186,498 research outputs found
Sul nocciolo duro (ma incompiuto) della “privatizzazione” del lavoro pubblico, in Gargiulo-A. Zoppoli (a cura di), Il lavoro pubblico in trent’anni di riforme, QDLM, 16, 2024, p. 67
No full textDecision Support system for risk management in robust fast tracking projects
No full textModern management strategies aim for the maximum reduction of projects duration. Two schedule compression techniques are usually used, applied to activities on the critical path of course. The Crashing consists in allocating more resources on a certain activity to make it end before; in this case we obtain an increase of total project cost. The Fast Tracking (FT) reduces the project duration by anticipating the beginning of activities originally scheduled to start sequentially. This method changes the relationship of activities. With fast tracking, activities that would normally be done in sequence are allowed to be done in parallel or with some overlap. This technique involves a relevant increase of project risk that is proportional to the achieved time compression. We believe that should be convenient to anticipate the analysis of fast tracking risks to identify effective, preventive and precautionary mitigation measures. So we try to define a decision support system to identify the risk related to Fast Tracking and Robust Fast Tracking and to choose the best policy to reduce time and to mitigate risks
GHG mitigation schemes and energy policies: A model-based assessment for the Italian economy
No full textWe build up a large scale dynamic general equilibrium model embodying a cap on pollutant emissions, an electricity sector and fuel consumption to analyse climate-energy policies for the Italian economy. Our results show how the trade-off between environmental quality and economic activity can be effectively overcome by recycling the revenues from the sales of emission permits in labour tax reductions. A tax combination aimed at reducing the consumption of fossil fuel, while simultaneously decreasing taxes on labour, is expansionary, but the final outcome is influenced by the underlying GHG emission policy. Tax incentives encouraging the use of clean energy sources, by discouraging the use of fossil fuel, produce a sizeable reallocation of emissions across sectors and are found to be expansionary. Overall the paper highlights the non-trivial interactions between the different fiscal tools in hand to meet the legally binding commitment on emission reduction, while limiting the potential negative fallout on the economy
IRIS: a method for reverse engineering of regulatory relations in gene networks
No full textAbstract Background The ultimate aim of systems biology is to understand and describe how molecular components interact to manifest collective behaviour that is the sum of the single parts. Building a network of molecular interactions is the basic step in modelling a complex entity such as the cell. Even if gene-gene interactions only partially describe real networks because of post-transcriptional modifications and protein regulation, using microarray technology it is possible to combine measurements for thousands of genes into a single analysis step that provides a picture of the cell's gene expression. Several databases provide information about known molecular interactions and various methods have been developed to infer gene networks from expression data. However, network topology alone is not enough to perform simulations and predictions of how a molecular system will respond to perturbations. Rules for interactions among the single parts are needed for a complete definition of the network behaviour. Another interesting question is how to integrate information carried by the network topology, which can be derived from the literature, with large-scale experimental data. Results Here we propose an algorithm, called inference of regulatory interaction schema (IRIS), that uses an iterative approach to map gene expression profile values (both steady-state and time-course) into discrete states and a simple probabilistic method to infer the regulatory functions of the network. These interaction rules are integrated into a factor graph model. We test IRIS on two synthetic networks to determine its accuracy and compare it to other methods. We also apply IRIS to gene expression microarray data for the Saccharomyces cerevisiae cell cycle and for human B-cells and compare the results to literature findings. Conclusions IRIS is a rapid and efficient tool for the inference of regulatory relations in gene networks. A topological description of the network and a matrix of gene expression profiles are required as input to the algorithm. IRIS maps gene expression data onto discrete values and then computes regulatory functions as conditional probability tables. The suitability of the method is demonstrated for synthetic data and microarray data. The resulting network can also be embedded in a factor graph model.</p
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