Estudio del gen Brr2 en la especificación neuronal de Drosophila melanogaster

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología. Fecha de lectura: 26-05-2014El cerebro humano está formado por más de 10.000 tipos celulares diferentes. Esta diversidad celular comienza a establecerse en el desarrollo embrionario durante el cual, células madre neurales o Neuroblastos dan lugar a células diferenciadas que conformarán los distintos tipos celulares. Utilizando la Cuerda Nerviosa Ventral de Drosophila como modelo, el presente trabajo está orientado a profundizar en el conocimiento de los mecanismos que rigen la especificación celular. El estudio realizado está centrado en la función del gen Brr2 en los procesos de especificación neuronal del Grupo Apterous, un grupo de cuatro neuronas que pertenecen al linaje tardío del Neuroblasto 5-6 y adquieren distintos destinos celulares determinados por la expresión de los genes de diferenciación terminal FMRFamida (FMRFa) y Neuropeptide like precursor 1 (Nplp1). Brr2 codifica una proteína del tipo DExD/H-box con actividad helicasa que juega un papel fundamental en el procesamiento del ARN. Aunque no ha sido descrita ninguna relación de Brr2 con procesos de especificación neuronal, recientemente ha sido relacionado con la expresión de FMRFamida. En este trabajo hemos podido determinar que Brr2 juega un papel fundamental en la especificación de los destinos celulares correspondientes a la expresión de los marcadores FMRFa y Nplp1 específicamente en el Grupo Apterous. Así mismo se ha podido identificar que Brr2 actúa a dos niveles en la activación de la ruta de señalización retrógrada BMP/TGF-β necesaria para la expresión de FMRFa en el Grupo Apterous: en los procesos de proyección axonal e inervación a su tejido diana y en la activación de los receptores BMP correspondientes a esta vía. Además, los resultados obtenidos han permitido sugerir que los mecanismos de procesamiento de ARN regulados por Brr2 están estrechamente relacionados con la adquisición de los destinos celulares del Grupo Apterous

An Expert System to Improve the Energy Efficiency of the Reaction Zone of a Petrochemical Plant

Energy is the most important cost factor in the petrochemical industry. Thus, energy efficiency improvement is an important way to reduce these costs and to increase predictable earnings, especially in times of high energy price volatility. This work describes the development of an expert system for the improvement of this efficiency of the reaction zone of a petrochemical plant. This system has been developed after a data mining process of the variables registered in the plant. Besides, a kernel of neural networks has been embedded in the expert system. A graphical environment integrating the proposed system was developed in order to test the system. With the application of the expert system, the energy saving on the applied zone would have been about 20%.Junta de Andalucía TIC-570

Doctrina del contrato del Estado

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Derecho, leída en 1976.El primer capítulo fija las nociones generales de la institución de los contratos del estado así como el derecho comparado en relación con los mismos. En el capítulo segundo contempla los elementos de la institución y en particular del estado-parte en los contratos y el administrado-contratante del estado. Los fines de la institución son estudiados en el capitulo tercero. La naturaleza jurídica y clases de contratos del estado es objeto de estudio en el capitulo cuarto. La generación del contrato del estado se estudia en el quinto capitulo. Por ultimo en el capitulo sexto estudia la ejecución del estado fijándose específicamente en el deber de cumplimiento de mutua colaboración en la ejecución del cumplimiento del contrato al interés publico y en la revisión del contrato del estado por alteración de las circunstancias económicas.Fac. de DerechoTRUEProQuestpu

Doctrina del contrato del Estado

El primer capítulo fija las nociones generales de la institución de los contratos del estado así como el derecho comparado en relación con los mismos. En el capítulo segundo contempla los elementos de la institución y en particular del estado-parte en los contratos y el administrado-contratante del estado. Los fines de la institución son estudiados en el capitulo tercero. La naturaleza jurídica y clases de contratos del estado es objeto de estudio en el capitulo cuarto. La generación del contrato del estado se estudia en el quinto capitulo. Por ultimo en el capitulo sexto estudia la ejecución del estado fijándose específicamente en el deber de cumplimiento de mutua colaboración en la ejecución del cumplimiento del contrato al interés publico y en la revisión del contrato del estado por alteración de las circunstancias económicas

An Approach to Detection of Tampering in Water Meters

Meter tampering is defined as a fraudulent manipulation which implies a service that is not billed by a utility company. It is a lack of consumption control for the utility company and a main problem because they represent an important loss of income. We have developed a methodology consists of a set of three algorithms for the detection of meter tampering in the Emasesa Company (a water distribution company in Seville and one of the most important of the country). The algorithms were generated and programmed after a data mining process from the database of the company and they detect three type of consumption patterns: Progressive drops, sudden drops and abnormally low consumption. The methodology has been tested with in situ inspections of the customers of a village of the province of Seville. Once carried out the inspections by the utility, the inspectors confirmed a good success rate taking into account that the detection of this type of fraud is very difficult because it is a noninvasive technique. Besides, this type of detections is a topic that, if we take a look at the state of the art, there are few references or works.Ministerio de Ciencia y Tecnología TEC2013-40767-

Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection

Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers

Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach

The rapidly growing world energy use already has concerns over the exhaustion of energy resources andheavy environmental impacts. As a result of these concerns, a trend of green and smart cities has beenincreasing. To respond to this increasing trend of smart cities with buildings every time more complex,in this paper we have proposed a new method to solve energy inefficiencies detection problem in smartbuildings. This solution is based on a rule-based system developed through data mining techniques andapplying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is alsoproposed to detect anomalies. The data mining system is developed through the knowledge extracted bya full set of building sensors. So, the results of this process provide a set of rules that are used as a partof a decision support system for the optimisation of energy consumption and the detection of anomaliesin smart buildings.Comisión Europea FP7-28522

Electricity clustering framework for automatic classification of customer loads

Clustering in energy markets is a top topic with high significance on expert and intelligent systems. The main impact of is paper is the proposal of a new clustering framework for the automatic classification of electricity customers’ loads. An automatic selection of the clustering classification algorithm is also highlighted. Finally, new customers can be assigned to a predefined set of clusters in the classificationphase. The computation time of the proposed framework is less than that of previous classification tech- niques, which enables the processing of a complete electric company sample in a matter of minutes on a personal computer. The high accuracy of the predicted classification results verifies the performance of the clustering technique. This classification phase is of significant assistance in interpreting the results, and the simplicity of the clustering phase is sufficient to demonstrate the quality of the complete mining framework.Ministerio de Economía y Competitividad TEC2013-40767-RMinisterio de Economía y Competitividad IDI- 2015004

Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant

The energy efficiency of industrial plants is an important issue in any type of business but particularly in the chemical industry. Not only is it important in order to reduce costs, but also it is necessary even more as a means of reducing the amount of fuel that gets wasted, thereby improving productivity, ensuring better product quality, and generally increasing profits. This article describes a decision system developed for optimizing the energy efficiency of a petrochemical plant. The system has been developed after a data mining process of the parameters registered in the past. The designed system carries out an optimization process of the energy efficiency of the plant based on a combined algorithm that uses the following for obtaining a solution: On the one hand, the energy efficiency of the operation points occurred in the past and, on the other hand, a module of two neural networks to obtain new interpolated operation points. Besides, the work includes a previous discriminant analysis of the variables of the plant in order to select the parameters most important in the plant and to study the behavior of the energy efficiency index. This study also helped ensure an optimal training of the neural networks. The robustness of the system as well as its satisfactory results in the testing process (an average rise in the energy efficiency of around 7%, reaching, in some cases, up to 45%) have encouraged a consulting company (ALIATIS) to implement and to integrate the decision system as a pilot software in an SCADA