Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented. The strategy of forecasting is implemented using two stages. In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel matrix (HSVD). In the second stage, an ARIMA model and two ANNs for one-step-ahead time series forecasting are used. The coefficients of the first ANN are estimated through the particle swarm optimization (PSO) learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP) learning algorithm. The proposed models are evaluated using a weekly time series of traffic accidents of Valparaíso, Chilean region, from 2003 to 2012. The best result is given by the combination HSVD-ARIMA, with a MAPE of 0 : 26%, followed by MA-ARIMA with a MAPE of 1 : 12%; the worst result is given by the MA-ANN based on PSO with a MAPE of 15 : 51%.

Health institutions are adopting new technologies for their processes through automation by means of the concept of "Internet of Things" (IoT). Hence, offering innovative tools, applications and technology for the collection of key data and information, which is then integrated and consolidated, covering the different systems and their collaborators. The necessity of receiving quality medical services is essential in the public Policy of any country. The increasing demand for having an adequate number of medical specialists, pharmacies and medications stock, dental and mental health coverage and other, together with the minimization of the waiting list and patient care time have been a crucial concern. Under this context, it is valuable to redesign the processes planning and its coordination through the use of Information & Communications Technology (ICT) and IoT that unifies the systems. Based on previous research, the general purpose is to generate a system model to examine healthcare quality of service and corroborate its effectiveness in a real environment. The aim of this paper focus on the development of a decision support model to define key areas where the inclusion of IoT would sustain the efficiency in health care service. The research methodology is based on case study, integrating planning processes, data analysis, scoring method that interacts with multicriteria approach. A pilot case study is pursued in health institutions in Chile, determining critical factors and the current automation level system appraisal to generate actions of improvement in processes that show poor service quality. The results give rise to the development of an investment plan that can be converted into action plans for a health institution.

In this work, the management of a high circulation road connecting two mainstream cities in Chile is tackled. The cities are connected through a coastal road and is permanently under congestion effects. This situation leads to much discontent in the users and negatively impacts both economically and environmentally as well. This problem setting arises in many other countries and is subject of a relevant body of research. To minimize the impact of congestion in this problem setting, a micro-simulation is performed in VISSIM. This model, incorporates the road and its users and represents their driving behavior and its impact on congestion. Throughout the experiments conducted, the emissions of CO2 are calculated; this allowed to define a set of congestion minimization strategies that also reduce emissions. These strategies were validated under a set of real-world scenarios and were able to solve a set of specific road management problems and optimize average travel time for users. The model, the strategies and a case study are introduced and discussed, also, future research directions are given.

This paper presents the investigation about a problem situation that Electric Distributor Companies are facing in Chile resulting from transit accidents. The number of vehicle crashes to power distribution poles and street lighting has grown. This situation causes discomfort to citizen and mainly to the neighbors due to power cuts and even on occasion , losses of human lives because of the accident that have occurred. Based on previous research, the accidents are not random nor chance dependent, but the majority of transit accident follow parameters or variables from the scenery where it occurs. In order to analyze the variables and the degree this variables affect the accidents, a model of Perceptron and Multipercetron Artificial Neural Networks and a Multiple Nonlinear Regression model are proposed. An empirical study was made; collecting data from a distributor company and from Chilean National Traffic Safety Commission, where the more frequent variables involved in accidents were determined to develop the mentioned models. These variables were investigated and also their influence on the occurrence of vehicle crashes to power distribution poles could be confirmed. With this data, the prediction of post crashes was developed, where through the application of the neural network and multiple nonlinear regression, revealed 95.7% of acceptable predictions. This study will bring benefits to power distribution companies considering a risk index in the streets, based on the number of crashes of poles per street; this will allow optimal decisions in future electrical distribution projects avoiding critical areas.

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