Driving business performance: innovation complementarities and persistence patterns

Complementarities between technological and non-technological innovation are crucial determinants of firm performance. Although innovation complementarity has been extensively tested in the empirical literature, it has not been analysed in conjunction with innovation persistence. This fact is mainly due to the lack of data sets able to provide adequate longitudinal information. The capacities to develop market-oriented behaviour and introduce new organisational innovations, together with technological innovation, are the drivers of a firm’s productivity and profitability. We find that these activities complement technological innovation and that their impact is greater when they persist over time, thus introducing a more general concept of innovation persistence. We present an empirical model based on a large new panel of Italian manufacturing firms covering the period 2000–2012 which enables us to determine the precise impacts of a firm’s innovative attitude, in a broad definition that incorporates non-technological innovation and persistence, on its productivity and profitability.     

In vivo characterization of renal auto-antigens involved in human auto-immune diseases: the case of membranous glomerulonephritis

Renal auto-immune diseases represent a major source of morbidity in humans. For many years the knowledge on mechanisms of auto-immunity involving the kidney has been uniquely based on animal models. However, these findings often could not be readily translated to humans owing to notably difference in antigen expression by human podocytes. One example is Heymann nephritis (HN), the experimental model of human membranous glomerulonephritis (MGN), which is obtained in rats by injecting antibodies against megalin, a protein that is not present in human glomeruli. Human studies could not be done in the past since sequencing required too much material exceeding what obtainable from tissue biopsies in vivo. Research is now on the way to identify auto-antigens and isolate specific auto-antibodies in humans. New technology developments based on tissue microdissection and proteomical analysis have facilitated the recent discoveries, allowing direct analysis of human tissue in vivo. Major advances on the pathogenesis of MGN, the prototype for the formation and glomerular deposition of auto-antibodies, are now in progress. Two independent groups have, in fact, demonstrated the existence of specific IgG(4) against phospholipase A2 receptor, aldose reductase and Mn-superoxide dismutase in glomerular eluates and in plasma of a prominent part of patients with MGN, suggesting a major role of these proteins as auto-antigens in human MGN. This review will focalize these aspects outlining the contribution of proteomics in most recent developments.     

Performance evaluation of efficient multi-objective evolutionary algorithms for design space exploration of embedded computer systems

Multi-objective evolutionary algorithms (MOEAs) have received increasing interest in industry because they have proved to be powerful optimizers. Despite the great success achieved, however, MOEAs have also encountered many challenges in real-world applications. One of the main difficulties in applying MOEAs is the large number of fitness evaluations (objective calculations) that are often needed before an acceptable solution can be found. There are, in fact, several industrial situations in which fitness evaluations are computationally expensive and the time available is very short. In these applications efficient strategies to approximate the fitness function have to be adopted, looking for a trade-off between optimization performance and efficiency. This is the case in designing a complex embedded system, where it is necessary to define an optimal architecture in relation to certain performance indexes while respecting strict time-to-market constraints. This activity, known as design space exploration (DSE), is still a great challenge for the EDA (electronic design automation) community. One of the most important bottlenecks in the overall design flow of an embedded system is due to simulation. Simulation occurs at every phase of the design flow and is used to evaluate a system which is a candidate for implementation. In this paper we focus on system level design, proposing an extensive comparison of the state-of-the-art of MOEA approaches with an approach based on fuzzy approximation to speed up the evaluation of a candidate system configuration. The comparison is performed in a real case study: optimization of the performance and power dissipation of embedded architectures based on a Very Long Instruction Word (VLIW) microprocessor in a mobile multimedia application domain. The results of the comparison demonstrate that the fuzzy approach outperforms in terms of both performance and efficiency the state of the art in MOEA strategies applied to DSE of a parameterized embedded system.     

Product interface design: A participatory approach based on virtual reality

The usability of the user interface is a key aspect for the success of several industrial products. This assumption has led to the introduction of numerous design methodologies addressed to evaluate the user-friendliness of industrial products. Most of these methodologies follow the participatory design approach to involve the user in the design process. Virtual Reality is a valid tool to support Participatory Design, because it facilitates the collaboration among designers and users.The present study aims to evaluate the feasibility and the efficacy of an innovative Participatory Design approach where Virtual Reality plays a ‘double role’: a tool to evaluate the usability of the virtual product interface, and a communication channel that allows users to be directly involved in the design process as co-designers.In order to achieve these goals, we conducted three experiments: the purpose of the first experiment is to determine the influence of the virtual interface on the usability evaluation by comparing “user–real product” interaction and “user–virtual product” interaction. Subsequently, we tested the effectiveness of our approach with two experiments involving users (directly or through their participation in a focus group) in the redesign of a product user interface. The experiments were conducted with two typologies of consumer appliances: a microwave oven and a washing machine.     

Large area forest stem volume mapping in the boreal zone using synergy of ERS-1/2 tandem coherence and MODIS vegetation continuous fields

ERS-1/2 tandem coherence was reported to have high potential for the mapping of boreal forest stem volume (e.g. Santoro et al., 2002, 2007a; Wagner et al., 2003; Askne & Santoro, 2005). Large-scale application of the data for forest stem volume mapping, however, is hindered by the variability of coherence with meteorological and environmental acquisition conditions. The traditional way of stem volume retrieval is based on the training of models, relating coherence to stem volume, with the aid of forest inventory data which is generally available for a few small test sites but not for large areas. In this paper a new approach is presented that allows model training using the MODIS Vegetation Continuous Fields canopy cover product (Hansen et al., 2003) without further need for ground data. A comparison of the new approach with the traditional regression-based and ground-data dependent model training is presented in this paper for a multi-seasonal ERS-1/2 tandem dataset covering several well known Central Siberian forest sites. As a test scenario for large-area application, the approach was applied to a multi-seasonal ERS-1/2 tandem dataset of 223 ERS-1 and ERS-2 image pairs covering Northeast China (~ 1.5 million km²) to map four stem volume classes (0-20, 20-50, 50-80, and > 80 m³/ha).     

Deadlock free routing algorithms for irregular mesh topology NoC systems with rectangular regions

The simplicity of regular mesh topology Network on Chip (NoC) architecture leads to reductions in design time and manufacturing cost. A weakness of the regular shaped architecture is its inability to efficiently support cores of different sizes. A proposed way in literature to deal with this is to utilize the region concept, which helps to accommodate cores larger than the tile size in mesh topology NoC architectures. Region concept offers many new opportunities for NoC design, as well as provides new design issues and challenges. One of the most important among these is the design of an efficient deadlock free routing algorithm. Available adaptive routing algorithms developed for regular mesh topology cannot ensure freedom from deadlocks. In this paper, we list and discuss many new design issues which need to be handled for designing NoC systems incorporating cores larger than the tile size. We also present and compare two deadlock free routing algorithms for mesh topology NoC with regions. The idea of the first algorithm is borrowed from the area of fault tolerant networks, where a network topology is rendered irregular due to faults in routers or links, and is adapted for the new context. We compare this with an algorithm designed using a methodology for design of application specific routing algorithms for communication networks. The application specific routing algorithm tries to maximize adaptivity by using static and dynamic communication requirements of the application. Our study shows that the application specific routing algorithm not only provides much higher adaptivity, but also superior performance as compared to the other algorithm in all traffic cases. But this higher performance for the second algorithm comes at a higher area cost for implementing network routers.     

Experimental setup for lensless imaging via soft x-ray resonant scattering

We have developed a setup for measuring holographically formed interference patterns using an integrated sample-mask design. The direct space image of the sample is obtained via a two-dimensional Fourier transform of the X-ray diffraction pattern. We present the details of our setup, commenting on the influence of geometrical parameters on the imaging capabilities. As an example, we present and discuss the results of test experiments on a patterned Co film.     

Rules and Strategies for Contextual Specialization of Constraint Logic Programs

We address the problem of specializing a constraint logic program w.r.t. a constrained atom which specifies the context of use of the program. We follow an approach based on transformation rules and strategies. We introduce a novel transformation rule, called contextual constraint replacement, to be combined with variants of the traditional unfolding and folding rules. We present a general Partial Evaluation Strategy for automating the application of these rules, and two additional strategies: the Context Propagation Strategy which is instrumental for the application of our contextual constraint replacement rule, and the Invariant Promotion Strategy for taking advantage of invariance properties of the computation. We show through some examples the power of our method and we compare it with existing methods for partial deduction of constraint logic programs based on extensions of Lloyd and Shepherdson's approach.     

Discovering invariants via simple component analysis

We propose a new technique combining dynamic and static analysis of programs to find linear invariants. We use a statistical tool, called simple component analysis, to analyze partial execution traces of a given program. We get a new coordinate system in the vector space of program variables, which is used to specialize numerical abstract domains. As an application, we instantiate our technique to interval analysis of simple imperative programs and show some experimental evaluations.