Network Structure Research Articles

Mathematical Limitations of Gravity Model in Constructing Regional Association Networks: A Case Study

This study evaluates the limitations of gravity models in constructing regional association networks, using China’s interprovincial economic connections as a case study. Comparison between a gravity-model-based simulated network and an actual network reveals significant topological differences. The gravity model overestimates the influence of larger, inward-oriented provinces and fails to accurately represent external connections. Attempts to refine the model with additional variables proved ineffective. Further theoretical analysis attributes these deficiencies to measurement bias from the model’s simplified binary perspective and information loss due to dimensional mismatch between pairwise predictions and complex network structures. These findings underscore the need for cautious application of gravity models and the development of more comprehensive analytical frameworks in regional network analysis.

Determination of heat transfer characteristics of tube bundles over heating plate by machine learning

In this study machine learning is used with the aid of a deep neural network algorithm to predict convective heat transfer characteristics for inline and staggered tube bundles, and correlation equations for Nusselt number and friction factor are derived. Machine learning algorithm's data were obtained from experimental work for various transverse pitch of the tube bundles, longitudinal pitch of the tube bundles and Reynolds number. 276 experimental data points were taken for both inline and staggered tube bundles. However, considering that the data obtained from the experimental study may be insufficient for training, a two-step data augmentation method and retraining with cross-validation was used to prevent data deficiency in the deep neural network structure. Thus, the unseen data in the experimental work were also predicted. The coefficient of determination for the DNN model predictions was obtained greater than 0.96. One correlation equation for Nusselt Number and three correlation equations for friction factor were proposed from the augmented data with machine learning. The R2 values of the correlation equations varied between 89 % and 99 %. As a result, machine learning methods successfully applied to predict the Nusselt number and friction factor of tube bundles consistent with the experimental data.

Frabrication of carboxymethylchitin nanofibers and fish gelatin hybrid gels with robust gel performance

Compared to animal gelatin, the application of fish gelatin (FG) in the food industry is limited due to its lower gel hardness, gelation temperature, and melting temperature. To address these issues, we developed hybrid gels using carboxymethyl chitin nanofibers (CMC-NFs) with FG to enhance its gel properties. The chitin was carboxymethylated by monochloroacetic acid, increasing the negative potential from −6 mV to −35 mV. This modified chitin was then defibrillated into CMC-NFs through high-pressure homogenization to improve its water dispersibility. The addition of 0.1% CMC-NFs significantly increased the hardness of FG gels from 533 g to 989 g, which was notably higher than the ∼600 g hardness of porcine gelatin. Additionally, the gelation and melting temperatures of FG reached 17 °C and 32 °C, respectively, which are very close to the 20 °C and 35 °C of porcine gelatin. Mechanistic studies revealed that FG and CMC-NFs (0.1%) molecules formed a synergistic gel network, facilitated by hydrogen bonding and hydrophobic interactions. This interaction significantly increased the α-helix content in the secondary structure of the FG protein from 15.26% to 21.18%, thereby enhancing the FG-dominated three-dimensional network structure with CMC-NF molecules. Increasing the concentration of CMC-NFs further elevated the gel's melting temperature while reducing its hardness due to phase separation. This study not only presents a viable method for enhancing the gelling properties of modified FG but also offers a feasible solution for expanding FG's applications in the food industry.

Physics-informed deep convolutional network for combined sea ice concentration and velocity prediction

Accurate prediction of short-term fluctuations in Arctic sea ice is important for safe use of Arctic shipping routes. While deep-learning models can improve the accuracy of sea-ice predictions, the accuracy and predictability of short-term sea-ice conditions are limited in most purely data-driven deep-learning models that consider a single aspect of sea ice, without considering the physical variation law between several sea ice factors. We propose dual-task prediction models for sea ice concentration (SIC) and sea ice velocity (SIV), and incorporate a loss function that simultaneously addresses dynamic constraints of SIC and SIV into each model, based on dynamics terms in the sea ice control equation. Comparative experiments are performed to determine which model structure best performs at predicting SIC and SIV. We report a dual-task branching structure to be more suitable for predicting SIC and SIV, and a post-decoder branch network structure to best predict SIC and SIV. Adding a sea ice dynamics equation to the loss function improves the model fit with dynamics law, improving the predictability and prediction accuracy of SIC and SIV.

The endoplasmic reticulum as an active liquid network

The peripheral endoplasmic reticulum (ER) forms a dense, interconnected, and constantly evolving network of membrane-bound tubules in eukaryotic cells. While individual structural elements and the morphogens that stabilize them have been described, a quantitative understanding of the dynamic large-scale network topology remains elusive. We develop a physical model of the ER as an active liquid network, governed by a balance of tension-driven shrinking and new tubule growth. This minimalist model gives rise to steady-state network structures with density and rearrangement timescales predicted from the junction mobility and tubule spawning rate. Several parameter-independent geometric features of the liquid network model are shown to be representative of ER architecture in live mammalian cells. The liquid network model connects the timescales of distinct dynamic features such as ring closure and new tubule growth in the ER. Furthermore, it demonstrates how the steady-state network morphology on a cellular scale arises from the balance of microscopic dynamic rearrangements.

Rheological analysis of network structure of raw natural rubber prepared by different processing techniques

The performance of raw natural rubber (NR) is dominated by its network structure, particular the levels of long chain branching (LCB) and entanglement. Here, three type of raw rubbers were prepared using different processing methods for commercial grade NRs. Linear and nonlinear viscoelastic behaviors, as well as stress relaxation, were analyzed to access their network structures. The third relative harmonic, phase angle, and first to second quarter-period integral ratio of stress curve were utilized as indicators for the nonlinearity of samples. By combining these values with flow activation energy and characteristic times, it can be confirmed that naturally coagulated NR showed higher elasticity than acid-coagulated NR due to high levels of LCB and entanglement, and hot air drying could lead to chain degradation. These findings correlated with molecular weight parameters, gel content and Mooney viscosity results. This research establishes a method for monitoring raw NR quality and predicting its properties.

Identification of dynamic networks community by fusing deep learning and evolutionary clustering

Community detection is a critical component of network analysis and a hot topic in social computing. Detecting community structure in dynamic networks has important theoretical and practical implications for understanding the intrinsic function of networks and predicting network behavior. However, the majority of existing dynamic community detection methods adopt shallow models, which have limited ability to excavate complex non-linear structures and tend to generate undesirable community structures. In order to obtain an accurate and robust community structure in dynamic networks, we are inspired by network representation learning and utilize the deep learning to detect evolving communities in dynamic networks. In this paper, we propose a novel dynamic community detection method by fusing Deep Learning and Evolutionary Clustering (DLEC). This work attempts to combine deep learning and evolutionary clustering into a unified framework. First, we propose a matrix construction strategy to fully reveal the inherent community structures via the underlying community memberships. Then, we develop a novel multi-layer deep autoencoder framework that consists of multiple non-linear functions to extract the latent deep representation of the dynamic network. Based on the evolutionary clustering framework, a graph regularization term is introduced to ensure the smoothness of the community evolution. Finally, we employ the K-means clustering algorithm on the low-dimensional network space to obtain the community structure. Extensive experimental results on synthetic and real-world networks show that the proposed DLEC algorithm can effectively detect high-quality communities in dynamic networks.

Rhizosphere microbial community assembly as influenced by reductive soil disinfestation to resist successive cropping obstacle.

Reductive soil disinfestation (RSD), which involves creating anaerobic conditions and incorporating large amounts of organic materials into the soil, has been identified as a reliable strategy for reducing soilborne diseases in successive cropping systems. However, limited research exists on the connections between soil microorganism composition and plant diseases under various types of organic material applications. This study aimed to evaluate the effects of distinct RSD strategies (control without soil amendment; RSD with 1500 kg ha-1 molasses powder; RSD with 3000 kg ha-1 molasses powder; RSD with 3000 kg ha-1 molasses powder and 37.5-41.3 kg ha-1 microbial agent) on the plant disease index, bacterial community composition and network structure in rhizosphere soil. RSD treatments significantly reduced the occurrence of black shank disease in tobacco and increased soil bacterial diversity. High amounts of molasses powder in RSD treatments further enhanced disease inhibition and reduced fungal abundance and Shannon index. RSD also increased the relative abundance of bacterial phylum Firmicutes and fungal phylum Ascomycota, while decreasing the relative abundance of bacterial phyla Chloroflexi and Acidobacteriota and fungal phylum Basidiomycota in rhizosphere soil. A multiple regression model identified bacterial positive cohesion as the primary factor influencing the plant disease index, with a greater impact than bacterial negative cohesion and community stability. The competition among beneficial bacteria for creating a healthy rhizosphere environment is likely a key factor in the success of RSD in reducing plant disease risk. RSD, especially with higher rates of molasses powder, is a viable strategy for controlling black shank disease in tobacco and promoting soil health by fostering beneficial microbial communities. This study provides guidelines for soil management and plant disease prevention. © 2024 Society of Chemical Industry.

Insecticide exposure can increase burrow network production and alter burrow network structure in soil dwelling insects (Agriotes spp.)

Insecticide treated seeds are commonly used to reduce yield losses from burrowing insect damage such as wireworms. Using temporal X-ray Computed Tomography (CT) of soil-filled bioassays, we aimed to quantify changes in burrow network production and structure as a measure of wireworm behavioural change in response to three types of insecticide treated maize seed; compound X (R&D product in field trial stage of development); tefluthrin and thiamethoxam. A biopesticide alternative treatment (neem), untreated maize seed and bare soil were also investigated. Insect health outcomes were also monitored to provide toxicity/mortality data. Wireworms exposed to compound X produced greater burrow networks than untreated maize and neem treatments, similar to that in volume of those produced in bare soil. Compound X exposure also elicited the production of more complex burrow structures, a function of the number of vertices, edges and faces of a shape (V-E+F) related to the number of interconnected branches, compared to any other treatments. Compound X, tefluthrin and thiamethoxam induced mortality at greater rates than neem or untreated, suggesting all three could have potential to manage wireworm populations and reduce yield loss, but only compound X modified burrowing behaviour. With soil biopores playing an important role in soil productivity and carbon sequestration, the wider implications of this increase in burrowing activity for food security and climate change warrants further exploration.

Alpha coherence is a network signature of cognitive recovery from disorders of consciousness.

Alpha (8-12 Hz) frequency band oscillations are among the most informative features in electroencephalographic (EEG) assessment of patients with disorders of consciousness (DoC). Because interareal alpha synchrony is thought to facilitate long-range communication in healthy brains, coherence measures of resting-state alpha oscillations may provide insights into a patients capacity for higher-order cognition beyond channel-wise estimates of alpha power. In multi-channel EEG, global coherence methods may be used to augment standard spectral analysis methods by both estimating the strength and identifying the structure of coherent oscillatory networks. We performed global coherence analysis in 95 separate clinical EEG recordings (28 healthy controls and 33 patients with acute or chronic DoC, 25 of whom returned for follow-up) collected between two academic medical centers. We found that posterior alpha coherence is associated with recovery of higher-level cognition. We developed a measure of network organization, based on the distance between eigenvectors of the alpha cross-spectral matrix, that detects recovery of posterior alpha networks. In patients who have emerged from a minimally conscious state, we showed that coherence-based alpha networks are reconfigured prior to restoration of alpha power to resemble those seen in healthy controls. This alpha network measure performs well in classifying recovery from DoC (AUC = 0.78) compared to common representations of functional connectivity using the weighted phase lag index (AUC = 0.50 - 0.57). Lastly, we observed that activity within these alpha networks is suppressed during positive responses to task-based EEG command-following paradigms, supporting the potential utility of this biomarker to detect covert cognition. Our findings suggest that restored alpha networks may represent a sensitive early signature of cognitive recovery in patients with DoC. Therefore, network detection methods may augment the utility of EEG assessments for DoC.

Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional–patient interaction intensity: a cohort study

AimInteractions between patients and healthcare professionals (HCP) during hospital admissions are complex and difficult to interrogate using traditional analysis of electronic patient record (EPR) data. The objective of this study...

Super-Tough, Non-Swelling Zwitterionic Hydrogel Sensor Based on the Hofmeister Effect for Potential Motion Monitoring of Marine Animals.

Hydrogel-based electronic devices in aquatic environments have sparked widespread research interest. Nevertheless, the challenge of developing hydrogel electronics underwater has not been profoundly surmounted because of the fragility and swelling of hydrogels in aquatic environments. In this work, a zwitterionic double network hydrogel comprisedof polyvinyl alcohol (PVA), poly(sulfobetaine methacrylate) (PSBMA), and sulfuric acid (H2SO4) demonstrates super-tough and non-swelling performance. The Hofmeister effect of H2SO4 and PSBMA induces the PVA chains to form numerous nanocrystalline domains, which serve as the primary physical crosslinking points and provide effective energy dissipation. H2SO4 induces a strong salting-out effect to facilitate PVA crystallization and the formation of a dense and stable network structure that inhibits swelling. The resulting hydrogel exhibits an ultra-high toughness of 4.61MJm-3, non-swelling, and long-term stability for up to a month in pure water and seawater. Based on this, a hydrogel-based seawater strain sensor has been developed to monitor the underwater movements of marine animal models. Reliable and stable sensing performance ensures real-time collection of underwater motion signals, despite the impacts of water flow and the interference of ions. This study provides a facile approach to designing super-tough and non-swelling hydrogels and further expands the application of underwater electronic devices.

Preparation of chitosan/cellulose nanofibril composite aerogel and its adsorption performance for Cu(II)-MO binary pollutant

Heavy metals and organic dyes commonly coexist in water, which pose a serious threat to human health. Herein, a functional aerogel for adsorption of Cu(II)-methyl orange binary-polluted system was prepared. Cellulose nanofibril (CNF) was prepared by 2,2,6,6-tetramethylpiperidinyloxy (TEMPO)-NaBr-NaClO system using abandoned pineapple leaves as the main raw material, and chitosan/cellulose nanofibril (CS/CNF) composite aerogel was constructed by sol-gel method combined with freeze-drying. The structure of composite aerogel was characterized by XRD, FTIR, TG and SEM. The fabricated aerogels were ultra-lightweight and exhibited a highly porous 3D network structure. The effects of adsorbent dosage, ionic strength, solution pH, adsorbent concentration, adsorption time, and temperature on the adsorption of Cu(II) and methyl orange (MO) by composite aerogel were studied. The adsorption of composite aerogel towards mono-polluted of Cu(II) and MO reached equilibrium after 100 min with a maximum adsorption capacity of 116.69 and 295.86 mg/g, respectively. The adsorption of Cu(II) and MO by CS/CNF aerogel was mainly achieved through electrostatic attraction, metal chelation and hydrogen bonding interactions. More importantly, the adsorption of Cu(II) by CS/CNF aerogel has an inhibitory effect on its adsorption of MO in Cu(II)-MO binary-polluted system.

Rupture Mechanics of Blood Clots: Influence of Fibrin Network Structure on the Rupture Resistance

Embolization is a leading cause of mortality, yet we know little about clot rupture mechanics. Fibrin provides the main structural and mechanical stability to blood clots. Previous studies have shown that altering the concentration of coagulation activators (thrombin or tissue factor (TF)) has a significant impact on fibrin structure and viscoelastic properties, but their effects on rupture properties are mostly unknown. Toughness, which corresponds to the ability to resist rupture, is independent of viscoelastic properties. We used varying TF concentrations to alter the structure and toughness of human plasma clots. We performed single-edge notch rupture tests to examine fibrin toughness under a constant strain rate and we assessed viscoelastic mechanics using rheology. We utilized fluorescent confocal and scanning electron microscopy (SEM) to quantify the fibrin network structure under varying TF concentrations. Our results revealed that increased TF concentration resulted in increased number of fibrin fibers with a reduction in network pore size, thinner and shorter fibrin fibers. Increasing TF concentration yielded a maximum toughness at mid-TF concentration, such that fibrin diameter and number of fibers underlie a complex role in influencing the rupture resistance of blood clots, resulting in a nonmonotonic relationship between TF and toughness. A simple mechanical model, built on our findings from our Fluctuating Spring (FS) computational model, adopted to estimate the fracture toughness (critical energy release rate) as a function of TF predicts trends that are in good agreement with experiments. The differences in mechanical responses point to the importance of studying the structure-function relationships of fibrin networks, which may be predictive of the tendency for embolization. Statement of significanceFibrin, a naturally occurring biomaterial, is the main mechanical and structural scaffold of blood clots that provides the necessary strength and stability to the clot, ensuring effective stemming of bleeding. The rupture of blood clots can result in the blockage of downstream vessels thereby blocking blood flow and oxygen supply. The fibrin network structure has been shown to influence the viscoelastic mechanical properties of clots, but has not been explored for fracture mechanics. Here, we modulate the fibrin network structure by varying the concentration of Tissue Factor (TF). Interestingly, the association between TF concentration and maximum toughness of the clots is non-monotonic. The variations in mechanical responses highlight the importance of studying the structure-function relationships of fibrin networks, as these may predict the tendency for embolization.

Soft sensor modeling method and application based on TSECIT2FNN-LSTM

To address the issue of low accuracy in soft sensor modeling of key variables caused by multi-variable coupling and parameter sensitivity in complex processes, this paper introduces a TSK-type-based self-evolving compensatory interval type-2 fuzzy Long short-term memory (LSTM) neural network (TSECIT2FNN-LSTM) soft sensor model. The proposed TSECIT2FNN-LSTM integrates the LSTM neural network with the interval type-2 fuzzy inference system to address long-term dependencies in sequence data by utilizing the gate mechanism of the LSTM neural network. The TSECIT2FNN-LSTM structure learning algorithm uses the firing strength of the network rule antecedent to decide whether to generate new rules to improve the rationality of the network structure. TSECIT2FNN-LSTM parameter learning utilizes the gradient descent method to optimize network parameters. However, unlike other interval type-2 fuzzy neural network gradient calculation processes, the error term in the LSTM node parameter gradient of TSECIT2FNN-LSTM is propagated backwards in the time dimension. Additionally, the error term is simultaneously transferred to the upper layer network to enhance network prediction accuracy and memory capabilities. The TSECIT2FNN-LSTM soft sensor model is utilized to predict the alcohol concentration in wine and the nitrogen oxide emission in gas turbines. Experimental results demonstrate that the proposed TSECIT2FNN-LSTM soft sensing model achieves higher prediction accuracy compared to other models.

Research on Low-Voltage Arc Fault Based on CNN–Transformer Parallel Neural Network with Threshold-Moving Optimization

Low-voltage arc fault detection can effectively prevent fires, electric shocks, and other accidents, reducing potential risks to human life and property. The research on arc fault circuit interrupters (AFCIs) is of great significance for both safety in production scenarios and daily living disaster prevention. Considering the diverse characteristics of loads between the normal operational state and the arc fault condition, a parallel neural network structure is proposed for arc fault recognition, which is based on a convolutional neural network (CNN) and a Transformer. The network uses convolutional layers and Transformer encoders to process the low-frequency current and high-frequency components, respectively. Then, it uses Softmax classification to perform supervised learning on the concatenated features. The method combines the advantages of both networks and effectively reduces the required depth and computational complexity. The experimental results show that the accuracy of this method can reach 99.74%, and with the threshold-moving method, the erroneous judgment rate can be lower. These results indicate that the parallel neural network can definitely detect arc faults and also improve recognition efficiency due to its lean structure.

Efficient Optimized YOLOv8 Model with Extended Vision.

In the field of object detection, enhancing algorithm performance in complex scenarios represents a fundamental technological challenge. To address this issue, this paper presents an efficient optimized YOLOv8 model with extended vision (YOLO-EV), which optimizes the performance of the YOLOv8 model through a series of innovative improvement measures and strategies. First, we propose a multi-branch group-enhanced fusion attention (MGEFA) module and integrate it into YOLO-EV, which significantly boosts the model's feature extraction capabilities. Second, we enhance the existing spatial pyramid pooling fast (SPPF) layer by integrating large scale kernel attention (LSKA), improving the model's efficiency in processing spatial information. Additionally, we replace the traditional IOU loss function with the Wise-IOU loss function, thereby enhancing localization accuracy across various target sizes. We also introduce a P6 layer to augment the model's detection capabilities for multi-scale targets. Through network structure optimization, we achieve higher computational efficiency, ensuring that YOLO-EV consumes fewer computational resources than YOLOv8s. In the validation section, preliminary tests on the VOC12 dataset demonstrate YOLO-EV's effectiveness in standard object detection tasks. Moreover, YOLO-EV has been applied to the CottonWeedDet12 and CropWeed datasets, which are characterized by complex scenes, diverse weed morphologies, significant occlusions, and numerous small targets. Experimental results indicate that YOLO-EV exhibits superior detection accuracy in these complex agricultural environments compared to the original YOLOv8s and other state-of-the-art models, effectively identifying and locating various types of weeds, thus demonstrating its significant practical application potential.

Fucoidan-Vegetable Oil Emulsion Applied to Myosin of Silver Carp: Effect on Protein Conformation and Heat-Induced Gel Properties.

How to improve the gel properties of protein has become a research focus in the field of seafood processing. In this paper, a fucoidan (FU)-vegetable oil emulsion was prepared, and the mechanism behind the effect of emulsion on protein conformation and the heat-induced gel properties was studied. The results revealed that the FU-vegetable oil complex caused the aggregation and cross-linking of myosin, as well as increased the surface hydrophobicity and total sulfhydryl content of myosin. In addition, the addition of the compound (0.3% FU and 1% vegetable oil) significantly improved the gel strength, hardness, chewiness, and water-holding capacity of the myosin gel (p < 0.05). In particular, when the addition of camellia oil was 1%, the gel strength, hardness, chewiness, and water-holding capacity had the highest values of 612.47 g.mm, 406.80 g, 252.75 g, and 53.56%, respectively. Simultaneously, the emulsion (0.3% FU-1% vegetable oil) enhanced the hydrogen bonds and hydrophobic interaction of the myosin gels. The image of the microstructure showed that the emulsion with 0.3% FU-1% vegetable oil improved the formation of the stable three-dimensional network structure. In summary, the FU-vegetable oil complex can promote unfolding of the protein structure and improve the gel properties of myosin, thus providing a theoretical basis for the development of functional surimi products.

Solution of the Steiner problem in the search for optimal network structure using population optimization methods

Subject of research: This paper considers an example of solving the Steiner problem for the design of utilities, in particular, power lines. Purpose of research: The study is electrical networks. The study is to assess the suitability of using genetic algorithms to find the network connection scheme with the lowest total active power losses. Methods and objects of research: Heuristic methods of population optimization are applied as a research method. Main results of research: As a result of the work, the suitability of applying genetic algorithms to solve the Steiner problem on the example of an electrical network to minimize total power losses has been evaluated.

Enhancing refrigerated chicken breasts preservation: Novel composite hydrogels incorporated with antimicrobial peptides, bacterial cellulose, and polyvinyl alcohol

Microbial contamination annually leads to substantial food resource loss. Effective food packaging can mitigate food contamination and waste, yet conventional materials such as plastics often lack bacteriostatic activity. This study aimed to synthesise FengycinA-M3@bacterial cellulose@polyvinyl alcohol composite hydrogels via dual cross-linking with hydrogen and borate bonding, with the goal of enhancing antibacterial properties and prolonging the preservation period of refrigerated chicken breast. The composite hydrogel was subjected to comprehensive characterisation for structural, mechanical, water absorption, slow peptide release, antimicrobial capacity, biocompatibility, and chicken breast freshness preservation. The results showed that the composite hydrogel had a porous network structure and excellent gel elasticity and biocompatibility. It was effective in inhibiting Staphylococcus aureus and Escherichia coli, and prolonged the storage time of frozen chicken breast for up to 12 days. These findings emphasise the potential of hydrogel food packaging to prolong storage periods and its suitability for food industry applications due to ease of manufacture.