Information Entropy for Evaluation of Wastewater Composition

Praus, Petr

doi:10.3390/w12041095

Cited by 7 publications

(4 citation statements)

References 45 publications

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“…The effect of wastewater constituents on the RE of OFX was shown in Figs. 10 and 11, where the studied concentration of impurities was determined based on the raw wastewater analysis results as reported in literature [22,[49][50][51].…”

Section: Effect Of Wastewater Constituentsmentioning

confidence: 99%

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Photocatalytic degradation of ofloxacin antibiotic wastewater using TS-1/C3N4 composite photocatalyst: Reaction performance optimisation and estimation of wastewater component synergistic effect by artificial neural network and genetic algorithm

Shang

Liu

Zhang

et al. 2022

Chemical Engineering Journal

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Section: Effect Of Wastewater Constituentsmentioning

confidence: 99%

“…Furthermore, water analysis results show that various constituents (such as cations, metal ions and anions) are present in antibiotic wastewater [22,23]. The wastewater components will probably consume the photo-generated reactive species and cause light-shielding effect in the photocatalytic degradation process of antibiotics.…”

Section: Introductionmentioning

confidence: 99%

Photocatalytic degradation of ofloxacin antibiotic wastewater using TS-1/C3N4 composite photocatalyst: Reaction performance optimisation and estimation of wastewater component synergistic effect by artificial neural network and genetic algorithm

Shang

Liu

Zhang

et al. 2022

Chemical Engineering Journal

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“…Signals from different catchments with different wastewater matrices are mixed beside the highly diluted inflow. Environmental conditions such as wastewater composition, temperature, or pH might impact virus decay and detection [ 4 , 5 ]. According to [ 6 , 7 , 8 , 9 ], there is an apparent information gain in sampling within the sewer network, rather than only at the endpoints.…”

Section: Introductionmentioning

confidence: 99%

Optimal Selection of Sampling Points within Sewer Networks for Wastewater-Based Epidemiology Applications

Yao,

Zhu,

Nogueira

et al. 2024

MPs

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Wastewater-based epidemiology (WBE) has great potential to monitor community public health, especially during pandemics. However, it faces substantial hurdles in pathogen surveillance through WBE, encompassing data representativeness, spatiotemporal variability, population estimates, pathogen decay, and environmental factors. This paper aims to enhance the reliability of WBE data, especially for early outbreak detection and improved sampling strategies within sewer networks. The tool implemented in this paper combines a monitoring model and an optimization model to facilitate the optimal selection of sampling points within sewer networks. The monitoring model utilizes parameters such as feces density and average water consumption to define the detectability of the virus that needs to be monitored. This allows for standardization and simplicity in the process of moving from the analysis of wastewater samples to the identification of infection in the source area. The entropy-based model can select optimal sampling points in a sewer network to obtain the most specific information at a minimum cost. The practicality of our tool is validated using data from Hildesheim, Germany, employing SARS-CoV-2 as a pilot pathogen. It is important to note that the tool’s versatility empowers its extension to monitor other pathogens in the future.

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“…In recent years, the research of NLP-based students' composition evaluation models has become a hot topic. Use of NLP technology effectively improves the evaluation efficiency and reduces the burden on teachers, because it is subjective, time consuming, and labor intensive to evaluate the compositions of students [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Students’ Composition Evaluation Model Based on Natural Language Processing Algorithm

Wang¹,

Deng²

2023

Int. J. Emerg. Technol. Learn.

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It is subjective, time consuming and labor intensive to evaluate students’ compositions. Use of natural language processing (NLP) technology effectively improves the evaluation efficiency and reduces the burden on teachers. In order to overcome the problems of traditional models, such as over-fitting and poor generalization ability, this research studied a students’ composition evaluation model based on an NLP algorithm. A students’ composition evaluation model based on a multi-task learning framework was proposed, which completed three sub-tasks simultaneously using the NLP algorithm. Three different encoding methods were used; namely, convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM), which captured text information from multiple perspectives. A new pairing pre-training mode was built, which aimed to help build an NLP-based students’ composition evaluation model based on the multi-task learning framework, thus alleviating the deviation caused by excessive correlation. The experimental results verified that the constructed model and the proposed method were effective.

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Information Entropy for Evaluation of Wastewater Composition

Cited by 7 publications

References 45 publications

Photocatalytic degradation of ofloxacin antibiotic wastewater using TS-1/C3N4 composite photocatalyst: Reaction performance optimisation and estimation of wastewater component synergistic effect by artificial neural network and genetic algorithm

Photocatalytic degradation of ofloxacin antibiotic wastewater using TS-1/C3N4 composite photocatalyst: Reaction performance optimisation and estimation of wastewater component synergistic effect by artificial neural network and genetic algorithm

Optimal Selection of Sampling Points within Sewer Networks for Wastewater-Based Epidemiology Applications

Students’ Composition Evaluation Model Based on Natural Language Processing Algorithm

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