University of Valencia

About: University of Valencia is a education organization based out in Valencia, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 27096 authors who have published 65669 publications receiving 1765689 citations. The organization is also known as: Universitat de València & UV.

World checklist of hornworts and liverworts.

Abstract: A working checklist of accepted taxa worldwide is vital in achieving the goal of developing an online flora of all known plants by 2020 as part of the Global Strategy for Plant Conservation. We here present the first-ever worldwide checklist for liverworts (Marchantiophyta) and hornworts (Anthocerotophyta) that includes 7486 species in 398 genera representing 92 families from the two phyla. The checklist has far reaching implications and applications, including providing a valuable tool for taxonomists and systematists, analyzing phytogeographic and diversity patterns, aiding in the assessment of floristic and taxonomic knowledge, and identifying geographical gaps in our understanding of the global liverwort and hornwort flora. The checklist is derived from a working data set centralizing nomenclature, taxonomy and geography on a global scale. Prior to this effort a lack of centralization has been a major impediment for the study and analysis of species richness, conservation and systematic research at both regional and global scales. The success of this checklist, initiated in 2008, has been underpinned by its community approach involving taxonomic specialists working towards a consensus on taxonomy, nomenclature and distribution.

Mapping burns and natural reforestation using thematic Mapper data

Abstract: Remote sensing techniques are specially suitable to detect and to map areas affected by forest fires. In this work, Landsat 5 Thematic Mapper (TM) data has been used to study a number of forest fires that occurred in the province of Valencia (Spain) and to monitor the vegetation regeneration over burnt areas. A reference area (non‐burnt forest) was established to assess the change produced by fire. The radiance in the thermal band (10.4–12.5 μm) and the normalized difference in reflectance between near 1R (0.76–0.90 μm) and middle IR (2.08–2.35 μm) were the most suitable parameters to map burnt areas. This index can also be used for monitoring vegetation regeneration in burnt areas. About a month after the fire, the burns show temperatures of 5–6 °C higher than those found in the reference area, and the vegetation index shows negative values whereas the reference area values remain positive. The differences between the burns and the reference area for the vegetation index decrease with time as ve...

An open access database for the evaluation of heart sound algorithms.

Abstract: In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.