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.     

基于平均飞行航迹的CCO减噪效用研究

近年来，通过优化飞行程序降低机场飞机噪声影响成为机场环境保护的重要研究方向。文章首先建立了基于飞机“噪声-功率-距离”数据的噪声计算模型，介绍了平均飞行航迹以及连续爬升运行(Continuous Climb Opera-tion, CCO)离场程序的相关理论，最后以大型国际机场为实例，使用飞机平均飞行航迹进行噪声预测，运用综合噪声模型计算出噪声影响面积并绘制噪声影响等值线图，比较了CCO离场相对常规的标准仪表离场(Standard Instru-ment Departure, SID)的降噪效果。结果表明，CCO离场程序可有效降低机场噪声影响，在高噪声级影响区域的降噪效果更佳。     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Reconstruction and hydration of hydrotalcite response to thermal activation temperature: Enhancement of properties for magnesia castables

Optimization binding system for refractory castables is significant to enhance the service performance. Hydrotalcite has been considered a promoter for high-temperature performance of basic castables, however, its binding property remains to be improved before practical application. In this work, the thermal activated Mg–Al hydrotalcites were incorporated in magnesia castables, and the mutual influence of pre-calcination temperature on the hydration, microstructure, and strength of castables was investigated. The obtained results indicated that the reconstruction of calcined hydrotalcite took place in the hydration process and effectively motivated the hydrolysis. Hydrate was thus promoted and a relatively dense microstructure of magnesia castables was confirmed by X-ray computed tomography analysis. Hydrotalcite pre-calcinated at 300 °C contributed to the highest early strength for castable, and the high-temperature properties also performed better than that of other pre-calcinated hydrotalcite-adding. The enhancement mechanisms of calcined hydrotalcite were attributed to the two following reasons: (ⅰ) the modified microstructure of magnesia castables from the early stage by hydration process, (ⅱ) the further enhanced sinterability inspired by the appropriate thermal activation effect.     

Radiation- and Photo-Induced Oxidation Pathways of Methionine in Model Peptide Backbone under Anoxic Conditions

Within the reactive oxygen species (ROS) generated by cellular metabolisms, hydroxyl radicals (HO^•) play an important role, being the most aggressive towards biomolecules. The reactions of HO^• with methionine residues (Met) in peptides and proteins have been intensively studied, but some fundamental aspects remain unsolved. In the present study we examined the biomimetic model made of Ac-Met-OMe, as the simplest model peptide backbone, and of HO^• generated by ionizing radiation in aqueous solutions under anoxic conditions. We performed the identification and quantification of transient species by pulse radiolysis and of final products by LC-MS and high-resolution MS/MS after γ-radiolysis. By parallel photochemical experiments, using 3-carboxybenzophenone (CB) triplet with the model peptide, we compared the outcomes in terms of short-lived intermediates and stable product identification. The result is a detailed mechanistic scheme of Met oxidation by HO^•, and by CB triplets allowed for assigning transient species to the pathways of products formation.     

基于数值模拟的挤压参数对AZ91镁合金管转角焊合室分流挤压焊合压力的影响规律研究

提出了一种镁合金管材转角焊合室分流挤压新工艺，该工艺可在有效延长焊合室长度和焊合时间前提下保证舌针刚度，从而保证管材尺寸精度，并且可通过转角剪切变形机制增加预焊合金属变形量和动态再结晶程度，从而有利于提高管材性能和焊缝焊合性能。利用有限元法揭示了转角焊合室分流挤压成形过程中金属的流动特征，应变分布特征和焊合室内的静水压力分布特征。结果表明，整个挤压过程无金属折叠，从而保证管材的表面质量；流经转角后预焊合金属变形量明显增加，有利于提高管材质量和焊缝质量。最后，研究揭示了坯料初始温度，挤压速度和模具转角对焊合室内静水压力的影响规律。结果表明，随着挤压速度的增加和模具转角的增大，转角焊合室内静水压力增大；随着坯料预热温度的增加，转角焊合室内静水压力呈先增大后减小的趋势。     

Non-Halogenated-Solvent Processed and Additive-Free Tandem Organic Solar Cell with Efficiency Reaching 16.67%

Organic solar cells (OSCs) have recently reached a remarkably high efficiency and become a promising technology for commercial application. However, OSCs with top efficiency are mostly processed by halogenated solvents and with additives that are not environmentally friendly, which hinders large-scale manufacture. In this study, high-performance tandem OSCs, based on polymer donors and two small-molecule acceptors with different bandgaps, are fabricated by solution processing with non-halogenated solvents without additive. Importantly, the two active layers developed from non-halogenated solvents show better phase segregation and charge transport properties, leading to superior performance than halogenated ones. As a result, a tandem OSC with high efficiency of up to 16.67% is obtained, showing unique advantages in future massive production.     

基于K-means和SOM的水下传感器数据采集方法

随着海洋资源勘探和海洋污染物监控工作的开展，水文数据的监测和采集等已经成为重要的研究方向。其中，水下无线传感器网络在水文数据采集过程中起着举足轻重的作用。本文研究的是水下无线传感器二维监测网络模型中，传感器节点数据采集的问题，其设计方法是通过自组织映射（Self-organizing mapping，SOM）对传感器节点进行路径最优化处理，结合优化的路径图形和K-means算法找到路径内部聚合点，利用聚合点和传感器的节点得到传感器通信半径内的数据采集点，最后通过SOM得到水下机器人（Autonomous underwater vehicle，AUV）到各个数据采集点采集数据的最优路径。经过实验验证，在水下1 200 m×1 750 m范围内布置52个传感器节点的情景下，数据采集点相比于传感器节点路径规划采用相同的采集顺序得到的路径优化了6.7%；对数据采集点重新进行自组织路径规划得到的路径比传感器结点路径的最优解提高了12.2%。增加传感器节点的数量，其结果也大致相同，因此采用该方法可以提高水下机器人采集数据的效率。