LT码新型度分布及其在电力线通信中的应用

Luby变换(Luby Transform,LT)码作为信道编码应用于电力线通信(Power Line Communication,PLC)，可实现电信号的可靠传输。度分布对LT码编译码性能的影响至关重要。为了得到更优的度分布，首先调整二进制指数分布(Binary Exponential Distribution,BED)中的度数比例，获得一种译码性能更优的改进的二进制指数分布(Improved BED,IBED)。然后，根据IBED在冗余度较小时译码成功率高，而冗余度增大后鲁棒孤子分布(Robust Soliton Distribution,RSD)的译码性能表现更佳的特点，通过求和归一化的方式将IBED与RSD两种度分布的优势进行有机结合，提出一种新型二进制鲁棒孤子分布(Binary RSD,BRSD)。仿真结果表明，与其他方法及传统的RSD相比，采用新度分布进行LT编码，可明显降低译码开销，并节约编译码耗时。将新型度分布应用于基于LT码的PLC系统中，能有效地抑制PLC信道中各种噪声对电信号的干扰，并提高通信效率。     

COMPARING PPP WITH TRADITIONAL PROCUREMENT: THE CASE OF SCHOOLS PROCUREMENT IN IRELAND

Advocates of public‐private partnerships (PPPs) argue that they can deliver public infrastructure more efficiently than traditional procurement through timelier completion and superior value for money. Despite these claims comparative analysis of the performance of both procurement methods has received scant attention in the PPP literature to date. This paper addresses this issue by providing an in‐depth, case‐based comparison of PPP versus traditional procurement in the schools sector in Ireland. Through detailed semi‐structured interviews with key stakeholders and an examination of the available documentation, we assess whether the key objectives of using PPP have been achieved. Overall, we find no evidence that PPP leads to faster delivery of infrastructure when the overall procurement process from contract notice to delivery is accounted for. In addition, we find only limited evidence to suggest that PPP results in better value for money.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

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.     

Are China’s regional agricultural productivities converging: How and why?

This article tests the hypotheses of convergence to a single level of total factor productivity (TFP), and a steady state of TFP growth rate in China’s agricultural sector. Based on multilateral TFP estimates we found that China’s agricultural sector has rebounded in recent years from a slower TFP growth in the 2005–2007 period. While convergence test results confirm a “catch-up” effect that provinces with lower TFP levels tend to grow faster than others, estimated rates of β convergence are conditional on how we capture the heterogeneity effect across regions. The rates of β convergence range from 0.016 to 0.039 under different model specifications. Estimates show that higher growth rates of educational attainment, R&D, and intermediate goods density (per unit of labor) can enhance TFP growth. Unfortunately, there is no evidence of an overall σ convergence, indicating that TFP levels are not converging except in the South region. It implies that to catch up with leading provinces, it would require extra efforts for those lagging behind by increasing their region-specific research investment, promoting rural educational attainment, and enhancing embodied technical change.     

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.     

正交实验设计方法在B to B电子商务体系质量改进中的应用

电子商务在全球快速地发展，中国企业面临着由B to C方式向B to B方式的转变，本文采用正交实验设计的方法来解决已建成电子商务体系的质量，以满足新的需求。     

跨国公司技术联盟:动因、效应及启示

跨国公司技术联盟是在经济全球化与一体化的国际背景下发展起来的，它被愈来愈多的人认为是一种知识联盟，是一种独立的组织形式。跨国公司进行技术联盟的动因在于：获取与企业核心技术相关的上下游技术、适应网络竞争的需要及促进研究与开发等。跨国公司技术联盟既给合作方带来一定的积极效应，同时也产生相当的负面影响。在当前，跨国公司技术联盟对我国企业进行技术联盟与合作有积极的借鉴意义。     

Identifying “best” applicants in recruiting using data envelopment analysis

Selecting the most promising candidates to fill an open position can be a difficult task when there are many applicants. Each applicant achieves certain performance levels in various categories and the resulting information can be overwhelming. We demonstrate how data envelopment analysis (DEA) can be used as a fair screening and sorting tool to support the candidate selection and decision-making process. Each applicant is viewed as an entity with multiple achievements. Without any a priori preference or information on the multiple achievements, DEA identifies the non-dominated solutions, which, in our case, represent the “best” candidates. A DEA-aided recruiting process was developed that (1) determines the performance levels of the “best” candidates relative to other applicants; (2) evaluates the degree of excellence of “best” candidates’ performance; (3) forms consistent tradeoff information on multiple recruiting criteria among search committee members, and, then, (4) clusters the applicants.     

Productivity Growth in the Chinese Economy by Industry and the Role of Foreign Capital

I. IntroductionIt is well known that China’s reform and open-up policy initiated in 1978 had an extremelylarge impact on the Chinese economy. This policy represented an about-face against thetraditional self-reconstruction strategy that China had maintained since the founding ofthe people’s Republic in 1949. Although China’s economy temporarily fell into confusionfor a few years after the Tian-an-men incident in 1989, China continued to record high GDPgrowth. Moreover, after Deng Xiao…