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<p>Predictive modeling in healthcare involves the development of data-driven and computational models which can predict what will happen, be it for a single individual or for an entire system. The adoption of predictive models can guide various stakeholders’ decision-making in the healthcare sector, and consequently improve individual outcomes and the cost-effectiveness of care. With the rapid development in healthcare of big data and the Internet of Things technologies, research in healthcare decision-making has grown in both importance and complexity. One of the complexities facing those who would build predictive models is heterogeneity of patient populations, clinical practices, and intervention outcomes, as well as from diverse health systems. There are many sub-domains in healthcare for which predictive modeling is useful such as disease risk modeling, clinical intelligence, pharmacovigilance, precision medicine, hospitalization process optimization, digital health, and preventive care. In my dissertation, I focus on predictive modeling for applications that fit into three broad and important domains of healthcare, namely clinical practice, public health, and healthcare system. In this dissertation, I present three papers that present a collection of predictive modeling studies to address the challenge of modeling heterogeneity in health care. The first paper presents a decision-tree model to address clinicians’ need to decide among various liver cirrhosis diagnosis strategies. The second paper presents a micro-simulation model to assess the impact on cardiovascular disease (CVD) to help decision makers at government agencies develop cost-effective food policies to prevent cardiovascular diseases, a public-health domain application. The third paper compares a set of data-driven prediction models, the best performing of which is paired together with interpretable machine learning to facilitate the coordination of optimization for hospital-discharged patients choosing skilled nursing facilities. This collection of studies addresses important modeling challenges in specific healthcare domains, and also broadly contribute to research in medical decision-making, public health policy and healthcare systems.</p>

The use of technology in assisting educators to use student data in well-devised ways to enhance the instruction received by students is gaining headway and the support of federal dollars across the nation. Since research has not provided insight as to whether or not reading coaches are using data technology tools with teachers, this mixed methods study sought to examine what behavioral intentions reading coaches have in using data technology tools with teachers, what variables may influence their behavioral intentions, and what trends may emerge in their views about using technology data tools with teachers. A mixed methods approach was deployed via a survey embedded in an email, and data from 61 Florida reading coaches from elementary, middle, and high schools in a large urban school district were examined using an adaptation of the Technology Acceptance Model (TAM). The results showed that collectively all reading coaches have a high level of behavioral intentions towards using a data technology tool with teachers. The study also showed that elementary, middle, and high school reading coaches vary in their degree of behavioral intentions in using a data technology tool based on different variables. Trends in data showed that reading coaches think data technology tools are helpful, but that trainings are needed and that technology tools should be user-friendly. Discussion is provided regarding the implications of the study results for all stakeholders.

Low-aspect-ratio reinforced concrete (RC) shear walls, characterized by height-to-length ratios of less than two, have been widely used as a seismic force-resisting system (SFRS) in a wide array of structures, ranging from conventional buildings to critical infrastructure systems such as nuclear facilities. Despite their extensive applications, recent research has brought to light the inadequate understanding of their seismic performance, primarily attributed to the intricate nonlinear flexure-shear interaction behaviour unique to these walls. In this respect, the current research dissertation aims to bridge this knowledge gap by conducting a comprehensive evaluation to quantify the seismic performance of low-aspect-ratio RC shear walls when used in different applications. Chapter 2 focuses on low-aspect-ratio RC shear walls that are employed in residential and industrial structures. Considering their significance, the seismic response modification factors of such walls, as defined in various standards, are thoroughly examined and evaluated utilizing the FEMA P695 methodology. The analysis revealed potential deficiencies in the current code-based recommendations for response modification factors. Consequently, a novel set of response modification factors, capable of mitigating the seismic risk of collapse under the maximum considered earthquake, is proposed. Such proposed values can be integrated into the forthcoming revisions of relevant building codes and design standards. While the FEMA P695 methodology offers a comprehensive approach to assessing building seismic performance factors, its practical implementation is associated with many challenges for practicing engineers. Specifically, the methodology heavily relies on resource-intensive and time-consuming incremental dynamic analyses, making it less feasible for routine engineering practices. To enhance its practicality, a data-driven framework is developed in Chapter 3, circumventing the need for such demanding analyses. This framework provides genetic programming-based expressions capable of producing accurate predictions of the median collapse intensities—a key metric in the acceptance criteria of the FEMA P695 methodology, for different structural systems. To demonstrate its use, the developed framework is operationalized to low-aspect-ratio RC shear walls and the predictive expression is evaluated considering several statistical and structural parameters, which showed its adequacy in predicting the median collapse intensities of such walls. Furthermore, the adaptability of this framework is showcased, highlighting its applicability across various SFRSs. Chapters 4 and 5 tackle the scarcity of experimental assessments pertaining to the seismic performance of low-aspect-ratio RC walls in nuclear facilities. The seismic hybrid simulation testing technique is employed herein to merge the simplicity of numerical simulations with the efficiency of experimental tests. Hybrid simulation can overcome obstacles related to physical specimen sizes, limited actuator capacities, and space constraints in most laboratories. In these two chapters, the experimental program delves into evaluating the seismic performance of three two-storey low-aspect-ratio nuclear RC walls under different earthquake levels, including operational, design, and beyond-design-level scenarios. Diverse design configurations, including the use of increased thickness boundary elements and different materials (i.e., normal- and high-strength reinforcement), are considered in such walls to provide a comprehensive understanding of several structural parameters and economic metrics. Key structural parameters, such as the force-displacement responses, multi-storey effects, lateral and rotational stiffnesses, ductility capacities, displacement components, rebar strains, crack patterns and damage sequences, are all investigated to provide direct comparisons between the walls in terms of their seismic performances. Additionally, economic metrics, including the total rebar weights, overall construction costs and the expected seismic repair costs, are considered in order to evaluate the seismic performance of the walls considering an economic perspective. The findings of this experimental investigation are expected to inform future nuclear design standards by enhancing the resilience and safety of their structures incorporating low-aspect-ratio RC shear walls. / Thesis / Doctor of Philosophy (PhD)

Standardized, collaborative decision-making processes have already been implemented at some network-relevant airports, and these can be further enhanced through data-driven approaches (e.g., data analytics, predictions). New cost-effective implementations will also enable the appropriate integration of small and medium-sized airports into the aviation network. The required data can increasingly be gathered and processed by the airports themselves. For example, Automatic Dependent Surveillance-Broadcast (ADS-B) messages are sent by arriving and departing aircraft and enable a data-driven analysis of aircraft movements, taking into account local constraints (e.g., weather or capacity). Analytical and model-based approaches that leverage these data also offer deeper insights into the complex and interdependent airport operations. This includes systematic monitoring of relevant operational milestones as well as a corresponding predictive analysis to estimate future system states. In fact, local ADS-B receivers can be purchased, installed, and maintained at low cost, providing both very good coverage of the airport apron operations (runway, taxi system, parking positions) and communication of current airport performance to the network management. To prevent every small and medium-sized airport from having to develop its own monitoring system, we present a basic concept with our approach. We demonstrate that appropriate processing of ADS-B messages leads to improved situational awareness. Our concept is aligned with the operational milestones of Eurocontrol’s Airport Collaborative Decision Making (A-CDM) framework. Therefore, we analyze the A-CDM airport London–Gatwick Airport as it allows us to validate our concept against the data from the A-CDM implementation at a later stage. Finally, with our research, we also make a decisive contribution to the open-data and scientific community.

[pt] As novas tecnologias da informação e comunicação, associadas à perda de protagonismo das mídias de massa tradicionais, impuseram às campanhas eleitorais novas formas de estruturação, de modo a atender às demandas de uma população cada vez mais habituada a consumir informação de forma rápida e simplificada. A necessária reprogramação da comunicação política fez surgir o que se entende como campanhas orientadas por dados, caracterizadas pela utilização de grandes volumes de dados pessoais na elaboração das estratégias de propaganda eleitoral. No Brasil, a positivação do impulsionamento de conteúdo on-line, em conjunto com a prática de difusão em massa de conteúdo eleitoral por aplicativos de mensagens instantâneas – observada de forma contundente nas eleições de 2018 – indicam a consolidação das campanhas orientadas por dados no cenário político-eleitoral nacional e as recentes reformas na Lei de Eleições revelam a intenção do legislador em adequar o regramento eleitoral à nova realidade das campanhas. As alterações legislativas, no entanto, não vieram acompanhada na necessária preocupação em promover uma ampla atualização da legislação eleitoral para fazer frente aos riscos que as práticas adotadas pelas campanhas orientadas por dados podem acarretar ao processo democrático, principalmente no tocante à autonomia do eleitor e à proteção de dados pessoais, demandando maiores estudos sobre o tema. / [en] New informational and communication technologies associated with traditional mass media s faltering prominence precipitated new methods of structuring electoral campaigns to meet the demands of a population increasingly used to consuming information in a fast and simplified way. The required reprogramming of political communication gave rise to what are understood as data-driven campaigns,characterized by the use of large volumes of personal data to drive electoral propaganda strategies. In Brazil, the legalization of online content boosting, along side mass dissemination of electoral content through instant messaging applications – strikingly observed in the 2018 elections – indicate the consolidation of data-driven campaigns inthe national political-electoral environment. Further, recent reforms in the Electoral Lawreveal legislators intent to adapt electoral rules to the new reality presented by these campaigns. The changes, however, were not accompanied by a much-needed, broader investigation and update of the electoral legislation to confront the risks that these data driven campaign practices bring to the democratic process, especially with regard to voter autonomy and protection of personal data.

A power system consists of several critical components necessary for providing electricity from the producers to the consumers. Monitoring the lifetime of power system components becomes vital since they are subjected to electrical currents and high temperatures, which affect their ageing. Estimating the component's ageing rate close to the end of its lifetime is the motivation behind our project. Knowing the ageing rate and life expectancy, we can possibly better utilize and re-utilize existing power components and their parts. In return, we could achieve better material utilization, reduce costs, and improve sustainability designs, contributing to the circular industry development of power system components. Monitoring the thermal distribution and the degradation of the insulation materials informs the estimation of the components' health state. Moreover, further study of the employed paper material of their insulation system can lead to a deeper understanding of its thermal characterization and a possible consequent improvement. Our study aims to create a model that couples the physical equations that govern the deterioration of the insulation systems of power components with modern machine learning algorithms.  As the data is limited and complex in the field of components' ageing, Physics-Informed Neural Networks (PINNs) can help to overcome the problem. PINNs exploit the prior knowledge stored in partial differential equations (PDEs) or ordinary differential equations (ODEs) modelling the involved systems. This prior knowledge becomes a regularization agent, constraining the space of available solutions and consequently reducing the training data needed.  This thesis is divided into two parts: the first focuses on the insulation system of power transformers, and the second is an exploration of the paper material concentrating on cellulose nanofibrils (CNFs) classification. The first part includes modelling the thermal distribution and the degradation of the cellulose inside the power transformer. The deterioration of one of the two systems can lead to severe consequences for the other. Both abilities of PINNs to approximate the solution of the equations and to find the parameters that best describe the data are explored. The second part could be conceived as a standalone; however, it leads to a further understanding of the paper material. Several CNFs materials and concentrations are presented, and this thesis proposes a basic unsupervised learning using clustering algorithms like k-means and Gaussian Mixture Models (GMMs) for their classification. / Ett kraftsystem består av många kritiska komponenter som är nödvändiga för att leverera el från producenter till konsumenter. Att övervaka livslängden på kraftsystemets komponenter är avgörande eftersom de utsätts för elektriska strömmar och höga temperaturer som påverkar deras åldrande. Att uppskatta komponentens åldringshastighet nära slutet av dess livslängd är motivationen bakom vårt projekt. Genom att känna till åldringshastigheten och den förväntade livslängden kan vi eventuellt utnyttja och återanvända befintliga kraftkomponenter och deras delar   bättre. I gengäld kan vi uppnå bättre materialutnyttjande, minska kostnaderna och förbättra hållbarhetsdesignen vilket bidrar till den cirkulära industriutvecklingen av kraftsystemskomponenter. Övervakning av värmefördelningen och nedbrytningen av isoleringsmaterialen indikerar komponenternas hälsotillstånd. Dessutom kan ytterligare studier av pappersmaterial i kraftkomponenternas isoleringssystem leda till en djupare förståelse av dess termiska karaktärisering och en möjlig förbättring.  Vår studie syftar till att skapa en modell som kombinerar de fysiska ekvationer som styr försämringen av isoleringssystemen i kraftkomponenter med moderna algoritmer för maskininlärning. Eftersom datan är begränsad och komplex när det gäller komponenters åldrande kan  fysikinformerade neurala nätverk (PINNs) hjälpa till att lösa problemet. PINNs utnyttjar den förkunskap som finns lagrad i partiella differentialekvationer (PDE) eller ordinära differentialekvationer (ODE) för att modellera system och använder dessa ekvationer för att begränsa antalet tillgängliga lösningar och därmed minska den mängd träningsdata som behövs.  Denna avhandling är uppdelad i två delar: den första fokuserar på krafttransformatorers isoleringssystem, och den andra är en undersökning av pappersmaterialet som används med fokus på klassificering av cellulosananofibriller (CNF). Den första delen omfattar modellering av värmefördelningen och nedbrytningen av cellulosan inuti krafttransformatorn. En försämring av ett av de två systemen kan leda till allvarliga konsekvenser för det andra. Både PINNs förmåga att approximera lösningen av ekvationerna och att hitta de parametrar som bäst beskriver datan undersöks. Den andra delen skulle kunna ses som en fristående del, men den leder till en utökad förståelse av själva pappersmaterialet. Flera CNF-material och koncentrationer presenteras och denna avhandling föreslår en simpel oövervakad inlärning med klusteralgoritmer som k-means och Gaussian Mixture Models (GMMs) för deras klassificering. / <p>QC 20231010</p>

Digitalisering sker idag både i privat som offentlig sektor där datadriven beslutsfattning är en av trenderna. En teknologi som vuxit fram i samband med digitaliseringen och som hjälper verksamheter utvecklas är Self Service Business Intelligence (SSBI). Offentliga verksamheters digitala utveckling går långsammare än för privata bolag. Studien syftar till att undersöka vilka utmaningar offentliga verksamheter ställs inför i sin användning av SSBI samt att presentera hanteringsförslag på dessa utmaningar. För att besvara studiens frågeställning och uppfylla dess syfte har en kvalitativ forskningsansats använts. Semistrukturerade intervjuer har genomförts där respondenterna har varit personer som arbetar på offentliga verksamheter alternativt mot offentliga verksamheter. Studien resulterade i fyra utmaningar som är vanligt förekommande inom offentlig verksamhet och som inte lyfts i tidigare litteratur. Dessa fyra är; diversifierade verksamheter, ledningen, lagar och säkerhet samt begränsad självständighet. För varje utmaning har förslag diskuterats för hur utmaningarna effektivt kan hanteras. Studiens slutsats kan vara hjälpsam för offentliga verksamheter i deras fortsatta utveckling mot att bli datadrivna i sin beslutsfattning. Med hjälp av datadriven beslutsfattning möjliggörs för offentliga verksamheter att arbeta mer hållbart och bli mer resurseffektiva. / Digitization is today taking place in both private and public sectors, wheredata driven decision making is one of the trends. Self Service BusinessIntelligence (SSBI) is a technology that has emerged in conjunction with thedigital development and is helping businesses to develop. However, thedigital development in public organizations tends to be slower than forprivate companies. Therefore, this study aims to examine the challengesfaced by public organizations in their use of SSBI and also to presentproposals for addressing these challenges.To answer the research question and fulfill the study's purpose, a qualitativeresearch approach has been used with an abductive thinking. Semistructured interviews have been conducted with respondents who work in orwith public organizations. The study resulted in four challenges that arecommon in public organizations and that have not been addressed inprevious literature. These four challenges are diversified organizations, themanagement, laws and security, limited self-reliance. For each challenge,proposals have been discussed for how the challenges can be effectivelyaddressed. This study conclusion can be helpful for public organizations inthe continued development towards becoming data driven decision making.With the help of data driven decision making, public organizations can workmore sustainably and become more resource efficient.

This thesis aims to investigate a digital, emerging business model, which just enjoys the highest attention in many industrial sectors. The industry 4.0 changes how original equipment manufacturers (OEMs) retain their competitiveness and offer innovative solutions to their customers. Therefore, this master thesis investigates the diverse opportunities of usage-based business models – namely Pay-per-X / Equipment-as-a-Service – from the perspective of a sensor-tech start-up. The company, which can act as a technical enabler for Pay-per-X, is located in Germany and has various markets leading machine and component manufacturers as customers. This projects’ goal is to get an in-depth understanding of the business model and how the company (with their technology) can participate in the market, which is estimated to have a size of 131.2m USD in 2025. Therefore, mainly qualitative research methods have been applied – however, quantitative sections enrich the analysis part of the thesis. Nine expert interviews were conducted, and a calculation tool was developed, which aims to easily convince OEMs of the new business models through modeling a business case, by incorporation of the most relevant parameters. Two use cases were analyzed in the fields of production machines and commercial vehicles. This is followed by a short excursus to a required Pay-per-X cloud software, where requirements were defined based on machine users’ needs. Based on these insights, a positioning strategy for the case company within this field is proposed later-on, which puts emphasis on how the firm can act as a technical enabler for Pay-per-X business models. / Syftet med denna avhandling är att undersöka en ny digital affärsmodell som just nu får stor uppmärksamhet inom många industrisektorer. Industri 4.0 förändrar hur tillverkare av originalutrustning behåller sin konkurrenskraft och erbjuder innovativa lösningar till sina kunder. Därför undersöker denna masteruppsats de olika möjligheterna med användningsbaserade affärsmodeller - nämligen Pay-per-X / Equipment-as-a-Service - ur ett startupföretag inom sensorteknik. Företaget, som kan fungera som en teknisk möjliggörare för Pay-per-X, är beläget i Tyskland och har olika marknadsledande maskin- och komponenttillverkare som kunder. Projektets mål är att få en djupgående förståelse för affärsmodellen och hur företaget (med sin teknik) kan delta på marknaden, som beräknas ha en storlek på 131,2 miljoner US-dollar år 2025. Därför har huvudsakligen kvalitativa forskningsmetoder tillämpats - men kvantitativa avsnitt berikar analysdelen av avhandlingen. Nio expertintervjuer genomfördes och ett beräkningsverktyg utvecklades, som syftar till att enkelt övertyga OEMs om de nya affärsmodellerna genom att modellera ett affärscase, genom att införliva de mest relevanta parametrarna. Två användningsfall analyserades inom områdena produktionsmaskiner och kommersiella fordon. Detta följs av en kort utflykt till en nödvändig Pay-per-X-molnmjukvara, där kraven definierades utifrån maskinanvändarnas behov. På grundval av dessa insikter föreslås senare en positioneringsstrategi för fallföretaget inom detta område, som lägger tonvikten på hur företaget kan fungera som en teknisk möjliggörare för Pay-per-X-affärsmodeller.

Hydropower is an important energy source since it is fossil-free, renewable, and controllable. Characteristics that become especially important as the reliance on intermittent energy sources increases. However, the dams for the hydropower plants are also associated with large risks as a dam failure could have fatal consequences. Dams are therefore monitored by several sensors, to follow and evaluate any changes in the dam. One of the most important dam surveillance measurements is seepage since it can examine internal erosion. Seepage is affected by several different parameters such as reservoir water level, temperature, and precipitation. Studies also indicate the existence of a time lag between the reservoir water level and the seepage flow, meaning that when there is a change in the reservoir level there is a delay before these changes are reflected in the seepage behaviour. Recent years have seen increased use of AI in dam monitoring, enabling more dynamic warning systems.  This master’s thesis aims to develop a model for dynamic warning signals by predicting seepage using reservoir water level, temperature, and precipitation. Furthermore, a snowmelt variable was introduced to account for the impact of increased water flows during the spring season. The occurrence of a time lag and its possible influence on the model’s performance is also examined. To predict the seepage, three models with different complexity are used – linear regression, support vector regression, and long short-term memory. To investigate the time lag, the linear regression and support vector regression models incorporate a static time lag by shifting the reservoir water level data up to 14 days. The time lag was further investigated using the long short-term memory model as well.  The results show that reservoir water level, temperature, and the snowmelt variable are the combination of input parameters that generate the best results for all three models. Although a one-day time lag between reservoir water level and seepage slightly improved the predictions, the exact duration and nature of the time lag remain unclear. The more complex models (support vector regression and long short-term memory) generated better predictions than the linear regression but performed similarly when evaluated based on the dynamic warning signals. Therefore, linear regression is deemed a suitable model for dynamic warning signals by seepage prediction.