This report presents FastBrush, an advanced implementation for real time brush simulation, which achieves high detail with a large amount of bristles, and is lightweight enough to be implemented for mobile devices. The final result of this system has far higher detail than available consumer painting applications. Paintbrushes have up to a thousand bristles. Adobe Photoshop is only able to simulate up to a hundred bristles in real-time, while FastBrush is able to capture the full detail of a brush with up to a thousand bristles in real-time on mobile devices. Simple multidimensional data driven modeling is used to create a deformation table, which enables calculating the physics of the brush deformations in near constant time for the entire brush, and thus the physics calculation overhead of a large number of bristles becomes negligible. The results show that there is a large potential for use of data driven models in high detail brush simulations. / Denna rapport presenterar FastBrush, en avancerad implementation för realtidssimulation av penselmålning som uppnår hög detalj med en stor mängd penselstrån, samt är snabb nog att implementeras för mobila enheter. Det slutgiltliga resultatet av denna implementation har mycket högre detail än nuvarande tillgängliga konsumentapplikationer. Penslar har ett tusen individuella penselstrån. Adobe Photoshop är begränsad till att simulera maximum ett hundra penselstrån, medan FastBrush kan uppnå fullständig detaljrik återgivning med upp till ett tusen penselstrån i realtid på mobila enheter. Enkel multidimensionell datadriven modellering används för att skapa en deformationstabell, vilket möjliggör att beräkna fysiken för penselns deformation i nära konstant tid, och därför blir de kostnaden av fysikkalkylationerna för ett högt antal individuella penselstrån försummbar. Resultaten visar att det finns stor potential för användning av datadrivna modeller i högdetaljerade penselsimulationer.

In general, at smaller companies, decisions are based on the intuition of their experts within their respective areas. The decision processes are dependent on several aspects, such as assumptions and context, and some on data. Over the last year, the increase in data flow has enabled SMEs to make a decision in a systematic and planned process referred to as data-driven decision-making(DDM). Small-medium enterprises (SME) companies have been affected by enabling aspects. However, research shows challenges for SMEs trying to develop their DDM. To address these challenges, this thesis aims to propose a process to assess and develop data-driven decision-making in an SME within the manufacturing industry. The study has been made with a qualitative approach. In addition, a case study of an SME within the manufacturing industry has been done to study the phenomenon in a real-life situation. The data collection was conducted by a literature review, interviews, and planned and unplanned observations. The literature review showed that different aspects affect the development of DDM. The aspects discussed were the decision-making process, technology and organisational factors such as general change, organisational culture, resistance to change, management and the last aspect, Data quality. A maturity assessment model was discussed to introduce the ability to assess a company's current state. The empirical data discussed two main aspects: the current state and the desired future state. The empirical findings showed that there were three main levels of decision-making in the current state: Operator level, Production level, and Management level. The desired state discusses data expectations, which provides a view of the company's perception of what data is and how it is used. In the analysis, there were two main challenging aspects identified from the empirical and theoretical data, and these were organisational and technological factors. The challenges related to technological factors were identified, such as digital adaptation, technological uncertainties and data quality. The challenges related to Organisational factors were the decision-making process, adaptation to change, organisational culture and data quality. Based on these challenges and the evaluation of the maturity model and application process, a different proposed application process was created to help organisations develop their DDM. Some of the challenges identified within the SME company connect to the challenges found in theory, and they bring future support that these challenges are present in real-life situations. An aspect that was identified as both a technological factor and an organization is the need for data quality and evaluation of it within the organisation. It shows that this is a critical aspect that must be considered when developing DDM.Keywords: Data-driven decision-making, Techno

<p>Mass spectrometry imaging (MSI) is a label-free technique that enables mapping hundreds of molecules in biological samples with high sensitivity and molecular specificity. MSI experiments usually sample a virtual grid of pixels on a sample surface. A full mass spectrum is acquired at each pixel. Typically, a single MSI experiment generates hundreds of thousands of spectra, each containing thousands of molecular features. The size of MSI data keeps increasing with MSI technology improvements in spatial resolution and molecular coverage. Subsequent interpretation of the vast and complex MSI data is a major bottleneck for deriving biological conclusions from the experimental results. In chapter 1, I review recently emerging computational methods in MSI for data analysis and “smart” experiments. I also provide a outlook for a future paradigm shift in MSI with transformative computational methods.<br> </p> <p>In my research, I have developed several approaches for the analysis and mining of the MSI data in a data-driven manner. In chapter 2, I introduce a vendor-neutral data processing pipeline for visualizing ion images from MSI data, which supports both standard and unconventional MSI acquisition strategies. In chapter 3, a spatial segmentation method is described. This method combines matrix factorization and manifold learning to enable the identification of distinct cells or tissue subregions in an unsupervised manner. In chapter 4, I describe a self-supervised approach for identifying and clustering colocalized molecules using contrastive learning, which helps analyze molecular pathways in biological samples. In chapter 5, I introduce a precise image registration method for studying individual fibers in mouse muscle tissue using multimodal MSI and immunofluorescence imaging. The locations of different types of muscle fibers obtained from immunofluorescence images are registered to MSI space, which enables biomarker discovery based on spatially resolved metabolomics and lipidomics data.</p> <p><br> Computational methods also provide powerful strategies for enhancing MSI capabilities, such as throughput, molecular coverage, and specificity. In chapter 6, I describe a “smart” sampling method for enhancing the experimental throughput of MSI. In collaboration with Prof. Dong Hye Ye’s group at Marquette University, we have developed a deep learning algorithm for sparse sampling (DLADS), which dynamically estimates molecularly informative tissue locations and guides sampling in MSI experiments. We coupled DLADS with nanospray desorption electrospray ionization (nano DESI) MSI platform through software and hardware integration. This approach preferentially samples informative tissue locations and reconstructs high-fidelity\ ion images with sparse MSI data, which improves the throughput of nano-DESI MSI experiments by 2.3-fold.<br>  </p>

Örebro University and Akademiska Hus have started an initiative towards smart buildings. Avery important role to this is Digital Twin for buildings. Digital twin for buildings is a virtualcopy of a physical building. And by adding a Data Driven Simulation System, an even moresmart building could be achieved. Given a humidity-, temperature-, illuminance- and motionsensor in a specific corridor at the Örebro University, this thesis will ascertain what can bedone by creating a Data Driven Simulation System and using these sensors to achieve thedesired smart building. In this thesis, a simulation was created with simulated sensors andpedestrians. The simulation is a clone of the real world, by using real life sensors andapplying the data to the simulated sensors, this was partially achieved.

Thesis advisor: Larry H. Ludlow / Teachers in the United States are increasingly tasked with using data to inform their classroom instruction both through federal policies, such as the Every Student Succeeds Act (ESSA, 2016), and state policies requiring the use of teacher-determined data-driven goals for performance evaluations (MA 603 CMR 35.07). Many teachers, however, report that they feel underprepared to engage in this type of work (Dunn et al., 2013), also called Data-Driven Decision Making (DDDM). In addition, there is currently a limited set of instruments to measure the construct of using data to inform classroom instruction and the instruments that currently exist measure this construct using a typical Classical Test Theory design.This work developed an instrument called the Using Data to Inform classroom Instruction (UDII) scale to measure teachers’ use of data to inform classroom instruction. It used the Rasch/Guttman Scenario (RGS) methodology, an approach that develops scenarios that reflect the rich lived experiences of individuals (Antipkina & Ludlow, 2020; Ludlow et al., 2014). The RGS approach utilizes the Rasch model, part of the family of Item Response Theory models, which conceptualizes a construct as a hierarchical continuum. Scenario items and people are plotted on the same variable map, which allows for the development of rich descriptions of individuals at particular raw score locations on the continuum. An interpretative variable map is included to help schools and districts use the results of the survey. This work adds to the growing body of literature utilizing the RGS approach, as well as the literature focused on the use of data to inform classroom instruction (or DDDM). The UDII scale can be utilized by schools and districts who are engaged in the work of using data to inform classroom instruction to identify the current skillsets of teachers and/or teams of teachers to provide differentiated support, or it can be used before and after an intervention focused on using data to inform classroom instruction to measure change. / Thesis (PhD) — Boston College, 2022. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

Background: With the increasing number of software users using social media forums, providing feedback about the OSS projects, the developer’s need to address this feedback to understand the requirements of an OSS project. As different tools support different structures for the feedback, the need to classify, prioritize and filter them into a fundamental set of categories persists. Some of the feedback includes images from users, along with the text. These images may vary from a screenshot of the bug, encountered by the user to a code snippet modification as required by the user. The significance of how these images help the developers in fixing the bug is not clear. Objectives: This thesis aims to identify the underlying advantages of using images in the feedback or bug report submitted by the user for an OSS project to the developers. The goal is to find the extent to which different image attributes help the developer’s in understanding the issue suggested in the feedback or bug report. The research also aims to classify the view of practitioners regarding which image attributes affect the most and to propose a simple DSS model that can possibly be used by users and developers while attaching images in the feedback or bug reports. Methods: In this research, we have conducted an empirical study using systematic mapping and a survey study. We identified 28 research articles form systematic mapping using a search string and snowballing process to extract different image attributes. To triangulate and verify the results of the systematic map, we have conducted an online questionnaire replied by 32 respondents experienced in contributing to the OSS community. The usability of the image attributes has been evaluated from the responses received. Both quantitative and descriptive statistical analysis techniques were used to analyze the results. Results: From the 28 research articles identified for the systematic mapping study, we have extracted 11 image attributes that influence the developers in interpreting the software requirements from the images attached to feedback or bug reports. Of the identified image attributes, image quality and image resolution are considered to be the most useful attributes by the survey respondents. Moreover, two new image attributes (timestamp and steps to reproduce) are reported from the survey study. Conclusions: The identification and validation of the image attributes suggest the potential use of images in feedback and bug reports. Furthermore, these image attributes provide additional information to the developers in understanding the software requirements from the users perspective clearly. We propose a simple DSS model that can be used by the users and the developers before attaching an image along with the feedback or the bug reports to the developing OSS communities to promote further usage of images in feedback and bug reports for OSS.

Empirical models based on linear and nonlinear potential theory that determine the forces on Wave Energy Converters (WECs) are essential as they can be used for structural, mechanical and control system design as well as performance prediction. In contrast to empirical modelling, Computational Fluid Dynamics (CFD) solves the mass and momentum balance equations for fluid domains. CFD and linear potential theory models represent two extreme in terms of capturing the full range of hydrodynamic effects. These are classified as white box models and the structure of these models is completely derived from first principles understanding of the system. In contrast black box models like a Artificial Neural Networks and Auto-Regressive with, Exogenous Input (ARX), map input and output behaviour of a system without any specific physics based structure. Grey box models do not strictly follow a first principles approach but are based on some observations of relationships between the hydrodynamic effects (e.g. buoyancy force) and system state (e.g. free surface height). The objective of this thesis is to propose a data driven grey box modelling approach, which is computationally efficient compared to high fidelity white box mod- els and still sufficiently accurate for the purpose of determining hydrodynamic forces on heaving WECs. In this thesis, a unique data driven approach that combines features from existing works in modelling of WEC and application of nonlinear hysteretic systems is developed. To that end a CFD based Numerical Wave Tank that could provide the data needed to populate the new modelling framework is used. A hull which hydrodynamically represents a Self Reacting Point Absorbers (SRPAs) with heave plate is subjected to pan-chromatic wave fields and is forced to oscillate concomitantly. The results provide evidence that a SRPA with heave plate exhibits nonlinear relationships with motion parameters including relative position, velocity and acceleration. These parameters show causal relationships with the hydrodynamic force. A simulation methodology to establish confidence in the components of a model framework is developed and the hydrodynamic forces on SRPAs with heave plate and bulbous tank have been analyzed and compared. Two sets of numerical simulation were conducted. Firstly, the WECs were restricted to all degrees of freedom and subjected to monochromatic waves and later the WECs were oscillated at various frequency in a quiescent numerical tank. These results were validated against existing experimental data. Earlier attempts by other authors to develop a data-driven model were limited to simple hulls and did not include rate dependent nonlinearities that develop for heave plates. These studies laid the foundation to current work. The model framework developed in this thesis accounts for the nonlinear relationship between force and parameters like velocity and acceleration along with hysteretic relationship between force and velocity. This modelling framework has a nonlinear static, a hysteresis (Bouc-Wen model) and a dynamic (ARX model) block. In this work the Bouc-Wen model is employed to model the hysteresis effect. Five different models developed from this modelling framework are analyzed; two are state dependent models, while the other three required training to identify dynamic order of model equations. These latter models (Hammerstein, rate dependent Hammerstein and rate dependent KGP models) have been trained and validated for various cases of fixed and oscillating HP cylinder. The results demonstrate significant improvement (max 39%) in prediction accuracy of hydrodynamic forces on a WEC with heave plate, for the model in which a rate dependent hysteresis block is coupled with Hammerstein or KGP models. / Graduate

AbstractThe increasing amount of data available from software systems has given a unique opportunity for software development organizations to make use of it in decision-making. There are several types of data such as bug reports, website interaction information, product usage extent or test results coming into software-intensive companies and there is a perceived lack of structure associated with the data. The data is mostly scattered and not in an organized form to be utilized further. The data, if analyzed in an effective way, can be useful for many purposes, especially in decision-making. The decisions can be on the level of business or on the level of product execution. In this paper, through a literature review, an interview study and a qualitative analysis we categorize different types data that organizations nowadays collect. Based on the categorization we order the different types of decisions that are generally taken in a software development process cycle. Combining the two we create a model to explain a recommended process of handling the surge of data and making effective use of it. The model is a tool to help both practitioners and academicians who want to have a clearer understanding of which type of data can best be used for which type of decisions. An outline of how further research can be conducted in the area is also highlighted.

The pervasive nature of technological advancements has increased the complexity, and thus the environmental volatility that span well across the borders of industries and na-tions. It could be argued that the need for firms to demonstrate dynamic capabilities are greater than ever before. In this conceptual study we take an exploratory approach to understand how dynamic capabilities are dynamically interconnected with digital trans-formation and the consequences this has on the business model(s) and further, the over-arching business strategy. This is realized through the introduction of a conceptual framework for “data-driven dynamic capabilities” which constitutes that there is a dynam-ic interconnectivity at play between the dynamic capabilities and digital transformation themes. Our findings suggest that the dynamic conflation between the two help fuel one another and that the firm’s business model(s) ought to be congruent with its data-driven dynamic capabilities. Further our findings suggest that there is a feedback loop between the firm’s overarching business strategy and its data-driven dynamic capabilities. Thus, the implications of this conceptual paper will be to create new value, adding knowledge and new theoretical trajectories into the field with the help of the integrative conceptual framework introduced in our study.

Debris-slides are one of the most frequently occurring geological hazards in metasedimentary rocks of the Anakeesta ridge in Great Smoky Mountains National Park (GRSM), which often depends on the influence of multiple causing factors or geo-factors such as geological structures, slope, topographic elevation, land use, soil type etc. or a combination of these factors. The main objective of the study was to understand the control of geo-factors in initiating debris-slides using different knowledge and data-driven methods in GIS platform. The study was performed in three steps: (1) Evaluation of geometrical relationship between geological discontinuity and topographic orientation in initiation of debris-slides, (2) Preparation of knowledge-driven debris-slide susceptibility model, and (3) Preparation of data-driven debris-slide susceptibility models and compare their efficacy. Performance of the models were evaluated mostly using area under Receiver Operating Characteristic (ROC) curve, which revealed that the models were statistically significant.