Forecasting China’s primary energy demand based on an improved AI model

An improved energy demand forecasting model is built based on the autoregressive distributed lag (ARDL) bounds testing approach and an adaptive genetic algorithm (AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization, and technological progress as the input variables. On the basis of historical annual data from 1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.     

Post-flooding disaster crop diversity recovery: a case study of Cowpea in Mozambique

To restore food security to a traditional African cropping system following a sudden loss of seed, genetic diversity must be re-established. This study examines the extent to which Cowpea diversity was reinstated two years after a flood disaster in Gaza Province, Mozambique. The contribution that seed from various sources made to the recovery was assessed using semi-structured interviews and morphological and molecular data. Data suggest that diversity had recovered to some extent yet there was evidence of a narrowing of the genetic base, with fewer rare alleles and differences in the distribution of allele frequencies. Although the main channels for accessing seed after the flood were seed relief and markets, these sources contributed to minimal and different diversity. It appears that diversity was regained primarily through social networking in the form of loans or gifts of seed from friends and relatives. The results of the study are discussed in relation to seed relief approaches.     

培养藻类制取生物能源的研究

能源紧张和环境污染是全世界所面临的共同问题,用藻类替代传统作物生产生物能源是生物质能的重要发展方向之一。文章介绍了国内外对藻类培养条件、光生物反应器型式、藻类基因工程、藻细胞收集方法以及能源藻类生产系统的研究成果。研究认为,以藻类为原料生产生物燃料具有良好的发展前景,并指出了今后能源藻类生长的重点研究方向。     

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。     

遗传投影寻踪回归在城市环境质量评价中的应用

引入投影降维的思想,将遗传投影寻踪与回归分析技术运用到城市环境质量评价中。将此技术与神经网络方法进行实例比较,投影寻踪回归方法不但可以合理地作出环境质量的综合评价,而且消除了神经网络方法中类别判断不够精确的影响。     

响应面法和神经网络优化Acinetobactersp．DNS32发酵基质

为了提高阿特拉津降解菌Acinetobactersp．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、K：HPO。）进行优化研究。响应曲面法确定3种成分的含量为玉米粉39．494g／L,豆饼粉25．638g／L和K。HPO。3．265g／L时,预测发酵活菌最大生物量为7．079×10^8CFU／mL,实测量为7．194×10^8CFU／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39．650g／L,豆饼粉为25．500g／L,K2HPO4为2．624g／L时,预测最大值为7．199×10^8CFU／mL,实测量为7．244×10。CFU／mL;最终确定培养基配方：玉米粉为39．650g／L,豆饼粉为25．500g／L,K2HPO4为2．624g／L,CaCO3为3．000g／L,MgSO4·7H2O和NaCl均为0．200g／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。     

慢生花生根瘤菌的遗传多样性研究

用ＲＥＰ１Ｒ１和ＲＥＰ２１ＤＮＡ为引物,对２２株四川土著慢生花生根瘤菌染色体ＤＮＡ的基因外回文重复序列（ＲｅｐｅｔｉｔｉｖｅＥｘｔｒｏｇｅｎｉｃｐａｌｉｎｄｒｏｍｉｃｓｅｑｕｅｎｃｅＲＥＰ）进行了ＰＣＲ扩增和凝胶电泳,并对电泳结果的指纹图型进行了相似性聚类分析．结果表明了四川土著慢生花生根瘤菌的遗传多样性．指纹图形是根瘤菌多样性研究中一种简便而有效的方法     

杉木地理种源遗传变异的RAPD分析

应用随机扩增多态性DNA技术对我国12个有代表性的杉木地理种源遗传多样性进行分析,从80个随要引物中筛选出20个进行扩增,共扩增出149个DNA片段,其中具多态性的片段有115个,占77．18％％,表明极木地理种源间具有丰富的DNA序列多态性、平均每个引物提供7．45个RAPD标记的信息量,扩增出的DNA片段大小一般为150到2000bp范围之间,12个杉木地理种源间遗传距离变幅为0．193－0．4467,聚类结果表明,在0．2664处,广东信宜,广西梧州,湖南会同,湖南江华,贵州锦屏,江西全南聚为一类,福建沙县,浙江开化,湖北咸宁,安徽休宁聚为一类,四川雅安,陕西南郑各为一类,图2表2参15     

Human Influence and Classical Biogeographic Predictors of Rare Species Occurrence

Biogeographic theory predicts that rare species occur more often in larger, less‐isolated habitat patches and suggests that patch size and connectivity are positive predictors of patch quality for conservation. However, in areas substantially modified by humans, rare species may be relegated to the most isolated patches. We used data from plant surveys of 81 meadow patches in the Georgia Basin of Canada and the United States to show that presence of threatened and endangered plants was positively predicted for patches that were isolated on small islands surrounded by ocean and for patches that were isolated by surrounding forest. Neither patch size nor connectivity were positive predictors of rare species occurrence. Thus, in our study area, human influence, presumably due to disturbance or introduction of competitive non‐native species, appears to have overwhelmed classical predictors of rare species distribution, such that greater patch isolation appeared to favor presence of rare species. We suggest conservation planners consider the potential advantages of protecting geographically isolated patches in human‐modified landscapes because such patches may represent the only habitats in which rare species are likely to persist. Influencia Humana y Predictores Biogeográficos Clásicos de la Ocurrencia de Especies Raras