This research study deals with lead and nickel simultaneous removal from aqueous solutions by the use of chitosan coated cobalt ferrite as adsorbent. Batch removal tests were performed in order to establish the main parameters that influence the sorption capacity, removal efficiency and the selectivity of this adsorbent. The values of sorption capacity for lead and nickel experimentally determined are: 56.23 mg/g and respectively 45.11 mg/g. Langmuir and Freundlich adsorption isotherms were used to interpret the sorption experimental data. The kinetic data were explored by pseudo-first order, pseudo-second order and intraparticle diffusion kinetic models. The experimental data were well fitted with the pseudo-second order model for both heavy metals. The main conclusion that can be drawn from this research is that this material can be successfully used for the removal of lead and nickel from binary aqueous solutions and wastewater.
In recent years, the need for communication increased in online social media. Propaganda is a mechanism which was used throughout history to influence public opinion and it is gaining a new dimension with the rising interest of online social media. This paper presents our submission to NLP4IF-2019 Shared Task SLC: Sentence-level Propaganda Detection in news articles. The challenge of this task is to build a robust binary classifier able to provide corresponding propaganda labels, propaganda or non-propaganda. Our model relies on a unified neural network, which consists of several deep leaning modules, namely BERT, BiLSTM and Capsule, to solve the sentencelevel propaganda classification problem. In addition, we take a pre-training approach on a somewhat similar task (i.e., emotion classification) improving results against the coldstart model. Among the 26 participant teams in the NLP4IF-2019 Task SLC, our solution ranked 12th with an F 1-score 0.5868 on the official test data. Our proposed solution indicates promising results since our system significantly exceeds the baseline approach of the task organizers by 0.1521 and is slightly lower than the winning system by 0.0454.
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