Enhanced electrochemical properties of Al-doped bulk manganese oxides synthesized by a facile liquid-phase method

Aluminum doped MnO₂ nanoparticles were synthesized by a simple liquid-phase process using potassium permanganate as oxidation agent, glycol as reducing agent. Specific capacitance of the optimal sample electrode can reach 290 F g^?1 after 10 cycles. The electrode also exhibits excellent cycle stability, retaining 86.6 % after 1,000 cycles. The infrared absorption bands of aluminum doped manganese oxide shift to high wave number for the reason that aluminum ion has smaller nuclear charge. The doping of aluminum strengthens the Mn–O bond and decreases the aggregation degree, thus the electrochemical properties are enhanced.     

一种色彩向量的降维表示方法

提出了一种依据人眼的颜色视觉机制对真彩色显微生物图像进行分割的色彩向量的降维表示方法。该方法将由红绿蓝三色彩刺激经Karhunen Lo埁ｖｅ变换得到的Ｉ1I2 I3 色彩空间中的I1强度信息摒弃 ,将色彩特征由三维I1I2 I3 色彩空间向量降维为二维I2 I3 色彩向量。并以此I2 I3 色彩向量作为图像的色彩表示特征 ,运用反向传播网络对真彩色显微生物图像进行了分割实验。实验表明该方法应用于分割真彩色显微生物图像时 ,与采用I1I2色彩向量的降维方法相比 ,可优化色彩表示特征 ,加快网络的收敛速度 ,提高图像分割的正确率     

相对幅度和相位对相位阵列系统的影响及消除

用扩散近似的解析模型分析了幅度相消相位调制系统对组织中微小异质子实现定位的原理；详细讨论了相对幅度、相对相位、异质子的大小和吸收系数对系统对系统定位精度的影响。提出了一种与相对幅度和相位无关的定位方法并从理论上解释了这种方法的原理和可行性。     

Rare earth element doping effect on the bonding and the transport property of δ-MoN

Combining non-equilibrium Green’s function technique with density functional theory, the rare earth element doping effect on the bonding and the transport property of δ-MoN were theoretically investigated. The Mo–N bond lengths become more uneven after dopings. Some Mo–N bonds were heavily lengthened by the La- and Gd-dopings, resulting in obvious damages of their bonding. Evident covalent-like La–N and Gd–N bonds were formed in La–MoN and Gd–MoN, respectively, while the Yb atom underwent an ionic-like interaction with its neighboring N atoms in Yb–MoN. A clear drop of the conductivity was found after La- and Gd-dopings. On the contrary, the conductivity was improved upon the Yb-doping. This case was rationalized from the carrier density and the scattering of the carriers. The backscattering effect was evident at the impurities. The La- and Gd-dopings could not effectively increase the carrier density near the Fermi level, while the Yb atom could offer f-carriers to transfer from the valence band to the conduction band.     

A novel composite electrode for oxygen evolution reaction

FeOOH nanowires were prepared and characterized with X-ray diffraction spectroscopy and scanning electron microscopy. A composite electrode consisting of FeOOH and Ni foam (FeOOH/Ni-foam) was fabricated, and its catalytic performance for oxygen evolution reaction (OER) was evaluated and compared with nanowire NiCo₂O₄/Ni-foam electrode. The mass current density of OER on FeOOH/Ni-foam is around three times of that on NiCo₂O₄/Ni-foam. Ni foam played a key role for the high activity of the FeOOH/Ni-foam. A synergistic mechanism of FeOOH and Ni was proposed to account for the superior catalytic performance of the FeOOH/Ni-foam electrode. Considering the low cost, abundant resource and environment-benign property of FeOOH, the FeOOH/Ni-foam electrode would be a promising anode for OER.     

Adaptive output feedback asymptotic stabilization of nonholonomic systems with uncertainties

A global adaptive output feedback control strategy is presented for a class of nonholonomic systems in generalized chained form with drift nonlinearity and unknown virtual control parameters. The purpose is to design a nonlinear output feedback switching controller such that the closed-loop system is globally asymptotically stable. By using the input-state scaling technique and an integrator back-stepping approach, an output feedback controller is given. A filter of observer gain is introduced for state and parameter estimates. Meanwhile, in order to avoid the over-parameters, a tuning function technique is utilized. A novel switching control strategy based on the output measurement of the first subsystem rather than time is used to overcome the uncontrollability of the x₀-subsystem in the origin. The proposed controller can guarantee that all the system states globally converge to the origin, while other signals maintain bounded. The numerical simulation testifies the effectiveness.     

Stability results for stochastic delayed recurrent neural networks with discrete and distributed delays

We present new conditions for asymptotic stability and exponential stability of a class of stochastic recurrent neural networks with discrete and distributed time varying delays. Our approach is based on the method using fixed point theory, which do not resort to any Liapunov function or Liapunov functional. Our results neither require the boundedness, monotonicity and differentiability of the activation functions nor differentiability of the time varying delays. In particular, a class of neural networks without stochastic perturbations is also considered. Examples are given to illustrate our main results.     

Polarization effect in parametric amplifier

Polarization effect in parametric amplifiers is studied. Coupled equations are derived from the basic propagation equations and numerical solutions are given for both one-wavelength-pump and two-wavelengthpump systems. Several parametric amplifiers driven by pumps at one wavelength and two wavelengths are analyzed and the polarization independent parametric amplifier is proposed.     

Experimental study of high-power pulse side-pumped Nd:YAG laser

The paper reports on the characterization of a compact and simple side-pumped 0.538 J×100 Hz pulse Nd:YAG laser. A side-pumping configuration with 100 laser diode bars is used in the laser head. We also experimentally studied the laser performance of the diode-pumped Nd:YAG laser head in the free running and Q-switched operation under different repetition rates.     

A Crop Image Segmentation and Extraction Algorithm Based on Mask RCNN

The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results.