Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors(TFs)in intricate regulatory networks in a cell-type specific manner.Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings.This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets,addressing batch effects and conserving biological variance.This integration spans a broad spectrum of tissues,including both below-and above-ground parts.Utilizing a rigorous approach for cell type annotation,we identified 47 distinct cell types or states,largely expanding our current view of plant cell compositions.We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression.Taken together,our study not only offers extensive plant cell atlas exploration that serves as a valuable resource,but also provides molecular insights into gene-regulatory programs that varies from different cell types.

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.

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

Early warning of core network capacity in space-terrestrial integrated networks

With the rapid development of low-orbit satellite com-munication networks both domestically and internationally,space-terrestrial integrated networks will become the future development trend.For space and terrestrial networks with limi-ted resources,the utilization efficiency of the entire space-terres-trial integrated networks resources can be affected by the core network indirectly.In order to improve the response efficiency of core networks expansion construction,early warning of the core network elements capacity is necessary.Based on the inte-grated architecture of space and terrestrial network,multidimen-sional factors are considered in this paper,including the number of terminals,login users,and the rules of users’migration during holidays.Using artifical intelligence(AI)technologies,the regis-tered users of the access and mobility management function(AMF),authorization users of the unified data management(UDM),protocol data unit(PDU)sessions of session manage-ment function(SMF)are predicted in combination with the num-ber of login users,the number of terminals.Therefore,the core network elements capacity can be predicted in advance.The proposed method is proven to be effective based on the data from real network.

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

Average Secrecy Capacity of the Reconfigurable Intelligent Surface-Assisted Integrated Satellite Unmanned Aerial Vehicle Relay Networks

Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.

Sensing and Communication Integrated Fast Neighbor Discovery for UAV Networks

In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.

Covert Communication in Integrated High Altitude Platform Station Terrestrial Networks

In recent years,Internet of Things(IoT)technology has emerged and gradually sprung up.As the needs of largescale IoT applications cannot be satisfied by the fifth generation(5G)network,wireless communication network needs to be developed into the sixth generation(6G)network.However,with the increasingly prominent security problems of wireless communication networks such as 6G,covert communication has been recognized as one of the most promising solutions.Covert communication can realize the transmission of hidden information between both sides of communication to a certain extent,which makes the transmission content and transmission behavior challenging to be detected by noncooperative eavesdroppers.In addition,the integrated high altitude platform station(HAPS)terrestrial network is considered a promising development direction because of its flexibility and scalability.Based on the above facts,this article investigates the covert communication in an integrated HAPS terrestrial network,where a constant power auxiliary node is utilized to send artificial noise(AN)to realize the covert communication.Specifically,the covert constraint relationship between the transmitting and auxiliary nodes is derived.Moreover,the closed-form expressions of outage probability(OP)and effective covert communication rate are obtained.Finally,numerical results are provided to verify our analysis and reveal the impacts of critical parameters on the system performance.

Integration strategies of hydrogen network in a refinery based on operational optimization of hydrotreating units

Inferior crude oil and fuel oil upgrading lead to escalating increase of hydrogen consumption in refineries.It is imperative to reduce the hydrogen consumption for energy-saving operations of refineries.An integration strategy of hydrogen network and an operational optimization model of hydrotreating(HDT)units are proposed based on the characteristics of reaction kinetics of HDT units.By solving the proposed model,the operating conditions of HDT units are optimized,and the parameters of hydrogen sinks are determined by coupling hydrodesulfurization(HDS),hydrodenitrification(HDN)and aromatic hydrogenation(HDA)kinetics.An example case of a refinery with annual processing capacity of eight million tons is adopted to demonstrate the feasibility of the proposed optimization strategies and the model.Results show that HDS,HDN and HDA reactions are the major source of hydrogen consumption in the refinery.The total hydrogen consumption can be reduced by 18.9%by applying conventional hydrogen network optimization model.When the hydrogen network is optimized after the operational optimization of HDT units is performed,the hydrogen consumption is reduced by28.2%.When the benefit of the fuel gas recovery is further considered,the total annual cost of hydrogen network can be reduced by 3.21×10~7CNY·a^(-1),decreased by 11.9%.Therefore,the operational optimization of the HDT units in refineries should be imposed to determine the parameters of hydrogen sinks base on the characteristics of reaction kinetics of the hydrogenation processes before the optimization of the hydrogen network is performed through the source-sink matching methods.

Integration and development of energy and information network in the Pan-Arctic region

The Global Energy Interconnection is an important strategic approach used to achieve efficient worldwide energy allocation.The idea of developing integrated power,information,and transportation networks provides increased power interconnection functionality and meaning,helps condense forces,and accelerates the integration of global infrastructure.Correspondingly,it is envisaged that it will become the trend of industrial technological development in the future.In consideration of the current trend of integrated development,this study evaluates a possible plan of coordinated development of fiber-optic and power networks in the Pan-Arctic region.Firstly,the backbone network architecture of Global Energy Interconnection is introduced and the importance of the Arctic energy backbone network is confirmed.The energy consumption and developmental trend of global data centers are then analyzed.Subsequently,the global network traffic is predicted and analyzed by means of a polynomial regression model.Finally,in combination with the current construction of fiber-optic networks in the Pan-Arctic region,the advantages of the integration of the fiber-optic and power networks in this region are clarified in justification of the decision for the development of a Global Energy Interconnection scheme.

Integration of energy recovery network including recycling residual pressure energy with pinch technology

Work exchange is a promising innovative technology in recovering residual pressure energy. However, at the systematic level, the comprehensive utilization of different energy resources in an energy system has become an issue of concern. In this work, a systematic approach is proposed, one that successively integrates heat, work and adjusts operation parameters. A detailed procedure for building a heat-work coupling transfer network is provided. The synthesis mainly consists of constructing a work exchange sub-network with pinch analysis based on positive displacement type work exchangers. Simultaneously, another kind of sub-network based on turbine-type work exchangers is built as a schematic comparison. The influence of applying a positive displacement work exchanger on the system is investigated. Finally, as a case study, a renovation design of a typical rectisol process in the coal-water slurry gasification section of an ammonia plant is presented. The results show that the added work exchanger has little impact on the existing heat exchange sub-network. Moreover,extra pressure energy is recovered by coupling the transfer network. It is concluded that the heat-work systematic design is a promising and powerful method to use energy more efficiently.

Study on Security Technologies for Wireles Network Integration

The network integration provides users with a new network with long connection time and a high data rate when needed, but it also brings the defects of all the networks that integrate together into the integrated network. This will cause all kinds of existing and some new security problems in the operation of the integrated network. A complete protection based on recovery is proposed in the paper. It uses the public-key algorithm to authorize and private-key algorithm to encrypt the communicating data. This solution can provide the system with reliable security, and avoid Denial of Service (DoS) of the user. This solution has been proposed lately, and we should further identify the correct action of all the layers and figure out how to react when a legal node is framed by multiple malicious nodes.

Structural Reliability Analysis Based on Support Vector Machine and Dual Neural Network Direct Integration Method

Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.

On the Motivation and Reasons of Popularity of Parody in Network Language From the Perspective of Conceptual Integration Theory

The Conceptual Integration Theory was first formally put forward in 1997 by Fauconnier and Turner. According to it, there is a conceptual blending network comprised of four mental spaces： Input space Ⅰ, Input space Ⅱ, generic space, and blended space. In the process of blending, common information or structures from input spaces are projected to the ＂generic space＂. Meanwhile, through partially cross-space mapping, those structures are selectively projected to the ＂blended space＂. By means of composition, completion, and elaboration, consequently ＂emergent structure＂ comes into being from the development of blending. This theory instantly became a fresh power in cognitive research field. With the rapid development of network technology and the popularization of the internet, network language makes tremendous progresses and spreads quickly, which reflects the social and cultural development. The uniqueness and effectiveness of network language creation, to a great extent, relies on various rhetorical devices, among which parody is frequently used and plays an important role. In recent years, studies about network language somehow concentrate a lot on the construction, word transformation, and features of network vocabulary, and cognitive analysis on the mechanism of parody in network language is rather limited and requires further exploration. This paper tends to probe into the motivation and the reasons ofparody＇s popularity in network language through some examples in light of Conceptual Integration Theory in hope of a better comprehension, appreciation, and application of parody in network language

The Integration of Network Teaching and Chinese Art History Education in Colleges

Based on the network teaching model,this article briefly summarizes the development of Chinese art history education in universities,analyzes the importance of integrating network teaching in Chinese art history lessons,and explores the integration strategy.

Jamming-Aided Secure Communication in Ultra-Dense LEO Integrated Satellite-Terrestrial Networks

The ultra-dense low earth orbit(LEO)integrated satellite-terrestrial networks(UDLEO-ISTN)can bring lots of benefits in terms of wide coverage,high capacity,and strong robustness.Meanwhile,the broadcasting and open natures of satellite links also reveal many challenges for transmission security protection,especially for eavesdropping defence.How to efficiently take advantage of the LEO satellite’s density and ensure the secure communication by leveraging physical layer security with the cooperation of jammers deserves further investigation.To our knowledge,using satellites as jammers in UDLEO-ISTN is still a new problem since existing works mainly focused on this issue only from the aspect of terrestrial networks.To this end,we study in this paper the cooperative secrecy communication problem in UDLEOISTN by utilizing several satellites to send jamming signal to the eavesdroppers.An iterative scheme is proposed as our solution to maximize the system secrecy energy efficiency(SEE)via jointly optimizing transmit power allocation and user association.Extensive experiment results verify that our designed optimization scheme can significantly enhance the system SEE and achieve the optimal power allocation and user association strategies.

Photonic integrated neuro-synaptic core for convolutional spiking neural network

Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions of a photonic spiking neural network(PSNN).However,they are separately implemented with different photonic materials and devices,hindering the large-scale integration of PSNN.Here,we propose,fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spike activation based on a distributed feedback(DFB)laser with a saturable absorber(DFB-SA).A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation.Furthermore,a fourchannel DFB-SA laser array is fabricated for realizing matrix convolution of a spiking convolutional neural network,achieving a recognition accuracy of 87%for the MNIST dataset.The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.

Influence of matrix physical properties on flow characteristics in dual network model

The tight oil formation develops with microfractures and matrix pores,it is important to study the influence of matrix physical properties on flow characteristics.At first,the representative fracture and matrix samples are selected respectively in the dual media,the fracture and matrix digital rocks are constructed with micro-CT scanning at different resolutions,and the corresponding fracture and matrix pore networks are extracted,respectively.Then,the modified integration method is proposed to build the dual network model containing both fracture and matrix pore-throat elements,while the geometric-topological structure equivalent matrix pores are generated to fill in the skeleton domain of fracture network,the constructed dual network could describe the geometric-topological structure characteristics of fracture and matrix pore-throat simultaneously.At last,by adjusting the matrix pore density and the matrix filling domain factor,a series of dual network models are obtained to analyze the influence of matrix physical properties on flow characteristics in dual-media.It can be seen that the matrix system contributes more to the porosity of the dual media and less to the permeability.With the decrease in matrix pore density,the porosity/permeability contributions of matrix system to dual media keep decreasing,but the decrease is not significant,the oil-water co-flow zone decreases and the irreducible water saturation increases,and the saturation interval dominated by the fluid flow in the fracture keeps increasing.With the decrease in matrix filling domain factor,the porosity/permeability contributions of matrix system to dual media decreases,the oil-water co-flow zone increases and the irreducible water saturation decreases,and the saturation interval dominated by the fluid flow in the fracture keeps increasing.The results can be used to explain the dual-media flow pattern under different matrix types and different fracture control volumes during tight oil production.

Blockchain Based Secure Relay Scheme for Space-Terrestrial Integrated Networks

The future 6G networks will integrates space and terrestrial networks to realize a fully connected world with extensive collaboration.However,how to build trust between multiple parties is a difficult problem for secure cooperation without a reliable third-party.Blockchain is a promising technology to solve this problem by converting the trust between multi-parties to the trust to the common shared data.Several works have proposed to apply the incentive mechanism in blockchain to encourage effective cooperation,but how to evaluate the cooperation performance and avoid breach of contract is not discussed.In this paper,a secure relay scheme is proposed based on the consortium blockchain system composed by different operators.In particular,smart contract checks the integrity of the message based on RSA accumulator,and executes transactions automatically when the message is delivered successfully.Detailed procedures are introduced for both uplink and downlink relay.Implementation based on Hyperledger Fabric proves the effectiveness of the proposed scheme and shows that the complexity of the scheme is low enough for practical deployment.

Dynamic QoS Mapping and Adaptive Semi-Persistent Scheduling in 5G-TSN Integrated Networks

The ubiquitous and deterministic communication systems are becoming indispensable for future vertical applications such as industrial automation systems and smart grids.5G-TSN(Time-Sensitive Networking)integrated networks with the 5G system(5GS)as a TSN bridge are promising to provide the required communication service.To guarantee the endto-end(E2E)QoS(Quality of Service)performance of traffic is a great challenge in 5G-TSN integrated networks.A dynamic QoS mapping method is proposed in this paper.It is based on the improved K-means clustering algorithm and the rough set theory(IKCRQM).The IKC-RQM designs a dynamic and loadaware QoS mapping algorithm to improve its flexibility.An adaptive semi-persistent scheduling(ASPS)mechanism is proposed to solve the challenging deterministic scheduling in 5GS.It includes two parts:one part is the persistent resource allocation for timesensitive flows,and the other part is the dynamic resource allocation based on the max-min fair share algorithm.Simulation results show that the proposed IKC-RQM algorithm achieves flexible and appropriate QoS mapping,and the ASPS performs corresponding resource allocations to guarantee the deterministic transmissions of time-sensitive flows in 5G-TSN integrated networks.