Martensitic transformation,crystal structure and shape memory effect in Ni55−xMn25Ga20Cox alloys

ABSTRACT

The effect of cobalt addition instead of nickel on the crystal structure, martensitic transformation behaviour and shape memory effect were investigated. Within the analysed range of chemical composition, a single non-modulated martensite was detected at ambient temperature. Cobalt addition modified the lattice parameters and, thus, affected the tetragonality of the martensite unit cell. The hysteresis of martensitic transformation was differently affected by the type of heat treatment applied. For furnace cooled samples, the hysteresis decreased from 50°C to 30°C; in the case of water quenched samples, the hysteresis sharply increased up to 60°C. The shape memory effect, measured as the recoverable strain upon annealing after compression tests, reached a fully recoverable deformation at 10?at.-% of cobalt.

This paper is part of a Thematic Issue on The Crystallographic Aspects of Metallic Alloys.     

Anti-Inflammatory Treatment with FTY720 Starting after Onset of Symptoms Reverses Synaptic Deficits in an AD Mouse Model

Therapeutic approaches providing effective medication for Alzheimer’s disease (AD) patients after disease onset are urgently needed. Previous studies in AD mouse models suggested that physical exercise or changed lifestyle can delay AD-related synaptic and memory dysfunctions when treatment started in juvenile animals long before onset of disease symptoms, while a pharmacological treatment that can reverse synaptic and memory deficits in AD mice was thus far not identified. Repurposing food and drug administration (FDA)-approved drugs for treatment of AD is a promising way to reduce the time to bring such medication into clinical practice. The sphingosine-1 phosphate analog fingolimod (FTY720) was approved recently for treatment of multiple sclerosis patients. Here, we addressed whether fingolimod rescues AD-related synaptic deficits and memory dysfunction in an amyloid precursor protein/presenilin-1 (APP/PS1) AD mouse model when medication starts after onset of symptoms (at five months). Male mice received intraperitoneal injections of fingolimod for one to two months starting at five to six months. This treatment rescued spine density as well as long-term potentiation in hippocampal cornu ammonis-1 (CA1) pyramidal neurons, that were both impaired in untreated APP/PS1 animals at six to seven months of age. Immunohistochemical analysis with markers of microgliosis (ionized calcium-binding adapter molecule 1; Iba1) and astrogliosis (glial fibrillary acid protein; GFAP) revealed that our fingolimod treatment regime strongly down regulated neuroinflammation in the hippocampus and neocortex of this AD model. These effects were accompanied by a moderate reduction of Aβ accumulation in hippocampus and neocortex. Our results suggest that fingolimod, when applied after onset of disease symptoms in an APP/PS1 mouse model, rescues synaptic pathology that is believed to underlie memory deficits in AD mice, and that this beneficial effect is mediated via anti-neuroinflammatory actions of the drug on microglia and astrocytes.     

NiTi形状记忆合金超声波焊接温度场数值计算与分析

NiTi形状记忆合金因具有优异的超弹性、形状记忆效应而被广泛应用。超声波焊接作为一种固相连接技术，在NiTi合金薄片材料的连接方面具有一定的优势，由于超声波焊接方法具有瞬时性且焊接过程十分复杂，所以难以通过试验观察的手段来研究其连接机理。针对NiTi形状记忆合金超声波焊接过程中温度难以监测的问题，采用ANSYS有限元分析软件建立NiTi合金的超声波焊接二维有限元分析模型，探究了超声波振幅对焊接温度场分布的影响规律，搭建热电偶测温平台采集焊接试验过程的温度数据对模型进行验证，结合数值模拟结果，分析了NiTi合金超声波焊接的连接机理。结果表明，焊接温度场与振幅呈正相关，在固定焊接条件下，振幅每增加5 μm，焊接最高温度约提高45℃。经试验测定，模拟结果与试验数据吻合较好，最高温度仅相差4℃，误差不超过最高温度的3%。将试验结果与模拟结果相结合，NiTi合金的超声波过程中铝没有熔化，接头为固相连接。     

基于 GA-SVM 算法的通信网络入侵信号自动识别技术

针对当前通信系统入侵行为自动识别技术存在入侵信号样本识别成功率较低、误识别率和漏识别率较高的问题,提出基于 GA-SVM 算法的通信网络入侵信号自动识别技术。利用混沌原理提取通信网络入侵的非平稳信号时域特征,并凭借自回归模型提取对应频域特征,捕捉邻域入侵信号间的非线性时空动作频率,评价相邻行为间的状态关联性,预测入侵信号后续行为,完成入侵信号的识别。实验表明,所提方法识别精度高、误识别率较低,漏识别率非常低,具有可应用于实际的理论价值。     

Progressive mean control chart is not a special case of an exponentially weighted moving average control chart

The progressive mean (PM) statistic is based on a simple idea of accumulating information of each subgroup by calculating the average progressively. Its weighting structure is based on a subgroup number that changes arithmetically, which makes the PM chart unique and efficient compared with the existing classical memory control charts. In a recent article (see reference 1), it was claimed that the PM chart is a special case of the exponentially weighted moving average (EMWA) chart. In this article, it is shown that even though the PM statistic can be written in the form of an EWMA statistic, the variance of the PM statistic is different from that of the EWMA statistic. Consequently, the limits of the PM chart are different from that of the EWMA chart. Therefore, it is found that the PM chart is not a special case of the EWMA chart; hence, the claim in reference 1 is incorrect. Furthermore, it is pointed out in this paper that no adaptive property in the weighting parameter of the PM statistic exists, further contradicting the claim in reference 1.     

基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Proton exchange membrane fuel cell (PEMFC) long-term prognostic facilitates reducing the time/cost of the durability tests and is a critical starting point for control/maintenance suggestions. Long short-term memory (LSTM) recurrent neural networks have excellent time series processing capabilities and are proved to be useful for the short-term prognostic of PEMFC. However, LSTM prognostic models usually suffer from accumulated errors and model recognition uncertainties, which make it difficult to break the historical degradation data limitations, resulting in unsatisfactory long-term prediction performance. To tackle the problem, this paper proposes a novel model named navigation sequence driven LSTM (NSD-LSTM) for long-term prognostic. In the strategy, a navigation sequence is firstly generated by using an autoregressive integrated moving average model with exogenous variables. The sequence is then fed iteratively into LSTM in the implementation stage to achieve long-term perdition. The proposed strategy is evaluated using the aging experimental data of two types of PEMFC under different operating conditions. The long-term prognostic performance of the proposed model and other two state-of-the-art prognostic models, namely, nonlinear autoregressive exogenous and echo state network, are evaluated through comparison experiments. The simulation and experimental results show that the proposed prognostic strategy has better long-term degradation trend prediction consistency and remaining useful life estimation robustness.     

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.     

境内外矿业资本市场信息发布差异研究：以三道庄钼矿为例

矿产资源作为矿业公司持有使用权的核心资产,由于境内外矿业资本市场要求不同,因此境内外发布的矿产资源信息存在较大差异。洛阳钼业是香港交易所和上海证券交所双重上市的矿业公司,根据香港交易所要求,由胜任人按照JORC规则发布独立技术审查报告;根据上海证券交易所等要求,按照我国标准编制核实报告并通过评审备案后,将其作为A股招股说明书的所载内容公开发布。本文以洛阳钼业所属上市资产三道庄钼矿为例,以公开的矿产资源信息为切入点,对境内外首发上市的矿产资源信息发布路径、发布内容以及矿产资源储量对比分析,认为境内外证券交易所矿产资源信息发布要求、采纳的矿产资源储量分类标准和矿产资源储量估算认定体系等要素是导致信息发布存在差异的主要原因,总结了矿业资本市场信息发布差异带来的三点启示,为完善我国矿业资本市场发展,促进矿业行业国际互联互通提供实证。     

采煤机滚筒截割记忆程控的灰关联度计算新方法

针对采煤机滚筒记忆程控调高和截割工作循环的特征，分析了这种指令式控制的特点，研究了采煤机在不同的截割循环中其姿态的改变引起滚筒位置基准产生的偏差和滚筒截割记忆程控的再现精度预测的必要性和偏差修正的方法.根据各截割循环滚筒位置高度参数序列相互之间及其分别与参考序列之间的关联程度的预测，分析了灰关联度的理论方法对序列参数特征敏感性和算法的递推性，提出了适合截割记忆程控序列参数特点的灰色预测的一种关联度新算法，即面积比值法，具有计算的递推性和对序列幅值总体变化趋势的敏感性.