Cooperative role of cobalt and gallium under the ethanol steam reforming on Co/CeGaOx

The performance of gallium promoted cobalt-ceria catalysts for ethanol steam reforming (ESR) was studied using H₂O/C₂H₅OH = 6/1 mol/mol at 500 °C. The catalysts were synthetized via cerium-gallium co-precipitation and wetness impregnation of cobalt. A detailed characterization by N₂-physisorption, XRD, H₂-TPR and TEM allowed the normalization of contact time and rationalization of the role of each catalysts component for ESR. The gallium promoted catalyst, Co/Ce₉₀Ga₁₀O_x, was more efficient for the ethanol conversion to H₂ and CO₂, and the production of oxygenated by-products (such as, acetaldehyde and acetone) than Co/CeO₂. The catalytic performance is explained assuming that: (i) bare ceria is able to dehydrogenate ethanol to ethylene; (ii) Ce–O–Ga interface catalyzes ethanol reforming; (iii) both Ce–O–Co and Ce–O–Ga interfaces takes part in acetone production; and (iv) cobalt sites further allow C–C scission. It is suggested that a cooperative role between Co and Ce–O–Ga sites enhance the H₂ and CO₂ yields under ESR conditions.     

超高层劲性结构及大型机械设备立体协同施工技术

基于某超高层建筑群项目的主体施工阶段,对超高层劲性结构建筑群项目施工过程中可能遇到的问题,诸如大型机械立体协同、钢结构与钢筋连接节点优化、钢结构桁架层施工等重难点进行总结分析,并提出相应的解决方案以供探讨,可为同类型工程提供参考经验。     

以用户为中心的可见光通信协作传输中训练资源分配算法

以用户为中心的可见光通信协作传输是近年来出现的新架构,这导致虚拟小区之间出现重叠。为避免导频污染问题,每个虚拟小区中的光接入点(AP)或者虚拟小区中选择相同AP的用户发送的训练序列应该是正交的。针对可见光通信中以用户为中心的协作网络,研究训练资源的正交分配问题,提出了一种新的导频分配算法,联合导频分配和用户选择问题,以期最大限度地增加虚拟小区内可被接入的用户数。分析和仿真结果表明,该导频分配方案可以有效改善导频污染问题,提高训练资源利用率,并且相比已有的导频分配方案,性能有所改进。     

Identifying cooperation for innovation―a comparison of data sources

ABSTRACT

The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidized collaborative R&D projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers.     

Assessment of the food control systems in the Sultanate of Oman

National food control systems are vital tools in governing the safety and quality of food intended for human consumption. This study of the Omani system was conducted to evaluate the effectiveness of the current food controls in place for protecting, in particular, the public health from emerging biological and chemical hazards. In response to this situation, a survey was undertaken within the different food safety authorities in Oman to examine the different elements of the national food control systems in terms of their existing food control management, food legislation, food inspection, food analysis laboratories and information, education and communications. Officials from the different authorities were interviewed and results were captured in prepared questionnaires. Overall examinations of the challenges, strength and weakness of the existing system have been highlighted. The findings of the study indicate significant progress is being made and the creation by the government of a national Centre for Food Safety and Quality is a significant positive step.     

基于空间合作关系的基站流量预测模型

针对传统的自回归积分移动平均（ARIMA）模型和长短时记忆（LSTM）单元在基站流量预测中没有利用基站（BS）间合作关系的问题，提出一种利用由用户群体在不同基站下访问产生的基站合作关系的流量预测（TPBC）算法。首先，通过基站之间的合作关系构建基站合作网络，并对此合作网络进行社区划分得到基站社区；然后，通过格兰杰因果关系检验方法寻找与目标基站同一社区且关系最紧密的若干基站，作为目标基站的合作基站；最后，使用LSTM和词嵌入层（Embedding）搭建混合神经网络，并根据目标基站和合作基站的流量信息进行流量预测。实验结果表明，TPBC在基站流量预测上的均方根误差（RMSE）相比ARIMA和LSTM分别减小了29.19%和27.47%。TPBC能有效提高基站流量预测准确率，在流量卸载和绿色节能等领域具有重要意义。     

Scheduling strategy for computational-intensive data flow in generalized cluster environments

A classical problem in the field of distributed computation and parallel processing concerns reasonable allocation of resources among computational-intensive data flows. We introduce the generalized cluster in this paper for processing large-scale scientific computations and to further explore a consecutive cooperation game-based dynamic scheduling strategy. We construct an abstract generalized cluster environment and summarize the types of data flows. We then convert the multi-objective scheduling problem into a multi-objective expectation function-based continuous cooperation game model and discuss its strategy and a solution for its kernel. We also propose a dynamic scheduling mechanism to address the instability of generalized clusters to ensure a reasonable, real-time adjusting allocation scheme by monitoring and compensating appropriately. Finally, we apply our method to a real world application to demonstrate our successful scheduling strategy that achieves superior results for overall cost, cost-performance index, and mean run time when compared with other methods.     

基于现代学徒制校企共建课程体系的探索与实践

以无人机应用技术专业为例,探索基于现代学徒制课程体系的构建。由构建基于现代学徒制课程体系的基本要求和依据出发,分析了现代学徒制专业课程体系特征,描述现代学徒制专业课程体系建设原则与思路,重点阐述了现代学徒制专业课程体系基本组成以及构建方法,为相关现代学徒制课程体系的构建提供参考。     

Mathematical foundations of moral preferences

One-shot anonymous unselfishness in economic games is commonly explained by social preferences, which assume that people care about the monetary pay-offs of others. However, during the last 10 years, research has shown that different types of unselfish behaviour, including cooperation, altruism, truth-telling, altruistic punishment and trustworthiness are in fact better explained by preferences for following one''s own personal norms—internal standards about what is right or wrong in a given situation. Beyond better organizing various forms of unselfish behaviour, this moral preference hypothesis has recently also been used to increase charitable donations, simply by means of interventions that make the morality of an action salient. Here we review experimental and theoretical work dedicated to this rapidly growing field of research, and in doing so we outline mathematical foundations for moral preferences that can be used in future models to better understand selfless human actions and to adjust policies accordingly. These foundations can also be used by artificial intelligence to better navigate the complex landscape of human morality.