The urgency of developing effective circularity assessment tools is reinforced at European and national levels, notably through initiatives like the EU Circular Economy Action Plan. However, existing metrics (e.g., MCI, BSI) and methodologies fall short of capturing the multifaceted nature of reuse scenarios. They struggle to address dynamic market conditions shaped by technological innovation and economic volatility, which are critical in determining the feasibility of reuse. Current reuse assessments rely heavily on resource-intensive audits conducted by specialised experts, limiting their scalability and accessibility. This paper presents the Reuse Viability Index (RVI), an innovative metric designed to evaluate and predict the sustainability of building component reuse. The RVI integrates all dimensions of sustainability-economic, environmental, social, and, uniquely, technical. The technical dimension is vital for construction reuse, encompassing parameters such as compliance with contemporary standards, deconstructability potential, and residual value. The primary objective of this research is to provide the genesis, development, and practical application of the RVI. The study seeks to answer a fundamental question: Can a single index be developed as a decision-making tool to measure and predict the reuse potential of building components in existing structures and at the project design stage for new constructions? The paper details the formulation of the RVI, explaining its conceptual basis and justification of the attributes critical for inclusion in such an index.

In 1939, Giuseppe Pagano, leading figure of Italian Rationalism, took a dozen photographs of Mussolini’s former editorial office, known as “Il Covo” or “the lair.” The sequence offers a compelling and unexpected lens through which to reconsider Pagano’s long-celebrated "Exhibition on Rural Architecture,” held at the 1936 Milan Triennale, and praised in historiography as an “anthropological inquiry”—a paradigm shift away from style and received canons in search for truth in the humble, everyday rural environments. Yet, when read alongside his portrayal of Mussolini’s lair, Pagano’s rural images appear far more ambivalent— a visual mode, as Devalle suggests, that combines equal parts realism and myth, whose tendency toward abstraction erases contingencies and class struggles to produced archetypical forms and gestures. This reading complicated the dominant historiography on Pagano’s photography, especially his status as precursor to Neorealist sensibilities, and situates Pagano’s visual practice within recent philosophical reflections on fascism’s capacity to transfigure “reality” through myth.

Digital self-control tools have emerged as technological interventions to address excessive smartphone usage and promote digital wellbeing. However, these tools face persistent challenges with user attrition and sustained engagement, compromising their long-term effectiveness. Current literature lacks understanding of the psychological mechanisms that drive user retention and meaningful interaction with DSCTs, particularly how intrinsic motivation and readiness to change influence sustained engagement patterns. This study investigates how intrinsic motivation and readiness to change, operationalized through users' daily goal priorities and observable behaviors, can be strategically leveraged to address user attrition in DSCTs and sustain user-nudge interactions to promote digital wellbeing over time. We conducted a quasi-experimental study (n=252) targeting passive users at risk of churning from a DSCT mobile application. Participants were randomly assigned to receive an invitation to reconfigure their nudge settings during daily check-ins (experimental group, n=138) or to a control condition (n=114, no intervention). The experimental group was further classified into acceptance and rejection subgroups based on their response to the intervention. Data collection included system usage logs, self-reported questionnaire responses, and semi-structured user interviews. We analyzed user-nudge interaction ratios, nudge configuration parameters, daily goal selections, and behavioral patterns using t-tests and Cohen's d for effect sizes, at P<.05. Of the experimental participants, 46% (63/138) accepted the nudge reconfiguration invitation. The acceptance group showed pre-existing behavioral indicators of higher readiness to change, including 21.53% shorter consecutive usage durations and 20.56% longer cooldown periods compared to the rejection group. Post-intervention, the acceptance group exhibited a temporary surge in user-nudge interaction from 24% to 65%, while the rejection group showed sustained decline below 20%. Behavioral divergence between groups widened significantly (Cohen's d increasing from -0.47 to -0.67, P=.002). Notably, acceptance group participants demonstrated significantly lower tendency to select leisure-oriented daily goals compared to the rejection group (15.6% vs 26.2%, P=.001). Self-reported measures of screen time goals and scrolling regret showed no predictive value for intervention acceptance (P>.1). Observable behaviors, rather than stated intentions, effectively predict intervention receptiveness in DSCTs. The study reveals a significant intention-behavior gap, highlighting that behavioral analytics provide superior predictive value compared to self-report measures. Sustainable DSCT engagement requires alignment with users' intrinsic motivation and readiness to change, as evidenced by pre-existing behavioral patterns. These findings suggest that effective DSCT design should incorporate adaptive systems that recognize and respond to users' evolving motivational states while preserving autonomy, rather than relying on static interventions or self-reported preferences.

Dwarf galaxies are the most common type of galaxy in the Universe and have emerged as powerful testbeds for our standard cosmological model LambdaCDM on small scales, where several tensions persist. This thesis focuses specifically on the phase-space distribution of dwarf satellites around massive hosts. Several nearby hosts, including the Milky Way, appear to have flattened, kinematically correlated satellite configurations that are uncommon among LambdaCDM analogs---the planes-of-satellites challenge. Robust tests require large, contamination-controlled satellite samples across diverse environments, which are challenging to obtain beyond the Local Group because dwarfs are intrinsically faint and generally have low surface brightness. This thesis contributes to the extension of phase-space studies beyond the Local Group by (i) evaluating and refining methods to quantify phase-space distributions, in particular lopsided satellite distributions and planes-of-satellites, and conducting statistical tests on such distributions in existing survey data (e.g., MATLAS, ELVES); (ii) expanding line-of-sight velocity coverage via VLT/MUSE spectroscopy of MATLAS candidates and establishing host membership; and (iii) developing an automated pipeline, combining classical methods with deep learning, to build a survey-scale catalog of dwarf galaxy candidates in the wide-field UNIONS survey. Across 68 host systems in MATLAS/ELVES, ~21% show significant lopsidedness with the signal strongest at larger projected radii, consistent with recent accretions and in line with LambdaCDM expectations. Follow-up on reported candidate planes generally revealed no significant tension with LambdaCDM when revisited with new data. An exception to this trend is the NGC 4490 group, where such a highly correlated structure was identified as uncommon in simulated analogs. In the MUSE program, we confirmed 75% of the MATLAS dwarfs in the sample as satellites; non-members tend to be late-types, supporting morphology as a membership prior. Our pipeline yielded dwarf probability scores for 1.5 million selected objects, producing the Galaxies OBserved as Low-luminosity Identified Nebulae (GOBLIN) catalog. GOBLIN contains ~43,000 high probability (>=0.8) dwarf candidates, which represents a significant increase in the number of high-confidence candidates in the local Universe. Taken together, the majority of our investigations into phase-space distributions of dwarfs revealed consistency with LambdaCDM given current data. There are, however, noteworthy exceptions, and several systems are still far from complete in terms of distance and velocity estimates. Such measurements are necessary to draw definitive conclusions. Our publicly available GOBLIN catalog contains a large sample of high-probability dwarf candidates, laying the foundation for targeted follow-up campaigns and future phase-space studies.

The kinetic inertness and low solubility of Au3(pyrazolate)3 complexes have made it difficult to integrate them into supramolecular assemblies. In this thesis, an investigation into the dynamic combinatorial chemistry of these complexes is presented, followed by strategies for their incorporation into molecular cages and a study of the host - guest chemistry of the resulting assemblies. The first research chapter, Chapter 2, describes the constitutional dynamic chemistry of Au3(pyrazolate)3 complexes. Ligand exchange reactions between"free" pyrazole ligands and Au3(pyrazolate)3 complexes were performed, and a pronounced autocatalytic behavior was observed. This led to the understanding that pyrazole ligands can act as catalysts for ligand scrambling between two Au3(pyrazolate)3 complexes. In addition, four crystal structures of heteroleptic Au3(pyrazolate)2(pyrazolate') are presented. Building on this foundation, Chapter 3 describes the synthesis of a dodecanuclear Au(I) cage. The cage consists of four Au3(pyrazolate)3 units, connected through an organic linker. The cage was first synthesized by a ligand exchange reaction, using an Au3(triazolate)3 complex as a gold source. Surprisingly, the same cage was also obtained in a direct synthesis from a reaction with AuCl(SMe2). The results highlight the importance of ligand design for the successful formation of Au3(pyrazolate)3-based cages. In Chapters 4 and 5, we demonstrate how the kinetic inertness of Au cyclic trinuclear complexes (CTCs) can be utilized in order to use them as pre-formed building blocks for cage synthesis. In Chapter 4, a stepwise approach, in which the use of metalloligands enabled the construction of a Fe24Au24Pd8 cage, is presented. With a molecular weight of 21 kDa and a diameter of approximately 4.1 nm, it is among the largest [Pd6L8]12+ cages reported. In addition, we demonstrate the use of a heteroleptic Au3(pyrazolate)2(pyrazolate') complex for the construction of a smaller Fe8Au12Pd2 cage. In Chapter 5, the preparation of three Au3(pyrazolate)3-based cages by combining coordination chemistry and dynamic covalent chemistry is described. First, two Au3(pyrazolate)3 complexes bearing peripheral aldehyde groups were synthesized. These complexes were used as building blocks for an imine condensation reaction, yielding one tetrahedral cage, containing four CTC units, and two prismatic cages, containing two CTC units each. The crystal structures of all three assemblies are reported. In addition, the host-guest chemistry with pi-acidic guests was studied: the tetrahedral cage was found to bind fullerenes with high affinity, while the prismatic cages encapsulate halogenated aromatic compounds. In Chapter 6, the combination of coordination chemistry and dynamic covalent chemistry for the synthesis of a new family of zirconium-based cages is presented. Pre-formed zirconium clusters were employed as rigid, preorganized building blocks, leading to the synthesis of four new cages: a compact [1+1] species, a [2+3] architecture, and two large tetrahedral [4+4] cages.

For decades, cancer biology research has primarily focused on intrinsic tumor mechanisms driven by genetic lesions. It is now clear that tumor cell behavior is shaped by additional layers of regulation, including epigenetic modifications and metabolic reprogramming, which confer high plasticity and render cancer cells highly responsive to cues from their surrounding microenvironment. Continuous cross-talk between tumor cells and the diverse components of the tumor microenvironment dictates tumorigenesis and is often skewed toward pro-tumoral signals, thereby fueling cancer progression. In B-cell lymphomas, a heterogeneous group of malignancies arising from mature B cells, immune-tumor interactions are particularly important, especially in early disease and indolent subtypes. The increasingly precise understanding of these interactions has recently driven the development of immune-based therapies, which have achieved transformative results in both lymphomas and solid tumors. Nevertheless, tumors continuously adapt and evade immune control, and relapsed or refractory cancers remain largely incurable despite recent advances. Overcoming these limitations requires novel therapeutic strategies and preclinical models that reproduce the complexity of human disease.

In this thesis, I describe a strategy to enhance anti-cancer immunity by targeting long-chain fatty acid (LCFA) elongation. By combining high-throughput drug screening with proteomics, lipidomics, and genetic engineering, we identified Phago Booster 1 (PB1), a small-molecule inhibitor of HSD17B12, a key enzyme in LCFA elongation. Targeting HSD17B12 selectively altered membrane organization in cancer cells, leading to the redistribution of surface receptors, including immune checkpoints and lipid transporters. In contrast, HSD17B12 inhibition enhanced the cytotoxic capacity of immune cells by increasing their glycolytic activity, thereby promoting activation. This divergent effect proved therapeutically relevant in vivo, leading to slower tumor growth and improved survival without notable toxicity. In parallel, I contributed to the development of a patient-derived lymphoma tissue explant platform designed to preserve native tumor architecture, stromal components, and immune cell diversity. We demonstrated that this system, termed lymphomoids, can support personalized medicine approaches by enabling the ex vivo assessment of patient-specific therapeutic responses. In a pilot cohort of 8 patients, drug sensitivity in lymphomoids successfully matched clinical outcomes in 89% of cases, highlighting its potential as a predictive tool for guiding treatment selection.

Together, these approaches illustrate how combining innovative therapeutic discovery with physiologically relevant disease models can uncover actionable strategies to overcome immune resistance and advance personalized cancer treatment.

Millions of people around the world suffer from neurodegenerative diseases. While specific treatments may be able to relieve some of the symptoms of neurodegenerative diseases, in most cases, these complex neurological disorders do not have a cure as we do not completely understand the cause of the disease. The current theory for the mechanism of neurodegenerative diseases is that the causative protein misfolds into a beta-sheet conformation triggering the formation of amyloid fibrils that aggregate into distinctive cellular inclusions. One such protein is alpha-synuclein (aSyn) which accumulates into fibrils in Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). When aSyn amyloid fibrils clump together in diseases such as PD and DLB, they form Lewy bodies. As amyloid fibrils are thought to be the causative agent of the disease, many therapeutic strategies are being developed to inhibit the formation of the fibrils. To facilitate structure-based drug design, several methods have emerged in recent years to generate disease fibrils in vitro and obtain the atomic resolution 3D structure using cryo-electron microscopy. Of the many structures which are emerging from these studies, one major question is if these in vitro-generated structures are the same as what is seen in human disease. In this research, I will use multiple electron microscopy approaches to understand the physiological structure of aSyn fibrils in DLB, which is the second most common form of dementia after Alzheimer's disease. My first aim is to determine the in vitro structure of aSyn fibrils extracted from DLB patients. Cryo-EM is a tool that allows us to get the high-resolution structure of the fibrils in native conditions by plunge freezing in liquid ethane. Several software packages have been developed for electron microscopy structure determination, including RELION (REgularised LIkelihood Optimization); My second aim is to use room-temperature correlative light and electron microscopy (CLEM) method on resin-embedded brain sections to obtain the global morphology of aSyn inclusions in DLB brain donors. The inclusions will be first localized in the brain slices by fluorescence microscopy, and the structure will be analyzed by electron microscopy and electron tomography. I will investigate the inclusions in different brain regions and multiple patients. This analysis will inform us about different inclusion types present in DLB brains and define the targets for high-resolution cryo-CLEM; My final aim is to obtain the high-resolution structure directly within post-mortem human brain samples by cryo-CLEM. This will provide a direct representation of the pathological fibril structure, which can be used for drug discovery and mouse models. Cryo-CLEM and CLEM will be used to study the fibrillar structures in different parts of the post-mortem human brain with DLB. By correlating the information from post-mortem human brains with in vitro fibril structures, I can identify the specific fibril strain which makes up the pathology seen in DLB and PD. The results of this study may lead to the discovery of new therapeutic approaches for synucleinopathies and, in particular, for the prevention or slowing of neurodegeneration associated with Lewy Body diseases.

This thesis systematically evaluates wind farm power efficiency under cyclic yaw control (CYC) across various layouts and inflow conditions via extensive wind tunnel testing. The core mechanism governing the efficacy of CYC in wind farm power improvement is investigated. Inspired by the kinematic similarity between yawing turbines and floating offshore wind turbines (FOWTs) in motions, the work expands to a second part on the power performance and wake characteristics of a FOWT under prescribed platform motions.

The first part of the thesis on CYC includes four studies. The first study, on a single and three-turbine farm, found that CYC accelerates wake recovery, leaving more power available downstream and thus increasing wind farm power production. However, the power of the wind farm does not increase monotonically with the yaw frequency (or Strouhal number) or amplitude. Intriguingly, CYC can even reduce turbulence intensity in the core wake region compared to the baseline.

The second study broadened the scope to different farm layouts and inflow turbulence intensities, also evaluating power fluctuations (a key grid power quality metric). Results show the optimal Strouhal number depends strongly on farm length rather than inflow turbulence intensity and spanwise spacing. The percentage of power gain due to CYC decreases as inflow turbulence increases, highlighting its key role in wind farm control. Notably, within a certain Strouhal range, CYC simultaneously increases mean power production and reduces power fluctuations.

The third study provided the mechanistic insight: periodic wake meandering induced by CYC is the primary driver for power improvement. The periodic wake dynamics sustain throughout the farm at low Strouhal numbers but are suppressed at high frequencies. Inflow conditions, especially turbulence intensity and length scales, can significantly affect the evolution of these CYC-generated wake dynamics. CYC can also alter interactions between wind farm wakes and the atmospheric boundary layer flow.

The fourth study validated an analytical approach based on phase-averaged wake analysis to predict the wake of a wind turbine under CYC. The model accounts for the impacts of CYC on wake velocity and deflection. It is validated experimentally for moderate Strouhal numbers, providing a tool for dynamic wake control scenarios.

The second part expands the scope of this thesis to FOWTs. Three studies on prescribed pitch, surge, and roll motions (at moderate Strouhal numbers) reveal distinct impacts on different motions. Pitch motion influences power and enhances wake recovery, with its generated wake structures affecting downstream turbines. In contrast, surge motion has minimal effect on power or wake characteristics but primarily impacts turbine power fluctuations. Roll motion accelerates wake recovery, increases turbulence intensity, and enhances lateral meandering. Under strong dynamics, roll can also affect thrust and cause wake deflection, presenting a challenge for FOWT analytical wake modeling.