Kalman最优平滑器在滑坡位移监测数据处理中的应用

针对传统方法中利用Kalman滤波器来处理物体的形变数据存在精度低的缺点,依据Kalman平滑比滤波的精度要高的原理,提出了将滑坡形变的估计问题视为Kalman平滑问题,而不仅仅是Kalman滤波问题;利用Kalman最优平滑器来处理滑坡位移监测数据,可使滑坡预测预报的精度得到较大提高,并在此基础上给出了一种固定区间平滑算法;最后,以龙羊峡龙西滑坡为例进行了实例分析,结果表明本文提出的方法是可行和有效的。此外,结论中针对实际运用中存在的问题提出了相应的建议。     

健康风险暴露评价研究进展

综述了国内外健康风险暴露评价的最新研究进展,重点讨论了对人体进行直接监测的生物监测技术和对环境中污染因子进行间接监测并利用数学模型进行暴露剂量计算的间接方法。生物监测方法通过测定人体生理介质(如血液、尿液)中的污染物质及其代谢产物含量确定人体对环境污染物的暴露情况,监测结果反映了风险因子通过所有暴露途径进入人体的总暴露剂量。为了利用生物监测结果评价人体暴露安全性,近几年建立了生物监测等效值的概念,推导确定化学物质的生物监测等效值发展迅速。环境监测和数学模型间接方法通过对不同暴露媒介中风险因子的浓度监测和特定暴露途径的量化研究,同时利用精确的暴露计算模型(如空气分散模型、地下水扩散模型)计算人体对污染物的暴露剂量。生物监测和环境监测技术及数学模型的发展使健康风险评价和管理的暴露参数更加精确,降低了风险评价的不确定性。还介绍了利用数学模拟和剂量重建等方法插补历史空白暴露数据的方法。     

大型排架施工安全监控机制研究

以大型排架为研究对象,研究排架从设计开始、搭设过程、排架使用过程、排架拆除等环节,从安全组织、技术措施、监督检查、安全许可等方面严格把关,对工程建设参建各方应该履行的安全生产管理职责、权限、工作内容及工作流程进行梳理和细化,设置控制节点,强化中间环节的安全检查,实现排架全生命过程的安全控制,研究成果以程序文件表现。大型排架施工过程监控程序文件中有工作流程8个,对应安全检查表10个,工作流程环环相扣,安全检查表间相互关联。通过实施大型排架施工过程监控程序文件,严格过程监督管理,保证高排架在搭设与使用中不发生安全生产事故,有效杜绝了类似于高排架垮塌伤人等群体性伤害安全事故。     

西安地铁车站深基坑变形规律FLAC模拟研究

开展西安地铁深基坑变形规律理论与监测的研究对指导西北地区深基坑信息化施工具有重要价值。本文以西安地铁2号线某车站深基坑工程为背景,完成了车站深基坑施工监测方案设计,对深基坑施工过程进行了FLAC计算模拟,重点研究了桩体变形、钢支撑轴力、基坑周边地表变形规律。结果表明,复杂环境下城市地铁车站站深基坑明挖施工时,现场监测是信息化安全施工的保证,采用钻孔灌注桩和钢支撑的复合围护方案作为车站深基坑的围护结构是合理的,土方分层开挖方式和钢支撑预应力施加是减少空间效应保证安全施工的重要措施。桩身水平位移特别是桩顶水平位移是围护结构变形特性的直接反映,围护桩变形最大的地方为基坑中部到三分之二基坑深度处。基坑围护结构附近的地面隆起量明显小于基坑中部的隆起量,随着开挖深度增大,隆起量逐渐由基坑中部最大转变为两边大中间小的型式。     

某市区重点行业职业病危害因素现状调查研究

对某市区重点行业15家企业职业病危害因素进行检测与评价,对普查结果进行综合分析。结果表明:15家企业工作场所化学有害因素职业卫生现状情况良好,但噪声危害程度较大;在6个重点行业中,电子制造行业有害因素合格率最低,为84%,其次是纺织行业,有害因素合格率为85%,其他化学原料及化学制品、化工、木制家具、石材加工四个行业合格率均较高;市直以上企业职业卫生现状情况优于区属企业,300-2000人之间较大规模的企业职业卫生现状情况优于300人以下的企业。同时,对企业提出了职业危害现状的改进措施与建议。     

中国煤矿员工文化程度及对安全的影响分析

我国煤矿员工文化程度低一直被看作是煤炭企业的安全生产的重要制约因素之一,而把事故归咎于矿工的文化水平也引起了广泛争议。本文通过广泛收集国内外相关文献信息,将我国煤矿员工与美国、加拿大和南非的煤矿员工及国内其他行业的员工文化程度进行对比,发现我国煤矿员工平均收教育年限较国外少3.3年,与国内其他高危行业持平,但低于全国劳动力平均受教育水平。在现场调研数据的基础上,分析了员工文化程度对安全生产的影响情况,发现煤矿员工的文化程度与企业总体安全文化水平呈正相关关系,而且文化程度与煤矿的安全生产状况互为因果。     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.     

Monitoring,imperfect detection,and risk optimization of a Tasmanian devil insurance population

Most species are imperfectly detected during biological surveys, which creates uncertainty around their abundance or presence at a given location. Decision makers managing threatened or pest species are regularly faced with this uncertainty. Wildlife diseases can drive species to extinction; thus, managing species with disease is an important part of conservation. Devil facial tumor disease (DFTD) is one such disease that led to the listing of the Tasmanian devil (Sarcophilus harrisii) as endangered. Managers aim to maintain devils in the wild by establishing disease‐free insurance populations at isolated sites. Often a resident DFTD‐affected population must first be removed. In a successful collaboration between decision scientists and wildlife managers, we used an accessible population model to inform monitoring decisions and facilitate the establishment of an insurance population of devils on Forestier Peninsula. We used a Bayesian catch‐effort model to estimate population size of a diseased population from removal and camera trap data. We also analyzed the costs and benefits of declaring the area disease‐free prior to reintroduction and establishment of a healthy insurance population. After the monitoring session in May–June 2015, the probability that all devils had been successfully removed was close to 1, even when we accounted for a possible introduction of a devil to the site. Given this high probability and the baseline cost of declaring population absence prematurely, we found it was not cost‐effective to carry out any additional monitoring before introducing the insurance population. Considering these results within the broader context of Tasmanian devil management, managers ultimately decided to implement an additional monitoring session before the introduction. This was a conservative decision that accounted for uncertainty in model estimates and for the broader nonmonetary costs of mistakenly declaring the area disease‐free.     

How can process safety and a risk management approach guide pandemic risk management?

The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.