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.     

基于DM2的体系结构可执行模型构建方法

基于元模型的体系结构设计思想和基于可执行模型的评估方法是体系结构领域研究的两个热点,但当前的研究没有将两者结合起来。文章基于DoDAF2.0提出的元模型数据(Meta-Model Data,DM2),将元模型的思想和基于可执行模型的评估方法相结合,分析了基于DM2的逻辑数据与可执行模型各构建要素的对应关系,构建了基于DM2的逻辑数据模型直接转可执行模型的过程框架,重点研究了如何直接从体系结构底层数据转可执行模型的方法,从而为进行基于元模型的体系结构可执行评估提供模型基础,也为进一步实现体系结构自动化验证评估提供技术支持。实例验证了文章提出的方法。     

C4ISR体系结构工程概念研究

通过分析国内外C4ISR系统体系结构开发、研究中存在的问题,提出了C4ISR体系结构工程的设想,即采用工程的概念、原理、技术和方法,从技术和管理两方面研究如何更好地开发、维护C4ISR系统的体系结构.定义了C4ISR体系结构工程的相关概念,提出并详细介绍了体系结构的生命周期.     

防空导弹网络化作战C4ISR系统体系结构研究

网络中心战是信息化战争中一种新的作战概念.按照网络中心战的概念,研究防空导弹网络化作战的C4ISR体系结构.提出了以火力单元的功能系统为基本单元,建立跟踪制导网、指挥控制网和拦截兵器网三层逻辑网结构,并定义各逻辑层的主要功能和基本组成.该结构对实现防空导弹网络中心战有指导意义.     

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.     

战争观念的语源原型

本文从汉字语源出发,根据荣格心理学的原型观念,通过汉字“战”的语源分析,探寻蕴含在汉字深处(因而也是汉文化底层)的战争观念。文字学认为,“战”()字中单为声符,戈为形符,而作为声符的单系“战”字的隐义,亦即“战”的战争原型。古文字研究表明“单”为鳄类爬行动物,象征着人性中凶猛残暴的兽性;而脑科学研究表明,大脑深处的主管攻击、暴力的部分正是原始脑的“爬虫复合体”(鳄类)。这些研究成果与无意识心理学中的阴影原型(人性中的兽性)不谋而合。因此通过文字学与心理学我们找到了战争冲动的原始意象,也从中发现了相应的化解方法。     

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。     

栅格数据处理中邻域型算法的并行优化方法

随着并行计算的成熟,众多数据密集型的栅格处理算法亟需利用并行计算来缩减执行时间.针对其中一类邻域型算法,构建了用于估计是时间代价的串行/并行时域模型,分析了各个组成的代价影响因素,提出了降低数据I/O代价的并行I/O方法和降低数据通信代价的光圈预测方法.实验证明,所提的两个优化方法可以使邻域型栅格处理算法的并行程序更加充分地利用并行计算资源,进而在一般并行化的基础上进一步提升其并行性能.