海洋活性胶原肽酶解液的脱色脱腥工艺

以富含胶原的海产品下脚料为原料,用海洋碱性蛋白酶894水解,得到具有清除羟自由基活性的酶解液.采用一次正交回归实验设计及结果分析,建立回归方程,研究了活性炭、β-环糊精、酵母三者用量及温度、pH和作用时间6因子及其可能存在交互作用的变化关系对该酶解液综合脱色脱腥效果的影响.结果表明,在给定取值范围内,pH对综合脱色脱腥效果影响高度显著,酵母添加量、pH与酵母添加量的交互作用影响显著,其它因子及其可能的交互作用影响不显著,得最佳工艺为:温度37℃、pH 4.0、活性炭0.8%、β-环糊精和酵母分别为0.1%,作用30 min.此时,综合脱色脱腥效果值为90.90,蛋白回收率为98.02%,羟自由基清除率的IC50浓度从原来的1.42 mg·mL-1增加到1.62 mg·mL-1.     

烟台芦洋湾鼠尾藻种群生物量结构的季节变化

2005年2月至2006年1月对烟台芦洋湾鼠尾藻(Sargassum thunbergii)的生长情况进行了生态学调查。实验设立固定样方观测了鼠尾藻种群结构的季节变化,采用逐步回归分析法分析了生境因子对鼠尾藻生物量的影响。结果表明,(1)鼠尾藻分布在低潮线以上50～125cm的中潮带和低潮带之间;(2)生物量消长和平均藻体长度消长的季节变化趋势一致,呈双峰曲线(7月份和12月份达到峰值,9月份达到最低值);(3)生活周期可以划分为4个时期:休止期、生长期、繁殖期和衰退期,6月中旬到7月下旬为有性生殖期;(4)营养生殖贯穿全年,并呈现一定的季节变化;(5)快速生长期内(5-7月),其大小级层次明显,近似正态分布;(6)水温为影响其生长的主要因素,其次为浪冲击度和人为干扰,干露对鼠尾藻的生长影响不显著。总之,本调查区域的鼠尾藻种群呈现明显的季节变化,并与其他海区种群差异显著,生长地区的环境因素是导致差异的根本原因。     

Predicting the growth of gilthead sea bream (Sparus aurata L.) farmed in marine cages under real production conditions using temperature- and time-dependent models

The growth of gilthead sea bream ( Sparus aurata ) has been studied considering five multiple exponential regression models using data from 20 lots of gilthead sea bream growing in 20 marine cages from a Mediterranean commercial fish farm. The final weight ( W _f) of fish was predicted in relation to the initial weight ( W _i), time ( n ) and temperature ( T ), or the sum of effective temperatures (∑ T _ef). The estimated weight results from the simulation using the five models have been compared with the real final weight values using the mean of the absolute values of the prediction errors in short and long term (the precision value). All models presented a high determination coefficient, above 96%, and good prediction values in the short term. Regression models were tested using data from six new cages. The best models for predicting the growth of sea bream long term were the ones where final weight is expressed in relation to the initial weight and the sum of effective temperature,     and     obtaining long-term prediction errors 12.9% and 10.7% respectively.     

Use of landscape pattern metrics and multiscale data in aquatic species distribution models: a case study of a freshwater mussel

The distributions of freshwater mussels are controlled by landscape factors operating at multiple spatial scales. Changes in land use/land cover (LULC) have been implicated in severe population declines and range contractions of freshwater mussels across North America. Despite widespread recognition of multiscale influences few studies have addressed these issues when developing distribution models. Furthermore, most studies have disregarded the role of landscape pattern in regulating aquatic species distributions, focusing only on landscape composition. In this study, the distribution of Rabbitsfoot (Quadrula cylindrica) in the upper Green River system (Ohio River drainage) is modeled with environmental variables from multiple scales: subcatchment, riparian buffer, and reach buffer. Four types of landscape environment metrics are used, including: LULC pattern, LULC composition, soil composition, and geology composition. The study shows that LULC pattern metrics are very useful in modeling the distribution of Rabbitsfoot. Together with LULC compositional metrics, pattern metrics permit a more detailed analysis of functional linkages between aquatic species distributions and landscape structure. Moreover, the inclusion of multiple spatial scales is necessary to accurately model the hierarchical processes in stream systems. Geomorphic features play important roles in regulating species distributions at intermediate and large scales while LULC variables appear more influential at proximal scales.     

Simulation of artificial neural network model for trunk sap flow of Pyrus pyrifolia and its comparison with multiple-linear regression

Trunk sap flow of tree is an important index in the irrigation decision of orchard. On the basis of the measured sap flow (SF) of pear tree (Pyrus pyrifolia) in the field, the multiple-linear regression for simulating the SF was obtained after analyzing the relationships between the SF and its affecting factors in this study and an artificial neural network (ANN) technique was applied to construct a nonlinear mapping to simulate the SF, then the simulated SF by two models was, respectively, compared to the measured value. Results showed that trunk SF had significant relationship with the vapour pressure deficit (VPD) in the single-variable analysis method but with soil volumetric water content (θ) using the ANN models with default of different variables. The correlation coefficient (R²), mean relative error (MRE) and root mean square error (RMSE) between the measured and simulated sap flows by the ANN model developed by taking VPD, solar radiation (S_r), air temperature (T), wind speed (W_s), θ, leaf area index (LAI) as the input variables were 0.953, 10.0% and 5.33 L d⁻¹, respectively, and the simulation precision of ANN model was superior to that of multiple-linear regression due to its better performance for the nonlinear relationship between trunk SF and its affecting factors, thus ANN model can simulate trunk sap flow and then may help the efficient water management of orchard.     

基于计算机视觉与敲击振动融合技术检测鸭蛋壳强度

通过对敲击振动特征频率矩阵变异系数和测定有裂纹蛋壳强度的探索性分析,获得鸭蛋壳强度特征频率的分离阈值λ=0.02.通过传感器数据关联.确定了进入模型的特征变量.采用后向消去法进行多元线性回归建立了无裂纹鸭蛋壳强度预测模型,结合蛋壳强度分离阈值得出基于特征层多传感器融合鸭蛋壳强度的系统模型.验证性试验表明该模型有较好适应性.对于各种颜色、形态鸭蛋的蛋壳强度都有较好的预测效果,误差均值小于3.1%,误差标准差为0.003.     

环境规制政策情境下农户认知对农业绿色生产意愿的影响——来自黑龙江省698个种植户数据的验证

为提高农业绿色生产意愿,引导农户从事农业绿色生产,结合对外部性理论、农户行为理论、计划行为理论等相关理论的分析,基于对黑龙江省13市47村698个种植户的调查数据,构建二元Logistic回归模型检验农户认知对农业绿色生产意愿的影响机制,并将环境规制作为调节变量,探究环境规制对农户认知—农业绿色生产意愿关系的调节效应....     

哑变量在燕山地区华北落叶松人工林生长模型中的应用1）

以燕山地区华北落叶松人工林为研究对象,在收集大量临时样地和解析木资料的基础上,筛选出合适的数据,根据承德市隆化县林管局、木兰林管局和塞罕坝机械林场3个地方的地区特征引入哑变量的概念,利用非线性回归的方法对华北落叶松人工林胸径、树高、材积生长过程进行拟合,结果显示3个地区华北落叶松人工林的胸径、树高、材积生长量没有显著差异。基于哑变量建立了3个地区华北落叶松人工林的胸径、树高、材积生长模型。结果表明：含有哑变量的Schumacher模型对华北落叶松人工林胸径、树高生长过程的拟合效果最好,决定系数R2分别达到了0．9941和0．9929,残差平方和分别为4．7339和6．8849;含有哑变量的二次函数对华北落叶松人工林的材积生长过程拟合最好,决定系数R2为0．9928,残差平方和为0．0023。通过对所建立生长模型的适应性检验,结果表明残差分布比较均匀,预估精度都达到95％以上,说明模型的预估效果比较好。