No / We introduce a new "marker-based" model for use in capturing equine movement. This model is informed by a sound biomechanical study of the animal and can be deployed in the pursuit of many undertakings. Unlike many other approaches, our method provides a high level of automation and hides the intricate biomechanical knowledge required to produce realistic results. Due to this approach, it is possible to acquire solved data with minimal manual intervention even in real-time conditions. The approach introduced can be replicated for the production of many other animals. The model is first informed by the veterinary world through studies of the subject's anatomy. Second, further medical studies aimed at understanding and addressing surface processes, inform model creation. The latter studies address items such as skin sliding. If not otherwise corrected these processes may hinder marker based capture. The resultant model has been tested in feasibility studies for practicality and subject acceptance during production. Data is provided for scrutiny along with the subject digitally captured through a variety of methods. The digital subject in mesh form as well as the motion capture model aid in comparison and show the level of accurateness achieved. The video reference and digital renders provide an insight into the level of realism achieved.

Purpose - This study explores how knowledge exchange between sales and marketing can enhance data-driven marketing initiatives for firms in the high-tech process industry. Additionally, the study aims to identify the factors that drive alignment and collaboration between sales and marketing interfaces from a dynamic capability perspective. Method - This master's thesis is an exploratory study with an inductive approach. 10 qualitative interviews were conducted with employees from a high-tech process automation company, specifically those working in marketing and sales roles. The interviews follow a semi-structured approach, and a thematic analysis was performed to examine the empirical findings. Findings - The study emphasizes the significance of collaboration, knowledge exchange, and functional alignment between sales and marketing, in the context of data-driven marketing and the sales lead generation. By applying a dynamic capability framework, the study sheds light on how firms can leverage knowledge exchange and functional alignment to capitalize on market opportunities and gain a competitive advantage.  Theoretical and practical contributions - The study delves into the underexplored realm of data-driven marketing within the high-tech process industry, particularly focusing on the intricate dynamics between sales and marketing functions during the lead generation process. Through its analysis, the research not only enriches theoretical understanding but also offers practical insights for managers in the high-tech process industry, providing some recommendations to enhance collaboration, knowledge exchange, and optimize data-driven marketing initiatives. / Syfte - Denna studie undersöker hur kunskapsutbyte mellan försäljning och marknadsföring kan förbättra datadrivna marknadsföring initiativ för företag inom högteknologisk processindustri. Dessutom syftar studien till att identifiera de faktorer som driver linjering och samarbete mellan försäljning och marknadsföring, ur ett perspektiv från teorier inom Dynamic Capabilities.  Metod - Denna uppsats är en explorativ studie med en induktiv ansats. 10 kvalitativa intervjuer har genomförts med anställda på ett företag inom högteknologisk process automation, specifikt de som arbetar inom marknadsföring och försäljning. Intervjuerna har följt en semi-strukturerad metod och en tematisk analys har genomförts för att undersöka de empiriska resultaten. Resultat - Studien betonar vikten av samarbete, kunskapsutbyte och funktionell linjeringen mellan försäljning och marknadsföring, i kontexten av datadriven marknadsföring och sales lead generation. I studien har teorier inom Dynamic Capabilities belyst studien, mer specifikt hur företag kan utnyttja kunskapsutbyte och funktionell linjeringen för att utnyttja marknadsmöjligheter och skapa konkurrensfördelar. Teoretiska och praktiska bidrag - Studien utforskar det underutvecklade området datadriven marknadsföring inom högteknologisk processindustri, med särskilt fokus på de komplexa dynamikerna mellan försäljnings- och marknadsförings funktioner under processen för sales lead generation. Genom sin analys berikar forskningen inte bara den teoretiska utan erbjuder också praktiska insikter för chefer inom högteknologisk processindustri, med rekommendationer för att förbättra samarbete, kunskapsutbyte och optimera datadrivna marknadsföring initiativ.

Artificial intelligence is a technology that many individuals view favourably intoday's world. Where the technology contributes to a variety of benefits when usedthat can help individuals perform specific tasks. It has been shown from previousresearch that individuals' willingness to use artificial intelligence is affected by thetrust they have in the technology. It discusses how working methods and processesare affected by artificial intelligence and how it is of utmost importance to promotethe individual's trust in the technology to later promote the way artificialintelligence is used. In the previous research, various methods and models havebeen identified and demonstrated to measure and build appropriate trust in AI aswell as for various variables that affect the individual's trust and thus willingnessto use artificial intelligence as a technology.The study aims to study trust in and use of artificial intelligence from a user'sperspective where the individual is in focus. The study thus aims to study theimpact of trust on the use of the technology, any variables that affect users' trustin the technology and what is important in the implementation and use of artificialintelligence to promote trust and the use of artificial intelligence. This is tocontribute with nuanced knowledge of what is required to promote effectivecollaboration between humans and artificial intelligence when performing varioustasks.The study contains a qualitative and inductive approach where empirical data wascollected through open individual interviews and observations where respondentsinteracted with an AI tool and where they performed simple use cases. Throughcontent analysis, the empirical material has created a nuanced andknowledgeexpanding view of the phenomenon and identified aspects andvariables with an impact on individuals' trust and use of artificial intelligence.These are areas that, according to respondents, have a major impact on decisionsto use or not use artificial intelligence tools.The results of the study show that there are aspects and variables around trust thathave a major impact on decisions to use AI tools. These are thus important to keepin mind when implementing AI tools to promote trust and thus the use of artificialintelligence

Understanding the behavior of unsaturated soils under multi-physics loading is crucial for addressing several challenges related to emerging geo-energy technologies, climate change, and geohazard mitigation. The main objective of this research is to enhance the state of the art for modeling unsaturated soils under multi-physics loading conditions through physics-based and data-driven approaches. Toward this objective, this dissertation is divided into two distinct yet interrelated parts. In the first part (Chapters 2, 3, and 4), thermodynamic principles are employed to develop new constitutive functions to describe soil suction and stress-dilatancy of unsaturated soils under temperature-dependent conditions. Building upon the insights gained from the first part, the second part of the dissertation (Chapter 5) presents a novel physics-informed data-driven constitutive model for unsaturated soils under thermo-hydro-mechanical (THM) conditions via a generative machine learning method. In Chapter 2, a novel model for temperature-dependent suction is developed by expanding the Gibbs free energy to address both internal chemical exchanges and the thermodynamic system as a whole. The suction model is then utilized in Chapter 3 to establish a stress-dilatancy relationship for unsaturated soils, considering the difference between net and effective stresses to derive equations for dissipative and internal energies. The proposed dilatancy model is extended in Chapter 4 to temperature-dependent conditions considering various sources of energy dissipation, including entropy, water flow, friction, and energies associated with volume change and soil grain rearrangement. Based on the insights gained from the first section, Chapter 5 develops a new physics-informed data-driven constitutive model by utilizing a hybrid Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) framework to predict the THM response of unsaturated soils. The hybrid VAE-GAN model integrates physical constraints such as physical consistency and conservation laws. Experimental data from triaxial tests under varying THM conditions were used to train, validate, and test the model. The findings of this research demonstrate the great yet unexplored potential of physics-informed data-driven models for modeling THM problems in unsaturated soils, offering a powerful tool for future research and practical applications.

Dynamic operation of dispatchable energy sources is crucial for enabling the efficient integration of intermittent renewable energy into the electricity grid. Coal-fired boilers have been required to increase transient operation as renewable energy expands in order to avoid excess renewable energy going to waste. The frequent transient operation required to meet residual energy demand has created a challenge for coal-fired units to operate efficiently. This work utilizes an Advanced Sensor Network (ASN) to calculate Net Unit Heat Rate (NUHR) of a coal-fired boiler in real time through combustion calculations and statistical correlations to provide the tools for optimizing dynamic operation. Real-time heating values that were necessary to determine fuel input energy to calculate accurate NUHR were found using both fundamental and data-driven methods. Real-time NUHR shows distinct shifts that reflect changes in process conditions that improve the ability to optimize transient operation. Data-driven heating value correlations had 24% lower Root Mean Square Error (RMSE) than the fundamental combustion calculation approach when compared to daily retrospective proximate analysis. The data-driven method RMSE improved by 7% with the inclusion of ASN data. The performance of the boiler was statistically compared before and after the inclusion of real-time NUHR. Models were fit to the NUHR results as a function of generation level and confidence bands for the model were used to determine statistical significance in the change in boiler performance. Student's t-tests were also used to compare data at common generation levels. Improvements in NUHR ranging between 0.4% and 1.3% were observed over the typical range of generation levels experienced by the plant. These improvements are estimated to result in yearly savings of about $200,000. The most significant increase in NUHR was at high loads where the plant spends less time at steady operation and was often transient. Overall, the real-time NUHR has enabled dynamic optimization to better control transient operation of the coal-fired boiler.

De plus en plus d’organisations scientifiques contemporaines intègrent dans leur processus des foules de participants assignés à des tâches variées, souvent appelés projets de science citoyenne. Ces foules sont une opportunité dans un contexte lié à une avalanche de données massives qui met les structures scientifiques face à leurs limites en terme de ressources et en capacités. Mais ces nouvelles formes de coopération sont déstabilisées par leur nature même dès lors que les tâches déléguées à la foule demandent une certaine inventivité - résoudre des problèmes, formuler des hypothèses scientifiques - et que ces projets sont amenés à être répétés dans l’organisation. A partir de deux études expérimentales basées sur une modélisation originale, cette thèse étudie les mécanismes gestionnaires à mettre en place pour assurer la performance des projets délégués à la foule. Nous montrons que la performance est liée à la gestion de deux types de capitalisation : une capitalisation croisée (chaque participant peut réutiliser les travaux des autres participants) ; une capitalisation séquentielle (capitalisation par les participants puis par les organisateurs). Par ailleurs cette recherche met en avant la figure d’une nouvelle figure managériale pour supporter la capitalisation, le « gestionnaire des foules inventives », indispensable pour le succès des projets. / A growing number of contemporary scientific organizations collaborate with crowds for diverse tasks of the scientific process. These collaborations are often designed as citizen science projects. The collaboration is an opportunity for scientific structures in a context of massive data deluge which lead organizations to face limits in terms of resources and capabilities. However, in such new forms of cooperation a major crisis is caused when tasks delegated to the crowd require a certain inventiveness - solving problems, formulating scientific hypotheses - and when these projects have to be repeated in the organization. From two experimental studies based on an original modeling, this thesis studies the management mechanisms needed to ensure the performance of projects delegated to the crowd. We show that the performance is linked to the management of two types of capitalization: a cross-capitalization (each participant can reuse the work of the other participants); a sequential capitalization (capitalization by the participants then by the organizers). In addition, this research highlights the figure of a new managerial figure to support the capitalization, the "manager of inventive crowds", essential for the success of the projects.

[pt] Este trabalho propõe um modelo de otimização robusta de pior caso orientado por dados aplicado na seleção de um portfólio de títulos de renda fixa. A gestão das carteiras implica na tomada de decisões financeiras e no gerenciamento do risco através da seleção ótima de ativos com base nos retornos esperados. Como estes são variáveis aleatórias incertas foi incluído um conjunto definido de incertezas estimadas diretamente no processo de otimização, chamados de cenários. Foi usado o modelo de ajuste de curvas Nelson e Siegel para construir as estruturas a termo das taxas de juros empregadas na precificação dos títulos, um ativo livre de risco e alguns ativos com risco de maturidades diferentes. Os títulos prefixados são marcados a mercado porque estão sendo negociados antes do prazo de vencimento. A implementação ocorreu pela simulação computacional usando dados de mercado e dados estimados que alimentaram o modelo.Com a modelagem de otimização robusta foram realizados diferentes testes como: analisar a sensibilidade do modelo frente às variações dos parâmetros verificando seus resultados e a utilização de um horizonte de janela rolante para simular o comportamento ao longo do tempo. Obtidas as composições ótimas das carteiras, foi feito o backtesting para avaliar o comportamento das alocações com o retorno real e também a comparação com o desempenho de umbenchmark. Os resultados dos testes mostraram a adequação do modelo da curva de juros e bons resultados de alocação do portfólio robusto, que apresentaram confiabilidade até em períodos de crise. / [en] This paper proposes a data-driven worst case robust optimization model applied in the selection of a portfolio of fixed income securities. The portfolio management implies in financial decision-making and risk management through the selection of optimal assets based on expected returns. As these are uncertain random variables, was included a defined set of estimated uncertainties directly in the optimization process, called scenarios. The Nelson and Siegel curve fitting model was used to construct the term structure of the interest rates employed in the pricing of securities, a risk-free asset and some risky assets of different maturities. The fixed-rate securities are marked to market because they are being traded before the maturity date. The implementation took place through computational simulation using market data and estimated data that fed the model. With robust optimization modeling were done different tests such as: analyze the sensitivity of the model to the variations of the parameters checking the results and the use of a rolling horizon scheme to simulate behavior over time. Once the optimal portfolio composition was obtained, the backtesting was done to evaluate the behavior of the allocations with the real return and also the comparison with the performance of a benchmark. The results of the tests showed the adequacy of the interest curve model and good allocation results of the robust portfolio, which presented reliability even in times of crisis.

Various research has shown the potential and robustness of deep learning-based approaches to synthesise novel motions of 3D characters in virtual environments, such as video games and films. The models are trained with the motion data that is bound to the respective character skeleton (rig). It inflicts a limitation on the scalability and the applicability of the models since they can only learn motions from one particular rig (domain) and produce motions in that domain only. Transfer learning techniques can be used to overcome this issue and allow the models to better adapt to other domains with limited data. This work presents a study of three transfer learning techniques for the proposed Objective-driven motion generation model (OMG), which is a model for procedurally generating animations conditioned on positional and rotational objectives. Three transfer learning approaches for achieving rig-agnostic encoding (RAE) are proposed and experimented with: Feature encoding (FE), Feature clustering (FC) and Feature selection (FS), to improve the learning of the model on new domains with limited data. All three approaches demonstrate significant improvement in both the performance and the visual quality of the generated animations, when compared to the vanilla performance. The empirical results indicate that the FE and the FC approaches yield better transferring quality than the FS approach. It is inconclusive which of them performs better, but the FE approach is more computationally efficient, which makes it the more favourable choice for real-time applications. / Många studier har visat potentialen och robustheten av djupinlärningbaserade modeller för syntetisering av nya rörelse för 3D karaktärer i virtuell miljö, som datorspel och filmer. Modellerna är tränade med rörelse data som är bunden till de respektive karaktärskeletten (rig). Det begränsar skalbarheten och tillämpningsmöjligheten av modellerna, eftersom de bara kan lära sig av data från en specifik rig (domän) och därmed bara kan generera animationer i den domänen. Kunskapsöverföringsteknik (transfer learning techniques) kan användas för att överkomma denna begränsning och underlättar anpassningen av modeller på nya domäner med begränsade data. I denna avhandling presenteras en studie av tre kunskapsöverföringsmetoder för den föreslagna måldriven animationgenereringsnätverk (OMG), som är ett neural nätverk-baserad modell för att procedurellt generera animationer baserade på positionsmål och rotationsmål. Tre metoder för att uppnå rig-agnostisk kodning är presenterade och experimenterade: Feature encoding (FE), Feature clustering (FC) and Feature selection (FS), för att förbättra modellens lärande på nya domäner med begränsade data. All tre metoderna visar signifikant förbättring på både prestandan och den visuella kvaliteten av de skapade animationerna, i jämförelse med den vanilla prestandan. De empiriska resultaten indikerar att både FE och FC metoderna ger bättre överföringskvalitet än FS metoden. Det går inte att avgöra vilken av de presterar bättre, men FE metoden är mer beräkningseffektiv, vilket är fördelaktigt för real-time applikationer.

Nowadays, becoming data-driven is the vision of almost all organizations. However, achieving this vision is not as easy as it may look like and there are many factors that affect, enable, support and sustain the data-driven ecosystem in an organization. Among these factors, this study focuses on data democracy which can be defined as the intra-organizational open data that aims to empower the employees getting faster and easier access to data in order to benefit from the business insight they need without the interfere of external help.  In the existing literature, while the importance of becoming data-driven has been widely discussed, when it comes to data democracy within organizations, there is a noticeable gap. As a result, this master’s thesis aims to justify the importance and role of the data democracy in becoming a data-driven organization, focusing on the case of a Swedish bank. Additionally, it intends to provide extra investigation on the role of data analytics tools in achieving data democracy.  The results of the study show that there is a strong connection between the benefits of the empowering different actors of the organization with the needed data knowledge, and the speeding up of the data-driven transformation journey. Based on the study, shared data and the availability of data to a larger number of stakeholders inside an organization result into a better understanding of different aspects of the problems, simplify the data-driven decision making and make the organization more data-driven. In the process of becoming data-driven, the organizations should provide the analytics tools not only to the data specialists but even to the non-data technical people. And by offering the needed support, training and collaboration possibilities between the two groups of employees (data specialists and non-data specialists), it should be attempted to enable the second group to extract the insight from the data, independently from the help of the data scientists.  An organization can succeed in the path of becoming data-driven when they invest on the reusable capabilities of its employees, by discovering the data science skills across various departments and turning their domain experts into citizen data scientists of the organization.

In an increasingly digital world, companies need to be at the forefront of development in order to gain market share and be competitive. Therefore this qualitative case study intends to investigate how Business intelligence should be implemented to increase the technology acceptance among the employees. Furthermore, it is investigated how data-driven a consulting company in Sweden is. An important factor when Business intelligence is about to be implemented and applied are the employees and the company culture. It is important to normalize the collection of data, in order to create a data culture where high quality data is collected. What is more, Business intelligence is strongly dependent on the data collected maintaining a high data quality, in order to be able to create relevant reports and thereby be able to support various decision-making. When implementing a new platform, many employees are affected. This means that the platform can generate both positive and negative reactions. / I en allt mer digitaliserad värld krävs det att företag ligger långt fram i utvecklingen för att kunna ta marknadsandelar och vara konkurrenskraftiga. Därmed ämnar denna kvalitativa fallstudie att undersöka hur Business intelligence kan implementeras för att öka acceptansen för IT-stöd hos de anställda, samt hur datadrivet ett svenskt konsultföretag är. En viktig faktor när Business intelligence ska implementeras och appliceras är de anställda och den kultur som företaget har. Det är viktigt att normalisera insamlandet av data, för att i förlängningen skapa en datakultur där data med hög kvalité samlas in. Vidare är Business intelligence starkt beroende av att den data som samlas in håller en hög datakvalité, för att kunna skapa relevanta rapporter och därigenom kunna ge förslag inför olika beslutsfattande. Vid en implementering av en ny plattform, är det många anställda som berörs. Det betyder att plattformen kan generera såväl positiva som negativa reaktioner.