This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition.

<p>Soft pneumatic actuators allow compliant force application and movement for a variety of tasks. While most soft actuators have compliance in directions perpendicular to their direction of force application, they are most often analyzed only in their direction of actuation. In this work, we show a characterization of a soft 3D printed bellows actuator that considers shear and axial deformations, modeling both active and passive degrees of freedom. We build a model based on actuator geometry and a parallel linear and torsional spring system which we fit to experimental data in order to obtain the model constants. We demonstrate this model on two complex parallel networks, a delta mechanism and a floating actuator mechanism, and show how this single actuator model can be used to better predict movements in parallel structures of actuators. These results verify that the presented model and modeling approach can be used to speed up the design and simulation of more complex soft robot models by characterizing both active and passive forces of their one degree-of-freedom soft actuators.<br> </p>

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Utilizing smartphone sensor data from crowdsen-sing (CS) campaigns for transportation planning (TP) requires highly reliable transport mode recognition. To address this, we present our RNN-based AI model MovDeep, which works on GPS, accelerometer, magnetometer and gyroscope data. It was trained on 92 hours of labeled data. MovDeep predicts six transportation modes (TM) on one second time windows. A novel postprocessing further improves the prediction results. We present a validation methodology (VM), which simulates unknown context, to get a more realistic estimation of the real-world performance (RWP). We explain why existing work shows overestimated prediction qualities, when they would be used on CS data and why their results are not comparable with each other. With the introduced VM, MovDeep still achieves 99.3 % F1 -Score on six TM. We confirm the very good RWP for our model on unknown context with the Sussex-Huawei Locomotion data set. For future model comparison, both publicly available data sets can be used with our VM. In the end, we compare MovDeep to a deterministic approach as a baseline for an average performing model (82 - 88 % RWP Recall) on a CS data set of 540 k tracks, to show the significant negative impact of even small prediction errors on TP.

[pt] O tempo de permanência (LoS) é uma das métricas mais utilizadas para avaliar o uso de recursos em Unidades de Terapia Intensiva (UTI). Esta tese propõe uma metodologia estruturada baseada em dados para abordar três principais demandas de gestores de UTI. Primeiramente, será proposto um modelo de predição individual do LoS em UTI, que pode ser utilizado para o planejamento dos recursos necessários. Em segundo lugar, tem-se como objetivo desenvolver um modelo para predizer o risco de permanência prolongada, o que auxilia na identificação deste tipo de paciente e assim uma ação mais rápida de intervenção no mesmo. Finalmente, será proposto uma medida de eficiência ajustada por case-mix capaz de realizar análises comparativas de benchmark entre UTIs. Os objetivos específicos são: (i) realizar uma revisão da literatura dos fatores que predizem o LoS em UTI; (ii) propor uma metodologia data-driven para predizer o LoS individual do paciente na UTI e o seu risco de longa permanência; e (iii) aplicar essa metodologia no contexto de um grande conjunto de UTIs de diferentes tipos de hospitais. Os resultados da revisão da literatura apresentaram os principais fatores de risco que devem ser considerados em modelos de predição. Em relação ao modelo preditivo, a metodologia proposta foi aplicada e validada em um conjunto de dados de 109 UTIs de 38 diferentes hospitais brasileiros. Este conjunto continha um total de 99.492 internações de 01 de janeiro a 31 de dezembro de 2019. Os modelos preditivos construídos usando a metodologia proposta apresentaram resultados precisos comparados com a literatura. Estes modelos propostos têm o potencial de melhorar o planejamento de recursos e identificar precocemente pacientes com permanência prolongada para direcionar ações de melhoria. Além disso, foi utilizado o modelo de predição proposto para construir uma medida não tendenciosa para benchmarking de UTIs, que também foi validada no conjunto de dados estudado. Portanto, esta tese propôs um guia estruturado baseado em dados para gerar predições para o tempo de permanência em UTI ajustadas ao contexto em que se deseja avaliar. / [en] The length of stay (LoS) in Intensive Care Units (ICU) is one of the most used metrics for resource use. This thesis proposes a structured datadriven methodology to approach three main demands of ICU managers. First, we propose a model to predict the individual ICU length of stay, which can be used to plan the number of beds and staff required. Second, we develop a model to predict the risk of prolonged stay, which helps identifying prolonged stay patients to drive quality improvement actions. Finally, we build a case-mix-adjusted efficiency measure (SLOSR) capable of performing non-biased benchmarking analyses between ICUs. To achieve these objectives, we divided the thesis into the following specific goals: (i) to perform a literature review and meta-analysis of factors that predict patient s LoS in ICUs; (ii) to propose a data-driven methodology to predict the numeric ICU LoS and the risk of prolonged stay; and (iii) to apply this methodology in the context of a big set of ICUs from mixed-type hospitals. The literature review results presented the main risk factors that should be considered in future prediction models. Regarding the predictive model, we applied and validated our proposed methodology to a dataset of 109 ICUs from 38 different Brazilian hospitals. The included dataset contained a total of 99,492 independent admissions from January 01 to December 31, 2019. The predictive models to numeric ICU LoS and to the risk of prolonged stay built using our data-driven methodology presented accurate results compared to the literature. The proposed models have the potential to improve the planning of resources and early identifying prolonged stay patients to drive quality improvement actions. Moreover, we used our prediction model to build a non-biased measure for ICU benchmarking, which was also validated in our dataset. Therefore, this thesis proposed a structured data-driven guide to generating predictions to ICU LoS adjusted to the specific environment analyzed.

The Swedish real estate market has been digitalized over the past decade with the current practice being to post your real estate advertisement online. A question that has arisen is how a seller can optimize their public listing to maximize the selling premium. This paper analyzes the use of three machine learning methods to solve this problem: Linear Regression, Decision Tree Regressor and Random Forest Regressor. The aim is to retrieve information regarding how certain attributes contribute to the premium value. The dataset used contains apartments sold within the years of 2014-2018 in the Östermalm / Djurgården district in Stockholm, Sweden. The resulting models returned an R2-value of approx. 0.26 and Mean Absolute Error of approx. 0.06. While the models were not accurate regarding prediction of premium, information was still able to be extracted from the models. In conclusion, a high amount of views and a publication made in April provide the best conditions for an advertisement to reach a high selling premium. The seller should try to keep the amount of days since publication lower than 15.5 days and avoid publishing on a Tuesday. / Den svenska bostadsmarknaden har blivit alltmer digitaliserad under det senaste årtiondet med nuvarande praxis att säljaren publicerar sin bostadsannons online. En fråga som uppstår är hur en säljare kan optimera sin annons för att maximera budpremie. Denna studie analyserar tre maskininlärningsmetoder för att lösa detta problem: Linear Regression, Decision Tree Regressor och Random Forest Regressor. Syftet är att utvinna information om de signifikanta attribut som påverkar budpremien. Det dataset som använts innehåller lägenheter som såldes under åren 2014-2018 i Stockholmsområdet Östermalm / Djurgården. Modellerna som togs fram uppnådde ett R²-värde på approximativt 0.26 och Mean Absolute Error på approximativt 0.06. Signifikant information kunde extraheras from modellerna trots att de inte var exakta i att förutspå budpremien. Sammanfattningsvis skapar ett stort antal visningar och en publicering i april de bästa förutsättningarna för att uppnå en hög budpremie. Säljaren ska försöka hålla antal dagar sedan publicering under 15.5 dagar och undvika att publicera på tisdagar.

Clinical observations of individual patients are the cornerstones for furthering our understanding of the human body, diseases, and therapeutics. Traditionally, clinical observations were communicated through publishing case reports and case series. The effort of identifying and investigating unusual clinical observations has always rested on the shoulders of busy clinicians. To date, there has been little effort dedicated to increasing the efficiency of identifying unique and uncommon patient observations that may lead to valuable discoveries. In this thesis, we propose and implement an augmented intelligence framework to identify potential novel clinical observations by combining machine analytics through outlier analysis with the judgment of subject-matter experts. Preeclampsia is a significant cause of maternal and perinatal mortality and morbidity, and advances in its management have been slow. Considering the complex etiological nature of preeclampsia, clinical observations are essential in advancing our understanding of the disease and therapeutic approaches. Thus, the objectives and studies in this thesis aim to answer the hypothesis that using outlier analysis in preeclampsia-related medical data would lead to identifying previously uninvestigated clinical cases with new clinical insight. This thesis combines three articles published or submitted for publication in peer-reviewed journals. The first article (published) is a systematic review examining the extent to which case reports and case series in preeclampsia have contributed new knowledge or discoveries. We report that under one-third of the identified case reports and case series presented new knowledge. In our second article (submitted for publication), we provide an overview of outlier analysis and introduce the framework of augmented intelligence using our proposed extreme misclassification contextual outlier analysis approach. Furthermore, we conduct a systematic review of obstetrics-related research that used outlier analysis to answer scientific questions. Our systematic review findings indicate that such use is in its infancy. In our third article (published), we implement the proposed augmented intelligence framework using two different outlier analysis methods on two independent datasets from separate studies in preeclampsia and hypertensive disorders of pregnancy. We identify several clinical observations as potential novelties, thus supporting the feasibility and applicability of outlier analysis to accelerate clinical discovery.

This project aimed to evaluate the effectiveness of the Auto Regressive Exogenous(ARX) model in forecasting stock prices and contribute to research on statisticalmodels in predicting stock prices. An ARX model is a type of linear regression modelused in time series analysis to forecast future values based on past values and externalinput signals. In this study, the ARX model was used to forecast the closing pricesof stocks listed on the OMX Stockholm 30 (OMXS30*) excluding Essity, Evolution,and Sinch, using historical data from 2016-01-01 to 2020-01-01 obtained from YahooFinance. The model was trained using the least squares approach with a control signal that filtersoutliers in the data. This was done by modeling the ARX model using optimizationtheory and then solving that optimization problem using Gurobi OptimizationSoftware. Subsequently, the accuracy of the model was tested by predicting prices in aperiod based on past values and the exogenous input variable. The results indicated that the ARX model was not suitable for predicting stock priceswhile considering short time periods.

Monoklonala antikroppsbaserade läkemedel (mAb) är ett av de snabbast växande segmenten på läkemedelsmarknaden, främst på grund av deras tillämpning inom onkologi, immunologi och hematologi. Traditionellt sker den industriella produktionen av mAb med fed-batch-odling. Detta är en relativt lätthanterlig process med mAb-utbyten på 5-10 g/L, men dess brist på kontroll över kritiska processparametrar (CPP) orsakar höga mAb-förluster på grund av att kvalitetsspecifikationer inte uppfylls. Ökande marknadskrav och regulatoriska förändringar pådriver läkemedelsindustrin iinnovation inom mAb-tillverkningsprocessen, för att nå kontinuerlig tillverkning. För närvarande, som ett övergångssteg till kontinuerlig tillverkning, sker investeringar i intensifierade fed-batch-odlingar. Dessa uppnår högre celldensiteter på cirka 25-30 g/L, men detta är fortfarande mycket lägre än motsvarande mAb-koncentrationer på 130 g/L som kan uppnås med perfusionsprocesser. Andra fördelar med perfusionsprocesser är att de tillåter flexibla produktionsanläggningar och möjliggör en nivå av processkontroll som skulle tillåta realtidstestning av release. För att upprätthålla en perfusionsprocess under de specificerade förhållandena som garanterar den önskade mAb-kvaliteten, måste CPP kontrolleras noggrant. Process Analytical Technologies (PAT) kan mäta CPP i realtid på ett icke-destruktivt sätt. Denna studie undersökte tillämpningen av två PAT, ArgusEye®-sensorerna och Time-gated Raman-spektroskopi, på perfusionsprocesser. Vi visade att ArgusEye®-sensorerna kan användas för att mäta IgG i perfusionsprover med ganska bra korrelation med referensmetoden. Vi har också visat att multivariata Raman-baserade modeller kan konstrueras för att förutsäga flera CPP, baserat på samma spektra. Framförallt belyser denna studie komplexiteten i tillämpningen av dessa PAT för att kontrollera perfusionsprocesser. För ArgusEye® drar vi slutsatsen att för att få exakta mätningar måste vi ta hänsyn till förändringarna i koncentrationen av värdcellsprotein under en perfusionsprocess, eftersom deras ospecifika bindning till sensorerna är den troliga orsaken till variationen i IgG-mätningarna. För de Raman-baserade modellerna, visar denna studie att en stor mängd data krävs för att bygga korrekta prediktionsmodeller, något som rapprterats om i litteraturen. Sammantaget visar denna rapport att dessa PAT har en stor tillämpningspotential, men de måste förbättras ytterligare innan de kan användas som automatiska återkopplingskontrollverktyg. / Monoclonal antibody-based therapeutics (mAb) are one of the fastest-growing segments in the pharmaceutical market, mainly due to their application in oncology, immunology, and hematology. Traditionally, the industrial production of mAb is done with fed-batch cultivation. This is a relatively easy to operate process with mAb yields of 5-10 g/L, but its lack of control over critical process parameters (CPP) causes high mAb losses due to unmet quality specifications. Driven by increasing market demands and regulatory changes, the pharmaceutical industry is innovating in the mAb manufacturing process to reach continuous manufacturing. Currently, as a transition step to continuous manufacturing, the pharmaceutical industry is investing in intensified fed-batch cultivations. They achieve higher cells densities and present yields around 25-30 g/L, but this is still much lower than the equivalent mAb titers of 130 g/L that can be achieved with perfusion processes. Other advantages of perfusion processes are that they allow the existence of flexible production facilities and enable a level of process control that would permit Real-Time Release Testing. To maintain a perfusion process under the specified conditions to guarantee the desired mAb quality, the CPP need to be closely controlled. Process Analytical Technologies (PAT) can measure CPP in real-time and non-destructively. This study evaluated the application of two PAT, the ArgusEye® sensors and Time-gated Raman spectroscopy, on perfusion processes. We showed that the ArgusEye® sensors can be used to measure IgG in perfusion samples with quite good correlation to the reference method. We have also shown that multivariate Raman-based models can be constructed to predict several CPP based on the same spectra. Most importantly, this study highlights the complexity of the application of these PAT to control perfusion processes. For the ArgusEye®, we conclude that to obtain accurate measurements, we need to account for the changes in the concentration of host cell protein during a perfusion process, as their unspecific binding to the sensors is the probable cause for the variation in the IgG measurements. For the Raman-based models, as previously reported in the literature, this study shows that a high volume of data is require to build accurate prediction models. Overall, this report shows that these PAT have a great potential of application, but they need to be further improved prior to their use as automatic feedback control tools.