Strange quark star in dilaton gravity

In this work, we first obtain the hydrostatic equilibrium equation in dilaton gravity. Then, we examine some of the structural characteristics of a strange quark star in dilaton gravity in the context of Einstein gravity. We show that the variations of dilaton parameter do not affect the maximum mass, but variations in the cosmological constant lead to changes in the structural characteristics of the quark star. We investigate the stability of strange quark stars by applying the MIT bag model with dilaton gravity. We also provide limiting values for the dilaton field parameter and cosmological constant. We also study the effects of dilaton gravity on the other properties of a quark star such as the mean density and gravitational redshift.We conclude that the last reported value for the cosmological constant does not affect the maximum mass of a strange quark star.     

GeoTxt: A scalable geoparsing system for unstructured text geolocation

In this article we present GeoTxt, a scalable geoparsing system for the recognition and geolocation of place names in unstructured text. GeoTxt offers six named entity recognition (NER) algorithms for place name recognition, and utilizes an enterprise search engine for the indexing, ranking, and retrieval of toponyms, enabling scalable geoparsing for streaming text. GeoTxt offers a flexible application programming interface (API), allowing for customized attribute and/or spatial ranking of retrieved toponyms. We evaluate the system on a corpus of manually geo‐annotated tweets. First, we benchmark the performance of the six NERs that GeoTxt provides access to. Second, we assess GeoTxt toponym resolution accuracy incrementally, demonstrating improvements in toponym resolution achieved (or not achieved) by adding specific heuristics and disambiguation methods. Compared to using the GeoNames web service, GeoTxt's toponym resolution demonstrates a 20% accuracy gain. Our results show that places mentioned in the same tweet do not tend to be geographically proximate.