Network Structure Research Articles

Advancements and Perspectives in Biodegradable Polyester Elastomers: Toward Sustainable and High-Performance Materials

While the traditional rubber industry faces the severe pressure of environmental pollution and carbon emissions, bio-based and biodegradable elastomers have become a hot topic in the field and drawn intensive research interest. Inspired by polyester resin, incorporating polyol or polycarboxylic acid as a branching unit into aliphatic polyester and/or introducing a monomer with a C=C bond to provide open-bond cross-linking in the fashion of common vulcanization to form three-dimensional network structures are two mainstream strategies for designing biodegradable polyester elastomers (BPEs). Both methods encounter more or fewer problems, such as poor mechanical and thermal properties due to the easy hydrolysis of the ester bond and space hinderance, or the potential harm of the remaining degraded small molecules with olefin bonds. This article provides an overview of recent endeavors aimed at addressing these challenges and prospects the probable future advancements in the field.

Tracing microbial community across endophyte-to-saprotroph continuum of Cinnamomum camphora (L.) Presl leaves considering priority effect of endophyte on litter decomposition

Endophytes typically coexist with plants in symbiosis and transition into the saprobic system as plant tissues senesce, participating in the decomposition process of litter. However, the dynamic changes of endophytic communities during this process and their role in litter decomposition remain unclear. This study tracked the microbial composition across the transition from live leaves to litter in Cinnamomum camphora (L.) Presl (C. camphora), evaluating the contribution of endophytes to litter decomposition by examining microbial diversity, community assembly, and co-occurrence networks along the endophyte-to-saprotroph spectrum. The results revealed increasing bacterial diversity but stable fungal diversity, and the diversity of endogenous microbes is mirrored this in the saprophytic phase. Bacterial community assembly was characterized by deterministic processes during the symbiotic phase, shifted to stochastic processes during the saprophytic phase. In contrast, fungal community assembly was predominantly driven by stochastic processes throughout the continuum. Out of the 49 keystone taxa identified, only Pseudorhodoplanes sinuspersici demonstrated a significant positive correlation with community assembly. All identified bacterial keystone taxa during the saprophytic phase originated from endophytic sources, and around 80% of the fungal keystone taxa in the initial stages of decomposition were similarly endophytic in origin. Additionally, 60% of the dominant bacterial taxa and 28% of the dominant fungal taxa at the commencement of decomposition were of endophytic descent. This suggests that endogenous microbes possess the potential to evolve into both keystone and dominant taxa during the saprophytic phase. Endogenous keystone and dominant microbes both exhibited significant correlations with microbial network, indicating their substantial ecological presence in microbial community. Both endogenous keystone and dominant taxa exerted significant potential influences on litter decomposition. Overall, during the saprophytic phase, endophytes are likely to influence the assemblage of microbial communities, the network structure, and decomposition-related functions. Specifically, it appears that bacterial endophytes may possess a greater adaptability to the decomposition processes of leaf litter compared to their fungal counterparts.

Nanoarchitectonics of a Skin-Like Polymeric Hydrogel with High Anti-Swelling and Self-Adhesion Performance for Underwater Communication.

Hydrogels are flexible materials characterized by a 3D network structure, which possess high water content and adjustable physicochemical properties. They have found widespread applications in tissue engineering, electronic skin, drug delivery, flexible sensors, and photothermal therapy. However, hydrogel networks often exhibit swelling behavior in aqueous environments, which can result in structural degradation and a loss of gel performance. In this study, polyacrylic acid is utilized as the primary network structure with the incorporation of the natural polymer chitosan. Furthermore, a conductive hydrogel exhibiting good mechanical strength similar to human skin and excellent anti-swelling properties is developed by integrating phytic acid into the hydrogel network. The as-prepared hydrogels exhibited maximum swelling in pure water, achieving an equilibrium swelling rate of 15%. Additionally, a dopamine-grafted polyacrylic acid binder is synthesized through a coupling reaction to enhance the adhesion of the hydrogels to various substrates. The hydrogels demonstrated strong adhesion properties with different substrates. Whether in the air or underwater, the hydrogel sensor effectively monitors human movement behaviors. Furthermore, by utilizing the sensing signals to send Morse code, the hydrogel sensor can facilitate underwater communication. This type of hydrogel sensor is anticipated to play a significant role in wearable sensing applications and underwater communication.

Integrating Formal and Relational Contracting: The Link Between Network Structure and Contract Design in Interlocal Collaboration Agreements

ABSTRACTLocal governments increasingly rely on contractual relationships with third parties for public service delivery, blending formal agreements with relational mechanisms to reduce risks and uncertainties. While most research on these contracts has focused on relationships between two parties, this work adopts a network perspective to analyze how the structure of these networks—especially close‐knit connections—influences contract design. We analyzed 6576 agreements between local governments in Iowa related to public safety from 2007 to 2017 and selected 300 to qualitatively code for contractual provisions. Our findings show that when there are strong shared connections between third parties, contracts tend to be less formal. These results expand the typical focus on two‐party relationships, showing how larger networks can shape the balance between formal and relational contracts in public service management.

Interdependence patterns of multi-frequency oscillations predict visuomotor behavior

Abstract We show that sensorimotor behavior can be reliably predicted from single-trial EEG oscillations fluctuating in a coordinated manner across brain regions, frequency bands and movement time epochs. We define high-dimensional oscillatory portraits to capture the interdependence between basic oscillatory elements, quantifying oscillations occurring in single-trials at specific frequencies, locations and time epochs. We find that the general structure of the element-interdependence networks (effective connectivity) remains stable across task conditions, reflecting an intrinsic coordination architecture and responds to changes in task constraints by subtle but consistently distinct topological reorganizations. Trial categories are reliably and significantly better separated using oscillatory portraits, than from the information contained in individual oscillatory elements, suggesting an inter-element coordination-based encoding. Furthermore, single-trial oscillatory portrait fluctuations are predictive of fine trial-to-trial variations in movement kinematics. Remarkably, movement accuracy appears to be reflected in the capacity of the oscillatory coordination architecture to flexibly update as an effect of movement-error integration.

Academic Research Trend Analysis in the Past Twenty-five Years of Electric Vehicle Industry by Network Visualisation

The study investigates the visualised network regarding the research focuses and trends of the electric vehicle industry by VOSviewer and Gephi software. A co-occurrence network was constructed based on cleaned data, with a threshold of five and 1000 selected keywords. There are three types of visualisations presented in coloured clusters, overlay and density. The VOSviewer illustrates a network with 991 nodes, 242,749 edges, and a total link strength of 1,884,874. Although three visualisations represent the same network, each contains a unique perspective of information, which is the categorisation of research topics, the time arrangement of the studies over the years, and the influential power. The analysis is based on the network graph, structure, keywords position and parameters. The study finds that research areas of the EV industry are significantly dense and overlapping. The sector is inferred to focus more on innovative EV technologies, which will guide researchers and stakeholders in exploring the field more.

Integrating life cycle assessment into supply chain optimization

Integrating Supply Chain Optimization (SCO) with Life Cycle Assessment (LCA) is essential for creating supply chains that are both economically efficient and environmentally sustainable. While SCO focuses on optimizing network structures and decisions related to product and service delivery, LCA systematically assesses the environmental impacts across the entire supply chain. The existing literature treats SCO and LCA as separate, sequential steps, often leading to inconsistencies in scope and challenges in data transfer and rescaling. Our research presents a novel Supply Chain Life Cycle Optimization (SCLCO) model that integrates SCO and LCA. Our SCLCO model is based on LCA data structures, incorporates multi-time period, closed-loop SCO decisions (e.g. reverse chain management, inventory control, network design), and is capable of considering the three pillars of sustainability: environmental, economic, and social. It includes harmonizing principles, terminology, and notation, thereby bridging the gap between the SCO and LCA communities through a generalized formulation. Computational experiments on a selected SCO model from Operations Research literature validate the SCLCO and demonstrate its effectiveness in providing valuable insights to both SCO and LCA practitioners and researchers. The results emphasize that the simultaneous execution of SCO and LCA in SCLCO minimizes the risk of overlooking decision impacts and facilitates data transfer from existing LCA databases.

Identifying the Impacts of Social Movement Mobilization on YouTube: Social Network Analysis

This study explores the potential of social media in improving education, engagement, and mobilization for climate change initiatives. Using the theoretical framework of resource mobilization and methods such as social network analysis (SNA) and bipartite networks, it examines how effective deployment of resources such as information, social capital, and organizational capabilities can help in the progression of collective movements. Social media platforms, particularly YouTube, significantly influences network structures by facilitating resource mobilization and driving essential engagement. This study extracted data from NodeXL and found that YouTube is an effective medium in disseminating climate change information and delivering educational content to a multilingual audience. Additionally, video affordances such as storytelling, audio–visual effects, and concise narratives enhance viewer interest and engagement, increasing resource mobilization effectiveness. This research offers insights into optimizing social media use for effective resource mobilization and engagement in climate change initiatives.

Characterizing the microbiome recruited by the endangered plant Firmiana danxiaensis in phosphorus-deficient acidic soil

Phosphorus (P)-deficient soils serve as crucial habitats for endangered plant species. Microbiomes play pivotal roles in soil element cycling and in determining a plant’s adaptability to the environment. However, the relationship between the endangered plant, microbiome, and soil stoichiometric traits, and how it affects plant adaption to P-deficient habitats remain largely unexplored. In this study, we investigated the microbiome (bacteria and fungi) in the rhizosphere of Firmiana danxiaensis, an endangered plant species growing exclusively in P-deficient acidic soils on Mt. Danxia, South China; the non-endangered coexisting tree species Pinus massoniana was used as a reference. Our results showed that soil traits in the rhizosphere of F. danxiaensis differed significantly from that of P. massoniana, including higher soil pH, lower C:N, and higher N:P. The rhizosphere of F. danxiaensis harbors higher microbial diversity and different microbial communities from P. massoniana. Using the machine learning approach, we characterized 76 bacterial and 20 fungal phylotypes dominated in F. danxiaensis rhizosphere, most of which had strong impacts on microbial ecological network structure (they accounted for only 0.33% node numbers but linked 21.2% of the nodes in the network); specifically, Udaeobacter spp., a highly abundant (constituting 4.07% of the total bacterial community) member of Verrucomicrobiota exclusively accumulated in the rhizosphere of F. danxiaensis but not P. massoniana, demonstrated a pronounced ecological prefers toward F. danxiaensis rhizosphere habitat (high pH, low C:N and high N:P) and potential antagonistic indication. In contrast, P. massoniana rhizosphere harbored more Subgroup2 of Acidobacteria and Gammaproteobacterial N-fixer. Taken together, this study provided novel evidence that endangered plants recruited a unique microbiome characterized by Udaeobacter spp. favoring high N habitat. It contributes not only to our understanding of microbiome recruitment by plants in P-deficient acidic soils, but also underscores the importance of microbiome in the conservation and population restoration of endangered plants.

A novel samarium(III) orotate complex [NaSm(orotate)4(H2O)10]·3H2O – crystal structure and vibrational spectra

Abstract A novel Sm(III) orotate complex, [NaSm(orotate)4(H2O)10]·3H2O, has been synthesized and its structure determined by single crystal X-ray diffraction and vibrational (IR and Raman) spectroscopic methods. The title compound crystallizes in the non-centrosymmetric orthorhombic space group Cmc21 with Z = 4. In the crystal structure, one of the two independent orotate ligands links the Sm(III) and Na(I) cations, forming chains. The second orotate ligand completes the coordination environment of the Na(I) ion, resulting in layers that are parallel to the ab plane. This arrangement leads to an extensive three-dimensional network structure, characterized by numerous intermolecular O–H⋯O and N–H⋯O interactions.

A Dual-Channel and Frequency-Aware Approach for Lightweight Video Instance Segmentation

Video instance segmentation, a key technology for intelligent sensing in visual perception, plays a key role in automated surveillance, robotics, and smart cities. These scenarios rely on real-time and efficient target-tracking capabilities for accurate perception and intelligent analysis of dynamic environments. However, traditional video instance segmentation methods face complex models, high computational overheads, and slow segmentation speeds in time-series feature extraction, especially in resource-constrained environments. To address these challenges, a Dual-Channel and Frequency-Aware Approach for Lightweight Video Instance Segmentation (DCFA-LVIS) is proposed in this paper. In feature extraction, a DCEResNet backbone network structure based on a dual-channel feature enhancement mechanism is designed to improve the model’s accuracy by enhancing the feature extraction and representation capabilities. In instance tracking, a dual-frequency perceptual enhancement network structure is constructed, which uses an independent instance query mechanism to capture temporal information and combines with a frequency-aware attention mechanism to capture instance features on different attention layers of high and low frequencies, respectively, to effectively reduce the complexity of the model, decrease the number of parameters, and improve the segmentation efficiency. Experiments show that the model proposed in this paper achieves state-of-the-art segmentation performance with few parameters on the YouTube-VIS dataset, demonstrating its efficiency and practicality. This method significantly enhances the application efficiency and adaptability of visual perception intelligent sensing technology in video data acquisition and processing, providing strong support for its widespread deployment.

Network Analysis of Internalizing and Externalizing Symptoms in Arab Gifted Children: A Cross-Sectional Study.

The current paper aimed to estimate the network structure of general psychopathology (internalizing and externalizing symptoms/disorders) among 239 gifted children in Jordan. This cross-sectional study with a convenience sampling method was conducted between September 2023 and October 2024 among gifted children aged 7-12. The Child Behavior Checklist (CBCL) was employed to assess six symptom clusters: conduct problems, attention-deficit/hyperactivity disorder (ADHD), and oppositional defiant problems as externalizing symptoms, and affective problems, anxiety issues, and somatic complaints as internalizing symptoms. We used the network analysis perspective by graphical least absolute shrinkage and selection operator (gLASSO) and the Extended Bayesian Information Criterion (EBIC). These methods were used to determine network structure and important nodes in the estimated network. "Sleeps less" (centrality strength = 2.04, edge weight = 0.33) was the central symptom in the affective cluster. In contrast, "worries" (centrality strength = 1.89, edge weight = 0.28) and "headaches" (centrality strength = 2.35, edge weight = 0.41) were pivotal in the anxiety and somatic clusters, respectively. The findings suggested that these symptoms had critical roles in the context of the general psychopathology among gifted children. Accordingly, the mentioned symptoms should be assessed and targeted among gifted children. Future studies could evaluate the results of targeting these symptoms on gifted children's well-being and daily functions.

Ultra-Short-Term Photovoltaic Power Prediction Based on BiLSTM with Wavelet Decomposition and Dual Attention Mechanism

Photovoltaic power generation relies on sunlight conditions, and traditional prediction models find it difficult to capture the deep features of power data, resulting in low prediction accuracy. In addition, there are problems such as outliers and missing values in the data collected on site. This article proposes an ultra-short-term photovoltaic power generation prediction model based on wavelet decomposition, a dual attention mechanism, and a bidirectional long short-term memory network (W-DA-BiLSTM), aiming to address the limitations of existing deep learning models in processing nonlinear data and automatic feature extraction and optimize for the common problems of outliers and missing values in on-site data collection. This model uses the quartile range method for outlier detection and multiple interpolation methods for missing value completion. In the prediction section, wavelet decomposition is used to effectively handle the volatility and nonlinear characteristics of photovoltaic power generation data, while the bidirectional long short-term memory network (LSTM) structure and dual attention mechanism enhance the model’s comprehensive learning ability for time series data. The experimental results show that compared with the SOTA method, the model proposed in this paper has higher accuracy and efficiency in predicting photovoltaic power generation and can effectively address common random fluctuations and nonlinear problems in photovoltaic power generation.

Deep neural networks have an inbuilt Occam’s razor

The remarkable performance of overparameterized deep neural networks (DNNs) must arise from an interplay between network architecture, training algorithms, and structure in the data. To disentangle these three components for supervised learning, we apply a Bayesian picture based on the functions expressed by a DNN. The prior over functions is determined by the network architecture, which we vary by exploiting a transition between ordered and chaotic regimes. For Boolean function classification, we approximate the likelihood using the error spectrum of functions on data. Combining this with the prior yields an accurate prediction for the posterior, measured for DNNs trained with stochastic gradient descent. This analysis shows that structured data, together with a specific Occam’s razor-like inductive bias towards (Kolmogorov) simple functions that exactly counteracts the exponential growth of the number of functions with complexity, is a key to the success of DNNs.

Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought Conditions

Grain supply chains remain stable in the face of natural disasters, and the resilience of the grain supply chain plays an important role. In a complex scenario of exposure to shocks, it is significant to identify the critical nodes of the grain supply chain and propose countermeasures accordingly to enhance the resilience of the grain supply chain. In this paper’s study, firstly, a triangular model of contradictory events is used to describe complex scenarios and obtain Bayesian network nodes. Secondly, the fragmentation of the scenario is based on the description of the scene, the scene stream is constructed, the event network is obtained, and the Bayesian network structure is built on the basis. Then, combining expert knowledge and D–S evidence theory, the Bayesian network parameters are determined, and the Bayesian network model is built. Finally, the key nodes of the grain supply chain are identified in the context of the 2022 drought data in the Yangtze River Basin in China, and, accordingly, a strategy for improving the resilience of the grain supply chain is proposed in stages. This study provides a new research perspective on issues related to grain supply-chain resilience and enriches the theoretical foundation of research related to supply-chain resilience.

Observation of the Smallest Three‐Dimensional Neutral Boron Cluster

Despite major progress in the investigation of boron cluster anions, direct experimental study of neutral boron clusters remains a significant challenge because of the difficulty in size selection. Here we report a size‐specific study of the neutral B9 cluster using threshold photoionization with a tunable vacuum ultraviolet free electron laser. The ionization potential of B9 is measured to be 8.45 ± 0.02 eV and it is found to have a heptagonal bipyramid D7h structure, quite different from the planar molecular wheel of the B9‐ anionic cluster. Chemical bonding analyses reveal superior stability of the bipyramidal structure arising from delocalized s and p bonding interactions within the B7 ring and between the B7 ring and the capping atoms. Photoionization of B9 breaks the single‐electron B‐B bond of the capping atoms, which undergo off‐axis distortion to enhance interactions with the B7 ring in the singlet ground state of B9+. The single‐electron B‐B bond of the capping atoms appears to be crucial in stabilizing the D7h structure of B9. This work opens avenues for direct size‐dependent experimental studies of a large variety of neutral boron clusters to explore the stepwise development of network structures.

The impact of matrix viscosity on the mechanical behavior of fiber reinforced composites

AbstractThe influence of matrix viscosity on the mechanical behavior of fiber reinforced composites was investigated in this paper. The glass fiber fabric was selected as reinforcement and the mixed solution composed of resin, curing agent and different viscosity of diluent was selected as matrix. The composites was fabricated by vacuum‐assisted resin infusion (VARI). The influence of matrix viscosity on bending, impact and creep properties of composites were studied by varying the mass fraction of 1,4‐butanediol diglycidyl ether (BDDGE) diluent, the least square fitting was used to fit the experimental result. The mathematical model for characterizing the relationship between the matrix viscosity and the bending strength was established, and the mass fraction of BDDGE diluent based on the optimal bending property was obtained. The failure mechanisms were analyzed by analyzing fiber reinforced composites properties, reaction principles and fracture morphologies. Result shows that the epoxy groups within the viscosity undergo a ring‐opening reaction to integrate the cross‐linking network structure of the epoxy resin. However, an excessive amount of diluent will result in an increase of number of flexible chain segments, which initially enhancing the mechanical properties of the specimens before causing a subsequent decline. The mathematical model was substantiated by experiment, which reveals that the viscosity is 185.3 mPa·s for the optimal bending properties.Highlights The bending strength and modulus reached the maximum at a viscosity of 180 mPa·s The creep curves demonstrate progressive damage and minute deformation. The ring‐opening reaction occurs with two oxygen atoms in the diluent BDDGE. The matrix viscosity of BDDGE diluent is 185.3 mPa·s for the optimal bending properties.

A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations

The rapid development of highways greatly affects the flow of people, finance, goods, and information between cities, and monitoring the OD flow of travel has become a very important task for intelligent transportation systems (ITS). The temporal dynamics and complex spatial correlations of OD traffic distribution, as well as the sparsity and incompleteness of data caused by uneven traffic distribution, make OD traffic prediction complex and challenging. This paper proposes a multi-task prediction model for OD traffic between highway stations. The model adopts a hard parameter shared multi-task learning network structure, which is divided into sub-task learning inflow trend modules, sub-task learning outflow trend modules, and main task learning modules for OD traffic. At the same time, the attraction intensity matrix between stations is constructed using the population density data as the external feature of the sub-task module for outlet outflow flow, and stronger constraints between tasks are introduced to achieve better fitting results. Finally, an OD flow prediction case experiment was conducted between stations on highways in Sichuan Province. The experimental results showed that the proposed model not only had higher accuracy in predicting results than other baseline models, but also had better effectiveness and robustness.

Observation of the Smallest Three-Dimensional Neutral Boron Cluster.

Despite major progress in the investigation of boron cluster anions, direct experimental study of neutral boron clusters remains a significant challenge because of the difficulty in size selection. Here we report a size-specific study of the neutral B9 cluster using threshold photoionization with a tunable vacuum ultraviolet free electron laser. The ionization potential of B9 is measured to be 8.45 ± 0.02 eV and it is found to have a heptagonal bipyramid D7h structure, quite different from the planar molecular wheel of the B9- anionic cluster. Chemical bonding analyses reveal superior stability of the bipyramidal structure arising from delocalized s and p bonding interactions within the B7 ring and between the B7 ring and the capping atoms. Photoionization of B9 breaks the single-electron B-B bond of the capping atoms, which undergo off-axis distortion to enhance interactions with the B7 ring in the singlet ground state of B9+. The single-electron B-B bond of the capping atoms appears to be crucial in stabilizing the D7h structure of B9. This work opens avenues for direct size-dependent experimental studies of a large variety of neutral boron clusters to explore the stepwise development of network structures.

Effect of Pre-Emulsified Flaxseed Oil Containing Rutin on the Quality of Nemipterus virgatus Surimi Gel: Gelatinization Properties, Storage Stability, and Protein Digestibility

Rinsing during surimi protein processing can result in the loss of essential nutrients, such as fats and minerals. Therefore, supplementing functional fats in a stable form can make up for the fat loss of surimi during the rinsing process. This research aimed to investigate the effects of incorporating pre-emulsified flaxseed oil with different concentrations of rutin (0, 0.5, 1.5, 2.5, and 3.5%, dissolved in flaxseed oil, w/v) to Nemipterus virgatus surimi on the gelatinization properties, lipid oxidation, and in vitro static simulated digestion characteristics of surimi gels. The results indicated that the addition of 1.5% rutin significantly improved the water-holding capacity and decreased the cooking loss rate of surimi gel (p < 0.05). The results of optical microscopy and scanning electron microscopy showed that the addition of 1.5% rutin promoted a denser network structure of surimi gel. Furthermore, the incorporation of rutin effectively slowed lipid oxidation in pre-emulsified flaxseed oil surimi gel. Compared with the gel group containing only pre-emulsified flaxseed oil, the addition of rutin significantly reduced the levels of volatile base nitrogen (TVB-N) and thiobarbituric acid reactive substances (TBARSs) in the gel and also mitigated the decline in acidity (p < 0.05). Moreover, the addition of rutin significantly inhibited the decrease in pH of surimi gel during storage (p < 0.05). In vitro static simulated digestion demonstrated that the addition of 1.5% rutin enhanced the protein digestibility from 71.2% to 77.2% of the surimi gel. Therefore, adding pre-emulsified oil containing an appropriate amount of rutin to surimi can not only compensate for the fat loss during the surimi rinsing process but also effectively improve the quality characteristics of surimi gels. This research will provide a theoretical basis for the effective addition of functional lipids in surimi products and the development of nutritious and healthy surimi products.