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Glacial diamictite may provide important information on paleoenvironment and average composition of the upper continental crust (UCC). In this study, we report sedimentary facies, petrological and geochemical characteristics of Neoproterozoic diamictite from a profile of the Luoquan Formation on the southern margin of the North China Block (NCB). Upwards the sampling profile, lithostratigraphic strata vary from massive diamictite with poorly sorted carbonate gravels to laminated diamictite with small gravels of terrestrial detrital materials. Along the profile, CaO-MgO-LOI-Sr values decrease with the increase of SiO2-Al2O3-K2O contents. All these petrological and geochemical variations indicate a change from lodgement till deposition in the proximal of ice sheet to ice-rafting deposition in glacial-marine environment with less dolomite to supply their source. Together with previous studies on diamictite from other outcrops on the NCB, the deposition of Luoquan diamictite reflects that the glaciation on the NCB vanished and the ice-rafting effect weakened with glacial transgression process. In addition, significant co-variations of various elements with La and Al2O3 confirm the significant conservation of most analyzed elements during the sedimentary processes to produce diamictite. [ABSTRACT FROM AUTHOR]

Surface defect detection is a complicated task to achieve both specific class and precise location of each defect. Specifically for industrial scenario, realizing efficient and accuracy-satisfactory surface defect automatic detection is still a big challenge. Therefore, a surface defect detection algorithm based on feature-enhanced YOLO (FE-YOLO) for practical industrial applications is proposed in this paper. For the purpose of efficient detection, we lighten YOLO model by combining deep separable convolution and dense join. And an improved feature pyramid network is proposed to enhance the spatial location correlation for multi-scale detection layer for the sake of high accuracy. Then, a new loss function of prediction box regression is established to boost the detection accuracy under the high Intersection over Union (IoU) threshold and accelerate model convergence. To select anchor boxes of different scale feature detection layers, we propose a statistical-based k-means++ algorithm, which can improve the quality of initial anchors and accelerate the convergence of the proposed model. Two industrial surface defect datasets, NEU-DET dataset and DeepPCB dataset, are used to verify the effectiveness of the proposed FE-YOLO algorithm. Experimental results demonstrate that FE-YOLO algorithm is lightened nearly 80% compared with YOLOV4. The detection speed is better than the other state-of-the-art surface defect detection algorithms. The defects detection accuracy respectively reaches 83.9% and 98.9% for the NEU-DET dataset and DeepPCB dataset, which are better than the state-of-the-art defect detection methods. The end-to-end fast and accurate detection for industrial surface defects is realized. [ABSTRACT FROM AUTHOR]

This paper presents a Cooperative Particle Swarm Optimizer with Depth First Search Strategy (DFS-CPSO), which has better seacrch capality than classical Particle Swarm Optimizer (PSO) in solving multimodal optimization problems. In order to improve the quality of information exchange, the Depth First Search (DFS) strategy is hybridized to Cooperative Particle Swarm Optimization(CPSO), which makes information transfer more effectively and generates better quality solution. Specifically, DFS strategy enables different components of solution vector to exchange information separately with PSO and increases the diversity of the population, so that the information of solution components could be preserved by multiple iterations in CPSO. Confirmatory experiments are performed to prove the effectiveness of employing the DFS strategy to CPSO. The comparative results demonstrate superior performance of DFS-CPSO in solving high dimensional multimodal functions than CPSO and other advanced methods. [ABSTRACT FROM AUTHOR]

Froth flotation is a complicated process which is difficult to establish its first-principle model. Due to the fluctuations in the grade of raw ore, adaptively adjusting the set-points is extremely important in the flotation process. The inappropriate set-points easily lead to the instability of the process. This paper presents a fuzzy association rule-based set-point adaptive optimization and control strategy for the antimony flotation process without knowing the system model. Firstly, a fuzzy neural network is constructed as a soft-sensor to estimate the feed grade online because of the lack of efficient measurement equipment. Then, fuzzy association rule is used to mine the hidden relationship between the feed grade with reagent dosages and the optimal set-points. Through data mining from the quantitative database, the fuzzy inference system generates the optimal set-points. To implement satisfactory tracking performance, predictive controller is used to compute the control inputs. Because the system dynamics is unknown, long short-term memory network model is established to predict the future behaviors of the process. Finally, simulations and experiments are carried out to demonstrate the effectiveness of the proposed strategy. Compared to the manual manipulation, which is widely used in flotation processes, our control strategy achieves a better control performance, and the concentrate grades are more in line with the process requirement. [ABSTRACT FROM AUTHOR]

Using carbon tetrachloride (CCl4) as extraction agent, the activated sludge from Tianjin Jizhuangzi Sewage Treatment Plant as inoculum, the test study on biodegradability of lubricants was carried out. The test flasks containing the mineral medium, the test oil and the inoculum were placed in incubation together with flasks containing poisoned blanks for periods of 0 and 21 days, respectively. Flasks containing the reference materials in place of the test oil were run in parallel. At the end of the incubation period, the contents of the flasks were subjected to sonic vibration, and were acidified and extracted by using CCl4. The extracts were then analysed by infra-red (IR) spectrometer to measure the maximum absorption of the C-H stretch of CH2-CH3 band at wavelength of 2 930 cm−1. The absorption values were used to calculate the residual oil contents of the poisoned and test flasks. Consequently the biodegradability of the test oil was calculated. The test results indicate that the differences in the biodegradability of test materials in different tests are within 5.5%, and consistent with the data described in Coordinating European Council (CEC) L-33-A-93. The biodegradability of lubricants can be evaluated by this method effectively. [ABSTRACT FROM AUTHOR]