This article presents a system which allows interaction between a physically disabled person and ... more This article presents a system which allows interaction between a physically disabled person and his environment. It contributes to achieve an Ambient Assisted Living (AAL). This system is controlled by voluntary muscular movements, particularly the orbicular ones. These movements are translated into instructions which are sent by means of a wireless link to a mobile robot that executes them. This robot includes a video camera in order to show the environment of the route that the robot follows on its way to the user. It also includes a subsystem that contains light and sound signals. This system can aid people with reduced mobility, extending the time that older people and disabled people can live in their home environment, increasing their autonomy and their confidence.
This work presents a new method, called heuristic selection (HS), to process algebraic equation s... more This work presents a new method, called heuristic selection (HS), to process algebraic equation systems (AEs). These systems are part of all chemical processes models that respect the state space models form. For given AEs, the proposed method produces a set of decision variables, a set of subsystems, a set of variables for iteration, a set of equations for verification,
This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any... more This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any plant is designed, the engineers define the design for reaching optimal operation under nominal conditions. However, uncertain variables or disturbance cannot be handled to obtain those nominal conditions. For this reason, the design contains control elements for compensating the disturbance effects. This compensation is only possible into a region defined by the characteristics of the process and the control elements. When the actual conditions are out of that region, the compensation is not enough, and the plant is not operable. A flexibility study determines the region into which the process is operable. This information can be useful for determining design modifications to improve the process flexibility. A flexibility study involves a complex mathematical model, which is even more complex for a MSF plant. To perform the task, in this work, a stationary simulator was developed for a real-world case study, and the region exploration was performed by Monte Carlo simulation. Results show, in terms of both robustness and speed of computation, that this approach can be a useful tool.
This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) de... more This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) desalination plant by using BNs (Bayesian networks). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the latter case, the diagnostic system determines the cause of the abnormal process state; i.e., it finds out which is the fault that is affecting the supervised process. ABN is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. A BN readily handles situations where some data entries are missing. This paper determines both the structure and parameters of a BN intended for a diagnostic system. The implemented system is evaluated by using a dynamic simulator, which was developed for a real MSF desalination plant. Besides, the diagnostic system performance is compared with the performances of two other diagnostic systems. The obtained results show some advantages for the BN based diagnostic system.
This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) des... more This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) desalination plant using artificial neural networks (ANNs). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal process state. The diagnostic system has an ANN for each potential fault. Every ANN processes the plant data looking for symptoms of their respective faults. At a given time, the result reported by an ANN is an index between 0 and 1. This number represents the certainty about the corresponding fault is affecting the plant. The higher is the value, the higher is the certainty of the affirmation. The structure of each ANN is simpler than those reported in the bibliography; however, the performance is better. These results are obtained due to a careful selection of the diagnostic system output and the use of a special training method. That training method calculates an appropriate value for the output of each ANN instead of setting it at 0 or 1 only. The new value of the output does not depend on the fault that causes the inputs but it does only on the degree of matching between the observed evolution and the expected one for the fault corresponding to each ANN. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.
This work presents a new method, called heuristic selection (HS), to process algebraic equation s... more This work presents a new method, called heuristic selection (HS), to process algebraic equation systems (AEs). These systems are part of all chemical processes models that respect the state space models form. For given AEs, the proposed method produces a set of decision variables, a set of subsystems, a set of variables for iteration, a set of equations for verification,
This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any... more This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any plant is designed, the engineers define the design for reaching optimal operation under nominal conditions. However, uncertain variables or disturbance cannot be handled to obtain those nominal conditions. For this reason, the design contains control elements for compensating the disturbance effects. This compensation is only possible into a region defined by the characteristics of the process and the control elements. When the actual conditions are out of that region, the compensation is not enough, and the plant is not operable. A flexibility study determines the region into which the process is operable. This information can be useful for determining design modifications to improve the process flexibility. A flexibility study involves a complex mathematical model, which is even more complex for a MSF plant. To perform the task, in this work, a stationary simulator was developed for a real-world case study, and the region exploration was performed by Monte Carlo simulation. Results show, in terms of both robustness and speed of computation, that this approach can be a useful tool.
This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any... more This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any plant is designed, the engineers define the design for reaching optimal operation under nominal conditions. However, uncertain variables or disturbance cannot be handled to obtain those nominal conditions. For this reason, the design contains control elements for compensating the disturbance effects. This compensation is only possible into a region defined by the characteristics of the process and the control elements. When the actual conditions are out of that region, the compensation is not enough, and the plant is not operable. A flexibility study determines the region into which the process is operable. This information can be useful for determining design modifications to improve the process flexibility. A flexibility study involves a complex mathematical model, which is even more complex for a MSF plant. To perform the task, in this work, a stationary simulator was developed for a real-world case study, and the region exploration was performed by Monte Carlo simulation. Results show, in terms of both robustness and speed of computation, that this approach can be a useful tool.
This work outlines the development of a fault diagnostic system to supervise a MSF desalination p... more This work outlines the development of a fault diagnostic system to supervise a MSF desalination plant during dynamic states (e.g., start-up and changes of operative conditions) using artificial neural networks (ANNs). This diagnostic system processes the plant data to determine whether the process state during a dynamic state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal process state. The diagnostic system has an ANN for each potential fault (equipment malfunctions or operator mistakes). Every ANN processes the plant data looking for symptoms of their respective faults. At a given time, the result reported by an ANN is an index between 0 and 1. This number represents the security about how the corresponding fault is affecting the plant. The higher this value, the higher the security of the affirmation. The structure of each ANN is simpler than that reported in the literature; however, the performance is better. These results were obtained due to a careful selection of the diagnostic system output and the use of a special training method. This training method calculates an appropriate value for the output of each ANN instead to set it at 0 or 1 only. The new value of the output does not depend on the fault provoking the inputs but only on the degree of matching between the observed evolution and the expected one for the fault corresponding to each ANN. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.
This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) de... more This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) desalination plant by using BNs (Bayesian networks). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the latter case, the diagnostic system determines the cause of the abnormal process state; i.e., it finds out which is the fault that is affecting the supervised process. ABN is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. A BN readily handles situations where some data entries are missing. This paper determines both the structure and parameters of a BN intended for a diagnostic system. The implemented system is evaluated by using a dynamic simulator, which was developed for a real MSF desalination plant. Besides, the diagnostic system performance is compared with the performances of two other diagnostic systems. The obtained results show some advantages for the BN based diagnostic system.
This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) des... more This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) desalination plant using artificial neural networks (ANNs). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal process state. The diagnostic system has an ANN for each potential fault. Every ANN processes the plant data looking for symptoms of their respective faults. At a given time, the result reported by an ANN is an index between 0 and 1. This number represents the certainty about the corresponding fault is affecting the plant. The higher is the value, the higher is the certainty of the affirmation. The structure of each ANN is simpler than those reported in the bibliography; however, the performance is better. These results are obtained due to a careful selection of the diagnostic system output and the use of a special training method. That training method calculates an appropriate value for the output of each ANN instead of setting it at 0 or 1 only. The new value of the output does not depend on the fault that causes the inputs but it does only on the degree of matching between the observed evolution and the expected one for the fault corresponding to each ANN. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.
A mathematical model for the optimization of the mechanical vapor compression (MVC) desalination ... more A mathematical model for the optimization of the mechanical vapor compression (MVC) desalination process is presented. The mathematical model involves the real physical constraints for the evaporation process. Nonlinear equations in terms of chemical-physical properties and design equations are used to model the process. A general algebraic modeling system (GAMS) is used to implement the model. The generalized reduced gradient algorithm CONOPT 2.041 is used as an NLP solver. The effects of some relevant process parameters on the system performance are studied. The output results from the proposed model were successfully compared with those of the literature.
This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any... more This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any plant is designed, the engineers define the design for reaching optimal operation under nominal conditions. However, uncertain variables or disturbance cannot be handled to obtain those nominal conditions. For this reason, the design contains control elements for compensating the disturbance effects. This compensation is only possible into a region defined by the characteristics of the process and the control elements. When the actual conditions are out of that region, the compensation is not enough, and the plant is not operable. A flexibility study determines the region into which the process is operable. This information can be useful for determining design modifications to improve the process flexibility. A flexibility study involves a complex mathematical model, which is even more complex for a MSF plant. To perform the task, in this work, a stationary simulator was developed for a real-world case study, and the region exploration was performed by Monte Carlo simulation. Results show, in terms of both robustness and speed of computation, that this approach can be a useful tool.
This work outlines the development of a fault diagnostic system to supervise a MSF desalination p... more This work outlines the development of a fault diagnostic system to supervise a MSF desalination plant during dynamic states (e.g., start-up and changes of operative conditions) using artificial neural networks (ANNs). This diagnostic system processes the plant data to determine whether the process state during a dynamic state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal process state. The diagnostic system has an ANN for each potential fault (equipment malfunctions or operator mistakes). Every ANN processes the plant data looking for symptoms of their respective faults. At a given time, the result reported by an ANN is an index between 0 and 1. This number represents the security about how the corresponding fault is affecting the plant. The higher this value, the higher the security of the affirmation. The structure of each ANN is simpler than that reported in the literature; however, the performance is better. These results were obtained due to a careful selection of the diagnostic system output and the use of a special training method. This training method calculates an appropriate value for the output of each ANN instead to set it at 0 or 1 only. The new value of the output does not depend on the fault provoking the inputs but only on the degree of matching between the observed evolution and the expected one for the fault corresponding to each ANN. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.
A mathematical model for the optimization of the mechanical vapor compression (MVC) desalination ... more A mathematical model for the optimization of the mechanical vapor compression (MVC) desalination process is presented. The mathematical model involves the real physical constraints for the evaporation process. Nonlinear equations in terms of chemical-physical properties and design equations are used to model the process. A general algebraic modeling system (GAMS) is used to implement the model. The generalized reduced gradient algorithm CONOPT 2.041 is used as an NLP solver. The effects of some relevant process parameters on the system performance are studied. The output results from the proposed model were successfully compared with those of the literature.
ABSTRACT The extraordinary events that occurred in the country between 2001-2007, with a sharp de... more ABSTRACT The extraordinary events that occurred in the country between 2001-2007, with a sharp decline in inequality and the levels of poverty and extreme poverty offers a unique opportunity to examine the most relevant factors to explain changes that benefit the poor ...
ABSTRACT The extraordinary events that occurred in the country between 2001-2007, with a sharp de... more ABSTRACT The extraordinary events that occurred in the country between 2001-2007, with a sharp decline in inequality and the levels of poverty and extreme poverty offers a unique opportunity to examine the most relevant factors to explain changes that benefit the poor ...
This article presents a system which allows interaction between a physically disabled person and ... more This article presents a system which allows interaction between a physically disabled person and his environment. It contributes to achieve an Ambient Assisted Living (AAL). This system is controlled by voluntary muscular movements, particularly the orbicular ones. These movements are translated into instructions which are sent by means of a wireless link to a mobile robot that executes them. This robot includes a video camera in order to show the environment of the route that the robot follows on its way to the user. It also includes a subsystem that contains light and sound signals. This system can aid people with reduced mobility, extending the time that older people and disabled people can live in their home environment, increasing their autonomy and their confidence.
This work presents a new method, called heuristic selection (HS), to process algebraic equation s... more This work presents a new method, called heuristic selection (HS), to process algebraic equation systems (AEs). These systems are part of all chemical processes models that respect the state space models form. For given AEs, the proposed method produces a set of decision variables, a set of subsystems, a set of variables for iteration, a set of equations for verification,
This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any... more This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any plant is designed, the engineers define the design for reaching optimal operation under nominal conditions. However, uncertain variables or disturbance cannot be handled to obtain those nominal conditions. For this reason, the design contains control elements for compensating the disturbance effects. This compensation is only possible into a region defined by the characteristics of the process and the control elements. When the actual conditions are out of that region, the compensation is not enough, and the plant is not operable. A flexibility study determines the region into which the process is operable. This information can be useful for determining design modifications to improve the process flexibility. A flexibility study involves a complex mathematical model, which is even more complex for a MSF plant. To perform the task, in this work, a stationary simulator was developed for a real-world case study, and the region exploration was performed by Monte Carlo simulation. Results show, in terms of both robustness and speed of computation, that this approach can be a useful tool.
This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) de... more This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) desalination plant by using BNs (Bayesian networks). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the latter case, the diagnostic system determines the cause of the abnormal process state; i.e., it finds out which is the fault that is affecting the supervised process. ABN is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. A BN readily handles situations where some data entries are missing. This paper determines both the structure and parameters of a BN intended for a diagnostic system. The implemented system is evaluated by using a dynamic simulator, which was developed for a real MSF desalination plant. Besides, the diagnostic system performance is compared with the performances of two other diagnostic systems. The obtained results show some advantages for the BN based diagnostic system.
This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) des... more This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) desalination plant using artificial neural networks (ANNs). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal process state. The diagnostic system has an ANN for each potential fault. Every ANN processes the plant data looking for symptoms of their respective faults. At a given time, the result reported by an ANN is an index between 0 and 1. This number represents the certainty about the corresponding fault is affecting the plant. The higher is the value, the higher is the certainty of the affirmation. The structure of each ANN is simpler than those reported in the bibliography; however, the performance is better. These results are obtained due to a careful selection of the diagnostic system output and the use of a special training method. That training method calculates an appropriate value for the output of each ANN instead of setting it at 0 or 1 only. The new value of the output does not depend on the fault that causes the inputs but it does only on the degree of matching between the observed evolution and the expected one for the fault corresponding to each ANN. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.
This work presents a new method, called heuristic selection (HS), to process algebraic equation s... more This work presents a new method, called heuristic selection (HS), to process algebraic equation systems (AEs). These systems are part of all chemical processes models that respect the state space models form. For given AEs, the proposed method produces a set of decision variables, a set of subsystems, a set of variables for iteration, a set of equations for verification,
This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any... more This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any plant is designed, the engineers define the design for reaching optimal operation under nominal conditions. However, uncertain variables or disturbance cannot be handled to obtain those nominal conditions. For this reason, the design contains control elements for compensating the disturbance effects. This compensation is only possible into a region defined by the characteristics of the process and the control elements. When the actual conditions are out of that region, the compensation is not enough, and the plant is not operable. A flexibility study determines the region into which the process is operable. This information can be useful for determining design modifications to improve the process flexibility. A flexibility study involves a complex mathematical model, which is even more complex for a MSF plant. To perform the task, in this work, a stationary simulator was developed for a real-world case study, and the region exploration was performed by Monte Carlo simulation. Results show, in terms of both robustness and speed of computation, that this approach can be a useful tool.
This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any... more This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any plant is designed, the engineers define the design for reaching optimal operation under nominal conditions. However, uncertain variables or disturbance cannot be handled to obtain those nominal conditions. For this reason, the design contains control elements for compensating the disturbance effects. This compensation is only possible into a region defined by the characteristics of the process and the control elements. When the actual conditions are out of that region, the compensation is not enough, and the plant is not operable. A flexibility study determines the region into which the process is operable. This information can be useful for determining design modifications to improve the process flexibility. A flexibility study involves a complex mathematical model, which is even more complex for a MSF plant. To perform the task, in this work, a stationary simulator was developed for a real-world case study, and the region exploration was performed by Monte Carlo simulation. Results show, in terms of both robustness and speed of computation, that this approach can be a useful tool.
This work outlines the development of a fault diagnostic system to supervise a MSF desalination p... more This work outlines the development of a fault diagnostic system to supervise a MSF desalination plant during dynamic states (e.g., start-up and changes of operative conditions) using artificial neural networks (ANNs). This diagnostic system processes the plant data to determine whether the process state during a dynamic state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal process state. The diagnostic system has an ANN for each potential fault (equipment malfunctions or operator mistakes). Every ANN processes the plant data looking for symptoms of their respective faults. At a given time, the result reported by an ANN is an index between 0 and 1. This number represents the security about how the corresponding fault is affecting the plant. The higher this value, the higher the security of the affirmation. The structure of each ANN is simpler than that reported in the literature; however, the performance is better. These results were obtained due to a careful selection of the diagnostic system output and the use of a special training method. This training method calculates an appropriate value for the output of each ANN instead to set it at 0 or 1 only. The new value of the output does not depend on the fault provoking the inputs but only on the degree of matching between the observed evolution and the expected one for the fault corresponding to each ANN. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.
This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) de... more This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) desalination plant by using BNs (Bayesian networks). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the latter case, the diagnostic system determines the cause of the abnormal process state; i.e., it finds out which is the fault that is affecting the supervised process. ABN is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. A BN readily handles situations where some data entries are missing. This paper determines both the structure and parameters of a BN intended for a diagnostic system. The implemented system is evaluated by using a dynamic simulator, which was developed for a real MSF desalination plant. Besides, the diagnostic system performance is compared with the performances of two other diagnostic systems. The obtained results show some advantages for the BN based diagnostic system.
This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) des... more This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) desalination plant using artificial neural networks (ANNs). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal process state. The diagnostic system has an ANN for each potential fault. Every ANN processes the plant data looking for symptoms of their respective faults. At a given time, the result reported by an ANN is an index between 0 and 1. This number represents the certainty about the corresponding fault is affecting the plant. The higher is the value, the higher is the certainty of the affirmation. The structure of each ANN is simpler than those reported in the bibliography; however, the performance is better. These results are obtained due to a careful selection of the diagnostic system output and the use of a special training method. That training method calculates an appropriate value for the output of each ANN instead of setting it at 0 or 1 only. The new value of the output does not depend on the fault that causes the inputs but it does only on the degree of matching between the observed evolution and the expected one for the fault corresponding to each ANN. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.
A mathematical model for the optimization of the mechanical vapor compression (MVC) desalination ... more A mathematical model for the optimization of the mechanical vapor compression (MVC) desalination process is presented. The mathematical model involves the real physical constraints for the evaporation process. Nonlinear equations in terms of chemical-physical properties and design equations are used to model the process. A general algebraic modeling system (GAMS) is used to implement the model. The generalized reduced gradient algorithm CONOPT 2.041 is used as an NLP solver. The effects of some relevant process parameters on the system performance are studied. The output results from the proposed model were successfully compared with those of the literature.
This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any... more This work addresses a flexibility study on a multi-stage flash (MSF) desalination plant. When any plant is designed, the engineers define the design for reaching optimal operation under nominal conditions. However, uncertain variables or disturbance cannot be handled to obtain those nominal conditions. For this reason, the design contains control elements for compensating the disturbance effects. This compensation is only possible into a region defined by the characteristics of the process and the control elements. When the actual conditions are out of that region, the compensation is not enough, and the plant is not operable. A flexibility study determines the region into which the process is operable. This information can be useful for determining design modifications to improve the process flexibility. A flexibility study involves a complex mathematical model, which is even more complex for a MSF plant. To perform the task, in this work, a stationary simulator was developed for a real-world case study, and the region exploration was performed by Monte Carlo simulation. Results show, in terms of both robustness and speed of computation, that this approach can be a useful tool.
This work outlines the development of a fault diagnostic system to supervise a MSF desalination p... more This work outlines the development of a fault diagnostic system to supervise a MSF desalination plant during dynamic states (e.g., start-up and changes of operative conditions) using artificial neural networks (ANNs). This diagnostic system processes the plant data to determine whether the process state during a dynamic state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal process state. The diagnostic system has an ANN for each potential fault (equipment malfunctions or operator mistakes). Every ANN processes the plant data looking for symptoms of their respective faults. At a given time, the result reported by an ANN is an index between 0 and 1. This number represents the security about how the corresponding fault is affecting the plant. The higher this value, the higher the security of the affirmation. The structure of each ANN is simpler than that reported in the literature; however, the performance is better. These results were obtained due to a careful selection of the diagnostic system output and the use of a special training method. This training method calculates an appropriate value for the output of each ANN instead to set it at 0 or 1 only. The new value of the output does not depend on the fault provoking the inputs but only on the degree of matching between the observed evolution and the expected one for the fault corresponding to each ANN. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.
A mathematical model for the optimization of the mechanical vapor compression (MVC) desalination ... more A mathematical model for the optimization of the mechanical vapor compression (MVC) desalination process is presented. The mathematical model involves the real physical constraints for the evaporation process. Nonlinear equations in terms of chemical-physical properties and design equations are used to model the process. A general algebraic modeling system (GAMS) is used to implement the model. The generalized reduced gradient algorithm CONOPT 2.041 is used as an NLP solver. The effects of some relevant process parameters on the system performance are studied. The output results from the proposed model were successfully compared with those of the literature.
ABSTRACT The extraordinary events that occurred in the country between 2001-2007, with a sharp de... more ABSTRACT The extraordinary events that occurred in the country between 2001-2007, with a sharp decline in inequality and the levels of poverty and extreme poverty offers a unique opportunity to examine the most relevant factors to explain changes that benefit the poor ...
ABSTRACT The extraordinary events that occurred in the country between 2001-2007, with a sharp de... more ABSTRACT The extraordinary events that occurred in the country between 2001-2007, with a sharp decline in inequality and the levels of poverty and extreme poverty offers a unique opportunity to examine the most relevant factors to explain changes that benefit the poor ...
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