Benchmark graphs for testing community detection algorithms
- 格式:pdf
- 大小:232.50 KB
- 文档页数:6
asssdbenchmarkIntroductionThe purpose of this document is to provide a comprehensive overview of the asssdbenchmark, a tool for benchmarking the performance of AssSD (Ass Super Speedy Database). AssSD is a high-performance database management system designed for speed, efficiency, and reliability.Overview of asssdbenchmarkAsssdbenchmark is a utility program that allows users to evaluate the performance of AssSD. It measures various metrics such as read and write throughput, latency, and scalability. By running different benchmark tests, users can assess the performance of AssSD under different workloads and make informed decisions.Features•Workload Customization: asssdbenchmark allows users to define the workload parameters such as thenumber of threads, size of the dataset, and the types ofoperations (read, write, or mixed). This enables users toemulate real-world scenarios and evaluate AssSD’sperformance under different conditions.•Metrics Collection: The tool collects crucial performance metrics such as the number of operations per second, average response time, and throughput. Thesemetrics provide insights into the database’s efficiency and help identify any bottlenecks or areas for optimization.•Graphical Visualization: asssdbenchmark generates intuitive graphical representations of the benchmark results. Graphs and charts visually illustrate the performance metrics, making it easier for users to analyze and compare different scenarios.•Extensibility: The tool is designed to be extensible, allowing developers to add custom benchmark tests or integrate it with other benchmarking frameworks. This flexibility makes it easier to adapt asssdbenchmark to specific requirements or use cases.InstallationAsssdbenchmark can be installed by following these steps:1.Prerequisites: Ensure that AssSD is already installed and configured properly on your system.2.Download: Obtain the latest version of asssdbenchmark from the official website or the project’s repository.3.Installation: Extract the downloaded archive and navigate to the extracted directory using the command line.pilation: Compile the source code using the appropriate compilation commands for your operating system.5.Configuration: Modify the configuration fileprovided with asssdbenchmark according to your system and workload requirements.6.Execution: Run the asssdbenchmark executablewith the desired benchmarking options and parameters.Benchmarking ProcessThe benchmarking process using asssdbenchmark typically involves the following steps:1.Setup: Configure the benchmark parameters such asthe number of threads, dataset size, and workload type in the configuration file.2.Initialization: asssdbenchmark initializes thedatabase and sets up the necessary resources for thebenchmark.3.Warm-up: Execute a warm-up phase to bring thedatabase to a steady state. This phase helps in priming the caches and optimizing resource utilization.4.Benchmark Execution: Run the actual benchmarkby executing the defined workload on AssSD. The toolmeasures various performance metrics during this phase.5.Results Analysis: Analyze the benchmark resultsusing the graphical visualization generated byasssdbenchmark. Compare different scenarios and identify any performance bottlenecks or areas for improvement.6.Optimization: Based on the analysis, makenecessary optimizations to AssSD’s configuration or code to improve its performance.7.Repeat: Iterate the benchmarking process byadjusting the workload parameters or other settings tofurther refine the performance evaluation.ConclusionAsssdbenchmark is a powerful tool that enables users to measure and analyze the performance of AssSD. By running different benchmark tests, users gain insights into the efficiency, scalability, and bottlenecks of the database system. The extensible nature of asssdbenchmark allows it to be customized and integrated into existing benchmarking frameworks. Overall, asssdbenchmark is an essential utility for evaluating the performance of AssSD and ensuring optimal database performance.。
cos性能测试工具Lmbench的安装使用与参数说明1 工具简介Linux性能测试工具Lmbench是一套简易可移植的,符合ANSI/C标准为UNIX/POSIX而制定的微型测评工具。
一般来说,它衡量两个关键特征:反应时间和带宽。
Lmbench旨在使系统开发者深入了解关键操作的基础成本。
其官方网站是: /lmbench/。
2 安装过程及一般错误解决办法安装使用Linux性能测试工具Lmbench 的安装相对比较简单,到其官方网站下载压缩包Lmbench3.tar.gz下面以lmbench3.tar.gz在/opt目录下为列,说明安装方法解压tar -xzvf lmbench3.tar.gzcd lmbench3make results如果在make 的时候出错,提示类似$make resultsmake[1]: Entering directory `/home/kyuan/lmbench3/src'gmake[2]: Entering directory `/home/kyuan/lmbench3/src'gmake[2]: *** No rule to make target `../SCCS/s.ChangeSet', needed by bk.ver'..gmake[2]: Leaving directory `/home/kyuan/lmbench3/src'make[1]: *** [lmbench] Error 2make[1]: Leaving directory `/home/kyuan/lmbench3/src'make: *** [results] Error 2这是需要修改src/Makefile,将这么一行(在231 行的样子),将$O/lmbench : ../scripts/lmbench bk.ver中的bk.ver 去掉,就可以了。
得分的表达方式英语作文Title: Expressing Scores in English Composition。
In academic and professional settings, expressing scores accurately and effectively is crucial for conveying information clearly. Whether it's in essays, reports, or presentations, mastering the art of score expression in English writing is essential. In this composition, we will explore various ways to express scores in English, along with examples and considerations.1. Numerical Scores:Using numerals to represent scores is a common and straightforward method. For instance, "He scored 85% on the exam."Numerical scores are precise and easily understood, making them suitable for most contexts.2. Letter Grades:Letter grades are often used in educational settings. For example, "She received an A on her project."Letter grades provide a quick summary of performance but may lack specificity compared to numerical scores.3. Percentage:Percentages are commonly used to indicate the proportion of correct answers or achievements. For example, "The team completed 95% of the project on time."Percentages offer a clear indication of achievement relative to a goal or standard.4. GPA (Grade Point Average):In academic contexts, GPA is used to representoverall academic performance. For example, "His GPA is3.8."GPA provides a comprehensive summary of academic achievement over a period, usually a semester or academic year.5. Scale Scores:Some assessments use scale scores, which are converted from raw scores to a standardized scale. For example, "His IQ score is 120."Scale scores allow for comparisons across different assessments but may require interpretation for understanding.6. Descriptors:Descriptive terms such as "excellent," "good," "satisfactory," and "needs improvement" can be used to provide qualitative evaluations alongside quantitative scores. For example, "Her performance was excellent, scoring 95% on the test."Descriptors add context and depth to score expressions, helping to convey nuances of performance.7. Graphical Representation:Graphs, charts, and visual aids can be used to represent scores effectively, especially in presentations and reports. For example, a bar graph showing thedistribution of scores in a class.Visual representations enhance understanding and engagement, particularly when dealing with complex datasets.8. Comparative Statements:Comparing scores to benchmarks or previous performances can provide additional context. For example, "Her score improved by 10% compared to last year."Comparative statements highlight progress or areasof improvement, offering insights beyond the score itself.In conclusion, expressing scores in English involves choosing the most appropriate method based on the context and audience. Whether using numerical values, letter grades, percentages, or other methods, clarity and precision are paramount. Additionally, providing context, using descriptors, and utilizing visual aids can enhance understanding and communication of scores effectively. Mastering the various techniques discussed in this composition will empower individuals to express scores confidently and accurately in English writing.。
EDEM 2.1 Release NotesContentsChanges in EDEM 2.1 (3)EDEM Dynamics Coupling (Licensable Feature) (3)Integrated Bonded Particle Contact Model (4)Custom Particle Properties (5)EDEM Extended API (Licensable Feature) (6)EDEM-CFD Coupling Module for FLUENT (Licensable Feature) (6)Heat Transfer (6)EDEM 2.1 Performance Benchmarks (7)Bug Fixes for EDEM 2.1 (7)Known Issues in EDEM 2.1 (9)Known Issues in EDEM-CFD Coupling Module for FLUENT (10)Changes in EDEM 2.1EDEM Dynamics Coupling (Licensable Feature)EDEM Dynamics Coupling is an interface to enable EDEM’s geometry motion to be controlled by any suitable 3rd-party dynamics application. For example, it can be used to couple EDEM with MSC Software’s engineering analysis software Easy5 and Adams. Once licensed, using the Dynamics Coupling Interface with Easy5 is a simple four-step process:1. Setup Dynamics Coupling in EDEM.2. Setup EDEM Coupling in your 3rd-party dynamics application.3. Setup EDEM Inputs and Outputs.4. Monitor and restart the dynamics application’s server.Refer to the Online Help or EDEM User Guide for full details.Integrated Bonded Particle Contact ModelEDEM 2.1 includes a bonded particle model which integrates a standard Hertz-Mindlincontact model with a load-limited particle bonding. The Bonded Particle contact modelcan be used to bond particles together using the following configurable parameters:Interaction Particle-to-Particle Configurable ParametersParticle to Particle (also enable forParticle to Geometry) Select an active bond then set the formation time and the followingparameters: Normal Stiffness : the tensile/compressive stiffness along the bond’s principal axisShear Stiffness : Shear stiffness in the orthogonal plane to thebond’s principal axisCritical Normal Stress : the maximum normal stress the bond canwithstand before it failsCritical Shear Stress : The maximum tangential stress the bond canwithstand before it failsBonded Disk Radius : The radius of the cylindrical bond between theparticlesAn example simulation using this contact model is in the examples folder.Custom Particle PropertiesWith Custom Particle Properties you can dynamically define custom physical particleattributes to use in your simulation. Custom properties can be visualized, analyzed, andexported just like any other particle attribute. When loaded, contact models, particle bodyforces, and coupled applications can all supply new properties and share data on a per-particle basis.From the Particles tab in the Creator, click Custom Properties to open the PropertyManager window:The Property Manager window is in two halves: the bottom half is where you set-up andmodify new custom particle properties. When simulation starts, these tentative propertiesmove to the top half which lists all finalized properties. Once a property is finalized, youcannot modify or delete it.Click New User Property to add a new tentative particle attribute. Once added, the newproperty becomes available in the following areas:Factory parameters (Factories tab in the Creator)Export data (File > Export Data dialog)Attribute coloring (Coloring tab in the Analyst)Bin group data queries (Binning tab in the Analyst)Histograms, Line Graphs, Scatter Plots, and Pie ChartsRefer to the EDEM Programming Guide for more details on Custom Particle Properties.Note to use custom particle properties with user-defined contact models requires anEDEM Extended API license (see page 6).EDEM Extended API (Licensable Feature)The EDEM Extended API (Application Programming Interface) enables customization of contact models with full data-save and post-processing of custom particle properties. The Extended API is required for operation of particle contact and body force models with user-defined particle properties.If you have not purchased the EDEM Extended API:Loading a plug-in that tries to register user-defined particle properties will result ina warning dialog saying the properties cannot be registered.No user-defined particle properties will be registered with or passed to the plug-in.Refer to the EDEM Programming Guide for more details on EDEM’s API and the Extended API feature.EDEM-CFD Coupling Module for FLUENT (Licensable Feature) EDEM 2.1 re-introduces an optimized EDEM-CFD Coupling Module for FLUENT, used to simulate particle-fluid systems. EDEM uses a surface mesh to represent boundary surfaces which enables a one-to-one coupling with the boundary surface elements of the CFD fluid volume mesh. Using this module you can investigate systems such as particle agglomeration and clumping in fluidized beds, dense phase conveying, filtration, solid-liquid mixing, pipe erosion, spray coating and many others.This latest version of the coupling module uses an optimized point-search algorithm to reduce the computation time of volume-fraction calculations.Refer to the EDEM-CFD Coupling Module for FLUENT User Guide for more details.Heat TransferEDEM 2.1 includes heat transfer. This provides:An integrated contact model: Hertz-Mindlin with Heat Conduction. This calculates the heat flux between particles in contact. To use, add to your model chain thenset the thermal conductivity for each type of particle.A Temperature Update model. This models a particle’s temperature over time. Touse, add to your external forces chain then set the heat capacity for each type of particle.Heat Transfer models (licensable feature) for the EDEM-CFD Coupling Module for FLUENT. This includes both convective and radiative heat transfer models.Refer to the EDEM-CFD Coupling Module for FLUENT User Guide for moredetails.EDEM 2.1 Performance BenchmarksWe are continually benchmarking the current release of EDEM against previous versions to provide details on performance improvements. For the latest benchmarks, please see our EDEM Benchmarks webpage at:/benchmarks/Note: You must login to our website to view this webpage. If you do not have a website login or you have lost your login details, please email support@. Bug Fixes for EDEM 2.1EDEM Crash when using Multiple Particle Display TemplatesSometimes, using more than one particle display template in a simulation would cause EDEM to crash. This bug was due to the configuration file and has now been fixed.Out-of-Memory and NaN ErrorsEDEM 2.1 uses improved quaternion calculations which fix several situations that could result in out-of-memory or NaN (not-a-number) errors.License Check-in ErrorsPreviously, running a simulation for a long period of time sometimes caused license check-in error messages to be displayed. This bug has now been fixed.Incorrect Particle Count in Bin Groups when Start Point greater than End Point Previously, setting any of the X,Y,Z start points of a bin group to be greater than the corresponding end point could result in an incorrect particle count. This bug has now been fixed.Dynamics Failing to Implement Moves-with-BodyStopping and starting a simulation with the Moves-with-Body geometry dynamics checkbox enabled could sometimes result in incorrect behavior. This bug has now been fixed.Collision Data Missing from Data ExportExporting collision data from simulations with multiple geometry elements could sometimes omit data for some elements. This bug has now been fixed.Incorrect Number-of-Contacts from Data ExportWith EDEM 2.0, exported contact data for more than one type of particle could be incorrect. This bug has now been fixed.Torque values in Nm Regardless of Unit SelectedWith EDEM 2.0, exporting or plotting geometry torque data always displayed values in Nm, regardless of the unit set using Options > Units. This bug has now been fixed. Changing the Domain During Simulation Removes Periodic BoundariesEnabling a Periodic Boundary checkbox then changing the model domain part-way through simulation previously removed the periodic boundary when the simulation was restarted. This bug has now been fixed.Typo in Example Source Plug-in Contact Model: Bonded ParticleThe plug-in bonded particle contact model supplied with EDEM 1.3.1 (BondedParticle.cpp) contained a typo in the code to calculate rolling friction, where variable nPhysicalCurvature1 was used instead of nPhysicalCurvature2. The code: if(!isZero(angVel2.lengthSquared())){CTorque torque2 = angVel2;torque2.normalise();torque2 *= -F_HM_n.length() * *nPhysicalCurvature1 * *nRollingFriction; T_damping_2 = torque2;}Has been corrected in EDEM 2.1 as follows:if(!isZero(angVel2.lengthSquared())){CTorque torque2 = angVel2;torque2.normalise();torque2 *= -F_HM_n.length() * *nPhysicalCurvature2 * *nRollingFriction; T_damping_2 = torque2;}GUI UpdatesThe EDEM GUI has been updated to fix various inconsistencies and to update icons, buttons, and dialogs. EDEM will now also remember its main window size and position. API Documentation UpdatesThe previous Programming Reference document has been greatly expanded into an EDEM 2.1 Programming Guide. This now includes details on the plug-in development process, the EDEM simulation sequence, and several new step-by-step examples.Known Issues in EDEM 2.1Deleting a .ppf file Removes Particles as well as Custom PropertiesWhen you use custom particle properties, a .ppf file is created containing information about the custom properties defined in the simulation. Deleting a .ppf file may result in particles being removed from your simulation.Particle Creation Mismatch in Solve ReportWhile a simulation is running, the number of particles reported as created in the Solve Report may differ from the factory’s Total Particles Created. When the simulation completes, these two figures collate correctly.Factory Particles Created Outside of GeometrySometimes, using an imported geometry section as a particle factory may result in a few particles being created outside of the geometry’s volume. These particles may eventually fall out of the domain, or they can be hidden from view using selection groups. Another workaround is to recreate the geometry or save the geometry as a different file format from your CAD package then re-import it into EDEM.Particle Explosion when Periodic Boundaries EnabledIf you enable periodic boundaries then define your simulation’s grid size to be only one grid cell wide, you may find particles “explode” when running the simulation. The workaround is to be sure you have at least two grid cells in the direction of the periodic boundary.You may find particles also explode when you enable a periodic boundary to set particles to re-enter the domain in the same space occupied by the particle factory. The workaround here is to be sure the factory is more than one particle’s diameter away from the boundary.Importing a Geometry File with Multiple Spaces in the FilenameSome geometry files will fail to import correctly when the filename has two or more periods (.) For example:wheel_v1.stp will import correctlywheel.v1.stp may not import correctlyThe workaround is to rename the geometry file before importing it into EDEM.Cannot Export Data to EnSightWhen creating a query to export data in EnSight format, some query options may not appear to be available at certain timesteps. For example, Particle or Contact queries. The workaround is to playback the simulation until particles are created, then select File > Export Data to define your query.Known Issues in EDEM-CFD Coupling Module for FLUENTOccasional Blank Graphics Display (Windows Only)There is an issue in FLUENT for Windows causing the graphics to sometimes not display. A workaround is add the switch -driver msw to the Fluent icon’s target:1. Right-click the Fluent icon from your desktop.2. Update the Target to end with –driver msw then click OK.FLUENT Crashes when Reading Multiple Case-and-Data FilesAttempting to read case and data files (File > Read > Case and Data) repeatedly can cause FLUENT to stop responding. A workaround is to restart FLUENT before opening another case and data file.Setting Changes not Saved in SimulatorWhen launching the Simulator from the EDEM Scheme Panel in FLUENT, you are unable to save any permanent changes to EDEM settings. To save permanent change to settings, start EDEM directly then make the changes there.Sharing Case Files Between Windows and Linux ClientsIf you want to use the same case file on both EDEM for Windows and for Linux, make a backup then use a text editor to update the case file to change file paths. For example, change:edem/path "/opt/Fluent.Inc/addons/edem/eulerian.jou"to:edem/path "C:\Fluent.Inc\addons\edem\eulerian.jou"Also search for and modify:f_inc <fluent_dir>and change the fcm-platform variable to either “WINDOWS” or “LINUX”.GCC_4.2.0 Library Error on Linux (32bit)Attempting to run the coupling module on Suse 10.2 Linux 32-bit or RedHat Enterprise Linux Workstation 4 may result in the following library error message:Opening library "/opt/DEMSolutions/lib/edem_udf" ...Error:/opt/Fluent.Inc/fluent6.3.26/lnx86/syslib/libgcc_s.so.1: version'GCC_4.2.0' not found (required by /usr/lib/libstdc++.so.6If you see this error message, type the following:# cd ../Fluent.Inc/fluent6.3.26/lnx86/syslib# mv libgcc_s.so.1 libgcc_s.so.1.old# ln -s /lib/libgcc_s.so.1 .FLUENT Segmentation Fault on LinuxSometimes FLUENT produces a segmentation fault when loading the EDEM panel on EDEM for Linux. If this happens, type the following then restart FLUENT:$ cd ~$ rm -rf .qt/$ exportLD_LIBRARY_PATH=/opt/DEMSolutions/EDEM_v2.1.0/lib:$LD_LIBRARY_PATH Licenses not Released Properly (Windows 64-bit, Suse and Redhat Linux)An EDEM license is sometimes checked-out every time a deck is loaded in FLUENT. If this happens, exit and restart FLUENT to release the license.Using Case Files from an Older Version of EDEM-FLUENT Coupling ModuleTo use case files created using an older version of the EDEM-FLUENT Coupling Module, make a backup then use a text editor to update the case file. Change the path for the udf/libname variable as follows:MS WindowsFrom:C:\Fluent.Inc\addons\edem\edem_udfto:C:\Program Files\DEM Solutions\EDEM v2.1.0\lib\edem_udfLinux (default)From:/opt/DEMSolutions/lib/edem_udfto:/opt/DEMSolutions/EDEM_v2.1.0/lib/edem_udfFLUENT Error Message when Uncoupling SimulationWhen changing the Coupling Method to Uncoupled, you may see the following error message in the FLUENT console:No Fluid Zones set for uncoupling from DEMThis message can be ignored as the simulation has been uncoupled correctly. To avoid the error message, select the coupled fluid zone before changing the coupling method to uncoupled.Cannot Save Simulator Changes from within FLUENTWhen launching the EDEM Simulator from within FLUENT, any changes you make in the Simulator apply to the current session only. To save your changes across sessions, start EDEM on its own then make and save your changes there.Error Loading Corresponding EDEM Timestep from FLUENT Case FileSometimes, when you load a coupled simulation into FLUENT, the timestep might be out of sync due to floating point errors. A workaround is to launch the EDEM Creator from the EDEM panel and set the Current Time manually.Loss of Display when using Cygwin X ServerWhen using the Cygwin X Server to access FLUENT and the Coupling Module remotely, you may occasionally lose your X-display connection. If this happens, either restart the X Server and try again or run FLUENT and the Coupling Module locally.。
第1篇Executive SummaryThis report presents the findings from a comprehensive data analysis of [Subject/Industry/Company Name]. The analysis was conducted using a variety of statistical and analytical techniques to uncover trends, patterns, and insights that are relevant to the decision-making process within the [Subject/Industry/Company Name]. The report is structured as follows:1. Introduction2. Methodology3. Data Overview4. Data Analysis5. Findings6. Recommendations7. Conclusion1. Introduction[Provide a brief overview of the report's purpose, the subject of the analysis, and the context in which the data was collected.]The objective of this report is to [state the objective of the analysis, e.g., identify market trends, assess customer satisfaction, or optimize business processes]. The data used in this analysis was sourced from [describe the data sources, e.g., internal databases, surveys, external market research reports].2. MethodologyThis section outlines the methods and techniques used to analyze the data.a. Data Collection- Describe the data collection process, including the sources of thedata and the methods used to collect it.b. Data Cleaning- Explain the steps taken to clean the data, such as removing duplicates, handling missing values, and correcting errors.c. Data Analysis Techniques- List the statistical and analytical techniques used, such asregression analysis, clustering, time series analysis, and machine learning algorithms.d. Tools and Software- Mention the tools and software used for data analysis, such as Python, R, Excel, and Tableau.3. Data OverviewIn this section, provide a brief overview of the data, including the following:- Data sources and types- Time period covered- Key variables and measures- Sample size and demographics4. Data AnalysisThis section delves into the detailed analysis of the data, using visualizations and statistical tests to illustrate the findings.a. Descriptive Statistics- Present descriptive statistics such as mean, median, mode, standard deviation, and variance for the key variables.b. Data Visualization- Use charts, graphs, and maps to visualize the data and highlight key trends and patterns.c. Hypothesis Testing- Conduct hypothesis tests to determine the statistical significance of the findings.d. Predictive Modeling- If applicable, build predictive models to forecast future trends or outcomes.5. FindingsThis section summarizes the key findings from the data analysis.- Highlight the most important trends, patterns, and insights discovered.- Discuss the implications of these findings for the[Subject/Industry/Company Name].- Compare the findings to industry benchmarks or past performance.6. RecommendationsBased on the findings, provide actionable recommendations for the [Subject/Industry/Company Name].- Outline specific strategies or actions that could be taken to capitalize on the insights gained from the analysis.- Prioritize the recommendations based on potential impact and feasibility.7. ConclusionConclude the report by summarizing the key points and reiterating the value of the data analysis.- Reiterate the main findings and their significance.- Emphasize the potential impact of the recommendations on the [Subject/Industry/Company Name].- Suggest next steps or future areas of analysis.Appendices- Include any additional information or data that supports the report but is not essential to the main narrative.References- List all the sources of data and any external references used in the report.---Note: The following is an example of how the report might be structured with some placeholder content.---Executive SummaryThis report presents the findings from a comprehensive data analysis of the e-commerce sales trends for XYZ Corporation over the past fiscal year. The analysis aimed to identify key patterns in customer behavior, sales performance, and market dynamics to inform strategic decision-making. The report utilizes a variety of statistical and analytical techniques, including regression analysis, clustering, and time series forecasting. The findings suggest several opportunities for improving sales performance and customer satisfaction.1. Introduction[Placeholder: Provide an introduction to the report, including the purpose and context.]2. Methodologya. Data CollectionData for this analysis was collected from XYZ Corporation's internal sales database, which includes transactional data for all online sales over the past fiscal year. The dataset includes information on customer demographics, purchase history, product categories, and sales performance metrics.b. Data CleaningThe data was cleaned to ensure accuracy and consistency. This involved removing duplicate entries, handling missing values, and correcting any inconsistencies in the data.c. Data Analysis TechniquesStatistical techniques such as regression analysis were used to identify correlations between customer demographics and purchase behavior. Clustering was employed to segment customers based on their purchasing patterns. Time series forecasting was used to predict future sales trends.d. Tools and SoftwarePython and R were used for data analysis, with Excel and Tableau for data visualization.3. Data OverviewThe dataset covers a total of 10 million transactions over the past fiscal year, involving over 1 million unique customers. The data includes information on over 5,000 product categories.4. Data Analysisa. Descriptive Statistics[Placeholder: Present descriptive statistics for key variables, such as average order value, customer acquisition cost, and customer lifetime value.]b. Data Visualization[Placeholder: Include visualizations such as line graphs for sales trends over time, bar charts for product category performance, and pie charts for customer segmentation.]c. Hypothesis Testing[Placeholder: Describe the hypothesis testing conducted, such as testing the relationship between customer age and spending habits.]d. Predictive Modeling[Placeholder: Outline the predictive models developed, such as a model to forecast sales based on historical data and external market indicators.]5. FindingsThe analysis revealed several key findings:- Customers aged 25-34 are the highest spenders.- The product category with the highest growth rate is electronics.- The company's customer acquisition cost is higher than the industry average.6. RecommendationsBased on the findings, the following recommendations are made:- Target marketing efforts towards the 25-34 age group.- Invest in marketing campaigns for the electronics product category.- Reduce customer acquisition costs by optimizing marketing channels.7. ConclusionThe data analysis provides valuable insights into XYZ Corporation's e-commerce sales performance and customer behavior. By implementing the recommended strategies, the company can improve its sales performance and enhance customer satisfaction.Appendices[Placeholder: Include any additional data or information that supports the report.]References[Placeholder: List all the sources of data and any external references used in the report.]---This template serves as a guide for structuring a comprehensive data analysis report. Adjust the content and format as needed to fit the specific requirements of your analysis and audience.第2篇Executive Summary:This report presents a comprehensive analysis of customer purchase behavior on an e-commerce platform. By examining various data points and employing advanced analytical techniques, we aim to uncover trends, patterns, and insights that can inform business strategies, enhance customer experience, and drive sales growth. The report is structuredinto several sections, including an overview of the dataset, methodology, results, and recommendations.1. Introduction1.1 Background:The rapid growth of e-commerce has transformed the retail landscape, offering businesses unprecedented opportunities to reach a global audience. Understanding customer purchase behavior is crucial for e-commerce platforms to tailor their offerings, improve customer satisfaction, and increase profitability.1.2 Objectives:The primary objectives of this analysis are to:- Identify key trends in customer purchase behavior.- Understand the factors influencing customer decisions.- Propose strategies to enhance customer satisfaction and drive sales.2. Dataset Overview2.1 Data Sources:The dataset used for this analysis is a combination of transactional data, customer demographics, and product information obtained from an e-commerce platform.2.2 Data Description:The dataset includes the following variables:- Customer demographics: Age, gender, location, income level.- Purchase history: Product categories purchased, purchase frequency, average order value.- Product information: Product category, price, brand, rating.- Transactional data: Purchase date, time, payment method, shipping address.3. Methodology3.1 Data Cleaning:Prior to analysis, the dataset was cleaned to address missing values, outliers, and inconsistencies.3.2 Data Exploration:Initial data exploration was conducted to identify patterns, trends, and relationships within the dataset.3.3 Statistical Analysis:Descriptive statistics were used to summarize the dataset and identify key characteristics of customer purchase behavior.3.4 Predictive Modeling:Advanced predictive models, such as regression analysis and clustering, were employed to identify factors influencing customer purchase decisions.3.5 Visualization:Data visualization techniques were used to present the results in an easily interpretable format.4. Results4.1 Customer Demographics:Analysis revealed that the majority of customers are between the ages of 25-34, with a slight male majority. Customers from urban areas tend to have higher average order values.4.2 Purchase Behavior:The dataset showed a strong preference for electronics and fashion products, with a significant number of repeat purchases in these categories. The average order value was highest during festive seasons and weekends.4.3 Influencing Factors:Several factors were identified as influential in customer purchase decisions, including product price, brand reputation, and customer reviews.4.4 Predictive Models:Predictive models accurately predicted customer purchase behavior based on the identified influencing factors.5. Discussion5.1 Key Findings:The analysis confirmed that customer demographics, product categories, and influencing factors play a significant role in shaping purchase behavior on the e-commerce platform.5.2 Limitations:The analysis was limited by the availability of data and the scope of the study. Further research could explore the impact of additional factors, such as marketing campaigns and social media influence.6. Recommendations6.1 Enhancing Customer Experience:- Implement personalized product recommendations based on customer purchase history.- Offer targeted promotions and discounts to encourage repeat purchases.6.2 Improving Marketing Strategies:- Allocate marketing budgets to products with high customer demand and positive reviews.- Develop targeted marketing campaigns for different customer segments.6.3 Product Development:- Invest in product development based on customer preferences and feedback.- Monitor market trends to stay ahead of the competition.7. ConclusionThis report provides valuable insights into customer purchase behavior on an e-commerce platform. By understanding the factors influencing customer decisions, businesses can tailor their strategies to enhance customer satisfaction and drive sales growth. The recommendations outlined in this report can serve as a roadmap for businesses looking to capitalize on the e-commerce market.References:- Smith, J., & Johnson, L. (2020). "Customer Purchase Behavior in E-commerce: A Review." Journal of E-commerce Studies, 15(2), 45-60.- Brown, A., & White, M. (2019). "The Role of Customer Demographics inE-commerce Success." International Journal of Marketing Research, 12(3), 78-95.Appendix:- Detailed data visualization plots and tables.- Code snippets for predictive modeling.---This template provides a comprehensive structure for an English reporton data analysis. You can expand on each section with specific data, insights, and recommendations tailored to your dataset and analysis objectives.第3篇---Executive SummaryThis report presents the findings of a comprehensive data analysis conducted on [Subject of Analysis]. The analysis aimed to [State the objective of the analysis]. The report outlines the methodology employed, the key insights derived from the data, and the recommendations based on the findings.---1. Introduction1.1 BackgroundProvide a brief background on the subject of analysis, including any relevant historical context or industry trends.1.2 ObjectiveClearly state the objective of the data analysis. What specificquestions or problems are you trying to address?1.3 ScopeDefine the scope of the analysis. What data sources were used? What time frame is covered?---2. Methodology2.1 Data CollectionExplain how the data was collected. Describe the data sources, data collection methods, and any limitations associated with the data.2.2 Data ProcessingDetail the steps taken to process the data. This may include data cleaning, data transformation, and data integration.2.3 Analytical TechniquesDescribe the analytical techniques used. This could include statistical analysis, predictive modeling, machine learning, or other relevant methods.2.4 Tools and SoftwareList the tools and software used in the analysis. For example, Python, R, SAS, SPSS, Excel, etc.---3. Data Analysis3.1 Descriptive StatisticsPresent descriptive statistics such as mean, median, mode, standard deviation, and variance to summarize the central tendency and spread of the data.3.2 Data VisualizationUse charts, graphs, and maps to visualize the data. Explain what each visualization represents and how it contributes to understanding the data.3.3 Hypothesis TestingIf applicable, discuss the hypothesis testing conducted. State the null and alternative hypotheses, the test statistics, and the p-values.3.4 Predictive ModelingIf predictive modeling was part of the analysis, describe the model built, the evaluation metrics used, and the model's performance.---4. Key Insights4.1 Major FindingsSummarize the major findings of the analysis. What trends, patterns, or relationships were discovered?4.2 ImplicationsDiscuss the implications of the findings for the business, industry, or research question at hand.4.3 LimitationsAcknowledge any limitations of the analysis. How might these limitations affect the validity or generalizability of the findings?---5. RecommendationsBased on the findings, provide actionable recommendations. These should be practical, specific, and tailored to the context of the analysis.5.1 Short-term RecommendationsOffer recommendations that can be implemented in the near term to address immediate issues or opportunities.5.2 Long-term RecommendationsProvide recommendations for strategies that can be developed over a longer period to support sustainable outcomes.---6. ConclusionReiterate the main findings and their significance. Emphasize the value of the analysis and how it contributes to the understanding of the subject matter.---7. AppendicesInclude any additional material that supports the report but is not essential to the main body. This could be detailed data tables, code snippets, or additional visualizations.---ReferencesList all the sources cited in the report, following the appropriate citation style (e.g., APA, MLA, Chicago).---8. About the AuthorProvide a brief biography of the author(s) of the report, including their qualifications and relevant experience.---9. Contact InformationInclude the contact information for the author(s) or the organization responsible for the report.---This template is designed to be flexible, allowing you to tailor the content to the specific requirements of your data analysis project. Remember to ensure that the report is clear, concise, and accessible to the intended audience.。
PROfLINE 2100HArMonICS & FLICKEr, CondUCTEd IMMUnITy TEST SySTEMS1981HArMonICS & FLICKEr, CondUCTEd IMMUnITy TEST SySTEMS3PROfLINE 2100 OvERvIEwThe ProfLine 2100 system is a complete and cost effective harmonics and flicker measurement test system to the latest IEC/EN standards. The programmable power generation capability of up to 45 kVA (90 kVA and 145 kVA sources comprise multiple 45 kVA units) provides more than ample power to cater for a wide range of Equipment Under Test (EUT). In addition to harmonics and flicker testing capability the AC/DC power source used in the system is capable of testing to a wide range of power quality immunity tests. In short, this system is a one stop power quality testing station that will help you meet your EMC responsibilities for compliance testing.Harmonics standard:IEC 61000-3-2 < 16 A per phaseIEC 61000-3-12 > 16 to 75 A per phaseFlicker standard:IEC 61000-3-3 < 16 A per phaseIEC 61000-3-11 < 75 A per phaseVoltage dip, interruption & variation:IEC 61000-4-11 < 16 A per phaseIEC 61000-4-34 > 16 A per phaseother immunity tests:IEC 61000-4-8 Power line magnetic fieldIEC 61000-4-13 Immunity to harmonics & inter-harmonicsIEC 61000-4-14 Repetitive voltage variationsIEC 61000-4-17 Ripple on DC input power portsIEC 61000-4-27 Voltage & Phase unbalance immunityIEC 61000-4-28 Frequency variationsIEC 61000-4-29 DC dips, variation and short interruptionALL ThE POwER LEvELS YOU NEED designed and widely used for compliance testing of equipment up to 45 kVA (90kVA and 145 kVA sources comprise multiple 45 kVA units), Teseq’s ProfLine 2100system is ideal for:Test houses requiring high precision tools for compliance and pre-compliance testing Manufacturers requiring AC & DC test tools for both in-house/self certification and product developmentRental companies requiring precise, reliable, portable harmonics & flicker systems for on-site customer testingProfLine 2100: highly modular compliance test power capability Programmable IEC compliant AC power sources accommodate wide range of 1- and 3-phase power levelsUltra-fast digital power analyzer provides high resolution acquisition for accuratemeasurementIEC 60725-compliant reference impedance ensures accurate flicker measurementAll electrical data is stored for complete evaluation and test replay analysisWindows-based operation speeds set-up, analysis, display and reportingContinuous pass/fail status monitoring4The high repetitive peak current. AC power source is designed for demanding non-linear load applications such as white goods, air-conditioners and other products with inductive or capacitive loads. The 45 kVA (90 kVA and 145 kVA sources comprise multiple 45 kVA units) source is specially designed with regenerative load withstand capability. It can handle power generated back to the source which is common in AC motor and motor control applications.3 kVA test system. Ideal for manufacturer not requiring the full 16 amps of the standard require-ments.5 kVA to 15 kVA test systems. Cater for manufacturers, test houses and rental companies requiring the full 16 amp range.1- and 3-phase configuration up to 45 kVA. This power house is ideal for the manufacturerand test houses that require the full range of low and high current testing such as required forcompressors, air conditioners and machine tools. ArrayFully featured 3x5 kVA harmonics and flickersystem including 3 phase power quality testingAC switch for compliant IEC 61000-4-11 testingdC to 500 Hz fundamental frequencyLow output impedanceSupports power magnetics applicationsIEC 61000-4-13 testing5hIGh ACCURACYMEASUREMENT vERIfIEDAt the heart of the ProfLine 2100 system is a fully compliant harmonics analyzer and flickermeter. DSP-based 1 M sample per second, no-gap/no overlap 200 mS data acquisition and powerful FFT analysis ensures full compliance harmonics testing based on IEC 61000-4-7. Direct PC bus access ensures higher data throughput than is found on most single box IEEE-488-based test system. Streaming real-time data display and storage allows measured data to be replayed and analyzed in complete confidence, speeding up fault detection.All EUT electrical parameters are monitored and stored continuously. Distortion, current har-monics and power consumption are checked against relevant IEC class test limits for pass/fail detection and dynamic class C and D test limit calculation.Independent verification has confirmed the following is correctly implemented: Measurement accuracy for electrical parameters such as voltage, current, harmonics and flickermeter is as per IEC requirementSoftware applies relaxation as and when the situation warrants it for pass/fail decision Compliance to all test equipment requirements as per IEC 61000-4-7 and IEC 61000-4-15A true measure of class. The unique concept for the ProfLine 2100 system measurement section is a cutting edge PC based analyzer. The measurement section is split into two parts, one being the advanced coupling unit CCN 1000 whilst the PC provides the digitization of the analogue signals, data processing and analysis. This approach has been extremely successful in keeping up with changes to the standards that demanded major increase in data processing and analysis capability.CCn 1000. This advanced coupling unit provides quick and easy single cable connection between the AC power source output and the EUT, plus the required isolation and signal con-ditioning. Precision, no-burden, active hall-effect current transformers ensure accurate current sensing over 4 A, 16 A and 40 A ranges simultaneously with 200 A peak capability for maximum resolution.6data Acquisition Unit Input ChannelsAll harmonics tests can be accessed from the ProfLine 2100’s single control and data display window on the PC. With a few mouse clicks the test can be set up and run quickly and easily.The operator is presented with a simple screen that shows the type of test to be run and the test duration, with clearly labelled buttons for the test to start or stop. Voltage and current time domain waveform displays are updated in real time during the test. All power analyzer parameters such as Vrms, Irms, Ifundemental, Ipeak, crest factor, real power, apparent power and power factor are clearly displayed throughout the test and updated in real time.The harmonics window displays instantaneous current harmonics and a line marking the appli-cable test limits. AC source voltage and EUT power are also monitored continuously throughout the entire test. Voltage distortion and current harmonics are checked against the IEC class limits for preliminary pass/fail detection. The continuous monitoring of EUT power consumption allows class C and D limits to be calculated dynamically.Harmonics analysis is implemented using the high performance DSP based plug-in A/D card connected directly to the CCN 1000 signal conditioning unit through a shielded cable. Each Power phase has four dedicated measurement channels- a total of 12 in 3-phase systems – ensuring accurate full compliance to the harmonics standard.The software will also automatically apply any relaxation of limits (e.g. POHC) should the situation warrant it and will indicate this in the test report.78All IEC harmonics tests can be accessed from ProfLine 2100’s single control and data display window on the PC. Steady state harmonic, transitory harmonic and inter-harmonic tests can be set up and run quickly and easily.Simple buttons start and stop automated testKey EUT electrical parameters updated continuouslyUser selectable test limitsTest progress clearly indicated, with preliminary pass/fail indication throughout AC voltage distortion continuously monitoredComplete test documentation including Word™ and Excel™ compatible data files Voltage and current waveform shown together in real timeUser-selectable real time display of individual current harmonicsEUT description and operator identification can be added to the test reportUser selectable measurement of inter-harmonics per IEC 61000-4-7Harmonics AnalysishARMONICS TEST SOfTwARE wIN 21009Seven simple steps to configure a harmonic test, configurationcan be saved for single step test start.Parameters required:1 Select harmonic test2 Select class A, B, C, D3 Select frequency 50/60 Hz4 Select test voltage5 Select limit, European or Japanese6 Select single or three phase7 Select test durationAll test parameters are displayed in real time, includingharmonic spectrum viewed against limit, test progress, voltage andcurrent waveforms.report can be viewed in Word™ format using inbuilt standardtemplate. Data files can be viewed with Excel™.fLICkER TEST MADE EASYFlicker tests are run from the same user interface as the harmonics module, making it familiar to the user. Set up is minimal and tests run can be started quickly.during each test run two graphical windows are displayed and updated continuously. One window will display the Vrms whilst the other can be user selected to display absolute voltage deviation or percentage, dt, dmax, dc, instantaneous Pst or Plt against their respective limits. At the end of the test sequence, both short-term flicker (Pst) and long-term (Plt) are calculated and a clear pass/fail indication is provided.Embedded in the ProfLine 2100 software is an IEC 61000-4-15 compliant single/three-channel flickermeter for 1- and 3-phase application. Single phase output configuration can use both the programmable and real IEC 60725-compliant output impedance to perform flicker measurement. Lumped reference impedance for 1- and 3-phase system impedances with varying current carrying capacity are available as an option.Power Source Reference Impendance Analyzer/Flickermeter EUTFlicker Test SoftwareStart and stop flicker tests with a single mouse clickTest progress clearly indicated with pass/fail indication throughoutPeak values displayed and updated in real timeUser-selectable test timeUser selectable parameters and data display optionCustomizable test limits for pre-compliance applicationReal time display of Vrms and one user selectable parameterEUT description and operator identification can be entered for inclusion in the test report24 dmax and inrush current test10Flicker Analysisreference impedance. For single phase systems the impedance is programmed into the source, therefore no physical impedance is required thus making the system more simple and lower costs. This approach is not possible in the three phase systems as it is not possible to separate the line and neutral impedances. Therefore the appropriate three phase impedance unit is supplied as part of the system.Test reports and data Logging. Reports can be printed at the end of each test report or retrospectively to support CE approval or for inclusion in a Technical Report File. The results file includes voltage and current waveform graphs, current harmonics spectrum and class limits and a complete flicker test analysis. The graph can be printed or stored in ASCII format on disc along with timing waveform data for use in detailed reporting or for further analysis using applications such as Excel.1112nSG 2200 AC Switch unit for complaint -4-11 and -4-34 testing. Available as either singleor three phase, these units use solid state IGBTs to rapidly switch between two sources of ACsupply. Typically this will be between the mains supply and a programmable AC source. TheAC source will be set at the lower voltage required for the test with the mains supplying thehigher.Controlled by Teseq WIN 2120 software and able to switch within the required 5 μs this deviceenables the standard to be fully met. Since the higher voltage level is supplied by the usersmains system, the inrush current is limited only by the mains supply and not by the equipment.The NSG 2200 is able to handle 50 amps rms current continuously and up to 500 amps inrush current.AC fast switching unit for standards specifiedin the IEC 61000-4-11Unit has two inputs, AC source and AC MainsAllows for single- or three-phase mode testingAC SwitchAC SwITChING UNIT13Magnetic field immunity testing. The power sources in the ProfLine 2100 systems makean ideal source for mains frequency magnetic field testing. Used in conjunction with the TeseqINA 2170 test coil and interface unit the supplies can be controlled by the WIN 2120 software togenerate the required fields and frequencies.Use of the clean sinusoidal programmable supply ensures that tests can be performed witheither 50 Hz or 60 Hz for different regions. Both the continuous and short duration tests can beeasily programmed at levels up to 100 A/m continuous and 300 A/m short duration dependingon the selection of source.Magnetic coilsIEC 61000-4-8 power frequency fieldAutomated test softwareAdjustable single loop antenna in 3 positionsNote: maximum and continuous coil field strengths can only be achieved using the correctlyspecified NSG 1007 AC/DC Power Source. INA 2170 coils can also be used for IEC 61000-4-9testing in conjunction with Teseq’s nSG 3060 generators.MAGNETIC fIELDIMMUNITY TEST COILSPROfLINE 2100: MORE ThANjUST hARMONICS & fLICkERProfLine 2100 has the hardware and software flexibility to test to beyond harmon-ics and flicker emission. The fully programmable AC power source with arbitrary waveform generation capability can be used in standalone mode in various applications for IEC 61000-4-X testing at pre or full-compliance. The ProfLine system has built in IEC 61000-4-13 immunity testing to harmonics and inter-harmonics standard which sets this system apart as a fully equipped test station for power quality.IEC 61000-4-8: Power frequency magnetic field immunity. Using the power source built into the ProfLine 2100 system the frequency and test level can be accurately controlled. This is ideal if your target market uses a different mains system to your local supply.Loop antenna, interface unit and control software (WIN 2120) are available as options.IEC 61000-4-11: AC Voltage dips, short interruptions and variations. The 1–5 µs rise and fall time and the 500 amp inrush current requirements of the standard for voltage dips and interruptions mean that a power source alone cannot meet the standard.The NSG 2200 AC switch can switch between a power source and the mains supply within the required time enabling the user to meet both requirements.IEC 61000-4-13: Immunity to harmonics and inter-harmonics. ProfLine 2100’s built in sweep generator provides full compliance testing to IEC 61000-4-13. Simple pre-programmed test levels at various test classes makes testing simple. At a click of the start button the two digitally controlled generators superimpose harmonics and inter-harmonics up to the 40th harmonics order (2 kHz for 50 Hz and 2.4 kHz for 60 Hz). The programmable AC power source generates combination waveforms better known as the flat top, overswing and meister curve, tests individual harmonics, and does a sweep to check for resonance points. The user can then go back to those resonance frequencies and test again. The operator can record any unusual behaviour at the observation section which will be included in the report. Pass/fail decision will be determined by the user based on the evaluation of the EUT during the test.1415IEC 61000-4-14: Voltage Fluctuation. A simple screen allows the operator to select the levelof severity of test to be run and the desired nominal test voltage and frequency. All voltagefluctuation test parameters can be customized by the user as required, ensuring the ProfLine2100 fully meets the standard. During testing, the EUT load current is measured continuously tohelp the operator observe and diagnose potential unit failures.IEC 61000-4-17: Ripple on DC input power ports. The test sequence implemented by thistest consists of the application of an AC ripple of specified peak to peak value as a percentageof the DC voltage and at a frequency determined as a multiple of the AC Line frequency. Theripple waveform consists of a sinusoidal linear waveshape. The user selectable severity levelscan easily meet the multiple of the power frequency of 1, 2, 3, 6 and at the user specified levelup to a staggering 20 times the power frequency at 25% Vdc-peak-peak.IEC 61000-4-27: Voltage and phase unbalance. This test is only for three-phase systemsas it involves voltage and phase unbalance between phases of a three phase supply network.Voltage unbalances are applied at different levels depending on product categories. The usermust determine the product class and select the appropriate test level. During the test run,voltage and phase changes are applied. The voltage levels and phase shifts are determined bythe values set in the data entry grid. Predefined test level are also provided to help the operatorwith the settings.Note: The ProfLine 2100 does not fully meet the IEC 61000-4-27 in respect of this particular test,1–5 µs rise fall rate not achievable and maximum output voltage is 300 V. So whilst it can meetthe 110% of U nom required by the product standards (110% of 230 V is 253 V) it does not reachthe 150% of U nom mentioned in the equipment standard (150% of 230 V is 345 V). 45 kVA unitshave a 400 Volt option.16IEC 61000-4-28: Frequency variation. The system provides an open field for the operator toenter the amount of frequency variation or simply load and amend the predefined tests levelprovided. Test parameters for the duration and frequency deviation can be easily customized,enabling ProfLine 2100 to meet this standard should there be changes to it in the future.IEC 61000-4-29: dC dips, variations and short interruptions. Pre-compliance test for DCvoltage dips can be set up quickly using the software. The test sequence implemented by this testconsists of a series of DC voltage dips (to less than DC nominal) or interruptions (dip to 0 V). It isalso possible to select voltage variations which cause the DC voltage to change at a programmedrate to a specified level and then return at the same or a different rate to the nominal DC level.These dips and variations can be applied at different levels and durations for different productcategories. The user must determine the product class and select the appropriate test file. Theselected levels and durations are visible on screen and can be edited and saved to a new setup fileif needed. This allows a library of test files for specific product categories to be created. Accordingto the standard, the use of a test generator with higher or lower voltage or current capability isallowed provided that the other specifications are preserved. The test generator steady statepower/current capability shall be at least 20% greater than the EUT power/current ratings.This means that for many EUT’s a 25 A capable generator is not needed. However, since the riseand fall time requirements may not be met under all circumstances, this is a pre-compliancetest only.IEC 61000-4-34: AC Voltage dips, short interruptions and variations. Similar to IEC61000-4-11 but applying to equipment requiring greater than 16 amps per phase, this standardcan be met by the higher power models in the range. Teseq is ready to advise you on the idealconfiguration and to discuss the limitations on the maximum current due to the selection of thevarious units in the system.SYSTEM SELECTION ChART1Requires option 2/32Current limited by source to 37 amps at 230 volts3Current limited by source to 62 amps at 230 volts4Requires option 8 (100 A/m continuous field)5Requires option 8 (100 A/m continuous field and 300 a/m for 3 seconds)6Requires option 117Pre-Compliance only, generator is not fully compliant with all aspects of the standard 816 to 37 amps at 230 volts916 to 62 amps at 230 volts PL 2115 plus option 11-3* Figures quoted are the maximum current available from the system. The current limit is in some cases due to the source and in some cases due to other equipment in the system. For information on the maximum power available from the sources please contact your local Teseq office.PL 2103/PL 210517Specifications subject to change without notice.All trademarks recognized.Teseq is an ISO-registered company. Its products are designed and manufactured under the strict quality and environmental requirements of the ISO 9001. This document has been carefully checked. However, Teseq does not assume any liability for errors, inaccuracies or changes due to technical developments.。
日常管理制度英文缩写Here are some key components of a robust Daily Management System:1. Standard Operating Procedures (SOPs): SOPs outline the steps and guidelines for performing specific tasks or activities within the organization. They help ensure consistency and quality in the execution of daily operations.2. Performance Metrics: Performance metrics are quantifiable measures that help track and evaluate progress towards achieving organizational goals. These metrics can include key performance indicators (KPIs), targets, and benchmarks.3. Daily Huddles: Daily huddles are brief meetings held at the beginning of each day to review priorities, goals, and updates on current projects. They provide an opportunity for team members to align on objectives and identify any potential roadblocks.4. Gemba Walks: Gemba walks involve senior leadership visiting the actual work area to observe operations, interact with employees, and gain a deeper understanding of processes. This practice promotes open communication, employee engagement, and continuous improvement.5. Visual Management: Visual management involves using visual cues such as charts, graphs, and boards to communicate information, monitor performance, and promote transparency. Visual management helps employees stay informed and motivated to achieve their targets.6. Root Cause Analysis: Root cause analysis is a problem-solving technique used to identify the underlying reasons for issues or defects. By digging deeper into the root causes, organizations can implement effective solutions and prevent similar problems from occurring in the future.7. PDCA Cycle: The PDCA (Plan-Do-Check-Act) cycle is a continuous improvement framework that involves planning, implementing, evaluating, and adjusting processes to drive ongoing performance improvements. The PDCA cycle fosters a culture of learning, experimentation, and innovation within the organization.8. 5S Methodology: The 5S methodology (Sort, Set in order, Shine, Standardize, Sustain) is a systematic approach to organizing the workplace for efficiency, safety, and productivity. By implementing 5S practices, organizations can reduce waste, improve workflows, and createa clean and organized work environment.9. Kaizen Events: Kaizen events are focused, short-term activities that bring together cross-functional teams to tackle specific challenges and drive continuous improvement. Kaizen events help identify opportunities for optimization, implement changes quickly, and promote collaboration among employees.10. Daily Management Reviews: Daily management reviews are regular meetings where leadership reviews performance data, discusses issues, and makes decisions to addressoperational challenges. These reviews provide a forum for setting priorities, allocating resources, and driving strategic initiatives.In conclusion, a well-designed Daily Management System is crucial for optimizing organizational performance, fostering a culture of continuous improvement, and achieving sustainable success in today's competitive business environment. By implementing the key components outlined above, companies can enhance operational efficiency, drive employee engagement, and deliver value to customers.。
Study Guide and Intervention and Practice WorkbookTo the Student This Study Guide and Intervention and Practice Workbook gives you additional examples and problems for the concept exercises in each lesson.The exercisesare designed to aid your study of mathematics by reinforcing important mathematical skills needed to succeed in the everyday world.The materials are organized by chapter andlesson, with one Study Guide and Intervention and Practice worksheet for every lesson in Glencoe Math Connects, Course 1.Always keep your workbook handy.Along with your textbook, daily homework, and class notes, the completed Study Guide and Intervention and Practice Workbook can help you in reviewing for quizzes and tests.To the Teacher These worksheets are the same ones found in the Chapter Resource Masters for Glencoe Math Connects, Course 1.The answers to these worksheets areavailable at the end of each Chapter Resource Masters booklet as well as in your T eacher Wraparound Edition interleaf pages.Copyright ©by The McGraw-Hill Companies, Inc.All rights reserved.Except as permitted under the United States Copyright Act, no part of thispublication may be reproduced or distributed in any form or by any means, orstored in a database or retrieval system, without prior written permission of thepublisher.Send all inquiries to:Glencoe/McGraw-Hill8787 Orion PlaceColumbus, OH 43240ISBN:978-0-07-881032-9MHID:0-07-881032-9Study Guide and Intervention and Practice Workbook, Course 1 Printed in the United States of America1 2 3 4 5 6 7 8 9 10 009 13 12 11 10 09 08 07Lesson/Title Page 1-1 A Plan for Problem Solving (1)1-1 A Plan for Problem Solving (2)1-2Prime Factors (3)1-2Prime Factors (4)1-3Powers and Exponents (5)1-3Powers and Exponents (6)1-4Order of Operations (7)1-4Order of Operations (8)1-5Algebra:Variables and Expressions (9)1-5Algebra:Variables and Expressions (10)1-6Algebra:Functions (11)1-6Algebra:Functions (12)1-7Problem-Solving Investigation:Guess and Check (13)1-7Problem-Solving Investigation:Guess and Check (14)1-8Algebra:Equations (15)1-8Algebra:Equations (16)1-9Algebra:Area Formulas (17)1-9Algebra:Area Formulas (18)2-1Problem-Solving investigation:Make a T able (19)2-1Problem-Solving investigation:Make a T able (20)2-2Bar Graphs and Line Graphs (21)2-2Bar Graphs and Line Graphs (22)2-3Interpret Line Graphs (23)2-3Interpret Line Graphs (24)2-4Stem-and-Leaf Plots (25)2-4Stem-and-Leaf Plots (26)2-5Line Plots (27)2-5Line Plots (28)2-6Mean (29)2-6Mean (30)2-7Median, Mode and Range (31)2-7Median, Mode and Range (32)2-8Selecting an Appropriate Display (33)2-8Selecting an Appropriate Display (34)2-9Integers and Graphing (35)2-9Integers and Graphing (36)3-1Representing Decimals (37)3-1Representing Decimals (38)3-2Comparing and Ordering Decimals (39)3-2Comparing and Ordering Decimals (40)3-3Rounding Decimals (41)3-3Rounding Decimals (42)3-4Estimating Sums and Differences (43)3-4Estimating Sums and Differences (44)3-5Adding and Subtracting Decimals (45)3-5Adding and Subtracting Decimals (46)3-6Multiplying Decimals by WholeNumbers (47)3-6Multiplying Decimals by WholeNumbers..............................................48Lesson/Title Page 3-7Multiplying Decimals (49)3-7Multiplying Decimals (50)3-8Dividing Decimals by WholeNumbers (51)3-8Dividing Decimals by WholeNumbers (52)3-9Dividing by Decimals (53)3-9Dividing by Decimals (54)3-10Problem-Solving Investigation:Reasonable Answers (55)3-10Problem-Solving Investigation:Reasonable Answers (56)4-1Greatest Common Factor (57)4-1Greatest Common Factor (58)4-2Simplifying Fractions (59)4-2Simplifying Fractions (60)4-3Mixed Numbers and ImproperFractions (61)4-3Mixed Numbers and ImproperFractions (62)4-4Problem-Solving Investigation:Makean Organized List (63)4-4Problem-Solving Investigation:Makean Organized List (64)4-5Least Common Multiple (65)4-5Least Common Multiple (66)4-6Comparing and Ordering Fractions (67)4-6Comparing and Ordering Fractions (68)4-7Writing Decimals as Fractions (69)4-7Writing Decimals as Fractions (70)4-8Writing Fractions as Decimals (71)4-8Writing Fractions as Decimals (72)4-9Algebra:Ordered Pairs andFunctions (73)4-9Algebra:Ordered Pairs andFunctions (74)5-1Rounding Fractions and MixedNumbers (75)5-1Rounding Fractions and MixedNumbers (76)5-2Problem-Solving Investigation:Act It Out (77)5-2Problem-Solving Investigation:Act It Out (78)5-3Adding and Subtracting Fractionswith Like Denominators (79)5-3Adding and Subtracting Fractionswith Like Denominators (80)5-4Adding and Subtracting Fractionswith Unlike Denominators (81)5-4Adding and Subtracting Fractionswith Unlike Denominators (82)5-5Adding and Subtracting MixedNumbers (83)CONTENTSiiiLesson/Title Page 5-5Adding and Subtracting MixedNumbers (84)5-6Estimating Products of Fractions (85)5-6Estimating Products of Fractions (86)5-7Multiplying Fractions (87)5-7Multiplying Fractions (88)5-8Multiplying Mixed Numbers (89)5-8Multiplying Mixed Numbers (90)5-9Dividing Fractions (91)5-9Dividing Fractions (92)5-10Dividing Mixed Numbers (93)5-10Dividing Mixed Numbers (94)6-1Ratios and Rates (95)6-1Ratios and Rates (96)6-2Ratio T ables (97)6-2Ratio T ables (98)6-3Proportions (99)6-3Proportions (100)6-4Algebra:Solving Proportions (101)6-4Algebra:Solving Proportions (102)6-5Problem-Solving Investigation:Lookfor a Pattern (103)6-5Problem-Solving Investigation:Lookfor a Pattern (104)6-6Sequences and Expressions (105)6-6Sequences and Expressions (106)6-7Proportions and Equations (107)6-7Proportions and Equations (108)7-1Percents and Fractions (109)7-1Percents and Fractions (110)7-2Circle Graphs (111)7-2Circle Graphs (112)7-3Percents and Decimals (113)7-3Percents and Decimals (114)7-4Probability (115)7-4Probability (116)7-5Constructing Sample Spaces (117)7-5Constructing Sample Spaces (118)7-6Making Predictions (119)7-6Making Predictions (120)7-7Problem-Solving Investigation:Solvea Simpler Problem (121)7-7Problem-Solving Investigation:Solvea Simpler Problem (122)7-8Estimating with Percents (123)7-8Estimating with Percents (124)8-1Length in the Customary System (125)8-1Length in the Customary System (126)8-2Capacity and Weight in theCustomary System (127)8-2Capacity and Weight in theCustomary System (128)8-3Length in the Metric System (129)8-3Length in the Metric System (130)8-4Mass and Capacity in the MetricSystem (131)Lesson/Title Page 8-4Mass and Capacity in the MetricSystem (132)8-5Problem-Solving Investigation:UseBenchmarks (133)8-5Problem-Solving Investigation:UseBenchmarks (134)8-6Changing Metric Units (135)8-6Changing Metric Units (136)8-7Measures of Time (137)8-7Measures of Time (138)8-8Measures of T emperature (139)8-8Measures of T emperature (140)9-1Measuring Angles (141)9-1Measuring Angles (142)9-2Estimating and Drawing Angles (143)9-2Estimating and Drawing Angles (144)9-3Angle Relationships (145)9-3Angle Relationships (146)9-4T riangles (147)9-4T riangles (148)9-5Quadrilaterals (149)9-5Quadrilaterals (150)9-6Problem-Solving Investigation:Draw a Diagram (151)9-6Problem-Solving Investigation:Draw a Diagram (152)9-7Similar and Congruent Figures (153)9-7Similar and Congruent Figures (154)10-1Perimeter (155)10-1Perimeter (156)10-2Circles and Circumferences (157)10-2Circles and Circumferences (158)10-3Area of Parallelograms (159)10-3Area of Parallelograms (160)10-4Area of T riangles (161)10-4Area of T riangles (162)10-5Problem-Solving Investigation:Make a Model (163)10-5Problem-Solving Investigation:Make a Model (164)10-6Volume of Rectangular Prisms (165)10-6Volume of Rectangular Prisms (166)10-7Surface Area of Rectangular Prisms..167 10-7Surface Area of Rectangular Prisms..168 11-1Ordering Integers (169)11-1Ordering Integers (170)11-2Adding Integers (171)11-2Adding Integers (172)11-3Subtracting Integers (173)11-3Subtracting Integers (174)11-4Multiplying Integers (175)11-4Multiplying Integers (176)11-5Problem-Solving investigation:Workbackward (177)11-5Problem-Solving investigation:Workbackward (178)ivLesson/Title Page 11-6Dividing Integers (179)11-6Dividing Integers (180)11-7The Coordinate Plane (181)11-7The Coordinate Plane (182)11-8T ranslations (183)11-8T ranslations (184)11-9Reflections (185)11-9Reflections (186)11-10Rotations (187)11-10Rotations (188)12-1The Distributive Property (189)12-1The Distributive Property (190)12-2Simplifying Algebraic Expressions (191)12-2Simplifying Algebraic Expressions (192)12-3Solving Addition Equations (193)12-3Solving Addition Equations (194)12-4Solving Subtraction Equations (195)12-4Solving Subtraction Equations (196)12-5Solving Multiplication Equations (197)12-5Solving Multiplication Equations (198)12-6Problem-Solving Investigation:Choose the Best Method ofComputation (199)12-6Problem-Solving Investigation:Choose the Best Method ofComputation (200)v1-1 Example 1 Exercises1-1PATTERNS Complete each pattern.1-2 Example 1 Example 2 Exercises1-2Tell whether each number is1-3Example 1 Example 2 Example 3 ExercisesWrite each product using an exponent.Example 1 Example 2ExercisesFind the value of each expression.Example 1 Example 2 Example 3 Example 4 ExercisesExample 1 Example 2 ExercisesComplete each function table.Example 1 ExerciseMixed Problem SolvingUse the guess and check strategy toExample 1 Example 2 ExercisesIdentify the solution of each equation from the list given.Example 1 Example 2 ExercisesFind the area of each rectangle.Example 1 ExerciseMixed Problem SolvingUse the make a table strategy to solveExample 1 Example 2 Exercises1.ANIMALSExample 1 ExercisesSPORTS For Exercises 1–3,Example 1 ExerciseMake a stem-and-leaf plot for each set of data.Example 1Example 2 ExercisesPRESIDENTS For Exercises 1–4,Example 1 Example 2 ExercisesFind the mean of the data represented in each model.Example 1 Example 2 ExercisesFind the median,Example 1Example 2 Exercises1.FOOD Which display makes it easier to see theExample 1 Example 2 ExercisesWrite an integer to represent each situation.Example 2 Example 1ExercisesWrite each decimal in word form.Example 1 Example 2 ExercisesExample 1 Example 2 ExercisesRound each decimal to the indicated place-value position.Example 1 Example 2 ExercisesEstimate using rounding.Example 1 Example 2 Exercises。
绩效评估报告英文Performance Evaluation Report1. IntroductionThis report presents the performance evaluation of [CompanyName/Department/Individual] for the [Time Period]. The purpose of this evaluation is to assess the overall performance and achievements against the set goals and objectives. The evaluation is based on a comprehensive analysis of key performance indicators, feedback from superiors and peers, and a review of the individual's or team's performance throughout the period.2. MethodologyThe evaluation was conducted through a combination of quantitative and qualitative assessment methods. Key performance indicators such as revenue generated, customer satisfaction ratings, and timely completion of tasks were measured to assess the quantitative performance. Qualitative feedback was collected through surveys, interviews, and performance discussions.3. Performance SummaryBased on the assessment, the overall performance of [CompanyName/Department/Individual] during the [Time Period] can be summarized as follows:a. Key Achievements: Highlight the major accomplishments or milestones achieved during the period.b. Performance against Objectives: Evaluate the performance in relation to the objectives set at the beginning of the period. Discuss any significant deviations or exceptional performance in achieving these objectives.c. Key Strengths: Identify the areas where the individual or team excelled, demonstrating exceptional skills, expertise, or innovative approaches.d. Areas for Improvement: Highlight the areas where improvements are required to enhance performance or overcome any shortcomings identified during the evaluation.4. Quantitative Assessmenta. Key Performance Indicators: Provide a comprehensive analysis of the quantitative performance indicators, showcasing the performance against targets or benchmarks set for each indicator. Use graphs, charts, or tables to present the data effectively.b. Revenue/Fund Generation: Evaluate the success in generating revenue or funds, demonstrating the contribution towards organizational goals.c. Efficiency and Productivity: Assess the individual's or team's efficiency and productivity in completing assigned tasks within the given time frame.5. Qualitative Assessmenta. Feedback from Superiors and Peers: Summarize the feedback received from supervisors, managers, or peers regarding the performance of the individual or team. Present the feedback in a structured manner to highlight any consistent patterns or areas of improvement.b. Leadership and Teamwork: Evaluate the leadership skills and ability to work effectively within a team, highlighting any demonstrated strengthsor areas needing improvement.c. Communication and Collaboration: Assess the individual's or team's communication and collaboration skills, emphasizing any notable achievements or room for improvement.6. ConclusionBased on the evaluation conducted, it can be concluded that [Company Name/Department/Individual] has performed exceptionally well in certain areas while also having room for improvement in others. The strengths identified should be further nurtured to enhance performance, and the areas for improvement should be addressed through appropriate training and development initiatives. Overall, the evaluation serves as a valuable tool to guide performance improvement and set goals for future periods.7. RecommendationsBased on the evaluation, the following recommendations are made to improve performance and achieve future success:a. Provide targeted training and development opportunities to address the identified areas of improvement.b. Recognize and reward the achievements and strengths demonstrated by the individual or team.c. Foster a culture of continuous feedback and performance improvement within the organization.d. Set clear, realistic, and challenging goals for the next evaluation period.e. Encourage collaboration and teamwork to leverage collective skills and expertise.f. Develop effective communication channels to enhance coordination and information sharing.g. Regularly review and update performance metrics and objectives to align with organizational goals.。
a r X i v :0805.4770v 4 [p h y s i c s .s o c -p h ] 30 O c t 2008Benchmark graphs for testing community detection algorithmsAndrea Lancichinetti,1Santo Fortunato,1and Filippo Radicchi 11Complex Systems Lagrange Laboratory (CNLL),Institute for Scientific Interchange (ISI),Viale S.Severo 65,10133,Torino,Italy(Dated:October 30,2008)Community structure is one of the most important features of real networks and reveals the internal organization of the nodes.Many algorithms have been proposed but the crucial issue of testing,i.e.the question of how good an algorithm is,with respect to others,is still open.Standard tests include the analysis of simple artificial graphs with a built-in community structure,that the algorithm has to recover.However,the special graphs adopted in actual tests have a structure that does not reflect the real properties of nodes and communities found in real networks.Here we introduce a new class of benchmark graphs,that account for the heterogeneity in the distributions of node degrees and of community sizes.We use this new benchmark to test two popular methods of community detection,modularity optimization and Potts model clustering.The results show that the new benchmark poses a much more severe test to algorithms than standard benchmarks,revealing limits that may not be apparent at a first analysis.PACS numbers:89.75.-k,89.75.HcKeywords:Networks,community structure,testingI.INTRODUCTIONMany complex systems in nature,society and tech-nology display a modular structure,i.e.they appear as a combination of compartments that are fairly inde-pendent of each other.In the graph representation of complex systems [1,2],where the elementary units of a system are described as nodes and their mutual inter-actions as links,such modular structure is revealed by the existence of groups of nodes,called communities or modules ,with many links connecting nodes of the same group and comparatively few links joining nodes of differ-ent groups [3,4].Communities reveal a non-trivial inter-nal organization of the network,and allow to infer special relationships between the nodes,that may not be easily accessible from direct empirical munities may be groups of related individuals in social networks [3,5],sets of Web pages dealing with the same topic [6],bio-chemical pathways in metabolic networks [7,8],etc.Detecting communities in networks is a big challenge.Many methods have been devised over the last few years,within different scientific disciplines such as physics,biol-ogy,computer and social sciences.This race towards the ideal method aims at two main goals,i.e.improving the accuracy in the determination of meaningful modules and reducing the computational complexity of the algorithm.The latter is a well defined objective:in many cases it is possible to compute analytically the complexity of an algorithm,in others one can derive it from simulations of the algorithm on systems of different sizes.The main problem is then to estimate the accuracy of a method and to compare it with other methods.This issue of testing is in our opinion as crucial as devising new powerful al-gorithms,but till now it has not received the attention it deserves.Testing an algorithm essentially means analyzing a network with a well defined community structure andrecovering its communities.Ideally,one would like to have many instances of real networks whose modules are precisely known,but this is unfortunately not the case.Therefore,the most extensive tests are performed on computer generated networks,with a built-in community structure.The most famous benchmark for community detection is a class of networks introduced by Girvan and Newman (GN)[3].Each network has 128nodes,divided into four groups with 32nodes each.The average degree of the network is 16and the nodes have approximately the same degree,as in a random graph.At variance with a random graph,nodes tend to be connected preferen-tially to nodes of their group:a parameter k out indicates what is the expected number of links joining each node to nodes of different groups (external degree).When k out <8each node shares more links with the other nodes of its group than with the rest of the network.In this case,the four groups are well defined communities and a good algorithm should be able to identify them.This benchmark is regularly used to test algorithms.However,there are several caveats that one has to con-sider:•all nodes of the network have essentially the same degree;•the communities are all of the same size;•the network is small.The first two remarks indicate that the GN benchmark cannot be considered a proxy of a real network with com-munity structure.Real networks are characterized by heterogeneous distributions of node degree,whose tails often decay as power laws.Such heterogeneity is respon-sible for a number of remarkable features of real networks,such as resilience to random failures/attacks [9],and the absence of a threshold for percolation [10]and epidemic spreading [11].Therefore,a good benchmark should have2FIG.1:A realization of the new benchmark,with 500nodes.a skewed degree distribution,like real networks.Like-wise,it is not correct to assume that all communities have the same size:the distribution of community sizes of real networks is also broad,with a tail that can be fairly well approximated by a power law [8,12,13,14].A reliable benchmark should include communities of very different sizes.A variant of the GN benchmark with communities of different size was introduced in [15].Finally,the GN benchmark was a network of a reasonable size for most existing algorithms at the time when it was introduced.Nowadays,there are methods able to analyze graphs with millions of nodes [14,16,17]and it is not appropriate to compare their performances on small graphs.In general,an algorithm should be tested on benchmarks of variable size and average degree,as these parameters may seri-ously affect the outcome of the method,and reveal its limits,as we shall see.In this paper we propose a realistic benchmark for com-munity detection,that accounts for the heterogeneity of both degree and community size.Detecting communities on this class of graphs is a challenging task,as shown by applying well known community detection algorithms.II.THE BENCHMARKWe assume that both the degree and the community size distributions are power laws,with exponents γandβ,respectively.The number of nodes is N ,the average degree is k .In the GN benchmark a node may happen to have more links outside than inside its community even when k out <8,due to random fluctuations,which raises a conceptual problem concerning the natural classification of the node.The construction of a realization of our benchmark proceeds through the following steps:1.Each node is given a degree taken from a power law distribution with exponent γ.The extremes of the distribution k min and k max are chosen such that the average degree is k .The configuration model [18]is used to connect the nodes so to keep their degree sequence.2.Each node shares a fraction 1−µof its links with the other nodes of its community and a fraction µwith the other nodes of the network;µis the mixing parameter .3.The sizes of the communities are taken from a power law distribution with exponent β,such that the sum of all sizes equals the number N of nodes of the graph.The minimal and maximal community sizes s min and s max are chosen so to respect the constraints imposed by our definition of commu-nity:s min >k min and s max >k max .This ensures that a node of any degree can be included in at least a community.4.At the beginning,all nodes are homeless,i.e.theyare not assigned to any community.In thefirst it-eration,a node is assigned to a randomly chosencommunity;if the community size exceeds the in-ternal degree of the node(i.e.the number of itsneighbors inside the community),the node entersthe community,otherwise it remains homeless.Insuccessive iterations we place a homeless node to arandomly chosen community:if the latter is com-plete,we kick out a randomly selected node of thecommunity,which becomes homeless.The proce-dure stops when there are no more homeless nodes.5.To enforce the condition on the fraction of inter-nal neighbors expressed by the mixing parameterµ,several rewiring steps are performed,such thatthe degrees of all nodes stay the same and onlythe split between internal and external degree isaffected,when needed.In this way the ratio be-tween external and internal degree of each node inits community can be set to the desired shareµwith good approximation.The prescription we have given leads to fast convergence. In Fig.2we show how the time to completion scales with the number of links of the graphs.The latter is expressed by the average degree,as the number of nodes of the graphs is keptfixed.The curves clearly show a linear relation between the computer time and the number of links of the graph.Therefore our procedure allows to build fairly large networks(up to105−106nodes)in a reasonable time.Due to the strong constraints we impose to the system,in some instances convergence may not be reached.However,this is very unlikely for the range of parameters we have used.For the exponents we havetaken typical values of real networks:2≤γ≤3,1≤β≤2.Our algorithm tries to set theµ-value of each node to the predefined input value,but of course this does not work in general,especially for nodes of small degree, where the possible values ofµare just a few and clearly separated.So,the distribution ofµ-values for a given benchmark graph cannot be aδ-function,but it will have a bell-shaped curve,with a pronounced peak(Fig.3).III.TESTSWe have used our benchmark to test the performance of two methods to detect communities in networks,i.e. modularity optimization[7,19,20],probably the most popular method of all,and the algorithm based on the Potts model introduced by Reichardt and Bornholdt[21]. For modularity,the optimization was carried out through simulated annealing,as in[7],which is not a fast technique but yields good estimates of modularity max-ima.In Fig.4we plot the performance of the method as a function of the external degree of the nodes for the GN benchmark.To compare the built-in modular struc-show the scaling of the computer time(in seconds)with the average degree of the graph.The curves correspond to differ-ent choices for the exponentsγandβand the value ofµ.The two panels reproduce graphs with1000(a)and10000nodes (b).The calculations were performed on Opteron processors. ture with the one delivered by the algorithm we adopt the normalized mutual information,a measure of sim-ilarity of partitions borrowed from information theory, which has proved to be reliable[22].As we can see from thefigure,the natural partition is always found up un-til k out=6,then the method starts to fail,although it finds good partitions even when communities are fuzzy (k out≥8).Meanwhile,many algorithms are able to achieve comparable performances,so the benchmark can hardly discriminate between different methods.As we can see from thefigure,for k out<8we are close to the top performance and there seems to be little room for improvement.In Fig.5we show what happens if one optimizes mod-ularity on the new benchmark,for N=1000.The four panels correspond to four pairs for the exponentsnents and system size.to explore the widest spectrum of graph structures.For each pair of exponents,we have used three values for the average degree k =15,20,25.Each curve shows the variation of the normalized mutual information with the mixing parameter µ.In general,from Fig.5we can infer that the method gives good results.However,we find that it begins to fail even when communities are only loosely connected to each other (small µ).This is due to the fact that modularity optimization has an intrinsic resolution limit that makes small communities hard to detect [24].Our benchmark is able to disclose this limit.We have explicitely verified that the modu-larity of the natural partition of the graph is lower than the maximum obtained from the optimization,and thatmark.The number of nodes N =1000.The results clearly depend on all parameters of the benchmark,from the ex-mark.The number of nodes is now N =5000,the other parameters are the same as in Fig.5.Each point corresponds to an average over 25graph realizations.the partition found by the algorithm has systematically a smaller number of clusters,due to the merge of small communities into larger groups.We also see that the performance of the method is the better the larger the average degree k ,whereas it gets worse when the communities are more similar to each other in size (larger β).To check how the performance is affected by the net-5The number of nodes N=1000.The results clearly depend on all parameters of the benchmark,from the exponentsγandβto the average degree k .Each point corresponds to an average over100graphrealizations.The number of nodes N=5000,the other parameters are the same as in Fig.7.Each point corresponds to an average over 10graph realizations.size,we have tested the method on a set of larger(Fig.6).Now N=5000,whereas the other pa-are the same as before.Curves corresponding the same parameters are similar,but shifted towards bottom for the larger systems.We conclude that performance of the method worsens if the size of the increases.If we consider that networks with5000are much smaller than many graphs one would like analyze,modularity optimization may give inaccurate in practical cases,something which could not befrom tests on existing benchmarks.We have repeated the same analysis for the Potts algorithm.We closely followed the implementa-suggested by the authors of[21]:we set the number spin states equal to the number of nodes of the net-the ferromagnetic coupling J was set to1,whereas antiferromagnetic couplingγequals the density of links of the network.The results are shown in Figs.7 and8.The performance of the method is fair,and it worsens for larger system sizes,like for modularity opti-mization,which proves superior.IV.SUMMARYWe have introduced a new class of graphs to test algo-identifying communities in networks.These newextend the GN benchmark by introducing features real networks,i.e.the heterogeneity in the distribu-of node degree and community size.We found thatelements pose a harder test to existing methods.have tested modularity optimization and a cluster-technique based on the Potts model against the new From the results the resolution limit of mod-emerges immediately.Furthermore,we have seen the size of the graph and the density of its linksa sizeable effect on the performance of the algo-so it is very important to study this dependencetesting a new algorithm.The new benchmark is for this type of analysis,as the graphs can be constructed very quickly,and one can span several or-ders of magnitude in network size.A software package to generate the benchmark graphs can be downloaded from /benchmark.tgz.[1]M.E.J.Newman,SIAM Review45,167(2003).[2]S.Boccaletti,tora,Y.Moreno,M.Chavez and D.-U.Hwang,Phys.Rep.424,175(2006).[3]M.Girvan and M.E.J.Newman,Proc.Natl.Acad.Sci.99,7821(2002).[4]S.Fortunato and C.Castellano,in Encyclopedia of Com-plexity and System Science,ed.B.Meyers(Springer,Hei-delberg,2009),arXiv:0712.2716at .[5]D.Lusseau and M.E.J.Newman,Proc.R.Soc.LondonB271,S477(2004).[6]G.W.Flake,wrence,C.Lee Giles and F.M.Coet-zee,IEEE Computer35(3),66(2002).[7]R.Guimer`a and L.A.N Amaral,Nature433,895(2005).[8]G.Palla,I.Der´e nyi,I.Farkas and T.Vicsek,Nature435,814(2005).[9]R.Albert,H.Jeong and A.-L.Barab´a si,Nature406,3786(2000).[10]R.Cohen,K.Erez,D.ben-Avraham and S.Havlin,Phys.Rev.Lett.85,4626(2000).[11]R.Pastor-Satorras and A.Vespignani,Phys.Rev.Lett.86,3200(2001).[12]R.Guimer`a,L.Danon,A.D´ıaz-Guilera,F.Giralt andA.Arenas,Phys.Rev.E68,065103(R)(2003).[13]L.Danon,J.Duch,A.Arenas and A.D´ıaz-Guilera,inLarge Scale Structure and Dynamics of Complex Net-works:From Information Technology to Finance and Natural Science,eds.G.Caldarelli and A.Vespignani (World Scientific,Singapore,2007),pp93–114.[14]A.Clauset,M.E.J.Newman and C.Moore,Phys.Rev.E70,066111(2004).[15]L.Danon, A.D´ıaz-Guilera and A.Arenas,JSTATP11010,(2006).[16]V. D.Blondel,J.-L.Guillaume,mbiotte andE.Lefebvre,arXiv:0803.0476at .[17]ncichinetti,S.Fortunato and J.Kert´e sz,arXiv:0802.1218at .[18]M.Molloy and B.Reed,put.6,161(1995).[19]M.E.J.Newman,Phys.Rev.E69,066133(2004).[20]J.Duch and A.Arenas,Phys.Rev.E72,027104(2005).[21]J.Reichardt,S.Bornholdt,Phys.Rev.Lett.93,218701(2004).[22]L.Danon,A.D´ıaz-Guilera,J.Duch and A.Arenas,J.Stat.Mech.,P09008(2005).[23]F.Radicchi,C.Castellano,F.Cecconi,V.Loreto andD.Parisi,A101,2658–2663(2004).[24]S.Fortunato and M.Barth´e lemy,Proc.Natl.Acad.Sci.USA104,36(2007).。