Abstract The Furong tin deposit in the central Nanling region, South China, consists of three main types of mineralization ores, i.e. skarn‐, altered granite‐ and greisen‐type ores, hosted in Carboniferous and Permian strata and Mesozoic granitic intrusions. Calcite is the dominant gangue mineral intergrown with ore bodies in the orefield. We have carried out REE, Mn, Fe, and Mg geochemical and C, and O isotopic studies on calcites to constrain the source and evolution of the ore‐forming fluids. The calcites from the Furong deposit exhibit middle negative Eu anomaly (Eu/Eu*= 0.311–0.921), except for one which has an Eu/Eu* of 1.10, with the total REE content of 5.49–133 ppm. The results show that the calcites are characterized by two types of REE distribution patterns: a LREE‐enriched pattern and a flat REE pattern. The LREE‐enriched pattern of calcites accompanying greisen‐type ore and skarn‐type ore are similar to those of Qitianling granite. The REE, Mn, Fe, and Mg abundances of calcites exhibit a decreasing tendency from granite rock mass to wall rock, i.e. these abundances of calcites associated with altered granite‐type and greisen‐type ores are higher than those associated with skarn‐type ores. The calcites from primary ores in the Furong deposit show large variation in carbon and oxygen isotopic compositions. The δ 13 C and δ 18 O of calcites are −0.4 to −12.7‰ and 2.8 to 16.4‰, respectively, and mainly fall within the range between mantle or magmatic carbon and marine carbonate. The calcites from greisen and altered granite ores in the Furong deposit display a negative correlation in the diagram of δ 13 C versus δ 18 O, probably owing to the CO 2 ‐degassing of the ore‐forming fluids. From the intrusion to wall‐rock, the calcites display an increasing tendency with respect to δ 13 C values. This implies that the carbon isotopic compositions of the ore‐bearing fluids have progressively changed from domination by magmatic carbon to sedimentary carbonate carbon. In combination with other geological and geochemical data, we suggest that the ore‐forming fluids represent magmatic origin. We believe that the fluids exsolved from fractionation of the granitic magma, accompanying magmatism of the Qitianling granite complex, were involved in the mineralization of the Furong tin polymetallic deposit.
The eggshell is the major source of protection for the inside of poultry eggs from microbial contamination. Timely detection of cracked eggs is the key to improving the edible rate of fresh eggs, hatching rate of breeding eggs and the quality of egg products. Different from traditional detection based on acoustics and vision, this paper proposes a nondestructive method of detection for eggshell cracks based on the egg electrical characteristics model, which combines static and dynamic electrical characteristics and designs a multi-layer flexible electrode that can closely fit the eggshell surface and a rotating mechanism that takes into account different sizes of eggs. The current signals of intact eggs and cracked eggs were collected under 1500 V of DC voltage, and their time domain features (TFs), frequency domain features (FFs) and wavelet features (WFs) were extracted. Machine learning algorithms such as support vector machine (SVM), linear discriminant analysis (LDA), decision tree (DT) and random forest (RF) were used for classification. The relationship between various features and classification algorithms was studied, and the effectiveness of the proposed method was verified. Finally, the method is proven to be universal and generalizable through an experiment on duck eggshell microcrack detection. The experimental results show that the proposed method can realize the detection of eggshell microcracks of less than 3 μm well, and the random forest model combining the three features mentioned above is proven to be the best, with a detection accuracy of cracked eggs and intact eggs over 99%. This nondestructive method can be employed online for egg microcrack inspection in industrial applications.
To control unbalanced vibration caused by a grinding wheel mass unbalance,a new active control strategy for suppressing grinding wheel unbalance vibration was proposed.Suppressing-vibration damping in the control scheme originated from the principle of a bearing-less motor generating a radial magnetic force.Firstly,the winding structure and the working principle of an induction-type two-winding electric spindle were investigated.Its radial control force model and the model of the forces exerted on a grinding wheel were also studied.The induction-type flexural electric spindle-grinding wheel dynamic model was built with the finite element method.An active control system for the unbalanced vibration of the electric spindle-grinding wheel was designed and simulated.The results showed that the control scheme plays a significant role in suppressing the unbalanced vibration of the grinding wheel.
Knowledge Tracing is the process of tracking and monitoring a learner's knowledge of a particular subject over time to identify gaps in knowledge and adjust instruction accordingly. In this paper, we present a simple idea showing another insight into how to approach this problem to get different outcomes. This work is focused on the data that is fed into Deep Knowledge Tracing (DKT) model. We propose to add a pre-trained layer between the input and the hidden layer. The introduced layer is designed to produce a better representation of the questions that are fed to the model. That representation should conserve the correlation between the questions. That information provides a hint to the DKT model to enhance prediction accuracy. The experiments show that it is possible to extract the correlation between the exercises from the sequence of answers a learner gave to a random sample of the exercises.
In order to make the thermo-hygrometer calibrator more intellectualized, an automatic indication recognition algorithm of dual-pointer meter is proposed. Firstly, Hough circle transform algorithm is used to segment the image and intercept the dial; secondly, the color feature of the image is used to detect the datum point and correct the image; then, the image of pointer is extracted by the improved Zhang thinning algorithm and progressive probabilistic Hough transform algorithm; finally, the algorithm is evaluated by the images of meter captured in an experimental platform of the thermo-hygrometer calibrator. The results show that the algorithm proposed in this paper can recognize the indication of the meter with high accuracy and robustness.
In service-oriented computing environments, many Web services are provided for users to build service-oriented systems. Since the performance of the same Web service is different from different users' perspectives, users have to personally select the optimal Web services according to quality-of-service(QoS) data observed by other similar users. However, users with low reputations will provide unreliable data, which will have a negative impact on service selection. Moreover, the QoS data vary over time due to changes in user reputation. Therefore, how to estimate a personalized reputation for each user at runtime remains a significant problem. To address this critical challenge, this paper proposes an online reputation estimation method, called OPRE, to efficiently provide a personalized reputation for each user. Based on the users' observed QoS data, OPRE employs matrix factorization and online learning techniques to estimate personalized reputations. The experimental results show that OPRE has high effectiveness compared to other approaches.
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