Abstract
In this paper, the gas dynamics within the pipelines is modelled as a repetitive process with smoothing. Controllability and observability criteria when the system is steered through initial and boundary data, which is achieved by an adequate choice of the homogeneity, are obtained. From the point of view of the technical applications, it seems to make more sense to consider boundary data controls as for instance in the management of high pressure gas networks. Stability criteria suitable computer simulations are also included.

Abstract
Serious Games is a field of research that has evolved substantially with valuable contributions to many application domains and areas. Patients often consider traditional rehabilitation approaches to be repetitive and boring, making it difficult for them to maintain their ongoing interest and to assure the completion of the treatment program. This paper reviews Serious Games and the natural and multimodal user interfaces for the health rehabilitation domain. Specifically, it details a framework for the development of Serious Games that integrates a rich set of features that can be used to improve the designed games with direct benefits to the rehabilitation process. Highlighted features include natural and multimodal interaction, social skills (collaboration and competitiveness) and progress monitoring. Due to the rich set of features supported by the framework, the games' rehabilitation efficacy can be enhanced primarily from an increase in the patient's motivation when exercising the rehabilitation tasks.

Abstract
This work presents a recommender system of economic news articles. Its objectives are threefold: (i) managing the vocabulary of the economic news domain to improve the system based on the seamlessly intervention of the documentalist (ii) automatically multi-classify the economic new articles and users profiles based on the domain vocabulary, and (iii) recommend the articles by comparing the multiclassification of the articles and profiles of the users. While several solutions exist to recommend news, multi-classify document and compare representations of items and profiles. They are not automatically adaptable to provide a mutual answer to previous points. Even more, existing approaches lacks substantial correlation with the human and in particular with the documentalist perspective. © Springer International Publishing Switzerland 2015.

Abstract
Robustness, real-time properties and resource efficiency are key properties to embedded devices of the CPS/IoT era. In this paper we propose a language approach RTFM-core, and show its potential to facilitate the development process and provide highly efficient and statically verifiable implementations. Our programming model is reactive, based on the familiar notions of concurrent tasks and (single-unit) resources. The language is kept minimalistic, capturing the static task, communication and resource structure of the system. Whereas C-source can be arbitrarily embedded in the model, and/or externally referenced, the instep to mainstream development is minimal, and a smooth transition of legacy code is possible. A prototype compiler implementation for RTFM-core is presented. The compiler generates C-code output that compiled together with the RTFM-kernel primitives runs on bare metal. The RTFM-kernel guarantees deadlock-lock free execution and efficiently exploits the underlying interrupt hardware for static priority scheduling and resource management under the Stack Resource Policy. This allows a plethora of well-known methods to static verification (response time analysis, stack memory analysis, etc.) to be readily applied. The proposed language and supporting tool-chain is demonstrated by showing the complete process from RTFM-core source code into bare metal executables for a lightweight ARM-Cortex M3 target.

Abstract

Abstract
Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.