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.

Coral Reef Monitoring, Reef Assessment Technologies, and Ecosystem-Based Management

Abstract: Coral reefs are exceptionally biodiverse and human dependence on their ecosystem services is high. Reefs experience significant direct and indirect anthropogenic pressures, and provide a sensitive indicator of coastal ocean health, climate change, and ocean acidification, with associated implications for society. Monitoring coral reef status and trends is essential to better inform science, management and policy, but the projected collapse of reef systems within a few decades makes the provision of accurate and actionable monitoring data urgent. The Global Coral Reef Monitoring Network has been the foundation for global reporting on coral reefs for two decades, and is entering into a new phase with improved operational and data standards incorporating the Essential Ocean Variables (EOVs) (www.goosocean.org/eov) and Framework for Ocean Observing developed by the Global Ocean Observing System. Three EOVs provide a robust description of reef health: hard coral cover and composition, macro-algal canopy cover, and fish diversity and abundance. A data quality model based on comprehensive metadata has been designed to facilitate maximum global coverage of coral reef data, and tangible steps to track capacity building. Improved monitoring of events such as mass bleaching and disease outbreaks, citizen science, and socio-economic monitoring have the potential to greatly improve the relevance of monitoring to managers and stakeholders, and to address the complex and multi-dimensional interactions between reefs and people. A new generation of autonomous vehicles (underwater, surface, and aerial) and satellites are set to revolutionize and vastly expand our understanding of coral reefs. Promising approaches include Structure from Motion image processing, and acoustic techniques. Across all systems, curation of data in linked and open online databases, with an open data culture to maximize benefits from data integration, and empowering users to take action, are priorities. Action in the next decade will be essential to mitigate the impacts on coral reefs from warming temperatures, through local management and informing national and international obligations, particularly in the context of the Sustainable Development Goals, climate action, and the role of coral reefs as a global indicator. Mobilizing data to help drive the needed behavior change is a top priority for coral reef observing systems.

Resistance distance in graphs and random walks

Abstract: We study the resistance distance on connected undirected graphs, linking this concept to the fruitful area of random walks on graphs. We provide two short proofs of a general lower bound for the resistance, or Kirchhoff index, of graphs on N vertices, as well as an upper bound and a general formula to compute it exactly, whose complexity is that of inverting an N×N matrix. We argue that the formulas for the resistance in the case of the Platonic solids can be generalized to all distance-transitive graphs. © 2001 John Wiley & Sons, Inc. Int J Quant Chem 81: 29–33, 2001

Analytic solution of the channel potential in undoped symmetric dual-gate MOSFETs

Abstract: We extend our previous Lambert function-based analytic solution for the surface potential of undoped-body single-gate bulk MOSFETs to offer an explicit analytic solution of the surface potential of undoped-body symmetric dual-gate devices. The error produced by the proposed solution compared to exact results is reasonably small for typical device dimensions and bias conditions.

Venezuelan rainfall data analysed by using a Bayesian space-time model

Abstract: We consider a set of data from 80 stations in the Venezuelan state of Guarico consisting of accumulated monthly rainfall in a time span of 16 years. The problem of modelling rainfall accumulated over fixed periods of time and recorded at meteorological stations at different sites is studied by using a model based on the assumption that the data follow a truncated and transformed multivariate normal distribution. The spatial correlation is modelled by using an exponentially decreasing correlation function and an interpolating surface for the means. Missing data and dry periods are handled within a Markov chain Monte Carlo framework using latent variables. We estimate the amount of rainfall as well as the probability of a dry period by using the predictive density of the data. We considered a model based on a full second-degree polynomial over the spatial co-ordinates as well as the first two Fourier harmonics to describe the variability during the year. Predictive inferences on the data show very realistic results, capturing the typical rainfall variability in time and space for that region. Important extensions of the model are also discussed.