回采工作面瓦斯涌出量遗传投影寻踪回归预测

为快速、准确预测回采工作面瓦斯涌出量,基于投影降维思想,建立一种遗传算法(GA)投影寻踪回归预测方法。选取煤层瓦斯原始含量、埋藏深度、煤层厚度、煤层倾角、工作面长度、推进速度、采出率、临近层瓦斯含量、临近层厚度、临近层层间距、岩层岩性、开采深度作为评价因子,对某矿15个学习样本进行训练,建立GA投影寻踪回归预测模型。利用该矿3个实测样本对模型进行检验,并与主成分分析和BP神经网络方法结果进行对比。研究表明:利用GA投影寻踪回归预测回采工作面瓦斯涌出量,平均误差为3.43%,最大误差为5.7%,精度优于其他2种方法。     

通过屋顶暴露方法模拟聚碳酸酯微塑料在长江水样的长期老化行为

由于较低的自然老化速率,微塑料在实际水体长期户外暴露过程中老化行为的数据仍较缺乏.通过聚碳酸酯（Polycarbonate,PC）微塑料在长江表层水样的18个月户外暴露试验以揭示自然老化过程对微塑料表面特征及对金属离子(Cr⁶⁺和Co²⁺)吸附行为的重要影响.结果显示,户外暴露改变了PC微塑料的理化性质,包括表面开裂、粒径降低、亲水性和熔点增加以及表面氧化（产生羰基和羟基）.同时,户外老化过程明显影响微塑料对金属离子的吸附行为.原始和低老化程度PC对金属离子的吸附主要通过物理作用控制,而高老化程度微塑料由于产生含氧功能团对金属离子的吸附主要由化学过程如络合和共价键等作用控制.并且,不同老化程度PC微塑料对Cr⁶⁺和Co²⁺的吸附性能不同.对于Cr⁶⁺,PC的吸附性能随其老化程度增加呈先升高后降低的趋势,然而对于Co²⁺,平衡吸附性能随老化程度增加呈增加的趋势.户外老化过程产生含氧官能团、破碎和开裂以及生物膜可能是影响吸附性能的主要因素.本研究证实了长期户外...     

废水排放对近海环境中抗生素抗性基因和微生物群落的影响

污水处理厂是水环境中抗生素抗性基因（ARGs）的重要来源.可移动遗传元件（MGEs）和微生物群落是影响ARGs增殖扩散的关键因素.为探究污水处理厂废水排放对近海环境中ARGs和微生物群落的影响,采用高通量荧光定量PCR （HT-qPCR）和高通量16S rRNA扩增子测序技术,对杭州湾上虞（SY）和嘉兴（JX）两个近岸纳污区（ERAs）及远岸湾区表层沉积物中ARGs、MGEs和微生物群落的组成和分布进行调查.结果表明,多重耐药类ARGs是所有样点中丰度最高的ARGs类型.纳污区沉积物中ARGs和MGEs多样性和丰度远远高于远岸湾区沉积物.JX纳污区沉积物中微生物群落丰富度和多样性高于SY纳污区及远岸湾区沉积物.PCoA结果显示,纳污区与远岸湾区沉积物中ARGs、MGEs和微生物群落分布存在显著的差异,说明长期的废水排放对近海环境中ARGs、MGEs和微生物群落影响较大.ARGs、MGEs和细菌属的共现网络显示,嗜冷杆菌属、假单胞菌属、亚硫酸杆菌属、假交替单胞菌属和芽孢杆菌属等12种菌属与ARGs和MGEs存在显著正相关.多重耐药类和β-内酰胺类ARGs的潜在宿主最多.     

基于遗传算法进行EMI滤波器电感的设计

通过电感的高频特性及其对共模插入损耗的影响分析,采用遗传算法和Ngspice仿真库设计出电感的选择方案,最后通过电路仿真和实验同时验证方案的有效性。该方案避免了对EMI滤波器高频段的复杂建模,降低了电感选择的难度,对电感元件的选择具有一定的指导意义。     

Effects of input uncertainty and variability on the modelled environmental fate of organic pollutants under global climate change scenarios

Global climate change (GCC) is expected to influence the fate, exposure and risks of organic pollutants to wildlife and humans. Multimedia chemical fate models have been previously applied to estimate how GCC affects pollutant concentrations in the environment and biota, but previous studies have not addressed how uncertainty and variability of model inputs affect model predictions. Here, we assess the influence of climate variability and chemical property uncertainty on future projections of environmental fate of six polychlorinated biphenyl congeners under different GCC scenarios using a spreadsheet version of the ChemCAN model and the Crystal Ball® software. Regardless of emission mode, results demonstrate: (i) uncertainty in degradation half-lives dominates the variance of modelled absolute levels of PCB congeners under GCC scenarios; (ii) when the ratios of predictions under GCC to predictions under present day climate are modelled, climate variability dominates the variance of modelled ratios; and (iii) the ratios also indicate a maximum of about a factor of 2 change in the long-term average environmental concentrations due to GCC that is forecasted between present conditions and the period between 2080 and 2099. We conclude that chemical property uncertainty does not preclude assessing relative changes in a GCC scenario compared to a present-day scenario if variance in model outputs due to chemical properties and degradation half-lives can be assumed to cancel out in the two scenarios.     

Transport of copper as affected by titania nanoparticles in soil columns

The effects of TiO₂ nanoparticles on the transport of Cu through four different soil columns were studied. For two soils (HB and DX), TiO₂ nanoparticles acted as a Cu carrier and facilitated the transport of Cu. For a third soil (BJ) TiO₂ nanoparticles also facilitated Cu transport but to a much lesser degree, but for a fourth soil (HLJ) TiO₂ nanoparticles retarded the transport of Cu. Linear correlation analysis indicated that soil properties rather than sorption capacities for Cu primary governed whether TiO₂ nanoparticles-facilitated Cu transport. The TiO₂-associated Cu of outflow in the Cu-contaminated soil columns was significantly positively correlated with soil pH and negatively correlated with CEC and DOC. During passage through the soil columns 46.6-99.9% of Cu initially adsorbed onto TiO₂ could be “stripped” from nanoparticles depending on soil, where Cu desorption from TiO₂ nanoparticles increased with decreasing flow velocity and soil pH.     

响应面法和神经网络优化Acinetobacter sp. DNS32发酵基质

为了提高阿特拉津降解菌Acinetobacter sp.DNS32的产量,分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分(玉米粉、豆饼粉、K2HPO4)进行优化研究。响应曲面法确定3种成分的含量为玉米粉39.494 g/L,豆饼粉25.638 g/L和K2HPO43.265 g/L时,预测发酵活菌最大生物量为7.079×108CFU/mL,实测量为7.194×108CFU/mL;人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39.650 g/L,豆饼粉为25.500 g/L,K2HPO4为2.624 g/L时,预测最大值为7.199×108CFU/mL,实测量为7.244×108CFU/mL;最终确定培养基配方:玉米粉为39.650 g/L,豆饼粉为25.500 g/L,K2HPO4为2.624 g/L,CaCO3为3.000 g/L,MgSO4.7H2O和NaCl均为0.200 g/L;优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36.6%。结果表明,在阿特拉津降解菌DNS32发酵培养基组分优化方面,响应面法和基于人工神经网络的遗传算法都是可行的,基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。