高烈度地震区隧道软硬围岩交接段刚柔并济抗减震技术研究*

为提高高烈度地震区隧道抗震性能，以某铁路隧道为研究背景，分析3种抗减震措施下隧道不同监测点隧道拱顶沉降、边墙收敛、衬砌结构PGA及最小安全系数，通过对比分析得到最优抗减震措施。结果表明:相比于工况1，工况2隧道拱顶沉降减小10.54%~81.10%，边墙收敛减小13.92%~78.77%，衬砌结构PGA减小31.42%~72.02%，最小安全系数增加18.04%~66.13%；相比于工况1，工况3结构拱顶沉降减小3.04%~18.02%，边墙收敛减小4.70%~32.00%，PGA增加13.95%~27.48%，最小安全系数增加7.49%~30.99%；工况4即“减震层+SFRC衬砌”刚柔并济法，相比于工况1，隧道拱顶沉降减小18.46%~83.98%，结构边墙收敛减小17.54%~85.47%，PGA减小30.00%~69.98%，最小安全系数增加47.95%~83.56%；4种工况抗减震性能由高到低依次为:工况4＞工况2＞工况3＞工况1。研究结果可为隧道软硬围岩交接段抗震设防提供理论参考。     

A proactive process risk assessment approach based on job hazard analysis and resilient engineering

Complex systems often experience a long period of incubation before accidents occur. Therefore, a proactive risk assessment is essential for process safety. The conventional job hazard analysis (JHA) method has been an effective tool to conduct a process risk assessment in the high-risk industrial field. However, the conventional JHA is inadequate for the proactive risk assessment since it is usually conducted during and before one specific operation process. Operations such as startup and maintenance are performed repeatedly on the lifecycle of a plant. Therefore, the risk reduction measures for the industrial process should include not only preventive actions obtained from the conventional JHA but also recovery ones. Resilience engineering (RE) has proven to be helpful for the recovery analysis of a complex system. The objective of this paper is to propose a proactive and comprehensive process risk assessment approach based on JHA and RE. The mechanism of applying RE to address operation process risk is illustrated. The integrated approach can provide guidelines to establish proactive risk reduction measures as well as maintain a low-risk level. Finally, a gas transmission startup process risk assessment case is presented to demonstrate its applicability.     

Forecasting China’s primary energy demand based on an improved AI model

An improved energy demand forecasting model is built based on the autoregressive distributed lag (ARDL) bounds testing approach and an adaptive genetic algorithm (AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization, and technological progress as the input variables. On the basis of historical annual data from 1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.     

Climate engineering and human rights

ABSTRACT

In this Forum, three scholars discuss how climate engineering will pose novel human rights challenges, and may well force reconsideration of how human rights are applied as a guide to action. Following a short introduction, the first section introduces three competing approaches to human rights, arguing views which emphasize fairness or attempt to maximize satisfaction are more promising than one viewing human rights as inviolable ‘side-constraints’. The second section draws lessons from climate migration that are relevant for climate engineering in terms of incorporating a human rights approach to duties, rights, and participation. The final section compares the ‘needs-based’ and ‘rights-based’ approaches to humanitarian work in the face of climate change and climate engineering, raising concerns for duty-bearers and right-holders. The Forum’s conclusion draws together points of overlap and suggests a path forward for policy and research on this topic.     

Using environmental and geographic data to optimize ex situ collections and preserve evolutionary potential

Maintenance of biodiversity through seed banks and botanical gardens, where the wealth of species’ genetic variation may be preserved ex situ, is a major goal of conservation. However, challenges can persist in optimizing ex situ collections if trade-offs exist among cost, effort, and conserving species evolutionary potential, particularly when genetic data are not available. We evaluated the genetic consequences of population preservation informed by geographic (isolation by distance [IBD]) and environmental (isolation by environment [IBE]) distance for ex situ collections for which population provenance is available. We used 19 genetic and genomic data sets from 15 plant species to assess the proportion of population genetic differentiation explained by geographic and environmental factors and to simulate ex situ collections prioritizing source populations based on pairwise geographic distance, environmental distance, or both. Specifically, we tested the impact prioritizing sampling based on these distances may have on the capture of neutral, functional, or putatively adaptive genetic diversity and differentiation. Individually, IBD and IBE explained limited population genetic differences across all 3 genetic marker classes (IBD, 10–16%; IBE, 1–5.5%). Together, they explained a substantial proportion of population genetic differences for functional (45%) and adaptive (71%) variation. Simulated ex situ collections revealed that inclusion of IBD, IBE, or both increased allelic diversity and genetic differentiation captured among populations, particularly for loci that may be important for adaptation. Thus, prioritizing population collections based on environmental and geographic distance data can optimize genetic variation captured ex situ. For the vast majority of plant species for which there is no genetic information, these data are invaluable to conservation because they can guide preservation of genetic variation needed to maintain evolutionary potential within collections.     

Incorporating putatively neutral and adaptive genomic data into marine conservation planning

The availability of genomic data for an increasing number of species makes it possible to incorporate evolutionary processes into conservation plans. Recent studies show how genetic data can inform spatial conservation prioritization (SCP), but they focus on metrics of diversity and distinctness derived primarily from neutral genetic data sets. Identifying adaptive genetic markers can provide important information regarding the capacity for populations to adapt to environmental change. Yet, the effect of including metrics based on adaptive genomic data into SCP in comparison to more widely used neutral genetic metrics has not been explored. We used existing genomic data on a commercially exploited species, the giant California sea cucumber (Parastichopus californicus), to perform SCP for the coastal region of British Columbia (BC), Canada. Using a RAD-seq data set for 717 P. californicus individuals across 24 sampling locations, we identified putatively adaptive (i.e., candidate) single nucleotide polymorphisms (SNPs) based on genotype–environment associations with seafloor temperature. We calculated various metrics for both neutral and candidate SNPs and compared SCP outcomes with independent metrics and combinations of metrics. Priority areas varied depending on whether neutral or candidate SNPs were used and on the specific metric used. For example, targeting sites with a high frequency of warm-temperature-associated alleles to support persistence under future warming prioritized areas in the southern coastal region. In contrast, targeting sites with high expected heterozygosity at candidate loci to support persistence under future environmental uncertainty prioritized areas in the north. When combining metrics, all scenarios generated intermediate solutions, protecting sites that span latitudinal and thermal gradients. Our results demonstrate that distinguishing between neutral and adaptive markers can affect conservation solutions and emphasize the importance of defining objectives when choosing among various genomic metrics for SCP.     

中义隧道片理化玄武岩段大变形控制技术研究*

为进一步探究高地应力隧道软岩大变形控制技术，以中义隧道主洞片理化玄武岩段为工程背景，提出大变形Ⅰ型支护、大变形Ⅱ型支护、大变形Ⅱ型支护（围岩加固）3种大变形控制方案，以现场试验段监测为辅助验证，采用数值仿真对3种控制方案的控制效果进行对比分析。结果表明:适宜的支护成环时间具有减缓大变形的作用；在衬砌各部位累计最大变形控制方面，控制方案3较其他方案衬砌最大变形最少减小20.8%，且变形时程曲线最终收敛；围岩最大日变形量控制方面，经过开挖断面的进一步优化以及边墙部位塑性区围岩自承能力的提高，控制方案3最大日变形量较其他支护方案至少减小20.8%。结果显示控制方案3能够稳定控制片理化玄武岩大变形，且效果最好，研究结果可为类似工程提供设计依据。     

氢安全研究现状及面临的挑战

氢能具有储运便捷、来源多样、洁净环保等突出优点,许多国家把发展氢能作为重要的能源战略。氢安全是氢能大规模商业化应用的重要保障。在分析国内外氢安全领域近年来最新研究进展的基础上,依次从氢泄漏与扩散、氢燃烧与爆炸、氢与金属材料相容性及氢风险评价等方面,系统总结了国内外氢安全研究面临的挑战,并对我国氢安全的发展提出了建议。     

应用高频气力分选机分选电子废料碎片的研究

进行了实验室高频气力分选机分选3～0.3 mm印刷电(线)路板模拟物料的研究.结果表明,在振频为1 621次/min,上升风量为120 m3/h,95 m3/h,60 m3/h,床面横向倾角6.64°,纵向倾角为1.07°时,金属回收率达93.95%;重产物金属品位为89.19%;轻产物中非金属的品位达99.33%.0.5～0.3 mm细粒物料的分选效果也是令人满意的.     

应急救援物资车辆运输线路的选择

为了给紧急救援物资支持保障中心的物资调度人员提供科学的车辆路线调度决策方法,提高应急救援工作的响应能力,基于应急条件下物资运输调度的时效性、安全性和经济性特征,运用旅行商理论,对上述3个决策属性分量进行了数学描述,构造了它们的目标函数,并进行了无量纲和权重聚合处理,继而按照多属性决策理论建立了用来评价备选线路决策效用函数的数学模型.利用期望效用属性合并所搜集的信息,将多属性决策问题转化成单属性决策问题,确立了选择具有最高期望效用方案的方法,最后进行了实例分析.结果表明,应急救援条件下的运输调度与正常环境下的企业运输调度决策目标之间存在明显的差异,前者虽然本质上属于图论中最短路线问题的范畴,但由于多个决策目标的存在,不能直接运用最短路线模型和Dijkstra算法求解.运用多属性决策理论建立的评价备选线路决策效用函数,是有关人员进行车辆路线安排的有效方法,但其相对优越性尚需做进一步的研究和探讨.