BUSCADOR RECOLECTA

When talking about poverty, a lot of energy is expended by academics and sociologists in the identification and classification of the poor. Less attention is paid to classifying the rich. The Center for Global Development created the Commitment to Development Index in 2003, which ranks countries according to their contribution to the reduction of poverty in developing countries. Since its first report, ‘‘Ranking the rich, the Index has been quite successful. However, it has also been subject to multiple criticisms. This paper proposes the use of an ordinal classification to rate, not rank, the performance of rich countries. An ordinal classification, where an ordinal scale labels the examples, can help discovering the level of each country’s commitment to development, automatically and independently from others’ performances. It could stimulate both advocacy from civil society and the determination of more coherent public policies in rich countries for poorer ones.The methodology used is Artificial Neural Networks, a common machine learning tool for successfully solving classification problems. Experiments yield robust results, showing better outcomes than other alternative ordinal classifiers, opening the possibility of developing a classification technique which could overcome the limitations of the current ranking technique.

This research has three main objectives. First, it examines influence of gamification on the behavioral intention to use an e-commerce platform. Second, it analyzes the role of the flow state given its importance in terms of behavior in online environments. Finally, the study aims to detect and analyze differences between Millennials and Generation X., Es la versión enviada del artículo. Se puede consultar la versión final en https://doi.org/10.1108/K-07-2018-0350

It is known that for a totally positive (TP) matrix, the eigenvalues are positive and distinct and the eigenvector associated with the smallest eigenvalue is totally nonzero and has an alternating sign pattern. Here, a certain weakening of the TP hypothesis is shown to yield a similar conclusion.

In current research, an enormous amount of information is constantly being produced, which poses a challenge for data mining algorithms. Many of the problems in extremely active research areas, such as bioinformatics, security and intrusion detection, or text mining, share the following two features: large data sets and class-imbalanced distribution of samples. Although many methods have been proposed for dealing with class-imbalanced data sets, most of these methods are not scalable to the very large data sets common to those research fields. In this paper, we propose a new approach to dealing with the class-imbalance problem that is scalable to data sets with many millions of instances and hundreds of features. This proposal is based on the divide-and-conquer principle combined with application of the selection process to balanced subsets of the whole data set. This divide-and-conquer principle allows the execution of the algorithm in linear time. Furthermore, the proposed method is easy to implement using a parallel environment and can work without loading the whole data set into memory. Using 40 class-imbalanced medium-sized data sets, we will demonstrate our method's ability to improve the results of state-of-the-art instance selection methods for class-imbalanced data sets. Using three very large data sets, we will show the scalability of our proposal to millions of instances and hundreds of features.

The aim of this study was to evaluate the effects of Tetraselmis chuii (TC) microalgae supplementation for sixty days on hematological, anthropometric and hormonal parameters in healthy young men. Forty-six men divided into a placebo group (PG; n = 16; 20.77 ± 2.7 years; 72.14 ± 7.18 kg; 1.76 ± 0.07 m), a group supplemented with 25 mg/day of TC (SG 25; n = 15; 20.40 ± 1.40 years; 71.28 ± 8.26 kg; 1.76 ± 0.05 m) and another group supplemented with 200 mg/day of TC (SG 200; n = 15; 20.83 ± 2.45 years; 72.30 ± 11.13 kg; 1.77 ± 0.08 m) participated in this double-blind study. PG ingested 200 mg/day of lactose powder. Participants underwent 4 assessments (baseline, month 1, month 2 and desadaptation) separated in time by an interval of thirty days. At SG 25 and SG 200, significant increases in percent muscle mass, erythropoietin, insulin-like growth factor 1, free testosterone, leukocytes, neutrophils and lymphocytes were observed (p < 0.05). Decreases in the levels of percent fat mass, platelets, hematocrit and mean corpuscular hemoglobin also occurred in these groups (p < 0.05). TC supplementation induced favorable changes on anthropometric, hematological and hormonal levels. In view of the data, it seems that the most effective dose was 25 mg/day of TC

Background and aims To investigate the efficacy and feasibility of three different 8 h time-restricted eating (TRE) schedules (i.e., early, late, and self-selected) compared to each other and to a usual-care (UC) intervention on visceral adipose tissue (VAT) and cardiometabolic health in men and women. Methods and results Anticipated 208 adults (50% women) aged 30–60 years, with overweight/obesity (25 ≤ BMI<40 kg/m2) and with mild metabolic impairments will be recruited for this parallel-group, multicenter randomized controlled trial. Participants will be randomly allocated (1:1:1:1) to one of four groups for 12 weeks: UC, early TRE, late TRE or self-selected TRE. The UC group will maintain their habitual eating window and receive, as well as the TRE groups, healthy lifestyle education for weight management. The early TRE group will start eating not later than 10:00, and the late TRE group not before 13:00. The self-selected TRE group will select an 8 h eating window before the intervention and maintain it over the intervention. The primary outcome is changes in VAT, whereas secondary outcomes include body composition and cardiometabolic risk factors. Conclusion This study will determine whether the timing of the eating window during TRE impacts its efficacy on VAT, body composition and cardiometabolic risk factors and provide insights about its feasibility.

Higher Education Institutions have to respond today to an increasing number of societal and economic demands. The European Commission, as well as different national European governments, are recommending and developing initiatives to provide universities with more institutional autonomy in order to allow them to better satisfy such needs. Two case studies at two different Spanish universities have been analysed using Grounded Theory methodology, which show that real autonomy (not only legal) may be difficult to effectively implement without rethinking, and eventually changing, the university governance system. By analysing the Spanish case, the article shows that a number of interesting paradoxes emerge from the public discourse and argues that the governance models based on a collegial decision-making process, particularly widespread in Southern European countries, may be a constraint on the necessary transformations and management changes.

Appropriate therapeutic modulation of endothelial proliferation and sprouting is essential for the effective inhibition of angiogenesis in cancer or its induction in cardiovascular disease. The current view is that an increase in growth factor concentration, and the resulting mitogenic activity, increases both endothelial proliferation and sprouting. Here, we modulate mitogenic stimuli in different vascular contexts by interfering with the function of the VEGF and Notch signalling pathways at high spatiotemporal resolution in vivo. Contrary to the prevailing view, our results indicate that high mitogenic stimulation induced by VEGF, or Notch inhibition, arrests the proliferation of angiogenic vessels. This is due to the existence of a bell-shaped dose-response to VEGF and MAPK activity that is counteracted by Notch and p21, determining whether endothelial cells sprout, proliferate, or become quiescent. The identified mechanism should be considered to achieve optimal therapeutic modulation of angiogenesis.

Childhood and adolescent obesity are currently among the greatest challenges for public health. Physical activity, physical fitness, and adherence to the Mediterranean diet (MD), representing powerful indicators of healthy lifestyles, are shown as determinant factors in the prevention and treatment of obesity. The aim of the present study has been to analyse the relationship between health-related physical fitness components, body composition, and adherence to MD in 387 Icelandic adolescents of 13–16-years old (54% boys). The ALPHA Fitness Test was used to measure physical fitness and body composition. The KIDMED questionnaire was used to assess the adherence to MD among participants. Associations between variables were tested according to gender and age using linear regression models and analysis of variance. Participants with high/medium adherence to MD showed significantly higher endurance scores in both the boys and the girls. Gender differences were found. The boys in high/medium MD categories had significantly lower fat percentages and ran a 4 × 10 m sprint faster than the girls. The girls scored higher than the boys in endurance and speed-agility tests. It can be concluded that a high and medium adherence to MD is associated with high and very high endurance in both the girls and the boys.

Los informes oficiales de delitos de odio en los Estados Unidos están subestimados en comparación con la cantidad real de incidentes de este tipo. Además, a pesar de las aproximaciones estadísticas, no hay in-formes oficiales de muchas ciudades estadounidenses sobre incidentes de odio. Aquí, mostramos inicialmente que la extracción de eventos y el aprendizaje multi-instancia, basados en inteligencia artificial (IA), aplica-dos a un conjunto de artículos de noticias locales, pueden predecir ca-sos de delitos de odio. Luego utilizamos el modelo entrenado de IA para detectar incidentes de odio en ciudades para las cuales el FBI carece de estadísticas. Finalmente, entrenamos modelos de IA para predecir homi-cidios y secuestros, comparamos las predicciones con los informes del FBI y establecemos que, de hecho, los incidentes de odio están subestima-dos en comparación con otros tipos de delitos en la prensa local. Es im-portante destacar que esta información no ha sido extraída de este lugar., Official reports of hate crimes in the United States are underestimated compared to the actual number of such incidents. Additionally, despite statistical approximations, many American cities lack official reports on hate incidents. Here, we initially demonstrate that event extraction and multi-instance learning, based on artificial intelligence (AI), applied to a set of local news articles, can predict hate crime cases. We then use the AI-trained model to detect hate incidents in cities for which the FBI lacks Official reports of hate crimes in the United States are underestimated compared to the actual number of such incidents. Additionally, despite statistical approximations, many American cities lack official reports on hate incidents. Here, we initially demonstrate that event extraction and multi-instance learning, based on artificial intelligence (AI), applied to a set of local news articles, can predict hate crime cases. We then use the AI-trained model to detect hate incidents in cities for which the FBI lacks