The Nature of Blame

My aim is to contribute to the contemporary debate on the nature of blame in two ways. First, I want to discuss and critically evaluate the most prominent attempts to answer the question: “What is blame?”. Second, I defend a theory on the nature of blame. According to this theory, blame is whatever mental state (cognitive, emotional, conative, or a mix of these) serves the function of signaling the blamer’s normative competence and normative commitments. A version of the theory of blame as signaling has been recently defended by D. Shoemaker and M. Vargas in their 2021. To my knowledge, their work has not been yet amply discussed. However, I think they are on the right track when they argue that the most important function of blame is that of signaling something. Finally, this dissertation also deals with a somewhat neglected topic in the philosophical discussion on blame. This topic is the status of non-moral blame. Does a theory of blame need to address cases of non-moral blame, too? Or, more radically, is non-moral blame to be considered as “proper” blame? In my opinion, a theory of blame that cannot account for cases of non-moral blame is at best incomplete. In the last chapter of this dissertation, I want to show that a theory of blame as signaling can also accommodate cases of non-moral blame

Comparing e-Fuels and Electrification for Decarbonization of Heavy-Duty Transports

The freight sector is expected to keep, or even increase, its fundamental role for the major modern economies, and therefore actions to limit the growing pressure on the environment are urgent. The use of electricity is a major option for the decarbonization of transports; in the heavy-duty segment, it can be implemented in different ways: besides full electric-battery powertrains, electricity can be used to supply catenary roads, or can be chemically stored in liquid or gaseous fuels (e-fuels). While the current EU legislation adopts a tailpipe Tank-To-Wheels approach, which results in zero emissions for all direct uses of electricity, a Well-To-Wheels (WTW) method would allow accounting for the potential benefits of using sustainable fuels such as e-fuels. In this article, we have performed a WTW-based comparison and modelling of the options for using electricity to supply heavy-duty vehicles: e-fuels, eLNG, eDiesel, and liquid Hydrogen. Results showed that the direct use of electricity can provide high Greenhouse Gas (GHG) savings, and also in the case of the e-fuels when low-carbonintensity electricity is used for their production. While most studies exclusively focus on absolute GHG savings potential, considerations of the need for new infrastructures, and the technological maturity of some options, are fundamental to compare the different technologies. In this paper, an assessment of such technological and non-technological barriers has been conducted, in order to compare alternative pathways for the heavy-duty sector. Among the available options, the flexibility of using drop-in, energy-dense liquid fuels represents a clear and substantial immediate advantage for decarbonization. Additionally, the novel approach adopted in this paper allows us to quantify the potential benefits of using e-fuels as chemical storage able to accumulate electricity from the production peaks of variable renewable energies, which would otherwise be wasted due to grid limitations

CrazyChoir: Flying Swarms of Crazyflie Quadrotors in ROS 2

This paper introduces CrazyChoir, a modular Python framework based on the Robot Operating System (ROS) 2. The toolbox provides a comprehensive set of functionalities to simulate and run experiments on teams of cooperating Crazyflie nano-quadrotors. Specifically, it allows users to perform realistic simulations over robotic simulators as, e.g., Webots and includes bindings of the firmware control and planning functions. The toolbox also provides libraries to perform radio communication with Crazyflie directly inside ROS 2 scripts. The package can be thus used to design, implement and test planning strategies and control schemes for a Crazyflie nano-quadrotor. Moreover, the modular structure of CrazyChoir allows users to easily implement online distributed optimization and control schemes over multiple quadrotors. The CrazyChoir package is validated via simulations and experiments on a swarm of Crazyflies for formation control, pickup-and-delivery vehicle routing and trajectory tracking tasks. CrazyChoir is available at https://github.com/OPT4SMART/crazychoir

Determinismo causale e responsabilità morale: un approccio semicompatibilista

Questo articolo si propone di analizzare il recente sforzo teorico di J.M. Fischer in merito alla compatibilità del determinismo causale con la responsabilità morale. Dopo l’analisi concettuale dei termini fondamentali della discussione contemporanea sul determinismo e la libertà del volere, metterò in luce l’originalità della teoria di Fischer basata sulla difesa del semicompatibilismo. Secondo tale prospettiva teorica è possibile essere responsabili del proprio agire anche nel caso in cui la verità del determinismo causale dovesse eliminare la presenza di possibilità alternative. Per sostenere questa tesi farò riferimento alla nozione di controllo (Fischer e Ravizza 1998) unitamente alla presentazione del controesempio elaborato da Frankfurt (1969) sulla non necessità di poter fare altrimenti per essere ritenuti responsabili del proprio agire.Infine, dopo aver affrontato alcune critiche agli esperimenti mentali elaborati a partire da quelli di Frankfurt, mi concentrerò sugli attacchi diretti alla compatibilità fra responsabilità e determinismo, vale a dire sulle critiche che pur non rifacendosi alla necessità della possibilità di fare altrimenti cercano di mettere in discussione la compatibilità fra responsabilità morale e determinismo causale

Sensitivity Analysis for a PEM Fuel Cell Model aimed to Optimization

The amount of current density produced by the stack is the key performance parameter for a fuel cell, given a well-defined quantity of reactants flowing through it. A Proton Exchange Membrane fuel cell (PEMFC) distributed parameters model is considered with all the aspects influencing the cell behavior. A sensitivity analysis is performed through a Monte Carlo Simulation to assess the impact on performances of key parameters. The Pareto plot obtained from such analysis allow to operate design variables reduction, aimed to those parameters that show small impact, so to decrease the problem complexity through an increased orthogonality of the input design matrix. The target of the activity is to obtain and validate a method able to reduce the time needed for a complete simulation, so to be able to realize an effective multi-disciplinary design optimization

FAStEN: an efficient adaptive method for feature selection and estimation in high-dimensional functional regressions

Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and feature selection is crucial for avoiding overfitting and achieving accurate predictions. We propose a new, flexible, and ultra-efficient approach to perform feature selection in a sparse high dimensional function-on-function regression problem, and we show how to extend it to the scalar-on-function framework. Our method combines functional data, optimization, and machine learning techniques to perform feature selection and parameter estimation simultaneously. We exploit the properties of Functional Principal Components, and the sparsity inherent to the Dual Augmented Lagrangian problem to significantly reduce computational cost, and we introduce an adaptive scheme to improve selection accuracy. Through an extensive simulation study, we benchmark our approach to the best existing competitors and demonstrate a massive gain in terms of CPU time and selection performance without sacrificing the quality of the coefficients' estimation. Finally, we present an application to brain fMRI data from the AOMIC PIOP1 study

Challenges and opportunities of process modelling renewable advanced fuels

The Paris COP21 held on December 2015 represented a step forward global GHG emission reduction: this led to intensify research efforts in renewables, including biofuels and bioliquids. However, addressing sustainable biofuels and bioliquid routes and value chains which can limit or reverse the ILUC (indirect land-use change effect) is of paramount importance. Given this background condition, the present study targets the analysis and modelling a new integrated biomass conversion pathway to produce renewable advanced fuels, enabling the issue of indirect land-use change (ILUC) of biofuels to be tackled. The bioenergy chain under investigation integrates the decentralized production of biogas through anaerobic digestion and its upgrading to biomethane, followed by a centralized conversion to liquid transport fuels, involving methane reforming into syngas, Fischer–Tropsch (FT) synthesis, and methanol synthesis. The methodology adopted in this work stem from extensive literature review of suitable bio/thermo-chemical conversion technologies and their process modelling using a commercial flow-diagram simulation software is carried out. The major significance of the study is to understand the different modelling approaches, to allow the estimation of process yields and mass/energy balances: in such a way, this work aims at providing guidance to process modellers targeting qualitative and quantitative assessments of biomass to biofuels process routes. Beyond FT products, additional process pathways have been also explored, such as MeOH synthesis from captured CO2 and direct methane to methanol synthesis (DMTM). The analysis demonstrated that it is possible to model such innovative integrated processes through the selected simulation tool. However, research is still needed as regards the DMTM process, where studies about modelling this route through the same tool have not been yet identified in the literature