THE EFFECT OF PERSON-ENVIRONMENT FIT ON THE PSYCHOLOGICAL WELL-BEING OF PSYCHIATRIC AIDES WORKING IN A STATE HOSPITAL (COPING RESOURCES, SOCIAL SUPPORT, OCCUPATIONAL STRESS, LOCUS-OF-CONTROL)

This study addressed two purposes: (1) to determine the effect of person-environment fit on the psychological well-being of psychiatric aides and (2) to determine what role the coping resources of social support and control have on the above relationship. Two hundred and ten psychiatric aides working in a state hospital in Texas responded to a questionnaire pertaining to these issues.^ Person-environment fit, as a measure of occupational stress, was assessed through a modified version of the Work Environment Scale (WES). The WES subscales used in this study were: involvement, autonomy, job pressure, job clarity, and physical comfort. Psychological well-being was measured with the General Well-Being Schedule which was developed by the National Center for Health Statistics. Co-worker and supervisor support were measured through the WES and finally, control was assessed through Rotter's Locus of Control Scale.^ The results of this study were as follows: (1) all person-environment (p-e) dimensions appeared to have linear relationships with psychological well-being; (2) the p-e fit - well-being relationship did not appear to be confounded by demographic factors; (3) all p-e fit dimensions were significantly related to well-being except for autonomy; (4) p-e fit was more strongly related to well-being than the environmental measure alone; (5) supervisor support and non-work related support were found to have additive effects on the relationship between p-e fit and well-being, however no interaction or buffering effects were observed; (6) locus of control was found to have additive effects in the prediction of well-being and showed interactive effects with work pressure, involvement and physical comfort; and (7) the testing of the overall study model which included many of the components mentioned above yielded an R('2) = .27.^ Implications of these findings are discussed, future research suggested and applications proposed. ^

THE EFFECT OF FAMILY ENVIRONMENT FACTORS IN THE ETIOLOGY OF CHILD ABUSE

Twenty-three abusing couples were compared with a matched group of 23 non-abusing couples in terms of stress levels and family environment factors (cohesion, expressiveness, conflict, independence, achievement orientation, organization, control) which might mediate the response of abuse to stress. Parents who had physically abused their children were found to have significantly greater stress, conflict, and control and a significantly lower level of cohesion, independence, and achievement orientation than non-abusing parents. However, none of the mediating effects of the family environment factors reached the level of significance. ^

THE EFFECT OF PERSON-ENVIRONMENT INTERACTION ON ANXIETY, DEPRESSION, AND SUBJECTIVE DISTRESS OF OPTOMETRY STUDENTS

The purpose of this study was to investigate whether an incongruence between personality characteristics of individuals and concomitant charcteristics of health professional training environments on salient dimensions contributes to aspects of mental health. The dimensions examined were practical-theoretical orientation and the degree of structure-unstructure. They were selected for study as they are particularly important attributes of students and of learning environments. It was proposed that when the demand of the environment is disparate from the proclivities of the individual, strain arises. This strain was hypothesized to contribute to anxiety, depression, and subjective distress.^ Select subscales on the Omnibus Personality Inventory (OPI) were the operationalized measures for the personality component of the dimensions studied. An environmental index was developed to assess students' perceptions of the learning environment on these same dimensions. The Beck Depression Inventory, State-Trait Anxiety Inventory and General Well-Being schedule measured the outcome variables.^ A congruence model was employed to determine person-environment (P-E) interaction. Scores on the scales of the OPI and the environmental index were divided into high, medium, and low based on the range of scores. Congruence was defined as a match between the level of personality need and the complementary level of the perception of the environment. Alternatively, incongruence was defined as a mismatch between the person and the environment. The consistent category was compared to the inconsistent categories by an analysis of variance procedure. Furthermore, analyses of covariance were conducted with perceived supportiveness of the learning environment and life events external to the learning environment as the covariates. These factors were considered critical influences affecting the outcome measures.^ One hundred and eighty-five students (49% of the population) at the College of Optometry at the University of Houston participated in the study. Students in all four years of the program were equally represented in the study. However, the sample differed from the total population on representation by sex, marital status, and undergraduate major.^ The results of the study did not support the hypotheses. Further, after having adjusted for perceived supportiveness and life events external to the learning environment, there were no statistically significant differences between the congruent category and incongruent categories. Means indicated than the study sample experienced significantly lower depression and subjective distress than the normative samples.^ Results are interpreted in light of their utility for future study design in the investigation of the effects of P-E interaction. Emphasized is the question of the feasibility of testing a P-E interaction model with extant groups. Recommendations for subsequent research are proposed in light of the exploratory nature of the methodology. ^

Growth rates of late Miocene corals from Crete (Greece)

Modern scleractinian corals are classical components of marine shallow warm water ecosystems. Their occurrence and diversity patterns in the geological record have been widely used to infer past climates and environmental conditions. Coral skeletal composition data reflecting the nature of the coral environment are often affected by diagenetic alteration. Ghost structures of annual growth rhythms are, however, often well preserved in the transformed skeleton. We show that these relicts represent a valuable source of information on growth conditions of fossil corals. Annual growth bands were measured in massive hemispherical Porites of late Miocene age from the island of Crete (Greece) that were found in patch reefs and level bottom associations of attached mixed clastic environments as well as isolated carbonate environments. The Miocene corals grew slowly, about 2-4 mm/yr, compatible with present-day Porites from high-latitude reefs. Slow annual growth of the Miocene corals is in good agreement with the position of Crete at the margin of the Miocene reef belt. Within a given time slice, extension rates were lowest in level bottom environments and highest in attached inshore reef systems. Because sea surface temperatures (SSTs) can be expected to be uniform within a time slice, spatial variations in extension rates must reflect local variations in light levels (low in the level bottom communities) and nutrients (high in the attached reef systems). During the late Miocene (Tortonian-early Messinian), maximum linear extension rates remained remarkably constant within seven chronostratigraphic units, and if the relationship of SSTs and annual growth rates observed for modern massive Indo-Pacific Porites spp. applies to the Neogene, minimum (winter) SSTs were 20°-21°C. Although our paleoclimatic record has a low resolution, it fits the trends revealed by global data sets. In the near future we expect this new and easy to use Porites thermometer to add important new information to our understanding of Neogene climate.

Depth profiles of pore-water and solid-phase constituents, respiration rates and grain size distribution in sediments of the Clarion-Clipperton Fracture Zone

Manganese nodules of the Clarion-Clipperton Fracture Zone (CCFZ) in the NE Pacific Ocean are highly enriched in Ni, Cu, Co, Mo and rare-earth elements, and thus may be the subject of future mining operations. Elucidating the depositional and biogeochemical processes that contribute to nodule formation, as well as the respective redox environment in both, water column and sediment, supports our ability to locate future nodule deposits and evaluates the potential ecological and environmental effects of future deep-sea mining. For these purposes we evaluated the local hydrodynamics and pore-water geochemistry with respect to the nodule coverage at four sites in the eastern CCFZ. Furthermore, we carried out selective leaching experiments at these sites in order to assess the potential mobility of Mn in the solid phase, and compared them with the spatial variations in sedimentation rates. We found that the oxygen penetration depth is 180 - 300 cm at all four sites, while reduction of Mn and NO3- is only significant below the oxygen penetration depth at sites with small or no nodules on the sediment surface. At the site without nodules, potential microbial respiration rates, determined by incubation experiments using 14C-labelled acetate, are slightly higher than at sites with nodules. Leaching experiments showed that surface sediments covered with big or medium-sized nodules are enriched in mobilizable Mn. Our deep oxygen measurements and pore-water data suggest that hydrogenetic and oxic-diagenetic processes control the present-day nodule growth at these sites, since free manganese from deeper sediments is unable to reach the sediment surface. We propose that the observed strong lateral contrasts in nodule size and abundance are sensitive to sedimentation rates, which in turn, are controlled by small-scale variations in seafloor topography and bottom-water current intensity.

Metadata Visualization of Cultural Heritage Information within a Collaborative Environment

Cultural content on the Web is available in various domains (cultural objects, datasets, geospatial data, moving images, scholarly texts and visual resources), concerns various topics, is written in different languages, targeted to both laymen and experts, and provided by different communities (libraries, archives museums and information industry) and individuals (Figure 1). The integration of information technologies and cultural heritage content on the Web is expected to have an impact on everyday life from the point of view of institutions, communities and individuals. In particular, collaborative environment scan recreate 3D navigable worlds that can offer new insights into our cultural heritage (Chan 2007). However, the main barrier is to find and relate cultural heritage information by end-users of cultural contents, as well as by organisations and communities managing and producing them. In this paper, we explore several visualisation techniques for supporting cultural interfaces, where the role of metadata is essential for supporting the search and communication among end-users (Figure 2). A conceptual framework was developed to integrate the data, purpose, technology, impact, and form components of a collaborative environment, Our preliminary results show that collaborative environments can help with cultural heritage information sharing and communication tasks because of the way in which they provide a visual context to end-users. They can be regarded as distributed virtual reality systems that offer graphically realised, potentially infinite, digital information landscapes. Moreover, collaborative environments also provide a new way of interaction between an end-user and a cultural heritage data set. Finally, the visualisation of metadata of a dataset plays an important role in helping end-users in their search for heritage contents on the Web.

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

La Planificación de Áreas Mineras en Declive : Una respuesta multidimensional = Planning Declining Mining Areas. A multidimensional response

La actividad minera tiene un gran impacto sobre el territorio, probablemente más que ninguna otra de las actividades humanas, ya que transforma el espacio en todas sus dimensiones: ecológica, ambiental, social y económica. Cuando la reducción de la rentabilidad de la explotación conduce al cierre de ésta, la repercusión sobre su entorno puede llegar a ser brutal. Pero las explotaciones mineras son muy distintas entre ellas y los efectos que su abandono producen sobre el espacio en la que se enclavan pueden ser diversos, por lo que la decisión sobre el futuro de estas áreas no es simple y evidente. Aquí se propone desarrollar una propuesta de clasificación tipológica de las minas y sus regiones con el objetivo de determinar las estrategias de intervención más adecuadas para el futuro de estos espacios y sus habitantes. En concreto se busca diferenciar los conceptos de Mina, Parque Minero, Espacio Minero y Región Minera, todos ellos fruto de la interacción de la huella de la actividad minera con el medio físico, los enclaves urbanizados, y la estructura socioeconómica de la región en la que se enclavan. Mining activity is having a great impact on the territory, probably more than any other human activity, which transforms the space in all of its dimensions, ecological, environmental, social and economic. When reducing the profitability of the operation leads to the conclusion thereof, the impact on the environment can be brutal. But mining are very different between them and the effects they produce on their abandonment in space that interlock can be diverse, so the decision on the future of these areas is not simple and obvious. This proposal aims to develop a typological classification of mines and their regions in order to determine the most appropriate intervention strategies for the future of these spaces and their inhabitants. Specifically, it seeks to differentiate the concepts of Mine, Mining Park, Space Miner and Mining Region, all the result of the interaction of the mining footprint with the physical environment, the urbanized enclaves, and the socio-economic structure of the region which interlock. El presente libro reúne las ponencias presentadas por los investigadores de la red REUSE dentro del 1er Simposio de Reutilización del Espacio Minero; evento organizado por la Universidad Federal de Minas Gerais (UFMG) en Belo Horizonte, entre el 1 y el 3 de octubre de 2012, en el marco del 1er Seminario Internacional de Reconversión de Territorios. La red REUSE es una red realizada gracias a la financiación del programa CYTED

Survey on student motivation and approval of educational environment at Universidad Politécnica de Madrid within the European Higher Education area implementation

A recent study elaborated by Vicerrectorado de Ordenación Académica y Planificación Estratégica of Technical University of Madrid (UPM) defines the satisfaction of the university student body as "the response that the University offers to the expectations and demands of service of the students, considered in a general way ". Besides an indicator of academic and institutional insertion of the student, the assessment of student engagement allows us to adapt the academic offer and the extension services of the University to the real needs of the students. The process of convergence towards the European Higher Education Area (EHEA) raises the need to form in competitions, that is to say, of developing in our students capacities and knowledge beyond the purely theoretical-practical thing. Therefore, the perception and experience of the educational process and environment by the students is an important issue to be addressed to accomplish their expectations and achieve a curriculum accordingly to EHEA expectations. The present study aims to explore the student motivation and approval of the educational environment at the UPM. To this end a total of 97 students enrolled in the undergraduate program of Civil Engineering, Computer Engineering and Agronomic Engineering at UPM were surveyed. The survey consisted of 40 questions divided in three blocks. The first one of 20 questions of personal character in that they were gathering, besides the sex and the age, the degree of fulfilment, implication and dedication with the institution and the academic tasks. In the second block we identify 10 questions related to the perception of the student on the teaching quality, and finally a block of 10 questions regarding the Bologna Process. The students personal motivation was moderately high, with a score of 3.6 (all scores are provided on a 5-point scale), being the most valuable items obtaining a university degree (4,3) and the friendship between students (4,2). Any significant difference was shown between sexes (P=0.23) since the averages for this block of questions were of 3.7±0.3 and 3.5±0.4 for women and men respectively. The students are moderately satisfied with their graduate studies with an average score of 3,2, being the questions that reflect a minor satisfaction the research profile of the teachers (2,8) and the organization of the Schools (2,9). The best valued questions are related to the usefulness and quality of the degrees, with 3,5 and 3,4 respectively, and to the interest of the courses within the degree (3,4). For sexes, the results of this block of questions are similar (3.1±0.3 and 3.2±0.3 for men and women respectively=0.79). Also, there were no differences (P=0.39) between the students who arrange work and studies or do not work (3.1±0.2 and 3.2±0.3 respectively). In conclusion, students at UPM present an acceptable degree of motivation and satisfaction with regard to the studies and services that offer their respective Schools. Both characteristics receive the same value both for men and for women and so much for students who arrange work and studies as for those who devote themselves only to studying. In a significant way, students who are more engaged and are in-class attendants present the major degree of satisfaction.Overall, there is a great lack of information regarding the Bologna Process. In fact to the majority, they would like to know more on what it is, what it means and what changes will involve its implementation.

Stability analysis of dam failures. Application to aurul tailing pond in Baia Mare (Romania)

La gestión de estériles de una explotación minera es un punto clave en el desarrollo económico de una actividad extractiva, y en especial, del entorno natural y social en el que se emplaza dicho proyecto. La minería de metales preciosos lleva asociada la construcción de balsas de residuos muy peligrosos, fruto de su proceso extractivo, como por ejemplo la cianuración en el caso del oro. Para un correcto funcionamiento de dichos emplazamientos es necesario escoger correctamente el método constructivo a partir de estudios de reconocimiento previos, como estudios de estabilidad geotécnica, contexto geológico de la zona, sismicidad, hidrología, etc. Así mismo, han de llevarse a cabo unas exhaustivas medidas de control y vigilancia para asegurar las condiciones de seguridad exigidas. La ruptura de la balsa de decantación de Aurul S.A. en Baia Mare (Rumania) el 30 de Enero del año 2000 ha sido escogido como caso de estudio de estabilidad de diques. ABSTRACT Tailing's management of a mining exploitation is a key point in the economical development of the extractive activity and, especially, of the natural and social environment of the site. Precious metals mining has high hazardous embankment construction associated, product of its extractive process, i.e. gold cyanidation. A correct operation of those sites makes necessary to choose a suitable construction method, based on previous studies as geotechnical stability studies, geological context of the area, seismicity, hydrology, etc. At the same time, exhaustive control and monitoring must be carried out in order to assure the required safety conditions. Aurul's decantation pond failure in Baia Mare (Romania), on 30th January 2000, has been chosen as a stability analysis case-study.

Management of irrigation frequency and nitrogen fertilization to mitigate GHG and NO emissions from drip-fertigated crops

Drip irrigation combined with split application of fertilizer nitrogen (N) dissolved in the irrigation water (i.e. drip fertigation) is commonly considered best management practice for water and nutrient efficiency. As a consequence, its use is becoming widespread. Some of the main factors (water-filled pore space, NH4+ and NO3−) regulating the emissions of greenhouse gases (i.e. N2O, CO2 and CH4) and NO from agroecosystems can easily be manipulated by drip fertigation without yield penalties. In this study, we tested management options to reduce these emissions in a field experiment with a melon (Cucumis melo L.) crop. Treatments included drip irrigation frequency (weekly/daily) and type of N fertilizer (urea/calcium nitrate) applied by fertigation. Crop yield, environmental parameters, soil mineral N concentrations and fluxes of N2O, NO, CH4 and CO2 were measured during 85 days. Fertigation with urea instead of calcium nitrate increased N2O and NO emissions by a factor of 2.4 and 2.9, respectively (P < 0.005). Daily irrigation reduced NO emissions by 42% (P < 0.005) but increased CO2 emissions by 21% (P < 0.05) compared with weekly irrigation. We found no relation between irrigation frequency and N2O emissions. Based on yield-scaled Global Warming Potential as well as NO cumulative emissions, we conclude that weekly fertigation with a NO3−-based fertilizer is the best option to combine agronomic productivity with environmental sustainability. Our study shows that adequate management of drip fertigation, while contributing to the attainment of water and food security, may provide an opportunity for climate change mitigation.

Mining mobile apps reviews to support release planning

The mobile apps market is a tremendous success, with millions of apps downloaded and used every day by users spread all around the world. For apps’ developers, having their apps published on one of the major app stores (e.g. Google Play market) is just the beginning of the apps lifecycle. Indeed, in order to successfully compete with the other apps in the market, an app has to be updated frequently by adding new attractive features and by fixing existing bugs. Clearly, any developer interested in increasing the success of her app should try to implement features desired by the app’s users and to fix bugs affecting the user experience of many of them. A precious source of information to decide how to collect users’ opinions and wishes is represented by the reviews left by users on the store from which they downloaded the app. However, to exploit such information the app’s developer should manually read each user review and verify if it contains useful information (e.g. suggestions for new features). This is something not doable if the app receives hundreds of reviews per day, as happens for the very popular apps on the market. In this work, our aim is to provide support to mobile apps developers by proposing a novel approach exploiting data mining, natural language processing, machine learning, and clustering techniques in order to classify the user reviews on the basis of the information they contain (e.g. useless, suggestion for new features, bugs reporting). Such an approach has been empirically evaluated and made available in a web-­‐based tool publicly available to all apps’ developers. The achieved results showed that the developed tool: (i) is able to correctly categorise user reviews on the basis of their content (e.g. isolating those reporting bugs) with 78% of accuracy, (ii) produces clusters of reviews (e.g. groups together reviews indicating exactly the same bug to be fixed) that are meaningful from a developer’s point-­‐of-­‐view, and (iii) is considered useful by a software company working in the mobile apps’ development market.

Life cycle assessment of concentrated solar power (CSP) and the influence of hybridising with natural gas

Purpose Concentrating Solar Power (CSP) plants based on parabolic troughs utilize auxiliary fuels (usually natural gas) to facilitate start-up operations, avoid freezing of HTF and increase power output. This practice has a significant effect on the environmental performance of the technology. The aim of this paper is to quantify the sustainability of CSP and to analyse how this is affected by hybridisation with different natural gas (NG) inputs. Methods A complete Life Cycle (LC) inventory was gathered for a commercial wet-cooled 50 MWe CSP plant based on parabolic troughs. A sensitivity analysis was conducted to evaluate the environmental performance of the plant operating with different NG inputs (between 0 and 35% of gross electricity generation). ReCiPe Europe (H) was used as LCA methodology. CML 2 baseline 2000 World and ReCiPe Europe E were used for comparative purposes. Cumulative Energy Demands (CED) and Energy Payback Times (EPT) were also determined for each scenario. Results and discussion Operation of CSP using solar energy only produced the following environmental profile: climate change 26.6 kg CO2 eq/KWh, human toxicity 13.1 kg 1,4-DB eq/KWh, marine ecotoxicity 276 g 1,4-DB eq/KWh, natural land transformation 0.005 m2/KWh, eutrophication 10.1 g P eq/KWh, acidification 166 g SO2 eq/KWh. Most of these impacts are associated with extraction of raw materials and manufacturing of plant components. The utilization NG transformed the environmental profile of the technology, placing increasing weight on impacts related to its operation and maintenance. Significantly higher impacts were observed on categories like climate change (311 kg CO2 eq/MWh when using 35 % NG), natural land transformation, terrestrial acidification and fossil depletion. Despite its fossil nature, the use of NG had a beneficial effect on other impact categories (human and marine toxicity, freshwater eutrophication and natural land transformation) due to the higher electricity output achieved. The overall environmental performance of CSP significantly deteriorated with the use of NG (single score 3.52 pt in solar only operation compared to 36.1 pt when using 35 % NG). Other sustainability parameters like EPT and CED also increased substantially as a result of higher NG inputs. Quasilinear second-degree polynomial relationships were calculated between various environmental performance parameters and NG contributions. Conclusions Energy input from auxiliary NG determines the environmental profile of the CSP plant. Aggregated analysis shows a deleterious effect on the overall environmental performance of the technology as a result of NG utilization. This is due primarily to higher impacts on environmental categories like climate change, natural land transformation, fossil fuel depletion and terrestrial acidification. NG may be used in a more sustainable and cost-effective manner in combined cycle power plants, which achieve higher energy conversion efficiencies.

Extracción de conocimientos de la diabetes tipo 1 utilizando la metodología de "Data Mining"

La diabetes mellitus es un trastorno en la metabolización de los carbohidratos, caracterizado por la nula o insuficiente segregación de insulina (hormona producida por el páncreas), como resultado del mal funcionamiento de la parte endocrina del páncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos químicos más pequeños mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polipéptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentración de glucosa en sangre, ya que las células no pueden metabolizarla; consumo de ácidos grasos mediante el hígado, liberando cuerpos cetónicos para aportar la energía a las células. Esta situación expone al enfermo crónico, a una concentración de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo múltiples problemas médicos: oftalmológicos, renales, cardiovasculares, cerebrovasculares, neurológicos… La diabetes representa un gran problema de salud pública y es la enfermedad más común en los países desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparición de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentación clínica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las células beta del páncreas (productoras de insulina) resultando necesaria la administración de insulina exógena. Dicho esto, el paciente con diabetes tipo 1 deberá seguir un tratamiento con insulina administrada por la vía subcutánea, adaptado a sus necesidades metabólicas y a sus hábitos de vida. Para abordar esta situación de regulación del control metabólico del enfermo, mediante una terapia de insulina, no serviremos del proyecto “Páncreas Endocrino Artificial” (PEA), el cual consta de una bomba de infusión de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisión, eficacia y seguridad en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. El PEA se instala mediante vía subcutánea, por lo que, el retardo introducido por la acción de la insulina, el retardo de la medida de glucosa, así como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducción de variables de entrada. De este modo lo que conseguimos, es una predicción de los estados futuros en los que se puede encontrar la glucosa del paciente, sirviéndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando parámetros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se están empleando actualmente en el PEA no están funcionando correctamente por la cantidad de información y variables que debe de manejar. Data Mining, también referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracción no trivial de información implícita, previamente desconocida y potencialmente útil. Todo ello, sirviéndonos las siguientes fases del proceso de extracción del conocimiento: selección de datos, pre-procesado, transformación, minería de datos, interpretación de los resultados, evaluación y obtención del conocimiento. Con todo este proceso buscamos generar un único modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con cálculos en tiempo real, a través de unos parámetros introducidos. Este trabajo busca extraer la información contenida en una base de datos de pacientes diabéticos tipo 1 obtenidos a partir de la experimentación clínica. Para ello emplearemos técnicas de Data Mining. Para la consecución del objetivo implícito a este proyecto hemos procedido a implementar una interfaz gráfica que nos guía a través del proceso del KDD (con información gráfica y estadística) de cada punto del proceso. En lo que respecta a la parte de la minería de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a través de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el código. Mediante estos dispositivos y lo expuesto en el proyecto se podrían implementar o incluso crear nuevas aplicaciones novedosas y muy útiles para este campo. Como conclusión del proyecto, y tras un exhaustivo análisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological … The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the “Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin–glucose model for each patient.

Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining

La gran cantidad de datos que se registran diariamente en los sistemas de base de datos de las organizaciones ha generado la necesidad de analizarla. Sin embargo, se enfrentan a la complejidad de procesar enormes volúmenes de datos a través de métodos tradicionales de análisis. Además, dentro de un contexto globalizado y competitivo las organizaciones se mantienen en la búsqueda constante de mejorar sus procesos, para lo cual requieren herramientas que les permitan tomar mejores decisiones. Esto implica estar mejor informado y conocer su historia digital para describir sus procesos y poder anticipar (predecir) eventos no previstos. Estos nuevos requerimientos de análisis de datos ha motivado el desarrollo creciente de proyectos de minería de datos. El proceso de minería de datos busca obtener desde un conjunto masivo de datos, modelos que permitan describir los datos o predecir nuevas instancias en el conjunto. Implica etapas de: preparación de los datos, procesamiento parcial o totalmente automatizado para identificar modelos en los datos, para luego obtener como salida patrones, relaciones o reglas. Esta salida debe significar un nuevo conocimiento para la organización, útil y comprensible para los usuarios finales, y que pueda ser integrado a los procesos para apoyar la toma de decisiones. Sin embargo, la mayor dificultad es justamente lograr que el analista de datos, que interviene en todo este proceso, pueda identificar modelos lo cual es una tarea compleja y muchas veces requiere de la experiencia, no sólo del analista de datos, sino que también del experto en el dominio del problema. Una forma de apoyar el análisis de datos, modelos y patrones es a través de su representación visual, utilizando las capacidades de percepción visual del ser humano, la cual puede detectar patrones con mayor facilidad. Bajo este enfoque, la visualización ha sido utilizada en minería datos, mayormente en el análisis descriptivo de los datos (entrada) y en la presentación de los patrones (salida), dejando limitado este paradigma para el análisis de modelos. El presente documento describe el desarrollo de la Tesis Doctoral denominada “Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining���. Esta investigación busca aportar con un enfoque de visualización para apoyar la comprensión de modelos minería de datos, para esto propone la metáfora de modelos visualmente aumentados. ABSTRACT The large amount of data to be recorded daily in the systems database of organizations has generated the need to analyze it. However, faced with the complexity of processing huge volumes of data over traditional methods of analysis. Moreover, in a globalized and competitive environment organizations are kept constantly looking to improve their processes, which require tools that allow them to make better decisions. This involves being bettered informed and knows your digital story to describe its processes and to anticipate (predict) unanticipated events. These new requirements of data analysis, has led to the increasing development of data-mining projects. The data-mining process seeks to obtain from a massive data set, models to describe the data or predict new instances in the set. It involves steps of data preparation, partially or fully automated processing to identify patterns in the data, and then get output patterns, relationships or rules. This output must mean new knowledge for the organization, useful and understandable for end users, and can be integrated into the process to support decision-making. However, the biggest challenge is just getting the data analyst involved in this process, which can identify models is complex and often requires experience not only of the data analyst, but also the expert in the problem domain. One way to support the analysis of the data, models and patterns, is through its visual representation, i.e., using the capabilities of human visual perception, which can detect patterns easily in any context. Under this approach, the visualization has been used in data mining, mostly in exploratory data analysis (input) and the presentation of the patterns (output), leaving limited this paradigm for analyzing models. This document describes the development of the doctoral thesis entitled "New Visualizations Schemes to Improve Understandability of Data-Mining Models". This research aims to provide a visualization approach to support understanding of data mining models for this proposed metaphor visually enhanced models.