Phase separation process is influenced by operational factors that can hardly be controlled. This paper demonstrates the results of a series of experiments aiming to solve these problems using polyvinyl-alcohol - poly-acrylic acid copolymer hydrogel micro-carrier for the adherence of microorganisms to achieve better settling properties of the biomass. The nitrification process was examined using hydrogel micro-carriers and conventional activated sludge flocks. The sedimentation properties of the two systems were compared indifferent conditions. Results show that the sedimentation properties of the immobilized system were more favorable than activated sludge flocks.

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Purpose Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning. Design/methodology/approach For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared. Findings The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models. Originality/value This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

Organic waste and the compost and vermicompost derived from it may have different agronomic values, but little work is available on this aspect of sewage sludge. A 75-day pot experiment with perennial ryegrass (Lolium perenne) as the test plant aimed to investigate the fertiliser value and organic matter replenishment capacity of digested sewage sludge (DS) and the compost (COM) and vermicompost (VC) made from it, applied in 1% and 3% doses on acidic sand and calcareous loam. The NPK content and availability, changes in organic carbon content and plant biomass, and the efficiency of the amendments as nitrogen fertilisers were investigated. The final average residual carbon content for DS, COM, and VC was 35 ± 34, 85 ± 46, and 55 ± 46%, respectively. The organic carbon mineralisation rate depended on the soil type. The additives induced significant N mineralisation in both soils: the average increment in mineral N content was 1.7 times the total added N on acidic sand and 4.2 times it on calcareous loam for the 1% dose. The agronomic efficiency of COM and VC as fertilisers was lower than that of DS. In the short term, DS proved to be the best fertiliser, while COM was the best for organic matter replenishment.