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Given the multi-disciplinary nature of energy efficiency, it is not surprising that researchers with different backgrounds, ranging from ecology to economics, have engaged in this line of research. Due to this, advice on how to promote energy efficiency differs depending on the adopted perspective. Despite the myriad of studies, there remains no consensus on which barriers and opportunities are the most important. In this work, we summarize the barriers and opportunity found in Spain with a questionnaire at the energy efficiency companies with the help of ANESE (Asociación Nacional de Empresas de Servicios Energéticos) in 2016.

The implementation of the European Higher Education Area (EHEA) raises the need for teaching based on individual characteristics, and in order to meet this requirement it is necessary for students to have flexible and individualized forms of learning, which promotes learning styles (LS) should be taken into consideration. It can not be forgotten that students? EE preferences are linked to performance and satisfaction with the study.

Estudio de la evolución de la RSC, responsabilidad social corporativa, en empresas de servicios energéticos y propuesta de marco integrador.

Global companies need to be able to forecast, plan and manage international issues, cope with multiple crises, influence and work with intergovernmental organizations, and know how to operate appropriately within diverse cultural and societal environments.

Creativity is a core 21st century skill for students. In this article, we suggest and encourage to follow the model of Standord in Design Thinking. There are dozens or more variations of design thinking models. But having a model is valuable as it provides educators a guiding approach and tools to promote more and better ideas. As creativity is viewed as multidimensional, there are many ways to conceptualize it, including divergent and creative expert performance. For divergent think, students are asked to generate as many of their own solutions as possible in response to a domain‐general task, and the responses are assessed for fluency, flexibility and originality. In contrast, creative expert performance is measured via expert judgments of performances within domains. We apply this methodology and measures in the following subjects: “Organización Industrial”, “Diseño de Máquinas II”, and “Bio‐Inspired Machine Learning”.

(SPA)Realizamos un estudio de los estilos cognitivos de nuestros estudiantes para entender su forma de innovar y colaborar para alcanzar dicha innovación. Para ello, primero, resumimos el inventario de la Teoría de Kirton Adaptador- Innovador (KM), y segundo, se lo aplicamos a nuestros estudiantes (más innovador o adaptador) en UPM. Aquí presentamos un estudio piloto llevado a cabo en ETSIDI (Escuela Técnica Superior Ingeniería y Diseño Industrial). (ENG)We study of the cognitive styles of our students to understand their process of collaboration and innovation. First, we summarize the inventory of the Kirton's adaptor-innovator Theory (KM), and second, we apply our test, to our students at UPM. In this work, we present a pilot study carried out at ETSIDI (School of Engineering and Industrial Design).

En este trabajo proponemos un modelo de referencia para poder investigar los efectos de las condiciones organizativas sobre la justicia social y la generación de un entorno donde se valore la voz de los empleados y estudiantes. Estos efectos pueden ser tan importantes como los efectos que sus políticas tienen en las comunidades y asociaciones de estudiantes o sus políticas sobre empleados. La universidad extiende sus relaciones informales hacia las asociaciones de estudiantes que pueden impactar positiva o negativamente en el compromiso de todas las personas implicadas a través de su influencia en los estados cognitivos, estados emocionales y su vida en la comunidad.

Mauelshagen et al. reveal that the intellectual capital required for effective environmental policy making is particular diverse and encourage the development of models than can understand the profile of individuals and teams when dealing with collaboration, knowledge transfer and innovation. Furthermore, stakeholders frequently kill novel ideas when those ideas do not fit with the organization?s identity, i.e. organizational actors' enduring sense of who the organization is and what it stands for. The challenges of radical innovation requires architectures that embrace and augment, "hot" cognitive processes and the underlying mechanisms of interactions and innovation is still limited. The objective of our work is to understand the cognitive style of our students when finding solutions as agents of changes, as could be climate change following Kirton Adaptation-Innovation Theory (KAI).

One of the characteristics of the European Space of Higher Education is to consider university degrees in terms of learning outcomes, and essentially expressed in forms of competence. Competencies represent a dynamic combination of attributes such as knowledge and its application, attitudes and responsibilities that describe the learning outcomes of a particular program. Transversal competences, such as competences towards environmental risk management, are part of the general characteristics of human action in economic and technical environments. The training, evaluation and development of professional competences can present different approaches and teaching-learning methodologies. In our study, we focus on learning from experience. With the evaluation of students? learning styles, we can begin to know how our students begin to develop their skills towards environmental risk management.

After a systematic literature review, we outline an analytical framework where tensions and creativity are managed in university-industry relationships. UTTOs act as intermediaries between industry, universities and researchers to assess the potential economic value of research findings, so that they can be translated into commercial products.Design thinking is presented as an umbrella of the interplay of different actors that enables the synthesis of paradoxical tensions. Interactions enable actors to upgrade their technological capabilities.By managing organizational paradox, a dynamic equilibrium fosters learning and creativity.

Koniocortex-Like Network is a novel category of Bio-Inspired Neural Networks whose architecture and properties are inspired in the biological koniocortex, the ?rst layer of the cortex that receives information from the thalamus. In the Koniocortex-Like Network competition and pattern classi?cation emerges naturally due to the interplay of inhibitory interneurons, metaplasticity and intrinsic plasticity. Recently proposed, it has shown a big potential for complex tasks with unsupervised learning. Now for the ?rst time, its competitive results are proved in a relevant standard real application that is the objective of state-ofthe-art research: the diagnosis of breast cancer data from the Wisconsin Breast Cancer Database

La gestión de riesgos medioambientales requiere integrar diversas competencias sobre aspectos económicos, sociales y económicos. Es importante permitir la colaboración entre diferentes. Después de revisar la teoría sobre desarrollo de competencias, observamos que es necesario un estilo de aprendizaje basado las habilidades interpersonales que facilitan la colaboración.

The Salta Province (Img. 1) has 155.000 km2. It is at NW of Argentina (NOA) having latitudes around 25ºS. Some winds from South or SE made climate less hot and bring rain from 400 to 800 mm/year (with peaks of 1200mm in high altitude places in SO), and rainfall is concentrated in the summer time. Altitude has great ranges (at NE are areas at 200m and at NW a PUNA region with summits higher than 6000m). With 1.200.000 inhabitants it has a low density of population, and the city of Salta concentrates more or less fifty per cent of the total.

Technology transfer is a key functio n or process of the global innovation systems. There is a wide variety of activities through which academics transfer their new knowledge. Some of these activities are more formal than others. Many prior studies have concentrated on more formal activities based on intellectual property rights as the main outcomes of universities such as patenting, licensing and the formation of spin-offs. After a systematic literature review, we outline an analytical framework that uses global chains as the mechanism for bridging global and national innovation systems. The merit of global chains is that they emphasize inter-firm and intra-organization interactions and networks along the production process of a given product. These are often neglected by innovation systems. But these interactions allow us to understand better the role of informal university technology transfer along global value chain and its risks. https://library.iated.org/view/MARTINRUBIO2016RIS

The extent of change in business process and smart manufacturing usage should be taken into account in every energy efficiency project in industries. A significant part of smart metering success depends upon making the business processes more systematic. Smart manufacturing in the dramatically intensified and pervasive application of networked information-based technologies through the manufacturing and supply chain enterprise. There is no doubt that the deployment os smart meters involves complex and different business models, the management of significant risks, and the need for research and developtment of new technologies, business models and organization engineering. Industrial energy audits play an important role in achieving cost optimization and peak load reduction goals for industry. In this paper, we present the impact of smart meters in industry. In essence, smart meters promotes the (r)evolution of smart manufacturing.

Nuestro estudio muestra cómo realizar y medir prácticas de forma creativa teniendo en cuento diferentes niveles organizativos: características individuales y nivel grupal (equipos de trabajo). Los procesos de acreditación de títulos de ingeniería en USA y en Europa muestran la relevancia que integrar ambos pensamientos: críticos y creativos. Con el impulso de los modelos de creatividad divergente y crítico, los profesores firmantes de este artículo tratamos de promover en la UPM ambos aspectos en nuestros títulos.

A pesar de que las compañías eléctricas alcanzan una mayor puntuación en la publicación de informes de Responsabilidad Social Corporativa (RSC), a menudo son acusados de "lavado verde" debido a su mala reputación ambiental. En suma, los informes RSC son el instrumento más generalizado para transmitir el comportamiento social de la empresa. Sin embargo, detectamos que es necesario investigar el compromiso real de los profesionales de las empresas hacia los informes de RSC. Las ESEs prestan atención y recursos al desarrollo y comunicación de los valores sobre responsabilidad social corporativa con los profesionales. Los directivos de las ESEs y sus profesionales son quienes desarrollan la RSC en los proyectos de eficiencia energética. No se trata simplemente de realizar informes, sino de un enfoque de comportamiento estratégico de todos los agentes que interaccionan de la empresa. La comunicación a través de valores de los directivos y profesionales con todos los agentes es clave para el éxito de las nuevas soluciones energéticas. Las prácticas de Recursos Humanos, relacionados con el comportamiento ciudadano organizativo, permiten que todos los empleados se comprometan con los valores de la RSC, generando una cultura en la empresa que permite integrar estas buenas prácticas en la operativa cotidiana.

The concept of designing smartness of new products and systems from a busi-ness perspective has been investigated in operations literature. The problem of understanding, designing, engineering and governing the technologies behind these new products requires new concepts.The emergence of these modern technologies causes a myriad of interconnected systems, which are working together to satisfy the necessities of modern life. Development of System of System Engineering (SoSE) is an attempt by the systems engineering and science community to fulfill this requirement.

En este artículo, revisamos los distintos tipos de comunicación de la RSC, desde la simple información hasta la participación en la generación de la RSC a través del diálogo. La gestión de la comunicación y la identificación con los intereses de los diferentes agentes permite diferenciar diferentes niveles de compromiso y colaboración, que incluye estrategias de información, persuasión y diálogo diferentes. Este diálogo puede ser virtual, por lo que analizamos las características para poder gestionarlo. Finalmente, revisamos la situación actual de un agente, en particular, los sindicatos en España, para considerarlos como agentes interesados en la generación de estrategias de dialogo virtual como herramienta para desarrollar la RSC. La escasa experiencia sindical en torno a la RSC puede verse desarrollada a través de estrategias de comunicación virtual que permite a los diferentes agentes contactar y dialogar de forma ágil. La vida real de la gestión de las decisiones estratégicas en RSC revela que está lejos de las teorías en donde se señala lo beneficioso que es implicar a los diferentes agentes. Las vías de comunicación siguen el camino de la información, llegar al diálogo parece difícil.

Teaching in context can be defined as teaching a mathematical idea or process by using a problem, situation, or data to enhance the teaching and learning process. The same problem or situation may be used many times, at different mathematical levels to teach different objectives.

In this paper a method based mainly on Data Fusion and Artificial Neural Networks to classify one of the most important pollutants such as Particulate Matter less than 10 micrometer in diameter (PM10) concentrations is proposed. The main objective is to classify in two pollution levels (Non-Contingency and Contingency) the pollutant concentration. Pollutant concentrations and meteorological variables have been considered in order to build a Representative Vector (RV) of pollution. RV is used to train an Artificial Neural Network in order to classify pollutant events determined by meteorological variables. In the experiments, real time series gathered from the Automatic Environmental Monitoring Network (AEMN) in Salamanca Guanajuato Mexico have been used. The method can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

At present, in the University curricula in most countries, the decision theory and the mathematical models to aid decision making is not included, as in the graduate program like in Doctored and Master´s programs. In the Technical School of High Level Agronomic Engineers of the Technical University of Madrid (ETSIA-UPM), the need to offer to the future engineers training in a subject that could help them to take decisions in their profession was felt. Along the life, they will have to take a lot of decisions. Ones, will be important and others no. In the personal level, they will have to take several very important decisions, like the election of a career, professional work, or a couple, but in the professional field, the decision making is the main role of the Managers, Politicians and Leaders. They should be decision makers and will be paid for it. Therefore, nobody can understand that such a professional that is called to practice management responsibilities in the companies, does not take training in such an important matter. For it, in the year 2000, it was requested to the University Board to introduce in the curricula an optional qualified subject of the second cycle with 4,5 credits titled " Mathematical Methods for Making Decisions ". A program was elaborated, the didactic material prepared and programs as Maple, Lingo, Math Cad, etc. installed in several IT classrooms, where the course will be taught. In the course 2000-2001 this subject was offered with a great acceptance that exceeded the forecasts of capacity and had to be prepared more classrooms. This course in graduate program took place in the Department of Applied Mathematics to the Agronomic Engineering, as an extension of the credits dedicated to Mathematics in the career of Engineering.

There is evidence that the climate changes and that now, the change is influenced and accelerated by the CO2 augmentation in atmosphere due to combustion by humans. Such ?Climate change? is on the policy agenda at the global level, with the aim of understanding and reducing its causes and to mitigate its consequences. In most countries and international organisms UNO (e.g. Rio de Janeiro 1992), OECD, EC, etc . . . the efforts and debates have been directed to know the possible causes, to predict the future evolution of some variable conditioners, and trying to make studies to fight against the effects or to delay the negative evolution of such. The Protocol of Kyoto 1997 set international efforts about CO2 emissions, but it was partial and not followed e.g. by USA and China . . . , and in Durban 2011 the ineffectiveness of humanity on such global real challenges was set as evident. Among all that, the elaboration of a global model was not boarded that can help to choose the best alternative between the feasible ones, to elaborate the strategies and to evaluate the costs, and the authors propose to enter in that frame for study. As in all natural, technological and social changes, the best-prepared countries will have the best bear and the more rapid recover. In all the geographic areas the alternative will not be the same one, but the model must help us to make the appropriated decision. It is essential to know those areas that are more sensitive to the negative effects of climate change, the parameters to take into account for its evaluation, and comprehensive plans to deal with it. The objective of this paper is to elaborate a mathematical model support of decisions, which will allow to develop and to evaluate alternatives of adaptation to the climatic change of different communities in Europe and Latin-America, mainly in especially vulnerable areas to the climatic change, considering in them all the intervening factors. The models will consider criteria of physical type (meteorological, edaphic, water resources), of use of the ground (agriculturist, forest, mining, industrial, urban, tourist, cattle dealer), economic (income, costs, benefits, infrastructures), social (population), politician (implementation, legislation), educative (Educational programs, diffusion) and environmental, at the present moment and the future. The intention is to obtain tools for aiding to get a realistic position for these challenges, which are an important part of the future problems of humanity in next decades.

The engineering careers models were diverse in Europe, and are adopting now in Spain the Bolonia process for European Universities. Separated from older Universities, that are in part technically active, Civil Engineering (Caminos, Canales y Puertos) started at end of 18th century in Spain adopting the French models of Upper Schools for state civil servants with exam at entry. After 1800 intense wars, to conserve forest regions Ingenieros de Montes appeared as Upper School, and in 1855 also the Ingenieros Agrónomos to push up related techniques and practices. Other Engineers appeared as Upper Schools but more towards private factories. These ES got all adapted Lower Schools of Ingeniero Tecnico. Recently both grew much in number and evolved, linked also to recognized Professions. Spanish society, into European Community, evolved across year 2000, in part highly well, but with severe discordances, that caused severe youth unemployment with 2008-2011 crisis. With Bolonia process high formal changes step in from 2010-11, accepted with intense adaptation. The Lower Schools are changing towards the Upper Schools, and both that have shifted since 2010-11 various 4-years careers (Grado), some included into the precedent Professions, and diverse Masters. Acceptation of them to get students has started relatively well, and will evolve, and acceptation of new grades for employment in Spain, Europe or outside will be essential. Each Grado has now quite rigid curricula and programs, MOODLE was introduced to connect pupils, some specific uses of Personal Computers are taught in each subject. Escuela de Agronomos centre, reorganized with its old name in its precedent buildings at entrance of Campus Moncloa, offers Grados of Agronomic Engineering and Science for various public and private activities for agriculture, Alimentary Engineering for alimentary activities and control, Agro-Environmental Engineering more related to environment activities, and in part Biotechnology also in laboratories in Campus Monte-Gancedo for Biotechnology of Plants and Computational Biotechnology. Curricula include Basics, Engineering, Practices, Visits, English, ?project of end of career?, Stays. Some masters will conduce to specific professional diploma, list includes now Agro-Engineering, Agro-Forestal Biotechnology, Agro and Natural Resources Economy, Complex Physical Systems, Gardening and Landscaping, Rural Genie, Phytogenetic Resources, Plant Genetic Resources, Environmental Technology for Sustainable Agriculture, Technology for Human Development and Cooperation.

Climate change is on the policy agenda at the global level, with the aim of understanding and reducing its causes and to mitigate its consequences. In most of the countries and international organisms UNO, OECD, EC, etc … the efforts and debates have been directed to know the possible causes, to predict the future evolution of some variable conditioners, and trying to make studies to fight against the effects or to delay the negative evolution of such. Nevertheless, the elaboration of a global model was not boarded that can help to choose the best alternative between the feasible ones, to elaborate the strategies and to evaluate the costs. As in all natural, technological and social changes, the best-prepared countries will have the best bear and the more rapid recover. In all the geographic areas the alternative will not be the same one, but the model should help us to make the appropriated decision. It is essential to know those areas that are more sensitive to the negative effects of climate change, the parameters to take into account for its evaluation, and comprehensive plans to deal with it. The objective of this paper is to elaborate a mathematical model support of decisions, that will allow to develop and to evaluate alternatives of adaptation to the climatic change of different communities in Europe and Latin-America, mainly, in vulnerable areas to the climatic change, considering in them all the intervening factors. The models will take into consideration criteria of physical type (meteorological, edaphic, water resources), of use of the ground (agriculturist, forest, mining, industrial, urban, tourist, cattle dealer), economic (income, costs, benefits, infrastructures), social (population), politician (implementation, legislation), educative (Educational programs, diffusion), sanitary and environmental, at the present moment and the future.

Diabetes is the most common disease nowadays in all populations and in all age groups. diabetes contributing to heart disease, increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Different techniques of artificial intelligence has been applied to diabetes problem. The purpose of this study is apply the artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining (DM) technique for the diabetes disease diagnosis. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with decision tree (DT), Bayesian classifier (BC) and other algorithms, recently proposed by other researchers, that were applied to the same database. The robustness of the algorithms are examined using classification accuracy, analysis of sensitivity and specificity, confusion matrix. The results obtained by AMMLP are superior to obtained by DT and BC.

This paper present an environmental contingency forecasting tool based on Neural Networks (NN). Forecasting tool analyzes every hour and daily Sulphur Dioxide (SO2) concentrations and Meteorological data time series. Pollutant concentrations and meteorological variables are self-organized applying a Self-organizing Map (SOM) NN in different classes. Classes are used in training phase of a General Regression Neural Network (GRNN) classifier to provide an air quality forecast. In this case a time series set obtained from Environmental Monitoring Network (EMN) of the city of Salamanca, Guanajuato, México is used. Results verify the potential of this method versus other statistical classification methods and also variables correlation is solved.

Over the last ten years, Salamanca has been considered among the most polluted cities in México. This paper presents a Self-Organizing Maps (SOM) Neural Network application to classify pollution data and automatize the air pollution level determination for Sulphur Dioxide (SO2) in Salamanca. Meteorological parameters are well known to be important factors contributing to air quality estimation and prediction. In order to observe the behavior and clarify the influence of wind parameters on the SO2 concentrations a SOM Neural Network have been implemented along a year. The main advantages of the SOM is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. The results show a significative correlation between pollutant concentrations and some environmental variables.

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively.

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours

The province of Salta is located the Northwest of Argentina in the border with Bolivia, Chile and Paraguay. Its Capital is the city of Salta that concentrates half of the inhabitants of the province and has grown to 600000 hab., from a small active Spanish town well founded in 1583. The city is crossed by the Arenales River descending from close mountains at North, source of water and end of sewers. But with actual growing it has become a focus of infection and of remarkable unhealthiness. It is necessary to undertake a plan for the recovery of the river, directed to the attainment of the well-being and to improve the life?s quality of the Community. The fundamental idea of the plan is to obtain an ordering of the river basin and an integral management of the channel and its surroundings, including the cleaning out. The improvement of the water?s quality, the healthiness of the surroundings and the improvement of the environment, must go hand by hand with the development of sport activities, of relaxation, tourism, establishment of breeding grounds, kitchen gardens, micro enterprises with clean production and other actions that contribute to their benefit by the society, that being a basic factor for their care and sustainable use. The present pollution is organic, chemical, industrial, domestic, due to the disposition of sweepings and sewer effluents that affects not only the flora and small fauna, destroying the biodiversity, but also to the health of people living in their margins. Within the plan it will be necessary to consider, besides hydric and environmental cleaning and the prevention of floods, the planning of the extraction of aggregates, the infrastructure and consolidation of margins works and the arrangement of all the river basin. It will be necessary to consider the public intervention at state, provincial and local level, and the private intervention. In the model it has been necessary to include the sub-model corresponding to the election of the entity to be the optimal instrument to reach the proposed objectives, giving an answer to the social, environmental and economic requirements. For that the authors have used multi-criteria decision methods to qualify and select alternatives, and for the programming of their implementation. In the model the authors have contemplated the short, average and long term actions. They conform a Paretooptimal alternative which secures the ordering, integral and suitable management of the basin of the Arenales River, focusing on its passage by the city of Salta.

This paper presents a System Safety application to reduce the economical impact hazards in growings produced by Rainfall. System Safety is an engineering subdiscipline oriented to identify and mitigate the possible hazards to a system under study. Inside the System Safety area, the FMECA (Failure Mode, Effects and Criticallity Analysis) is a popular tool to analyze and identify the failures and weaknesses points of any system. Basically, it consist on identifying systematically the failure modes of a system to mitigate them as much as possible. The idea is to study three different kind of growings (stone fruits in the south of Spain, wheat production in Castilla Leon and Olive trees production in Andalucia) using this methodology in order to identify all the hazardous situations produced by rainfall. Applying the state of the art weather forecast techniques, this information would help farmers to prevent and mitigate the identified hazardous situations. The aim of the work is to prevent the economical hazards as are defined in the System Safety area: "Any real or potential condition that can cause injury, illness, or death to personnel; damage to or loss of a system, equipment or property; or damage to the environment", so the study is not reduced to the analysis of catastrophical situations but aboutany kind of economical damage produced by rainfall.

The Universidad Politécnica of Madrid (UPM) includes schools and faculties that were for engineering degrees, architecture and computer science, that are now in a quick EEES Bolonia Plan metamorphosis getting into degrees, masters and doctorate structures. They are focused towards action in machines, constructions, enterprises, that are subjected to machines, human and environment created risks. These are present in actions such as use loads, wind, snow, waves, flows, earthquakes, forces and effects in machines, vehicles behavior, chemical effects, and other environmental factors including effects of crops, cattle and beasts, forests, and varied essential economic and social disturbances. Emphasis is for authors in this session more about risks of natural origin, such as for hail, winds, snow or waves that are not exactly known a priori, but that are often considered with statistical expected distributions giving extreme values for convenient return periods. These distributions are known from measures in time, statistic of extremes and models about hazard scenarios and about responses of man made constructions or devices. In each engineering field theories were built about hazards scenarios and how to cover for important risks. Engineers must get that the systems they handle, such as vehicles, machines, firms or agro lands or forests, obtain production with enough safety for persons and with decent economic results in spite of risks. For that risks must be considered in planning, in realization and in operation, and safety margins must be taken but at a reasonable cost. That is a small level of risks will often remain, due to limitations in costs or because of due to strange hazards, and maybe they will be covered by insurance in cases such as in transport with cars, ships or aircrafts, in agro for hail, or for fire in houses or in forests. These and other decisions about quality, security for men or about business financial risks are sometimes considered with Decision Theories models, using often tools from Statistics or operational Research. The authors have done and are following field surveys about risk consideration in the careers in UPM, making deep analysis of curricula taking into account the new structures of degrees in the EEES Bolonia Plan, and they have considered the risk structures offered by diverse schools of Decision theories. That gives an aspect of the needs and uses, and recommendations about improving in the teaching about risk, that may include special subjects especially oriented for each career, school or faculty, so as to be recommended to be included into the curricula, including an elaboration and presentation format using a multi-criteria decision model.

The authors are from UPM and are relatively grouped, and all have intervened in different academic or real cases on the subject, at different times as being of different age. With precedent from E. Torroja and A. Páez in Madrid Spain Safety Probabilistic models for concrete about 1957, now in ICOSSAR conferences, author J.M. Antón involved since autumn 1967 for euro-steel construction in CECM produced a math model for independent load superposition reductions, and using it a load coefficient pattern for codes in Rome Feb. 1969, practically adopted for European constructions, giving in JCSS Lisbon Feb. 1974 suggestion of union for concrete-steel-al.. That model uses model for loads like Gumbel type I, for 50 years for one type of load, reduced to 1 year to be added to other independent loads, the sum set in Gumbel theories to 50 years return period, there are parallel models. A complete reliability system was produced, including non linear effects as from buckling, phenomena considered somehow in actual Construction Eurocodes produced from Model Codes. The system was considered by author in CEB in presence of Hydraulic effects from rivers, floods, sea, in reference with actual practice. When redacting a Road Drainage Norm in MOPU Spain an optimization model was realized by authors giving a way to determine the figure of Return Period, 10 to 50 years, for the cases of hydraulic flows to be considered in road drainage. Satisfactory examples were a stream in SE of Spain with Gumbel Type I model and a paper of Ven Te Chow with Mississippi in Keokuk using Gumbel type II, and the model can be modernized with more varied extreme laws. In fact in the MOPU drainage norm the redacting commission acted also as expert to set a table of return periods for elements of road drainage, in fact as a multi-criteria complex decision system. These precedent ideas were used e.g. in wide Codes, indicated in symposia or meetings, but not published in journals in English, and a condensate of contributions of authors is presented. The authors are somehow involved in optimization for hydraulic and agro planning, and give modest hints of intended applications in presence of agro and environment planning as a selection of the criteria and utility functions involved in bayesian, multi-criteria or mixed decision systems. Modest consideration is made of changing in climate, and on the production and commercial systems, and on others as social and financial.

The FMECA (Failure Mode Effects and Criticality Analysis) is a broadly extended System Safety tool applied in industries as Aerospace in order to prevent hazards. This methodology studies the different failure modes of a system and try to mitigate them in a systematic procedure. In this paper this tool is applied in order to mitigate economical impact hazards derived from Rainfalls to olive trees growing in Granada (Spain), understanding hazard from the System Safety perspective (Any real or potential condition that can cause injury, illness, or death to personnel; damage to or loss of a system, equipment or property; or damage to the environment). The work includes a brief introduction to the System Safety and FMECA methodologies, applying then these concepts to analyze the Olive trees as a system and identify the hazards during the different stages of the whole life cycle plant production

The authors of U.P.M. have had relations with Argentine Universities and Administrations, in part with J.M.Cisneros that is Professor at Universidad Nacional de Rio Cuarto in Prov. de Córdoba Arg., and have applied Decision Theories with Mathematic tools for the planning of defences and uses of soils in these areas and especially of La Colacha bassin enlarged with larger areas around it as “Cuenca de los Arroyos Menores” at West and South of Rio Cuarto. These large flat lands have loess soils brought by winds from Los Andes that are much far at West, there is a very old line of not high mountains at West near the study area from which rivers flow with little slopes to somehow lower flat areas at West with some marshes. Rivers through loess get regressive erosions with rains forming kilometres long ditches in the loess called “cárcavas” some meters deep and quite large across otherwise flat agricultural lands, and J.M. Cisneros, that has numerous publications about local soil uses, has directed diverse genie rural works to halt the regression of some of them. Part of the study is about protection against floods and “cárcavas”. There are famous experiences and records of higher inundations of large areas by rivers. Latitude of Rio Cuarto is 31º25’ South, that area is temperate refreshed by south winds, Zonda föhn winds from West being hotter and drier, and there are monsoons and so winter in July is dry and summer and December has rains, that average 829mm per year. Wind erosion and fires occur frequently on these loess soils in winter. This is an excellent area for agriculture but some actual uses may be not sustainable in long term such as excesses with genetically engineered soy, and the authors have studied aided with mathematical models (ELECTRE I, PROMETHEE and Expert Choice) the preferences of various alternative systems of land use (Autochthonous forest, High value forest, Traditional farms, Erosion control Crop with agriculture use, and Erosion control Crop with industrial use (biomass) ) in view of diverse Criteria (Water erosion index, Eolian erosion index, Implementation facility, Water resources, Economical Benefits, Hand power, Environmental Impacts, social Acceptance), as contribution for territorial planning, in part in relation with local official agencies

The guiding principle of process automation and soft computing is to achieve more robust, traceable and low cost solutions which incorporate the required intelligence to information technologies, thus enabling human centered functionalities. The application of Artificial Intelligence (IA) and Neural systems to the financial and banking industries has performed well in the areas of Risk Management improvement and Bankruptcy prediction. This paper contributes to analyze the synergies between logic and neural based approaches as the basis to enhance bankruptcy prediction models development.

The goal of the Project group created by U.P.M. in collaboration with Foreign Universities, Research Institutions and Companies is the development of an intelligent mechatronic system for the use of precision and sustainable agriculture. The project as a whole includes the following components: photographing and decoding of the soil surface; fertility determination and formation of the fertility map; generation of the controlling signal for mechatronic dosing device; intelligent dosing of fertilizers; simulation, prototype and testing; human-machine interaction and training preparation.

Over the last ten years, Salamanca has been considered among the most polluted cities in México. Nowadays, there is an Automatic Environmental Monitoring Network (AEMN) which measures air pollutants (Sulphur Dioxide (SO2), Particular Matter (PM10), Ozone (O3), etc.), as well as environmental variables (wind speed, wind direction, temperature, and relative humidity), and it takes a sample of the variables every minute. The AEM Network is mainly based on three monitoring stations located at Cruz Roja, DIF, and Nativitas. In this work, we use the PFCM (Possibilistic Fuzzy c Means) clustering algorithm as a mean to get a combined measure, from the three stations, looking to provide a tool for better management of contingencies in the city, such that local or general action can be taken in the city according to the pollution level given by each station and the combined measure. Besides, we also performed an analysis of correlation between pollution and environmental variables. The results show a significative correlation between pollutant concentrations and some environmental variables. So, the combined measure and the correlations can be used for the establishment of general contingency thresholds.

Rainfall is one of the most important events in daily life of human beings. During several decades, scientists have been trying to characterize the weather, current forecasts are based on high complex dynamic models. In this paper is presented a local rainfall forecast system based on Time Series analysis and Neural Networks. This model tries to complement the currently state of the art ensembles, from a locally historical perspective, where the model definition is not so dependent from the exact values of the initial conditions. After several year taking data, expert meteorologists proposed this approximation to characterize the local weather behavior, that is being automated by this system in different stages. However the whole system is introduced, it is focused on the different rainfall events situation classification as well as the time series analysis and forecast

At present, the water resources are a strategic element each time more necessary and limited becoming a source of conflicts. For that, it is fundamental to create an independent and competent entity with good reputation and social acceptation. This entity must be able to obtain, store and process all data dispersed in different entities creating a network for these purposes. Finally, it must be able to organize different branches between the government and the final users. Using one of the wellknown Multicriteria Decision Methods(MCDM) with several realistic alternatives and several criteria identified in expert seminars in Salta and Madrid, we have obtained hopeful results and more recently new modifications introduced have generated better results.

The purpose of this work is to analyze the illusion of movement that appears when seeing certain static images. This analysis is accomplished by using a biologically plausible neural network that learned (in a unsupervised manner) to identify the movement direction of shifting training patterns. Some of the biological features that characterizes this neural network are: intrinsic plasticity to adapt firing probability, metaplasticity to regulate synaptic weights and firing adaptation of simulated pyramidal networks. After analyzing the results, we hypothesize that the illusion is due to cinematographic perception mechanisms in the brain due to which each visual frame is renewed approximately each 100 msec. Blurring of moving object in visual frames might be interpreted by the brain as movement, the same as if we present a static blurred object.

Telecommunications network operators are on a constant challenge to provide new services which require ubiquitous broadband access. In an attempt to do so, they are faced with many problems such as the network coverage or providing the guaranteed Quality of Service (QoS). Network planning is a multi-objective optimization problem which involves clustering the area of interest by minimizing a cost function which includes relevant parameters, such as installation cost, distance between user and base station, supported traffic, quality of received signal, etc. On the other hand, service assurance deals with the disorders that occur in hardware or software of the managed network. This paper presents a large number of multicriteria techniques that have been developed to deal with different kinds of problems regarding network planning and service assurance. The state of the art presented will help the reader to develop a broader understanding of the problems in the domain.

In the decision-making, in many cases, we need to know the situation in a next step, if another alternative was adopted or which would be the best alternative, in that moment, with the situation that is envisaged. Decision making for election of regional water resources management entities shows a high complexity, due to much interrelation between aspects, decision makers and planning procedures. We make the decision following the Multi-criteria Discrete Decision Methods and we combine this procedure with the Bayesian methods and dynamic programming. The new decisional matrix, weights and the utility functions are obtained under uncertainty. We combine uncertainty and decision-making. An objective function, relating effects of decisions to actions, often cannot be specified in advance. This is an approach of MCDM to decide in conflict situations.

Making use of biologically plausible artificial neural networks that implement Grossberg’s presynaptic learning rule, we simulate the possible effects of calcium dysregulation in the neuron’s activation function, to represent the most accepted model of Alzheimer's Disease: the calcium dysregulation hypothesis. According to Cudmore and Turrigiano calcium dysregulation alters the shifting dynamic of the neuron’s activation function (intrinsic plasticity). We propose that this alteration might affect the stability of synaptic weights in which memories are stored. The results of the simulation supported the theoretical hypothesis, implying that the emergence of Alzheimer's disease's symptoms such as memory loss and learning problems might be correlated to intrinsic neuronal plasticity impairment due to calcium dysregulation.

A comparison between a linear regression model and a Non-linear regressionmodel is presented in this work for forecasting of pollution levels due to SO2 in Salamanca city, Gto. Prediction is performed by means of an Adaptive Linear Neural Network (ADALINE) and a Generalized Regression NeuralNetwork (GRNN). Prediction experiments are realized for 1, 12 and 24 hours in advance, and the results for linear regression have been satisfactory. The performance estimation of both models are determined using the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Obtained results are compared. The final results indicated that ADALINE outperforms the past approach using GRNN.

This work gives an overview of the broad field of computational swarm intelligence and its applications in swarm robotics. Computational swarm intelligence is modelled on the social behavior of animals and its principle application is as an optimization technique. Swarm robotics is a relatively new and rapidly developing field which draws inspiration from swarm intelligence. It is an interesting alternative to classical approaches to robotics because of some properties of problem solving present in social insects, which is flexible, robust, decentralized and self-organized. This work highlights the possibilities for further research.

Our objective is the design and simulation of an efficient system for detection of signals in communications in terms of speed and computational complexity. The proposed scheme takes advantage of two powerful frameworks in signal processing: Wavelets and Neural Networks. The decision system will take a decision based on the computation of the a priori probabilities of the input signal. For the estimation of such probability density functions, a Wavelet Neural Network (WNN) has been chosen. The election has arosen under the following considerations: (a) neural networks have been established as a general approximation tool for fitting nonlinear models from input/output data and (b) the increasing popularity of the wavelet decomposition as a powerful tool for approximation. The integration of the above factors leads to the wavelet neural network concept. This network preserve the universal approximation property of wavelet series, with the advantage of the speed and efficient computation of a neural network architecture. The topology and learning algorithm of the network will provide an efficient approximation to the required probability density functions.

Animal swarms in nature are able to adapt to dynamic changes in their envi-ronment, and through cooperation they can solve problems that are crucial for their survival. Only by means of local interactions with other members of the swarm and with the environment, they can achieve a common goal more efficiently than it would be done by a single individual. This problem-solving behavior that results from the multiplicity of such interactions is referred to as Swarm Intelligence. The mathematical models of swarming behavior in nature were initially proposed to solve optimization problems. Nevertheless, this decentralized approach can be a valuable tool for a variety of applications, where emerging global patterns represent a solution to the task at hand. Methods for the solution of difficult computational problems based on Swarm Intelligence have been experimentally demonstrated and reported in the literature. However, a general framework that would facilitate their design does not exist yet. In this dissertation, a new general design methodology for Swarm Intelligence tools is proposed. By defining a discrete space in which the members of the swarm can move, and by modifying the rules of local interactions and setting the adequate objective function for solutions evaluation, the proposed methodology is tested in various domains. The dissertation presents a set of case studies, and focuses on two general approaches. One approach is to apply Swarm Intelligence as a tool for optimization and feature extraction, and the other approach is to model multi-agent systems such that they resemble swarms of animals in nature providing them with the ability to autonomously perform a task at hand. Artificial swarms are designed to be autonomous, scalable, robust, and adaptive to the changes in their environment. In this work, the methods that exploit one or more of these features are presented. First, the proposed methodology is validated in a real-world scenario seen as a combinatorial optimization problem. Then a set of novel tools for feature extraction, more precisely the adaptive edge detection and the broken-edge linking in digital images is proposed. A novel data clustering algorithm is also proposed and applied to image segmentation. Finally, a scalable algorithm based on the proposed methodology is developed for distributed task allocation in multi-agent systems, and applied to a swarm of robots. The newly proposed general methodology provides a guideline for future developers of the Swarm Intelligence tools. Los enjambres de animales en la naturaleza son capaces de adaptarse a cambios dinamicos en su entorno y, por medio de la cooperación, pueden resolver problemas ´ cruciales para su supervivencia. Unicamente por medio de interacciones locales con otros miembros del enjambre y con el entorno, pueden lograr un objetivo común de forma más eficiente que lo haría un solo individuo. Este comportamiento problema-resolutivo que es resultado de la multiplicidad de interacciones se denomina Inteligencia de Enjambre. Los modelos matemáticos de comportamiento de enjambres en entornos naturales fueron propuestos inicialmente para resolver problemas de optimización. Sin embargo, esta aproximación descentralizada puede ser una herramienta valiosa en una variedad de aplicaciones donde patrones globales emergentes representan una solución de las tareas actuales. Aunque en la literatura se muestra la utilidad de los métodos de Inteligencia de Enjambre, no existe un entorno de trabajo que facilite su diseño. En esta memoria de tesis proponemos una nueva metodologia general de diseño para herramientas de Inteligencia de Enjambre. Desarrollamos herramientas noveles que representan ejem-plos ilustrativos de su implementación. Probamos la metodología propuesta en varios dominios definiendo un espacio discreto en el que los miembros del enjambre pueden moverse, modificando las reglas de las interacciones locales y fijando la función objetivo adecuada para evaluar las soluciones. La memoria de tesis presenta un conjunto de casos de estudio y se centra en dos aproximaciones generales. Una aproximación es aplicar Inteligencia de Enjambre como herramienta de optimización y extracción de características mientras que la otra es modelar sistemas multi-agente de tal manera que se asemejen a enjambres de animales en la naturaleza a los que se les confiere la habilidad de ejecutar autónomamente la tarea. Los enjambres artificiales están diseñados para ser autónomos, escalables, robustos y adaptables a los cambios en su entorno. En este trabajo, presentamos métodos que explotan una o más de estas características. Primero, validamos la metodología propuesta en un escenario del mundo real visto como un problema de optimización combinatoria. Después, proponemos un conjunto de herramientas noveles para ex-tracción de características, en concreto la detección adaptativa de bordes y el enlazado de bordes rotos en imágenes digitales, y el agrupamiento de datos para segmentación de imágenes. Finalmente, proponemos un algoritmo escalable para la asignación distribuida de tareas en sistemas multi-agente aplicada a enjambres de robots. La metodología general recién propuesta ofrece una guía para futuros desarrolladores deherramientas de Inteligencia de Enjambre.

Artificial Neural Networks design and training algorithms are based many times on the optimization of an objective error function used to provide an evaluation of the performances of the network. The value of the error depends basically on the weight values of the different connections between the neurons of the network. The learning methods modify and update the different weight values following a strategy that tends to minimize the final error in the network performance. The neural network theory identifies the weight values as a representation of the synaptic weights in the biological neural networks, and their ability to change their values can be interpreted as a kind of artificial plasticity inspired by the demonstrated biological counterpart process. The biological metaplasticity is related to the processes of memory and learning as an inherent property of the biological neuron connections, and consists in the capacity of modifying the learning mechanism using the information present in the network itself. In such a way, Artificial MetaPlasticity (AMP), is interpreted as the ability to change the efficiency of artificial plasticity depending on certain elements used in the training. A very efficient AMP model (as a function of learning time and performance) is the approach that connects metaplasticity and Shannon’s information theory, which establishes that less frequent patterns carry more information than frequent patterns. This model defines AMP as a learning procedure that produces greater modifications in the synaptic weights when less frequent patterns are presented to the network than when frequent patterns are used, as a way of extracting more information from the former than from the latter. In this doctoral thesis the AMP theory is implemented using different Artificial Neural Network (ANN), models and different learning paradigms. The networks are used as classifiers or predictors of synthetic and real data sets in order to be able to compare and evaluate the results obtained with several state of the art methods. The AMP theory is implemented over two general learning methods: • Supervised training: The BackPropagation Algorithm (BPA), is one of the best known and most used algorithms to training the neural networks. This algorithm compares the ideal results with the real results obtained at the networks output and calculates an error value. This value is used to modify the weight values in order to get a final trained network that minimizes the differences between the ideal and the real results. The BPA has been successfully applied to several patter classification problems in areas such as: medicine, bioinformatic, banking, climatological predictions, etc. However the classic algorithm has shown some limitations that prevent this method to reach an optimal efficiency level (convergence, speed problems and classification accuracy). Artificial Metaplasticity modification to the classic BPA, is in this case implemented in a Multilayer Perceptron (MLP), neural network. The Artificial Metaplasticity on MultiLayer Perceptron (AMMLP) model was applied in the ANNs training phase. During the training phase the AMMLP algorithm updates the weights assigning higher values to the less frequent activations than to the more frequent ones. AMMLP achieves a more efficient training and improves MLP performance. The suggested AMMLP algorithm was applied to different problems related to pattern classification or prediction in different areas and considering different methods for obtaining the information from the data set. Modeling this interpretation in the training phase, the hypothesis of an improved training shows a much more efficient training maintaining the ANN performance. This algorithm has achieved deeper learning on several multidisciplinary data sets without the need of a deep network. • Unsupervised training: Koniocortex-Like Network (KLN) is a novel category of bio-inspired neural networks whose architecture and properties are inspired in the biological koniocortex, the first layer of the cortex that receives information from the thalamus. In the KLN competition and pattern classification emerges naturally due to the interplay of inhibitory inter-neurons, metaplasticity and intrinsic plasticity. This behavior resembles a Winner Take All (WTA) mode of operation, where the most active neuron “wins”, i.e. fires, while neighboring ones remain silent. Although a winning neuron is identified by calculation in many artificial neural networks models, in biological neural networks the winning neuron emerges from a natural dynamic process. Recently proposed, it has shown a big potential for complex tasks with unsupervised learning. Now for the first time, its competitive results are proved in several relevant real applications. The simulations show that the unsupervised learning that emerges from individual neurons properties is comparable and even surpasses results obtained with several advanced state-of-the-art supervised and unsupervised learning algorithms.