Numerical investigation on rock fragmentation under decoupled charge blasting

Blasting using decoupled charge is extensively applied in rock excavation and rock fragmentation. In this study, the rock fragmentation induced by blasting using decoupled charge is investigated by combined finite element modelling and image-processing. After calibrating the numerical model developed in LS-DYNA against the fragment morphology and fragmentation size distribution (FSD) in three air-coupling blasts and three water-coupling blasts, a series of cubic single-hole models are constructed to simulate rock cracking induced by decoupled charge blasting with various decoupling ratios, distinct coupling mediums and different decoupled charge modes. The simulated fracture networks are obtained by blanking the damaged elements whose damage level is over the threshold of crack formation, and the resulting crack patterns are image-processed using ImageJ to identify fragment size. Then, the blast-created FSDs are characterized by a three-parameter generalized extreme value function, and the FSDs with decoupling ratios, coupling mediums and different decoupled charge modes are quantitatively analyzed and compared. The results show that rock fragmentation becomes finer and the FSD range gets narrower with the decrease in decoupling ratio. Meanwhile, smaller fragment sizes and narrower FSD spans are obtained when changing coupling material from air to water and altering radial decoupling to axial decoupling.acceptedVersio

Endocannabinoid system unlocks the puzzle of autism treatment

Autism spectrum disorder (ASD) is a serious neurodevelopmental disorder and characterized by early childhood-onset impairments in social interaction and communication, restricted and repetitive patterns of behavior or interests. So far there is no effective treatment for ASD, and the pathogenesis of ASD remains unclear. Genetic and epigenetic factors have been considered to be the main cause of ASD. It is known that endocannabinoid and its receptors are widely distributed in the central nervous system, and provide a positive and irreversible change toward a more physiological neurodevelopment. Recently, the endocannabinoid system (ECS) has been found to participate in the regulation of social reward behavior, which has attracted considerable attention from neuroscientists and neurologists. Both animal models and clinical studies have shown that the ECS is a potential target for the treatment of autism, but the mechanism is still unknown. In the brain, microglia express a complete ECS signaling system. Studies also have shown that modulating ECS signaling can regulate the functions of microglia. By comprehensively reviewing previous studies and combining with our recent work, this review addresses the effects of targeting ECS on microglia, and how this can contribute to maintain the positivity of the central nervous system, and thus improve the symptoms of autism. This will provide insights for revealing the mechanism and developing new treatment strategies for autism

A feature preserved mesh simplification algorithm

Large-volume mesh model faces challenge in rendering, storing, and transmission due to large size of polygon data. Mesh simplification is one of solutions to reduce the data size. This paper presents a mesh simplification method based on feature extraction with curvature estimation to triangle mesh. The simplified topology preserves good geometrical features in the area with distinct features, that is, coarse simplified mesh in the flat region and fine simplified mesh around the areas of crease and corner. Sequence of mesh simplification is controlled on the basis of geometrical feature sensitivity, which results in reasonable simplification topology with less data size. This algorithm can decrease the size of the file by largely simplifying flat areas and preserving the geometric feature as well

Robust Position Control of PMSM Using Fractional-Order Sliding Mode Controller

A new robust fractional-order sliding mode controller (FOSMC) is proposed for the position control of a permanent magnet synchronous motor (PMSM). The sliding mode controller (SMC), which is insensitive to uncertainties and load disturbances, is studied widely in the application of PMSM drive. In the existing SMC method, the sliding surface is usually designed based on the integer-order integration or differentiation of the state variables, while in this proposed robust FOSMC algorithm, the sliding surface is designed based on the fractional-order calculus of the state variables. In fact, the conventional SMC method can be seen as a special case of the proposed FOSMC method. The performance and robustness of the proposed method are analyzed and tested for nonlinear load torque disturbances, and simulation results show that the proposed algorithm is more robust and effective than the conventional SMC method

Pollen tube emergence is mediated by ovary-expressed ALCATRAZ in cucumber

Pollen tube guidance within female tissues of flowering plants can be divided into preovular guidance, ovular guidance and a connecting stage called pollen tube emergence. As yet, no female factor has been identified to positively regulate this transition process. In this study, we show that an ovary-expressed bHLH transcription factor Cucumis sativus ALCATRAZ (CsALC) functions in pollen tube emergence in cucumber. CsALC knockout mutants showed diminished pollen tube emergence, extremely reduced entry into ovules, and a 95% reduction in female fertility. Further examination showed two rapid alkalinization factors CsRALF4 and CsRALF19 were less expressed in Csalc ovaries compared to WT. Besides the loss of male fertility derived from precocious pollen tube rupture as in Arabidopsis, Csralf4 Csralf19 double mutants exhibited a 60% decrease in female fertility due to reduced pollen tube distribution and decreased ovule targeting efficiency. In brief, CsALC regulates female fertility and promotes CsRALF4/19 expression in the ovary during pollen tube guidance in cucumber. Pollen tube growth is guided towards ovules. Here the authors show that a bHLH transcriptional factor CsALC functions in pollen tube emergence towards ovules to regulate female fertility in cucumber and promotes the expression of two rapid alkalinization factors CsRALF4/19 in the ovary

Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

BACKGROUND: Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. METHODOLOGY/PRINCIPAL FINDINGS: Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. CONCLUSIONS/SIGNIFICANCE: The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice

The impacts of new energy vehicles on fleet average oil consumption of passenger vehicles in China

Using authoritative data and calculation formulas, this paper reveals exactly the role of new energy vehicles (NEV) in the decrease of fleet average oil consumption (FAOC) of passenger vehicles in China. NEV is the reason for the gap among the accounted FAOC, the real FAOC of passenger vehicles and the FAOC of conventional energy vehicles (CEV). Specifically, the NEV multipliers result in the difference between the accounted FAOC and the real FAOC of passenger vehicles, while the low oil consumption and output of NEV result in the difference between the real FAOC of passenger vehicles and the FAOC of CEV. NEV is accelerating the reduction of the FAOC of passenger vehicles in China