基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Enhanced thermal conductivity and stability of boron nitride/phenyl silicone rubber composites via surface modification and grain alignment

Journal of Materials Science - With the extensive use of high-power electronic appliances, polymer-based thermal insulation composites with excellent thermal properties are utilized in the field of...     

亲水作用色谱-串联质谱法测定动物源运动食品中3种氨基糖苷类抗生素的残留量

该研究建立了一种亲水交互作用色谱-串联质谱（HILIC-MS/MS）法测定动物源运动食品中潮霉素B、新霉素、安普霉素3种氨基糖苷类抗生素残留量的方法。结果表明，样品经Sielc Obelisc R柱分离，采用0.1%甲酸水溶液-乙腈梯度洗脱，可以实现3种目标物组分的分离。在此条件下，3种氨基糖苷类抗生素在5～500 ng/mL的质量浓度范围内线性关系良好，相关系数R2为0.999 5～0.999 9，检出限均为15 μg/kg，定量限均为50 μg/kg，保留时间的日间和日内相对标准偏差（RSD）分别为3.5%～7.9%和3.5%～4.1%，峰面积的日间和日内RSD分别为3.6%～7.4%和3.2%～3.9%，加标回收率为85.7%～93.6%，回收率试验结果的RSD为3.1%～5.2%。该方法可以满足动物源运动食品中3种氨基糖苷类抗生素的检测需求。     

铁磁偶合的Gd（Ⅲ）Cu（Ⅱ）配合物──组装分子基铁磁体的一种重要元件

铁磁偶合的Ｇｄ（Ⅲ）Ｃｕ（Ⅱ）配合物──组装分子基铁磁体的一种重要元件缪明明，廖代正，王耕霖（南开大学化学系天津３０００７１）摘要分子基磁体的设计是自然界向化学家挑战的一个热点课题。铁磁偶合的Ｇｄ（Ⅲ）Ｃｕ（Ⅱ）配合物是组装分子基磁体的一种重要元件。...     

辐射敏感综合症--AT病研究进展

共济失调性毛细血管扩张综合症(AT)是一种单基因突变引起的遗传性疾病，表现为高辐射敏感性、进行性神经退变、免疫缺陷、早衰及易患癌症等。本文综述了AT病的研究概况，包括AT临床症状、AT基因的定位、结构与功能、辐射对AT细胞信号传导的影响、AT基因组不稳定性及基因治疗。     

Studies on the treatment of brilliant green solution by combination microwave induced oxidation with CoFe2O4

In this work the synergistic effects of microwave (MW) irradiation induced oxidation processes and CoFe₂O₄ were studied for the degradation of brilliant green (BG) from aqueous solutions. Under the optimum condition, the ratio of solid to liquid was 1:167 (0.3 g CoFe₂O₄ mixed with 50 mL of 20 mg L⁻¹ BG solution), MW power 600 W, and the time of the irradiation 2 min. And the decolorization rate could reach up to 100%. Further investigation showed that BG was degraded by MW-induced oxidation combined with CoFe₂O₄ surface adsorption. The CoFe₂O₄ could increase the efficiencies of MW degradation and be used repeatedly. The experimental results indicated that the method of MW degradation BG in the presence of CoFe₂O₄ could reduce reaction time and increase product yield.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

职校生在线学习中的自我导向学习、注意力对学习绩效的影响 ——兼论在线学习时间的调节作用

在线学习作为一种开放、灵活、不受时空限制的学习方式在职业学校逐渐得到应用,尤其在停课不停学期间,在线学习作为主流的学习方式,保证了正常的教学活动开展.学生的在线学习绩效,是在线学习质量的重要评价因素.以职校生为研究对象,使用结构方程模型进行验证性因素分析,探索在线学习时间、自我导向学习和注意力对学生在线学习绩效的影响.研究结果表明,自我导向学习通过注意力的中介作用对学习绩效有显著影响,并且在线学习时间在其中起到调节作用.     

过氧化氢分光光度法测定湿法提钒工艺中钒

为了提高湿法提钒工艺过程控制效率,采用以过氧钒离子(VO(O₂)⁺)显色特性为基础的过氧化氢分光光度法测定钒含量,实现了样品中钒含量的现场快速测定。首先通过在试样溶液中加入适量氢氧化钠溶液,调整溶液pH值至9～10,然后加入过氧化氢将低价态钒全部氧化至钒(V),再加入硫酸调整溶液至酸性,钒(V)与过氧化氢在室温下形成稳定的红棕色过氧钒离子(VO(O₂)⁺),于450 nm处测定吸光度。钒质量浓度在2.0~400 mg/L内与吸光度值符合比尔定律。采用实验方法测定3种湿法冶金提钒液体试样(包括钒浸取溶液、钒盐沉淀后溶液以及废水等)中钒,结果的相对标准偏差(RSD,n=10)分别为0.38%、3.5%和7.6%;与高锰酸钾氧化-硫酸亚铁铵滴定法结果相一致。