Grid System Analysis of Urban Flora of Bukhara City (Uzbekistan)

This article presents information on the study of the flora of Uzbekistan based on grid system mapping. The urban flora of the city of Bukhara was researched in it. As a result of research, the territory of Bukhara city was divided into 85 indexes based on 1 × 1 km2 grid mapping system. The diversity and density of species in the indexes are determined. The influence of anthropogenic factors on the diversity of species in the indexes is determined.

A Long-Time-Step-Permitting Tracer Transport Model on the Regular Latitude–Longitude Grid

If an explicit time scheme is used in a numerical model, the size of the integration time step is typically limited by the spatial resolution. This study develops a regular latitude–longitude grid-based global three-dimensional tracer transport model that is computationally stable at large time-step sizes. The tracer model employs a finite-volume flux-form semiLagrangian transport scheme in the horizontal and an adaptively implicit algorithm in the vertical. The horizontal and vertical solvers are coupled via a straightforward operator-splitting technique. Both the finite-volume scheme's onedimensional slope-limiter and the adaptively implicit vertical solver's first-order upwind scheme enforce monotonicity. The tracer model permits a large time-step size and is inherently conservative and monotonic. Idealized advection test cases demonstrate that the three-dimensional transport model performs very well in terms of accuracy, stability, and efficiency. It is possible to use this robust transport model in a global atmospheric dynamical core.

Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models

Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.

Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.

Intelligent Power Grid Load Transferring Based on Safe Action-Correction Reinforcement Learning

When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changingthe states of tie-switches and load demands. Computation speed is one of the major performance indicators inpower grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault powergrids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient.The tedious training process of the reinforcement learning model can be conducted offline, so the model showssatisfactory performance in real-time operation, indicating that it is suitable for fast load transfer. Consideringthat the reinforcement learning model performs poorly in satisfying safety constraints, a safe action-correctionframework is proposed to modify the learning model. In the framework, the action of load shedding is correctedaccording to sensitivity analysis results under a small discrete increment so as to match the constraints of line flowlimits. The results of case studies indicate that the proposed method is practical for fast and safe power grid loadtransfer.

Adaptive Sparse Grid Discontinuous Galerkin Method:Review and Software Implementation

This paper reviews the adaptive sparse grid discontinuous Galerkin(aSG-DG)method for computing high dimensional partial differential equations(PDEs)and its software implementation.The C++software package called AdaM-DG,implementing the aSG-DG method,is available on GitHub at https://github.com/JuntaoHuang/adaptive-multiresolution-DG.The package is capable of treating a large class of high dimensional linear and nonlinear PDEs.We review the essential components of the algorithm and the functionality of the software,including the multiwavelets used,assembling of bilinear operators,fast matrix-vector product for data with hierarchical structures.We further demonstrate the performance of the package by reporting the numerical error and the CPU cost for several benchmark tests,including linear transport equations,wave equations,and Hamilton-Jacobi(HJ)equations.

Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

A typhoon-induced storm surge numerical model with GPU acceleration based on an unstructured spherical centroidal Voronoi tessellation grid

Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal areas.Accurate and timely issuance of storm surge warnings to take appropriate countermeasures is an important means to reduce storm surge-related losses.Storm surge numerical models are important for storm surge forecasting.To further improve the performance of the storm surge forecast models,we developed a numerical storm surge forecast model based on an unstructured spherical centroidal Voronoi tessellation(SCVT)grid.The model is based on shallow water equations in vector-invariant form,and is discretized by Arakawa C grid.The SCVT grid can not only better describe the coastline information but also avoid rigid transitions,and it has a better global consistency by generating high-resolution grids in the key areas through transition refinement.In addition,the simulation speed of the model is accelerated by using the openACC-based GPU acceleration technology to meet the timeliness requirements of operational ensemble forecast.It only takes 37 s to simulate a day in the coastal waters of China.The newly developed storm surge model was applied to simulate typhoon-induced storm surges in the coastal waters of China.The hindcast experiments on the selected representative typhoon-induced storm surge processes indicate that the model can reasonably simulate the distribution characteristics of storm surges.The simulated maximum storm surges and their occurrence times are consistent with the observed data at the representative tide gauge stations,and the mean absolute errors are 3.5 cm and 0.6 h respectively,showing high accuracy and application prospects.

Performance Assessment of a Real PV System Connected to a Low-Voltage Grid

The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean energy.In the past,generation systems depended on non-renewable sources such as oil,coal,and gas.Therefore,this paper assesses the performance of a 51 kW PV solar power plant connected to a low-voltage grid to feed an administrative building in the 6th of October City,Egypt.The performance analysis of the considered grid-connected PV system is carried out using power system simulator for Engineering(PSS/E)software.Where the PSS/E program,monitors and uses the power analyzer that displays the parameters and measures some parameters such as current,voltage,total power,power factor,frequency,and current and voltage harmonics,the used inverter from the type of grid inverter for the considered system.The results conclude that when the maximum solar radiation is reached,the maximum current can be obtained from the solar panels,thus obtaining the maximum power and power factor.Decreasing total voltage harmonic distortion,a current harmonic distortion within permissible limits using active harmonic distortion because this type is fast in processing up to 300 microseconds.The connection between solar stations and the national grid makes the system more efficient.

A Wind Power Prediction Framework for Distributed Power Grids

To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article combines wind turbine monitoring data with numerical weather prediction(NWP)data to create a suitable wind power prediction framework for distributed grids.First,high-precision NWP of the turbine range is achieved using weather research and forecasting models(WRF),and Kriging interpolation locates predicted meteorological data at the turbine site.Then,a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve,and historical power is reconstructed using variational mode decomposition(VMD)filtering to form input variables in chronological order.Finally,input variables of a single turbine enter the temporal convolutional network(TCN)to complete initial feature extraction,and then integrate the outputs of all TCN layers using Long Short Term Memory Networks(LSTM)to obtain power prediction sequences for all turbine positions.The proposed method was tested on a wind farm connected to a distributed power grid,and the results showed it to be superior to existing typical methods.

Electromechanical Transient Modeling Analysis of Large-Scale New Energy Grid Connection

The synchronous virtual machine uses inverter power to imitate the performance of the conventional synchronous machine.It also has the same inertia,damping,frequency,voltage regulation,and other external performance as the generator.It is the key technology to realize new energy grid connections’stable and reliable operation.This project studies a dynamic simulation model of an extensive new energy power system based on the virtual synchronous motor.A new energy storage method is proposed.The mathematical energy storage model is established by combining the fixed rotor model of a synchronous virtual machine with the charge-discharge power,state of charge,operation efficiency,dead zone,and inverter constraint.The rapid conversion of energy storage devices absorbs the excess instantaneous kinetic energy caused by interference.The branch transient of the critical cut set in the system can be confined to a limited area.Thus,the virtual synchronizer’s kinetic and potential energy can be efficiently converted into an instantaneous state.The simulation of power system analysis software package(PSASP)verifies the correctness of the theory and algorithm in this paper.This paper provides a theoretical basis for improving the transient stability of new energy-connected power grids.

Sparse-Grid Implementation of Fixed-Point Fast Sweeping WENO Schemes for Eikonal Equations

Fixed-point fast sweeping methods are a class of explicit iterative methods developed in the literature to efficiently solve steady-state solutions of hyperbolic partial differential equations(PDEs).As other types of fast sweeping schemes,fixed-point fast sweeping methods use the Gauss-Seidel iterations and alternating sweeping strategy to cover characteristics of hyperbolic PDEs in a certain direction simultaneously in each sweeping order.The resulting iterative schemes have a fast convergence rate to steady-state solutions.Moreover,an advantage of fixed-point fast sweeping methods over other types of fast sweeping methods is that they are explicit and do not involve the inverse operation of any nonlinear local system.Hence,they are robust and flexible,and have been combined with high-order accurate weighted essentially non-oscillatory(WENO)schemes to solve various hyperbolic PDEs in the literature.For multidimensional nonlinear problems,high-order fixed-point fast sweeping WENO methods still require quite a large amount of computational costs.In this technical note,we apply sparse-grid techniques,an effective approximation tool for multidimensional problems,to fixed-point fast sweeping WENO methods for reducing their computational costs.Here,we focus on fixed-point fast sweeping WENO schemes with third-order accuracy(Zhang et al.2006[41]),for solving Eikonal equations,an important class of static Hamilton-Jacobi(H-J)equations.Numerical experiments on solving multidimensional Eikonal equations and a more general static H-J equation are performed to show that the sparse-grid computations of the fixed-point fast sweeping WENO schemes achieve large savings of CPU times on refined meshes,and at the same time maintain comparable accuracy and resolution with those on corresponding regular single grids.

EASE-Grid投影风云卫星产品地理信息写入方法

风云卫星遥感数据服务网的卫星遥感产品数据集中,风云三系列气象卫星遥感产品数据集中很多采用的是等面积可伸缩地球网格(EASE-Grid)投影方式进行处理,实际应用中对使用者具有较高的数据处理能力要求,不利于遥感产品数据集的省级应用。基于数据集使用中的这些问题,该文以FY3D雪水当量数据集产品为例,采用程序化方法对EASE-Grid投影产品数据集的地理信息进行写入,通过构建地理坐标系参考对象和地理信息目录,将数据矩阵中写入地理信息并以GeoTiff格式文件输出。结果表明,经过该方法处理过的产品数据可与矢量文件实现准确的经纬度信息的匹配,降低了数据分析处理的难度。该方法具有较好的适用性,对于EASE-Grid的三种不同的投影方式均适用,可在一定程度上提高卫星遥感产品数据集的省级科研与应用水平。

“GRIDS”教学法在工程热力学教学中的创新与实践

针对工程热力学课程传统教学课堂沉闷、教学效率低、专业拓展少、课时吃紧、思政入耳不入心和学生创新能力培养不足等问题,依托省级线上线下混合式一流课程,开展基于“GRIDS”教学法的教学创新与实践。混合式教学模式让线下教学融入更多高阶知识点、针对性案例,让课程内容更具弹性;“GRIDS”每一个字母代表一种教学方法;分组案例汇报拓展学科前沿,培养学生团队合作能力;雨课堂随堂测验;4种思政范式让思政“铭于心而践于行”;讨论式互动培养学生批判性学习思维;学习卡片帮助学生把握重难点。各教学方法的形成性评价是过程化考核依据。教学方法形成合力,将教学痛点逐个击破,形成闭环,并持续改进。

东湖杂交羊产羔性状的GWAS分析及候选基因GRID 2的验证

旨在研究影响东湖杂交羊产羔性状的关键基因并挑选关键基因的变异位点进行验证,以期为东湖杂交奶绵羊多羔性状的选育提供分子标记。本研究从西北农林科技大学金昌奶绵羊试验示范基地采集身体健康无疾病的东湖杂交二代奶绵羊168只进行全基因组重测序,GEMMA和SnpEff软件进行全基因组关联分析,KOBAS 3.0数据库对注释的基因进行富集分析,LDBlockShow软件对显著性最强SNPs周边的连锁区域进行连锁不平衡分析。使用竞争性等位基因特异性PCR(kompetitive allele-specific PCR,KASP)技术对GRID 2基因进行SNP的分型检测,统计该基因两个多态位点的遗传多样性参数,并将其与产羔性状进行关联分析。结果显示,全基因组关联分析确定了6号染色上1163个达到显著性水平的SNPs位点,通过对候选位点的注释,确定与产羔性状相关的候选基因BMPR 1B、PDLIM5、PDHA2、UNC 5C、GRID2、NFKB1、TSPAN5、STPG2、PPP 3CA。同时结合已有报道和最强关联区域的注释,选择GRID 2上的两个SNPs(g.35685218A>G和g.35685499C>T)作为候选位点。通过统计学分析发现,本研究中涉及的两个位点均处于P>0.05的哈代-温伯格平衡状态,表明这两个位点均没有受到过强选择,并且这两个位点与东湖杂交羊的产羔性状有显著关联,两个位点的野生型均处于优势状态,且两个位点在胎次增加时均值也增加。综上所述,GRID 2基因2个SNPs对东湖杂交奶绵羊产羔性状有显著影响,可以作为分子标记辅助选择的候选位点。本研究为提高东湖杂交奶绵羊产羔率,促进奶绵羊的遗传育种进展提供了坚实的理论依据。

基于GRID-TIN方法的露天煤矿地表模型的仿真研究

露天矿地表具有独特的表面形态特征,现有DEM难以对其地貌形态特征进行准确描述,无法满足露天矿地表地形数值模拟模型的需求。为此,以露天煤矿典型地形为研究对象,基于GRID-TIN方法和面向对象技术构建出露天矿地表数值模拟模型;用MATLAB、SOLIDWORKS等软件,对煤矿进行三维真实地形的计算机模拟与仿真,形成地形实体,具有一定的实践意义和应用价值。

Development and simulation of a gridded thermionic cathode electron gun for a high-energy photon source

A gridded thermionic cathode electron gun was developed for the linear accelerator of the High Energy Photon Source(HEPS).An electron gun should provide a large maximum bunch charge with a wide adjustable range.To satisfy these requirements,the shape of the electrode was optimized using a multi-objective genetic algorithm.A large bunch charge with an adjustable range was achieved using the grid-limited gun,the flow of which was analyzed using 3-D simulations.The electron gun has been manufactured and tested,and the measured data of the grid-limited current and simulation results are compared and discussed in this study.

A study of the influence of different grid structures on plasma characteristics in the discharge chamber of an ion thruster

The grid structure has significant effects on the discharge characteristics of an ion thruster.The discharge performances of a 30 cm diameter ion thruster with flat,convex and concave grids are studied.The analysis results show that the discharge chamber with a convex grid has a larger’magnetic-field free area’than the others,and the parallelism of the magnetic-field isopotential lines and anode is generally the same in the three models.Plasma densities of the three structures at the grid outlet are in the range of 3.1×1016-6.9×1017m-3.Along the thruster axis direction,the electron temperature in the chamber with the convex and concave grids is in the range of 3.3-3.5 eV,while that with a flat grid is lower,in the range of 3.1-3.5 eV.In addition,the convex and the concave grids have better uniform distribution of electron temperature.Moreover,the collision frequency ratios show that the axial degree of ionization of the three models is the highest,and the flat grid has the highest discharge efficiency,followed by the convex grid and the concave grid is the least efficient.The test and simulation results of the 30 cm diameter ion thruster with the convex grid show that the measurement and calculation results are 3.67 A and 3.44 A,respectively,and the error above mainly comes from the ignorance of the doubly charged ions and parameter settings in the model.The comparison error between the simulation and measurement of beam current density is mainly caused by the actual thermal deformation of the grids during the discharge process,which leads to the change in electric potential distribution and variation of the focusing characteristics of the grids.Upon consideration of discharge performance and the thermal grid gap variation,it can be concluded that the flat and concave grids are more suitable for small-diameter ion thrusters,while the convex grid is a more reasonable choice for the higher-power and larger-diameter thrusters.

Review of trans-Mediterranean power grid interconnection:a regional roadmap towards energy sector decarbonization

Climate change is becoming an important issue in all fields of infrastructure development.Electricity plays a core role in the decarbonized energy system’s path to a regional zero-emission pattern.A well-built trans-Mediterranean backbone grid can hedge the profound evolution of regional power generation,transmission,and consumption.To date,only Turkey and the Maghreb countries(i.e.,Morocco,Algeria,and Tunisia)are connected with the Continental European Synchronous Area.Other south-and east-shore countries have insufficient interconnection infrastructures and synchronization difficulties that have proven to be major hurdles to the implementation of large-scale solar and wind projects and achievement of climate goals.This study analyzes the current trans-boundary grid interconnections and power and carbon emission portfolios in the Mediterranean region.To align with the recently launched new climate target‘Fit for 55’program and the accelerated large-scale renewables target,a holistic review of projected trans-Mediterranean grids and their market,technical,and financial obstacles of implementation was conducted.For south-and east-shore countries,major legal and regulatory barriers encompassing non-liberalized market structure,regulation gaps of taxation and transmission tariffs,and the private sector’s access rights need to be removed.Enhancement of domestic grids,substations,and harmonized grid codes and frequency,voltage,and communication technology standards among all trans-Mediterranean countries are physical prerequisites for implementing the Trans-Mediterranean Electricity Market.In addition,the mobilization of capital instruments along with private and international investments is indispensable for the realization of supranational transmission projects.As the final section of the decarbonization roadmap,the development of electric appliances,equipment,and vehicles with higher efficiency is inevitable in the decarbonized building,transportation,and industry sectors.

A Federated Named Entity Recognition Model with Explicit Relation for Power Grid

The power grid operation process is complex,and many operation process data involve national security,business secrets,and user privacy.Meanwhile,labeled datasets may exist in many different operation platforms,but they cannot be directly shared since power grid data is highly privacysensitive.How to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart grid.Therefore,this paper proposes federated learning named entity recognition method for the power grid field,aiming to solve the problem of building a named entity recognition model covering the entire power grid process training by data with different security requirements.We decompose the named entity recognition(NER)model FLAT(Chinese NER Using Flat-Lattice Transformer)in each platform into a global part and a local part.The local part is used to capture the characteristics of the local data in each platform and is updated using locally labeled data.The global part is learned across different operation platforms to capture the shared NER knowledge.Its local gradients fromdifferent platforms are aggregated to update the global model,which is further delivered to each platform to update their global part.Experiments on two publicly available Chinese datasets and one power grid dataset validate the effectiveness of our method.