电感耦合等离子体原子发射光谱法(ICP-OES)测定地下水中多种元素

应用电感耦合等离子体原子发射光谱法(ICP-OES)测定地下水中钾、钠、钙、镁、铁、锰元素的含量,使用分光光度法测定氟的含量,测定的相对标准偏差为0.7%~2.8%,加标回收率为92.1%~105.2%。结果表明,大部分地区地下水中钠和氟化物的含量均偏高,常量与微量元素的组成含量差别不大。     

液相色谱-原子荧光光谱联用法测定地下水中的三价砷和五价砷

建立了一种利用液相色谱-原子荧光光谱联用法测定地下水中三价砷和五价砷含量的方法。流动相为磷酸二氢钾和磷酸氢二钠,流速1.0mL/min,方法的标准曲线线性相关性好,进样后在10min内分离、测定完毕,操作自动化程度高,重现性好,方法准确可靠,检出限低(As~(3+)、As~(5+)都是0.02μg/L),可同时测定地下水中As~(3+)、As~(5+)的含量。     

基于SAO-VMD-FFT的激光诱导荧光光谱信号信噪比提升方法

针对基于激光诱导荧光光谱法(LIFs)的便携式场地地下水检测技术装备在原位检测地下水中典型重金属时,光传感信号含噪声严重,影响后续定量分析准确度的问题,提出了一种雪消融优化器(SAO)寻优算法优化的变分模态分解(VMD)结合快速傅里叶变换(FFT)的LIFs光传感信号信噪比提升算法。利用SAO算法对VMD分解关键参数分解模态个数K和惩罚因子α进行优化,实现含噪信号的自适应最佳VMD分解,得到一系列本征模态分量。进一步结合FFT算法寻找频率突变点从而筛选出相关模态。累计相关模态得到信噪比提升后的信号。通过仿真和实测信号分析,结果表明与EMD、WTD和S-G等常用算法相比,本文算法能更好地去除LIFs信号中的噪声分量,保留有用信息,验证了该方法的可行性和有效性,可为后续利用LIFs传感信号原位定量分析地下水中重金属元素含量提供支持。     

基于易损性与冗余度分析的构件重要性评价方法

为合理评价构件在结构中的重要性,以构件材料的弹性模量为基本参数,并考虑构件刚度变化产生的影响,基于构件应变能对材料弹性模量的敏感性,建立了构件冗余度及其易损性评价指标,以衡量构件的脆弱性和冗余特性。在此基础上将构件的重要性指标定义为构件易损性系数与冗余度系数的比值,实现构件重要性的分类量化。结果表明,采用的构件重要性评价指标能正确反映构件在结构中的重要性,重要性系数高的构件为结构的高易损-低冗余构件,是结构的关键部位和薄弱环节所在,其失效后对结构整体性能的影响较大。加强高易损-低冗余关键构件对于提高结构的整体性能和抗连续倒塌的能力具有重要作用。     

Quantum-Like Interdependence Theory Advances Autonomous Human–Machine Teams (A-HMTs)

As humanity grapples with the concept of autonomy for human–machine teams (A-HMTs), unresolved is the necessity for the control of autonomy that instills trust. For non-autonomous systems in states with a high degree of certainty, rational approaches exist to solve, model or control stable interactions; e.g., game theory, scale-free network theory, multi-agent systems, drone swarms. As an example, guided by artificial intelligence (AI, including machine learning, ML) or by human operators, swarms of drones have made spectacular gains in applications too numerous to list (e.g., crop management; mapping, surveillance and fire-fighting systems; weapon systems). But under states of uncertainty or where conflict exists, rational models fail, exactly where interdependence theory thrives. Large, coupled physical or information systems can also experience synergism or dysergism from interdependence. Synergistically, the best human teams are not only highly interdependent, but they also exploit interdependence to reduce uncertainty, the focus of this work-in-progress and roadmap. We have long argued that interdependence is fundamental to human autonomy in teams. But for A-HMTs, no mathematics exists to build from rational theory or social science for their design nor safe or effective operation, a severe weakness. Compared to the rational and traditional social theory, we hope to advance interdependence theory first by mapping similarities between quantum theory and our prior findings; e.g., to maintain interdependence, we previously established that boundaries reduce dysergic effects to allow teams to function (akin to blocking interference to prevent quantum decoherence). Second, we extend our prior findings with case studies to predict with interdependence theory that as uncertainty increases in non-factorable situations for humans, the duality in two-sided beliefs serves debaters who explore alternatives with tradeoffs in the search for the best path going forward. Third, applied to autonomous teams, we conclude that a machine in an A-HMT must be able to express itself to its human teammates in causal language however imperfectly.