A new deterministic model of strange stars

The observed evidence for the existence of strange stars and the concomitant observed masses and radii are used to derive an interpolation formula for the mass as a function of the radial coordinate. The resulting general mass function becomes an effective model for a strange star. The analysis is based on the MIT bag model and yields the energy density, as well as the radial and transverse pressures. Using the interpolation function for the mass, it is shown that a mass-radius relation due to Buchdahl is satisfied in our model. We find the surface redshift ($Z$) corresponding to the compactness of the stars. Finally, from our results, we predict some characteristics of a strange star of radius 9.9 km.Comment: one new figures and minor revisions have been done. To appear in Eur.Phys.J.

Possible existence of wormholes in the central regions of halos

An earlier study (Rahaman, etal., 2014 and Kuhfittig, 2014) has demonstrated the possible existence of wormholes in the outer regions of the galactic halo, based on the Navarro-Frenk-White (NFW) density profile. This paper uses the Universal Rotation Curve (URC) dark matter model to obtain analogous results for the central parts of the halo. This result is an important compliment to the earlier result, thereby confirming the possible existence of wormholes in most of the spiral galaxies. \ufffd 2014 Elsevier Inc

Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment

It has become a necessity for people to communicate with each other to complete their needs. The exchange of information conveyed in communication often cannot be directly assessed, especially online news. They just get news and are unable to filter out inappropriate stuff. The media website conveys a great deal of information. Popular news websites are one source for keeping up with the newest news. It requires a significant amount of work to deliver news on prominent websites and to choose content that is not incorrect. To crawl the web and analyse enormous data, massive computer power is required, and solutions to lower the process's space and temporal complexity must be created.Data mining is seen to be a solution to the aforementioned difficulties since it extracts particular information based on defined attributes. This research investigated a model to determine the content of false news information in Indonesian popular news. Firstly, preprocessing process from dataset that collected from keaggle. Secondly, we try use classification methods to determined which the optimal method to classify fake news. Thirdly, we use another public dataset for testing method. Furthermore, five machine learning classifiers are compared: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC), and Random Forest (RF). These classifications are utilized independently before being compared based on receiver operating characteristic curves and accuracy. The experimental result shows that DTC has the lowest accuracy of 75.33% and SVM has the highest accuracy of 83.55%.