[diss] Perustieteiden korkeakoulu / SCI

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  • Advancing research methodologies in digital phenotyping for mental health
    (2026) Ikäheimonen, Arsi
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-02-13
    Digital phenotyping is an evolving field that fuses the disciplines of data science, behavioral science, and medicine. Digital phenotyping research utilizes data from personal digital devices and online platforms to investigate various behavioral, social, and health-related aspects of an individual's life. By analyzing these data, research aims to find novel insights into behavior, health, and well-being. These insights may lead to new complementary methods and applications for more effective healthcare solutions. As a new field, several factors hinder digital phenotyping from reaching its full potential. This thesis aims to advance the field of digital phenotyping through two perspectives: methodological and practical. From a methodological perspective, the thesis focuses on the barriers due to technical and research methodology-related challenges. In turn, the practical perspective evaluates the potential of using digital phenotyping to monitor and predict depressive symptom severity using data collected from outpatients diagnosed with ongoing depressive episodes. This thesis comprises five research articles, two of which focus on advancing the methodology, and the remaining three focus on practical aspects. The first article introduces Niimpy, an open-source Python toolbox for analyzing behavioral data. The second article outlines a datadriven workflow that facilitates research and improves its generalizability. The third article examines how accurately digital phenotyping, using smartphone-sensed data, can assess depression severity. The fourth article inspects the feasibility of digital phenotyping by analyzing the behavioral differences between the healthy control and patient cohorts. The fifth article explores the inherent variability in depressive symptomology and associated mobile-sensed behaviors. Taken together, these articles provide standardized tools and data analysis pipelines that facilitate behavioral data analysis, thereby lowering the barrier to entry into digital phenotyping research. This thesis proposes concrete, actionable guidelines for improving research replicability and reproducibility through the use of analysis workflows. The work demonstrates that machine learning models, using digital phenotyping data, can predict future depression severity. In addition, the feasibility of using smartphone data is supported by both study groups, healthy controls and patients diagnosed with depression, providing behavioral data with no differences in participation adherence or data quantity. Finally, the thesis demonstrates how behavioral differences, both between and within the participants diagnosed with depression, are connected with their self-reported symptoms. To conclude, this thesis advances digital phenotyping research by introducing tools and workflows to facilitate analysis. Furthermore, by building on previous work in the field, the thesis contributes to the research by presenting research outcomes obtained through the analysis of depressive outpatient behavioral data.
  • Hydrogel-based 3D culture systems for modeling breast cancer
    (2026) Heilala, Maria
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-30
    Breast cancer is a significant cause of cancer-related death in women. To develop more targeted therapies for advanced breast cancer, a detailed knowledge of mechanisms involved in breast cancer progression is required. Hydrogel-based 3D culture systems are particularly promising models for studying microenvironment-regulated changes in cellular phenotypes during metastasis. This thesis compiles three publications that investigated how cells can be guided to model prometastatic phenotypes by tailoring the properties of biopolymer-based hydrogel scaffolds. In Publication I, fibrin gels were used to study the interaction between fibrin and circulating tumor cell (CTC)-like cells representing triple negative breast cancer (TNBC). The phenotypes observed in fibrin gels were clearly distinct from those in Matrigel and suspension culture, evidenced by differences in gene and protein expression and cell morphology. By varying fibrin concentration and degradation rate, the association between fibrin stiffness and the regulation of cellular protrusions was revealed. In addition to cancer cells, immunosuppressive immune cells are associated with poor prognosis in breast cancer. In Publications II and III, plant-derived nanofibrillar cellulose (NFC) gels were used as the hydrogel scaffolds. In Publication II, unmodified NFC gels with varying stiffnesses were used for breast cancer patient-derived explant culture (PDEC). It was discovered that soft matrix influenced the balance of T cells and promoted macrophage polarization to M2-like phenotype through the fibroblast growth factor 2 (FGF2) secreted by the cancer cells. Publication III investigated the immunomodulatory effects of NFC chemical modifications on primary immune cells. NFC surface chemistry was found to influence the gene expression profiles of peripheral blood mononuclear cells (PBMCs) and the differentiation of monocytes to M2-like macrophages. Notably, macrophages generated in phosphorylated NFC gel expressed M2 marker CD206 at similar levels as M2a macrophages differentiated with interleukin-4 (IL-4) treatment in conventional 2D culture. Moreover, the cytokine profile was differentially modulated in cells in phosphorylated NFC gel compared to cells in 2D culture. In conclusion, diverse prometastatic phenomena were recapitulated in the hydrogel-based 3D culture models developed in this thesis. Importantly, the models expanded the phenotypes captured by the 2D culture setup and the commercial Matrigel matrix. These findings contribute to the development of more versatile breast cancer models, which may lead to the discovery of novel treatment modalities to prevent and treat metastatic disease.
  • Beyond critical points: Critical manifolds in self-organizing systems
    (2026) Sormunen, Silja
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-29
    Self-organized criticality describes the spontaneous evolution of a system to the verge of a phase transition, where the system balances between two macroscopically distinct phases. At this critical threshold, microscopic dynamics give rise to universal behavior characterized by scale-invariant spatial and temporal correlations spanning the whole system. Traditionally, self-organized criticality has been depicted as a one-dimensional phenomenon in which a single parameter is driven to its critical value. However, as the number of parameters in most real-world systems is vast, the states of the system corresponding to criticality can be expected to form a highdimensional manifold. This thesis explores the consequences of this multidimensionality, illustrating the topological and dynamical flexibility inherent in the self-organized critical state. Positioned at the intersection of network science and dynamical systems theory, this doctoral thesis makes both theoretical and methodological contributions to the study of self-organized criticality and its signatures. Using simulations and theoretical analysis, the first study demonstrates the phenomenon of a self-organized critical drift, where the self-organization process drives an adaptive network to drift on the critical manifold. The second study shows that this topological flexibility corresponds to dynamical richness, providing the first demonstration of an adaptive system self-organizing to multicriticality, where the system resides simultaneously on the verge of two different phase transitions. Finally, the third study addresses the methodological challenge of identifying the power-law distribution, a core hallmark of criticality. Power laws are notoriously difficult to identify, and this task is further complicated when data is subsampled. Our results show that two state-of-the-art methods fail to reliably distinguish between subsamples from power-law and some other heavy-tailed distributions, calling for caution in their use. Together, the results of this thesis provide a novel framework for understanding self-organized criticality that both opens new perspectives and unifies research directions previously regarded as competing. In previous research, it has been suggested that the brain would tune itself to criticality, but a lack of consensus on the phase transition in question has slowed down progress in the field. By demonstrating the inherent flexibility of the criticality hypothesis, this thesis shows how a system can seamlessly move to a new transition without leaving another, providing new insight into systems that may rely on multiple phase transitions to support complex adaptive behavior. As this work concerns the theoretical foundations of self-organized criticality, the findings have broad relevance across disciplines, generally applying to all self-organizing systems from biological brains and artificial neural networks to ecosystems and human contact networks.
  • Bayesian ordinary differential equation and Gaussian process modeling of biomedical data
    (2026) Timonen, Juho
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-23
    Probabilistic models of biomedical data account for uncertainty and variability in biomedical systems by describing the data and unknown quantities through probability distributions. Probability-based models are particularly valuable in biomedical contexts, where data are often noisy, incomplete, or heterogeneous, and where capturing uncertainty is critical for decisionmaking. This thesis focuses on complex probabilistic models, particularly those involving Gaussian processes (GPs) and ordinary differential equations (ODEs). The aim is to develop interpretable and practical models for biomedical data and address computational challenges that arise from both the complexity of the models and the large scale of the data involved. The first line of research advances inference techniques for Bayesian ODE models. Such models are useful for example for modeling intracellular regulation mechanisms, distribution of a drug in the body, or spreading of infectious diseases. However, simulating the time evolution of such models typically requires computationally intensive numerical solvers, which provide only approximate solutions and require that the functional form defining the ODE system is known. In practice, however, the kinetic parameters—and often the structure—of the system are unknown and must be inferred from data, introducing significant computational complexity. While the numerical approximation errors of ODE solvers have been extensively studied in classical numerical analysis, their impact on probabilistic inference has received less attention. This thesis proposes a computationally efficient workflow for obtaining reliable Markov chain Monte Carlo (MCMC)–based Bayesian inference results for nonlinear ODE models. It highlights and analyses the limitations of common adaptive ODE solvers in probabilistic contexts and introduces a scalable method for probabilistic network inference under structural uncertainty of the system. The second line of research focuses on interpretable, nonparametric modeling of longitudinal data using additive GPs. Such data typically involve a mix of relevant and irrelevant continuous and categorical predictor variables. In this thesis, flexible yet explainable models of longitudinal data are constructed using additive Gaussian processes. The thesis proposes novel modifications to the GP covariance functions to enhance the interpretability of model components. To accommodate large datasets, it is shown how to apply a reduced rank approximation scheme for GPs whose covariance depends on both categorical and continuous predictor variables. To increase model parsimony and interpretability, model reduction techniques are developed, adapted and evaluated for their performance in identifying relevant predictors and retaining predictive power while simplifying the model.While the core contributions are methodological, the developed techniques are applied to realworld biomedical problems, including the inference of gene regulatory networks involved in cell differentiation and the modeling of biomarkers associated with the development of type 1 diabetes
  • Hybrid Josephson junctions and electrothermal effects in graphene devices
    (2026) Haque, Mohammad Tasnimul
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-23
    In this thesis, I explore hybrid graphene-based Josephson junction (SGS) devices integrated with superconducting microwave cavities. The field of quantum computing has advanced rapidly in recent times, with realisations of various hardware platforms such as ion-traps, ultracold atoms, spin qubits, topological systems, and Josephson junction-based superconducting qubits, each aiming to reach the fault-tolerant quantum computing regime. Alongside these developments, two-dimensional materials such as graphene exhibit unique mechanical and electronic properties, such as atomically thin, high carrier mobility, and gate-tunable proximity-induced supercurrent, which makes SGS junctions an attractive architecture for long-lived, magnetic-field-resilient quantum circuits. Critical current fluctuations are a key dominant factor for decoherence in a Josephson junction. In the first experiment, I investigate low-frequency 1/f noise in the critical current of a hexagonal boron nitride encapsulated edge-contacted SGS junction. Fluctuations in the inductance of the SGS junction were tracked using microwave reflection measurements, and then the critical current noise was extracted from these measured fluctuations. We find significant fluctuations in the critical current, on the order of per unit band of 1 Hz. The critical current noise grows away from the charge neutrality point, which we attribute to the variation of the proximity-induced gap in the SGS junction. In a second set of experiments, I study various electrothermal effects that emerged in graphene devices. A superconductor–insulator–normal-metal–insulator–superconductor (SINIS) junction is formed from a monolayer graphene flake and coupled to a superconducting cavity. The device exhibits thermal self-oscillations arising from a highly nonlinear temperature-dependent resistance. We show that the modelling of these thermal oscillations provides a method to evaluate electronphonon coupling in graphene. In addition, these oscillations can be harnessed for the parametric amplification of microwave signals. In another device, we demonstrate the generation of a thermoelectric current in a graphene-based Cooper pair splitter, which is formed from two graphene quantum dots connected to an aluminium superconductor. Finally, I investigate microwave quantum optomechanics utilizing the Josephson capacitance of a Cooper pair box and find several orders of magnitude enhancement in both the optomechanical and the cross-Kerr couplings. Mediated by the Josephson capacitance, this three-partite system reaches the single photon ultrastrong coupling regime, enabling the generation of non-classical states of light and mechanical motion, as well as providing a platform for a single phonon counter. Together, these results demonstrate the potential of graphene–superconductor hybrids as a versatile building block for future quantum circuits, combining the design flexibility of superconducting platforms with the exceptional properties of two-dimensional materials.
  • Enabling cryogenic technologies for superconducting quantum devices
    (2026) Hätinen, Joel
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-23
    Low-temperature refrigerators cool systems down to cryogenic temperatures near absolute zero, where thermal noise and decoherence are suppressed. This allows quantum phases, such as superconductivity, to emerge in certain materials and enables the harnessing of individual quantum states for scientific and high-performance applications. However, the refrigerators used for these purposes are large and rely on cryoliquids, such as scarce and expensive 3He, which can be a limiting factor depending on the technological application. To enable more scalable, costeffective cryogenic platforms, new refrigeration technologies must be developed. To this end, chip-scale coolers based on superconducting tunnel junctions have been envisioned to provide a fully solid-state alternative. Proof-of-principle operation of these coolers has been demonstrated at temperatures below 1.5 K, but to link them with commercially available 4He pulse tube cryocoolers, stage operating above 2.0 K is required. Additionally, thermally isolating and electrically conducting methods are needed to cascade coolers operating at different temperature ranges. In this thesis, the fundamental components of a multi-stage chip-scale cooler operating at temperatures compatible with 4He pulse tube cryocoolers are developed. A superconducting flipchip assembly fabricated with In-bumps was characterized in the sub-kelvin temperature range, and the inter-chip thermal resistance was found to be suitable for chip-scale cooling applications. A through-chip signal routing method utilizing ALD TiN-based TSVs was developed, and the demonstrated critical temperature of 2.0 K enables dissipationless DC transport for multi-chip assemblies, such as cascaded coolers. Additionally, ALD MoCx was shown to exhibit a superconducting transition temperature up to 4.4 K and high conformality, showing promise as a TSV-compatible material. The key achievement of electronic cooling of Al thin film from a bath temperature of 2.4 K down to 1.6 K was demonstrated using Nb-based superconducting tunnel junctions, probed by an onchip junction thermometer. Thermal model calculations highlighted the emergence of superconductivity in the Al beneath the cooler junctions, persisting up to a bath temperature of 2.4 K: one kelvin higher than the nominal critical temperature of the Al thin film. The single-stage cooler operating above 2.0 K enables solid-state on-chip cooling from 4He pulse-tube compatible temperature without the use of magnetic fields. Additionally, Al- and V-based tunnel junctions were fabricated at the wafer scale using degenerately doped Si as the normal electrode. The junctions exhibited suitable low-temperature electrical characteristics for cooling applications. From superconducting interconnects to tunnel-junction components supporting high cooling power density above 1 K, the achievements presented in this thesis enable modular design of chip-scale cascade coolers. This technology is envisioned to support the scaling of several superconducting quantum devices from proof-of-principle to multi-component systems beyond experimental lab environments.
  • Thermometry based on a superconducting qubit
    (2026) Lvov, Dmitrii
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-20
    In this thesis, we demonstrate the operation of a thermometer based on a superconducting transmon qubit. We experimentally show that by measuring the qubit’s population distribution and its respective effective temperature, it is possible to measure temperature of an object with which the qubit is thermalized. However, due to the qubit’s quantum nature, it is very sensitive to any other source of excitation present in the system, which can be treated as a separate noise source or a heat bath. Consideration of a qubit coupled to several uncorrelated noise sources constitutes an important part of the thesis. We investigate the applicability range of the qubit thermometer in terms of dynamic range, operation speed and precision. While the upper bound on the dynamic temperature range is mainly defined by the material properties of the superconductor used in the qubit fabrication, namely, by the superconducting gap, the lower temperature is set by coupling to parasitic excitation sources. The population measurements of a transmon qubit are based on an algorithm which uses π-pulses for swapping the populations of the three lowest energy levels of the transmon. The largest impact on the precision of the measurement is defined by the signal-to-noise ratio and the quality of the qubit control pulses. The precision limit is ultimately defined by the quantum Cramér-Rao bound, highlighting the statistical origin of this thermometry method. Finally, we utilize the transmon qubit for thermometry of a mesoscopic heat bath located on the same chip. The heat bath is a normal metal resistor, whose temperature was controlled with normal metal/insulator/superconductor (NIS) junctions. By coupling the transmon to the resistor capacitively and performing the population measurements, we could observe linear dependence between the qubit effective temperature and the temperature of the resistor. Moreover, while the designed mechanism of the qubit-resistor coupling was photonic, we managed to observe another channel of interaction. At large bias voltages applied to the NIS-junction, which sets the temperature of the resistor, the junction starts to emit nonequilibrium phonons that can break Cooper pairs in the superconducting qubit. This is a nonlocal effect, in which nonequilibrium quasiparticles lead both to change of the qubit population distribution and significant suppression of the relaxation time.
  • Electrical properties and transport characteristics of single-walled carbon nanotube bundles
    (2026) Khan, Md Abu Taher
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-16
    As silicon approaches its fundamental performance limits, single-walled carbon nanotubes (SWCNTs) are emerging as a promising alternative channel material for transistors. Floating catalyst chemical vapor deposition (FC-CVD) is a highly effective technique for growing highly crystalline, defect-free SWCNTs while enabling their direct deposition from the reactor onto the wafer. During growth in an FC-CVD reactor, carbon nanotubes agglomerate due to Brownian diffusion, forming bundles, as predicted by simple aerosol models. In this dissertation, a fast and clean dry deposition method was developed to fabricate hundreds of functional SWCNT field-effect transistors (FETs), aiming to obtain a large number of single-bundle FETs. This method enabled the characterization of the pristine electrical properties of assynthesized FC-CVD SWCNTs and was also used to investigate the transport characteristic and electrical performance of SWCNT bundles. FETs were fabricated using two sets of FC-CVD-grown SWCNT bundles: Small Bundle Small Diameter (SBSD) SWCNTs and Large Bundle Large Diameter (LBLD) SWCNTs. The mean diameters of SBSD and LBLD SWCNTs were 4.1 ± 2.1 nm and 7.1 ± 3.7 nm, respectively, with nanotubes having mean diameters of 1.4 nm and 1.9 nm, respectively. Electron diffraction (ED) analysis determined that as-synthesized SBSD and LBLD SWCNTs contained metallic fractions of 38% and 46.3%, respectively. Due to the presence of a large fraction of metallic SWCNTs, bundles consist of a mixture of metallic and semiconducting nanotubes, leading to the expectation that FETs would lose their semiconducting switching efficiency. Interestingly, our experimental data show the opposite. The fraction of semiconducting FETs (s-FETs) was higher than the fraction of assynthesized semiconducting SWCNTs. The SBSD SWCNT FETs exhibited a semiconducting fraction of 71.5% out of 1,887 functioning FETs, while the LBLD SWCNT FETs showed a semiconducting fraction of 62% out of a total of 1,839 functioning FETs. The charge carrier mobility of SWCNT s-FETs was extracted using two rigorous methods: the peak transconductance (𝜇𝜇𝑃𝑃𝑃𝑃𝑃𝑃) and the Y-function (𝜇𝜇𝑌𝑌𝑌𝑌) methods. The ohmic-contact SBSD SWCNT s-FET exhibited mean 𝜇𝜇𝑃𝑃𝑃𝑃𝑃𝑃 and 𝜇𝜇𝑌𝑌𝑌𝑌 mobility values of 1,061 cm2V-1S-1 and 2,817 cm2V-1S-1, respectively, while the ohmic-contact LBLD SWCNT s-FET exhibited mean 𝜇𝜇𝑃𝑃𝑃𝑃𝑃𝑃 and 𝜇𝜇𝑌𝑌𝑌𝑌 mobility values of 1,854 cm2V-1S-1 and 5,378 cm2V-1S-1, respectively. These values are several orders of magnitude higher than the performance of SWCNTs grown using other methods. Similarly, the mobility of ohmic-contact single-junction SWCNT s-FETs was extracted for both SBSD and LBLD SWCNTs. Compared to single-bundle FETs, the mean 𝜇𝜇𝑃𝑃𝑃𝑃𝑃𝑃 and 𝜇𝜇𝑌𝑌𝑌𝑌 of single-junction SBSD SWCNT s-FETs decreased about fourfold to 255 cm2V-1S-1 and 737 cm2V-1S-1, respectively, while for single-junction LBLD SWCNT s-FETs, these values decreased about threefold to 856 cm2V-1S-1 and 1,732 cm2V-1S-1, respectively. Furthermore, both SBSD and LBLD SWCNT s-FETs exhibited an on-off ratio of up to 108.
  • Exploring movement and dance in virtual reality
    (2026) Laattala, Markus
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-16
    Virtual Reality (VR) allows the creation of new types of unique, interactive, and immersive experiences. However, designing for the medium brings its own strengths and limitations. While a player in a virtual world can see and hear anything, their material body's movement is still limited by both their physical capabilities and the space they are in. Moreover, today's mainstream consumer VR devices still struggle to accurately capture subtle human movements or provide realistic force feedback when interacting with virtual objects. These limitations highlight the need for more advanced interaction methods tailored to immersive environments. This dissertation investigates these types of novel techniques for user movement in virtual environments and explores how motion can be guided in ways that are unique to Virtual Reality. From the five publications included in this dissertation, Publications I, II, and III focus on dance experiences. Publication I introduces and evaluates a new way of instructing choreographies, allowing the user to anticipate incoming moves in games and other experiences. Publication II explores solutions for creating multiplayer dance games in Virtual Reality with limited dance space and movement tracking. Publication III examines the dance experience in VR compared to Mixed Reality, where a virtual partner is embedded in the user's real surroundings and the user sees their own body similar to real-life. The remaining two publications IV and V examine how familiar ovement actions from traditional video games can be reimagined in more embodied forms that suit the physical and technical realities of VR. To achieve these goals, this dissertation employs a mixed-methods approach to exploring and developing novel movement- and dance-based game mechanics and interactions, describing the design processes and rationale, and evaluating the mechanics and interactions through user studies that combine quantitative data with qualitative interviews. This dissertation explores different novel VR experiences and highlights unexplored design space for movement-based experiences in VR. It also demonstrates how the medium's technological limitations and non-physical nature can serve as strengths, inspiring more creative approaches to movement design and a deeper focus on user experience in VR. The main contributions of this dissertation are the design insights gathered from the projects as well as the projects themselves that serve as groundwork for future dance-centered and movement-centered games, applications, and research in VR.
  • Students’ topic-specific difficulties in learning data structures and algorithms
    (2026) Tilanterä, Artturi
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-16
    Data structures and algorithms (DSA) are fundamental concepts in computer science, as they provide basis for e cient computation. An introductory DSA course is mandatory ffi for students in an undergraduate computing curriculum similarly to an introductory programming (IP) course. Investigating students’ difficulties in learning DSA would advance teaching of this common topic. Knowledge of misconceptions, students’ understandings which deviate from the scientific consensus, could be utilized to increase the effectiveness of DSA instruction. This dissertation investigated students’ misconceptions and other difficulties specific in learning DSA. A systematic literature review produced a catalog of 70 students’ misconceptions related to DSA subtopics and recursion. Previously, a questionnaire instrument was designed to diagnose whether a certain student’s difficulties relate to missing knowledge of IP or DSA. In this dissertation, the instrument was evaluated by think-aloud interviews with DSA students, confirming that the instrument can differentiate difficulties between IP and DSA, but not diagnose difficulties on IP/DSA subtopics reliably. Students’ misconceptions of Dijkstra’s single-source shortest paths algorithm were studied extensively with mixed methods. A key instrument were visual algorithm simulation (VAS) exercises, where student simulates the execution of Dijkstra’s algorithm by interacting with a graph visualization. Several methods were used to analyze students’ errors during VAS activity: think-aloud interviews, automatic detection of exercise states with certain properties, and modeling systematically repeating errors with algorithms. Based on the observations, students confused elementary graph algorithm concepts, or their simulations resembled other graph algorithms instead of Dijkstra’s algorithm. This thesis provides instructors with a catalog of DSA misconceptions from the literature and new misconceptions of Dijkstra’s algorithm, both utilizable in DSA course instruction. The catalog points to future directions of DSA misconception research. Furthermore, the VAS-relevant mixed methods methodology supports investigation of students’ difficulties in visual algorithm simulation exercises.
  • Perspectives on capillary bridges Quasi-two-dimensional droplets and bridges made of living cells
    (2026) Kärki, Tytti
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-09
    Liquid-solid and liquid-liquid interactions are central to fundamental research, impacting fields from physics to biology. Liquids can range from simple, like water, to complex, including living systems. The interactions often involve coupled capillary effects that are challenging to quantify separately. Confining the system offers a way to reduce complexity, albeit more readily accomplished in theoretical models than in experiments. This thesis presents perspectives on capillary bridges, including both confined systems of liquid droplets and living tissues, to explore capillary interactions and their implications in three-dimensional spaces. Publication I focused on liquid-solid interactions on superhydrophobic surfaces, where minimal contact between the droplet and solid is provided by trapped air, called a plastron, in between the droplet and solid surface with microscopic pillars. By measuring friction forces and plastron thickness simultaneously, a friction component originating from the plastron was identified. This effect resulted in significant dissipations at larger droplet velocities, reducing the performance of the surfaces. Therefore, the results from this work facilitate improved design of water-repellent coatings. Publication II transitions three-dimensional (3D) droplets into a quasi-two-dimensional (Q2D) framework. Gravity effects were retained despite the geometric transition, resulting in droplet sizes and shapes determined by capillary length, and falling and sliding droplets similarly as to 3D. Publication III focused on liquid-liquid interactions utilizing the Q2D geometry to investigate liquidliquid interactions. The confined geometry provided optimal conditions to investigate vapor and thin film mediated mass transfer that resulted in complex Marangoni effects. Both long- and short-range vapor effects were identified, influencing droplet motion and thin film formation. The results from Publication II and III together demonstrated effective Q2D droplet analogies, from static to dynamic ones, valuable for studying various interactions. In Publication IV, confined geometry was applied to living tissues, examining complex liquid-solid interactions by creating living capillary bridges that resemble inert liquid bridges. Dynamics driven by cellular activities caused transitions from quasi-static to unstable bridge states, leading to selforganized rupturing. This transition was influenced by the competition between cell growth and cell flow, suggesting a mechanical interplay affecting tissue integrity in confinement. This thesis presents perspectives on capillary bridges and demonstrates that they are valuable techniques for studying various liquid-solid and liquid-liquid interactions, particularly in scenarios involving complex liquids, interactions, and even living matter. The key advantages include welldefined and controlled interfaces and optical access, facilitating easier study of 3D problems and the possibility of discovering new effects due to boundary conditions from the confinement.
  • Understanding the (de)polarized social media
    (2026) Xia, Yan
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-09
    Political polarization poses a serious societal challenge around the globe, which has only grown more pronounced with the rise of digital communication. As increasingly central venues for political discourse, social media platforms provide unprecedented opportunities for studying polarization dynamics through unobtrusive observations. Building on prior research that assessed whether social media is polarized and to what extent, this thesis delves into polarized debates to understand how polarization persists and deepens in online discussions. A case study of climate change discussions on Twitter points to information spreading as a crucial reinforcing mechanism of polarization: not only does content circulate primarily within like-minded groups, but the most viral themes also function to enhance ingroup bonds and preclude outgroup engagement. In addition to the empirical analysis, a technical framework is presented for automatically decomposing layers of polarized debates from traces of information diffusion, while its applicability to social media data is systematically evaluated. The second part of this thesis addresses the more challenging question of how polarization can be mitigated. Despite the success of various depolarization experiments, empirical investigation into depolarization on social media remains scarce. To fill this gap, this thesis examines depolarization in real-world settings following cross-party interactions or external threats. The results show limited evidence of depolarization after a cross-party interaction in Reddit political discussions. Even in the face of a substantial external threat, political debates on Twitter were selectively depolarized, with consensus reached among a subset of actors on a subset of arguments. The contribution of this thesis is two-fold. In rendering a realistic depiction of (de)polarized social media discourse, the findings reveal novel insights into the depth of polarization in the system and its striking resilience to depolarization. Through a critical review of the research methods employed, the thesis presents a broader reflection on the merits and limits of observational analysis in computational social science, while underscoring the value of methodological triangulation and integration. In doing so, it not only advances present understandings of political polarization, but also informs future inquiries into the phenomenon.
  • Topological effects in polymer networks for adhesion, resilience, and sustainability
    (2026) Savolainen, Henri
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-01-19
    The present thesis treats the topic of functional properties of hydrogels, i.e., aqueous polymeric networks. Therein topological effects of the networks have a strong influence on the mechanical properties such as strength, adhesion, and resilience due to the geometrical interactions and packings between the chains. The benefit of such approach is that the control of chain arrangement is universal to all polymer types. This thesis studies topological effects to cause triggerable adhesion, resilience, and strong hydrogels. As a result, polymer networks can be influenced not only by their chemical compositions but via entanglements and topological rearrangements. The first part of the thesis shows a hydrogel that allows self-adhesion upon irradiation in Publication I. The hydrogel contains gold nanoparticles that convert the light into heat, causing heat-sensitive poly(N-isopropyl acrylamide) to coil at the site of irradiated fracture and therefore adhere topologically. Usually, self healing materials act via dynamic/physical interactions. This approach allows for the combination of self-recovery and resilience, properties which are normally contradictive. The second part further focuses on resilience in Publication II. Therein, instead of the classically aimed toughening by dissipation, the aim was to minimize energy dissipation in consecutive mechanical loading and deloading. The concept allows effective storing of mechanical energy. By swelling the hydrogel in a monomer solution of the same composition as the previously created hydrogel network, dense entanglements and also stretching of the previous networks can be achieved. The process can be repeated up to five times until resilience is lost. The high Young’s modulus and strength of the hydrogel has been applied to locust-inspired jumping mechanism to allow for burst-like energy release. Publication III focuses on a biomass derived monomer to create a covalent adaptive network that can be 3D printed. The hydrogel based on poly(itaconic acid) is crosslinked with a novel condensate that allows the hydrogel to be solvent resistant but also melt at elevated temperatures. As the hydrogel melts, the rheological properties are tunable for optimizing for 3D printing. We demonstrate that shapes can be printed which do not dissolve in aqueous media that mimic the pH and salinity of the body.
  • Image interpretation methods for high-resolution scanning probe microscopy
    (2025) Kurki, Lauri
    School of Science | Doctoral thesis (article-based) | Defence date: 2026-12-19
    Interfaces play a central role in both natural and technological systems, and their properties are strongly influenced by atomic-scale interactions. Scanning probe microscopy (SPM), in particular scanning tunnelling microscopy (STM) and atomic force microscopy (AFM), has enabled direct imaging of surfaces and adsorbed molecules with atomic resolution. Advances in tip functionalization have further improved spatial resolution, revealing chemical bonds and molecular structures in great detail. However, interpretation of SPM images remains challenging: threedimensional adsorption geometries distort image contrast, and chemical identification often requires extensive quantum mechanical simulations. This thesis addresses these challenges using both traditional and data-driven approaches. The traditional methodology is employed to support experimental studies of confined water dimers within molecular networks and of organosilicon compounds synthesized on surfaces. To overcome the limitations of manual structure identification, the thesis also develops machine learning models trained on simulated datasets for automated structure discovery for STM and AFM. These models show that machine learning can significantly reduce the time required for molecular identification. The thesis highlights both the strengths and limitations of SPM simulation-based analysis and extends machine learning approaches in SPM to high-resolution STM imaging. While current models remain sensitive to noise and experimental artifacts, the results provide a step toward fully automated structure discovery.
  • Deep learning for chemical reactions
    (2025) Astero, Maryam
    School of Science | Doctoral thesis (article-based) | Defence date: 2025-12-16
    Modeling chemical reactions is a central challenge in computational chemistry. Capturing the transformation of reactants into products requires analysis at two complementary levels. At the local level, reactivity is governed by atomic and bond changes—such as formation, cleavage, or valence shifts—that define the immediate chemical events. At the global level, reactions are classified into broader categories, including substitution, addition, and rearrangement, which describe the overall transformation. A comprehensive framework should integrate both perspectives, as each conveys distinct yet interdependent aspects of chemical reactivity. Bridging these levels relies on a foundational task: atom mapping, the one-to-one correspondence between atoms in the reactants and products. Atom mapping provides a consistent structural alignment, which underpins downstream tasks such as reaction center prediction and reaction classification. Although atom mapping is fundamental to reaction modeling, existing methods often fail to incorporate it effectively. Existing approaches either rely on external mapping tools, which introduce noise and undermine end-to-end learnability, or focus narrowly on tasks such as reaction classification or edit prediction, often at the expense of interpretability and crosstask coherence. These limitations motivate the development of graph-based learning frameworks that integrate atom alignment, reaction center identification, and reaction classification within a unified architecture. This dissertation introduces a progression of such frameworks. The first, AMNet, formulates atom mapping as a deep graph matching task. By incorporating symmetry-aware constraints grounded in the Weisfeiler–Lehman test, AMNet resolves structural ambiguities and yields reliable molecular alignments. Building on this, SAMMNet introduces multitask learning with an auxiliary atom-type prediction objective. This self-supervised design improves mapping fidelity, enhances generalization, and addresses challenges posed by imbalanced reaction data. The final framework, MARCC, unifies atom mapping, reaction center identification, and reaction classification in a single multi-task architecture. Leveraging mapping-guided cross-attention and a dual-graph representation, MARCC achieves stateof-the-art performance on the USPTO-50K benchmark, with improved interpretability through explicit structural alignment. Together, these contributions advance reaction modeling by tightly coupling local and global reasoning within scalable graph neural architectures. Beyond setting new benchmarks for atom mapping, reactivity prediction, and classification, this work establishes building blocks for end-to-end reaction understanding systems, with applications in automated synthesis planning, mechanistic inference, and chemical pathway modeling.
  • Hearing as intended: How differences in listening conditions affect sound translation
    (2025) Riionheimo, Janne
    School of Science | Doctoral thesis (article-based) | Defence date: 2025-12-12
    One primary objective in sound production is to ensure reliable translation from mixing rooms to end-listening environments. Film and music makers should be confident that their mixes are heard as intended in the final playback conditions, whether in cinemas, homes, or on personal devices. However, variations in room acoustics, loudspeaker configurations, and playback systems introduce challenges that can alter perceived timbre, spatiality, and overall loudness. This thesis investigates how room acoustics and reproduction systems affect the perceptual translation of sound character across diverse listening conditions. The research is based on two controlled listening experiments: one focusing on movie sound reproduction in cinemas and mixing rooms, and the other on spatial sound reproduction in music contexts, including headphone listening. Listening environments were captured using spatial impulse-response measurements and recreated in a laboratory setting with a spherical loudspeaker array or headphones. Sound professionals evaluated perceptual attributes such as clarity, immersiveness, and brightness through carefully designed listening tests, which were analysed using statistical methods. The results show that room acoustics and reproduction format significantly influence how sound translates between mixing rooms and playback spaces. In cinema environments, excessive reverberation reduces dialogue clarity, making speech sound muddy or distant. In contrast, the frequency response and brightness dominate the perceived differences in music. Moderate reverberation can enhance listener envelopment, particularly with slower music, and adding artificial reverberation to dialogue can improve spatial translation across rooms. In spatial music reproduction, multichannel formats, such as 7.1 surround, better preserve the spatial sound character across different rooms compared to stereo, especially when sound objects are precisely positioned. Moderate reverberation (around 0.25 s) was also found to enhance clarity and proximity, and to reduce phantom-centre colouration, whereas overly dry environments can compromise listener comfort. The findings further highlight mixing challenges such as phantom-centre problems, coherent summing, and down-mixing issues that affect translation in both loudspeaker and binaural headphone reproduction. While timbral balance and loudness are primarily determined by system calibration, spatial translation depends more strongly on room acoustics, especially in the absence of surround speakers. This thesis discusses various calibration approaches and target-curve choices and examines how calibration, room acoustics, and speaker configuration influence the translation of sound character across listening environments. The work provides practical insights for optimising mixing and reproduction to improve translation.
  • Long-term scenario modelling for sustainable climate change mitigation and adaption
    (2025) Freistetter, Nadine-Cyra
    School of Science | Doctoral thesis (article-based) | Defence date: 2025-12-05
    Global land systems can either mitigate or exacerbate climate change and its impacts. To utilise and protect land effectively, there is a need for timely, accessible foresight on landbased climate change impacts and mitigation strategies. This dissertation examines interactions between human-managed land systems and climate change through computational modelling and scenario analysis extending to 2100. Across four papers (I–IV), it delivers regional and global foresight under climatic and societal scenarios, and introduces two novel, lightweight, open-source models to bridge the gap between highly detailed, computationally expensive integrated assessment models and simple ones. Specifically, Paper I investigates worst-case climate change impacts on hazardous driving and walking conditions in Northern Europe. Using a road surface energy balance model driven by regional climate projections, it is found that while the slip season may shorten with warming, its intensity could increase. Paper II presents the fast global land-use model CLASH, which integrates forestry, dynamic vegetation, and optimisation models into a single framework. A carbon stock maximisation experiment illustrates the utility of CLASH, reaffirming that reduced animal products consumption and tropical forest conservation are key mitigation levers. Paper III links CLASH to global energy, materials, and climate systems to form the complex yet lightweight integrated assessment model SuCCESs. A Monte Carlo experiment demonstrates its stochastic capabilities, revealing moderate sensitivity of fossil emissions and low sensitivity of land-use emissions to key parameters. Paper IV builds on Papers II and III, using SuCCESs to explore a wide scenario space informed by current uncertainties in land-based mitigation potential research. The analysis highlights critical system interactions, particularly between land and climate policy: When land is freed (e.g., via reduced product demand), policy must incentivise its use for mitigation. Conversely, if land use is constrained through preservation, mitigation incentives become redundant, as land-use change emissions are sufficiently avoided. In summary, this dissertation pairs global climate mitigation with local impacts, underscoring the need for harmonised cross-sectoral policies. Future work is called to strengthen stakeholder integration and expand regional data to improve scenario relevance and policy robustness.
  • On imbalanced data and text classification
    (2025) Avela, Aleksi
    School of Science | Doctoral thesis (article-based) | Defence date: 2025-12-05
    The vast amounts of information available today call for smart ways to analyze and make decisions based on data. One of the most prominent approaches is machine learning, that is, algorithms which utilize data for discovering patterns and learning to make optimal decisions. This thesis focuses on one important category of machine learning: classification, in which the aim is to learn rules that can be used for predicting the classes or labels of observations. On top of classification in general, this thesis considers two sub-problems of it – both separately and mixed together – which are imbalanced data and text classification. Imbalanced data refers to classification tasks where one or some of the classes are notably rarer compared to the other class(es). Observations belonging to a rare class are typically the ones that have a high value, but, without modifications, many classification algorithms struggle with finding these rare observations. Text classification refers to applying classification algorithms to tasks involving natural languagedocuments. The thesis includes an introduction to classification and the analysis of text data and three publications. The first publication presents an application of text classification for measuring the economic sentiment in Finland based on news titles. The second publication considers imbalanced data and text data together and introduces a new method for addressing both challenges simultaneously. The third publication discusses the – perhaps surprisingly challenging – question of how different classifiers should be evaluated and compared when dealing with imbalanced data.
  • Synthesis of silica nanofibers for visible light scattering applications
    (2025) Lin, Zhen
    School of Science | Doctoral thesis (article-based) | Defence date: 2025-11-28
    Silica nanoparticles have been widely studied for their various morphologies and broad optical applications. Among them, curly silica nanofibers (NFs) have attracted increasing interest due to their random morphologies and strong visible light scattering abilities. However, their synthesis, typically via the water-in-oil emulsion method, still lacks a comprehensive understanding of the underlying growth mechanism. In this thesis, silica NFs with randomly curly morphologies are synthesized via the water-in-oil emulsion method. NFs sprout from small aqueous droplets stabilized by sodium citrate in 1-pentanol, where the hydrolysis and condensation of precursors drive silica growth. Due to their sufficiently small size, both the aqueous droplets and the growing silica undergo Brownian motion. However, these motions are non-synchronized because of differences in size and density, making the droplets more sensitive to Brownian motion than the silica. Consequently, the silica growth follows the stochastic trajectories of the droplets, ultimately leading to randomly curly morphologies of NFs. This mechanism also explains the observed negative correlation between NF curvature and diameter, enabling morphological control through temperature adjustment. The resulting NFs are assembled into porous network films that exhibit strong visible light scattering and high whiteness, with broadband reflectance exceeding 0.8, outperforming both commercial papers and films composed of conventional nanospheres or nanorods. In addition, these NFs are embedded into polymer matrixes to fabricate films with strain-induced tunable haze. Upon the application and release of strain, the formation and disappearance of internal cavities within the film change the refractive index contrast, thereby modulating visible light scattering and haze. Finally, silica fluorescent nanofibers (FNFs) are synthesized, which can be excited by ultraviolet A (UVA) light. Taking advantage of high whiteness of NFs when dry, which transforms to transparency when wet, along with the fluorescent properties of FNFs, a two-factor authentication (2FA) optical security system is constructed for encryption application. This system responds to liquid and UVA light in sequence, thereby requiring two independent and sequential keys for decryption, providing a relatively secure and advanced strategy for information protection application. In summary, this thesis offers insights into the synthesis of randomly curly silica NFs, reveals the Brownian motion-driven mechanism underlying their formation and morphological control, studies their visible light scattering properties including whiteness and haze, and demonstrates their functional application in optical security. Together, these findings provide a theoretical foundation and valuable reference for future research in this field.
  • Theorizing in strategy research with analytically structured history approach using a relational database method - Essays on Telecom Finland’s strategizing between 1981–1998
    (2025) Aalto, Eero
    School of Science | Doctoral thesis (article-based) | Defence date: 2025-11-24
    This dissertation builds on our analytically structured history approach, which applies a relational database method in organizational and management research. The approach aims to fulfill the demands of conceptual rigor necessary for advancing theory, while also ensuring historical veracity essential for conducting authentic historical analysis. With the dissertation, I extend the methodological foundations of our analytically structured history approach into a theoretical project that examines how our historical approach can be integrated with the theorizing process to advance theoretical understanding. I argue that such integration requires a respect for both the core characteristics of historical analysis—namely, historical evidence, contextual analysis and nonreductionist view—and the essential features of theorizing, including theoretical frameworks, the seek for parsimony and a reductionist orientation. The overarching aim of the dissertation is to contribute to the ongoing discussions on integrating historical methods into organizational and management research. Since the 1990s, there have been increasing calls for a “historical turn” in the field to integrate historical methods more closely into research practice. These calls acknowledge the untapped potential of historical approaches to enrich and extend theoretical understanding. Nevertheless, historical methods have remained largely peripheral in theory development. This is due, in part, to still existing methodical divide between historical methods and rigorous standards for producing generalizable theoretical claims. The three essays in the dissertation demonstrate how historically authentical analysis of extensive digitized archival records can be combined with conceptually grounded and parsimonious theoretical arguments. The research context is the telecommunications industry in Finland between 1981 and 1998— a period during which drastic changes in technological, market, and regulatory environments occurred in the industry. The essays focus on a distinct aspect of strategic decision-making: regulatory strategy (Essay 1), internationalization strategy (Essay 2) and strategic change (Essay 3). While each essay contributes distinctively our understanding of strategy, I synthesize the essays to derive implications detailing how the strengths of our approach can be applied to theorizing process. I contribute by outlining how historical contextualism can be centered around multilevel historical analysis that captures the contextually embedded dynamics from the relationships, interactions and multidirectional links within and across levels. These opportunities for theoretical contributions are exemplified with the essays and examples of resource and capability legacies and dynamics of historical contingencies. The main conclusion of this dissertation is that bridging the methodical divide between historical methods and organizational and management research lies at the intersection of historical contextualism and reductionist position in theorizing. I demonstrate that achieving this integration needs to emphasize the trustworthiness and accuracy of theoretical claims while maintaining the authenticity of historical analysis.