Good water quality is important for normal production processes in industrial aquaculture. However, in situ or real-time monitoring is generally not available for many aquacultural systems due to relatively high monitoring costs. Therefore, it is necessary to predict water quality parameters in industrial aquaculture systems to obtain useful information for managing production activities. This study used back propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector machine (SVM), and least squares support vector machine (LSSVM) to simulate and predict water quality parameters including dissolved oxygen (DO), pH, ammonium-nitrogen (NH₃-N), nitrate nitrogen (NO₃-N), and nitrite-nitrogen (NO₂-N). Published data were used to compare the prediction accuracy of different methods. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting DO were 0.60, 0.99, 0.99, and 0.99, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting pH were 0.56, 0.84, 0.99, and 0.57. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting NH₃-N were 0.28, 0.88, 0.99, and 0.25, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting NO3-N were 0.96, 0.87, 0.99, and 0.87, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM predicted NO₂-N with correlation coefficients of 0.87, 0.08, 0.99, and 0.75, respectively. SVM obtained the most accurate and stable prediction results, and SVM was used for predicting the water quality parameters of industrial aquaculture systems with groundwater as the source water. The results showed that the SVM achieved the best prediction effect with accuracy of 99% for both published data and measured data from a typical industrial aquaculture system. The SVM model is recommended for simulating and predicting the water quality in industrial aquaculture systems.

Problems such as water scarcity and pollution frequently occur in coastal zones. This study investigated the grey water footprint and the sustainability and intensity of grey water footprint in Yantai between 2014 and 2019 by taking both surface water and groundwater into consideration. The research results indicated that the Yantai grey water footprint firstly increased and then decreased between 2014 and 2019. The lowest grey water footprint in 2019 was 744 million m³. The agricultural grey water footprint accounted for a large proportion of the total grey water footprint. Although the sustainability of grey water footprint fluctuates in Yantai, it maintains well. The Yantai grey footprint intensity gradually decreased to <10 m³/10,000 CNY. The economic benefit of grey water footprint and utilization efficiency of water resources have been improved yearly. The quality of the water environment in Yantai has also been improved. The research of this paper provides some useful information for water resources protection and sustainable utilization in coastal cities.

In the Xining area of Huangshui River, China, the concentrations of globally alarming toxic heavy metals such as chromium (Cr), arsenic (As), lead (Pb), cadmium (Cd), and zinc (Zn) in the surface sediments were measured to determine the ecological risks to the riverine ecosystem. Overall, the concentrations of heavy metals in the surface sediments were relatively high. The results indicated that the concentrations of Pb/Zn/Cr were lower than 120/250/200mg·kg⁻¹ which was the risk screening value of each heavy metal for soil contamination of agricultural land (GB15618-2018). The concentrations of As/Cd were higher than 120/3.0mg·kg⁻¹ which was the risk intervention value of As/Cd for soil contamination of agricultural land (GB15618-2018). Their values were arranged in the following decreasing order: As (151.23818.55mg·kg⁻¹)>Cr (44.18201.70mg·kg⁻¹)>Pb (29.10121.95mg·kg⁻¹)>Zn (1.4586.18mg·kg⁻¹)>Cd (4.366.21mg·kg⁻¹). The concentrations of As, Cr Pb, and Cd greatly exceeded the background contents of elements in Qinghai soils. While Zn was lower than background. The contamination factor (CF), the geoaccumulation index (I_geo), and the potential ecological risk index (RI) of As, Cr, Cd, and Pb followed the descending order of Cd>As>Pb>Cr>Zn. The order of the enrichment factor (EF) was as follows: As>Cd>Pb>Cr>Zn. The contamination level of heavy metals implied that the condition is frightening and probably severely affecting the aquatic ecology. Cd and As were the main contributing elements to the ecological risk of the sediments in the Xining area of the Huangshui River, which should be mainly studied and prevented.

Tailings collected from the tailing reservoir at Huogeqi Copper Mine, located in Inner Mongolia, China, were used in a leachate study to evaluate the acid potential, neutralization potential, and possibility for producing acid mine drainage (AMD) from the site. The speciation of Cu, Pb, and Zn contained in the tailings was also determined during the leachate study to further access the potential migration abilities of these metals. The results showed that the tailings did not produce significant AMD as the pH of the leachate ranged from 7 to 9 and decreased with time. The Cu, Pb, and Zn concentrations were high, ranging from 439.1 to 4527 mg/kg in the tailings and from 0.162 to 7.964 mg/L in the leachate, respectively. Concentrations of metals in the leachate and tailings were positively correlated. Over 60% of the Cu in the tailing samples existed in an oxidizable form. Most of the Pb also existed in its oxidized form, as did the silicate and Zn. Metals usually have higher mobility in their exchangeable and oxidizable forms and as such represent a higher potential risk to the environment. Results of risk assessment code also revealed that metals in tailings exerted medium to high risks to the environment.

The reliability of the transportation network is studied by simulating the attack on the important edges. The important edges are deliberately attacked, and the impedance of the attacking edges is set to infinity. Then the influence of the characteristics of single failure edge on network reliability change and the influence of failure edge number on network reliability change are studied. Two test schemes are designed to compare the changes of network reliability in three failure experiments. At last, for the transportation network ofZhengzhou-Europe International Block train, the simulation experiment of the transportation network reliability is curried out to demonstrate the proposed method. The reliability of the transportation network is studied through numerical simulation, which is helpful to improve the reliability of the transportation network and reduce the huge economic loss caused by the failure.

The disc property is formulated for domains in $\mathbb{C}^n$. Holomorphic Lipschitz functions enjoy a gain in the order of Lipschitz regularity along the complex tangential direction on domains with disc property. Disc property is studied on various domains of infinite type. As applications, the local version of Stein's theorem is obtained on these domains, including the worm domains.