Heritage and food history. A critical assessment

The heritagization of food is based on cultural constructions creating or strenghtening several identity markers. As the topic of heritage is one the most recently addressed within the wide bibliography of food studies, in particular food history has been analysing how societies and groups historically produced food heritages. This essay chooses the Unesco international food labelling as a lens to analyze limits and critic points of the institutional heritagizazion. Firstly, the essay will examine the territorial identity paradigms involved in Unesco institutional procedures and their articulation on different spatial levels, which tend to overlap and intersect each other in complex geographies. Secondly, it will analyze risks and problems of the constant recourse to past and history - related to issues of authenticity, tradition, nostalgia - aimed at legitimizing food heritage and identity claims

Security and border making in 19th-century southern Italy

The article focuses on the border region between two states in pre-unification Italy, the Kingdom of the Two Sicilies and the Papal States. Although negotiations to define the border precisely started only following the cholera epidemic of 1836–7, the early 19th century already saw the start of greater control of the territory and of the borders by the ‘administrative monarchies’. Analysed through the lens of securitisation, movement control processes reveal a variable geography of ‘security spaces’ and freedom of movement for different social groups, where state security and collective security needs overlappe

Chapter 2 Heritage and food history

Food Heritage and Nationalism in Europe contends that food is a fundamental element of heritage, and a particularly important one in times of crisis. Arguing that food, taste, cuisine and gastronomy are crucial markers of identity that are inherently connected to constructions of place, tradition and the past, the book demonstrates how they play a role in intangible, as well as tangible, heritage. Featuring contributions from experts working across Europe and beyond, and adopting a strong historical and transnational perspective, the book examines the various ways in which food can be understood and used as heritage. Including explorations of imperial spaces, migrations and diasporas; the role of commercialisation processes, and institutional practices within political and cultural domains, this volume considers all aspects of this complex issue.　Arguing that the various European cuisines are the result of exchanges, hybridities and complex historical processes, Porciani and the chapter authors offer up a new way of deconstructing banal nationalism and of moving away from the idea of static identities. Suggesting a new and different approach to the idea of so-called national cuisines, Food Heritage and Nationalism in Europe will be a compelling read for academic audiences in museum and heritage studies, cultural and food studies, anthropology and history. Chapters 1,2,4,6 and 12 of this book are available for free in PDF format as Open Access from the individual product page at www.routledge.com. They have been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 licens

DOMAIN-AWARE MULTIFIDELITY LEARNING FOR DESIGN OPTIMIZATION

Accurate physics-based models are essential to the design and optimization of engineering systems, to compute key performance indicators associated with alternative design solutions. The implementation of high-fidelity models in simulation-based design optimization poses significant challenges due to the relevant computational cost frequently associated with their execution. However, real world engineering systems can rely on the availability of multiple models or approximations of their physics, representations characterized by different computational complexity and accuracy. Those alternative models can be cheaper to evaluate and can thus be exploited to enhance the efficiency of the optimization task. Multifidelity methods allow to combine multiple sources of information at different levels of fidelity, potentially exploiting the affordability of low fidelity evaluations to massively explore the design space, then enriching the accuracy through a reduced number of high-fidelity queries [1]. Many multifidelity optimization methods combine data from multiple models into a probabilistic surrogate, frequently delaying the identification of promising design alternatives that could rather be more efficiently captured if domain specific expertise were also used to inform the search [2]. To address this challenge, we present original domain-aware multifidelity frameworks to accelerate design optimization and improve the quality of the solution. In particular, our strategy is based on an active learning scheme that combines data-driven and physics-informed utility functions, to include the expert knowledge about the specific physical phenomena during the search for the optimal design. This allows to tailor the selection of the physical model to evaluate and increase the efficiency of the learning process, using at best a limited amount of high-fidelity data to sensitively improve the design solution. We discuss several applications of the proposed framework for aerospace design optimization problems, considering atmospheric flight at low and high altitudes for both aeronautics and space applications. [1] Peherstorfer, B., Willcox, K., Gunzburger, M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization. Siam Review (2018) 60(3): 550–591. [2] Di Fiore, F., Maggiore, P. Mainini L. Multifidelity domain-aware learning for the design of re-entry vehicles. Structural and Multidisciplinary Optimization (2021) 64: 3017–303

Non-Myopic Multifidelity Bayesian Optimization

Bayesian optimization is a popular framework for the optimization of black box functions. Multifidelity methods allows to accelerate Bayesian optimization by exploiting low-fidelity representations of expensive objective functions. Popular multifidelity Bayesian strategies rely on sampling policies that account for the immediate reward obtained evaluating the objective function at a specific input, precluding greater informative gains that might be obtained looking ahead more steps. This paper proposes a non-myopic multifidelity Bayesian framework to grasp the long-term reward from future steps of the optimization. Our computational strategy comes with a two-step lookahead multifidelity acquisition function that maximizes the cumulative reward obtained measuring the improvement in the solution over two steps ahead. We demonstrate that the proposed algorithm outperforms a standard multifidelity Bayesian framework on popular benchmark optimization problems

NM-MF: Non-Myopic Multifidelity Framework for Constrained Multi-Regime Aerodynamic Optimization

The exploration and trade-off analysis of different aerodynamic design configurations requires solving optimization problems. The major bottleneck to assess the optimal design is the large number of time-consuming evaluations of high-fidelity computational fluid dynamics (CFD) models, necessary to capture the non-linear phenomena and discontinuities that occur at higher Mach number regimes. To address this limitation, we introduce an original non-myopic multifidelity Bayesian framework aimed at including expensive high-fidelity CFD simulations for the optimization of the aerodynamic design. Our scheme proposes a novel two-step lookahead policy to maximize the improvement of the solution quality considering the rewards of future steps, and combines it with utility functions informed by the fluid dynamic regime and the information extracted from data, to wisely select the aerodynamic model to interrogate. We validate the proposed framework for the case of a constrained drag coefficient optimization problem of a NACA 0012 airfoil, and compare the results to other popular multifidelity and single-fidelity optimization frameworks. The results suggest that our strategy outperforms the other approaches, allowing to significantly reduce the drag coefficient through a principled selection of limited evaluations of the high-fidelity CFD model

From Counter-Revolution as a Project to Counter-Revolution as a Network

The counter-revolution has traditionally been interpreted in relation to the glorious histories of the past, namely through the discourses and narratives produced by the contemporary liberal state. The conceptual core that supports this approach is that which dictates that a line of development always corresponds to a line of opposition. It follows, therefore, that the counter-revolution is a sort of reaction – the opposite of the revolution or, in other words, a revolution in reverse. However, to paraphrase the Savoyard Joseph De Maistre, the counter-revolution was not «a revolution in reverse, but the opposite of the revolution»1. It was, indeed, another way of looking at things 373 and facing the present, but without linking its existence to the changes proposed by the revolution; instead, it rested on its own intrinsic logic, entrenched long before the revolution was a fact. Essentially, the counter-revolution did not draw its meaning from the opposition, but from the defense of a logic that existed before the revolution’s appearance on the scen

Domain-Aware Active Learning for Multifidelity Optimization

Bayesian optimization is a popular strategy for the optimization of black-box objective functions [1]. In many engineering applications, the objective can be evaluated with multiple representations at different levels of fidelity, to enhance a trade-off between cost and accuracy. Accordingly, multifidelity methods have been proposed in a Bayesian framework to efficiently combine information sources, using low-fidelity models to enable the exploration of design alternatives, and improve the accuracy of the solution through limited high-fidelity evaluations [2]. Most multifidelity methods based on active learning search the optimal design considering only the information extracted from the surrogate model. This can preclude the evaluation of promising design configurations that can be captured only including the knowledge of the particular physical phenomena involved [3]. To address this issue, this presentation discusses original domain-aware multifidelity Bayesian frameworks to accelerate design analysis and optimization performances. In particular, our strategy comes with an active learning scheme to adaptively sample the design space, combining statistical data from the surrogate model with physical information from the specific domain. Our formulation introduces physics-informed utility functions as additional contributions to the acquisition functions. This permits to enhance the active learning with a physicsbased insight and to realize a form of domain awareness which is beneficial to the efficiency and accuracy of the optimization task. The presentation will discuss several applications and implementations of the proposed approach for single discipline and multidisciplinary aerospace design optimization problems. [1] Snoek, J., Larochelle, H.. Adams, R.P. Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems. (2012) 25. [2] Peherstorfer, B., Willcox, K., Gunzburger, M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization. Siam Review (2018) 60(3): 550–591. [3] Di Fiore, F., Maggiore, P. Mainini L. Multifidelity domain-aware learning for the design of re-entry vehicles. Structural and Multidisciplinary Optimization (2021

Multifidelity modeling for the design of re-entry capsules

The design and optimization of space systems presents many challenges associated with the variety of physical domains involved and their coupling. A practical example is the case of satellites and space vehicles designed to re-enter the atmosphere upon completion of their mission [1]. For these systems, aerodynamics and thermodynamics phenomena are strongly coupled and relate to structural dynamics and vibrations, chemical non equilibrium phenomena that characterize the atmosphere, specific re-entry trajectory, and geometrical shape of the body. Blunt bodies are common geometric configurations used in planetary re-entry (e.g. Apollo Command Module, Mars Viking probe, etc.). These geometries permit to obtain high aerodynamic resistance to decelerate the vehicle from orbital speeds along with contained aerodynamic lift for trajectory control. The large radius-of-curvature of the bodies’ nose allows to reduce the heat flux determined by the high temperature effects behind the shock wave. The design and optimization of these bodies would largely benefit from accurate analyses of the re-entry flow field through high-fidelity representations of the aerodynamic and aerothermodynamic phenomena. However, those high-fidelity representations are usually in the form of computer models for the numerical solutions of PDEs (e.g. Navier-Stokes equations, heat equations, etc.) which require significant computational effort and are commonly excluded from preliminary multidisciplinary design and trade-off analysis. This work addresses the integration of high-fidelity computer-based simulations for the multidisciplinary design of space systems conceived for controlled re-entry in the atmosphere. In particular, we discuss the use of multifidelity methods to obtain efficient aerothermodynamic models of the re-entering vehicles. Multifidelity approaches allow to accelerate the exploration and evaluation of design alternatives through the use of different representations of a physical system/process, each characterized by a different level of fidelity and associated computational expense [2, 3]. By efficiently combining less-expensive information from low-fidelity models with a principled selection of few expensive simulations, multifidelity methods allow to incorporate high-fidelity costly information for multidisciplinary design analysis and optimization [4–7]. This presentation proposes a multifidelity Bayesian optimization framework leveraging surrogate models in the form of gaussian processes, which are progressively updated through acquisition functions based on expected improvement. We introduce a novel formulation of the multifideltiy expected improvement including both data-driven and physics-informed utility functions, specifically implemented for the case of the design optimization of an Orion-like atmospheric re-entry vehicle. The results show that the proposed formulation gives better optimization results (lower minimum) than single fidelity Bayesian optimization based on low-fidelity simulations only. The outcome suggests that the multifidelity expected improvement algorithm effectively enriches the information content with the high-fidelity data. Moreover, the computational cost associated with 100 iterations of our multifidelity strategy is sensitively lower than the computational burden of 6 iterations of a single fidelity framework invoking the high-fidelity model. References [1] Gallais, P., Atmospheric re-entry vehicle mechanics, Springer Science and Business Media, 2007. [2] Peherstorfer, B., Willcox, K., and Gunzburger, M., “Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization,” SIAM Review, Vol. 60, 2018, pp. 550–591. [3] Fernandez-Godino, G., Park, C., Kim, N., and Haftka, R., “Issues in Deciding Whether to Use Multifidelity Surrogates,” AIAA Journal, 2019, p. 16. [4] Mainini, L., and Maggiore, P., “A Multifidelity Approach to Aerodynamic Analysis in an Integrated Design Environment,” AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, AIAA, 2012. [5] Goertz, S., Zimmermann, R., and Han, Z. H., “Variable-fidelity and reduced-order models for aero data for loads predictions,” Computational Flight Testing, 2013, pp. 99–112. [6] Meliani, M., Bartoli, N., Lefebvre, T., Bouhlel, M.A., J., Martins, and Morlier, J., “Multi-fidelity efficient global optimization: Methodology and application to airfoil shape design,” AIAA Aviation 2019 Forum, AIAA, 2019. [7] Beran, P., Bryson, D., Thelen, A., Diez, M., and Serani, A., “Comparison of Multi-Fidelity Approaches for Military Vehicle Design,” AIAA Aviation 2020 Forum, AIAA, 2020

Gli Invisibili: Polizia politica e agenti segreti nell’Ottocento borbonico

[English]:During the nineteenth century, the police control represented, for the European continent, first one of the pivotal components of the international system developed by Metternich and then a function to be reshaped in view of the political crisis due to the events of 1848. In the Kingdom of the Two Sicilies, in the aftermath of the French Decade, the police were the focus of an intense reflection, inclined to rethink it beyond the Napoleonic model, whose outcome was not at all obvious. The issues that emerged in this context concerning the nature and limits of police power were destined to remain, in the following decades, the subject of a debate developed in the broader framework of the Italian peninsula. The revolutionary turmoil crossing the Kingdom, in particular following the 1848 revolutions, nevertheless placed in the foreground the urgency of deploying suitable devices and instruments to make the prevailing task of police control a defense of the status quo. Moreover, facing the global scale of the liberal-democratic threat, the Bourbon police rearranged political surveillance in a transnational sense, resorting to secret agents and spies, but also to consuls and diplomats, on the trail of exiles and conspirators in a European and Mediterranean dimension./ [Italiano]: Nel corso dell’Ottocento il controllo poliziesco si profilò, per il continente europeo, prima come una delle componenti essenziali del sistema internazionale messo a punto da Metternich e poi come una funzione da rimodulare a fronte della crisi politica legata agli eventi del 1848. Nel Mezzogiorno, all’indomani del Decennio francese, la polizia fu al centro di un’intensa riflessione, incline a ripensarla al di fuori del modello napoleonico, il cui esito non era affatto scontato. I nodi problematici emersi in questo contesto riguardo alla natura e ai limiti del potere di polizia erano destinati a restare, nei decenni successivi, oggetto di un dibattito sviluppato nel più ampio quadro della penisola italiana. I fermenti rivoluzionari che attraversarono il Regno delle Due Sicilie, in particolare a seguito delle rivoluzioni quarantottesche, posero tuttavia in primo piano l’urgenza di dispiegare dispositivi e strumenti idonei in primo luogo a fare della difesa dello status quo il compito prevalente del controllo poliziesco. Inoltre, a fronte del respiro globale della minaccia liberal-democratica, la polizia borbonica riarticolò la sorveglianza politica in senso transnazionale, ricorrendo ad agenti segreti e spie, ma anche a consoli e diplomatici, sulle tracce di esuli e cospiratori in una dimensione europea e mediterranea