Metodologia para avaliação de desempenho ambiental em aeroportos e sua aplicação no Aeroporto Internacional de Viracopos, Campinas (SP)

Pós-graduação em Geologia Regional - IGCE

Estratégia competitiva e o papel da função produção: uma análise em empresas do polo calçadista de Birigui

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Influência do tratamento térmico sobre a tenacidade de um aço com AISI SAE 1045 com médio teor de carbono, avaliada por ensaios de impacto

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Caracterização estrutural do sistema reprodutor masculino e do hepatotâncreas dos diferentes morfotipos de Macrobrachium amazonicum

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Resíduos sólidos urbanos em área de proteção aos mananciais - município de São Lourenço da Serra - SP: compostagem e impacto ambiental

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Riscos potenciais de impacto ambiental negativo da suinocultura na bacia do córrego Bebedouro, Uberlândia-MG

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Políticas de apoio a gestão da arborização urbana: uma análise do programa município verde azul

Pós-graduação em Agronomia - FEIS

Diagnóstico hidroambiental para incremento da qualidade ambiental no município de Votorantim, SP

Pós-graduação em Ciências Ambientais - Sorocaba

Politiche Antitrust e crescita economica

Negli ultimi anni le istituzioni e la regolamentazione hanno svolto un ruolo sempre più importante nell’analisi della crescita economica. Tuttavia, non è facile interpretare le istituzioni e gli effetti dei regolamenti sulla crescita attraverso indicatori che tendono a “misurare” le istituzioni. Lo scopo di questa ricerca è analizzare la relazione di lungo periodo tra la crescita economica e la regolamentazione e il ruolo della regolamentazione antitrust sulla crescita economica. La stima econometrica dei modelli di crescita con la concorrenza e gli indicatori di potere di mercato si base su un dataset appositamente costruito che copre 211 Paesi, su un arco temporale massimo di 50 anni (da 1960 a 2009). In particolare, cerchiamo di identificare un quadro analitico volto a integrare l’analisi istituzionale ed economica al fine di valutare il ruolo della regolamentazione e, più in generale, il ruolo delle istituzioni nella crescita economica. Dopo una revisione della letteratura teorica ed empirica sulla crescita e le istituzioni, vi presentiamo l’analisi dell'impatto normativo (RIA) in materia di concorrenza, e analizziamo le principali misure di regolamentazione, la governance e le misure antitrust. Per rispondere alla nostra domanda di ricerca si stimano modelli di crescita prendendo in considerazione tre diverse misure di regolamentazione: la Regulation Impact (RI), la Governance (GOV), e la libertà economica (LIB). Nel modello a effetti fissi, RI, gli effetti della legislazione antitrust sulla crescita economica sono significativi e positivi, e gli effetti di durata antitrust sono significativi, ma negativi. Nel pannel dinamico, GOV, gli effetti dell’indicatore di governance sulla crescita sono notevoli, ma negativo. Nel pannel dinamico, LIB, gli effetti della LIB sono significativi e negativi.

Runoff Water Quality and Pollutant Mass Loads From Extensive Green Roofs

Green roof mitigation of volume and peak flow-rate of stormwater runoff has been studied extensively. However, due to the common practice of green roof fertilization, there is the potential for introduction of nutrients into local bodies of water. Therefore, this study compares green roof runoff quality with the water quality of precipitation and runoff from a bare shingle roof. The runoff from a demonstration-scale extensive green roof was analyzed during the summer of 2011 for its effect on runoff volume and analyzed during eleven storm events in the fall and winter for concentrations of copper, cadmium, zinc, lead, nitrogen species, total nitrogen, total organic carbon, sulfate, orthophosphate, and other monovalent and divalent ions. The green roof reduced the overall volume of runoff and served as a sink for NO3 - and NH4 +. However, the green roof was also a source for the pollutants PO4 3-, SO4 2-, TOC, cations, and total nitrogen. Metals such as zinc and lead showed trends of higher mass loads in the bare roof runoff than in precipitation and green roof runoff, although results were not statistically significant. The green roof also showed trends, although also not statistically significant, of retaining cadmium and copper. With the green roof serving as a source of phosphorous species and a sink for nitrogen species, and appearing to a retain metals and total volume, the life cycle impact analysis shows minimum impacts from the green roof, when compared with precipitation and bare roof runoff, in all but fresh water eutrophication. Therefore, the best environments to install a green roof may be in coastal environments.

Action Research and Learning Analytics in Higher Education

Teaching is a dynamic activity. It can be very effective, if its impact is constantly monitored and adjusted to the demands of changing social contexts and needs of learners. This implies that teachers need to be aware about teaching and learning processes. Moreover, they should constantly question their didactical methods and the learning resources, which they provide to their students. They should reflect if their actions are suitable, and they should regulate their teaching, e.g., by updating learning materials based on new knowledge about learners, or by motivating learners to engage in further learning activities. In the last years, a rising interest in ‘learning analytics’ is observable. This interest is motivated by the availability of massive amounts of educational data. Also, the continuously increasing processing power, and a strong motivation for discovering new information from these pools of educational data, is pushing further developments within the learning analytics research field. Learning analytics could be a method for reflective teaching practice that enables and guides teachers to investigate and evaluate their work in future learning scenarios. However, this potentially positive impact has not yet been sufficiently verified by learning analytics research. Another method that pursues these goals is ‘action research’. Learning analytics promises to initiate action research processes because it facilitates awareness, reflection and regulation of teaching activities analogous to action research. Therefore, this thesis joins both concepts, in order to improve the design of learning analytics tools. Central research question of this thesis are: What are the dimensions of learning analytics in relation to action research, which need to be considered when designing a learning analytics tool? How does a learning analytics dashboard impact the teachers of technology-enhanced university lectures regarding ‘awareness’, ‘reflection’ and ‘action’? Does it initiate action research? Which are central requirements for a learning analytics tool, which pursues such effects? This project followed design-based research principles, in order to answer these research questions. The main contributions are: a theoretical reference model that connects action research and learning analytics, the conceptualization and implementation of a learning analytics tool, a requirements catalogue for useful and usable learning analytics design based on evaluations, a tested procedure for impact analysis, and guidelines for the introduction of learning analytics into higher education.

Predicting dependencies using domain-based coupling

Software dependencies play a vital role in programme comprehension, change impact analysis and other software maintenance activities. Traditionally, these activities are supported by source code analysis; however, the source code is sometimes inaccessible or difficult to analyse, as in hybrid systems composed of source code in multiple languages using various paradigms (e.g. object-oriented programming and relational databases). Moreover, not all stakeholders have adequate knowledge to perform such analyses. For example, non-technical domain experts and consultants raise most maintenance requests; however, they cannot predict the cost and impact of the requested changes without the support of the developers. We propose a novel approach to predicting software dependencies by exploiting the coupling present in domain-level information. Our approach is independent of the software implementation; hence, it can be used to approximate architectural dependencies without access to the source code or the database. As such, it can be applied to hybrid systems with heterogeneous source code or legacy systems with missing source code. In addition, this approach is based solely on information visible and understandable to domain users; therefore, it can be efficiently used by domain experts without the support of software developers. We evaluate our approach with a case study on a large-scale enterprise system, in which we demonstrate how up to 65 of the source code dependencies and 77% of the database dependencies are predicted solely based on domain information.

Management of fever and neutropenia in paediatric cancer patients: room for improvement?

PURPOSE OF REVIEW Fever and neutropenia is the most common complication in the treatment of childhood cancer. This review will summarize recent publications that focus on improving the management of this condition as well as those that seek to optimize translational research efforts. RECENT FINDINGS A number of clinical decision rules are available to assist in the identification of low-risk fever and neutropenia however few have undergone external validation and formal impact analysis. Emerging evidence suggests acute fever and neutropenia management strategies should include time to antibiotic recommendations, and quality improvement initiatives have focused on eliminating barriers to early antibiotic administration. Despite reported increases in antimicrobial resistance, few studies have focused on the prediction, prevention, and optimal treatment of these infections and the effect on risk stratification remains unknown. A consensus guideline for paediatric fever and neutropenia research is now available and may help reduce some of the heterogeneity between studies that have previously limited the translation of evidence into clinical practice. SUMMARY Risk stratification is recommended for children with cancer and fever and neutropenia. Further research is required to quantify the overall impact of this approach and to refine exactly which children will benefit from early antibiotic administration as well as modifications to empiric regimens to cover antibiotic-resistant organisms.

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Agile Construction and Evolution of Product-Line Architectures

Software Product Line Engineering (SPLE) has proved to have significant advantages in family-based software development, but also implies the up¬front design of a product-line architecture (PLA) from which individual product applications can be engineered. The big upfront design associated with PLAs is in conflict with the current need of "being open to change". However, the turbulence of the current business climate makes change inevitable in order to stay competitive, and requires PLAs to be open to change even late in the development. The trend of "being open to change" is manifested in the Agile Software Development (ASD) paradigm, but it is spreading to the domain of SPLE. To reduce the big upfront design of PLAs as currently practiced in SPLE, new paradigms are being created, one being Agile Product Line Engineering (APLE). APLE aims to make the development of product-lines more flexible and adaptable to changes as promoted in ASD. To put APLE into practice it is necessary to make mechanisms available to assist and guide the agile construction and evolution of PLAs while complying with the "be open to change" agile principle. This thesis defines a process for "the agile construction and evolution of product-line architectures", which we refer to as Agile Product-Line Archi-tecting (APLA). The APLA process provides agile architects with a set of models for describing, documenting and tracing PLAs, as well as an algorithm to analyze change impact. Both the models and the change impact analysis offer the following capabilities: Flexibility & adaptability at the time of defining software architectures, enabling change during the incremental and iterative design of PLAs (anticipated or planned changes) and their evolution (unanticipated or unforeseen changes). Assistance in checking architectural integrity through change impact analysis in terms of architectural concerns, such as dependencies on earlier design decisions, rationale, constraints, and risks, etc.Guidance in the change decision-making process through change im¬pact analysis in terms of architectural components and connections. Therefore, APLA provides the mechanisms required to construct and evolve PLAs that can easily be refined iteration after iteration during the APLE development process. These mechanisms are provided in a modeling frame¬work called FPLA. The contributions of this thesis have been validated through the conduction of a project regarding a metering management system in electrical power networks. This case study took place in an i-smart software factory and was in collaboration with the Technical University of Madrid and Indra Software Labs. La Ingeniería de Líneas de Producto Software (Software Product Line Engi¬neering, SPLE) ha demostrado tener ventajas significativas en el desarrollo de software basado en familias de productos. SPLE es un paradigma que se basa en la reutilización sistemática de un conjunto de características comunes que comparten los productos de un mismo dominio o familia, y la personalización masiva a través de una variabilidad bien definida que diferencia unos productos de otros. Este tipo de desarrollo requiere el diseño inicial de una arquitectura de línea de productos (Product-Line Architecture, PLA) a partir de la cual los productos individuales de la familia son diseñados e implementados. La inversión inicial que hay que realizar en el diseño de PLAs entra en conflicto con la necesidad actual de estar continuamente "abierto al cam¬bio", siendo este cambio cada vez más frecuente y radical en la industria software. Para ser competitivos es inevitable adaptarse al cambio, incluso en las últimas etapas del desarrollo de productos software. Esta tendencia se manifiesta de forma especial en el paradigma de Desarrollo Ágil de Software (Agile Software Development, ASD) y se está extendiendo también al ámbito de SPLE. Con el objetivo de reducir la inversión inicial en el diseño de PLAs en la manera en que se plantea en SPLE, en los último años han surgido nuevos enfoques como la Ingeniera de Líneas de Producto Software Ágiles (Agile Product Line Engineering, APLE). APLE propone el desarrollo de líneas de producto de forma más flexible y adaptable a los cambios, iterativa e incremental. Para ello, es necesario disponer de mecanismos que ayuden y guíen a los arquitectos de líneas de producto en el diseño y evolución ágil de PLAs, mientras se cumple con el principio ágil de estar abierto al cambio. Esta tesis define un proceso para la "construcción y evolución ágil de las arquitecturas de lineas de producto software". A este proceso se le ha denominado Agile Product-Line Architecting (APLA). El proceso APLA proporciona a los arquitectos software un conjunto de modelos para de¬scribir, documentar y trazar PLAs, así como un algoritmo para analizar vel impacto del cambio. Los modelos y el análisis del impacto del cambio ofrecen: Flexibilidad y adaptabilidad a la hora de definir las arquitecturas software, facilitando el cambio durante el diseño incremental e iterativo de PLAs (cambios esperados o previstos) y su evolución (cambios no previstos). Asistencia en la verificación de la integridad arquitectónica mediante el análisis de impacto de los cambios en términos de dependencias entre decisiones de diseño, justificación de las decisiones de diseño, limitaciones, riesgos, etc. Orientación en la toma de decisiones derivadas del cambio mediante el análisis de impacto de los cambios en términos de componentes y conexiones. De esta manera, APLA se presenta como una solución para la construcción y evolución de PLAs de forma que puedan ser fácilmente refinadas iteración tras iteración de un ciclo de vida de líneas de producto ágiles. Dicha solución se ha implementado en una herramienta llamada FPLA (Flexible Product-Line Architecture) y ha sido validada mediante su aplicación en un proyecto de desarrollo de un sistema de gestión de medición en redes de energía eléctrica. Dicho proyecto ha sido desarrollado en una fábrica de software global en colaboración con la Universidad Politécnica de Madrid e Indra Software Labs.