Use of surface-washing agents for the treatment of oiled shorelines: Research advancements, technical applications and future challenges

Modified porous cellulose beads (MCBs) with typical sizes ranging from 2 to 3 mm were successfully fabricated via acidic precipitation from the cellulose dissolved in ionic liquid, followed by surface-grafting with aminoguanidine hydrochloride using glutaric anhydride as a coupling agent. The nano-sized calcium carbonate (30% wt on dry cellulose) was added to cellulose solution prior to the precipitation in acidic medium in an attempt to promote the formation of the pore structure of cellulose beads. Modified cellulose powders (MCPs) (size 150 μm) were prepared by precipitating the solution of the cellulose which reacted with glutaric anhydride homogeneously in ionic liquid, followed by grafting with aminoguanidine hydrochloride. The pseudo-second-order kinetic model and the Langmuir isotherm model described the adsorption process well. The maximum adsorption capacities of MCBs towards Hg(II) and Cu(II) were 581.4 and 94.88 mg/g; whereas the maximum adsorption capacities of MCPs for Hg(II) and Cu(II) were 625 and 98.52 mg/g, respectively. The results demonstrated that both MCBs and MCPs are highly effective and promising as green-based adsorbents for the removal of heavy metal ions in aqueous systems, Hg2+ in particular.

Porous cellulose beads reconstituted from ionic liquid for adsorption of heavy metal ions from aqueous solutions

Exploring the use of ceramic disk filter coated with Ag/ZnO nanocomposites as an innovative approach for removing Escherichia coli from household drinking water.

Water Quality Assessment for Wastewater Reclamation Using Principal Component Analysis

Risk assessment of trace metal-polluted coastal sediments on Hainan Island: A full-scale set of 474 geographical locations covering the entire island.

The spatial variability of acid-volatile sulfide (AVS) and its influential factors were studied through regression analysis to explain the spatial distribution of AVS and to predict the variability of metal availability under changing conditions simply and effectively. The AVS equation is used to derive oxidation-reduction potential (Eh), sulfate-reducing bacteria (SRB), organic carbon (OC), and total sulfur (TS). The relationships of these variables with AVS were then analyzed. Moreover, their effect on AVS was quantified through linear regression (LR) and principal component regression (PCR). These two regression equations were analyzed using a histogram of residual values and by comparing mean relative error (MRE) and root-mean-square error (RMSE) values. LR (Model 1) and PCR (Model 2) models were established as well. The MRE and RMSE values in the PCR model were 21.9 and 25.9%, respectively. In terms of these values, the PCR model is more accurate than the LR model. Furthermore, its predictive results were more reasonable. In conclusion, the PCR model can be used to predict the AVS concentrations based on the OC, Eh, SRB, and TS values. This model simplifies and facilitates the evaluation of metal toxicity under field conditions and can thus be used to manage sediments contaminated with metals.

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A Field Study of Factors that Affect Acid-Volatile Sulfide Concentrations

Abstract With the development of cloud computing, more and more security problems like “fuzzy boundary” are exposed. To solve such problems, unsupervised anomaly detection is increasingly used in cloud security, where density estimation is commonly used in anomaly detection clustering tasks. However, in practical use, the excessive amount of data and high dimensionality of data features can lead to difficulties in data calibration, data redundancy, and reduced effectiveness of density estimation algorithms. Although auto-encoders have made fruitful progress in data dimensionality reduction, using auto-encoders alone may still cause the model to be too generalized and unable to detect specific anomalies. In this paper, a new unsupervised anomaly detection method, MemAe-gmm-ma, is proposed. MemAe-gmm-ma generates a low-dimensional representation and reconstruction error for each input sample by a deep auto-encoder. It adds a memory module inside the auto-encoder to better learn the inner meaning of the training samples, and finally puts the low-dimensional information of the samples into a Gaussian mixture model (GMM) for density estimation. MemAe-gmm-ma demonstrates better performance on the public benchmark dataset, with a 4.47% improvement over the MemAe model standard F1 score on the NSL-KDD dataset, and a 9.77% improvement over the CAE-GMM model standard F1 score on the CIC-IDS-2017 dataset. 

/pdf/research-on-unsupervised-anomaly-data-detection-method-based-13z17fqh.pdf

Y. Y. Liang

Papers

A Field Study of Factors that Affect Acid-Volatile Sulfide Concentrations

Research on unsupervised anomaly data detection method based on improved automatic encoder and Gaussian mixture model