激光诱导击穿光谱(LIBS)技术在低碳钢铁冶金行业的应用

激光诱导击穿光谱技术(Laser induced breakdown spectroscopy,简称LIBS)是一种基于激光等离子体的发射光谱技术,具有样品制备简单,分析迅速,多元素同时分析,现场在线和远程分析等独特优势,已在钢铁冶金领域取得了越来越广泛的应用与发展。综述了近年来LIBS技术在钢铁冶金领域的应用研究进展,从冶金生产的全流程进行介绍,包括矿石、熔融钢水/铁水、钢铁产品及炉渣和废气分析。同时,还总结了LIBS技术在钢铁冶金领域应用的优点和缺点,并对该技术的应用前景及未来发展方向进行展望。     

地外样品研究中电子探针元素面扫描分析技术的应用

由于月壤等地外样品十分珍贵,在实验室研究中优先使用原位、微区、无损的元素分析方法。电子探针元素面扫描是地外样品研究中常用的分析方法之一。该方法可获取样品整体或者感兴趣区域的多种元素分布数据,应用于矿物相识别与含量估算,锆石等定年矿物的快速定位,矿物环带、出溶、反应边结构等特殊岩相和矿物接触关系等分析和研究。本研究中以嫦娥五号月壤、月球陨石、火星陨石研究为例,介绍了目前元素面扫描的应用方法。此外,本文还对比、分析了电子探针面扫描技术与其它面扫描技术的优缺点和适用范围。未来十年,我国将实施一系列月球、火星、小行星等天体采样返回任务。电子探针元素面扫描分析未来将在这些地外样品研究中广泛使用。同时,建议行星科学家围绕所关心的科学问题,合理搭配多种分析方法以实现各种技术优势互补和样品科学价值最大化,服务我国月球与深空探测任务科学产出和行星科学发展。     

桑白皮中21种无机元素的测定及其质量评价

为准确了解不同产区桑白皮中多种元素分布及含量情况,用65%硝酸溶液对其样品进行微波消解处理,后采用电感耦合等离子体质谱（ICP-MS/MS）法测定桑白皮样品中21种元素的含量。得到21种元素的线性关系良好,R2 > 0.999,方法学考察试验的RSD均小于3%,加样回收率在91.32%~108.84%之间,RSD均小于4%;Al、Ga、Sr、Ba相比而言含量较大,且不同产区的特异性较明显;主成分分析中17批样品得到4个主成分,累计方差贡献率达 87.380%,确定V、Co、Al、Li、As、Se、Pb、U、Zn、Cr、Cd、Ga、Ba、Cs、Ni等15种元素为桑白皮药材的特征元素;相关性分析可知,元素间的相关性较强, P≤0.05的元素与主成分分析的特征元素基本一致。参照2020年版《中国药典》中对中药材中有害元素的限量规定,发现桑白皮样品中Pb、Cd、As、Cu有害元素的含量均未超过限量要求。ICP-MS/MS能准确快速测定桑白皮中21种元素含量情况,这对评价不同产区桑白皮质量,完善桑白皮质量标准具有一定参考价值。     

水中硝酸盐氮稳定同位素比值测定前处理方法的优化

水体中过量的硝酸盐导致会严重的水生生态恶化和环境污染问题。氮稳定同位素技术为水体污染来源的判断及水生系统氮素转化机理研究提供了强有力的工具。在前人基础上,通过开发Cu2+-Cd复合催化结合超声波辅助加速反应,优化硝酸盐氮同位素比值测定前处理方法。考察了Cu2+添加量,超声功率以及反应时间变化对NO3--N转化生成N2O气体及其氮同位素比值的影响,在单因素实验基础上采用正交实验优化得到了最佳反应条件,并采用不同15N同位素比值的KNO3标准溶液结合气体预浓缩装置与稳定同位素质谱仪联用系统对新方法进行了验证。新方法单次反应体系中氮最低量为1.0 μg,其中自然丰度和高丰度样品δ15N分析精度小于1‰,富集样品的15N分析精度可达0.1 atom%以内(CV<1%);且所有标准样品的15N测定值与参考值基本一致。将优化后的方法应用于不同来源水样中硝酸盐氮稳定同位素比值测定,均可获得较好的精度,较原方法提高了前处理效率,且精度更优。综上,建立的方法准确可靠,操作简单,耗时短,适用于水中硝酸盐氮稳定同位素比值测定的批量、快速前处理。     

波束开角对多普勒测频结果的影响*

具有不同波束开角的声学多普勒测速设备未标定的测速结果存在偏差。基于水底椭圆散射模型研究了波束开角对声学多普勒测速结果的影响,对提高声学测速性能具有实际意义。水底椭圆散射模型下,足印上不同方位传播损失和散射强度存在差异,导致频谱不对称,测频结果偏小。利用不对称系数量化频谱不对称程度,若波束开角增大,方位不对称系数随之增大,测频偏差增大,推导了波束开角导致的测频偏差解析式。分析仿真和外场试验数据测频结果可知,不同多普勒频移下测频偏差和理论计算值相符,不同波束开角下实际测频偏差与理论公式计算的测频偏差均随波束开角增大而增大。     

Energetic Bio-Activation of Some Organic Molecules and Their Antioxidant Activity in the Pulp of the Moroccan Argan Tree «Argania spinosa L.»

Argania spinosa L. Skeels is an emblematic tree in Morocco, known worldwide for its medicinal and nutritional value. Its fruits contain kernels used to prepare an edible oil, the leaves are used to feed livestock, and its wood is used as fuel. If the oil acquires high importance, the other components of the fruit of the argan are undervalued. Our objective is to invest the waste of the argan industry. Particularly, our study aimed to assess the effect of thermal activation of argan pulp on its therapeutic value, its phenolic profile and its functional and physicochemical properties. After heat treatment, the HPLC analysis for the average total phenolic content varied from 2% to 37%, depending on temperature. The antioxidant activity was increased with heat treatment. Higher values of antioxidant activity, polyphenol and pigment content were recorded at 70 °C. Functional properties analysis indicated that water solubility index and water absorption capacity were significantly affected by heat stress. Physicochemical analysis showed that moisture content, titratable acidity and soluble solids were affected.     

Quantum-Inspired Complex-Valued Language Models for Aspect-Based Sentiment Classification

Aiming at classifying the polarities over aspects, aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. The vector representations of current models are generally constrained to real values. Based on mathematical formulations of quantum theory, quantum language models have drawn increasing attention. Words in such models can be projected as physical particles in quantum systems, and naturally represented by representation-rich complex-valued vectors in a Hilbert Space, rather than real-valued ones. In this paper, the Hilbert Space representation for ABSA models is investigated and the complexification of three strong real-valued baselines are constructed. Experimental results demonstrate the effectiveness of complexification and the outperformance of our complex-valued models, illustrating that the complex-valued embedding can carry additional information beyond the real embedding. Especially, a complex-valued RoBERTa model outperforms or approaches the previous state-of-the-art on three standard benchmarking datasets.     

Quantitative and Qualitative Analysis of the Anti-Proliferative Potential of the Pyrazole Scaffold in the Design of Anticancer Agents

The current work presents an objective overview of the impact of one important heterocyclic structure, the pyrazole ring, in the development of anti-proliferative drugs. A set of 1551 pyrazole derivatives were extracted from the National Cancer Institute (NCI) database, together with their growth inhibition effects (GI%) on the NCI’s panel of 60 cancer cell lines. The structures of these derivatives were analyzed based on the compounds’ averages of GI% values across NCI-60 cell lines and the averages of the values for the outlier cells. The distribution and the architecture of the Bemis–Murcko skeletons were analyzed, highlighting the impact of certain scaffold structures on the anti-proliferative effect’s potency and selectivity. The drug-likeness, chemical reactivity and promiscuity risks of the compounds were predicted using AMDETlab. The pyrazole ring proved to be a versatile scaffold for the design of anticancer drugs if properly substituted and if connected with other cyclic structures. The 1,3-diphenyl-pyrazole emerged as a useful scaffold for potent and targeted anticancer candidates.     

Monitoring of Spirulina Flakes and Powders from Italian Companies

Microalgae and microalgae-derived compounds have great potential as supplements in the human diet and as a source of bioactive products with health benefits. Spirulina (Arthrospira platensis (Nordstedt) Gomont, or Spirulina platensis) belongs to the class of cyanobacteria and has been studied for its numerous health benefits, which include anti-inflammatory properties, among others. This work was aimed at comparing some spirulina products available on the Italian market. The commercial products here analyzed consisted of spirulina cultivated and processed with different approaches. Single-component spirulina products in powder and flake form, free of any type of excipient produced from four different companies operating in the sector, have been analyzed. The macro- and micromorphological examination, and the content of pigments, phycobiliproteins, phenols, and proteins have shown differences regarding the morphology and chemical composition, especially for those classes of particularly unstable compounds such as chlorophylls and carotenoids, suggesting a great influence of both culture conditions and processing methods.     

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.