临海航天发射场设施设备腐蚀控制策略研究

针对临海航天发射场设施设备金属结构腐蚀严重的问题,基于金属腐蚀机理,对发射场设施设备的差异性腐蚀损伤进行了统计和梳理,得出腐蚀成因,并提出腐蚀控制策略和研究方向的建议,为进一步提升我国航天发射场维修保障能力提供理论和技术支撑.     

数字地震仪器的管理维护及在观测应用中的几点体会

“九五”期间 ,我国建立了数字地震观测台网。在模拟地震观测向数字地震观测的转变中 ,从仪器的管理维护到地震的观测分析有许多地方需要不断地改进与完善。我台用数字地震仪观测一年多来 ,积累了一定经验 ,摸索了一些排除故障解决问题的方法。总结于此 ,以便于借鉴。     

城市道路养护作业对驾驶员安全驾驶的影响

从市政养护的满意度、驾驶员行为调查以及城市交通标志识别等方面展开调查,发现私家车、公交车和出租车对道路养护作业的满意度存在显著差异,3类车型的驾驶员在安全驾驶行为上也存在明显差异。私家车驾驶员的驾驶失误行为明显高于出租车和公交车驾驶员。违规越多的驾驶员,其驾驶攻击性行为越强。同时道路养护中的常见标志对不同类型的驾驶员产生不同的信息量。私家车驾驶员对于交通标志的理解程度明显高于出租车和公交车驾驶员。     

模拟降雨条件下生物可利用磷在地表径流中的流失和预测

本文通过模拟降雨径流实验,运用两种生物可利用磷（ＢＡＰ）的化学浸提方法,在１．２ｍｍ．ｍｉｎ＾－１的大暴雨条件下,研究在施肥在未施肥两种情况下,ＢＡＰ的径流流失方式和过程,并建立模型ＢＡＰ的预测。结果表明,运用两种ＢＡＰ的化学浸提的结果差异性不显著（ｐ＝０．０５）,并具１：１的线性相关关系;ＢＡＰ的流失通过溶解态磷（ＤＰ）和颗粒态生物可利用磷（ＢＰＰ）两种形态方式,随着降雨径流时间的延长,累积的Ｂ     

废水废气污染源在线监测系统日常维护及故障处理

伴随着我国社会现代化程度和城市化进程的不断加深,社会得到了非常好的发展,但同时也产生了大量的废气和废水,对生态环境造成了严重破坏。因此,为了将这个问题良好的解决,应建立一套具有较高科学性和合理性的废水废气污染源在线监测系统,并加大日常维护及故障处理的力度,从根源减少人们在工作生活中对生态环境造成的影响。因此,本文针对废水废气污染源在线监测系统日常维护及故障处理的有效措施,展开详细的分析,以期能够为我国生态环境可持续发展目标的实现贡献应有的力量。     

配电设备的风险评估与状态检修决策方法

针对当前配电设备采用的定期检修方式未将可靠性与经济性综合考虑的问题,提出1种基于风险评估结果确定配电设备状态检修决策的方法。首先,根据配电设备健康状态评估结果计算配电设备实时健康指数,利用指数模型对配电设备的实时故障率进行计算;然后,采用最小路法计算配网中所有设备的平均实时故障率,并结合停电时用户损失严重度、配电设备损失严重度和环境损失严重度确定配电设备故障后果严重度;最后,利用风险偏好型效用函数量化计算配电设备的经济运行风险水平,计算各配电设备的相对风险值,并以相对风险值为指标确定各配电设备的状态检修策略。结合算例对所提的方法进行验证,结果表明该方法合理有效。     

储罐底板维修决策方法研究

储罐底板腐蚀是储罐失效的主要原因之一,对底板的腐蚀检测方法目前有很多种。每种方法都有自己的优势,但同时也存在着相应的弊端。通过在实践应用中对比漏磁检测与超声测厚检测方法,分别指出两种检测方法的优势与实践应用中存在的问题,提出应综合应用两种方法来提高效率和确保准确性。为更好地利用检测技术保障储罐安全提出参考建议。     

Predicting the conservation status of data‐deficient species

There is little appreciation of the level of extinction risk faced by one‐sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data‐deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species’ life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity. Predección del Estado de Conservación de Especies con Deficiencia de Datos     

Mechanistic understanding of human–wildlife conflict through a novel application of dynamic occupancy models

Crop and livestock depredation by wildlife is a primary driver of human–wildlife conflict, a problem that threatens the coexistence of people and wildlife globally. Understanding mechanisms that underlie depredation patterns holds the key to mitigating conflicts across time and space. However, most studies do not consider imperfect detection and reporting of conflicts, which may lead to incorrect inference regarding its spatiotemporal drivers. We applied dynamic occupancy models to elephant crop depredation data from India between 2005 and 2011 to estimate crop depredation occurrence and model its underlying dynamics as a function of spatiotemporal covariates while accounting for imperfect detection of conflicts. The probability of detecting conflicts was consistently <1.0 and was negatively influenced by distance to roads and elevation gradient, averaging 0.08–0.56 across primary periods (distinct agricultural seasons within each year). The probability of crop depredation occurrence ranged from 0.29 (SE 0.09) to 0.96 (SE 0.04). The probability that sites raided by elephants in primary period t would not be raided in primary period t + 1 varied with elevation gradient in different seasons and was influenced negatively by mean rainfall and village density and positively by distance to forests. Negative effects of rainfall variation and distance to forests best explained variation in the probability that sites not raided by elephants in primary period t would be raided in primary period t + 1. With our novel application of occupancy models, we teased apart the spatiotemporal drivers of conflicts from factors that influence how they are observed, thereby allowing more reliable inference on mechanisms underlying observed conflict patterns. We found that factors associated with increased crop accessibility and availability (e.g., distance to forests and rainfall patterns) were key drivers of elephant crop depredation dynamics. Such an understanding is essential for rigorous prediction of future conflicts, a critical requirement for effective conflict management in the context of increasing human–wildlife interactions.     

基于改进JHA铜冶炼维修作业风险辨识与信息化应用

基于能量意外释放论,提出改进的JHA风险辨识方法。在按设备结构划分维修作业后,不划分作业步骤,而是根据风险来源进行辨识,实现了辨识的实用性和系统全面性。基于辨识结果,建立信息化维修作业风险信息数据库,并作为《检修项目指令书》安全措施的信息支撑。实践表明,改进的JHA辨识方法是有效的,信息化应用改变了经验式的《检修项目指令书》,提高了风险管控的有效性。