Incidents occuring during anesthesia for total hip arthroplasty: a comparison of general versus regional anaesthesia

Background: The type of anesthesia to be used for total hip arthroplasty (THA) is still a matter of debate. We compared the occurrence of per- and post-anesthesia incidents in patients receiving either general (GA) or regional anesthesia (RA). Methods: We used data from 29 hospitals, routinely collected in the Anaesthesia Databank Switzerland register between January 2001 and December 2003. We used multi-level logistic regression models. Results: There were more per- and post-anesthesia incidents under GA compared to RA (35.1% vs 32.7 %, n = 3191, and 23.1% vs 19.4%, n = 3258, respectively). In multi-level logistic regression analysis, RA was significantly associated with a lower incidence of per-anesthetic problems, especially hypertension, compared with GA. During the post-anesthetic period, RA was also less associated with pain. Conversely, RA was more associated with post-anesthetic hypotension, especially for epidural technique. In addition, age and ASA were more associated with incidents under GA compared to RA. Men were more associated with per-anesthetic problems under RA compared to GA. Whereas increased age (>67), gender (male), and ASA were linked with the choice of RA, we noticed that this choice depended also on hospital practices after we adjusted for the other variables. Conclusions: Compared to RA, GA was associated with an increased proportion of per- and post-anesthesia incidents. Although this study is only observational, it is rooted in daily practice. Whereas RA might be routinely proposed, GA might be indicated because of contraindications to RA, patients' preferences or other surgical or anaesthesiology related reasons. Finally, the choice of a type of anesthesia seems to depend on local practices that may differ between hospitals.

Incidents occurring during anesthesia for total hip arthroplasty: a comparison of general versus regional anaesthesia

Background: The type of anesthesia to be used for total hip arthroplasty (THA) is still a matter of debate. We compared the occurrence of per- and post-anesthesia incidents in patients receiving either general (GA) or regional anesthesia (RA). Methods: We used data from 29 hospitals, routinely collected in the Anaesthesia Databank Switzerland register between January 2001 and December 2003. We used multi-level logistic regression models. Results: There were more per- and post-anesthesia incidents under GA compared to RA (35.1% vs 32.7 %, n = 3191, and 23.1% vs 19.4%, n = 3258, respectively). In multi-level logistic regression analysis, RA was significantly associated with a lower incidence of per-anesthetic problems, especially hypertension, compared with GA. During the post-anesthetic period, RA was also less associated with pain. Conversely, RA was more associated with post-anesthetic hypotension, especially for epidural technique. In addition, age and ASA were more associated with incidents under GA compared to RA. Men were more associated with per-anesthetic problems under RA compared to GA. Whereas increased age (>67), gender (male), and ASA were linked with the choice of RA, we noticed that this choice depended also on hospital practices after we adjusted for the other variables. Conclusions: Compared to RA, GA was associated with an increased proportion of per- and post-anesthesia incidents. Although this study is only observational, it is rooted in daily practice. Whereas RA might be routinely proposed, GA might be indicated because of contraindications to RA, patients' preferences or other surgical or anaesthesiology related reasons. Finally, the choice of a type of anesthesia seems to depend on local practices that may differ between hospitals.

From Empirical Bayes to Full Bayes: Methods for Analysing Traffice Safety Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this slight, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

From Empirical Bayes to Full Bayes: Methods for Analyzubg Traffic Safey Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this light, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

Outcome evaluation of ankle osteoarthritis treatments using spatio-temporal gait parameters and plantar pressure during unconstrained long distance walking : P25

Introduction: Ankle arthrodesis (AD) and total ankle replacement (TAR) are typical treatments for ankle osteoarthritis (AO). Despite clinical interest, there is a lack of their outcome evaluation using objective criteria. Gait analysis and plantar pressure assessment are appropriate to detect pathologies in orthopaedics but they are mostly used in lab with few gait cycles. In this study, we propose an ambulatory device based on inertial and plantar pressure sensors to compare the gait during long-distance trials between healthy subjects (H) and patients with AO or treated by AD and TAR. Methods: Our study included four groups: 11 patients with AO, 9 treated by TAR, 7 treated by AD and 6 control subjects. An ambulatory system (Physilog®, CH) was used for gait analysis; plantar pressure measurements were done using a portable insole (Pedar®-X, DE). The subjects were asked to walk 50 meters in two trials. Mean value and coefficient of variation of spatio-temporal gait parameters were calculated for each trial. Pressure distribution was analyzed in ten subregions of foot. All parameters were compared among the four groups using multi-level model-based statistical analysis. Results: Significant difference (p <0.05) with control was noticed for AO patients in maximum force in medial hindfoot and forefoot and in central forefoot. These differences were no longer significant in TAR and AD groups. Cadence and speed of all pathologic groups showed significant difference with control. Both treatments showed a significant improvement in double support and stance. TAR decreased variability in speed, stride length and knee ROM. Conclusions: In spite of a small sample size, this study showed that ankle function after AO treatments can be evaluated objectively based on plantar pressure and spatio-temporal gait parameters measured during unconstrained walking outside the lab. The combination of these two ambulatory techniques provides a promising way to evaluate foot function in clinics.

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.

Systems medicine and integrated care to combat chronic noncommunicable diseases

We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.

Exploring the strategic impact of technological change - Studies on the role of Internet in magazine publishing

Over the past few decades, turbulent change has characterized the situation in the media industry. It has been noted that digitalization and new media are strongly influencing the industry: it is changing the existing market dynamics and requires new strategies. Prior research on the impact of digitalization and the Internet has emphasized news-focused media such as newspaper publishing and broadcasting, yet magazine publishing is very seldom the focus of the research. This study examines how the Internetimpacts magazine publishing. The work presents a multi-level analysis on the role and impact of the Internet on magazine products, companies and industry. The study is founded on strategic management, technology management and media economics literature. This study consists of two parts. The first part introduces the research topic and discusses the overall results of the study. The second part comprises five research publications. Qualitative research methods are used throughout. The results of the study indicate that the Internet has not had a disruptive effect on magazine publishing, and that its strategic implications could rather be considered complementary to the print magazine and the business as a whole. It seems that the co-specialized assets, together with market-related competencies and unchanged core competence have protected established firms from the disruptive effect of the new technology in magazine publishing. In addition, it seems that the Internet offers a valuable possibility to build and nourish customer relationships. The study contributes tomedia management and economics research by moving from product- or industry-level investigations towards a strategic-management perspective.

Tuning the design procedures for laser processed microwave mechanics

This research has been focused at the development of a tuned systematic design methodology, which gives the best performance in a computer aided environment and utilises a cross-technological approach, specially tested with and for laser processed microwave mechanics. A tuned design process scheme is also presented. Because of the currently large production volumes of microwave and radio frequency mechanics even slight improvements of design methodologies or manufacturing technologies would give reasonable possibilities for cost reduction. The typical number of required iteration cycles could be reduced to one fifth of normal. The research area dealing with the methodologies is divided firstly into a function-oriented, a performance-oriented or a manufacturability-oriented product design. Alternatively various approaches can be developed for a customer-oriented, a quality-oriented, a cost-oriented or an organisation-oriented design. However, the real need for improvements is between these two extremes. This means that the effective methodology for the designers should not be too limited (like in the performance-oriented design) or too general (like in the organisation-oriented design), but it should, include the context of the design environment. This is the area where the current research is focused. To test the developed tuned design methodology for laser processing (TDMLP) and the tuned optimising algorithm for laser processing (TOLP), seven different industrial product applications for microwave mechanics have been designed, CAD-modelled and manufactured by using laser in small production series. To verify that the performance of these products meets the required level and to ensure the objectiveness ofthe results extensive laboratory tests were used for all designed prototypes. As an example a Ku-band horn antenna can be laser processed from steel in 2 minutes at the same time obtaining a comparable electrical performance of classical aluminium units or the residual resistance of a laser joint in steel could be limited to 72 milliohmia.

Moottoripolttoaineiden ominaisuuksien mallintaminen

Työn tavoitteena on selvittää voidaanko neuroverkkoa käyttää mallintamaan ja ennustamaan polttoaineen vaikutusta nykyaikaisen auton päästöihin. Näin pystyttäisiin vähentämään aikaa vievien ja kalliiden koeajojen tarvetta. Työ tehtiin Lappeenrannan teknillisen yliopiston ja Fortum Oy:n yhteistyöprojektissa. Työssä tehtiin kolme erilaista mallia. Ensimmäisenä tehtiin autokohtainen malli, jolla pyrittiin ennustamaan autokohtaista käyttäytymistä. Toiseksi kokeiltiin mallia, jossa automalli oli yhtenä syötteenä. Kolmantena yritettiin kiertää eräitä aineiston ongelmia käyttämällä "sumeutettuja" polttoaineiden koostumuksia. Työssä käytettiin MLP-neuroverkkoa, joka opetettiin backpropagation algoritmilla. Työssä havaittiin ettei käytettävissä olleella aineistolla ja käytetyillä malleilla pystytä riittävällä tarkkuudella mallintamaan polttoaineen vaikutusta päästöihin. Aineiston ongelmia olivat mm. suuret mittausvarianssit, aineiston pieni määrä sekä aineiston soveltumattomuus neuroverkolla mallintamiseen.

Systems Medicine and Integrated Care to Combat Chronic Noncommunicable Diseases.

We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.

Trasporti e sviluppo territoriale negli spazi intermedi : l'esempio della regione transfrontaliera Insubrica

Avec cette thèse de doctorat nous proposons une réflexion transversale concernant les relations entre infrastructures de transport et développement territorial dans des espaces dits « intermédiaires ». Le concept d'espace intermédiaire, relativement nouveau en géographie, est conçu en fonction d'une double approche : celle des infrastructures où les espaces intermédiaires constituent des zones de transit obligées entre des pôles urbains hiérarchiquement supérieurs (par rapport une échelle horizontale) et celle des frontières où les espaces intermédiaires constituent des territoires de coopération entre différents niveaux politico-institutionnels (par rapport à une échelle verticale). Cette problématique de recherche est traitée aussi bien du point de vue théorique qu'avec des études de cas portant sur les effets des nouvelles infrastructures de transports dans la région transfrontalière insubrique (entre le Canton du Tessin et la Lombardie). L'objectif visé est de défendre un scénario d'organisation spatiale polycentrique à plusieurs niveaux comme solution pour le développement durable et cohérent de ces espaces intermédiaires. Ainsi, pour le « niveau macro », nous proposons une analyse des changements d'accessibilité spatiale et des potentiels de développement territorial pour les agglomérations concernées par la mise en service du nouveau tunnel ferroviaire de base du Monte Ceneri (TBC) et de la nouvelle ligne Lugano/Como-Mendrisio-Varese-Malpensa (FMV) à l'horizon 2020. Pour le « niveau meso », nous analysons les effets de la nouvelle ligne FMV en termes de potentiel de densification polycentrique autours des gares ferroviaires. Pour le « niveau micro », nous proposons une analyse sur les comportements de mobilité ainsi que des améliorations ciblées du système de transport pour la ville de Mendrisio visant à promouvoir le développement polycentrique de cette commune. De plus, un système d'analyse permettant de mettre en lien les divers facteurs explicatifs dans l'analyse des relations entre les nouvelles infrastructures de transport et les effets sur la mobilité et le développement territorial est également élaboré et testé dans notre recherche. -- With this Ph.D. thesis we investigate the relationship between transport infrastructures and territory development inside the so called "in-between spaces". The idea of "in-between space", relatively novel in geography, is the formal outcome of a double approach: the one of the infrastructures, saying that these spaces can be described as areas of constrained transit between urban centres of superior hierarchical level (on a horizontal scale), and the one of the borders, stating that in-between spaces are areas of cooperation between various political-institutional levels. The above mentioned research issues are deepened both at theoretical and empirical level, being the latter based on field studies of the cross-boundary Western-Lombard area (between the Swiss canton of Ticino and the Italian region of Lombardy). This research pursues the goal of defending the argument that a multi-level polycentric spatial scenario can be a possible solution fora sustainable development of the above described in- between areas. From a "macro" perspective, what we submit here is an analysis on the expected changes in spatial accessibility and on the potential territorial development for the built-up areas influenced by the construction of the new train tunnel of the Monte Ceneri (TBC) and of the new railway line Lugano/Como-Mendrisio-Varese-Malpensa (FMV). At a "meso" level we analyse the effects exerted by the new FMV line taking into account the potential densification of the areas surrounding the railway stations. Finally, at a "micro" level, we analyse the mobility behaviours in the town of Mendrisio and we propose some possible improvements to the local public transport system, with the scope to promote a polycentric development of this municipality. Moreover, we developed and tested an analytic system able to define the existing links between the various explaining factors characterizing the relationship between new transport infrastructures and effects on mobility. -- With this Ph.D. thesis we investigate the relationship between transport infrastructures and territory development inside the so called "in-between spaces". The idea of "in-between space", relatively novel in geography, is the formal outcome of a double approach: the one of the infrastructures, saying that these spaces can be described as areas of constrained transit between urban centres of superior hierarchical level (on a horizontal scale), and the one of the borders, stating that in-between spaces are areas of cooperation between various political-institutional levels. The above mentioned research issues are deepened both at theoretical and empirical level, being the latter based on field studies of the cross-boundary Western-Lombard area (between the Swiss canton of Ticino and the Italian region of Lombardy). This research pursues the goal of defending the argument that a multi-level polycentric spatial scenario can be a possible solution for a sustainable development of the above described in- between areas. From a "macro" perspective, what we submit here is an analysis on the expected changes in spatial accessibility and on the potential territorial development for the built-up areas influenced by the construction of the new train tunnel of the Monte Ceneri (TBC) and of the new railway line Lugano/Como-Mendrisio-Varese-Malpensa (FMV). At a "meso" level we analyse the effects exerted by the new FMV line taking into account the potential densification of the areas surrounding the railway stations. Finally, at a "micro" level, we analyse the mobility behaviours in the town of Mendrisio and we propose some possible improvements to the local public transport system, with the scope to promote a polycentric development of this municipality. Moreover, we developed and tested an analytic system able to define the existing links between the various explaining factors characterizing the relationship between new transport infrastructures and effects on mobility.

Opinion Mining

In this thesis we study the field of opinion mining by giving a comprehensive review of the available research that has been done in this topic. Also using this available knowledge we present a case study of a multilevel opinion mining system for a student organization's sales management system. We describe the field of opinion mining by discussing its historical roots, its motivations and applications as well as the different scientific approaches that have been used to solve this challenging problem of mining opinions. To deal with this huge subfield of natural language processing, we first give an abstraction of the problem of opinion mining and describe the theoretical frameworks that are available for dealing with appraisal language. Then we discuss the relation between opinion mining and computational linguistics which is a crucial pre-processing step for the accuracy of the subsequent steps of opinion mining. The second part of our thesis deals with the semantics of opinions where we describe the different ways used to collect lists of opinion words as well as the methods and techniques available for extracting knowledge from opinions present in unstructured textual data. In the part about collecting lists of opinion words we describe manual, semi manual and automatic ways to do so and give a review of the available lists that are used as gold standards in opinion mining research. For the methods and techniques of opinion mining we divide the task into three levels that are the document, sentence and feature level. The techniques that are presented in the document and sentence level are divided into supervised and unsupervised approaches that are used to determine the subjectivity and polarity of texts and sentences at these levels of analysis. At the feature level we give a description of the techniques available for finding the opinion targets, the polarity of the opinions about these opinion targets and the opinion holders. Also at the feature level we discuss the various ways to summarize and visualize the results of this level of analysis. In the third part of our thesis we present a case study of a sales management system that uses free form text and that can benefit from an opinion mining system. Using the knowledge gathered in the review of this field we provide a theoretical multi level opinion mining system (MLOM) that can perform most of the tasks needed from an opinion mining system. Based on the previous research we give some hints that many of the laborious market research tasks that are done by the sales force, which uses this sales management system, can improve their insight about their partners and by that increase the quality of their sales services and their overall results.

DAI-DEPUR: an integrated and distributed architecture for wastewater treatment plants supervision

The activated sludge process - the main biological technology usually applied towastewater treatment plants (WWTP) - directly depends on live beings (microorganisms), and therefore on unforeseen changes produced by them. It could be possible to get a good plant operation if the supervisory control system is able to react to the changes and deviations in the system and can take thenecessary actions to restore the system’s performance. These decisions are oftenbased both on physical, chemical, microbiological principles (suitable to bemodelled by conventional control algorithms) and on some knowledge (suitable to be modelled by knowledge-based systems). But one of the key problems in knowledge-based control systems design is the development of an architecture able to manage efficiently the different elements of the process (integrated architecture), to learn from previous cases (spec@c experimental knowledge) and to acquire the domain knowledge (general expert knowledge). These problems increase when the process belongs to an ill-structured domain and is composed of several complex operational units. Therefore, an integrated and distributed AIarchitecture seems to be a good choice. This paper proposes an integrated and distributed supervisory multi-level architecture for the supervision of WWTP, that overcomes some of the main troubles of classical control techniques and those of knowledge-based systems applied to real world systems