This document provides an overview and comparison of two approaches to measuring efficiency in the agricultural sector: data envelopment analysis (DEA) and stochastic frontier analysis (SFA). DEA is a non-parametric method that uses linear programming to construct an efficiency frontier from the data. SFA is a parametric econometric method that accounts for random effects and technical inefficiency through an error term. The document discusses the key characteristics and assumptions of each model and compares their advantages and disadvantages, such as DEA's flexibility but sensitivity to outliers, and SFA's ability to distinguish noise from inefficiency but risk of misspecification.
9. the efficiency of volatility financial model withikhwanecdc
This document summarizes a study that investigates the effectiveness of volatility financial models with the presence of additive outliers via Monte Carlo simulation. The study simulates data using an ARMA(1,0)-GARCH(1,2) model with different sample sizes of 500, 1000, and 1400, both with and without 10% additive outliers added. The effectiveness of the models is evaluated based on error metrics and information criteria. The results indicate that the effectiveness of the ARMA-GARCH model diminishes as sample size increases in the presence of additive outliers.
This short note describes a relatively simple methodology, procedure or approach to increase the performance of already installed industrial models used for optimization, control, simulation and/or monitoring purposes. The method is called Excess or X-Model Regression (XMR) where the concept of “excess modeling” or an X-model is taken from the field of thermodynamics to describe the departure or residual behaviour of real (non-ideal) gases and liquids from their ideal state (Kyle, 1999; Poling et. al., 2001; Smith et. al., 2001). It has also been applied to model the non-ideal or nonlinear behaviour of blending motor gasoline octanes with its synergistic and antagonistic interactional effects (Muller, 1992).
The fundamental idea of XMR is to calibrate, train, fit or estimate, using actual data and multiple linear regression (MLR) or ordinary least squares (OLS), the deviations of the measured responses from the existing model responses. The existing model may be a glass, grey or black-box model (known or unknown, linear or nonlinear, implicit/open or explicit/closed) depending on the use of the model. That is, for optimization and control the model structure and parameters are available given that derivative information is required although for simulation and monitoring, the model may only be observed through the dependent output variables given the necessary independent input variables.
This document summarizes a presentation about a farm in Somerset, Pennsylvania called Laurel Vista Farm. It discusses the farm's history of growing potatoes, green beans, and sweet onions and how the farm has modernized its equipment and operations over time. It then talks about the barriers small farms face in doing value-added processing and distribution. It proposes the idea of a shared commercial kitchen / food hub called RFEC that would help farms aggregate, process, store, and distribute value-added products to overcome these barriers and sell products year-round. It then introduces Greg Boulos and Andrew Ellsworth to present more details about RFEC.
The document discusses issues related to agriculture in the WTO. It provides background on the establishment of the WTO and the Agreement on Agriculture (AoA). Key points covered include commitments made by countries on domestic support, market access and export subsidies. It discusses how developed countries continue to heavily subsidize their agriculture contrary to WTO provisions. This has negatively impacted farmers in developing countries by restricting market access and allowing suppressed prices. The document also outlines India's positions and recommendations for the negotiations to achieve a more equitable framework.
This document discusses the implications of the World Trade Organization (WTO) on India's agricultural sector. It provides background on GATT, the predecessor to WTO, and explains key aspects of the WTO including its formation, purpose, and differences from GATT. The document then discusses India's large and historically important agricultural industry. It outlines issues such as low productivity and government interventions that impact the sector. Finally, it analyzes India's commitments under the WTO Agreement on Agriculture, including maintaining quantitative restrictions on imports and not providing direct export subsidies.
Adding value refers to the difference between the price received for a finished good or service and the costs of inputs required to produce it. This value added can be calculated by taking revenue and subtracting total costs. A business owner created Tyrrell's Potato Chips by taking potatoes, a commodity, and adding value through distinctive flavors, packaging, and hand-fried preparation to create a premium branded product. Businesses can add value through branding, customer service, product features, efficiency, convenience, and unique selling points. Signs that a small business is adding value include strong gross profit margins, repeat business from satisfied customers, and good brand recognition.
The document discusses value-added products in the food processing sector, noting that value is added through activities like grading, sorting, cutting, and packaging agricultural products to increase their value and price. Major areas of food processing include fruits and vegetables, dairy, and fisheries, with examples given of processed products in each category like juices, cheeses, and prepared fish dishes. The processing adds value by developing products that meet consumer needs and demands.
value addition and processing of agri-productssurabhi mishra
- The document discusses opportunities and challenges for value addition and processing of agricultural products in India.
- It outlines high levels of post-harvest losses on farms and in supply chains, as well as low levels of agro-processing and value addition compared to other countries.
- The document advocates for strategies like expanding processing levels, modernizing food processing sectors, and promoting seamless value chains to reduce losses and add more value to agricultural commodities in India.
9. the efficiency of volatility financial model withikhwanecdc
This document summarizes a study that investigates the effectiveness of volatility financial models with the presence of additive outliers via Monte Carlo simulation. The study simulates data using an ARMA(1,0)-GARCH(1,2) model with different sample sizes of 500, 1000, and 1400, both with and without 10% additive outliers added. The effectiveness of the models is evaluated based on error metrics and information criteria. The results indicate that the effectiveness of the ARMA-GARCH model diminishes as sample size increases in the presence of additive outliers.
This short note describes a relatively simple methodology, procedure or approach to increase the performance of already installed industrial models used for optimization, control, simulation and/or monitoring purposes. The method is called Excess or X-Model Regression (XMR) where the concept of “excess modeling” or an X-model is taken from the field of thermodynamics to describe the departure or residual behaviour of real (non-ideal) gases and liquids from their ideal state (Kyle, 1999; Poling et. al., 2001; Smith et. al., 2001). It has also been applied to model the non-ideal or nonlinear behaviour of blending motor gasoline octanes with its synergistic and antagonistic interactional effects (Muller, 1992).
The fundamental idea of XMR is to calibrate, train, fit or estimate, using actual data and multiple linear regression (MLR) or ordinary least squares (OLS), the deviations of the measured responses from the existing model responses. The existing model may be a glass, grey or black-box model (known or unknown, linear or nonlinear, implicit/open or explicit/closed) depending on the use of the model. That is, for optimization and control the model structure and parameters are available given that derivative information is required although for simulation and monitoring, the model may only be observed through the dependent output variables given the necessary independent input variables.
This document summarizes a presentation about a farm in Somerset, Pennsylvania called Laurel Vista Farm. It discusses the farm's history of growing potatoes, green beans, and sweet onions and how the farm has modernized its equipment and operations over time. It then talks about the barriers small farms face in doing value-added processing and distribution. It proposes the idea of a shared commercial kitchen / food hub called RFEC that would help farms aggregate, process, store, and distribute value-added products to overcome these barriers and sell products year-round. It then introduces Greg Boulos and Andrew Ellsworth to present more details about RFEC.
The document discusses issues related to agriculture in the WTO. It provides background on the establishment of the WTO and the Agreement on Agriculture (AoA). Key points covered include commitments made by countries on domestic support, market access and export subsidies. It discusses how developed countries continue to heavily subsidize their agriculture contrary to WTO provisions. This has negatively impacted farmers in developing countries by restricting market access and allowing suppressed prices. The document also outlines India's positions and recommendations for the negotiations to achieve a more equitable framework.
This document discusses the implications of the World Trade Organization (WTO) on India's agricultural sector. It provides background on GATT, the predecessor to WTO, and explains key aspects of the WTO including its formation, purpose, and differences from GATT. The document then discusses India's large and historically important agricultural industry. It outlines issues such as low productivity and government interventions that impact the sector. Finally, it analyzes India's commitments under the WTO Agreement on Agriculture, including maintaining quantitative restrictions on imports and not providing direct export subsidies.
Adding value refers to the difference between the price received for a finished good or service and the costs of inputs required to produce it. This value added can be calculated by taking revenue and subtracting total costs. A business owner created Tyrrell's Potato Chips by taking potatoes, a commodity, and adding value through distinctive flavors, packaging, and hand-fried preparation to create a premium branded product. Businesses can add value through branding, customer service, product features, efficiency, convenience, and unique selling points. Signs that a small business is adding value include strong gross profit margins, repeat business from satisfied customers, and good brand recognition.
The document discusses value-added products in the food processing sector, noting that value is added through activities like grading, sorting, cutting, and packaging agricultural products to increase their value and price. Major areas of food processing include fruits and vegetables, dairy, and fisheries, with examples given of processed products in each category like juices, cheeses, and prepared fish dishes. The processing adds value by developing products that meet consumer needs and demands.
value addition and processing of agri-productssurabhi mishra
- The document discusses opportunities and challenges for value addition and processing of agricultural products in India.
- It outlines high levels of post-harvest losses on farms and in supply chains, as well as low levels of agro-processing and value addition compared to other countries.
- The document advocates for strategies like expanding processing levels, modernizing food processing sectors, and promoting seamless value chains to reduce losses and add more value to agricultural commodities in India.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScscpconf
1) The document presents a new technique for fault detection and isolation that uses neural networks to generate models of normal and faulty system behaviors. A decision tree is then used to evaluate residuals and isolate faults.
2) The technique is demonstrated on a benchmark process for an electro-pneumatic valve actuator. Neural networks are used to generate models of the actuator's normal and 19 possible faulty behaviors.
3) A decision tree structure is proposed to simplify online fault diagnosis by only evaluating the most significant residuals needed at each step to isolate faults. This reduces computational effort compared to evaluating all residuals.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
Cost based industrial rectifying sampling inspectionIAEME Publication
This document summarizes a research paper that develops a new cost-based model for rectifying sampling inspection. The model generalizes an existing model by Schmidt and Taylor by using a more general formula for the average proportion defective over time. The expected total cost is derived under the model. The research applies the model to real-world data from an industrial oil producer, finding the new model reduces expected costs by 65.9% compared to the existing model. The paper concludes the new model is more flexible and accurate when failure times are not uniformly distributed.
Application of Semiparametric Non-Linear Model on Panel Data with Very Small ...IOSRJM
-This research work investigated the behaviour of a new semiparametric non-linear (SPNL) model on
a set of panel data with very small time point (T = 1). The SPNL model incorporates the relationship between
individual independent variable and unobserved heterogeneity variable. Five different estimation techniques
namely; Least Square (LS), Generalized Method of Moments (GMM), Continuously Updating (CU), Empirical
Likelihood (EL) and Exponential Tilting (ET) Estimators were employed for the estimation; for the purpose of
modelling the metrical response variable non-linearly on a set of independent variables. The performances of
these estimators on the SPNL model were examined for different parameters in the model using the Least
Square Error (LSE), Mean Absolute Error (MAE) and Median Absolute Error (MedAE) criteria at the lowest
time point (T = 1). The results showed that the ET estimator which provided the least errors of estimation is
relatively more efficient for the proposed model than any of the other estimators considered. It is therefore
recommended that the ET estimator should be employed to estimate the SPNL model for panel data with very
small time point.
The document discusses using machine learning techniques to analyze traffic accident data from Porto Alegre, Brazil between 2000-2013. It compares decision trees, random forests, and logistic regression for predicting whether accidents resulted in injuries. Random forests and logistic regression performed similarly and better than decision trees. Motorcycles and accident type were highly predictive of injuries, while factors like weather had low relevance. The models could be improved with additional data on drivers, weather, and traffic conditions.
Automated well test analysis ii using ‘well test auto’Alexander Decker
This document describes an automated computer program called WELL TEST AUTO that was developed to fully automate well test analysis and interpretation. The program selects reservoir models and estimates parameter values. It was tested on 10 datasets, including simulated and actual field data. Selected results from 3 of the datasets are presented, showing that the program correctly identified the reservoir model and provided acceptable estimates of parameters like permeability and skin. The program implements an artificial intelligence approach to automate the entire well test interpretation workflow in a visual basic program.
20.18 Optimization Problems In Air Pollution ModelingKelly Lipiec
This document discusses the use of optimization problems and adjoint equations in air pollution modeling. It notes that mathematical models are needed to design reliable control strategies to keep pollution levels under critical levels. Optimization is required to determine how and where to reduce emissions in an optimal way. The document outlines the formulation of air pollution models using systems of partial differential equations and describes how data assimilation can be used to obtain initial concentration fields and optimize model parameters, emissions, and deposition rates. It also discusses how adjoint equations and variational data assimilation have been successfully applied in meteorology to compute gradients and find optimal initial conditions.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
Presented by Oswaldo Carrillo, CIFOR, at Online Workshop Capacity Building on the IPCC 2013 Wetlands Supplement, FREL Diagnostic and Uncertainty Analysis, April 15th, 2020
A survey of industrial model predictive control technology (2003)Yang Lee
This document provides a survey of model predictive control (MPC) technology as of 1999-2000, summarizing information from MPC vendors. It begins with a brief history of MPC, including early developments like LQG control in the 1960s. The survey describes general MPC algorithms and how various vendors approach aspects like modeling and optimization. It summarizes MPC applications by industry and envisions future opportunities for the technology.
The document provides an overview of an advanced econometrics and Stata training course. It includes the schedule, which covers topics like single and multi regression, panel data models, time series models, stochastic frontier approach (SFA), data envelopment analysis (DEA), and difference-in-differences (DID). The document also discusses efficiency concepts, performance appraisal techniques, methods for estimating efficiency frontiers like SFA and DEA, and considerations for specifying functional forms.
Statistical modelling is of prime importance in each and every sphere of data analysis. This paper reviews the justification of fitting linear model to the collected data. Inappropriateness of the fitted model may be due two reasons 1.wrong choice of the analytical form, 2. Suffers from the adverse effects of outliers and/or influential observations. The aim is to identify outliers using the deletion technique. In I extend the result of deletion diagnostics to the ex- changeable model and reviews some results of model analytical form checking and the technique illustrated through an example.
1) The document proposes a mathematical formulation to optimize the design and process planning stages of product development concurrently rather than sequentially.
2) It represents the design and process planning stages using functions and constraints. The objective is to minimize quality loss considering customer requirements, product specifications, part dimensions, and process capability.
3) A numerical example of optimizing the design and manufacturing of a low-pass electrical circuit is provided to demonstrate that the proposed concurrent approach leads to better solutions than the traditional sequential approach.
Data Envelopment Analysis is a linear programming technique that assigns efficiency scores to firms engaged in producing similar outputs employing similar inputs. Extremely efficient firms are potential Outliers. The method developed detects Outliers, implementing Stochastic Threshold Value, with computational ease. It is useful in data filtering in BIG DATA problems.
This document evaluates the MATLAB toolbox MATCONT for constructing bifurcation diagrams of chemical process systems. MATCONT is a relatively new software that allows for the continuation of static and dynamic equilibria of nonlinear systems. The document demonstrates MATCONT's capabilities using a well-studied example of a nonlinear ethanol fermentation process that exhibits rich dynamic behavior including multiplicity, oscillations, and chaos. The document concludes that MATCONT is a robust, flexible, and user-friendly tool recommended for bifurcation analysis of nonlinear systems in both research and teaching.
Integrate fault tree analysis and fuzzy sets in quantitative risk assessmentIAEME Publication
This document discusses integrating fault tree analysis and fuzzy sets in quantitative risk assessment. It proposes using fuzzy sets to make the probabilities in a fault tree analysis more precise by accounting for uncertainty. The document provides background on fault tree analysis and fuzzy set theory. It then presents a case study of applying fuzzy fault tree analysis to a flammable liquid storage tank system to evaluate the risk of overpressure in the tank.
Bio inspired use case variability modelling, ijseaijseajournal
Background.Feature Model (FM) is the most important technique used to manage the variability through products in Software Product Lines (SPLs). Often, the SPLs requirements variability is by using variable use case modelwhich is a real challenge inactual approaches: large gap between their concepts and those of
real world leading to bad quality, poor supporting FM, and the variability does not cover all requirements modeling levels.
Aims. This paper proposes a bio-inspired use case variability modeling methodology dealing with the
above shortages.
The chemistry of the actinide and transactinide elements (set vol.1 6)Springer
Actinium is the first member of the actinide series of elements according to its electronic configuration. Actinium closely resembles lanthanum chemically. The three most important isotopes of actinium are 227Ac, 228Ac, and 225Ac. 227Ac is a naturally occurring isotope in the uranium-actinium decay series with a half-life of 21.772 years. 228Ac is in the thorium decay series with a half-life of 6.15 hours. 225Ac is produced from 233U with applications in medicine.
Transition metal catalyzed enantioselective allylic substitution in organic s...Springer
This document provides an overview of computational studies of palladium-mediated allylic substitution reactions. It discusses the history and development of quantum mechanical and molecular mechanical methods used to study the structures and reactivity of allyl palladium complexes. In particular, density functional theory methods like B3LYP have been widely used to study reaction mechanisms and factors controlling selectivity. Continuum solvation models have also been important for properly accounting for reactions in solvent.
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1) The document presents a new technique for fault detection and isolation that uses neural networks to generate models of normal and faulty system behaviors. A decision tree is then used to evaluate residuals and isolate faults.
2) The technique is demonstrated on a benchmark process for an electro-pneumatic valve actuator. Neural networks are used to generate models of the actuator's normal and 19 possible faulty behaviors.
3) A decision tree structure is proposed to simplify online fault diagnosis by only evaluating the most significant residuals needed at each step to isolate faults. This reduces computational effort compared to evaluating all residuals.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
Cost based industrial rectifying sampling inspectionIAEME Publication
This document summarizes a research paper that develops a new cost-based model for rectifying sampling inspection. The model generalizes an existing model by Schmidt and Taylor by using a more general formula for the average proportion defective over time. The expected total cost is derived under the model. The research applies the model to real-world data from an industrial oil producer, finding the new model reduces expected costs by 65.9% compared to the existing model. The paper concludes the new model is more flexible and accurate when failure times are not uniformly distributed.
Application of Semiparametric Non-Linear Model on Panel Data with Very Small ...IOSRJM
-This research work investigated the behaviour of a new semiparametric non-linear (SPNL) model on
a set of panel data with very small time point (T = 1). The SPNL model incorporates the relationship between
individual independent variable and unobserved heterogeneity variable. Five different estimation techniques
namely; Least Square (LS), Generalized Method of Moments (GMM), Continuously Updating (CU), Empirical
Likelihood (EL) and Exponential Tilting (ET) Estimators were employed for the estimation; for the purpose of
modelling the metrical response variable non-linearly on a set of independent variables. The performances of
these estimators on the SPNL model were examined for different parameters in the model using the Least
Square Error (LSE), Mean Absolute Error (MAE) and Median Absolute Error (MedAE) criteria at the lowest
time point (T = 1). The results showed that the ET estimator which provided the least errors of estimation is
relatively more efficient for the proposed model than any of the other estimators considered. It is therefore
recommended that the ET estimator should be employed to estimate the SPNL model for panel data with very
small time point.
The document discusses using machine learning techniques to analyze traffic accident data from Porto Alegre, Brazil between 2000-2013. It compares decision trees, random forests, and logistic regression for predicting whether accidents resulted in injuries. Random forests and logistic regression performed similarly and better than decision trees. Motorcycles and accident type were highly predictive of injuries, while factors like weather had low relevance. The models could be improved with additional data on drivers, weather, and traffic conditions.
Automated well test analysis ii using ‘well test auto’Alexander Decker
This document describes an automated computer program called WELL TEST AUTO that was developed to fully automate well test analysis and interpretation. The program selects reservoir models and estimates parameter values. It was tested on 10 datasets, including simulated and actual field data. Selected results from 3 of the datasets are presented, showing that the program correctly identified the reservoir model and provided acceptable estimates of parameters like permeability and skin. The program implements an artificial intelligence approach to automate the entire well test interpretation workflow in a visual basic program.
20.18 Optimization Problems In Air Pollution ModelingKelly Lipiec
This document discusses the use of optimization problems and adjoint equations in air pollution modeling. It notes that mathematical models are needed to design reliable control strategies to keep pollution levels under critical levels. Optimization is required to determine how and where to reduce emissions in an optimal way. The document outlines the formulation of air pollution models using systems of partial differential equations and describes how data assimilation can be used to obtain initial concentration fields and optimize model parameters, emissions, and deposition rates. It also discusses how adjoint equations and variational data assimilation have been successfully applied in meteorology to compute gradients and find optimal initial conditions.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
Presented by Oswaldo Carrillo, CIFOR, at Online Workshop Capacity Building on the IPCC 2013 Wetlands Supplement, FREL Diagnostic and Uncertainty Analysis, April 15th, 2020
A survey of industrial model predictive control technology (2003)Yang Lee
This document provides a survey of model predictive control (MPC) technology as of 1999-2000, summarizing information from MPC vendors. It begins with a brief history of MPC, including early developments like LQG control in the 1960s. The survey describes general MPC algorithms and how various vendors approach aspects like modeling and optimization. It summarizes MPC applications by industry and envisions future opportunities for the technology.
The document provides an overview of an advanced econometrics and Stata training course. It includes the schedule, which covers topics like single and multi regression, panel data models, time series models, stochastic frontier approach (SFA), data envelopment analysis (DEA), and difference-in-differences (DID). The document also discusses efficiency concepts, performance appraisal techniques, methods for estimating efficiency frontiers like SFA and DEA, and considerations for specifying functional forms.
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1) The document proposes a mathematical formulation to optimize the design and process planning stages of product development concurrently rather than sequentially.
2) It represents the design and process planning stages using functions and constraints. The objective is to minimize quality loss considering customer requirements, product specifications, part dimensions, and process capability.
3) A numerical example of optimizing the design and manufacturing of a low-pass electrical circuit is provided to demonstrate that the proposed concurrent approach leads to better solutions than the traditional sequential approach.
Data Envelopment Analysis is a linear programming technique that assigns efficiency scores to firms engaged in producing similar outputs employing similar inputs. Extremely efficient firms are potential Outliers. The method developed detects Outliers, implementing Stochastic Threshold Value, with computational ease. It is useful in data filtering in BIG DATA problems.
This document evaluates the MATLAB toolbox MATCONT for constructing bifurcation diagrams of chemical process systems. MATCONT is a relatively new software that allows for the continuation of static and dynamic equilibria of nonlinear systems. The document demonstrates MATCONT's capabilities using a well-studied example of a nonlinear ethanol fermentation process that exhibits rich dynamic behavior including multiplicity, oscillations, and chaos. The document concludes that MATCONT is a robust, flexible, and user-friendly tool recommended for bifurcation analysis of nonlinear systems in both research and teaching.
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This document discusses integrating fault tree analysis and fuzzy sets in quantitative risk assessment. It proposes using fuzzy sets to make the probabilities in a fault tree analysis more precise by accounting for uncertainty. The document provides background on fault tree analysis and fuzzy set theory. It then presents a case study of applying fuzzy fault tree analysis to a flammable liquid storage tank system to evaluate the risk of overpressure in the tank.
Bio inspired use case variability modelling, ijseaijseajournal
Background.Feature Model (FM) is the most important technique used to manage the variability through products in Software Product Lines (SPLs). Often, the SPLs requirements variability is by using variable use case modelwhich is a real challenge inactual approaches: large gap between their concepts and those of
real world leading to bad quality, poor supporting FM, and the variability does not cover all requirements modeling levels.
Aims. This paper proposes a bio-inspired use case variability modeling methodology dealing with the
above shortages.
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This document reviews solid-state NMR techniques that have been used to determine the molecular structures of amyloid fibrils. It discusses five categories of NMR techniques: 1) homonuclear dipolar recoupling and polarization transfer via J-coupling, 2) heteronuclear dipolar recoupling, 3) correlation spectroscopy, 4) recoupling of chemical shift anisotropy, and 5) tensor correlation methods. Specific techniques described include rotational resonance, dipolar dephasing, constant-time dipolar dephasing, REDOR, and fpRFDR-CT. These techniques have provided insights into the hydrogen-bond registry, spatial organization, and backbone torsion angles of amyloid fibrils.
This document discusses principles of ionization and ion dissociation in mass spectrometry. It covers topics like ionization energy, processes that occur during electron ionization like formation of molecular ions and fragment ions, and ionization by energetic electrons. It also discusses concepts like vertical transitions, where electronic transitions occur much faster than nuclear motions. The document provides background information on fundamental gas phase ion chemistry concepts in mass spectrometry.
Higher oxidation state organopalladium and platinumSpringer
This document discusses the role of higher oxidation state platinum species in platinum-mediated C-H bond activation and functionalization. It summarizes that the original Shilov system, which converts alkanes to alcohols and chloroalkanes under mild conditions, involves oxidation of an alkyl-platinum(II) intermediate to an alkyl-platinum(IV) species by platinum(IV). This "umpolung" of the C-Pt bond facilitates nucleophilic attack and product formation rather than simple protonolysis back to alkane. Subsequent work has validated this mechanism and also demonstrated that platinum(IV) can be replaced by other oxidants, as long as they rapidly oxidize the
Principles and applications of esr spectroscopySpringer
- Electron spin resonance (ESR) spectroscopy is used to study paramagnetic substances, particularly transition metal complexes and free radicals, by applying a magnetic field and measuring absorption of microwave radiation.
- ESR spectra provide information about electronic structure such as g-factors and hyperfine couplings by measuring resonance fields. Pulse techniques also allow measurement of dynamic properties like relaxation.
- Paramagnetic species have unpaired electrons that create a magnetic moment. ESR detects transition between spin energy levels induced by microwave absorption under an applied magnetic field.
This document discusses crystal structures of inorganic oxoacid salts from the perspective of periodic graph theory and cation arrays. It analyzes 569 crystal structures of simple salts with the formulas My(LO3)z and My(XO4)z, where M are metal cations, L are nonmetal triangular anions, and X are nonmetal tetrahedral anions. The document finds that in about three-fourths of the structures, the cation arrays are topologically equivalent to binary compounds like NaCl, NiAs, and FeB. It proposes representing these oxoacid salts as a quasi-binary model My[L/X]z, where the cation arrays determine the crystal structure topology while the oxygens play a
Field flow fractionation in biopolymer analysisSpringer
This document summarizes a study that uses flow field-flow fractionation (FlFFF) to measure initial protein fouling on ultrafiltration membranes. FlFFF is used to determine the amount of sample recovered from membranes and insights into how retention times relate to the distance of the sample layer from the membrane wall. It was observed that compositionally similar membranes from different companies exhibited different sample recoveries. Increasing amounts of bovine serum albumin were adsorbed when the average distance of the sample layer was less than 11 mm. This information can help establish guidelines for flow rates to minimize fouling during ultrafiltration processes.
1) The document discusses phonons, which are quantized lattice vibrations in crystals that carry thermal energy. It describes modeling crystal vibrations using a harmonic lattice approach.
2) Normal modes of the lattice vibrations can be described as a set of independent harmonic oscillators. Quantum mechanically, these normal modes are quantized as phonons with discrete energy levels.
3) Phonons can be thought of as quasiparticles that carry momentum and energy in the crystal lattice. Their propagation is described using a phonon field approach rather than independent normal modes.
This chapter discusses 3D electroelastic problems and applied electroelastic problems. For 3D problems, it presents the potential function method for solving problems involving a penny-shaped crack and elliptic inclusions. It derives the governing equations and introduces potential functions to obtain the general static and dynamic solutions. For applied problems, it discusses simple electroelastic problems, laminated piezoelectric plates using classical and higher-order theories, and piezoelectric composite shells. It also presents a unified first-order approximate theory for electro-magneto-elastic thin plates.
Tensor algebra and tensor analysis for engineersSpringer
This document discusses vector and tensor analysis in Euclidean space. It defines vector- and tensor-valued functions and their derivatives. It also discusses coordinate systems, tangent vectors, and coordinate transformations. The key points are:
1. Vector- and tensor-valued functions can be differentiated using limits, with the derivatives being the vector or tensor equivalent of the rate of change.
2. Coordinate systems map vectors to real numbers and define tangent vectors along coordinate lines.
3. Under a change of coordinates, components of vectors and tensors transform according to the Jacobian of the coordinate transformation to maintain geometric meaning.
This document provides a summary of carbon nanofibers:
1) Carbon nanofibers are sp2-based linear filaments with diameters of around 100 nm that differ from continuous carbon fibers which have diameters of several micrometers.
2) Carbon nanofibers can be produced via catalytic chemical vapor deposition or via electrospinning and thermal treatment of organic polymers.
3) Carbon nanofibers exhibit properties like high specific area, flexibility, and strength due to their nanoscale diameters, making them suitable for applications like energy storage electrodes, composite fillers, and bone scaffolds.
Shock wave compression of condensed matterSpringer
This document provides an introduction and overview of shock wave physics in condensed matter. It discusses the assumptions made in treating one-dimensional plane shock waves in fluids and solids. It briefly outlines the history of the field in the United States, noting that accurate measurements of phase transitions from shock experiments established shock physics as a discipline and allowed development of a pressure calibration scale for static high pressure work. It describes some of the practical applications of shock wave experiments for providing high-pressure thermodynamic data, understanding explosive detonations, calibrating pressure scales, and enabling studies of materials under extreme conditions.
Polarization bremsstrahlung on atoms, plasmas, nanostructures and solidsSpringer
This document discusses the quantum electrodynamics approach to describing bremsstrahlung, or braking radiation, of a fast charged particle colliding with an atom. It derives expressions for the amplitude of bremsstrahlung on a one-electron atom within the first Born approximation. The amplitude has static and polarization terms. The static term corresponds to radiation from the incident particle in the nuclear field, reproducing previous results. The polarization term accounts for radiation from the atomic electron and contains resonant denominators corresponding to intermediate atomic states. The full treatment allows various limits to be taken, such as removing the nucleus or atomic electron, reproducing known results from quantum electrodynamics.
Nanostructured materials for magnetoelectronicsSpringer
This document discusses experimental approaches to studying magnetization and spin dynamics in magnetic systems with high spatial and temporal resolution.
It describes using time-resolved X-ray photoemission electron microscopy (TR-XPEEM) to image the temporal evolution of magnetization in magnetic thin films with picosecond time resolution. Results are presented showing the changing domain structure in a Permalloy thin film following excitation with a magnetic field pulse. Different rotation mechanisms are observed depending on the initial orientation of the magnetization with respect to the applied field.
A novel pump-probe magneto-optical Kerr effect technique using higher harmonic generation is also discussed for addressing spin dynamics in magnetic systems with femtosecond time resolution and element selectivity.
This document discusses nanomaterials for biosensors and implantable biodevices. It describes how nanostructured thin films have enabled the development of more sensitive electrochemical biosensors by improving the detection of specific molecules. Two common techniques for creating nanostructured thin films are described - Langmuir-Blodgett films and layer-by-layer films. These techniques allow for the precise control of film thickness at the nanoscale and have been used to immobilize biomolecules like enzymes to create biosensors. Recent research is also exploring how these nanostructured films and biomolecules can be used to create implantable biosensors for real-time monitoring inside the body.
Modern theory of magnetism in metals and alloysSpringer
This document provides an introduction to magnetism in solids. It discusses how magnetic moments originate from electron spin and orbital angular momentum at the atomic level. In solids, electron localization determines whether magnetic properties are described by localized atomic moments or collective behavior of delocalized electrons. The key concepts of metals and insulators are introduced. The document then presents the basic Hamiltonian used to describe magnetism in solids, including terms for kinetic energy, electron-electron interactions, spin-orbit coupling, and the Zeeman effect. It also discusses how atomic orbitals can be used as a basis set to represent the Hamiltonian and describes the symmetry properties of s, p, and d orbitals in cubic crystals.
This chapter introduces and classifies various types of damage that can occur in structures. Damage can be caused by forces, deformations, aggressive environments, or temperatures. It can occur suddenly or over time. The chapter discusses different damage mechanisms including corrosion, excessive deformation, plastic instability, wear, and fracture. It also introduces concepts that will be covered in more detail later such as damage mechanics, fracture mechanics, and the influence of microstructure on damage and fracture. The chapter aims to provide an overview of damage types before exploring specific mechanisms and analyses in later chapters.
This document summarizes research on identifying spin-wave eigen-modes in a circular spin-valve nano-pillar using Magnetic Resonance Force Microscopy (MRFM). Key findings include:
1) Distinct spin-wave spectra are observed depending on whether the nano-pillar is excited by a uniform in-plane radio-frequency magnetic field or by a radio-frequency current perpendicular to the layers, indicating different excitation mechanisms.
2) Micromagnetic simulations show the azimuthal index φ is the discriminating parameter, with only φ=0 modes excited by the uniform field and only φ=+1 modes excited by the orthogonal current-induced Oersted field.
3) Three indices are used to label resonance
MySQL InnoDB Storage Engine: Deep Dive - MydbopsMydbops
This presentation, titled "MySQL - InnoDB" and delivered by Mayank Prasad at the Mydbops Open Source Database Meetup 16 on June 8th, 2024, covers dynamic configuration of REDO logs and instant ADD/DROP columns in InnoDB.
This presentation dives deep into the world of InnoDB, exploring two ground-breaking features introduced in MySQL 8.0:
• Dynamic Configuration of REDO Logs: Enhance your database's performance and flexibility with on-the-fly adjustments to REDO log capacity. Unleash the power of the snake metaphor to visualize how InnoDB manages REDO log files.
• Instant ADD/DROP Columns: Say goodbye to costly table rebuilds! This presentation unveils how InnoDB now enables seamless addition and removal of columns without compromising data integrity or incurring downtime.
Key Learnings:
• Grasp the concept of REDO logs and their significance in InnoDB's transaction management.
• Discover the advantages of dynamic REDO log configuration and how to leverage it for optimal performance.
• Understand the inner workings of instant ADD/DROP columns and their impact on database operations.
• Gain valuable insights into the row versioning mechanism that empowers instant column modifications.
AI in the Workplace Reskilling, Upskilling, and Future Work.pptxSunil Jagani
Discover how AI is transforming the workplace and learn strategies for reskilling and upskilling employees to stay ahead. This comprehensive guide covers the impact of AI on jobs, essential skills for the future, and successful case studies from industry leaders. Embrace AI-driven changes, foster continuous learning, and build a future-ready workforce.
Read More - https://bit.ly/3VKly70
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Introducing BoxLang : A new JVM language for productivity and modularity!Ortus Solutions, Corp
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
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"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdfleebarnesutopia
So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation.
In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
2. 4 E. Silva et al.
1.1 Introduction
The agricultural policy analysis, mainly, the agricultural productivity, is often
based on support models like mathematical programming (non-parametric, non-
stochastic) models or econometric (stochastic, parametric) models. These models
are very useful for decision support because they give an orientation of what are the
main characteristics of agricultural farms and how it is possible to solve some of the
problems found.
The deterministic production frontier is an approach where all observations are
in one side of the frontier and all deviations from the frontier are attributed to
inefficiency. On the other hand, in the stochastic approach, all observations are on
both sides of the frontier, and it is possible to separate between random errors and
differences in inefficiency.
The most popular approaches to calculate efficiency are: (1) the non-parametric
techniques (Charnes et al. 1978), the Data Envelopment Analysis (DEA) based on
the linear programming tools; and (2) parametric techniques (Aigner et al. 1977;
Meeusen and van den Broeck 1997), the Stochastic Frontier Analysis (SFA) –
stochastic frontier production (SFP) – based on econometric tools.
1.2 The Data Envelopment Analysis: DEA
The use of optimization tools to calculate efficiency with Data Envelopment
Analysis (DEA) was developed by Charnes et al. (1978) from earlier work by Farrell
(1957). This method has been used to estimate the efficiency in the organizational
units in several areas (Cooper 1999).
DEA is a non-parametric method to estimate efficiency, involves the use of
technical linear programming to construct a non-parametric piecewise surface (or
frontier) over data, for it to be able to calculate efficiency relative to his surface
(Coelli 1996a). Any farm that lies below the frontier is considered to be inefficient.
DEA permits to construct a best-practice benchmark from the data on inputs and
outputs (Jaforullah and Whiteman 1999). In opposite, parametric techniques, as
econometric methods, construct a stochastic frontier.
DEA involves the concept of efficiency, and Farrell (1957) had decomposed
the efficiency into (1) technical efficiency and (2) allocative efficiency. The tech-
nical efficiency measures the maximum equiproportional reduction in all inputs,
which still allows continued production of given outputs. The allocative effi-
ciency reflects the ability of a firm to use the inputs in optimal proportions,
given their respective prices. These two concepts form the concept of economics
efficiency (Coelli 1995). The allocative inefficiency measures the magnitude of
consequent loss. Similar considerations are applied to economics efficiency and
inefficiency.
3. 1 Efficiency Measures in the Agricultural Sector: The Beginning 5
Therefore, the overall measure of technical efficiency can be disaggregated into
three components: (1) pure technical efficiency due to producing within an isoquant
frontier, (2) congestion due to overutilization of inputs and (3) scale efficiency due
to deviations from constant returns to scale (Weersink et al. 1990).
Technical efficiency under constant returns to scale is estimated by relating
each observation to the frontier under constant returns to scale (CRS). Technical
efficiency under variable returns to scale (TEVRS) and technical efficiency under
constant returns to scale (TECRS) are equal for farms that operate in the region
of constant returns to scale, i.e. these farms have a scale efficiency of one. As
a consequence, these farms have a scale efficiency (SCAL) of one (Lansink and
Reinhard 2004).
One of the most popular computer programs used to solve DEA problems is
the Data Envelopment Analysis Computer Program (DEAP) developed by Coelli
(1996b). This program is based on the optimization model used by Charnes
et al. (1978), but considering the input components ikxik and a scaling constant
l (normally 100%):
Max W Efa D
sX
rD1
rayra
s:t: W
mX
iD1
viaxia D l
sX
rD1
rkyrk Ä
mX
iD1
vikxik k D 1; : : : ; n
rk; vik 0 i D 1; : : : ; m r D 1; : : : ; s (1.1)
yrk is the level of output r used by decision-making unit k, xik is the level of input i
used by decision-making unit k and rk and ik are the non-negativevariable weights
associated to the solution of decision-making unit k, of output (r) and inputs (i),
respectively; s is the number of outputs considered and m the number of inputs
considered.
The DEA approach has been applied to the agricultural field to estimate the
efficiency by different researchers in different parts of the world, such as Arzubi
and Berbel (2002), Reinhard and Thijssen (2000), Reinhard et al. (2000), Jaforullah
and Whiteman (1999), Fraser and Cordina (1999), Gonzalez et al. (1996), F¨are and
Whittaker (1995) and Weersink et al. (1990).
1.3 Stochastic Frontier Analysis (SFA) or Production (SFP)
The Stochastic Frontier Analysis (SFA) is a parametric approach which was
originally and independently proposed by Aigner et al. (1977) and Meeusen and
van den Broeck (1977) as pointed out by Battese and Coelli (1988). The SFA
4. 6 E. Silva et al.
involves an unobservable random variable associated with the technical inefficiency
in production of individual firms. In addition to the random error in a traditional
regression model (Battese and Broca 1997), the SFA considers that an error term
has two components: one to account for random effects and another to account for
the technical inefficiency effects (Coelli 1995).
The most common different functional forms for stochastic frontier are the
tanslogarithmic and the Cobb-Douglas production functions.
One of the most common software to solve parametric problems is FRONTIER
version 4.1, developed by Coelli (1996b), often selected to estimate the models.
The efficiency model (model I) is proposed by Battese and Coelli (1995), and the
vit is the technical inefficiency variable which is defined by
vit D fexp Œ Á .t T /g vi ; with i D 1; : : : ; N; and t D 1; : : : ; T (1.2)
where Á is an unknown parameter to be estimated; and vi with i D 1, 2, : : : , n,
are independent and identically distributed non-negative random variables, which
are assumed to account for technical inefficiency in production and obtained by
truncations (at zero) of the normal distribution, with unknown mean and unknown
variance, 2
.
This model specifies that the technical inefficiency effects for the sample in
the earlier periods of the panel are a deterministic exponential function of the
inefficiency effects for the corresponding firms in the last years of the panel (i.e.
vit D vi, given the data for the i-th firm is available in the period, T).
Given the Eq. (1.2), the expectation of the mean technical efficiency is
TE D exp . vi /; (1.3)
and it is estimated considering the technical inefficiency effects (vi).
The model I only permits to determine the technical efficiency, but to know more
about inefficiency, Battese and Coelli (1995) presented the model II (inefficiency
model) which permits to incorporate in model I the variable that could cause ineffi-
ciency in the firms. In this model, the technical inefficiency effects are defined by
uit D zit ı C wit ; with i D 1; : : : ; N; and t D 1; : : : ; T (1.4)
where zit is a (1 M) vector of explanatory variables associated with technical
inefficiency effects; ı is a (M 1) vector of unknown parameters to be estimated;
and wit are the unobservable random errors which are assumed to be independently
distributed and obtained by truncation of a normal distribution with unknown mean
and variance, 2
, such that uit is non-negative .wit zit ı/.
This approach is widely used by many authors such as Pascoe et al. (2001),
Torres et al. (2002), Munzir and Heidhues (2002), Reinhard et al. (1999), Reinhard
et al.(2000), Alvarez and Gonzalez (1999), Webster et al. (1998), Franco (2001),
Daryanto et al. (2002), Lawson et al.(2004), Battese and Coelli (1988), Battese and
Broca (1997), Battese and Coelli (1992), Brummer (2001), Hallam and Machado
(1996) and Venˆancio and Silva (2004).
5. 1 Efficiency Measures in the Agricultural Sector: The Beginning 7
1.4 DEA and SFA Approaches: A Comparison
DEA is very useful to calculate efficiency (Lansink and Reinhard 2004) and
provides a simple way to calculate the efficiency gap that separates each producer’s
behaviour from the best practice, which can be assessed from actual observations of
the inputs and outputs of efficient firms (Reig-Mart´ınez and Picazo-Tadeo 2004).
The DEA model allows the comparison of a firm to a benchmark (set of best
producers), and then the measure of efficiency is relative to the best producer in that
group of firms; it is not necessarily the maximum real output per input used.
Sharma et al. (1999) argue that the main advantage of DEA is the fact that
avoids the parametric specification of technology such as the assumptions for the
distribution of the inefficiency term.
The DEA approach has the advantage of considering many inputs and many
outputs simultaneously. This has as a consequence the increasing of efficiency
with the number of variables: more variables, higher efficiency (Reig-Mart´ınez
and Picazo-Tadeo 2004; Silva et al. 2004). DEA does not require a parametric
specification of a functional form to construct the frontier (Silva et al. 2004),
and it was considered by Coelli and Perelman (1999) as the main advantage of
DEA; it is very easy to perform because it does not require a priori knowledge
on the functional form of the frontier and benchmarking (best-practice reference)
to real firms (Lauwers and van Huylenbroecks 2003). DEA does not impose any
assumptions about functional form; hence, it is less prone to misspecification and
does not take into account the random error and consequently is not subject to
the problems of assuming an underlying distribution about error term (Pascoe and
Mardle 2000). To these authors, DEA does not take account of statistical noise,
and the efficiency estimates may be biased if the production process is largely
characterized by stochastic elements.
The mathematical programming models are non-stochastic, and so they cannot
have the values, of inefficiency and noise, separately. It is a non-parametric
technique and not as effective as SFA, as a specification error (Reig-Mart´ınez
and Picazo-Tadeo 2004). The major limitations of DEA is that it is difficult,
conceptually, to separate the effects of uncontrollable environmental variables and
measurements error, from the effect of differences in farm management and the
presence of outliers (Silva et al. 2004).
The DEA model is deterministic and attributes all the deviations from the
frontier to inefficiencies; a frontier estimated by DEA is likely to be sensitive to
measurement errors or other noise in data (Sharma et al. 1999) and may attribute
stochastic noise to the inefficiency scores and thus may be more sensitive to outliers
(Lauwers and van Huylenbroecks 2003).
DEA has more flexibility in that they avoid a parametric specification of
technology and assumptions about the distribution of efficiency, whilst allowing
curvature conditions to be imposed easily (Lansink and Reinhard 2004).
The main DEA disadvantage to Coelli and Perelman (1999) is that when the
calculation of shadow prices are desired, only a range of prices can be derived
6. 8 E. Silva et al.
for the efficient firms. The production surface constructed by DEA is a series of
intersecting planes. The efficient frontier points that define this frontier surface
(primarily) lie at the intersections of these planes. Hence, when one attempts to
measure shadow prices for these efficient points, only a range of price ratios can be
observed (corresponding to the slopes of the planes involved).
A disadvantage of the DEA approach is that there are no single objective criteria
(it is different from a CRS to a VRS) against assessment of the model, and the
models only provide a reasonable representation of the actual frontier (or set of
frontiers) (Pascoe and Mardle 2000).
The econometric approach is stochastic, and then it allows distinguishing the
two effects: statistical noise from productive inefficiency. It is also parametric and
can mix the effect of misspecification of functional form (can be flexible) with
inefficiency. The flexible functional form could imply the multicollinearity, and
some theoretical conditions could be violated (Reinhard et al. 2000)
One of the most important characteristics of econometric models (SFA) is that it
allows a specification in the case of panel data and the construction of confidence
intervals (Reinhard et al. 2000).
The SFA models only have in this functional form one output and various inputs.
SFA allows a correction for stochastic events, but assumes a parametric specification
for the production technology, which can confuse efficiency results. SFA calculated
by a translog specification, the curvature conditions (concavity inputs) are not
globally satisfied, SFA makes an explicit assumption about the distribution of the
inefficiency term (Lansink and Reinhard 2004).
Stochastic estimations incorporate a measure of random error but impose an
explicit functional form and distribution assumption of data (Pascoe and Mardle
2000). SFP approach produces a set of statistics against which the models can be
judged in terms of goodness of fit and alternative methods can be discriminated
against.
One advantage of parametric methods is that they permit the testing of hypothe-
ses such as those relating to the significance of included inputs and/or outputs,
returns to scale and so on (Coelli and Perelman 1999).
The advantage of DEA over SFA is that the technological frontier is constructed
without imposing a parametric functional form for inefficiency or on a deviation
from it (Reig-Mart´ınez and Picazo-Tadeo 2004).
Sharma et al. (1999) found that DEA is more sensitive to outliers and other noise
in the data, but DEA results are more robust than those obtained from the parametric
approach.
The most important point of frontier approach is that it deals with stochastic noise
and allows statistical test of hypotheses pertaining to production structure and the
degree of inefficiency. The imposed structural form and the explicit distributional
assumption for the inefficiency term are the worst point of parametric techniques
(Sharma et al. 1999).
There are several differences between these two kinds of tools. One of the
differences pointed by Reinhard et al. (2000) was about the construction of a
production frontier and the calculation of efficiency relative to the frontier.
7. 1 Efficiency Measures in the Agricultural Sector: The Beginning 9
Coelli and Perelman (1999) point as advantages of parametric and non-
parametric approaches the influence of outliers, although the stochastic frontier
method attempts to account this problem, the translog distance function estimates
obtained by this tool, has not been very successful.
In some situations in DEA, when there are non-stochastic elements affecting a
firm’s level of production, the greater DEA flexibility allows a better representation
in the level of the distribution of technical efficiency (Pascoe and Mardle 2003). In
this case, the imposition of a single production frontier (SFA model) may result that
some inefficient firms probably are efficient.
Paul et al. (2004) compare the stochastic and determinist methods and conclude
that in terms of level, the SFP (Stochastic Frontier Production) measures suggest
more scale economies and technical efficiency, and DEA measures more “technical
progress” or shifts out of the technological frontier over time.
In the construction of confidence intervals, Brummer (2001) points that intervals
for SFA are wider than DEA, because the assumptions of DEA are more restric-
tive (deterministic structure). The SFP model estimated a higher mean technical
efficiency compared with DEA models (Pascoe and Mardle 2000).
Reinhard et al. (2000) compared the SFA and DEA approaches and concluded
that both methods can estimate efficiency scores, but only SFA incorporates noise.
DEA is a deterministic model and could not identify the environmental detrimental
variables in the model.
In summary, the main difference between SFA and DEA is about the way
the production possibility is estimated; that means, DEA does not require a
functional form and SFA does. The DEA (deterministic) approach ignores the
random effects (noise), but SFA approach takes it into account; then in the DEA
model, any deviation is considered inefficiency but in SFA is considered noise
and inefficiency. Both approaches give the same indication of the characteristics
that affect efficiency, and the outliers can affect the scores of efficiency. Although
the scores of inefficiency are similar, usually the value of the DEA approach is
lower than the one obtained by SFA. The SFA approach allows distinguishing the
inefficiency from the error term: it is identified what is due to inefficiency and what
is due to random disturbances such as measurement of error, luck, bad weather, pest
or diseases.
Both approaches seem useful, and their use will depend on the objectives of the
analysis.
1.5 Why This Book?
The aim of this book is to aggregate several articles on efficiency measures and
techniques for the agricultural sector.
Increasing efficiency in the agricultural sector and in rural communities can
improve the financial situation of farms and communities, given the continuous
pressure on margins of agricultural products. This fact has been highly recognized
8. 10 E. Silva et al.
and studied by researchers since many years. But, in the current context of economic
crises in Europe, these themes are, more than ever, especially pertinent as several
southern countries need to increase exportation and decrease importation.
This book starts by describing techniques like Data Envelopment Analysis
(DEA) and Stochastic Frontier Analysis (SFA) and proceeds by presenting several
applications exploring the particularities of the agriculture sector as the abundance
of subsidies, strong competition between regions worldwide and the need for
efficient production systems. The introductory chapters make possible for the
nonexpert reader to understand the book, and clear applications lead the reader to
practical problems and solutions.
Applications include the estimation of technical efficiency in agricultural grazing
systems (dairy, beef and mixed) and specifically for dairy farms. The conclusions
indicate that it is now necessary to help small dairy farms in order to make them
more efficient. These results can be compared with the technical efficiency of a
sample of Spanish dairy processing firms presented by Magdalena Kapelko and
co-authors.
In the work by Silva and Venˆancio, SFA is used for the estimation of inefficiency
models. Another approach involved the assessing of the importance of subsidies in
farms efficiency as they are so relevant for farmers. Another application studies the
level of technical efficiency in the Andalusia oil industry implementing environmen-
tal and non-discretionary variables.
There are also articles on applying DEA techniques to agriculture using R soft-
ware and making it more user friendly. The “Productivity Analysis with R” (PAR)
framework establishes a user-friendly data envelopment analysis environment with
special emphasis on variable selection, aggregation and summarization.
The editors have confidence that this book is useful and informative for students
and researchers. Feel free to send feedback to amendes@uac.pt.
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