Innovation in Industrial Districts: Evidence from Italy

The aim of this paper is to show that Italian manufacturing firms belonging to Marshallian industrial districts carry out a higher innovative effort than is usually acknowledged. The empirical analysis makes use of a panel of 1,218 district and non-district firms belonging to traditional sectors. Data refers to the years 1992 and 1995. We have estimated an augmented Cobb-Douglas production function. The estimates make it possible to empirically identify three different determinants of firms' productivity: (i) the intentional innovative activity; (ii) the “district effect”; and (iii) the joint district and innovation effect. The results show that firms' membership in industrial districts and product innovations are key factors in explaining the productivity of firms working in traditional Italian sectors.     

Quality of Service Networking for High Performance Grid Applications

This paper reports on different efforts to provide quality of service (QoS) Networking to Grid applications done in the context of the MB-NG, GRS and DataTAG EU projects. These are leading edge network research projects involving more that 50 researchers in the UK, Europe and North America, concerned with the development and testing of protocols and standards for the next generation of high speed networks. We have implemented and tested the Differentiated Services Architecture (DiffServ) in a multi-domain, 2.5 Gbits/s network (the first such deployment) defining appropriate Service Level Agreements (SLAs) to be used between administrative domains to guarantee end-to-end Quality of Service. We characterised several hardware implementations of DiffServ and concluded on their appropriateness for several network scenarios. Since current and future Grid applications will have to use modified mechanisms of congestion control we have evaluated old and new TCP implementations over a Differentiated Services Networks. These quality of service tests have also included innovative MPLS (Multi-Protocol Label Switching) experiments to establish guaranteed bandwidth connections to Grid applications in a fast and efficient way. We have also developed a software based bandwidth broker architecture for Grids based on IETF standards which allows applications to transparently request dynamic and advanced reservations and implemented it in a real experimental network. We finally report on experiences delivering Quality of Service networking to high performance applications like Particle Physics data transfer and High Performance Computation. This includes quantitative results on the performance improvements that QoS brought to real data transfers in the context of High Performance Computing.     

Formal methods integration for the specification of dependable distributed systems

This paper describes a real-world case study in the specification and analysis of dependable distributed systems. The case study is an automated transport system with safety requirements. In order to manage the complexity of the problem of specifying the dynamic behavior of the whole system, a compositional approach is used, based on the integration of the trace logic of the Communicating Sequential Processes (CSP) theory, and stochastic Petri nets (SPNs). It is argued that the integration of different formal methods is a useful approach in the definition of practical engineering methodologies for the specification, design and analysis of complex dependable distributed systems.     

Inductive inference from noisy examples using the hybrid finitestate filter

Recurrent neural networks processing symbolic strings can be regarded as adaptive neural parsers. Given a set of positive and negative examples, picked up from a given language, adaptive neural parsers can effectively be trained to infer the language grammar. In this paper we use adaptive neural parsers to face the problem of inferring grammars from examples that are corrupted by a kind of noise that simply changes their membership. We propose a training algorithm, referred to as hybrid finite state filter, which is based on a parsimony principle that penalizes the development of complex rules. We report very promising experimental results showing that the proposed inductive inference scheme is indeed capable of capturing rules, while removing noise.     

Scale-effects on mean and standard deviation of the mechanical properties of condensed matter: an energy-based unified approach

The size effects on the mean values of the mechanical properties of condensed matter and on the related variances are analysed by means of a unified approach based on the multiscale character of energy dissipation. In particular, the scaling law for fragmentation energy density is obtained taking into account the self-similarity of fragments. It is based on a generalization of the three classical comminution laws that has been performed to evaluate the energy dissipation, computing volume and surface area of the particles for one- two- and three-dimensional fragmented objects. The result is general and can be applied to different fractal energy dissipation mechanisms, e.g., plasticity. Based on this approach, the scaling laws for mean and standard deviation values of the main mechanical properties of materials can be derived, like Young's and shear elastic moduli, ultimate normal and shear stresses and strains, fracture energy and toughness.     

Scaling of energy dissipation in crushing and fragmentation: a fractal and statistical analysis based on particle size distribution

An extensive experimental investigation on concrete specimens under crushing and fragmentation over a large scale range (1:10) – exploring even very small specimen dimensions (1 cm) – was carried out to evaluate the influence of fragment size distribution on energy density dissipation and related size effect. To obtain a statistically significant fragment production as well as the total energy dissipated in a given specimen, the experimental procedure was unusually carried out up to a strain of approximately –95%, practically corresponding to the initial fragment compaction between the loading platens. The experimental fragment analysis suggests a fractal law for the distribution in particle size; this simply means that fragments derived from a given specimen appear geometrically self-similar at each observation scale. In addition, clear size effects on dissipated energy density are experimentally observed. Fractal concepts permit to quantify the correlation between fragment size distribution and size effect on dissipated energy density, the latter being governed by the total surface area of produced fragments. The experimental results agree with the proposed multi-scale interpretation satisfactorily.     

A Novel Human Neutralizing mAb Recognizes Delta,Gamma and Omicron Variants of SARS-CoV-2 and Can Be Used in Combination with Sotrovimab

The dramatic experience with SARS-CoV-2 has alerted the scientific community to be ready to face new epidemics/pandemics caused by new variants. Among the therapies against the pandemic SARS-CoV-2 virus, monoclonal Antibodies (mAbs) targeting the Spike glycoprotein have represented good drugs to interfere in the Spike/ Angiotensin Converting Enzyme-2 (ACE-2) interaction, preventing virus cell entry and subsequent infection, especially in patients with a defective immune system. We obtained, by an innovative phage display selection strategy, specific binders recognizing different epitopes of Spike. The novel human antibodies specifically bind to Spike-Receptor Binding Domain (RBD) in a nanomolar range and interfere in the interaction of Spike with the ACE-2 receptor. We report here that one of these mAbs, named D3, shows neutralizing activity for virus infection in cell cultures by different SARS-CoV-2 variants and retains the ability to recognize the Omicron-derived recombinant RBD differently from the antibodies Casirivimab or Imdevimab. Since anti-Spike mAbs, used individually, might be unable to block the virus cell entry especially in the case of resistant variants, we investigated the possibility to combine D3 with the antibody in clinical use Sotrovimab, and we found that they recognize distinct epitopes and show additive inhibitory effects on the interaction of Omicron-RBD with ACE-2 receptor. Thus, we propose to exploit these mAbs in combinatorial treatments to enhance their potential for both diagnostic and therapeutic applications in the current and future pandemic waves of coronavirus.