一种基于功率谱重心的自适应特征信息提取方法

提出一种基于功率谱重心的自适应特征信息提取方法.该方法通过对不同状态转换过程中的灵敏度分布进行分析获得特征功率谱,然后计算特征功率谱的重心频率以及自适应加权系数,对特征功率谱进行自适应加权计算获得特征信息.文中给出了将自适应特征信息提取方法应用于磨煤机噪声信号特征提取的实例,对比分析了采用不同特征信息提取方法的效果.仿真结果表明,所提出的自适应特征信息提取方法能够准确剔除无效信息,自主适应运行工况变化,具有较好的灵敏度和线性度,为提高软测量模型的预测精度和泛化性能提供了保证.     

Spin dynamics and implications for superconductivity: some problems with thed-wave scenario

We review the spin dynamics of the normal state of the cuprates with special emphasis on neutron data in both the YBa₂Cu₃O_7– and La_2–x Sr _x CuO₄ systems. When realistic models of the Fermi surface shapes are incorporated, along with a moderate degree of spin fluctuations, we find good semiquantitative agreement with experiment for both cuprates. Building on the success of this Fermi-liquid-based scheme, we explore the implications ford-wave pairing from a number of vantage points. We conclude that our present experimental and theoretical understanding is inadequate to confirm or refute thed-wave scenario.     

Engineering Transition Metal Layers for Long Lasting Anionic Redox in Layered Sodium Manganese Oxide

Oxygen-redox-based-layered cathode materials are of great importance in realizing high-energy-density sodium-ion batteries (SIBs) that can satisfy the demands of next-generation energy storage technologies. However, Mn-based-layered materials (P2-type Na-poor Na_y[A_xMn_1−x]O₂, where A = alkali ions) still suffer from poor reversibility during oxygen-redox reactions and low conductivity. In this work, the dual Li and Co replacement is investigated in P2-type-layered Na_xMnO₂. Experimentally and theoretically, it is demonstrated that the efficacy of the dual Li and Co replacement in Na_0.6[Li_0.15Co_0.15Mn_0.7]O₂ is that it improves the structural and cycling stability despite the reversible Li migration from the transition metal layer during de-/sodiation. Operando X-ray diffraction and ex situ neutron diffraction analysis prove that the material maintains a P2-type structure during the entire range of Na⁺ extraction and insertion with a small volume change of ≈4.3%. In Na_0.6[Li_0.15Co_0.15Mn_0.7]O₂, the reversible electrochemical activity of Co³⁺/Co⁴⁺, Mn³⁺/Mn⁴⁺, and O^2-/(O₂)^n- redox is identified as a reliable mechanism for the remarkable stable electrochemical performance. From a broader perspective, this study highlights a possible design roadmap for developing cathode materials with optimized cationic and anionic activities and excellent structural stabilities for SIBs.     

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.     

Ka频段卫通相控阵封装天线设计与实现

针对卫星通信对终端天线低剖面、低功耗、极化可变及发射控制等需求,采用平板相控阵架构、极化可变阵列、稀疏布阵等技术,研制了一台基于单板集成封装技术的Ka频段卫星通信相控阵天线。该天线满足高轨同步卫星宽带应用需求和适航符合性验证试验DO-160G要求,经长时间跑车和飞行试验,其性能优良,可靠性高。     

Effect of Sintering Temperature on Magnetic and Electrical Properties of GdBaCo<Subscript>2</Subscript><Emphasis Type="Italic">O</Emphasis><Subscript>5 + <Emphasis Type="Italic">δ</Emphasis></Subscript>

In this study, we report the results of an investigation into the sintering temperature dependence of magnetic and transport properties for GdBaCo₂ O _{5 + δ} synthesized through a sol-gel method. The lowering of sintering temperature leads to the increase of oxygen content and the reduction of grain size. The increase of oxygen content results in the enhancement of magnetic interactions and the weakening of Coulomb repulsion effect, while the reduction of grain size improves the magnetoresistance effect. Metal-insulator transition accompanied with spin-state transition is observed in all samples.     

Redox and pH Dual Responsive Polymer Based Nanoparticles for In Vivo Drug Delivery

Responsive nanomaterials have emerged as promising candidates as drug delivery vehicles in order to address biomedical diseases such as cancer. In this work, polymer‐based responsive nanoparticles prepared by a supramolecular approach are loaded with doxorubicin (DOX) for the cancer therapy. The nanoparticles contain disulfide bonds within the polymer network, allowing the release of the DOX payload in a reducing environment within the endoplasm of cancer cells. In addition, the loaded drug can also be released under acidic environment. In vitro anticancer studies using redox and pH dual responsive nanoparticles show excellent performance in inducing cell death and apoptosis. Zebrafish larvae treated with DOX‐loaded nanoparticles exhibit an improved viability as compared with the cases treated with free DOX by the end of a 3 d treatment. Confocal imaging is utilized to provide the daily assessment of tumor size on zebrafish larva models treated with DOX‐loaded nanoparticles, presenting sustainable reduction of tumor. This work demonstrates the development of functional nanoparticles with dual responsive properties for both in vitro and in vivo drug delivery in the cancer therapy.     

A Metal–Polyphenol Network Coated Nanotheranostic System for Metastatic Tumor Treatments

As a characteristic trait of most tumor types, metastasis is the major cause of the death of patients. In this study, a photothermal agent based on gold nanorod is coated with metal (Gd³⁺)‐organic (polyphenol) network to realize combination therapy for metastatic tumors. This nanotheranostic system significantly enhances antitumor therapeutic effects in vitro and in vivo with the combination of photothermal therapy (PTT) and chemotherapy, also can remarkably prevent the invasion and metastasis due to the presence of polyphenol. After the treatment, an 81% decrease in primary tumor volumes and a 58% decrease in lung metastasis are observed. In addition, the good performance in magnetic resonance imaging, computerized tomography, and photothermal imaging of the nanotheranostic system can realize image‐guided therapy. The multifunctional nanotheranostic system will find a great potential in diagnosis and treatment integration in tumor treatments, and broaden the applications of PTT treatment.     

Walnut-inspired microsized porous silicon/graphene core–shell composites for high-performance lithium-ion battery anodes

Silicon is considered an exceptionally promising alternative to the most commonly used material, graphite, as an anode for next-generation lithium-ion batteries, as it has high energy density owing to its high theoretical capacity and abundant storage. Here, microsized walnut-like porous silicon/reduced graphene oxide (P-Si/rGO) core–shell composites are successfully prepared via in situ reduction followed by a dealloying process. The composites show specific capacities of more than 2,100 mAh·g^?1 at a current density of 1,000 mA·g^?1, 1,600 mAh·g^?1 at 2,000 mA·g^?1, 1,500 mAh·g^?1 at 3,000 mA·g^?1, 1,200 mAh·g^?1 at 4,000 mA·g^?1, and 950 mAh·g^?1 at 5,000 mA·g^?1, and maintain a value of 1,258 mAh·g^?1 after 300 cycles at a current density of 1,000 mA·g^?1. Their excellent rate performance and cycling stability can be attributed to the unique structural design: 1) The graphene shell dramatically improves the conductivity and stabilizes the solid–electrolyte interface layers; 2) the inner porous structure supplies sufficient space for silicon expansion; 3) the nanostructure of silicon can prevent the pulverization resulting from volume expansion stress. Notably, this in situ reduction method can be applied as a universal formula to coat graphene on almost all types of metals and alloys of various sizes, shapes, and compositions without adding any reagents to afford energy storage materials, graphene-based catalytic materials, graphene-enhanced composites, etc.

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