, a large coupling strength, in the case of a heterogeneous network, increases the resilience of the network by facilitating the phase transition at a large fraction of inactive oscillators. Furthermore, a larger standard deviation of the natural frequencies always leads to a more fragile network. We derive the macroscopic evolution equations for the order parameters and the stability curve using the first-order moment expansion around the mean-field. In addition, we also deduce the critical fraction of inactive units and the critical inertial strength analytically that matches with the simulation results. Interestingly, we find that the critical inertial strength is reciprocally related to the square of the mean frequency of the network.

Interplay of Spatial and Topological Defects in Polymer Networks.

Polymer networks are widely used in applications, and the formation of a network and its gel point can be predicted. However, the effects of spatial and topological heterogeneity on the resulting network structure and ultimately the mechanical properties, are less understood. To address this challenge, we generate in silico random networks of cross-linked polymer chains with controlled spatial and topological defects. While all fully reacted networks investigated in this study have the same number of end-functionalized polymer strands and cross-linkers, we vary the degree of spatial and topological heterogeneities systematically. We find that spatially heterogeneous cross-linker distributions result in a reduction in the network's primary loops with increased spatial heterogeneity, the opposite trend as observed in homogeneous networks. By performing molecular dynamics simulations, we investigated the mechanical properties of the networks. Even though spatially heterogeneous networks have more elastically active strands and cross-linkers, they break at lower extensions than the homogeneous networks and sustain slightly lower maximum stresses. Their shear moduli are higher, i.e., stiffer, than theoretically predicted, and higher than their homogeneous gel counterparts. Our results highlight that topological loop defects and spatial heterogeneities result in significantly different network structures and, ultimately, different mechanical properties.

DEVELOPMENT OF AN ONTOLOGICAL MODEL OF DEEP LEARNING NEURAL NETWORKS

This research paper examines the challenges and prospects associated with the integration of artificial neural networks and knowledge bases. The focus is on leveraging this integration to address practical problems. The paper explores the development, training, and integration of artificial neural net- works, emphasizing their adaptation to knowledge bases. This adaptation involves processes such as in- tegration, communication, representation of ontological structures, and interpretation by the knowledge base of the artificial neural network's representation through input and output. The paper also delves into the direction of establishing an intellectual environment conducive to the development, training, and integration of adapted artificial neural networks with knowledge bases. The knowledge base embedded in an artificial neural network is constructed using a homogeneous semantic network, and knowledge processing employs a multi-agent approach. The representation of artificial neural networks and their specifications within a unified semantic model of knowledge representation is detailed, encompassing text-based specifications in the language of knowledge representation with theoretical semantics. The models shared with the knowledge base include dynamic and other types that vary in their capabilities for knowledge representation. Furthermore, the paper conducts an analysis of approaches to creating artificial neural networks across various libraries of the high-level programming language Python. It explores techniques for developing arti- ficial neural networks within the Python development environment, investigating the key features and func- tions of these libraries. A comparative analysis of neural networks created in object-oriented programming languages is provided, along with the development of an ontological model for deep learning neural net- works.

The Emotion Regulation of Acupuncture in Chronic Low Back Pain: A Clinical Neuroimaging Protocol.

Acupuncture is effective for patients with chronic low back pain (CLBP), which can relieve pain intensity and regulate negative emotional states such as pain-related anxiety and depression. Previous studies mainly discuss the analgesic mechanism of acupuncture treatment of CLBP, but there are multiple dimensions to pain, including sensation, emotion and cognition. Therefore, this study aims to investigate the central mechanism of acupuncture for CLBP from the perspective of emotional regulation by functional magnetic resonance imaging (fMRI). A total of 72 patients with CLBP will be recruited in the study and randomly assigned to the verum acupuncture group or the sham acupuncture group. The trail will last for 18 weeks including a 2-week baseline, a 4-week treatment and a 12-week for follow-up period. The primary outcomes are the visual analog scale (VAS) and the Japanese Orthopaedic Association Scores (JOA) score. The secondary outcomes are the 12-item short form health survey (SF-12), the state trait anxiety inventory (STAI), the self-rating anxiety scale (SAS) and self-rating depression scale (SDS). The VAS, JOA, STAI SAS and SDS will be collected at baseline, week 2, week 4, and after follow-up. The SF-12 will be evaluated at baseline, week 2 and week 4. Functional magnetic resonance imaging (MRI) data will be collected at baseline and the end of treatment. Emotion-related brain regions will be chosen as regions of interest (ROIs). The gray matter volume (GMV), amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), functional connectivity (FC), and large-scale functional brain network based on these ROIs will be analyzed within and between the two groups. This study will verify the emotional regulation of acupuncture and explore the mechanism of acupuncture for emotion regulation in patients with CLBP. https://www.chictr.org.cn/showproj.html?proj=195486, identifier: ChiCTR2300070557.