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  • Public defence: 2020-04-07 13:00
    Gallinaro, Julia
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science. Bernstein Center Freiburg and Faculty of Biology, University of Freiburg, Germany.
    Neuronal assembly formation and non-random recurrent connectivity induced by homeostatic structural plasticity2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Plasticity is usually classified into two distinct categories: Hebbian or homeostatic. Hebbian is driven by correlation in the activity of neurons, while homeostatic relies on a negative feedback signal to control neuronal activity. Since correlated activity leads to strengthened synaptic contacts and formation of cell assemblies, Hebbian plasticity is considered to be the basis of learning and memory. Stronger synapses, on the other hand, promote stronger correlation. This positive feedback loop can lead to instability and homeostatic plasticity is thought to play a role of stabilization. The experimentally observed time scales of homeostatic plasticity, however, are too slow to compensate for the fast Hebbian changes. Therefore, the exact way multiple types of plasticity interact in the brain remains to be elucidated. In this thesis, we will show that homeostatic plasticity can also have interesting effects on network structure. We will show that homeostatic structural plasticity has a Hebbian effect on the network level, and it comprises two separate time scales, a faster for learning and a slower for forgetting. Using a model of classical conditioning task, we will show that this rule can perform pattern completion, and that network response upon stimulation is gradual, reflecting the strength of the memory. Furthermore, we will show that networks grown with homeostatic structural plasticity and a broad distribution of target rates exhibit non-random features similar to some of those found in cortical networks. These include a broad distribution of in- and outdegrees, an over-abundance of bidirectional motifs and scaling of synaptic weights with the number of presynaptic partners. Overall, we will use simulations of spiking neural networks and mathematical tools to show that there is more to homeostatic plasticity than just controlling network stability. It remains an open question, however, the extent to which homeostatic plasticity can be accounted for structural features found in the brain.

  • Public defence: 2020-04-15 10:00 Stockholm
    Safavi Nick, Arash
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Casting of Metals.
    Pores, inclusions and electromagnetic stirring: Topics from the continuous casting of steel2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis deals with two topics of relevance to the continuous casting of steel,in view of their importance as regards the quality of the final solidified structure.The first concerns the precipitation of gas pores and inclusions in the interden-dritic region of the solidifying metal. Motivated by experimental results thatindicate the formation of pore-inclusion clusters in the final cast structure, a the-oretical model is developed to describe how thus might occur; the model makesuse of the basic principles of fluid mechanics and heat transfer, with asymptoticmethods then being used in order to obtain solutions. In particular, it is foundthat soluto-thermocapillary drift in a direction perpendicular to the direction ofcasting, as a consequence of the dependence of surface tension at the pore-metalinterface on temperature and sulphur concentration, could explain cluster forma-tion. The second is a theoretical study concerning longitudinal electromagneticstirring (EMS), which is often used in the continuous casting of blooms in order toimprove product quality. Via an analysis of the three-dimensional (3D) Maxwellequations for the components of the magnetic flux density, a flaw is found inthe way that the components of the stirring Lorentz force have previously beencalculated; this is corrected and the new results are confirmed by comparison ofsolutions obtained from asymptotic analysis and time-dependent 3D computa-tions using finite-element methods. The analysis identifies the importance of theproduct of the bloom width and the wave vector of the applied field as a keydimensionless parameter.

  • Public defence: 2020-04-16 09:00 F3, Stockholm
    Johansson, Petter
    KTH, School of Engineering Sciences (SCI), Applied Physics.
    Molecular Processes in Dynamic Wetting2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The spreading of liquids onto and over surfaces is a fundamental process in nature. It is present in all forms and sizes: From rivers carving through landscapes, to our blood stream transporting nutrients to cells, and even single water molecules moving through channels into these cells. We now have a good understanding of how fluid movement works inside the fluid itself. However, we do not fully understand the processes close to the contact line, where the liquid is spreading onto the surface. We are forced to make assumptions about this behaviour and none of these assumptions have yet proven to be universally valid.

    As everything in nature, liquid spreading is a fundamentally molecular process. This thesis summarises my work on applying this lens to the process. By studying molecules we begin at the smallest combined building blocks of nature and do not have to make any prior assumptions of the involved processes. Instead, we simply observe their behaviour. This is accomplished through the use of molecular dynamics simulation, which are an atomistic form of computer experiments. We use a realistic model of water molecules as our base liquid, since this captures realistic effects such as hydrogen bonding which are not present when using simpler models. Combined with large-scale systems which minimise the influence of finite-size effects, we have a realistic treatment of complex liquid systems.

    We find that the molecular processes of wetting have an important influence on large-scale wetting. Most importantly, the hydrogen bonding nature of water to realistic substrates yields the no-slip condition often used as a boundary condition for models of wetting. Furthermore, since molecular processes are thermal in nature they create energy barriers which impede contact line advancement. We show how these barriers are created and how they can be diminished, for example in the case of electrowetting. This highlights that understanding the molecular behaviour of fluids remains an important field of study.

  • Public defence: 2020-04-20 15:00 Stockholm
    Pitt, Christine
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
    Automated Text Analysis of Online Content in Marketing: Dictionray-Based Methods and Artificial Intelligence2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Far more than products or services, words are the most fundamental element in the exchanges between sellers and buyers. Understanding the words that constitute the text that is created when sellers and buyers interact with each other is therefore critical for marketing decision makers. This has become especially relevant in the age of the internet, and particularly with the advent of social media. In the pre-computer age, the content analysis of text was a time-consuming, laborious and frequently error-prone tool for marketing scholars and practitioners to use. Now, powerful computers and software enable the content analysis of text to be performed rapidly, and with little human effort or error. Two fundamental types of tools that enable the automated analysis of text are those that are dictionary-based and those that are artificial intelligence-based. The former automated text analysis tools rely on pre-constructed dictionaries, and then scan a piece of text in order to count and match the words in it to obtain scores on the dimensions of interest. Artificial intelligence-based automated text analysis tools employ machine learning algorithms to recognize patterns in text. They compare text to other pre-classified texts, having been trained by human experts to recognize the desired dimensions of a construct, and can “learn” to do this more effectively the more they are used. 

    The service dominant logic perspective on marketing holds that value is co-created by both sellers and buyers. This enables the identification of two fundamental marketing focus activities. First, sellers and buyers engage in acts of creation; second, sellers and buyers engage in acts of experience. On a wide range of forums, both buyers and sellers create text about these marketing focus activities. This text lends itself to analysis by the two categories of automated text analysis tools. Therefore, the central question is: How can automated textual analysis tools enable marketing practitioners and scholars to gain insights from different types of textual data? 

    Marketing scholars have recently given more attention to the use of automated text analysis tools in marketing research. These efforts have included overviews of the approach, suggestions on choosing amongst methods, and considerations of the sampling and statistical issues unique to automated text analysis. Less emphasis has been placed on specifically examining the use of the two different types of automated text analysis tools (dictionary-based and artificial intelligence based) in exploring the text generated by sellers and buyers in the context of the focal marketing activities of creation and experience. The current research therefore explores the following four research questions:


    • RQ1: What insights can an artificial intelligence-based automated text analysis tool deliver from depth interviews with respondents engaged in a creative focus activity?
    • RQ2: What insights can an artificial intelligence-based automated text analysis tool deliver from online reviews by respondents engaged in an experience focus activity?
    • RQ3: What insights can a dictionary-based automated text analysis tool deliver from online reviews by respondents engaged in an experience focus activity?
    • RQ4: What insights can a dictionary-based automated text analysis tool deliver from online interviews by respondents engaged in a creation focus activity


    The empirical part of this research covered four papers, all of which involved analyzing textual data with the two categories of automated text analysis tools. Two of these papers used artificial intelligence-based automated text analysis tools in both the creation and experience settings, and the other two used dictionary-based automated text analysis tools, again, in these settings. 

    The overall contribution to the body of knowledge is to provide evidence of the applicability of both artificial intelligence-based- and dictionary-based automated text analysis tools in two fundamental marketing focus activities, namely, creation and experience. The individual papers also 


    further our understanding of the use of automated text analysis to study comparisons between groups, as well as correlation between traits and ways of speaking within samples of text.

    The document is organised as an overall introduction to the research narrative of four related published papers. The document opens with a chapter providing an overview of automated text analysis in marketing, the statement of the overall research problem, and the identification of four 

    research sub-questions. This is followed by a chapter on the literature review. Next is a chapter on the methodology used in the studies. The fourth chapter considers the four papers in more detail, acknowledging their limitations, identifying the implications for marketing practice, and suggesting avenues for future research by marketing scholars. The four papers follow under Chapter 5 at the end. Three of these papers have either been published or accepted for publication; the other is in the second round of revision and resubmission.

  • Public defence: 2020-04-21 10:00 Live streaming, Stockholm
    Sedlak Mosesson, Michal
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Solid Mechanics.
    Modelling of intergranular stress corrosion cracking mechanism2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    When assessing nuclear power plant life, stress corrosion cracking (SCC) plays an important role. Stress corrosion cracking in nuclear power plants is well recognized and heavily researched. Still due to its complicated nature it is not completely understood. There are many different damage mechanisms behind SCC. The focus in this thesis is on the slip-oxidation model. In the slip-oxidation model, the aggressive ions are diffused to the crack tip. In the crack tip the aggressive ions act as a catalyst to slow down the repassivation rate of the oxide film. At the crack tip the localized anodic dissolution occurred until an oxide film was produced to repassivate the corrosion process. Due to the constant stresses applied, the oxide film ruptured, and new virgin material was exposed to be dissolved and finally repassivated. This process is consequently repeated.   The first part of the work introduces a new formulation of a cohesive element with extended environmental degradation capability, which is essential to create the later SCC models. The new degradation method was evaluated against a Hydrogen Embrittlement (HE) experiment showing improved agreement with the experiment compared to the literature. The effect of different susceptibility zones at the crack tip was also investigated, showing that a uniform degradation throughout the susceptible zone is more influenced by the χ parameter than a triangular susceptible zone.  In the second part a phenomenological SCC model was created with the purpose to model primary water conditions in pressurized water reactors (PWR). It used the slip-oxidation model for considering SCC in boiling water reactors (BWR) under normal water chemistry (NWC).   The PWR model was implicit, coupled with a segregated solution scheme including a diffusion equation based on Fick’s second law and a cohesive zone description for the fracture mechanics part. The degradation was modelled with an anodic slip-dissolution equation that uses the effective cohesive traction and concentration as the main parameters. The model was evaluated against experiments on the effects of cold work on intergranular stress corrosion cracking (IGSCC). The model showed good agreements for both shifting amount of cold work illustrated by only changing the yield stress in the bulk material and for shifting the stress intensity factor. The model versatility was also shown by simulating IGSCC in Alloy600, also with good agreements.   The BWR model was multi-physical including a slip-oxidation, diffusion model and had adaptive oxide thickness developed into the cohesive element framework. The oxide thickness was derived from the slip-oxidation model and updated in every structural iteration to fully couple the fracture properties of the cohesive element. The cyclic physics of the slip oxidation model was replicated. The model results agreed with experiments in the literature for changes in the stress intensity factor, yield stress representing cold work and environmental factors such as conductivity and corrosion potential. The adaptive model was also expanded into a duplex oxide model with an inner and outer oxide. The model showed agreeing results with literature and the model was used to simulate different oxide growth kinetics

    The full text will be freely available from 2020-04-21 10:55
  • Public defence: 2020-04-21 15:00 Stockholm
    Dabirian, Amir
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Industrial Marketing and Entrepreneurship.
    Unpacking Employer Branding in the Information Technology Industry2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Attracting and retaining the best talent is a concern, particularly for knowledge-based firms in high-turnover industries, which rely on a limited supply of highly qualified individuals (Ewing, Pitt, De Bussy, & Berthon, 2002). In 2014, 36% of global employers criticized talent shortages, and in a 2015 study, 73% of CEOs reported being concerned about the availability of workers with key skills (Mosley, 2015). Employer branding is a key human resource and marketing strategy that contributes to the company’s brand, enhances the firm’s reputation as a great place for employees to work, and attracts a new workforce (Ahmad & Daud, 2016). An employer brand’s and its employer branding value propositions’ (EBV) ability to attract new employees and increase retention will provide benefits for the entire organization.

    EBV defines the employer’s attractiveness (Berthon et al., 2005), is a key aspect of the employer branding process, and provides differentiation for the firm (Alnıaçık & Alnıaçık, 2012; Backhaus & Tikoo, 2004; Berthon et al., 2005; Leekha Chhabra & Sharma, 2014; Moroko & Uncles, 2008) to attract and retain employees. Existing research viewed employer branding and its EBV from one or two views—employee or employer—and lacked multiview approaches to employer branding and employer attractiveness. This research focused on a holistic approach and addressed the question: “How do different EBVs affect the perceptions of employer attractiveness? To answer this question holistically, the following research subquestions emerged:


    RQ1: How do employees perceive the EBV of employer attractiveness?

    RQ2: How do current and former employees perceive the EBV of employer attractiveness?

    RQ3: How do potential employees perceive the EBV of employer attractiveness?

    RQ4: How do employers manage how employees perceive EBV?


    This research consisted of four empirical papers and focused on the information technology (IT) industry context. The first study focused on employee views from all industries, whereas the second study concentrated on the IT industry and compared current and former employees. Study 3 considered potential employees in the IT industry and operationalized the employee attractiveness construct and EBVs. The final study explored EBVs from the employer’s view in an IT firm and compared its employees’ views regarding the psychological contract. The design of this research is a mixed approach with descriptive and exploratory methodologies. IBM Watson’s artificial intelligence content analysis was used in Studies 1, 2, and 4.

    Contributions to the body of knowledge includes operationalizing the employee attractiveness construct as a set of EBVs. This research viewed EBVs holistically and extended the set of EBVs from five to eight value propositions for the IT industry. It also defined employer brand intelligence as a tool for practitioners to develop insights for their employer brand.

    The document is organized with an introductory chapter describing the overall research approach, followed by a literature review chapter, methodology chapter, and summary of findings and contributions. The four papers are included in the final chapter.

  • Public defence: 2020-04-22 09:00 Stockholm
    Paschen, Jeannette
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
    Creating market knowledge from big data: Artificial intelligence and human resources2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The abundance of social media use and the rise of the Internet-of-Things (IoT) has given rise to big data which offer great potential for enhanced market knowledge for marketers. In the literature, market knowledge has been associated with positive marketing performance. The literature also considers market knowledge as an antecedent to insight which in turn is a strategic asset that can yield a sustained competitive advantage. In summary, market knowledge is important due to its relationship with performance and as a pre-requisite to insight.

    Market knowledge (as an outcome) results from market knowledge creation processes which encompasses the activities to create market knowledge. Market knowledge is created by integrating resources, specifically information technology and human resources.

    With respect to information technology, the unique characteristics of big data - volume, variety, veracity, velocity and value (the five V’s) - make traditional information technologies ill-suited to turn big data into information and ultimately market knowledge. Artificial intelligence (AI) has been discussed as one important information technology for creating market knowledge from big data. The literature suggests that AI is having a profound impact on the creation of market knowledge from big data and calls for more research on understanding the value potential of AI.

    Regarding human resources, the primacy of human contributions to the creation of market knowledge has been established in the literature. However, scholars and practitioners alike suggest that AI will change the nature and role of human contributions to creating market knowledge. The literature also suggests that the aspect of AI and human resources in market knowledge has not been adequately studied to date.

    Hence, the research problem in this thesis is formulated as “How do marketers create market knowledge from big data using artificial intelligence and human resources?” This research problem is addressed via five research questions (RQs):

    RQ 1: How does artificial intelligence contribute to creating market knowledge from big data?

    RQ 2: How does artificial intelligence impact the creation of market knowledge from big data and what are the implications for human resources?

    RQ 3: How do artificial intelligence and human resources interact in creating market knowledge from big data?

    RQ 4: What are the mutual contributions of artificial intelligence and human resources in creating market knowledge from big data?

    RQ 5: What are the contributions of artificial intelligence and human resources to different activities in creating market knowledge from big data?

    The research in this thesis encompasses two studies and three papers. The three papers are published or forthcoming in peer-reviewed journals. The research adopts an interpretivist paradigm and follows a qualitative research approach. The findings provide three key contributions to the body of knowledge and to theory. First, this thesis provides a non-technical understanding of what AI is, how it works and its implications for market knowledge, thus addressing a gap in the marketing literature.

    Second, this thesis posits that AI is a resource that meets the criteria of being 'valuable', 'rare', 'in-imitable', and 'organized' (VRIO) postulated by resource-based theory (RBT). The value of AI as a resource occurs in transforming big data into information and also AI transforming information into knowledge. Human resources are an important capability that improve the productivity of AI as a resource. This thesis provides empirical evidence that the nature of contributions offered by AI as a resource and human capabilities differ and explains how they differ.

    Third, this thesis contributes to resource-based theory. It proposes a conceptual model and puts forward five propositions regarding the relationship of AI as a resource, human capabilities and market knowledge. This model and the propositions can be tested in future scholarly work.

    This thesis opens with a chapter providing an introduction to the research area, followed by a literature review, a methodology chapter and a chapter discussing the findings and contributions to theory and practice, and outlining opportunities for future research. The papers and studies underpinning this thesis are presented in the last chapter of this thesis.

  • Public defence: 2020-04-24 10:00
    Harahap, Fumi
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology.
    Exploring synergies between the palm oil industry and bioenergy production in Indonesia2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Climate change along with increasing demand for food and fuel call for sustainable use of natural resources. One way to address these concerns is through efficient use of resources, which is also vital for the achievement of the Sustainable Development Goals and the Paris Agreement. In this context, the sustainable and efficient use of resources in the palm oil industry is an interesting case to scrutinise. This is particularly important for Indonesia, the leading palm oil producer in the world. Large quantities of oils and biomass are generated from oil palm plantations and processing, presenting the potential for the development of bio-based production systems. However, at present, sustainability is a matter of great concern in this industry, including land use issues and the fact that large portions of the residues generated are untreated, releasing greenhouse gas emissions, and imposing environmental threats.

    This doctoral thesis aims at exploring how resource efficiency can be enhanced in the palm oil industry. Three research questions are posed to address the objective. The first question examines the sectoral policy goals of biofuel, agriculture, climate, and forestry and their requirements for land. The second question is focused on new industrial configurations for efficient use of palm oil biomass for bioenergy production. The final question summarises the role of enhancing resource efficiency in the palm oil industry with regards to meeting the national bioenergy targets, which include 5.5 GWe installed capacity and biofuel blending with fossil fuels (30% biodiesel blending with diesel and 20% ethanol blending with gasoline) in the transport, industry, and power sectors. The research questions are explored using three main methods: policy coherence analysis, techno-economic analysis, and a spatio-temporal optimisation model (BeWhere Indonesia).

    The thesis identifies areas in which policy formulation, in terms of sectoral land allocation, can be improved. Adjustments and improvements in policy formulation and implementation are crucial for land allocation. The inconsistencies in the use of recognised land classifications in the policy documents, the unclear definition of specific land categories, and the multiple allocation of areas, should be addressed immediately to ensure coherent sectoral policies on land allocation. This can lead to more effective policy implementation, reduce pressure on land, enhance synergies, and resolve conflicts between policy goals.

    The transition towards a more sustainable palm oil industry requires a shift from current traditional practices. Such transition involves efficient use of palm oil biomass resources through improved biomass conversion technologies and integration of palm oil mills with energy production in biorefinery systems. The upgrading of the conventional production systems can serve multiple purposes including clean energy access and production of clean fuels for the transport, industry, and power sectors, ultimately helping the country meet its renewable energy and sustainable development targets, along with reduced emissions. More specifically, the efficient use of biomass and co-production of bioenergy carriers in biorefineries can enable Indonesia to reach its targets for bioenergy installed capacity and bio-based blending.

    At present, many government policies in Indonesia are working in the right direction. Nevertheless, various barriers still need to be overcome so that resource efficiency can be improved. This includes harnessing the full potential of bioenergy in the palm oil industry. There is room for enhancing the sustainability of the palm oil industry in Indonesia with adjustments to existing policies and practices, as shown in this thesis. First, guidance across sectoral policies can help to coordinate the use of basic resources. Second, the shift from traditional practices requires a strategy that includes improvement in agricultural practices (i.e., higher yields), infrastructure for biomass conversion technologies together with improved grid connectivity, and adoption of a biorefinery system. Strengthening policy support is needed to promote such a comprehensive shift. Third, various programmes can forge partnerships between oil palm plantations, the palm oil mills, and energy producers to ensure the development of sustainable industrial practices. A sustainable palm oil industry will improve resource and cost efficiency, and help open international markets for Indonesian products. This could pave the way for an enhanced role for the Indonesian palm oil industry in global sustainability efforts.