Analysis of Multi-Element Blended Course Teaching and Learning Mode Based on Student-Centered Concept under the Perspective of “Internet+”

The integration of Internet and education has changed students’ learning environment and affected their learning behavior, which poses a greater challenge to the traditional teaching mode. Through the SWOT analysis of the “student centered” multi-element blended teaching mode in the era of “Internet + education”, it is concluded that the adaptability of learners themselves and the mismatch between teachers’ educational ideas and this teaching model delay the development of education to a certain extent. Some suggestions are put forward, such as strengthening the supervision and guidance, implementing the teaching and learning model scientifically, improving teachers’ ideology and comprehensive quality, and making full use of the characteristics of Internet opening, sharing and collaboration to construct the public service system and platform of national educational resources

Real-space sampling of terahertz waveforms with sub-nanometer spatial resolution

Terahertz scanning tunneling microscopy (THz-STM) has emerged as a potent technique for probing ultrafast nanoscale dynamics with exceptional spatiotemporal precision, whereby the acquisition of THz near-field waveforms holds paramount significance. While substantial efforts have been dedicated to retrieving the waveform utilizing the photoemission current or a molecular sensor, these methods are challenged by intensive thermal effects or complex sample preparations. In this study, we introduce a universal approach for real-time characterization of THz near-field waveforms within the tunnel junction, achieving sub-nanometer spatial resolution. Utilizing the gating mechanism intrinsic to the STM junction, coherent scanning of a gated strong THz pulse over a weak THz pulse is achieved, facilitating direct measurement of the waveform. Notably, employing a custom-built Carrier-Envelope Phase (CEP) shifter, THz-CEP has been successfully characterized in the tunnel junction. Furthermore, THz spectral imaging through point-to-point sampling of THz waveforms on a triatomic Au (111) step has been demonstrated, highlighting the sub-nanometer spatial resolution of our sampling methodology.Comment: 26 pages and 4 figures for the manuscript; 16 pages and 7 figures for the Supporting Informatio

Multi-energy X-ray linear-array detector enabled by the side-illuminated metal halide scintillator

Conventional scintillator-based X-ray imaging typically captures the full spectral of X-ray photons without distinguishing their energy. However, the absence of X-ray spectral information often results in insufficient image contrast, particularly for substances possessing similar atomic numbers and densities. In this study, we present an innovative multi-energy X-ray linear-array detector that leverages side-illuminated X-ray scintillation using emerging metal halide Cs3Cu2I5. The negligible self-absorption characteristic not only improves the scintillation output but is also beneficial for improving the energy resolution for the side-illuminated scintillation scenarios. By exploiting Beer's law, which governs the absorption of X-ray photons with different energies, the incident X-ray spectral can be reconstructed by analyzing the distribution of scintillation intensity when the scintillator is illuminated from the side. The relative error between the reconstructed and measured X-ray spectral was less than 5.63 %. Our method offers an additional energy-resolving capability for X-ray linear-array detectors commonly used in computed tomography (CT) imaging setups, surpassing the capabilities of conventional energy-integration approaches, all without requiring extra hardware components. A proof-of-concept multi-energy CT imaging system featuring eight energy channels was successfully implemented. This study presents a simple and efficient strategy for achieving multi-energy X-ray detection and CT imaging based on emerging metal halides

SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation

High-resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. Firstly, these algorithms tend to focus on local information, neglecting non-local information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a Superpixel High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in high-resolution SAR mode. Based on the concept of superpixel techniques, we initially combine non-convex and non-local total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into a Deep Unfolded Network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the Deep Unfolded Network is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results