Network Structure Research Articles

Novel pH-sensitive gellan gum-ε-polylysine hydrogel microspheres for sulforaphene delivery.

This study aimed to improve the stability and utilization of sulforaphene (SFE) and to enhance the intestinal stability and pH-sensitive release of SFE in the gastrointestinal tract. To achieve this objective, calcium chloride (CaCl2) was used as a crosslinking agent to fabricate novel SFE-loaded gellan gum (GG)-ε-polylysine (ε-PL) pH-sensitive hydrogel microspheres by using the ionic crosslinking technique. The molecular docking results of GG, ε-PL, and SFE were good and occurred in the natural state. The loading efficiency (LE) of all samples was above 70%. According to the structural characterization results, GG and ε-PL successfully embedded SFE in a three-dimensional network structure through electrostatic interaction. The swelling characteristics and in vitro release results revealed that the microspheres were pH-sensitive, and SFE was mainly retained inside the hydrogel microsphere in the stomach, and subsequently released in the intestine. The result of cytotoxicity assay showed that the hydrogel microspheres were non-toxic and had an inhibitory effect on human colon cancer Caco-2 cells. Thus, the hydrogel microspheres could improve SFE stability and utilization and achieve the intestinal targeted delivery of SFE. © 2024 Society of Chemical Industry.

Influence of high field strength ions on the crystallization characteristics and properties of slag glass-ceramics

Blast furnace slag glass–ceramics were prepared by melting using rare-earth-containing blast furnace slag (REBFS) as the main raw material and Fe2O3 and Cr2O3 as composite nucleating agents. By adding different TiO2 contents, the study comprehensively revealed the effects of high field strength ions on the glass network structure, crystalline properties, elemental distribution and physicochemical properties. Raman spectroscopy and differential scanning calorimetry revealed that TiO2 acted primarily as a network modifier in the glass network. The appropriate TiO2 content disrupted the glass network, decreasing the crystallization activation energy (EC) of the glass–ceramic. However, excess TiO2 repaired the network structure, thereby increasing the EC. The combined effect of the Cr and Ti substantially improved the crystallization characteristics of the glass-ceramics. Cr2O3 induced the formation of spinel, which promoted the crystallization of the main crystalline augite phase. The self-aggregation of the high field strength Ti4+ ion induced the formation of a titanium- and cerium- containing titanite phase, which also contributed to grain refinement. This titanite phase effectively increased the Vickers hardness and acid and alkali resistances of the glass-ceramics to 9.16GPa and over 98.5%, respectively. The excellent physicochemical properties render REBFS glass–ceramics as being promising materials for various construction applications. This research provides theoretical support for comprehensively utilizing blast furnace slag and provides theoretical guidance for preparing slag glass–ceramics possessing desirable physicochemical properties.

A graph convolutional network model based on regular equivalence for identifying influential nodes in complex networks

The identification of influential nodes in complex networks is an interesting problem. Traditional approaches, such as metric- and machine learning-based approaches, often fail to adequately consider the network structure or node features, limiting their applications in certain scenarios. Deep learning-based methods, particularly those using graph neural networks (GNNs), have yielded remarkable results in this field. However, there is a lack of utilization of the regular equivalence-based similarity between nodes to feature construction, which is critical to deep learning-based methods. Nevertheless, regular equivalence-based similarity is extremely suitable to the quantification of the similarity between influential nodes, as these nodes usually have similar structural properties but do not require common neighbors. Building upon graph convolutional network (GCN, a specific type of GNN) and the regular equivalence-based similarity, this study proposes a novel method for identifying influential nodes in complex networks. Particularly, the regular equivalence-based similarities between nodes are considered to construct node features; a GCN is enhanced to improve the identification of influential nodes. Experiments on several datasets indicate that the proposed method is superior to state-of-the-art methods, demonstrating the advantages of the proposed feature construction based on regular equivalence-based similarity over the identification of influential nodes.

Examining physical and virtual sexual and drug risk venue co-attendance among PrEP-eligible Latinx sexual minority men

Disparities in Latinx sexual minority men (LSMM) pre-exposure prophylaxis (PrEP) initiation and adherence demonstrate the need for innovative strategies to improve outcomes along the PrEP Care Continuum. The purpose of this study was to examine physical and virtual risk venue co-attendance among LSMM of varying stages of PrEP use living in South Florida, an Ending the HIV Epidemic (EHE) priority jurisdiction. Venue co-attendance may be leveraged to support equitable dissemination and implementation (D&I) of HIV and drug use preventive interventions to LSMM. Ninety-four LSMM completed surveys on PrEP use and identified physical and virtual sexual and drug risk venues (where men seek sex partners and/or have sex or access or use drugs). Bivariate analyses examined differences in demographics and venue endorsement by PrEP status. Exponential random graph models examined endorsement of venues by PrEP status. One-mode projection visualizations portray the structure of venue co-attendance networks by PrEP status. Participants identified 49 physical and virtual sexual and drug risk venues. A homophily effect of PrEP status on venue endorsement was identified for both sexual and drug risk venues. Network size and density differed by PrEP status and whether the venue was identified as a sexual or drug risk location. Online dating apps, bars, and saunas had the highest centrality in each network type suggesting their potential role in implementing preventive programming. This study identified specific sexual and drug risk venues that may be leveraged to both disseminate targeted PrEP and harm reduction information and to implement interventions to improve PrEP uptake and adherence among LSMM living in EHE priority jurisdictions.

Biomimetic design of polyisoprene rubber with terminal synergistic effects leading to mechanical properties and creep resistance boost

The enhancement of polyisoprene rubber performance through biomimetic simulation of the terminal effect of natural rubber has been explored. So far, the presence of synergistic effects between different terminal groups and the challenge of controlling the relaxation behavior of non-covalent bonds while preserving their advantages remain open questions. In this study, we successfully prepared natural rubber biomimetic polymers with different functionalized terminals. Specifically, we constructed terminal-olefin cross-linking points at one end of the polymer chains, while dissociable oligopeptide assemblies were formed at the other end. Investigation of the viscoelastic behavior of the polymers and the structure of the rubber network confirmed the existence of synergistic effects between different terminal groups. The terminal-olefin cross-linking points effectively anchored the rubber network and stabilized the non-covalent bonds. Simultaneously, the non-covalent bonds formed by the oligopeptides acted as weak bonds, dissipating energy and improving the dimensional stability and mechanical properties of the vulcanized rubber.

A dandelion structure of eigenvector preferential attachment networks

In this paper we introduce a new type of preferential attachment network, the growth of which is based on the eigenvector centrality. In this network, the agents attach most probably to the nodes with larger eigenvector centrality which represents that the agent has stronger connections. A new network is presented, namely a dandelion network, which shares some properties of star-like structure and also a hierarchical network. We show that this network, having hub-and-spoke topology is not generally scale free, and shows essential differences with respect to the Barabási–Albert preferential attachment model. Most importantly, there is a super hub agent in the system (identified by a pronounced peak in the spectrum), and the other agents are classified in terms of the distance to this super-hub. We explore a plenty of statistical centralities like the nodes degree, the betweenness and the eigenvector centrality, along with various measures of structure like the community and hierarchical structures, and the clustering coefficient. Global measures like the shortest path statistics and the self-similarity are also examined.

Three-dimensional shape and connectivity of physical networks

Data describing the three-dimensional structure of physical networks is increasingly available, leading to a surge of interest in network science to explore the relationship between the shape and connectivity of physical networks. We contribute to this effort by standardizing and analyzing 15 data sets from different domains. Each network is made of tube-like objects bound together at junction points, which we treat as nodes, with the connections between them considered as links. We divide these networks into three categories: lattice-like networks, trees, and linked trees. The degree distribution of these physical networks is bounded, with most nodes having degrees one or three. Characterizing the physical properties of links, we show that links have an elongated shape and tend to follow a nearly straight trajectory, while a small fraction of links follow a winding path. These typical node and link properties must be reflected by physical network models. We also measure how confined a link is in space by comparing its trajectory to a randomized null model, showing that links that are central in the abstract network tend to be physically confined by their neighbors. The fact that the shape and connectivity of the physical networks are intertwined highlights that their three-dimensional layout must be taken into account to understand the evolution and function of physical networks.

Exploring patients with COPD self-care behaviours and self-efficacy and their interconnections: A network analysis.

The aim of this study was to investigate self-care behaviours of patients with chronic obstructive pulmonary disease (COPD), understand their complex interconnections and identify key behaviours influencing self-care and self-efficacy. An observational, cross-sectional study design. The outpatient department of two tertiary hospital. A convenience sample of patients with a diagnosis of COPD were included (n = 222). Self-care behaviours were assessed using the Chinese version of self-care of chronic obstructive pulmonary disease inventory based on the Middle-Range Theory of Self-Care of Chronic Illness. Patient self-care efficacy was evaluated using Chinese version of Self-Care Self-Efficacy Scale in Chronic Obstructive Pulmonary Disease. Employing network analysis, associations between behaviours (nodes) and their interrelationships (edges) were deciphered. The study was reported following the STROBE checklist. Among the spectrum of self-care behaviours, patients exhibited the most pronounced deficiencies in behaviours directed to enhancing breathing, monitoring extra-respiratory symptoms and problem-solving. Within the network, the most central emerging behaviour was the modification of prescribed therapy at the worsening of symptoms, as suggested by healthcare providers, which drives all self-care behaviours. The confidence in being able to do something to relieve symptoms, despite difficulties, was a bridging activator of self-care. The network structure underscored the prominence of self-care self-efficacy in driving self-care maintenance, monitoring, and management behaviours. Education programs for COPD should prioritise enhancing breathing, symptom monitoring and problem-solving skills. The crucial self-care behaviour in COPD involves adjusting treatments in response to symptom changes. Patient confidence in symptom alleviation acts as a catalyst for self-care engagement. Targeted assessments addressing these aspects could enhance educational interventions, ultimately improving COPD patient outcomes. This study has been registered in the Chinese Clinical Trials Registry (registration number: ChiCTR2200059764; registration date: 11 May 2022).

Improving physical stability of microalgae protein-based emulsions under acidic and neutral conditions via carboxymethyl chitosan complexation

The emulsification stability of microalgae protein (MP) is limited to strongly alkaline conditions, restricting its applications in food processing. This study aims to investigate the capability of carboxymethyl chitosan (CMCS) to improve MP's emulsification stability over a wider pH range. Results indicated soluble MP-CMCS complexes formed at pH 2, 4, and 7, while aggregation of the complexes occurred at pH 8. The complexes stabilized emulsions exhibited smaller droplet sizes and higher absolute zeta potential at pH 2, 4, and 7 compared to pH 8. After 2 weeks of storage, emulsions remained stable at pH 2, 4, and 7, with significant delamination at pH 8. Laser confocal microscopy confirmed uniform droplet distribution at pH 2 and 7, with slight fusion at pH 4. The complexes stabilized emulsions exhibited higher viscosity and shear stress than MP stabilized emulsions at pH 2, 4, and 7. The stronger viscoelastic properties and higher storage moduli (G') values of MP-CMCS complexes under acidic and neutral conditions indicated stronger intermolecular interactions compared to alkaline conditions. The increase in G' and loss moduli (G") values for emulsions at pH 8 under stress highlighted the significant impact on network structure strength and viscosity in these emulsions. This study elucidated the binding interactions between MP and CMCS under various pH conditions, and demonstrated a feasible approach to improving MP's emulsification stability over a wider pH range.

Enhanced elastic stability of a topologically disordered crystalline metal-organic framework.

By virtue of their open network structures and low densities, metal-organic frameworks (MOFs) are soft materials that exhibit elastic instabilities at low applied stresses. The conventional strategy for improving elastic stability is to increase the connectivity of the underlying MOF network, which necessarily increases the material density and reduces the porosity. Here we demonstrate an alternative paradigm, whereby elastic stability is enhanced in a MOF with an aperiodic network topology. We use a combination of variable-pressure single-crystal X-ray diffraction measurements and coarse-grained lattice-dynamical calculations to interrogate the high-pressure behaviour of the topologically aperiodic system TRUMOF-1, which we compare against that of its ordered congener MOF-5. We show that the topology of the former quenches the elastic instability responsible for pressure-induced framework collapse in the latter, much as irregularity in the shapes and sizes of stones acts to prevent cooperative mechanical failure in drystone walls. Our results establish aperiodicity as a counter-intuitive design motif in engineering the mechanical properties of framework structures that is relevant to MOFs and larger-scale architectures alike.

Controlled Interlayer Binding and Healing Improve the Transverse Properties of Upcycled Disposable Waste Wood.

Recovery and reuse of bulk waste wood are particularly challenging because of usage defects and contaminations. Here, we present a robust and efficient strategy for regenerating used wood veneers into high-performance structural materials through micro/nano interface manipulation. Our approach involves using cellulose-based interlayers to bind together two waste wood plates without an external adhesive by partially dissolving and regenerating the interlayer using a solution of ionic liquids and dimethyl sulfoxide. The mechanical properties of the regenerated wood exceed that of natural wood, displaying over a 16 and 20 times increase in transverse tensile strength and modulus, respectively, and 4-6 times improvement in longitudinal tensile strength and modulus. Nanoscale mechanical analyses show that the improvement is possible as a result of several factors, including the robust network structure of the interlayer, the good adhesion at the wood-interlayer interface, the compacted wood structure, and the low stiffness and deformation gradients between the interlayer and the wood structure. The interlayers can be created from waste papers and wood particles by taking advantage of the nanofibrillar structure of cellulose.

A multilevel backbone extraction framework

As networks grow in size and complexity, backbones become an essential network representation. Indeed, they provide a simplified yet informative overview of the underlying organization by retaining the most significant and structurally influential connections within a network. Network heterogeneity often results in complex and intricate structures, making it challenging to identify the backbone. In response, we introduce the Multilevel Backbone Extraction Framework, a novel approach that diverges from conventional backbone methodologies. This generic approach prioritizes the mesoscopic organization of networks. First, it splits the network into homogeneous-density components. Second, it extracts independent backbones for each component using any classical Backbone technique. Finally, the various backbones are combined. This strategy effectively addresses the heterogeneity observed in network groupings. Empirical investigations on real-world networks underscore the efficacy of the Multilevel Backbone approach in preserving essential network structures and properties. Experiments demonstrate its superiority over classical methods in handling network heterogeneity and enhancing network integrity. The framework is adaptable to various types of networks and backbone extraction techniques, making it a versatile tool for network analysis and backbone extraction across diverse network applications.

Research on Isomorphic Task Transfer Algorithm Based on Knowledge Distillation in Multi-Agent Collaborative Systems

In response to the increasing number of agents and changing task scenarios in multi-agent collaborative systems, existing collaborative strategies struggle to effectively adapt to new task scenarios. To address this challenge, this paper proposes a knowledge distillation method combined with a domain separation network (DSN-KD). This method leverages the well-performing policy network from a source task as the teacher model, utilizes a domain-separated neural network structure to correct the teacher model’s outputs as supervision, and guides the learning of agents in new tasks. The proposed method does not require the pre-design or training of complex state-action mappings, thereby reducing the cost of transfer. Experimental results in scenarios such as UAV surveillance and UAV cooperative target occupation, robot cooperative box pushing, UAV cooperative target strike, and multi-agent cooperative resource recovery in a particle simulation environment demonstrate that the DSN-KD transfer method effectively enhances the learning speed of new task policies and improves the proximity of the policy model to the theoretically optimal policy in practical tasks.

Ethanediamine Intercalation Induced Hydrogen Bond Network in Vanadium Oxide for Ultralong‐Life Aqueous Ammonium Ion Batteries

Aqueous ammonium‐ion batteries (AAIBs) have received tremendous attention as a potential energy technology, but their development is severely challenged by the fact that the as‐reported electrode materials are usually unable to meet the requirements of high capacity and high stability simultaneously. Herein, an organic‐inorganic hybrid material of ethanediamine (EDA) intercalated vanadium oxide (VO‐EDA) is synthesized as a high‐performance anode material for AAIBs. The intercalated EDA molecules not only act as an electron donor to bind with NH4+, but also form hydrogen bonding network structures with vanadium oxides to facilitate charge/ion transfer. As a result, this hybrid material provides a high specific capacity of 104.4 mAh g−1 at 0.5 A g−1 and good cycling stability after 5000 cycles 10 A g−1 with a coulombic efficiency of ~100%. Moreover, the ammonium‐ion full cell based on VO‐EDA anode and NiHCF cathode achieves a specific capacity of 55 mAh g−1 at 0.1 A g−1 and impressive cycling stability with 88.6% capacity retention after 10000 cycles at 5 A g−1.

Quantifying stability and resilience of eco-social keystone species complexes for coastal marine ecosystems of the Caribbean Sea and eastern Pacific: applications in conservation and monitoring programmes.

The local stability and resilience of 13 eco-social keystone species complexes (eco-social KSCs)-considered as conservation and monitoring units-were quantified in coastal marine ecosystems located in the Caribbean and eastern Pacific. Based on Routh-Hurwitz's criterion and Levins' criteria, the eco-social KSCs corresponding to Islas Marietas National Park (Mexico) emerged as the most locally stable and resilient ecosystem. To the contrary, the eco-social KSCs determined for Guala Guala Bay (Chile) and Xcalak Reef National Park (Caribbean) were the least stable and resilient, respectively. In terms of sensitivity, the eco-social KSCs corresponding to El Cobre Bay (Chile) presented the greatest number of sensitive components. The ecological section of the KSCs is formed by a tri-trophic network, dominating self-negative feedbacks. In the case of the socio-economic section, the fisher could exhibit the three types of self-feedbacks, and instead, the demand should be controlled. The identification of eco-social KSCs and the quantification of their stabilities and resiliences allow us to approach ecosystem-based fisheries management under a climate change context. Therefore, we suggest assessing and monitoring the persistence of the eco-social KSCs herein analysed over time, as a way to conserve the fundamental network structure of these ecosystems intervened by fishing.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Spatially explicit predictions of food web structure from regional-level data.

Knowledge about how ecological networks vary across global scales is currently limited given the complexity of acquiring repeated spatial data for species interactions. Yet, recent developments in metawebs highlight efficient ways to first document possible interactions within regional species pools. Downscaling metawebs towards local network predictions is a promising approach to using the current data to investigate the variation of networks across space. However, issues remain in how to represent the spatial variability and uncertainty of species interactions, especially for large-scale food webs. Here, we present a probabilistic framework to downscale a metaweb based on the Canadian mammal metaweb and species occurrences from global databases. We investigated how our approach can be used to represent the variability of networks and communities between ecoregions in Canada. Species richness and interactions followed a similar latitudinal gradient across ecoregions but simultaneously identified contrasting diversity hotspots. Network motifs revealed additional areas of variation in network structure compared with species richness and number of links. Our method offers the potential to bring global predictions down to a more actionable local scale, and increases the diversity of ecological networks that can be projected in space. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Hydrogen‐Bonded Organic Frameworks‐based Electrolytes with Controllable Hydrogen Bonding Networks for Solid‐State Lithium Batteries

The lack of stable solid‐state electrolytes (SSEs) with high‐ionic conductivity and rational design of electrode/electrolyte interfaces remains challenging for solid‐state lithium batteries. Here, for the first time, a high‐performance solid‐state lithium–oxygen battery is developed based on the Li‐ion‐conducted hydrogen‐bonded organic framework (LHOF) electrolyte and the core‐shell HOF‐DAT@CNT cathode with a few layers of HOF‐DAT on surface of carbon nanotubes. Benefiting from the abundant dynamic hydrogen bonding network in LHOF‐DAT SSEs, fast Li+ ion transport (2.2 × 10–4 S cm–1), a high Li+ transfer number (0.88), and a wide electrochemical window of 5.05 V are achieved. Symmetric batteries constructed with LHOF‐DAT SSEs exhibit a stably cycled duration of over 1400 h, which mainly stems from the jumping sites that promote a uniformly high rate of Li+ flux and the hydrogen‐bonding network structure that can relieve the structural changes during Li+ transport. LHOF‐DAT SSEs‐based Li–O2 batteries exhibit high specific capacity (10335 mAh g−1), and stable cycling life up to 150 cycles. Moreover, the solid‐state lithium metal battery with LHOF‐DAT SSEs endow good rate capability (128.8 mAh g–1 at 1 C), long‐term discharge/charge stability (210 cycles). The design of LHOF‐DAT SSEs opens an avenue for the development of novel SSEs‐based solid‐state lithium batteries.

Hydrogen-Bonded Organic Frameworks-based Electrolytes with Controllable Hydrogen Bonding Networks for Solid-State Lithium Batteries.

The lack of stable solid-state electrolytes (SSEs) with high-ionic conductivity andrational design of electrode/electrolyte interfaces remains challenging for solid-state lithium batteries. Here, for the first time, a high-performance solid-state lithium-oxygen battery is developed based on the Li-ion-conducted hydrogen-bonded organic framework (LHOF) electrolyte and the core-shell HOF-DAT@CNT cathode with a few layers of HOF-DAT on surface ofcarbon nanotubes. Benefiting from the abundant dynamic hydrogen bonding network inLHOF-DAT SSEs, fast Li+ ion transport (2.2 × 10-4 S cm-1), a high Li+ transfer number (0.88), and a wide electrochemical window of 5.05 V are achieved. Symmetric batteries constructed with LHOF-DAT SSEs exhibit a stably cycled duration of over 1400 h, which mainly stems from the jumping sites that promote a uniformly high rate of Li+ flux and the hydrogen-bonding network structure that can relieve the structural changes during Li+ transport. LHOF-DAT SSEs-based Li-O2 batteries exhibit high specific capacity (10335 mAh g-1), and stable cycling life up to 150 cycles. Moreover, the solid-state lithium metal battery with LHOF-DAT SSEs endow good rate capability (128.8 mAh g-1 at 1 C), long-term discharge/charge stability (210 cycles). The design of LHOF-DAT SSEs opens an avenue for the development of novel SSEs-based solid-state lithium batteries.

Exploring Innovative Structures and Mechanisms in Urban Grassroots Governance: A Network Governance Perspective

This paper examines the "Five Chiefs System" as a grassroots governance model in Chongqing, China, exploring innovative mechanisms in urban grassroots governance from a networked governance perspective. Through a literature review, this study summarizes existing research on innovation in urban grassroots governance, highlighting approaches in technological governance, process re-engineering, and structural research. Building on theories of governance and networked governance, and using the "Five Chiefs System" in Chongqing as a case study, the paper reveals discrepancies between the system and its operational goals as well as the principles of co-governance in practice. Consequently, an analysis of the adaptability of the "Five Chiefs System" and networked governance leads to the exploration of innovative structures and mechanisms for the networked governance of the "Five Chiefs System." This involves developing a decentralized network structure with diverse entities and establishing mechanisms for integration, trust, coordination, and maintenance within networked governance.

Network of Categories: A Method to Aggregate Egocentric Network Survey Data into a Whole Network Structure

Network of Categories: A Method to Aggregate Egocentric Network Survey Data into a Whole Network Structure