Effectiveness of Students’ Self-Regulated Learning during the COVID-19 Pandemic

Self-regulated learning means that learners can set their own learning goals, determine content and progress, choose skills and methods, monitor the entire process, and conduct self-assessment. During the COVID-19 Pandemic, students’ self-regulated learning became the main learning method, but whether this learning method is effective remains to be tested. This study used 8th grade students from four middle schools in Changyuan County, Henan Province of China as objects, and explored the effects of self-regulated learning for students in special periods through educational experiments, and on this basis, proposed methods that would be more suitable for students self-regulated learning. A total of 2,536 students in the 8th-grade of the four middle schools, including 1,270 in the experimental group and 1,266 in the control group were selected. Through SPSS20.0, the pre- and post-test results of the two groups are analyzed and found: (i). Under a special period of background, self-regulated learning of some subjects is effective; (ii). Compared with self-regulated watching teachers live on the Internet platform Learning method, using protocol-guided learning for self-regulated learning, students learn better. Therefore, we suggest that: (i). Teachers should guide students to carry out self-regulated learning according to the characteristics of the discipline; (ii). Teachers should choose self-regulated learning materials and methods suitable for students according to their academic conditions

Neural Gradient Regularizer

Owing to its significant success, the prior imposed on gradient maps has consistently been a subject of great interest in the field of image processing. Total variation (TV), one of the most representative regularizers, is known for its ability to capture the sparsity of gradient maps. Nonetheless, TV and its variants often underestimate the gradient maps, leading to the weakening of edges and details whose gradients should not be zero in the original image. Recently, total deep variation (TDV) has been introduced, assuming the sparsity of feature maps, which provides a flexible regularization learned from large-scale datasets for a specific task. However, TDV requires retraining when the image or task changes, limiting its versatility. In this paper, we propose a neural gradient regularizer (NGR) that expresses the gradient map as the output of a neural network. Unlike existing methods, NGR does not rely on the sparsity assumption, thereby avoiding the underestimation of gradient maps. NGR is applicable to various image types and different image processing tasks, functioning in a zero-shot learning fashion, making it a versatile and plug-and-play regularizer. Extensive experimental results demonstrate the superior performance of NGR over state-of-the-art counterparts for a range of different tasks, further validating its effectiveness and versatility

Posterior Circulation Mechanical Thrombectomy through Primitive Trigeminal Artery: A Case Report

Introduction: Primitive trigeminal artery (PTA) is a rare intracranial vascular malformation, and mechanical thrombectomy and revascularization via PTA are rarely reported. Case Presentation: We reported a case of mechanical thrombectomy through PTA in a patient who presented with sudden slurred speech and had a National Institutes of Health Stroke Scale score of 12. Digital subtraction angiography of the cerebral vasculature showed PTA formation in the right internal carotid artery cavernous segment, with acute occlusion of the distal basilar artery at the PTA junction, and bilateral vertebral arteries and proximal basilar artery were underdeveloped. Therefore, we chose mechanical thrombectomy via PTA, but unfortunately, the vessel failed to recanalize. Follow-up at 1-month post-procedure indicated that the patient had passed away. We present the endovascular process and analyze and summarize the reasons for the failure to provide a reference for subsequent mechanical thrombectomy via PTA. Conclusions: PTA increases the risk of ischemic stroke and adds to the complexity of mechanical thrombectomy post-stroke. However, in certain situations, PTA can be used as a thrombectomy channel to increase the first-line possibility of timely endovascular treatment to save ischemic brain tissue

Ultra-Strong Long-Chain Polyamide Elastomers With Programmable Supramolecular Interactions and Oriented Crystalline Microstructures

Polyamides are one of the most important polymers. Long-chain aliphatic polyamides could bridge the gap between traditional polyamides and polyethylenes. Here we report an approach to preparing sustainable ultra-strong elastomers from biomass-derived long-chain polyamides by thiol-ene addition copolymerization with diamide diene monomers. The pendant polar hydroxyl and non-polar butyrate groups between amides allow controlled programming of supramolecular hydrogen bonding and facile tuning of crystallization of polymer chains. The presence of thioether groups on the main chain can further induce metal–ligand coordination (cuprous-thioether). Unidirectional step-cycle tensile deformation has been applied to these polyamides and significantly enhances tensile strength to over 210 MPa while maintaining elasticity. Uniaxial deformation leads to a rearrangement and alignment of crystalline microstructures, which is responsible for the mechanical enhancement. These chromophore-free polyamides are observed with strong luminescence ascribed to the effect of aggregation-induced emission (AIE), originating from the formation of amide clusters with restricted molecular motions

Tensor Compressive Sensing Fused Low-Rankness and Local-Smoothness

A plethora of previous studies indicates that making full use of multifarious intrinsic properties of primordial data is a valid pathway to recover original images from their degraded observations. Typically, both low-rankness and local-smoothness broadly exist in real-world tensor data such as hyperspectral images and videos. Modeling based on both properties has received a great deal of attention, whereas most studies concentrate on experimental performance, and theoretical investigations are still lacking. In this paper, we study the tensor compressive sensing problem based on the tensor correlated total variation, which is a new regularizer used to simultaneously capture both properties existing in the same dataset. The new regularizer has the outstanding advantage of not using a trade-off parameter to balance the two properties. The obtained theories provide a robust recovery guarantee, where the error bound shows that our model certainly benefits from both properties in ground-truth data adaptively. Moreover, based on the ADMM update procedure, we design an algorithm with a global convergence guarantee to solve this model. At last, we carry out experiments to apply our model to hyperspectral image and video restoration problems. The experimental results show that our method is prominently better than many other competing ones. Our code and Supplementary Material are available at https://github.com/fsliuxl/cs-tctv

Anomaly Detection for Hyperspectral Images Based on Anisotropic Spatial-Spectral Total Variation and Sparse Constraint

A novel anomaly detection method for hyperspectral images (HSIs) is proposed based on anisotropic spatial-spectral total variation and sparse constraint. HSIs are assumed to be not only smooth in spectral dimension but also piecewise smooth in spatial dimension. The proposed method adopts the anisotropic spatial-spectral total variation model which combines 2D spatial total variation and 1D spectral variation to explore the spatial-spectral smooth property of HSIs. Meanwhile, the sparse property of anomalies is exploited for its low probability in the image. To utilize both the spatial and spectral information of HSIs, we preserve the original cubic form of HSIs and divide the HSIs into three 3D arrays, each representing the background, the anomaly, and the noise respectively. By using anisotropic spatial-spectral total variation regularization on the background component and sparse constraint on the anomaly component, this anomaly detection problem has therefore been formulated as a constraint optimization problem whose solution has been derived by alternately using Split Bregman Method and Go Decomposition (GoDec) Method. Experimental results on hyperspectral datasets illustrate that our proposed method has a better detection performance than state-of-the-art hyperspectral anomaly detection methods

Discharge voltage prediction of UHV AC transmission line–tower air gaps by a machine learning model

Full-scale discharge tests of transmission line–tower air gaps are costly and time-consuming, and they cannot exhaustively simulate all the gap configurations in practical engineering. In this paper, a machine learning model established by support vector machine is introduced to predict the switching impulse discharge voltages of the ultra-high-voltage (UHV) AC transmission line–tower air gaps. The three-dimensional finite element models of a UHV cup-type tower and a UHV compact transmission line were established for electric field calculation, and some features were extracted from the hypothetic discharge channel and the shortest path between the bundled conductor and the tower. These features under a given voltage were normalised and input to the SVM model, while the output is two binary values, respectively, representing gap withstanding or breakdown. Trained by experimental data of one type of the UHV transmission line–tower gaps, the SVM model is able to predict the discharge voltages of another gap type. The mean absolute percentage errors of the two engineering gap types, under different gap distances, are 8.31 and 4.86%, respectively, which are acceptable for engineering applications. The results provide a possible way to obtain the discharge voltages of complicated engineering gaps by mathematical calculations

Static voltage sharing technology of multi-break mechanical switch for hybrid HVDC breaker

The hybrid high-voltage direct current (HVDC) breaker combines mechanical and power electronics switching that enables it to interrupt power flows within a few milliseconds. Mechanical switch is a key component of hybrid HVDC breaker and has a number of serially connected interrupter units which ideally would divide the voltage equally. The static voltage distribution characteristics and voltage sharing design of a multi-break mechanical switch were discussed in this study. A finite-element model was developed to study the static voltage distribution characteristics and capacitance parameters of multi-break mechanical switch (which actually consists of resistance and capacitance parameters under direct current) as a preliminary study. Comparisons were made under the simulation of vertical and U-shaped arrangement forms. The results indicate that the static voltage distribution of the high-voltage terminal is at least more than 65%, whereas the severe non-uniform voltage distribution can be well improved by means of the method proposed in this study

Hyperspectral Image Denoising via Nonlocal Spectral Sparse Subspace Representation

Hyperspectral image (HSI) denoising based on nonlocal subspace representation has attracted a lot of attention recently. However, most of the existing works mainly focus on refining the representation coefficient images (RCIs) using certain nonlocal denoiser but ignore the understanding why these pseudoimages have a similar spatial structure as the original HSI. In this work, we revisit such vein from the respective of principal component analysis (PCA). Inspired by an alternative sparse PCA, we propose a spectral sparse subspace representation strategy to simultaneously learn low-dimensional spectral subspace and novel RCIs with sparse loadings. It turns out that the resulting RCIs possess a more significant spatial structure due to the adaptive sparse combination of spectral bands. A simple nonlocal low-rank approximation is then employed to further remove the residual noise of the RCIs. Finally, the entire denoised HSI is obtained by inverse spectral sparse PCA. Extensive experiments on the simulated and real HSI datasets show that the proposed nonlocal spectral sparse subspace representation method, dubbed as NS3R, has excellent performance both in denoising effect and running time compared with many other state-of-the-art methods