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Article Highlights• Larger bubbles are generated in electrolyte solutions compared with water at similar conditions • Increasing the salt concentrations makes the bubbles slightly larger • Bubble diameter decreases with increasing heat flux in all three electrolyte solutions • Larger bubbles appear during boiling of NaCl solutions since it has higher surface tension Abstract Bubble departure diameters during saturated pool boiling to pure water and three different electrolyte solutions including NaCl, KNO 3 and KCl aqueous solutions are experimentally measured. Variable heat fluxes up to 90 kW/m 2 and different salt concentrations from 10.6 to 69.6 kg/m 3 are applied in order to investigate their effects on the bubble size during pool boiling around the horizontal rod heater. Visual observations demonstrate that larger vapor bubbles generate on the heat transfer surface at higher salt concentrations and lower heat fluxes in all of the solutions tested while in distilled water bubbles become slightly larger with increasing heat flux. Furthermore, the effects of different important physical properties like surface tension, viscosity, and density of the solutions on the bubble departure diameter are also discussed. NaCl solutions have surface tension higher than the other electrolyte solutions. Furthermore, the addition of NaCl to distilled water slightly increases the viscosity of the solution whereas other salts have no measurable effect on the viscosity. Therefore, larger bubbles are expected to appear on the heat transfer surface during the boiling of NaCl solutions, which is in agreement with the experimental results.

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Named Entity Recognition is an essential task in natural language processing to detect entities and classify them into predetermined categories. An entity is a meaningful word, or phrase that refers to proper nouns. Named Entities play an important role in different NLP tasks such as Information Extraction, Question Answering and Machine Translation. In Machine Translation, named entities often cause translation failures regardless of local context, affecting the output quality of translation. Annotating named entities is a time-consuming and expensive process especially for low-resource languages. One solution for this problem is to use word alignment methods in bilingual parallel corpora in which just one side has been annotated. The goal is to extract named entities in the target language by using the annotated corpus of the source language. In this paper, we compare the performance of two alignment methods, Grow-diag-final-and and Intersect Symmetrisation heuristics, to exploit the annotation projection of English-Brazilian Portuguese bilingual corpus to detect named entities in Brazilian Portuguese. A NER model that is trained on annotated data extracted from the alignment methods, is used to evaluate the performance of aligners. Experimental results show the Intersect Symmetrisation is able to achieve superior performance scores compared to the Grow-diag-final-and heuristic in Brazilian Portuguese.