Research librarians in a virtual environment : new skills, new services

This paper highlights both the new functions taken on by UOC research librarians and the new skills that this professional profile requires, based on the experience of the UOC Virtual Library. By setting up a series of bibliometric units, the Library has been able to integrate itself into the University through bibliometric studies and other research support services. A group of research librarians provides support to researchers from the start of the research process to the assessment of their scientific output. They also provide support for the University's strategic decision-making through the analysis of bibliometric data.

Research librarians in a virtual environment : new skills, new services

This poster highlights both the new functions taken on by UOC research librarians and the new skills that this professional profile requires, based on the experience of the UOC Virtual Library. By setting up a series of bibliometric units, the Library has been able to integrate itself into the University through bibliometric studies and other research support services. A group of research librarians provides support to researchers from the start of the research process to the assessment of their scientific output. They also provide support for the University's strategic decision-making through the analysis of bibliometric data.

Strategy for optimizing DNA amplification in a peripheral blood PCR assay used for diagnosis of human brucellosis.

We studied two of the possible factors which can interfere with specific DNA amplification in a peripheral-blood PCR assay used for the diagnosis of human brucellosis. We found that high concentrations of leukocyte DNA and heme compounds inhibit PCR. These inhibitors can be efficiently suppressed by increasing the number of washings to four or five and decreasing the amount of total DNA to 2 to 4 microg, thereby avoiding false-negative results.

Application of a support system to the design of wastewater treatment plants

This paper presents a case study that explores the advantages that can be derived from the use of a design support system during the design of wastewater treatment plants (WWTP). With this objective in mind a simplified but plausible WWTP design case study has been generated with KBDS, a computer-based support system that maintains a historical record of the design process. The study shows how, by employing such a historical record, it is possible to: (1) rank different design proposals responding to a design problem; (2) study the influence of changing the weight of the arguments used in the selection of the most adequate proposal; (3) take advantage of keywords to assist the designer in the search of specific items within the historical records; (4) evaluate automatically thecompliance of alternative design proposals with respect to the design objectives; (5) verify the validity of previous decisions after the modification of the current constraints or specifications; (6) re-use the design records when upgrading an existing WWTP or when designing similar facilities; (7) generate documentation of the decision making process; and (8) associate a variety of documents as annotations to any component in the design history. The paper also shows one possible future role of design support systems as they outgrow their current reactive role as repositories of historical information and start to proactively support the generation of new knowledge during the design process

Underfunding of stroke research: a Europe-wide problem.

BACKGROUND AND PURPOSE: Previous studies in the United States and the United Kingdom have shown that stroke research is underfunded compared with coronary heart disease (CHD) and cancer research despite the high clinical and financial burden of stroke. We aimed to determine whether underfunding of stroke research is a Europe-wide problem. METHODS: Data for the financial year 2000 to 2001 were collected from 9 different European countries. Information on stroke, CHD, and cancer research funding awarded by disease-specific charities and nondisease-specific charity or government- funded organizations was obtained from annual reports, web sites, and by direct communication with organizations. RESULTS: There was marked and consistent underfunding of stroke research in all the countries studied. Stroke funding as a percentage of the total funding for stroke, CHD, and cancer was uniformly low, ranging from 2% to 11%. Funding for stroke was less than funding for cancer, usually by a factor of > or =10. In every country except Turkey, funding for stroke research was less than that for CHD. CONCLUSIONS: This study confirms that stroke research is grossly underfunded, compared with CHD and cancer, throughout Europe. Similar data have been obtained from the United States suggesting that relative underfunding of stroke research is likely to be a worldwide phenomenon.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Bridge to life: the Lifebridge B2T extracorporeal life support system in an in vitro trial.

Extracorporeal life support systems (ECLS) have become common in cardiothoracic surgery, but are still "Terra Incognita" in other medical fields due to the fact that perfusion units are normally bound to cardiothoracic centres. The Lifebridge B2T is an ECLS that is meant to be used as an easy and fast-track extracorporeal cardiac support to provide short-term perfusion for the transport of a patient to a specialized centre. With the Lifebridge B2T it is now possible to provide extracorporeal bypass for patients in hospitals without a perfusion unit. The Lifebridge B2T was tested on three calves to analyze the handling, performance and security of this system. The Lifebridge B2T safely can be used clinically and can provide full extracorporeal support for patients in cardiac or pulmonary failure. Flows up to 3.9 +/- 0.2l/min were reached, with an inflow pressure of -103 +/- 13mmHg, using a 21Fr. BioMedicus (Medtronic, Minneapolis, MN, USA) venous cannula. The "Plug and Play" philosophy, with semi-automatic priming, integrated check-list, a long battery time of over two hours and instinctively designed user interface, makes this device very interesting for units with high-risk interventions, such as catheterisation labs. If a system is necessary in an emergency unit, the Lifebridge can provide a high security level, even in centres not acquainted with cardiopulmonary bypass.

"A renewed sense of purpose": Mothers' and fathers' experience of having a child following a recent stillbirth.

Background Most research has focused on mothers¿ experiences of perinatal loss itself or on the subsequent pregnancy, whereas little attention has been paid to both parents¿ experiences of having a child following late perinatal loss and the experience of parenting this child. The current study therefore explored mothers¿ and fathers' experiences of becoming a parent to a child born after a recent stillbirth, covering the period of the second pregnancy and up to two years after the birth of the next baby.MethodIn depth interviews were conducted with 7 couples (14 participants). Couples were eligible if they previously had a stillbirth (after 24 weeks of gestation) and subsequently had another child (their first live baby) who was now under the age of 2 years. Couples who had more than one child after experiencing a stillbirth and those who were not fluent in English were excluded. Qualitative analysis of the interview data was conducted using Interpretive Phenomenological Analysis.ResultsFive superordinate themes emerged from the data: Living with uncertainty; Coping with uncertainty; Relationship with the next child; The continuing grief process; Identity as a parent. Overall, fathers' experiences were similar to those of mothers', including high levels of anxiety and guilt during the subsequent pregnancy and after the child was born. Coping strategies to address these were identified. Differences between mothers and fathers regarding the grief process during the subsequent pregnancy and after their second child was born were identified. Despite difficulties with bonding during pregnancy and at the time when the baby was born, parents' perceptions of their relationship with their subsequent child were positive.ConclusionsFindings highlight the importance of tailoring support systems not only according to mothers' but also to fathers' needs. Parents¿, and particularly fathers', reported lack of opportunities for grieving as well as the high level of anxiety of both parents about their baby's wellbeing during pregnancy and after birth implies a need for structured support. Difficulties experienced in bonding with the subsequent child during pregnancy and once the child is born need to be normalised.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Field Testing of Integral Abutments, Interim Report, HR-399, 1998

Integral abutment bridges are constructed without an expansion joint in the superstructure of the bridge; therefore, the bridge girders, deck, abutment diaphragms, and abutments are monolithically constructed. The abutment piles in an integral abutment bridge are vertically orientated, and they are embedded into the pile cap. When this type of a bridge experiences thermal expansion or contraction, horizontal displacements are induced at the top of the abutment piles. The flexibility of the abutment piles eliminates the need to provide an expansion joint at the inside face to the abutments: Integral abutment bridge construction has been used in Iowa and other states for many years. This research is evaluating the performance of integral abutment bridges by investigating thermally induced displacements, strains, and temperatures in two Iowa bridges. Each bridge has a skewed alignment, contains five prestressed concrete girders that support a 30-ft wide roadway for three spans, and involves a water crossing. The bridges will be monitored for about two years. For each bridge, an instrumentation package includes measurement devices and hardware and software support systems. The measurement devices are displacement transducers, strain gages, and thermocouples. The hardware and software systems include a data-logger; multiplexers; directline telephone service and computer terminal modem; direct-line electrical power; lap-top computer; and an assortment of computer programs for monitoring, transmitting, and management of the data. Instrumentation has been installed on a bridge located in Guthrie County, and similar instrumentation is currently being installed on a bridge located in Story County. Preliminary test results for the bridge located in Guthrie County have revealed that temperature changes of the bridge deck and girders induce both longitudinal and transverse displacements of the abutments and significant flexural strains in the abutment piles. For an average temperature range of 73° F for the superstructure concrete in the bridge located in Guthrie County, the change in the bridge length was about 1 118 in. and the maximum, strong-axis, flexural-strain range for one of the abutment piles was about 400 micro-strains, which corresponds to a stress range of about 11,600 psi.

El camino hacia la integración de sistemas de gestión de la información científica : el TCD (Trinity College Dublin) como estudio de caso. CERIF y euroCRIS

Pablo de Castro, Director de GrandIR, describió la visión que el Grupo euroCRIS tiene de la infraestructura integrada de gestión de la información científica, compuesta por un sistema CRIS institucional, un repositorio de publicaciones y un repositorio de datos y software, y presentó el modelo de infraestructura integrada del Trinity College Dublin (TCD) como estudio de caso internacional. El sistema CRIS del TCD (TCD Research Support System o RSS), desde su primera versión en 2002, está basado en el estándar CERIF, un modelo de descripción de la actividad científica que está adquiriendo una progresiva relevancia como base de los sistemas CRIS en Europa, particularmente en el Reino Unido. Se citaron en la presentación los ensayos para incorporar CERIF al modelo de datos del software ePrints de repositorios, habilitándolo así para soportar parte de las tareas de recolección de información que realiza un CRIS, y la progresiva cobertura de CERIF a ámbitos tales como la gestión de datos de investigación.

Task Support in Scenario Process: Case LUT 2016

This thesis attempts to find whether scenario planning supports the organizational strategy as a method for addressing uncertainty. The main issues are why, what and how scenario planning fits in organizational strategy and how the process could be supported to make it more effective. The study follows the constructive approach. It starts with examination of competitive advantage and the way that an organization develops strategy and how it addresses the uncertainty in its operational environment. Based on the conducted literature review, scenario methods would seem to provide versatile platform for addressing future uncertainties. The construction is formed by examining the scenario methods and presenting suitable support methods, which results in forming of the theoretical proposition for supporter scenario process. The theoretical framework is tested in laboratory conditions, and the results from the test sessions are used a basis for scenario stories. The process of forming the scenarios and the results are illustrated and presented for scrutiny

International bioenergy trade- scenario study on international biomass market in 2020

The markets of biomass for energy are developing rapidly and becoming more international. A remarkable increase in the use of biomass for energy needs parallel and positive development in several areas, and there will be plenty of challenges to overcome. The main objective of the study was to clarify the alternative future scenarios for the international biomass market until the year 2020, and based on the scenario process, to identify underlying steps needed towards the vital working and sustainable biomass market for energy purposes. Two scenario processes were conducted for this study. The first was carried out with a group of Finnish experts and thesecond involved an international group. A heuristic, semi-structured approach, including the use of preliminary questionnaires as well as manual and computerised group support systems (GSS), was applied in the scenario processes.The scenario processes reinforced the picture of the future of international biomass and bioenergy markets as a complex and multi-layer subject. The scenarios estimated that the biomass market will develop and grow rapidly as well as diversify in the future. The results of the scenario process also opened up new discussion and provided new information and collective views of experts for the purposes of policy makers. An overall view resulting from this scenario analysis are the enormous opportunities relating to the utilisation of biomass as a resource for global energy use in the coming decades. The scenario analysis shows the key issues in the field: global economic growth including the growing need for energy, environmental forces in the global evolution, possibilities of technological development to solve global problems, capabilities of the international community to find solutions for global issues and the complex interdependencies of all these driving forces. The results of the scenario processes provide a starting point for further research analysing the technological and commercial aspects related the scenarios and foreseeing the scales and directions of biomass streams.