Adiabatic cooling of ions in the penning trap

An ion cloud in a Penning trap can be cooled by adiabatic expansion by reducing the trap's magnetic and electric fields. We treat the ion cloud as a classical gas and obtain the relations between the temperature and the trapping fields. This cooling method may be useful in trapping and cooling of antiprotons with the aim of measuring the gravitational accleration of anti-protons and other experiments on heavy ions.     

Supercritical Propanol-Water Synthesis and Comprehensive Size Characterisation of Highly Crystalline anatase TiO2 Nanoparticles

Highly crystalline anatase TiO₂ nanoparticles have been synthesised in less than 1 min in a supercritical propanol-water mixture using a continuous flow reactor. The synthesis parameter space (T, P, concentration) has been explored and the average particle size can be accurately controlled within 10-18 nm with narrow size distributions (2-3 nm). At subcritical conditions amorphous products are obtained, whereas a broad range of T and P in the supercritical regime gives 11-14 nm particles. At high temperature and pressure, the particles size increase to 18 nm. The nanoparticles have been extensively characterised with powder X-ray diffraction (PXRD), transmission electron microscopy (TEM) and small-angle X-ray scattering (SAXS) with excellent agreement on size and size distribution parameters. The SAXS analysis suggests disk-shaped particles with diameters that are approximately double the height. For comparison, a series of conventional autoclave sol-gel syntheses have been carried out. These also produce phase-pure anatase nanoparticles, but with much broader size distributions and at much longer synthesis times (hours). The study demonstrates that synthesis in supercritical fluids is a very promising method for manipulating the size and size distribution of nanoparticles, thus removing one of the key limitations in many applications of nanomaterials.     

Electrostatic layer-by-layer assembled carbon nanotube multilayer film and its electrocatalytic activity for O2 reduction

Multilayer films of shortened multiwalled carbon nanotubes (MWNTs) are homogeneously and stably assembled on glassy carbon electrodes with the layer-by-layer (LBL) method, based on electrostatic interaction of positively charged poly(diallyldimethylammonium chloride) and negatively charged and shortened MWNTs. The film assembly and electrochemical property as well as the electrocatalytic activity toward O2 reduction of the MWNT multilayer film are studied. Scanning electron microscopy, the quartz crystal microbalance technique, ultraviolet-visible-near-infrared spectroscopy, and cyclic voltammetry are used for characterization of film assembly. Experimental results revealed that film growth is uniform, almost with the same coverage of the MWNTs in each layer, and that the assembled MWNTs are mainly in the form of small bundles or single tubes on the electrodes. Electrochemical studies indicate that the LBL assembled MWNT films possess a remarkable electrocatalytic activity toward O2 reduction in alkaline media. This property, combined with the well-dispersed, porous and conductive features of the MWNT film illustrated with the LBL method, suggests the potential application of the MWNT film for constructing an efficient alkaline air electrode for energy conversions.     

X射线荧光光谱(XRF)法测定绿泥石中镁、铝、硅、磷、钾、钙、钛、铁元素含量

建立了X射线荧光光谱法测定绿泥石中镁、铝、硅、磷、钾、钙、钛、铁元素含量的快速分析方法。以标准物质及标准物质与基准试剂氧化镁、氧化钙人工混合配制标样的方法建立标准工作曲线,重点讨论了熔剂和脱模剂的选取。最佳熔样条件：采用8.0g四硼酸锂和偏硼酸锂混合熔剂(质量比67: 33)+0.8g样品并添加溴化锂作为脱模剂,熔样温度1100℃,熔样时间10min。该方法相对标准偏差(n=12)均小于5.57%,绿泥石样品测定结果与化学法一致,硅酸盐标准物质测定结果均满足不确定度要求。     

粉末压片-X荧光光谱法测定过磷酸钙中硫含量

硫是过磷酸钙中重要营养指标之一,为准确快速测定过磷酸钙中硫的含量,试验采用粉末压片-X荧光光谱法,将过磷酸钙试样充分干燥后研磨至粒度小于74 μm,采用硼酸镶边,在压力18 Mpa条件下保压30 s,制成样片。通过在过磷酸钙样品中添加不同质量的纯物质硫酸钙（质量分数范围1.52 %～17.21 %）,经过专用混匀设备混合均匀后,与试样压片相同条件下压制标准样片,作为过磷酸钙中硫的标准样品,建立硫标准曲线,曲线线性相关系数R2为0.9995,采用经验系数法校正干扰,建立了粉末压片-波长色散X射线荧光光谱法测定过磷酸钙中硫含量的方法, 方法检出限为0.002 %。对3个不同硫含量的过磷酸钙样品采用本实验方法重复测量7次,RSD在1.4 %~3.1 %,方法精密度性好,同时用高温燃烧红外光谱法和电感耦合等离子发射光谱法对比,三者测量结果相对极差小于2.0 %,测量结果无显著性差异。此方法不需要对样品进行熔融或溶解,样品制备简单,数据准确度和稳定性好,分析效率高,适合大批量样品中硫的测定。     

振动噪声等高图的电驱动桥噪声预测及优化*

在项目开发前期通过优化电驱动桥扭矩特性的设计,可以规避噪声大问题。根据电驱动桥台架在对应扭矩下的振动噪声特性,提出了一套稳定高效的测试流程和分析方法。首先设计了一套完整的试验流程,制定了精准的数据分析方法。然后绘制出能够全面反映电驱动桥振动噪声特性的等高图。最后利用电驱动桥台架的振动噪声等高图,准确评估电驱动桥加速工况下振动噪声风险,为主机厂和电驱动桥零部件企业提供电驱动桥扭矩特性设计前期指导。利用该方法成功识别到某电驱动桥匀速及加速工况下电机及齿轮的啸叫问题。通过优化电驱动桥扭矩特性设计,电机48阶噪声峰值降低了8.5dB(A),确认该方法准确可靠,具备推广应用价值。     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

Multi-Classifier Fusion Based on MI–SFFS for Cross-Subject Emotion Recognition

With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain’s electrical activity associated with different emotions. The aim of this research is to improve the accuracy by enhancing the generalization of features. A Multi-Classifier Fusion method based on mutual information with sequential forward floating selection (MI_SFFS) is proposed. The dataset used in this paper is DEAP, which is a multi-modal open dataset containing 32 EEG channels and multiple other physiological signals. First, high-dimensional features are extracted from 15 EEG channels of DEAP after using a 10 s time window for data slicing. Second, MI and SFFS are integrated as a novel feature-selection method. Then, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) are employed to classify positive and negative emotions to obtain the output probabilities of classifiers as weighted features for further classification. To evaluate the model performance, leave-one-out cross-validation is adopted. Finally, cross-subject classification accuracies of 0.7089, 0.7106 and 0.7361 are achieved by the SVM, KNN and RF classifiers, respectively. The results demonstrate the feasibility of the model by splicing different classifiers’ output probabilities as a portion of the weighted features.     

KEADA: Identifying Key Classes in Software Systems Using Dynamic Analysis and Entropy-Based Metrics

Software maintenance is indispensable in the software development process. Developers need to spend a lot of time and energy to understand the software when maintaining the software, which increases the difficulty of software maintenance. It is a feasible method to understand the software through the key classes of the software. Identifying the key classes of the software can help developers understand the software more quickly. Existing techniques on key class identification mainly use static analysis techniques to extract software structure information. Such structure information may contain redundant relationships that may not exist when the software runs and ignores the actual interaction times between classes. In this paper, we propose an approach based on dynamic analysis and entropy-based metrics to identify key classes in the Java GUI software system, called KEADA (identifying KEy clAsses based on Dynamic Analysis and entropy-based metrics). First, KEADA extracts software structure information by recording the calling relationship between classes during the software running process; such structure information takes into account the actual interaction of classes. Second, KEADA represents the structure information as a weighted directed network and further calculates the importance of each node using an entropy-based metric OSE (One-order Structural Entropy). Third, KEADA ranks classes in descending order according to their OSE values and selects a small number of classes as the key class candidates. In order to verify the effectiveness of our approach, we conducted experiments on three Java GUI software systems and compared them with seven state-of-the-art approaches. We used the Friedman test to evaluate all approaches, and the results demonstrate that our approach performs best in all software systems.