Cell Cycle Control in Eukaryotes: a BioSpi model

This paper presents a stochastic model of the cell cycle control in eukaryotes. The framework used is based on stochastic process algebras for mobile systems. The automatic tool used in the simulation is the BioSpi. We compare our approach with classical ODE specications

Sustainability and Urban Planning Processes. An Integrated Tool for Sustainable Urban Management.

In the last decades in Italy the debate on the urban planning crisis (Balducci, 1991; Urbani, 2000) had showed a shift from practices informed by politics and negotiation to new positions where planning methods are characterized by a communicative adaptive evaluation of a set of options about land uses and transformations. Within this framework the need emerged of new approaches to planning able to fulfil community expectations. In the light of recent developments in the economic sector and his branches, and primarily in business management, various tools for urban policy making have been recently adopted and implemented in many Italian local authorities: - Implementation of certificated systems for environmental management (Varese Ligure was first Italian municipality to obtain an ISO 14001 certification in 1999); - Use of control and evaluation systems like environmental and strategic plan design aiming at the integration of these practices in a single comprehensive tool, articulated within three phases (organizational, social accounting (18 municipalities have already test these tools and a bill is discussed for their insert in public authorities management); - Employment of participatory practices in the government of environmental problems (Local Agenda 21 processes is hitting an advanced level of implementation both in the municipal and in the provincial level especially in regions like the Emily and Romagna, the Marches, Tuscany, Liguria); - Use of means of communication addressed both to internal members of public authorities and to stakeholders and local community (for example environmental and social statements drawing up by local authorities or sustainability reports like that compiled within 21st Olympic Games organization). However, the analysis of many case-studies showed often the use of these tools it is not directly coordinated with urban planning instruments. In this paper the authors propose a tentative framework for a sustainable decisional and operative) cyclicly. The objective is, as far as these practices are promoted by main international and European agendas and declarations, to connect by this tool local government choices to most important policies on sustainable development.

Inferring rate coefficents of biochemical reactions from noisy data with KInfer

Dynamical models of inter- and intra-cellular processes contain the rate constants of the biochemical reactions. These kinetic parameters are often not accessible directly through experiments, but they can be inferred from time-resolved data. Time resolved data, that is, measurements of reactant concentration at series of time points, are usually affected by different types of error, whose source can be both experimental and biological. The noise in the input data makes the estimation of the model parameters a very difficult task, as if the inference method is not sufficiently robust to the noise, the resulting estimates are not reliable. Therefore "noise-robust" methods that estimate rate constants with the maximum precision and accuracy are needed. In this report we present the probabilistic generative model of parameter inference implemented by the software prototype KInfer and we show the ability of this tool of estimating the rate coefficients of models of biochemical network with a good accuracy even from very noisy input data

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g., social, citations, and trademark networks) aim to implement various data mining tasks including classification, clustering, recommendation, anomaly detection, and link prediction. The commitment and efforts of artificial intelligence research in network biology are motivated by the fact that machine learning techniques are often prohibitively computational demanding, low parallelizable, and ultimately inapplicable, since biological network of realistic size is a large system, which is characterised by a high density of interactions and often with a non-linear dynamics and a non-Euclidean latent geometry. Currently, graph embedding emerges as the new learning paradigm that shifts the tasks of building complex models for classification, clustering, and link prediction to learning an informative representation of the graph data in a vector space so that many graph mining and learning tasks can be more easily performed by employing efficient non-iterative traditional models (e.g., a linear support vector machine for the classification task). The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a comprehensive summary of the main graph embedding algorithms in light of the recent burgeoning interest in geometric deep learning

Balanced Budget Government Spending in a Small Open Regional Economy

This paper investigates the impact of a balanced budget fiscal policy expansion in a regional context within a numerical dynamic general equilibrium model. We take Scotland as an example where, recently, there has been extensive debate on greater fiscal autonomy. In response to a balanced budget fiscal expansion the model suggests that: an increase in current government purchase in goods and services has negative multiplier effects only if the elasticity of substitution between private and public consumption is high enough to move downward the marginal utility of private consumers; public capital expenditure crowds in consumption and investment even with a high level of congestion; but crowding out effects might arise in the short-run if agents are myopic.regional computable general equilibrium analysis, fiscal federalism, fiscal policy.

Forward Looking and Myopic Regional Computable General Equilibrium Models. How Significant is the Distinction?

We present a stylized intertemporal forward-looking model able that accommodates key regional economic features, an area where the literature is not well developed. The main difference, from the standard applications, is the role of saving and its implication for the balance of payments. Though maintaining dynamic forward-looking behaviour for agents, the rate of private saving is exogenously determined and so no neoclassical financial adjustment is needed. Also, we focus on the similarities and the differences between myopic and forward-looking models, highlighting the divergences among the main adjustment equations and the resulting simulation outcomes.Myopic and Forward-looking Behaviour, Computable General Equilibrium Models, Regional Adjustment.

Rebound Effects from Increased Efficiency in the Use of Energy by UK Households*

In this paper, we use CGE modelling techniques to identify the impact on energy use of an improvement in energy efficiency in the household sector. The main findings are that 1) when the price of energy is measured in natural units, the increase in efficiency yields only to a modification of tastes, changing as a result, the composition of household consumption; 2) when households internalize efficiency, the improvement in energy efficiency reduces the price of energy in efficiency units, providing a source of improved competitiveness as the nominal wage and the price level both fall; 3) the short-run rebound can be greater than the long run rebound if the household demand elasticity is the same for both time frames, however, the short run rebound is always lower than in the long-run if the demand for energy is relatively more elastic in the long-run; 4) the introduction of habit formation changes the composition of household consumption, modifying the magnitude of the household rebound only in the short-run. In this period, household and economy wide rebound are lowest for external habit formation and highest when consumers' preferences are defined using a conventional utility function.Energy efficiency; Rebound effects; Households energy consumption; CGE models.

An Attention Module for Object Detection in Cluttered Images

In this paper, we propose a visual attention module that automatically detects the regions of an input previously unseen image, which are more likely occupied by a known object. The module can be integrated in many object recognition systems for reducing the image space in which to search the object, and the computational costs. The strategy has been tested on two public real-world image databases showing good performances. Moreover, we measured the usefulness of this selective visual attention by comparing the performances of the SIFT recognition algorithm with and without the proposed attention module

Automatic calibration of CODESA-3D using PEST

We describe here our experience in using the Model Independent Pameter ESTimation (PEST) free software tool [Doherty, 2002] to perform the automatic calibration of the COupled DEnsity-dependent variably SAturated flow and miscible transport (CODESA-3D) groundwater model [Gambolati et al., 1999]. Generally speaking, calibration of a model requires that a suitable method of spatial parameter characterization be defined in order to adjust model parameters until model outputs correspond well to specific laboratory and/or field measurements of the system which is simulated. In particular, for groundwater models the adjustable parameters are usually given by main hydrogeological properties (e.g. hydraulic permeability) and/or system excitations (e.g. abstraction volumes) while control data are represented by piezometric heads and/or salt concentrations measured in the field. Model calibration is a complex task. To perform it for a 3D fully-distributed physically-based hydrological model we need to build up a chain of interdependent software tools and data through the interdisciplinary expertise of GIS experts, modelers and hydrogeologists (Figure 1). The newly generated optimization model is comprised by the two pieces of software CODESA-3D and PEST with the latter wrapping the former up. The optimization model is not restricted in its use solely to the calibration of the groundwater model, through this tool modeler can gain valuable insight into the strengths and weakness of the input dataset allowing future data gathering to be undertaken in an optimal manner. In addition, lessons learned will be applicable also to the estimation of the degree of uncertainty associated with a given calibrated model prediction and to make decisions regarding appropriate levels of model complexity. In the following we discuss in detail the optimization model development and test using synthetic observations generated by the groundwater model itself

The importance of graduates to the Scottish economy : a “micro to macro" approach

There have been numerous attempts to assess the overall impact of Higher Education Institutions on regional economies in the UK and elsewhere. There are two disparate approaches focussing on: demand-side effects of HEIs, exerted through universities’ expenditures within the local economy; HEIs’ contribution to the “knowledge economy”. However, neither approach seeks to measure the impact on regional economies that HEIs exert through the enhanced productivity of their graduates. We address this lacuna and explore the system-wide impact of the graduates on the egional economy. An extensive and sophisticated literature suggests that graduates enjoy a significant wage premium, often interpreted as reflecting their greater productivity relative to non-graduates. If this is so there is a clear and direct supply-side impact of HEI activities on regional economies through the employment of their graduates. However, there is some dispute over the extent to which the graduate wage premium reflects innate abilities rather than the impact of higher education per se. We use an HEI-disaggregated computable general equilibrium model of Scotland to estimate the impact of the growing proportion of graduates in the Scottish labour force that is implied by the current participation rate and demographic change, taking the graduate wage premium in Scotland as an indicator of productivity enhancement. We conduct a range of sensitivity analyses to assess the robustness of our results. While the detailed results do, of course, vary with alternative assumptions about future graduate retention rates and the size of the graduate wage premium, for example, they do suggest that the long-term supply-side impacts of HEIs provide a significant boost to regional GDP. Furthermore, the results suggest that the supply-side impacts of HEIs are likely to be more important than the expenditure impacts that are the focus of most “impact” studies