Nowadays, the utilization of online auction platforms is becoming increasingly prevalent. Online auction provides a common and practical way for global buyers to compete fairly. Nevertheless, the anonymous environment...
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While green synthesized Mn2O3 has been used to activate peroxydisulfate (PDS) to degrade estrogens, effective application in real wastewater is less common due to variation in environmental matrices in wastewater. Her...
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While green synthesized Mn2O3 has been used to activate peroxydisulfate (PDS) to degrade estrogens, effective application in real wastewater is less common due to variation in environmental matrices in wastewater. Here, green synthesized Mn2O3 was used as a Fenton-like catalyst to activate PDS for estriol (E3) degradation in wastewater. The results show that the high concentration of K+ and humic acid in wastewater could inhibit the activation process of the Mn2O3/PDS system, resulting in low removal efficiency of E3 in wastewater. However, when the concentration of PDS was increased to 15 mM, the removal efficiency of E3 in medical wastewater can reach 100 %, because the high PDS concentration increases the main reactive oxygen species singlet oxygen (1O2) in medical wastewater. XRD and SEM-EDS analysis indicate that the crystal structure of Mn2O3 is stable, with a consistent rice grain-like morphology before and after reaction. XPS results show no obvious changes in the percentages of Mn(II), Mn(III) and Mn(IV) before and after reaction, indicating that Mn2O3 has good stability when degrading E3 in medical wastewater. Based on density functional theory (DFT) calculations, liquid chromatography-mass spectrometry (LC-MS), and ecological structure-activity relationship (ECOSAR) modelingdataanalysis, the reactive oxygen species produced by the Mn2O3/PDS system mainly attack the benzene ring structure of E3, where the toxicity of its intermediate products declines significantly after breaking the benzene ring structure. Overall, this work provides greater understanding of the E3 degradation pathway and the toxicity of its degradation products.
In mineral flotation, concentrate and tailing grade are indices directly relating to plant economics. Realizing online detection of grade is costly and difficult. In China, offline analysis is employed in most flotati...
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ISBN:
(纸本)9789881563804
In mineral flotation, concentrate and tailing grade are indices directly relating to plant economics. Realizing online detection of grade is costly and difficult. In China, offline analysis is employed in most flotation plants, resulting in delayed detection and control. A grade prediction model based on nonlinear autoregressive networks with exogenous inputs (NARX) and ensemble learning is herein proposed. Concentrate and tailing grades are influenced by many factors in the complex flotation process. The prediction model structure is determined by mechanism analysis in that process. Based on the correlation analysis between process conditions and the grade, process condition input and feedback delays are determined. To improve the model accuracy and generalization ability, a NARX neural network was trained as the base learner and support vector regression was the second learner. A grade prediction model was established and shown to effectively track grade dynamic fluctuations with high prediction accuracy and stability.
The relevance of electronic testing in comparison with other forms of control and assessment of the mastery of the material covered in the discipline is considered. An analysis was carried out of 6 popular services fo...
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As academic research becomes more and more international, close cooperation between different research institutions has become a key factor in promoting the development of science and technology. Research teams and in...
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With the continuous increase of offshore wind farms and other offshore regional buildings, the contradiction between them and the navigation safety in the densely distributed areas of offshore ship tracks is increasin...
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With the continuous increase of offshore wind farms and other offshore regional buildings, the contradiction between them and the navigation safety in the densely distributed areas of offshore ship tracks is increasingly obvious. It is urgent to build a safe distance model based on the actual sea condition to reduce the mutual influence. Based on the drift distance of ships in the boundary water areas of the wind farm under the actual meteorological and hydrological conditions, as well as the tolerable collision probability, reliability model and the normal distribution trend of ships in the ship routes, and the probability model of safe distance between ship routes and wind farm is built in the end. Based on the example and the tolerable collision probability of ships, the safe distance is analyzed, the analysis results show that the calculation model can determine the safe distance according to the control requirements of the collision probability between the ship and the offshore wind farm. In addition, the collision probability and the safe distance obtained are basically consistent with the safe distance published by the local maritime authority in combination with the changes of the output results caused by the velocity difference of the ship. The model lays a theoretical foundation on offshore wind farm site selection optimization, route boundary demarcation, ship safety and maritime supervision.
In order to accurately and effectively measure the carbon emissions of port ships, a method based on AIS data is proposed to measure the spatiotemporal characteristics and carbon emissions of container port integrated...
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The increased need for accurately modeling the input-output characteristics of linear time-periodic (LTP) systems necessitates novel identification and control algorithms as well as new test benches for their experime...
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Even if the performances of bioprocesses can be significantly improved by model-based control, there often remains a tradeoff between model complexity and control robustness. This paper proposes an original data -driv...
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Even if the performances of bioprocesses can be significantly improved by model-based control, there often remains a tradeoff between model complexity and control robustness. This paper proposes an original data -driven strategy for fast design of dynamic bioprocess models with minimal complexity (i.e., minimal number of bioreactions). Maximum likelihood principal component analysis (MLPCA) is applied to infer the minimal reaction scheme from a 25-state mammalian cell culture database. Then, a systematic algorithm is used to provide a continuous kinetic model formulation assuming all rates to occur simultaneously, which may be far from true cell metabolic conditions sometimes presenting discontinuous metabolic switches. A robust model predictive formulation is therefore adopted to reduce the impact of model structural uncertainty on the process performances. Additional numerical results show that the proposed strategy presents excellent performances in presence of unexpected metabolic switches.
Social media platforms are home to massive amounts of content created by users, which can offer insightful information on attitudes, beliefs, and feelings. In order to extract valuable information from the abundance o...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Social media platforms are home to massive amounts of content created by users, which can offer insightful information on attitudes, beliefs, and feelings. In order to extract valuable information from the abundance of data, sentiment analysis is essential. This work presents a novel hybrid sentiment analysis model using the Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Approach (RoBERTa) models. The strategy tries to improve sentiment classification performance by utilizing the advantages of both models. The complexities of social media text are often difficult for traditional sentiment analysis models, like rule-based systems and machine learning algorithms like logistic regression and support vector machines, to capture because of issues with contextual understanding, linguistic complexity, inherent data variability, and noise. This hybrid sentiment analysis, on the other hand, excels at understanding natural language semantics and gathering contextual information. Labelled data and an innovative training approach are combined to train the model. Reporting accuracy metrics is part of the performance evaluation process when using a test dataset. Additionally, a thorough classification report and confusion matrix are produced. A test dataset performance evaluation yields an accuracy of 82%.
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