999 research outputs found
The dynamics of socially responsible product differentiation and the habit formation of socially responsible consumers
In our model of socially responsible (SR) product differentiation two duopolists (a zero profit socially concerned producer and a profit maximizing producer) compete over prices and (costly) âsocially and environmentally responsibleâ features of their products under a given law of motion of consumerâs habits. In a continuous time model in which the location of the zero profit socially responsible entrant is fixed and the profit maximizing producer (PMP) limits himself to price competition without SR imitation, we show that the optimal dynamic PMPâs price is always lower than his optimal static price since the PMP knows that, by leaving too much market share to his competitor, he will reinforce the habit of socially responsible consumption and loose further market share in the future. We also inspect the properties of equilibria when the PMP can imitate the entrantâs SR and we find that, in this case, the threshold triggering a PMP strategy of SR imitation and minimum price differentiation is higher in the dynamic than in the static case, depending on the PMPâs shadow cost of changes in consumer social responsibility.product differentiation, social responsibility, fair trade
Let us buy sustainable! The impact of cash mobs on sustainable consumption: Experimental results
Cash mob is a practice where groups of people gather at local shops to buy a given product (usually with a strong sustainable feature) and make their decisions visible to the general public. With our paper we aim to assess the effectiveness of the cash mob as a behavioural tool and provide a better understanding of the behavioural triggers of consumersâ decision making process. We run a laboratory experiment where we mimic sustainable consumption and the cash mob treatment is embedded in a sequential game structure with/without an environmental frame. We find that the cash mob treatment has a positive gross effect, that is, the share of sustainable consumers is significantly higher in treated sessions. We also document a significant effect of expectations about the number of those eliciting a sustainable behaviour depending on participantsâ previous choices. Our results suggest that cash mob-like mechanisms can help to solve social dilemmas like sustainable consumption with entirely private solutions (not based on punishment like taxes but on positive action), and with no costs for government budgets
dynamic modeling of wind turbines experimental tuning of a multibody model
Abstract This work is part of a research project funded by the Italian Ministry of the University and Research (MIUR), under the call for "National Interest Research Projects 2015 (PRIN 2015)", titled "Smart Optimized Fault Tolerant WIND turbines (SOFTWIND)". Within this project, the research unit of the University of Perugia (UniPG) aims to develop dynamic modeling and simulation methodologies and fatigue behavior evaluation ones for wind turbine as a whole. The development of these methodologies will be aimed at predicting the life of generic wind turbines, also providing important and fundamental parameters for optimizing their control, aimed at reducing the failures of these machines. In the present paper, a small turbine, developed at the Department of Engineering of the University of Perugia, will be analyzed. The multibody modeling technique adopted and the experimental activity conducted in the wind tunnel of UniPG, needed for the tuning of the model, will be described. The analysis of both model behavior and experimental data has allowed for the definition of a robust multibody modeling technique that adopts a freeware code (NREL - FAST), universally considered to be a reference in this field. The goodness of the model guarantees the capabilities of the simulation environment to analyze the real load scenario and the fatigue behavior of this kind of device
Wind turbine systematic yaw error: Operation data analysis techniques for detecting IT and assessing its performance impact
The widespread availability of wind turbine operation data has considerably boosted the research and the applications for wind turbine monitoring. It is well established that a systematic misalignment of the wind turbine nacelle with respect to the wind direction has a remarkable impact in terms of down-performance, because the extracted power is in first approximation proportional to the cosine cube of the yaw angle. Nevertheless, due to the fact that in the wind farm practice the wind field facing the rotor is estimated through anemometers placed behind the rotor, it is challenging to robustly detect systematic yaw errors without the use of additional upwind sensory systems. Nevertheless, this objective is valuable because it involves the use of data that are available to wind farm practitioners at zero cost. On these grounds, the present work is a two-steps test case discussion. At first, a new method for systematic yaw error detection through operation data analysis is presented and is applied for individuating a misaligned multi-MW wind turbine. After the yaw error correction on the test case wind turbine, operation data of the whole wind farm are employed for an innovative assessment method of the performance improvement at the target wind turbine. The other wind turbines in the farm are employed as references and their operation data are used as input for a multivariate Kernel regression whose target is the power of the wind turbine of interest. Training the model with pre-correction data and validating on post-correction data, it is estimated that a systematic yaw error of 4⊠affects the performance up to the order of the 1.5% of the Annual Energy Production
Two-neutron transfer in nuclei close to the dripline
We investigate the two-neutron transfer modes induced by (t,p) reactions in
neutron-rich oxygen isotopes. The nuclear response to the pair transfer is
calculated in the framework of continuum-Quasiparticle Random Phase
Approximation (cQRPA). The cQRPA allows a consistent determination of the
residual interaction and an exact treatment of the continuum coupling. The
(t,p) cross sections are calculated within the DWBA approach and the form
factors are evaluated by different methods : macroscopically, following the
Bayman and Kallio method, and fully microscopically. The largest cross section
corresponds to a high-lying collective mode built entirely upon continuum
quasiparticle states.Comment: 12 pages, 7 figure
Sampling properties of directed networks
For many real-world networks only a small "sampled" version of the original
network may be investigated; those results are then used to draw conclusions
about the actual system. Variants of breadth-first search (BFS) sampling, which
are based on epidemic processes, are widely used. Although it is well
established that BFS sampling fails, in most cases, to capture the
IN-component(s) of directed networks, a description of the effects of BFS
sampling on other topological properties are all but absent from the
literature. To systematically study the effects of sampling biases on directed
networks, we compare BFS sampling to random sampling on complete large-scale
directed networks. We present new results and a thorough analysis of the
topological properties of seven different complete directed networks (prior to
sampling), including three versions of Wikipedia, three different sources of
sampled World Wide Web data, and an Internet-based social network. We detail
the differences that sampling method and coverage can make to the structural
properties of sampled versions of these seven networks. Most notably, we find
that sampling method and coverage affect both the bow-tie structure, as well as
the number and structure of strongly connected components in sampled networks.
In addition, at low sampling coverage (i.e. less than 40%), the values of
average degree, variance of out-degree, degree auto-correlation, and link
reciprocity are overestimated by 30% or more in BFS-sampled networks, and only
attain values within 10% of the corresponding values in the complete networks
when sampling coverage is in excess of 65%. These results may cause us to
rethink what we know about the structure, function, and evolution of real-world
directed networks.Comment: 21 pages, 11 figure
Data describing the effects of the Macrolide Antibiotic Clarithromycin on preclinical mouse models of Colorectal Cancer
On Counting Triangles through Edge Sampling in Large Dynamic Graphs
Traditional frameworks for dynamic graphs have relied on processing only the
stream of edges added into or deleted from an evolving graph, but not any
additional related information such as the degrees or neighbor lists of nodes
incident to the edges. In this paper, we propose a new edge sampling framework
for big-graph analytics in dynamic graphs which enhances the traditional model
by enabling the use of additional related information. To demonstrate the
advantages of this framework, we present a new sampling algorithm, called Edge
Sample and Discard (ESD). It generates an unbiased estimate of the total number
of triangles, which can be continuously updated in response to both edge
additions and deletions. We provide a comparative analysis of the performance
of ESD against two current state-of-the-art algorithms in terms of accuracy and
complexity. The results of the experiments performed on real graphs show that,
with the help of the neighborhood information of the sampled edges, the
accuracy achieved by our algorithm is substantially better. We also
characterize the impact of properties of the graph on the performance of our
algorithm by testing on several Barabasi-Albert graphs.Comment: A short version of this article appeared in Proceedings of the 2017
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Mining (ASONAM 2017
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