Traditional risk factors and lifetime risk of acute coronary events

Objective: To investigate the association between the four traditional coronary heart disease (CHD) risk factors (hypertension, smoking, hypercholesterolemia, and diabetes) and outcomes of first ACS. Methods: Data were drawn from the ISACS Archives. The study participants consisted of 70953 patients with first ACS, but without prior CHD. Primary outcomes were patient’ age at hospital presentation and 30-day all-cause mortality. The risk ratios for mortality among subgroups were calculated using a balancing strategy by inverse probability weighting. Trends were evaluated by Pearson's correlation coefficient (r). Results: For fatal ACS (n=6097), exposure to at least one traditional CHD-risk factor ranged from 77.6% in women to 74.5% in men. The presence of all four CHD-risk factors significantly decreased the age at time of ACS event and death by nearly half a decade compared with the absence of any traditional risk factors in both women (from 67.1±12.0 to 61.9±10.3 years; r=-0.089, P<0.001) and men (from 62.8±12.2 to 58.9±9.9 years; r=-0.096, P<0.001). By contrast, there was an inverse association between the number of traditional CHD-risk factors and 30-day mortality. The mortality rates in women ranged from 7.7% with four traditional CHD-risk factors to 16.3% with no traditional risk factors (r=0.073, P<0.001). The corresponding rates in men were 4.8% and 11.5% (r=0.078, P<0.001), respectively. The risk ratios among individuals with at least one CHD-risk factors vs. those with no traditional risk factors were 0.72 (95%CI:0.65-0.79) in women and 0.64 (95%CI:0.59-0.70) in men. This association was consistent among patient subgroups managed with guideline-recommended therapeutic options. Conclusions: The vast majority of patients who die for ACS have traditional CHD-risk factor exposure. Patients with CHD-risk factors die much earlier in life, but they have a lower relative risk of 30-day mortality than those with no traditional CHD-risk factors, even in the context of equitable evidence‐based treatments after hospital admission

Essays in political economy

This dissertation explores the interplay between norms, preferences, and information across three distinct chapters. It investigates how information derived from commemoration, news, or social media interacts with social norms, such as trust and pro-social behavior, and political preferences, including regime support in autocracy and demand for regulation. Chapter 1 explores the transmission of social norms through collective memory in transient communities. Utilizing novel data on online donation ads for small personal items as a proxy for pro-social behavior, the study reveals that individuals are less likely to engage in such behavior when reminded about past repression through commemoration. This emphasizes the crucial role of collective memory in shaping historical legacies, even in transient communities. Examining the demand for state regulation during the COVID-19 pandemic, Chapter 2 challenges existing theories by incorporating fear alongside trust. Analyzing survey data from 61 Russian regions, the study finds that fear of the virus increases demand for regulation. The findings highlight a critical scope condition: the impact of trust on regulation is conditional on fear, with high levels of fear decreasing the effect of trust. This offers insights into how fear of social threats shapes support for state intervention, especially in crises. Concluding the dissertation, Chapter 3 establishes an empirical link between exposure to information on casualties, contrasting war propaganda, and war and regime support in Russia after the full-scale invasion of Ukraine in February 2022. Analyzing the changes in social media engagement in response to verified information on Russian war fatalities in Ukraine, the study reveals that accurate information on the human cost of war disrupts the spread of war propaganda and has the potential to erode support for the autocrat at war. This underscores the broader implications of countering false narratives in the context of independent media and misinformation

Spectroscopic investigation on photoreactivity, structure and polymorphism of organic molecular crystals

The study of the spectroscopic phenomena in organic solids, in combination with other techniques, is an effective tool for the understanding of the structural properties of materials based on these compounds. This Ph.D. work was dedicated to the spectroscopic investigation of some relevant processes occurring in organic molecular crystals, with the goal of expanding the knowledge on the relationship between structure, dynamics and photoreactivity of these systems. Vibrational spectroscopy has been the technique of choice, always in combination with X-ray diffraction structural studies and often the support of computational methods. The vibrational study of the molecular solid state reaches its full potential when it includes the low-wavenumber region of the lattice-phonon modes, which probe the weak intermolecular interactions and are the fingerprints of the lattice itself. Microscopy is an invaluable addition in the investigation of processes that take place in the micro-meter scale of the crystal micro-domains. In chemical and phase transitions, as well as in polymorph screening and identification, the combination of Raman microscopy and lattice-phonon detection has provided useful information. Research on the fascinating class of single-crystal-to-single-crystal photoreactions, has shown how the homogeneous mechanism of these transformations can be identified by lattice-phonon microscopy, in agreement with the continuous evolution of their XRD patterns. On describing the behavior of the photodimerization mechanism of vitamin K3, the focus was instead on the influence of its polymorphism in governing the product isomerism. Polymorphism is the additional degree of freedom of molecular functional materials, and by advancing in its control and properties, functionalities can be promoted for useful applications. Its investigation focused on thin-film phases, widely employed in organic electronics. The ambiguities in phase identification often emerging by other experimental methods were successfully solved by vibrational measurements

Looking at the big picture: defining a method to incorporate multiple pressure into fisheries management considerations

In the Mediterranean Sea the scientific advice aimed to maintain the long-term productivity of fish stocks is achieved by single species-stock assessment. Ecosystem-oriented advice requires knowledge on the relations between species biology and the environment that surrounds and a method to forecast the biological response to future scenarios. Taking as a case study the Common cuttlefish in the Adriatic Sea, we collected knowledge and we implemented a probabilistic Risk Assessment to describe the sources of error associated to the single species stock assessment. We observe that Bayesian Belief Networks can be used to summarize outputs of ecological models and to link them to expert based conceptual models. We gathered the knowledge on the ecosystem and anthropic pressures and their relationship with biological process by the means of literature review, single species stock assessment, machine learning models and bayesian meta analysis. We then implement a semi-quantitative extension of a risk assessment based on a hierarchical composite indicator describing stock assessment considerations and a Bayesian belief network to model population dynamic and environmental/ecosystem considerations. The proposed approach combines Risk Table, model weighing, ecological models results and Bayesian Belief Network to identify which is the most relevant source of uncertainty in the single species stock assessment. The Bayesian Belief Network is used to model management and environmental scenarios tracking the risk probability that growth performance of common cuttlefish is impaired. Food web, and to a less extent temperature, can impact the growth performance of cuttlefish. Furter research is needed to explicitly model the biomass dynamic as a function of alternative biological parameters accounting for food web status

Towards a sustainable approach for Cultural Heritage Conservation: developing micro-structured green materials and analytical protocols for their performance assessment

Cleaning is one of the most important and delicate procedures that are part of the restoration process. When developing new systems, it is fundamental to consider its selectivity towards the layer to-be-removed, non-invasiveness towards the one to-be-preserved, its sustainability and non-toxicity. Besides assessing its efficacy, it is important to understand its mechanism by analytical protocols that strike a balance between cost, practicality, and reliable interpretation of results. In this thesis, the development of cleaning systems based on the coupling of electrospun fabrics (ES) and greener organic solvents is proposed. Electrospinning is a versatile technique that allows the production of micro/nanostructured non-woven mats, which have already been used as absorbents in various scientific fields, but to date, not in the restoration field. The systems produced proved to be effective for the removal of dammar varnish from paintings, where the ES not only act as solvent-binding agents but also as adsorbents towards the partially solubilised varnish due to capillary rise, thus enabling a one-step procedure. They have also been successfully applied for the removal of spray varnish from marble substrates and wall paintings. Due to the materials' complexity, the procedure had to be adapted case-by-case and mechanical action was still necessary. According to the spinning solution, three types of ES mats have been produced: polyamide 6,6, pullulan and pullulan with melanin nanoparticles. The latter, under irradiation, allows for a localised temperature increase accelerating and facilitating the removal of less soluble layers (e.g. reticulated alkyd-based paints). All the systems produced, and the mock-ups used were extensively characterised using multi-analytical protocols. Finally, a monitoring protocol and image treatment based on photoluminescence macro-imaging is proposed. This set-up allowed the study of the removal mechanism of dammar varnish and semi-quantify its residues. These initial results form the basis for optimising the acquisition set-up and data processing

Low-power heterogeneous architectures for efficient and predictable autonomous cyber-physical systems

In today’s rapidly evolving technological landscape, cyber-physical systems have pervaded various aspects of our daily lives, from autonomous vehicles and healthcare to industrial automation and smart cities. Such applications span a wide range in criticality, performance, and memory footprint, under tight cost and power constraints. High-end applications rely on power-hungry Systems-on-Chip (SoCs) featuring powerful processors, large LPDDR/DDR3/4/5 memories, and supporting full-fledged Operating Systems (OS). On the contrary, low-end applications typically rely on Ultra-Low-Power µcontrollers with a "close to metal" software environment and simple real-time micro-kernel-based runtimes. Emerging applications and trends of cyber-physical systems require the "best of both worlds": cheap and low-power SoC systems able to (i) run increasingly complex multi-tasking workloads with large memory footprints within a few hundred mW power budget, (ii) offer a well-known and agile software environment based on full-fledged OS while (iii) providing extreme energy efficient processing capabilities and (iv) not compromising on time-predictability. In this context, this work presents a threefold contribution. First, the thesis introduces Shaheen, a 22nm low-power (<200mW) heterogeneous SoC designed for autonomous nano-unmanned aerial vehicles, an emerging class of cyber-physical systems. Shaheen features an application-class RV64 host processor with hardware virtualization support, enabling the secure consolidation of a real-time and a full-blown OS onto the same platform. Furthermore, it integrates a flexible cluster of eight RV32 cores, providing state-of-the-art energy-efficient performance for low-power artificial intelligence algorithms. Secondly, this work discusses enhancements to Shaheen’s memory hierarchy, demonstrating the trade-off between low-power and high-end off-chip memories. Lastly, it focuses on the time predictability of AXI-based architectures in safety-critical applications. Namely, it introduces (i) a novel fine-grained methodology for modeling the typical resources composing modern heterogeneous SoCs and (ii) a complete mathematical analysis to upper bound the response time of the interactions between the agents in the system

Wireless solutions for the industrial internet of things

The fourth industrial revolution is paving the way for Industrial Internet of Things applications where industrial assets (e.g., robotic arms, valves, pistons) are equipped with a large number of wireless devices (i.e., microcontroller boards that embed sensors and actuators) to enable a plethora of new applications, such as analytics, diagnostics, monitoring, as well as supervisory, and safety control use-cases. Nevertheless, current wireless technologies, such as Wi-Fi, Bluetooth, and even private 5G networks, cannot fulfill all the requirements set up by the Industry 4.0 paradigm, thus opening up new 6G-oriented research trends, such as the use of THz frequencies. In light of the above, this thesis provides (i) a broad overview of the main use-cases, requirements, and key enabling wireless technologies foreseen by the fourth industrial revolution, and (ii) proposes innovative contributions, both theoretical and empirical, to enhance the performance of current and future wireless technologies at different levels of the protocol stack. In particular, at the physical layer, signal processing techniques are being exploited to analyze two multiplexing schemes, namely Affine Frequency Division Multiplexing and Orthogonal Chirp Division Multiplexing, which seem promising for high-frequency wireless communications. At the medium access layer, three protocols for intra-machine communications are proposed, where one is based on LoRa at 2.4 GHz and the others work in the THz band. Different scheduling algorithms for private industrial 5G networks are compared, and two main proposals are described, i.e., a decentralized scheme that leverages machine learning techniques to better address aperiodic traffic patterns, and a centralized contention-based design that serves a federated learning industrial application. Results are provided in terms of numerical evaluations, simulation results, and real-world experiments. Several improvements over the state-of-the-art were obtained, and the description of up-and-running testbeds demonstrates the feasibility of some of the theoretical concepts when considering a real industry plant

Study and development of BaCe0.65Zr0.20Y0.1503-Δ (BCZY) – Gd0.2Ce0.8O2-Δ (GDC) dense ceramic membranes sysyem for high temperature H2 purification

The first main conclusion drawn from this dissertation concerns the amount of Pt deposited on the asymmetric layer of membrane produced by tape casting porosity shaping method. Three different amounts were investigated (0.15, 1.5 and 4.5 mg cm-2 ). The most optimal performance, based on H2 permeation performances, was attained when 1.5 mg cm-2 of Pt was deposited on the porous layer, resulting in a 0.642 mL min-1 cm-2 permeated H2 when 80% H2 in He was employed as the feed. Pt deposition method is influenced by the concentration of the Pt precursor, which results in different morphology of the catalyst. The second development focused on further optimization on tape casting membranes concerning the solvent employed for the Pt catalyst deposition. The same concentration of Pt was employed, depositing 1.5 mg cm-2 on the porous side of the membrane, but a mixture of acetone and water was employed as solvent. This mixture allowed the suppression of effects leading to poorly dispersed particles. As a result, it was possible to achieve 0.74 mL min-1 cm-2 at 750°C with 50% H2 in He. Lastly, first-ever permeation performance measurements into an innovative ceramic membrane type for hydrogen separation was investigated. In-depth research was done on a group of hierarchically-structured BaCe0.65Zr0.20Y0.15O3-δ(BCZY) - Gd0.2Ce0.8O2-δ(GDC) membranes produced by freeze casting porosity shaping method. Membranes were investigated observing the effect of deposition solvent and the effect of porous layer thickness. Employing a mixture of Acetone and water resulted in better hydrogen permeation at temperatures (T > 650°C), reaching 0.26 mL min-1 cm-2 at 750°C with 50% H2 in He. The reduction of porous layer thickness led to a hydrogen flow of 0.33 mL min-1 cm-2 , at 750°C with 50% H2 in He

PDEs for neural networks with internal states

In the context of mathematical neuroscience, the Integrate and Fire model undoubtedly enjoys great fame and a vast literature. Yet, its peculiar mathematical structure makes the study of this equation challenging and always open-ended. The classical model consists of an equation that describes the dynamics of a network of neurons based on the membrane potential of the cells. A network can be interconnected with excitatory or inhibitory linkages or disconnected, in which case the equation will be linear. We are interested in the asymptotic behaviour of such networks in the linear case, where mathematical tools such as the relative entropy, the integral method and Harris theory have been useful in proving the convergence towards the steady state. In the first extension of the classical Integrate and Fire model we propose, we replace the punctual boundary condition with a non-local term, introducing a randomness parameter. For this new system, we prove long-time convergence via Harris theory and relative entropy with Poincaré inequality independent of the random parameter. Furthermore, we study the asymptotic convergence of the solutions of this model to those of the classical one. In the second extension, we deal with the incorporation of a variable for the adaptation current. First, we study the dynamics of this last variable alone, analysing the regularity of the stationary solution in dependence on the parameters and the asymptotic behaviour by means of the different methods of relative entropy with compactness argument and integral method. We then investigate the dynamics of the two-dimensional model through numerical simulations and we make comparison with a similar Fokker-Planck equation with partial diffusion and non-linearity. A number of numerical simulations accompany the study of each analysed model, allowing its theoretical results to be supported or anticipated.Dans le contexte des neurosciences mathématiques, le modèle Intègre et Tire jouit sans aucun doute d'une grande renommée et d'une vaste littérature. Cependant, sa structure mathématique particulière rend l'étude de cette équation difficile et toujours ouvert. Le modèle classique consiste en une équation qui décrit la dynamique d'un réseau neuronal en fonction du potentiel de membrane des cellules. Un réseau peut être interconnecté par des liens excitateurs ou inhibiteurs, ou déconnecté, auquel cas l'équation sera linéaire. Nous nous intéressons au comportement asymptotique de ces réseaux dans le cas linéaire, où des outils mathématiques tels que l'entropie relative, la méthode integrale et la théorie de Harris ont été utiles pour démontrer la convergence vers l'état stationnaire. Dans la première extension du modèle classique d'Intègre et Tire que nous proposons, nous remplaçons la condition au bord ponctuelle par un terme non local, en introduisant un paramètre aléatoire. Pour ce nouveau système, nous prouvons la convergence en temps long au moyen de la théorie de Harris et de l'entropie relative avec une inégalité de Poincaré indépendante du paramètre aléatoire. De plus, nous étudions la convergence asymptotique des solutions de ce modèle vers celles du modèle classique. Dans la deuxième extension, nous traitons de l'ajout d'une variable pour le courant d'adaptation. Nous étudions d'abord la dynamique de cette variable seule, en analysant la régularité de la solution stationnaire en fonction des paramètres et le comportement asymptotique à l'aide des différentes méthodes de l'entropie relative avec argument de compacité et de la méthode intégrale. Nous étudions ensuite la dynamique du modèle bidimensionnel au moyen de simulations numériques et nous proposons des comparaisons avec une équation de Fokker-Planck similaire avec diffusion partielle et non-linéarité. Un certain nombre de simulations numériques accompagnent l'étude de chaque modèle analysé, ce qui permet d'étayer ou d'anticiper les résultats théoriques.Nel contesto delle neuroscienze matematiche, il modello di Integrate and Fire gode indubbiamente di grande fama e di una vasta letteratura. Eppure, la sua peculiare struttura matematica rende lo studio di questa equazione stimolante e sempre aperto. Il modello classico consiste in un'equazione che descrive la dinamica di una rete di neuroni in funzione del potenziale di membrana delle cellule. Una rete può essere interconnessa con legami eccitatori o inibitori o disconnessa, nel qual caso l'equazione sarà lineare. Noi siamo interessati al comportamento asintotico di tali reti nel caso lineare, dove strumenti matematici come l'entropia relativa, il metodo integrale e la teoria di Harris si sono rivelati utili per dimostrare la convergenza verso lo stato stazionario. Nella prima estensione del modello classico di Integrate and Fire che proponiamo, sostituiamo la condizione al bordo puntuale con un termine non locale, inserendo un parametro di casualità. Per questo nuovo sistema, dimostriamo la convergenza allo stato stazionario tramite la teoria di Harris e dell'entropia relativa con disuguaglianza di Poincaré indipendente dal parametro casuale. Inoltre, studiamo la convergenza asintotica delle soluzioni di questo modello a quelle del classico. Nella seconda estensione ci occupiamo di incorporare una variabile per la corrente di adattazione. In primo luogo, studiamo la dinamica di quest'ultima variabile sola, analizzando la regolarità della soluzione stazionaria in dipendenza dai parametri e studiando il comportamento asintotico tramite i differenti metodi dell'entropia relativa con argomento di compattezza e metodo integrale. Indaghiamo poi la dinamica del modello bidimensionale tramite delle simulazioni numeriche e lo confrontiamo con un'equazione di Fokker-Planck similare con diffusione parziale e nonlinearità. Alcune simulazioni numeriche accompagnano lo studio di ogni modello analizzato, permettendo così di supportarne o anticiparne i risultati teorici

Multifaceted applications of crown ethers exploiting nitroxide labelling- cation sensing, synthesis of paramagnetic rotaxanes and dissipative processes of radical molecular machines

Crown ethers are one of the most studied class of macrocycles for host-guest interactions. They have been employed in a wide range of possible applications in the field of biochemistry as biomimetic models, ionophores, metal sensing and mechanically interlocked molecules (MIMs). In the field of cation sensing an alternative approach consists in introducing radical probes as an active moiety in the complexation of a crown ether structure. In these systems, EPR spectroscopy acquires a key role in the detection of binding events thanks to its high sensitivity to the chemical environment surrounding the paramagnetic units. To this aim nitroxides based on aza-crown ethers were prepared and employed as selective sensors for the detection of inorganic and organic cations by EPR analysis of the corresponding host-guest complexes, particularly with Alkali, Alkali-earth and di-benzyl ammonium cations. The possibility to extend spin probe methodology in stable nitroxide macrocycle involved in molecular machines, like rotaxanes, makes the radical behaving as a direct recognizing unit for a guest hosted inside containing more than one station. In the present investigation, a [2]rotaxane based on a synthetic radical aza-crown ether derivative was studied in an aerobic oxidation reaction of alcohol to aldehyde as a mechanically blocked catalyst. Lastly, it is discussed a novel methodology for the synthesis of a series of 24-crown-8 chiral derivatives containing ethers, alcohols and carboxylic groups as side chains. Furthermore, these macrocycles were tested as catalysts for the synthesis of MGI-1 type Rotaxanes using the Metal-free active template approach, and the parameters to induct a face-selectivity of the macrocycles were also investigated