Preface

Different from traditional tax audit, supervision with self-audit is a combination of audit by the taxpayer himself and audit by the tax authority. This paper mainly studies a taxpayer’s optimal policy of tax evasion under supervision with self-audit and its related properties, in order to deduce some effective suggestions and theoretical bases to restrain tax evasion. Assuming that only a certain proportion of evaded tax can be discovered when the audit is executed, the authors first formulate a static model with self-audit. This model is divided into two stages. At the first stage, taxpayers declare their taxes, then the tax authority chooses audit objects, based on a known probability, and announces the result; if the taxpayer is chosen, he will enter the second stage, during which he has a chance to pay the evaded tax and the corresponding late fees and then is audited by the tax authority. The authors show the existence and uniqueness of the optimal amount of tax evasion at the first stage and the optimal proportion to self-expose at the second stage. The authors also discuss the related properties of the interior solution, and do elasticity analyses on some parameters. Besides, the authors extend the static model into the corresponding two-period model, and study the existence and uniqueness of the solution of the extended model. Finally, under the assumption that tax evasion can only be discovered with a certain probability when the audit is executed, the authors formulate another static model with self-audit and investigate its properties. This research is partially supported by the National Natural Science Foundation of China under Grant Nos. 60674082, 70221001, and 70731003.     

聚集诱导发光材料在生物成像、疾病诊断及治疗的应用

 聚集诱导发光（AIE）从2001年提出到现在,经历了迅猛的发展。研究AIE的发光机理、设计合成新型的AIE分子,应用到人类生活的各个领域中是当前的研究热点。在生物医学应用上,AIE分子相比于传统的结构平面刚性的荧光材料具有优势。根据AIE的发光机理--分子内运动受限,研究者设计出多种点亮型的AIE探针,它们可以在生物检测中提供更低的背景和更可靠的信号。同时,由于未结合检测物的AIE探针具有低背景,使用AIE探针的应用还具有无需洗涤步骤的优点,大大节省了操作时间及减少检测样品的损失。检测形成的AIE聚集体具有优异的光稳定性和抗光漂白性,可以实现长时间的追踪和监测。目前,AIE生物探针在生物分子检测、细胞器成像、细菌成像、细胞追踪、血管成像、体内肿瘤成像与治疗等方面已经取得了众多的成果。本文介绍了AIE的提出、AIE的工作机理,AIE生物探针的构建,总结了AIE探针在生物成像、疾病诊断及治疗等不同方面的应用。     

Structures of Textile-apparel Supply Chain： Concepts and Cases

In recent years, supply chain management （SCM） has been in popularity as a new management philosophy for all industries, including textile and apparel industries. The textile-apparel supply chain is relatively complex because it encompasses many participants such as yarn manufacturers, fabric manufacturers, garment manufacturers and retailers. Although many scholars are engaged in researching SCM in textile and apparel industries, a systematic classification of textile-apparel chain does not exist. The paper proposes three types of textile-apparel chain, nominated vertical integration chain, traditional sourcing chain and 3P-hub （third party as the hub） chain. Different coordinators exist in different types of chain. Three Hong Kong headquartered companies, Esquel Group, TAL Apparel Ltd., and Li ＆ Fang （Trading） Ltd. are used as cases responding to each type of the structures respectively.     

Tax Evasion: Models with Self-Audit

Different from traditional tax audit, supervision with self-audit is a combination of audit by the taxpayer himself and audit by the tax authority. This paper mainly studies a taxpayer＇s optimal policy of tax evasion under supervision with self-audit and its related properties, in order to deduce some effective suggestions and theoretical bases to restrain tax evasion. Assuming that only a certain proportion of evaded tax can be discovered when the audit is executed, the authors first formulate a static model with self-audit. This model is divided into two stages. At the first stage, taxpayers declare their taxes, then the tax authority chooses audit objects, based on a known probability, and announces the result; if the taxpayer is chosen, he will enter the second stage, during which he has a chance to pay the evaded tax and the corresponding late fees and then is audited by the tax authority. The authors show the existence and uniqueness of the optimal amount of tax evasion at the first stage and the optimal proportion to self-expose at the second stage. The authors also discuss the related properties of the interior solution, and do elasticity analyses on some parameters. Besides, the authors extend the static model into the corresponding two-period model, and study the existence and uniqueness of the solution of the extended model. Finally, under the assumption that tax evasion can only be discovered with a certain probability when the audit is executed, the authors formulate another static model with self-audit and investigate its properties.     

Designing a Hybrid Intelligent Mining System for Credit Risk Evaluation

In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.     

Genome sequencing and analysis of Aspergillus oryzae

The genome of Aspergillus oryzae, a fungus important for the production of traditional fermented foods and beverages in Japan, has been sequenced. The ability to secrete large amounts of proteins and the development of a transformation system have facilitated the use of A. oryzae in modern biotechnology. Although both A. oryzae and Aspergillus flavus belong to the section Flavi of the subgenus Circumdati of Aspergillus, A. oryzae, unlike A. flavus, does not produce aflatoxin, and its long history of use in the food industry has proved its safety. Here we show that the 37-megabase (Mb) genome of A. oryzae contains 12,074 genes and is expanded by 7-9 Mb in comparison with the genomes of Aspergillus nidulans and Aspergillus fumigatus. Comparison of the three aspergilli species revealed the presence of syntenic blocks and A. oryzae-specific blocks (lacking synteny with A. nidulans and A. fumigatus) in a mosaic manner throughout the genome of A. oryzae. The blocks of A. oryzae-specific sequence are enriched for genes involved in metabolism, particularly those for the synthesis of secondary metabolites. Specific expansion of genes for secretory hydrolytic enzymes, amino acid metabolism and amino acid/sugar uptake transporters supports the idea that A. oryzae is an ideal microorganism for fermentation.     

A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting

In this paper, a KELM-based ensemble learning approach, integrating Granger causality test, grey relational analysis and KELM(Kernel Extreme Learning Machine), is proposed for the exchange rate forecasting. The study uses a set of sixteen macroeconomic variables including, import,export, foreign exchange reserves, etc. Furthermore, the selected variables are ranked and then three of them, which have the highest degrees of relevance with the exchange rate, are filtered out by Granger causality test and the grey relational analysis, to represent the domestic situation. Then, based on the domestic situation, KELM is utilized for medium-term RMB/USD forecasting. The empirical results show that the proposed KELM-based ensemble learning approach outperforms all other benchmark models in different forecasting horizons, which implies that the KELM-based ensemble learning approach is a powerful learning approach for exchange rates forecasting.     

Entrepreneurship Measurement and Comparison: Holistic Acceptability Global Entrepreneurship Index

Journal of Systems Science and Complexity - The Global Entrepreneurship and Development Institute annually publishes the Global Entrepreneurship Index (GEI) to show entrepreneurship of each...     

Development of a Three-Dimensional Measuring System for Neonates＇ Head and Facial Morphology

A web camera based multi-camera convergent close-range photogrammetric system is developed to obtain the neonates＇ head and facial morphology. The data will then be used to develop a secure and good-fitting eye-patch protector for neonates, particularly whoa they are exposed to bright lights such as phototherapy light. Measurements obtained by the system are evaluated and validated against data obtained from optical scanning. Results show that the photogrammetric system meets the requirements of measuring accuracy and safety for neonate in the neonatal units.     

A Hybrid Approach for Studying the Lead-Lag Relationships Between China’s Onshore and Offshore Exchange Rates Considering the Impact of Extreme Events

Understanding the characteristics of the dynamic relationship between the onshore Renminbi (CNY) and the offshore Renminbi (CNH) exchange rates considering the impact of some extreme events is very important and it has wide implications in several areas such as hedging. For better estimating the dynamic relationship between CNY and CNH, the Granger-causality test and Bry-Boschan Business Cycle Dating Algorithm were employed in this paper. Due to the intrinsic complexity of the lead-lag relationships between CNY and CNH, the empirical mode decomposition (EMD) algorithm is used to decompose those time series data into several intrinsic mode function (IMF) components and a residual sequence, from high to low frequency. Based on the frequencies, the IMFs and a residual sequence are combined into three components, identified as short-term composition caused by some market activities, medium-term composition caused by some extreme events and the long-term trend. The empirical results indicate that when it only matters the short-term market activities, CNH always leads CNY; while the medium-term impact caused by those extreme events may alternate the lead-lag relationships between CNY and CNH.