苯乙炔与二苯丁二炔的共聚及共聚物的表征

本文以(Ph_3P)_2PdCl_2作催化剂,合成出聚二苯丁二炔和二苯丁二炔与苯乙炔共聚物。均聚物及共聚物产率都在90%以上,均聚物分子量为900,二苯丁二炔与苯乙炔最佳摩尔比为1/2(数均分子量为1205)。结构分析表明,均聚物及共聚物均具有反—反式结构。     

降低执政成本提高执政成效

降低执政成本,提高执政成效,是执政党追求的永恒目标,也是执政过程中始终面临的重大课题。从政治意义上看,执政成效关系到党的兴衰成败、生死存亡;从经济意义上看,执政成效是执政党保持执政合法性的重要条件。降低执政成本、提高执政成效是一个系统工程,要提高执政的成本效益意识;建立一支高素质的领导干部队伍;精兵简政,降低政府行政成本;加强制度建设。     

不断增强旅团党委依法从严治军的本领

从严治军是军队建设的铁律。旅团党委是部队建设的“一线指挥部”,应当树立依法指导和开展工作的法制观念,严格职责管理权限,突出作风纪律建设这个重点,把提高部队战斗力作为根本标准,不断增强依法从严治军、加强部队正规化建设的本领。     

Impacts of logistics information on sales: Evidence from Alibaba

Facing fierce competition from rivals, sellers in online marketplaces are eager to improve their sales by delivering items faster and more reliably. Because logistics quality can be known only after a transaction, sellers must identify effective ways to communicate logistics information to consumers. Drawing on the accessibility-diagnosticity framework, we theorize that the sales impacts of logistics information depend on its relative diagnostic value. Using data on 1493 items with 505,785 consumer reviews from an online marketplace, we examine how sales are affected by three information sources for logistics services: online word of mouth (WOM) about logistics, self-reported logistics services, and expected delivery time. We use an instrumental variable method to address the endogeneity issue between sales and WOM. We find that, ceteris paribus, consumers give more weight to WOM about logistics and delivery time when they make purchase decisions but less weight to self-reported logistics service. The effects of logistics information on sales are asymmetric for large and small sellers.     

一种基于QFD与ANP的装备作战需求分析方法

针对当前在装备论证过程中缺乏合适的方法和工具将模糊的军事需求科学合理地映射到定量的作战使用性能上去的问题,提出一种基于质量功能部署(QFD)与网络分析法(ANP)的装备作战需求分析方法.该方法能够辅助论证人员深刻理解和合理判断论证中各种因素之间的相互影响关系,通过结构化的装备作战需求分析过程得到能正确反映军事需求的作战使用性能重要度排序.通过实例验证了方法的有效性和实用性.     

一种完全分布式的空间服务及组合模型

针对服务组合中控制流和数据流的不同协调机制,可将组合模型分为4种类型,分别是控制流集中式且数据流集中式、控制流集中式且数据流分布式、控制流分布式且数据流集中式、控制流分布式且数据流分布式.空间服务组合流程需处理较大规模的数据流,采用控制流分布式和数据流分布式的协调机制能有效降低组合过程中系统整体通信规模并减少空间服务组合响应时间.通过对各种不同组合类型建模的定量分析对上述观点进行了正确性论证.模拟实验证明,定量分析结果可信.     

A model of supply-chain decisions for resource sharing with an application to ventilator allocation to combat COVID-19

We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top-most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst-case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk-aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp-up consideration can be based on a cost-benefit analysis.     

一种新型的超声换能器驱动与回波检测电路设计

通过对超声换能器的工作原理分析,设计了一种新型的超声换能器驱动与回波检测电路。驱动电路由功率放大器、脉冲变压器及一定的补偿电路组成;回波检测电路由前置放大器、带通滤波器和自动电平控制电路组成。试验结果显示,此电路性能良好,能明显提高超声换能器的测距范围。     

努力争当知识型开拓型有为型自律型领导型干部

在新的历史起点上,我们的干部要努力争当学习力强、知识面广、业务上精的知识型干部;争当视野开阔、勇于创新、充满激情的开拓型干部;争当心无旁骛想干事、素质过硬能干事、不辱使命干成事的有为型干部;争当善于律心、心理不失衡,长于律言、言语不失规,严于律行、行为不失范的自律型干部;争当能说会道、让人信服,公道正派、让人诚服,硕果...     

Multi-scale traffic vehicle detection based on faster R-CNN with NAS optimization and feature enrichment

It well known that vehicle detection is an important component of the field of object detection. However, the environment of vehicle detection is particularly sophisticated in practical processes. It is compara-tively difficult to detect vehicles of various scales in traffic scene images, because the vehicles partially obscured by green belts, roadblocks or other vehicles, as well as influence of some low illumination weather. In this paper, we present a model based on Faster R-CNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes. First, we proposed a Retinex-based image adaptive correction algorithm (RIAC) to enhance the traffic images in the dataset to reduce the influence of shadow and illumination, and improve the image quality. Second, in order to improve the feature expression of the backbone network, we conducted Neural Architecture Search (NAS) on the backbone network used for feature extraction of Faster R-CNN to generate the optimal cross-layer connection to extract multi-layer features more effectively. Third, we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets, and improve the robustness of the model for challenging targets such as small scale and severe occlusion. In the imple-mentation of the model, K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model. Our model has been trained and tested on the UN-DETRAC dataset, and the obtained results indicate that our method has art-of-state detection performance.