University of Talca

About: University of Talca is a education organization based out in Talca, Chile. It is known for research contribution in the topics: Model predictive control & Population. The organization has 2885 authors who have published 5976 publications receiving 74004 citations.

Encountering Development: The Making and Unmaking of the Third World

Abstract: Preface to the 2012 Edition vii Preface xlv CHAPTER 1: Introduction: Development and the Anthropology of Modernity 3 CHAPTER 2: The Problematization of Poverty: The Tale of Three Worlds and Development 21 CHAPTER 3: Economics and the Space of Development: Tales of Growth and Capital 55 CHAPTER 4: The Dispersion of Power: Tales of Food and Hunger 102 CHAPTER 5: Power and Visibility: Tales of Peasants, Women, and the Environment 154 CHAPTER 6: Conclusion: Imagining a Postdevelopment Era 212 Notes 227 References 249 Index 275

Model Predictive Control for Power Converters and Drives: Advances and Trends

Abstract: Model predictive control (MPC) is a very attractive solution for controlling power electronic converters. The aim of this paper is to present and discuss the latest developments in MPC for power converters and drives, describing the current state of this control strategy and analyzing the new trends and challenges it presents when applied to power electronic systems. The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm. This paper summarizes the most recent research concerning these elements, providing details about the different solutions proposed by the academic and industrial communities.

PLIP: fully automated protein–ligand interaction profiler

Abstract: The characterization of interactions in protein-ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we present the protein-ligand interaction profiler (PLIP), a novel web service for fully automated detection and visualization of relevant non-covalent protein-ligand contacts in 3D structures, freely available at projects.biotec.tu-dresden.de/plip-web. The input is either a Protein Data Bank structure, a protein or ligand name, or a custom protein-ligand complex (e.g. from docking). In contrast to other tools, the rule-based PLIP algorithm does not require any structure preparation. It returns a list of detected interactions on single atom level, covering seven interaction types (hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges and halogen bonds). PLIP stands out by offering publication-ready images, PyMOL session files to generate custom images and parsable result files to facilitate successive data processing. The full python source code is available for download on the website. PLIP's command-line mode allows for high-throughput interaction profiling.

Semantics and complexity of SPARQL

Abstract: SPARQL is the W3C candidate recommendation query language for RDF. In this paper we address systematically the formal study of SPARQL, concentrating in its graph pattern facility. We consider for this study simple RDF graphs without special semantics for literals and a simplified version of filters which encompasses all the main issues. We provide a compositional semantics, prove there are normal forms, prove complexity bounds, among others that the evaluation of SPARQL patterns is PSPACE-complete, compare our semantics to an alternative operational semantics, give simple and natural conditions when both semantics coincide and discuss optimization procedures.

Essentials of oral cancer.

Abstract: Oral cancer is one of the 10 most common cancers in the world, with a delayed clinical detection, poor prognosis, without specific biomarkers for the disease and expensive therapeutic alternatives. This review aims to present the fundamental aspects of this cancer, focused on squamous cell carcinoma of the oral cavity (OSCC), moving from its definition and epidemiological aspects, addressing the oral carcinogenesis, oral potentially malignant disorders, epithelial precursor lesions and experimental methods for its study, therapies and future challenges. Oral cancer is a preventable disease, risk factors and natural history is already being known, where biomedical sciences and dentistry in particular are likely to improve their poor clinical indicators.