毫米波三维异质异构集成技术的进展与应用

三维异质异构集成技术是实现电子信息系统向着微型化、高效能、高整合、低功耗及低成本方向发展的最重要方法,也是决定信息化平台中微电子和微纳系统领域未来发展的一项核心高技术。文章详细介绍了毫米波频段三维异质异构集成技术的优势、近年来的发展趋势以及面临的挑战。利用硅基MEMS 光敏复合薄膜多层布线工艺可实现异质芯片的低损耗互连,同时三维集成高性能封装滤波器、高辐射效率封装天线等无源元件,还能很好地处理布线间的电磁兼容和芯片间的屏蔽问题。最后介绍了一款新型毫米波三维异质异构集成雷达及其在远距离生命体征探测方面的应用。     

Piezoelectric properties and microstructure of ceramicrete-based piezoelectric composites

Inorganic piezoelectric ceramic composite is the potential sensing element for long-term structural health monitoring due to its excellent durability and compatibility. In this study, a Ceramicrete-based piezoelectric composite is proposed preliminarily, in which the magnesium potassium phosphate cement is used as the matrix and the lead zirconate titanate particle is utilized as the functional phase. Piezoelectric properties test and microstructure analysis are performed to evaluate the testing samples. Results show that the piezoelectric performance of the composite increase with the increase of piezoelectric ceramic particle size. The value of the piezoelectric strain factor (d₃₃) can reach 83.8 pC/N, while the corresponding piezoelectric voltage factor (g₃₃) is 50.1 × 10^-3 V•m/N at the 50th day after polarization. Microstructure analysis illustrates that the interfacial transition zone (ITZ) between the matrix and the particles is dense. Moreover, the influence of aging on the composite is attributed to the continuous hydration after polarization. It indicates that the composites have a higher piezoelectric performance, which can be regarded as a promising sensing element material.     

Deacetylated Cellulose Acetate/Polyurethane Composite Nanofiber Membranes with Highly Efficient Oil/Water Separation and Excellent Mechanical Properties

Nowadays, oil pollution has become more serious, which causes great threats both to the ecological environment and human life. In this study, a novel type of multifunctional deacetylated cellulose acetate/polyurethane (d-M_CA:M_TPU) composite nanofiber membranes for oil/water separation are successfully fabricated by electrospinning, which show super-amphiphilicity in air, super-hydrophilicity in oil, and oleophobicity in water. All the d-M_CA:M_TPU composite nanofiber membranes with different mass ratios can be used as water-removing, oil-removing, and emulsion separation substance only by gravity driving force. The highest separation flux for water and oil reaches up to 37 000 and 74 000 L m⁻² h⁻¹, respectively, and all the separation efficiencies are more than 99%. They have outstanding comprehensive mechanics performance, which can be controlled by simply adjusting the mass ratios. They show excellent antifouling and self-cleaning ability, endowing powerful cyclic stability and reusability. Those results show that d-M_CA:M_TPU composite nanofiber membranes have great application prospects in oil/water separation.     

In Silico Analysis of the Molecular-Level Impact of SMPD1 Variants on Niemann-Pick Disease Severity

Sphingomyelin phosphodiesterase (SMPD1) is a key enzyme in the sphingolipid metabolism. Genetic SMPD1 variants have been related to the Niemann-Pick lysosomal storage disorder, which has different degrees of phenotypic severity ranging from severe symptomatology involving the central nervous system (type A) to milder ones (type B). They have also been linked to neurodegenerative disorders such as Parkinson and Alzheimer. In this paper, we leveraged structural, evolutionary and stability information on SMPD1 to predict and analyze the impact of variants at the molecular level. We developed the SMPD1-ZooM algorithm, which is able to predict with good accuracy whether variants cause Niemann-Pick disease and its phenotypic severity; the predictor is freely available for download. We performed a large-scale analysis of all possible SMPD1 variants, which led us to identify protein regions that are either robust or fragile with respect to amino acid variations, and show the importance of aromatic-involving interactions in SMPD1 function and stability. Our study also revealed a good correlation between SMPD1-ZooM scores and in vitro loss of SMPD1 activity. The understanding of the molecular effects of SMPD1 variants is of crucial importance to improve genetic screening of SMPD1-related disorders and to develop personalized treatments that restore SMPD1 functionality.     

Enhanced electromagnetic microwave absorption of Fe/C/SiCN composite ceramics targeting in integrated structure and function

The Fe/C/SiCN composite ceramics were synthesized by polymer-derived method to obtain the integration of structure and functions. The electromagnetic waves (EMW) absorption properties at X and Ku bands were investigated. The addition of nano-sized Fe particles improved the magnetic loss and impedance matching, and the carbon nanotubes generated by the iron in-situ catalysis increased the internal relaxation polarization and interfacial polarization, which together improved the EMW absorption properties significantly. In particular, the Fe/C/SiCN-9 showed the optimum reflection loss (RL) of ?31.06 dB at 10.03 GHz with an effective absorption bandwidth (EAB, RL < ?10 dB) of 3.03 GHz at 2.51 mm, indicating the excellent EMW absorption properties of Fe/C/SiCN composite ceramics.     

总变差模型在书画打印宣纸印刷质量预测中的应用

为了建立适用于书画打印宣纸印刷质量的预测模型,本研究测量了14种书画打印宣纸的粗糙度、白度、不透明度、定量、光泽度和针对宣纸特别设定的帘纹深浅以及帘纹疏密度等表面物理参量,并在相同条件下,使用喷墨打印设备输出并测量印品色度值,利用总变差模型构建去除帘纹色差的测定方法,得到与人眼视觉特征相符的色差。运用GRNN广义回归神经网络结合书画打印宣纸表面物理参量与宣纸去帘纹后的色差值,建立预测模型。结果表明,该模型能够在仅测量书画打印宣纸表面物理参量的情况下,便能较为准确地预测书画打印宣纸印刷质量,为书画打印宣纸印刷前的选纸工作提供指导依据。     

A PV installation framework concerning electricity variable rates

This paper assesses building integrated photovoltaic (BIPV) installation parameters based on the profit generated by a photovoltaic system. It takes into consideration a home building case study and it investigates its monthly energy demand based on a specific location and a typical occupancy. The capability of a photovoltaic (PV) system to generate more profit occurs when solar intensity is maximum while the electric energy price is at its highest rate. The paper traces a framework that encompasses different aspects such as energy demand, energy price, and solar intensity. This framework identifies profit alternatives according to different installation parameters. A tool that predicts a PV installation hourly electric energy production is developed. The profit generated is simulated for home buildings located in Beirut (Lebanon) and Xihua (China), both at 33.8° latitude north. The paper highlights a new approach for BIPV installations, taking into account weather conditions, energy demand, and electric energy utility rates.     

Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: A machine learning approach

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.     

基于启发式BP算法的软件缺陷预测模型

针对软件缺陷预测时缺陷数据集中存在的类别分布不平衡问题，结合上采样算法SMOTE与Edited Nearest Neighbor (ENN) 数据清洗策略，提出了一种基于启发式BP神经网络算法的软件缺陷预测模型。模型中采用上采样算法SMOTE增加少数类样本以改善项目中的数据不平衡状况，并针对采样后数据噪声问题进行ENN数据清洗，结合基于启发式学习的模拟退火算法改进四层BP神经网络后建立分类预测模型，在AEEEM数据库上使用交叉验证对提出的方案进行性能评估，结果表明所提出的算法能够有效提高模型在预测类不平衡数据时的分类准确度。