Problem of Network Transmission Based on Linux Server and Its Solution

Using Linux server as a gateway between the local area network and the Internet may lead to slow pace in network transmission and even data loss. In this study,the above problem and its solution were discussed.

Reconstructing early transmission networks of SARS-CoV-2 using a genomic mutation model

The coronavirus disease 2019(COVID-19)pandemic has greatly damaged human society,but the origins and early transmission patterns of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pathogen remain unclear.Here,we reconstructed the transmission networks of SARS-CoV-2 during the first three and six months since its first report based on ancestor-offspring relationships using BANAL-52-referenced mutations.We explored the position(i.e.,root,middle,or tip)of early detected samples in the evolutionary tree of SARS-CoV-2.In total,6799 transmission chains and 1766 transmission networks were reconstructed,with chain lengths ranging from 1-9 nodes.The root node samples of the 1766 transmission networks were from 58 countries or regions and showed no common ancestor,indicating the occurrence of many independent or parallel transmissions of SARS-CoV-2 when first detected(i.e.,all samples were located at the tip position of the evolutionary tree).No root node sample was found in any sample(n=31,all from the Chinese mainland)collected in the first 15 days from 24 December 2019.Results using six-month data or RaTG13-referenced mutation data were similar.The reconstruction method was verified using a simulation approach.Our results suggest that SARS-CoV-2 may have already been spreading independently worldwide before the outbreak of COVID-19 in Wuhan,China.Thus,a comprehensive global survey of human and animal samples is essential to explore the origins of SARS-CoV-2 and its natural reservoirs and hosts.

FOUR-PARAMETER AUTOMATIC TRANSMISSION TECHNOLOGY FOR CONSTRUCTION VEHICLE BASED ON ELMAN RECURSIVE NEURAL NETWORK

From the viewpoint of energy saving and improving transmission efficiency, the ZL50E wheel loader is taken as the study object. And the system model is analyzed based on the transmission system of the construction vehicle. A new four-parameter shift schedule is presented, which can keep the torque converter working in the high efficiency area. The control algorithm based on the Elman recursive neural network is applied, and four-parameter control system is developed which is based on industrial computer. The system is used to collect data accurately and control 4D180 power-shift gearbox of ZL50E wheel loader shift timely. An experiment is done on automatic transmission test-bed, and the result indicates that the control system could reliably and safely work and improve the efficiency of hydraulic torque converter. Four-parameter shift strategy that takes into account the power consuming of the working pump has important operating significance and reflects the actual working status of construction vehicle.

Best-retransmission count selection for environment optimization over wireless heterogeneous networks

This paper proposes an environment-aware best- retransmission count selected optimization control scheme over IEEE 802.11 multi-hop wireless networks. The proposed scheme predicts the wireless resources by using statistical channel state and provides maximum retransmission count optimization based on wireless channel environment state to improve the packet delivery success ratio. The media access control （MAC） layer selects the best-retransmission count by perceiving the types of packet loss in wireless link and using the wireless channel charac- teristics and environment information, and adjusts the packet for- warding adaptively aiming at improving the packet retransmission probability. Simulation results show that the best-retransmission count selected scheme achieves a higher packet successful delivery percentage and a lower packet collision probability than the corresponding traditional MAC transmission control protocols.

Effects of information transmission delay and channel blocking on synchronization in scale-free Hodgkin-Huxley neuronal networks

In this paper,we investigate the evolution of spatiotemporal patterns and synchronization transitions in dependence on the information transmission delay and ion channel blocking in scale-free neuronal networks.As the underlying model of neuronal dynamics,we use the Hodgkin-Huxley equations incorporating channel blocking and intrinsic noise.It is shown that delays play a significant yet subtle role in shaping the dynamics of neuronal networks.In particular,regions of irregular and regular propagating excitatory fronts related to the synchronization transitions appear intermittently as the delay increases.Moreover,the fraction of working sodium and potassium ion channels can also have a significant impact on the spatiotemporal dynamics of neuronal networks.As the fraction of blocked sodium channels increases,the frequency of excitatory events decreases,which in turn manifests as an increase in the neuronal synchrony that,however,is dysfunctional due to the virtual absence of large-amplitude excitations.Expectedly,we also show that larger coupling strengths improve synchronization irrespective of the information transmission delay and channel blocking.The presented results are also robust against the variation of the network size,thus providing insights that could facilitate understanding of the joint impact of ion channel blocking and information transmission delay on the spatiotemporal dynamics of neuronal networks.

Reliability Strategies for Data Transmission in Wireless Mesh Network

Wireless Mesh Network (WMN) is a new-type wireless network. Its core idea is that any of its wireless equipment can act as both an Access Point (AP) and a router. Each node in the network can send and receive signals as well as directly communicate with one or several peer nodes. One important issue to be considered in wireless Mesh networks is how to secure reliable data transmission in multi-hop links. To solve the problem, the 3GPP system architecture proposes two functionalities: ARQ and HARQ. This paper presents two HARQ schemes, namely hop-by-hop and edge-to-edge, and three ARQ schemes: hop-by-hop, edge-to-edge, and last-hop. Moreover, it proposes three solutions for WMNs from the perspective of protocol stock design: layered cooperative mechanism, relay ARQ mechanism and multi-hop mechanism.

ZTE's Optical Transmission Products Used in National Backbone Network of Indonesia

1 Technical Features of ZTE’sDWDM SystemThe toll backbone wave division equipmentof ZXWM M900 backbone transmissionplatform is well designed forlarge-capacity optical transmission. It canfully satisfy the networking and managementrequirements of diversified users and be fit forvarious toll backbone networks. The systemsupports up to 40 working wavelengths and up to400 Gb/s transmission capacity, and thewavelength selection and interval are in strictcompliance with ITU-T Recommendations. It

A Least-squares-based Iterative Method with Better Convergence for PF/OPF in Integrated Transmission and Distribution Networks

The limitations of the conventional master-slavesplitting(MSS)method,which is commonly applied to power flow and optimal power flow in integrated transmission and distribution(I-T&D)networks,are first analyzed.Considering that the MSS method suffers from a slow convergence rate or even divergence under some circumstances,a least-squares-based iterative(LSI)method is proposed.Compared with the MSS method,the LSI method modifies the iterative variables in each iteration by solving a least-squares problem with the information in previous iterations.A practical implementation and a parameter tuning strategy for the LSI method are discussed.Furthermore,a LSI-PF method is proposed to solve I-T&D power flow and a LSIheterogeneous decomposition(LSI-HGD)method is proposed to solve optimal power flow.Numerical experiments demonstrate that the proposed LSI-PF and LSI-HGD methods can achieve the same accuracy as the benchmark methods.Meanwhile,these LSI methods,with appropriate settings,significantly enhance the convergence and efficiency of conventional methods.Also,in some cases,where conventional methods diverge,these LSI methods can still converge.

Operational Risk Assessment for Integrated Transmission and Distribution Networks

To satisfy the requirements of accurate operationalrisk assessment of integrated transmission and distribution networks (I-T&D), an integrated operational risk assessment (IORA) algorithm is proposed. Specific cases demonstrate thatan I-ORA is necessary because it provides accurate handlingof the coupling between transmission and distribution networks,accurate analysis of power supply mode (PSM) changes ofimportant users and helps to improve security and stability ofpower grid operations. Two key technical requirements in theI-ORA algorithm are realized, i.e., integrated topology analysisand integrated power flow calculation. Under a certain contingency, integrated topology analysis is used to assess the risksof substation power cuts, network split and PSM changes ofimportant users, while the integrated power flow calculation,based on the self-adaptive Levenburg-Marquard method andNewton method, can be implemented to assess risks of heavyload/overload and voltage deviation. In addition, the graphicsprocessing unit is used to parallelly process some computationintensive steps. Numerical experiments show that the proposedI-ORA algorithm can realize accurate assessment for the entireI-T&D. In addition, the efficiency and convergence are satisfying,indicating the proposed I-ORA algorithm can significantly benefitreal practice in the coordination operation of I-T&D in the future.

Detection of influential nodes with multi-scale information

The identification of influential nodes in complex networks is one of the most exciting topics in network science.The latest work successfully compares each node using local connectivity and weak tie theory from a new perspective.We study the structural properties of networks in depth and extend this successful node evaluation from single-scale to multi-scale.In particular,one novel position parameter based on node transmission efficiency is proposed,which mainly depends on the shortest distances from target nodes to high-degree nodes.In this regard,the novel multi-scale information importance(MSII)method is proposed to better identify the crucial nodes by combining the network's local connectivity and global position information.In simulation comparisons,five state-of-the-art algorithms,i.e.the neighbor nodes degree algorithm(NND),betweenness centrality,closeness centrality,Katz centrality and the k-shell decomposition method,are selected to compare with our MSII.The results demonstrate that our method obtains superior performance in terms of robustness and spreading propagation for both real-world and artificial networks.

Life Experience Method of Application Writing under the Internet+ Field

Network virtual social life and social life of the intimate contact, gaining experience through the network are growing acceptance of a new kind of practice writing behavior. In this paper, closely related to the new direction of life experience of the Internet age forms, and characteristics of practical writing life experience development under the network environment and study of teaching strategy analysis, think the Internet+ provided by the social forms life mirror, fits the practical writing “material needs” obtaining through the network to the indirect way to gain life experience of social forms, and has become a new kind of practical writing teaching methods. Network life experience is not only a practical writing, and also will use literature writing, news writing, such as writing behavior.

Transmission Network Expansion Planning Considering Uncertainties in Loads and Renewable Energy Resources

This paper proposes a novel method for transmission network expansion planning(TNEP)that take into account uncertainties in loads and renewable energy resources.The goal of TNEP is to minimize the expansion cost of candidate lines without any load curtailment.A robust linear optimization algorithm is adopted to minimize the load curtailment with uncertainties considered under feasible expansion costs.Hence,the optimal planning scheme obtained through an iterative process would be to serve loads and provide a sufficient margin for renewable energy integration.In this paper,two uncertainty budget parameters are introduced in the optimization process to limit the considered variation ranges for both the load and the renewable generation.Simulation results obtained from two test systems indicate that the uncertainty budget parameters used to describe uncertainties are essential to arrive at a compromise for the robustness and optimality,and hence,offer a range of preferences to power system planners and decision makers.

A Scenario-Based Robust Transmission Network Expansion Planning Method for Consideration of Wind Power Uncertainties

This paper uses a novel scenario generation method for tackling the uncertainties of wind power in the transmission network expansion planning(TNEP)problem.A heuristic moment matching(HMM)method is first applied to generate the typical scenarios for capturing the stochastic features of wind power,including expectation,standard deviation,skewness,kurtosis,and correlation of multiple wind farms.Then,based on the typical scenarios,a robust TNEP problem is presented and formulated.The solution of the problem is robust against all the scenarios that represent the stochastic features of wind power.Three test systems are used to verify the HMM method and is compared against Taguchi’s Orthogonal Array(OA)method.The simulation results show that the HMM method has better performance than the OA method in terms of the trade-off between robustness and economy.Additionally,the main factors influencing the planning scheme are studied,including the number of scenarios,wind farm capacity,and penalty factors,which provide a reference for system operators choosing parameters.

Risk-based maintenance scheduling of generating units in the deregulated environment considering transmission network congestion

In restructured power systems,the traditional approaches of unit maintenance scheduling(UMS)need to undergo major changes in order to be compatible with new competitive structures.Performing the maintenance on generating units may decrease the security level of transmission network and result in electricity shortage in power system;as a result,it can impose a kind of cost on transmission network as called security cost.Moreover,taking off line a generating unit for performing maintenance can change power flow in some transmission lines,and may lead to network congestion.In this study,generating unit maintenance is scheduled considering security and congestion cost with N-1 examination for transmission lines random failures.The proposed UMS approach would lead to optimum operation of power system in terms of economy and security.To achieve this goal,the optimal power flow(OPF)compatible with market mechanism is implemented.Moreover,the electricity price discovery mechanism as locational marginal pricing(LMP)is restated to analyze the impacts of UMS on nodal electricity price.Considering security and congestion cost simultaneously,this novel approach can reveal some new costs which are imposed to transmission network on behalf of generation units;as a result,it provides a great opportunity to perform maintenance in a fair environment for both generating companies(GenCo)and transmission companies(TransCo).At the end,simulation results on nine-bus test power system demonstrate that by using this method,the proposed UMS can guarantee fairness among market participants including GenCos and TransCo and ensure power system security.

Transmission network expansion planning with embedded constraints of short circuit currents and N-1 security

An approach of transmission network expan-sion planning with embedded constraints of short circuit currents and N-1 security is proposed in this paper.The problem brought on by the strong nonlinearity property of short circuit currents is solved with a linearization method based on the DC power flow.The model can be converted to a mixed-integer linear programming problem,realizing the optimization of planning model that considers the constraints of linearized short circuit currents and N-1 security.To compensate the error caused by the assump-tions of DC power flow,the compensation factor is pro-posed.With this factor,an iterative algorithm that can compensate the linearization error is then presented.The case study based on the IEEE 118-bus system shows that the proposed model and approach can be utilized to:opti-mize the construction strategy of transmission lines;ensure the N-1 security of the network;and effectively limit the short circuit currents of the system.

Advances in research of HIV transmission networks

Transmission network analysis is a crucial evaluation tool aiming to explore the characteristics of the human immunodeficiency virus epidemic,develop evidence-based prevention strategies,and contribute to various areas of human immunodeficiency virus/acquired immunodeficiency syndrome prevention and control.Over recent decades,transmission networks have made tremendous strides in terms of modes,methods,applications,and various other aspects.Transmission network methods,including social,sexual,and molecular transmission networks,have played a pivotal role.Each transmission network research method has its advantages,as well as its limitations.In this study,we established a systematic review of these aforementioned transmission networks with respect to their definitions,applications,limitations,recent progress,and synthetic applications.

Distributionally Robust Co-optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation

Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation.This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation.The proposed model includes distributionally robust joint chance constraints,which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set.The proposed formulation yields a twostage robust optimization model with variable bounds of the uncertain sets,which is hard to solve.By applying the affine decision rule,second-order conic reformulation,and duality,we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers.Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.

Transmission Network Expansion Planning Considering the Generators’Contribution to Uncertainty Accommodation

This paper presents an optimization for transmission network expansion planning(TNEP)under uncertainty circumstances.This TNEP model introduces the approach of parameter sets to describe the range that all possible realizations of uncertainties in load and renewable generation can reach.While optimizing the TNEP solution,the output of each generator is modeled as an uncertain variable to linearly respond to changes caused by uncertainties,which is constrained by the extent to which uncertain parameters may change the operational range of generators,and network topology.This paper demonstrates that the robust optimization approach is effective to make the problem with uncertainties tractable by converting it into a deterministic optimization,and with the genetic algorithm,the optimal TNEP solution is derived iteratively.Compared with other robust TNEP results tested on IEEE 24-bus systems,the proposed method produces a least-cost expansion plan without losing robustness.In addition,the contribution that each generator can make to accommodate with every uncertainty is optimally quantified.Effects imposed by different uncertainty levels are analyzed to provide a compromise of the conservativeness of TNEP solutions.