面向突发性水污染事件的三维可视化系统

针对突发性水污染事件的突发性、复杂性与潜在的次生衍生危害等特点,设计并开发了面向突发性水污染事件的三维可视化系统。对建设三维可视化系统的需求进行了分析,提出了系统的四层体系结构,基于三维地理信息系统World Wind组件,采用三维可视化环境建模、海量数据缓存机制以及基础地理信息集成等信息技术,开发了系统的基本应用与高级应用,将系统应用到2005年发生在松花江流域的突发性水污染事件中。实例表明:所设计的三维可视化系统具有集成性、可扩展性和较好三维表现力等特点,支持海量数据与复杂应用的集成与三维展示,为突发性水污染事件的空间模拟和决策支持提供支撑。     

如何确定高加速寿命试验的应力极限

气锤驱动的高加速寿命试验,主要目的是确定试验样品的应力极限(包括工作极限和破坏极限),并通过改进试验所暴露的缺陷,按预期目标拓宽产品的应力极限。本文对一些应力极限的名词术语作了解释性说明,并具体论述了如何确定应力极限及快速温变循环试验的高低温温度极值。     

大型会展对举办地旅游形象的提升路径及效应分析——以上海世博会为例

大型会展对提升举办地旅游形象效应成为旅游学界当前关注的热点。以上海世博会为例，采用案例分析法和文献研究法，在分析过程中结合定量分析和定性分析，研究大型会展对旅游地的形象效应。研究表明，上海世博会主要通过聚焦作用下的旅游形象建设、光晕遮蔽作用下的网络宣传、世博宣传片与上海旅游形象联手营销、旅游企业的积极行为以及政府的支持与配合等5种途径提升上海都市商务会展、都市文化、都市休闲等旅游形象，提出利用大型会展的聚焦作用和光晕遮蔽作用提高举办地知名度，将大型会展宣传与举办地旅游形象宣传相结合，将大型会展建设融人到城市的旅游形象建设之中等3个方面提高旅游地形象。     

Specific features of seedage in Norway spruce (Picea abies karst.) in the northern Moscow region

Climatic characteristics of the northern Moscow region and the periodicity of seedage in Norway spruce were analyzed retrospectively. The relationship between the intensity of seedage and weather conditions in the period before seedage was revealed.     

Wet weather impact on trihalomethane formation potential in tributaries to drinking water reservoirs

During rain storm events, land surface runoff and resuspension of bottom sediments cause an increase in Trihalomethane (THM) precursors in rivers. These precursors, when chlorinated at water treatment facilities will lead to the formation of THMs and hence impact drinking water resources. In order to evaluate the wet weather impact on the potential formation of THMs, river samples were collected before, during and after three rain storms ranging from 15.2 to 24.9 mm precipitation. The samples were tested for THM formation potential and other indicators including UV254 absorbance, turbidity and volatile suspended solid (VSS). Average levels of THMs increased from 61 μg/l during dry weather to 131 μg/l during wet weather, and then went back to 81 μg/l after rain ended. Wet weather values of THM are well above the maximum contaminant level (MCL) 80 μg/l, set by EPA for drinking water. THM indicators also exhibited similar trends. Average levels increased from 0.6 to 1.8 abs; 2.6 to 6 ntu; and 7.5 to 15 mg/l respectively for UV254, turbidity and VSS. A positive correlation was observed between THM formation and THM indicators. The t-test of significance (p-value) was less than 0.05 for all indicators, and R values ranged from 0.85 to 0.92 between THMs and the indicators, and 0.72 to 0.9 among indicators themselves.     

建筑物消防系统可靠性分析

为评估建筑物消防系统的可靠性,首先将系统分为探测报警系统和灭火系统等部分,再用事件树法分析事故发生时消防系统不同部分的反应情况.通过贝叶斯理论及历史统计数据得到系统失效率的不确定性概率分布,建立消防系统可靠性随时间变化的数学模型; 用蒙特卡罗方法模拟求得系统可靠性的时间函数并对模型的不确定性参数进行敏感性分析.该方法将统计数据与经验公式、理论方法相结合,并利用蒙特卡罗方法处理模型中的不确定性,不仅能够有效估计消防系统的可靠性,还可对其他类似系统的可靠性进行分析,并通过敏感性分析为进一步减少估计的不确定性提出合理建议.     

贵广I回工程HOM3系统软件缺陷改进方法

通过分析贵广I回工程HOM3光测量系统在极控和直流滤波器保护系统中的软件,找到了软件在处理激光管告警信号和通讯故障信息的差异和缺陷,并提出了修改方法。修改方法经过实时数字仿真系统(RTDS)仿真验证,能够区分激光管告警和通讯故障两种不同的顺序事件记录(SER)信息,并准确定位发生通讯故障的通道,对提高工程安全稳定运行具有重要意义。     

基于灰色模糊集理论的船舶搁浅概率分析

为深入分析船舶搁浅概率,在随机性和模糊性的基础上,考虑了系统的灰色性,建立灰色模糊集,模部为正态模糊数,灰部以系统的灰度熵来表示。建立了船舶航行计划失效造成船舶搁浅的事件树,构造了船舶搁浅概率的灰色模糊隶属度函数,根据各基本事件的失效概率来分析因航行计划失效造成船舶搁浅事故的灰色模糊概率分布情况。结果表明,与经典概率论和模糊概率相比,采用灰色模糊概率更能全面地反映船舶搁浅概率的可能性与不确定性。并采用RRW法和RAW法对基本事件的重要度进行了分析,找出了事故主要诱因及对降低事故风险价值最大的基本事件。     

基于SA-ELM的爆破震动速度预测

针对爆破震动速度与其影响因素之间的复杂非线性,结合模拟退火算法(SA)的全局寻优性,提出了一种新的SA-ELM算法.以矿山周边建筑物爆破震动实测数据作为训练样本,选取总药量、最大段药量、测点与爆破点距离、地面震动特性、建筑物震动特性等8个影响因素作为输入变量,建立了爆破震动速度预测的SA-ELM模型.模型训练值和预测值与实测值的均方误差(MSE)分别为0.20和3.26,平均相对误差控制在5％以内,显示出该模型具有良好的训练精度和泛化能力.对比传统ELM模型,SA-ELM模型不但提高了精度和泛化能力,而且降低了隐层节点数变化对训练结果的影响,提高了模型的稳定性.     

Estimated Injury Risk for Specific Injuries and Body Regions in Frontal Motor Vehicle Crashes

Objective: Injury risk curves estimate motor vehicle crash (MVC) occupant injury risk from vehicle, crash, and/or occupant factors. Many vehicles are equipped with event data recorders (EDRs) that collect data including the crash speed and restraint status during a MVC. This study's goal was to use regulation-required data elements for EDRs to compute occupant injury risk for (1) specific injuries and (2) specific body regions in frontal MVCs from weighted NASS-CDS data.

Methods: Logistic regression analysis of NASS-CDS single-impact frontal MVCs involving front seat occupants with frontal airbag deployment was used to produce 23 risk curves for specific injuries and 17 risk curves for Abbreviated Injury Scale (AIS) 2+ to 5+ body region injuries. Risk curves were produced for the following body regions: head and thorax (AIS 2+, 3+, 4+, 5+), face (AIS 2+), abdomen, spine, upper extremity, and lower extremity (AIS 2+, 3+). Injury risk with 95% confidence intervals was estimated for 15–105 km/h longitudinal delta-Vs and belt status was adjusted for as a covariate.

Results: Overall, belted occupants had lower estimated risks compared to unbelted occupants and the risk of injury increased as longitudinal delta-V increased. Belt status was a significant predictor for 13 specific injuries and all body region injuries with the exception of AIS 2+ and 3+ spine injuries. Specific injuries and body region injuries that occurred more frequently in NASS-CDS also tended to carry higher risks when evaluated at a 56 km/h longitudinal delta-V. In the belted population, injury risks that ranked in the top 33% included 4 upper extremity fractures (ulna, radius, clavicle, carpus/metacarpus), 2 lower extremity fractures (fibula, metatarsal/tarsal), and a knee sprain (2.4–4.6% risk). Unbelted injury risks ranked in the top 33% included 4 lower extremity fractures (femur, fibula, metatarsal/tarsal, patella), 2 head injuries with less than one hour or unspecified prior unconsciousness, and a lung contusion (4.6–9.9% risk). The 6 body region curves with the highest risks were for AIS 2+ lower extremity, upper extremity, thorax, and head injury and AIS 3+ lower extremity and thorax injury (15.9–43.8% risk).

Conclusions: These injury risk curves can be implemented into advanced automatic crash notification (AACN) algorithms that utilize vehicle EDR measurements to predict occupant injury immediately following a MVC. Through integration with AACN, these injury risk curves can provide emergency medical services (EMS) and other patient care providers with information on suspected occupant injuries to improve injury detection and patient triage.