A field trial for remediation of multi-metal contaminated soils using the combination of fly ash stabilization and Zanthoxylumbungeanum- Lolium perenne intercropping system.

Insitu stabilization and phytoextraction are considered as two convenient and effective technologies for the remediation of toxic elements (TEs) in soils. However, the effectiveness of these two remediation technologies together on the bioavailability and phytoextraction of TEs in field trials has not been explored yet. Specifically, the remediation potential of fly ash (FA; as stabilizing agent) and ryegrass (as a TE accumulator) intercropped with a target crop for soil polluted with multiple TEs has not been investigated yet, particularly in long-term field trials. Therefore, in this study, a six-month combined remediation field experiment of FA stabilization and/or ryegrass intercropping (IR) was carried out on the farmland soils contaminated with As, Cd, Cr, Cu, Hg, Ni, Pb and Zn where Zanthoxylumbungeanum (ZB) trees as native crops were grown for years. The treatments include soil cultivated alone with ZB untreated- (control) and treated-with FA (FA), produced by burning lignite in Shaanxi Datong power plant, China, soil cultivated with ZB and ryegrass untreated- (IR) and treated-with FA (FA + IR). This was underpinned by a large-scale survey in Daiziying (China), which showed that the topsoils were polluted by Cd, Cu, Hg and Pb, and that Hg and Pb contents in the Zanthoxylumbungeanum fruits exceeded their allowable limits. The TEs contents in the studied FA were lower than their total element contents in the soil. The DTPA-extractable TEs contents of the remediation modes were as follows: FA < FA + IR < IR < control. Notably, TEs contents in the ZB fruits were lowest under the FA + IR treatment, which were decreased by 27.6% for As, 42.3% for Cd, 16.7% for Cr, 30.5% for Cu, 23.1% for Hg, 15.5% for Ni, 33.2% for Pb and 38.1% for Zn compared with the control treatment. Whereas the FA + IR treatment enhanced TEs contents in ryegrass shoots and roots, and the TEs contents in ryegrass shoots were below their regulatory limits for fodder crops. The findings confirmed that the combined remediation strategy, i.e., FA (with low content of TEs) stabilization effect and intercropping of ZB (target crop) and ryegrass (accumulating plant) could provide a prospective approach to produce target plants within safe TEs thresholds with greater economic benefits, while remediating soils polluted with multiple TEs and mitigating the potential ecological and human health risk. Those results are of great applicable concern.

Elevated whole blood arsenic level is associated with type 2 diabetes in coal-burning areas in Guizhou.

The potential association between coal-burning arsenic exposure and type 2 diabetes (T2D) was examined through a case control study, conducted in coal-burning arsenic poisoning areas in the Guizhou Province. This study included patients diagnosed with type 2 diabetes. Control subjects without type 2 diabetes were recruited randomly after gender and age 1:1 matching. All subjects completed questionnaire surveys and underwent physical examination and whole blood arsenic level testing. The whole blood arsenic level was associated with a significant increase in the risk of type 2 diabetes (75th versus 25th, adjusted OR = 1.76, 95% CI: 1.03-3.01). However, a nonlinear relationship was observed between the blood arsenic level and type 2 diabetes. The risk of type 2 diabetes increased with blood arsenic levels above 3.69 µg/L (Log As ≥0.57). The subgroup analysis revealed that blood arsenic levels were associated with significantly increased risk of type 2 diabetes in people who ever smoked (P < .05), particularly those who smoked ≥15 years (adjusted OR = 3.15, 95% CI: 1.9-7.28). Therefore, prolonged arsenic exposure, even at a low level, is associated with a higher prevalence of type 2 diabetes in a nonlinear pattern. Blood arsenic levels less than 3.69 µg/L may be considered safe with respect to the risk of T2D. However, smoking, particularly smoking ≥15 years, may be associated with the development of diabetes in patients with arsenic exposure.

Design of a novel MEMS gyroscope array.

This paper reports a novel four degree-of-freedom (DOF) MEMS vibratory gyroscope. A MEMS gyroscope array is then presented using the novel gyroscope unit. In the design of the proposed 4-DOF MEMS vibratory gyroscope, the elements of the drive-mode are set inside the whole gyroscope architecture, and the elements of sense-mode are set around the drive-mode, which thus makes it possible to combine several gyroscope units into a gyroscope array through sense-modes of all the units. The complete 2-DOF vibratory structure is utilized in both the drive-mode and sense-mode of the gyroscope unit, thereby providing the desired bandwidth and inherent robustness. The gyroscope array combines several gyroscope units by using the unique detection mass, which will increase the gain of sense-mode and improve the sensitivity of the system. The simulation results demonstrate that, compared to a single gyroscope unit, the gain of gyroscope array (n = 6) is increased by about 8 dB; a 3 dB bandwidth of 100 Hz in sense-mode and 190 Hz in drive-mode are also provided. The bandwidths of both modes are highly matched with each other, providing a bandwidth of 100 Hz for the entire system, thus illustrating that it could satisfy the requirements in practical applications.

A Stochastic Damage Model for Bond Stress-Slip Relationship of Rebar-Concrete Interface under Monotonic Loading.

The stochastic bond stress-slip behavior is an essential topic for the rebar-concrete interface. However, few theoretical models incorporating stochastic behavior in current literature can be traced. In this paper, a stochastic damage model based on micro-mechanical approach for bond stress-slip relationship of the interface under monotonic loading was proposed. In order to describe the mechanical behaviors of the rebar-concrete interface, a microscopic damage model was proposed. By introducing a micro-element consists of parallel spring element, friction element and a switch element, the model is formulated. In order to reflect the randomness of the bond stress-slip behavior contributed by the micro-fracture in the interface, a series of paralleled micro-elements are adopted with the failure threshold of individual spring element is set as a random variable. The expression of both mean and variance for the bond stress-slip relationship was derived based on statistical damage mechanics. Furthermore, by utilizing a search heuristic global optimization algorithm (i.e., a genetic algorithm), parameters of the proposed model are able to be identified from experimental results, which a lognormal distribution has adopted. The prediction was verified against experimental results, and it reveals that the proposed model is capable of capturing the random nature of the micro-structure and characterizing the stochastic behavior.

Stacked ensemble extreme learning machine coupled with Partial Least Squares-based weighting strategy for nonlinear multivariate calibration.

With its simple theory and strong implementation, extreme learning machine (ELM) becomes a competitive single hidden layer feed forward networks for nonlinear multivariate calibration in chemometrics. To improve the generalization and robustness of ELM further, stacked generalization is introduced into ELM to construct a modified ELM model called stacked ensemble ELM (SE-ELM). The SE-ELM is to create a set of sub-models by applying ELM repeatedly to different sub-regions of the spectra and then combine the predictions of those sub-models according to a weighting strategy. Three different weighting strategies are explored to implement the proposed SE-ELM, such as the Winner-takes-all (WTA) weighting strategy, the constraint non-negative least squares (CNNLS) weighing strategy and the partial least squares (PLS) weighting strategy. Furthermore, PLS is suggested to be selected as the optimal weighting method that can handle the multi-colinearity among the predictions yielded by all the sub-models. The experimental assessment of the three SE-ELM models with different weighting strategies is carried out on six real spectroscopic datasets and compared with ELM, back-propagation neural network (BPNN) and Radial basis function neural network (RBFNN), statistically tested by the Wilcoxon signed rank test. The obtained experimental results suggest that, in general, all the SE-ELM models are more robust and more accurate than traditional ELM. In particular, the proposed PLS-based weighting strategy is at least statistically not worse than, and frequently better than the other two weighting strategies, BPNN, and RBFNN.