Perceived emotional intelligence as a predictor of depressive symptoms after a one year follow-up during adolescence

Research to date has identified various risk factors in the emergence of depressive disorders in adolescence. There are very few studies, however, which have analyzed the role of perceived emotional intelligence in depressive symptoms longitudinally during adolescence. This work aimed to analyze longitudinal relationships between perceived emotional intelligence and depressive symptoms in adolescence, developing an explanatory model of depression following a one-year follow-up. A longitudinal study was carried out with two waves separated by one year, with a sample of 714 Spanish adolescents. The instruments consisted of self-report measures of depressive symptoms and perceived emotional intelligence. Results underlined gender differences in depressive symptoms and emotional intelligence, and indicated that greater emotional intelligence was associated with a lower presence of depressive symptoms after a one year follow-up. A multiple partial mediation model was developed to explain longitudinally depressive symptoms based on perceived emotional intelligence skills and depressive symptoms. These contributions underscore the need to design programs to prevent depression in adolescence through the promotion of emotional intelligence.peer-reviewe

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Zinc intake, status and indices of cognitive function in adults and children: a systematic review and meta-analysis

In developing countries, deficiencies of micronutrients are thought to have a major impact on child development; however, a consensus on the specific relationship between dietary zinc intake and cognitive function remains elusive. The aim of this systematic review was to examine the relationship between zinc intake, status and indices of cognitive function in children and adults. A systematic literature search was conducted using EMBASE, MEDLINE and Cochrane Library databases from inception to March 2014. Included studies were those that supplied zinc as supplements or measured dietary zinc intake. A meta-analysis of the extracted data was performed where sufficient data were available. Of all of the potentially relevant papers, 18 studies met the inclusion criteria, 12 of which were randomised controlled trials (RCTs; 11 in children and 1 in adults) and 6 were observational studies (2 in children and 4 in adults). Nine of the 18 studies reported a positive association between zinc intake or status with one or more measure of cognitive function. Meta-analysis of data from the adult’s studies was not possible because of limited number of studies. A meta-analysis of data from the six RCTs conducted in children revealed that there was no significant overall effect of zinc intake on any indices of cognitive function: intelligence, standard mean difference of <0.001 (95% confidence interval (CI) –0.12, 0.13) P=0.95; executive function, standard mean difference of 0.08 (95% CI, –0.06, 022) P=0.26; and motor skills standard mean difference of 0.11 (95% CI –0.17, 0.39) P=0.43. Heterogeneity in the study designs was a major limitation, hence only a small number (n=6) of studies could be included in the meta-analyses. Meta-analysis failed to show a significant effect of zinc supplementation on cognitive functioning in children though, taken as a whole, there were some small indicators of improvement on aspects of executive function and motor development following supplementation but high-quality RCTs are necessary to investigate this further

Childhood intelligence predicts premature mortality : Results from a 40-year population-based longitudinal study

Acknowledgements This study was supported by a grant from the Luxembourg Fonds National de la Recherche (VIVRE FNR/06/09/18) and a PhD scholarship awarded to the first author by the Fonds National de la Recherche.Peer reviewedPostprin

Application of Computational Intelligence Techniques to Process Industry Problems

In the last two decades there has been a large progress in the computational intelligence research field. The fruits of the effort spent on the research in the discussed field are powerful techniques for pattern recognition, data mining, data modelling, etc. These techniques achieve high performance on traditional data sets like the UCI machine learning database. Unfortunately, this kind of data sources usually represent clean data without any problems like data outliers, missing values, feature co-linearity, etc. common to real-life industrial data. The presence of faulty data samples can have very harmful effects on the models, for example if presented during the training of the models, it can either cause sub-optimal performance of the trained model or in the worst case destroy the so far learnt knowledge of the model. For these reasons the application of present modelling techniques to industrial problems has developed into a research field on its own. Based on the discussion of the properties and issues of the data and the state-of-the-art modelling techniques in the process industry, in this paper a novel unified approach to the development of predictive models in the process industry is presented