外军装备维修及对我军弹药维修的启示

首先介绍了外军装备维修的现状和发展趋势,之后指出了我军弹药维修存在的问题,最后得出对我军弹药维修保障发展的启示。认为我军弹药维修保障应向军民一体化方向发展,开展全寿命保障,提高保障信息化水平,进行弹药两级维修体制的论证,并倡导绿色维修的新理念。     

湿地基质及阴极面积对人工湿地型微生物燃料电池去除偶氮染料同步产电的影响

本研究采用人工湿地型微生物燃料电池处理偶氮染料X-3B,实现降解偶氮染料同步产电的效果.为了构建性能最优的人工湿地型微生物燃料电池(CW-MFC)系统,本研究主要从湿地基质和阴极面积两个方面研究系统构型对去除X-3B同步产电的影响,提高系统性能.研究表明以粒径10 mm、孔隙率30%的小石子作为湿地基质构造的CW-MFC系统微生物生物量最大,去除X-3B效果最好,脱色率高达92.70%,但其产电性能最差.较小的粒径和孔隙率使底层微生物生物量增加,促进X-3B的去除,但随着湿地基质粒径和孔隙率的减小,导致阴阳极营养物质不足,系统传质阻力增加,抑制了系统产电性能.X-3B的去除效果随着阴极面积的增加而提高直到阴极面积为594 cm~2时取得最大脱色率99.41%.当阴极面积继续增加时,CW-MFC系统产电性能上升趋势趋于平缓,X-3B去除效果呈现下降趋势,这是因为阴极反应过快导致更多的阳极电子输送到阴极用于产生电流,与X-3B发生反应的电子减少,阳极成为提高CW-MFC系统性能的限制因素.     

基于距离相关系数和支持向量机回归的PM2.5浓度滚动统计预报方案

针对目前空气质量统计预报方法存在的主要缺陷,本文提出了距离相关系数和支持向量机回归相结合的统计预报方案DC-SVR.利用淮安市2013年1—12月PM_(2.5)观测资料和常规气象观测资料,首先在选入预报当日气象要素的基础上,增加选取前期污染物和气象要素作为预报因子,再采用距离相关系数分季节从预报因子中筛选出重要预报因子,最后采用支持向量机回归对PM_(2.5)浓度值进行逐日滚动统计预报.研究发现,淮安地区气温和气压对PM_(2.5)的距离相关性要高于其他气象要素,夏秋季PM_(2.5)与气象要素的距离相关性较春冬季好.基于距离相关系数和支持向量机回归建立DC-SVR模型,PM_(2.5)的试预报值和实测值的全年相关系数高达0.76,平均偏差仅为1.13μg·m~(-3),平均绝对误差为23.47μg·m~(-3).通过与支持向量机回归、人工神经网络的统计预报效果对比,DC-SVR模型有效降低预报因子维数且能自适应选取最佳参数,预报精度显著优于其他3种统计预报方案,可为业务化预报提供参考.     

江汉湖群典型湖泊生态系统健康评价——以梁子湖、洪湖、长湖、斧头湖、武湖为例

湖泊生态系统健康评价对湖泊生态健康状况评定、环境问题诊断和湖泊生态系统管理具有重要意义.根据江汉湖群实际情况和生态特点,选取江汉湖群五大湖泊—梁子湖、长湖、洪湖、斧头湖和武湖为研究对象,采用野外调查、资料收集并结合GIS技术的研究方法,从生物、水质、生境特征及生态压力4个方面选择15个代表性评价指标,构建江汉湖群典型湖泊生态系统健康评价指标体系,并利用基于模糊综合评价模型的评价方法,对江汉湖群五大湖泊生态系统健康进行评价.结果表明,江汉湖群五大湖泊生态健康综合评价得分值为5.47~7.46,梁子湖得分最高,其余依次为斧头湖、洪湖、长湖和武湖.从湖泊生态系统健康等级看,五大湖泊中梁子湖和斧头湖处于比较健康状态,而洪湖、长湖和武湖处于亚健康状态.通过实例分析表明,本研究建立的湖泊生态系统健康评价方法能够较客观系统地评价江汉湖群生态系统健康状况,具有较高的可行性和可靠性,可作为江汉湖群健康诊断的评价方法.本文研究结果可为江汉湖群的分类管理提供较为重要的参考依据.     

论可持续发展视野中的农村社会支持

本文阐述了如何可持续发展观来指导农村社会支持,并提出在农村社会支持活动中,引进社会保险、社会服务、生态保护和心理培育等新的机制,运用综合性评价指标体系对各种农村社会支持项目及其结果进行经济效益、社会效益和心理效益三个方面的分析。     

Framing of Geoengineering Affects Support for Climate Change Mitigation

The growing recognition that climate change mitigation alone will be inadequate has led scientists and policymakers to discuss climate geoengineering. An experiment with a US sample found, contrary to previous research, that reading about geoengineering did not reduce conservatives’ skepticism about the existence of anthropogenic climate change. Moreover, depending on how it is framed, geoengineering can reduce support for mitigation among both conservatives and non-conservatives. When geoengineering is framed as a major solution, people worry less about climate change, leading to reduced mitigation support. When framed as disastrous, people perceived geoengineering as riskier, also leading to a decrease in mitigation support. A more moderate framing of geoengineering as a partial solution is less susceptible to moral hazard effects. Overall, results suggest that geoengineering will not lessen political polarization over anthropogenic climate change, and could undercut support for mitigation efforts. Instead, framing geoengineering as a small piece to solving a big puzzle seems to be the best strategy to weaken this potential moral hazard.     

A spatially explicit backcasting approach for sustainable land-use planning

General backcasting as a decision support and planning method starts from desired future states and simulates developments backwards until reaching the present state. Development pathways that reveal steps to be taken to reach a certain future state, and milestones that serve as interim goals, are created during the process. Backcasting has hitherto only been applied in workshops or as a theoretical framework and no spatially explicit backcasting model has previously been established. This paper presents the development of a spatially explicit backcasting model. The proposed model first creates a future scenario utilizing an agent-based model and then simulates backwards. It is implemented using the programming language Python. The model has been applied to a case study for sustainable land-use planning in Salzburg, Austria. The results of the model run show a successful backcasting of land-use classes from a future state back to the present, in 10 year time steps.     

Climate policy support under political consensus: exploring the varying effect of partisanship and party cues

By creating attitudinal rifts among partisan voters, political polarization is expected to negatively affect chances of effectively mitigating climate change. While such expectations generally have found support, less attention has been paid to the opposite claim that political consensus should eliminate the partisan dimension in climate change politics. This study tests this claim by studying how party identification, and party cues specifically, affects public policy attitudes in a context defined by political consensus. Using data from a large online access panel in Sweden, party identification and party cues are shown to matter for policy attitudes even in a consensus context. This effect is not limited to certain issues but is found across a wide range of policies, and the effect of party cues, for a given issue, varies across parties. The implications of this study and areas for future work are discussed.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

中国环境质量拐点:基于EKC的实证判断

促进环境质量改善是"十三五"时期实现绿色发展和最终全面建成小康社会的重要目标和任务。随着经济进入新常态和环境治理政策的强势推进,中国环境质量是否已经跨越峰值和进入持续改善的通道?本文基于环境库兹涅茨曲线这一理论工具,依据环境统计数据和国际经验事实判断中国是否到达环境库兹涅茨曲线的拐点和具备促使环境质量持续改善的经济特征。首先,应用中国各省份1998—2013年的面板数据,对人均收入水平与主要大气污染物排放的关系进行回归拟合发现,人均烟粉尘排放自1998年以来持续下降,人均二氧化硫、氮氧化物排放均在"十二五"中前期到达峰值。大部分东部省份已经越过环境库兹涅茨曲线的拐点,但环境质量改善仍然缓慢。多数中部省份处于峰值阶段,而西部省份大都处于经济增长与环境质量恶化的矛盾阶段。其次,基于污染物排放的驱动因素框架,将中国当前的经济特征与美、日、韩三国跨越环境库兹涅茨曲线拐点的经验进行比较,结论表明中国目前已基本具备了跨越环境库兹涅茨曲线拐点的经济驱动条件,但是相对滞后的城镇化进程将会导致污染物排放峰值的扁平化和波动性。以主要污染物排放在2014年的基础上削减30%—40%作为环境质量全面改善的转折点判断,中国环境质量将在2025年左右趋于全面改善。最后,为了促进环境质量全面改善,认为应该根据各地区经济发展与环境污染形势的不同,实施差别化环境治理政策。同时积极防范新一轮城镇化过程中的环境风险,进一步强化区域环境污染的协同治理。