Permit markets,carbon prices and the creation of innovation clusters

Innovation clusters combining public and private effort to develop breakthrough technologies promise greater technological advances to slow down climate change. We use a multi-country model with an emission trading system to examine whether and how international climate policy can incentivize countries to create such innovation clusters. We find that a minimal carbon price is needed to attract applied research firms, but countries may nevertheless fail to invest in complementary research infrastructure. We construct a mechanism that leads to innovation clusters when emissions targets are set before uncertainty surrounding technological developments is resolved. It is a combination of low permit endowments for the country with the lowest costs to build the needed infrastructure, compensation for this country by profits from permit trade, and maximal possible permit endowments for the remaining countries. We outline how the EU-ETS can be further refined according to this mechanism.     

The impact of research collaboration on academic performance: An empirical analysis for some European countries

The aim of this paper is to investigate the impact of internal and external research collaborations on the scientific performance of academic institutions. The data are derived from the international SCOPUS database. We consider both quantity (the number of publications) and quality indicators (the field-weighted citation impact and the share of publications in the 10% most-cited articles) to evaluate universities' performance in some European countries (Germany, France, Italy, the UK and Russia). To this end, we develop a non-overlapping generations model to evidence the theoretical idea of research externalities between academic institutions. Moreover, we implement an empirical model to determine the extent to which the impact of internal and external collaborations on universities' performance is sensitive to the geographical dimension of the data.     

Exploring job crafting: Diagnosing and responding to the ways employees adjust their jobs

Recent research has developed an in-depth understanding of how workers change their jobs on their own to improve their performance and well-being, a process called job crafting. This research suggests that managers, coworkers, and organizations need a better understanding of how to manage job crafting to capitalize on its benefits and reduce costs. This article will help organizations diagnose the occurrence of job crafting, recognize the differences between goal-oriented approaches to job crafting as opposed to withdrawal-oriented avoidance job crafting, and identify the seven specific types of job crafting: work role expansion, social expansion, work role reduction, work organization, adoption, metacognition, and withdrawal. We conclude with recommendations for managers, coworkers, and organizations on how to diagnose and manage job crafting.     

Is it U.S. GAAP or IFRS? Understanding how R&D costs affect ratio analysis

While financial reporting standards under U.S. GAAP and IFRS are fundamentally similar, differences do exist that may affect our analysis of company financial statements. This is particularly true when comparing a U.S. company following U.S. GAAP to a firm that uses IFRS. To illustrate, we compare research and development (R&D) accounting methods under both sets of standards and illustrate how they affect the analysis of financial results of firms in a specific industry—automotive manufacturers. Our results provide insight into settings in which differences in R&D accounting may have the greatest impact on financial analysis.     

Some issues in blockchain for accounting and the supply chain,with an application of distributed databases to virtual organizations

This paper reviews some recent blockchain‐based applications for information capture, distribution and preservation. As part of that review, this paper examines two key concerns with current blockchain designs for accounting and supply chain transactions: data independence and multiple semantic models for the same information distribution problem. Blockchain applications typically integrate database, application and presentation tiers all in the same ledger. This results in a general inability to query information in the ledger and other concerns. Further, since most applications appear to be private blockchain applications, there is a concern of agents needing to accommodate multiple blockchains depending on who their trading partners are and what they request. Finally, this paper uses a distributed database to design a ‘blockchain‐like’ system for virtual organizations.     

基于AD9361的掌上型LTE RRU设计与开发

当前小体积远端射频单元（Remote Radio Unit,RRU）主要是皮站级别的Pico-RRU，其重量和体积仍然难以满足掌上型的要求。为此，采用高集成度的AD9361芯片来实现长期演进（Long Term Evolution，LTE）宽带收发信机功能，配合时钟同步模块、放大器模块和控制接口电路等一起构建低成本的掌上型RRU。主要解决了基于AD9516的时钟同步、低相位噪声设计和高灵敏度接收机设计三个技术难点，成功实现了掌上型、高性能、低成本的设计目标。整个RRU样机尺寸为18 cm×10 cm×2 cm，质量约500 g，量产成本千元以内。测试表明，该RRU下行误差向量幅度（Error Vector Magnitude，EVM）小于3%，优于3GPP要求的8%；10 MHz带宽时上行接收机灵敏度优于3GPP要求的-93.5 dBm。     

天地一体化信息网络中天基卫星网络架构设计

为了充分利用各种空间设施资源，提升天基信息网络覆盖与传输能力，分别从天基信息网络体系架构和逻辑功能架构角度进行了系统性设计。与以往天基网络的设计不同，该设计更注重各类卫星网络的深度融合应用，为此将网络虚拟化技术引入天基卫星网络。同时，还提出了解决新架构下卫星组网难题的方法，将电磁涡旋、卫星灵活载荷和边缘计算技术设计到天基网络。仿真表明，所提出的架构能有效提升频谱利用率和减少卫星网络信令开销。     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.