The use of quadratic discriminant analysis (QDA) or its regularized version (RQDA) for classification is often not recommended, due to its well-acknowledged high sensitivity to the estimation noise of the covariance matrix. This becomes all the more the case in unbalanced data settings for which it has been found that R-QDA becomes equivalent to the classifier that assigns all observations to the same class. In this paper, we propose an improved R-QDA that is based on the use of two regularization parameters and a modified bias, properly chosen to avoid inappropriate behaviors of R-QDA in unbalanced settings and to ensure the best possible classification performance. The design of the proposed classifier builds on a refined asymptotic analysis of its performance when the number of samples and that of features grow large simultaneously, which allows to cope efficiently with the high-dimensionality frequently met within the big data paradigm. The performance of the proposed classifier is assessed on both real and synthetic data sets and was shown to be much higher than what one would expect from a traditional R-QDA.

The radio magnetotellucic (RMT) method originates from (Tikhonov, 1950) and (Cagniard,1953) who discovered the possibility to estimate resistivity in the Earth’s subsurface using naturally occurring telluric currents. This through measuring the surface impedance of electromagnetic waves. Later (Paal, 1965) makes use of electromagnetic plane-waves originating from radio transmitters that operates in the very low frequency range (10 − 30kHz). These plane waves artificially induce telluric currents which can then be used to estimate resistivity in shallow subsurface. It is hence the source of the electromagnetic wave inducing the telluric current that named the RMT method accordingly. It’s then shown by (Bastani, 2001) that the signal can be measured in an even broader band (10 − 250kHz) to gain a better vertical resolution. Finally, frequency of the electromagnetic wave is affecting the skin depth and one can therefore estimate the depth of resistivity layers in the subsurface. In this thesis a data processing algorithm has been developed, based on the work in (Bastani, 2001), that processes RMT field data. Impedance, resistivity among other quantities is calculated. One can then export this data and use it in existing modeling software. New processing parameters has been implemented and its effect on the data set investigated. The software is implemented in matlab, and tested with synthetic data and data measured at Blötberget, Sweden. However, work is still to be done due to field equipment malfunction during measurements at Blötberget. This rendering an, at least partly, faulty data set. Therefore one cannot completely exclude calibration issues until a clean data set is taken. / Den radio magnetotelluriska (RMT) metoden har sitt ursprung i (Tikhonov, 1950) och (Cagniard, 1953) arbete där strukturen hos geologisk resistivitet uppskattas med hjälp av naturligt förekommande elektriska jordströmmar. Detta genom att mäta den elektromagnetiska vågimpedansen vid markytan. Sedan använder (Paal, 1965) sig av elektromagnetiska planvågor från lågfrekventa radiosändare (10 − 30kHz), som artificiellt inducerar jordströmmar, för att uppskatta resistiviteten i mer ytligt underliggande geologisk struktur. Det är således källan till den inducerade jordströmmen som namngett RMT-metoden. Det är sedan påvisat av (Bastani,2001) att man kan använda sig av frekvenser i ett bredare spektrum (10 − 250kHz) för att få ökad vertikal upplösning. Slutligen påverkar frekvensen hos den elektromagnetiska vågen inträngningsdjupet och på så sätt tillåts det att uppskatta resistiviteten vid ett särskilt djup. I denna studie har en databehandlingsalgorithm utvecklats, baserat på arbete utfört av (Bastani, 2001), som bearbetar RMT fältdata. Impedans och resistivitet samt andra storheter beräknas för att sedan kunna exporteras och användas i existerande modelleringsprogramvara. Programvaran är skriven i matlab, och dess funktionalitet har prövats med syntetisk data samt data från Blötberget, Sverige. Även påverkan av olika processparametrar har undersökts. Detta har gjorts med framgång, men arbete återstår för att fullständigt verifiera korrekt implementering då utrustningen som användes för insamling av fältdata vid Blötberget felade. Därför kan kalibreringsproblem i programvaran inte helt uteslutas.

La plupart des médicaments développés pour les maladies du SNC n’atteignent pas leur cible en raison des propriétés uniques de la BHE, nécessitant la mise en place de stratégies de délivrance comme l'utilisation d'un processus physiologique, le RMT. Des peptides ciblant le LDLR (exprimé à la BHE et impliqué dans ces processus) ont été développés. Les objectifs de cette thèse ont été de caractériser le trafic intracellulaire et la capacité de transport de différentes formes de ces peptides dans différents modèles in vitro y compris dans un modèle de BHE.Les résultats obtenus dans une lignée cellulaire surexprimant le LDLR tagué GFP par imagerie en fluorescence montrent que les différentes formes de ces peptides lient le LDLR à la membrane plasmique d’où ils sont internalisés et adressés aux lysosomes sans interférer avec l’endocytose des LDL. Ils permettent l’adressage aux lysosomes de petites molécules (fluorochrome) et de protéines qui leur sont fusionnées, ces résultats indiquent qu’ils pourraient être utilisés pour cibler des molécules thérapeutiques aux lysosomes de cellules exprimant les LDLR. Dans le modèle in vitro de BHE, les peptides sont internalisés via le LDLR à partir du pôle apical et suivent un transport intracellulaire similaire aux LDL, étant déroutés de la voie de dégradation vers les lysosomes pour être transportés jusqu’au compartiment abluminal comme précédemment décrit pour le LDL et la transferrine. Ces données indiquent donc que les peptides ciblant le LDLR sont des candidats vecteurs intéressants pour compléter/améliorer le panel de peptide/anticorps existant et permettre le ciblage et le transport de molécules thérapeutiques à travers la BHE. / Many drugs are ineffective in treating CNS diseases due in part to unique properties of the BBB, requiring the establishment of delivery strategies such as the use of a physiological process, as the RMT. Peptides targeting the LDLR (expressed in the BBB and involved in these processes) have been developed. The objectives of this thesis were to characterize the intracellular traffic and transport capacity of different shapes of these peptides in various in vitro models including a model of BBB.The results obtained in a cell line overexpressing the LDLR tagged GFP by fluorescence imaging shows that the various forms of these peptides bind plasma membrane LDLR, where they are internalized and sent to lysosomes without interfering with LDL endocytosis. They allow lysosomal targeting of small molecules (fluorochrome) and proteins that are fused to them. These results indicate that it might be used to target therapeutic compounds to cells expressing LDLR lysosomes. In the in vitro BBB model, the peptides are internalized via the LDLR from the apical pole and follow a similar intracellular transport than LDL, being diverted from the lysosomal degradation pathway to be transported to the abluminal compartment as previously described for LDL and transferrin. These data indicate that the LDLR-targeting peptides seems useful vectors candidates to complete/improve the existing peptide/antibodies panel and allow the targeting and the transport of therapeutic molecules through the BBB.

SAMMANFATTNING Datum:                      2024-05-29   Nivå:                          Kandidatuppsats i företagsekonomi, 15 Hp Institution:                 Akademin för Ekonomi, Samhälle och Teknik, Mälardalens Universitet Författare:            Nimo Mahamed Elmi          Hoodo Muhumud             Nimo Hassan                                                         02/07/13                           02/07/05                     02/03/02 Titel:                          Rykteshantering inom banksektorn.  Handledare:               Dariusz Osowski Nyckelord:                 Rykteshantering, digitalisering, AI, legitimitet, RMT Forskningsfråga:       Hur har digitaliseringen, speciellt artificiell intelligens påverkat                               hanteringen av svenska bankers digitala rykte?  Syfte:       Syftet med studien är att utforska fenomenet rykte och dess hantering i en digitaliserad affärsmiljö. Utgångspunkten för studien är att göra en fördjupad undersökning av hur AI har påverkat hanteringen av ryktesrisker. Metod:     Uppsatsen är en kvalitativ studie med strukturerad intervjumetod. Insamlingen av empirin gjordes genom intervjumetodik och gör empirin primärkälla. Slutsats:     AI och digitaliseringen generellt har förändrat banksektorn på många sätt. Bland   annat övervakningen där avancerade algoritmer och system används för att förbättra säkerheten och hanteringen av ryktesrisker. Den har också effektiviserat bankernas arbetsprocess och avlastat personalens arbetsbörda. Slutligen har den även ökat banktillgängligheten genom att erbjuda kunderna digitala plattformar som förenklat kommunikationen mellan organisation och intressent. Genom dessa strategier etablerar bankerna en hållbar digital närvaro. / ABSTRACT Date:           2024-05-29 Level:        Bachelor thesis in Business Administration, 15 cr Institution:       School of Business, Society and Engineering, Mälardalen University Authors:         Nimo Mahamed Elmi                     Hoodo Muhumud                 Nimo Hassan                                                               02/07/13                         02/07/05                      02/03/02 Title:                      Reputation management in banking sector. Supervisor:             Dariusz Osowski Keywords:               Reputation management, digitalization, AI, legitimacy, RMT Research question:   How has digitalization, especially artificial intelligence, affected the management of Swedish bank´s digital reputation? Purpose:                 The aim of the study is to explore the phenomenon of reputation and its management in a digitized business environment. The starting point for the study is to conduct an in-depth investigation into how AI has affected the management of reputational risks. Method:              The study is a qualitative study with a semi structured interview method. The collection of the empirical evidence was done through interview methodology and makes the empirical evidence a primary source. Conclusion:         AI and digitization in general have changed the banking sector in many ways. Among other things, monitoring where advanced algorithms and systems are used to improve security and the management of reputational risks. It has also streamlined the banks' work process and relieved the staff's workload. Finally, it has also increased banking accessibility by offering customers digital platforms that have simplified communication between organization and stakeholders. Through these strategies, the banks establish a sustainable digital presence.

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abstract: In recent years, there are increasing numbers of applications that use multi-variate time series data where multiple uni-variate time series coexist. However, there is a lack of systematic of multi-variate time series. This thesis focuses on (a) defining a simplified inter-related multi-variate time series (IMTS) model and (b) developing robust multi-variate temporal (RMT) feature extraction algorithm that can be used for locating, filtering, and describing salient features in multi-variate time series data sets. The proposed RMT feature can also be used for supporting multiple analysis tasks, such as visualization, segmentation, and searching / retrieving based on multi-variate time series similarities. Experiments confirm that the proposed feature extraction algorithm is highly efficient and effective in identifying robust multi-scale temporal features of multi-variate time series. / Dissertation/Thesis / M.S. Computer Science 2013

Geophysical methods are non-invasive and allow an effective way of understanding subsurface structures and their physical properties. One of the main challenges is the often non-uniqueness of the geophysical models and that several different models can explain a dataset to an agreeable fit. Moreover, noise and limitations in resolution, which are inherent to field data, are additional obstacles for obtaining a true physical property model of the subsurface. Facing all these challenges, geophysicists have dedicated their efforts for decades to recover models that represent, as close as possible, the true subsurface. Joint inversion and integration of multiple geophysical data are two main approaches that I studied to better resolve subsurface structures. I further used these approaches, together with new software and hardware implementations for data acquisition and inversion, for near-surface applications.  In this thesis, radio-magnetotelluric (RMT), boat-towed RMT, boat-towed controlled source MT (CSMT), electrical resistivity tomography (ERT), and first-arrival traveltime tomography are jointly used for quick clay investigations and fracture zone delineation under shallow water-bodies. The joint approach, as compared with any individual method, shows a better ability to both resolve the geological targets and to assist in understanding the subsurface geology that hosts these targets. For examples: by performing the joint inversion of lake-floor ERT and boat-towed RMT data, a fracture zone is better delineated with greater details compared with single inversion; by employing boat-towed CSMT measurements and jointly inverting with boat-towed RMT data, the subsurface structures, especially at greater depth, are better resolved than by inverting each dataset alone. During my PhD studies, two types of new implementations were employed. (1) Boat-towed data acquisition system was implemented to expand the RMT and CSMT method from land to shallow-water applications. This is significant since many large-scale underground infrastructures are likely to cross these water zones (for example multi-lane train or bypass tunnels, such as the Stockholm bypass). (2) The modification of a well-structured code EMILIA allows joint inversion of boat-towed RMT and lake-floor ERT datasets, and the modification of another well-structured code MARE2DEM can accurately model high frequency CSMT data and handle joint inversion of boat-towed RMT and boat-towed CSMT datasets. Thus, the code modification as another type of new implementation guarantees the success of near-surface applications using the boat-towed RMT and CSMT data acquisition systems. Studies conducted during my PhD work, included under the SEG-GWB (the Society of Exploration Geophysicists - Geoscientists Without Borders) program and the TRUST (TRansparent Underground STructure) umbrella project, are useful for near-surface applications including, for examples, engineering purposes such as planning of underground infrastructures, site characterization in connection with energy or waste storage, and geohazard investigations.

Radio Communication technologies are undergoing drastic demand over the past two decades. The precious radio resource, electromagnetic radio spectrum, is in vain as technology advances. It is required to come up with a solution to improve its wise uses. Cognitive Radio enabled by Software-Defined Radio brings an intelligent solution to efficiently use the Radio Spectrum. It is a method to aware the radio communication system to be able to adapt to its radio environment like signal power and free spectrum holes. The approach will pose a question on how to efficiently detect a signal. In this thesis different spectrum sensing algorithm will be explained and a special concentration will be on new sensing algorithm based on the Eigenvalues of received signal. The proposed method adapts blind signal detection approach for applications that lacks knowledge about signal, noise and channel property. There are two methods, one is ratio of the Maximum Eigenvalue to Minimum Eigenvalue and the second is ratio of Signal Power to Minimum Eigenvalue. Random Matrix theory (RMT) is a branch of mathematics and it is capable in analyzing large set of data or in a conclusive approach it provides a correlation points in signals or waveforms. In the context of this thesis, RMT is used to overcome both noise and channel uncertainties that are common in wireless communication. Simulations in MATLAB and real-time measurements in LabVIEW are implemented to test the proposed detection algorithms. The measurements were performed based on received signal from an IF-5641R Transceiver obtained from National Instruments.

The resolution power of single Very Low Frequency (VLF) data and multi-frequency Radiomagnetotelluric (RMT) data in delineating conductive structures typical for the sedimentary cover and crystalline basement in Scandinavia is studied with a view to future developments of the technique to increasing the frequency range into the LW radio band. Airborne and ground VLF data are interpreted and correlated with RMT measurements made on the ground to better understand the resolution power of VLF data. To aid in this understanding single and multifrequency VLF and RMT responses for some typical resistivity structures are analyzed. An analytic model is presented for obtaining unique transfer functions from measurements of the electromagnetic components on board an air-plane or on the ground. Examples of 2D inversion of ground and airborne VLF profiles in Sweden are shown to demonstrate the quantitative interpretation of VLF data in terms of both lateral and depth changes of the resistivity in the uppermost crust. Geothermal resources are ideal targets for Electromagnetic (EM) methods since they produce strong variations in underground electrical resistivity. Modelling of Magnetotelluric (MT) data in SW Iceland indicates an alteration zone beneath the surface, where there are no obvious geothermal manifestations, in between Hengill and Brennisteinsfjoll geothermal systems. It suggests that a hydrothermal fluid circulation exists at depth. It also proves that the MT method, with its ability to map deep conductive features can play a valuable role in the reconnaissance of deep geothermal systems in active rift regimes such as in Iceland. A damped nonlinear least-squares inversion approach is employed to invert Controlled Source Tensor MT (CSTMT) data for azimuthal anisotropy in a 1D layered earth. Impedance and tipper data are inverted jointly. The effects of near-surface inhomogeneities are parameterized in addition to each layer parameter(s). Application of the inversion algorithm to both synthetic and field data shows that the CSTMT method can be used to detect azimuthal anisotropy under realistic conditions with near surface lateral heterogeneities.