Cost Function Analysis Applied to Different Kinetic Release Models of Arrabidaea chica Verlot Extract from Chitosan/Alginate Membranes.

This work focuses on the mathematical analysis of the controlled release of a standardized extract of A. chica from chitosan/alginate (C/A) membranes, which can be used for the treatment of skin lesions. Four different types of C/A membranes were tested: a dense membrane (CA), a dense and flexible membrane (CAS), a porous membrane (CAP) and a porous and flexible membrane (CAPS). The Arrabidae chica extract release profiles were obtained experimentally in vitro using PBS at 37 °C and pH 7. Experimental data of release kinetics were analyzed using five classical models from the literature: Zero Order, First Order, Higuchi, Korsmeyer-Peppas and Weibull functions. Results for the Korsmeyer-Peppas model showed that the release of A. chica extract from four membrane formulations was by a diffusion through a partially swollen matrix and through a water filled network mesh; however, the Weibull model suggested that non-porous membranes (CA and CAS) had fractal geometry and that porous membranes (CAP and CAPS) have highly disorganized structures. Nevertheless, by applying an explicit optimization method that employs a cost function to determine the model parameters that best fit to experimental data, the results indicated that the Weibull model showed the best simulation for the release profiles from the four membranes: CA, CAS and CAP presented Fickian diffusion through a polymeric matrix of fractal geometry, and only the CAPS membrane showed a highly disordered matrix. The use of this cost function optimization had the significant advantage of higher fitting sensitivity.

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy.

Nowadays, electric vehicles have gained great popularity due to their performance and efficiency. Investment in the development of this new technology is justified by increased consciousness of the environmental impacts caused by combustion vehicles such as greenhouse gas emissions, which have contributed to global warming as well as the depletion of non-oil renewable energy source. The lithium-ion battery is an appropriate choice for electric vehicles (EVs) due to its promising features of high voltage, high energy density, low self-discharge, and long life cycles. In this context, State of Charge (SoC) is one of the vital parameters of the battery management system (BMS). Nevertheless, because the discharge and charging of battery cells requires complicated chemical operations, it is therefore hard to determine the state of charge of the battery cell. This paper analyses the application of Artificial Neural Networks (ANNs) in the estimation of the SoC of lithium batteries using the NASA's research center dataset. Normally, the learning of these networks is performed by some method based on a gradient, having the mean squared error as a cost function. This paper evaluates the substitution of this traditional function by a measure of similarity of the Information Theory, called the Maximum Correntropy Criterion (MCC). This measure of similarity allows statistical moments of a higher order to be considered during the training process. For this reason, it becomes more appropriate for non-Gaussian error distributions and makes training less sensitive to the presence of outliers. However, this can only be achieved by properly adjusting the width of the Gaussian kernel of the correntropy. The proper tuning of this parameter is done using adaptive strategies and genetic algorithms. The proposed identification model was developed using information for training and validation, using a dataset made available in a online repository maintained by NASA's research center. The obtained results demonstrate that the use of correntropy, as a cost function in the error backpropagation algorithm, makes the identification procedure using ANN networks more robust when compared to the traditional Mean Squared Error.

Economies of integration and sources of change in productivity in the Chilean water and sewerage industry: a translog cost function approach.

This paper researches the existence of economies of scale and scope in the Chilean water and sewerage industry using a translog cost function approach over the period 2010-2017. It also provides estimates of productivity growth and its determinants, technical change and scale effect. The results suggest that on average, the water industry operates under increasing economies of scale, which implies that further increases in water companies' scale of operations may be cost beneficial. Economies of scope between water and sewerage services were found for the average company, which suggests that the joint provision of these services may lead to cost savings. An average annual productivity increase of 2.5% was reported with scale effect being the major driver. Full private companies performed better than concessionary and public water companies. The results of our study can be of greater interest to the policy-makers who want to make decisions on introducing policy reforms such as mergers, unbundling of the water and sewerage supply chain, or developing incentives to improve companies' productivity.

Employment effects of COVID-19 across Chilean regions: An application of the translog cost function.

Estimating an aggregated translog cost function for the period 2013-2018, and using alternative scenarios of product loss based on expert projections, this article provides a preliminary forecast of the regional employment effects of COVID-19 across Chilean regions. The total estimated loss in the average scenario was around 705,000 jobs (577,000 in the optimistic and 870,000 in the pessimistic scenarios). Relative impacts were spatially heterogeneous, ranging from 1.5% (Antofagasta Region) to 13.6% (Los Lagos Region) of total regional jobs in the average scenario. Estimated impacts may inform regionally-targeted social protection and economic stimulus policies at a time in which the virus has not fully spread and total regional employment impacts have not been realized. In any region and scenario, estimated losses were sizeable and call for rapid and spread implementation of job and production protection initiatives recently passed as well as others still being discussed in congress.

Este artículo ofrece un pronóstico preliminar de los efectos de COVID­19 en el empleo regional en las regiones de Chile, para lo cual estima una función de costos translog agregados para el período 2013­2018 y utiliza escenarios alternativos de pérdida de producto basados en las proyecciones de expertos. La pérdida total estimada en el escenario promedio fue de alrededor de 705.000 puestos de trabajo (577.000 en el escenario optimista y 870.000 en el pesimista). Los impactos relativos fueron heterogéneos espacialmente, y variaron entre el 1,5% (Región de Antofagasta) y el 13,6% (Región de Los Lagos) del total de empleos regionales en el escenario promedio. Los impactos estimados pueden informar políticas a nivel regional específicas en cuanto a protección social y estímulo económico en un momento en que el virus no se ha propagado plenamente y no ha tenido lugar la totalidad de los impactos sobre el empleo en la región. En todas las regiones y escenarios, las pérdidas estimadas fueron considerables y exigen la aplicación rápida y generalizada de las iniciativas de protección del empleo y la producción aprobadas recientemente, así como otras que todavía se están debatiendo en el congreso.

Flexible versus common technology to estimate economies of scale and scope in the water and sewerage industry: an application to England and Wales.

The water industry presents several structures in different countries and also within countries. Hence, several studies have been conducted to evaluate the presence of economies of scope and scale in the water industry leading to inconclusive results. The lack of a common methodology has been identified as an important factor contributing to divergent conclusions. This paper evaluates, for the first time, the presence of economies of scale and scope in the water industry using a flexible technology approach integrating operational and exogenous variables of the water companies in the cost functions. The empirical application carried out for the English and Welsh water industry evidenced that the inclusion of exogenous variables accounts for significant differences in economies of scale and scope. Moreover, completely different results were obtained when the economies of scale and scope were estimated using common and flexible technology methodological approaches. The findings of this study reveal the importance of using an appropriate methodology to support policy decision-making processes to promote sustainable urban water activities.