Dominance of rock exposure and soil depth in leaf trait networks outweighs soil quality in karst limestone and dolomite habitats

Leaf trait networks(LTNs)visualize the intricate linkages reflecting plant trait-functional coordination.Typical karst vegetation,developed from lithological dolomite and limestone,generally exhibits differential communities,possibly due to habitat rock exposure,soil depth,and soil physicochemical properties variations,leading to a shift from plant trait variation to functional linkages.However,how soil and habitat quality affect the differentiation of leaf trait networks remains unclear.LTNs were constructed for typical dolomite and limestone habitats by analyzing twenty-one woody plant leaf traits across fifty-six forest subplots in karst mountains.The differences between dolomite and limestone LTNs were compared using network parameters.The network association of soil and habitat quality was analyzed using redundancy analysis(RDA),Mantle's test,and a random forest model.The limestone LTN exhibited significantly higher edge density with lower diameter and average path length when compared to the dolomite LTN.It indicates LTN differentiation,with the limestone network displaying a more compact architecture and higher connectivity than the dolomite network.The specific leaf phosphorus and leaf nitrogen contents of dolomite LTN,as well as the leaf mass and leaf carbon contents of limestone LTN,significantly contributed to network degree and closeness,serving as crucial node traits regulating LTN connectedness.Additionally,both habitat LTNs significantly correlated with soil nitrogen and phosphorus,stoichiometric ratios,pH,and organic carbon,as well as soil depth and rock exposure rates,with soil depth and rock exposure showing greater relative importance.Soil depth and rock exposure dominate trait network differentiation,with the limestone habitat exhibiting a more compact network architecture than the dolomite habitat.

Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

Outage Probability Analysis for D2D-Enabled Heterogeneous Cellular Networks with Exclusion Zone:A Stochastic Geometry Approach

Interference management is one of the most important issues in the device-to-device(D2D)-enabled heterogeneous cellular networks(HetCNets)due to the coexistence of massive cellular and D2D devices in which D2D devices reuse the cellular spectrum.To alleviate the interference,an efficient interference management way is to set exclusion zones around the cellular receivers.In this paper,we adopt a stochastic geometry approach to analyze the outage probabilities of cellular and D2D users in the D2D-enabled HetCNets.The main difficulties contain three aspects:1)how to model the location randomness of base stations,cellular and D2D users in practical networks;2)how to capture the randomness and interrelation of cellular and D2D transmissions due to the existence of random exclusion zones;3)how to characterize the different types of interference and their impacts on the outage probabilities of cellular and D2D users.We then run extensive Monte-Carlo simulations which manifest that our theoretical model is very accurate.

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions

Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.

Multiscale Characteristics and Connection Mechanisms of Attraction Networks:A Trajectory Data Mining Approach Leveraging Geotagged Data

Urban tourism is considered a complex system,and multiscale exploration of the organizational patterns of attraction networks has become a topical issue in urban tourism,so exploring the multiscale characteristics and connection mechanisms of attraction networks is important for understanding the linkages between attractions and even the future destination planning.This paper uses geotagging data to compare the links between attractions in Beijing,China during four different periods:the pre-Olympic period(2004–2007),the Olympic Games and subsequent‘heat period’(2008–2013),the post-Olympic period(2014–2019),and the COVID-19(Corona Virus Disease 2019)pandemic period(2020–2021).The aim is to better understand the evolution and patterns of attraction networks at different scales in Beijing and to provide insights for tourism planning in the destination.The results show that the macro,meso-,and microscales network characteristics of attraction networks have inherent logical relationships that can explain the commonalities and differences in the development process of tourism networks.The macroscale attraction network degree Matthew effect is significant in the four different periods and exhibits a morphological monocentric structure,suggesting that new entrants are more likely to be associated with attractions that already have high value.The mesoscale links attractions according to the common purpose of tourists,and the results of the community segmentation of the attraction networks in the four different periods suggest that the functional polycentric structure describes their clustering effect,and the weak links between clusters result from attractions bound by incomplete information and distance,and the functional polycentric structure with a generally more efficient network of clusters.The pattern structure at the microscale reveals the topological transformation relationship of the regional collaboration pattern,and the attraction network structure in the four different periods has a very similar importance profile structure suggesting that the attraction network has the same construction rules and evolution mechanism,which aids in understanding the attraction network pattern at both macro and micro scales.Important approaches and practical implications for planners and managers are presented.

Exploring reservoir computing:Implementation via double stochastic nanowire networks

Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

Protocol-Based Non-Fragile State Estimation for Delayed Recurrent Neural Networks Subject to Replay Attacks

Dear Editor, This letter focuses on the protocol-based non-fragile state estimation problem for a class of recurrent neural networks(RNNs). With the development of communication technology, the networked systems have received particular attentions. The networked system brings advantages such as easy to implement.

Dynamical behaviors in discrete memristor-coupled small-world neuronal networks

The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each other.The memory characteristic of memristors makes them suitable for simulating neuronal synapses with plasticity.In this paper,a memristor is used to simulate a synapse,a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is explored.We explore the influence of system parameters on the dynamical behaviors of the discrete small-world network,and the system shows a variety of firing patterns such as spiking firing and triangular burst firing when the neuronal parameterαis changed.The results of a numerical simulation based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network,and the higher the reconnection probability and number of the nearest neurons,the more significant the synchronization state of the neurons.In addition,by increasing the coupling strength of memristor synapses,synchronization performance is promoted.The results of this paper can boost research into complex neuronal networks coupled with memristor synapses and further promote the development of neuroscience.

Resilient Satellite Communication Networks Towards Highly Dynamic and Highly Reliable Transmission

As the key infrastructure of space-ground integrated information networks,satellite communication networks provide high-speed and reliable information transmission.In order to meet the burgeoning service demands of the IoT and the Internet,the low-latency LEO satellite network has developed rapidly.However,LEO satellites face inherent problems such as small coverage,fast moving speed and short overhead time,which will be more severe when serving high-dynamic users,e.g.high-speed rails and airplanes.The heterogeneous network composed of GEO,MEO and LEO satellites can provide various services,whose network management and resource allocation are also more challenging.

Hybrid Seagull and Whale Optimization Algorithm-Based Dynamic Clustering Protocol for Improving Network Longevity in Wireless Sensor Networks

Energy efficiency is the prime concern in Wireless Sensor Networks(WSNs) as maximized energy consumption without essentially limits the energy stability and network lifetime. Clustering is the significant approach essential for minimizing unnecessary transmission energy consumption with sustained network lifetime. This clustering process is identified as the Non-deterministic Polynomial(NP)-hard optimization problems which has the maximized probability of being solved through metaheuristic algorithms.This adoption of hybrid metaheuristic algorithm concentrates on the identification of the optimal or nearoptimal solutions which aids in better energy stability during Cluster Head(CH) selection. In this paper,Hybrid Seagull and Whale Optimization Algorithmbased Dynamic Clustering Protocol(HSWOA-DCP)is proposed with the exploitation benefits of WOA and exploration merits of SEOA to optimal CH selection for maintaining energy stability with prolonged network lifetime. This HSWOA-DCP adopted the modified version of SEagull Optimization Algorithm(SEOA) to handle the problem of premature convergence and computational accuracy which is maximally possible during CH selection. The inclusion of SEOA into WOA improved the global searching capability during the selection of CH and prevents worst fitness nodes from being selected as CH, since the spiral attacking behavior of SEOA is similar to the bubble-net characteristics of WOA. This CH selection integrates the spiral attacking principles of SEOA and contraction surrounding mechanism of WOA for improving computation accuracy to prevent frequent election process. It also included the strategy of levy flight strategy into SEOA for potentially avoiding premature convergence to attain better trade-off between the rate of exploration and exploitation in a more effective manner. The simulation results of the proposed HSWOADCP confirmed better network survivability rate, network residual energy and network overall throughput on par with the competitive CH selection schemes under different number of data transmission rounds.The statistical analysis of the proposed HSWOA-DCP scheme also confirmed its energy stability with respect to ANOVA test.

Singular optical propagation properties of two types of one-dimensional anti-PT-symmetric periodic ring optical waveguide networks

Two types of one-dimensional(1D)anti-PT-symmetric periodic ring optical waveguide networks,consisting of gain and loss materials,are constructed.The singular optical propagation properties of these networks are investigated.The results show that the system composed of gain materials exhibits characteristics of ultra-strong transmission and bidirectional reflection.Conversely,the system composed of loss materials demonstrates equal transmittance and reflectance at some frequencies.In both the systems,a new type of total reflection phenomenon is observed.When the imaginary part of the refractive indices of waveguide segments is smaller than 10-5,the system shows bidirectional transparency with the transmittance tending to be 1 and reflectivity to be smaller than 10-8 at some bands.When the refractive indices of the waveguide segments are real,the system will be bidirectional transparent at the full band.These findings may deepen the understanding of anti-PT-symmetric optical systems and optical waveguide networks,and possess potential applications in efficient optical energy storage,ultra-sensitive optical filters,ultra-sensitive all-optical switches,integrated optical chips,stealth physics,and so on.

Graph theoretical analysis of limestone fracture network damage patterns based on uniaxial compression test

The topological attributes of fracture networks in limestone,subject to intense hydrodynamics and intricate geological discontinuities,substantially influence the mechanical and hydraulic characteristics of the rock mass.The dynamical evolution of fracture networks under stress is crucial for unveiling the interaction patterns among fractures.However,existing models are undirected graphs focused on stationary topology,which need optimization to depict fractures'dynamic development and rupture process.To compensate for the time and destruction terms,we propose the damage network model,which defines the physical interpretation of fractures through the ternary motif.We focus primarily on the evolution of node types,topological attributes,and motifs of the fracture network in limestone under uniaxial stress.Observations expose the varying behavior of the nodes'self-dynamics and neighbors'adjacent dynamics in the fracture network.This approach elucidates the impact of micro-crack behaviors on large brittle shear fractures from a topological perspective and further subdivides the progressive failure stage into four distinct phases(isolated crack growth phase,crack splay phase,damage coalescence phase,and mechanical failure phase)based on the significance profile of the motif.Regression analysis reveals a positive linear and negative power correlation between fracture network density and branch number to the rock damage resistance,respectively.The damage network model introduces a novel methodology for depicting the interaction of two-dimensional(2D)projected fractures,considering the dynamic spatiotemporal development characteristics and fracture geometric variation.It helps dynamically characterize properties such as connectivity,permeability,and damage factors while comprehensively assessing damage in rock mass fracture networks.

A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and convergence.In this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration.Due to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward functions.Regarding the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the flows.In addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+MADDPG.Introducing a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network environment.The LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

Impact of asymptomatic infected individuals on epidemic transmission dynamics in multiplex networks with partial coupling

The theory of network science has attracted great interest of many researchers in the realm of biomathematics and public health,and numerous valuable epidemic models have been developed.In previous studies,it is common to set up a one-to-one correspondence between the nodes of a multi-layer network,ignoring the more complex situations in reality.In the present work,we explore this situation by setting up a partially coupled model of a two-layer network and investigating the impact of asymptomatic infected individuals on epidemics.We propose a self-discovery mechanism for asymptomatic infected individuals,taking into account situations such as nucleic acid testing in the community and individuals performing self-antigen testing during the epidemic.Considering these factors together,through the microscopic Markov chain approach(MMCA)and extensive Monte Carlo(MC)numerical simulations,we find that the greater the coupling between the networks,the more information dissemination is facilitated.In order to control the epidemics,more asymptomatic infected individuals should be made aware of their infection.Massive adoption of nucleic acid testing and individual adoption of antigenic self-testing can help to contain epidemic outbreaks.Meanwhile,the epidemic threshold of the proposed model is derived,and then miscellaneous factors affecting the epidemic threshold are also discussed.Current results are conducive to devising the prevention and control policies of pandemics.

EECLP: AWireless Sensor Networks Energy Efficient Cross-Layer Protocol

Recent advancements in wireless communications have allowed the birth of novel wireless sensor networks(WSN).A sensor network comprises several micro-sensors deployed randomly in an area of interest.A micro-sensor is provided with an energy resource to supply electricity to all of its components.However,the disposed energy resource is limited and battery replacement is generally infeasible.With this restriction,the sensors must conserve energy to prolong their lifetime.Various energy conservation strategies for WSNs have been presented in the literature,from the application to the physical layer.Most of these solutions focus only on optimizing a single layer in terms of energy consumption.In this research,a novel cross-layer technique for WSNs’effective energy usage is presented.Because most energy consumption factors exist in the Medium Access Control(MAC)layer and network layer,our EECLP protocol(Energy Efficient Cross-Layer Protocol for Wireless Sensor Networks)integrates these two layers to satisfy energy efficiency criteria.To gain access to the transmission channel,we implement a communication regime at the MAC layer based on CSMA/CA(Carrier Sense Multiple Access/Collision Avoidance)techniques.Next,depending on the activity and a standby period,we employ the RTS/CTS(Request to Send/Clear to Send)method to prevent collisions and resolve hidden node concerns by utilizing the network allocation vector(NAV)to calculate the transmission duration.Employing a greedy strategy,we establish chains amongst cluster members to mitigate the issue of high energy consumption in routing data.An objective function was utilized to determine the optimal cross-chain path based on the distances to the base station(BS)and residual energy(RE).The simulation,testing,and comparison of the proposed protocol to peer protocols have shown superior outcomes and a prolonged network lifespan.Using the suggested protocol,the network lifetime increases by 10%compared to FAMACO(Fuzzy and Ant Colony Optimization based MAC/Routing Cross-layer)protocol,and it increases by 90%and 95%compared to IFUC(Improved Fuzzy Unequal Clustering)and UHEED(Unequal Hybrid Energy Efficient and Distributed)protocols successively.

Effects of different doses of glucose and fructose on central metabolic pathways and intercellular wireless communication networks in humans

Fructose and glucose are often widely used in food processing and may contribute to many metabolic diseases.To observe the effects of different doses of glucose and fructose on human metabolism and cellular communication,volunteers were given low,medium,and high doses of glucose and fructose.Serum cytokines,glucose,lactate,nicotinamide adenine dinucleotide(NADH)and metabolic enzymes were assayed,and central carbon metabolic pathway networks and cytokine communication networks were constructed.The results showed that the glucose and fructose groups basically maintained the trend of decreasing catabolism and increasing anabolism with increasing dose.Compared with glucose,low-dose fructose decreased catabolism and increased anabolism,significantly enhanced the expression of the inflammatory cytokine interferon-γ(IFN-γ),macrophage-derived chemokine(MDC),induced protein-10(IP-10),and eotaxin,and significantly reduced the activity of isocitrate dehydrogenase(ICDH)and pyruvate dehydrogenase complexes(PDHC).Both medium and high doses of fructose increase catabolism and anabolism,and there are more cytokines and enzymes with significant changes.Furthermore,multiple cytokines and enzymes show strong relevance to metabolic regulation by altering the transcription and expression of enzymes in central carbon metabolic pathways.Therefore,excessive intake of fructose should be reduced to avoid excessive inflammatory responses,allergic reactions and autoimmune diseases.