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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Brigita Dalecka; Martins Strods; Talis Juhna; Gunaratna Kuttuva Rajarao;

    Discharge of organic load from treated wastewater may cause environmental eutrophication. Recently, fungi have gained much attention due to their removal of pharmaceutical substances by enzymatic degradation and adsorption. However, the fungal effect in removing nutrients is less investigated. Therefore, two fungal species, the white-rot fungus T. versicolor as a laboratory strain and the mold A. luchuensis as an environmental isolate from the municipal wastewater treatment plant, were studied to determine the fungal potential for phosphorus, nitrogen, and the total organic carbon removal from municipal wastewater, carrying out a batch scale experiment to a fluidized bed pelleted bioreactor. During the batch scale experiment, the total removal (99.9 %) of phosphorus by T. versicolor was attained after a 6 hours-long incubation period while the maximal removal efficiency (99.9 %) for phosphorus from A. luchuensis was gained after an incubation period of 24 hours. Furthermore, both fungi showed that the pH adjustment to 5.5 kept the concentration of nitrogen constant and stabilized the total organic carbon reduction process for the entire incubation period. The results from the fluidized bed bioreactor demonstrated opposite tendencies on a nutrient removal comparing to a batch experiment where no significant effect on phosphorus, nitrogen, and total organics carbon reduction was observed. The obtained results from this study of batch and fluidized bed bioreactor experiments are a promising starting point for a successful fungal treatment optimization and application to wastewater treatment. QC 20210107

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Microbiological Rese...arrow_drop_down
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    Microbiological Research
    Article . 2020 . Peer-reviewed
    License: Elsevier Non-Commercial
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Microbiological Rese...arrow_drop_down
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      Microbiological Research
      Article . 2020 . Peer-reviewed
      License: Elsevier Non-Commercial
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Khays, Samir;

    Anticipating the future positions of the surrounding vehicles is a crucial task foran autonomous vehicle in order to drive safely. To foresee complex manoeuvresfor longer time horizons, a framework that relies on high-level properties ofmotion and is able to incorporate, e.g. contextual features, is needed. In thisthesis, the problem of predicting the trajectories of the surrounding vehicles ona highway is tackled by using machine learning. The objective is to evaluate theperformance of recurrent neural networks for trajectory prediction, specificallylong-short term memory neural networks. Moreover, the goal is to investigateif contextual features can improve the predictions.The problem of predicting future trajectories is solved by using two differentapproaches, which are compared by using the same framework. The firstapproach is based on the vehicle states of the surrounding vehicles relative tothe ego-vehicle, where the reference system is in the ego-vehicle. The secondapproach is based on the velocities of the vehicles relative to the ground, wherethe reference system is in the ground. The results show that, with the proposedarchitecture, the latter approach results in a lower RMSE in the longitudinaldirection compared with the former approach. The results also show that theproposed models, overall, outperform a simple model, which is based on polynomialfitting, particularly in the lateral direction where the proposed modelsare significantly better than the polynomial models. Furthermore, contextualfeatures do not improve the predictions significantly. However, the results indicatethat contextual information has a positive impact on the predictions inspecific scenarios. Att kunna förutse framtida rörelser av kringliggande fordon är en viktig uppgiftför att ett autonomt fordon ska kunna köra säkert. För att kunna förutspå komplexamanövreringar för längre tidshorisonter, behövs ett ramverk baserat påavancerade egenskaper hos rörelser och som kan integrera t.ex. kontextuell information.I detta examensarbete betraktas problemet att förutspå trajektorierav kringliggande fordon på motorväg med hjälp av maskininlärning. Målet äratt utvärdera prestandan av recurrent neural networks för denna uppgift, specifiktlong-short term memory neural networks. Målet är också att undersöka omkontextuell information kan förbättra prediktionerna.Problemet att prediktera framtida trajektorier är löst genom att användatvå olika tillvägagångssätt. Det ena tillvägagångssättet är baserat på fordonstillståndenav de kringliggande fordonen relativt ego-fordonet, där referenssystemetär i ego-fordonet. Det andra tillvägagångssättet är baserat på hastigheternaav fordonen relativt marken, där referenssystemet är i marken. Resultatetvisar att det sistnämnda tillvägagångssättet resulterar i ett lägre RMSE i denlongitudinella riktningen, med den föreslagna arkitekturen, jämfört med detförstnämnda. Resultaten visar även att de framtagna modellerna, totalt sett,presterar bättre än en simpel modell som är baserad på polynomanpassning,speciellt i lateral riktning där de framtagna modellerna är betydligt bättreän polynomanpassningarna. Det visar sig också att kontextuell informationinte förbättrar prediktionerna signifikant, däremot indikerar resultaten att detpåverkar prediktionerna positivt i specifika scenarier.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Péter Várnai; Dimos; V. Dimarogonas;

    In this paper, we aim towards providing a practical framework for learning to satisfy signal temporal logic (STL) task specifications for systems with partially unknown dynamics. We consider STL tasks whose satisfaction can be guaranteed by enforcing a priori known temporal specifications imposed on the atomic propositions that compose them. First, a neural network is trained offline as a control policy to satisfy such temporal specifications while also minimizing a target cost, such as the input energy of the system. The obtained controller then serves as a guide that aids exploration while learning to satisfy any specific STL task optimally using policy improvement, greatly increasing the sample efficiency of the procedure. The promise of the approach towards a versatile STL learning framework is demonstrated through simulations.  QC 20201125

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    https://doi.org/10.1109/cdc400...
    Other literature type . Conference object . 2019 . Peer-reviewed
    License: IEEE Copyright
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      https://doi.org/10.1109/cdc400...
      Other literature type . Conference object . 2019 . Peer-reviewed
      License: IEEE Copyright
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Seema Paul; Jesper Oppelstrup; Roger Thunvik; John Mango Magero; +2 Authors

    This study explored two-dimensional (2D) numerical hydrodynamic model simulations of Lake Victoria. Several methods were developed in Matlab to build the lake topography. Old depth soundings taken in smaller parts of the lake were combined with more recent extensive data to produce a smooth topographical model. The lake free surface numerical model in the COMSOL Multiphysics (CM) software was implemented using bathymetry and vertically integrated 2D shallow water equations. Validated by measurements of mean lake water level, the model predicted very low mean flow speeds and was thus close to being linear and time invariant, allowing long-time simulations with low-pass filtered inflow data. An outflow boundary condition allowed an accurate simulation to achieve the lake’s steady state level. The numerical accuracy of the linear measurement of lake water level was excellent. QC 20191106. QC 20200129 PhD project

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    Fluids
    Other literature type . Article . 2019 . Peer-reviewed
    License: CC BY
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    Fluids
    Article . 2019
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    Article . 2019
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Fluidsarrow_drop_down
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      Other literature type . Article . 2019 . Peer-reviewed
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      Article . 2019
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      Article . 2019
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Flodmark, Erik; Sävendahl, Carl;

    Sweden has the ambition to be the world leading country leveraging the opportunities of digitalization in the healthcare sector. In parallel, the Swedish Research Council highlights that conducting more clinical studies is essential to improve the healthcare. Henceforth, considering the need for increased operational efficiency as an enabler for increased clinical activity, a digital transformation of the industry was identified as a potential catalyst. The study thus utilizes a cognitive work analysis framework to investigate the potential benefits and risks of digitizing the functional operations at a contract management department for clinical studies at a Swedish university hospital. The aim is thereafter to determine the appropriate properties necessary to consider managing a digital transformation. The analysis identified three key benefits from a digitization. 1) transparent data sharing, 2) standardized contract management and 3) efficient operations. These three aspects are currently insufficient at the department hindering the objective of increasing the clinical activity. The study found that a digital transformation would be suitable in order to mitigate these insufficiencies and consequently facilitating the achievement of the objectives. Thereafter, the study found the key properties to consider managing a digital transformation to be interoperability, quality, adaptability and usability. In addition, safety was found critical to be considered in the transformation as the contract management department acts under rigid laws and regulations on ethics and patient security with which digitized processes must comply. The results contribute to the field of cognitive systems engineering. However, the study has limitations regarding the reliability and generalizability of the results. The findings are based on a single case study, which may not be representative for the industry in general nor for university hospitals in particular. In addition, since no actual digitalization effort was performed at the organization during the study, appropriate properties key to consider in the digital transformation are speculative by design. Consequently, it is necessary to study an actual implementation process in future research and whether the proposed considerations are sufficient in order to realize the suggested benefits of such a digitalization. Sverige har ett övergripande mål att vara det ledande landet när det gäller att dra nytta av digitaliseringens möjligheter inom sjukvården. Dessutom understryker Vetenskapsrådet att det är centralt för förbättrad sjukvård att öka antalet kliniska studier i landet. Följaktligen, med tanke på behovet av en ökad operativ effektivitet, identifierade författarna det av intresse att studera digitalisering av branschen. Studien tillämpar således ett kognitivt ramverk för arbetsanalys i syfte att undersöka de potentiella fördelarna eller riskerna med att digitalisera den funktionella verksamheten hos en kontrakthanteringsavdelning för kliniska studier vid ett stort svenskt universitetssjukhus. Målsättningen är därefter att ta fram lämpliga egenskaper som är nödvändiga att beakta vid hanteringen av den digitala transformationen. Kontrakthanteringsavdelningen fanns att inneha brister i sina arbetsprocesser gällande transparens, effektivitet och standardisering vilket hindrar målet avseende ökad klinisk aktivitet. Studien visade att en digital transformation skulle vara nödvändig för att motverka dessa brister, samt för att möjliggöra en uppskalning av organisationen. Ett annat specifikt förbättringsområde som skulle underlättas av en digital transformation visade sig vara förbättrad synkronisering mellan arbetsprocesser. Vidare fann studien att de mest kritiska egenskaperna nödvändiga att beakta, vid hantering av en digital transformation, skulle vara interoperabilitet, kvalitet, anpassningsförmåga och användbarhet. Dessutom är säkerhet en egenskap som visat sig vara kritisk att beakta vid digitalisering då kontrakthanteringsavdelningen lyder under stränga lagar och föreskrifter beträffande etik och patientsäkerhet. Resultaten bidrar till forskningsområdet cognitive systems engineering. Studien har dock vissa begränsningar gällande tillförlitlighet och generaliserbarhet. Resultaten är baserade på en enfallstudie, som eventuellt inte är representativ för branschen i allmänhet eller för universitetssjukhus i synnerhet. Dessutom, då ingen digitaliseringsinsats utfördes under studien är de viktiga egenskaperna att beakta i den digitala transformationen enbart spekulativa. Således är det i framtida forskning viktigt att studera en faktisk implementation och då studera om föreslagna beaktanden är tillräckliga för att utnyttja digitaliseringens möjligheter.

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    Publikationer från KTH
    Bachelor thesis . 2021
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      Publikationer från KTH
      Bachelor thesis . 2021
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    Authors: Augier, Pierre; Mohanan, Ashwin Vishnu; Bonamy, Cyrille;

    FluidDyn is a project to foster open-science and open-source in the fluid dynamics community. It is thought of as a research project to channel open-source dynamics, methods and tools to do science. We propose a set of Python packages forming a framework to study fluid dynamics with different methods, in particular laboratory experiments (package fluidlab), simulations (packages fluidfft, fluidsim and fluidfoam) and data processing (package fluidimage). In the present article, we give an overview of the specialized packages of the project and then focus on the base package called fluiddyn, which contains common code used in the specialized packages. Packages fluidfft and fluidsim are described with greater detail in two companion papers [4, 5]. With the project FluidDyn, we demonstrate that specialized scientific code can be written with methods and good practices of the open-source community. The Mercurial repositories are available in Bitbucket (https://bitbucket.org/fluiddyn/). All codes are documented using Sphinx and Read the Docs, and tested with continuous integration run on Bitbucket Pipelines and Travis. To improve the reuse potential, the codes are as modular as possible, leveraging the simple object-oriented programming model of Python. All codes are also written to be highly efficient, using C++, Cython and Pythran to speedup the performance of critical functions. FluidDyn project QC 20220322

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    Journal of Open Research Software
    Other literature type . Article . 2019 . Peer-reviewed
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      Journal of Open Research Software
      Other literature type . Article . 2019 . Peer-reviewed
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    Authors: Kaalen, Stefan;

    Several manufacturers of road vehicles today are working on developing autonomous vehicles. One subject that is often up for discussion when it comes to integrating autonomous road vehicles into the infrastructure is the safety aspect. There is in the context no common view of how safety should be quantified. As a contribution to this discussion we propose describing each potential hazardous event of a vehicle as a Semi-Markov Process (SMP). A reliability-based method for using the semi-Markov representation to calculate the probability of a hazardous event to occur is presented. The method simplifies the expression for the reliability using the Laplace-Stieltjes transform and calculates the transform of the reliability exactly. Numerical inversion algorithms are then applied to approximate the reliability up to a desired error tolerance. The method is validated using alternative techniques and is thereafter applied to a system for automated steering based on a real example from the industry. A desired evolution of the method is to involve a framework for how to represent each hazardous event as a SMP. Flertalet tillverkare av vägfordon jobbar idag på att utveckla autonoma fordon. Ett ämne ofta på agendan i diskussionen om att integrera autonoma fordon på vägarna är säkerhet. Det finns i sammanhanget ingen klar bild över hur säkerhet ska kvantifieras. Som ett bidrag till denna diskussion föreslås här att beskriva varje potentiellt farlig situation av ett fordon som en Semi-Markov process (SMP). En metod presenteras för att via beräkning av funktionssäkerheten nyttja semi-Markov representationen för att beräkna sannolikheten för att en farlig situation ska uppstå. Metoden nyttjar Laplace-Stieltjes transformen för att förenkla uttrycket för funktionssäkerheten och beräknar transformen av funktionssäkerheten exakt. Numeriska algoritmer för den inversa transformen appliceras sedan för att beräkna funktionssäkerheten upp till en viss feltolerans. Metoden valideras genom alternativa tekniker och appliceras sedan på ett system för autonom styrning baserat på ett riktigt exempel från industrin. En fördelaktig utveckling av metoden som presenteras här skulle vara att involvera ett ramverk för hur varje potentiellt farlig situation ska representeras som en SMP.

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    Authors: Hickman, Björn;

    Today, brokers within the stock market brokerage industry are having difficulties with accurately forecasting the trading volume that is conducted by their customers. This is especially a problem during periods of exceptionally high or low trading volumes. Solving this problem would lead to both monetary savings in terms of server costs and operational planning issues. This thesis uses three Machine Learning models (Random Forest Regressor, Linear Regression, and Support Vector Regression) to predict daily trading volume. In Machine Learning, features are variables that act as explanatory variables for the dependent variable, in this case, the daily trading volume. The primary focus of this study is to evaluate and analyze which types of feature categories are the most important. Therefore, this study uses a variety of features divided into five different categories (Temporal, Historical, Market, External, and Customer). The results from the models trained using each individual feature category are compared against each other. Secondly, this study also focuses on analyzing the performance of all feature categories together. A Naive model of a 20-day rolling average is used as a benchmark to evaluate the results. The findings of this study indicate that Machine Learning models perform better than the proposed Naive approach when predicting daily stock market trading volume. However, the difference is of a small nature. Further, the Historical feature category is the category that performs best and can therefore be argued to be the most important category when predicting daily trading volume. However, the results of this study are not of statistical significance. The findings of this study can be relevant to the research field and can be used in future studies to further investigate the feature importance in stock market trading volume prediction. Idag har företag inom industrin för aktiemäklare svårigheter att på ett träffsäkert sätt prognostisera sina kunders handelsvolymer. Detta är särskilt ett problem under perioder med extremt höga eller låga volymer. Att lösa detta problem skulle leda till både monetära besparingar i form av serverkostnader, och även lösa operationella planeringsproblem. Denna studie använder tre olika maskininlärningsmodeller (Random Forest Regressor, Linear Regression, och Support Vector Regression) för att förutspå handelsvolym. Denna studie har som primärt fokus att utvärdera och analysera vilka typer av data som är av vikt i syfte att förutspå kommande daglig aktiehandelsvolym. Denna studie använder därmed en mängd olika variabler indelat i fem grupper (Tid, Historik, Marknad, Extern, Kund). Modellerna tränas individuellt med varje grupp och resultatet jämförs inbördes för att besvara studiens frågeställningar. Studien fokuserar även på att analysera resultatet av att träna modellerna på samtliga grupper tillsammans. För att utvärdera resultatet används en naiv modell med 20 dagars rullande medelvärde. Resultatet från denna studie indikerar att användning av maskininlärning presterar bättre än den använda naiva modellen, för att förutspå daglig handelsvolym på aktiemarknaden. Skillnaden i resultat är dock liten. Vidare visar studiens resultat att den grupp av variabler som presterar bäst är kategorin Historik. Därmed kan det sägas att denna grupp av variabler är den viktigaste gruppen för att förutspå daglig handelsvolym, av grupperna använda i denna studie. Det går dock inte att säga att resultaten i denna studie är signifikanta. Resultaten och slutsatserna från denna studie bidrar till forskningsområdet och resultaten kan i framtiden användas för att fortsätta undersöka vilka variabler som är av intresse när det kommer till att förutspå daglig handelsvolym på aktiemarknaden.

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    Authors: Isabelle Matthiesen; Dimitrios Voulgaris; Polyxeni Nikolakopoulou; Thomas E. Winkler; +1 Authors

    Microphysiological systems mimic the in vivo cellular ensemble and microenvironment with the goal of providing more human-like models for biopharmaceutical research. In this study, the first such model of the blood-brain barrier (BBB-on-chip) featuring both isogenic human induced pluripotent stem cell (hiPSC)-derived cells and continuous barrier integrity monitoring with <2 min temporal resolution is reported. Its capabilities are showcased in the first microphysiological study of nitrosative stress and antioxidant prophylaxis. Relying on off-stoichiometry thiol–ene–epoxy (OSTE+) for fabrication greatly facilitates assembly and sensor integration compared to the prevalent polydimethylsiloxane devices. The integrated cell–substrate endothelial resistance monitoring allows for capturing the formation and breakdown of the BBB model, which consists of cocultured hiPSC-derived endothelial-like and astrocyte-like cells. Clear cellular disruption is observed when exposing the BBB-on-chip to the nitrosative stressor linsidomine, and the barrier permeability and barrier-protective effects of the antioxidant N-acetylcysteine amide are reported. Using metabolomic network analysis reveals further drug-induced changes consistent with prior literature regarding, e.g., cysteine and glutathione involvement. A model like this opens new possibilities for drug screening studies and personalized medicine, relying solely on isogenic human-derived cells and providing high-resolution temporal readouts that can help in pharmacodynamic studies. QC 20220330

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    Authors: Li, Shuo;

    Vehicle breakdowns can lead to fatal accidents, increase costs and reduce productivity. Therefore, robust and accurate fault diagnosis and prediction systems are critical to ensure the proper operation of vehicles. Many researchers have used machine learning for the prediction of vehicle breakdowns. However, previous work has typically predicted the failure of a specific component or predicted whether a breakdown will occur without predicting the specific type of breakdown. This thesis used ensemble models to predict breakdowns that have not yet occurred through vehicle breakdown data. The model proposed in the thesis is able to predict six common types of vehicle breakdowns. This study faces the problem of data imbalance that may cause some of the breakdowns difficult to predict. This work used two methods to balance the classes and various ensemble models to solve this problem and compares the results with baseline models such as Support Vector Machine (SVM) and Random Forest Classifier (RF) to demonstrate the powerful predictive power of ensemble models. Fordonshaverier kan leda till dödsolyckor, öka kostnaderna och minska produktiviteten. Därför är det viktigt att ha ett robust och exakt system för feldiagnostik och felförutsägelse för att fordonen ska kunna fortsätta att fungera som de ska. Många forskare har använt maskininlärning för att förutsäga fordonshaverier. Tidigare arbete har dock vanligtvis förutsett felet på en specifik komponent eller förutsett om ett fel kommer att inträffa utan att förutsäga den specifika typen av haverier. I denna avhandling används ensemblemodeller för att förutsäga haverier som ännu inte har inträffat med hjälp av data om haverier i fordon. Den modell som föreslås i avhandlingen kan förutsäga sex vanliga typer av fordonshaverier. Denna studie står inför problemet med obalans i data som kan göra det svårt att förutsäga en del av haverierna. I arbetet används två metoder för att balansera klasserna och olika ensemblemodeller för att lösa detta problem och resultaten jämförs med basmodeller som SVM och RF för att visa på ensemblemodellernas kraftfulla förutsägelseförmåga.

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    Authors: Brigita Dalecka; Martins Strods; Talis Juhna; Gunaratna Kuttuva Rajarao;

    Discharge of organic load from treated wastewater may cause environmental eutrophication. Recently, fungi have gained much attention due to their removal of pharmaceutical substances by enzymatic degradation and adsorption. However, the fungal effect in removing nutrients is less investigated. Therefore, two fungal species, the white-rot fungus T. versicolor as a laboratory strain and the mold A. luchuensis as an environmental isolate from the municipal wastewater treatment plant, were studied to determine the fungal potential for phosphorus, nitrogen, and the total organic carbon removal from municipal wastewater, carrying out a batch scale experiment to a fluidized bed pelleted bioreactor. During the batch scale experiment, the total removal (99.9 %) of phosphorus by T. versicolor was attained after a 6 hours-long incubation period while the maximal removal efficiency (99.9 %) for phosphorus from A. luchuensis was gained after an incubation period of 24 hours. Furthermore, both fungi showed that the pH adjustment to 5.5 kept the concentration of nitrogen constant and stabilized the total organic carbon reduction process for the entire incubation period. The results from the fluidized bed bioreactor demonstrated opposite tendencies on a nutrient removal comparing to a batch experiment where no significant effect on phosphorus, nitrogen, and total organics carbon reduction was observed. The obtained results from this study of batch and fluidized bed bioreactor experiments are a promising starting point for a successful fungal treatment optimization and application to wastewater treatment. QC 20210107

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    Microbiological Research
    Article . 2020 . Peer-reviewed
    License: Elsevier Non-Commercial
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      Microbiological Research
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    Authors: Khays, Samir;

    Anticipating the future positions of the surrounding vehicles is a crucial task foran autonomous vehicle in order to drive safely. To foresee complex manoeuvresfor longer time horizons, a framework that relies on high-level properties ofmotion and is able to incorporate, e.g. contextual features, is needed. In thisthesis, the problem of predicting the trajectories of the surrounding vehicles ona highway is tackled by using machine learning. The objective is to evaluate theperformance of recurrent neural networks for trajectory prediction, specificallylong-short term memory neural networks. Moreover, the goal is to investigateif contextual features can improve the predictions.The problem of predicting future trajectories is solved by using two differentapproaches, which are compared by using the same framework. The firstapproach is based on the vehicle states of the surrounding vehicles relative tothe ego-vehicle, where the reference system is in the ego-vehicle. The secondapproach is based on the velocities of the vehicles relative to the ground, wherethe reference system is in the ground. The results show that, with the proposedarchitecture, the latter approach results in a lower RMSE in the longitudinaldirection compared with the former approach. The results also show that theproposed models, overall, outperform a simple model, which is based on polynomialfitting, particularly in the lateral direction where the proposed modelsare significantly better than the polynomial models. Furthermore, contextualfeatures do not improve the predictions significantly. However, the results indicatethat contextual information has a positive impact on the predictions inspecific scenarios. Att kunna förutse framtida rörelser av kringliggande fordon är en viktig uppgiftför att ett autonomt fordon ska kunna köra säkert. För att kunna förutspå komplexamanövreringar för längre tidshorisonter, behövs ett ramverk baserat påavancerade egenskaper hos rörelser och som kan integrera t.ex. kontextuell information.I detta examensarbete betraktas problemet att förutspå trajektorierav kringliggande fordon på motorväg med hjälp av maskininlärning. Målet äratt utvärdera prestandan av recurrent neural networks för denna uppgift, specifiktlong-short term memory neural networks. Målet är också att undersöka omkontextuell information kan förbättra prediktionerna.Problemet att prediktera framtida trajektorier är löst genom att användatvå olika tillvägagångssätt. Det ena tillvägagångssättet är baserat på fordonstillståndenav de kringliggande fordonen relativt ego-fordonet, där referenssystemetär i ego-fordonet. Det andra tillvägagångssättet är baserat på hastigheternaav fordonen relativt marken, där referenssystemet är i marken. Resultatetvisar att det sistnämnda tillvägagångssättet resulterar i ett lägre RMSE i denlongitudinella riktningen, med den föreslagna arkitekturen, jämfört med detförstnämnda. Resultaten visar även att de framtagna modellerna, totalt sett,presterar bättre än en simpel modell som är baserad på polynomanpassning,speciellt i lateral riktning där de framtagna modellerna är betydligt bättreän polynomanpassningarna. Det visar sig också att kontextuell informationinte förbättrar prediktionerna signifikant, däremot indikerar resultaten att detpåverkar prediktionerna positivt i specifika scenarier.

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    Authors: Péter Várnai; Dimos; V. Dimarogonas;

    In this paper, we aim towards providing a practical framework for learning to satisfy signal temporal logic (STL) task specifications for systems with partially unknown dynamics. We consider STL tasks whose satisfaction can be guaranteed by enforcing a priori known temporal specifications imposed on the atomic propositions that compose them. First, a neural network is trained offline as a control policy to satisfy such temporal specifications while also minimizing a target cost, such as the input energy of the system. The obtained controller then serves as a guide that aids exploration while learning to satisfy any specific STL task optimally using policy improvement, greatly increasing the sample efficiency of the procedure. The promise of the approach towards a versatile STL learning framework is demonstrated through simulations.  QC 20201125

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    https://doi.org/10.1109/cdc400...
    Other literature type . Conference object . 2019 . Peer-reviewed
    License: IEEE Copyright
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      https://doi.org/10.1109/cdc400...
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    Authors: Seema Paul; Jesper Oppelstrup; Roger Thunvik; John Mango Magero; +2 Authors

    This study explored two-dimensional (2D) numerical hydrodynamic model simulations of Lake Victoria. Several methods were developed in Matlab to build the lake topography. Old depth soundings taken in smaller parts of the lake were combined with more recent extensive data to produce a smooth topographical model. The lake free surface numerical model in the COMSOL Multiphysics (CM) software was implemented using bathymetry and vertically integrated 2D shallow water equations. Validated by measurements of mean lake water level, the model predicted very low mean flow speeds and was thus close to being linear and time invariant, allowing long-time simulations with low-pass filtered inflow data. An outflow boundary condition allowed an accurate simulation to achieve the lake’s steady state level. The numerical accuracy of the linear measurement of lake water level was excellent. QC 20191106. QC 20200129 PhD project

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    Authors: Flodmark, Erik; Sävendahl, Carl;

    Sweden has the ambition to be the world leading country leveraging the opportunities of digitalization in the healthcare sector. In parallel, the Swedish Research Council highlights that conducting more clinical studies is essential to improve the healthcare. Henceforth, considering the need for increased operational efficiency as an enabler for increased clinical activity, a digital transformation of the industry was identified as a potential catalyst. The study thus utilizes a cognitive work analysis framework to investigate the potential benefits and risks of digitizing the functional operations at a contract management department for clinical studies at a Swedish university hospital. The aim is thereafter to determine the appropriate properties necessary to consider managing a digital transformation. The analysis identified three key benefits from a digitization. 1) transparent data sharing, 2) standardized contract management and 3) efficient operations. These three aspects are currently insufficient at the department hindering the objective of increasing the clinical activity. The study found that a digital transformation would be suitable in order to mitigate these insufficiencies and consequently facilitating the achievement of the objectives. Thereafter, the study found the key properties to consider managing a digital transformation to be interoperability, quality, adaptability and usability. In addition, safety was found critical to be considered in the transformation as the contract management department acts under rigid laws and regulations on ethics and patient security with which digitized processes must comply. The results contribute to the field of cognitive systems engineering. However, the study has limitations regarding the reliability and generalizability of the results. The findings are based on a single case study, which may not be representative for the industry in general nor for university hospitals in particular. In addition, since no actual digitalization effort was performed at the organization during the study, appropriate properties key to consider in the digital transformation are speculative by design. Consequently, it is necessary to study an actual implementation process in future research and whether the proposed considerations are sufficient in order to realize the suggested benefits of such a digitalization. Sverige har ett övergripande mål att vara det ledande landet när det gäller att dra nytta av digitaliseringens möjligheter inom sjukvården. Dessutom understryker Vetenskapsrådet att det är centralt för förbättrad sjukvård att öka antalet kliniska studier i landet. Följaktligen, med tanke på behovet av en ökad operativ effektivitet, identifierade författarna det av intresse att studera digitalisering av branschen. Studien tillämpar således ett kognitivt ramverk för arbetsanalys i syfte att undersöka de potentiella fördelarna eller riskerna med att digitalisera den funktionella verksamheten hos en kontrakthanteringsavdelning för kliniska studier vid ett stort svenskt universitetssjukhus. Målsättningen är därefter att ta fram lämpliga egenskaper som är nödvändiga att beakta vid hanteringen av den digitala transformationen. Kontrakthanteringsavdelningen fanns att inneha brister i sina arbetsprocesser gällande transparens, effektivitet och standardisering vilket hindrar målet avseende ökad klinisk aktivitet. Studien visade att en digital transformation skulle vara nödvändig för att motverka dessa brister, samt för att möjliggöra en uppskalning av organisationen. Ett annat specifikt förbättringsområde som skulle underlättas av en digital transformation visade sig vara förbättrad synkronisering mellan arbetsprocesser. Vidare fann studien att de mest kritiska egenskaperna nödvändiga att beakta, vid hantering av en digital transformation, skulle vara interoperabilitet, kvalitet, anpassningsförmåga och användbarhet. Dessutom är säkerhet en egenskap som visat sig vara kritisk att beakta vid digitalisering då kontrakthanteringsavdelningen lyder under stränga lagar och föreskrifter beträffande etik och patientsäkerhet. Resultaten bidrar till forskningsområdet cognitive systems engineering. Studien har dock vissa begränsningar gällande tillförlitlighet och generaliserbarhet. Resultaten är baserade på en enfallstudie, som eventuellt inte är representativ för branschen i allmänhet eller för universitetssjukhus i synnerhet. Dessutom, då ingen digitaliseringsinsats utfördes under studien är de viktiga egenskaperna att beakta i den digitala transformationen enbart spekulativa. Således är det i framtida forskning viktigt att studera en faktisk implementation och då studera om föreslagna beaktanden är tillräckliga för att utnyttja digitaliseringens möjligheter.

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