How Brazil’s Agrarian Dynamics Shape Development Cooperation in Africa

Submitted version of Bulletin articleThis article shows how Brazil’s history of agrarian dynamics shapes development cooperation. In particular, Brazil’s dualistic agrarian structure frames policy discourse, and shapes development cooperation thinking and practice. Given Brazil’s recent experience of rural poverty reduction, the article argues that a focus on ‘family farming’ is potentially the most productive form of engagement in development cooperation. This is illustrated through an analysis of Brazilian cooperation promoted by the Ministry of Agrarian Development (MDA), and in particular its More Food International Programme. While Brazilian family farms are very different to those found in Africa, there can be a productive exchange of experience, expertise and equipment. Key lessons from the Brazilian experience is the need for state backing and support, providing social security for the poor, offering financial support and technical expertise for family farming and the existence of effective social mobilisation by civil society.ESRC, DFI

Distributed Fault-Tolerant Control for Networked Robots in the Presence of Recoverable/Unrecoverable Faults and Reactive Behaviors

The paper presents an architecture for distributed control of multi-robot systems with an integrated fault detection, isolation, and recovery strategy. The proposed solution is based on a distributed observer-controller schema where each robot, by communicating only with its direct neighbors, is able to estimate the overall state of the system; such an estimate is then used by the controllers of each robot to achieve global missions as, for example, centroid and formation tracking. The information exchanged among the observers is also used to compute residual vectors that allow each robot to detect failures on anyone of the teammates, even if not in direct communication. The proposed strategy considers both recoverable and unrecoverable actuator faults as well as it properly manages the possible activation of reactive local control behaviors of the robots (e.g., the activation of obstacle avoidance strategy), which generate control inputs different from those required by the global mission control. In particular, when the robots are subject to recoverable faults, those are managed at a local level by computing a proper compensating control action. On the other side, when the robots are subject to unrecoverable faults, the faults are isolated from anyone of the teammates by means of a distributed fault detection and isolation strategy; then, the faulty robots are removed from the team and the mission is rearranged. The proposed strategy is validated via numerical simulations where the system properly identifies and manages the different cases of recoverable and unrecoverable actuator faults, as well as it manages the activation of local reactive control in an integrated case study

Predicting economic resilience of territories in Italy during the COVID-19 first lockdown

This paper aims to predict the economic resilience to crises of territories based on local pre-existing socioeco-nomic characteristics. Specifically, we consider the case of Italian municipalities during the first wave of the COVID-19 pandemic, leveraging a large-scale dataset of cardholders performing transactions in Point-of-Sales. Based on a set of machine learning classifiers, we show that network-based measures and variables related to the social, economic, demographic and environmental dimensions are relevant predictors of the economic resilience of Italian municipalities to the crisis. In particular, we find accurate classification performance both in balanced and un-balanced scenarios, as well as in the case we restrict the analysis to specific geographical areas. Our analysis predicts that territories with larger income per capita, soil consumption, concentration of real estate activities and commuting network centrality in terms of closeness and Pagerank constitute the set of most affected areas, experiencing the strongest reduction of economic activities during the COVID-19 pandemic. Overall, we provide an application of an early-warning system able to provide timely evidence to policymakers about the detrimental effects generated by natural disasters and severe crisis episodes, thus contributing to optimize public decision support systems

Electromagnetic inversion for subsurface applications under the distorted Born approximation

The problem of reconstructing dielectric permittivity of a buried object from the knowledge of the scattered field is considered for a two-dimensional rectangular geometry at a fixed frequency. The linearization of the mathematical relationship between the dielectric permittivity function and the scattered field about a constant reference profile function and the approximation of actual internal field with the unperturbed field leads to the so-called Distorted Born Approximation. To analyze the limitations and capabilities of the linear inversion algorithms, we investigate the class of the retrievable profiles. This analysis makes it possible to point out that a very reduced number of independent data is available, so requiring to employ regularization techniques in order to perform in a reliable and stable way the linear inversions. In this paper we present a general algorithm consisting in a regularized Singular Value Decomposition of the matrix resulting from a discretization of the problem. Finally, numerical results of linear inversions are given

Random Graph-Homomorphisms and Logarithmic Degree

A graph homomorphism between two graphs is a map from the vertex set of one graph to the vertex set of the other graph, that maps edges to edges. In this note we study the range of a uniformly chosen homomorphism from a graph G to the infinite line Z. It is shown that if the maximal degree of G is `sub-logarithmic', then the range of such a homomorphism is super-constant. Furthermore, some examples are provided, suggesting that perhaps for graphs with super-logarithmic degree, the range of a typical homomorphism is bounded. In particular, a sharp transition is shown for a specific family of graphs C_{n,k} (which is the tensor product of the n-cycle and a complete graph, with self-loops, of size k). That is, given any function psi(n) tending to infinity, the range of a typical homomorphism of C_{n,k} is super-constant for k = 2 log(n) - psi(n), and is 3 for k = 2 log(n) + psi(n)

Vision based robot-to-robot object handover

This paper presents an autonomous robot-to-robot object handover in the presence of uncertainties and in the absence of explicit communication. Both the giver and receiver robots are equipped with an eye-in-hand depth camera. The object to handle is roughly positioned in the field of view of the giver robot's camera and a deep learning based approach is adopted for detecting the object. The physical exchange is performed by recurring to an estimate of the contact forces and an impedance control, which allows the receiver robot to perceive the presence of the object and the giver one to recognize that the handover is complete. Experimental results, conducted on a couple of collaborative 7 DoF manipulators in a partially structured environment, demonstrate the effectiveness of the proposed approach

Bouguer gravity field of the Tuscan Archipelago (central Italy)

In this paper, we present a new Bouguer gravity map of the Northern Tuscan offshore (central Italy), based on original gravity data acquired on the islands of the Tuscan Archipelago. Our dataset integrates 274 unpublished gravity field measurements with 126 available marine gravity data of the northern Tyrrhenian Sea. The Bouguer anomaly map shows a westward and southward increase of the regional gravity field associated with the uplift of the Moho boundary from central Apennines towards the Tyrrhenian Sea. At a local scale, several Bouguer anomalies are well associated with the igneous plutons of the Elba, Montecristo and Capraia islands, as a result of a deep density contrast between the granitoid intrusive rocks and the embedding metamorphic basement. The presented Bouguer anomaly map represents a useful tool for future studies of the complex geological and geodynamical setting of the Tuscan Archipelago and of the buried and deep igneous structures

Protein structure analysis of the interactions between SARS-CoV-2 spike protein and the human ACE2 receptor: from conformational changes to novel neutralizing antibodies

The recent severe acute respiratory syndrome, known as Coronavirus Disease 2019 (COVID-19) has spread so much rapidly and severely to induce World Health Organization (WHO) to declare a state of emergency over the new coronavirus SARS-CoV-2 pandemic. While several countries have chosen the almost complete lock-down for slowing down SARS-CoV-2 spread, the scientific community is called to respond to the devastating outbreak by identifying new tools for diagnosis and treatment of the dangerous COVID-19. With this aim, we performed an in silico comparative modeling analysis, which allows gaining new insights into the main conformational changes occurring in the SARS-CoV-2 spike protein, at the level of the receptor-binding domain (RBD), along interactions with human cells angiotensin-converting enzyme 2 (ACE2) receptor, that favor human cell invasion. Furthermore, our analysis provides (1) an ideal pipeline to identify already characterized antibodies that might target SARS-CoV-2 spike RBD, aiming to prevent interactions with the human ACE2, and (2) instructions for building new possible neutralizing antibodies, according to chemical/physical space restraints and complementary determining regions (CDR) mutagenesis of the identified existing antibodies. The proposed antibodies show in silico high affinity for SARS-CoV-2 spike RBD and can be used as reference antibodies also for building new high-affinity antibodies against present and future coronaviruses able to invade human cells through interactions of their spike proteins with the human ACE2. More in general, our analysis provides indications for the set-up of the right biological molecular context for investigating spike RBD–ACE2 interactions for the development of new vaccines, diagnostic kits, and other treatments based on the targeting of SARS-CoV-2 spike protein