Browsing by Author "Mahamood, S."

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    Enhanced degradation of paracetamol by UV-C supported photo-Fenton process over Fenton oxidation
    (2011) Manu, B.; Mahamood, S.
    For the treatment of paracetamol in water, the UV-C Fenton oxidation process and classic Fenton oxidation have been found to be the most effective. Paracetamol reduction and chemical oxygen demand (COD) removal are measured as the objective functions to be maximized. The experimental conditions of the degradation of paracetamol are optimized by the Fenton process. Influent pH 3, initial H 2O 2 dosage 60 mg/L, [H 2O 2]/[Fe 2+] ratio 60 : 1 are the optimum conditions observed for 20 mg/L initial paracetamol concentration. At the optimum conditions, for 20 mg/L of initial paracetamol concentration, 82% paracetamol reduction and 68% COD removal by Fenton oxidation, and 91% paracetamol reduction and 82% COD removal by UV-C Fenton process are observed in a 120 min reaction time. By HPLC analysis, 100% removal of paracetamol is observed at the above optimum conditions for the Fenton process in 240 min and for the UV-C photo-Fenton process in 120 min. The methods are effective and they may be used in the paracetamol industry. IWA Publishing 2011.
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    Enhanced degradation of paracetamol by UV-C supported photo-Fenton process over Fenton oxidation
    (2011) Manu, B.; Mahamood, S.
    For the treatment of paracetamol in water, the UV-C Fenton oxidation process and classic Fenton oxidation have been found to be the most effective. Paracetamol reduction and chemical oxygen demand (COD) removal are measured as the objective functions to be maximized. The experimental conditions of the degradation of paracetamol are optimized by the Fenton process. Influent pH 3, initial H 2O 2 dosage 60 mg/L, [H 2O 2]/[Fe 2+] ratio 60 : 1 are the optimum conditions observed for 20 mg/L initial paracetamol concentration. At the optimum conditions, for 20 mg/L of initial paracetamol concentration, 82% paracetamol reduction and 68% COD removal by Fenton oxidation, and 91% paracetamol reduction and 82% COD removal by UV-C Fenton process are observed in a 120 min reaction time. By HPLC analysis, 100% removal of paracetamol is observed at the above optimum conditions for the Fenton process in 240 min and for the UV-C photo-Fenton process in 120 min. The methods are effective and they may be used in the paracetamol industry. © IWA Publishing 2011.
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    The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
    (Association for Computational Linguistics (ACL), 2021) Gehrmann, S.; Adewumi, T.; Aggarwal, K.; Ammanamanchi, P.S.; Anuoluwapo, A.; Bosselut, A.; Chandu, K.R.; Clinciu, M.; Das, D.; Dhole, K.D.; Du, W.; Durmus, E.; Dušek, O.; Emezue, C.; Gangal, V.; Gârbacea, C.; Hashimoto, T.; Hou, Y.; Jernite, Y.; Jhamtani, H.; Ji, Y.; Jolly, S.; Kale, M.; Kumar, D.; Ladhak, F.; Madaan, A.; Maddela, M.; Mahajan, K.; Mahamood, S.; Majumder, B.P.; Martins, P.H.; McMillan-Major, A.; Mille, S.; van Miltenburg, E.; Nadeem, M.; Narayan, S.; Nikolaev, V.; Niyongabo, R.A.; Osei, S.; Parikh, A.; Perez-Beltrachini, L.; Rao, N.R.; Raunak, V.; Rodriguez, J.D.; Santhanam, S.; Sedoc, J.; Sellam, T.; Shaikh, S.; Shimorina, A.; Sobrevilla Cabezudo, M.A.S.; Strobelt, H.; Subramani, N.; Xu, W.; Yang, D.; Yerukola, A.; Zhou, J.
    We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for which we are organizing a shared task at our ACL 2021 Workshop and to which we invite the entire NLG community to participate. © 2021 Association for Computational Linguistics