1,720,985 research outputs found
Apprendimento di modelli grafici esplorativi per la valutazione in ambito socio-sanitario: il caso dell'assistenza informale
No full textThe present work is part of a wider project of work and study intended to research and describe variables, indicators, and their relationships associated with the experience of families which, together or through one of their members, live through exceptional experiences, such as for example hospitalisation, or much more commonly and on a more daily level, access to the most elementary social and health services. In particular the level of satisfaction perceived from hospital use with regard to care received will be explored and described. Naturally such events take on different characteristics depending on the demographic, epidemiological, social and economic typology of the family involved. The database used in the work project is the one obtained in the sample surveys which ISTAT (Central Institute of Statistics) organised in Italy on health conditions and the use made of social and health services by Italian families. Since 1980 ISTAT has organised and carried out these surveys through families in order to learn about health conditions, use of social and health services, and some habits or lifestyles including risk factors(senza ‘s (risk’s factors) (smoking, alcohol, etc.). These surveys had at most a three-year time limit and are characterised by the fact that they were carried out over the whole national territory, each time involving more than 23,000 families and over 93,000 people (ISTAT....). For the present analysis the data were selected from the Multipurpose Survey on Families carried out by ISTAT in 1998 called “Families, social subjects and infancy conditions”. In the papaer we concentrate on Bayesian approach where our interest is turned in the construction of the tipology of network(learning9direct towardsthe complex system. operationally the problem was to offer the most probable (MAP) model from a complete database in the context of bayesian Network
A comparative study on high-dimensional bayesian regression with binary predictors
No full textBayesian regression models have been widely studied and adopted in the statistical literature. Many studies consider the development of reliable priors to select the relevant variables and derive accurate posterior predictive distributions. Moreover in the context of small high-dimensional data, where the number of observations is very small with respect to the number of predictors, sparsity is assumed and many parameters can be set to values close to zero without affecting the fit of the model. Aim of this work is to develop a comparative analysis to empirically evaluate the performances of several Bayesian regression approaches in these contexts. In this study we assume that the predictors can be expressed only as binary variables coding the presence or the absence of a particular characteristic of the system. This binary structure is often present in many real studies, in particular in laboratory experimentation and in very high-dimension genome wide association studies
The relationship between brand constructs and motivational patterns in crowdfunding decisions. Evidence from university crowdfunding
No full textPurpose: This study aims to expand the existing body of knowledge on crowdfunding (CF) motivational patterns with special reference to intangible factors, which most scholars assume to be the most important ones, especially in non-investment-based CF. The purpose is to understand how the presence of an established brand in a CF campaign can affect backers’ funding choices and the reasons behind them. To this end, the authors combine principles from identification, brand relationship and self-determination theories. Design/methodology/approach: The authors considered the (altruistic in nature) domain of CF for social causes as the most widespread type of branded CF and chose the relevant empirical setting of “research CF” run by universities which seem to be more and more interested in connecting branding and fundraising efforts through the active involvement of their “relational circles”. Accordingly, the authors surveyed an extensive sample of students as a primary stakeholder group of potentially engaged backers from one of the first Italian universities to launch a CF program and used structural equation modelling to test the research hypotheses. Findings: The authors found that, despite the CF domain considered, the choices made by backers (counterintuitively, women, in particular) manifest themselves as mostly self-oriented. This is partly explained by brand identification, which fully mediates the effect of brand pride and partially mediates the effect of brand respect (BR) on funding intention. Moreover, BR also directly drives CF choices. Originality/value: This study portrays a remarkably different CF playground compared with conventional campaigns for both project proponents and backers with several theoretical and managerial implications
Multi-scenario analysis in the Adriatic Sea: A GIS-based Bayesian network to support maritime spatial planning
No full textOceans are changing faster than even observed before. Unprecedented climate variability is interacting with long-term trends, all against a backdrop of rising anthropogenic use of marine space. The growth of maritime activities is taking place without the full understanding of complex interactions between natural and human-induced changes, leading to a progressive decline of biodiversity and degradation of marine ecosystems. Against this complex interplay, marine managers and policy makers are increasingly calling for new approaches and tools allowing a multi-scenario assessment of environmental impacts arising from the complex interaction between natural and anthropogenic drivers, also in consideration of multiple marine plans objectives. Responding to this need, for the Adriatic Sea we developed a GIS-based Bayesian Network to evaluate the probability (and related uncertainty) of cumulative impacts under four ‘what-if’ scenarios representing different marine management options and climate conditions. We addressed issues concerning consequences of potential planning measures, as well as management programmes required to achieve environmental status targets, as required by relevant EU acquis. Results from the scenario analysis highlighted that an integrated approach to maritime spatial planning is required, combining more sustainable management options of marine spaces and resources with climate adaptation strategies. This approach to planning would allow to reduce human pressures on the marine environment and rise resilience of natural ecosystems to climate and human-induced disturbances, which would result in an overall decrease of cumulative impacts
Pareto-based multi-objective optimization algorithms to design energy-efficient static daylight solutions
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A Neural Network Model for Lead Optimization of MMP12 Inhibitors
No full textLead Optimization is a complex process, whereby a large number of interacting entities give rise to molecular structures whose properties should be optimized in order to be considered for drug development. We will study molecular systems that are characterized by high dimensionality and dynamically interacting networks with the goal of discovering the optimal molecules with respect to the set of essential properties. Currently, the research involves the screening and the identification of molecule with desirable properties from large molecule libraries. Lead Optimization is a multi-objective optimization problem. The classical approaches involving in-vitro laboratory analysis are time consuming and very expensive. To address this problem, we propose in this paper an in-silico approach: Lead Optimization based on Neural Network (NN) model in order to help the chemist in the lab experimentation by requiring a small set of real laboratory tests. We propose and estimate a predictive network model to derive a simultaneous optimal multi-response property following a single and multi-objective optimization procedure. We adopt different architectures in this study and we compare our procedure with other state-of-the-art method showing the better performance of our approach
The relationship between brand constructs and motivational patterns in crowdfunding decisions
No full textCrowdfunding (CF) platforms are emerging as new source of resources to support either business or not-for-profit entrepreneurial projects. This phenomenon has received increasing attention by academic scholars. One of the most important existing streams of literature is the one of backers’ motivations. To the best of our knowledge, no study has so far considered the possible role of brand constructs in backers’ funding decisions. This is due to the typical CF setting, where project proponents usually don’t have a strong brand to rely on and backers have no significant reason to feel emotionally connected to a given CF platform. However, the scenario is changing: companies and other organizations seem to be increasingly intrigued by the idea of using CF as a marketing tool. We aim to deepen our understanding of this very recent phenomenon by analyzing a special empirical setting, which is the one of CF platforms created by Universities to fund (above all) their scientific research projects. These projects have mostly to do with the progress and well-being of society, so we should expect more of other-oriented reasons for funding. Nevertheless, since all the stakeholders of a given University (starting from students) could have strong reasons to conceive themselves as “in-groups” we expect this can affect the CF intention (as a brand supportive behavior) as well as the reasons behind it
Model-based lead molecule design
No full text“Lead molecule” is a chemical compound deemed as a good candidate for drug discovery. Designing a lead molecule for optimization involves a complex phase in which researchers look for compounds that satisfy pharmaceutical properties and can then be investigated for drug development and clinical trials. Finding the optimal lead molecule is a hard problem that commonly requires searching in high dimensional and large experimental spaces. In this paper we propose to discover the optimal lead molecule by developing an evolutionary model-based approach where different classes of statistical models can achieve relevant information. The analysis is conducted comparing two different chemical representations of molecules: the amino-boronic acid representation and the chemical fragment representation. To deal with the high dimensionality of the fragment representation we adopt the Formal Concept Analysis and we then derive the evolutionary path on a reduced number of fragments. This approach has been tested on a particular data set of 2500 molecules and the achieved results show the very good performance of this strategy
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