Abstract
All over the world, energy companies are investing in technologies to make better use of renewable energy to generate electric power. Wind energy is the renewable energy source that had a higher growing in the last decades and can be considered a hope in future based on clean and sustainable energy. The development of tools that allows operators and maintenance personnel to correlate machinery related information with other operational information such as machine speed, electrical load, and wind speed, are needed.

Abstract
In this paper it is proposed an efficient contingency screening and ranking algorithm for assessment and classification of multiple contingencies of an Electric Power System using the Rough Set theory. The developed methodology produces a classification of the system operation in five possible states: normal, alert, emergency, in-extremis and restorative. These different operating states correspond to six levels of security: secure, alert, correctively secure, alert no correctable, emergency and restorative. The states can be classified horizontally as secure, that correspond to the normal state and insecure for the remaining ones. The developed computer programs, the SECURsySTEM were applied to the IEEE 118 busbars test power network and results are presented and analyzed.

Abstract
Maintenance management is a subject that, instead of reducing importance, with the increase of equipment reliability, it increases its role in the companies and obliges the increase of the level of demand of professionals involved because of the new technical and environmental demands. Sometimes, scientific developments anticipate the company's needs while other times it is the company that challenges science. The maintenance area is an example that offers challenges to both science and companies in order to optimize the performance of equipment and facilities. This is also the case of wind generators, because their expansion, evolution, maintenance and reliability guarantee, needs to be adequately articulated in order to maximize production time and, obviously, to optimize maintenance interventions. It is because of this kind of challenge that the authors are developing new methodologies in the area of wind generators that aims to optimize the cycles of production and, consequently, reduce other kinds of energy production. The new features include on-line measures and the corresponding on-time treatment, using algorithms based on time-series forecasting and wireless technology to transmit the signals. The prediction models uses regression techniques based on SVR, ARMA and ARIMA models, modified according to this specific case. The weather, conditions and the technical and construction characteristics of wind generators are only some variables that we have in account in the models that are under development. But, if these conditions are important, it is also very important to collect, read and treat data from sensors placed in wind generators that, because their geographic dispersion, and difficulty of transmission, must be solved adequately and conjugated with the above referred algorithms, in order to implement an adequate system. This is the ambit of the present article that reports a wide approach of a subject that usually is managed separately, this is, the hardware from one side and the prediction algorithms from other side. This is possible because the team has being researching and developing algorithms and an information system, since many years ago, around the terology subject that is a wider vision of maintenance. Then, the new methodologies, above mentioned, will be, later, incorporated through new predictive maintenance modules in an integrated maintenance management system called SMIT (Terology Integrated Modular System). The base of SMIT is accessed through a client-server system and a browser system that includes the main modules of a traditional system, as well as a fault diagnosis module, a non-periodic maintenance planning module and a generic oncondition maintenance module, among other innovations.

Abstract
Technology has allowed new insights and creativity into the learning and teaching process and also enables to try out in a much more flexible, dynamic way other learning experiences. This constitutes in itself a more engaging role for teachers as well as students helping them to explore and create new areas, opening up, at the same time, new perspectives into the process and maximizing the availability and use of the current tools which the same technology provides. The facilitation in the investigation work development of projects renders the learning and teaching activities really appealing, allowing at the same time levels of effectiveness in this extremely modern and global era. This study integrates technology in the learning process and evaluation of its various outcomes. This case study describes the experience of two teachers with their class along a semester using computer aided technology for instruction and assessment. The task involved the lighting design of monuments with the purpose of attracting the on lookers' attention, enhancing the beauty and attributes of the façade of monuments or buildings considered relevant or of public interest. An approach of reinforcement learning applied to the historical monuments light configurations will be described.

Abstract
Wind generators maintenance can be performed in a systematic way because they are built with equipments with known reliability and maintenance parameters known that can be used in the planned maintenance. However, it is possible to increase the reliability of the system so as to rationalize the costs, by developing new management models and new algorithms based on on-line monitoring of several parameters, namely vibrations, electrical variables, among others. It is because of this kind of challenge that the authors are developing new methodologies in the area of wind generators that, as we will demonstrate, can contribute to a better environment. The methodology proposed aims to optimize the cycles of production and, consequently, reduce other kinds of energy production. The new methodologies will be, later, incorporated through new predictive maintenance modules in an integrated maintenance management system called SMIT (Terology Integrated Modular System). The base of SMIT is accessed through a client-server system and a browser system that includes the main modules of a traditional system, as well as a fault diagnosis module, a non-periodic maintenance planning module and a generic on-condition maintenance module, among other innovations. The new features will include, in the case of wind generators, on-line measures and the corresponding on-time treatment, using algorithms based on time-series forecasting.

Abstract
Maintenance management is a subject that, instead of reducing importance with the increase of equipment reliability, it increases its role in the companies and obliges the increase of the level of demand of professionals involved because of the new technical and environmental demands. Sometimes, scientific developments anticipate the company's needs while other times it is the company that challenges science. The maintenance area is an example that offers challenges to both science and companies in order to optimize the performance of equipment and facilities. However, if during the industrial era the maintenance had to respond to industry, nowadays, and in parallel with the increase of equipment performance, the maintenance has the obligation to aid in order to convert technology in becoming more environmentally friendly. It is because of this kind of challenge that the authors are developing new methodologies, almost antagonistic, because of the areas in development, namely diesel engines and wind generators, and as we will demonstrate, areas which are compatible and can contribute to a better environment. In the case of wind generators, the methodology aims to optimize the cycles of production and consequently, reduce the other kinds of energy production. On the other hand, the methodologies for maintenance of diesel engines are based on environmental indicators that can predict the "health state" taking into account restrictions including health human factors among others. The new methodologies will later be incorporated through new predictive maintenance modules in an integrated maintenance management system called SMIT (Terology Integrated Modular System). The SMIT system includes the main modules of a traditional system, as well as a fault diagnosis. a non-periodic maintenance planning and a generic on-condition maintenance module, among other innovations. The new features will include, in the case of wind generators, on-line measures and the corresponding on-time treatment, using algorithms based on time-series forecasting and wireless technology to transmit the signals. In the case of diesel engines, the algorithms are based on Markov chains and hidden Markov chains, with an approach that is offering good results, which proves the validity of the methodology and the innovation itself. It is based on these developments and the new researches mentioned so far that this paper is built upon, and we believe will be a contribution to the maintenance management area.