个性化学习路径推荐综述

近年来，伴随着现代信息技术的迅猛发展，以人工智能为代表的新兴技术在教育领域得到了广泛应用，引发了学习理念和方式的深刻变革.在这种大背景下，在线学习超越了时空的限制，为学习者“随时随地”学习提供了更多的可能性，从而得到了蓬勃发展.然而，在线学习中师生时间、空间分离的特征，导致教师无法及时掌握学生的学习状态，一定程度上制约了在线学习中教学质量的提升.面对多元化的学习需求及海量学习资源，如何迅速完成学习目标、降低学习成本、合理分配学习资源等问题成为限制个人和时代发展的重大问题.然而，传统的“一刀切”的教育模式已经不能满足人们获取知识的需求了，需要一个更高效、更科学的个性化教育模式，以帮助学习者以最小的学习成本最大限度地完成学习目标.基于以上背景，如何自动高效识别学习者特征，高效地组织和分配学习资源，为每一位学习者规划个性化路径，成为面向个体的精准化教育资源匹配机制研究中亟待解决的问题.系统地综述并分析了当前个性化学习路径推荐的研究现状，并从多学科领域的角度分析了对于同一问题的不同研究思路，同时也归纳总结了当前研究中最为主流的核心推荐算法.最后，强调当前研究存在的主要不足之处.     

HPCL法测定左亚叶酸钠的异构体

目的:常用的亚叶酸类制剂包括亚叶酸钙、亚叶酸钠及其他们的左旋单体左亚叶酸钙、左亚叶酸钠,亚叶酸类制剂在体内真正起活性作用的部分为左亚叶酸.在制备中一般通过2种单体成盐时不同溶解度进行分离,但现有测定含量的检测方法均无法分离左旋或右旋单体,容易造成含量测定误差.结论:因此我们用HPLC法对注射用左亚叶酸钠异构体测定方法进行了研究,该法测定重复性好,结果准确可靠.     

Optimized decomposition and two-step nonlinear integration model with error correction strategy coupled interval prediction for digital currency price forecast

Digital currency price prediction is vital to both sellers and purchasers. Over these years, decomposition and integration models have been applied more and more to realize the goal of precise prediction, however, many of them tend to neglect the reconstruction of features or the residual series. Altogether, one of the biggest drawbacks of the decomposition and integration framework is the method applied requires manual parameter setting whether it is for decomposition or integration. Still, for the results, they are merely satisfied with the point prediction which brings high uncertainty. In this paper, an optimized feature reconstruction decomposition and two-step nonlinear integration method is proposed which gives consideration to feature reconstruction, nonlinear integration, optimization and interval prediction. The original data series is decomposed through improved variational mode decomposition based approximate entropy feature reconstruction system. Then, improved particle swarm optimization-gated recurrent unit (iPSO-GRU) is utilized in the first and second nonlinear integration part separately. Meanwhile, the residual series is given attention, if it is not a white noise series, the residual will be the input of iPSO-GRU whose result will be added back to the second integration result to form the point prediction result. Based on the point prediction result, interval prediction estimate will be generated as well via maximum likelihood function. This study chooses three kinds of digital currency as cases and the results show that the MAPE values of point prediction are all below 3.5%, and CP values of interval prediction are all 1 with suitable MWP. In addition, compared with other benchmark models, the proposed model shows better performance.

     

Improved thermal stability of the near-infrared Al-modulated Zn3Ga2GeO8: Cr3+ phosphors for plant growth applications

The exploration of the high thermal stability near-infrared (NIR) phosphors is significantly crucial for the development of plant lighting. However, NIR phosphors suffer from the poor chemical and thermal stability, which severely limits their long-term operation. Here, the successful improvement of luminous intensity (149.5%) and thermal stability at 423 K of Zn₃Ga₂GeO₈ (ZGGO): Cr³⁺ phosphors is achieved for the introduction of Al³⁺ ions into the host. The release of carriers in deep traps inhibits the emission loss for the thermal disturbance. Furthermore, an NIR light emitting diodes (LEDs) lamp is explored by combining the optimized Zn₃Ga_1.1675Al_0.8GeO₈: 0.0325Cr³⁺ phosphors with a commercial 460 nm blue chip, and the emission band can match well with the absorption bands of photosynthetic pigments and the phytochrome (P_R and P_FR) of plants. The explored LEDs lamp further determines the growth and the pheromone content of the involved plants for the participation of the NIR emission originated from Cr³⁺ ions. Our work provides a promising NIR lamp as plant light with improved thermal stability for long-term operation.     

Er3+-Yb3+ ions doped fluoro-aluminosilicate glass-ceramics as a temperature-sensing material

The transparent Er³⁺-Yb³⁺-doped fluoro-aluminosilicate glass-ceramic (GC) was prepared by melt-quenching. The crystal phase, morphology, and up-conversion (UC) luminescence of as-produced GC were characterized by X-ray diffraction, scanning electron microscopy, and fluorescence spectrophotometry, respectively. The results show that BaYF₅ nanocrystals were uniformly distributed in the glass matrix of the as-produced GC. When the as-produced GC was subjected to heat treatment, the crystallinity was increased, but the crystal identity remains unchanged. Such heat-treatment doubled the intensity of the UC luminescence, and this enhancement was ascribed to the increased incorporation of both Er³⁺ and Yb³⁺ ions into the lower phonon energy environment of BaYF₅ nanocrystals. Furthermore, the heat-treated GC was stable against further crystallization, and consequently its UC luminescence was stable at the application temperature. The heat-treated GC was found to possess an outstanding temperature-sensing capability.     

Micromembrane absorber with deep-permeation nanostructure assembled by flowing synthesis

A micromembrane adsorber with deep-permeation nanostructure (DPNS) has been successfully fabricated by flowing synthesis. The nanoparticles are in-situ assembled in membrane pores and immobilized in each membrane pore along the direction of membrane thickness. The nanoparticles with a lower size and thinner size distribution can be achieved owing to the confined space effect of the membrane pores. As a concept-of-proof, the nano ZIF-8 and ZIF-67 are fabricated in porous membrane pores for methyl orange (MO) and rhodamine B (RhB) adsorption. The adsorption rate is increased significantly owing to the enhanced contact and mass transfer in the confined space. The adsorption capacity for the RhB is also increased, since the size of the nanoparticles assembled in membrane pores is smaller with more active sites exposed. This micromembrane adsorber with DPNS has good reusability and can provide a promising prospect for industrial application.