A Bi‐Lineage Conducive Scaffold for Osteochondral Defect Regeneration

Because cartilage and bone tissues have different lineage‐specific biological properties, it is challenging to fabricate a single type of scaffold that can biologically fulfill the requirements for regeneration of these two lineages simultaneously within osteochondral defects. To overcome this challenge, a lithium‐containing mesoporous bioglass (Li‐MBG) scaffold is developed. The efficacy and mechanism of Li‐MBG for regeneration of osteochondral defects are systematically investigated. Histological and micro‐CT results show that Li‐MBG scaffolds significantly enhance the regeneration of subchondral bone and hyaline cartilage‐like tissues as compared to pure MBG scaffolds, upon implantation in rabbit osteochondral defects for 8 and 16 weeks. Further investigation demonstrates that the released Li+ ions from the Li‐MBG scaffolds may play a key role in stimulating the regeneration of osteochondral defects. The corresponding mechanistic pathways involve Li+ ions enhancing the proliferation and osteogenic differentiation of bone mesenchymal stem cells (BMSCs) through activation of the Wnt signalling pathway, as well as Li+ ions protecting chondrocytes and cartilage tissues from the inflammatory osteoarthritis (OA) environment through activation of autophagy. These findings suggest that the incorporation of Li+ ions into bioactive MBG scaffolds is a viable strategy for fabricating bi‐lineage conducive scaffolds that enhance regeneration of osteochondral defects.     

显像（示）管阴极像黑洞状缺陷形成的原因及消除对策

本文通过有关理论依据和试验数据，着重阐述了在显像（示）管测试中，阴极像中心黑洞状缺陷产生的原因以及消除这种缺陷的处理方法。通过这种处理方法处理过的显像（示）管，其整体质量水平（尤其是真空度）将会得到明显的提高。     

特征提取在硅内部微/纳米级体缺陷检测中的应用

提出了一种利用特征提取来对硅中微/纳米级体缺陷的激光散射图样进行分析,以获得缺陷大小信息的分析方法.给出了该方法中第一特征值和第二特征值的定义,指出通过所提出的第一特征值及第二特征值即可迅速地判断出缺陷的大小量级所在.这种分析方法具有分析速度快,在体缺陷小于1μm时分辨率高,且可使系统实现自动化等优点.     

Bi2O3蒸汽掺杂的Ba1—xSixTiO3半导化陶瓷的PTCR效应

施主掺杂的钛酸钡陶瓷的ＰＴＣＲ效应来源于晶界上的钡空位。在Ｂｉ２Ｏ３蒸汽两次掺杂的样品中发现了大于８个量级的ＰＴＣＲ效应。在烧成过程中的Ｂｉ２Ｏ３蒸汽掺杂时，高氧分压下可以产生更高的钡空位浓度，因而样品具有更高的ＰＴＣＲ效应。     

Plasma immersion ion implantation for SOI synthesis: SIMOX and ion-cut

We have demonstrated feasibility to form silicon-on-insulator (SOI) substrates using plasma immersion ion implantation (PIII) for both separation by implantation of oxygen and ion-cut. This high throughput technique can substantially lower the high cost of SOI substrates due to the simpler implanter design as well as ease of maintenance. For separation by plasma implantation of oxygen wafers, secondary ion mass spectrometry analysis and cross-sectional transmission electron micrographs show continuous buried oxide formation under a single-crystal silicon overlayer with sharp Si/SiO₂ interfaces after oxygen plasma implantation and high-temperature (1300°C) annealing. Ion-cut SOI wafer fabrication technique is implemented for the first time using PIII. The hydrogen plasma can be optimized so that only one ion species is dominant in concentration and there are minimal effects by other residual ions on the ion-cut process. The physical mechanism of hydrogen induced silicon surface layer cleavage has been investigated. An ideal gas law model of the microcavity internal pressure combined with a two-dimensional finite element fracture mechanics model is used to approximate the fracture driving force which is sufficient to overcome the silicon fracture resistance.     

Mechanism of defect formation in low-dose oxygen implanted silicon-on-insulator material

The defects and microstructure of low-dose (<0.7 × 10¹⁸ cm⁻²), oxygen-implanted silicon-on-insulator (SIMOX) material were investigated as a function of implant dose and annealing temperature by plan-view and cross-sectional transmission electron microscopy. The threading-dislocations in low-dose (0.2∼0.3×10¹⁸ cm⁻²), annealed SIMOX originate from unfaulting of long (∼10 μm), shallow (0.3 μm), extrinsic stacking faults generated during the ramping stage of annealing. As dose increases, the defect density is reduced and the structure of the buried oxide layer evolves dramatically. It was found that there is a dose window which gives a lower defect density and a continuous buried oxide with a reduced density of Si islands in the buried oxide.     

500kv覆冰绝缘子串的工频和操作冲击电压特性试验

为了给重冰地区的500kV输电线路的绝缘设计提供依据,在武汉水利电力学院高压实验室建立了大型人工覆冰装置,对500kV线路绝缘子进行了一系列人工覆冰和放电试验。结果表明,覆冰绝缘子串的电气强度与覆冰受污染的程度有极大关系。当覆冰融化水的电导率(γ)较大时,27片XP—21或28片XP—16的500kV绝缘子串无论工频还是操作冲击闪络电压都有很大的降低,在额定耐受试验电压下已有可能发生闪络,但当覆冰是清洁的,其γ<75μS/cm时,则仍有足够的抗电强度。试验还确定了覆冰类型、电压极性、串长等因素对放电电压的影响。得出了长串的工频耐受电压梯度低于短串的结论。这就解释了为什么冰闪事故大多发生在超高压线路而在较低电压线路中很少发生的现象。文中还对覆冰绝缘子长串的放电规律和闪络机理进行了分析,提出了新的观点。     

Characterization of stepwise flash evaporated CuIn3Se5 films

CuIn₃Se₅ thin films were grown by stepwise flash evaporation from the polycrystalline powder source. Bulk CuIn₃Se₅ was synthesized by melt-quench technique, starting from the stoichiometric mixture of constituent elements. Phase purity of the synthesized material was confirmed by powder X-ray diffraction. Structural investigations by transmission electron microscopy (TEM) show that the films grown at 370 K and above were polycrystalline in nature. Compositional analysis by Rutherford backscattering spectrometry (RBS) revealed that the films have near stoichiometric composition. Analysis of optical transmittance data yielded a band gap value of 1.26±0.02 eV.     

椭圆封头压制过程中易产生的各类缺陷分析

借鉴各制造厂的实际经验和压制工艺,对椭圆封头压制中易产生的各类缺陷进行了分析。     

Semi-supervised Software Defect Prediction Using Task-Driven Dictionary Learning

We present a semi-supervised approach for software defect prediction.The proposed method is designed to address the special problematic characteristics of software defect datasets,namely,lack of labeled samples and class-imbalanced data.To alleviate these problems,the proposed method features the following components.Being a semi-supervised approach,it exploits the wealth of unlabeled samples in software systems by evaluating the confidence probability of the predicted labels,for each unlabeled sample.And we propose to jointly optimize the classifier parameters and the dictionary by a task-driven formulation,to ensure that the learned features (sparse code) are optimal for the trained classifier.Finally,during the dictionary learning process we take the different misclassification costs into consideration to improve the prediction performance.Experimental results demonstrate that our method outperforms several representative stateof-the-art defect prediction methods.