Frog, Bat, and Dung Beetle Diversity in the Cloud Forest and Coffee Agroecosystems of Veracruz, Mexico

Abstract:  We compared the species diversity of copronecrophagous beetles (Scarabaeinae) , bats, and frogs in tropical montane cloud forest (original vegetation) and shaded coffee plantations (an agroecosystem common to the region) for a landscape in central Veracruz, Mexico. We sampled in three tropical montane cloud forest fragments and in three coffee plantations with traditional polyculture shade between 1998 and 2001. The three indicator groups responded differently to the transformation of tropical montane cloud forest into shaded coffee plantations. The species richness of frogs was one-fifth less in coffee plantations than in forest fragments, and only one-third of the frog species occurred in both forest fragments and coffee plantations. The number of beetle species and their abundance was significantly greater in coffee plantations than in the forest fragments, whereas species richness and species composition of bats were virtually the same in both habitats. The majority of the abundant species remained as such in both communities, but species that were less abundant were not scarce in both habitats. We attributed differences in the species assemblages to the differing degrees of penetrability of the borders of the two habitat types (especially for the coffee plantations) and to the differences in life-history traits among species. Shaded coffee plantations form a matrix that envelops the remaining fragments of cloud forest. Together they connect the forest fragments with the other habitats of the landscape and represent a highly functional resource for the preservation of biodiversity that serves as a complement to but not a substitute for cloud forest in this notably modified landscape.     

Using the expert model PERPEST to translate measured and predicted pesticide exposure data into ecological risks

An important topic in the registration of pesticides and the interpretation of monitoring data is the estimation of the consequences of a certain concentration of a pesticide for the ecology of aquatic ecosystems. Solving these problems requires predictions of the expected response of the ecosystem to chemical stress. Up until now, a dominant approach to come up with such a prediction is the use of simulation models or safety factors. The disadvantage of the use of safety factors is a crude method that does not provide any insight into the concentration–response relationships at the ecosystem level. On the other hand, simulation models also have serious drawbacks like that they are often very complex, lack transparency, their implementation is expensive and there may be a compilation of errors, due to uncertainties in parameters and processes. In this paper, we present the expert model prediction of the ecological risks of pesticides (PERPEST) that overcomes these problems. It predicts the effects of a given concentration of a pesticide based on the outcome of already performed experiments using experimental ecosystems. This has the great advantage that the outcome is more realistic. The paper especially discusses how this model can be used to translate measured and predicted concentrations of pesticides into ecological risks, by taking data on measured and predicted concentrations of atrazine as an example. It is argued that this model can be of great use to evaluate the outcome of chemical monitoring programmes (e.g. performed in the light of the Water Framework Directive) and can even be used to evaluate the effects of mixtures.     

Inhibiting effects by Fe2O3 on combustion and explosion characteristics of ABS resin

The global increase in the use of, and reliance on, plastics has prompted the demand for acrylonitrile-butadiene-styrene (ABS) resin in various fields. With this increased requirement, numerous failures have occurred in the ABS process. Those incidents, resulting from electrostatic discharge, powder accumulation, heat accumulation, construction sparks, and plant fires, have caused dust fire and explosions.In this study, the ABS resin was gleaned from the site and tested for its explosion parameters, including minimum ignition temperature of dust cloud (MIT_C), minimum ignition energy (MIE), and minimum explosion concentration (MEC). To improve loss prevention in the manufacturing process, ferric oxide (Fe₂O₃) as an inert additive was added in the ABS powder. According to the MIE test, Fe₂O₃ has an apparent inhibiting effect on dust explosion for the ABS dust. With the proportion of Fe₂O₃ increased from 25 to 50 mass% in ABS, the MIE increased from 67 to 540 mJ. The explosion tests via 20-L apparatus indicated that Fe₂O₃ mixed with ABS could not increase the MEC significantly. However, the explosion pressure dropped by increasing in the ratio of Fe₂O₃ in ABS. This inerting strategy of ABS was deemed to substantially lessen the probability and severity of fire and explosion.     

考虑驾驶员反应时间的车辆碰撞预警模型

为了保证车辆在行驶过程中的安全性,提出了一种考虑驾驶员反应时间的车辆碰撞预警模型,改进了传统模型中驾驶员反应时间定值化的缺点。首先,依据车辆的制动过程分析了驾驶员反应时间对制动距离的影响。其次,设计驾驶员反应时间的模糊推理算法,选取驾龄、疲劳强度和应变能力3个主要因素作为评价指标来计算反应时间。最后,采用分等级的预警策略建立考虑驾驶员反应时间的碰撞预警模型,并通过Carsim-Matlab/Simulink联合仿真与传统模型进行对比分析。结果表明,设计的预警模型可以对不同类型的驾驶员进行差异化碰撞预警,在30 km/h和80 km/h两种车速下实际停车距离与理论值的最大误差为8%。     

云对太阳紫外辐射的影响

云对到达地面的太阳紫外辐射有着重要的影响,根据在香河综合观测站１年的观测资料,利用相关分析,得到了计算云天条件下太阳紫外总辐射的一种经验公式,计算结果比较令人满意。对阴天太阳紫外总辐射的减弱作了简单的分析。     

层状云酸化过程的数值模拟

修正和发展了一维层状云物理化学模式,该模式包括了详细的气?液相反应和光辐射传输,在我国东部地区平均污染水平下模拟了降水酸化过程?结果表明:光解系数在云顶为无云时的2～3倍,在云底则为无云时的0.2倍;自由基对S(Ⅳ)的氧化在降水后期可超过H₂O₂,并致使SO2-₄和NO-₃浓度在降水后期有逐渐增长的趋势;由于H₂SO₄和HNO₃的不断生成以及NH3和气溶胶的洗脱,地面雨水酸度持续上升?      

化工火灾事故调查方法研究

为克服化工火灾调查中人为因素的影响,增强事故调查的科学性和准确性,在总结化工火灾现场特点的基础上,提出化工火灾事故调查时应遵循的假设推理法,通过证据收集、分析资料、建立假设、检验假设和选择假设,构建化工火灾事故调查的证据系统,包括实物证据,位置证据、文件证据和人证,从火灾动力学角度分析了火灾痕迹的影响因素。结果表明:假设推理法重视证据收集,综合使用归纳推理和演绎推理,有助于避免主观判断的影响;采用系统方法收集证据,扩大证据收集的范围,列举证据清单和优先考虑敏感性证据的方法有助于提高调查效率;火灾行为、材料的热响应和灭火行动对火灾痕迹的形成有直接影响,分析时应充分考虑,以免作出错误的判断。     

重大危险源事故风险预警技术研究

重大危险源事故风险预警技术是事故控制技术中的一大研究重点。本文通过对比分析安全生产领域常用事故预警模型的优缺点,总结了事故预警模型的发展趋势,提出了基于模糊综合评判技术和动态模糊神经网络技术的重大危险源事故风险预警模型,详细陈述其构建过程,最后对液化石油气蒸气云爆炸事故应用该模型,得到了较好的预测效果,实现了风险程度的定量化预警。该预警模型具有快速的自学习能力和容错能力,能够同时处理多种风险因素、自动生成模糊规则并满足预警系统的实时性要求,可应用于重大危险源在线监控预警系统,为企业端和政府端监管者提供有效的重大危险源事故风险定量化预警信息,为遏制重大灾难事故的发生、减少死亡人、数受伤人数和直接经济损失提供先进的理论和技术支撑。     

山区村镇洪灾承灾能力评估——以神农架林区为例

针对我国山区村镇洪灾特点,以神农架林区为对象,提出了基于云模型的洪灾承灾能力评价方法。针对山区村镇条件和山洪特点,构建评估指标体系;建立基于逆向云发生器和综合云算法结合专家打分区间和最可能值确定指标权重、使用条件云发生器获得量化指标的评价水平、耦合得到评价结果的评价方法,并对全区八个乡镇的山洪承灾能力进行了评估,为林区规划提供科学依据。结果表明,林区各村镇山洪承灾能力总体较低,孕灾环境恶劣,专项应灾能力明显落后,其中松柏镇、阳日镇、红坪镇和下谷坪乡属于低水平,木鱼镇与新华镇属于较低水平,宋洛乡属于中等水平,九湖镇为高水平。案例结果表明该方法能够直观地将客观数据和主观认识反映为综合结果,有利于获得可靠的结论。     

建筑安全事故应急措施推理研究

为了加强对建筑安全事故历史案例经验的利用,提高建筑安全事故应急管理的信息化水平,对如何利用智能化技术制定事故应急措施进行研究。将规则推理引入案例推理中,建立了建筑安全事故应急措施推理模型。通过计算综合相似度,在案例库中检索出相似案例的基础上,根据关键属性运用规则推理对相似案例的应急措施进行适用性修改。计算特征属性权重时,利用灰色关联分析法对传统粗糙集方法进行改进,对冗余属性提出了灰色重要度的概念,避免了冗余属性权重为0的情况。选取基坑事故案例对模型进行实例分析,结果表明:建筑安全事故应急措施推理模型具有普遍适用性,可应用于事故应急管理。