Enhanced photoelectric properties of PbSnSe thin films via quick oxygen ion-implantation sensitization

Journal of Materials Science - Although chalcogenide materials continue to generate considerable interest due to great potentials for various optoelectronic devices, annealing for a long time in...     

西藏燕麦主要种植区灌溉定额空间分布及影响因素

燕麦为西藏自治区典型牧草之一,由于种植区地域辽阔,灌溉试验结果受限,西藏燕麦主要种植区的灌溉定额尚不明确。本文在西藏燕麦主要种植区内选取28个典型站点进行资料收集,遵循农业气候相似原则进行区域划分,基于水量平衡法揭示了西藏燕麦主要种植区灌溉定额的空间分布特征,并根据统计学原理分析了其影响因素。研究表明:燕麦主要种植区的灌溉定额呈由西藏中部至东部呈现先递增后递减的趋势,50%水文年下的燕麦灌溉定额在56~265 mm之间变化。降雨量是影响研究区内燕麦灌溉定额的主要因素(R²为0.515),ET₀次之(R²为0.152);其它气象因素中,日照时数对研究区燕麦灌溉定额影响较大(R²为0.462),且呈正相关关系;相对湿度对燕麦灌溉定额影响较小。西藏燕麦主要种植区的灌溉定额及其空间分布可为西藏自治区灌溉用水管理提供支撑。     

基于形态特征提取的急性下壁心肌梗死BiLSTM网络辅助诊断算法

急性下壁心肌梗死是一种病发急、进展快、致死率高的心脏疾病,该文提出一种新颖的基于形态特征提取的BiLSTM神经网络分类的急性下壁心肌梗死辅助诊断算法,可大幅度提高医生对急性下壁心肌梗死疾病的诊断效率并有助于及时确诊.算法包括:对胸痛中心数据库心拍信号进行降噪及心拍分割;根据临床心内科医学诊断指南提取了12导联波形距离特征和分导联波形幅值特征;依据提取的特征搭建LSTM与BiLSTM神经网络进行心拍的分类识别;使用PTB公开数据库和胸痛中心数据库多临床中心进行交叉验证.实验结果表明,加入胸痛中心真实临床数据后,基于形态特征提取BiLSTM神经网络的急性下壁心肌梗死辅助诊断算法准确率达到99.72％,精度达到99.53％,灵敏度达到100.00％,同时F1-Score达到99.76.该算法比其他现有算法准确率提高至少1％,该项研究具有非常重要的临床应用价值.     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Optical design of the Lyman alpha based beam emission spectroscopy(LAB)diagnostic on the HL-2A tokamak

In this article, we present the optical design of a novel diagnostic on the HL-2 A tokamak, i.e. the20-channel edge Lyman-alpha beam emission spectroscopy, which is a promising solution for edge density turbulence research on tokamaks, as it offers the possibility of density fluctuation measurement with a 3.3 mm spatial resolution while maintains a high temporal resolution of1 μs. The optical path, including the reflective collection optics, the high-dispersion spectrometer, and the linear detector array, is carefully optimized to obtain a good image quality and a high throughput. The maximum root mean square radius of the collection optics is 64 μm.The detected photon flux is estimated to be about 10~(11) photons/s/channel.     

High energy storage and ultrafast discharge in NaNbO3-based lead-free dielectric capacitors via a relaxor strategy

Dielectric capacitors with decent energy storage and fast charge-discharge performances are essential in advanced pulsed power systems. In this study, novel ceramics (1-x)NaNbO₃-xBi(Ni_2/3Nb_1/3)O₃(xBNN, x = 0.05, 0.1, 0.15 and 0.20) with high energy storage capability, large power density and ultrafast discharge speed were designed and prepared. The impedance analysis proves that the introducing an appropriate amount of Bi(Ni_0·5Nb_0.5)O₃ boosts the insulation ability, thus obtaining a high breakdown strength (E_b) of 440 kV/cm in xBNN ceramics. A high energy storage density (W_total) of 4.09 J/cm³, recoverable energy storage density (W_rec) of 3.31 J/cm³, and efficiency (η) of 80.9% were attained in the 0.15BNN ceramics. Furthermore, frequency and temperature stability (fluctuations of W_rec ≤ 0.4% over 5–100 Hz and W_rec ≤ 12.3% over 20–120 °C) were also observed. The 0.15BNN ceramics exhibited a large power density (19 MW/cm³) and ultrafast discharge time (~37 ns) over the range of ambient temperature to 120 °C. These enhanced performances may be attributed to the improved breakdown strength and relaxor behavior through the incorporation of BNN. In conclusion, these findings indicate that 0.15BNN ceramics may serve as promising materials for pulsed power systems.     

Preparation of high-activity coal char-based catalysts from high metals containing coal gangue and lignite for catalytic decomposition of biomass tar

The potential of using high metals containing coal gangue and lignite to prepare high-activity coal char-based catalysts is investigated for effective biomass tar decomposition. Loose structure and rough surface are formed for these char-based catalysts with heterogeneous distribution of a large number of inorganic particles. In the biomass tar decomposition, the performance of the coal char-based catalysts is significantly influenced by the content of the metals in the raw materials and coal gangue char (GC) with the ash content as high as 50.80% exhibits the highest activity in this work. A high biomass tar conversion efficiency of 93.5% is achieved at 800 °C along with a significant increase in the fuel gas product. During the five-time consecutive tests, the catalytic performance of GC increases a little at the second or third times reuse and remains relatively stable, showing the remarkable stability of the catalyst in biomass tar decomposition applications.