基于人工免疫算法的离散隐马尔科夫故障诊断模型优化

目的解决离散隐马尔科夫模型在行星齿轮箱故障诊断中的自适应性和泛化性问题。方法建立人工免疫优化模型,将包含易被误判样本的多样本集作为抗原,以其正确识别率为适应度函数,不断对初始观测矩阵进行增殖、变异和选择,获得识别率最高时的初始观测矩阵,利用人工免疫算法对隐马尔科夫故障诊断模型的初始观测矩阵进行优化。结果将该方法应用于行星齿轮箱的故障诊断中,通过不同工况下的对比试验、单样本和多样本优化对比试验,验证了优化后的隐马尔科夫故障诊断模型的诊断效果。结论优化后的隐马尔科夫故障诊断模型具有更好的适应性,诊断精度显著提高。     

基于空间马尔科夫链的关中地区生态安全时空演变分析

生态系统内部和生态系统之间都不是封闭的,各要素间均存在着相互作用。利用已获得的陕西关中地区生态安全格网化评价结果数据,运用空间马尔科夫链对该区域的生态安全时空演变进行了分析,得出以下四个结论：①区域背景在关中地区生态安全趋同时空演变过程中起着相当重要作用;②不同区域背景在区域生态安全等级转移中所起的作用也各不相同;③一个区域生态安全等级向上或向下转移的概率与该区域和周围邻居之间的差异程度不成比例;④空间马尔可夫转移概率矩阵为＂区域趋同的时空演变＂现象提供了空间上的解释。实践表明,空间马尔可夫链方法为定量分析地理环境对区域生态安全变化的空间效应提供了方法依据,值得进一步研究和讨论。     

From a literature review to a conceptual framework for sustainable supply chain management

Academic and corporate interest in sustainable supply chain management has risen considerably in recent years. This can be seen by the number of papers published and in particular by journal special issues. To establish the field further, the purpose of this paper is twofold. First, it offers a literature review on sustainable supply chain management taking 191 papers published from 1994 to 2007 into account. Second, it offers a conceptual framework to summarize the research in this field comprising three parts. As starting point related triggers are identified. This allows putting forward two distinct strategies: (1) supplier management for risks and performance, and (2) supply chain management for sustainable products. It is evident that research is still dominated by green/environmental issues. Social aspects and also the integration of the three dimensions of sustainability are still rare.Both practitioners in companies and academics might find the review useful, as it outlines major lines of research in the field. Further, it discusses specific features of sustainable supply chains as well as limitations of existing research; this should stimulate further research.     

Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model

Nitrous oxide (N₂O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N₂O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N₂O submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N₂O emissions from the nitrification and denitrification processes, which are modelled as the product of a potential rate with three dimensionless factors related to soil water content, nitrogen content and temperature. These equations involve a total set of 15 parameters, four of which are site-specific and should be measured on site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior information on the model parameters based on the literature review, and assigned them uniform probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was subsequently developed to update the parameter distributions against a database of seven different field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm. This site-specific calibration significantly reduced the spread in parameter distribution, and the uncertainty in the N₂O simulations. The model’s root mean square error (RMSE) was also abated by 73% across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently applied simultaneously to all data sets, to obtain better global estimates for the parameters initially deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the uncalibrated model. These global parameter values may be used to obtain more realistic estimates of N₂O emissions from arable soils at regional or continental scales.     

Carbonaceous aerosols in PM10 and pollution gases in winter in Beijing

An intensive observation of organic carbon (OC) and element carbon (EC) in PM10 and gaseous materials (SO2, CO, and O3,) was conducted continuously to assess the characteristics of wintertime carbonaceous aerosols in an urban area of Beijing, China. Results showed that the averaged total carbon (TC) and PM10 concentrations in observation period are 30.2±120.4 and 172.6±198.3 μ/m3, respectively. Average OC concentration in nighttime (24.9±19.6 μ/m3) was 40% higher than that in daytime (17.7±10.9 μ/m3). Average EC concentrations in daytime (8.8±15.2 μ/m3) was close to that in nighttime (8.9±15.1 μ/m3). The OC/EC ratios in nighttime ranging from 2.4 to 2.7 are higher than that in daytime ranging from 1.9 to 2.0. The concentrations of OC, EC, PM10 were low with strong winds and high with weak winds. The OC and EC were well correlated with PM10, CO and SO2, which implies they have similar sources. OC and EC were not well correlated with O3. By considering variation of OC/EC ratios in daytime and night time, correlations between OC and O3, and meteorological condition, we speculated that OC and EC in Beijing PM10 were emitted as the primary particulate form. Emission of motor vehicle with low OC/EC ratio and coal combustion sources with high OC/EC ratio are probably the dominant sources for carbonaceous aerosols in Beijing in winter. A simple method was used to estimate the relative contribution of sources to carbonaceous aerosols in Beijing PM10. Motor vehicle source accounts for 80% and 68%, while coal combustion accounts for 20% and 32% in daytime and nighttime, respectively in Beijing. Averagely, the motor vehicle and coal combustion accounted for 74% and 26%, respectively, for carbonaceous aerosols during the observation period. It points to the motor vehicle is dominant emission for carbonaceous aerosols in Beijing PM10 in winter period, which should be paid attention to control high level of PM10 in Beijing effectively.     

Identifying the factors influencing the performance of reverse supply chains (RSC)

This paper aims to extract the factors influencing the performance of reverse supply chains (RSCs) based on the structure equation model (SEM). We first introduce the definition of RSC and describe its current status and follow this with a literature review of previous RSC studies and the technology acceptance model . We next develop our research model and 11 hypotheses and then use SEM to test our model and identify those factors that actually influence the success of RSC. Next, we use both questionnaire and web‐based methods to survey five companies which have RSC operation experience in China and Korea. Using the 168 responses, we used measurement modeling test and SEM to validate our proposed hypotheses. As a result, nine hypotheses were accepted while two were rejected. We found that ease of use, perceived usefulness, service quality, channel relationship and RSC cost were the five most important factors which influence the success of RSC. Finally, we conclude by highlighting our research contribution and propose future research.     

长株潭城市群地类转移的碳传导效应与预测

土地利用碳排放是影响城市碳达峰、碳中和实现的重要因素.基于土地利用遥感数据和碳排放估算模型,得到长株潭城市群的土地利用碳排放量,借助转移矩阵分析了长株潭城市群土地利用转移的碳传导效应.此外,采用马尔科夫模型预测2030年和2060年的长株潭土地利用碳排放量.结果表明：①1995~2018年长株潭城市群土地利用净碳排放从810.84×10⁴ t增加到2015.41×10⁴ t,碳源/汇比整体呈上升趋势.其中,建设用地是主要的碳排放源,林草地是主要的碳汇.②不同时段地类转移引致的碳传导最终均表现为净碳排放,在时间上呈现先增加后减少的态势.其中以林地和耕地向建设用地转移产生的碳传导最为显著,涉及草地、水域和未利用地的碳传导效应微弱.③预测结果表明,长株潭城市群的土地利用碳排放预测量处于持续上涨态势,如若仍按目前趋势发展,则如期实现"双碳目标"存在难度.政府需要在加强林地的碳吸收能力以提升生态系统碳汇增量和遏制建设用地的无序扩张以减少碳源两方面着力,加快长株潭城市群的绿色低碳建设.上述结果为长株潭城市群开展低碳导向的城市土地利用调控提供了重要参考.     

全球价值链视角下中国农产品贸易隐含氮、磷、钾研究

基于GTAP数据库提供的多区域投入产出表,采用全球价值链方法测算中国农产品贸易中隐含的化肥转移,并按照农产品最终消费的地理位置将其分解为四部分。研究发现：（1）中国农产品进口为国内节约640万t化肥（占我国化肥用量的13%）,同时引起全球化肥用量节约285万t,为缓解全球资源和环境压力做出贡献;（2）中国农产品贸易深度参与全球价值链,进口农产品中隐含的化肥有12%会再次出口到全球,意味着农产品贸易背后隐含的资源到达中国后会再次出口,形成多次跨境转移。建议在全球价值链视角下更为客观地估算农产品贸易隐含的化肥及其他资源环境问题,倡导共同承担贸易引致的资源环境问题。     

村镇简易隔震支座数值模拟及施工应用

为改变农村建筑隔震技术的落后性,对现有技术成熟的橡胶隔震支座的尺寸和形状进行改良,板材依旧使用钢板,使其在满足农村建筑隔震要求的同时也兼顾工程的经济性,相较于为农村开发的纤维增强塑料板支座,这种隔震支座的理论体系和工艺更成熟,更适合大范围生产。为论证改良后的支座是否符合隔震需求,使用ABAQUS对其进行模拟,并将结果同试验结果进行对比,最后以昆明市富民县赤鹫镇项目探讨支座的可行性。经过模拟及赤鹫镇项目得出,简易隔震支座的力学性能及经济性都能满足农村建筑隔震要求,可以在广大农村地区进行推广。     

A new method for broad-scale modeling and projection of plant assemblages under climatic,biotic, and environmental cofiltering

There is increasing interestin broad-scale analysis, modeling, and prediction of the distribution and composition of plant species assemblages under climatic, environmental, and biotic change, particularly for conservation purposes. We devised a method to reliably predict the impact of climate change on large assemblages of plant communities, while also considering competing biotic and environmental factors. To this purpose, we first used multilabel algorithms in order to convert the task of explaining a large assemblage of plant communities into a classification framework able to capture with high cross-validated accuracy the pattern of species distributions under a composite set of biotic and abiotic factors. We applied our model to a large set of plant communities in the Swiss Alps. Our model explained presences and absences of 175 plant species in 608 plots with >87% cross-validated accuracy, predicted decreases in α, β, and γ diversity by 2040 under both moderate and extreme climate scenarios, and identified likely advantaged and disadvantaged plant species under climate change. Multilabel variable selection revealed the overriding importance of topography, soils, and temperature extremes (rather than averages) in determining the distribution of plant species in the study area and their response to climate change. Our method addressed a number of challenging research problems, such as scaling to large numbers of species, considering species relationships and rarity, and addressing an overwhelming proportion of absences in presence–absence matrices. By handling hundreds to thousands of plants and plots simultaneously over large areas, our method can inform broad-scale conservation of plant species under climate change because it allows species that require urgent conservation action (assisted migration, seed conservation, and ex situ conservation) to be detected and prioritized. Our method also increases the practicality of assisted colonization of plant species by helping to prevent ill-advised introduction of plant species with limited future survival probability.