传感器阵列对浓香型白酒发酵中黄水的检测研究

黄水是白酒在固态发酵过程的副产物,含有大量发酵产物,黄水的成分一定程度反映了固态发酵的信息。基于白酒固态发酵只能在线检测温度这一现状,设计了针对白酒副产物黄水的一种传感器阵列检测装置,用于检测黄水的总酸和残余葡萄糖。该传感器阵列由电导率传感器、pH传感器和氧化还原传感器构成。采用主成份分析、判别函数分析印证了传感器数据对不同样品的区分作用,再采用多元线性回归对黄水样品建立了预测模型,结果显示:该传感器阵列装置对黄水的总酸和残余葡萄糖的预测偏差分别为0.39和0.45。     

海口市冬季大气颗粒物PM2.5中多环芳烃源解析

于2014年冬季在海口市两个采样点采集大气PMz5样品,采用超声萃取和GC/MS分析了PM2.5中16种PAHs,探讨了其含量分布特征,并运用因子分析/多元线性回归对PAHs来源进行定性和定量源解析.结果表明,冬季PM2.5中PAHs质量浓度为3.191～15.539ng/m3,两个采样点平均质量浓度为6.771 ng/m3;高温热解和交通污染混合源、生物质燃烧、天然气是PM25中PAHs的3类主要来源,其贡献率分别为74％、10％、16％.     

基于最优加权组合模型的枯季径流预测研究

【目的】研究最优的枯季径流预测模型,为流域水资源管理提供依据。【方法】建立基于差分自回归移动平均(ARIMA)、人工神经网络(ANN)和多元线性回归(MLR)3个单项模型的简单平均组合和最优加权组合预测模型,并将单项预报模型和组合模型应用到石羊河流域支流西营河的枯季径流预测中,采用相关系数、确定性系数以及均方根误差对各模型预测精度进行比较。【结果】单项预测模型中,仅ARIMA模型通过了确定性系数检验;最优加权组合模型的预测精度较简单平均组合模型高;组合预测模型中,仅ARIMA-MLR和ARIMA-ANN最优加权组合模型的确定性系数高于所有单项预测模型。【结论】最优加权组合模型的精度不但取决于各单项预测模型的精度,也与其之间的相关性有关,适合西营河枯季径流预测的最优加权组合模型是ARIMA-MRL和ARIMA-ANN组合模型。     

土地整治项目快速估算模型的建立

为提高土地整治预算效率,快速、合理、有效地预测工程投资造价,通过对8个典型代表省的166个土地整治项目进行投资分析,基于客土回填、农用井、渠道+管道组合类型分别建立单位面积投资与各单项工程投资之间的多元回归模型,并对该模型进行检验及实例验证分析。结果表明:客土回填、农用井和渠道+管道组合3种类型的回归模型分别为y=1 689.099+59.459x_1、y=1 870.219-13.13x_2+28.04x_3和y=5 269.069-42.265x_2-15.033x_3,所建立的多元回归模型能够对全国的土地整治项目进行准确估算,模型总体回归显著,满足精度要求,具有较好的实用性。     

廊道式人工湿地处理污水过程中氨氮的去除效果研究

通过廊道式人工湿地(CCW)的生产性试验工程,研究了该处理系统的性能及系统内部氨氮的变化规律.结果表明,CCW处理系统的去除效率高且稳定,氨氮的去除率平均为88%,质量去除率平均为(6.94±2.07)gN·m-2·d-1,系统内部第三单元的氨氮质量去除率最高;CCW处理系统的氨氮质量负荷率小于10gN·m-2·d-1时,质量去除率与质量负荷率呈明显的线性相关,质量负荷率大于等于20gN·m-2·d-1时,进水浓度和出水浓度之间呈明显线性相关;处理系统各处理单元及系统整体的体积去除率常数(Kv值)均大于其他湿地处理系统;CCW处理系统氨氮浓度变化与pH值的变化趋势并不一致.     

Determination of lignin,holocellulose, and organic solvent extractives in fresh leaf,litterfall, and organic material on forest floor using near-infrared reflectance spectroscopy

We report the results of a study regarding the near-infrared reflectance spectra of various leaf stages from fresh to senescing, and to decomposing leaf. A broad absorbance feature increased in the region of 1100–1400nm with the advance of the leaf senescence and leaf decomposition. A decrease was seen in the region over 1440nm during the senescence and decomposition process. These differences of spectra showed the changes in constituents of leaf in terms of the degree of the senescence and decomposition. A comparison of multiple linear regression between the near-infrared reflectance spectra and proximate chemical analyses showed that near-infrared reflectance spectroscopy achieved a certain level of useful accuracy. We consider that near-infrared reflectance spectroscopy has the potential to predict the contents of carbon fractions in plant materials, and that this method can replace previous methods due to faster determination of carbon fractions, and its ability to significantly increase the number of samples that can be collected and measured.     

Clinical and immunological response of lymphoma dogs following chemotherapy and irradiation

The effect of chemotherapy or chemotherapy followed by total body irradiation and autologous bone marrow transplantation on clinical status and lymphocyte function was evaluated in 79 dogs with spontaneous lymphoma. Advanced disease led to the early deaths of 28 dogs (35%), and 24 dogs (30%) administered chemotherapy had a mean survival time of 85 days (range 33–199 days). Survival for eight dogs receiving chemotherapy and BCG was comparable to that produced by chemotherapy only. Thirteen dogs administered chemotherapy followed by irradiation and autologous marrow grafts had survival times ranging from 59 to > 807 days (median 222 days) with median unmaintained clinical remission lasting 151 days. Most treated dogs experienced improvement in health, reduction in lymph node sizes and normalization of liver function. Lymphocyte reactivity to mitogens and streptolysin-O antigen by untreated and treated lymphoma dogs was depressed (P<0.05) compared to response of normal lymphocytes. Mixed leukocyte reactivity of irradiated lymphoma dogs was impaired in the first 150 days post-irradiation and returned to normal values after 150 days. Lymphocyte responses of irradiated normal dogs paralleled those seen in lymphoma dogs. Mixed leukocyte reactivity was not correlated with tumor rejection since eight of nine dogs evaluated following irradiation relapsed during a period of vigorous reactivity to allogeneic stimulating cells.     

Suspended sediment load prediction of river systems: An artificial neural network approach

Information on suspended sediment load is crucial to water management and environmental protection. Suspended sediment loads for three major rivers (Mississippi, Missouri and Rio Grande) in USA are estimated using artificial neural network (ANN) modeling approach. A multilayer perceptron (MLP) ANN with an error back propagation algorithm, using historical daily and weekly hydroclimatological data (precipitation P_(t), current discharge Q_(t), antecedent discharge Q_(t−1), and antecedent sediment load SL_(t−1)), is used to predict the suspended sediment load SL_(t) at the selected monitoring stations. Performance of ANN was evaluated using different combinations of input data sets, length of record for training, and temporal resolution (daily and weekly data). Results from ANN model were compared with results from multiple linear regressions (MLR), multiple non-linear regression (MNLR) and Autoregressive integrated moving average (ARIMA) using correlation coefficient (R), mean absolute percent error (MAPE) and model efficiency (E). Comparison of training period length was also made (4, 3 and 2 years of training and 1, 2 and 3 years of testing, respectively). The model efficiency (E) and R² values were slightly higher for the 4 years of training and 1 year of testing (4 * 1) for Mississippi River, indifferent for Missouri and slightly lower for Rio Grande River. Daily simulations using Input 1 (P_(t), Q_(t), Q_(t−1), SL_(t−1)) and three years of training and two years of testing (3 * 2) performed better (R² and E of 0.85 and 0.72, respectively) than the simulation with two years of training and three years of testing (2 * 3) (R² and E of 0.64 and 0.46, respectively). ANN predicted daily values using Input 1 and 3 * 2 architecture for Missouri (R² = 0.97) and Mississippi (R² = 0.96) were better than those of Rio Grande (R² = 0.65). Daily predictions were better compared to weekly predictions for all three rivers due to higher correlation within daily than weekly data. ANN predictions for most simulations were superior compared to predictions using MLR, MNLR and ARIMA. The modeling approach presented in this paper can be potentially used to reduce the frequency of costly operations for sediment measurement where hydrological data is readily available.     

基于光谱分析的温室番茄叶片叶绿素含量预测

通过分析叶片光谱反射率与其叶绿素含量的相关关系表明,温室番茄叶片的光谱反射率与其叶绿素含量的敏感波段为510～625 nm和690～710 nm,最佳波长为526 nm、609 nm和697 nm.利用最佳波长处的光谱反射率与多种建模方法如多元回归分析MLR(Multilinear Regression)、偏最小二乘回归分析PLSR(Partial Least Square Regression)建立了预测叶片叶绿素含量的模型,建模相关系数很相近,均大于0.740,但是PLSR在一定程度上消除了多重相关性的影响,模型验证时的相关系数(0.768)大于MLR模型(0.740);本研究在NDVI的基础上提出了一个新的光谱指数NDCI,并基于NDCI建立了叶片叶绿素含量的预测模型(Rc=0.753),获得了比较好的预测效果(Rv=0.761),为作物长势检测仪的开发奠定了基础.