1,721,108 research outputs found
Supplemental Material, Appendix_1 - The Complex Dynamic Evolution of Cultural Vibrancy in the Region of Halland, Sweden
No full textSupplemental Material, Appendix_1 for The Complex Dynamic Evolution of Cultural Vibrancy in the Region of Halland, Sweden by Paolo Massimo Buscema, Guido Ferilli, Christer Gustafsson and Pier Luigi Sacco in International Regional Science Review</p
Supplemental Material, Appendix_2 - The Complex Dynamic Evolution of Cultural Vibrancy in the Region of Halland, Sweden
No full textSupplemental Material, Appendix_2 for The Complex Dynamic Evolution of Cultural Vibrancy in the Region of Halland, Sweden by Paolo Massimo Buscema, Guido Ferilli, Christer Gustafsson and Pier Luigi Sacco in International Regional Science Review</p
Data Mining of Determinants of Intrauterine Growth Retardation Revisited Using Novel Algorithms Generating Semantic Maps and Prototypical Discriminating Variable Profiles
No full textApplications of Mathematics in Models, Artificial Neural Networks and Arts
No full textThis book is set up in a non-traditional way, yet it takes a systematic approach. There are four parts. The first part is historical and deals with the changes that have taken place in recent years in the relationship between mathematics and sociology: an analysis is made of Paul F. Lazarsfeld’s mathematical models, models of simulation and artificial societies, and models of artificial neural networks. The second part compares and contrasts mathematical models that come from physics and linguistics and those that come from sociology and economics. The third part analyzes models of artificial neural networks in detail. The final part examines the relationship between mathematics and the arts
Encoding and simulating the past. A machine learning approach to the archaeological information
No full textThe encoding of the spatial-temporal archeological, historical and anthropological records can be considered an ideal-typical representation of the human reasoning and thus also an artificial membrane interposed between the researcher and the past. These membranes are here considered artificial networks and can undergo interrogation processes through the most advanced analytical tools for learning and modeling complex configurations. The aim of this paper is to synthesize recent advances in Artificial Intelligence and Computer Science and – at the same time – to support the connectionists and symbolic computational paradigms as a new epistemic frontier in the automatic annotation of tangible and intangible heritage as well in the contemporary theories and methods of the archeological thought
Tackling climate change through energy efficiency: Mathematical models to offer evidence-based recommendations for public policy
No full textPromoting and increasing rates of energy efficiency is a promising method of reducing CO2 emissions and avoiding the potentially devastating effects of climate change. The question is: How do we induce a cultural or a behavioural change whereby people nationally and globally adopt more energy-efficient lifestyles? We propose a new family of mathematical models, based on a statistical mechanics extension of discrete choice theory, that offer a set of formal tools to systematically analyse and quantify this problem. An application example is to predict the percentage of people choosing to buy new energy-efficient light bulbs instead of the old incandescent versions; in particular, through statistical evaluation of survey responses, the models can identify the key driving factors in the decision-making process, for example, the extent to which people imitate each other. These tools and models that allow us to account for social interactions could help us identify tipping points that may be used to trigger structural changes in our society. The results may provide tangible and deliverable evidence-based policy options to decision makers. We believe that these models offer an opportunity for the research community, in both the social and the physical sciences, and decision makers, both in the private and the public sectors, to work together towards preventing the potentially devastating social, economic and environmental effects of climate change. © 2010 Springer Science+Business Media B.V
Artificial Neural Networks, and Evolutionary Algorithms as a systems biology approach to a data-base on fetal growth restriction
No full textA New Risk Chart for Acute Myocardial Infarction by a Innovative Algoritm
No full textAcute myocardial infarction (AMI) is complex disease; its pathogenesis is not completely understood and several variables are involved in the disease.. The aim of this paper was to assess: 1) the predictive capacity
of Artificial Neural Networks (ANNs) in consistently distinguishing the two different conditions (AMI or control). 2) the identification of those variables with the maximal relevance for AMI. Genetic variances in inflammatory genes and clinical and classical risk factors in 149 AMI patients and 72 controls were investigated. From the data base of this case/control study 36 variables were selected. TWIST system, an evolutionary algorithm able to remove redundant and noisy information from complex data sets, selected 18 variables. Fitness, sensitivity, specificity, overall accuracy of the association of these variables with AMI risk were investigated. Our findings showed that ANNs are useful in distinguishing risk factors selectively associated with the disease. Finally, the new variable cluster, including classical and genetic risk factors, generated a new risk chart able to discriminate AMI from controls with an accuracy of 90%. This approach may be used to assess individual AMI risk in unaffected subjects with increased risk of the disease such as first relative with positive parental history of AMI
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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