Simón Bolívar University

About: Simón Bolívar University is a education organization based out in Caracas, Venezuela. It is known for research contribution in the topics: Population & Crystallization. The organization has 5912 authors who have published 8294 publications receiving 126152 citations.

Toward an integrative understanding of social behavior: new models and new opportunities.

Abstract: Social interactions among conspecifics are a fundamental and adaptively significant component of the biology of numerous species. Such interactions give rise to group living as well as many of the complex forms of cooperation and conflict that occur within animal groups. Although previous conceptual models have focused on the ecological causes and fitness consequences of variation in social interactions, recent developments in endocrinology, neuroscience, and molecular genetics offer exciting opportunities to develop more integrated research programs that will facilitate new insights into the physiological causes and consequences of social variation. Here, we propose an integrative framework of social behavior that emphasizes relationships between ultimate-level function and proximate-level mechanism, thereby providing a foundation for exploring the full diversity of factors that underlie variation in social interactions, and ultimately sociality. In addition to identifying new model systems for the study of human psychopathologies, this framework provides a mechanistic basis for predicting how social behavior will change in response to environmental variation. We argue that the study of non-model organisms is essential for implementing this integrative model of social behavior because such species can be studied simultaneously in the lab and field, thereby allowing integration of rigorously controlled experimental manipulations with detailed observations of the ecological contexts in which interactions among conspecifics occur.

Augmented Reality-Based Simulators as Discovery Learning Tools: An Empirical Study

Abstract: This paper reports empirical evidence on having students use AR-SaBEr, a simulation tool based on augmented reality (AR), to discover the basic principles of electricity through a series of experiments. AR-SaBEr was enhanced with knowledge-based support and inquiry-based scaffolding mechanisms, which proved useful for discovery learning in Web-based simulation environments. Learning performance factors evaluated included students' learning behavior while interacting with the system, AR-SaBEr's learning effectiveness, and students' motivation in using the AR-based simulation environment. The study suggests that AR simulators can be exploited as effective learning environments for learning the basic principles of electricity. It would, however, be necessary to consider a number of scaffolding options to improve the learning effectiveness of AR-based simulation environments when used with students who have low levels of either: 1) self-regulation skills, or 2) motivation to engage in AR-based simulation activities.

Arbuscular mycorrhizal infection in Cyperaceae and Gramineae from natural, disturbed and restored savannas in La Gran Sabana, Venezuela.

Abstract: The mycorrhizae of a tropical savanna growing in oligotrophic and stony soils were compared with those of a disturbed area that had been reclaimed with introduced species and of an area that was disturbed but not revegetated. All were compared with natural regeneration in a savanna that had been disturbed 12 years previously. Arbuscular mycorrhizae (AM) were common in savannas. Cyperaceae species, which were codominant with Graminaea, showed high levels of infection frequency (45%) like the Gramineae (61%). Arbuscules observed in the Cyperaceae indicated functionality. There were few plants in disturbed, nonrevegetated sites, but those present had AM. Observations of roots from soil monoliths showed that AM were present in disturbed areas, but compared with natural, succesional and revegetated savanna had a lower infection frequency (48–59% vs 75%), lower intensity (10–15% vs 25%) and a lower percentage of arbuscules (0.7–0.8% vs 2.3%). The percentage of vesicles was also lower in succesional savanna than in natural savanna (1.6% vs 4.8%). The revegetated site had the highest percentage of vesicles (6.6%). Although a high frequency of mycorrhizal infection has been reestablished in disturbed areas, the intensity and structure of the infection suggests that mycorrhizal function has not been restored to the original levels. We hypothesize that neither plants nor fungi have adapted to the new edaphic conditions.

Cardiac arrhythmia detection from ECG combining convolutional and long short-term memory networks

Abstract: Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to initiate appropriate preventive measures and to improve the analysis of cardiac disease. To this end, this paper introduces a new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings. Methods: The proposed approach used a combination of Convolution Neural Networks (CNNs) and a sequence of Long Short-Term Memory (LSTM) units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and we selected the final prediction for classification. Results were cross-validated on the Physionet Challenge 2017 training dataset, which contains 8,528 single lead ECG recordings lasting from 9s to just over 60s. Results: Using the proposed structure and no explicit feature selection, 10-fold stratified cross-validation gave an overall F-measure of 0.83.10±0.015 on the held-out test data (mean ± standard deviation over all folds) and 0.80 on the hidden dataset of the Challenge entry server.