Network Structure Research Articles

Mapping the Mpox discourse: A network and sentiment analysis

Mpox, a zoonotic disease re-emerging from animals to humans, poses a risk of evolving into a global pandemic due to its high infectivity and potential asymptomatic transmission. This study maps the structure and configuration of mpox-related discussions on Twitter/X, identifies key influencers and top hashtags, and analyzes public sentiment. Data were collected using NodeXL Pro from May 7, 2022, to January 15, 2023, with the keyword “Monkeypox” and visualized using Gephi. Social network analysis ranked nodes by betweenness centrality scores to highlight key communicators, and the YifanHu layout algorithm visualized the network. Influential users, source topics, and hashtags were identified, and sentiment analysis was conducted using Azure Machine Learning tools. The analysis identified 11,397 mpox-related tweets. The network structure resembled a community with diverse participants. Influential users included health and science journalists, writers, academics, medical doctors, and public figures. News media and organizational websites were the top information sources, emphasizing the need for accessible scientific information. “Monkeypox” and “Mpox” were the most prevalent hashtags. Negative sentiments dominated the discussion. This analysis provides insights into network structure, key influencers, information sources, and public sentiment, aiding tailored health initiatives to address misinformation and advocate valid health information and emergency responses.

Classification of Crack Coordinates through Wavelet - Analysis and CNN: Analysis of Data Preparation and Network Structure

For the two-dimensional localization of acoustic emission events (AE), artificial intelligence (AI) was applied. AE events were generated by breaking pencil leads on a granite plate measuring with dimensions of 50 cm × 50 cm × 6 cm. A total of 128 measurements were carried out at each of the 81 positions on the top surface of the granite plate. The recorded signals with 16 channels were processed using continuous wavelet transformation (CWT). The resulting RGB images served as input data for a convolutional neural network (CNN). The transformed images of the measured AE signals were converted into a four-dimensional input by considering them as deeper input, allowing for improved data capture. The result was analysed using a binary classifier according to the one-versus-all method. However, due to limited computational capacity, processing the four-dimensional data posed a challenge, resulting in significant programming effort. The results of this investigation provide insights into the potential of using AI for the localization of AE events.

High Resource Overlap and a Consistently Generalised Pattern of Interactions in a Bat-Flower Network in a Seasonally Dry Landscape.

Pollination is an ecosystem process that is crucial to maintain biodiversity and ecosystem function. Bats are important pollinators in the tropics and are an integral part of complex plant-pollinator interaction networks. However, network analysis-based approaches are still scarce at the plant species and bat community levels. We used metabarcoding to identify plant taxa present in pollen from fur and faecal samples collected across 1 year from three nectar-feeding bat roosts in central Mexico. We calculated the frequency of occurrence of plant taxa and assembled a zoocentric network of bat-plant interactions. We constructed a year-long network, encompassing the entire period of sampling, two seasonal networks comprising the wet and dry seasons, and six individual networks from sampling at two-month intervals across the year. Four species of nectar-feeding bats interacted with 36 plant species from 16 families. We found highly generalised interaction patterns across networks corresponding with opportunistic feeding behaviour by bats, with little seasonal variation in network structure. There was high resource overlap between bat species, and bats visited a diverse range of plant species even during periods with a high abundance of particular resources in the landscape. The diverse diet of nectar-feeding bats emphasises the importance of floristically rich natural habitats in the landscape to provide reliable foraging resources year-round in a seasonally variable system. While a generalised network structure is thought to increase robustness, further research is necessary to understand how fluctuations in pollinator abundance and diversity in the face of land use and climate change may impact bat-flower networks and the consequences to plant communities.

A novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical uncertainty: a real case study.

Organ transplantation is one of the most complicated and challenging treatments in healthcare systems. Despite the significant medical advancements, many patients die while waiting for organ transplants because of the noticeable differences between organ supply and demand. In the organ transplantation supply chain, organ allocation is the most significant decision during the organ transplantation procedure, and kidney is the most widely transplanted organ. This research presents a novel method for assessing the efficiency and ranking of qualified organ-patient pairs as decision-making units (DMUs) for kidney allocation problem in the existence of COVID-19 pandemic and uncertain medical and logistical data. To achieve this goal, two-stage network data envelopment analysis (DEA) and credibility-based chance constraint programming (CCP) are utilized to develop a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method. The main benefits of the developed method can be summarized as follows: considering internal structures in kidney allocation system, investigating both medical and logistical aspects of the problem, the capability of expanding to other network structures, and unique efficiency decomposition under uncertainty. Moreover, in order to evaluate the validity and applicability of the proposed approach, a validation algorithm utilizing a real case study and different confidence levels is used. Finally, the numerical results indicate that the developed approach outperforms the existing kidney allocation system.

Internet enterprise management, network structure and network capabilities: Basis for internet industry technology innovation framework

Internet enterprise management, network structure and network capabilities: Basis for internet industry technology innovation framework

Fractional-order spike timing dependent gradient descent for multi-layer spiking neural networks

Accumulated detailed knowledge about the neuronal activities in human brains has brought more attention to bio-inspired spiking neural networks (SNNs). In contrast to non-spiking deep neural networks (DNNs), SNNs can encode and transmit spatiotemporal information more efficiently by exploiting biologically realistic and low-power event-driven neuromorphic architectures. However, the supervised learning of SNNs still remains a challenge because the spike-timing-dependent plasticity (STDP) of connected spiking neurons is difficult to implement and interpret in existing backpropagation learning schemes. This paper proposes a fractional-order spike-timing-dependent gradient descent (FO-STDGD) learning model by considering a derived nonlinear activation function that describes the relationship between the quasi-instantaneous firing rate and the temporal membrane potentials of nonleaky integrate-and-fire neurons. The training strategy can be generalized to any fractional orders between 0 and 2 since the FO-STDGD incorporates the fractional gradient descent method into the calculation of spike-timing-dependent loss gradients. The proposed FO-STDGD model is tested on the MNIST and DVS128 Gesture datasets and its accuracy under different network structure and fractional orders is analyzed. It can be found that the classification accuracy increases as the fractional order increases, and specifically, the case of fractional order 1.9 improves by 155% relative to the case of fractional order 1 (traditional gradient descent). In addition, our scheme demonstrates the state-of-the-art computational efficacy for the same SNN structure and training epochs.

Important role of Chloroflexi: Improved the cooperation of anammox community under PFOS stress

Perfluorooctane sulfonic acid (PFOS) has received extensive attention in recent years due to its persistence and biotoxicity. As an advanced biological nitrogen removal process, anaerobic ammonium oxidation (anammox) is considered to be the most promising process to replace traditional nitrogen removal technology in the future. However, it is still unclear how PFOS affects microbial community and network structure in anammox system. The study found that PFOS had little influence on the nitrogen removal efficiency. Through microbial community and network analysis, the results showed that the typical anammox bacteria (Candidatus_Kuenenia, Candidatus_Jettenia, and Candidatus_Brocadia) enriched with the increasing concentrations of PFOS. Overall network complexity and proportion of positive links was significantly increased under 10 mg/L PFOS. Especially the subnetwork of Chloroflexi owned 100 % positive links under 10 mg/L PFOS, which accounted for 53.62 % of the corresponding overall network. The genus OLB13 and norank_f__Anaerolineaceae from Chloroflexi could cooperate with anammox bacteria to resist the impact of PFOS and further maintained the stability of anammox system. This study provided new insights into the effects of perfluoroalkyl compounds on anammox system.

Substitutional/positional disorder of biguanide and guanylurea in the structure of a decavanadate complex [(Bg)(HV10O285-)]0.4[(HGU+)(V10O286-)]0.6(H2Met2+)2(H3O+)·8H2O.

A hydrated salt of decavanadate containing diprotonated metforminium(2+) (H2Met2+), hydronium (H3O+) and either neutral biguanide (Bg) or monoprotonated guanylurea (HGU+) exhibits a previously seen complex charge-stabilized hydrogen-bonded network [Chatkon et al. (2022). Acta Cryst. B78, 798-808]. Charge balance is achieved in two ways through substitutional disorder: a 0.6 occupied HGU+ cation is paired with a V10O286- anion, and a 0.4 occupied neutral Bg molecule is paired with a HV10O285- anion, with the remaining charge in both cases balanced by two H2Met2+ dications and one H3O+ monocation. Bg/HGU+ moieties exhibit bifurcated N-H...O hydrogen bonding to the H3O+ cation and are substitutionally/positionally disordered along with the H3O+ cation about an inversion center. The HGU+ V10O286- synthon seen in the previous study occurs again. Bg exhibits bifurcated hydrogen bonding from two amino groups to two rows of cluster O atoms running diagonally across the equatorial plane of the HV10O285- anion with a return hydrogen bond from the cluster H atom to the imino N atom of the Bg. Thus, a Bg...cluster synthon similar to the HGU+...cluster synthon previously reported is found. The disordered moieties occupy spaces with excess volume in the 3-D network structure. Interestingly, when the crystallographic unit cell of the current compound, whose X-ray data was collected at 100 K, is compared with that of a previous compound exhibiting the same supramolecular framework, unit-cell parameter c does not shorten as a and b expectantly do because of the lower data collection temperature. The lack of contraction on unit-cell parameter c is possibly due to the supramolecular structure.

A Coupled Spatial-Network Model: A Mathematical Framework for Applications in Epidemiology.

There is extensive evidence that network structure (e.g., air transport, rivers, or roads) may significantly enhance the spread of epidemics into the surrounding geographical area. A new compartmental modeling framework is proposed which couples well-mixed (ODE in time) population centers at the vertices, 1D travel routes on the graph's edges, and a 2D continuum containing the rest of the population to simulate how an infection spreads through a population. The edge equations are coupled to the vertex ODEs through junction conditions, while the domain equations are coupled to the edges through boundary conditions. A numerical method based on spatial finite differences for the edges and finite elements in the 2D domain is described to approximate the model, and numerical verification of the method is provided. The model is illustrated on two simple and one complex example geometries, and a parameter study example is performed. The observed solutions exhibit exponential decay after a certain time has passed, and the cumulative infected population over the vertices, edges, and domain tends to a constant in time but varying in space, i.e., a steady state solution.

Effect of ZnO/Li2O ratio on the structure and properties of Li2O-ZnO-SiO2-P2O5 glass-ceramics sealants

In order to meet the sealing temperature of copper alloy below 1000 °C, a kind of Li2O-ZnO-SiO2-P2O5-(B2O3+Al2O3+K2O) (LZS) glass-ceramics with high expansion coefficient and high resistivity was synthesized via the melting method and then crystallized through controlled cooling. The network structure, thermal properties, crystal phase transition, thermal expansion coefficient, and high-temperature resistivity have been investigated. As the mass ratio of ZnO/Li2O increases, the network structure of the LZS glass is strengthened, leading to an increase in the glass transition and crystallization temperature. Moreover, the predominant crystal phases in LZS glass-ceramics transit from Li2SiO3 to Zn[Zn0.1Li0.6Si0.3]SiO4 and subsequently to Zn2SiO4. At a ZnO/Li2O ratio of 3, a cristobalite phase with a high expansion coefficient emerges. The change in crystal phase results in a variation in the coefficient of thermal expansion (CTE) of glass-ceramics, ranging from 13.2 to 11.2×10-6 K-1. At a ZnO/ Li2O ratio of 3, the softening temperature of glass-ceramics is minimized. Moreover, as the ZnO/Li2O mass ratio increases, the resistance of crystallized glass decreases first and then increases. The maximum resistivity is achieved at a ZnO/Li2O mass ratio of 5. By optimizing the mass ratio of ZnO/Li2O, it is determined that when the mass ratio of ZnO/Li2O is 3, the CTE is 12.4×10-6 K-1, and the softening temperature (Ts) is 679.8 °C, which is the best material for sealing applications.

Higher-order Network Information Propagation Model Based on Social Impact Theory

User relationships are becoming increasingly diverse in current online social networks. This paper utilizes simplicial complexes in higher-order networks to depict multiple user relationships and characterize the topology of social networks. By employing social impact theory and factors such as user interest and information aging to define state transition rules among users, we establish a UAPR information propagation model based on simplicial complexes to simulate the information propagation process in higher-order social networks. The proposed model is simulated on three types of synthetic networks and four real-world simplicial complexes, demonstrating its ability to accurately describe the dynamics and influences of information propagation. Furthermore, our results indicate that different network structures, user interest values, information aging speeds, and intimacy levels significantly influence the information transmission process.

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.

Modeling and analysis of COVID-19 spreading based on complex network theory

Complex networks can effectively describe interactions within real-world complex systems. In researches of epidemic spreading, scientists constructed various physical contact networks between individuals on the microscopic scale and the metapopulation networks on the macroscopic scale. These different types of network structures significantly impact the propagation dynamics of epidemic in human society. For instance, population flows in global airline networks influence the speed and arrival time of epidemics across large-scale space. In this paper we review the epidemic spreading models on various network structures, including fully mixed networks, three types of lower-order networks, three types of higher-order networks, metapopulation networks, and multiple strains competitive epidemic spreading models. We also provide an overview of the application of complex network theory in the COVID-19 pandemic, covering topics of prediction, prevention, and control of the epidemic. Finally, we discuss the strengths and limitations of these models and propose perspectives for future research.

Construction, Assessment, and Protection of Green Infrastructure Networks from a Dynamic Perspective: A Case Study of Dalian City, Liaoning Province, China

The green infrastructure (GI) network, an important nature-based solution (NBS) strategy, is pivotal for sustainable urban development. However, current research perspectives focus on constructing a static GI network, and research on evaluating and protecting GI networks in the context of spatiotemporal changes has been limited. This study aims to comprehensively characterise the spatio-temporal changes in the GI network and protect its efficiency. We selected Dalian City as a case study and applied the Future Land-use Simulation (FLUS) model to predict its land use for 2030 and 2040. Based on predicted and historical land-use data, the 1990–2040 GI network of Dalian City was constructed utilising the Integrated valuation of ecosystem services and trade-offs (InVEST), morphological spatial pattern analysis (MSPA) and minimum cumulative resistance (MCR) model, while its spatiotemporal changes were evaluated. The results revealed that the hub area exhibited positive growth over time, whereas the link lengths showed an opposite trend. Furthermore, the overall position of the GI network shifted toward the southwest. The flatness indicator detected that the direction of the GI network shape gradually became less pronounced. Consequently, the structure of the GI network shifted from multi-centre to mono-centre. Additionally, priority protection for hubs is mainly in the northeast, while links are primarily in the coastal regions. The urgency of protection for both increases over time. The study developed a dynamic GI network construction and assessment method, providing a scientific basis and reference for future rational GI network planning, ecological protection planning, and related land policy formulation.

Structure of plant-frugivorous bird interaction networks in two high Andean forests of southwestern Colombia

Structure of plant-frugivorous bird interaction networks in two high Andean forests of southwestern Colombia

Australia's recently established predators restore complexity to food webs simplified by extinction.

Since prehistory, humans have altered the composition of ecosystems by causing extinctions and introducing species. However, our understanding of how waves of species extinctions and introductions influence the structure and function of ecological networks through time remains piecemeal. Here, focusing on Australia, which has experienced many extinctions and introductions since the Late Pleistocene, we compared the functional trait composition of Late Pleistocene (130,00-115,000 years before present [ybp]), Holocene (11,700-3,000 ybp), and current Australian mammalian predator assemblages (≥70% vertebrate meat consumption; ≥1kg adult body mass). We then constructed food webs for each period based on estimated prey body mass preferences. We found that introduced predators are functionally distinct from extinct Australian predators, but they rewire food webs toward a state that closely resembles the Late Pleistocene, prior to the megafauna extinctions. Both Late Pleistocene and current-day food webs consist of an apex predator and three smaller predators. This leads to food web networks with a similar total number of links, link densities, and compartmentalizations. However, this similarity depends on the presence of dingoes: in their absence, food webs become simplified and reminiscent of those following the Late Pleistocene extinctions. Our results suggest that recently established predators, even those implicated in species extinctions and declines, can restore complexity to food webs simplified by extinction.

Cluster partition-fuzzy broad learning-based fast detection and localization framework for false data injection attack in smart distribution networks

The distributed renewable energy generations, as accessible and easily targets for attackers, introduce an extra false data injection attack (FDIA) threat in the smart distribution networks. Scattered attack points and complex attack features hinder the elimination of potential threats. In this context, an FDIA fast detection and pinpoint localization framework is proposed. This framework identifies abnormal signals and attacked nodes from the unique topology structure and status contiguity of smart distribution networks, namely, spatial-temporal correlations of power grids, by using a cluster partition-fuzzy broad learning system (CP-FBLS). Unlike most existing FDIA detection methods, which are dedicated to high accuracy but neglect the urgent need for rapid detection in smart distribution networks, the proposed CP-FBLS framework maintains the fast computational nature of a fuzzy broad learning system (FBLS), while avoiding the accuracy degradation caused by high-dimension of data in large-scale smart distribution networks. Moreover, the multi-layer structure of the proposed framework recognizes the location of FDIA, bridging the research gap of attack localization. To comprehensively evaluate the proposed strategy, datasets containing various FDIA types are constructed. Numerical simulations based on the above datasets in modified IEEE 34-bus and 123-bus distribution systems are implemented. The results of the case studies showed that the proposed method can achieve 98.43% accuracy with 0.34ms detection time, realizing rapid detection and localization of various FDIAs with satisfactory accuracy.

Improving I-ELM structure through optimal addition of hidden nodes: Compact I-ELM

Incremental extreme learning machines (I-ELMs) can automatically determine the structure of neural networks and achieve high learning speeds. However, during the process of adding hidden nodes, unnecessary hidden nodes that have little relevance to the target may be added. Several studies have proposed methods to overcome this problem by measuring the relevance between hidden nodes and outputs and adding or removing hidden nodes accordingly. Random hidden nodes have the advantage of creating diverse patterns, but they encounter a problem in which hidden nodes that generate patterns with little or no relevance to the target can be added, thereby increasing the number of hidden nodes. Unlike in existing I-ELMs, which use random hidden nodes, we propose a compact I-ELM algorithm that initially adds linear regression nodes and subsequently applies a method to ensure that the hidden nodes have patterns differing from the existing ones. Based on benchmark data, we confirmed that the proposed method constructs a compact neural network structure with fewer hidden nodes compared to the existing I-ELM systems.

Fault location method for new distribution networks based on waveform matching of time–frequency travelling waves

AbstractSome existing fault location methods for distribution networks rely too much on the local wave head information of the time or frequency domain signals, making it difficult to adapt to the increasingly complex structure and operating conditions of the distribution network after the new energy access. For improvement, the travelling wave (TW) time and frequency ranges that can effectively avoid waveform distortion caused by new energy access are analysed. The one‐to‐one matching relationship between the TW waveforms in these ranges and the fault positions is revealed. A fault TW time–frequency matrix with specific time and frequency windows is constructed, and a new distribution network fault location method is proposed based on the matching technique of waveform features, which realises the accurate fault location by exploring the proportionality between the cumulative trend of the matrix energy amplitude deviation and the fault point position. Simulation test results show that the proposed method is not affected by the complex structure of distribution networks such as new energy access and overhead‐cable line mixing on fault location and flexibly transforms the fault location problem into a time–frequency TW waveform matching problem, which improves the accuracy and robustness of the fault location for new distribution networks to a degree.

Fasteners quantitative detection and lightweight deployment based on improved YOLOv8.

Currently, research on on-board real-time quantitative detection of rail fasteners is few. Therefore, this paper proposes and validates an improved YOLOv8 based method for quantitative detection of rail fasteners, leveraging the capabilities of edge miniaturized artificial intelligence (AI) computing devices. First, the lightweight MobileNetV3 is employed as the backbone network for our model to increase detection speed, and the SA attention mechanism is integrated at the end of the backbone network to enhance the feature of the fasteners. Then the deformable convolution is introduced to reconstruct the bottleneck structure of the neck network, which can segment fasteners without compromising accuracy. Subsequently, the optimized network model is utilized on a Jetson AGX Xavier edge AI computing device by the TensorRT acceleration method. Segmentation results are then extracted at the pixel level for quantitative analysis of fastener breakage degree and deflection angle, so as to correct the detection results. Experimental results show that the size of the improved lightweight network volume is reduced by 28% compared to the original YOLOv8 model, and the frame rate on the edge AI computing device is lifted by 71.87%, i.e., 55 f/s. Furthermore, the model is refined based on quantitative analysis results, resulting in an mAP0.5 of 97.0%, and real-time quantitative detection of rail fasteners is realized.