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BACKGROUND Serpentine is an important group of minerals, widely used in the chemical and building industries, agriculture, metallurgy, environmental protection, and other fields. Due to the high requirements for the mining grade of serpentine ores in China, many abandoned tailings have been formed, resulting in a large amount of waste causing environmental pollution. According to the different content of serpentine in the waste tailings, different industrial products can be produced, which can improve the natural environment and diversify serpentine enterprises. These works are based on the ability to determine serpentine contents quickly and accurately. At present, the determination method of serpentine ores is based on the industry standard HG/T 3575—2006 Analysis Method of Serpentine Ores. The content of magnesium is measured first, and the content of serpentine mineral is obtained after conversion. The analysis steps are tedious, and the analysis time is long. The existing detection methods obviously cannot meet the urgent needs of the market and enterprises. For a long time, the precision of the quantitative analysis of serpentine by X-ray diffraction has not been controlled quantitatively. The precision comparison between laboratories also lacks a corresponding basis. OBJECTIVES It is urgent to improve the detection level of X-ray diffraction and further improve the detection technology of serpentine mineral content. The purpose of this study is to lay a foundation for the establishment of the standard method for the quantitative analysis of serpentine phase in the future and provide a basis for the evaluation of analytical methods and the monitoring of analytical quality. METHODS This study refers to the method widely used internationally to express precision with repeatability limit and reproducibility limit, and to judge the reliability of analysis results in practice. According to the requirements of GB/T 6379.2—2004 standard, 10 samples with different serpentine content are selected and prepared for uniformity test. The serpentine content ranges from 5.00% to 84.78%, which basically covers the content range of serpentine deposits in China, meeting the requirements of the research program. The purpose of the uniformity test is to reduce the error of quantitative analysis, especially in the case of low content, because the uniformity of the sample has a great impact on the accuracy of the test results. The measured value of Fmeasured is less than the critical value of Fcritical, indicating that the analyzed samples are uniform. Eight laboratories are selected to use the qualified samples for the precision collaborative test. According to the requirements of the quantitative limit and the optimal measurement range, and the results of the precision, accuracy and recovery test, the standard curve method has a small error, accurate test data, high recovery rate and stronger applicability for samples with serpentine mineral content of less than 20.00%. For high-content samples with serpentine mineral content of 20% or more, the parameters of the K value method all meet the requirements, considering the fast and convenient determination, it is more suitable for the application of the K value method. The laboratories participating in the collaborative test use the uniformly supplied analytical pure corundum reagent as the reference material, and use the X-ray diffraction standard curve method (provide the calibration curve) for samples with serpentine mineral < 20%, while use the X-ray diffraction K value method (provide K value) for samples with serpentine mineral ≥20% under the same test conditions. Only the data passing the test of Grubbs and Cochran can participate in the calculation of precision parameters, and the mathematical function relationship between the precision and content of X-ray diffraction standard curve method and K-value method is obtained. RESULTS The experimental results show that the critical value of Fcritical under 95% confidence is 3.50 for the collaborative test, while the measured value of Fmeasured after statistical calculation is less than 3.50, which proves that the 10 samples are relatively uniform and are qualified as the precision collaborative sample. Moreover, the data of the collaborative test are all less than 5% of the critical value, and all of them pass the Grubbs test and Cochran test. All of the data can be included in the calculation of precision parameters, which also proves the high consistency of the test results of different instruments in different laboratories. CONCLUSIONS The calculation results of precision parameters show that the precision parameters of X-ray diffraction standard curve method and K-value method are positively correlated with the content of serpentine minerals, that is, the higher the content of serpentine minerals, the greater the repeatability limit and reproducibility limit, and vice versa. The sample with the lowest content of serpentine has the lowest repeatability limit and reproducibility limit, which also verifies that the error of X-ray diffraction for phase quantitative analysis mainly comes from the uniformity error of the sample. It confirms that among all factors affecting the results of X-ray diffraction quantitative analysis, the homogeneity of the sample is the most critical factor, and the influence of other factors can be overcome by changing the test conditions and other corresponding methods. If the sample is uniform enough, the error of quantitative analysis can be controlled. The research on the precision of the quantitative analysis method of serpentine phase provides technical support and basis for the establishment of the standard method of the quantitative analysis of serpentine phase, the evaluation of the analysis method and the monitoring of the analysis quality in the future.

Deep convolutional neural networks (DCNNs) have achieved impressive performance in image recognition, object detection, etc. Nevertheless, they are susceptible to adversarial attacks and interferential noise. Adversarial attacks can mislead DCNN models by manipulating input data with small perturbations, causing security risks to intelligent system applications. Comparatively, these small perturbations have very limited perceptual impact on humans. Therefore, the research on brain-inspired adversarial robust models has gained increasing attention. Beginning from the adversarial attack concepts and schemes, we present a review of the conventional adversarial attack and defense methods and compare the advantages and differences between brain-inspired robust neural networks and the conventional adversarial defense methods. We further review the existing adversarial robust DCNN models, including methods inspired by the early visual systems and supervised by neural signals. Representative examples have validated the efficacy of brain-inspired methods for designing adversarial robust models, which may benefit the further research and development of brain-inspired robust deep convolutional neural networks and the intelligent system applications. [ABSTRACT FROM AUTHOR]

Target detection has always been a hotspot in image processing/computer vision research, and small-target detection is a frequently encountered problem in the field of target detection. With the continuous innovation of target detection technology, people always hope that the detection of small targets can reach the real-time accuracy of large-target detection. In this paper, a small-target detection model based on dual-core convolutional neural networks (CNN) is proposed, which is mainly used for the intelligent detection of books in the production line of printed books. The model is mainly composed of two modules, including a region prediction module and suspicious target search module. The region prediction module uses a CNN to predict suspicious region blocks in a large context. The suspicious target search module uses a different CNN from the above to find tiny targets in the predicted region blocks. Comparative testing of four small book target samples using this model shows that this model has better book small-target detection accuracy compared to other models. [ABSTRACT FROM AUTHOR]

Featured Application: A general framework for functional integration evaluation of human brain is proposed, which effectively combines whole-brain functional connectivity analysis with effective connectivity analysis based on Dynamic Causal Modeling. Much effort has been devoted towards the identification of brain areas recruited during driving—as one of the most common motor skills of human beings. However, how driving experience impacts on the brain's intrinsic functional architecture has not been fully investigated. Using resting-state fMRI data collected from 20 taxi drivers and 20 nondrivers, this paper asks whether there exists specific brain network integration encoding driving behavior. First, to address this, we proposed a general framework combining whole-brain functional connectivity analysis with effective connectivity analysis based on spectral Dynamic Causal Modeling. The validation results indicated that the application of this framework could effectively discover the brain network that best explained the observed BOLD fluctuations. Second, by segmenting supplementary motor area (SMA) into pre-SMA and SMA proper sub-regions, we used the above framework and discovered a hierarchical architecture with pre-SMA located at the higher level in both driver and control groups. Third, we further evaluated the possibility that driving behavior could be encoded by directed connections among the hierarchy, and found that the effective connectivity from pre-SMA to left superior frontal gyrus could distinguish drivers from nondrivers with a sensitivity of 80%. Our findings provide a new paradigm for analyzing the brain's intrinsic functional integration, and may shed new light on the theory of neuroplasticity that training and experience can remodel the patterns of correlated spontaneous brain activity between specific processing regions. Meanwhile, from a methodological advantage perspective, our proposed framework takes the functional connectivity results as a prior, enabling subsequent spectral DCM to efficiently assess functional integration at a whole-brain scale, which is not available by only using other DCM methods, such as stochastic DCM or the State-of-the-Art multimodal DCM. [ABSTRACT FROM AUTHOR]

The visual quality and spatial distribution of architectural styles represent a city's image, influence inhabitants' living conditions, and may have positive or negative social consequences which are critical to urban sensing and designing. Conventional methods of identifying architectural styles rely on human labor and are frequently time-consuming, inefficient, and subjective in judgment. These issues significantly affect the large-scale management of urban architectural styles. Fortunately, deep learning models have robust feature expression abilities for images and have achieved highly competitive results in object detection in recent years. They provide a new approach to supporting traditional architectural style recognition. Therefore, this paper summarizes 22 architectural styles in a study area which could be used to define and describe urban architectural styles in most Chinese urban areas. Then, this paper introduced a Faster-RCNN general framework of architectural style classification with a VGG-16 backbone network, which is the first machine learning approach to identifying architectural styles in Chinese cities. Finally, this paper introduces an approach to constructing an urban architectural style dataset by mapping the identified architectural style through continuous street view imagery and vector map data from a top-down building contour map. The experimental results show that the architectural style dataset created had a precision of 57.8%, a recall rate of 80.91%, and an F1 score of 0.634. This dataset can, to a certain extent, reflect the geographical distribution characteristics of a wide variety of urban architectural styles. The proposed approach could support urban design to improve a city's image. [ABSTRACT FROM AUTHOR]