Versions and Latest Releases
Version 16: with the newest release of version 16d, we introduce a new input style, called Desirable Inputs Model. In this new model, we allow some input style (called IGood) which are larger the better. Examples include number of electric vehicles in an environmental model, the number of test takers in vaccine development model, etc. For more details, go to newsletter 20.
DEA-Solver-Pro Version 14d- Newsletter17
The latest release of version 14 is 14d, with a new feature SBM Bounded Model as an extention to SBM Max of version 13, which replaced SBM Variation model of version 12. In the real world, there are cases where input resources and/od output expansion are restricted by external constraints. SBM Bounded Model takes care of such situations, so that the outcome of SBM Bounded Model becomes more realistic than before. Note that these SBM models essentially represent KAIZEN improvement. For more details, go to newsletter 17.
Financial Benchmarking Of Transportation Companies In The New York Stock Exc...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/financial-benchmarking-of-transportation-companies-in-the-new-york-stock-exchange-nyse-through-data-envelopment-analysis-dea-and-visualization/
In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution.
Financial benchmarking of transportation companies in the New York Stock Exch...Gurdal Ertek
In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution.
http://research.sabanciuniv.edu.
Differential Evolution Algorithm for Optimal Power Flow and Economic Load Dis...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Structural sizing and shape optimisation of a load celleSAT Journals
Abstract This paper presents a structural application of a sizing and shape based on a reliability-related multi-factor optimisation. The application to a load cell design confirmed that this method is highly effective and efficient in terms of sizing and shape optimisation. A simple model of the S-type load cell is modelled in finite element analysis software and the systematic optimisation method is applied. Structural responses and geometrical sensitivities are analysed by a FE method, and reliability performance is calculated by a reliability loading-case index (RLI). The evaluation indices of performances and loading cases are formulated, and an overall performance index is presented to quantitatively evaluate a design. Index Terms: Multifactor optimisation, Finite element analysis, Load cell
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
DEA-Solver-Pro Version 14d- Newsletter17
The latest release of version 14 is 14d, with a new feature SBM Bounded Model as an extention to SBM Max of version 13, which replaced SBM Variation model of version 12. In the real world, there are cases where input resources and/od output expansion are restricted by external constraints. SBM Bounded Model takes care of such situations, so that the outcome of SBM Bounded Model becomes more realistic than before. Note that these SBM models essentially represent KAIZEN improvement. For more details, go to newsletter 17.
Financial Benchmarking Of Transportation Companies In The New York Stock Exc...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/financial-benchmarking-of-transportation-companies-in-the-new-york-stock-exchange-nyse-through-data-envelopment-analysis-dea-and-visualization/
In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution.
Financial benchmarking of transportation companies in the New York Stock Exch...Gurdal Ertek
In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution.
http://research.sabanciuniv.edu.
Differential Evolution Algorithm for Optimal Power Flow and Economic Load Dis...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Structural sizing and shape optimisation of a load celleSAT Journals
Abstract This paper presents a structural application of a sizing and shape based on a reliability-related multi-factor optimisation. The application to a load cell design confirmed that this method is highly effective and efficient in terms of sizing and shape optimisation. A simple model of the S-type load cell is modelled in finite element analysis software and the systematic optimisation method is applied. Structural responses and geometrical sensitivities are analysed by a FE method, and reliability performance is calculated by a reliability loading-case index (RLI). The evaluation indices of performances and loading cases are formulated, and an overall performance index is presented to quantitatively evaluate a design. Index Terms: Multifactor optimisation, Finite element analysis, Load cell
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
I have done this analysis using SAS on a dataset with 5000 records. I have used CART and Logistic regression to build a predictive model to identify customers which are likely to shift to competitors network.
A Weighted Duality based Formulation of MIMO SystemsIJERA Editor
This work is based on the modeling and analysis of multiple-input multiple-output (MIMO) system in downlink communication system. We take into account a recent work on the ratio of quadratic forms to formulate the weight matrices of quadratic norm in a duality structure. This enables us to achieve exact solutions for MIMO system operating under Rayleigh fading channels. We outline couple of scenarios dependent on the structure of eigenvalues to investigate the system behavior. The results obtained are validated by means of Monte Carlo simulations.
We will test whether :
a) Sequential Deep Neural Networks (DNNs) can predict the stock market (S&P 500) better than OLS regression;
b) DNNs using smooth Rectified Linear activation functions perform better than the ones using Sigmoid (Logit) activation functions.
Designed to construct a statistical model describing the impact of a two or more quantitative factors on a dependent variable. The fitted model may be used to make predictions, including confidence limits and/or prediction limits. Residuals may also be plotted and influential observations identified.
Exploring Support Vector Regression - Signals and Systems ProjectSurya Chandra
Our team competed in a Kaggle competition to predict the bike share use as a part of their capital bike share program in Washington DC using a powerful function approximation technique called support vector regression.
SOLVING A MULTI-OBJECTIVE REACTIVE POWER MARKET CLEARING MODEL USING NSGA-IIijait
This paper presents an application of elitist non-dominated sorting genetic algorithm (NSGA-II) for solving a multi-objective reactive power market clearing (MO-RPMC) model. In this MO-RPMC model, two objective functions such as total payment function (TPF) for reactive power support from generators/synchronous condensers and voltage stability enhancement index (VSEI) are optimized
simultaneously while satisfying various system equality and inequality constraints in competitive electricity markets which forms a complex mixed integer nonlinear optimization problem with binary variables. The proposed NSGA-II based MO-RPMC model is tested on standard IEEE 24 bus reliability test system. The results obtained in NSGA-II based MO- RPMC model are also compared with the results obtained in real coded genetic algorithm (RCGA) based single-objective RPMC models.
In Machine Learning in Credit Risk Modeling, we provide an explanation of the main Machine Learning models used in James so that Efficiency does not come at the expense of Explainability.
(Contact Yvan De Munck for more info or to receive other and future updates on the subject @yvandemunck or yvan@james.finance)
ASSESSMENT OF INTRICATE DG PLANNING WITH PRACTICAL LOAD MODELS BY USING PSO ecij
This paper presents the optimal sizing and placement of DG by assuming practical load models. The particle swarm optimization technique is used to minimize the multi-objective fitness function (MOFF). This MOFF has considered the performance indices such as a voltage difference index, active power loss index and reactive power loss index. Most of the studies have considered the constant load for distribution system planning which may mislead the exact assessment of the system performance. Thus the voltage dependency of load models is found in a highly demanding issue in updating researches. Keeping in view the urgent need of precise and flawless distribution system planning the effect of different load models on the total load, voltage profile, active and reactive power loss has been evaluated and presented in this paper. The efficacy of the proposed method has been executed by implementing it on the 33-bus radial test system.
This paper proposes performance based macro modeling of analog circuit using Multi Output Modeling (MOM) with the help of Support Vector Machine (SVM). SVM models the analog circuits and provides a relation between multi-input and multi-output parameters. In this work, Voltage Controlled Oscillator (VCO) is modeled as a test circuit which is designed in Cadence Virtuoso GPDK 45nm technology. From the Spice simulation results, the feasible dataset has been extracted from the complete dataset. Then, the VCO output frequency and phase noise is modeled by the width of the transistors which are the input parameters of the transistors. After tuning the model properly by k-cross validation method, the accuracy was found to be 96.1% which is good enough to make it use for the circuit synthesis purpose.
I have done this analysis using SAS on a dataset with 5000 records. I have used CART and Logistic regression to build a predictive model to identify customers which are likely to shift to competitors network.
A Weighted Duality based Formulation of MIMO SystemsIJERA Editor
This work is based on the modeling and analysis of multiple-input multiple-output (MIMO) system in downlink communication system. We take into account a recent work on the ratio of quadratic forms to formulate the weight matrices of quadratic norm in a duality structure. This enables us to achieve exact solutions for MIMO system operating under Rayleigh fading channels. We outline couple of scenarios dependent on the structure of eigenvalues to investigate the system behavior. The results obtained are validated by means of Monte Carlo simulations.
We will test whether :
a) Sequential Deep Neural Networks (DNNs) can predict the stock market (S&P 500) better than OLS regression;
b) DNNs using smooth Rectified Linear activation functions perform better than the ones using Sigmoid (Logit) activation functions.
Designed to construct a statistical model describing the impact of a two or more quantitative factors on a dependent variable. The fitted model may be used to make predictions, including confidence limits and/or prediction limits. Residuals may also be plotted and influential observations identified.
Exploring Support Vector Regression - Signals and Systems ProjectSurya Chandra
Our team competed in a Kaggle competition to predict the bike share use as a part of their capital bike share program in Washington DC using a powerful function approximation technique called support vector regression.
SOLVING A MULTI-OBJECTIVE REACTIVE POWER MARKET CLEARING MODEL USING NSGA-IIijait
This paper presents an application of elitist non-dominated sorting genetic algorithm (NSGA-II) for solving a multi-objective reactive power market clearing (MO-RPMC) model. In this MO-RPMC model, two objective functions such as total payment function (TPF) for reactive power support from generators/synchronous condensers and voltage stability enhancement index (VSEI) are optimized
simultaneously while satisfying various system equality and inequality constraints in competitive electricity markets which forms a complex mixed integer nonlinear optimization problem with binary variables. The proposed NSGA-II based MO-RPMC model is tested on standard IEEE 24 bus reliability test system. The results obtained in NSGA-II based MO- RPMC model are also compared with the results obtained in real coded genetic algorithm (RCGA) based single-objective RPMC models.
In Machine Learning in Credit Risk Modeling, we provide an explanation of the main Machine Learning models used in James so that Efficiency does not come at the expense of Explainability.
(Contact Yvan De Munck for more info or to receive other and future updates on the subject @yvandemunck or yvan@james.finance)
ASSESSMENT OF INTRICATE DG PLANNING WITH PRACTICAL LOAD MODELS BY USING PSO ecij
This paper presents the optimal sizing and placement of DG by assuming practical load models. The particle swarm optimization technique is used to minimize the multi-objective fitness function (MOFF). This MOFF has considered the performance indices such as a voltage difference index, active power loss index and reactive power loss index. Most of the studies have considered the constant load for distribution system planning which may mislead the exact assessment of the system performance. Thus the voltage dependency of load models is found in a highly demanding issue in updating researches. Keeping in view the urgent need of precise and flawless distribution system planning the effect of different load models on the total load, voltage profile, active and reactive power loss has been evaluated and presented in this paper. The efficacy of the proposed method has been executed by implementing it on the 33-bus radial test system.
This paper proposes performance based macro modeling of analog circuit using Multi Output Modeling (MOM) with the help of Support Vector Machine (SVM). SVM models the analog circuits and provides a relation between multi-input and multi-output parameters. In this work, Voltage Controlled Oscillator (VCO) is modeled as a test circuit which is designed in Cadence Virtuoso GPDK 45nm technology. From the Spice simulation results, the feasible dataset has been extracted from the complete dataset. Then, the VCO output frequency and phase noise is modeled by the width of the transistors which are the input parameters of the transistors. After tuning the model properly by k-cross validation method, the accuracy was found to be 96.1% which is good enough to make it use for the circuit synthesis purpose.
A General Method for Estimating a Linear Structural Equation System
The substantially upgraded new version marks the golden jubilee of a seminal development in the history of Structure Equation Modeling (SEM). A little over a half century ago Professor Karl Jöreskog published a monograph in the Educational Testing Service (ETS) Research Bulletin series entitled A General Method for Estimating a Linear Structural Equation System, along with the LISREL software program.
祺荃企業有限公司 您可以信賴的軟體供應商
國內外原版軟體代理及經銷 | 教育訓練 | 軟體購買諮詢 | Devs Paradise | 線上商店(Store)
Cheer Chain Enterprise Co., Ltd. distributes and sells software with the aim of offering clients guidance when choosing software, as well as technical support !!!
Distribution of Software | Training Courses | Consulting Services
Focused Analysis of Qualitative Interviews with MAXQDA
Step by Step
Focused Analysis of Qualitative Interviews with MAXQDA
Authors: Stefan Rädiker, Udo Kuckartz
Pages: 125
Released: 2020
Language: English
ISBN: 978-3-948768072
DOI: 10.36192/978-3-948768072
All-in-One Website Security Scanner
Find and detect vulnerabilities at the earliest stage using Acunetix automated web vulnerability scannerFind vulnerabilities in your websites and web APIs
Find vulnerabilities in your websites and web APIs
Highest detection rating of over 4500 vulnerabilities in custom, commercial, and open source apps with nearly 0% false positives.
AcuSensor (IAST) allows you to find and test hidden inputs not discovered during black-box scanning (DAST)
Advanced Crawling & Authentication support gives you the ability to crawl JavaScript websites and SPAs
NativeJ is a powerful Java EXE maker. The executable generated by NativeJ is uniquely customized to launch your Java application under Windows. NativeJ is not a compiler! Think of NativeJ-generated executables as supercharged "binary batch files"
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
Edraw Max - All-In-One Diagram Software
Edraw Max is a versatile diagram software, with features that make it perfect not only for professional-looking flowcharts, org charts, network diagrams and mind maps, but also building plans, business charts, workflows, fashion designs, UML diagrams, electrical engineering diagrams, directional maps and database model diagrams.
EdrawSoft Edraw Max 特別版是一整合圖示繪製軟體,新穎小巧,功能強大,可以很方便的繪製各種專業的流程圖、組織結構圖、網路拓撲圖、傢俱設計圖、商業圖表等。
應用領域:流程圖、網路拓撲圖、組織結構圖、工作流程圖、UML,軟體設計、商業圖表、2D, 3D 圖形、計畫 / 報表、地圖,方向圖、資料庫等。
購買及下載請聯絡
祺荃企業有限公司 - 您可以信賴的軟體供應商
www.cheerchain.com.tw | info@cheerchain.com.tw
Tel : 886-4-2386-3559 Fax : 886-4-2386-3159
線上購買 : http://www.appcenter.com.tw/
Atlas.ti 8 質性分析軟體新功能介紹!
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
購買請洽 祺荃企業有限公司-您可以信賴的軟體供應商
www.cheerchain.com.tw or www.appcenter.com.tw
Email : info@cheerchain.com.tw Phone : +8864-23863559
NEW VERSION OUT NOW! We are happy to announce that ATLAS.ti 8 is released now! Completely re-designed in nearly every aspect, ATLAS.ti 8 Windows is poised to set new standards for computer-assisted qualitative data analysis. What's new? Find out about the new powerfull features of ATLAS.ti 8 here http://bit.ly/2hDIK0H.
**ATLAS.ti licenses purchased after April 1, 2015 qualify for a FREE UPGRADE
New Features
These are some of the powerful new features:
Under the hood: Clean separation of data layer, application logic, and user interface, latest technology for safe and reliable performance.
Unicode throughout
Undo/Redo (100 steps)
Direct import of Twitter, Endnote, Evernote data
Powerful Visual Query Editor for creating and modifying SmartCodes and SmartGroups
Full project search (former “Word cruncher”) significantly improved with dynamic fade-in/fade-out hit categories
Elegant and trememdously useful new network layout options
Network groups
Memo comments
State-of-the-art, highly intuitive user interface with ribbons, tabbed views, flexible navigation areas.
All tool windows can be freely positioned
Multiple documents
More powerful “margin” than ever, many new interactive functions.
Features Yet To Come
At the time of the RC1 release, the following areas are still missing or incomplete:
Project exchange between ATLAS.ti Mac and ATLAS.ti Windows
Teamwork scenario with central, shared project directories
Non-English user interface
Some specific functionalities (see below)
Functionality still to be added:
Transcription
Document editing
Print documents with margin
Global filters
Interrater reliability
Relative values in code-doc table
XML converter
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
購買請洽 祺荃企業有限公司-您可以信賴的軟體供應商
www.cheerchain.com.tw or www.appcenter.com.tw
Email : info@cheerchain.com.tw Phone : +8864-23863559
Maxqda12 features -detailed feature comparison for more information about each product
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
AntiVirus Business Edition 2016 and Internet Security Business Edition 2016 Protection from emerging threats and protection for your data, identity, PCs and servers
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
德國 combit List & label 21 報表工具軟體新功能介紹
Highlights
Report Designer can be redistributed at no extra costDesigner can be launched with .NET without a single line of codeFree choice of development environment: .NET, Visual Studio, C#, etc.One of the most feature-rich report generators on the marketAdds crosstabs, charts, gauges, forms, labels, barcodes, web reports...Winner of multiple awards from customers and media worldwide
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
購買請洽 祺荃企業有限公司-您可以信賴的軟體供應商
www.cheerchain.com.tw or www.appcenter.com.tw
Email : info@cheerchain.com.tw Phone : +8864-23863559
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
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Newsletter 20.pdf
1. Newsletter 20
1
: How can we deal with big data set comprised of several thousand
DMUs?
: You can divide them into several groups consisting of a few
hundred DMUs and solve each group for finding efficient DMUs in the
group. Then, merge the efficient DMUs into one set. Solve the set and
find the final efficient DMUs. Using the finalist, re-evaluate each group.
Version 16.0: Desirable Inputs Model
Generally speaking, in DEA, inputs (I) indicate input resources and
the smaller the better, while outputs (O) correspond to productions
induced by inputs (I), which are the larger the better. Among outputs,
there exist undesirable outputs (OBad) incidental to outputs, e.g. CO2,
which are the smaller the better. In this version, we propose a new
input style, called Desirable inputs (IGood), which are the larger the
better. We show four potential examples of (IGood).
(a)In accordance with the increasing concern on environmental
pollution, spread of Electric cars is worldwide required. We
compare the energy consumption issue of countries. We consider
“Number of Electric Cars” as a Good Input (IGood). Other IO items
are (I) Total consumption of Energy, (O) GDP and (OBad)
Pollutants (e.g. CO2). DMUs are Country 1 to Country N.
(b)We consider the case that there are many new projects in a
company and the manager wishes to evaluate the efficiency of
projects. For this purpose, a certain number of evaluators is
assigned to each project and they report Cost (Input), Return
DEA-Solver-Pro Newsletter
No. 20
(Jan 7, 2022)
2. Newsletter 20
2
(Output), Risk (Undesirable Output) for the assigned project. In this
case, we consider the number of evaluators is a Desirable input
(IGood) and the larger the better.
(c)We point to “Women’s Rights Movements” in a country where
women’s right is not fully guaranteed. In such a country, suppose
local governments where men and women are serving as officers.
They are inputs to office, while women are (IGood) and men are
(I). As outputs, we assume (O) Service and (OBad) Claims.
(d)Suppose that, inspired by COVID-19, many medical enterprises
are eager to develop vaccine. In this case, (IGood) Number of test
takers, (I) Cost, (O) Recovery, (OBad) Failure. As DMUs, we
assume potential Vaccine1 to Vaccine N.
We reasoned that such situations occur in many fields of enterprise.
We formulate this situation in the framework of SBM.
In Version 15.1, we added the CRS (constant returns-to-scale)
version to NegativeData_SBM models. Furthermore, as for Malmquist
models, we added the cumulative and the adjusted Malmquist indices.
In DEA, we often encounter negative data, e.g. financial data. In
response to many requests, in this Version 15.0, we updated SBM
models to cope with negative data. Three clusters are added under
the Variable Returns-to-Scale (VRS) model. They are (1)
NegativeData_SBM, (2) NegativeData_SBM_Max and (3)
NegativeData_Malmquist. Now, we can deal with positive and
negative data in a single model. The efficiency scores are units
invariant and translation invariant.
3. Newsletter 20
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In Version 14.0, we introduced SBM_Bounded models as an
extension of the SBM_Max models. In real world applications, there
are cases where input-reduction and/or output expansion are
restricted by some external constraints. For example, suppose that
expansion of inpatient visits, as an output of hospitals, is bounded by
3% of the current value due to physical environments. Input reduction
bound and output expansion bound differ for every input and every
output. The SBM_Bounded models handle these cases as the
constraints to slacks which are obtained by SBM_Max models. Hence,
the projection of inefficient DMUs thus obtained indicates more
practical and useful improvement (Kaizen) plan. In Figure 14.0, we
illustrate the model in the input side projection. DMUs A, B, C and D
are on the efficient frontier while P and S are inefficient. P will be
projected to R by the SBM_Max model, whereas the bounded area
forces it to stop at Q. The bounded area is determined by Input 1
reduction rate and Input 2 reduction rate which are assumed to be
common to all DMUs. DMU S is projected to T inside its bounded area
and on the efficient frontier.
Input 1
Input 2
A
B
C
D
●P
● Q
●S
T ●
R
0
4. Newsletter 20
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Figure 14.0: SBM_Bounded model
In Version 14.0, we have revised the SBM_Max models.
In Version 13.2, we added Super-Efficiency score to the SBM_Max
model.
In Version 13.0, we replaced SBM_Variation models in the earlier
version by SBM_Max models. The SBM models usually report the
worst efficiency scores for inefficient DMUs. This means that the
projected point is the farthest one on the associated efficient frontier.
In contrast, SBM_Max models look for the nearest point on the
associated efficient frontier. Hence, the efficiency score is, in a sense,
maximized as contrasted to the ordinary SBM (Min) models. This
indicates that we can attain an efficient status with less input
reductions and less output expansions than the ordinary SBM (Min)
models. The relationship among SBM-Min, SBM-Max and CCR
models can be depicted by the figure below. The new SBM_Max
models attain more accurate nearest point on the efficient frontiers
than the SBM_Variation in the previous version, whereas the
computational time increases depending on the number of efficient
DMUs. We can say that the projection by SBM_Max model represents
a practical “KAIZEN” (Improvement) by DEA. This model includes
input-, output- and non-oriented versions under the constant and
variable returns-to-scale assumptions.
5. Newsletter 20
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Figure 13.1: SBM-Min, CCR and SBM-Max
In Version 12.0, we introduced Directional Distance (Function)
Model that computes the efficiency of DMUs along the given direction.
We offer two types of efficiency, i.e., ordinary and super efficiency.
The Directional Distance model (DD model) has three variations: (1)
Input- (2) Output- and (3) Non-oriented. Each variation has two
returns-to-scale (RTS): (a) CRS (Constant RTS) and (b) VRS
(Variable RTS).
The DD model deals with two kinds of input and three kinds of output
as follows.
(a)Directional (Ordinary) inputs (I)
The heading of this class is (I) and its value is denoted by
( )
( 1, , ; 1, , )
I
ij
x i I j n
= =
where 1, ,
i I
= corresponds to input and 1, ,
j n
=
indicates DMU. The component of directional vector is defined as
( ) ( )
I I
ij ij
d x
= .
Input 1
Input 2
O
6. Newsletter 20
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(b)Non-directional inputs (IN)
The heading of this class is (IN) and denoted by ( )
( 1, , ; 1, , )
IN
ij
x i IN j n
= =
where IN is the number of (IN) inputs. The component of directional
vector is defined as ( )
0
IN
ij
d = . This class has no directional effect on the
efficiency score, but associates with it through the intensity vector.
This class is the secondary input and can be empty.
(c)Directional (Good) output (O)
The heading of this class is (O) and denoted by ( )
( 1, ,O; 1, , )
O
ij
y i j n
= =
where O is the number of good outputs. The component of directional
vector is defined as ( ) ( )
O O
ij ij
d y
= .
(d)Non-directional outputs (ON)
The heading of this class is (ON) and denoted by
( )
( 1, ,ON; 1, , )
ON
ij
y i j n
= =
where ON is the number of non-directional
outputs. The component of directional vector is defined as ( )
0
ON
ij
d = .
This class has no directional effect on the efficiency score, but
associates with it through the intensity vector. This class is the
secondary output and can be empty.
(e)Undesirable outputs (OBad)
The heading of this class is (OBad) and denoted by
( )
( 1, ,OBad; 1, , )
OBad
ij
y i j n
= =
where OBad is the number of undesirable
outputs. The component of directional vector is defined as
( ) ( )
OBad OBad
ij ij
d y
= . This class can be empty.
In Version 11.0, we introduced a new model called “Resampling in
DEA”. This model deals with data variations and gives confidence
7. Newsletter 20
7
intervals of DEA scores. Input/output values are subject to change for
several reasons, e.g., measurement errors, hysteretic factors,
arbitrariness and so on. Furthermore, these variations differ in their
input/output items and their decision-making units (DMU). Hence,
DEA efficiency scores need to be examined by considering these
factors. Resampling based on these variations is necessary for
gauging the confidence interval of DEA scores. We propose four
resampling models. The first one assumes downside and upside
measurement error rates for each input/output, which are common to
all DMUs. We resample data following the triangular distribution that
the downside and upside errors indicate around the observed data.
The second model evaluates the downside and upside error rates
from historical data. The third model utilizes historical data, e.g., past-
present, for estimating data variations, imposing chronological order
weights which are supplied by Lucas series (a variant of Fibonacci
series). This model provides different downside and upside error rates
for each DMU. The last one deals with future prospects. This model
aims at forecasting the future efficiency score and its confidence
interval for each DMU. Table 11.1 exhibits a sample of forecast DEA
scores and their confidence intervals, while Figure 11.1 shows 95%
confidence interval of forecast scores.
Table 11.1: Forecast DEA score and confidence interval
DMU Forecast 97.50% 90% 80% 75% 50% 25% 20% 10% 2.50%
H1 0.946 1.122 1.092 1.062 1.036 0.925 0.880 0.870 0.849 0.818
H2 1.043 1.128 1.098 1.079 1.073 1.049 0.906 0.883 0.845 0.815
H3 0.708 0.743 0.722 0.709 0.704 0.683 0.662 0.657 0.645 0.625
H4 0.809 0.828 0.809 0.796 0.792 0.769 0.747 0.742 0.728 0.708
H5 0.719 0.748 0.727 0.714 0.709 0.689 0.668 0.663 0.651 0.633
H6 1.067 1.185 1.146 1.124 1.115 1.084 1.055 1.049 1.033 0.993
H7 0.900 0.918 0.894 0.879 0.874 0.849 0.826 0.820 0.805 0.782
H8 0.795 0.877 0.835 0.809 0.798 0.762 0.730 0.722 0.705 0.680
H9 0.689 0.736 0.707 0.689 0.681 0.655 0.630 0.623 0.606 0.582
H10 0.801 1.065 0.981 0.914 0.891 0.791 0.748 0.740 0.720 0.689
H11 0.866 1.010 0.947 0.914 0.902 0.861 0.826 0.819 0.798 0.771
H12 1.013 1.014 0.992 0.961 0.951 0.911 0.878 0.870 0.850 0.822
8. Newsletter 20
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Figure 11.1: Forecast DEA score and 95% confidence interval
In Version 10.0, we introduced the “Non-convex DEA” model. In
DEA, we are often puzzled by the large difference between the
constant-returns-scale (CRS) and variable returns-to-scale (VRS)
scores, and by the convexity production set syndrome in spite of the
S-shaped curve often observed in many real data sets. See Figure
10.1. In this model, we propose a solution to these problems.
0.000
0.200
0.400
0.600
0.800
1.000
1.200
1.400
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12
97.50% DEA 2.50%
0
1
2
3
4
5
6
7
8
0 1 2 3 4 5 6 7 8 9 10 11
y
x
9. Newsletter 20
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Figure 10.1: S-shaped data
In Version 9.0, we introduced a model called “DNSBM” (dynamic
SBM with network structure). We have already introduced the network
SBM (NSBM) and the dynamic SBM (DSBM) models separately.
Hence, this model is a composite of these two models. Vertically, we
deal with multiple divisions connected by links of network structure
within each period and, horizontally, we combine the network
structure by means of carry-over activities between two succeeding
periods. See Figure 9.1 below. This model can evaluate (1) the overall
efficiency over the observed entire period, (2) dynamic change of
period (term) efficiency (3) dynamic change of divisional efficiency
and (4) divisional Malmquist index. The model can be implemented in
input-, output- or non-(both) oriented forms under the CRS or VRS
assumptions on the production possibility set. We can impose the
initial condition on the carry-overs.
We can formally deal with bad carry-overs, such as nonperforming
loans and dead stock, in this model. We modified the data formats of
Dynamic SBM (DSBM) and Network SBM (NSBM) so that they are
compatible with DNSBM model. Please see User’s Guide Version
10.0.
10. Newsletter 20
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Figure 9.1: Dynamic model with network structure
In Version 8.0 we introduced a model called “EBM” (epsilon-based
measure of efficiency) for measuring technical efficiency. DEA is a
data driven tool for measuring efficiency of decision making units
(DMU) and shows a sharp contrast to so-called “parametric methods”
such as stochastic frontier analysis (SFA). The latter methods assume
specific production function forms to be identified. This assumption is
not so reasonable in several instances and aspects. Since DEA can
deal with multiple inputs vs. multiple outputs relations in a single
framework, it is becoming a method of choice for efficiency evaluation.
However, DEA has several shortcomings to be explored further. In
DEA, we have two measures of technical efficiency with different
characteristics: radial and non-radial. Historically, the radial measure,
represented by the CCR model was the first DEA model, whereas the
non-radial model, represented by the SBM model was a latecomer.
For instance, in the input-oriented case, the CCR deals mainly with
proportionate reduction of input resources. In other words, if the
Carry- Over
Carry- Over
Carry- Over
Output
Output 2
Output 3
Division 2
Division 3
Division 1
Link 2→3
Link 1→2
Input 2
Division 2
Division 3
Division 1
Input 3
Input 1
Output 1
Output 2
Output 3
Link 2→3
Link 1→2
Input 2
Input 3
Period t
Input 1
Period t+1
11. Newsletter 20
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organisational unit under study has two inputs, this model aims at
obtaining the maximum rate of reduction with the same proportion, i.e.
a radial contraction in the two inputs that can produce the current
outputs. In contrast, the non-radial models put aside the assumption
of proportionate contraction in inputs and aim at obtaining maximum
rates of reduction in inputs that may discard varying proportions of
original input resources.
In this EBM, we propose a composite model which combines both
models in a unified framework. This model has two parameters: one
scalar and one vector. In order to determine these two parameters,
we introduce a new affinity index associated with inputs or outputs.
We apply the principal component analysis (PCA) to thus defined
affinity matrix. These two parameters are utilized to unify radial and
non-radial models in a single model.
In Version 7.0, we reinforced our collection of DEA models by the
addition of Dynamic DEA models. In DEA, there are several methods
for measuring efficiency changes over time, e.g. the Window analysis
and the Malmquist index. However, they usually neglect carry-over
activities between two consecutive terms and only focus on the
separate period independently aiming local optimization in a single
period, even if these models can take into account the time change
effect. In the actual business world, a long time planning and
investment is a subject of great concern. For these cases, single
period optimization model is not suitable for performance evaluation.
To cope with long time point of view, the dynamic DEA model
incorporates carry-over activities into the model as depicted below
and enables us to measure period specific efficiency based on the
long time optimization during the whole period.
12. Newsletter 20
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Input t
Output t
Term
t
Term
t+1
Carry-Over
Output t+1
Input t+1
Figure 7.1: Dynamic model
We enforced Network SBM models by adding the uniqueness
check of the divisional efficiencies.
In Version 6.0 we added Network DEA (Network SBM) models.
Network DEA intends to fortify DEA models in the following way.
Traditional DEA models evaluate the relative efficiency of DMUs
regarding multiple-inputs vs. multiple-outputs correspondences.
Figure below (left) exhibits such traditional DEA models. However,
looking into the inside of the DMU, we often find inter-related divisions
as exemplified in Figure below (right). In this example, an output from
Division 1 (Div1) comes to Division 2 (Div2) as an input. The three red
lines are intermediate products. We call then as “link.” Traditional DEA
models cannot deal with links formally, since every activity should
belong to inputs or outputs, but not both. These inner structures form
a network, and the Network DEA treats these inter-related
organizations formally. Using this model, we can identify the relative
efficiency of each division as well as the overall efficiency of the DMU.
Network DEA serves to clarify the internally-related efficiencies which
are usually regarded as “black box” and have not been investigated
so far.
13. Newsletter 20
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In Version 5.0, we added our collection of DEA models by the
Hybrid model and the Weighted SBM model, and modified the Non-
separable Outputs model in the Undesirable Outputs cluster. We also
added worksheets “Decomposition” to the SBM-family and Non-
separable Output models. This includes the decomposition of
efficiency score into inefficiency of each input/output factor.
The Hybrid model deals with radial and non-radial inputs/outputs in
a unified framework. The Weighted SBM model puts weights to slacks
in accordance with their importance to management.
In Version 4.1, we enriched our collection of DEA models by the
addition of Undesirable Outputs models. In accordance with the
global environmental conservation awareness, undesirable outputs of
production and social activities, e.g., air pollutants and hazardous
waste, have been widely recognized as societal evils. Thus,
development of technologies with less undesirable outputs is an
important subject of concern in every area of production. DEA usually
assumes that producing more outputs relative to less input resources
Input Output
14. Newsletter 20
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is a criterion of efficiency. In the presence of undesirable outputs,
however, technologies with more good (desirable) outputs and less
bad (undesirable) outputs relative to less input resources should be
recognized as efficient. The Undesirable Model deals with this
problem. Please see Newsletter 5.
All DEA models can be classified into four types: (1) Radial, (2)
Non-Radial and Oriented, (3) Non-Radial and Non-Oriented and (4)
Composite of Radial and Non-radial. ‘Radial’ means that a
proportionate change of input/output values is the main concern and
hence it neglects the existence of slacks (input excesses and output
shortfalls remaining in the model) as secondary or freely disposable,
whereas ‘Non-Radial’ deals with slacks directly and does not stick to
a proportionate change of input/output. ‘Oriented’ indicates the input
or output orientation in evaluating efficiency, i.e., the main target of
evaluation is either input reduction or output expansion. For example,
input oriented models first aim to reduce input resources to the
efficient frontier as far as possible, and then to enlarge output
products as the second objective. ‘Non-Oriented’ models deal with
input reduction and output expansion at the same time.
They are classified into the four categories as displayed below.
Category Cluster or Model
Radial CCR, BCC, IRS, DRS, AR, ARG, NCN, NDSC, BND, CAT,
SYS, Bilateral, Scale Elasticity, Congestion, Window,
Malmquist-Radial, FDH, NonConvex-Radial, Resampling –
SuperRadial DD-I(O)-C(V), SuperDD-I(O)-C(V), DD-C(V),
SuperDD-C(V)
Non-Radial and Oriented SBM-Oriented, Super-efficiency-Oriented, Malmquist,
NetworkSBM(Oriented), DynamicSBM(Oriented),
DynamicNetworkSBM(Oriented), NonConvex-SBM-I(O),
Resampling-SuperSBM(Oriented), SBM_Max(Oriented)
Super-SBM_Max (Oriented), SBM_Bounded (Oriented),
NegativeData_SBM (Oriented), NegativeData_SBM_Max
(Oriented), NegativeData_Malmquist (Oriented)
Non-Radial and Non-Oriented Cost, New-Cost, Revenue, New-Revenue, Profit, New-Profit,
Ratio, SBM-NonOriented, Super-SBM-NonOriented, Malmquist-
C (V, GRS), Undesirable Outputs, Weighted SBM ,
NetworkSBM(NonOriented), DynamicSBM(NonOriented),
Bilateral, DynamicNetworkSBM(NonOriented), NonConvex-
SBM-NonOriented, Resampling-SuperSBM(NonOriented), SBM
16. Newsletter 20
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30 Malmquist-Radial Malmquist-Radial-I-C, Malmquist-Radial-I-V,
Malmquist-Radial-I-GRS, Malmquist-Radial-O-C,
Malmquist-Radial-O-V, Malmquist-Radial-O-GRS
31 Scale Elasticity Elasticity-I, Elasticity-O
32 Congestion Congestion
33 Undesirable outputs BadOutput-C, BadOutput-V, BadOutput-GRS,
NonSeparable-C, NonSeparable-V, NonSeparable-
GRS
34 Hybrid Hybrid-C, Hybrid-V, Hybrid-I-C, Hybrid-I-V, Hybrid-
O-C, Hybrid-O-V
35 Network DEA Oriented NetworkSBM-I(O)-C(V)
36 Network DEA Non-Oriented NetwrokSBM-C(V)
37 Dynamic DEA Oriented DynamicSBM-I(O)-C(V)
38 Dynamic DEA Non-oriented DynamicSBM-C(V)
39 EBM Oriented EBM-I(O)-C(V)
40 EBM Non-Oriented EBM-C(V)
41 DynamicNetworkSBM
Oriented
DNSBM-I-C(V), DNSBM-O-C(V)
42 DynamicNetworkSBM
NonOriented
DNSBM-C(V)
43 NonConvex-SBM NonConvex-SBM-I(O),-NonOriented
44 NonConvex-Radial NonConvex-Radial-I(O)
45 Rsampling-Triangular Resampling-(Super)SBM, Resampling-(Super)Radial
46 Resampling-Triangular-
Historical
Resampling-(Super)SBM, Resampling-(Super)Radial
47 Resampling-PastPresent Resampling-(Super)SBM, Resampling-(Super)Radial
48 Resampling-
PastPresentFuture
Resampling-(Super)SBM, Resampling-(Super)Radial
49 Directional Distance
Oriented
DD-I(O)-C(V), SuperDD-I(O)-C(V)
50 Directional Distance Non-
oriented
DD-C(V), SuperDD-C(V)
51 SBM_Max SBM_Max-I(O)-C(V), -C(V), -GRS
52 Super-SBM_Max Super-SBM_Max (Oriented), Super-SBM_Max
(NonOriented)
53 SBM_Bounded SBM_Bounded (Oriented), SBM_Bounded
(NonOriented)
54 NegativeData_SBM NegativeData_SBM-I(O)-C(V), NegativeData_SBM-
C(V), NegativeData_SuperSBM-C(V)
55 NegativeData_SBM_Max NegativeData_SBM_Max-I(O)-C(V),
NegativeData_SBM_Max-C(V)
56 NegativeData_Malmquist NegativeData_Malmquist_SBM-I(O)-C(V),
NegativeData_Malmquist_SBM-C(V)
57 Desirable Inputs GoodInpt-C, GoodInput-V
The meanings of the extensions -C, -V and –GRS are as follows.
Every DEA model assumes a returns to scale (RTS) characteristics
that is represneted by the range (L, U) of the sum of the intensity
vectorλ. The constant RTS (-C) corresponds to (L=0, U=infinity) and
17. Newsletter 20
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the variable RTS (-V) to (L=1, U=1), respectively.
In the models with the extension GRS, we have to supply L and U
from keyboard, the defaults being L=0.8 and U=1.2. The increasing
RTS (IRS) corresponds to (L=1, U=infinity) and the decreasing RTS
(DRS) to (L=0, U=1), respectively. It is recommended to try several
sets of (L, U) in order to identify how the RTS chracteristics exerts an
influence on the efficiency score. In the Non-Oriented EBM models
(EBM-C and EBM-V), we must supply L and U from the keyboard, the
defaults being L=U=1.
In the Resampling models, the number of replicas must be supplied
through the keyboard.