Study of Ecological Water Demand of Rivers in Shenyang City, Northeastern China

The ultimate goal of the protection and restoration of water ecosystem is intended to facilitate the healthiness of ecosystems in rivers and lakes, and the basic premise of river healthiness is to ensure the ecological water demand of rivers. Shenyang City is the economic and cultural center of Northeastern China, and most rivers in the territory of Shenyang City are plagued with the ecology and environment problems such as dry-up of river way, wetland shrinkage and groundwater overdraft. In protection and restoration of water ecosystem, Shenyang City should first study the ecological water demand of rivers. In this paper, we first defined the connotation, composition and calculation method of ecological water demand of rivers and selected eight major rivers in Shenyang City for ecological water demand study and calculation according to the characteristics of the rivers and water resources conditions in Shenyang City. We calculated the basic eco-environmental water demand and consumptive water demand for evaporation and seepage of river ways respectively and thus obtained the total eco-environmental water demand for eight rivers. In total eco-environmental water demand of rivers, consumptive water demand for evaporation and seepage of river ways accounts for 51% and the eco-environmental water demand of rivers accounts for 49%.

Cascade model for continuous prediction of silicon content of molten iron with coupled state variable nodes

With the goal of achieving advanced and multi-step prediction of silicon content of molten iron in the blast furnace ironmaking process,a path adaptive optimization seeking strategy coupled with simulated annealing algorithm and genetic algorithm was proposed from the perspective of innovative intelligent algorithm application.It was further coupled with wavelet neural network algorithm to deeply explore the nonlinear and strong coupling relationship between the information of big data samples and construct a cascade model for continuous prediction of silicon content of molten iron with the intelligent research results of state variables such as permeability index as the node and silicon content forecast as the output.In the model construction process,the 3r criterion was used for non-anomaly estimation of abnormal data to build a time-aligned sample set for multi-step forecasting of iron content,the normalization method was used to eliminate the influence of dimensionality of sample information,and the spearman correlation analysis algorithm was used to eliminate the time delay between state variables,control variables,and silicon content of molten iron in the blast furnace smelting process.The results show that permeability and theoretical combustion temperature as the key state variable nodes have real-time correlation with the silicon content of molten iron,and there are accurate forecasting results on the optimal path with the endpoint of molten iron silicon content prediction.The path finding based on the improved genetic algorithm of simulated annealing has good effect on the downscaling and depth characterization of sample data and improves the data ecology for the application of wavelet neural network algorithm.The accuracy of the real-time continuous forecasting model for the silicon content of molten iron reaches 95.24%;the hit rate of continuous forecasting one step ahead reaches 91.16%,and the hit rate of continuous forecasting five steps ahead is 87.41%.This model,which can realize the nodal dynamics of state variables,has better promotion value.

Environmental heavy metals and cardiovascular diseases:Status and future direction

Cardiovascular disease(CVD)and environmental degradation are leading global health problems of our time.Recent studies have linked exposure to heavy metals to the risks of CVD and diabetes,particularly in populations from low-and middle-income countries,where concomitant rapid development occurs.In this review,we 1)assessed the totality,quantity,and consistency of the available epidemiological studies,linking heavy metal exposures to the risk of CVD(including stroke and coronary heart disease);2)discussed the potential biological mechanisms underlying some tantalizing observations in humans;and 3)identified gaps in our knowledge base that must be investigated in future work.An accumulating body of evidence from both experimental and obser-vational studies implicates exposure to heavy metals,in a dose-response manner,in the increased risk of CVD.The limitations of most existing studies include insufficient statistical power,lack of comprehensive assessment of exposure,and cross-sectional design.Given the widespread exposure to heavy metals,an urgent need has emerged to investigate these putative associations of environmental exposures,either independently or jointly,with incident CVD outcomes prospectively in well-characterized cohorts of diverse populations,and to determine potential strategies to prevent and control the impacts of heavy metal exposure on the cardiometabolic health outcomes of individuals and populations.

Occupational exposure to heavy metals, alcohol intake, and risk of type 2 diabetes and prediabetes among Chinese male workers

Objective: Both exposure to heavy metals and alcohol intake have been related to the risk of type 2 diabetes (T2D). In this study, we aimed to assess the potential interactions between metal exposure and alcohol intake on the risk of T2D and prediabetes in a cohort of Chinese male workers. Methods: We conducted a cross-sectional analysis of 26,008 Chinese male workers in an occupational cohort study from 2011 to 2013. We assessed metal exposure and alcohol consumption at baseline in these workers who were aged ≥20 years. Based on occupations which were categorized according to measured urine metal levels, multiple logistic regression analyses were used to evaluate the independent and joint effects of metal and alcohol exposure on the risk of T2D and prediabetes. Results: Risks of T2D (Ptrend= 0.0 0 1 ) and prediabetes (Ptrend = 0.001) were significantly elevated with increasing number of standard drinks per week, years of drinking, and lifetime alcohol consumption. An adjusted odds ratio (OR) of 6.1 (95% confidence interval [CI]: 4.8-7.8) was observed for the smelting/refining workers (highest metal exposure levels) who had the highest lifetime alcohol consumption (>873 kg)(Pinteraction = 0.018), whereas no statistically significant joint effect was found for prediabetes (Pinteraction =0.515). Conclusions: Both exposures to metal and heavy alcohol intake were associated with the risk of diabetes in this large cohort of male workers. There was a strong interaction between these two exposures in affecting diabetes risk that needs to be confirmed in future studies.

Effect of basicity on metallurgical properties of magnesium fluxed pellets

Magnesium fluxed pellets are the focus of blast furnace burden research for reducing environmental load.The pelletizing,roasting and metallurgical properties of a Chinese fine magnetite ore with the addition of magnesium flux were experimentally tested,and the effects of basicity on the consolidation behavior,compressive strength,and reducibility of magnesium fluxed pellets were systematically clarified.Then,the mechanisms were analyzed by means of thermodynamics calculation and scanning electron microscopy-energy-dispersive spectrometry analysis methods.The results show that the consolidation behavior of magnesium fluxed pellets during roasting process was obviously promoted with increasing the basicity of the magnesium fluxed pellets.The compressive strength increased firstly and then decreased,reaching the maximum value of 2352 N/pellet with the basicity of 1.0.The reduction degree increased gradually with enhancing the basicity owing to the fact that the decomposition of the added CaCO^could increase the porosity of pellets,thereby increasing the CO diffusion in pellet during reduction.Simultaneously,the reduction swelling index was improved with increasing the basicity because the generated calcium feirite could effectively suppress the growth of iron whiskers.

Prediction of compressive strength based on visualization of pellet microstructure data

In recent years,with the wide application of image data visual extraction technology in the field of industrial engineering,the development of industrial economy has reached a new situation.To explore the interaction between the pellet microstructure and compressive strength,firstly,the pellet microstructure needed for the experiment was obtained using a Leica DM4500P microscope.The area proportions of hematite,calcium ferrite,magnetite,calcium silicate and pore in pellet microstructure were extracted by visual extraction technology of image data.Moreover,the relationship between the area proportions of mineral components and compressive strength was established by backpropagation neural network(BPNN),generalized regression neural network(GRNN)and beetle antennae search-generalized regression neural network(BAS-GRNN)algorithms,which proves that the pellet microstructure can be used as the prediction standard of compressive strength.The errors of BPNN and BAS-GRNN are 5.13%and 3.37%,respectively,both of which are less than 5.5%.Therefore,through data visualization,we are able to discuss the connection between various components of pellet microstructure and compressive strength and provide new research ideas for improving the compressive strength and metallurgical performance of pellet.