New clues for understanding the magnetic behavior of UPt3

We present results of a fairly complete study on which dopants M result in single-phase U_1−xM_xPt₃ samples. The resulting new samples. M = Y, Lu, Sc, Hf, Zr, have been characterized by low temperature magnetic susceptibility and specific heat for x 0.07. Based on a previously proposed model, the c/a ratios of the present alloys would be expected to result in magnetic behavior as observed in U_1−xTh_xPt₃ and U(Pt_1−xPd_x)₃. On the contrary, no magnetism is found; the Sc-doped samples actually show a suppression of spin fluctuations. A new proposal is made to explain which properties are critical for the magnetic behavior of UPt₃, as well as its doped derivatives.     

基于数据融合技术的大型圆钢锻件超声缺陷识别的研究

对大型圆钢锻件常见缺陷进行了超声识别技术研究，利用感知器算法实现了典型缺陷自动识别，并根据不同类型、位置和姿态的超声换能器，对不同缺陷类型的敏感程度不同，实现了二组换能器在决策级上的数据融合，大大提高了缺陷识别可靠度。     

厚大断面球墨铸铁件凝固装置的研制

自行研制了能够模拟厚大铸件冷却环境的小型凝固装置，通过控制不同的环境温度和冷却速度，来获取厚大断面球墨铸铁件凝固过程温度场的分布和变化规律。与空冷凝固时间相比，延长了300％的冷却凝固时间，验证了这一装置在实验中的有效性。     

转轮体铸造工艺的优化设计

本文重点总结了水轮机的重要部件转轮体常发生的主要铸造质量问题,分析了产生缩孔缩松、裂纹、尺寸超差等质量问题的主要直接原因,并提出了铸造工艺优化设计方案。     

随机共振在民航发动机转子振动中的应用研究

为有效降低航空发动机转子噪声对民航发动机转子振动信号提取的干扰,提高转子振动信号提取的准确性,将随机共振用于转子振动信号的处理.随机共振是一种在菲线性系统中噪声对目标信号起促进作用的反直观现象.首先介绍双稳随机共振的基本原理及其数学模型,再对某型民航发动机转子振动监测数据进行随机共振数值仿真.仿真结果表明,改进方法能有效提取发动机转子振动信号,并在强噪声条件下的转子振动信号提取中具有突出优势,且能监测到振动数据采集时的短时失效现象,较好地反应了转子的真实振动情况.     

电镀废水处理站DTCR辅助氢氧化物沉淀工艺改造

某废水处理站改造前采用传统氢氧化物沉淀工艺处理高浓度含铬、氰镉电镀废水,运行不稳定,出水不能达标排放。工艺改造后增加了高分子重金属捕集沉淀剂DTCR辅助去除重金属,在含铬废水DTCR投加量为50 mg/L、含氰镉废水DTCR投加量为10 mg/L条件下,出水总铬、镉分别稳定在0.5 mg/L、0.2 mg/L以下,远低于《电镀污染物排放标准》(GB 21900—2008)限值。     

重载电力机车车体的设计与强度计算

针对自主设计研发的总功率为9600kW的6轴新型重载电力机车的突出问题,要解决在纵向牵引制动载荷增加的条件下,车体挠度明显增大、工作应力大大增加,同时又要合理减重.为此,利用ANSY有限元软件,针对新型重载电力机车的特点,首先借鉴国内外车体结构技术标准和重载机车的实际运行状态研究确定了机车的载荷工况,并对车体结构的强度和剐度进行了有限元分析;根据各种工况下应力的分布特点,并基于抗疲劳破坏的观点,分析了车体结构所存在的问题,并提出了相应的改进方案.比较分析的结果表明,改进方案既保证了车体的刚度与强度,又收到了比较明显的减重效果,是一种比较合理的改进结构.     

Electrochemical performances of B doped and undoped diamond-like carbon (DLC) films deposited by femtosecond pulsed laser ablation for heavy metal detection using square wave anodic stripping voltammetric (SWASV) technique

Pure diamond-like carbon (DLC) thin films and boron-doped DLC thin films have been deposited on silicon substrates using femtosecond pulsed laser. The amorphous carbon materials (DLC), have been deposited at room temperature by ablating graphite targets with an amplified Ti:sapphire laser of 800 nm wavelength and a pulse duration of 150 fs in high vacuum conditions. Doping with boron has been performed by ablating alternatively graphite and boron targets.In this study, the DLC films were used as working electrodes for the electrochemical detection of trace heavy metals namely, Cd²⁺, Pb²⁺, Ni²⁺ and Hg²⁺, by using square wave anodic stripping voltammetry (SWASV) technique. Four metals were detected at −1.3 V deposition potential, and 90 s deposition time. The DLC films have been characterized by multiwavelength Raman spectrometry and high resolution scanning electron microscopy. The effect of the boron doping on the electrochemical behavior has been shown. The a-C:B 8%/Si₃N₄ electrode gives the more sensitive detection. The four metals are detected simultaneously with a detection limit of 1 μg/L or 2 μg/L and a dynamic range from 1 or 2 to 25 μg/L for every metal, as presented in third table of this article. The different sensitivities obtained are 6.2, 20.0, 1.2 and 6.6 μA/ppb or μA μg⁻¹ L for Cd²⁺, Pb²⁺, Ni²⁺ and Hg²⁺, respectively.     

Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model

A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from 10 variables acquired by gray relational analysis. The assessment models for Cd and Cu concentration employed five and six input variables, respectively. The results showed that the GDFNN for estimating Cu and Cd concentrations in rice performed well at prediction with a compact network structure using the training, validation, and testing sets (for Cu, fuzzy rules=9, R² greater than 0.75, and RMSE less than 2.5; for Cd, fuzzy rules=9, R² greater than 0.75, and RMSE less than 1.0). The final GDFNN model was then compared with a back-propagation (BP) neural network model, adaptive-network-based fuzzy interference systems (ANFIS), and a regression model. The accuracies of GDFNN model prediction were usually slightly better than those of the other three models. This demonstrates that the GDFNN model is more suitable for predicting heavy metal concentrations in rice.     

Stochastic volatility models with leverage and heavy-tailed distributions: A Bayesian approach using scale mixtures

This paper studies a heavy-tailed stochastic volatility (SV) model with leverage effect, where a bivariate Student-t distribution is used to model the error innovations of the return and volatility equations. Choy et al. (2008) studied this model by expressing the bivariate Student-t distribution as a scale mixture of bivariate normal distributions. We propose an alternative formulation by first deriving a conditional Student-t distribution for the return and a marginal Student-t distribution for the log-volatility and then express these two Student-t distributions as a scale mixture of normal (SMN) distributions. Our approach separates the sources of outliers and allows for distinguishing between outliers generated by the return process or by the volatility process, and hence is an improvement over the approach of Choy et al. (2008). In addition, it allows an efficient model implementation using the WinBUGS software. A simulation study is conducted to assess the performance of the proposed approach and its comparison with the approach by Choy et al. (2008). In the empirical study, daily exchange rate returns of the Australian dollar to various currencies and daily stock market index returns of various international stock markets are analysed. Model comparison relies on the Deviance Information Criterion and convergence diagnostic is monitored by Geweke’s convergence test.