Travel behavior models are essential for transportation planning and policydevelopment, addressing challenges like traffic congestion, environmentalimpact, and equitable access. By analyzing how individuals make travelchoices, these models support decisions related to infrastructure investmentand resource allocation. These models cover various aspects of travel, including activity planning, route selection and travel time and areconstantly being revised. One of the key ground of improvements are theemergence of novel data sources, significantly advancing the understandingof travel behavior and overall transportation planning. This thesis fitswithin the stream of studies that investigates travel behavior modelsusing novel data sources to guide policies that enhance mobility, supportsustainability, and promote equity in transportation systems, by means of 4distinct papers.

Paper 1 focuses on adapting mobile network data to Scaper, a dynamicdiscrete choice model. The Scaper framework, originally designed foractivity generation and scheduling based on travel survey data, is tailoredto handle the big data source and adapt accordingly. The study developsprobabilistic models by integrating observed and latent states to infertrip attributes from cell tower observations. It employs a backwardinduction method to compute the expected value function, using StochasticExpectation-Maximization for parameter estimation. This paper offersa methodological contribution, demonstrating the potential of how toeffectively adapt an activity based model Scaper to new data sources. Toillustrate the usefulness of this framework, we emphasize its application in Paper 2. This new framework is used for assessing mobility inequality andsegregation before and during COVID-19 in Stockholm. This shows howwe can use these models and data to further investigate mobility patternsduring times of crisis and to envision a more resilient transport system thatpromotes equity.

In line with the thesis’s scope of integrating sustainability into research,we use route choice models and GPS traces to investigate cycling behavior. Paper 3 primarily focuses on cyclists’ route preferences in the Netherlands.Notably, cyclists, including commuters, do not always choose the shortestpath. Instead, various factors influence their decisions, raising the importantquestion: how can we design infrastructure that aligns with cyclists’preferences and encourages more frequent cycling? the detailed GPS tracesallowed for to investigate various aspects of the route beyond distance,for instance, number of junctions, traffic lights, presence of nature, etc.This paper utilizes two approaches to address this question. The firstis theory-driven based on logit models, the Path Size Logit (PSL) andthe Pairwise Combinatorial Logit (PCL), both rooted in random utilitymaximization principles and designed to account for route overlap amongchoices. The second is a data-driven approach using deep learning topredict route choices through a one-dimensional Convolutional NeuralNetwork. We conducted a sensitivity analysis to uncover key patternsin the deep learning model, offering insights into the factors influencingroute preferences. By comparing these two approaches, we emphasize theirstrengths and limitations while showing how GPS data integrates with themto uncover key factors influencing cyclists’ route choices. This paper guidespolicymakers in designing efficient and appealing cycling routes.

Paper 4 expands the scope by incorporating GPS data alongsidesociodemographic information to examine cycling behaviors, particularlyin a cross-border context. Data were collected from three cities, namely,Braga, Istanbul and Tallinn. The focus is on travel time: What are theaverage and range of travel time for cyclists in different cities? How dofactors such as age, and gender influence travel time? Are there differencesbetween different cities? Travel time is a crucial variable for travel demandiimodeling but more so for cyclists, as they do not always prioritize speed. Alonger trip isn’t necessarily worse; it might even be preferred if the shorteralternative is more exhausting. Novel data sources like GPS traces collectedover period of months in three different cities provides the opportunity tounderstand these complex and comparative behavioral contexts. Cyclingunderscores not only the value of time but also the quality of time spentengaging in the activity. It’s within this context that travel time modelingbecomes particularly important to investigate. Using a survival analysisapproach, specifically the Latent Class Accelerated Failure Time (LCAFT)model, Paper 4 reveals how distance, trip purpose and bike type influencethe travel time of cycling and identifies potential latent classes in differentage groups and gender.

Skeletal muscle is crucial for enabling movement, maintaining posture, and stabilising joints. These functions are largely related to the morphology and mechanical properties of skeletal muscle. To quantify these properties in vivo, medical imaging techniques have been widely used, with ultrasonography and magnetic resonance imaging (MRI) being the most commonly used imaging modalities. This thesis presents four studies using different imaging techniques to quantify the morphology and mechanical properties of skeletal muscle. 

In the first study, we used three-dimensional freehand ultrasound (3DFUS) and MRI to quantify 3D skeletal muscle morphological parameters, including muscle volume, fascicle length, and pennation angle. We demonstrated that 3DFUS provided reliable and repeatable measurements, with strong agreement with MRI-based measurements. Given its lower cost and better accessibility, we suggest that 3DFUS could serve as a viable alternative to MRI for quantifying skeletal muscle morphology. 

In the second and third studies, we used two elastography techniques, magnetic resonance elastography (MRE) and ultrasound shear wave elastography (SWE), to quantify the mechanical properties of skeletal muscle. We incorporated diffusion tensor imaging to determine the fascicle orientation and integrated this information into the direct inversion of the wave equation in MRE. This approach allowed for the quantification of anisotropic mechanical properties under the assumption that skeletal muscle behaves as an incompressible transversely isotropic material. This approach was first validated through comparison with ex vivo rheometry measurements, demonstrating a good agreement between the two techniques, and then applied in vivo to the medial gastrocnemius (MG), demonstrating muscle anisotropy as well. We also compared this technique with a commercial ultrasound SWE system, which assumes skeletal muscle to be isotropic, by measuring both ex vivo muscle samples and the MG in vivo. By quantifying shear wave velocity using both elastography techniques, we observed a moderate to strong correlation between SWE and MRE in ex vivo muscle samples and a strong correlation in the MG in vivo. These findings suggested that the isotropy assumption in commercial ultrasound SWE systems does not substantially affect the quantification of muscle mechanical properties. 

In the fourth study, we used MRI to evaluate changes in calf muscle morphology and intramuscular fat content 12 months after the first botulinum neurotoxin type A (BoNT-A) injection in children with cerebral palsy (CP), who were naive to muscle tone reduction therapy. Our findings showed that the calf muscle growth was not impaired 12 months after BoNT-A injection, as indicated by increased absolute muscle volume and unchanged normalized muscle volume. However, the calf muscle growth was compromised by concurrent intramuscular fat infiltration, evidenced by increased intramuscular fat content. 

The ultrasonography and MRI techniques presented in this thesis provide the biomechanics field with different options for quantifying skeletal muscle morphology and mechanical properties. These techniques not only contribute to the medical imaging methodological development but also offer practical implications for clinical assessments and rehabilitation strategies.

With the increase in renewables integration in power systems, the demand for over current (OC) capability is increased. Until the fault clearance, converters in the power system must be able to withstand the increased currents without getting tripped by their internal protection based on thermal limits. This duration is typically 200 ms. In this thesis, various techniques have been proposed to remove the heat generated during OCs as soon as possible fromSilicon-Carbide (SiC) devices, hence increasing the OC duration. These techniques include implementing heat-absorbing materials, microchannel (MC) cooling on the top and bottom of the chip, and gate voltage augmentation during OCs. It is concluded that any cooling method (except gate voltage augmentation) gives the highest OC capability when it is implemented on top of the chip. MC cooling has the potential to increase OC capability duration until a few seconds, depending on the design of the MC block. Similarly, OC capability is significantly improved by using copper as heat-absorbing material on top and bottom (with a comparatively large block of copper) of the chip up to a few seconds, depending on the amount of OC. Even increasing the thickness of metallization on top of the chip can lead to increased OC capability. One application of the power modules with increased OC capability is in power flow controllers (PFCs). With the increase in meshing, controllability and flexibility to control the current and power in a high-voltage direct current (HVDC) system are reduced. By injecting a small amount of voltage, a PFC can change the current distribution. Existing topologies have been studied in detail by PLECS simulations and compared with respect to the number of capacitors, the control range of the PFC, the shape of voltage waveforms inserted by the PFC on the lines, number of devices, the directionality of the current, simplicity of the topology, total power semiconductor rating and losses, and protection of the topologies for external faults. A new topology, which is among the most simple topologies, has been proposed. Further, internal and external fault cases for the proposed topology have been investigated in detail. The simulations are verified by a scaled-down prototype in the lab. Simulations and experiments have been compared with respect to their per unit (pu) system and the experimental results are aligned with the simulation results.

Modern control systems provide services that are indispensable for society, e.g., transportation, energy production, healthcare etc. Hence, it is important to guarantee safe and reliable functioning of such systems. However, they are increasingly relying on networking technologies, which makes them susceptible to cyberattacks that could potentially jeopardise their safety. Moreover, such systems typically have a complex distributed architecture and dynamic behaviour. Hence, it is hard to ensure correctness and safety of their design. Formal methods are used to tackle system complexity and guarantee correctness of the design via abstract mathematical modelling and rigorous verification. Various formal modelling techniques have been successfully used in the design of safety-critical systems in different domains. However, they primarily focused on modelling and verification of system safety. Since modern safety-critical systems are increasingly becoming the subject of cyberattacks, formal modelling techniques should be extended to address the emerging problem of safety-security interactions. In this thesis, we propose a rigorous approach to modelling the impact of cyberattacks on safety of networked control systems. Our approach integrates graphical modelling in Systems Modelling Language – SysML and formal specification and verification in the Event-B framework. Graphical models provide support in visualising system architecture and interactions between the components as well as facilitate the analysis of safety and security interactions by the interdisciplinary teams. Modelling and proof- based verification in Event-B allows us to formally identify the cyberattacks that jeopardise system safety. To bridge the gap between the graphical and formal modelling, we developed software automatically translating graphical system models into formal specifications in Event-B. We believe that this thesis makes both theoretical and practical contributions towards an integration of safety and security engineering, which is necessary for the development of modern trustworthy networked control systems.

Biology and medicine have seen groundbreaking discoveries, from ion channels to induced pluripotent stem cells, resulting in a paradigm shift. The advancements in physical sciences and engineering have always been pivotal in unlocking mysteries of biology and highlighting that the new frontiers lie in deepening our understanding at the single-cell and single-molecule levels. Applying different physical and engineering principles sheds new light on our understanding of complex biological systems at the single-cell and single-molecule level, enabling the development of various technologies such as single-molecule detection, organ-on-chip platforms, and organoids. The development of these technologies offers valuable insights into disease progression and personalized therapeutic strategies. The advancements in micro and nanofabrication propel the development of sensing platforms and biological devices that pave the way for novel solutions, ensuring the best of both worlds. This thesis aims to contribute to advancing the fields of single-molecule sensing and cell therapy by integrating biological discoveries and engineering advancements to develop novel engineering toolboxes. 

The first part of this thesis introduces and describes two approaches for single-molecule sensing and detection, specifically tunneling nanogaps and solid-state nanopore-based sensing platforms. The first work reports the custom measurement setup built during the project, which facilitates automated probing and testing arrays with hundreds of tunnel junctions in liquid with integrated microfluidics, current in the pA range, and at sampling rates up to 200 kHz. This setup highlights key electrical and microfluidic components and design choices to achieve a scalable measurement method, providing a platform for further studies and development in this field and enabling the potential for dynamic sensing. The second work in this thesis investigates the fabrication and electrical behavior of tunnel junctions in various gaseous and liquid media by feedback-controlled electromigration of microfabricated gold nanoconstrictions. This work maps the conductance stability and characteristics of the resulting tunnel junctions, highlighting various considerations and challenges in working with on-chip integrated tunnel junctions to guide future efforts. 

In the third work, we shift our focus to solid-state nanopores and demonstrate that the nanopores fabricated by controlled dielectric breakdown could be localized at the site of femtosecond laser exposure on a pristine silicon nitride membrane. We analyze the sensing potential of these nanopores by the translocation of double-stranded DNA through the pores. The fourth work uses the solid-state nanopore platform to detect and study the binding of Estrogen Receptor Alpha to the Estrogen Receptor Elements on the DNA. The work on tunnel junction and solid-state nanopore-based sensing modalities holds potential for further development in the field of single-(bio)molecule sensing.

The second part of this thesis presents a microfluidic chip platform that enables simple and fast reprogramming of somatic cells, such as fibroblasts, into induced pluripotent stem cells (iPSCs). These iPSCs can then be differentiated further into functional ectodermal cell types towards neural lineage, resulting in neural stem cells on the chip. Furthermore, using bulk-RNA sequencing, we observed that the microfluidic platform boosted commitment toward generating neural stem cells while reducing biological variability compared to a conventional well plate. Our method provides a simple platform with considerably reduced reagent requirements, cellular input, and manual labor, leading to substantial cost savings and holding potential for the highly controlled generation of clinical-grade iPSCs and differentiated cells for cellular therapeutics.

Autonomous vehicles (AVs) must navigate uncertain environments while ensuring safety, particularly in scenarios involving risk and occlusions. This thesis develops structured approaches to risk- and occlusion-aware motion planning, integrating theoretical advancements with real-world validation.

To address risk in motion planning, we introduce a framework that quantifies both the probability and severity of safety violations, enabling AVs to reason about risk while maintaining operational efficiency. Complementing this, we investigate pedestrian-aware motion planning in urban environments, incorporating a harm-based risk model to balance safety and progress in interactions with vulnerable road users.

Occlusions pose a major challenge by limiting direct visibility of critical road users. We develop a method for tracking and reasoning about hidden obstacles using reachability analysis and formal logics. By incorporating prior observations, our approach systematically refines possible states of occluded agents, reducing unnecessary conservatism. For high-speed driving, we refine velocity bounds on occluded traffic participants, preventing worst-case assumptions that could lead to excessive braking. Additionally, we explore vehicle-to-everything (V2X) communication to enhance situational awareness, enabling AVs to infer and share information about occluded regions in real time.

Finally, we propose an occlusion-aware planning framework that integrates tree-based motion planning with reachability-based occlusion tracking. This enables AVs to proactively reason about future observations—or their absence—ensuring robust decision-making under limited sensing. By reducing overly conservative constraints while maintaining safety guarantees, our approach addresses key issues in occlusion-aware motion planning.

Together, these contributions advance the ability of AVs to operate safely and efficiently in demanding environments, supporting scalable real-world deployment.

Combating climate change and achieving a sustainable future are the main objectives of the Sustainable Development Goals, requiring low-carbon energy systems. The SDGs, adopted by the United Nations in 2015, aim to create a more equitable and sustainable world by addressing pressing global challenges such as climate change. This thesis explores and provides insights into reducing greenhouse gas (GHG) emissions by focusing on energy systems modeling for infrastructure planning, strategies, and targets in developing economies. By identifying key research gaps, this thesis offers practical methods, insights, and open data into GHG reductions through the integration of renewables in specific locations. 

This dissertation uses an open-source energy modeling system generator to develop long-term models addressing the challenges of transitioning to low-carbon energy systems in Ecuador, Kenya, and the State of Goa (India). These real-world case applications show how financial, technological, and policy factors influence the adoption of renewable energy in specific geographical locations. In addition, by utilizing an open-source framework, it is possible to integrate cross-sectoral systems, such as the Climate, Land, Energy, and Water systems (CLEWs), to analyze the interaction between energy, land, and water resources.

Research article I focuses on the impact of discount rates on long-term energy planning and how variations in these rates can influence decision-making processes. In addition, the discount rate is separated into global/social and individual/hurdle rates for electricity supply technologies. Research article II presents a whole energy system model tailored for a peculiar location in India, the state of Goa. The state lacks local electricity supply capacity and relies on allocated coal power plants. The study emphasizes achieving emission reduction goals in one scenario, accounting for a high share of renewables by integrating local stakeholders’ knowledge.

In the third research article, the geographic location is Kenya. A Climate, Land, Energy, and Water (CLEWs) model is developed focusing on land and energy systems, specifically the cooking and agricultural sectors. Special attention is given to clean cooking technologies and reducing crop imports, with the variability of input parameters being cataloged and discussed. This detailed cataloging is novel in this kind of models, as it extends beyond the energy sector to include parameters from land, agriculture, and water systems, and their interlinkages with the cooking and agricultural sectors. Such variability in inputs also highlights the associated uncertainties in these parameters. 

In contrast, in research article IV, the methodological approach goes beyond cataloging and discussing to identify the most influential and non-influential parameters driving modeling results through a global sensitivity analysis (GSA). This sensitivity analysis is performed on a CLEWs model for Kenya, which is the result of merging two existing models, one related to cooking and agriculture (research article III) and another related to the whole energy system. Combining these models addresses the limitation of each in isolation and provides a more comprehensive view of cross-system interlinkages. The GSA approach not only reveals which parameters are influential on model results but also helps to better understand model behavior and the interactions across energy, land, and water systems.

Through these four scientific articles, this thesis highlights how financial barriers and cross-sectoral complexities can either hinder or enable low-carbon energy transitions. It catalogs and discusses the uncertainty surrounding the techno-economic representations of land, energy, and water systems and demonstrates the influence of specific parameters on energy systems optimization models via sensitivity analysis. Ultimately, this integrated approach offers a blueprint for policymakers and stakeholders in other developing economies searching to balance financial viability with resource interdependencies on the path to reduce emissions.

Magnetic confinement fusion represents one of the most promising pathways to achieving sustainable and clean energy production. In this approach, strong magnetic fields are used to confine hot plasma within a device preventing it from coming into direct contact with the vessel walls. However, plasma-wall interactions remain an unavoidable challenge, as some heat and particles inevitably escape confinement, particularly during energetic transient events. These interactions pose a significant threat to the integrity of plasma-facing components (PFCs), which are subjected to extreme thermal and particle loads. Among the various forms of damage caused by such loads, melt damage is particularly concerning due to its potential to severely degrade the performance and longevity of PFCs. 

To address these challenges, the MEMOS-U physics model was developed to simulate macroscopic melt motion in fusion environments. MEMOS-U simplifies the computational heavy thermoelectric magnetohydrodynamic equations by employing the shallow water approximation, which reduces the dimensionality of the problem. MEMOS-U has been validated against a series of dedicated tokamak experiments, demonstrating its ability to capture the essential features of melt motion in fusion environments.

Building on the MEMOS-U model, the MEMENTO code was developed as a modern numerical implementation designed to further enhance the predictive capabilities of melt motion simulations. MEMENTO leverages the AMReX framework to create and maintain a non-uniform, adaptive grid, enabling efficient simulations of large PFCs over long time scales. The code includes solvers for heat transfer, fluid dynamics, and current propagation, all of which are fully coupled to accurately model the interplay between thermal loading, melt motion, and electromagnetic effects. 

The MEMENTO code has been validated against experimental data from dedicated controlled melting experiments carried out in the ASDEX-Upgrade and WEST tokamaks. Predictive studies with MEMENTO have provided valuable insights into the potential melt damage in future tokamaks. In summary, MEMENTO represents a significant advancement in the modeling of macroscopic melt motion in fusion environments. By implementing the MEMOS-U physics model in a new code, MEMENTO provides a reliable and computationally efficient tool able to accurately predict melt damage in future reactors for regimes that could not be probed before. 

Our ability to lubricate machine components effectively sets the boundaries for the technologies we can reliably deploy. This is patently clear in fields like wind power, wave and tidal power, and space. These applications face challenges due to low-speed oscillating motions where conventional lubrication methods struggle. By means of ionic material design, we probe these limitations with the objective of enabling progress in key machine technologies towards sustainable development. Our research shows that ionic liquids (ILs), when used as grease additives, can delay lubricant ejection from fretting contacts and provide remarkable lubricity. Nuclear magnetic resonance reveals that the ability of the IL to enact this effect depends on whether it is sequestered with the other grease components (thickener – base oil blend) or is readily available and mobile. Wide angle x-ray scattering shows that when these ionic liquids are subjected to pressures in the GPa range they have similar structural compliance and liquid-to-solid transitions when compared to a conventional PAO synthetic oil, and that these can be modified by changing the structure of the constituent ions. These findings illustrate that the effect of lubricant retention within the contact is linked to the ability of the IL to reach the surfaces and strongly adsorb, and that distinct behaviors under pressure may be achieved by tuning the architecture of the ionic species. We then explore the impact of these effects at the component level in oscillating bearings, using a custom-built frameless bearing test rig. Furthermore, these ILs are shown to have remarkable performance in vacuum environments, reducing wear by multiple orders of magnitude when compared to heritage space lubricants, and offering a pathway towards PFAS-free vacuum lubricants. The unique versatility and potential of these non-halogenated ILs is further highlighted when we then show that it is possible to use carbon capture and biomass products to synthesize ionic lubricants. Overall, it is shown that ionic materials can be leveraged to expand the limits set by our current technology in lubrication practice.

Photovoltaic (PV) systems could be a promising option for accelerating sustainable transition in the power sector. However, it is not straightforward to implement solar PV in Sweden. While the huge gap between current and desired solar capacity generates great opportunities for solar PV technologies, the challenges arise regarding accurate performance prediction, optimization of sizing and installation, optimization for high latitude regions and integration with other technologies. This study focuses on a multi-dimensional probe into the potential and feasibility of PV systems in Sweden with a case study of Stockholm. The techno-economic potential of PV systems is evaluated regarding weather, space, infrastructure, operation configuration and economics. The results reveal the technical and economic feasibility of PV systems in Swedish contexts, despite limitations on existing infrastructure. The research highlights the significant PV generation loss due to snow conditions. The annual electricity generation loss is found to be 14.7%, which is greater than most prior research findings. Regarding this significant snow loss, bifacial PV can reduce snow-induced PV generation losses by up to 6 percentage points under heavy snow conditions. It also outperforms monofacial PV with lower levelized cost of electricity (LCoE) and shorter payback year in Sweden. Wall-mounted PV could also be an alternative. Compared to fixed-tilt PV, wall-mounted PV can achieve comparable annual benefits due to higher generation during the snow season when the electricity price is rather high. Future projections indicate an anticipated increase in PV generation by approximately 5% compared to historical periods. The change in PV generation is expected to be relatively minor during future periods, with an estimated variation of less than 30 kWh/kWp by 2100. Additionally, an optimal tilt angle has been determined for Sweden, applicable across all cities, which could enhance PV generation by 3-6% compared to the common installation angle.

The increasing demand for wireless data traffic poses a significant challenge for current cellular networks, requiring each new technology generation to enhance network capacity and coverage, and spectral efficiency (SE) per connected device. Massive multiple-input multiple-output (MIMO) technology has emerged as a key component of 5G and leverages a large number of antennas at each access point (AP) to spatially multiplex many user equipments (UEs) over the same time-frequency resources. Looking beyond 5G, the new cell-free massive MIMO technology has gained considerable attention due to its ability to exploit spatial macro diversity and achieve higher interference resilience. Unlike traditional cellular networks, the cell-free architecture consists of a dense deployment of distributed APs that collaboratively serve UEs across a large coverage area without predefined cell boundaries. This architecture improves the mobile network coverage and aims to provide a more uniform quality of service throughout the network. However, the primary challenges of cell-free massive MIMO include the high computational complexity required for signal processing and the substantial fronthaul capacity needed for information exchange between APs. Moreover, another major challenge is handover management to cope with changing channel conditions and UE mobility; since in a cell-free network handover needs to consider how to dynamically evolve the serving set of APs to each UE, which is more complicated than in a cellular network where each UE is served by a single AP and handover means changing the serving AP.

In this doctoral thesis, we provide distributed solutions to research problems related to power allocation and mobility management to address some of the inherent challenges of the cell-free network architecture. Additionally, we introduce a new method for characterizing unknown interference in wireless networks. Moreover, we propose efficient optimization procedures in the context of multicast beamforming optimization and establish a novel method for rank reduction in conjunction with semidefinite relaxation (SDR).

For the problem related to power allocation, a distributed machine learning-based solution that provides a good trade-off between SE performance and applicability for implementation in large-scale networks is developed with reduced fronthaul requirements and computational complexity as compared to a centralized solution, where the power allocation for all APs is computed at a central processor. The solution is divided in a way that enables each AP, or group of APs, to separately decide on the power coefficients to the UEs based on the locally available information at the AP without exchanging information with the other APs, however, still attempting to achieve a network wide optimization objective. 

Regarding mobility management, a new soft handover procedure is devised for updating the serving sets of APs and assigning pilot signals to each UE in a dynamic scenario considering UE mobility. The algorithm is tailored to reduce the required number of handovers per UE and changes in pilot assignment. Numerical results show that our proposed solution identifies the essential refinements since it can deliver comparable SE to the case when the AP-UE association is completely redone.

As for interference modeling, we developed a new Bayesian-based technique to model the distribution of the unknown interference arising from scheduling variations in neighbouring cells. The method is shown to provide accurate statistical modeling of the unknown interference power and an effective tool for robust rate allocation in the uplink with a guaranteed target outage performance. The method was later extended to account for the unknown interference of neighbouring clusters in a cell-free network architecture.

Many wireless communication applications require sending the same data to multiple UEs; for example, in streaming live events, distributing software updates, or training of federated learning models. Physical-layer multicasting presents an efficient transmission topology to exploit the beamforming capabilities at the transmitting nodes and broadcast nature of the wireless channel to satisfy the demand for the same content from several UEs. The uniform service quality and improved coverage of the cell-free network architecture are particularly suitable for this transmission topology. In this regard, we propose a novel successive elimination algorithm coupled with SDR to extract a near-global optimal rank-1 beamforming solution to the max-min fairness (MMF) multicast problem in a cell-free massive MIMO network. A specifically tailored optimization algorithm is then designed, leveraging the alternating direction method of multipliers (ADMM) and offering significant improvements in computational requirements.