Chaque jour des milliers de camions transportant des centaines de milliers de tonnes de marchandises dangereuses par diverses modalités. Toutefois, le terme “dangereux” indique une adversité intrinsèque qui caractérise ces produits transportés, et qui peuvent se manifester lors d'un accident entraînant la fuite d'une substance dangereuse. Dans une telle situation, les conséquences peuvent nuire à l'environnement et létal pour l'humain.L'importance des marchandises dangereuses revient aux bénéfices économiques considérables générés. En fait, on ne peut nier la contribution du transport des produits dérivés de combustibles fossiles, ce qui représente plus de 60% des marchandises dangereuses transportées en Europe. Eni, la société italienne leader de pétrochimie, gère chaque jour une flotte d'environ 1.500 camions, qui effectuent de nombreuses expéditions. Pourtant la distribution de produits pétroliers est une activité à grande risques, et tout accident lors du transport peut entraîner de graves conséquences.Consciente des enjeux, la division Eni R&M - Logistique Secondaire, historiquement actif au siège de Gênes, collabore depuis 2002 avec le DIBRIS à l'Université de Gênes, et le CRC à Mines ParisTech, dans le but d'étudier les améliorations possibles en matière de sûreté dans le transport de marchandises dangereuses. Au fil des ans, cette collaboration a permis le développement d'un système d'information et décisionnel. Le composant principal de ce système est une plate-forme de surveillance de la flotte Eni appelé TIP (Transport Integrated Platform), pour livrer les produits vers les points de distributions. Ces véhicules sont équipés d'un dispositif capable de transmettre des flux de données en temps réel en utilisant un modem GPRS. Les données transmises peuvent être de nature différente et contenir des informations sur l'état du véhicule, le produit et les événements détectés durant l'expédition. Ces données sont destinées à être reçues par des serveurs centralisés puis traitées et stockées, afin de soutenir diverses applications du TIP.Dans ce contexte, les études menées tout au long de la thèse sont dirigés vers le développement d'une proposition visant à réduire davantage les risques liés au transport de marchandises dangereuses. En d'autres termes, un modèle basé sur le compromis entre les facteurs économiques et sûretés pour le choix de l'itinéraire. L'objectif est motivé par la nécessité de soutenir les règlements et les normes de sécurité existantes, car ils ne garantissent pas totalement contre les accidents entrainant des marchandises dangereuses.L'objectif est effectué en prenant en compte le système existant comme base pour l'élaboration d'un système de transport intelligent (STI) regroupant plusieurs plates-formes logicielles. Ces plates-formes doivent permettre aux planificateurs et aux décideurs de suivre en temps réel leur flotte, à évaluer les risques et tous les itinéraires possibles, de simuler et de créer différents scénarios, et d'aider à trouver des solutions à des problèmes particuliers.Tout au long de cette thèse, je souligne la motivation pour ce travail de recherche, les problématiques, et les défis de transport de marchandises dangereuses. Je présente le TIP comme le noyau de l'architecture proposée du STI. Pour les besoins de la simulation, les véhicules virtuels sont injectés dans le système. La gestion de la collecte des données a été l'objet d'une amélioration technique pour plus de fiabilité, d'efficacité et d'évolutivité dans le cadre de la surveillance en temps réel. Enfin, je présente une explication systématique de la méthode d'optimisation des itinéraires considérant les critères économiques et de risques. Le risque est évalué en fonction de divers facteurs notamment la fréquence d'accidents entrainant des marchandises dangereuses, et ses conséquences. La quantification de l'incertitude dans l'évaluation des risques est modélisée en utilisant la théorie des ensembles flous. / Everyday thousands of trucks transporting hundreds of thousands of tons of dangerous goods by various modalities and both within and across nations. However, the term “dangerous” indicates an intrinsic adversity that characterize these products, which can manifest in an accident leading to release of a hazardous substance (e.g. radioactive, flammable, explosive etc.). In this situation, the consequences can be lethal to human beings, other living organisms and damage the environment and public/private properties.The importance of dangerous goods boils down to the significant economic benefits that generates. In fact, one cannot deny the contribution of the transport of all fossil fuel derived product, which represents more than 60% of dangerous goods transported in Europe. Eni, the Italian leading petrochemical company, every day operates a fleet of about 1,500 trucks, which performs numerous trips from loading terminals to filling stations. Distribution of petroleum products is a risky activity, and an accident during the transportation may lead to serious consequences.Aware of what is at stake, the division Eni R&M - Logistics Secondary, historically active in Genoa headquarters, is collaborating since 2002 with the DIBRIS department at University of Genoa, and the CRC at Mines ParisTech, with the purpose of studying possible improvements regarding safety in transport of dangerous goods, particularly petroleum products. Over years, this collaboration has led to the development of different technologies and mainly to an information and decision support system. The major component of this system is a platform for monitoring Eni fleet, at the national level, to deliver the products to the distribution points, called the Transport Integrated Platform (TIP). These vehicles are equipped with a device capable of transmitting data stream in real-time using a GPRS modem. The data transmitted can be of different nature and contain information about the state of the vehicle and occurred events during the trip. These data are intended to be received by centralized servers then get processed and stored, in order to support various applications within the TIP.With this in mind, the studies undertaken throughout the thesis are directed towards the development of a proposal to further minimize the risk related to the transportation of dangerous goods. In other words, a trade-off based model for route selection taking into consideration economic and safety factors. The objective is prompted by the need to support existent regulations and safety standards, which does not assure a full warranty against accidents involving dangerous goods.The goal is carried out by considering the existent system as basis for developing an Intelligent Transportation System (ITS) aggregating multiple software platforms. These platforms should allow planners and decision makers to monitor in real-time their fleet, to assess risk and evaluate all possible routes, to simulate and create different scenarios, and to assist at finding solutions to particular problems.Throughout this dissertation, I highlight the motivation for such research work, the related problem statements, and the challenges in dangerous goods transport. I introduce the TIP as the core for the proposed ITS architecture. For simulation purposes, virtual vehicles are injected into the system. The management of the data collection was the subject of technical improvement for more reliability, efficiency and scalability in real-time monitoring of dangerous goods shipment. Finally, I present a systematic explanation of the methodology for route optimization considering both economic and risk criteria. The risk is assessed based on various factors mainly the frequency of accident leading to hazardous substance release and its consequences. Uncertainty quantification in risk assessment is modelled using fuzzy sets theory.

The thesis aims to examine the impact of climate change on line rating and to investigate the possibility of a potential increase of capacity of an overhead line. The line rating of an overhead line determines how much current can be transmitted in the line. The weather parameters which affect the line rating are ambient temperature, solar radiation, wind speed, and wind direction. If the line rating is adapted to weather conditions, it is important to be able to predict how the weather will change in the future. Therefore, the impact of climate change on weather parameters is investigated. The ambient temperature and solar radiation are expected to change between different scenarios. However, it is unclear how wind speed and wind direction will be affected. Climate scenarios are designed that take these findings into account. The results show that wind speed has, by a large margin from other weather parameters, the largest impact on the dynamic line rating. This is followed by the wind's angle of attack to the conductor, ambient temperature, and finally solar radiation. For the designed climate scenarios, the dynamic line rating is almost the same in each case, which means that the calculated change in ambient temperature and solar radiation has no significant effect on the line rating. To further increase the capacity of the overhead line, the line could be upgraded with a conductor with a larger cross-sectional area.

Sweden's maternal and childcare services have received recognition for maintaining high standards and employing effective risk-reduction strategies, leading to improved childbirth outcomes. However, these services face contemporary challenges exacerbated by evolving public health dynamics, including the increasing prevalence of obesity among Swedish adults, which mirrors trends in maternal healthcare and poses significant risks during pregnancy. Two significant risks during pregnancy are gestational diabetes mellitus (GDM) and preeclampsia. The rise in these pathophysiological conditions emphasizes the necessity for specialized maternal care approaches. In response, there is growing interest in utilizing technological innovations like glucose and blood pressure sensors for enhanced monitoring and proactive healthcare solutions during pregnancy.  This thesis investigates the intersection of maternal healthcare, technological advancements, and public health challenges in Sweden, aiming to identify innovative approaches to maternal care and explore the potential of glucose and blood pressure sensors in improving outcomes. To achieve it, an extensive literature review established a robust foundation, focusing on women's and maternity care trends and the impact of BMI on pregnancy outcomes. Interviews with healthcare professionals from diverse fields provided further insights, leading to an in-depth cause analysis and technology assessment. Subsequently, a mockup was developed based on the gathered information, utilizing JavaScript for program development and incorporating user feedback. Real-world data acquisition was explored using APIs, particularly Dexcom, for glucose monitoring. The design process employed Figma, ensuring visual appeal and functional integration, followed by comprehensive evaluation involving functional, usability, and compatibility testing to refine the mockup interface in alignment with stakeholder expectations.  The results of this study reveal two primary insights into maternal healthcare enhancement: Firstly, through a comprehensive literature review and stakeholder interviews, key challenges and potential solutions were identified, emphasizing the importance of interdisciplinary collaboration. Secondly, developing and evaluating a mockup integrating sensor technology demonstrated promising prospects for real-time monitoring and management, highlighting the potential for improving maternal health outcomes. These findings underscore the importance of integrating self-monitoring technology into maternal healthcare to enhance monitoring capabilities and address contemporary challenges in pregnancy care. / Sveriges mödra- och barnavårdstjänster har fått erkännande för att upprätthålla höga standarder och använda effektiva riskminskningsstrategier, vilket har lett till förbättrade förlossningsresultat. Emellertid möter dessa tjänster samtida utmaningar förvärrade av utvecklande folkhälsodynamik, inklusive den ökande förekomsten av fetma bland svenska vuxna, vilket speglar trender inom mödrahälsovård och innebär betydande risker under graviditeten. Två av dessa risker är graviditetsdiabetes mellitus och preeklampsi. Ökningen av dessa patofysiologiska tillstånd understryker behovet av nyanserade mödravårdsmetoder. Som svar finns det ett växande intresse för att använda teknologiska innovationer som glukos- och blodtryckssensorer för förbättrad övervakning och proaktiva hälsolösningar under graviditeten. Denna avhandling undersöker korsningen mellan mödrahälsovård, tekniska framsteg och folkhälsoutmaningar i Sverige, med målet att identifiera innovativa angreppssätt för mödraomsorg och utforska potentialen hos glukos- och blodtryckssensorer för att förbättra resultat.  För att uppnå detta etablerades en gedigen litteraturgenomgång som fokuserade på trender inom kvinnors och mödravård samt BMI:s påverkan på graviditetsresultat. Intervjuer med vårdpersonal från olika områden gav ytterligare insikter, vilket ledde till en djupgående orsaksanalys och teknisk bedömning. Därefter utvecklades en prototyp baserad på den insamlade informationen, med användning av JavaScript för programutveckling och inkorporering av användarfeedback. Verklig datainsamling utforskades med hjälp av API:er, särskilt Dexcom, för glukosövervakning. Designprocessen använde Figma för visuellt tilltalande och funktionell integration, följt av omfattande utvärderingar som involverade funktions-, användbarhets- och kompatibilitetstester för att förbättra prototypens gränssnitt i linje med intressenternas förväntningar.  Resultaten av denna studie belyser två primära insikter om förbättringar inom mödrahälsovård: För det första identifierades nyckelutmaningar och potentiella lösningar genom en omfattande litteraturgenomgång och intervjuer med intressenter, vilket betonar vikten av tvärvetenskapligt samarbete. För det andra visade utveckling och utvärdering av en prototyp som integrerar sensorteknik lovande framtidsutsikter för realtidsövervakning och hantering, vilket belyser potentialen för att förbättra mödrahälso-resultat. Dessa resultat understryker vikten av att integrera självövervakningsteknik i mödrahälsovård för att förbättra övervakningsmöjligheter och adressera samtida utmaningar inom graviditetsvård.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.