Emergence of photoluminescence on bulk MoS<Subscript>2</Subscript> by laser thinning and gold particle decoration

We demonstrate a facile and effective approach to significantly improve the photoluminescence of bulk MoS₂ via laser thinning followed by gold particle decoration. Upon laser thinning of exfoliated bulk MoS₂, photoluminescence emerges from the laser-thinned region. After further treatment with an AuCl₃ solution, gold particles self-assemble on the laser-thinned region and thick edges, further increasing the fluorescence of bulk MoS₂ 28 times and the Raman response 3 times. Such fluorescence enhancement can be attributed to both surface plasmon resonance and p-type doping induced by gold particles. The combination of laser thinning and AuCl₃ treatment enables the functionalization of bulk MoS₂ for optoelectronic applications. It can also provide a viable strategy for mask-free and area-selective p-type doping on single MoS₂ flakes.

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Evaluation of hydrogels for soft tissue adhesives <Emphasis Type="Italic">in vitro</Emphasis> and <Emphasis Type="Italic">in vivo</Emphasis> analyses

In this study, natural materials (sodium alginate, dextran, gelatin and carboxymethyl chitosan) were modified to get aldehyde components and amino components. Upon mixing the two-component solutions together, four kinds of Schiff base hydrogels formed successfully within 5-300 s and could seal the wound tissue. The cytotoxicity tests of hydrogel extraction solution confirmed that the hydrogels are nontoxic materials. The adhesive ability was evaluated in vivo by measuring the adhesive strength after sealing the skin incisions on the back of rats. All the hydrogels showed higher adhesive strength than that of commercial fibrin glue and the blank control. The histological staining observation by hematoxylin and eosin staining (HE) and Masson’s trichrome staining (MTC) methods suggested that the hydrogels had good biocompatibility and biodegradation in vivo. They have only normal initial inflammation to skin tissue and could improve the formation of new collagen in the incision section. So, the prepared hydrogels were both safe and effective tissue adhesive, which had the great potentials to be used as skin tissue adhesive.     

Electrosprayed porous Fe3O4/carbon microspheres as anode materials for high-performance lithium-ion batteries

Nano Research - Porous Fe3O4/carbon microspheres (PFCMs) were successfully fabricated via a facile electrospray method and subsequent heat treatment, using ferrous acetylacetonate, carbon nanotubes...     

雷达侦察系统参数测量的建模与仿真

在雷达侦察系统工作过程中,对表征敌方雷达信号特征的参数进行测量是后续进行信号分选和辐射源识别的前提条件;首先介绍了相位差测频和相位干涉仪测向的基本原理,然后对常用于表征雷达信号特征的参数的测量过程建立了数学模型,最后对建立的数学模型进行了仿真验证;仿真的结果表明,建立的数学模型能够有效地模拟雷达侦察系统对雷达信号参数的测量。     

某型导弹发射装置平衡机的改装设计

根据某试飞器发射装置改装方案,在对平衡机工作原理研究的基础上对某型导弹发射装置平衡机进行了改装设计;根据平衡机的设计原则,分析了平衡机的工作原理,计算了带弹和不带弹定向器的重量力矩,由此进行了平衡机的弹簧结构参数和平衡性能参数计算;通过对平衡力矩曲线和高低机负载曲线进行分析讨论,文章设计的方案中,平衡机的平衡力矩曲线与理想情况相符,切不平衡力矩曲线变化较为平缓,设计结果符合方案要求,且改装工作量小。     

海底沉积物岩芯横向声学测量系统的标定

水平轴向测量技术相对成熟,但较难获取海底沉积物的无扰动分层声学特性,采用横向测量技术可以解决这一问题。文章研究了海底沉积物岩芯横向测量技术,将水作为海底沉积物测量的参考标准,进行测量系统的标定。提出了等效换能器的概念,将样品以外的声传播介质整合成等效换能器的一部分,标定测量等效换能器声延时和声传播初始能量。以水为标准介质,分别采用33、80、100 kHz三种频率的声学换能器,以及75、90、110 mm三种管径的聚氯乙烯(Polyvinyl Chloride,PVC),测量所得的水中的声速与标准计算水中的声速具有一致性,其中以采用80 kHz频率声学换能器和90 mm管径PVC管测量时的声速误差最小,选用此状态进行标定,得出当隔水耦合测量法换能器相对距离为142.02 mm时,标定的等效换能器声延时为45.88±0.08 μs,等效换能器声传播初始能量(以接收换能器的接收电压表示)为0.86±0.03 V。应用标定的等效换能器声延时和声传播初始能量测量海底沉积物。计算得到声速和声衰减系数符合黏土质粉砂的声学特性。     

基于机器视觉的某种弹弹体硬度检测技术研究

目的 解决某种弹弹体硬度检测过程中人工读取硬度圆直径存在的问题。方法 应用材料的布氏硬度检测原理,设计基于机器视觉的某种弹弹体硬度检测装置,并制定某种弹弹体硬度的视觉检测算法。根据所提出的算法应用Maltlab实现对某种弹弹体硬度圆特征的提取和直径的计算。结果 通过T检验法对所提出的机器视觉检验方法与人工检测的结果进行对比分析,其结果为2种检测结果无差异性。结论 本文所设计的机器视觉检测装置和提出的视觉检测算法的具有较高的可靠性和正确性,可以应用于某种弹弹体的硬度检测。     

叶片-轮盘榫联结构的接触分析

叶片与轮盘之间的榫联结构存在接触和摩擦组合运动,在较高的热-机械载荷作用下容易发生微动磨损并导致疲劳破坏.本文采用有限元法对叶片-轮盘榫联结构进行接触分析,计算不同摩擦系数和不同转速情况下的叶片榫头和轮盘榫槽之间的接触压力、接触滑动距离.结果表明,摩擦系数增大,榫联结构接触面上的接触压力和滑动距离减小;转速增加,则接触压力和滑动距离增大.     

MHSACAE-CNN在噪声下的电机轴承故障诊断

电机的运行情况复杂,实际运行工况下会有大量的噪声,导致其轴承故障诊断精度下降。为了改善这一问题,提出了一种基于多头自注意力机制的一维全卷积自编码网络（One-dimensional Fully Convolutional Autoencoding Network Basedon Multi-head Self-attention,MHSACAE）与卷积神经网络（Convolutional Neural Network,CNN）结合的轴承故障诊断方法。该方法先采用MHSACAE网络进行降噪,再通过CNN进行故障诊断。其中MHSACAE去噪网络采用无监督训练的方式,充分考虑了实际工况和序列数据内在联系,在实现对噪声的滤除效果的同时,最大限度地保留下了原始的故障信息,使得CNN可以实现在噪声情况下对电机轴承故障的高精度诊断。通过与其他轴承故障诊断方法在噪声情况下进行对比,证明提出的方法具有更好的效果。     

XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous detection model that dynamically trains small subsets to these issues. First, this research introduces a deep neural network (DNN)-based GANomaly for semi-supervised learning. Second, this paper presents the proposed adaptive algorithm for the DNN-based GANomaly, which is validated with four subsets of the adaptive dataset. Finally, this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations, reconstruction error visualization, and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage, semi-supervised learning, and adaptive learning. Compared to other single-class classification techniques, the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13% and 8% of F1 scores and 4.17% and 11.51% for accuracy, respectively. Furthermore, experiments of the proposed adaptive learning reveal mostly improved results over the initial values. An analysis and monitoring system based on the combination of the three explainable methodologies is also described. Thus, the proposed method has the potential advantages to be applied in practical industry, and future research will explore handling unbalanced real-time datasets in various scenarios.