On Valuing Information in Adaptive‐Management Models

Abstract: Active adaptive management looks at the benefit of using strategies that may be suboptimal in the near term but may provide additional information that will facilitate better management in the future. In many adaptive‐management problems that have been studied, the optimal active and passive policies (accounting for learning when designing policies and designing policy on the basis of current best information, respectively) are very similar. This seems paradoxical; when faced with uncertainty about the best course of action, managers should spend very little effort on actively designing programs to learn about the system they are managing. We considered two possible reasons why active and passive adaptive solutions are often similar. First, the benefits of learning are often confined to the particular case study in the modeled scenario, whereas in reality information gained from local studies is often applied more broadly. Second, management objectives that incorporate the variance of an estimate may place greater emphasis on learning than more commonly used objectives that aim to maximize an expected value. We explored these issues in a case study of Merri Creek, Melbourne, Australia, in which the aim was to choose between two options for revegetation. We explicitly incorporated monitoring costs in the model. The value of the terminal rewards and the choice of objective both influenced the difference between active and passive adaptive solutions. Explicitly considering the cost of monitoring provided a different perspective on how the terminal reward and management objective affected learning. The states for which it was optimal to monitor did not always coincide with the states in which active and passive adaptive management differed. Our results emphasize that spending resources on monitoring is only optimal when the expected benefits of the options being considered are similar and when the pay‐off for learning about their benefits is large.     

A new approach to estimate nuclide ratios from measurements with activities close to background

Measurements of low-level radioactivity often give results of the order of the detection limit. For many applications, interest is not only in estimating activity concentrations of a single radioactive isotope, but focuses on multi-isotope analyses, which often enable inference on the source of the activity detected (e.g. from activity ratios). Obviously, such conclusions become questionable if the measurement merely gives a detection limit for a specific isotope. This is particularly relevant if the presence of an isotope, which shows a low signal only (e.g. due to a short half-life or a small transition probability), is crucial for gaining the information of interest.     

Bayesian sample size determination for inspection of general corrosion of process components

A Bayesian approach-based method is proposed for calculating the minimum size of a sample to assess, with a specified precision, the integrity of process components suffering from general corrosion. The proposed method ensures that the error in the posterior estimate of the mean does not exceed a pre-defined acceptable margin of error at a specified confidence level. An analytical formula to estimate the sample size is introduced. The sample size obtained using the proposed method is smaller than a sample size obtained using the classical method with same confidence level. This reduces sampling inspection cost without affecting the precision of the estimate.     

苏南水网地区农田钾素资源概况及潜力

苏南水网地区,土壤供钾潜力较低,加之复种指数较,雨水过多,土壤钾素匮缺严重,其中因径流和渗漏损失的钾素养分占钾肥施入量的５４．４％。而我国钾矿资源贫四肥的生产量还不到需要量的１／１０,随着作物产量的提高和农产品品质的改善,作物的需钾量将渐增大,因此,单纯依靠钾肥来满足作物对钾素日益增长的需要是远远不够的,迫切需要开发利用富钾资源以缓解钾素的供需矛盾。苏南水网地区的水资源,秸秆,绿肥,塘泥等均为丰富     

Dynamic risk assessment using failure assessment and Bayesian theory

To ensure the safety of a process system, engineers use different methods to identify the potential hazards that may cause severe consequences. One of the most popular methods used is quantitative risk assessment (QRA) which quantifies the risk associated with a particular process activity. One of QRA's major disadvantages is its inability to update risk during the life of a process. As the process operates, abnormal events will result in incidents and near misses. These events are often called accident precursors. A conventional QRA process is unable to use the accident precursor information to revise the risk profile. To overcome this, a methodology has been proposed based on the work of Meel and Seider (2006). Similar to Meel and Seider (2006) work, this methodology uses Bayesian theory to update the likelihood of the event occurrence and also failure probability of the safety system. In this paper the proposed methodology is outlined and its application is demonstrated using a simple case study. First, potential accident scenarios are identified and represented in terms of an event tree, next, using the event tree and available failure data end-state probabilities are estimated. Subsequently, using the available accident precursor data, safety system failure likelihood and event tree end-state probabilities are revised. The methodology has been simulated using deterministic (point value) as well as probabilistic approach. This Methodology is applied to a case study demonstrating a storage tank containing highly hazardous chemicals. The comparison between conventional QRA and the results from dynamic failure assessment approach shows the significant deviation in system failure frequency throughout the life time of the process unit.     

Brassinosteroid alleviates phenanthrene and pyrene phytotoxicity by increasing detoxification activity and photosynthesis in tomato

The present study was carried out to investigate the effects of exogenously applied 24-epibrassinolide (BR) on growth, gas exchange, chlorophyll fluorescence characteristics, lipid peroxidation and antioxidant systems of tomato seedlings grown under different levels (0, 10, 30, 100 and 300 μM) of phenanthrene (PHE) and pyrene (PYR) in hydroponics. A concentration-dependent decrease in growth, photosynthetic pigment contents, net photosynthetic rate (Pn), stomatal conductance (Gs), maximal quantum yield of PSII (Fv/Fm), effective quantum yield of PSII (Φ_PSII), photochemical quenching coefficient (qP) has been observed following PHE and PYR exposure. By contrast, non-photochemical quenching coefficient (NPQ) was increased. PHE was found to induce higher stress than PYR. However, foliar or root application of BR (50 nM and 5 nM, respectively) alleviated all those depressions with a sharp improvement in the activity of photosynthetic machinery. The activities of guaicol peroxidase (GPOD), catalase (CAT), ascorbate peroxidase (APX) and glutathione reductase (GR) as well as content of malondialdehyde (MDA) were increased in a dose-dependent manner under PHE or PYR treatments. Compared with control the highest increments of GPOD, CAT, APX, GR and MDA by PHE/PYR alone treatments were observed following 300 μM concentration, which were 67%, 87%, 53%, 95% and 74% by PHE and 42%, 53%, 30%, 86% and 62% by PYR, respectively. In addition, both reduced glutathione (GSH) and oxidized glutathione (GSSG) were induced by PHE or PYR. Interestingly, BR application in either form further increased enzymatic and non enzymatic antioxidants in tomato roots treated with PHE or PYR. Our results suggest that BR has an anti-stress effect on tomato seedlings contaminated with PHE or PYR and this effect is mainly attributed by increased detoxification activity.     

几种模拟处理方式污泥淋出液重金属与养分特征

为了减少城市污泥重金属对农田的污染,更好地实现污泥农业资源化利用,通过盆栽实验研究不同处理(对照、黑网、附Fe黑网、附Fe黑网+K2SO4及附Fe黑网+玉米)对城市污泥淋出液重金属和养分含量等的影响。结果表明,黑网可降低污泥淋出液的Zn﹑Cd总量,且没有减少污泥淋出液中氮、磷和钾的总量。Fe(OH)3可使淋出液中Zn总量降低,但同时也显著减少了淋出液中的磷总量。K2SO4可降低淋出液中的Cu总量,且促进Fe结合磷的释放。玉米的种植可使污泥的总重降低,同时玉米籽粒和茎叶重金属含量达到饲料标准。综合来看,任何处理每次淋出液的Cu、Zn、Pb、Cd浓度均符合农田灌溉水标准,淋出液氮、磷、钾量占原污泥中氮、磷、钾总量的比例(0.98%～9.88%)远远大于重金属元素(Cu,Zn,Pb,Cd)占原污泥对应重金属总量的比例(0.04%～0.41%),污泥淋出液作为农田灌溉水进行肥水利用将是污泥农业资源化利用的适合途径,同时黑网+玉米处理可能是较理想的污泥综合处理利用方式。     

基于贝叶斯网络的危化品爆炸事故电力系统风险评估模型

为提高危化品爆炸事故电力应急预警的准确性,建立基于贝叶斯网络的危化品爆炸事故电力系统风险评估模型.基于危化品爆炸事故电力应急典型情景分析,建立综合考虑突发事件、承灾载体和应急管理等风险因素的贝叶斯网络结构.应用概率刻画风险因素信息的不确定性及其相互影响,定量分析事件后果.结合一般条件和典型情景等的应用实例,分析评价方法...     

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm

● A hydrodynamic-Bayesian inference model was developed for water pollution tracking. ● Model is not stuck in local optimal solutions for high-dimensional problem. ● Model can estimate source parameters accurately with known river water levels. ● Both sudden spill incident and normal sewage inputs into the river can be tracked. ● Model is superior to the traditional approaches based on the test cases. Water quality restoration in rivers requires identification of the locations and discharges of pollution sources, and a reliable mathematical model to accomplish this identification is essential. In this paper, an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge. The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method (MCMC). The methodological framework is tested using a designed case of a sudden wastewater spill incident (i.e., source location, flow rate, and starting and ending times of the discharge) and a real case of multiple sewage inputs into a river (i.e., locations and daily flows of sewage sources). The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites. It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space. In comparison, the inverse models based on the popular random walk Metropolis (RWM) algorithm and microbial genetic algorithm (MGA) do not produce reliable estimates for the two scenarios even after multiple simulation runs, and they fall into locally optimal solutions. Since much more water level data are available than water quality data, the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data, especially for rivers receiving significant sewage discharges.     

防风网防尘技术及其在我国大型煤炭港口的应用与发展对策

防风网是控制煤炭起尘与扩散的一种有效手段,其防尘效果主要与防风网的结构、设置方式有关.虽然防风网的防尘效果已得到广泛认可,但防风网并未广泛应用于我国大型煤炭港口,其原因主要在于:①我国煤堆场规模较大,而防风网的有效庇护区范围有限,仅在其下风向、约20倍网高距离的范围内,因此限制了防风网的应用;②防风网造价较为昂贵,每100 m的造价多在106元以上.为推动防风网防尘技术在我国大型煤炭港口的应用,应加强对适宜煤堆场规模、防风网的有效庇护距离、新型防风网材料等技术问题的研究,并选择我国北方沿海大型煤码头开展防尘示范工程.