1,721,072 research outputs found
Link prediction and feature relevance in knowledge networks: A machine learning approach
No full textWe propose a supervised machine learning approach to predict partnership formation between universities. We focus on successful joint R&D projects funded by the Horizon 2020 programme in three research domains: Social Sciences and Humanities, Physical and Engineering Sciences, and Life Sciences. We perform two related analyses: link formation prediction, and feature importance detection. In predicting link formation, we consider two settings: one including all features, both exogenous (pertaining to the node) and endogenous (pertaining to the network); and one including only exogenous features (thus removing the network attributes of the nodes). Using out-of-sample cross-validated accuracy, we obtain 91% prediction accuracy when both types of attributes are used, and around 67% when using only the exogenous ones. This proves that partnership predictive power is on average 24% larger for universities already incumbent in the programme than for newcomers (for which network attributes are clearly unknown). As for feature importance, by computing super-learner average partial effects and elasticities, we find that the endogenous attributes are the most relevant in affecting the probability to generate a link, and observe a largely negative elasticity of the link probability to feature changes, fairly uniform across attributes and domains
Datanet: A Stata routine for organising a dataset for network analysis purposes
No full textThis paper presents and applies a new user-written Stata program, datanet, which facilitates the dataset organisation for network analysis purposes. Given a fixed number of units (or nodes) belonging to the same group (there will be a variable denoting group membership), possibly connected one each other or possibly not, this routine creates a new dataset containing all their possible couplings to then be easily exploited using Stata network analysis commands. So far, to our knowledge, no routine has been developed in Stata which executes this type of procedure
Machine learning prediction of academic collaboration networks
No full textWe investigate the different roles played by nodes’ network and non-network attributes in explaining the formation of European university collaborations from 2011 to 2016, in three European Research Council (ERC) domains: Social Sciences and Humanities (SSH), Physical and Engineering Sciences (PE), Life Sciences (LS), as well as multidisciplinary collaborations. On link formation in collaboration networks, existing research has not yet compared and simultaneously examined both network and non-network attributes. Using four machine learning predictive algorithms (LASSO, Neural Network, Gradient Boosting, and Random Forest) our results show that, over various model specifications: (i) best model link formation accuracy is larger than 80%, (ii) among the non-network attributes, public funding plays an important role in PE and LS, (iii) network attributes count more than non-network attributes for the formation, sensibly increasing accuracy, (iv) feature-importance scores show a different ordering in the four domains, thus signalling different modes of knowledge production and transmission taking place within these different scientific communities
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
Currency Unions and Global Value Chains: The Impact of the Euro on the Italian Value Added Exports
Full text linkMany estimates of the effect of the common currency on trade have been made, although a clear answer has yet to be given. This work analyses the trade effect of the euro by providing a twofold contribution. First, one of the main stylised facts that has emerged from the recent literature is that trade flows in gross terms can differ substantially from those measured in value added terms. Accordingly, we focus on the structure of global value chains rather than conventional gross trade. To this aim, we provide an estimate of the value added trade flows that would have existed between Italy and its main trading partners if Italy had not joined the monetary union and show how, and to what extent, international production sharing has been affected. Second, we use a methodology that is different from traditional, parametric ones. Specifically, we apply the synthetic control method to construct appropriate counterfactuals and estimate the causal impact of the euro. Our empirical analysis provides a relevant case for considering value added in addition to gross trade since it shows that the euro facilitated the forward integration of Italian exports, whereas it slowed down backward integration. Overall, these results suggest that the euro had an impact on Italian global value chain participation by altering value added flows across member as well as non-member states, with great heterogeneity in the results across value added trade components and sectors
Tecniche estetiche coadiuvanti la Chirurgia Ortognatica: La lipoaspirazione cervico- facciale ed il face -lift
No full textPOZZI EDITORE, ROM
Quadriceps muscles activity during gait: comparison between PFPS subjects and healthy control.
No full textSoccer-related craniomaxillofacial injuries
Full text linkThe authors assessed the rate of craniomaxillofacial fractures in soccer and the areas where they occur, describing above all the injury pattern of this sport. Over a 5-year period (1995-2000) 46 cases of 329 with fractures associated with different sports activities have been surgically operated at the maxillofacial surgery department of the Policlinico "Umberto I" Hospital, University "La Sapienza" of Rome. All data collected have been selected on the basis of sex, age, anatomic site of the fracture, and the practiced sport. Information on injury patterns, severity, and play circumstances have been documented. The department examined 7 sports disciplines, but soccer was responsible for sports-related maxillofacial fractures in 34 of 46 cases (73.9%). All 34 fractures occurred to men. In soccer, the zygomatic and nasal regions are mainly involved. In fact the authors examined zygomatic fractures in 15 cases and nasal fractures in 10 cases. Direct contact between players generally causes soccer-related maxillofacial fractures: head-elbow impacts (21 cases) or head-head impacts (14 cases). The male:female ratio is 6.6:1, while the average age is 25 years for males and 23 years for females. In comparison with other sports (rugby, football, etc.) where physical contact occurs more frequently and the higher incidence of traumatic events justifies the use of protective measures, soccer is not a particularly violent sport. In soccer, maxillofacial traumas are caused by violent impacts between players that take place mainly when the ball is played with the forehead. In this moment there can be an elbow-head impact or a head-head impact. The authors believe that the low incidence of fractures, severity of the lesions, and discomfort caused by possible protective masks make their use unjustified. The data collected during this study witness that in soccer 21 of 34 cases of maxillofacial fractures are caused by elbow-head impacts. This fact suggests a preventive strategy against violent behavior in soccer play. Because the use of any sort of helmet proved impossible, the introduction of more severe penalties and a greater respect for the rules of the game by the players could reduce the percentage of impacts during matches. Impacts cause the most serious and frequent lesions in the maxillofacial region
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