Artificial intelligence–based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method

Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile , Annona squamosal , Camellia sinensis (tea), and coriander ( Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (MP-A and MP-B), and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (CC). The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS–M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN–M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.     

An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge

We present a chemical discovery robot for the efficient and reliable discovery of supramolecular architectures through the exploration of a huge reaction space exceeding ten billion combinations. The system was designed to search for areas of reactivity found through autonomous selection of the reagent types, amounts, and reaction conditions aiming for combinations that are reactive. The process consists of two parts where reagents are mixed together, choosing from one type of aldehyde, one amine and one azide (from a possible family of two amines, two aldehydes and four azides) with different volumes, ratios, reaction times, and temperatures, whereby the reagents are passed through a copper coil reactor. Next, either cobalt or iron is added, again from a large number of possible quantities. The reactivity was determined by evaluating differences in pH, UV‐Vis, and mass spectra before and after the search was started. The algorithm was focused on the exploration of interesting regions, as defined by the outputs from the sensors, and this led to the discovery of a range of 1‐benzyl‐(1,2,3‐triazol‐4‐yl)‐N‐alkyl‐(2‐pyridinemethanimine) ligands and new complexes: [Fe(L¹)₂](ClO₄)₂ ( 1 ); [Fe(L²)₂](ClO₄)₂ ( 2 ); [Co₂(L³)₂](ClO₄)₄ ( 3 ); [Fe₂(L³)₂](ClO₄)₄ ( 4 ), which were crystallised and their structure confirmed by single‐crystal X‐ray diffraction determination, as well as a range of new supramolecular clusters discovered in solution using high‐resolution mass spectrometry.     

基于径向基函数网络的宣城市空气质量预测

提出了一种应用人工神经网络进行空气质量预测的方法,即采用径向基函数神经网络进行短期的空气质量预测;并采用了主成分分析方法降低神经网络学习矩阵维数,浓缩预测信息,降维去噪.选取宣城市气象局2003年到2005年地面气象观测资料作为预测因子,宣城市环境保护监测中心提供的PM10、SO2浓度值作为预测对象,进行训练学习和预测验证.研究结果表明:将该方法应用于空气质量预测,效果良好,具有较强的实用性和推广能力.     

西双版纳地区橡胶林生物量的遥感估算

以西双版纳地区橡胶林为对象,设置93个900 m2样地并测量每株胸径等数据,根据经验式计算样地生物量,并分析了生物量与样地调查时间接近的2010年Landsat TM影像的反射率和植被指数的相关性,建立生物量估算模型,计算了该地区橡胶林生物量.结果表明:①生物量与可见光波段(TM1,TM2,TM3)(P<0.01)和中红外波段(TM5,TM7)反射率呈显著负相关(P<0.05),与NDVI,RVI和GI植被指数则呈显著正相关(P<0.01);②GI植被指数是生物量的最佳估算因子;③橡胶林生物量变化在1～291 t.hm-2之间,平均值为(75.90±58.75)t.hm-2,总生物量为1.97×107 t;④生物量在景洪市和勐腊县南部地区较高,且随海拔升高呈现出先增加后减少趋势,最大值出现在600～700 m.     

稻纵卷叶螟人工饲料的研究

 以稻纵卷叶螟为研究对象,按不同配方配制了6种人工饲料分别用来饲养稻纵卷叶螟,以筛选出适合饲养稻纵卷叶螟的人工饲料。结果表明,在温度为24~26 ℃,相对湿度大于80%,光照条件大于14 h的条件下,其中饲料D、E、F饲养稻纵卷叶螟二龄幼虫只能完成二龄发育,少数幼虫存活到三龄或三龄以上;饲料C饲养稻纵卷叶螟二龄幼虫也只能完成三龄发育;而饲料A和B饲养的稻纵卷叶螟二龄幼虫能完成整个世代发育,存活率分别为26.17%和36.40%,接近对照组新鲜水稻叶饲养的存活率36.67%,且蛹质量和新鲜水稻叶饲养的幼虫没有显著差异,但与对照相比,幼虫历期延长4~6 d、化蛹率下降8%~9%、羽化率下降11%~12%。     

于桥水库菹草过度生长对水质的影响及成因分析

通过对于桥水库水生植物种群群落和生物量的调查,分析水质监测成果和影响菹草生长的因素,研究菹草过度生长对水库水质的影响,探究于桥水库菹草疯长发生成因.对Lambert-Beer定律公式进行变换,分析水下太阳辐照度与水深和透明度的关系.结果表明:在菹草死亡腐败期,过量菹草衰亡释放的高营养盐和有机质对水质影响较大,恶化程度近1倍,持续时间约为1周;导致于桥水库出现菹草疯长现象的主要原因是光照度、透明度、水深和水温等条件;当透明度一定时,4月水位提高1,m,水下太阳辐照度将降低56.5%,菹草所能获得的光照能量大大减少,从而抑制了菹草过度生长.以Lambert-Beer定律为理论依据,提出采用提高水位、改变高水位运行时间抑制菹草过度生长的新方法.     

舟山海岛废弃采石场人工植被恢复

在矿山人工植被恢复过程中,人工植被恢复坡面植物的状况能很好的反应出工程的恢复效果.选取浙江舟山具有代表性的庆丰采石场为主要研究对象,对人工恢复坡面植物群落、周边原生植被以及土壤物理性质进行观察,研究表明,豆科及禾本科植物适合作为先锋物种进行早期人工恢复坡面的水土保持,且在群落中占据着主要的优势;在植被演替过程中,菊科及禾本科植物入侵明显,但是周围灌木、乔木的乡土种较难入侵;随着时间的进行,草本植物种类显著增加,群落多样性增加,坡面生态系统日趋稳定,其中坡中、坡下的植被恢复效果明显优于坡上;坡度对于植被恢复     

人工水平冻结冻土帷幕强制解冻温度场数值分析

结合某车站北端头盾构到达人工水平冻结加固工程,运用数值计算软件ADINA建立了人工强制解冻模型,研究了水平冻土帷幕在人工强制解冻条件下温度场的分布规律,分别对强制解冻过程中热水循环温度、热水循环时间,以及解冻管间距的敏感性进行了分析。结果表明：提高循环热水温度、延长热水循环时间以及尽量利用原有水平冻结管的最短间距均能够缩短冻土帷幕人工强制解冻的完成时间,进而提出了盾构到达水平冻结冻土帷幕人工强制解冻施工技术参数指标。     

退化喀斯特地区植被恢复过程中苔藓的先锋作用

在贵州花江喀斯特高原生态综合治理试验示范区内采集苔藓植物样品,鉴别了主要苔藓种类,测定苔藓植物的持水性能,分析了苔藓、岩石接触面形成土壤的化学性质。结果表明：试验区内苔藓植物共有11科26属65种,以藓类植物为主;自然状态下苔藓植物含水率为36 %～898 %;新鲜苔藓持水率为2123 %～5159 %;苔藓、岩石接触面的土壤属于土壤养分分级的一级标准,各项养分指标亦明显高于对照样地。苔藓植物对喀斯特石漠生境有极强的适应性,是石漠化及其他退化生态系统进行人工生态恢复中理想的物种选择。     

人工湿地技术削减雨水初期径流污染负荷研究进展

拟从技术特点、技术流程、技术原理、技术参数、技术使用中存在的问题以及技术的应用前景等方面对雨水初期径流污染人工湿地技术进行分析和描述.介绍了该技术在国内和国外的研究现状,并展望了其未来研究方向.以期为我国城市雨水初期径流污染控制工程提供参考依据.