University of Udine
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Robust pricing of equity-Indexed annuities under uncertain volatility and stochastic interest rate
No full textIn this paper, we propose a novel methodology for pricing equity-indexed annuities featuring cliquet-style payoff structures and early surrender risk, using advanced financial modeling techniques. Specifically, the market is modeled by an equity index that follows an uncertain volatility framework, while the dynamics of the interest rate are captured by the Hull-White model. Due to the inherent complexity of the market dynamics under consideration, we develop a numerical algorithm that employs a tree-based framework to discretize both the interest rate and the underlying equity index, enhanced with local volatility optimization. Extensive numerical experiments demonstrate the high effectiveness of the proposed algorithm, which has been tested against a machine learning-based approach and yields consistent results with substantially lower computational cost. Furthermore, the numerical framework is employed to analyze key features of the insurance contract, including the delineation of the optimal exercise region when early surrender risk is incorporated
Life cycle assessment of biogas production
No full textThis chapter delves into the holistic assessment of the environmental impacts associated with the entire biogas system processes, from the provision of the feedstock to the various biogas technologies and the different end-use applications of biogas. Therefore, the methodological framework of life cycle assessment (LCA) is presented to quantify greenhouse gas emissions, energy consumption, and other environmental metrics. In addition, the most important aspects for carrying out a LCA of biogas processes are highlighted, and an overview of the current legal framework and its impact on assessment methodologies is given. Examples are used to show both the most sustainable production and utilization pathways for biogas in the future and introduce methodological and technical requirements for the further development of biogas systems. Finally, an outlook is given on possible developments of biogas technologies toward an overarching technology to reduce greenhouse gas emissions from organic waste streams, implement a circular economy, and link with a sustainable bioeconomy
Sociolinguistic Dynamics of Slovenian Linguistic Communities in Friuli, Italy: An Analysis of Communicative Practices and Linguistic Attitudes
No full textThis article analyzes the sociolinguistic dynamics of Slovenian-speaking communities across Natisone, Torre, Resia Valleys, and Val Canale in Friuli, northeastern Italy. Drawing on a com-prehensive sociolinguistic survey, the study investigates key indicators of language vitality, including self-reported competence, domains of use, and intergenerational transmission. We explore speakers’ attitudes and perceptions toward their local Slovenian varieties and Standard Slovenian, situated within a complex multilingual context that involves contact with Italian, Friulian, and German. The research highlights how differing historical trajectories, contact lev-els, and varying degrees of institutional support – particularly through formal education – have shaped the contemporary repertoire and maintenance challenges of these linguistic communi-ties. The findings offer a nuanced understanding of the factors driving language shift and main-tenance in minority language setting
Defect analysis by computed tomography in metallic materials: Optimisation, uncertainty quantification and classification
No full textThis paper presents a methodology to optimise post-processing parameters in X-ray Computed Tomography (CT) for defect detection in metallic materials. The approach addresses three main goals: minimisation of systematic errors in defect reconstruction, quantification of uncertainty, and reliable defect classification. The proposed methodology aims to remove the systematic error that impacts defect reconstruction, thereby improving the accuracy of defect size and morphology assessment, which is essential for fatigue life prediction, particularly in materials produced through additive manufacturing (AM). An iterative comparison between CT-based defect and fractographic measurements is involved to identify the optimal CT post-processing parameters, such as the grey threshold (GT). The methodology was applied to 11 dog-bone-shaped titanium alloy samples (5.5 mm nominal gauge diameter) produced via electron beam melting. The optimisation procedure resulted in a GT value that was 134% of that obtained using a commercial algorithm, effectively removing the systematic uncertainty associated with Murakami's parameter area. The uncertainty of various defect features, such as equivalent diameter, sphericity and aspect ratio, was calculated by propagating the remaining stochastic uncertainty of area. An unsupervised K-means algorithm categorised unlabelled defects into three major types often encountered in AM: gas pores, keyholes, and lack of fusion. Finally, the labelled defects were processed through a support vector machine to infer the analytical form of the decision boundaries, achieving an accuracy of 99%
The effect of stocking density and enrichment on hair cortisol, hair dehydroepiandrosterone (sulphate) and their ratio in growing-finishing pigs
No full textUnderstanding how husbandry practices affect chronic stress in growing-finishing pigs is essential to improve their welfare. The objective of this study was therefore to investigate the effect of two important practices, i.e., stocking density and enrichment, within different husbandry systems by studying concentrations of hair cortisol and hair dehydroepiandrosterone (sulphate) (DHEA(S)) and their ratio, as markers for chronic stress. Hereto, in six experiments, various organic and conventional systems were studied in which the stocking density and the level of enrichment varied. We found that a lower stocking density generally resulted in lower hair cortisol and DHEA(S) concentrations, but the effect of stocking density on the hair cortisol/DHEA(S) ratio was less clear. Access to enrichment only tended to result in higher DHEA(S) concentrations in one of the experiments. Furthermore, sex tended to affect or affected hair cortisol, DHEA(S) and/or the ratio only in some of the experiments. These results suggest that a lower stocking density is beneficial for growing-finishing pigs as they seemed to be less chronically stressed. That the enrichment items did not beneficially affect hair cortisol and DHEA(S) was likely due to the relatively small contrast between the control and enriched condition, as the pigs in the control condition already had access to straw. As not much studies have investigated hair DHEA(S) concentrations in pigs, more research is needed to get more insight of this hormone in relation to chronic stress and the effect of sex in pigs
Modeling moisture buffering of innovative plasters from material properties to room scale
No full textPassive moisture buffering can stabilize indoor microclimates and reduce cooling energy use, yet reliable prediction across scales remains challenging. This study presents a simplified characterization-to-modeling workflow that links laboratory measurements to room-scale performance. Step-response tests in a dynamic vapour sorption (DVS) analyzer were interpreted with a Fickian diffusion model fitted via root-mean-square error minimization, yielding effective moisture diffusivities of D ∼1.84 × 10−8 m2s−1 for the lime plaster and D ∼1.17 × 10−9 m2s−1 for a plaster containing 20 % calcium alginate beads. Vapour-resistance factor functions derived from these fits and from sorption isotherms were validated against NORDTEST MBV dynamic measurements, with the best agreement for both mass change (Δm) and MBV at a surface resistance of 0.1 m2KW−1 (errors: 0.67 % lime; 1.59 % alginate). Year-long building simulations then quantified room-scale impacts: the alginate-enhanced plaster attenuated indoor RH fluctuations more effectively than lime, with attenuation factors spanning from 0.2 to 0.8 depending on the moment of the year and the simulated scenario. Energy analyses showed substantial reductions in latent cooling (up to ∼100 % in scenarios with wider indoor relative humidity control band) and up to 11.2 % lower total cooling energy versus lime, with similar sensible loads. The results demonstrate a practical, scalable pathway from material properties to performance, and highlight alginate-enhanced plasters as promising passive components for humidity stabilization and energy-efficient building design and retrofits
Exact and metaheuristic approaches to minimizing makespan in parallel machine scheduling with conflicting jobs
No full textLeveraging machine learning for high-dimensional option pricing within the uncertain volatility model
No full textThis paper explores the application of Machine Learning techniques for pricing high-dimensional options within the framework of the Uncertain Volatility Model (UVM). The UVM is a robust framework that accounts for the inherent unpredictability of market volatility by setting upper and lower bounds on volatility and the correlation among underlying assets. By leveraging historical data and extreme values of estimated volatilities and correlations, the model establishes a confidence interval for future volatility and correlations, thus providing a more realistic approach to option pricing. By integrating advanced Machine Learning algorithms, we aim to enhance the accuracy and efficiency of option pricing under the UVM, especially when the option price depends on a large number of variables, such as in basket or path-dependent options. In this paper, we consider two approaches based on Machine Learning. The first one, termed GTU, evolves backward in time, dynamically selecting at each time step the most expensive volatility and correlation for each market state. Specifically, it identifies the particular values of volatility and correlation that maximize the expected option value at the next time step, and therefore, an optimization problem must be solved. This is achieved through the use of Gaussian Process regression, the computation of expectations via a single step of a multidimensional tree and the Sequential Quadratic Programming optimization algorithm. The second approach, referred to as NNU, leverages neural networks and frames pricing in the UVM as a control problem. Specifically, we train a neural network to determine the most adverse volatility and correlation for each simulated market state, generated via random simulations. The option price is then obtained through Monte Carlo simulations, which are performed using the values for the uncertain parameters provided by the neural network. The numerical results demonstrate that the proposed approaches can significantly improve the precision of option pricing and risk management strategies compared with methods already in the literature, particularly in high-dimensional contexts