Effect of outdoor temperature, heat primes and anchoring on belief in global warming

It is generally acknowledged that global warming is occurring, yet estimates of future climate change vary widely. Given this uncertainty, when asked about climate change, it is likely that people’s judgments may be affected by heuristics and accessible schemas. Three studies evaluated this proposition. Study 1 revealed a significant positive correlation between the outdoor temperature and beliefs in global warming. Study 2 showed that people were more likely to believe in global warming when they had first been primed with heat-related cognitions. Study 3 demonstrated that people were more likely to believe in global warming and more willing to pay to reduce global warming when they had first been exposed to a high vs. a low anchor for future increases in temperature. Together, results reveal that beliefs about global warming (and willingness to take actions to reduce global warming) are influenced by heuristics and accessible schemas. Several practical implications are discussed.     

试论地震灾害对经济社会发展的影响

通过收集整理我国大陆显著地震灾害事件与社会经济发展的相关资料,分析讨论不同经济体制下地震灾害对经济社会发展、产业结构调整等方面的影响,并对地震灾后恢复重建提出对策建议。     

人工神经网络方法在拟建小区域环境质量评价中的应用

人工神经网络的评价方法用于小区域环境质量评价中,根据本地区特点因地制宜地选择环境质量参数,代入模型中进行环境质量评价及预测,对用于环境质量评价的BP人工神经网络模型进行了改进,即对网络模型的训练样本进行了扩充,从而提高了模型的抗干扰能力和准确性.将改进了的BP人工神经网络模型应用于四川省资阳市沱江二桥拟建项目小区域的大气、地表水环境质量评价中, 对该市小区域大气、地表水环境质量状况进行评价,评价结果表明,BP人工神经网络模型用于环境质量评价是可行的,且评价结论客观,评价模型普遍适用.     

基于人工免疫原理的事故预防研究

介绍生物免疫识别模式,分析事故预防过程的实质是抑制潜在危险转化成事故的控制过程,分析比较事故预防与生物免疫识别模式在作用、生存环境、动作触发源等的共性,构建了基于免疫识别模式的事故预防系统并建立了事故预防数学模型;定义事故预防系统的识别率、失效率、误判率并给出了数学表达式,指出监测生产系统各环节状态信息是否符合系统的安全要求就是识别潜在危险的过程,是事故预防的关键,基于人工免疫原理的事故预防数学模型具有较强的健壮性、自适应性和动态防护性等特点。     

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。     

稠油污水回用锅炉处理工艺的应用

新疆油田公司稠油污水处理普遍采用成熟的离子调整旋流反应水处理技术,污水处理后达到回用锅炉水质标准SY/T 0097-2000《稠油油田采出水用于蒸汽发生器给水处理设计规范》,供给油田专用高压注汽锅炉用水。采用相同技术建成的采油一厂稠油处理站污水回用锅炉处理工艺投用后,处理后污水回用锅炉,减少了含油污水的外排,充分利用了稠油污水的热能,取得了良好的经济和社会效益。     

充分利用环保倒逼机制促进产业结构调整升级

淄博市污染聚集,环境质量易受外来因素影响,产业结构不合理,污染物排放量大,排放强度高。但其充分发挥环保的倒逼作用,通过建立健全环保政策法规体系,严格执行高于省及国家标准的地方环保标准,大力发展高新技术和现代服务业,加快改造传统产业,极大地促进了产业结构升级和经济发展方式的转变。     

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.     

基于人工蜂群算法与BP神经网络的水质评价模型

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。     

人工浮床对汾江河水质净化的研究

对佛山市汾江河内布置浮床的水域的7个断面进行采样监测,分析了布置浮床侧与未布置浮床侧各水质指标的差异,以考察人工浮床对汾江河水质的净化效果。同时比较了圆币草、水罂粟、梭鱼草、狐尾草、美人蕉和鸢尾6种浮床植物的生物量和氮磷吸收量。结果表明,布置人工浮床一侧的水质TN、TP、COD和NH4＋-N含量均显著低于未布置浮床一侧的水质,表明人工浮床对水体水质的有净化效果。总面积约4 900 m2的浮床植物经过3个月的生长,从水体中共吸收了192.5 kg的氮和76.1 kg的磷。6种浮床植物中,狐尾草的净增生物量最高,达到64.2 kg/m2;圆币草次之,为62.2 kg/m2。狐尾草和圆币草对氮磷的吸收能力在6种植物中处于较高水平,每平方米狐尾草和圆币草从水体中分别吸收了51.61g氮、19.79 g磷和46.90 g氮、22.93 g磷。综合比较得出,狐尾草和圆币草在生物量和氮磷吸收量上均保持在较高水平,是较好的浮床植物。本研究为人工浮床在南方类似河流中的应用及植物选择提供了参考依据。