5,985 research outputs found
Vector meson production in pp collisions at , measured with the ALICE detector
Vector mesons are key probes of the hot and dense state of strongly
interacting matter produced in heavy ion collisions. Their dileptonic decay
channel is particularly suitable for these studies, since dileptons have
negligible final state interactions in hadronic matter. A preliminary
measurement of the and differential cross sections was
performed by the ALICE experiment in pp collisions at TeV, through
their decay in muon pairs. The and rapidity regions covered in this
analysis are GeV and .Comment: 4 pages, 4, figures, proceedings of the Quark Matter 2011 conferenc
Editorial: new challenges In theory and practice of corporate governance
The aim of international conference “New Challenges in Corporate Governance: Theory And Practice” is to move the field closer to a global theory by advancing our understanding of corporate governance, which combines insights from the literature on firm governance bundles with insights from the national governance systems literature, investigating new perspectives and challenges for corporate governance and outlining possible scenarios of its development
Social innovation practices: focus on success factors for crowdfunding
This article explores what are the success factor for interaction with platforms of crowdfunding in Italy. Through a Principal Component Analysis we outline three variables and through a multiple regression analysis we demonstrate that the interaction on crowdfunding is positive correlated with socio-economic propensity and cultural level. The analysis has been conducted on a sample of 316 of projects funded in the Crowdfunding platform Produzioni dal Basso, the first platform born in Italy. We draw on SD logic and relationship marketing to underscore the importance of networks of actors and integration to create a co-creation of value. This view emphasizes the social and economic factors that influence, and are influenced by, crowdfundin
Enterprise Risk Management, Corporate Governance And Systemic Risk: Some Research Perspectives
The general goal of Enterprise Risk Management (ERM) processes is to
generate economic value through the coverage of firm business risk, on
the one hand, and by exploiting the positive side of uncertainty
conditions, on the other hand.
The increasing attention attributed to ERM in the creation of
economic value has led to even greater interactions between risk
management mechanisms and the corporate governance system.
In other words, in the last two decades, the relationships between
corporate governance and ERM increased since the ERM processes have
been considered more and more as critical drivers to combine strategic
objectives with relative low volatility of company performance. The basic
idea is that a good corporate governance system must deal about specific
risks along with their interactions and, at the same time, the firm’s
business risk as a whole. Moreover, an efficient and effective ERM
system provides clear information about linkages between strategic
opportunities and risk exposure and offers tools able to manage in an
optimal way the negative side of business risk (or downside risk) as wellas its positive side (or upside risk).
Accordingly, extant studies concerning the relationships between
ERM and corporate governance have been focusing on a micro-level of
analyses (i.e., the individual organization) and, specifically, on a firm’s
benefits that stem from the adoption of proper ERM processes that are
consistent with corporate governance goals and are able to sustain the
increase of economic value while maintaining a bearable business risk
over time.
From our initial analyses, a gap in literature arises. We argue that
the interdependence between ERM and corporate governance may be
analyzed from a broader point of view as well (i.e., the firm and its task
environment composed by its suppliers, customers, and partners). In
particular, our research idea is to enlarge traditional studies about
interrelations between corporate governance and ERM taking into
account whether such interrelations could be a driver of risk transfer
from the focal organization to other organizations that belong to its task
environment. Moreover, this study aims to deepen the mechanisms by
which the transfer of risk from a focal organization to its task
environment may foster the emergence of systemic risk, i.e., a macro risk
coming from domino and/or network effects.
Therefore, our paper aims to find new research areas by combining
micro and macro issues tied to corporate governance, ERM and systemic
risk.
The starting point of our work is the three following assumptions:
1) The compliance of a firm to ERM processes as well as to corporate
governance rules implies the reduction as much as possible of firm
business risk;
2) The reduction of the firm business risk leads to externalizing the
firm business risk through risk-sharing mechanisms;
3) The risk-sharing may arise like a driver of systemic risk
especially in those industries featured by strong network interrelations.
Starting from the above assumptions, the paper goal is to open a
new research area which combines four academic fields (ERM, corporate
governance, corporate finance, and macro-finance). So far, our initial
findings tell us that the following research questions arise:
RQ1: What are the conditions under which the transfer of business
risk towards organizations that belong to a firm task environment is
likely to become a source of systemic risk in a specific industry?
RQ2: How does the capital structure of a focal firm affect its
propensity to transfer business risk not only to commercial but also to
financial stakeholders included in firm task environment?
RQ3: How does the transfer of business risk influence the capital
cost of the focal firm as well as of the organizations that absorbed such
risk
Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference
The preservation of concrete dams is a key issue for researchers and practitioners in dam engineering because of the important role played by these infrastructures in the sustainability of our society. Since most of existing concrete dams were designed without considering their dynamic behaviour, monitoring their structural health is fundamental in achieving proper safety levels. Structural Health Monitoring systems based on ambient vibrations are thus crucial. However, the high computational burden related to numerical models and the numerous uncertainties affecting the results have so far prevented structural health monitoring systems for concrete dams from being developed. This study presents a framework for the dynamic structural health monitoring of concrete gravity dams in the Bayesian setting. The proposed approach has a relatively low computational burden, and detects damage and reduces uncertainties in predicting the structural behaviour of dams, thus improving the reliability of the structural health monitoring system itself. The application of the proposed procedure to an Italian concrete gravity dam demonstrates its feasibility in real cases
How Noisy Data Affects Geometric Semantic Genetic Programming
Noise is a consequence of acquiring and pre-processing data from the
environment, and shows fluctuations from different sources---e.g., from
sensors, signal processing technology or even human error. As a machine
learning technique, Genetic Programming (GP) is not immune to this problem,
which the field has frequently addressed. Recently, Geometric Semantic Genetic
Programming (GSGP), a semantic-aware branch of GP, has shown robustness and
high generalization capability. Researchers believe these characteristics may
be associated with a lower sensibility to noisy data. However, there is no
systematic study on this matter. This paper performs a deep analysis of the
GSGP performance over the presence of noise. Using 15 synthetic datasets where
noise can be controlled, we added different ratios of noise to the data and
compared the results obtained with those of a canonical GP. The results show
that, as we increase the percentage of noisy instances, the generalization
performance degradation is more pronounced in GSGP than GP. However, in
general, GSGP is more robust to noise than GP in the presence of up to 10% of
noise, and presents no statistical difference for values higher than that in
the test bed.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation
Conference (GECCO 2017), Berlin, German
Bamboo trusses with low cost and high ductility joints
Innovative solutions of joints for bamboo trusses are presented. Experimental tests show the performances and the high level of ductility of the proposed technique, joined with simplicity in the concept of the joints, low level of technology and low cost of all used materials. It can permit a proper dissemination and a sustainable maintenance in developing countries
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