Utifrån ett folkhälso- och samhällsperspektiv är livsmedelsproduktionen den största orsaken till globala miljöförändringar och även en riskfaktor för ohälsa. Samtidigt är kost en grundläggande bestämningsfaktor för hälsa. Restauranger har ett stort inflytande till att påverka en hållbar konsumtion och produktion, och för att gynna det globala ekosystemet. Samtidigt bär restauranger ett ansvar attservera miljövänlig mat, vilket är fördelaktigt ur ett folkhälsoperspektiv. I denna studien var syftet att studera restaurangchefers attityder och tillämpningar kring att servera mat utifrån ett ekologiskt hållbarhetsperspektiv. Metoden var en kvalitativ ansats med semistrukturerade intervjuer som analyserades utifrån en innehållsanalys. Resultaten visade överlag positiva attityder kring att handlar åvaror som är säsongsbaserade, närproducerade och ekologiska i syfte om att värna om miljö-och hälsa. När det kommer till framtida utmaningar och lösningar fanns en oro kring att kunna tillfredsställa framtida generationers behov, kopplat till miljöförändringar. Nedskärningen av köttinstustrin lyftes av restaurangcheferna som den största utmaningen, i relation till att förbättra miljön och således folkhälsan. Det visade sig också att användandet av mat utifrån ett ekologiskt hållbarhetsperspektiv i vissa fall hämmas av ekonomiska resurser. Avgörande faktorer i relation till att servera mat utifrån ett ekologiskt hållbarhetsperspektiv var både ekonomiska resurser och kunskaper kring ämnet. Däremot visade resultatet att kunskaper i de flesta fall var mer avgörande än ekonomiska tillgångar. Med detta resultat, kan slutsatsen dras att det finns ett ytterligare kunskapsbehov samt ekonomiskt behov inom ämnet för att restauranger ska agera mer ekologiskt hållbart. / From a public health and society perspective, food production is the biggest cause of global environmental change and also a risk factor for ill health. At the same time, diet is a fundamental determinant of health. Restaurants have a major influence on sustainable consumption and production, on benefiting the global ecosystem. At the same time, restaurants have a responsibility to serve environmentally friendly food, which is beneficial from a public health perspective. In this study, the purpose was to study restaurant managers' attitudes and applications around serving food from an ecological sustainability perspective. The method was a qualitative approach with semi-structured interviews that were analyzed on the basis of a content analysis. The results showed generally positive attitudes towards buying raw products that are seasonal, locally produced and organic in order to protect the environment and health. When it comes to future challenges and solutions, there was a concern about being able to satisfy the needs of future generations, linked to environmental changes. The cut of the meat industry was highlighted by the restaurant managers as the biggest challenge, in relation to improving the environment and thus public health. It also turned out that the use of food froman ecological sustainability perspective is in some cases hampered by financial resources. Crucial factors in relation to serving food from an ecological sustainability perspective were both financial resources and knowledge of the subject. On the other hand, the results showed that knowledge was in most cases more decisive than financial assets. With this result, it can be concluded that there is an additional need for knowledge as well as financial need in the subject for restaurants to act more ecologically sustainable.

This study set out to investigate the parent-infant interventions that are run by a community-based organisation on the outskirts of Johannesburg’s Alexandra township, South Africa. Community-based interventions that support the parent-infant dyad present an ideal opportunity to shape the development of youth as they aim to foster secure attachment relationships thereby providing the bedrock for future growth. This research specifically explores caregivers’ perceptions of the Baby Mat project in order to understand why some caregivers make optimal use of this intervention whereas others do not. It also gives insight into why some caregivers who are referred for parent-infant psychotherapy on the Baby Mat fail to take up this offer. In addition, it identifies needs caregivers have that are not being met by the Baby Mat. Data for this study was collected by holding a focus group with 11 caregivers in group discussion. The results of the data analysis indicate that caregivers are increasingly having to navigate the transition to motherhood alone, and are often overwhelmed with anxiety. Possibly this is because the support gleaned from extended families has diminished over the last few decades in South Africa. Consequently caregivers are often very receptive to the Baby Mat, which they see in the role of “grandmother”. By visiting the Baby Mat, caregivers realise that they are not alone in the challenges they face and often leave the mat feeling more hopeful about their problems. Yet several factors block them from making full use of this intervention. The primary one is their socially and economically weak position. They are also concerned that actions that they would rather avoid might be taken when facilitators on the mat learn of the abuse they are exposed to. Having limited resources, they are often looking for information and guidance and when this need cannot be met, frustration follows. Generally they want people running relevant interventions to come to them, as opposed to their going out to seek support. This may explain their failure to take up parent-infant psychotherapy. It is also was evident that the caregivers want to reach out to each other.

Världens befolkning och städer växer. I takt med detta ökar efterfrågan på tillgångar av mat, energi och vatten och det finns efterfrågan på tillvägagångssätt som tar hänsyn till både synergier och konflikter mellan dessa. Ett projekt som syftade till att skapa kunskap inom dessa samband genom att använda så kallade Urban Living Labs, ULLs, var det transnationella projektet CRUNCH. Urban Living Labs kan beskrivas som en slags samling tillvägagångssätt som betonar experimentella tillvägagångssätt och en hög nivå av deltagande och samskapande. Men ULLs har visat sig kunna se mycket olika ut och den här studien är ett bidrag till den växande empirin inom ämnet. Studien analyserade hur en av de deltagande städerna inom CRUNCH arbetat med samverkan och samskapande och vilka hinder och möjligheter ULL har som tillvägagångssätt för deltagande, samverkan och samskapande. Detta gjordes genom en kvalitativ fallstudie av Uppsalas ULL Rosendal och analyserades genom teorier om deltagande och kollaborativ governance. Studien fann att deltagandet var smalt och främst skedde genom konsultation och information. De främsta möjligheterna till samarbete verkade vara de inledande villkoren och ett ömsesidigt beroende mellan parterna för att få finansiering till att utveckla sina idéer. De främsta hindren verkade finnas i en obalans i resurser vad gäller finansiering och möjligheter att delta. Men det kanske allra främsta hindret var dock en bristande delad förståelse av begreppet ULL. Begreppet sattes snarare som en ”stämpel” på projekt som redan fanns utan att tillföra dem något extra i form av deltagande eller samverkan. / The world's population and cities are growing. As the demand for food, energy and water resources increases there is a demand for approaches that consider both synergies and conflicts between them. One project that aimed to create knowledge in this nexus by using something called Urban Living Labs, ULLs, was the transnational project CRUNCH. Urban Living Labs can be described as a collection of approaches that emphasizes experimental approaches and a high level of participation and co-creation. But ULLs have been shown to take a variety of different forms and this study is a contribution to the growing empirical evidence in the subject. The study analysed how one of the participating cities within CRUNCH worked with collaboration and co-creation and what obstacles and opportunities ULL has as an approach for participation, collaboration, and co-creation. This was done through a qualitative case study of Uppsala's ULL Rosendal and analysed through theories of participation and collaborative governance. The study found that participation was narrow and mainly took place through consultation and information. The main opportunities for cooperation seemed to be the initial starting conditions and an interdependence between the partners to get funding to develop their ideas. The main obstacles seemed to be resource imbalances in terms of funding and means to participate. But perhaps the main obstacle was a lack of shared understanding of the main concept of ULL. The term was rather applied as a label on projects that already existed, without adding anything extra to them in terms of participation or collaboration.

In the past decade, characterizing spiking neuron models has been extensively researched as an essential issue in computational neuroscience. In this thesis, we examine the estimation problem of two different neuron models. In Chapter 2, We propose a modified Izhikevich model with an adaptive threshold. In our two-stage estimation approach, a linear least squares method and a linear model of the threshold are derived to predict the location of neuronal spikes. However, desired results are not obtained and the predicted model is unsuccessful in duplicating the spike locations. Chapter 3 is focused on the parameter estimation problem of a multi-timescale adaptive threshold (MAT) neuronal model. Using the dynamics of a non-resetting leaky integrator equipped with an adaptive threshold, a constrained iterative linear least squares method is implemented to fit the model to the reference data. Through manipulation of the system dynamics, the threshold voltage can be obtained as a realizable model that is linear in the unknown parameters. This linearly parametrized realizable model is then utilized inside a prediction error based framework to identify the threshold parameters with the purpose of predicting single neuron precise firing times. This estimation scheme is evaluated using both synthetic data obtained from an exact model as well as the experimental data obtained from in vitro rat somatosensory cortical neurons. Results show the ability of this approach to fit the MAT model to different types of reference data.

It is currently a well-established fact that the dynamics of interacting fluid compartments of the central nervous system (CNS) may play a role in the CNS fluid physiology and pathology of a number of neurological disorders, including neurodegenerative diseases associated with accumulation of waste products in the brain. However, the mechanisms and routes of waste clearance from the brain are still unclear. One of the main components of this interacting cerebral fluids dynamics is blood flow. In the last decades, mathematical modeling and fluid dynamics simulations have become a valuable complementary tool to experimental approaches, contributing to a deeper understanding of the circulatory physiology and pathology. However, modeling blood flow in the brain remains a challenging and demanding task, due to the high complexity of cerebral vascular networks and the difficulties that consequently arise to describe and reproduce the blood flow dynamics in these vascular districts. The first part of this work is devoted to the development of efficient numerical strategies for blood flow simulations in complex vascular networks. In cardiovascular modeling, one-dimensional (1D) and lumped-parameter (0D) models of blood flow are nowadays well-established tools to predict flow patterns, pressure wave propagation and average velocities in vascular networks, with a good balance between accuracy and computational cost. Still, the purely 1D modeling of blood flow in complex and large networks can result in computationally expensive simulations, posing the need for extremely efficient numerical methods and solvers. To address these issues, we develop a novel modeling and computational framework to construct hybrid networks of coupled 1D and 0D vessels and to perform computationally efficient and accurate blood flow simulations in such networks. Starting from a 1D model and a family of nonlinear 0D models for blood flow, with either elastic or viscoelastic tube laws, this methodology is based on (i) suitable coupling equations ensuring conservation principles; (ii) efficient numerical methods and numerical coupling strategies to solve 1D, 0D and hybrid junctions of vessels; (iii) model selection criteria to construct hybrid networks, which provide a good trade-off between accuracy in the predicted results and computational cost of the simulations. By applying the proposed hybrid network solver to very complex and large vascular networks, we show how this methodology becomes crucial to gain computational efficiency when solving networks and models where the heterogeneity of spatial and/or temporal scales is relevant, still ensuring a good level of accuracy in the predicted results. Hence, the proposed hybrid network methodology represents a first step towards a high-performance modeling and computational framework to solve highly complex networks of 1D-0D vessels, where the complexity does not only depend on the anatomical detail by which a network is described, but also on the level at which physiological mechanisms and mechanical characteristics of the cardiovascular system are modeled. Then, in the second part of the thesis, we focus on the modeling and simulation of cerebral blood flow, with emphasis on the venous side. We develop a methodology that, departing from the high-resolution MRI data obtained from a novel in-vivo microvascular imaging technique of the human brain, allows to reconstruct detailed subject-specific cerebral networks of specific vascular districts which are suitable to perform blood flow simulations. First, we extract segmentations of cerebral districts of interest in a way that the arterio-venous separation is addressed and the continuity and connectivity of the vascular structures is ensured. Equipped with these segmentations, we propose an algorithm to extract a network of vessels suitable and good enough, i.e. with the necessary properties, to perform blood flow simulations. Here, we focus on the reconstruction of detailed venous vascular networks, given that the anatomy and patho-physiology of the venous circulation is of great interest from both clinical and modeling points of view. Then, after calibration and parametrization of the MRI-reconstructed venous networks, blood flow simulations are performed to validate the proposed methodology and assess the ability of such networks to predict physiologically reasonable results in the corresponding vascular territories. From the results obtained we conclude that this work represents a proof-of-concept study that demonstrates that it is possible to extract subject-specific cerebral networks from the novel high-resolution MRI data employed, setting the basis towards the definition of an effective processing pipeline for detailed blood flow simulations from subject-specific data, to explore and quantify cerebral blood flow dynamics, with focus on venous blood drainage.

In this thesis we develop a mathematically rigorous framework of the so-called ''classical limit'' of quantum systems and their semi-classical properties. Our methods are based on the theory of strict, also called C*- algebraic deformation quantization. Since this C*-algebraic approach encapsulates both quantum as classical theory in one single framework, it provides, in particular, an excellent setting for studying natural emergent phenomena like spontaneous symmetry breaking (SSB) and phase transitions typically showing up in the classical limit of quantum theories. To this end, several techniques from functional analysis and operator algebras have been exploited and specialised to the context of Schrödinger operators and quantum spin systems. Their semi-classical properties including the possible occurrence of SSB have been investigated and illustrated with various physical models. Furthermore, it has been shown that the application of perturbation theory sheds new light on symmetry breaking in Nature, i.e. in real, hence finite materials. A large number of physically relevant results have been obtained and presented by means of diverse research papers.

This thesis examines various methods for estimating the spatial distribution of solar radiation, and in particular its diffuse and direct components in mountainous regions. The study area is the Province of Bolzano (Italy). The motivation behind this work is that radiation components are an essential input for a series of applications, such as modeling various natural processes, assessing the effect of atmospheric pollutants on Earth's climate, and planning technological applications converting solar energy into electric power. The main mechanisms that should be considered when estimating solar radiation are: absorption and scattering by clouds and aerosols, and shading, reflections and sky obstructions by terrain. Ground-based measurements capture all these effects, but are unevenly distributed and poorly available in the Italian Alps. Consequently they are inadequate for assessing spatially distributed incoming radiation through interpolation. Furthermore conventional weather stations generally do not measure radiation components. As an alternative, decomposition methods can be applied for splitting global irradiance into the direct and diffuse components. In this study a logistic function was developed from the data measured at three alpine sites in Italy and Switzerland. The validation of this model gave MAB = 51 Wm^-2, and MBD = -17 Wm^-2 for the hourly averages of diffuse radiation. In addition, artificial intelligence methods, such as artificial neural networks (ANN), can be applied for reproducing the functional relationship between radiation components and meteorological and geometrical factors. Here a multilayer perceptron ANN model was implemented which derives diffuse irradiance from global irradiance and other predictors. Results show good accuracy (MAB in [32,43] Wm^-2, and MBD in [-7,-25] Wm^-2) suggesting that ANN are an interesting tool for decomposing solar radiation into direct and diffuse, and they can reach low error and high generality. On the other hand, radiative transfer models (RTM) can describe accurately the effect of aerosols and clouds. Indeed in this study the RTM libRadtran was exploited for calculating vertical profiles of direct aerosol radiative forcing, atmospheric absorption and heating rate from measurements of black carbon, aerosol number size distribution and chemical composition. This allowed to model the effect of aerosols on radiation and climate. However, despite their flexibility in including as much information as available on the atmosphere, RTM are computationally expensive, thus their operational application requires optimization strategies. Algorithms based on satellite data can overcome these limitations. They exploit RTM-based look up tables for modeling clear-sky radiation, and derive the radiative effect of clouds from remote observations of reflected radiation. However results strongly depend on the spatial resolution of satellite data and on the accuracy of the external input. In this thesis the algorithm HelioMont, developed by MeteoSwiss, was validated at three alpine locations. This algorithm exploits high temporal resolution METEOSAT satellite data (1 km at nadir). Results indicate that the algorithm is able to provide monthly climatologies of both global irradiance and its components over complex terrain with an error of 10 Wm^-2. However the estimation of the diffuse and direct components of irradiance on daily and hourly time scale is associated with an error exceeding 50 Wm^-2, especially under clear-sky conditions. This problem is attributable to the low spatial and temporal resolution of aerosol distribution in the atmosphere used in the clear-sky scheme. To quantify the potential improvement, daily averages of accurate aerosol and water vapor data were exploited at the AERONET stations of Bolzano and Davos. Clear-sky radiation was simulated by the RTM libRadtran, and low values of bias were found between RTM simulations and ground measurements. This confirmed that HelioMont performance would benefit from more accurate local-scale aerosol boundary conditions. In summary, the analysis of different methods demonstrates that algorithms based on geostationary satellite data are a suitable tool for reproducing both the temporal and the spatial variability of surface radiation at regional scale. However better performances are achievable with a more detailed characterization of the local-scale clear-sky atmospheric conditions. In contrast, for plot scale applications, either the logistic function or ANN can be used for retrieving solar radiation components.

Approximation theory is a fundamental tool in order to study the representation theory of a ring R. Roughly speaking, it consists in determining suitable additive or abelian subcategories of the whole module category Mod-R with nice enough functorial properties. For example, torsion theory is a well suited incarnation of approximation theory. Of course, such an idea has been generalised to the additive setting itself, so that both Mod-R and other interesting categories related with R may be linked functorially. By the seminal work of Beilinson, Bernstein and Deligne (1982), the derived category of the ring turns out to admit useful torsion theories, called t-structures: they are pairs of full subcategories of D(R) whose intersection, called the heart, is always an abelian category. The so-called standard t-structure of D(R) has as its heart the module category Mod-R itself. Since then a lot of results devoted to the module theoretic characterisation of the hearts have been achieved, providing evidence of the usefulness of the t-structures in the representation theory of R. In 2020, following a research line promoted by many other authors, Saorin and Stovicek proved that the heart of any compactly generated t-structure is always a locally finitely presented Grothendieck categories (actually, this is true for any t-structure in a triangulated category with coproducts). Essentially, this means that the hearts of D(R) come equipped with a finiteness condition miming that one valid in Mod-R. In the present thesis we tackle the problem of characterising when the hearts of certain compactly generated t-structures of a commutative ring are even locally coherent. In this commutative context, after the works of Neeman and Alonso, Jeremias and Saorin, compactly generated t-structures turned out to be very interesting over a noetherian ring, for they are in bijection with the Thomason filtrations of the prime spectrum. In other words, they are classified by geometric objects, moreover their constituent subcategories have a precise cohomological description. However, if the ascending chain condition lacks, such classification is somehow partial, though provided by Hrbek. The crucial point is that the constituents of the t-structures have a different description w.r.t. that available in the noetherian setting, yet if one copies the latter for an arbitrary ring still obtains a t-structure, but it is not clear whether it must be compactly generated. Consequently, pursuing the study of the local coherence of the hearts given by a Thomason filtration, we ended by considering two t-structures. Our technique in order to face the lack of the ascending chain condition relies on a further approximation of the hearts by means of suitable torsion theories. The main results of the thesis are the following: we prove that for the so-called weakly bounded below Thomason filtrations the two t-structures have the same heart (therefore it is always locally finitely presented), and we show that they coincide if and only they are both compactly generated. Moreover, we achieve a complete characterisation of the local coherence for the hearts of the Thomason filtrations of finite length.

We aim at generalizing the celebrated portfolio optimization problem "à la Merton", where the asset evolution is steered by a self-exciting jump-diffusion process. We first define the rigorous mathematical framework needed to introduce the stochastic optimal control problem we are interesting in. Then, we provide a proof for a specific version of the Dynamic Programming Principle (DPP) with respect to the general class of self-exciting processes under study. After, we state the Hamilton-Jacobi-Bellman (HJB) equation, whose solution gives the value function for the corresponding optimal control problem. The resulting HJB equation takes the form of a Partial-Integro Differential Equation (PIDE), for which we prove both existence and uniqueness for the solution in the viscosity sense. We further derive a suitable numerical scheme to solve the HJB equation corresponding to the portfolio optimizationproblem. To this end, we also provide a detailed study of solution dependence on the parameters of the problem. The analysis is performed by calibrating the model on ENI asset levels during the COVID-19 worldwide breakout. In particular, the calibration routine is based on a sophisticated Sequential Monte Carlo algorithm.

The problem of the relationship between brain function, characterized by functional magnetic resonance imaging, and physiological fluctuations by means of cardiac / respiratory oscillations is one of the most debated topics in the last decade. In recent literature, a great number of studies are found that focus on both practical and conceptual aspects about this topic. In this work, we start with reviewing two distinct approaches in considering physiology - related sequences with respect to functional magnetic resonance imaging: one treating physiology - related fluctuations as generators of noise, the other considering them as carriers of cognitively relevant information. In chapter 2 – “Physiology – related effects in the BOLD signal at rest at 4T”, we consider physiological quantities as generators of noise, and discuss conceptual flaws researchers have to face when dealing with data de-noising procedures. We point out that it can be difficult to show that the procedure has achieved its stated aim, i.e. to remove only physiology - related components from the data. As a practical solution, we present a benchmark for assessing whether correction for physiological noise has achieved its stated aim, based on the principle of permutation testing. In chapter 3 – “Directional relationships between BOLD activity and autonomic nervous system fluctuations revealed by fast fMRI acquisition”, on the other hand, we will consider autonomic indicants derived from physiological time - series as meaningful components of the BOLD signal. There, we describe a FMRI experiment building on this, where the goal was to localize brain areas whose activity is directionally related to autonomic one, in a top - down modulation fashion. In chapter 4 we recap the conclusions we found from the two approaches and we summarize the general contributions of our findings. We point out that bringing together the distinct approaches we reviewed lead us to mainly two contributions. On one hand we thought back the validity of almost established procedures in FMRI resting - state pre-processing pipelines. On the other we were able to say something new about general relationship between BOLD and autonomic activity, resting state fluctuations and deactivation theory.