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• Adopting a Durkheim-Bernsteinian approach to explain how a strongly framed moral system is established to promote performance. • Discussing the limitations of Bourdieu's and Foucault's frameworks to specify the moralities of Chinese schooling. • Understanding the operation of Chinese schooling through the lens of collective cultural beliefs in society. • Depicting how rituals for high-achieving students serve to facilitate commitment, while disengaging low-achieving students. This paper contributes to the discussion on how Chinese 'unpromising' pedagogic practices featured with strong framing over teacher-student relations contribute to students' educational involvement. Based on semi-structured interviews with 120 students from 4 secondary schools in Eastern China, this qualitative study employs Bernstein's concept of the 'regulative order' to examine the regulative dimension of schooling inscribed in school rituals. The Bernsteinian approach offers both an internal perspective on specific configurations of school rituals and an external perspective on socio-cultural meanings attached to them. The present study finds that the regulative order in China's schooling comes to be in a double-faced position. On the one hand, the regulative order imbued with the notion of 'impersonality', enacted through consensual rituals that make explicit authority relationships, serves as a 'semiotic mediator' in effecting a desirable separation from home and commitment to the school code. On the other hand, it imposes 'symbolic violence' on low-achieving students, through differentiating rituals which deepen existing academic divisions. It argues that the strongly framed regulative order is encapsulated within Confucian educational ideals and the distribution of its different aspects among students reflects a moral order characteristic with exam-oriented meritocracy in society. [ABSTRACT FROM AUTHOR]

Abstract: The spherical parallel machine has become the world''s academic and industrial focus of the field in recent years due to its simple and economical manufacture as well as its structural compactness especially suitable for areas where space gesture changes. This paper dwells upon its present research and development home and abroad. The newer machine (RGRR-II) can rotate around the axis z within 360° and the axis y1 from -90° to +90°. It has the advantages such as less moving parts (only 3 parts), larger ratio of work space to machine size, zero mechanic coupling, no singularity. Constructing rotation machine with spherical parallel 2-DOF rotation join (RGRR-II) may realize semispherical movement with zero dead point and extent the range. Control card (PA8000NT Series CNC) is installed in the computer. The card can run the corresponding software which realizes radar movement control. The machine meets the need of radars in plane and satellite which require larger detection range, lighter weight and compacter structure. [Copyright &y& Elsevier]

Abstract: Square and circular structure elements are presented, which are used to image de-noising and granularity detection. The different de-noising effects by using the two structure elements were gotten. Moreover, given only one kind of structure elements, we changed the size of the structure element, then, the difference of de-noising effects is very apparent. It is compared with the detection in one-dimensional direction. The range of detected granules’ size and shape can be expanded by using two dimensional structure elements. Meanwhile, the use of mathematical morphology to a certain extent overcomes the interference of noise. Taking one parasite egg micro image as a simulation example, experimental results show that the image granularities, that we detected by using different structure elements, have great difference, and two-dimensional detection is better than one-dimensional direction detection. [Copyright &y& Elsevier]

Salient object detection is still very challenging especially in images with complex or cluttered background. In this paper, we present an efficient and discriminative framework to address it. In specially, a discriminative similarity metric is first proposed by measuring the chi-square distance in a new constructed feature space. Then, we apply it to calculate a background based coarse saliency map by introducing distribution prior to remove foreground noises in the image boundaries. Based on manifold ranking, a robust saliency propagation mechanism is further developed to highlight salient object and simultaneously suppress background region by setting appropriate sink points. Finally, several simple refinement techniques are utilized to generate pixel-wise and smooth saliency maps. Extensive experimental results show the superior performance of the proposed method in terms of different evaluation metrics. In addition, the proposed framework can be also applied to the existing saliency propagation methods for significant performance boosting. We also believe that it is a good choice for subsequent applications based on the achieved high performance and acceptable computational overhead. [ABSTRACT FROM AUTHOR]

Various multivariate control charts (CCs) are applied to monitor compositional data (CoDa) processes post an isometric log-ratio (ilr) transformation aimed at assessing in-control or out-of-control (OOC) conditions. While optimal multivariate CCs effectively detect shifts in the overall mean vector, challenges arise when shifts occur in specific variables rather than the overall mean vector. This complexity in signal interpretation using traditional multivariate CCs prompts the need for improved approaches. To address this issue, this study introduces the application of a multilayer perceptron neural network (MLPNN) with back-propagation (BP) learning to interpret OOC signals in Hotelling's T 2 CC for CoDa. The proposed model aids practitioners in identifying atypical variables responsible for OOC situations instead of focusing solely on mean shifts. This capability to detect atypical variables enhances process control strategies, leading to more efficient industrial operations. The model's performance is assessed through two cases: one involving p = 3 -part CoDa and another with p = 5 -part CoDa. Shifts are introduced by altering variable means using various combinations. For comparison, the study also presents results obtained from multivariate data analysis using MLPNN with BP. The results demonstrate that the MLPNN consistently provides more accurate outcomes in the case of CoDa than multivariate data. Applying the ilr transformation improves the MLPNN's efficacy in accurately interpreting OOC signals within the CoDa domain. An application is reported to interpret the OOC signal during the working hours of machine operators in an industry. • The Hotelling T C 2 CC to CoDa using ilr transformation. • Analysis of OOC signal in T C 2 CC for CoDa using the BP technique in an MLPNN. • Identification of atypical variables responsible for OOC situations. • The performance assessment in diverse scenarios. [ABSTRACT FROM AUTHOR]

RGB-D salient object detection aims to separate salient object from an image aided by depth. While a number of effective approaches have been proposed, difficulties still exist, which is due to two challenges: (1) It is difficult to fully and effectively fuse RGB and depth features especially in challenging scenes; (2) How to enhance the semantic information of low-level feature and enrich the spatial information of high-level feature. Most of the existing approaches design separate modules to address them. In this paper, a unified discriminative feature fusion module is proposed to be used for both multimodal and multiscale feature fusion. The module can also increase the semantic information in low-level features and enrich the spatial information in high-level features. A multi-scale contextual perception module is embedded in the network to accurately localize objects at different scales. Unlike other methods, the depth branch in the network uses pure convolution for complementary feature extraction. This paper conducted a comparison with 14 state-of-the-art methods on 8 datasets, and the experimental results suggest that the proposed approach is more effective and superior. [ABSTRACT FROM AUTHOR]