化工废气排放量灰色预测的GPSM（1）模型

在对ＧＩＭ（１）模型研究的基础上，提出了一种化工废气排放的单变量灰色幂级数曲线预测新模型「简称（ＧＰＳＭ（１）」，并建立了辨识模型参数的优化方法。结果表明，ＧＰＳＭ（１）在有线性输出的平稳系统中，比ＧＭ（１，１）更能反映原始资料提供的信息，系统分辨率高，同态性好，预测精度理想，为环境系统的拟合、预测分析和决策开辟了新途径，从而拓宽了ＧＭ（１，１）模型在环境科学领域中的应用范围。     

基于GM(1,1)模型的铁路行车事故预测

铁路行车事故的发生具有偶然性 ,但可利用预测理论的方法加以预测。笔者简述了灰色理论中GM(1,1)预测模型的建模过程和精度检验的方法及步骤 ,在某铁路分局 1995～ 2 0 0 2年间的铁路行车事故统计的基础上 ,应用GM(1,1)预测模型预测了该铁路分局 2 0 0 4年的铁路行车事故发生趋势 ,经精度检验表明 ,模型的精度等级合格 ,其预测结果为铁路行车事故的防范和降低事故损失提供了指导和科学依据。     

GM（1,1）模型简化算法及其在噪声影响预测中的应用

对ＧＭ（１，１）模型经差分形式化及令定写成直线方程形式。据最小二乘法原理，原方程的参数辨识可借助线性回归方法来实现。从而使ＧＭ（１，１）模型的应用更显普及和实用化。     

转基因作物对鱼类的生态毒理效应研究综述

转基因作物潜在的健康和环境风险一直以来颇受争议,转基因作物加工成动物饲料后可能会诱导动物产生免疫应激反应,影响动物的生长发育、繁殖等。鱼类是水生脊椎动物的代表,已广泛应用于水环境的监测,但目前转基因作物对鱼类的饲用安全性研究还相对较少。文章基于转基因作物作为鱼饲料原料对鱼类生态毒理学效应的研究现状,综述了转基因作物对鱼的生长表现、生理生化、脏器功能及发育、组织病理以及行为活动等方面的生态毒理学效应,分析了当前研究中存在的问题,并对今后的研究趋势进行了探讨。     

灰色系统理论在湖泊水质预测建模中的应用

本文将灰色系统理论应用于湖泊水质预测建模研究,提出了湖泊水质预测的建模方法,并建立了某湖化学需氧量(COD)的GM(1,1)预测模型。      

Dispersal and persistence of genetically modified oilseed rape around Japanese harbors

Background, aim, and scope

The possibility of gene transfer from genetically modified oilseed rape (OSR) to its cultivated or wild relatives is of concern since its commercial cultivation, because of its potential weediness and impact on the environment. Introgression of modified genes can affect conservation of agricultural crops, because there are many cultivars and wild Brassicaceae that may cross with genetically modified OSR (Brassica napus) in Japan. Japan imports more than 2 million tons of OSR a year from Canada and other countries. Since volunteers of GM OSR were found around harbors in 2004, a lot of feral GM OSR was discovered in Japan. To consider the way how to keep domestic Brassicaceae from GM contamination, we surveyed and analyzed the dispersal and persistence of GM OSR around Japanese harbors. We present the cause and abundance of GM OSR in Japan by this paper.

Materials and methods

Survey of the feral OSR was performed several times a year at different seasons when domestic OSR either grows or does not around port areas. Detection of herbicide tolerance in feral B. napus was done by test papers that cross react with the modified gene product. Two kinds of herbicide tolerance (glyphosate and glufosinate) were tested.

Results

The feral B. napus were discovered around all 13 harbors that import rapeseeds from foreign countries. Genetically modified, herbicide-tolerant OSR were frequently found in the surveyed populations. Two kinds of herbicide-tolerant OSR (glyphosate- and glufosinate-tolerant) were discovered in a natural condition 40 km from port to an oil factory where 60,000 tons of OSR seed are processed a year. The cause of voluntary growth of OSR is seed spillage during transportation by trucks from harbors to oil factories and other processing facilities. Some of the feral OSR growing along the roadsides of transport paths exhibited perennial growth spilling their seeds around the places. Alteration of the generation of feral GM OSR was discovered for the first time in Japan as a result of this study. We studied the yearly change of feral OSR abundances focusing on Yokkaichi port over the 4 years since 2004. The rate of GM OSR increases year to year, and reaches nearly 90% in 2008.

Discussion

The possibility and consequences of gene transfer from the genetically modified OSR to domestic species (B. rapa and B. juncea) were discussed in relation to impact on domestic agriculture and on environments. Evolutional meaning of the gene transfer was also discussed with respect to the gene construct of GM OSR. This study shows the importance of another pathway of modified gene transfer to non-GM relative species by seed transportation in addition to pollen transfer from commercial cultivation of genetically modified OSR.

Conclusions and recommendations

We identified unintended dispersal and persistence of GM OSR around Japanese harbors that import OSR from Canada and other countries. Both glyphosate- and glufosinate-tolerant feral B. napus were discovered. The cause of volunteer OSR is spillage of the seeds during transportation by truck to oil factory. The feral GM OSR sometimes showed perennial growth in Japanese phonological conditions which are not observed for domestic Brassicaceae. In addition, we confirmed an alteration of generations by feral GM OSR in Japan. The possibility of cross pollination and GM gene introgression to domestic varieties can occur in these environments. To improve the situation, each responsible organization, company, administration, or government should establish measures how to stop the dispersal and persistence of GM OSR in nature. Also, the GM plant developers are responsible for revising this situation.     

灰色Elman神经网络在火灾事故预测中的应用研究

针对我国火灾事故现状,结合灰色理论和神经网络的特点,提出灰色Elman神经网络火灾事故预测模型。依据我国1998—2007年火灾事故统计数据,分别选用GM(1,1)模型和灰色Elman神经网络模型对1998—2005年数据(火灾起数)进行拟合,对2006年、2007年数据进行(火灾起数)预测。结果表明:灰色Elman神经网络火灾事故预测模型符合火灾事故的特点;有效地解决了传统灰色预测模型在火灾事故预测中误差大稳定性差的缺陷,提高了预测精度;可对火灾事故进行预测与分析,为消防安全管理提供依据,以最大限度地减少火灾事故的发生。     

基于灰色BP网络的火灾事故预测

将BP网络模型与灰色系统预测方法相结合,利用人工神经网络对GM(1,1)模型进行残差修正,建立了火灾事故预测模型.该模型兼具灰色系统与BP网络的优点,不但能体现火灾复杂的灰色系统行为,又能根据每次学习误差的不同调整学习速率,加快收敛速度.与单一的GM(1,1)模型相比,该模型预测结果具有更高的精度.     

硫化矿石堆自燃的灰色预测研究

采用灰色预测模型GM(1,1)对硫化矿石堆的自燃进行预测.根据相同间隔时间的矿石温度,通过灰色计算得出下一个或以后几个时间段的温度,将预测的温度与已有试验数据进行比较;参照硫化矿的发火周期,预测几天后矿石堆的自热状态,从而指导工作人员采取相应的防、灭火措施.实例验证表明,GM(1,1)适用于硫化矿的自燃预测.本研究对矿山安全建设工作具有一定的参考价值.     

Modified grey model for estimating traffic tunnel air quality

This study compared three forecasting models based on the mean absolute percentage errors (MAPE) of their accuracy in forecasting air pollution in a traffic tunnel: the Grey model (GM), the combination model used four sample point and five sample point prediction with GM (1,1)(GM(1,1)^4 + 5), and the modified grey model (MGM). An MGM was combined using the four points of the original sequence using the original grey prediction GM (1,1) for short-term forecasting. The proposed method cannot only enhance the prediction accuracy of the original grey model, but can also solve the jump data forecasting problem something for which the original grey model is inappropriate. The MAPE was applied to the models, and the MGM found the proposed method to be simple and efficient. The MAPE of MGM, calculated over 3 h of forecasts, were as follows: 10.12 (Upwind), 10.07 (Middle) and 7.68 (Downwind) for CO; 10.79 (Upwind), 6.05 (Middle) and 5.98 (Downwind) for NO _x , and 11.67 (Upwind), 7.32 (Middle) and 4.56 (Downwind) for NMHC. The MGM model results reveal that the combined forecasts can significantly decrease the overall forecasting error. Results of this demonstrate that MGM can accurately forecast air pollution in the Kaohsiung Chung–Cheng Tunnel.