污水管网优化设计程序的比较与分析

比较了流速法和坡度法两种常用污水管网优化设计程序在不同地形条件下的应用效果.结果表明,在地形较为平缓地区,两者均能得到较好的结果,其中流速法在埋深上更具优势;而在地形较陡地区,坡度法能充分利用有利地形控制埋深和坡度,优化设计效果较好.     

桑美台风风暴潮及其对张网作业影响的探讨

以2000年"桑美"台风风暴潮及此阶段张网作业的有关资料为依据,对风暴潮增水的形成机制、浙沪沿海风暴潮各阶段特征以及对张网作业的影响进行了探讨;对风暴潮增水在张网作业的积极作用与负面影响以及2000年八月半水张网作业丰收原因进行讨论.分析表明,在杭州湾滩浒岛附近海域,因风暴潮增水导致张网作业CPUE平均增幅为9.3%,最大增幅达23.5%.本文是一则风暴潮有利方面的报道,中国迄今尚属首次.     

新型农村社会养老保险收入再分配效应研究

通过政策分析发现,新农保制度的收入再分配效应主要体现在政府补贴方面,在本质上是财政的再分配,其中个人账户部分体现为代内再分配,基础养老金部分体现为代际再分配。新农保制度几乎对所有制度内人群都具有正的收入再分配效应。以净转移额来度量新农保收入再分配效应的程度,通过建立模型进行测算发现:在现行财政补贴政策下,新农保的收入再分配偏向于选择较高档次缴费的人群、缴费困难群体、寿命长的人群、女性农民、长期缴费的人群以及农村计划生育家庭;政府缴费补贴越多,个人账户投资收益率越高,基础养老金调整系数越大,参保人养老金的净转入额就越多;"捆绑式缴费"政策产生了逆向的收入再分配效应。     

Real-time risk assessment of explosion on offshore platform using Bayesian network and CFD

Combustion or explosion accident resulting from accidental hydrocarbon release poses a severe threat to the offshore platform's operational safety. Much attention has been paid to the risk of an accident occurring over a long period, while the real-time risk that escalates from a primary accident to a serious one was ignored. In this study, a real-time risk assessment model is presented for risk analysis of release accidents, which may escalate into a combustion or explosion. The proposed model takes advantage of Fault Tree-Event Tree (FT-ET) to describe the accident scenario, and Bayesian network (BN) to obtain the initial probability of each consequence and describe the dependencies among safety barriers. Besides, Computational Fluid Dynamics (CFD) is applied to handle the relationship between gas dispersion and time-dependent risk. Ignition probability model that considering potential ignition sources, gas cloud, and time series are also integrated into this framework to explain the likelihood of accident evolution. A case of release accidents on a production platform is used to test the availability and effectiveness of the proposed methodology, which can be adopted for facilities layout optimization and ignition sources control.     

基于贝叶斯网络的页岩气集输管道失效概率计算方法研究

页岩气集输管道运行压力和出砂量在生产过程中衰减显著，这导致管道失效概率不断变化，针对这一问题，采用贝叶斯网络方法，建立了页岩气集输管道失效概率动态计算模型。首先，分析页岩气气质特征、管道运行工况及失效原因，利用逻辑门的连接关系，建立了页岩气集输管道失效故障树；其次，基于贝叶斯网络与失效故障树的结构映射关系，将失效故障树转化成贝叶斯网络结构；然后，通过贝叶斯网络的参数学习，实现模型求解；最后，进行了实例应用。研究结果表明:该模型不仅可有效计算页岩气集输管道的失效概率，还能确定影响管道失效的关键风险因素，并且可通过调整节点的状态及概率分布，实现页岩气集输管道失效概率的更新。     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

A Bayesian-weighted approach to predicting the number of newly discovered rare species

In natural ecological communities, most species are rare and thus susceptible to extinction. Consequently, the prediction and identification of rare species are of enormous value for conservation purposes. How many newly found species will be rare in the next field survey? We took a Bayesian viewpoint and used observed species abundance information in an ecological sample to develop an accurate way to estimate the number of new rare species (e.g., singletons, doubletons, and tripletons) in an additional unknown sample. A similar method has been developed for incidence-based data sets. Five seminumerical tests (3 abundance cases and 2 incidence cases) showed that our proposed Bayesian-weight estimator accurately predicted the number of new rare species with low relative bias and low relative root mean squared error and, accordingly, high accuracy. Finally, we applied the proposed estimator to 6 conservation-directed empirical data sets (3 abundance cases and 3 incidence cases) and found the prediction of new rare species was quite accurate; the 95% CI covered the true observed value very well in most cases. Our estimator performed similarly to or better than an unweighted estimator derived from Chao et al. and performed consistently better than the naïve unweighted estimator. We recommend our Bayesian-weight estimator for conservation applications, although the unweighted estimator of Chao et al. may be better under some circumstances. We provide an R package RSE (r are s pecies e stimation) at https://github.com/ecomol/RSE for implementation of the estimators.     

Resilience-based approach to safety barrier performance assessment in process facilities

The performance assessment of safety barriers is essential to find vulnerable elements in a safety barrier system. Traditional performance assessment approaches mainly focus on using several static indicators for quantifying the performance of safety barriers. However, with the increasing complexity of the system, emerging hazards are highly uncertain, making it challenging for the static indicators to assess the performance of safety barriers. This paper proposes a resilience−based performance assessment method for safety barriers to overcome this problem. Safety barriers are classified according to their functions first. The dynamic Bayesian network (DBN) is then introduced to calculate the availability function under normal and disruption conditions. The ratio of the system's availability, when affected by the disruption, to the initial availability, is used to determine the absorption capacity of the system. The ratio of the quantity of availability recovery to the total quantity of system represents the adaptation and restoration capacity of the system. The system's resilience is represented by the sum of absorption, adaptation, and restoration capacities. The wax oil hydrogenation process is used to demonstrate the applicability of the proposed methodology.     

Cost shifting and other perverse incentives in biodiversity offsetting in India

Biodiversity offsetting aims to compensate for development‐induced biodiversity loss through commensurate conservation gains and is gaining traction among governments and businesses. However, cost shifting (i.e., diversion of offset funds to other conservation programs) and other perverse incentives can undermine the effectiveness of biodiversity offsetting. Additionality—the requirement that biodiversity offsets result in conservation outcomes that would not have been achieved otherwise—is fundamental to biodiversity offsetting. Cost shifting and violation of additionality can go hand in hand. India's national offsetting program is a case in point. Recent legislation allows the diversion of offset funds to meet the country's preexisting commitments under the United Nations Framework Convention on Climate Change (UNFCCC) and United Nations Convention on Biological Diversity (CBD). With such diversions, no additional conservation takes place and development impacts remain uncompensated. Temporary additionality cannot be conceded in light of paucity of funds for preexisting commitments unless there is open acknowledgement that fulfillment of such commitments is contingent on offset funds. Two other examples of perverse incentives related to offsetting in India are the touting of inherently neutral offsetting outcomes as conservation gains, a tactic that breeds false complacency and results in reduced incentive for additional conservation efforts, and the clearing of native vegetation for commercial plantations in the name of compensatory afforestation, a practice that leads to biodiversity decline. The risks accompanying cost shifting and other perverse incentives, if not preempted and addressed, will result in net loss of forest cover in India. We recommend accurate baselines, transparent accounting, and open reporting of offset outcomes to ensure biodiversity offsetting achieves adequate and additional compensation for impacts of development.     

基于贝叶斯网络的养老院火灾风险评估方法研究

为预防养老院火灾事故,结合事故树法(FTA)和贝叶斯网络法(BN),建立了一套养老院火灾风险定量评估模型。首先,采用事故树法建立潜在的养老院火灾事故场景;其次,考虑到养老院火灾事故场景中不确定因素的影响,将事故树模型转化为贝叶斯网络模型,并结合有人员伤亡的养老院火灾事故发生发展实际优化模型;最后,以某市养老院为例,结合调研、文献及统计数据确定先验概率及条件概率,并用GENIE 2.0软件实现贝叶斯图形化,分析验证该模型逻辑可行性。结果表明:通过该模型和方法,不仅可以预测养老院火灾事故中各场景发生发展概率,还能对各风险因素敏感度和最大致因链进行分析,提高了风险因素辨识和评价的准确性,可以为养老院火灾风险分析和防控提供参考。