C-Shaped Cartilage Development Using Wharton's Jelly-Derived Hydrogels to Assemble a Highly Biomimetic Neotrachea for use in Circumferential Tracheal Reconstruction

A highly biomimetic neotrachea with C-shaped cartilage rings has promising clinical applications in the treatment of circumferential tracheal defects (CTDs) owing to its structure and physiological function. However, to date, most fabricated tracheal cartilages are O-shaped. In this study, finite element analysis demonstrates C-shaped cartilage rings that exhibit better compliance than O-shaped. Hydrogel is developed using methacryloyl-modified decellularized Wharton's jelly matrix (DWJMA) for the regeneration of C-shaped cartilage rings. This novel hydrogel possesses adjustable physicochemical properties and favorable cytocompatibility. When loaded with chondrocytes, DWJMA hydrogels support the optimal cartilage regeneration both in vitro and in vivo. More importantly, a highly biomimetic neotrachea simultaneously simulating the structural and physiological properties of the normal trachea is regenerated via modular assembly of several individual C-shaped cartilage rings. The results demonstrate the highly biomimetic neotrachea have better patency (88.6 ± 6.1% vs 74.4 ± 9.4%, p < 0.05), improve the survival rate, alleviate weight loss and mucoid impaction, than its O-shaped counterpart when used for the treatment of CTDs in a rabbit model. Therefore, this study proposes a novel hydrogel for the regeneration of C-shaped cartilage and provides new insights into the treatment of CTDs using a highly biomimetic neotrachea with C-shaped cartilage rings.     

原岩应力的边界元位移反分析确定方法

围岩位移量在岩石地下工程中比较容易测得，利用数值计算方法通过反分析的手段可以由位移反算出原岩应力，并通过边界元法阐述了原岩应力位移反分析法的原理。     

二氧化钒的相变调控特性及可重构超表面天线应用研究

对二氧化钒(VO₂)薄膜的相变调控特性及方法进行了理论分析和实验探索。采用电激励方式研究了不同电极对相变程度的影响,发现电极尺寸和形状与绝缘状态下电阻的变化密切相关,从而影响相变程度。此外,相变程度也受不同调控方式的影响,可以用焦耳热原理解释。进一步将VO₂薄膜与超表面结构相结合,设计实现了大范围频率可重构的超表面天线。相比传统开关,VO₂薄膜在毫米波频段具有隔离度高、损耗低的优势,可为进一步研制高性能可调器件和天线提供重要的技术指导。     

昆钢大红山铁中矿再选实验研究

昆钢大红山铁中矿含铁品位50.68％,有用矿物主要成分为赤铁矿、磁铁矿,脉石矿物主要为绿泥石、石英,且矿物中微细粒级矿物占比相对较大,品位较高,-74μm粒级占总重量的89.05％,铁分布率为92.77％,-38 μm占到总量的51.07％,平均铁品位达到54.58％.针对昆钢大红山铁中矿的性质,采用弧面铺展流膜分选机...     

关于用户兴趣度的判定树算法的优化

判定树学习有很多算法,着重研究对引入用户兴趣度参数的ID3算法在面对多值属性时的快速分类的优化,提高计算速度,节约计算时间。     

醋酸纳米纤维夹心材料的制备及截滤性能研究

利用静电纺丝技术,在熔喷非织造布上分别沉积1、3、5、7、9min质量分数为10%的醋酸(CA)纳米纤维膜,并以纳米纤维膜为芯、制备熔喷非织造物为表里层的夹心材料。采用扫描电镜对CA纳米纤维膜及夹心材料进行形貌表征,测试孔径大小及分布,并对透气性和滤菌等截滤性能进行测试。结果表明:在实验范围内,纳米纤维连续均匀,直径分布在250～300nm之间;夹心材料孔径变小、孔隙分布均匀,孔径由10μm下降到3.5μm以下;纺丝时间对夹心材料透气性及滤菌性能有显著影响,但纺丝时间在1～5min范围内,对透气率影响较少,而滤菌效果明显改善;在纺丝时间为5min时,夹心材料透气率仅下降38mm/s,细菌过滤效率由27%左右提升至80%以上,可应用于医用口罩及其他防护产品。     

离子轰击模拟中子辐照在核材料研究中的应用

简要综述了离子轰击模拟中子辐照对材料，尤其是对堆用Ｚｒ合金的结构和性能影响的相关性，方法特点和研究。讨论了离子品种，能量，辐照剂量，束流密度和温度等实验参量的选择及其对材料组织，结构，力学和耐腐蚀性能等的影响。离子轰击模拟堆内中子辐照研究核材料是１种具有成熟理论体系，高效，安全和经济的好方法。     

在挠性印制板中埋嵌无源和有源元件

文章介绍了在挠性印制板中埋嵌无源和有源元件的方法。通过化学镀Ni(P)电阻可以实现薄膜电阻的埋嵌,并能保证一定的弯折性。通过对芯片进行背面减薄和倒装连接,可以实现有源芯片的埋嵌。随后还对埋嵌元件的可靠性进行了测试。     

K-doped CuO/ZnO with dual active centers of synergism for highly efficient dimethyl carbonate synthesis

The application of heterogeneous catalysts in dimethyl carbonate (DMC) synthesis from methanol is hindered by low activation efficiency of methanol to methoxy intermediates (CH₃O*), which is the key intermediate for DMC generation. Herein, a catalyst of alkali metal K anchored on the CuO/ZnO oxide is rationally designed for offering Lewis acid–base pairs as dual active centers to improve the activation efficiency of methanol. Characterizations of CO₂-TPD, NH₃-TPD, XPS, and DRIFTS revealed that the addition of Lewis base K observably boosted the dissociation of methanol and combined with Lewis acid CuO/ZnO oxide to adsorb the formed CH₃O* stably, thus synergistically promoted the transesterification. Finally, the CuO/ZnO-9%K₂O catalyst exhibited the optimal catalytic activity, achieving a high yield of 74.4% with an excellent selectivity of 98.9% for DMC at a low temperature of 90°C. The strategy of constructing Lewis acid–base pairs provides a reference for the design of heterogeneous catalysts.     

基于主成分分析和循环神经网络的入侵检测模型

针对网络数据特征维度高、现有的入侵检测方法准确率低的问题,该文提出了一种基于主成分分析(PCA)和循环神经网络(RNN)的入侵检测方法PCA-RNN。该方法先对网络数据进行预处理,通过主成分分析法对数据进行特征降维和降噪,找出含有最大信息的主成分特征子集,然后对处理后的数据使用循环神经网络进行分类训练。实验使用基于Python的TensorFlow平台,并采用NSL-KDD作为实验数据集。实验结果表明,与常用的基于机器学习和深度学习方法的入侵检测技术相比较,该文提出的入侵检测方法可有效地提高检测的准确性。