基于数字图像相关方法的石墨高温杨氏模量测试

目的为了得到石墨在高温环境下的杨氏模量,为飞行器热防护系统和高温热结构的设计提供可靠的技术保障。方法基于数字图像相关(DIC)方法和通电电阻加热技术,建立一套测量材料在高温环境下力学性能的测试系统。利用该系统,测量高温下石墨试样的表面应变场和应力-应变曲线,计算相应温度下的杨氏模量。在1400℃实验环境下,采用基于DIC与高温引伸计的方法同时测量超高温陶瓷试件的拉伸应变数据,并进行比较验证。结果在1400℃实验环境下,采用基于DIC与高温引伸计方法测得超高温陶瓷的应变-时间曲线吻合良好,方差为1.3×10-7。1200~1900℃高温环境下,石墨的杨氏模量随温度的升高呈线性增长趋势。结论采用基于DIC方法准确有效,该方法可方便快速地实现对石墨材料在高温环境下杨氏模量的测量。另外,该方法也可应用于其他导电材料杨氏模量的测试。     

结构应力逆向推演模型及其不确定度定量分析

目的 针对现有结构安全在线监测与评估方法存在的短板,结合人工智能方法探索新的解决方案。提出一种基于有限测点应力逆向推演整体结构应力分布的方法,以数据驱动的模式,基于神经网络技术搭建算法模型。方法 以结构有限元仿真数据为基础,运用相关性分析方法,获取代表结构响应特征的有限个测点,利用神经网络方法构建基于有限测点应力推演结构全场应力分布的算法模型。结果 以科学试验平台的连接器结构为对象,开展该算法模型的应用研究,并且对该应用实例下的算法模型开展不确定度分析,推演结果的相对不确定度u95rel为8.6%。结论 该算法模型的推演结果正确反映了结构总体响应特征。从建模过程角度分析,该算法模型的不确定度来源主要包括相关性分析方法、神经网络建模以及模型收敛条件3个方面。     

数字化控制系统信息显示特征对操纵员信息 捕获绩效的影响及优化研究

随着计算机技术和仪控技术的发展,数字化仪控系统逐步在大型控制室得到应用,主控室信息界面随之也出现了 “巨量信息与有限显示”的矛盾。通过分析数字化主控室信息显示的特点,基于认知心理学的相关理论,通过实验研究 信息显示率、信息提供率、数据更新率对操纵员信息捕获绩效的影响。实验结果表明:对操纵员信息捕获绩效影响最主 要的因素是信息显示率,其次是信息提供率,最后是数据更新率;信息显示率与信息提供率对被试信息捕获绩效的影响 具有较高的灵敏度,而数据更新率的变化对被试信息捕获绩效的影响并不显著;信息显示的最佳匹配组合是信息显示率 为56.4 bit,信息提供率为9.4 bit,数据更新率为3 s。研究成果可为数字化主控室信息显示的设计提供理论基础。     

Diagnosis of famine: a discursive contribution

This paper recommends the incorporation of an additional discursive dimension in famine diagnosis that draws on the number of reports referring to famine in the United Nations Office for the Coordination of Humanitarian Affairs (OCHA)'s ReliefWeb database. Present‐day diagnostic tools already apply the principle of triangulation with multiple indicators; the addition of a discursive diagnostic dimension would enable even more refined analysis, allowing more forcefully for the incorporation of the aspect of change. The newly devised discursive famine indices are used both to identify famines—in Ethiopia (2000), Malawi (2002), and Somalia (2011)—and to analyse key socioeconomic determinants of famine. The study finds that income (or poverty) together with state fragility appear to be the major determinants of cross‐country variations in famine reporting, while political regimes do not appear to have any independent effect. The indices appear largely robust with regard to concerns about cross‐country, semantic, and temporal biases.     

Roles of digital technology in China's sustainable forestry development

China has the fifth largest forest area in the world and any change in China's forestry development will have inevitable impacts on global ecological sustainability. China has undergone excessive logging of natural forests and also made tremendous efforts in afforestation during the past half century. China's forestry is now going through a variety of transitions and several forestry programs have been implemented to drive forestry transitions. The goal of these actions is to protect ecological services of forests and sustain China's forestry development. These forestry programs are spatially sophisticated and cannot be successfully implemented without accurate and transparent forest/forestry information. A variety of digital technologies, including forest modeling, remote sensing, geographic information systems, global positioning systems, and visualization, have been applied in handling diverse information in China's forestry. Digital forestry is not just a theoretical concept in China. Our digital forestry experience in northeast China suggests that digital technology is both usable and useful in China's forestry development. Digital technology is playing an important interactive role in China's top-down forestry administration system. The analog-to-digital transition in technology is expected to lead to the success of forestry programs and forestry transitions in China.     

数字滤波法在点源和非点源污染负荷分割中的应用

以分辨流域点源和非点源污染负荷为研究目标,在傅立叶分析的基础上,提出了从污染负荷时间序列中分辨点源和非点源污染负荷的新方法——数字滤波法,探讨了数字滤波方程的参数与滤波次数的关系,并将该方法应用到东江流域COD_Mn负荷的分割上. 应用实例表明,分割的点源和非点源负荷系列曲线符合点源和非点源负荷的产生特点,使用者能够在污染负荷分割过程中通过滤波参数的选取比较方便地加入自己的经验. 一般取较大滤波参数时,只需要3次滤波就能得出较满意的结果.      

Bayesian Networks and Adaptive Management of Wildlife Habitat

Abstract: Adaptive management is an iterative process of gathering new knowledge regarding a system's behavior and monitoring the ecological consequences of management actions to improve management decisions. Although the concept originated in the 1970s, it is rarely actively incorporated into ecological restoration. Bayesian networks (BNs) are emerging as efficient ecological decision‐support tools well suited to adaptive management, but examples of their application in this capacity are few. We developed a BN within an adaptive‐management framework that focuses on managing the effects of feral grazing and prescribed burning regimes on avian diversity within woodlands of subtropical eastern Australia. We constructed the BN with baseline data to predict bird abundance as a function of habitat structure, grazing pressure, and prescribed burning. Results of sensitivity analyses suggested that grazing pressure increased the abundance of aggressive honeyeaters, which in turn had a strong negative effect on small passerines. Management interventions to reduce pressure of feral grazing and prescribed burning were then conducted, after which we collected a second set of field data to test the response of small passerines to these measures. We used these data, which incorporated ecological changes that may have resulted from the management interventions, to validate and update the BN. The network predictions of small passerine abundance under the new habitat and management conditions were very accurate. The updated BN concluded the first iteration of adaptive management and will be used in planning the next round of management interventions. The unique belief‐updating feature of BNs provides land managers with the flexibility to predict outcomes and evaluate the effectiveness of management interventions.     

常用抗干扰元器件在电器中的分析与应用

本文结合作者的实践经验,对家用电器及电动工具中常用抗干扰器件EMI电容和铁氧体磁环进行分析与测试,得出相应结论,并实例验证结论,对工程应用具有实践和指导意义。     

Priority screening of toxic chemicals and industry sectors in the U.S. toxics release inventory: a comparison of the life cycle impact-based and risk-based assessment tools developed by U.S. EPA

Life Cycle Impact Assessment (LCIA) and Risk Assessment (RA) employ different approaches to evaluate toxic impact potential for their own general applications. LCIA is often used to evaluate toxicity potentials for corporate environmental management and RA is often used to evaluate a risk score for environmental policy in government. This study evaluates the cancer, non-cancer, and ecotoxicity potentials and risk scores of chemicals and industry sectors in the United States on the basis of the LCIA- and RA-based tools developed by U.S. EPA, and compares the priority screening of toxic chemicals and industry sectors identified with each method to examine whether the LCIA- and RA-based results lead to the same prioritization schemes. The Tool for the Reduction and Assessment of Chemical and other environmental Impacts (TRACI) is applied as an LCIA-based screening approach with a focus on air and water emissions, and the Risk-Screening Environmental Indicator (RSEI) is applied in equivalent fashion as an RA-based screening approach. The U.S. Toxic Release Inventory is used as the dataset for this analysis, because of its general applicability to a comprehensive list of chemical substances and industry sectors. Overall, the TRACI and RSEI results do not agree with each other in part due to the unavailability of characterization factors and toxic scores for select substances, but primarily because of their different evaluation approaches. Therefore, TRACI and RSEI should be used together both to support a more comprehensive and robust approach to screening of chemicals for environmental management and policy and to highlight substances that are found to be of concern from both perspectives.     

Integrating soil carbon cycling with that of nitrogen and phosphorus in the watershed model SWAT: Theory and model testing

In this paper we describe and test a sub-model that integrates the cycling of carbon (C), nitrogen (N) and phosphorus (P) in the Soil Water Assessment Tool (SWAT) watershed model. The core of the sub-model is a multi-layer, one-pool soil organic carbon (S_C) algorithm, in which the decomposition rate of S_C and input rate to S_C (through decomposition and humification of residues) depend on the current size of S_C. The organic N and P fluxes are coupled to that of C and depend on the available mineral N and P, and the C:N and N:P ratios of the decomposing pools. Tillage explicitly affects the soil organic matter turnover rate through tool-specific coefficients. Unlike most models, the turnover of soil organic matter does not follow first order kinetics. Each soil layer has a specific maximum capacity to accumulate C or C saturation (S_x) that depends on texture and controls the turnover rate. It is shown in an analytical solution that S_x is a parameter with major influence in the model C dynamics. Testing with a 65-yr data set from the dryland wheat growing region in Oregon shows that the model adequately simulates the S_C dynamics in the topsoil (top 0.3 m) for three different treatments. Three key model parameters, the optimal decomposition and humification rates and a factor controlling the effect of soil moisture and temperature on the decomposition rate, showed low uncertainty as determined by generalized likelihood uncertainty estimation. Nonetheless, the parameter set that provided accurate simulations in the topsoil tended to overestimate S_C in the subsoil, suggesting that a mechanism that expresses at depth might not be represented in the current sub-model structure. The explicit integration of C, N, and P fluxes allows for a more cohesive simulation of nutrient cycling in the SWAT model. The sub-model has to be tested in forestland and rangeland in addition to agricultural land, and in diverse soils with extreme properties such high or low pH, an organic horizon, or volcanic soils.