Quantile regression methods for recursive structural equation models

Two classes of quantile regression estimation methods for the recursive structural equation models of Chesher (2003) are investigated. A class of weighted average derivative estimators based directly on the identification strategy of Chesher is contrasted with a new control variate estimation method. The latter imposes stronger restrictions achieving an asymptotic efficiency bound with respect to the former class. An application of the methods to the study of the effect of class size on the performance of Dutch primary school students shows that (i.) reductions in class size are beneficial for good students in language and for weaker students in mathematics, (ii) larger classes appear benecial for weaker language students, and (iii.) the impact of class size on both mean and median performance is negligible.

JujubeNet: A high-precision lightweight jujube surface defect classification network with an attention mechanism

Surface Defect Detection (SDD) is a significant research content in Industry 4.0 field. In the real complex industrial environment, SDD is often faced with many challenges, such as small difference between defect imaging and background, low contrast, large variation of defect scale and diverse types, and large amount of noise in defect images. Jujubes are naturally growing plants, and the appearance of the same type of surface defect can vary greatly, so it is more difficult than industrial products produced according to the prescribed process. In this paper, a ConvNeXt-based high-precision lightweight classification network JujubeNet is presented to address the practical needs of Jujube Surface Defect (JSD) classification. In the proposed method, a Multi-branching module using Depthwise separable Convolution (MDC) is designed to extract more feature information through multi-branching and substantially reduces the number of parameters in the model by using depthwise separable convolutions. What’s more, in our proposed method, the Convolutional Block Attention Module (CBAM) is introduced to make the model concentrate on different classes of JSD features. The proposed JujubeNet is compared with other mainstream networks in the actual production environment. The experimental results show that the proposed JujubeNet can achieve 99.1% classification accuracy, which is significantly better than the current mainstream classification models. The FLOPS and parameters are only 30.7% and 30.6% of ConvNeXt-Tiny respectively, indicating that the model can quickly and effectively classify JSD and is of great practical value

Chinese expert consensus on minimally invasive interventional treatment of trigeminal neuralgia

Background and purposeTrigeminal neuralgia is a common condition that is associated with severe pain, which seriously affects the quality of life of patients. When the efficacy of drugs is not satisfactory or adverse drug reactions cannot be tolerated, minimally invasive interventional therapy has become an important treatment because of its simple operation, low risk, high repeatability and low cost. In recent years, minimally invasive interventional treatments, such as radiofrequency thermocoagulation (RF) of the trigeminal nerve and percutaneous microcompression (PMC), have been widely used in the clinic to relieve severe pain in many patients, however, some related problems remain to be addressed. The Pain Association of the Chinese Medical Association organizes and compiles the consensus of Chinese experts to standardize the development of minimally invasive interventional treatment of trigeminal neuralgia to provide a basis for its clinical promotion and application.Materials and methodsThe Pain Association of the Chinese Medical Association organizes the Chinese experts to compile a consensus. With reference to the evidence-based medicine (OCEBM) system and the actual situation of the profession, the Consensus Development Committee adopts the nominal group method to adjust the recommended level.ResultsPrecise imaging positioning and guidance are the keys to ensuring the efficacy and safety of the procedures. RF and PMC are the most widely performed and effective treatments among minimally invasive interventional treatments for trigeminal neuralgia.ConclusionsThe pain degree of trigeminal neuralgia is severe, and a variety of minimally invasive intervention methods can effectively improve symptoms. Radiofrequency and percutaneous microcompression may be the first choice for minimally invasive interventional therapy